# Understanding Long Tail Driver Mutations in Cancer

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2020 · $410,835

## Abstract

PROJECT SUMMARY/ABSTRACT
The transition to genomically driven oncology has begun, catalyzed in part by efforts to rationally design
effective therapies targeting the specific molecular aberrations on which individual tumors depend. This has
led, inexorably, to the prospective clinical sequencing of patients with active disease to guide their cancer care.
Nevertheless, a fundamental gap remains. The shift toward larger panel and whole exome sequencing has led
to the identification of increasing numbers of somatic mutations in even presumed actionable cancer genes,
the vast majority of which are in the so-called long right tail and lack biological or clinical validation. This
significantly impairs our ability to use findings generated by prospective profiling to guide patient care. We have
recently shown that such long-tail driver mutations can be the genetic basis of extraordinary responses to
systemic cancer therapy. We went on to show that a systematic survey utilizing population-scale cancer
genome data coupled to computational methodologies reveals similar long-tail drivers of both biological and
therapeutic significance. These findings underscore the importance of long-tail driver mutations in cancer, but
without a systematic approach for rapidly prioritizing and functionally and clinically validating these somatic
mutations, the gap in our understanding of the clinically actionable genome will widen. We propose to
overcome this urgent clinical challenge by establishing a robust and sophisticated framework for elucidating
novel driver mutations in the long tail. We will first establish a comprehensive computational framework that
identifies and prioritizes long-tail driver mutations that leverages not only population-scale data but integrates
orthogonal measures of selection. We will then apply these methods to a cohort of greater than 50,000
prospectively sequenced active cancer patients at our Center, all possessing detailed clinical, outcome, and
treatment response data, results from which can lead to the enrollment of patients on genotype-directed clinical
trials. Finally, we will perform functional studies of novel long-tail driver mutations revealed by these analyses
in genes for which there is an open basket study at our institution, thereby establishing a co-clinical framework
by which laboratory functional validation can be paired with patient treatment response. Together, these
studies seek to establish a computational-experimental framework for identifying functional mutations in the
long tail that expand the treatment options for molecularly defined populations of cancer patients.

## Key facts

- **NIH application ID:** 9850517
- **Project number:** 5R01CA204749-04
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Nikolaus Schultz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $410,835
- **Award type:** 5
- **Project period:** 2017-02-06 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9850517

## Citation

> US National Institutes of Health, RePORTER application 9850517, Understanding Long Tail Driver Mutations in Cancer (5R01CA204749-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9850517. Licensed CC0.

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