Tumor and Immune Cell Heterogeneity Pre- and Post-Combined Chemotherapy and Immune Checkpoint Blockade
Abstract
Lung cancer is the leading cause of cancer death worldwide. Personalized therapies have been widely praised for striking, recent mortality improvements for patients with non-small cell lung cancer. However, small cell lung cancer, the most aggressive form of lung cancer and one that accounts for 13% of all lung cancer diagnoses in the United States, remains saddled with subpar, one-size-fits-all treatment approaches. Despite recent expansions in approved drug classes for the treatment of small cell lung cancer patients, the median overall survival is just over 1 year from diagnosis, and fewer than 10% of patients are alive 5 years after their initial diagnosis. We have recently highlighted that, despite a shared diagnosis of small cell lung cancer, patients with this disease are marked by significant heterogeneity in their gene expression profiles. Our preliminary evidence suggests that these gene expression profiles fall into one of four categories and that each category assignment can be used to predict better response to therapies for small cell lung cancer. Further, we have shown that, even within a single patient’s tumor, there is diversity among cancer cells and their associated immune cells, while fluctuations in this diversity can be monitored to predict synergistic behavior between therapies, as well as impending resistance to therapies. Our proposal aims to evaluate these preliminary findings in a real-world clinical context. We will evaluate samples from patients treated with the latest drug combination that is the standard of care treatment for small cell lung cancer patients. Our analyses will provide unprecedented resolution into the features of the cancer cells and immune cells as they experience this treatment to assess how these features relate to response and/or resistance to these therapies. In doing so, our proposal will directly address two LCRP Areas of Emphasis: (1) develop or optimize prognostic or predictive markers to assist with therapeutic decision-making and (2) understand mechanisms of resistance to treatment (primary and secondary). Specifically, we will aim to determine whether our gene expression categorization approach can predict those patients that will best respond to this combination (carboplatin/cisplatin and etoposide plus durvalumab) as predicted by our preclinical and clinical data to date. Furthermore, we expect that, on treatment, real-time evaluations of changes in patient tumors and immune cells will influence ongoing responses to therapy and thus predict secondary resistance. Together, these findings could immediately influence therapeutic decision-making and offer the potential of the first predictive biomarker(s) for small cell lung cancer treatment. Finally, we will attempt to extend our classification and monitoring efforts to patient tumor biopsies as well as patient blood samples. If successful, this could offer an inexpensive and minimally invasive strategy to offer patient screening, surveillance, and biomarker selection for treatment selection. As SCLC is strongly linked to tobacco use and tobacco use is historically increased in the Veteran population, the development of less morbid and less costly SCLC surveillance strategies that increase the efficiency and effectiveness of treatment delivery offers an obvious benefit to Veterans as well as their families and the population at large. In short, this could mean fewer invasive procedures, an improved ability to distinguish between useful and futile therapies, and increased therapeutic options, all of which enhance the quality of both healthcare delivery and, most importantly, Veterans’ lives.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2210543
Entities
People
- Lauren Byers
Organizations
- The University of Texas MD Anderson Cancer Center
- United States Army