Prediction of Cancer Outcomes Can Be Improved By Genetic Analysis of Tumor Interview with:

Ana I. Vazquez PhD Department of Epidemiology and Biostatistics Michigan State University East Lansing, Michigan

Dr. Ana I. Vazquez

Ana I. Vazquez PhD
Department of Epidemiology and Biostatistics
Michigan State University
East Lansing, Michigan What is the background for this study? What are the main findings?

Response: Precise predictions of whether a tumor is likely to spread would help clinicians and patients choose the best course of treatment. But current methods fall short of the precision needed. We tested whether breast cancer survival predictions could be improved by profiling primary tumor samples with genomic technologies. We found that predictions based on clinical information, such as cancer stage and subtype, improve when they incorporate comprehensive data on which genes are active in tumor samples compared to non-cancerous tissues from the same patient. This is also true for genome-wide methylation data, which maps the parts of the DNA that carry molecular “tags” that influence gene activation. If developed for use in the clinic, our approach could spare some patients from unneeded chemotherapy. What should readers take away from your report?

Response: Predictions of cancer outcome using clinical information can be significantly improved by incorporating data from thousands of genes, and this improvement is even greater than the gains provided by some commercial genetic test. This widely available test estimates the risk of cancer recurrence, but focuses on a handful of pre selected genes that influence cancer progression. Our study shows that genome-wide approach provides more powerful predictions. To our surprise, it also revealed that that genome-wide methylation data was more predictive than any single source of clinical information currently used by doctors What recommendations do you have for future research as a result of this study?

Response: We developed and tested our methods using breast cancer samples from the The Cancer Genome Atlas project of the NIH. To be applied by clinicians, the method would need to be validated using data from thousands of patients, rather than the hundreds that were available. We are currently investigating how to incorporate other factors into our models, including treatment regimes. Ultimately, we hope this will help doctors and patients match the best course of treatment with the individual characteristics of each tumor. Is there anything else you would like to add?

Response: Our methods have not yet been developed for use in the clinic. Thank you for your contribution to the community.

Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles
Ana I. Vazquez, Yogasudha Veturi, Michael Behring, Sadeep Shrestha, Matias Kirst,Marcio F. R. Resende, Jr., Gustavo de los Campos
GENETICS July 1, 2016 vol. 203 no. 3 1425-1438; DOI:10.1534/genetics.115.185181

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