Cancer:  Susceptibility, Diagnosis and Individualized Therapies

Some general resources are listed below while those on specific types of cancer are included in the specific categories listed under 'Cancer' in the left navigation panel and at the top of this page.

The Epigenomics of Cancer.

Jones, PA & Baylin, SB, 2007. Cell 128(4):683-692.

Aberrant gene function and altered patterns of gene expression are key features of cancer. Growing evidence shows that acquired epigenetic abnormalities participate with genetic alterations to cause this dysregulation. Here, we review recent advances in understanding how epigenetic alterations participate in the earliest stages of neoplasia, including stem/precursor cell contributions, and discuss the growing implications of these advances for strategies to control cancer.

Journal Link | PMID

Comment:

A good introduction to basic concepts of epigenomics plus a clear illustration of its roles in cancer development and the possible resistance to chemotherapy.  This paper is very well illustrated for self-study and for use as a source of lecture material.

Translating cancer genomics into clinical oncology

Ramaswamy, S, 2004. N. Engl. J. Med. 350(18):1814-1816.

Intro. Paragraph

Clinical medicine is in the midst of a revolution that is being driven by an increasing understanding of the human genome and advances in molecular biotechnology. This revolution promises to transform clinical practice from population-based risk assessment and empirical treatment to a predictive, individualized model based on the molecular classification of disease and targeted therapy. The expectation is, of course, that personalized approaches to clinical care will increase the efficacy of treatment while decreasing its toxicity and cost. Nowhere is this transformation more apparent than in oncology. Cancer is a complex disease. Our current taxonomy of cancers, which is based mostly on histopathology, includes more than 200 distinct entities arising from diverse types of cells. In addition, tumors have somatic mutations and epigenetic changes, many of which are specific to individual neoplasms; these molecular abnormalities influence the expression of genes that control a tumor's growth, invasiveness, metastatic potential, and responsiveness or resistance to chemotherapy. The genetic complexity of cancer probably explains the clinical diversity of histologically similar tumors, but it has been difficult to study this diversity with traditional methods, which are best suited to investigating one gene at a time. The advent of DNA microarray technology, however, permits the quantitative measurement of complex, multigene expression patterns in cancer diversity with traditional methods, which are best suited to investigating one gene at a time. The advent of DNA microarray technology, however, permits the quantitative measurement of complex, multigene expression patterns in cancer.

Journal Link | PMID

Comments

A good overview of the application of microarrays to clinical oncology.  The brevity of this article limits its coverage of clinical and experimental detail relative to that of the more recent Quackenbush review (below), but this may be of benefit for those wanting a very quick overview.  Its single illustration is very good.

Microarray Analysis and Tumor Classification

Quackenbush, J., 2006, N. Engl. J. Med. 354(23):2463-2472

Intro Paragraphs

DNA microarray analysis was first described in the mid-1990s as a means to probe the expression of thousands of genes simultaneously and was quickly adopted by the research community for the study of a wide range of biologic processes. Most of the early studies had a simple and powerful design: to compare two biologic classes in order to identify the differential expression of the genes in them — genes with potential relevance to a wide range of biologic processes, such as the progression of cancer, the causes of asthma, heart disease, and neuropsychiatric disorders and the analysis of factors associated with infertility.

Soon after microarrays were introduced, many researchers realized that the technique could be used to find new subclasses in disease states and identify biologic markers (biomarkers) associated with disease and that even the expression patterns of the genes could be used to distinguish subclasses of disease. This realization resulted in a proliferation of searches for patterns of expression that could be used to classify types of tumors and predict the outcome and response to chemotherapy. An example is the Netherlands breast-cancer study, which sought to distinguish between patients who had the same stage of disease but a different response to treatment and a different overall outcome. The study was motivated by the observation that the best clinical predictors of metastasis, including lymph-node status and histologic grade, did not adequately predict clinical outcome, with the result that many patients receive chemotherapy or hormonal therapy regardless of whether they need this additional treatment. The study searched for gene-expression signatures that would indicate which patients would benefit from adjuvant chemotherapy. By profiling tumors of young patients who had received only surgical treatment and searching for correlations with clinical outcome, a signature of poor prognosis consisting of 70 genes was identified and was predictive of a short interval to distant metastasis in patients with tumors that were lymph-node–negative. The analysis showed that microarray-based signatures could outperform clinically based predictions of outcome in identifying patients who would benefit most from adjuvant therapy. These initial results led to a more extensive study that showed that the 70-gene classification profile was a more powerful predictor of disease outcome in young patients with breast cancer than were standard systems based on clinical and histologic criteria.

Journal Link | PMID

Comments

This is an excellent, well-illustrated review of the use of gene expression microarrays in the diagnosis and staging of cancers.  In addition to figures which give a good technical overview there is a glossary table defining the various types of 'omics' and the differences in their applications to clinical situations.

Cancer Treatment Gets Personal: An Interactive Poster

Ramaswamy, S & the editorial staff, 2006. Science 312(5777):1162a

Intro. Paragraph

Science, with the assistance of scientific advisor Sridhar  of the Center for Cancer Research, Massachusetts General Hospital, has created a poster to accompany its 26 May 2006 special issue on the new science of cancer. The poster is designed to help readers understand the conceptual framework of the new, patient-centered model of cancer care that is emerging, and how it might ultimately be implemented. This interactive online version of the poster includes additional topics and Web links not covered in the print version, and is available free to all site visitors. (A PDF version of the print poster is available to individual and institutional subscribers to Science.)  There is also a link to related cancer resources printed in Science.

Journal Link

Comments

This poster is a good summary in itself but the interactive website is a very good at illustrating the application of genomics to the clinical aspects of cancer.  The points covered include the technical aspects of cancer diagnosis and staging plus the clinical approaches, both 'classical' and molecular.  The interactive nature of the website allows the viewer to follow one's interest in proceeding through the material.  In addition to the references included in the interactive video the link to cancer resources provide a wealth of documentation and details for the presented material.

Cancer pharmacogenomics: international trends

Yamayoshi, Y et al., 2005. Int. J. Clin. Oncol. 10(1):5-13.

Abstract:

It is well known that inter-individual variability exists in the responses to many drugs. Many nongenetic factors, such as age, sex, diet, and organ function, are known to affect the therapeutic effects of drugs. However, recent advances in pharmacogenomics have revealed that genetic polymorphisms also significantly influence both the efficacy and the toxicity of drugs. Mutations in the genes encoding drug-metabolizing enzymes, transporters, and target molecules may alter their expression, activity, or affinity to drugs, thereby influencing the drugs' pharmacokinetics and pharmacodynamics. Numerous studies have reported on the correlations between therapeutic outcomes and polymorphisms in drug-metabolizing enzymes, transporters, target molecules, and DNA repair enzymes. These pharmacogenomic discoveries are expected to be useful for the individualization and optimization of cancer chemotherapy.

Journal Link | PMID

Comment:

A nice, concise review of 13 polymorphic genes vary in patient populations resulting in significantly different drug responses.  These generally common examples illustrate the theoretical and practical value of understanding and applying individualized medical therapies.