Virtual alignment of pathology image series for multi-gigapixel whole slide images.

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.

Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T cells.

Despite repeated associations between T cell infiltration and outcome, human ovarian cancer remains poorly responsive to immunotherapy. We report that the hallmarks of tumor recognition in ovarian cancer-infiltrating T cells are primarily restricted to tissue-resident memory (TRM) cells. Single-cell RNA/TCR/ATAC sequencing of 83,454 CD3+CD8+CD103+CD69+ TRM cells and immunohistochemistry of 122 high-grade serous ovarian cancers shows that only progenitor (TCF1low) tissue-resident T cells (TRMstem cells), but not recirculating TCF1+ T cells, predict ovarian cancer outcome. TRMstem cells arise from transitional recirculating T cells, which depends on antigen affinity/persistence, resulting in oligoclonal, trogocytic, effector lymphocytes that eventually become exhausted. Therefore, ovarian cancer is indeed an immunogenic disease, but that depends on ∼13% of CD8+ tumor-infiltrating T cells (∼3% of CD8+ clonotypes), which are primed against high-affinity antigens and maintain waves of effector TRM-like cells. Our results define the signature of relevant tumor-reactive T cells in human ovarian cancer, which could be applicable to other tumors with unideal mutational burden.

Immunosuppressive niche engineering at the onset of human colorectal cancer.

The evolutionary dynamics of tumor initiation remain undetermined, and the interplay between neoplastic cells and the immune system is hypothesized to be critical in transformation. Colorectal cancer (CRC) presents a unique opportunity to study the transition to malignancy as pre-cancers (adenomas) and early-stage cancers are frequently resected. Here, we examine tumor-immune eco-evolutionary dynamics from pre-cancer to carcinoma using a computational model, ecological analysis of digital pathology data, and neoantigen prediction in 62 patient samples. Modeling predicted recruitment of immunosuppressive cells would be the most common driver of transformation. As predicted, ecological analysis reveals that progressed adenomas co-localized with immunosuppressive cells and cytokines, while benign adenomas co-localized with a mixed immune response. Carcinomas converge to a common immune "cold" ecology, relaxing selection against immunogenicity and high neoantigen burdens, with little evidence for PD-L1 overexpression driving tumor initiation. These findings suggest re-engineering the immunosuppressive niche may prove an effective immunotherapy in CRC.

IgA-Dominated Humoral Immune Responses Govern Patients' Outcome in Endometrial Cancer.

Recent studies suggest that B cells could play an important role in the tumor microenvironment. However, the role of humoral responses in endometrial cancer remains insufficiently investigated. Using a cohort of 107 patients with different histological subtypes of endometrial carcinoma, we evaluated the role of coordinated humoral and cellular adaptive immune responses in endometrial cancer. Concomitant accumulation of T, B, and plasma cells at tumor beds predicted better survival. However, only B-cell markers corresponded with prolonged survival specifically in high-grade endometrioid type and serous tumors. Immune protection was associated with class-switched IgA and, to a lesser extent, IgG. Expressions of polymeric immunoglobulin receptor (pIgR) by tumor cells and its occupancy by IgA were superior predictors of outcome and correlated with defects in methyl-directed DNA mismatch repair. Mechanistically, pIgR-dependent, antigen-independent IgA occupancy drove activation of inflammatory pathways associated with IFN and TNF signaling in tumor cells, along with apoptotic and endoplasmic reticulum stress pathways, while thwarting DNA repair mechanisms. Together, these findings suggest that coordinated humoral and cellular immune responses, characterized by IgA:pIgR interactions in tumor cells, determine the progression of human endometrial cancer as well as the potential for effective immunotherapies. SIGNIFICANCE: This study provides new insights into the crucial role of humoral immunity in human endometrial cancer, providing a rationale for designing novel immunotherapies against this prevalent malignancy. See related commentary by Osorio and Zamarin, p. 766.

IgA transcytosis and antigen recognition govern ovarian cancer immunity.

Most ovarian cancers are infiltrated by prognostically relevant activated T cells1-3, yet exhibit low response rates to immune checkpoint inhibitors4. Memory B cell and plasma cell infiltrates have previously been associated with better outcomes in ovarian cancer5,6, but the nature and functional relevance of these responses are controversial. Here, using 3 independent cohorts that in total comprise 534 patients with high-grade serous ovarian cancer, we show that robust, protective humoral responses are dominated by the production of polyclonal IgA, which binds to polymeric IgA receptors that are universally expressed on ovarian cancer cells. Notably, tumour B-cell-derived IgA redirects myeloid cells against extracellular oncogenic drivers, which causes tumour cell death. In addition, IgA transcytosis through malignant epithelial cells elicits transcriptional changes that antagonize the RAS pathway and sensitize tumour cells to cytolytic killing by T cells, which also contributes to hindering malignant progression. Thus, tumour-antigen-specific and -antigen-independent IgA responses antagonize the growth of ovarian cancer by governing coordinated tumour cell, T cell and B cell responses. These findings provide a platform for identifying targets that are spontaneously recognized by intratumoural B-cell-derived antibodies, and suggest that immunotherapies that augment B cell responses may be more effective than approaches that focus on T cells, particularly for malignancies that are resistant to checkpoint inhibitors.

Histoecology: Applying Ecological Principles and Approaches to Describe and Predict Tumor Ecosystem Dynamics Across Space and Time.

Cancer cells exist within a complex spatially structured ecosystem composed of resources and different cell types. As the selective pressures imposed by this environment determine the fate of cancer cells, an improved understanding of how this ecosystem evolves will better elucidate how tumors grow and respond to therapy. State of the art imaging methods can now provide highly resolved descriptions of the microenvironment, yielding the data required for a thorough study of its role in tumor growth and treatment resistance. The field of landscape ecology has been studying such species-environment relationship for decades, and offers many tools and perspectives that cancer researchers could greatly benefit from. Here, we discuss one such tool, species distribution modeling (SDM), that has the potential to, among other things, identify critical environmental factors that drive tumor evolution and predict response to therapy. SDMs only scratch the surface of how ecological theory and methods can be applied to cancer, and we believe further integration will take cancer research in exciting new and productive directions. Significance: Here we describe how species distribution modeling can be used to quantitatively describe the complex relationship between tumor cells and their microenvironment. Such a description facilitates a deeper understanding of cancers eco-evolutionary dynamics, which in turn sheds light on the factors that drive tumor growth and response to treatment.

Hybrid Automata Library: A flexible platform for hybrid modeling with real-time visualization.

The Hybrid Automata Library (HAL) is a Java Library developed for use in mathematical oncology modeling. It is made of simple, efficient, generic components that can be used to model complex spatial systems. HAL's components can broadly be classified into: on- and off-lattice agent containers, finite difference diffusion fields, a GUI building system, and additional tools and utilities for computation and data collection. These components are designed to operate independently and are standardized to make them easy to interface with one another. As a demonstration of how modeling can be simplified using our approach, we have included a complete example of a hybrid model (a spatial model with interacting agent-based and PDE components). HAL is a useful asset for researchers who wish to build performant 1D, 2D and 3D hybrid models in Java, while not starting entirely from scratch. It is available on GitHub at https://github.com/MathOnco/HAL under the MIT License. HAL requires the Java JDK version 1.8 or later to compile and run the source code.

EvoFreq: visualization of the Evolutionary Frequencies of sequence and model data.

BACKGROUND: High throughput sequence data has provided in depth means of molecular characterization of populations. When recorded at numerous time steps, such data can reveal the evolutionary dynamics of the population under study by tracking the changes in genotype frequencies over time. This necessitates a simple and flexible means of visualizing an increasingly complex set of data. RESULTS: Here we offer EvoFreq as a comprehensive tool set to visualize the evolutionary and population frequency dynamics of clones at a single point in time or as population frequencies over time using a variety of informative methods. EvoFreq expands substantially on previous means of visualizing the clonal, temporal dynamics and offers users a range of options for displaying their sequence or model data. CONCLUSIONS: EvoFreq, implemented in R with robust user options and few dependencies, offers a high-throughput means of quickly building, and interrogating the temporal dynamics of hereditary information across many systems. EvoFreq is freely available via https://github.com/MathOnco/EvoFreq.