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.

ΔNp63 regulates MDSC survival and metabolism in triple-negative breast cancer

Triple-negative breast cancer (TNBC) contributes greatly to mortality of breast cancer, demanding new targetable options. We have shown that TNBC patients have high ΔNp63 expression in tumors. However, the function of ΔNp63 in established TNBC is yet to be explored. In current studies, targeting ΔNp63 with inducible CRISPR knockout and Histone deacetylase inhibitor Quisinostat showed that ΔNp63 is important for tumor progression and metastasis in established tumors by promoting myeloid-derived suppressor cell (MDSC) survival through tumor necrosis factor alpha. Decreasing ΔNp63 levels are associated with decreased CD4+ and FOXP3+ T-cells but increased CD8+ T-cells. RNA sequencing analysis indicates that loss of ΔNp63 alters multiple MDSC properties such as lipid metabolism, chemotaxis, migration, and neutrophil degranulation besides survival. We further demonstrated that targeting ΔNp63 sensitizes chemotherapy. Overall, we showed that ΔNp63 reprograms the MDSC-mediated immunosuppressive functions in TNBC, highlighting the benefit of targeting ΔNp63 in chemotherapy-resistant TNBC.

The RNA binding proteins LARP4A and LARP4B promote sarcoma and carcinoma growth and metastasis

RNA-binding proteins (RBPs) are emerging as important regulators of cancer pathogenesis. We reveal that the RBPs LARP4A and LARP4B are differentially overexpressed in osteosarcoma and osteosarcoma lung metastases, as well as in prostate cancer. Depletion of LARP4A and LARP4B reduced tumor growth and metastatic spread in xenografts, as well as inhibiting cell proliferation, motility, and migration. Transcriptomic profiling and high-content multiparametric analyses unveiled a central role for LARP4B, but not LARP4A, in regulating cell cycle progression in osteosarcoma and prostate cancer cells, potentially through modulating key cell cycle proteins such as Cyclins B1 and E2, Aurora B, and E2F1. This first systematic comparison between LARP4A and LARP4B assigns new pro-tumorigenic functions to LARP4A and LARP4B in bone and prostate cancer, highlighting their similarities while also indicating distinct functional differences. Uncovering clear biological roles for these paralogous proteins provides new avenues for identifying tissue-specific targets and potential druggable intervention.

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.

GLIMS: A two-stage gradual-learning method for cancer genes prediction using multi-omics data and co-splicing network

Identifying cancer genes is vital for cancer diagnosis and treatment. However, because of the complexity of cancer occurrence and limited cancer genes knowledge, it is hard to identify cancer genes accurately using only a few omics data, and the overall performance of existing methods is being called for further improvement. Here, we introduce a two-stage gradual-learning strategy GLIMS to predict cancer genes using integrative features from multi-omics data. Firstly, it uses a semi-supervised hierarchical graph neural network to predict the initial candidate cancer genes by integrating multi-omics data and protein-protein interaction (PPI) network. Then, it uses an unsupervised approach to further optimize the initial prediction by integrating the co-splicing network in post-transcriptional regulation, which plays an important role in cancer development. Systematic experiments on multi-omics cancer data demonstrated that GLIMS outperforms the state-of-the-art methods for the identification of cancer genes and it could be a useful tool to help advance cancer analysis.

Single-cell RNA sequencing reveals epithelial cells driving brain metastasis in lung adenocarcinoma

Brain metastases (BM) of lung adenocarcinoma (LUAD) are the most common intracranial malignancy leading to death. However, the cellular origins and drivers of BM from LUAD have not been clarified. Cellular composition was characterized by single-cell sequencing analysis of primary lung adenocarcinoma (pLUAD), BM and lymph node metastasis (LNM) samples in GSE131907. Our study briefly analyzed the tumor microenvironment (TME), focusing on the role of epithelial cells (ECs) in BM. We have discovered a population of brain metastasis-associated epithelial cells (BMAECs) expressing SPP1, SAA1, and CDKN2A, and it has been observed that this population is mainly composed of aneuploid cells from pLUAD, playing a crucial role in brain metastasis. Our study concluded that both LNM and BM in LUAD originated from pLUAD lesions, but there is currently insufficient evidence to prove a direct association between BM lesions and LNM lesions, which provides inspiration for further investigation of the TME in BM.

