Cancer – an overview

  • PMID: 29952494

Cancer is characterized by proliferation of cells that have managed to evade central endogenous control mechanisms. Cancers are grouped according to their organ or tissue of origin, but increasingly also based on molecular characteristics of the respective cancer cells. Due to the rapid technological advances of the last years, it is now possible to analyze the molecular makeup of different cancer types in detail within short time periods. The accumulating knowledge about development and progression of cancer can be used to develop more precise diagnostics and more effective and/or less toxic cancer therapies. In the long run, the goal is to offer to every cancer patient a therapeutic regimen that is tailored to his individual disease and situation in an optimal way.

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April 10, 2018

Genomic analysis of 33 cancer types completed

At a glance.

  • Using molecular and clinical information from more than 10,000 tumors, researchers finished a detailed genomic analysis of 33 types of cancer.
  • This better understanding of how, where, and why cancer develops will inform the development of novel and more personalized treatment approaches.

Squamous cell carcinoma, a type of skin cancer

Cancer is caused by changes to DNA that affect the way cells grow and divide. There are at least 200 forms of cancer, with many subtypes. Identifying the changes in each cancer’s complete set of DNA—its genome—and understanding how these changes interact to drive the disease will lay the foundation for improving cancer prevention, early detection, and tailored treatments.

The Cancer Genome Atlas (TCGA) was launched in 2005 by NIH’s National Human Genome Research Institute (NHGRI) and National Cancer Institute (NCI) to map the key genomic changes in 33 types of cancer . The multi-institution collaboration focused not only on cancer genome sequencing, but also on different types of molecular data collection and analysis, such as investigating gene and protein expression profiles (when they are turned on or off) and associating them with clinical and imaging data. With over $300 million in total funding, the project involved more than 150 researchers at more than two dozen institutions.

The PanCancer Atlas sums up the work accomplished by TCGA in a collection of 27 papers across a suite of Cell journals. Three summary papers published on April 5, 2018, recap the core findings, and companion papers report more in-depth explorations.

The first summary paper describes a technique called molecular clustering that groups cancers based on their molecular characteristics rather than their tissue of origin. The scientists analyzed gene expression, DNA modifications, protein expression, and other data from about 10,000 tissue samples representing 33 different types of cancer. The team identified 28 distinct clusters based on molecular similarities. Although most of these clusters could be linked to tissue of origin, many contained different cancer types. The most diverse group had 25 cancer types. These findings could help guide the treatment of many cancer patients whose tumors are of unknown origin.

The second paper presents findings on oncogenesis, the processes that lead to cancer development and progression. The authors focused on three critical oncogenic processes: the DNA mutations that drive cancers; the influence of DNA alterations on gene and protein expression; and the interplay of tumors with their surroundings, particularly immune cells. The results will help in the development of new treatments for a wide range of cancers.

The final paper details genomic alterations in 10 key signaling pathways that control the stages of the cell’s life cycle, growth, and death. The researchers found that 89% of tumors had at least one significant alteration in these pathways. About 57% of tumors had at least one alteration that could be targeted with currently known drugs and 30% had multiple targetable alterations. These findings will help researchers explore treatments with more tailored approaches, such as using a combination of drugs to target multiple pathways at the same time.

“TCGA was the first project of its scale to characterize—at the molecular level—cancer across a breadth of cancer types,” says Dr. Carolyn Hutter, NHGRI team lead for TCGA. “At the project’s infancy 10 years ago, it wasn’t even possible, much less on such a scale, to do the types of characterization and analysis that were being proposed. It was a hugely ambitious project.”

“The PanCancer Atlas effort complements the over 30 tumor-specific papers that have been published by TCGA in the last decade and expands upon earlier pan-cancer work that was published in 2013,” says Dr. Jean Claude Zenklusen, director of the TCGA Program Office at NCI.

Related Links

  • The Genomics of Cervical Cancer
  • Genomic Diversity of Metastases Among Men With Prostate Cancer
  • Novel Approach Gives Insights Into Tumor Development
  • Study Uncovers Alterations in Head and Neck Cancers
  • Gene Changes Identified in Most Common Lung Cancer
  • Genome Study Yields Insights Into Bladder Cancer
  • Genomic Analysis of Endometrial Tumors
  • New Insights Into Breast Cancer
  • Colon and Rectal Cancers Surprisingly Similar
  • A Detailed Look at Ovarian Cancer
  • A Genomic Survey of Melanoma
  • Mutations Affect Acute Myeloid Leukemia Treatment Outcomes
  • Study Reveals Large Number of Cancer Genes
  • The Genetic Underpinnings of Cancer
  • The Cancer Genome Atlas

References:  Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Hoadley KA, Yau C, Hinoue T, Wolf DM, Lazar AJ, Drill E, Shen R, Taylor AM, Cherniack AD, Thorsson V, Akbani R, Bowlby R, Wong CK, Wiznerowicz M, Sanchez-Vega F, Robertson AG, Schneider BG, Lawrence MS, Noushmehr H, Malta TM; Cancer Genome Atlas Network, Stuart JM, Benz CC, Laird PW. Cell . 2018 Apr 5;173(2):291-304.e6. doi: 10.1016/j.cell.2018.03.022. PMID: 29625048. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A, Bertrand D, Gibbs DL, Weerasinghe A, Huang KL, Tokheim C, Cortés-Ciriano I, Jayasinghe R, Chen F, Yu L, Sun S, Olsen C, Kim J, Taylor AM, Cherniack AD, Akbani R, Suphavilai C, Nagarajan N, Stuart JM, Mills GB, Wyczalkowski MA, Vincent BG, Hutter CM, Zenklusen JC, Hoadley KA, Wendl MC, Shmulevich L, Lazar AJ, Wheeler DA, Getz G; Cancer Genome Atlas Research Network. Cell . 2018 Apr 5;173(2):305-320.e10. doi: 10.1016/j.cell.2018.03.033. PMID: 29625049. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S, Chakravarty D, Daian F, Gao Q, Bailey MH, Liang WW, Foltz SM, Shmulevich I, Ding L, Heins Z, Ochoa A, Gross B, Gao J, Zhang H, Kundra R, Kandoth C, Bahceci I, Dervishi L, Dogrusoz U, Zhou W, Shen H, Laird PW, Way GP, Greene CS, Liang H, Xiao Y, Wang C, Iavarone A, Berger AH, Bivona TG, Lazar AJ, Hammer GD, Giordano T, Kwong LN, McArthur G, Huang C, Tward AD, Frederick MJ, McCormick F, Meyerson M; Cancer Genome Atlas Research Network, Van Allen EM, Cherniack AD, Ciriello G, Sander C, Schultz N. Cell . 2018 Apr 5;173(2):321-337.e10. doi: 10.1016/j.cell.2018.03.035. PMID: 29625050. The entire collection of papers comprising the PanCancer Atlas are available through a portal on .

Funding:  NIH’s National Human Genome Research Institute (NHGRI) and National Cancer Institute (NCI).

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  • v.18(3); Jul-Sep 2019

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Inflammation and Cancer

Nitin singh.

Department of Pedodontics and Preventive Dentistry, Chandra Dental College and Hospital, Safedabad, Barabanki, Uttar Pradesh, India

Deepak Baby

1 Department of Conservative and Endodontics, P.S.M Dental College and Research Centre, Akkikavu, Thrissur, Kerala, India

Jagadish Prasad Rajguru

2 Department of Oral Pathology and Microbiology, Hi-Tech Dental College and Hospital, Bhubaneswar, Odisha, India

Pankaj B Patil

3 Department of Oral and Maxillofacial Surgery, School of Dental Sciences, Krishna Institute of Health Sciences Deemed to be University, Karad, Maharashtra, India

Savita S Thakkannavar

4 Department of Oral Pathology and Microbiology, Tatyasaheb Kore Dental College and Research Centre, New Pargaon, Kolhapur, Maharashtra, India

Veena Bhojaraj Pujari

5 Department of Oral Medicine and Radiology, Tatyasaheb Kore Dental College and Research Centre, New Pargaon, Kolhapur, Maharashtra, India

Inflammation is often associated with the development and progression of cancer. The cells responsible for cancer-associated inflammation are genetically stable and thus are not subjected to rapid emergence of drug resistance; therefore, the targeting of inflammation represents an attractive strategy both for cancer prevention and for cancer therapy. Tumor-extrinsic inflammation is caused by many factors, including bacterial and viral infections, autoimmune diseases, obesity, tobacco smoking, asbestos exposure, and excessive alcohol consumption, all of which increase cancer risk and stimulate malignant progression. In contrast, cancer-intrinsic or cancer-elicited inflammation can be triggered by cancer-initiating mutations and can contribute to malignant progression through the recruitment and activation of inflammatory cells. Both extrinsic and intrinsic inflammations can result in immunosuppression, thereby providing a preferred background for tumor development. The current review provides a link between inflammation and cancer development.


L’inflammation est souvent associée au développement et à la progression du cancer. Les cellules responsables de l’inflammation associée au cancer sont génétiquement stables et ne subissent donc pas l’émergence rapide d’une pharmacorésistance; par conséquent, le ciblage de l’inflammation représente une stratégie attrayante à la fois pour la prévention du cancer et pour le traitement du cancer. L’inflammation tumorale extrinsèque est causée par de nombreux facteurs, notamment: infections bactériennes et virales, maladies auto-immunes, obésité, tabagisme, exposition à l’amiante et consommation excessive d’alcool, le tout qui augmentent le risque de cancer et stimulent la progression maligne. En revanche, l’inflammation intrinsèque au cancer ou provoquée par le cancer peut être déclenchée par des mutations initiant un cancer et peuvent contribuer à la progression maligne par le recrutement et l’activation de cellules inflammatoires. Tous les deux les inflammations extrinsèques et intrinsèques peuvent entraîner une immunosuppression, fournissant ainsi un fond préféré pour le développement de la tumeur. le l’examen actuel établit un lien entre l’inflammation et le développement du cancer.


The presence of leukocytes within tumors, observed in the 19 th century by Rudolf Virchow, provided the first indication of a possible link between inflammation and cancer. Yet, it is only during the past decade that clear evidence has been obtained that inflammation plays a critical role in tumorigenesis.[ 1 ]

However, when inflammation becomes chronic or lasts too long, it can prove harmful and may lead to disease. The role of pro-inflammatory cytokines, chemokines, adhesion molecules, and inflammatory enzymes has been linked with chronic inflammation [ Figure 1 ].[ 2 ]

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Different faces of inflammation and its role in tumorigenesis

Chronic inflammation has been found to mediate a wide variety of diseases, including cardiovascular diseases, cancer, diabetes, arthritis, Alzheimer's disease, pulmonary diseases, and autoimmune diseases.[ 3 ]

The current review, however, will be restricted to the role of chronic inflammation in cancer. Chronic inflammation has been linked to various steps involved in tumorgenesis, including cellular transformation, promotion, survival, proliferation, invasion, angiogenesis, and metastasis.[ 4 ]

Only a minority of all cancers are caused by germline mutations, whereas the vast majority (90%) are linked to somatic mutations and environmental factors. Many environmental causes of cancer and risk factors are associated with some form of chronic inflammation. Up to 20% of cancers are linked to chronic infections, 30% can be attributed to tobacco smoking and inhaled pollutants (such as silica and asbestos), and 35% can be attributed to dietary factors (20% of cancer burden is linked to obesity).[ 5 ]

Recent efforts have shed new light on molecular and cellular circuits linking inflammation and cancer. Two pathways have been schematically identified: in the intrinsic pathway, genetic events causing neoplasia initiate the expression of inflammation-related programs that guide the construction of an inflammatory microenvironment, and in the extrinsic pathway, inflammatory conditions facilitate cancer development.[ 6 ]

The triggers of chronic inflammation that increase cancer risk or progression include infections (e.g., Helicobacter pylori for gastric cancer and mucosal lymphoma; papillomavirus and hepatitis viruses for cervical and liver carcinomas, respectively), autoimmune diseases (e.g., inflammatory bowel disease for colon cancer), and inflammatory conditions of uncertain origin (e.g., prostatitis for prostate cancer). Cancer-related inflammation, the seventh hallmark of cancer, links to genetic instability.[ 7 ]

It was in 1863 that Rudolf Virchow noted leukocytes in neoplastic tissues and made a connection between inflammation and cancer. He suggested that the “lymphoreticular infiltrate” reflected the origin of cancer at sites of chronic inflammation. Over the past 10 years, our understanding of the inflammatory microenvironment of malignant tissues has supported Virchow's hypothesis, and the links between cancer and inflammation are starting to have implications for prevention and treatment.[ 8 ]


Inflammation is the body's response to tissue damage, caused by physical injury, ischemic injury (caused by an insufficient supply of blood to an organ), infection, exposure to toxins, or other types of trauma. The body's inflammatory response causes cellular changes and immune responses that result in repair of the damaged tissue and cellular proliferation (growth) at the site of the injured tissue. Inflammation can become chronic if the cause of the inflammation persists or certain control mechanisms in charge of shutting down the process fail. When these inflammatory responses become chronic, cell mutation and proliferation can result, often creating an environment that is conducive to the development of cancer. The so-called “perfect storm” is an extreme challenge that cancer patients face. This is true for the onset of cancer but also even more important for the advancement of the disease. Various signaling pathways are key contributors in creating epigenetic changes on the outside of the cell, switching on these internal mutations. Therefore, treating the inflammatory causes is always important.

