NIH grant: Advancing and Validating Single-Cell Long-Read Sequencing for Broad Adoption in Cancer Molecular Profiling (1R33CA315144-01)

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NIH grant 1R33CA315144-01: Advancing and Validating Single-Cell Long-Read Sequencing for Broad Adoption in Cancer Molecular Profiling
Institute: {'code': 'CA', 'abbreviation': 'NCI', 'name': 'National Cancer Institute', 'admin_org_id': '12500008', 'admin_funding_url': ''}. PI: OLGA  ANCZUKOW-CAMARDA. Award: $452458.
Start: 2026-08-01T00:00:00.
PROJECT SUMMARY/ABSTRACT
The future of personalized cancer medicine requires going beyond detecting mutations and copy number
changes to also considering downstream transcriptional and post-transcriptional processes that influence cellular
cancer phenotypes. Alternative splicing is a key, final step in gene expression regulation prior to translation,
dictating cellular function and plasticity through expression of diverse transcript and protein isoforms from a
single gene. The genetic and epigenetic insults that accumulate in tumors are upstream of splicing, and therefore
therapies based on splicing have the potential to more proximally and specifically influence cancer cell
phenotype. Splicing dysregulation is a hallmark of cancer and a robust indicator of cancer risk and has been
causally linked to tumorigenesis. However, how splicing contributes to tumor heterogeneity and impacts drug
response and resistance remains understudied, which hinders exploiting splicing vulnerabilities for clinical
benefit. This knowledge gap is a result of an important technical gap: the cancer research field lacks robust,
validated tools for mapping and analyzing full-length spliced isoforms at the single-cell resolution in tumors. To
meet this need, we will build and validate enhanced tools for single-cell long-read isoform transcriptomics (scLRseq)
(Aim 1). We will address specific shortcomings of currently emerging scLR-seq approaches, including
suboptimal read length/depth and the absence of bioinformatics pipelines customized for integrating single-cell
and LR-seq data. We will rigorously validate our advanced scLR-seq platform using orthogonal methods,
including fluorescent in situ hybridization and through comparison to bulk short- and long-read data from sorted
cells. Next, we will demonstrate the robustness of scLR-seq in detecting intra-tumor splicing heterogeneity, and
the broad applicability of scLR-seq across tumor types (Aim 2). We will apply scLR-seq in a patient-derived
xenograft (PDX) model of breast cancer and cisplatin resistance which contains at least five clonal populations
that are heterogeneous in their response to therapy and in spliced isoform content. We will use a PDX model
because cancer arises within, and is shaped by, the whole organism; tumor progression and treatment response
are systemic processes that in vitro systems cannot fully replicate. Thus, in vivo models are required for
comprehensive validation of our platform. To demonstrate effectiveness of scLR-seq across tumor types, we will
use patient’s lung and malignant peripheral sheath nerve tumors, selected for their divergent cell type lineages
and compositions. We will define the splicing landscape in cancer cells, tumor-resident immune cells, cancerassociated
fibroblasts, and circulating blood cells. Finally, we will develop open-access, web-based data sharing
and visualization tools for single cell isoform expression (Aim 3). By bringing scLR-seq and its associated
computational methods to technical and analytical maturity, we will encourage and facilitate broad adoption by
the cancer research community. This project will accelerate translation of splicing biology into the clinic,
advancing precision oncology through enabling new strategies for early detection, intervention, and prevention.

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