# Using patient-derived models to understand drug responses in SCLC

> **NIH NIH U01** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $613,419

## Abstract

PROJECT SUMMARY
Small cell lung cancer (SCLC) afflicts more than 30,000 patients per year and is rapidly fatal in 95% of cases,
with median survival is less than one year. Belying this grim prognosis, treatment-naive SCLC is highly
sensitive to chemotherapy, with response rates in excess of 70% for etoposide/platinum. However, relapse is
nearly inevitable, and relapsed SCLC presents two obstacles that have been insurmountable for at least 30
years: cross-resistance to chemotherapy, and absence of biomarker-driven targeted therapy.
Following relapse, resistance often extends beyond etoposide/platinum, and a disease that was once highly
chemosensitive becomes inexorably progressive. However, the molecular determinants of cross-resistance in
SCLC remain unclear. Although critically important, cross-resistance is difficult to study experimentally, as it
requires a model system that faithfully reproduces clinical outcomes.
Topotecan is the only approved second-line therapy, but NCCN guidelines list 10 agents of nearly equivalent
efficacy. None are particularly effective in unselected patients, and although there is significant molecular
heterogeneity in SCLC, this does not guide patient selection. As novel targets and therapeutic regimens
emerge, biomarker discovery will require a model system that recapitulates the molecular features of patient
tumors, so that molecular heterogeneity can be parsed into clinically meaningful subgroups.
We have generated a panel of 44 SCLC patient-derived xenograft models (PDXs) from biopsy specimens and
circulating tumor cells (CTCs). Our panel includes successive models from individual patients at time points
before and after specific lines of therapy, with detailed information about the corresponding clinical response.
For both standard chemotherapy and experimental agents in clinical trial, these models faithfully mirror patient
responses. However, unlike the patient experience, multiple strategies can be compared for identical tumors.
We propose to use these models to directly compare standard first and second-line chemotherapy with two
experimental regimens that have given promising results in the clinic or in preclinical assays: olaparib plus
temozolomide, in a phase I/II trial at MGH, and a combined Mcl-1/Bcl-2 inhibitors. Individually, these PDX
population trials are designed to reveal biomarkers of sensitivity and mechanisms of resistance for promising
experimental therapies. Collectively, they present a novel opportunity to model cross-resistance through
comparative analysis with reference to the clinical histories of each model.
The successful completion of this work will establish a large collection of PDX models with comprehensive
molecular an functional profiles. In addition, these experiments will investigate the molecular determinants of
cross-resistance following chemotherapy, a problem that has beleaguered management of SCLC for over
three decades.

## Key facts

- **NIH application ID:** 10247067
- **Project number:** 5U01CA220323-04
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** NICHOLAS J DYSON
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $613,419
- **Award type:** 5
- **Project period:** 2018-09-17 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10247067

## Citation

> US National Institutes of Health, RePORTER application 10247067, Using patient-derived models to understand drug responses in SCLC (5U01CA220323-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10247067. Licensed CC0.

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