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Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.

Immunotherapy is used to treat almost all patients with advanced non-small cell lung cancer (NSCLC); however, identifying robust predictive biomarkers remains challenging. Here we show the predictive capacity of integrating medical imaging, histopathologic and genomic features to predict immunotherapy response using a cohort of 247 patients with advanced NSCLC with multimodal baseline data obtained during diagnostic clinical workup, including computed tomography scan images, digitized programmed death ligand-1 immunohistochemistry slides and known outcomes to immunotherapy. Using domain expert annotations, we developed a computational workflow to extract patient-level features and used a machine-learning approach to integrate multimodal features into a risk prediction model. Our multimodal model (area under the curve (AUC) = 0.80, 95% confidence interval (CI) 0.74–0.86) outperformed unimodal measures, including tumor mutational burden (AUC = 0.61, 95% CI 0.52–0.70) and programmed death ligand-1 immunohistochemistry score (AUC = 0.73, 95% CI 0.65–0.81). Our study therefore provides a quantitative rationale for using multimodal features to improve prediction of immunotherapy response in patients with NSCLC using expert-guided machine learning.

The use of digital pathology for the histomorphologic profiling of pathological specimens is expanding the precision and specificity of quantitative tissue analysis at an unprecedented scale; thus, enabling the discovery of new and functionally relevant histological features of both predictive and prognostic significance. In this study, we apply quantitative automated image processing and computational methods to profile the subcellular distribution of the multi-functional transcriptional regulator, Kaiso (ZBTB33), in the tumors of a large racially diverse breast cancer cohort from a designated health disparities region in the United States. Multiplex multivariate analysis of the association of Kaiso’s subcellular distribution with other breast cancer biomarkers reveals novel functional and predictive linkages between Kaiso and the autophagy-related proteins, LC3A/B, that are associated with features of the tumor immune microenvironment, survival, and race. These findings identify effective modalities of Kaiso biomarker assessment and uncover unanticipated insights into Kaiso’s role in breast cancer progression.

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