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.

A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer.

BACKGROUND: Despite evidence indicating the dominance of cell-of-origin signatures in molecular tumor patterns, translating these genome-wide patterns into actionable insights has been challenging. This study introduces breast cancer cell-of-origin signatures that offer significant prognostic value across all breast cancer subtypes and various clinical cohorts, compared to previously developed genomic signatures. METHODS: We previously reported that triple hormone receptor (THR) co-expression patterns of androgen (AR), estrogen (ER), and vitamin D (VDR) receptors are maintained at the protein level in human breast cancers. Here, we developed corresponding mRNA signatures (THR-50 and THR-70) based on these patterns to categorize breast tumors by their THR expression levels. The THR mRNA signatures were evaluated across 56 breast cancer datasets (5040 patients) using Kaplan-Meier survival analysis, Cox proportional hazard regression, and unsupervised clustering. RESULTS: The THR signatures effectively predict both overall and progression-free survival across all evaluated datasets, independent of subtype, grade, or treatment status, suggesting improvement over existing prognostic signatures. Furthermore, they delineate three distinct ER-positive breast cancer subtypes with significant survival in differences-expanding on the conventional two subtypes. Additionally, coupling THR-70 with an immune signature identifies a predominantly ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive cases, as well as an ER-negative subgroup with notably poor outcome, characterized by a 15-fold shorter survival. CONCLUSIONS: The THR cell-of-origin signature introduces a novel dimension to breast cancer biology, potentially serving as a robust foundation for integrating additional prognostic biomarkers. These signatures offer utility as a prognostic index for stratifying existing breast cancer subtypes and for de novo classification of breast cancer cases. Moreover, THR signatures may also hold promise in predicting hormone treatment responses targeting AR and/or VDR.

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.

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.

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.

A field guide to cultivating computational biology.

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Recounting the FANTOM CAGE-Associated Transcriptome.

Long noncoding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes, including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly defined human transcriptome, inclusive of over 109,000 coding and noncoding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue-specific transcription profiles for distinct classes of coding and noncoding genes, (b) perform differential expression analysis across thirteen cancer types, identifying novel noncoding genes potentially involved in tumor pathogenesis and progression, and (c) confirm the prognostic value for several enhancer lncRNAs expression in cancer. Our resource is instrumental for the systematic molecular characterization of lncRNA by the FANTOM6 Consortium. In conclusion, comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs.

Comparative transcriptomics of primary cells in vertebrates.

Gene expression profiles in homologous tissues have been observed to be different between species, which may be due to differences between species in the gene expression program in each cell type, but may also reflect differences in cell type composition of each tissue in different species. Here, we compare expression profiles in matching primary cells in human, mouse, rat, dog, and chicken using Cap Analysis Gene Expression (CAGE) and short RNA (sRNA) sequencing data from FANTOM5. While we find that expression profiles of orthologous genes in different species are highly correlated across cell types, in each cell type many genes were differentially expressed between species. Expression of genes with products involved in transcription, RNA processing, and transcriptional regulation was more likely to be conserved, while expression of genes encoding proteins involved in intercellular communication was more likely to have diverged during evolution. Conservation of expression correlated positively with the evolutionary age of genes, suggesting that divergence in expression levels of genes critical for cell function was restricted during evolution. Motif activity analysis showed that both promoters and enhancers are activated by the same transcription factors in different species. An analysis of expression levels of mature miRNAs and of primary miRNAs identified by CAGE revealed that evolutionary old miRNAs are more likely to have conserved expression patterns than young miRNAs. We conclude that key aspects of the regulatory network are conserved, while differential expression of genes involved in cell-to-cell communication may contribute greatly to phenotypic differences between species.

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.

A robust and interpretable gene signature for predicting the lymph node status of primary T1/T2 oral cavity squamous cell carcinoma.

Oral cavity squamous cell carcinoma (OSCC) affects more than 30 000 individuals in the United States annually, with smoking and alcohol consumption being the main risk factors. Management of early-stage tumors usually includes surgical resection followed by postoperative radiotherapy in certain cases. The cervical lymph nodes (LNs) are the most common site for local metastasis, and elective neck dissection is usually performed if the primary tumor thickness is greater than 3.5 mm. However, postoperative histological examination often reveals that many patients with early-stage disease are negative for neck nodal metastasis, posing a pressing need for improved risk stratification to either avoid overtreatment or prevent the disease progression. To this end, we aimed to identify a primary tumor gene signature that can accurately predict cervical LN metastasis in patients with early-stage OSCC. Using gene expression profiles from 189 samples, we trained K-top scoring pairs models and identified six gene pairs that can distinguish primary tumors with nodal metastasis from those without metastasis. The signature was further validated on an independent cohort of 35 patients using real-time polymerase chain reaction (PCR) in which it achieved an area under the receiver operating characteristic (ROC) curve and accuracy of 90% and 91%, respectively. These results indicate that such signature holds promise as a quick and cost effective method for detecting patients at high risk of developing cervical LN metastasis, and may be potentially used to guide the neck treatment regimen in early-stage OSCC.