Chronic inflammation has been linked to various steps involved in tumorigenesis, including cellular transformation, promotion, survival, proliferation, invasion, angiogenesis, and metastasis.


Cancer defines malignant neoplasms characterized by metastatic growth. It may occur in almost every organ and tissue relating to a variety of etiologic factors, such as genomic instability and environmental stress.[ 9 ]

However, cancer development is still accepted as a multistep process, during which genetic alterations confer specific types of growth advantages; therefore, it drives the progressive transformation from normal cells to malignant cancer cells. Malignant growth is characterized by several key changes: self-sufficiency of growth signals, insensitivity to antigrowth signals, escaping from apoptosis, unregulated proliferation potential, enhanced angiogenesis, and metastasis. Each of these shifts is complicated and accomplished by combined efforts of various signaling processes. In later discussion, we will find that inflammation may contribute to the formation of these cancer phenotypes.[ 10 ]


Chronic inflammation is characterized by sustained tissue damage, damage-induced cellular proliferation, and tissue repair. Cell proliferation in this context is usually correlated with “metaplasia,” a reversible change in cell type. “Dysplasia,” a disorder of cellular proliferation leading to atypical cell production, follows and is regarded as the previous event of carcinoma because it was usually found adjacent to the site of neoplasm.[ 11 ]


The chronic inflammatory microenvironment is predominated by macrophages. Those macrophages, together with other leukocytes, generate high levels of reactive oxygen and nitrogen species to fight infection.[ 12 ] However, in a setting of continuous tissue damage and cellular proliferation, the persistence of these infection-fighting agents is deleterious. They may produce mutagenic agents, such as peroxynitrite, which react with DNA and cause mutations in proliferating epithelial and stroma cells. Macrophages and T-lymphocytes may release tumor necrosis factor-alpha (TNF-α) and macrophage migration inhibitory factor to exacerbate DNA damage.[ 13 ]

Migration inhibitory factor impairs p53-dependent protective responses, thus causing the accumulation of oncogenic mutations. Migration inhibitory factor also contributes to tumorigenesis by interfering Rb-E2F pathway.


The bacterium H. pylori is known to colonize the human stomach and induce chronic atrophic gastritis, intestinal metaplasia, and gastric cancer. H. pylori infection is a major risk factor for gastric cancer development, which is one of the most challenging malignant diseases worldwide with limited treatments.[ 14 ]

The multistep pathogenesis of gastric cancer is the best highlighted by Correa sequence that explains the progressive pathway to gastric cancer characterized by distinct histological changes. This model predicts that infection with H. pylori triggers an inflammatory response resulting in chronic, and then, atrophic, gastritis. This is followed by intestinal metaplasia which can be further classified into complete and incomplete subtypes. At this point, some patients will then proceed to gastric cancer via the intermediate stage of dysplasia [ Figure 2 ].[ 15 ]

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Correa sequence

The improvement or elimination of atrophy and intestinal metaplasia with H. pylori eradication could potentially inhibit gastric carcinogenesis. It is noteworthy to mention that gastric cancer can still develop even after successful eradication therapy. H. pylori eradication does not result in the regression of all precancerous lesions, which may depend on the degree and extent of preneoplastic changes at the time of eradication.[ 14 ]


The inflammatory microenvironment of tumors is characterized by the presence of host leukocytes both in the supporting stroma and in tumor areas.[ 16 ] Tumor-infiltrating lymphocytes may contribute to cancer growth and spread and to the immunosuppression associated with malignant disease.


Tumor-associated macrophages (TAM) are a major component of the infiltrate of most, if not all tumors. TAM derives from circulating monocytic precursors and is directed into the tumor by chemoattractant cytokines called chemokines. Many tumor cells also produce cytokines called colony-stimulating factors that prolong the survival of TAM. When appropriately activated, TAM can kill tumor cells or elicit tissue destructive reactions centered on the vascular endothelium. However, TAM also produces growth and angiogenic factors as well as protease enzymes which degrade the extracellular matrix. Hence, TAM can stimulate tumor cell proliferation, promote angiogenesis, and favor invasion and metastasis.[ 17 ]

Dendritic cells

Dendritic cells have a crucial role in both the activation of antigen-specific immunity and the maintenance of tolerance, providing a link between innate and adaptive immunity. Tumor-associated dendritic cells (TADCs) usually have an immature phenotype with defective ability to stimulate T-cells.[ 18 ]

This distribution of TADC is clearly different from that of TAM, which is evenly scattered in tumor tissue. The immaturity of TADC may reflect lack of effective maturation signals, prompt migration of mature cells to lymph nodes, or the presence of maturation inhibitors. TADC is likely to be poor inducers of effective responses to tumor antigens.


Natural killer cells are rare in the tumor microenvironment. The predominant T-cell population has a “memory” phenotype. The cytokine profile of these tumor-infiltrating T-cells has not been studied systematically, but in some tumors (e.g. Kaposi's sarcoma, Hodgkin's disease, bronchial carcinoma, and cervical carcinoma), they produce mainly interleukins (ILs) 4 and 5 and not interferon. IL-4 and 5 are cytokines associated with the T-helper type 2 (Th2) cells, whereas interferon is associated with Th1 responses.[ 19 ]


To address the details of transition from inflammation to cancers and the further development of inflammation-associated cancers, it is necessary to investigate specific roles of key regulatory molecules involved in this process.

Pro-inflammatory cytokines

The cytokine network of several common tumors is rich in inflammatory cytokines, growth factors, and chemokines but generally lacks cytokines involved in specific and sustained immune responses.[ 20 ]

There is now evidence that inflammatory cytokines and chemokines, which can be produced by the tumor cells and/or tumor-associated leukocytes and platelets, may contribute directly to malignant progression. Many cytokines and chemokines are inducible by hypoxia, which is a major physiological difference between tumor and normal tissue. Examples are TNF, IL-1 and 6, and chemokines.

The immune response to tumors is constituted by cytokines produced by tumor cells as well as host stromal cells. Tumor-derived cytokines, such as Fas ligand, vascular endothelial growth factor (VEGF), and transforming growth factor-h, may facilitate the suppression of immune response to tumors. Moreover, inflammatory cytokines have also been reported to facilitate the spectrum of tumor development.[ 21 ]

Tumor necrosis factor

TNF is a multifunctional cytokine that plays important roles in diverse cellular events such as cell survival, proliferation, differentiation, and death. As a pro-inflammatory cytokine, TNF is secreted by inflammatory cells, which may be involved in inflammation-associated carcinogenesis. TNF exerts its biological functions through activating distinct signaling pathways such as nuclear factor-κB (NF-κB) and c-Jun N-terminal kinase (JNK). NF-κB is a major cell survival signal that is antiapoptotic while sustained JNK activation contributes to cell death. The crosstalk between the NF-κB and JNK is involved in determining cellular outcomes in response to TNF. TNF is a double-edged sword that could be either pro- or antitumorigenic. On one hand, TNF could be an endogenous tumor promoter because TNF stimulates cancer cells' growth, proliferation, invasion and metastasis, and tumor angiogenesis. On the other hand, TNF could be a cancer killer. The property of TNF in inducing cancer cell death renders it a potential cancer therapeutic.[ 22 ]

TNF can be detected in malignant and/or stromal cells in human ovarian, breast, prostate, bladder, and colorectal cancer, lymphomas, and leukemias, often in association with ILs-1 and 6 and macrophage colony-stimulating factor.[ 23 ]

Interleukins 1 and 6 in cancer regulation

IL-6 is a pleiotropic cytokine that plays important roles in immune response, inflammation, and hematopoiesis. It is produced by a variety of normal cells including monocytes and macrophages but is also expressed by multiple tumor tissue types, such as breast, prostate, colorectal, and ovarian cancer. IL-6 may also play an important role in various aspects of tumor behavior, including apoptosis, tumor growth cell proliferation, migration and invasion, angiogenesis, and metastasis.[ 24 ]

IL-10, initially termed “cytokine synthesis inhibitor” or “cytokine inhibitory factor” due to its inhibitory action on cytokine production by T helper cells, is produced by almost all leukocytes, as well as numerous human tumor cells including breast, kidney, colon, pancreas, malignant melanomas, and neuroblastomas. IL-10 is essential to suppress tumor-promoting inflammation mediators, thereby facilitating tumor growth and metastasis. Specifically, TAMs produce IL-10 and are also associated with in-tumor immunosuppression, thereby providing a suitable microenvironment for cancer growth.[ 25 ]

In mouse models of metastasis, treatment with an IL-1 receptor antagonist (which inhibits the action of IL-1) significantly decreased tumor development, suggesting that local production of this cytokine aids the development of metastasis. Moreover, mice deficient in IL-1 were resistant to the development of experimental metastasis.[ 26 ]

Inflammatory cytokines are major inducers of a family of chemoattractant cytokines called chemokines that play a central role in leukocyte recruitment to sites of inflammation. Most tumors produce chemokines of the two major groups α (or CXC) and β.

Typically, CXC chemokines are active on neutrophils and lymphocytes, whereas CC chemokines act on several leukocyte subsets including monocytes, eosinophils, dendritic cells, lymphocytes, and natural killer cells but not neutrophils.[ 27 ]

Human and murine tumors also frequently secrete CXC chemokines such as IL-8. These chemokines are potent neutrophil attractants, yet neutrophils are rare in tumors. However, both IL-8 and a related chemokine called “gro” induce proliferation and migration of melanoma cell.


Tumor necrosis factor blockade.

TNF antagonists (etanercept [Enbrel] and infliximab [Remicade]) have been licensed for a clinical trial in the treatment of rheumatoid arthritis and Crohn's disease, with over 70,000 patients now treated. Thalidomide inhibits the processing of mRNA for TNF and VEGF, and continuous low-dose thalidomide has shown activity in patients with advanced myeloma. The role of etanercept in ameliorating the adverse effects of other cancer therapies is also being evaluated. There are also ongoing and planned clinical trials with infliximab. As with other “biological” approaches to cancer treatment, anti-TNF therapy may be optimal in an adjuvant setting with minimal disease.[ 28 ]

Chemokine antagonism

Chemokine receptors belong to a family of receptors (transmembrane G-protein-coupled receptors) which is already a target of pharmacological interest. Tumors driven by chemokines and those where chemokines are implicated in metastasis (e.g. seeding to lymph nodes) may be an appropriate target for chemokine antagonists now under development.[ 29 ]

IL-6 is a major growth factor for myeloma cells. In advanced disease, there is an excess of IL-6 production, and raised serum concentrations are associated with plasmablastic proliferative activity and short survival.

Nonsteroidal anti-inflammatory agents

Nonsteroidal anti-inflammatory drugs (NSAIDs) are nonselective or selective COX-1/2 inhibitors, which are wildly prescribed for pain killing, fever reduction, and even anti-inflammation.

Patients on NSAIDs are at reduced risk of colon cancer. This may also be true for cancers of the esophagus, stomach, and rectum, and in rodents experimental bladder, breast, and colon cancer. Colon cancer is reduced when NSAIDs are administered concurrently with carcinogens. NSAIDs inhibit cyclooxygenase enzymes and angiogenesis.[ 30 ]

The mechanisms involved in the association between NSAIDs and distant metastasis inhibition remain incompletely investigated. One possible explanation is that NSAIDs inhibit COX2. Abnormally high COX2 expression is observed in multicancers. Disordered COX2/PGE pathway is involved in multicancer processes, including carcinogenesis, proliferation, and metastatic spread; in addition, inhibition of COX2/PGE pathway with NSAIDs can restrain cancer cell lines.

