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.

Public Health Relevance

Small cell lung cancer (SCLC) afflicts over 30,000 patients per year, and is rapidly fatal in 95% of cases. In most cases, chemotherapy is the only option to prolong survival. We have developed a method to extract SCLC tumor cells from patient blood samples and grow them in mice. The responses of these mouse-born human tumors to cancer therapy mirror the responses of the patients themselves, and can be used compare the effectiveness of different treatments. Here we will use these tumor models to compare approved therapies for SCLC with promising experimental agents. This work may provide the basis for new therapeutic approaches in SCLC.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01CA220323-01A1
Application #
9602353
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Forry, Suzanne L
Project Start
2018-09-17
Project End
2023-08-31
Budget Start
2018-09-17
Budget End
2019-08-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code