# Quantitative and functional characterization of therapeutic resistance in cancer

> **NIH NIH U54** · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · 2021 · $1,976,036

## Abstract

Overall – Project Summary
 Despite tremendous advances in our understanding of cancer pathogenesis, the treatment of individual
patients with either conventional chemotherapy or targeted agents remains highly empiric. Current efforts to
predict drug efficacy are generally focused on genetic and transcriptional markers of pathway activation or drug
binding, such as resistance mutations that sterically hinder small molecule binding or activate parallel or
orthogonal signaling pathways. These markers exist in a very small fraction of all cancers, such that most
patients are treated with little or no understanding of whether they will respond to an individual
therapy. This results in many patients receiving ineffective and/or unnecessarily toxic therapies. There is a
desperate need to change this paradigm. The ideal for characterizing therapeutic sensitivity would allow
for: real-time decision making, identification of rare subpopulations with therapeutic resistance, analysis of
very small samples (e.g. MRD), and maintains viability individual cells for downstream assays to characterize
phenotypic, genotypic, transcriptional and other determinants of sensitivity. The overall goal of our U54
application is to address this need using new strategies for predicting therapeutic response in which
paired phenotypic and genomic properties are measured at the single-cell level. Phenotypic properties
will include both physical parameters (e.g. mass, mass accumulation rate) and molecular markers (e.g. protein
secretion, surface immunophenotype) that are rapidly affected by effective therapeutics and precede longer-
term phenotypes (e.g. loss of viability). Because these properties are measured for each single cell, clonal
architectures based on therapeutic response will be established across each tumor sample by incorporating
molecular and physical parameter data from large numbers of cells. In settings of deep treatment response,
pre-treatment and MRD samples will be compared to define the effects of therapy on clonal architecture. The
cells that exhibit particular functional properties (e.g. phenotypic non-responders) will be isolated and analyzed
for genomic determinants of these properties. These data will then be incorporated into mathematical models
to design and optimize therapeutic approaches that overcome the heterogeneity within individual tumors
responsible for treatment failure. By pursuing this approach, our center will establish a framework that
enables an iterative cycle between novel single-cell measurements from clinically-relevant specimens
and computational approaches that result in testable predictions.

## Key facts

- **NIH application ID:** 10162303
- **Project number:** 5U54CA217377-05
- **Recipient organization:** MASSACHUSETTS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** DOUGLAS A LAUFFENBURGER
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,976,036
- **Award type:** 5
- **Project period:** 2017-06-07 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10162303

## Citation

> US National Institutes of Health, RePORTER application 10162303, Quantitative and functional characterization of therapeutic resistance in cancer (5U54CA217377-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10162303. Licensed CC0.

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