Mutual promotion relationship between cancer metastasis and cancer-associated thrombosis is possibly another one of the underlying mechanisms. Abnormally high constitutive level of tissue factor (TF), one key regulator of hemostasis, is expressed by metastatic cancer cells, cancer microparticles, and cancer-associated monocytes and macrophages. TF can promote thrombosis formation by activating the extrinsic pathway of coagulation cascade. Furthermore, inflammation induced by thrombosis could result in endothelial damage that results in the vascular leak, facilitating the escape of cancer cells from blood vessels. Consequently, NSAIDs may disrupt the relationship between cancer metastasis and cancer-associated thrombosis via the suppression of platelet function, which is detrimental for the disseminated cancer cells in the bloodstream.[ 31 ]


Overall, this review provides evidence for a strong link between chronic inflammation and cancer. Thus, inflammatory biomarkers as described here can be used to monitor the progression of the disease. These biomarkers can also be exploited to develop new anti-inflammatory drugs to prevent and treat cancer. These drugs can also be used as an adjuvant to the currently available chemotherapy and radiotherapy, which by themselves activate NF-κB and mediate resistance. Numerous anti-inflammatory agents including those identified from natural sources have been shown to exhibit chemopreventive activities.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.


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New research suggests that fungi in the gut may affect how tumors respond to cancer treatments. In mice, when bacteria were eliminated with antibiotics, fungi filled the void and impaired the immune response after radiation therapy, the study found.

FDA has approved belumosudil (Rezurock) for the treatment of chronic graft-versus-host disease (GVHD). The approval covers the use of belumosudil for people 12 years and older who have already tried at least two other therapies.

In lab studies, the antibiotic novobiocin showed promise as a treatment for cancers that have become resistant to PARP inhibitors. The drug, which inhibits a protein called DNA polymerase theta, will be tested in NCI-supported clinical trials.

A drug called avasopasem manganese, which has been found to protect normal tissues from radiation therapy, can also make cancer cells more vulnerable to radiation treatment, a new study in mice suggests.

While doctors are familiar with the short-term side effects of immune checkpoint inhibitors, less is known about potential long-term side effects. A new study details the chronic side effects of these drugs in people who received them as part of treatment for melanoma.

Cholesterol-lowering drugs known as PCSK9 inhibitors may improve the effectiveness of cancer immune checkpoint inhibitors, according to studies in mice. The drugs appear to improve the immunotherapy drugs’ ability to find tumors and slow their growth.

Researchers have developed a nanoparticle that trains immune cells to attack cancer. According to the NCI-funded study, the nanoparticle slowed the growth of melanoma in mice and was more effective when combined with an immune checkpoint inhibitor.

A comprehensive analysis of patients with cancer who had exceptional responses to therapy has revealed molecular changes in the patients’ tumors that may explain some of the exceptional responses.

Researchers are developing a new class of cancer drugs called radiopharmaceuticals, which deliver radiation therapy directly and specifically to cancer cells. This Cancer Currents story explores the research on these emerging therapies.

FDA has recently approved two blood tests, known as liquid biopsies, that gather genetic information to help inform treatment decisions for people with cancer. This Cancer Currents story explores how the tests are used and who can get the tests.

Cancer cells with a genetic feature called microsatellite instability-high (MSI-high) depend on the enzyme WRN to survive. A new NCI study explains why and reinforces the idea of targeting WRN as a treatment approach for MSI-high cancer.

Efforts to contain the opioid epidemic may be preventing people with cancer from receiving appropriate prescriptions for opioids to manage their cancer pain, according to a new study of oncologists’ opioid prescribing patterns.

The gene-editing tool CRISPR is changing the way scientists study cancer, and may change how cancer is treated. This in-depth blog post describes how this revolutionary technology is being used to better understand cancer and create new treatments.

FDA’s approval of pembrolizumab (Keytruda) to treat people whose cancer is tumor mutational burden-high highlights the importance of genomic testing to guide treatment, including for children with cancer, according to NCI Director Dr. Ned Sharpless.

Patients with acute graft-versus-host disease (GVHD) that does not respond to steroid therapy are more likely to respond to the drug ruxolitinib (Jakafi) than other available treatments, results from a large clinical trial show.

NCI is developing the capability to produce cellular therapies, like CAR T cells, to be tested in cancer clinical trials at multiple hospital sites. Few laboratories and centers have the capability to make CAR T cells, which has limited the ability to test them more broadly.

An experimental drug may help prevent the chemotherapy drug doxorubicin from harming the heart and does so without interfering with doxorubicin’s ability to kill cancer cells, according to a study in mice.

In people with blood cancers, the health of their gut microbiome appears to affect the risk of dying after receiving an allogeneic hematopoietic stem cell transplant, according to an NCI-funded study conducted at four hospitals across the globe.

A novel approach to analyzing tumors may bring precision cancer medicine to more patients. A study showed the approach, which analyzes gene expression using tumor RNA, could accurately predict whether patients had responded to treatment with targeted therapy or immunotherapy.

Bone loss associated with chemotherapy appears to be induced by cells that stop dividing but do not die, a recent study in mice suggests. The researchers tested drugs that could block signals from these senescent cells and reverse bone loss in mice.

Some experts believe that proton therapy is safer than traditional radiation, but research has been limited. A new observational study compared the safety and effectiveness of proton therapy and traditional radiation in adults with advanced cancer.

In people with cancer, the abscopal effect occurs when radiation—or another type of localized therapy—shrinks a targeted tumor but also causes untreated tumors in the body to shrink. Researchers are trying to better understand this phenomenon and take advantage of it to improve cancer therapy.

An experimental drug, AMG 510, that targets mutated forms of the KRAS protein completely shrank tumors in cancer mouse models and data from a small clinical trial show that it appears to be active against different cancer types with a KRAS mutation.

Researchers have engineered an oncolytic virus to kill cancer cells and boost the immune response against tumors. In a new study, the virus provided T cells around tumors with a hormone they need for their own cell-killing functions.

FDA has approved entrectinib (Rozlytrek) for the treatment of children and adults with tumors bearing an NTRK gene fusion. The approval also covers adults with non-small cell lung cancer harboring a ROS1 gene fusion.

A new NCI-supported study showed that altering cancer cell metabolism by feeding mice a diet very low in the nutrient methionine improved the ability of chemotherapy and radiation therapy to shrink tumors.

An NCI-funded clinical trial is testing the immunotherapy drug nivolumab (Opdivo) in people who have advanced cancer and an autoimmune disease, such as rheumatoid arthritis, lupus, or multiple sclerosis, who are often excluded from such trials.

Researchers have identified a protein called CD24 that may be a new target for cancer immunotherapy. The protein is a ‘don’t eat me’ signal that prevents immune cells called macrophages from engulfing and eating cells.

Injecting cells undergoing necroptosis, a form of cell death, into tumors in mice kickstarted an immune response against the tumors, researchers have found. When combined with immunotherapy, the treatment was effective at eliminating tumors in mice.

Researchers have identified proteins that may play a central role in transforming T cells from powerful destroyers to depleted bystanders that can no longer harm cancer cells. The findings could lead to strategies for boosting cancer immunotherapies.

Did you know that NCI supports clinical trials of new treatments for pet dogs with cancer? Learn more about NCI’s comparative oncology studies and how they may also help people with cancer.

Researchers have discovered a potential way to turn on one of the most commonly silenced tumor-suppressor proteins in cancer, called PTEN. They also found a natural compound, I3C, that in lab studies could flip the on switch.

New findings from a clinical trial suggest that a single dose of radiation therapy may control painful bone metastases as effectively as multiple lower doses of radiation therapy.

The expanding use of cancer immunotherapy has revealed a variety of side effects associated with this treatment approach. Researchers are now trying to better understand how and why these side effects occur and develop strategies for better managing them.

The investigational immunotherapy drug bintrafusp alfa (also called M7824), a bifunctional fusion protein, shrank the tumors of some patients with advanced HPV-related cancers, according to results from a phase 1 clinical trial.

A new study provides insight into how cancer immunotherapy works and suggests ways to enhance the treatment’s effectiveness. The NCI-led study, published in Science, examined the effect of high potassium levels on T cells.

Pain is a common and much-feared symptom among people with cancer and long-term survivors. As more people survive cancer for longer periods, there is a renewed interest in developing new, nonaddictive approaches for managing their chronic pain.

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Herbs for Cancer Treatment pp 53–150 Cite as

Types of Cancer

  • Bhupendra Koul 2  
  • First Online: 08 January 2020

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1 Citations

Cancer is an extremely fatal disease, not only in developed but also in developing countries as it affects six million lives each year worldwide. According to the American Cancer Society each year 0.5% of US population is diagnosed with cancer. Annually, billions of dollars are invested on cancer research, yet the success rates are not satisfactory. Cancer cells arise due to the imbalance in the body functions and they invade and infect the normal cells. Cancer is not one disease; rather it is a group of various diseases. The present chapter includes explicit information on cancer types such as cancer of blood, lungs, colon and rectum, prostate, skin, breast, uterus, thyroid, lymphatic system, etc. Cancers are being treated by various methods which include chemotherapy, precision medicine, radiation therapy, surgery, stem cell transplant, hormone therapy, immune therapy, and targeted therapy. In some cases, cancer can be treated by single method, but mostly several methods are used to cure the disease. Moreover, the treatment specifically depends upon the stage and type of cancer. In chemotherapy and radiotherapy, survival rates are usually very low because of their undesirable side effects on human health. On the contrary, herbal therapy alone and in combination with the routine cancer treatment regimes has shown some promising potential, as it is free from side effects.

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  • Published: 04 December 2020

RNA sequencing: new technologies and applications in cancer research

  • Mingye Hong 1   na1 ,
  • Shuang Tao 2   na1 ,
  • Ling Zhang 3 ,
  • Li-Ting Diao 2 ,
  • Xuanmei Huang 1 ,
  • Shaohui Huang 1 ,
  • Shu-Juan Xie 2 ,
  • Zhen-Dong Xiao 2 &
  • Hua Zhang   ORCID: 1  

Journal of Hematology & Oncology volume  13 , Article number:  166 ( 2020 ) Cite this article

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Over the past few decades, RNA sequencing has significantly progressed, becoming a paramount approach for transcriptome profiling. The revolution from bulk RNA sequencing to single-molecular, single-cell and spatial transcriptome approaches has enabled increasingly accurate, individual cell resolution incorporated with spatial information. Cancer, a major malignant and heterogeneous lethal disease, remains an enormous challenge in medical research and clinical treatment. As a vital tool, RNA sequencing has been utilized in many aspects of cancer research and therapy, including biomarker discovery and characterization of cancer heterogeneity and evolution, drug resistance, cancer immune microenvironment and immunotherapy, cancer neoantigens and so on. In this review, the latest studies on RNA sequencing technology and their applications in cancer are summarized, and future challenges and opportunities for RNA sequencing technology in cancer applications are discussed.

Cancer remains one of the major malignant diseases that endangers human life and health and comprises complex biological systems that require accurate and comprehensive analysis. Since the first appearance of high-throughput sequencing in 2005 [ 1 ], it has become possible to understand life activities at the molecular level and to conduct detailed research to elucidate the genome and transcriptome. As an essential part of high-throughput sequencing, RNA sequencing (RNA-seq), especially single-cell RNA sequencing (scRNA-seq), provides biological information on a single tumor cell, analyzes the determinants of intratumor expression heterogeneity and identifies the molecular basis of formation of many oncological diseases [ 2 , 3 ]. Thus, RNA sequencing offers invaluable insights for cancer research and treatment. With the advent of the era of precision medicine, RNA sequencing will be widely used for research on many different types of cancer. This review summarizes the history of the development of RNA sequencing and focuses on the latest studies of RNA sequencing technology in cancer applications, especially single-cell RNA sequencing and spatial transcriptome sequencing. In addition, we provide a general introduction to the current bioinformatics analysis tools used for RNA sequencing and discuss future challenges and opportunities for RNA sequencing technology in cancer applications.

