Determining patterns of cancer drug resistance from autopsy patients
Author(s)Martin, Elizabeth E.,M. Eng.Massachusetts Institute of Technology.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Gad Getz and J. Christopher Love.
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Determining patterns of drug resistance is fundamentally required for improving clinical outcome of cancer treatment. The ability to study multiple samples from different metastatic sites of the same patient is a clinically and analytically challenging task, which has become possible with the advent of "rapid" autopsy procedures (<10 hours from death) conducted on cancer patients through the Massachusetts General Hospital (MGH) Rapid Autopsy Program. The dataset of whole-exome, whole genome and transcriptome sequencing data from advanced cancer samples uniquely captures genomic and transcriptomic information from multiple lesions of the same patient for advanced study of how resistance develops on the systemic level. Additionally, previously collected cell-free DNA samples enable the establishment of both a spatial and temporal picture of cancer drug resistance and progression.Using RNA expression and pathway analysis, we can also identify unique transcriptional programs and differentially expressed genes between distinct clones within one patient as well as compare genetically similar clones across patents. This thesis integrates genomic and transcriptomic data through advanced clonal reconstruction methods, as well as clinical information such as cancer type, treatment history, and lesion location and response to investigate how the patient developed resistance to anti-cancer therapy. This thesis concentrates on findings in two cohorts of rapid autopsy patients: ER+ metastatic breast cancer, with a focus on patients treated with a CDK4/6 inhibitor, and cholangiocarcinoma, with a focus on patients with FGFR2-fusions treated with an FGFR-inhibitor.In the ER+ breast cancer cohort, we identified multiple known and potentially novel resistance mechanisms in separate branches of the phylogenetic tree, often converging on distinct mutations in the same resistance genes. In cholangiocarcinoma, we found high levels of inter- and intra-tumoral heterogeneity in several patients, with a convergence on FGFR2 activating mutations as a form of resistance to FGFR-inhibitor therapy. The richness of the rapid autopsy dataset allows us to develop a fuller picture of convergent resistance mechanisms to therapy in cancer.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from student-submitted PDF of thesis.Includes bibliographical references (pages 97-102).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.