Deficiency of factor-inhibiting HIF creates a tumor-promoting immune microenvironment.

Hypoxia signaling influences tumor development through both cell-intrinsic and -extrinsic pathways. Inhibiting hypoxia-inducible factor (HIF) function has recently been approved as a cancer treatment strategy. Hence, it is important to understand how regulators of HIF may affect tumor growth under physiological conditions. Here we report that in aging mice factor-inhibiting HIF (FIH), one of the most studied negative regulators of HIF, is a haploinsufficient suppressor of spontaneous B cell lymphomas, particular pulmonary B cell lymphomas. FIH deficiency alters immune composition in aged mice and creates a tumor-supportive immune environment demonstrated in syngeneic mouse tumor models. Mechanistically, FIH-defective myeloid cells acquire tumor-supportive properties in response to signals secreted by cancer cells or produced in the tumor microenvironment with enhanced arginase expression and cytokine-directed migration. Together, these data demonstrate that under physiological conditions, FIH plays a key role in maintaining immune homeostasis and can suppress tumorigenesis through a cell-extrinsic pathway.

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

MOTIVATION: Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy. RESULTS: In this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/ESQmodel.

Considerations for Evaluating Older Adults with Cancer for Depression: A Qualitative Survey of Experts.

OBJECTIVES: The objective of this study was to engage national experts in geriatric psychiatry and oncology in qualitative interviews to develop consensus regarding how older adult cancer survivors (OACS) experience depressive symptoms, and how best to assess OACs for depression. METHODS: Expert clinicians in geriatric oncology disciplines were interviewed about approaches to assessing depression in OACs. Interviews were audio-recorded and transcribed, and conducted until thematic saturation was achieved. Thematic Content Analysis was utilized to identify key themes. RESULTS: Experts (N = 8) were board certified geriatric psychiatrists and oncologists with specialization in geriatric medicine. Two conceptual domains were identified: Key indicators of depression in OACs (e.g. anhedonia; loss of meaning and purpose; loneliness and social withdrawal) and unique considerations for depression assessment in OACs (e.g. alternative phrasing to "depression," disentangling mood and cancer or treatment-related side effects). CONCLUSIONS: The approaches identified tended to depart from traditional diagnostic criteria for depression. CLINICAL IMPLICATIONS: Results provide additional insight into the limitations of existing depression measures for OACs. The themes and practices identified in the present study suggest that a revised measure of depression for OACs may be useful. Future research will continue to shed light on best practices for depression assessment in OACs.

The role of cholesterol and its oxidation products in tuberculosis pathogenesis.

Mycobacterium tuberculosis causes tuberculosis (TB), one of the world's most deadly infections. Lipids play an important role in M. tuberculosis pathogenesis. M. tuberculosis grows intracellularly within lipid-laden macrophages and extracellularly within the cholesterol-rich caseum of necrotic granulomas and pulmonary cavities. Evolved from soil saprophytes that are able to metabolize cholesterol from organic matter in the environment, M. tuberculosis inherited an extensive and highly conserved machinery to metabolize cholesterol. M. tuberculosis uses this machinery to degrade host cholesterol; the products of cholesterol degradation are incorporated into central carbon metabolism and used to generate cell envelope lipids, which play important roles in virulence. The host also modifies cholesterol by enzymatically oxidizing it to a variety of derivatives, collectively called oxysterols, which modulate cholesterol homeostasis and the immune response. Recently, we found that M. tuberculosis converts host cholesterol to an oxidized metabolite, cholestenone, that accumulates in the lungs of individuals with TB. M. tuberculosis encodes cholesterol-modifying enzymes, including a hydroxysteroid dehydrogenase, a putative cholesterol oxidase, and numerous cytochrome P450 monooxygenases. Here, we review what is known about cholesterol and its oxidation products in the pathogenesis of TB. We consider the possibility that the biological function of cholesterol metabolism by M. tuberculosis extends beyond a nutritional role.

Integrated single cell analysis reveals co-evolution of malignant B cells and tumor micro-environment in transformed follicular lymphoma.

Histological transformation of follicular lymphoma (FL) to aggressive forms is associated with poor outcome. Phenotypic consequences of this evolution and its impact on the tumor microenvironment (TME) remain unknown. We perform single-cell whole genome sequencing (scWGS) and transcriptome sequencing (scWTS) of 11 paired pre/post-transformation patient samples and scWTS of additional samples from patients without transformation. Our analysis reveals evolutionary dynamics of transformation at single-cell resolution, highlighting a shifting TME landscape, with an emerging immune-cell exhaustion signature, co-evolving with the shifting malignant B phenotype in a regulatory ecosystem. Integration of scWGS and scWTS identifies malignant cell pathways upregulated during clonal tumor evolution. Using multi-color immunofluorescence, we transfer these findings to a TME-based transformation biomarker, subsequently validated in two independent pretreatment cohorts. Taken together, our results provide a comprehensive view of the combined genomic and phenotypic evolution of malignant cells during transformation and shifting crosstalk between malignant cells and the TME.

Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers.

BACKGROUND: The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug-associated transcriptional cell states. RESULTS: We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. CONCLUSIONS: Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration, and re-treatment strategies.

Spatially Resolved Tumor Microenvironment Predicts Treatment Outcomes in Relapsed/Refractory Hodgkin Lymphoma.

PURPOSE: About a third of patients with relapsed or refractory classic Hodgkin lymphoma (r/r CHL) succumb to their disease after high-dose chemotherapy followed by autologous stem-cell transplantation (HDC/ASCT). Here, we aimed to describe spatially resolved tumor microenvironment (TME) ecosystems to establish novel biomarkers associated with treatment failure in r/r CHL. PATIENTS AND METHODS: We performed imaging mass cytometry (IMC) on 71 paired primary diagnostic and relapse biopsies using a marker panel specific to CHL biology. For each cell type in the TME, we calculated a spatial score measuring the distance of nearest neighbor cells to the malignant Hodgkin Reed Sternberg cells within the close interaction range. Spatial scores were used as features in prognostic model development for post-ASCT outcomes. RESULTS: Highly multiplexed IMC data revealed shared TME patterns in paired diagnostic and early r/r CHL samples, whereas TME patterns were more divergent in pairs of diagnostic and late relapse samples. Integrated analysis of IMC and single-cell RNA sequencing data identified unique architecture defined by CXCR5+ Hodgkin and Reed Sternberg (HRS) cells and their strong spatial relationship with CXCL13+ macrophages in the TME. We developed a prognostic assay (RHL4S) using four spatially resolved parameters, CXCR5+ HRS cells, PD1+CD4+ T cells, CD68+ tumor-associated macrophages, and CXCR5+ B cells, which effectively separated patients into high-risk versus low-risk groups with significantly different post-ASCT outcomes. The RHL4S assay was validated in an independent r/r CHL cohort using a multicolor immunofluorescence assay. CONCLUSION: We identified the interaction of CXCR5+ HRS cells with ligand-expressing CXCL13+ macrophages as a prominent crosstalk axis in relapsed CHL. Harnessing this TME biology, we developed a novel prognostic model applicable to r/r CHL biopsies, RHL4S, opening new avenues for spatial biomarker development.

Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure.

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.