# A multimodal approach for precision immuno-oncology in lymphoma treated with CAR-T cells

> **NIH NIH K08** · SLOAN-KETTERING INST CAN RESEARCH · 2024 · $277,685

## Abstract

PROJECT SUMMARY/ABSTRACT
Autologous CD19-directed chimeric antigen receptor T-cells (CAR-T) have resulted in extraordinary response
rates in relapsing and refractory large B-cell lymphoma (LBCL). However, over 60% of CD19-CAR-T recipients
will experience disease recurrence or progression. Most of these patients will die from their disease.
Mechanisms of CAR-T treatment failure are partially understood and biomarkers informing patient outcomes
and management have limited clinical utility. Our central hypothesis is that orthogonal modalities (e.g., clinical,
molecular, genomic, and radiomic [quantitative measures from medical images]) complement one another,
together providing information on resistance mechanisms and patient outcomes beyond that accessible
through any individual modality. We present results suggesting that machine learning is an effective
methodology for synthesizing and modeling multiple sources of data together. Cancer cells harness genomic
heterogeneity to evade pressure applied by immunotherapies, such as immune checkpoint inhibitors. Our
preliminary data also demonstrate that TP53 genomic alterations strongly determine response to CAR-T.
Furthermore, using transcriptomic profiling, we found that cancer cellular pathways required for effective
transmission of CAR-T cytotoxic signals are distorted in TP53-altered lymphoma. These early findings provide
a proof-of-concept for the utility of genomics to inform disease biology and risk after CAR-T.
We hypothesize that tumor genetic aberrations in cellular pathways used by CAR-T cells to exert cytotoxicity
drive treatment resistance by rendering cancer cells insensitive to CAR-T stimuli and supporting immune
escape. In Aim 1, we will use comprehensive genotypic and phenotypic tumor profiling before and after CAR-T
to study the role of a priori determined genes and pathways in mediating inherent and acquired treatment
resistance. We also hypothesize that orthogonal modalities for patient and tumor profiling are complementary,
and their integration into a unified, multimodal model could accurately predict CAR-T outcomes. In Aim 2, we
will synthesize data from multiple modalities and use machine learning algorithms to predict CAR-T response
and identify novel biomarkers.
To meet our goals, we have compiled one of the largest CAR-T patient and sample biobanks. A group of
leading experts in immunology, genetics, pathology, radiology, machine learning, and bioinformatics will guide
the candidate in this multidisciplinary work. If successful, we expect our combinatorial approach to uncover
genetic features underlying inherent and acquired CAR-T resistance and identify new druggable targets.
Furthermore, our machine learning approach will support treatment personalization by establishing decision
support systems and identifying biomarkers of high-risk patients. Finally, we will introduce novel methodologies
for modeling CAR-T outcomes, which are extendable to other forms of treatment.

## Key facts

- **NIH application ID:** 10900821
- **Project number:** 5K08CA282987-02
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Roni Shouval
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $277,685
- **Award type:** 5
- **Project period:** 2023-08-07 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10900821

## Citation

> US National Institutes of Health, RePORTER application 10900821, A multimodal approach for precision immuno-oncology in lymphoma treated with CAR-T cells (5K08CA282987-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10900821. Licensed CC0.

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