# PROJECT 1: TIME-Based Spatiotemporal Cancer Immunograms Predictive for Immunotherapy-Targeted Therapy Sequential Combinations

> **NIH NIH U54** · INSTITUTE FOR SYSTEMS BIOLOGY · 2024 · $772,064

## Abstract

Project 1 Summary/Abstract
Combining immunotherapy with other therapy regimens, particularly targeted therapy, is a highly active area of
exploration with the goal of improving anti-tumor efficacy and extending therapeutic benefits to more patients or
tumor types. As the first mutation-immune co-targeted therapy, the simultaneous combination of anti-PD-L1 with
BRAFV600MUT and MEK inhibitors (so-called “triplet” therapy) has been approved for patients with BRAFV600MUT
melanoma. However, the data on this triplet appear mixed, with other trials not meeting key endpoints,
suggesting that simultaneous combination is not optimal. Our recent work in syngeneic murine melanoma
models showed uniformly, across tumor models of distinct driver mutations and cancer histologies, that a
regimen of 1-week anti-PD-1/L1 (± anti-CTLA-4) pretreatment augments the efficacy of triplet therapy by
enhancing MAPKi durability and dramatically suppressing melanoma brain metastasis. The improved therapy
efficacy resulted from the promotion of pro-inflammatory polarization of tumor-associated macrophages and the
elicitation of robust T cell clonal expansion and clonotypic convergence within the tumor-immune
microenvironment (TIME) induced by the anti-PD-1/L1 lead-in. This is consistent with observations in the clinical
trial data that prior immunotherapy before MAPKi is associated with improved progression-free survival. These
results highlight the vital role of the sequence/timing of each therapy component in the rational design of
combination therapies and also point to the need for a mechanistic understanding of the early-stage impact of
each combinatorial therapy component on the TIME.
However, the design of such sequential combination therapy trials is challenging because of the sheer number
of variables (sequence order, dosing, and timing) to be tested. The level of complexity calls for a predictive
framework to significantly reduce the parameter space and inform the identification of effective sequential
immunotherapy-targeted inhibitor combinations. Herein, we hypothesize that a spatiotemporal, multi-omics
analysis of early-stage (few days) monotherapy-induced changes in the TIME can provide deep insights
for greatly simplifying the design of immunotherapy-targeted inhibitor sequential combination trials. The
goal of Project 1 is to provide a data set that can be mined to inform the design of effective sequential combination
regimens. We will leverage state-of-the-art, spatial multi-omics tissue profiling tools to build a spatiotemporal
“movie” of the evolving TIME in established syngeneic melanoma tumor models, and their associated brain
metastases, after treatment with each of the combinatorial therapy components. The resultant spatiotemporal
multi-omic data will be analyzed to extract a number of highly informative TIME features from which agent-based
models (Project 2) for predicting effective sequential combination regimens can be constructed. Retrospective
studi...

## Key facts

- **NIH application ID:** 10916306
- **Project number:** 5U54CA274509-03
- **Recipient organization:** INSTITUTE FOR SYSTEMS BIOLOGY
- **Principal Investigator:** James R. Heath
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $772,064
- **Award type:** 5
- **Project period:** 2022-09-22 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916306

## Citation

> US National Institutes of Health, RePORTER application 10916306, PROJECT 1: TIME-Based Spatiotemporal Cancer Immunograms Predictive for Immunotherapy-Targeted Therapy Sequential Combinations (5U54CA274509-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10916306. Licensed CC0.

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