# Genetic Predictors of Prostate Cancer Survival

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $68,173

## Abstract

Prostate cancer (PC) has a very heterogenous clinical course. Many men have an indolent PC that can be
safely watched for years without progression. Alternatively, other men have an aggressive form of PC and can
progress very rapidly. Understanding PC risk stratification and separating indolent from aggressive disease is a
crucial unmet clinical need. Increasing data suggest that genetic single nucleotide polymorphisms (SNPs) may
aid in this effort. Indeed, this is the fundamental basis of the parent R01 (Klein PI; Freedland site PI).
 In line with that overarching view, this supplement supports Dr. Asilonu's development to focus on two areas
of risk stratification: 1) Using genetic SNP data; 2) using epidemiological data (obesity and diabetes status).
 In Aim 1, we aim to build upon our genetic analyses proposed in the parent grant. Specifically, we will use
Mendelian Randomization (MR) to determine causal relationships between type 2 diabetes and obesity with
prostate cancer survival. Building upon recent work classifying type 2 diabetes into different subtypes, we will
ask use genetic predictors of these diabetes subtypes, as well as predictors of obesity, as instrumental variables
in the Mendelian Randomization analysis. By comparing the effect size of SNPs on these variabes with the
effect size of these same SNPs on prostate cancer survival (derived from data from the parent grant), we will
determine the extent to which these subtypes of diabetes and obesity directly lead to worse prostate cancer
survival.
 In Aim 2, we will build upon the novel finding by Dr. Freedland and his team that diabetes and obesity appear
to act synergistically to create a more aggressive PC. This was not seen for overall PC risk, but specifically for
aggressive PC. This synergistic interaction was noted for both diagnosis of high-grade PC and PC mortality after
surgery for early-stage disease. Based upon these findings, we hypothesize that diabetes and obesity will
interact to synergistically increase the risk of aggressive PC (high-grade PC and PC mortality) but not low-grade
PC. We further hypothesize these associations will be independent of screening patterns and access to care
suggesting a biological basis. We will test this hypothesis using nationwide data from the Veterans Affairs (VA)
Health System. We received a separate grant to create a nationwide database to study the potential link between
obesity and race in predicting aggressive PC. The vast majority of work for this other grant is now complete. This
creates a great opportunity for Dr. Asilonu to build upon his current statistical knowledge and complete the
complicated analyses proposed in this supplement.
 From the research proposed, Dr. Asilonu will develop new skills in advanced statistical analyses, genetic
analyses, working with nationwide data, and manuscript preparation. We also built-in didactic work to help him
prepare grants. At the end of the nearly 3-year supplement, not only will we hav...

## Key facts

- **NIH application ID:** 10533696
- **Project number:** 3R01CA244948-02S1
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** ROBERT J. KLEIN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $68,173
- **Award type:** 3
- **Project period:** 2021-01-15 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10533696

## Citation

> US National Institutes of Health, RePORTER application 10533696, Genetic Predictors of Prostate Cancer Survival (3R01CA244948-02S1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10533696. Licensed CC0.

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