# A pan-cancer atlas of driver mutations in >100,000 patients based on a hypothesis-driven combined computational and experimental approach

> **NIH NIH K99** · DANA-FARBER CANCER INST · 2021 · $133,195

## Abstract

PROJECT SUMMARY
Most mutations in cancer genomes are random passengers that do not contribute to oncogenesis, whereas
only a few are drivers critical for tumor development. Existing cancer therapies interfere directly with the
biology of drivers, which have been characterized extensively in protein-coding regions but remain largely
uncharacterized outside coding regions. Most tumors harbor a combination of several driver mutations, but it is
unclear how multiple events are coordinated in tumor development. The applicant's long-term goal is to
advance cancer medicine by identifying new drug targets and clinical markers for therapies in complex
pathways. The overall objectives in this application are to (i) reveal the biological role of noncoding drivers, (ii)
capture the coordination of driver events at a pathway level, and (iii) profile the effects of noncoding drivers on
cancer gene expression. The central hypothesis is that refining the biological assumptions of computational
methods will enhance their statistical power. The rationale is that defining the biology of noncoding drivers and
their combination will offer a strong foundation for new therapies. The central hypothesis will be tested in three
specific aims: 1) Determine the impact of integrating biological mechanisms into statistical methods for
localizing noncoding drivers; 2) Evaluate mechanisms by which promoter mutations increase the expression of
cancer genes; and 3) Assess the coordination of multiple driver events in tumor development. The proposed
research is innovative, in the applicant's opinion, because it will allow for an unbiased characterization of driver
mutations across the entire genome, address the limitations of existing cancer genomics methods in noncoding
regions, and facilitate the usage of statistical concepts for non-computational scientists. The proposal is
significant because it will enable a systematic interrogation of noncoding drivers and their combinations.
Ultimately, this will pave the way for new targeted therapies. Dr. Dietlein will be mentored by Dr. Van Allen, an
Associate Professor of Medicine at Harvard Medical School with considerable experience in cancer genomics
methods that require statistical innovation for clinically focused questions. His co-mentor, Dr. Meyerson, is a
Professor of Genetics and Medicine at Harvard Medical School and a pioneer in developing targeted therapies
based on driver mutations. Additional support will be provided by 4 computational and 2 experimental
collaborators. Dr. Dietlein's training plan contains four goals, which will be pursued by hands-on experiential
training, conference meetings, and structured coursework: 1) Acquire computational skills for interpreting
drivers in noncoding regions; 2) Experimental techniques to validate driver mutations by CRISPR interference;
3) Develop professional leadership skills for interdisciplinary teams of scientists; and 4) Use machine-learning
methods for interpreting drivers in...

## Key facts

- **NIH application ID:** 10276520
- **Project number:** 1K99CA262152-01
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Felix Dietlein
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $133,195
- **Award type:** 1
- **Project period:** 2021-08-16 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10276520

## Citation

> US National Institutes of Health, RePORTER application 10276520, A pan-cancer atlas of driver mutations in >100,000 patients based on a hypothesis-driven combined computational and experimental approach (1K99CA262152-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10276520. Licensed CC0.

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