The cytokine Meteorin-like inhibits anti-tumor CD8+ T cell responses by disrupting mitochondrial function.

Tumor-infiltrating lymphocyte (TIL) hypofunction contributes to the progression of advanced cancers and is a frequent target of immunotherapy. Emerging evidence indicates that metabolic insufficiency drives T cell hypofunction during tonic stimulation, but the signals that initiate metabolic reprogramming in this context are largely unknown. Here, we found that Meteorin-like (METRNL), a metabolically active cytokine secreted by immune cells in the tumor microenvironment (TME), induced bioenergetic failure of CD8+ T cells. METRNL was secreted by CD8+ T cells during repeated stimulation and acted via both autocrine and paracrine signaling. Mechanistically, METRNL increased E2F-peroxisome proliferator-activated receptor delta (PPARδ) activity, causing mitochondrial depolarization and decreased oxidative phosphorylation, which triggered a compensatory bioenergetic shift to glycolysis. Metrnl ablation or downregulation improved the metabolic fitness of CD8+ T cells and enhanced tumor control in several tumor models, demonstrating the translational potential of targeting the METRNL-E2F-PPARδ pathway to support bioenergetic fitness of CD8+ TILs.

Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.

PURPOSE: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient outcomes. MATERIALS/METHODS: An international cohort of 117 oligometastatic castration-sensitive prostate cancer (omCSPC) patients, comprising 34 from John Hopkins Hospital (JHH) and 83 from Baskent University (BU), were treated with stereotactic ablative radiation therapy (SABR) MDT with both pre- and post-MDT PSMA-PET/CT scans acquired. PET radiomic features were analyzed from a CT-PET fusion defined gross tumor volume ((GTV) or zone 1), and a 5 mm expansion ring area outside the GTV (zone 2). A total of 1748 PET radiomic features were extracted from these zones. The six most significant features selected using the Chi2 method, along with five clinical parameters (age, Gleason score, number of total lesions, untreated lesions, and pre-MDT prostate-specific antigen (PSA)) were extracted as inputs to the models. Various machine learning models, including Random Forest, Decision Tree, Support Vector Machine, and Naïve Bayesian, were employed for 2-year MFS prediction and tested using leave-one-out and cross-institution validation. RESULTS: Six radiomic features, including Total Energy, Entropy, and Standard Deviation from pre-PSMA-PET zone 1, Total Energy and Contrast from post-PSMA-PET zone 1, and Entropy from pre-PSMA-PET zone 2, along with five clinical parameters were selected for predicting 2-year MFS. In a leave-one-out test with all the patients, random forest achieved an accuracy of 80 % and an AUC of 0.82 in predicting 2-year MFS. In cross-institution validation, the model correctly predicted 2-year MFS events with an accuracy of 75 % and an AUC of 0.77 for patients from JHH, and an accuracy of 78 % and an AUC of 0.80 for BU patients, respectively. CONCLUSION: Our study demonstrated the promise of using pre- and post-MDT PSMA-PET-based imaging biomarkers for MFS prediction for omCSPC patients.

ChatGPT for digital pathology research.

The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discuss how domain-specific approaches substantially minimise the risk of inaccurate responses, enhancing the reliability and accuracy of information extraction. We also highlight the broader implications of such tools, particularly in streamlining access to scientific research and democratising access to computational pathology techniques for scientists with little coding experience. This Viewpoint calls for an enhanced integration of domain-specific text-generation AI tools in academic settings to facilitate continuous learning and adaptation to the dynamically evolving landscape of medical research.

Emergence of transmissible SARS-CoV-2 variants with decreased sensitivity to antivirals in immunocompromised patients with persistent infections.

We investigated the impact of antiviral treatment on the emergence of SARS-CoV-2 resistance during persistent infections in immunocompromised patients (n=15). All patients received remdesivir and some also received nirmatrelvir-ritonavir or monoclonal antibodies. Sequence analysis showed that nine patients carried viruses with mutations in the nsp12 (RNA dependent RNA polymerase), while four had viruses with nsp5 (3C protease) mutations. Infectious SARS-CoV-2 with a double mutation in nsp5 (T169I) and nsp12 (V792I) was recovered from respiratory secretions 77 days after initial COVID-19 diagnosis from a patient treated with remdesivir and nirmatrelvir-ritonavir. In vitro characterization confirmed its decreased sensitivity to remdesivir and nirmatrelvir, which was overcome by combined antiviral treatment. Studies in golden Syrian hamsters demonstrated efficient transmission to contact animals. This study documents the isolation of SARS-CoV-2 carrying resistance mutations to both nirmatrelvir and remdesivir from a patient and demonstrates its transmissibility in vivo.

Obesogenic High-Fat Diet and MYC Cooperate to Promote Lactate Accumulation and Tumor Microenvironment Remodeling in Prostate Cancer.

Cancer cells exhibit metabolic plasticity to meet oncogene-driven dependencies while coping with nutrient availability. A better understanding of how systemic metabolism impacts the accumulation of metabolites that reprogram the tumor microenvironment (TME) and drive cancer could facilitate development of precision nutrition approaches. Using the Hi-MYC prostate cancer mouse model, we demonstrated that an obesogenic high-fat diet (HFD) rich in saturated fats accelerates the development of c-MYC-driven invasive prostate cancer through metabolic rewiring. Although c-MYC modulated key metabolic pathways, interaction with an obesogenic HFD was necessary to induce glycolysis and lactate accumulation in tumors. These metabolic changes were associated with augmented infiltration of CD206+ and PD-L1+ tumor-associated macrophages (TAM) and FOXP3+ regulatory T cells, as well as with the activation of transcriptional programs linked to disease progression and therapy resistance. Lactate itself also stimulated neoangiogenesis and prostate cancer cell migration, which were significantly reduced following treatment with the lactate dehydrogenase inhibitor FX11. In patients with prostate cancer, high saturated fat intake and increased body mass index were associated with tumor glycolytic features that promote the infiltration of M2-like TAMs. Finally, upregulation of lactate dehydrogenase, indicative of a lactagenic phenotype, was associated with a shorter time to biochemical recurrence in independent clinical cohorts. This work identifies cooperation between genetic drivers and systemic metabolism to hijack the TME and promote prostate cancer progression through oncometabolite accumulation. This sets the stage for the assessment of lactate as a prognostic biomarker and supports strategies of dietary intervention and direct lactagenesis blockade in treating advanced prostate cancer. SIGNIFICANCE: Lactate accumulation driven by high-fat diet and MYC reprograms the tumor microenvironment and promotes prostate cancer progression, supporting the potential of lactate as a biomarker and therapeutic target in prostate cancer. See related commentary by Frigo, p. 1742.

Genomic Determinants Associated with Modes of Progression and Patterns of Failure in Metachronous Oligometastatic Castration-sensitive Prostate Cancer.

BACKGROUND AND OBJECTIVE: Oligometastatic castration-sensitive prostate cancer (omCSPC) represents an early state in the progression of metastatic disease for which patients experience better outcomes in comparison to those with higher disease burden. Despite the generally more indolent nature, there is still much heterogeneity, with some patients experiencing a more aggressive clinical course unexplained by clinical features alone. Our aim was to investigate correlation of tumor genomics with the mode of progression (MOP) and pattern of failure (POF) following first treatment (metastasis-directed and/or systemic therapy) for omCSPC. METHODS: We performed an international multi-institutional retrospective study of men treated for metachronous omCSPC who underwent tumor next-generation sequencing with at least 1 yr of follow-up after their first treatment. Descriptive MOP and POF results are reported with respect to the presence of genomic alterations in pathways of interest. MOP was defined as class I, long-term control (LTC; no radiographic progression at last follow-up), class II, oligoprogression (1-3 lesions), or class III, polyprogression (≥4 lesions). POF included the location of lesions at first failure. Genomic pathways of interest included TP53, ATM, RB1, BRCA1/2, SPOP, and WNT (APC, CTNNB1, RNF43). Genomic associations with MOP/POF were compared using χ2 tests. Exploratory analyses revealed that the COSMIC mutational signature and differential gene expression were also correlated with MOP/POF. Overall survival (OS) was calculated via the Kaplan-Meier method from the time of first failure. KEY FINDINGS AND CLINICAL IMPLICATIONS: We included 267 patients in our analysis; the majority had either one (47%) or two (30%) metastatic lesions at oligometastasis. The 3-yr OS rate was significantly associated with MOP (71% for polyprogression vs 91% for oligoprogression; p = 0.005). TP53 mutation was associated with a significantly lower LTC rate (27.6% vs 42.3%; p = 0.04) and RB1 mutation was associated with a high rate of polyprogression (50% vs 19.9%; p = 0.022). Regarding POF, bone failure was significantly more common with tumors harboring TP53 mutations (44.8% vs25.9%; p = 0.005) and less common with SPOP mutations (7.1% vs 31.4%; p = 0.007). Visceral failure was more common with tumors harboring either WNT pathway mutations (17.2% vs 6.8%, p = 0.05) or SPOP mutations (17.9% vs 6.3%; p = 0.04). Finally, visceral and bone failures were associated with distinct gene-expression profiles. CONCLUSIONS AND CLINICAL IMPLICATIONS: Tumor genomics provides novel insight into MOP and POF following treatment for metachronous omCSPC. Patients with TP53 and RB1 mutations have a higher likelihood of progression, and TP53, SPOP, and WNT pathway mutations may have a role in metastatic organotropism. PATIENT SUMMARY: We evaluated cancer progression after a first treatment for metastatic prostate cancer with up to five metastases. We found that mutations in certain genes were associated with the location and extent of further metastasis in these patients.

Comparison of three machine learning algorithms for classification of B-cell neoplasms using clinical flow cytometry data.

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.

Semi-Supervised, Attention-Based Deep Learning for Predicting TMPRSS2:ERG Fusion Status in Prostate Cancer Using Whole Slide Images.

IMPLICATIONS: Our study illuminates the potential of deep learning in effectively inferring key prostate cancer genetic alterations from the tissue morphology depicted in routinely available histology slides, offering a cost-effective method that could revolutionize diagnostic strategies in oncology.

Distinct mesenchymal cell states mediate prostate cancer progression.

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

Divergent immune microenvironments in two tumor nodules from a patient with mismatch repair-deficient prostate cancer.

Patients with prostate cancer (PC) generally do not respond favorably to immune checkpoint inhibitors, which may be due to a low abundance of tumor-infiltrating lymphocytes even when mutational load is high. Here, we identified a patient who presented with high-grade primary prostate cancer with two adjacent tumor nodules. While both nodules were mismatch repair-deficient (MMRd), exhibited pathogenic MSH2 and MSH6 alterations, had a high tumor mutational burden (TMB), and demonstrated high microsatellite instability (MSI), they had markedly distinct immune phenotypes. The first displayed a dense infiltrate of lymphocytes ("hot nodule"), while the second displayed significantly fewer infiltrating lymphocytes ("cold nodule"). Whole-exome DNA analysis found that both nodules shared many identical mutations, indicating that they were derived from a single clone. However, the cold nodule appeared to be sub-clonal relative to the hot nodule, suggesting divergent evolution of the cold nodule from the hot nodule. Whole-transcriptome RNA analysis found that the cold nodule demonstrated lower expression of genes related to antigen presentation (HLA) and, paradoxically, classical tumor immune tolerance markers such as PD-L1 (CD274) and CTLA-4. Immune cell deconvolution suggested that the hot nodule was enriched not only in CD8+ and CD4 + T lymphocytes, but also in M1 macrophages, activated NK cells, and γδ T cells compared to the cold nodule. This case highlights that MMRd/TMB-high PC can evolve to minimize an anti-tumor immune response, and nominates downregulation of antigen presentation machinery (HLA loss) as a potential mechanism of adaptive immune evasion in PC.

Concomitant assessment of PD-1 and CD56 expression identifies subsets of resting cord blood Vδ2 T cells with disparate cytotoxic potential.

Vγ9Vδ2 T lymphocytes are programmed for broad antimicrobial responses with rapid production of Th1 cytokines even before birth, and thus thought to play key roles against pathogens in infants. The process regulating Vδ2 cell acquisition of cytotoxic potential shortly after birth remains understudied. We observed that perforin production in cord blood Vδ2 cells correlates with phenotypes defined by the concomitant assessment of PD-1 and CD56. Bulk RNA sequencing of sorted Vδ2 cell fractions indicated that transcripts related to cytotoxic activity and NK function are enriched in the subset with the highest proportion of perforin+ cells. Among differentially expressed transcripts, IRF8, previously linked to CD8 T cell effector differentiation and NK maturation, has the potential to mediate Vδ2 cell differentiation towards cytotoxic effectors. Our current and past results support the hypothesis that distinct mechanisms regulate Vδ2 cell cytotoxic function before and after birth, possibly linked to different levels of microbial exposure.

TP53 structure-function relationships in metastatic castrate-sensitive prostate cancer and the impact of APR-246 treatment.

PURPOSE: Despite well-informed work in several malignancies, the phenotypic effects of TP53 mutations in metastatic castration-sensitive prostate cancer (mCSPC) progression and metastasis are not clear. We characterized the structure-function and clinical impact of TP53 mutations in mCSPC. PATIENTS AND METHODS: We performed an international retrospective review of men with mCSPC who underwent next-generation sequencing and were stratified according to TP53 mutational status and metastatic burden. Clinical outcomes included radiographic progression-free survival (rPFS) and overall survival (OS) evaluated with Kaplan-Meier and multivariable Cox regression. We also utilized isogenic cancer cell lines to assess the effect of TP53 mutations and APR-246 treatment on migration, invasion, colony formation in vitro, and tumor growth in vivo. Preclinical experimental observations were compared using t-tests and ANOVA. RESULTS: Dominant-negative (DN) TP53 mutations were enriched in patients with synchronous (vs. metachronous) (20.7% vs. 6.3%, p < 0.01) and polymetastatic (vs. oligometastatic) (14.4% vs. 7.9%, p < 0.01) disease. On multivariable analysis, DN mutations were associated with worse rPFS (hazards ratio [HR] = 1.97, 95% confidence interval [CI]: 1.31-2.98) and overall survival [OS] (HR = 2.05, 95% CI: 1.14-3.68) compared to TP53 wild type (WT). In vitro, 22Rv1 TP53 R175H cells possessed stronger migration, invasion, colony formation ability, and cellular movement pathway enrichment in RNA sequencing analysis compared to 22Rv1 TP53 WT cells. Treatment with APR-246 reversed the effects of TP53 mutations in vitro and inhibited 22Rv1 TP53 R175H tumor growth in vivo in a dosage-dependent manner. CONCLUSIONS: DN TP53 mutations correlated with worse prognosis in prostate cancer patients and higher metastatic potential, which could be counteracted by APR-246 treatment suggesting a potential future therapeutic avenue.

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer.

BACKGROUND: While the efficacy of neoadjuvant chemotherapy (NACT) in treating triple-negative breast cancer (TNBC) is generally accepted, not all patients derive benefit from this preoperative treatment. Presently, there are no validated biomarkers to predict the NACT response, and previous attempts to develop predictive classifiers based on gene expression data have not demonstrated clinical utility. However, predictive models incorporating biological constraints have shown increased robustness and improved performance compared to agnostic classifiers. METHODS: We used the preoperative transcriptomic profiles from 298 patients with TNBC to train and test a rank-based classifier, k-top scoring pairs, to predict whether the patient will have pathological complete response (pCR) or residual disease (RD) following NACT. To reduce overfitting and enhance the signature's interpretability, we constrained the training process to genes involved in the Notch signaling pathway. Subsequently, we evaluated the signature performance on two independent cohorts with 75 and 71 patients. Finally, we assessed the prognostic value of the signature by examining its association with relapse-free survival (RFS) using Kaplan‒Meier (KM) survival estimates and a multivariate Cox proportional hazards model. RESULTS: The final signature consists of five gene pairs, whose relative ordering can be predictive of the NACT response. The signature has a robust performance at predicting pCR in TNBC patients with an area under the ROC curve (AUC) of 0.76 and 0.85 in the first and second testing cohorts, respectively, outperforming other gene signatures developed for the same purpose. Additionally, the signature was significantly associated with RFS in an independent TNBC patient cohort even after adjusting for T stage, patient age at the time of diagnosis, type of breast surgery, and menopausal status. CONCLUSION: We introduce a robust gene signature to predict pathological complete response (pCR) in patients with TNBC. This signature applies easily interpretable, rank-based decision rules to genes regulated by the Notch signaling pathway, a known determinant in breast cancer chemoresistance. The robust predictive and prognostic performance of the signature make it a strong candidate for clinical implementation, aiding in the stratification of TNBC patients undergoing NACT.

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.

Structural variants (SVs) represent a major source of genetic variation associated with phenotypic diversity and disease susceptibility. While long-read sequencing can discover over 20,000 SVs per human genome, interpreting their functional consequences remains challenging. Existing methods for identifying disease-related SVs focus on deletion/duplication only and cannot prioritize individual genes affected by SVs, especially for noncoding SVs. Here, we introduce PhenoSV, a phenotype-aware machine-learning model that interprets all major types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse genomic features and employs a transformer-based architecture to predict their impacts under a multiple-instance learning framework. With phenotype information, PhenoSV further utilizes gene-phenotype associations to prioritize phenotype-related SVs. Evaluation on extensive human SV datasets covering all SV types demonstrates PhenoSV's superior performance over competing methods. Applications in diseases suggest that PhenoSV can determine disease-related genes from SVs. A web server and a command-line tool for PhenoSV are available at https://phenosv.wglab.org .

Tumor-Specific CD8+ T Cells from the Bone Marrow Resist Exhaustion and Exhibit Increased Persistence in Tumor-Bearing Hosts as Compared to Tumor Infiltrating Lymphocytes.

Immunotherapy is now an integral aspect of cancer therapy. Strategies employing adoptive cell therapy (ACT) have seen the establishment of chimeric antigen receptor (CAR)-T cells using peripheral blood lymphocytes as well as tumor infiltrating lymphocytes (TILs) with significant clinical results. Despite these successes, the limitations of the current strategies are also emerging and novel approaches are needed. The bone marrow (BM) is an immunological niche that houses T cells with specificity for previously encountered antigens, including tumor-associated antigens from certain solid cancers. This study sought to improve our understanding of tumor-specific BM T cells in the context of solid tumors by comparing them with TILs, and to assess whether there is a rationale for using the BM as a source of T cells for ACT against solid malignancies. Herein, we demonstrate that T cells from the BM appear superior to TILs as a source of cells for cellular therapy. Specifically, they possess a memory-enriched phenotype and exhibit improved effector function, greater persistence within a tumor-bearing host, and the capacity for increased tumor infiltration. Taken together, these data provide a foundation for further exploring the BM as a source of tumor-specific T cells for ACT in solid malignancies.

CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species.

Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture.

Optimal fluid volume for detecting malignancy in serous effusions: a single institution experience.

INTRODUCTION: Detection of malignant cells in serous fluids is an indicator of advanced stage of malignancy and is critical in clinical management decisions and prompt treatment initiation. The minimum volume which is ideal for detecting malignancy in serous fluid is not well established. In this study, we aim to identify optimal volume that will be ideal for adequate cytopathological diagnosis. MATERIALS AND METHODS: A total of 1597 samples of serous fluids from 1134 patients were included in the study. Samples were diagnosed based on International System for Reporting Serous Fluid Cytopathology (ISRSFC). Clinicopathologic results from different diagnostic groups were compared and statistically analyzed. RESULTS: Pleural fluids comprised 890 (55.7%) specimens, followed by 456 (28.6%) peritoneal, 128 (8%) ascites, and 123 (7.7%) pericardial fluid specimens. The majority were negative for malignancy (1138, 71.3%), followed by malignant (376, 23.5%), atypical (59, 3.7%), and suspicious for malignancy (24, 1.5%). Malignancy was identified in sample with volumes from 5 mL to 5000 mL. Rate of detection of malignant cells increased significantly with higher sample volumes. For malignancy detection the optimal volume for overall serous fluid is 70 mL. Pericardial fluid is an exception, with lower mean volume and significantly lower proportion of cases with malignant diagnosis. CONCLUSIONS: Our study indicates that higher fluid volumes have a higher rate of malignancy detection and a low false-negative rate. We recommend a minimum of 70 mL of serous fluid for optimal cytopathologic examination and malignancy detection. Pericardial fluid is an exception, with lower mean volume and thus lower requirement.

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously.

MOTIVATION: Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. RESULTS: Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. AVAILABILITY AND IMPLEMENTATION: The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.

Distinct mesenchymal cell states mediate prostate cancer progression.

Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

REPAC: analysis of alternative polyadenylation from RNA-sequencing data.

Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, availability of these data are limited compared to RNA-sequencing data. We developed REPAC, a framework for the analysis of APA from RNA-sequencing data. Using REPAC, we investigate the landscape of APA caused by activation of B cells. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA.