# An informatics framework for single-cell multi-omics from clinical specimens

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2024 · $44,716

## Abstract

PROJECT SUMMARY
Intra-tumor heterogeneity is a significant barrier to precision oncology. Emerging single-cell and spatial profiling
approaches have enabled basic research into tumor heterogeneity. However, the application of these
emerging approaches to the clinical decision process is limited. There is a critical need for predictive models
that integrate these novel data with existing genomics approaches and histology, to generate actionable
clinical recommendations. This proposal builds on my lab’s recent work, using single-cell RNA sequencing
(scRNA-seq) to map the cellular hierarchies of complex tumors. Our preliminary data extend these studies to
single-cell multi-omics, integrating single-cell assay for transposase-accessible chromatin (scATAC-seq) and
spatial transcriptomics (ST). Our long-term goal is to develop models of malignant progression based on
sequencing data from patient biopsies and deploy them to support clinical decisions. The overall objective of
this project is to develop algorithms to integrate heterogeneous single-cell and imaging data to support therapy
selection, trained on data from multiple cancers and broadly applicable pan-cancer. The rationale for this work
is that these algorithms will be applied to pre-treatment biopsies to predict progression and to recommend
appropriate therapy combinations. In Aim 1 we will develop and validate algorithms to model clonal
composition, phylogeny, and evolutionary trajectory. This will be used to rigorously identify combinatorial
chemotherapy targets and monitor emerging treatment-resistant clones. In Aim 2, we integrate scRNA-seq with
ST as training data to develop a predictive model of gene expression and cellular composition, based on
imaging data alone. We validate these algorithms internally, on prospective cohorts, and in situ in adjacent
tissue. In Aim 3, we develop predictive models of two clinical problems that are challenging in many cancers:
1) the response to ionizing radiation, 2) the emergence of hypermutation at recurrence. Here, we exploit
modern deep-and-wide learning approaches to identify genomic predictors of outcome that are tailored to a
patient’s clinical context. We will validate this approach using both internal and external controls. Algorithms
will be implemented in clinician dashboards in an existing system and the evaluation of clinical support will take
place at two sites: the University of California, San Francisco and the University of Pittsburgh. We anticipate
that this project will identify novel prognostic signatures, enable risk stratification, disease monitoring, and the
selection of precision therapies. These studies will significantly advance our ability to apply single-cell and
spatial profiling in the clinical setting.

## Key facts

- **NIH application ID:** 10974879
- **Project number:** 3R01LM013897-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Aaron Antonio Diaz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $44,716
- **Award type:** 3
- **Project period:** 2022-07-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10974879

## Citation

> US National Institutes of Health, RePORTER application 10974879, An informatics framework for single-cell multi-omics from clinical specimens (3R01LM013897-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10974879. Licensed CC0.

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