# The MSK Genomic Data Analysis Center for Tumor Evolution

> **NIH NIH U24** · SLOAN-KETTERING INST CAN RESEARCH · 2022 · $416,304

## Abstract

Abstract
The MSK Genomic Data Analysis Center for Tumor Evolution seeks to implement tools, best practices and
analytical workflows for studying cancer evolution from cancer genome and transcriptome sequencing data.
Over the last 15 years, survey sequencing of patient populations of many cancer types has elucidated novel
driver mutations which are mechanistically responsible for disease pathogenesis. The Cancer Genome Atlas
(TCGA) and individual laboratory efforts have broadened the understanding of biological processes impacted by
somatic mutation and revealed new therapeutic targets that have achieved clinical impact. However, most of this
work has been based on bulk DNA sequencing from primary tumors and single biopsies from patients. It is well
understood that cancer is an evolutionary process during which clonal expansions within patients generates
heterogeneity and phenotypic diversity of cell populations across metastatic sites over time (with or without
therapeutic intervention). Indeed, the same targeted therapies developed based on mutation discoveries often
select for resistant clones, keeping durable cures out of reach. We will develop analytical methods, tools and
software infrastructure to study cancer progression through the lens of evolution, shifting emphasis from analysis
of primary tumors to dynamic analyses over clinical trajectories. We expect our program will advance the ability
to study clinical trajectories of patients in a more comprehensive approach, including temporal, spatial and single
cell analysis to better represent the full clonal repertoires of tumors and to study the determinants of how and
why tumors evolve. We use tools, well established in our laboratories, in three key areas: i) variant interpretation
from metastatic and post-treatment samples for discovery of therapeutic resistance mutations (Aim 1); ii) multi-
sample analysis across anatomic space, and/or time series data from serial biopsy or cell free DNA to track and
model clonal dynamics (Aim 2); iii) single cell approaches for clonal decomposition and clone-specific
phenotyping within patients (Aim 3). Our team is well positioned to carry out our objectives having developed
leading software infrastructures supporting TCGA and clinical sequencing through MSK-IMPACT, development
of clinically approved assays for longitudinal monitoring of patients through cell free DNA sequencing (MSK-
ACCESS) and through study of clonal evolution at bulk and single cell resolution. We will implement and improve
tools to support each of these aims, including Cancer Hotspots, OncoKB, and cBioPortal for Aim 1, PyClone
and fitClone for Aim 2 and CloneAlign and CellAssign for Aim 3, tailoring and customizing software to support
investigations into the dynamic and evolutionary nature of human cancers. These tools comprise a software
infrastructure focused on cancer evolution through variant allele interpretation, multi-sample analysis and single
cell investigation. Our infra...

## Key facts

- **NIH application ID:** 10469512
- **Project number:** 5U24CA264028-02
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Nikolaus Schultz
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $416,304
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10469512

## Citation

> US National Institutes of Health, RePORTER application 10469512, The MSK Genomic Data Analysis Center for Tumor Evolution (5U24CA264028-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10469512. Licensed CC0.

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