The collaboration of a large team was essential for this interdisciplinary work: Mansooreh Ahmadian, Miriam D. Post, Rebecca J. Wolsky, Christian Rickert, Kimberly R. Jordan, Junxiao Hu, Jennifer K. Richer, Lindsay W. Brubaker, Nicole Marjon, Kian Behbakht, @mjsikora.bsky.social /11

This paper came out of an on-going collaboration between CU Boulder and CU Anschutz led by @aaronclauset.bsky.social and Benjamin Bitler in which computational approaches are being applied to study ovarian cancer. /10

Overall, our study demonstrates the importance of investigating cell-cell interactions throughout the TIME in HGSC and provides a framework for hypothesis generation and testing. We discuss limitations and opportunities for further research in the paper. /9

We also ran Cox regressions for the features included in the random forest models to contextualize our predictive analysis, eg, finding that contact between B cells and M1 macrophages correlates with better patient outcomes. /8

For example, we find that features related to CD163+ cell interactions are among the most important features extracted by the model. We dig deeper into these and related results in the paper. /7

We then used feature importance scores to identify the subset of features that best predict outcomes. This strategy can be used to generate mechanistic hypotheses and inspire future lines of research into novel treatments for HGSC. /6

All models performed better than chance at predicting high/low progression-free survival. The best model (model 8, mean AUC = 0.71) used a combination of TIME composition and spatial features. /5

We then trained 15 random forest models to predict patient outcome from clinical features, immunohistochemical features, TIME composition, spatial structure, and cell interaction features. /4

We used multiplexed ion beam imaging (MIBI) to image the tumor immune microenvironment (TIME) of samples, identifying more than 160,000 cells across 23 cell types, and used spatial networks to generate features related to their patterns of interaction. /3

HGSC is the most common form of ovarian cancer. Unfortunately, survival and recurrence rates and treatment options have not improved much in 30 years. Here, we use machine learning to generate new hypotheses about what microenvironment features predict patient outcomes. /2