The development of RNA sequencing technologies

It was not until 1953 when Watson and Crick proposed the double-helix structure did people truly realize at the molecular level that the essence of life is the result of gene interactions [ 4 ]. The continuous development of RNA sequencing has ushered transcriptome analysis into a new era, with higher efficiency and lower cost. The timeline of RNA sequencing technologies is shown in Fig.  1 .

figure 1

The development timeline of RNA sequencing technologies

The first-generation sequencing technology is also called Sanger sequencing. The chain termination method was initiated by Sanger in 1977, followed by the chemical degradation method developed by Maxam and Gilbert [ 5 , 6 ]. The same year, Sanger determined the 5368 bp genome of phage φX174, which is the first DNA genome sequenced [ 7 ]. The DNA microarray has aided significant progress in many fields since it was first introduced. However, microarrays require prior knowledge of gene sequences and are unable to identify novel gene expression [ 8 ]. After the first high-throughput sequencing platform appeared in 2005 [ 1 ], multiple next-generation sequencing platforms followed (Table 1 , Figs.  2 , 3 ). The accuracy and reproducibility among different platforms depended on several factors, including the inherent features of the platform and the corresponding analysis pipelines [ 9 , 10 ]. Pyrosequencing that was no longer supported after 2016, developed by 454 Life Sciences, used a “sequencing by synthesis” method [ 1 , 11 , 12 , 13 ]. The ion torrent sequencing platform is also based on the “sequencing by synthesis” method, which outperforms pyrosequencing with respect to sensitivity. SOLiD (Sequencing by Oligonucleotide Ligation and Detection) exhibits high accuracy, as each base is sequenced twice, but the read length is short [ 11 , 12 , 13 ]. DNBS (DNA nanoball sequencing) enables large collection of DNA nanoballs for simultaneous sequencing. Illumina-based sequencing technology represents a “reversible terminator sequencing” method. High-throughput sequencing has the advantage of fast speed, low sequencing cost and high accuracy, otherwise known as next-generation sequencing (NGS). Compared to microarray, it can detect unknown gene expression sequences but is time intensive [ 14 ].

figure 2

RNA extraction and template preparation before RNA-sequencing. RNA was extracted from tissues, and after fragmentation, fragmented DNA molecules were converted into cDNA by reverse transcription then amplified by emulsion PCR or bridge PCR to prepare sequencing library

figure 3

Three kinds of sequencing methods. These methods contain sequencing by synthesis, sequencing by reversible terminator and sequencing by ligation. And their different mechanisms are shown in detail

In addition to NGS, there is third-generation sequencing, which allows for long-read sequencing of individual RNA molecules [ 15 ]. Single-molecule RNA sequencing enables the generation of full-length cDNA transcripts without clonal amplification or transcript assembly. Thus, third-generation sequencing is free from the shortcomings generated by PCR amplification and read mapping. It can greatly reduce the false positive rate of splice sites and capture the diversity of transcript isoforms [ 15 ]. Single-molecule sequencing platforms comprise Pacific Biosciences (PacBio) single-molecule real-time (SMRT) sequencing [ 16 ], Helicos single-molecule fluorescent sequencing [ 17 ] and Oxford Nanopore Technologies (ONT) nanopore sequencing [ 18 ]. Furthermore, RNA-seq recently evolved from bulk sequencing to single-cell sequencing. Single-cell RNA sequencing was first published in 2009 to profile the transcriptome at single-cell resolution [ 19 ]. Drop-Seq and InDrop were initially reported in 2015 by analyzing mouse retina cell and embryonic stem cell transcriptomes, identifying novel cell types. Sci-RNA-seq, single-cell combinatorial indexing RNA sequencing, was developed in 2017, and SPLiT-seq (split-pool ligation-based transcriptome sequencing) was first reported in 2018. Both approaches use a combinatorial indexing strategy in which attached RNAs are labeled with barcodes that indicate their cellular origin [ 20 , 21 ].

Though single-cell data enable single-cell transcriptomics, it may lose spatial information during single-cell isolation. To solve this problem, spatial transcriptomics has emerged. Spatial transcriptomics employs unique positional barcodes to visualize RNA distributions in RNA sequencing of tissue sections and was first published in 2016 [ 22 ]. Slide-seq, reported in 2019, uses DNA barcode beads with specific positional information [ 23 ]. Geo-seq was introduced in 2017 and integrated scRNA-seq with laser capture microdissection (LCM), which can isolate individual cells [ 24 ]. In situ sequencing refers to targeted sequencing of RNA fragments in morphologically preserved tissues or cells without RNA extraction, including in situ cDNA synthesis by padlock probes or stably cross-linked cDNA amplicons in fluorescent in situ RNA sequencing (FISSEQ) and in situ amplification by rolling-circle amplification (RCA) [ 25 , 26 ]. Furthermore, various new technologies based on RNA-seq have been developed for specific applications. For example, a type of targeted RNA sequencing, CaptureSeq, employs biotinylated oligonucleotide probes and results in the enrichment of certain transcripts to identify gene fusion [ 27 , 28 ].

Computational analysis of RNA sequencing data

Computational analysis tools for RNA sequencing have dramatically increased during the past decade. The choice of a particular tool should be based on the purpose and accuracy of application [ 29 , 30 , 31 ]. A general RNA sequencing data analysis process involves the quality control of raw data, read alignment and transcript assembly, expression quantification and differential expression analysis (Fig.  4 ).

figure 4

Bioinformatics tools commonly used in RNA-seq data analysis. These tools are primarily used in the four main processes of RNA-seq data analysis, including quality control, read alignment and transcript assembly, expression quantification and differential expression analysis

The first step of data analysis is to assess and clean the raw sequencing data, which is usually provided in the form of FASTQ files [ 32 ]. Quality control visually reflects the quality of the sequencing and purposefully discards low-quality reads, eliminates poor-quality bases and trims adaptor sequences [ 31 ]. Common tools include FASTQ [ 33 ], NGSQC [ 34 ], RNA-SeQC [ 35 ], Trimmomatic [ 36 ], PRINSEQ [ 37 ] and Soapnuke [ 38 ].

The next step is to map the clean reads to either a genome or a transcriptome. There are some mapping tools available, including Tophat2 [ 39 ], HISAT2 [ 40 ], STAR [ 41 ], BWA [ 42 ] and Bowtie [ 43 ]. After alignment, another type of software, such as Cufflinks [ 44 ], StringTie [ 45 ], Trinity [ 46 ], SOAPdenovoTrans [ 47 ] and Trans-AByS [ 48 ] can be used to assemble transcripts from short-reads. When the transcript model is established, its expression can be quantified at the gene, transcript and exon levels. Commonly used software for gene-level quantification includes FeatureCount [ 49 ] and HTSeq-count [ 50 ]. Transcript level quantitative software includes Cufflinks [ 44 ], eXpress [ 51 ] and RSEM [ 52 ]. DEXSeq is a software for exon level quantification [ 53 ]. In addition, there are some alignment-free quantification tools such as Kallisto [ 54 ], Sailfish [ 55 ] and Salmon [ 56 ], which have the advantage of marked computational resource saving. After normalizing, an expression matrix is generated, and statistical methods can be used to identify differentially expressed genes. DESeq2 [ 57 ] and edgeR [ 58 ] are commonly used to perform this task.

Applications of RNA-sequencing in cancer research

Genomic data, such as RNA-seq, have become widely available due to the popularity of high-throughput sequencing technology [ 59 ]. As an important part of next-generation sequencing, RNA sequencing has made great contributions in various fields, especially cancer research, including studies on differential gene expression analysis and cancer biomarkers, cancer heterogeneity and evolution, cancer drug resistance, the cancer microenvironment and immunotherapy, neoantigens, etc. (Fig.  5 ).

figure 5

Applications of RNA-seq in differential expression analysis and cancer biomarkers, cancer heterogeneity and drug resistance, cancer immune microenvironment, immunotherapy and neoantigen. a Differential expression analysis by RNA sequencing can identify potential biomarkers, including fusion transcript, lncRNA, miRNA and circRNA. b The heterogeneity and drug resistance of cancer cells identified by RNA-seq. c Novel molecular signature, regulatory protein and unknown subtypes in cancer infiltrating immune cells and potential resistance effector in immunotherapy can be identified by RNA-seq; d Neoantigen profiling by RNA-seq and TCR modification targeted neoantigens

Differential gene expression analysis and cancer biomarkers

Differential gene expression analysis is one of the most common applications of RNA sequencing [ 60 ]. Samples from different backgrounds (different species, tissues and periods) can be used for RNA sequencing to identify differentially expressed genes, revealing their function and potential molecular mechanisms [ 61 ]. More importantly, differential gene expression analysis facilitates the discovery of potential cancer biomarkers [ 62 ]. Many studies have shown that gene fusions are closely related to oncogenesis and are appreciated as both ideal cancer biomarkers and therapeutic targets [ 63 ]. Gene fusions in clinical samples are primarily detected by RNA-CaptureSeq. Compared to whole transcriptome sequencing, RNA-CaptureSeq has significantly higher sequencing depth [ 27 , 64 , 65 ]. It has been reported that the NUP98-PHF23 fusion gene is likely to be a novel therapeutic target in acute myeloid leukemia (AML) [ 66 ]. Recently, a variety of recurrent gene fusions, including ESR1-CCDC170, SEC16A-NOTCH1, SEC22B-NOTCH2 and ESR1-YAP1, have been identified in breast cancer, indicating that recurrent gene fusion is one of the key drivers for cancer [ 67 ]. Several novel configurations of  BRAF, NTRK3 and RET gene fusions have been identified in colorectal cancer [ 68 ]. These fusions may promote the development of malignancy and provide new targets for personalized treatment [ 68 ]. In addition, some special genomic factors have been discovered as biomarkers by RNA sequencing, including miRNA, lncRNA and circRNA, which are widely present in various types of cancer [ 69 , 70 , 71 ]. A recent example is circRNA_0001178 and circRNA_0000826, which are biomarkers of colorectal cancer metastasis to the liver [ 72 ]. By applying both RNA sequencing and small RNA sequencing, a study on pancreatic cancer identified differential expression of simple repetitive sequences (SSRs) and demonstrated that the frequency of SSR motifs changed dramatically, which is expected to become a tumor biomarker [ 73 ]. In addition to nucleic acid biomarkers, RNA-seq combined with immunohistochemistry and western blot has also identified certain proteins as cancer biomarkers, such as nuclear COX2 (cyclooxygenase2) in combination with HER2 (human epidermal growth factor receptor type 2), which may serve as potential biomarkers for the diagnosis and prognosis of colorectal cancer [ 74 ]. Similar examples identified using RNA-seq profiling analysis include ISG15 (Interferon-stimulated gene 15) in nasopharyngeal carcinoma [ 75 ] and DMGDH (dimethylglycine dehydrogenase) in hepatocellular carcinoma [ 76 ]. Data-mining analysis of RNA sequencing data and other clinical data has identified that the isoforms of peroxiredoxins also can be expected the prognostic biomarkers for predicting overall survival and relapse-free survival in breast cancer [ 77 ]. Increasing differentially expressed genes are being identified by RNA sequencing, and new potential cancer biomarkers are being continuously discovered (Table 2 ). However, sufficient clinical practice is needed to confirm the diagnostic and predictive applications of these biomarkers in cancer.

RNA-seq could detect early mutations as well as high molecular risk mutations, thus can discover novel cancer biomarkers and potential therapeutic targets, monitoring of diseases and guiding targeted therapy during early treatment decisions. Tumor mutation burden (TMB) is considered as a potential biomarker for immune checkpoint therapy and prognosis [ 78 , 79 ]. RNA-seq can be used to explore the application value of TMB in diffuse glioma [ 78 ]. Through the RNA-seq, MET exon 14 mutation and isocitrate dehydrogenase 1 (IDH1) mutation were identified as new potential therapeutic targets in lung adenocarcinoma and chondrosarcoma patients, respectively [ 80 , 81 ]. Several studies have shown that RNA sequencing can effectively improve the detection rate on the basis of DNA sequencing, provide more comprehensive detection results and achieve a better curative effect for targeted therapy [ 82 ]. In addition, it has been proved that IDH mutation is a good prognostic marker for glioma by RNA-seq [ 83 ]. Targeted therapy is also considered to enhance or replace cytotoxic chemotherapy regimen in cancer including AML [ 84 , 85 , 86 ].

ScRNA-seq also has some new discoveries in diagnosis. For example, scRNA-seq data can be used to infer copy number variations (CNV) and to distinguish malignant from non-malignant cells. The infer CNV algorithm, which was used in the study of glioblastoma, uses averaging relative expression levels over large genomic regions to infer chromosome copy number variation [ 87 ]. Similar examples include head and neck cancer [ 88 ] and human oligodendroglioma [ 89 ]. It is reported that RNA sequence of tumor-educated blood platelets (TEPs) can also become a blood-based cancer diagnosis method [ 90 ]. It should be noted that the lack of detailed functional implications of the identified RNAs in platelets in the field of platelet RNA research is also an urgent problem to be solved [ 91 ].

Cancer heterogeneity and evolution

Heterogeneity has always existed during the transformation of normal cells to cancer cells. The continuous accumulation of heterogeneity may reflect the evolution of cancer [ 109 ]. Early RNA sequencing detected all RNA transcripts in a given tissue or cell group, ignoring differences in individual cells. Transcriptome profiling of single-cell RNA sequencing solves this problem by providing single-cell resolution of the transcriptome [ 3 ]. In melanoma, single-cell RNA-seq was used to analyze 4645 tumor cells from 19 patients, including cancer cells, immune cells, mesenchymal cells and endothelial cells. Transcriptomic data from different single cells revealed that heterogeneity of cells within the same cancer is associated with cell cycle, spatial background and drug resistance [ 110 ]. A recent single-cell RNA-seq study of 49 samples of metastatic lung cancer revealed changes in plasticity induced by non-small cell lung cancer treatment, providing new directions for clinical treatment [ 111 ]. Single-cell RNA sequencing also integrates a variety of information in a single cancer cell, deciphering the secrets of cancer heterogeneity and evolution [ 112 ]. Compared with scRNA-seq, another emerging technology spatial transcriptome sequencing incorporates information on the spatial location of cells. In prostate cancer, using spatial transcriptomics technology, the transcriptome of nearly 6750 tissue regions was analyzed, revealing the whole-tissue gene expression heterogeneity of the entire multifocal prostate cancer and accurately describing the range of cancer foci [ 113 ]. In a study of breast cancer tissues, the results of spatial transcriptome sequencing revealed that gene expression among different regions was surprisingly highly heterogeneous [ 22 ]. In recent years, single-nucleus RNA sequencing (snRNA-seq) has also received extensive attention due to its solving the problem that single-cell RNA sequencing cannot be applied to frozen specimens and cannot obtain all cell types in a given tissue [ 114 , 115 ]. The emerging technology of RNA sequencing will contribute to research on cancer heterogeneity and evolution.

Cancer drug resistance

Drug resistance is a main reason leading to cancer treatment failure. However, the molecular mechanisms underlying drug resistance are still poorly understood [ 116 ]. RNA sequencing became a vital tool for revealing the mechanisms of cancer drug resistance. In breast cancer, single-cell RNA sequencing identified a tumor-infiltrating immunosuppressive immature myeloid cell that leads to drug resistance [ 117 ]. Another study identified a new COX7B gene related to platinum resistance and a surrogate marker CD63 in cancer cells by single-cell RNA-seq [ 118 ]. RNA sequencing has also demonstrated that cancer cells that wake up from a dormant state produce large amounts of BORIS (brother of the regulator of imprinted sites), which can regulate the expression of survival genes in drug-resistant neuroblastoma cells [ 119 ]. Identifying special molecules that mediate these processes could help us understand the occurrence of drug resistance. Single-cell transcriptomics can be used to study different modes of chemoresistance in tumor cells and has shown that pre-existing drug-resistant cells can be selected through higher phenotypic intratumoral heterogeneity, while phenotypic homogeneous cells use other mechanisms to trans-differentiate under drug-selection [ 120 ]. In one study of pancreatic ductal adenocarcinoma, human pancreatic cancer (PANC-1) cells and gemcitabine-resistant PANC-1 cell lines were compared by RNA sequencing, and two circRNAs were identified as both novel biomarkers and potential therapeutic targets for gemcitabine resistant patients [ 121 ]. RNA-seq has also conducted in-depth research on the drug resistance of hematological malignancies. Through RNA-seq, it has been found that non-coding RNAs and fusion genes play an important role in mediating the drug resistance of hematological malignancies [ 122 ]. A good example is to compare the circRNA expression profile of the drug-resistant acute myeloid leukemia cell with its parent cell, and determine the circRNAs involved in drug resistance [ 123 ]. Similarly, the novel MEF2D-BCL9 fusion transcript identified by RNA-seq was found to increase HDAC9 (histone deacetylase 9) expression and to enhance the resistance to dexamethasone in acute lymphocytic leukemia (ALL) [ 124 ]. Leukemia stem cells (LSCs), a rare cell population assumed to be responsible for relapse, is crucial to improve the prognosis of patients [ 125 , 126 ]. RNA-seq analysis showed that LSCs have a unique lncRNA signature with functional relevance and therapeutic potential, providing an explanation for chemotherapy resistance and disease recurrence [ 127 ].

The cancer microenvironment and immunotherapy

The immune system plays a critical role in the cancer microenvironment, affecting several stages of cancer development, including tumorigenesis, progression and metastasis, through tumor-infiltrating lymphocytes (TILs) [ 128 ]. TILs and their interactions with malignant cells and stromal cells make up the cancer immune microenvironment. Due to the heterogeneity of cancer, it is difficult to define the exact pro- or anti-cancer function of certain immune cells. Cancer heterogeneity also causes the varied clinical efficacy observed in patients treated with immunotherapies due to different responses of different subclones [ 129 ]. Transcriptomic profiling by RNA-seq, in particular scRNA-seq, provides comprehensive information on cellular activity and interactions among cells in the tumor microenvironment (TME). ScRNA-seq enables genomic and molecular profiling of high quantity and quality individual immune cells and assessment of cellular heterogeneity to depict the immune system spectrum in the cancer microenvironment [ 130 , 131 , 132 ]. ScRNA-seq data demonstrated that compared to normal tissues, cancer tissues exhibited significantly higher heterogeneity in the immune microenvironment, and a continuity in T cell activation resulting from polyclonal T cells and heterogeneous antigen-presenting cells has been identified [ 133 ].

ScRNA-seq of tumor-infiltrating T cells in metastatic melanoma identified transcription factor NFATC1 (nuclear factor of activated T cells 1) as a potential molecular signature of T cell exhaustion programs and revealed the depletion of low-exhaustion T cells in expanded clones of T cells [ 110 ]. Combining scRNA-seq with assembled T cell receptor (TCR) sequences, 11 T cell subsets, such as CD8  +  T cells and CD8  +  FOXP3  +  regulatory-like cells, and their genomic signatures, were identified in hepatocellular carcinoma (HCC), providing valuable insights for understanding the immune landscape of infiltrating T cells in HCC [ 134 ]. Cancer infiltrating T cells also play an anti-tumor role through impairment of an autophagy protein, LC3 (microtubule-associated protein 1A/1B-light chain 3, often short for LC3)-associated phagocytosis (LAP), demonstrating the role of autophagy in oncogenesis and suppression revealed by scRNA-seq [ 135 ]. In addition to solid tumors, scRNA-seq of acute myeloid leukemia patients detected diverse immunomodulatory genes that suppress T cell function [ 136 ]. By CSOmap, a computational tool for scRNA-seq, the CCL4-CCR8 directed interaction between Tregs and Texs, as well as reduced proliferation of Texs, was characterized [ 137 ]. Notably, findings also revealed that tumor-infiltrating T cells exhibited more interactions among themselves than with T cells from peripheral blood and different interactions between tumors and T cells, indicating a varied response to immunotherapy and a potential trend for immune escape [ 137 ].

In the cancer immune microenvironment, neutrophils, in addition to T cells, are also key components of cancer progression and cancer drug resistance [ 138 , 139 , 140 , 141 ]. Through scRNA-seq of murine sarcomas and certain human cancers, neutrophils with CSF3R (colony stimulating factor 3 receptor) expression were found to be a part of type 1 antitumor immunity associated with unconventional CD4 − CD8 − αβ T cells (UTCαβ) in anti-cancer immunity, indicating better prognosis [ 142 ]. ScRNA-seq of metastatic breast cancer and CD45 cells from primary cancer identified neutrophils as pro- and anti-tumorigenic or metastatic, in which pro-tumorigenic and metastatic neutrophils are induced by IL11 expressing cancer subclones, resulting in polyclonal metastasis [ 143 ]. This observation also provides new insight into anti-cancer immunotherapy by targeting neutrophils [ 143 ]. With scRNA-seq of CD4 and CD8 T cells, several crucial pathways with anti-cancer function were revealed [ 144 ].

The balance between immune reaction and immune tolerance is the basis of immune homeostasis, which is also involved in anti-cancer immunity and oncogenesis. scRNA-seq of monocytes and dendritic cells (DCs) separated from a single lymph node melanoma metastasis revealed a conserved homeostatic module regulated by suppressor-of-cytokine-2 (SOCS2) protein and IFNγ [ 145 ]. SOCS2 serves an essential regulatory role in anti-tumor immunity and T cell priming through DCs. This highly conserved homeostatic program establishes a connection between autoimmune prevention and immune surveillance in cancer [ 145 ].

Immunotherapies, especially immune checkpoint blockade (ICB), has opened a new chapter for anti-cancer therapy with remarkable responses from targeting programmed death 1 (PD1), programmed death-ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) [ 146 , 147 , 148 ]. However, only a few patients benefit from ICB, and severe side effects were observed [ 149 , 150 ]. Obviously, various unknown determinants are correlated with the outcome of immunotherapies in addition to well-known factors such as PD1/PD-L1/CTLA-4 expression and mismatch repair deficiency [ 151 , 152 , 153 , 154 , 155 ]. Therefore, it is paramount to identify potential effectors for ICB efficacy. By analyzing RNA-seq data from melanoma patients who underwent anti-PD1 and anti-CTLA4 treatment, a potential ICB resistance effector SERPINB9 (a member of the serine protease inhibitor (serpin) family) and the connection between cytotoxic T lymphocytes (CTL) infiltration level and ICB response were characterized [ 156 ]. A sub-population cells with immunotherapy persistence have been identified by scRNA-seq and were found to have stem cell-like states with the expression of stem cell antigen-1 (Sca-1) and Snai1 [ 157 ].

Another immunotherapy, myeloid-targeted immunotherapy, is based on the complexity of tumor-infiltrating myeloid cells, including DCs and tumor-associated macrophages (TAMs) revealed by scRNA-seq [ 158 ]. Through scRNA-seq of immune cells from colorectal cancer patients, C1QC  +  and SPP1  +  TAMs, two subsets of TAMs, were identified, and the mechanism of myeloid-targeted immunotherapy, such as anti-CSF1R (colony stimulating factor 1 receptor) and CD40 agonist, was revealed [ 159 ]. Intracellular staining and sequencing (INs-seq), a novel technology integrating scRNA-seq and intracellular protein activity measurements, revealed novel Arg1  +  Trem2  +  regulatory myeloid (Mreg) cells and demonstrated that depletion of Trem2 led to deduction of exhausted CD8 T cells with increased NK and cytotoxic T cells and cancer suppression by reducing accumulation of intratumoral Mreg cells [ 160 ].

Cancer neoantigens

Neoantigens, human leukocyte antigen (HLA)-bound peptides derived from cancer-specific somatic mutations or gene fusions during tumor growth, are another crucial regulator of the clinical response to immunotherapy [ 161 ]. Higher intratumor neoantigen heterogeneity and clonal neoantigen burden increases sensitivity to ICB and contributes to better clinical outcome in patients with melanoma and advanced non-small cell lung cancer [ 162 ]. This kind of antigen is an optimal target for anti-cancer immunotherapy, enhancing neoantigen-specific T cell activity, and a vaccine targeting personal neoantigens for melanoma patients has been developed [ 163 , 164 ]. Given all these promising features of personalized medicine targeting neoantigens in tumors, massive parallel profiling of tumor neoantigen burden is necessary for improving clinical efficacy and a deeper understanding of the neoantigen landscape. An RNA-seq-based transcriptomic approach is an efficient tool for neoantigen profiling in many studies. It was revealed that homology of neoantigen and somatic-mutation induced pathogens are important in response prediction in anti-CTLA4 treated melanoma [ 165 ]. In addition to melanoma, other studies found reduced neoantigen load in triple-negative and HER2 breast cancers [ 166 ], diverse neoantigen abundance in non-small-cell lung cancer patients with different treatment strategies [ 167 ], a decreased ratio of neoantigen expression to predicted neoantigens in recurrent glioma due to immune selection pressure [ 168 ], a negative correlation between neoantigen abundance and clinical outcome in selected solid tumors [ 169 ] and different neoantigen landscapes in immune filtration and T cell dysfunction based on histology in salivary gland carcinoma (SGC) patients [ 170 ].

