Interpretable Modeling of Immunotherapy Response Factors

Immunotherapy, which treats cancer by either stimulating or suppressing the immune system, has been extraordinarily effective for some cancers, such as breast cancer and B-cell lymphoma. A type of immunotherapy, checkpoint inhibitors work by blocking the ability of cancer cells to evade immune system detection. However, not all patients respond to checkpoint inhibitors, even those with the same tumor types, and the complexity of biological networks and diversity of patients makes it difficult for clinicians to understand why a patient does not respond to treatment. This thesis integrates RNA and whole-exome seqeuencing (WES) data into an interpretable machine learning model and investigates genetic factors that may separate responders from nonresponders. We discovered that both data types contribute to response separation and that certain gene sets may be especially important factors for predicting response. Further analysis to elucidate how much individual genes contribute to significant gene sets and response needs to be performed.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Ting, Britney A.

Advisor dc:contributor.advisor

• Fraenkel, Ernest

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151634

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151634