High-grade serous ovarian cancer (HGSC) is the most lethal gynecologic malignancy, with a five-year survival rate of less than 30% for metastatic disease. Our lab has identified mutational processes as predictors of survival and response to therapy, along with a working model to predict homologous recombination deficiency from hematoxylin and eosin (H&E) whole-slide images. Our collaborators in diagnostic radiology have discovered robust associations between BRCA mutational status and qualitative features on contrast-enhanced computed tomography (CE-CT). These two imaging modalities, however, have yet to be combined with genomic information to improve stratification of HGSC patients. Based on these preliminary data, I will test the hypothesis that combined mesoscopic information in CE-CT and microscopic information in H&E can be used to infer known mutational subtypes and also to identify novel patient strata. I have curated a cohort of 118 HGSC patients with matched targeted panel-based genome sequencing, scanned H&E whole-slide images, and segmented pre-treatment CE-CT images for this purpose.
In Specific Aim 1, I will develop a machine learning model to integrate CE-CT and H&E imaging to predict mutational subtype from these ubiquitous imaging modalities.
In Specific Aim 2, I will develop an end-to-end deep learning model to integrate the complementary information from CE-CT, H&E, and genome sequencing for survival analysis using a Cox Proportional Hazards model. I anticipate that this work will (1) identify refined stratification of HGSC patients using this multimodal prognostic signature and (2) develop a general-purpose machine learning model to integrate CE-CT, H&E, and genomic sequencing for cancer patient survival analysis. This research will be conducted at Memorial Sloan Kettering Cancer Center under the mentorship of Dr. Sohrab Shah. The training plan that Dr. Shah and I have developed will prepare me well for a future as a physician- scientist conducting machine learning research for cancer patient prognosis.
Mutational subtypes of high-grade serous ovarian cancer stratify patients by clinical outcome, yet genomic sequencing omits spatial information with potential prognostic relevance. In the proposed project, I aim to (1) infer mutational subtype by combining radiologic and histologic imaging and (2) identify refined patient strata by integrating radiologic and histologic imaging with genomic sequencing. Completion of this work will identify new strata of ovarian cancer patients and result in new machine learning models to integrate cancer imaging with genomic sequencing.