A neoantigen prediction program, Neopepsee, based on RNA-seq data and somatic mutation, can be utilized to detect potential neoantigens for personal vaccine development with reduced false-positive rate compared to binding affinity prediction [ 171 ]. ScanNeo is another prediction computational pipeline based on RNA-seq that aims to identify insertion and deletion derived neoantigens, which was validated in prostate cancer [ 172 ], and ASNEO, which identifies personal-specific alternative splicing derived neoantigens [ 173 ]. Several neoantigens have been identified to be related to cancer prognosis and might be potential targets of immunotherapies, such as the TP53 neoantigen for HCC patients [ 174 ]. For the anti-neoantigen immunotherapy in cancer, a new strategy involving neoantigen-specific TCRs modification has been proposed, and scRNA-seq has been applied to isolate neoantigen-specific TCRs for further clinical application [ 175 ].

Conclusions and perspectives

High-throughput RNA-seq technology has been a major tool to explore the transcriptome. The rapid development of RNA-seq technology not only saves time and cost but also sheds light on many new research fields. However, there are still limitations of RNA-seq technology that need to be improved.

For short-read length RNA-seq technologies, bias and imperfections are primarily generated in sequencing library preparation and short read assembly. It is difficult for these methods to correctly identify multiple isoforms from a certain gene. To overcome the disadvantage of short read length, improved read coverage and sequencing depth is required. Long-read length RNA-seq technologies avoid shortcomings in template amplification, reduce the false positive rate in splice junction detection and enable the identification of unannotated longer transcripts, overcoming the common limitations of short-read sequencing [ 176 , 177 ]. However, this method suffers from the drawback of reduced throughput, higher cost and higher sequencing error rate, especially insertion-deletion errors. To reduce random errors, PacBio circular consensus-sequencing (CCS) was developed to increase sequencing depth by rereading molecules several times. However, it also reduces the identification rate of unique isoforms. In addition, the sensitivity of long-read sequencing for identification of differentially expressed genes is lower compared to short-read sequencing [ 178 , 179 , 180 ]. Thus, hybridization of long-read and short-read sequencing has been reported to yield a more comprehensive and accurate analysis [ 181 ].

Improvements in the throughput of RNA sequencing technology have resulted in billions of sequencing reads, bringing great challenges to the computational process, such as data storage, transmission, quality control and data analysis, including read mapping, transcript assembly and read normalization. Therefore, it is important for bioinformatics to keep pace with the continuous developments of RNA-seq technologies. Notably, bias could be produced due to differences in read data handling, necessitating the improvement of current bioinformatics pipelines.

RNA-seq measures gene expression by the read counts, which always containing missing values, thus results in information loss of specific gene and negative impact on downstream analysis. To overcome this problem, missing data need to be imputed and analyzed by several methods, such as optimal clustering with missing values [ 182 ]. For scRNA-seq, the proportion of genes with zero or low expression varies across cells due to biological or technical bias. For example, batch effects can come from cells captured and sequenced in different conditions [ 183 ]. Imputation methods, such as SAVER, MAGIC and kNN-smoothing, are recommended for scRNA-seq [ 184 ]. Another method named batch effects correction with unknown subtypes for scRNA-seq data (BUSseq) utilizes Bayesian hierarchical model and can also be used to correct batch effects and missing data [ 185 ].

Combination of data from multi-omics sequencing can undoubtedly expand the application of RNA-seq. For example, Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) was developed by utilizing hyperactive Tn5 transposase to identify open chromatin region and transcriptional factor (TF) binding sites [ 186 ]. The integration of ATAC-seq and RNA-seq enables the reveal of TF-targeted genes and their transcripts [ 187 , 188 ]. Chromatin conformation capture analysis (3C) technology and its several derivatives including circular chromosome conformation capture (4C), carbon copy chromosome conformation capture (5C), ChIP-Loop, Hi-C and capture Hi-C were developed and improved to detect chromatin structure as well as unknown interacting regions [ 189 , 190 , 191 ]. It has been reported that combined analysis of RNA-seq and chromatin structure can detect structure variation-related differentially expressed genes [ 192 , 193 , 194 ].

Epitranscriptomics is a crucial part of gene expression, and methylation of adenosine at the N6 position (m6A) is the most abundant [ 195 ]. Traditional RNA-seq needs reverse transcription before sequencing and thus easily loses the information of transcriptome complexity. This shortcoming can be overcome by directly sequencing native RNA molecules using methods such as nanopore sequencing. Transcript modifications could be inferred from the current signal as the modified RNA molecules passing nanopore cause a characteristic temporary current blockade, which enables the detection of diverse modifications such as m6A or 5-methylcytosine (m5C) [ 196 , 197 , 198 ].

ScRNA-seq is a powerful technology to facilitate further exploration in cancer research and also has been employed in the detection of cancer stem cell subpopulation, metabolic switch in cancer-draining lymph nodes and therapy-induced adaption of cancer cells [ 111 , 199 , 200 ]. Combined with cell sorting or ligand-receptor interaction, scRNA-seq was utilized in cellular interaction, cell spatial organization as well as molecular crosstalk characterization [ 137 , 201 , 202 ]. Coupling of parallel CRISPR (clustered regularly interspaced short palindromic repeats)-pooled screen, scRNA-seq enables the simultaneous analysis of genomic perturbation and transcriptional activity to detect heterogeneous cell type as well as crucial factors of complexity regulatory mechanism [ 203 , 204 , 205 ]. ScNT-seq, single-cell metabolically labeled new RNA tagging sequencing, brings RNA-seq into time resolution by identifying RNAs transcribed at different stage [ 206 ]. Utilizing SNP-based demultiplexing of scRNA-seq data, MIX-Seq was developed to study cancer cell reaction to pharmacologic treatment [ 207 ]. Another technology, snRNA-seq, is invaluable for detecting cellular heterogeneity of cancer and has been employed to identification of a sub-population of adipocytes regulating cancer genesis [ 208 ].

Taken together, RNA-seq has been applied in an impressively wide range of cancer research. All applications in cancer research rely on the boost of advanced RNA-seq technologies, especially the combination of scRNA-seq and spatial transcriptomics as well as data from multi-omics, which will bring RNA-seq technologies into single-cell resolution and tissue-level transcriptomics, providing new insight into cancer diagnosis, treatment and prevention.

Availability of data and materials

Not applicable.


  • RNA sequencing

Single-cell RNA sequencing

Sequencing by Oligonucleotide Ligation and Detection

DNA nanoball sequencing

Next-generation sequencing

Pacific Biosciences single-molecule real-time

Oxford Nanopore Technologies

Single-cell combinatorial indexing RNA sequencing

Split-pool ligation-based transcriptome sequencing

Laser capture microdissection

Fluorescent in situ RNA sequencing

Rolling-circle amplification

Acute myeloid leukemia

Simple repetitive sequences


Human epidermal growth factor receptor type 2

Interferon-stimulated gene 15

Dimethylglycine dehydrogenase

Tumor mutation burden

Isocitrate dehydrogenase

Copy number variations

Tumor-educated blood platelets

Single-nucleus RNA-sequencing

Brother of the regulator of imprinted sites

Acute lymphocytic leukemia

Leukemia stem cells

Tumor-infiltrating lymphocytes

Tumor microenvironment

Nuclear factor of activated T cells 1

T cell receptor

Hepatocellular carcinoma

Microtubule-associated protein 1A/1B-light chain 3-associated phagocytosis

Colony stimulating factor 3 receptor

Unconventional CD4 − CD8 −  αβ T cells

Dendritic cells

Suppressor-of-cytokine-2 protein

Immune checkpoint blockade

Targeting programmed death 1

Programmed death-ligand 1

Cytotoxic T-lymphocyte-associated protein 4

Cytotoxic T lymphocytes

Stem cell antigen-1

Tumor-associated macrophages

Colony stimulating factor 1 receptor

Intracellular staining and sequencing

Regulatory myeloid cells

Human leukocyte antigen

Salivary gland carcinoma

Circular consensus-sequencing

Assay for Transposase-Accessible Chromatin using sequencing

Transcriptional factor

Chromatin conformation capture analysis

Circular chromosome conformation capture

Carbon copy chromosome conformation capture

N6 position


Clustered regularly interspaced short palindromic repeats

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This work was supported by the National Natural Science Foundation of China (81300398 and 81974436); Key-Area Research and Development Program of Guangdong Province (2020B1111030005); The Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2019ZT08Y485); Key Program of Marine Economy Development (Six Marine Industries) Special Foundation of Department of Natural Resources of Guangdong Province (GDNRC [2020]070); The Fundamental Research Funds for the Central Universities (19ykzd06) and the Opening Fund of Guangdong Key Laboratory of Marine Materia Medica (LMM2020-4).

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Mingye Hong and Shuang Tao have contributed equally to this work

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Institute of Laboratory Medicine, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, School of Medical Technology, Guangdong Medical University, Dongguan, 523808, China

Mingye Hong, Xuanmei Huang, Shaohui Huang & Hua Zhang

Biotherapy Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China

Shuang Tao, Li-Ting Diao, Shu-Juan Xie & Zhen-Dong Xiao

Health Science Center, The University of Texas, Houston, 77030, USA

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M.H. and S.T. wrote and edited this manuscript and created figures and tables. L.Z., L-T. D., X-M. H., S-H. H., S-J. X., Z-D. X and H.Z. reviewed and revised the manuscript. Z-D. X and H.Z. provided direction and guidance throughout the preparation of the manuscript. All authors read and approved the final manuscript.

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cancer and its types research paper

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Stress and cancer: mechanisms, significance and future directions

  • Anabel Eckerling   ORCID: 1 ,
  • Itay Ricon-Becker   ORCID: 1 ,
  • Liat Sorski   ORCID: 1 ,
  • Elad Sandbank   ORCID: 1 &
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Nature Reviews Cancer volume  21 ,  pages 767–785 ( 2021 ) Cite this article

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The notion that stress and cancer are interlinked has dominated lay discourse for decades. More recent animal studies indicate that stress can substantially facilitate cancer progression through modulating most hallmarks of cancer, and molecular and systemic mechanisms mediating these effects have been elucidated. However, available clinical evidence for such deleterious effects is inconsistent, as epidemiological and stress-reducing clinical interventions have yielded mixed effects on cancer mortality. In this Review, we describe and discuss specific mediating mechanisms identified by preclinical research, and parallel clinical findings. We explain the discrepancy between preclinical and clinical outcomes, through pointing to experimental strengths leveraged by animal studies and through discussing methodological and conceptual obstacles that prevent clinical studies from reflecting the impacts of stress. We suggest approaches to circumvent such obstacles, based on targeting critical phases of cancer progression that are more likely to be stress-sensitive; pharmacologically limiting adrenergic–inflammatory responses triggered by medical procedures; and focusing on more vulnerable populations, employing personalized pharmacological and psychosocial approaches. Recent clinical trials support our hypothesis that psychological and/or pharmacological inhibition of excess adrenergic and/or inflammatory stress signalling, especially alongside cancer treatments, could save lives.

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cancer and its types research paper

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The authors thank I. Ben-Ami Bartal for fruitful discussions and for critiques of the manuscript, and are grateful to the Emerson Collective, the Israel Cancer Research Fund and the Israel Science Foundation for their support.

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(SNS). Part of the autonomic nervous system that is involuntarily activated by stressors (for example, a dangerous or stressful situation) and orchestrates the ‘fight or flight’ response through adrenergic innervation of the adrenal medulla and of various organs (for example, the heart) through systemic and local release of adrenaline and noradrenaline, respectively.

A neuroendocrine system with negative feedback that increases systemic glucocorticoid (for example, cortisol) levels in various circumstances, including stressful conditions. Hypothalamic corticotropin-releasing hormone (CRH) elevates systemic release of adrenocorticotropic hormone (ACTH) from the anterior pituitary, which triggers the release of glucocorticoids from the adrenal cortex, which also trigger negative feedback through the pituitary and hypothalamic levels.

