# Modeling the SCLC Phenotypic Space

> **NIH NIH U54** · VANDERBILT UNIVERSITY · 2020 · $548,781

## Abstract

SUMMARY – PROJECT 1
Small cell lung cancer (SCLC) is a highly aggressive, incurable tumor. SCLC phenotypic heterogeneity has
been associated with disease aggressiveness, yet there have been no clinical advances based on patient
tumor stratification, and the uniform standard-of-care, based on combination chemo-radiation therapy
unchanged for over half a century, remains largely ineffective. Recently, several groups including ourselves
have independently identified phenotypic cell subpopulations in SCLC across a variety of experimental
systems including human cell lines, patient-derived xenografts and primary tumors, as well as tumors from
SCLC genetically engineered mouse models (GEMMs). Yet, there is no global understanding of SCLC
phenotypic diversity across systems that could enable integration of findings, leverage GEMMs for translational
purposes, and produce insights into its impact on treatment evasion. In this Project, we propose to address this
challenge by developing a global blueprint of SCLC phenotypic space, clarifying the bias imposed to this space
by genomic alterations, and understanding phenotype transition or selection dynamics in response to drugs. In
Aim 1 we develop a workflow to infer SCLC phenotypic heterogeneity from bulk-level transcriptomics data,
which we then validate experimentally at the single-cell level. We define a gene ontology metric to identify
biological similarities and differences between phenotypes across model systems. The resulting phenotype
map will inform studies aimed at connecting model systems to patients. In Aim 2, we propose to link the SCLC
phenotypic heterogeneity space to genomic alterations, by statistical correlations validated with experiments
that mechanistically induce cells to switch phenotypes through gene manipulation. Since in the clinic SCLC
biopsies or surgery are rarely performed beyond initial diagnosis, we then propose liquid biopsies of circulating,
cell-free DNA as a clinical proxy for the primary tumor, allowing a connection between these genomic
alterations and phenotypic diversity of SCLC tumors. By bridging this gap, predictions about patient response
to specific treatments could eventually be made. In Aim 3, we investigate the relative role of transitions vs.
selection in supporting SCLC phenotypic plasticity and drug treatment evasion. To this end, we use DNA
barcoding and information theory techniques to quantify rates of diversification of SCLC phenotypes in
response to drug treatment. Specifically, we map trajectories of cells within the SCLC phenotype space as
cells adapt and evade treatment. In summary, we propose to develop a comprehensive view of SCLC
phenotypic heterogeneity, linking transcriptomic, genomic, and functional features of SCLC cells across
diverse experimental model systems and patient primary tumor specimens. We will link these observations to
clinically measurable variables, and develop a unified map of phenotypic response dynamics in response to
thera...

## Key facts

- **NIH application ID:** 9901489
- **Project number:** 5U54CA217450-03
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Vito Quaranta
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $548,781
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9901489

## Citation

> US National Institutes of Health, RePORTER application 9901489, Modeling the SCLC Phenotypic Space (5U54CA217450-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9901489. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*