# Deep Discovery and Clinical Interpretation of Germline and Somatic Cancer Drivers

> **NIH NIH U24** · WASHINGTON UNIVERSITY · 2020 · $350,204

## Abstract

PROJECT SUMMARY
Large-scale cancer sequencing efforts provide a unique opportunity for the discovery of germline and somatic
driver alterations influencing cancer susceptibility, initiation, progression, and clinical response. Detecting such
alterations is fundamentally and technically challenging for several reasons including: 1) the combinatorially
enormous number of ways that a genome can be altered, 2) the presence of various sized repeats, highly
homologous gene families, and other contextual influences on alignment and detection accuracy, 3) systematic
errors inherent in current sequencing technologies and tumor preservation techniques, and 4) intratumoral and
intertumoral heterogeneity including clonality, purity, and lymphocyte infiltration. As a result, the full
complement of driver events for the typical tumor still defies identification and, in many cases, no drivers can
be found. Our recent work has also demonstrated that some types of indels/SVs such as complex indels,
ITD/PTD (internal/partial tandem duplications), and homopolymer indels are often missed by existing
approaches. Beyond detection challenges, functional interpretation of the impact of genomic alterations
requires strategies that integrate WGS/exome, RNA-seq, and protein data to reveal translational, splicing, and
protein structural effects. In addition, cooperative dynamics between germline and somatic alterations are
usually missed, as these events have been analyzed independently. As cancer sequencing projects expand to
include well-curated clinical phenotypes, methods necessary to understand the pathogenicity and druggability
of driver alterations that underlie phenotypes such as drug resistance or exceptional responders are also
urgently needed. To fully harness the power of large-scale cancer genomics and to facilitate advances in
personalized medicine, our group proposes to focus on two core competencies, coding and non-coding
mutations, outlined in the RFA. In collaboration with other GDACs, GDC, and AWGs, we will extend
computational approaches that we have successfully established and applied for TCGA and ICGC projects to
detect and functionally and clinically interpret germline and somatic drivers using sequencing data from GCC
along with curated clinical data.

## Key facts

- **NIH application ID:** 10003015
- **Project number:** 5U24CA211006-05
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Li Ding
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $350,204
- **Award type:** 5
- **Project period:** 2016-09-14 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10003015

## Citation

> US National Institutes of Health, RePORTER application 10003015, Deep Discovery and Clinical Interpretation of Germline and Somatic Cancer Drivers (5U24CA211006-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10003015. Licensed CC0.

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