Padigela, H. | bioRxiv Science

bioRxiv2024

Spatial mapping of immunosuppressive cancer-associated fibroblast gene signatures in H&E-stained images using additive multiple instance learning

The relative abundance of cancer-associated fibroblast (CAF) subtypes influences a tumors response to treatment, especially immunotherapy. However, the extent to which the underlying tumor composition associates with CAF subtype-specific gene expression is unclear. Here, we describe an interpretable machine learning (ML) approach, additive multiple instance learning (aMIL), to predict bulk gene expression signatures from H&E-stained whole slide images (WSI), focusing on an immunosuppressive LRRC15+ CAF-enriched TGF{beta}-CAF signature. aMIL models accurately predicted TGF{beta}-CAF across various cancer types. Tissue regions contributing most highly to slide-level predictions of TGF{beta}-CAF were evaluated by ML models characterizing spatial distributions of diverse cell and tissue types, stromal subtypes, and nuclear morphology. In breast cancer, regions contributing most to TGF{beta}-CAF-high predictions ("excitatory") were localized to cancer stroma with high fibroblast density and mature collagen fibers. Regions contributing most to TGF{beta}-CAF-low predictions ("inhibitory") were localized to cancer epithelium and densely inflamed stroma. Fibroblast and lymphocyte nuclear morphology also differed between excitatory and inhibitory regions. Thus, aMIL enables a data-driven link between tissue phenotype and transcription, offering biological interpretability beyond typical black-box models.

cancer biology↗

bioRxiv2023

Cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

cancer biology↗