# Dissecting and Predicting Lethal Prostate Cancer using Biologically Informed Artificial Intelligence

> **NIH NIH P50** · DANA-FARBER CANCER INST · 2024 · $442,025

## Abstract

PROJECT SUMMARY – PROJECT THREE
Treatment strategies for intermediate and high-risk localized prostate cancer (PCa) include surgery or
radiation with or without hormonal therapy. Multiple molecular factors, including germline and somatic
alterations in DNA repair genes and tissue-based transcriptional biomarkers, have biological and prognostic
relevance in these clinical settings yet are rarely used today to guide treatment decisions. Determination of
the interacting and co- occurring molecular features that jointly drive indolent or aggressive clinical outcomes
in this setting is urgently needed to enable molecularly guided therapeutic strategies and biologically
grounded predictive models for clinical use. Furthermore, complex molecular states may converge on
histopathological patterns to augment these predictions, but these properties are difficult to quantify,
integrate, and generalize across diverse patient populations. The advent of large and diverse patient cohorts
with clinically embedded molecular characterization, digital histopathology techniques, and key outcome
measures, along with innovations in computation and deep learning to analyze and interpret these data, has
created an opportunity to profoundly expand the discovery and translational potential of molecular,
pathologic, and phenotypic data for patients with localized PCa. Our overarching hypothesis is that
interacting molecular, pathologic, and phenotypic features define prognostic outcomes in intermediate and
high-risk localized PCa after surgery, and that biologically guided interpretable deep learning, paired with
harmonized cohorts representative of PCa diversity, will transform our understanding of indolent versus
potentially lethal localized PCa and deliver on the promise of precision cancer medicine. Toward that end, the
specific aims of this proposal are: 1) Dissect the interacting germline and somatic properties that mediate
localized PCa using biologically guided neural networks; 2) Determine the convergent spatial histopathologic
properties of molecularly and clinically distinct forms of PCa; 3) Develop and validate a clinical grade
molecular prognostic model guided by biological networks in real-world and clinical trial settings. For these
aims, we will build on our team’s extensive expertise in PCa genomics, computer science, and medical and
urologic oncology. Critically, we will embed our approaches in the context of harmonized and representative
PCa cohorts. The ability to understand why some intermediate and high-risk localized prostate cancers are
phenotypically aggressive, and therefore predict which PCa will progress following curative-intent treatment
in this manner, would significantly advance basic PCa research and clinical translation. Broadly, this project
will strive to transform precision cancer medicine for prostate cancer and serve as a model for the creation,
development, and application of these emerging methodologies across cancer types and contex...

## Key facts

- **NIH application ID:** 10916204
- **Project number:** 5P50CA272390-02
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Eliezer M Van Allen
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $442,025
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916204

## Citation

> US National Institutes of Health, RePORTER application 10916204, Dissecting and Predicting Lethal Prostate Cancer using Biologically Informed Artificial Intelligence (5P50CA272390-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10916204. Licensed CC0.

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