# Dissecting Therapeutic Resistance and Progression in Metastatic Melanoma Through Clinical Computational Oncology

> **NIH NIH K08** · DANA-FARBER CANCER INST · 2021 · $250,795

## Abstract

Project Summary
 The development of targeted therapy (BRAF/MEKi) and immune checkpoint blockade (ICB) targeting the
co-inhibitory receptors CTLA-4 and PD-1 have revolutionized the treatment of metastatic melanoma. However,
only a subset of patients maintain durable responses, and many people experience substantial side effects of
therapy. Predicting therapeutic response in individual patients remains a critical and unresolved issue.
Furthermore, the series of key genomic and epigenetic events driving progression and resistance to therapy is
incompletely understood. The guiding hypothesis of this proposal is that (a) resistance to ICB and targeted
therapy is mediated by tumor intrinsic and extrinsic mechanisms, some of which may be elucidated by
systematic multi-modal molecular characterization of the tumor and tumor microenvironment; and (b)
applying modern machine-learning and statistical approaches to molecular and clinical data from patient
tumors will inform development of new therapeutic approaches and predictive models to improve patient
care.
 Identifying and validating predictors of intrinsic resistance to BRAF/MEKi and ICB across large human
cohorts has been limited to date. Aim 1 of this proposal applies genomic and transcriptomic characterization of
pre-treatment tumors to large cohorts of patients treated with BRAF/MEKi, PD-1i, and CTLA-4i in order to
discover and to validate molecular and clinical markers of response and resistance. Machine learning
approaches will integrate these markers into parsimonious models predicting response. A differential analysis
using mutual information will be conducted to reveal markers that predict differential response to therapy.
 A significant proportion of patients do not respond or maintained sustained responses to immunotherapy,
and there is a critical need to characterize the acquisition or selection of drivers that confer resistance to
immunotherapy. Aim 2 of this proposal develops algorithms using molecular characterization of longitudinally
collected tumor samples across multiple anatomic sites to discover genomic and epigenetic drivers of
progression and resistance to immunotherapy using phylogenetic analysis as the backbone of discovery.
 Finally, the ability to detect novel tumor driver mutations present at low frequencies is strongly dependent
on cohort size. Aim 3 of this proposal leverages all genomically characterized melanomas to perform a meta-
analysis using state-of-the-art and novel algorithms to discover novel driver mutations present at low frequencies
with a focus on tumor subsets that lack known targetable drivers.
 These studies will expand the actionable landscape of genomic and epigenetic alterations in metastatic
melanoma, advance our understanding of intrinsic and acquired resistance to targeted and immunotherapies in
melanoma, and establish a framework to predict response in individual patients, which may impact patient care
in melanoma and have applicability in other di...

## Key facts

- **NIH application ID:** 10229579
- **Project number:** 5K08CA234458-05
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** David Liu
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $250,795
- **Award type:** 5
- **Project period:** 2018-09-18 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10229579

## Citation

> US National Institutes of Health, RePORTER application 10229579, Dissecting Therapeutic Resistance and Progression in Metastatic Melanoma Through Clinical Computational Oncology (5K08CA234458-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10229579. Licensed CC0.

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