# PROJECT 2: Development and Refinement of Predictive Models for Designing Immunotherapy Combination Treatments

> **NIH NIH U54** · INSTITUTE FOR SYSTEMS BIOLOGY · 2024 · $740,955

## Abstract

Project 2 Project Summary
Cutaneous melanoma became an early example for treatment with targeted therapy with the clinical
development of the first BRAFV600MUT-specific inhibitor (BRAFi), vemurafenib1. Resistance to BRAFi is common2,
and was initially ascribed to cancer cell intrinsic factors that reactivate MAPK pathway signaling3–7. BRAFi in
combination with MEKi8 was developed to combat such resistance, but only a quarter of patients treated with
this combination survive for five years9. In fact, recent data suggest that cancer cell-extrinsic factors, including
immune factors3,10–13, can play important roles in resistance development to MAPK pathway inhibitors, thus
highlighting the role of the tumor-immune microenvironment (TIME). Strikingly, in syngeneic melanoma models
that develop resistance against both MAPKi and immune checkpoint blockade (ICB), lead-in ICB can ‘prime’
both the primary tumor and distal metastases for eradication when the ICB is subsequently combined with
MAPKi14. While this suggests that immune based strategies, such as ICB or adoptive cell therapy (ACT), can
serve as sequential combinatorial agents to prevent MAPKi resistance. However, it also significantly complicates
the design of candidate treatment regimens, since multiple sequences and sequence timings need to be tested.
This can make clinical trials design impractical. We propose to develop methods that apply iterative and active
learning to deep phenotyping with spatial and temporal multi-omics assays to yield predictive in silico models
that can provide guidance for designing sequential immunotherapy - targeted inhibitor combination therapies. .
A key element of Project 2 is the iterative development of multiscale Agent Based Models (ABMs) as a virtual
representation of the TIME. ABMs are initially constructed from existing data, including preliminary results from
biobanked tumor specimens and public omics data bases, and from our extensive experience within the Cancer
Genome Atlas (TCGA). They are then evolved through a systems biology-inspired iterative cycle of quantitative
experimentation, analysis, modeling, and validation, drawing from experimental data from both Projects 1 and 2.

## Key facts

- **NIH application ID:** 10916309
- **Project number:** 5U54CA274509-03
- **Recipient organization:** INSTITUTE FOR SYSTEMS BIOLOGY
- **Principal Investigator:** VESTEINN THORSSON
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $740,955
- **Award type:** 5
- **Project period:** 2022-09-22 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916309

## Citation

> US National Institutes of Health, RePORTER application 10916309, PROJECT 2: Development and Refinement of Predictive Models for Designing Immunotherapy Combination Treatments (5U54CA274509-03). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10916309. Licensed CC0.

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