# Phylogenetic approaches to the quantification and serilization of variants enabling cancer theraputic resistance and the reconstruction of etilogically relevant ancestral states

> **NIH NIH F31** · YALE UNIVERSITY · 2021 · $46,036

## Abstract

PROJECT SUMMARY/ABSTRACT
Cancer progression is an evolutionary process, enabling tumors to evade our best therapeutics and, ultimately,
to recur and metastasize. The evolution of therapeutic resistance remains enigmatic in part because it must be
reconstructed from patient biopsies which are prescribed by patient care, rather than from design to empower
discovery. Some mechanisms of resistance in select cancer-therapy systems have been discovered, but this
knowledge is the result of extensive work and is subject to obsolescence as therapeutic strategies themselves
evolve, necessitating systematic approaches to reveal how therapy is subverted. Our goal is to leverage tumor
phylogenies to examine the evolutionary forces operating on variants responsible for therapeutic resistance.
To expose the timing of specific SNVs and quantify their contribution to therapeutic resistance, I will perform
deconvolution of selection from the underlying mutation rate on therapy-exposed branches of tumor
phylogenies to obtain cancer effect sizes for SNVs in the context of therapy. I employed this approach in a
preliminary analysis of EGFR L850R-driven, erlotinib-treated lung adenocarcinoma, and it revealed that the
EGFR T790M mutation in has a vast effect size—roughly 45× higher in the context of therapy than the L858R
mutation driving primary progression has during primary progression. The results support molecular and
clinical observations that EGFR T790M confers strong erlotinib resistance and suggest vulnerability of EGFR
L850R-driven cancer lineages to simultaneous, orthogonal treatments. Application of this method to less well
characterized cancer-therapeutic systems will enable detection of currently unknown enablers of resistance.
Development of these approaches to illuminate the evolutionary dynamics of therapeutic resistance will reveal
the series and timing of mutations enabling resistance. To fully utilize this knowledge, we will perform analyses
of somatic variant epistasis on cancer chronograms, resolving the gene interactions and evolutionary
trajectories that lead to therapeutic resistance. Further, we will learn from convergences in the mechanisms of
therapeutic resistance, modifying a Markov Chain Monte Carlo approach to the Bayesian reconstruction of
cancer chronograms, to quantify—on average—the time until specific mutations reach high frequency.
Finally, we will develop synergistic phylogenetic and machine-learning strategies that characterize the etiology
of unobservable ancestral features of cancer. For instance, our initial analysis of a lung adenocarcinoma
patient receiving erlotinib preceded by cisplatin found that platin-induced mutational signature peaked on the
same branch of the phylogeny as the EGFR c.2369C→T substitution causing the T790M resistance mutation,
suggesting that preceding erlotinib therapy with cisplatin can supply exactly the genetic heterogeneity needed
by the tumor to evade erlotinib therapy. These approaches will also...

## Key facts

- **NIH application ID:** 10141576
- **Project number:** 1F31CA257288-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Jeffrey Nicholas Fisk
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $46,036
- **Award type:** 1
- **Project period:** 2021-08-11 → 2023-08-10

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10141576

## Citation

> US National Institutes of Health, RePORTER application 10141576, Phylogenetic approaches to the quantification and serilization of variants enabling cancer theraputic resistance and the reconstruction of etilogically relevant ancestral states (1F31CA257288-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10141576. Licensed CC0.

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