Endogenous host-derived molecules that are released by damaged and dying cells. They are recognized by pattern recognition receptors on numerous cells, which lead to migration and activation of various immune cells and consequent innate and adaptive immune responses.

A family of molecules that are characterized by a catechol and an amine group in their chemical structure, and function as neurotransmitters and hormones within the body. These include dopamine, noradrenaline and adrenaline, all of which are synthesized from the amino acid tyrosine.

An experimental stress paradigm, where the animal is placed in a confined space (a tube-shaped apparatus perforated for air exchange) that prevents free movement but does not press or induce pain to the animal. Such restraint can last minutes to hours and can be repeated daily for several weeks as a chronic stress paradigm.

Refers to experimental methods for ablation of sympathetic nerves (also called sympathectomy), by either surgical cut of sympathetic nerve fibres or chemical ablation (for example, using 6-hydroxydopamine).

A class of cell surface G-protein-coupled receptors that bind different prostaglandins and are expressed on various cell types, including immune cells; for example, prostaglandin E 2 binds to the prostaglandin E 2 receptor 1–4 subtypes.

(T H 1 cell). A CD4 + T cell that participates in the pro-inflammatory type 1 or cellular immune response against intracellular pathogens and malignant cells. Naive T cells are differentiated into the type 1 phenotype following exposure to interleukin-12 (IL-12), and are known for the secretion of interferon-γ (IFNγ), which is also involved in the effector functions of cytotoxic T cells.

(T H 2 cell). A CD4 + T cell that participates in type 2 or humoral immune response against extracellular pathogens (for example, helminths) and allergens. Naive T cells are differentiated into a type 2 phenotype following exposure to interleukin-4 (IL-4), and are known for the secretion of IL-4, IL-13 and IL-5, and promotion of the production of antibodies.

A class of drugs with antagonistic activity towards β-adrenergic receptors (β-ARs). The drugs vary in specificity to the different β-ARs (β 1 -AR, β 2 -AR and β 3 -AR) and are classified as selective or non-selective to a certain receptor subtype.

An experimental stress paradigm in which the home cage of rodents is placed in a lit room in a 45° tilted position, starting before the onset of the animals’ dark period, resulting in reduced available floor space and disruption of the dark–light cycle.

An experimental stress paradigm where a weight is attached to the tail of rodents (usually rats, up to 2.5% of total body weight), which are then placed in a room temperature water tank for few minutes, followed by a rest period. This swim–rest cycle is usually repeated several times.

An experimental stress paradigm in which a midline abdominal incision is performed under anaesthesia, and often the small intestine is externalized and left hydrated in a soaked gauze pad for 30 min. The intestine is then internalized and the abdomen is sutured.

An experimental stress paradigm where an intruder rodent (a non-cage-mate animal) is introduced into a home cage populated with several stable cage-mates. The intruder is usually attacked by the residents cage-mates and/or displays submissive behaviour.

An experimental stress paradigm that is executed in an apparatus containing an electrified grid floor, in which the animal is exposed to electric shocks of varying intensity and duration. The paradigm can be acute or chronic, and is also used for fear-conditioning.

The ratio of the probability of events in a treatment group to the probability of events in a control group.

The tendency to publish a study based on its results (positive rather than negative findings or significant rather than non-significant findings). Existence of this bias can be statistically assessed in meta-analyses by Egger’s linear regression test.

A non-profit organization (maintaining no conflict of interests), which, among other activities, publishes methodologies and guidelines to produce high-quality systematic reviews and meta-analyses.

(CpG-C). A synthetic oligodeoxynucleotide (ODN) that functions as a Toll-like receptor 9 (TLR9) agonist and induces a physiological host-dependent activation of the immune system.

(GLA-SE). A synthetic agonist of Toll-like receptor 4 (TLR4). For administration, GLA is dissolved in an oil–water stable emulsion that serves as an adjuvant delivery system.

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Eckerling, A., Ricon-Becker, I., Sorski, L. et al. Stress and cancer: mechanisms, significance and future directions. Nat Rev Cancer 21 , 767–785 (2021).

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cancer and its types research paper

  • Open access
  • Published: 02 January 2024

The current status of clinical trials on cancer and age disparities among the most common cancer trial participants

  • Shuang Zhao 1 ,
  • Miao Miao 1 ,
  • Qingqing Wang 1 ,
  • Haijuan Zhao 1 ,
  • Han Yang 1 &
  • Xin Wang 1  

BMC Cancer volume  24 , Article number:  30 ( 2024 ) Cite this article

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Metrics details

To illustrate the status of all cancer clinical trials and characterize clinical trial enrollment disparities in the most common cancer.

Clinical trial data were extracted from website. All searched clinical trials were included in the current status analysis of clinical trials on cancer. Among all the clinical trials, only trials addressing single disease sites of breast, prostate, colorectal, or lung (BPCRL) cancer were included in the age disparities analysis. The difference in median age (DMA) between the trial participant median age and the population-based disease-site-specific median age was calculated for each trial.

A total of 7747 clinical trials were included in the current status analysis of clinical trials on cancer. The number of registered trials had been increasing from 2008 to 2021 (AAPC = 50.60, 95% CI 36.60, 66.00, P  < 0.05). Of the 7747 trials, 1.50% (116) of the studies were clinical trials for the elderly aged 60 years or older. 322 trials were included in the age disparities analysis. For all trials, the median DMA was − 8.15 years ( P 25 , P 75 , − 10.83 to − 2.98 years, P  < 0.001). The median DMA were − 9.55 years ( P 25 , P 75 , − 11.63 to − 7.11 years), − 7.10 years ( P 25 , P 75 , − 9.80 to − 5.70 years), − 9.75 years ( P 25 , P 75 , − 11.93 to − 7.35 years), 3.50 years ( P 25 , P 75 , 0.60 to 4.55 years), respectively, for breast cancer, colorectal cancer, lung cancer and prostate cancer.

The numbers of registered clinical trials show an upward trend. Age disparities between trial participants and diagnosed disease population are present in BPCRL cancer trials and appear to be increasing over time. Equitable participation in clinical trials on the basis of age is crucial for advancing medical knowledge and evaluating the safety and efficacy of new treatments that are generalizable to aging populations.

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Population aging has substantially contributed to the increasing number of new cancer cases worldwide. In 2018, assessment of global cancer burden showed that 2.3 million new cases occurred in adults aged 80 or older worldwide (13% of all cancer cases). Projections suggest that by 2050, an estimated 6.9 million new cancers will be diagnosed in this age group (20.5% of all cancer cases) [ 1 ]. The elderly are the fastest-growing segments of the world's population. Despite shouldering a disproportionate burden of disease and consumption of prescription drugs, the elderly are vastly underrepresented in clinical trials. In particular, older adults over the age of 75 are chronically underrepresented in cancer clinical trials [ 2 ]. Ensuring adequate population representation in clinical trials is critical to generate sufficient data on the safety and efficacy of interventions in all age groups. Unfortunately, many drug trials do not include older adults because of concerns about the safety and efficacy of drugs in the older population [ 3 , 4 , 5 ]. Randomised controlled trials (RCTs) often establish standard clinical practices. The applicability of the trial results could be compromised by the under-representation of elderly patients [ 6 , 7 , 8 ].

The lack of diverse participants in trials is an ethical and scientific issue, because it could limit the application of future therapies. Increasing representation of diverse participants into clinical trials is essential for assessing drug effectiveness and safety. Clinical trials that do not adequately represent the diversity of the population, particularly those most affected by certain diseases, may lead to the results not being generalizable.

Accordingly, it is necessary to analyse the current state of cancer clinical trials and explore the gap between the age of enrollment in clinical trials and the true median age of cancer patients at diagnosis. A study examined age disparities among modern oncologic clinical trials for breast, prostate, colorectal, and lung cancer (the 4 most common disease sites), characterizing the differences between trial participants and the population by disease site. In this analysis, 302 randomised clinical trials before 2017 were included. And this study found trial participants were significantly younger than the population by disease site [ 9 ]. Further research is needed to determine whether the age gap improves over time. Therefore, in this study, we first illustrated the status of all clinical trials on cancer and then characterized the clinical trial enrollment disparities in the most common cancer, focusing on improving enrollment to be more representative of the trial population.

Materials and methods

Data acquisition and processing.

In our study, we used the dataset, which is one of the most comprehensive clinical research databases globally. The database was frequently selected by other studies to characterize study populations and trends in clinical care and research [ 10 , 11 ].

The trial status in registry included active, not recruiting, by invitation, recruiting, suspended, terminated, withdrawn, completed, and unknown. We included completed clinical trials in our analysis. Oncology clinical trials up to September 13, 2022 were searched on the website. During the search process, the following advanced search parameters were utilized: other terms: “cancer”; study type: “all studies”; status: “completed”; study phase: “early phase 1, phase 1, phase 2, phase 3, phase 4” and study results: “with results”. A total of 7747 clinical trials were yielded. All 7747 clinical trials were used to analyse the current status of cancer clinical trials. Among all the clinical trials, the phase 3 trials targeting therapeutic intervention were screened to analyse age disparities among cancer trial participants (Fig.  1 ). Due to all trial-level data were publicly available, informed consent was not obtained.

figure 1

Flowchart of clinical trial screening, eligibility, and inclusion

Data evaluation

To provide information on the development and status quo of cancer treatment in the clinical setting, study entries were sorted in ascending order according to the date on which the study record first posted on Furthermore, common standardized study parameters were evaluated, such as study type, study phases, ages eligible for study or molecular profile restriction [ 12 ]. To provide a more in-depth description for the dataset, we also evaluated the study entries and categorized them according to additional parameters such as recruiting country, sponsoring country, or industry sponsor.

Trials targeting a single disease site of breast, prostate, colorectal or lung cancer were eligible for the analysis of age disparities among participants in the BPCRL cancer trials. Clinical trials that did not provide the median age of participants were not included in the study analysis. To avoid information bias, the two individuals performed clinical trial screening and parameter identification independently. Finally, the two individuals compared decisions and resolved disagreements through discussion.

Statistical analyses

Statistical analyses were performed at the trial level (each trial is an observation). Trial factors were summarized by frequency and percentage. To analyse the trend of the number of trial registrations over time, we calculated the annual percentage of change (APC) and average annual percent change (AAPC) using the method of joinpoint regression analysis (Joinpoint version, February, 2022). Joinpoint regression fits a piecewise linear regression model, which is a special case of linear spline [ 13 ]. Early phase 1, phase 1, and phase 2 were combined as early phase, and phases 3 and 4 were combined as advanced phase [ 14 ]. The median age of each trial was compared with the median age of the relevant disease site according to the NCI Surveillance, Epidemiology, and End Results (SEER) database [ 9 , 15 ]. The median age of SEER by disease site was also matched to the trial enrollment time [ 9 ]. For each trial, the difference in median age (DMA) was calculated as the trial median age minus the population median age. Independent-samples Mann–Whitney U and Kruskal–Wallis tests were used to compare the DMA of different groups. P  < 0.05 was set as significant and all P values were 2-sided. Analyses were performed using R 3.6.1.

The current status analysis of clinical trials on cancer

From 2008 to September 2022, a total of 7 747 clinical trials were registered on 1 551 146 participants were enrolled in the 7 747 clinical trials. Of these, only 17 185 participants were 60 years of age or older, and 1.50% (116) of the studies were clinical trials for the elderly aged 60 years or older. Of the 7747 trials designated with a phase, 79.02% were early phase 1, phase 1, and phase 2. The advanced phase included 1625 clinical trials, accounting for 20.98% (Fig.  2 ).

figure 2

Detailed trial phases of the 7747 clinical trials on cancer

For the trend analysis of the number of trial registrations over time, the APC of the number of trial registrations showed two periods, both of which showed continuous upward trends (2008–2010: APC = 761.02, P  < 0.001; 2010–2021: APC = 9.70, P  = 0.001). The number of registered trials had been increasing from 2008 (3 clinical trials) to 2021 (760 clinical trials) (AAPC = 50.60, 95% CI 36.60, 66.00, P  < 0.05). (Fig.  3 ).

figure 3

The annual percent change (APC) and average annual percent change (AAPC) of the number of clinical trials during 2008–2021. The solid lines represent the fitted values of the joinpoint regression. The annual percentage change (APC) P value corresponds to testing whether the APC is different from zero. Average annual percent change (AAPC) P value corresponds to testing whether the AAPC is different from zero

Age disparities analysis among BPCRL cancer trial participants

Three hundred twenty-two trials were included in the age disparities analysis (Fig.  1 ); these trials collectively enrolled a total of 293 267 patients. For all trials, the median DMA was − 8.15 years ( P 25 , P 75 , − 10.83 to − 2.98 years, P  < 0.001; Table 1 ). The median DMA were − 9.55 years ( P 25 , P 75 , − 11.63 to − 7.11 years), − 7.10 years ( P 25 , P 75 , − 9.80 to − 5.70 years), − 9.75 years ( P 25 , P 75 , − 11.93 to − 7.35 years), 3.50 years ( P 25 , P 75 , 0.60 to 4.55 years), respectively, for breast cancer, colorectal cancer, lung cancer and prostate cancer.

There was no significant age differences between industry-funded trials, with a median DMA of − 8.32 years for industry-sponsored trials compared with − 7.00 years for non-industry-sponsored trials ( P  = 0.169; Table 1 ). In addition, there was no significant difference for age disparity between international multicenter trials and non-international multicenter trials ( P  = 0.113). Trials with age restriction enrollment criterion (42 of 322 trials; 13.04%) or restricting molecular profile criterion (171 of 322 trials; 53.11%) were associated with larger DMA (Table 1 ). Among therapy trials, those that conducted with targeted therapy were associated with a larger DMA, followed by chemotherapy and surgery.

Similarly, sensitivity analyses of US-only trials showed a median DMA of -6.35 years ( P 25 , P 75 , − 10.00 to − 0.33 years, P  = 0.010). The median DMA were − 10.30 years ( P 25 , P 75 , − 12.00 to − 1.50 years), − 6.60 years ( P 25 , P 75 , − 10.10 to − 5.90 years), − 6.70 years ( P 25 , P 75 , − 7.00 to − 5.00 years), 2.00 years ( P 25 , P 75 , -1.65 to 4.60 years), respectively, for breast cancer, colorectal cancer, lung cancer and prostate cancer (Table 2 ).

Our research illustrated the status of all cancer clinical trials and analysed the enrollment disparities among the most common cancer trial participants. We found an upward trend in the number of registered clinical trials. And the age gap between trial participants and the diagnosed disease population was present in BPCRL cancer trials.

From 2008 to 2022, there was an upward trend in the number of clinical trial registrations, which could be due to an increase in the number of clinical trials conducted or an increasing number of journals, government funding agencies, universities, and hospitals required trials to be registered. Among all clinical trials, relatively few had been conducted in the elderly population only, which was consistent with other reported data result [ 16 ]. The Annual Report on the Progress of Clinical Trials for New Drug Registration in China showed that the trend in the number and proportion of clinical trials in the elderly population remained consistent when comparing data from the last three years. Clinical trials conducted only in the elderly population accounted for no more than 0.2% of all clinical trials in all years [ 16 ]. This reflected the enrollment disparities in clinical trials.

Enrollment disparities in clinical trials have been recognized for many years. While two-thirds of cancer patients are over 65 years old, only about 25% of cancer trial participants reach that age [ 17 ]. Ethan et al. reported on the factors associated with age disparities among cancer clinical trial participants. They found that trial participants were significantly younger than the population by disease site and the age gap was greater for industry-funded trial participants, which was consistent with our findings [ 9 ]. We also found that age disparities between trial participants and the diagnosed disease population appeared to be widening following the BPCRL cancer trial reported by Ethan et al. in 2017. This is true not only in the field of cancer, but also in other disease areas, such as cardiovascular diseases (CVDs). CVDs are the leading cause of death globally, causing an estimated 17.9 million deaths each year [ 18 ]; and 65% of those diagnosed are over 65 years of age. Despite these statistics, only 42.5% of participants in clinical trials for cardiovascular disease are over the age of 65, and 12.3% are over the age of 75 [ 19 ]. The participation of these older populations in clinical trials is also low in research on Alzheimer’s disease, arthritis, epilepsy and many other diseases [ 20 ].

The key reasons that the age of clinical trial participants is lower than the age of diagnosis in the population are typically due to a combination of challenges and barriers faced by both sponsors and older adults. These barriers include comorbidities and polypharmacy. Both may affect the attainment of trial safety or efficacy endpoints. Operational challenges include difficulties in recruitment or retention patients, obtaining informed consent, financial constraints, communication issues (e.g., hearing difficulties and visual impairment), and physical inflexibility, which may limit transportation options to clinical sites. This barrier has led to limitations in age-based exclusion criteria and a preference for including younger participant with a low risk of adverse outcomes in clinical trials [ 21 ]. In addition, older adults are more likely to experience adverse effects due to changes in pharmacokinetics (PK) and pharmacodynamics (PD), possible comorbidities, and concomitant therapies that may interact with investigational drugs. These adverse effects may be more severe or less tolerate and have more serious consequences compare with younger participants [ 22 ].

The inclusion of elderly patients in clinical trials is undoubtedly important. Decentralized clinical trials (DCT) could reduce barriers and facilitate appropriate participation of older participants. By conducting clinical trials remotely, participants could participate in the research from their own comfortable homes. A recent survey reported that 74% of seniors preferred this option to a clinic visit [ 23 ]. The Food and Drug Administration (FDA) also issues DCT draft guidance and encourages sponsors to broaden cancer clinical trial eligibility criteria to enhance the generalizability of trial results and develop strategies for recruiting patients that reflect the intended population [ 24 , 25 ].

This study had several strengths. Our study described the status of cancer clinical trials up to 2022 and analysed the age disparities among BPCRL cancer trial participants, which provides evidence to support the inclusion of more elderly patients in clinical trials. Further, in the data acquisition and evaluation, two individuals independently performed trial screening and parameter identification to avoid the information bias. Meanwhile, our research had several limitations. First, selection bias could arise from not including clinical trials registered in other registries, such as International Standard Randomised Controlled Trial Number (ISRCTN) registry, European Union Drug Regulating Authorities Clinical Trials (EudraCT) Database. Further validations in different clinical trial registries are necessary to increase the strength of medical evidence. Secondly, we only considered completed clinical trials with results, and thus we were not able to analyse whether the age disparities improved in more recently initiated trials. It is required to conduct further studies from a wider range of data sources. Third, the disease sites included in this study represent the most common cancer, and these sites may not be representative of the entire cancer trial. Additionally, the median age of the population by disease site was based on US SEER data. The majority of included trials (230 of 322; 71.43%) were multinational, and 48 trials (14.91%) were enrolled in a country other than the United States. Therefore, our study was limited by the extrapolation of US demographics to other countries. Nevertheless, we performed sensitivity analyses of US-only trials and obtained the same conclusions. Moreover, the median age, as an indicator, provides limited information on the exact proportion of elderly patients in a certain study. The median was chosen because of the heterogeneity in the age distribution reported by each trial and was compared as a common indicator for each trial, which was also consistent with the previous studies [ 9 ]. Lastly, SEER captures patients with relevant diagnoses, not just those treated; the median age of SEER may also disproportionately exclude older cancer patients due to a number of possible factors. Consequently, this analysis may underestimate the extent of age differences among trial participants.


Our study demonstrated future cancer clinical trials need to include a wider range of patients on the basis of age. Equitable participation in clinical trials contributed to advancing medical knowledge and evaluating the safety and efficacy of new treatments that are generalizable to aging populations.

Availability of data and materials

The data that support the findings of this study are openly available in the Database at


Breast, prostate, colorectal, or lung

Difference in median age

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Average annual percent change

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International Standard Randomised Controlled Trial Number

European Union Drug Regulating Authorities Clinical Trials

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This study was supported by grants from National High Level Hospital Clinical Research Funding (BJ-2023–208) and the capital health research and development of special project (2022-2Z-4055).

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Shuang Zhao, Miao Miao, Qingqing Wang, Haijuan Zhao, Han Yang & Xin Wang

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Z.S. and M.M. conceived the idea for the study Z.S. and Y.H. performed trial screening and parameter identification. Z.S. performed the data analyses. Z.S., M.M., W.Q.Q., Z.H.J. and Y.H. interpreted the results of the data analyses. Z.S. wrote the manuscript. Z.S., M.M., W.Q.Q., Z.H.J., Y.H. and W.X. made critical revision of the manuscript. All authors read and approved the final manuscript.

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Zhao, S., Miao, M., Wang, Q. et al. The current status of clinical trials on cancer and age disparities among the most common cancer trial participants. BMC Cancer 24 , 30 (2024).

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What to Know About the Science of Reading

An effort to overhaul how children learn to read, known as the science of reading movement, is sweeping the country. Here’s where it stands.

A third-grader sits on a bean bag chair with a book at Good Springs Elementary School in Nevada.

By Dana Goldstein

During an era of intense politicization of education, there has been rare bipartisan consensus on one issue: the need to overhaul how children learn to read.

Over the past five years, more than 40 states have passed laws that aim to revamp literacy instruction. And on Wednesday, Gov. Kathy Hochul of New York announced a proposal to require schools to use “scientifically proven” reading curriculums by 2025, and to invest $10 million in retraining teachers.

The effort sweeping the country is known as the science of reading movement. Here’s what to know about it, and where it stands.

What is the science of reading?

There is no single definition of the science of reading. But the key idea is that teaching strategies should align with a wide body of cognitive research on how young children learn to read. That research , amassed over decades, shows that in addition to a broad vocabulary, children need to understand phonics, or the relationship between letters and the sounds of spoken language.

While some children seem to pick up reading naturally, research shows that many need explicit, carefully sequenced instruction in the letter combinations and spelling patterns that form the English language. Without explicit teaching, some students — including children who are read to every day in homes filled with books — will not become proficient and confident readers.

Proponents of the science of reading, including leading brain researchers and parents of children with dyslexia, have pushed hard to change instruction over the past decade.

How does it differ from other teaching strategies?

The science of reading represents a significant shift for the nation’s school system. For the past two decades, a school of thought known as balanced literacy dominated how colleges prepared future teachers for the classroom and how those teachers taught.

The scholarly roots of balanced literacy are in the education and English departments of universities. Brain researchers, examining reading with M.R.I. machines, worked in other departments. As is common in academia, the two groups rarely shared ideas or collaborated.

Balanced literacy emphasizes the importance of surrounding children with books and allowing them to spend quiet time reading literature that interests them. It includes some phonics, but the instruction is less structured. Letter-sound relationships may be introduced as they come up in stories or through classroom games, instead of in a sequence designed to build foundational skills.

Balanced literacy curriculums have often relied on teaching strategies that have been discredited , such as coaching children to guess difficult words by using pictures and the first letter, instead of sounding out the entire word from beginning to end. Educators and researchers have said that technique leaves children ill-prepared to tackle more difficult texts, without illustrations, as they get older.

Critics have also argued that balanced literacy shortchanges vocabulary and knowledge building, by allowing teachers and students too much freedom to select reading material, instead of guiding them to challenging texts that build knowledge in various subjects such as social studies, science and the arts.

Three major American leaders of the balanced literacy movement — Lucy Calkins, Irene Fountas and Gay Su Pinnell — have rolled out changes to their reading curriculums under political and financial pressure from the science of reading movement.

But they have also warned about what they see as risks of too much phonics instruction and have pointed out that past phonics-centric reform efforts, such as the Reading First program under President George W. Bush, yielded limited results.

Is the science of reading movement working?

In some big ways, yes. States are passing laws requiring instructional change, and curriculums are being revised to include more foundational phonics and richer reading material. Many schools are also retraining teachers.

Universities are shifting, too, by distancing themselves from balanced literacy theorists and promising to change the way they prepare future educators.

New York has been a focal point. Under Mayor Eric Adams of New York City, who is dyslexic, the nation’s largest school district has required schools to abandon balanced literacy and select from one of several reading curriculums better aligned to cognitive research.

But there are many challenges to the overhaul, some of which could affect whether the efforts achieve a key goal: raising students’ reading test scores.

Teachers need to not only be retrained — an investment of time and money — but also be brought along with the efforts so they feel invested in the work. Another big cost is that classroom libraries need to be replaced in many elementary schools, as there are few books within them designed to build children’s phonics skills. Outdated curriculums need to replaced.

Across the country, initial research on these efforts has been hopeful, but limited in scope.

Dana Goldstein covers education and families for The Times.  More about Dana Goldstein


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