Return of research results from pharmacogenomic versus disease susceptibility studies: what’s drugs got to do with it?

One of the most controversial ethical issues in genomics research is the return of individual research results to research subjects. As new technologies, including whole-genome sequencing, provide an increased opportunity for researchers to find clinically relevant research results, the questions related to if, when and how individual research results should be returned become more central to the ethical conduct of genomic research. In the absence of federal guidance on this issue, many groups and individuals have developed recommendations and suggestions to address these questions. Most of these recommendations have focused on the return of individual results from disease susceptibility studies. However, in addition to predicting the development of disease, genomic research also includes predicting an individual’s response to drugs, especially the risk of developing adverse events. This article evaluates and compares the return of individual research results from disease susceptibility studies versus pharmacogenomic studies

Genomics Education for the Public: Perspectives of Genomic Researchers and ELSI Advisors

Aims: For more than two decades genomic education of the public has been a significant challenge. As genomic information becomes integrated into daily life and routine clinical care, the need for public education is even more critical. We conducted a pilot study to learn how genomic researchers and ethical, legal, and social implications advisors who were affiliated with large-scale genomic variation studies have approached the issue of educating the public about genomics. Methods/Results: Semi-structured telephone interviews were conducted with researchers and advisors associated with the SNP/HAPMAP studies and the Cancer Genome Atlas Study. Respondents described approach(es) associated with educating the public about their study. Interviews were audio-recorded, transcribed, coded, and analyzed by team review. Although few respondents described formal educational efforts, most provided recommendations for what should/could be done, emphasizing the need for an overarching entity(s) to take responsibility to lead the effort to educate the public. Opposing views were described related to: who this should be; the overall goal of the educational effort; and the educational approach. Four thematic areas emerged: What is the rationale for educating the public about genomics?; Who is the audience?; Who should be responsible for this effort?; and What should the content be? Policy issues associated with these themes included the need to agree on philosophical framework(s) to guide the rationale, content, and target audiences for education programs; coordinate previous/ongoing educational efforts; and develop a centralized knowledge base. Suggestions for next steps are presented. Conclusion: A complex interplay of philosophical, professional, and cultural issues can create impediments to genomic education of the public. Many challenges, however, can be addressed by agreement on a guiding philosophical framework(s) and identification of a responsible entity(s) to provide leadership for developing/overseeing an appropriate infrastructure to support the coordination/integration/sharing and evaluation of educational efforts, benefiting consumers and professionals

Participation in Cancer Pharmacogenomic Studies: A Study of 8456 Patients Registered to Clinical Trials in the Cancer and Leukemia Group B (Alliance)

Clinically annotated specimens from cancer clinical trial participants offer an opportunity for discovery and validation of pharmacogenomic findings. The purpose of this observational study is to better understand patient/institution factors that may contribute to participation in the pharmacogenomic component of prospective cancer clinical trials

IRB perspectives on the return of individual results from genomic research

Return of individual research results from genomic studies is a hotly debated ethical issue in genomic research. However, the perspective of key stakeholders—Institutional Review Board (IRB) reviewers—has been missing from this dialogue. This study explores the positions and experiences of IRB members and staff regarding this issue

Intrinsic Breast Tumor Subtypes, Race, and Long-Term Survival in the Carolina Breast Cancer Study

Previous research identified differences in breast cancer-specific mortality across four "intrinsic" tumor subtypes: luminal A, luminal B, basal-like, and human epidermal growth factor receptor 2 positive/estrogen receptor negative (HER2+/ER−)

Molecular classification of head and neck squamous cell carcinomas using patterns of gene expression

The prognostication of head and neck squamous cell carcinoma (HNSCC) is largely based upon the tumor size and location and the presence of lymph node metastases. Here we show that gene expression patterns from 60 HNSCC samples assayed on cDNA microarrays allowed categorization of these tumors into four distinct subtypes. These subtypes showed statistically significant differences in recurrence-free survival and included a subtype with a possible EGFR-pathway signature, a mesenchymal-enriched subtype, a normal epithelium-like subtype, and a subtype with high levels of antioxidant enzymes. Supervised analyses to predict lymph node metastasis status were approximately 80% accurate when tumor subsite and pathological node status were considered simultaneously. This work represents an important step toward the identification of clinically significant biomarkers for HNSCC

The molecular portraits of breast tumors are conserved acress microarray platforms

Background Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. Results A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. Conclusion This study validates the breast tumor intrinsic subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile

The molecular portraits of breast tumors are conserved across microarray platforms

BACKGROUND: Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. RESULTS: A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. CONCLUSION: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile