Cisplatin resistance-related transcriptome and methylome integration identifies PCDHB4 as a novel prognostic biomarker in small cell lung cancer.

Platinum-based chemo-resistance is the major issue for the treatment of small cell lung cancer (SCLC). The integrative analysis of multi-omics data is a reliable approach for discovering novel biomarkers associated with chemo-resistance. Here, multi-omics integrative analysis and Cox regression found that higher expression of PCDHB4 was associated with poorer survival of SCLC patients who received chemotherapy. PCDHB4 gene was hypomethylated and upregulated in SCLC, which was validated in the levels of promoter methylation, mRNA, and protein expression. Mechanistically, using bulk RNA-seq data, functional enrichment analysis indicated that higher PCDHB4 expression was associated with lower immune infiltration. The analysis of single-cell RNA-seq (scRNA-seq) found that SCLC cells with PCDHB4 expression exhibited the characteristics of stemness and EMT. In addition, the high expression and hypomethylation of PCDHB4 were also significantly associated with poor survival in lung squamous cell carcinoma. In summary, PCDHB4 is a potential prognostic biomarker of platinum-based chemotherapy in SCLC.

TGF-ß neutralization attenuates tumor residency of activated T cells to enhance systemic immunity in mice.

A tissue resident-like phenotype in tumor infiltrating T cells can limit systemic anti-tumor immunity. Enhanced systemic anti-tumor immunity is observed in head and neck cancer patients after neoadjuvant PD-L1 immune checkpoint blockade (ICB) and transforming growth factor ß (TGF-ß) neutralization. Using T cell receptor (TCR) sequencing and functional immunity assays in a syngeneic model of oral cancer, we dissect the relative contribution of these treatments to enhanced systemic immunity. The addition of TGF-ß neutralization to ICB resulted in the egress of expanded and exhausted CD8+ tumor infiltrating lymphocytes (TILs) into circulation and greater systemic anti-tumor immunity. This enhanced egress associated with reduced expression of Itgae (CD103) and its upstream regulator Znf683. Circulating CD8+ T cells expressed higher Cxcr3 after treatment, an observation also made in samples from patients treated with dual TGF-ß neutralization and ICB. These findings provide the scientific rationale for the use of PD-L1 ICB and TGF-ß neutralization in newly diagnosed patients with carcinomas prior to definitive treatment of locoregional disease.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion.

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Increased prevalence of hybrid epithelial/mesenchymal state and enhanced phenotypic heterogeneity in basal breast cancer.

Intra-tumoral phenotypic heterogeneity promotes tumor relapse and therapeutic resistance and remains an unsolved clinical challenge. Decoding the interconnections among different biological axes of plasticity is crucial to understand the molecular origins of phenotypic heterogeneity. Here, we use multi-modal transcriptomic data-bulk, single-cell, and spatial transcriptomics-from breast cancer cell lines and primary tumor samples, to identify associations between epithelial-mesenchymal transition (EMT) and luminal-basal plasticity-two key processes that enable heterogeneity. We show that luminal breast cancer strongly associates with an epithelial cell state, but basal breast cancer is associated with hybrid epithelial/mesenchymal phenotype(s) and higher phenotypic heterogeneity. Mathematical modeling of core underlying gene regulatory networks representative of the crosstalk between the luminal-basal and epithelial-mesenchymal axes elucidate mechanistic underpinnings of the observed associations from transcriptomic data. Our systems-based approach integrating multi-modal data analysis with mechanism-based modeling offers a predictive framework to characterize intra-tumor heterogeneity and identify interventions to restrict it.

AutoCancer as an automated multimodal framework for early cancer detection.

Current studies in early cancer detection based on liquid biopsy data often rely on off-the-shelf models and face challenges with heterogeneous data, as well as manually designed data preprocessing pipelines with different parameter settings. To address those challenges, we present AutoCancer, an automated, multimodal, and interpretable transformer-based framework. This framework integrates feature selection, neural architecture search, and hyperparameter optimization into a unified optimization problem with Bayesian optimization. Comprehensive experiments demonstrate that AutoCancer achieves accurate performance in specific cancer types and pan-cancer analysis, outperforming existing methods across three cohorts. We further demonstrated the interpretability of AutoCancer by identifying key gene mutations associated with non-small cell lung cancer to pinpoint crucial factors at different stages and subtypes. The robustness of AutoCancer, coupled with its strong interpretability, underscores its potential for clinical applications in early cancer detection.

Moderate-intensity aerobic exercise training improves CD8+ tumor-infiltrating lymphocytes effector function by reducing mitochondrial loss.

Aerobic exercise training (AET) has emerged as a strategy to reduce cancer mortality, however, the mechanisms explaining AET on tumor development remain unclear. Tumors escape immune detection by generating immunosuppressive microenvironments and impaired T cell function, which is associated with T cell mitochondrial loss. AET improves mitochondrial content and function, thus we tested whether AET would modulate mitochondrial metabolism in tumor-infiltrating lymphocytes (TIL). Balb/c mice were subjected to a treadmill AET protocol prior to CT26 colon carcinoma cells injection and until tumor harvest. Tissue hypoxia, TIL infiltration and effector function, and mitochondrial content, morphology and function were evaluated. AET reduced tumor growth, improved survival, and decreased tumor hypoxia. An increased CD8+ TIL infiltration, IFN-γ and ATP production promoted by AET was correlated with reduced mitochondrial loss in these cells. Collectively, AET decreases tumor growth partially by increasing CD8+ TIL effector function through an improvement in their mitochondrial content and function.

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.

Sarcoma_CellminerCDB: A tool to interrogate the genomic and functional characteristics of a comprehensive collection of sarcoma cell lines.

Sarcomas are a diverse group of rare malignancies composed of multiple different clinical and molecular subtypes. Due to their rarity and heterogeneity, basic, translational, and clinical research in sarcoma has trailed behind that of other cancers. Outcomes for patients remain generally poor due to an incomplete understanding of disease biology and a lack of novel therapies. To address some of the limitations impeding preclinical sarcoma research, we have developed Sarcoma_CellMinerCDB, a publicly available interactive tool that merges publicly available sarcoma cell line data and newly generated omics data to create a comprehensive database of genomic, transcriptomic, methylomic, proteomic, metabolic, and pharmacologic data on 133 annotated sarcoma cell lines. The reproducibility, functionality, biological relevance, and therapeutic applications of Sarcoma_CellMinerCDB described herein are powerful tools to address and generate biological questions and test hypotheses for translational research. Sarcoma_CellMinerCDB (https://discover.nci.nih.gov/SarcomaCellMinerCDB) aims to contribute to advancing the preclinical study of sarcoma.

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients.

Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data are available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-world healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer.

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

NOT gated T cells that selectively target EGFR and other widely expressed tumor antigens.

Here, we show that a NOT gated cell therapy (Tmod) can exploit antigens such as epidermal growth factor receptor (EGFR) and human leukocyte antigen-E (HLA-E) which are widely expressed on cancer cells. Noncancerous cells-despite high expression of these antigens-are protected from cytotoxicity by the action of an inhibitory receptor ("blocker") via a mechanism that involves blocker modulation of CAR surface expression. The blocker is triggered by the product of a polymorphic HLA allele (e.g., HLA-A∗02) deleted in a significant subset of solid tumors via loss of heterozygosity. Moreover, Tmod constructs that target mouse homologs of EGFR or HLA-E for activation, and a mouse-equivalent of HLA-A∗02 for inhibition, protect mice from toxicity caused by the CAR alone. The blocker also controls graft vs. host response in allogeneic T cells in vitro, consistent with the use of Tmod cells for off-the-shelf therapy without additional gene-editing.

Pan-cancer analysis of the interplay between mutational signatures and cellular signaling.

Cancer is a multi-faceted disease with intricate relationships between mutagenic processes, alterations in cellular signaling, and the tissue microenvironment. To date, these processes have been largely studied in isolation. A systematic understanding of how they interact and influence each other is lacking. Here, we present a framework for systematically characterizing the interaction between pairs of mutational signatures and between signatures and signaling pathway alterations. We applied this framework to large-scale data from TCGA and PCAWG and identified multiple positive and negative interactions, both cross֊tissue and tissue֊specific, that provide new insights into the molecular routes observed in tumorigenesis and their respective drivers. This framework allows for a more fine-grained dissection of common and distinct etiology of mutational signatures. We further identified several interactions with both positive and negative impacts on patient survival, demonstrating their clinical relevance and potential for improving personalized cancer care.

ALK inhibitors suppress HCC and synergize with anti-PD-1 therapy and ABT-263 in preclinical models.

Hepatocellular carcinoma (HCC) currently lacks effective therapies, leaving a critical need for new treatment options. A previous study identified the anaplastic lymphoma kinase (ALK) amplification in HCC patients, raising the question of whether ALK inhibitors could be a viable treatment. Here, we showed that both ALK inhibitors and ALK knockout effectively halted HCC growth in cell cultures. Lorlatinib, a potent ALK inhibitor, suppressed HCC tumor growth and metastasis across various mouse models. Additionally, in an advanced immunocompetent humanized mouse model, when combined with an anti-PD-1 antibody, lorlatinib more potently suppressed HCC tumor growth, surpassing individual drug efficacy. Lorlatinib induced apoptosis and senescence in HCC cells, and the senolytic agent ABT-263 enhanced the efficacy of lorlatinib. Additional studies identified that the apoptosis-inducing effect of lorlatinib was mediated via GGN and NRG4. These findings establish ALK inhibitors as promising HCC treatments, either alone or in combination with immunotherapies or senolytic agents.

Single-cell transcriptomic-informed deconvolution of bulk data identifies immune checkpoint blockade resistance in urothelial cancer.

Interactions within the tumor microenvironment (TME) significantly influence tumor progression and treatment responses. While single-cell RNA sequencing (scRNA-seq) and spatial genomics facilitate TME exploration, many clinical cohorts are assessed at the bulk tissue level. Integrating scRNA-seq and bulk tissue RNA-seq data through computational deconvolution is essential for obtaining clinically relevant insights. Our method, ProM, enables the examination of major and minor cell types. Through evaluation against existing methods using paired single-cell and bulk RNA sequencing of human urothelial cancer (UC) samples, ProM demonstrates superiority. Application to UC cohorts treated with immune checkpoint inhibitors reveals pre-treatment cellular features associated with poor outcomes, such as elevated SPP1 expression in macrophage/monocytes (MM). Our deconvolution method and paired single-cell and bulk tissue RNA-seq dataset contribute novel insights into TME heterogeneity and resistance to immune checkpoint blockade.

Modeling the heterogeneous apoptotic response of caspase-mediated signaling in tumor cells.

Resisting apoptosis is a hallmark of cancer. For this reason, it may be possible to force cancer cells to die by targeting components along the apoptotic signaling pathway. However, apoptosis signaling is challenging to understand due to dynamic and complex behaviors of ligands, receptors, and intracellular signaling components in response to cancer therapy. In this work, we forecast the apoptotic response based on the combined impact of these features. We expanded a previously established mathematical model of caspase-mediated apoptosis to include extracellular activation and receptor dynamics. In addition, three potential threshold values of caspase-3 necessary for the activation of apoptosis were selected to forecast which cells become apoptotic over time. We first vary ligand and receptor levels with the number of intracellular signaling proteins remaining consistent. Then, we vary the intracellular protein molecules in each simulated tumor cell to forecast the response of a heterogeneous population. By leveraging the benefits of computational modeling, we investigate the combined effect of several factors on the onset of apoptosis. This work provides quantitative insights for how the apoptotic signaling response can be forecasted, and precisely triggered, amongst heterogeneous cells via extracellular activation.

Tumor niche network-defined subtypes predict immunotherapy response of esophageal squamous cell cancer.

Despite the promising outcomes of immune checkpoint inhibitors (ICIs), resistance to ICI presents a new challenge. Therefore, selecting patients for specific ICI applications is crucial for maximizing therapeutic efficacy. Herein, we curated 69 human esophageal squamous cell cancer (ESCC) patients' tumor microenvironment (TME) single-cell transcriptomic datasets to subtype ESCC. Integrative analyses of the cellular network and transcriptional signatures of T cells and myeloid cells define distinct ESCC subtypes characterized by T cell exhaustion, and interleukin (IL) and interferon (IFN) signaling. Furthermore, this approach classifies ESCC patients into ICI responders and non-responders, as validated by whole tumor transcriptomes and liquid biopsy-based single-cell transcriptomes of anti-PD-1 ICI responders and non-responders. Our study stratifies ESCC patients based on TME transcriptional network, providing novel insights into tumor niche remodeling and potentially predicting ICI responses in ESCC patients.

Tumor invasiveness is regulated by the concerted function of APC, formins, and Arp2/3 complex.

Tumor cell invasion is the initial step in metastasis, the leading cause of death from cancer. Invasion requires protrusive cellular structures that steer the migration of leader cells emanating from the tumor mass toward neighboring tissues. Actin is central to these processes and is therefore the prime target of drugs known as migrastatics. However, the broad effects of general actin inhibitors limit their therapeutic use. Here, we delineate the roles of specific actin nucleators in tuning actin-rich invasive protrusions and pinpoint potential pharmacological targets. We subject colorectal cancer spheroids embedded in collagen matrix-a preclinical model mirroring solid tumor invasiveness-to pharmacologic and/or genetic treatment of specific actin arrays to assess their roles in invasiveness. Our data reveal coordinated yet distinct involvement of actin networks nucleated by adenomatous polyposis coli, formins, and actin-related protein 2/3 complex in the biogenesis and maintenance of invasive protrusions. These findings may open avenues for better targeted therapies.

Frequent loss of FAM126A expression in colorectal cancer results in selective FAM126B dependency.

Most advanced colorectal cancer (CRC) patients cannot benefit from targeted therapy due to lack of actionable targets. By mining data from the DepMap, we identified FAM126B as a specific vulnerability in CRC cell lines exhibiting low FAM126A expression. Employing a combination of genetic perturbation and inducible protein degradation techniques, we demonstrate that FAM126A and FAM126B function in a redundant manner to facilitate the recruitment of PI4KIIIα to the plasma membrane for PI4P synthesis. Examination of data from TCGA and GTEx revealed that over 7% of CRC tumor samples exhibited loss of FAM126A expression, contrasting with uniform FAM126A expression in normal tissues. In both CRC cell lines and tumor samples, promoter hypermethylation correlated with the loss of FAM126A expression, which could be reversed by DNA methylation inhibitors. In conclusion, our study reveals that loss of FAM126A expression results in FAM126B dependency, thus proposing FAM126B as a therapeutic target for CRC treatment.

Genomic analysis of human brain metastases treated with stereotactic radiosurgery reveals unique signature based on treatment failure.

Stereotactic radiosurgery (SRS) has been shown to be efficacious for the treatment of limited brain metastasis (BM); however, the effects of SRS on human brain metastases have yet to be studied. We performed genomic analysis on resected brain metastases from patients whose resected lesion was previously treated with SRS. Our analyses demonstrated for the first time that patients possess a distinct genomic signature based on type of treatment failure including local failure, leptomeningeal spread, and radio-necrosis. Examination of the center and peripheral edge of the tumors treated with SRS indicated differential DNA damage distribution and an enrichment for tumor suppressor mutations and DNA damage repair pathways along the peripheral edge. Furthermore, the two clinical modalities used to deliver SRS, LINAC and GK, demonstrated differential effects on the tumor landscape even between controlled primary sites. Our study provides, in human, biological evidence of differential effects of SRS across BM's.

The role of Testis-Specific Protein Y-encoded-Like 2 in kidney injury.

Renal ischemia-reperfusion injury (IRI) is a major cause of acute kidney injury (AKI). Recent findings suggest that Testis-Specific Protein Y-encoded-Like 2 (TSPYL2) plays a fibrogenic role in diabetes-associated renal injury. However, the role of TSPYL2 in IRI-induced kidney damage is not entirely clear. In this study, we found that the expression of TSPYL2 was upregulated in a mouse model of AKI and in the hypoxia/reoxygenation (H/R) cell model. Knockdown of TSPYL2 attenuated kidney injury after IRI. More specifically, the knockdown of TSPYL2 or aminocarboxymuconate-semialdehyde decarboxylase (ACMSD) alleviated renal IRI-induced mitochondrial dysfunction and oxidative stress in vitro and in vivo. Further investigation showed that TSPYL2 regulated SREBP-2 acetylation by inhibiting SIRT1 and promoting p300 activity, thereby promoting the transcriptional activity of ACMSD. In conclusion, TSPYL2 was identified as a pivotal regulator of IRI-induced kidney damage by activating ACMSD, which may lead to NAD+ content and the damaging response in the kidney.