Author Interviews, Brigham & Women's - Harvard, Prostate Cancer, Weight Research / 10.06.2019
Fat Distribution Linked to Advanced and Fatal Prostate Cancer
MedicalResearch.com Interview with:
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Dr. Dickerman[/caption]
Barbra Dickerman, PhD
Research Fellow
Department of Epidemiology
Harvard T.H. Chan School of Public Health
Boston, MA
MedicalResearch.com: What is the background for this study?
Response: Obesity is associated with a higher risk of advanced prostate cancer and poorer prognosis after diagnosis. However, emerging evidence suggests that the specific distribution of body fat may be an important prognostic factor for prostate cancer outcomes. In this original investigation, we analyzed body fat distribution on computed tomography imaging and the risk of being diagnosed with, and dying from, prostate cancer. This study was conducted among 1,832 Icelandic men with over a decade of follow-up in the Age, Gene/Environment Susceptibility-Reykjavik Study.
Dr. Dickerman[/caption]
Barbra Dickerman, PhD
Research Fellow
Department of Epidemiology
Harvard T.H. Chan School of Public Health
Boston, MA
MedicalResearch.com: What is the background for this study?
Response: Obesity is associated with a higher risk of advanced prostate cancer and poorer prognosis after diagnosis. However, emerging evidence suggests that the specific distribution of body fat may be an important prognostic factor for prostate cancer outcomes. In this original investigation, we analyzed body fat distribution on computed tomography imaging and the risk of being diagnosed with, and dying from, prostate cancer. This study was conducted among 1,832 Icelandic men with over a decade of follow-up in the Age, Gene/Environment Susceptibility-Reykjavik Study.

Jasleen Grewal, BSc.
Genome Sciences Centre
British Columbia Cancer Research Centre
Vancouver, British Columbia, Canada
MedicalResearch.com: What is the background for this study?
Response: Cancer diagnosis requires manual analysis of tissue appearance, histology, and protein expression. However, there are certain types of cancers, known as cancers of unknown primary, that are difficult to diagnose based purely on their appearance and a small set of proteins. In our precision medicine oncogenomics program, we needed an accurate approach to confirm diagnosis of biopsied samples and determine candidate tumour types for where the primary site of the cancer was uncertain. We developed a machine learning approach, trained on the gene expression data of over 10,688 individual tumours and healthy tissues, that has been able to achieve this task with high accuracy.
Genome sequencing offers a high-resolution view of the biological landscape of cancers. RNA-Seq in particular quantifies how much each gene is expressed in a given sample. In this study, we used the entire transcriptome, spanning 17,688 genes in the human genome, to train a machine learning method for cancer diagnosis. The resultant method, SCOPE, takes in the entire transcriptome and outputs an interpretable confidence score from across a set of 40 different cancer types and 26 healthy tissues.