RNA modification-related genes illuminate prognostic signature and mechanism in esophageal squamous cell carcinoma

Emerging studies have demonstrated the link between RNA modifications and various cancers, while the predictive value and functional mechanisms of RNA modification-related genes (RMGs) in esophageal squamous cell carcinoma (ESCC) remain unclear. Here we established a prognostic signature for ESCC based on five RMGs. The analysis of ESCC clinical samples further verified the prognostic power of the prognostic signature. Moreover, we found that the knockdown of NSUN6 promotes ESCC progression in vitro and in vivo, whereas the overexpression of NSUN6 inhibits the malignant phenotype of ESCC cells. Mechanically, NSUN6 mediated tRNA m5C modifications selectively enhance the translation efficiency of CDH1 mRNA in a codon dependent manner. Rescue assays revealed that E-cadherin is an essential downstream target that mediates NSUN6's function in the regulation of ESCC progression. These findings offer additional insights into the link between ESCC and RMGs, as well as provide potential strategies for ESCC management and therapy.

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

The transcriptional landscape of glycosylation-related genes in cancer

Changes in glycosylation patterns have been associated with malignant transformation and clinical outcomes in several cancer types, prompting ongoing research into the mechanisms involved and potential clinical applications. In this study, we performed an extensive transcriptomic analysis of glycosylation-related genes and pathways, using publicly available bulk and single cell transcriptomic datasets from tumor samples and cancer cell lines. We identified genes and pathways strongly associated with different tumor types, which may represent novel diagnostic biomarkers. By using single cell RNA-seq data, we characterized the contribution of different cell types to the overall tumor glycosylation. Transcriptomic analysis of cancer cell lines revealed that they present a simplified landscape of genes compared to tissue. Lastly, we describe the association of different genes and pathways with the clinical outcome of patients. These results can serve as a resource for future research aimed to unravel the role of the glyco-code in cancer.

NatD epigenetically activates FOXA2 expression to promote breast cancer progression by facilitating MMP14 expression

N-α-acetyltransferase D (NatD) mediates N-α-terminal acetylation of histone H4 (Nt-Ac-H4), but its role in breast cancer metastasis remains unknown. Here, we show that depletion of NatD directly represses the expression of FOXA2, and is accompanied by a significant reduction in Nt-Ac-H4 enrichment at the FOXA2 promoter. We show that NatD is commonly upregulated in primary breast cancer tissues, where its expression level correlates with FOXA2 expression, enhanced invasiveness, and poor clinical outcomes. Furthermore, we show that FOXA2 promotes the migration and invasion of breast cancer cells by activating MMP14 expression. MMP14 is also upregulated in breast cancer tissues, where its expression level correlates with FOXA2 expression and poor clinical prognosis. Our study shows that the NatD-FOXA2-MMP14 axis functions as a key signaling pathway to promote the migratory and invasive capabilities of breast cancer cells, suggesting that NatD is a critical epigenetic modulator of cell invasion during breast cancer progression.

ABHD12 contributes to tumorigenesis and sorafenib resistance by preventing ferroptosis in hepatocellular carcinoma

Sorafenib induces ferroptosis, making it a useful treatment against advanced liver hepatocellular carcinoma (LIHC). However, sorafenib resistance is extremely common among LIHC patients. Here, we used a comprehensive approach to investigate the effects of ABHD12, which regulates tumorigenesis and sorafenib resistance in LIHC. We validated ABHD12 expression was upregulated in LIHC tissue, which correlated with worse overall survival and related to tumor size or stage. ABHD12 facilitated a pro-tumorigenic phenotype involving increased cell proliferation, migration, and clonogenicity as well as sorafenib resistance. Knockout of ABHD12 sensitized liver cancer cells to sorafenib-induced ferroptosis. Co-delivery of sorafenib and ABHD12 inhibitor into a nude mouse model enhanced therapeutic efficacy for LIHC. Our study demonstrates that ABHD12 contributes to tumor growth and sorafenib resistance in liver cancer, which indicate the promising potential of ABHD12 in diagnosis and prognosis as well as highlight the potential therapeutic applications for co-delivery of sorafenib and ABHD12 inhibitor.

A machine learning and directed network optimization approach to uncover TP53 regulatory patterns

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells

Tumor suppressor p53 plays a pivotal role in suppressing cancer, so various drugs has been suggested to upregulate its function. However, drug resistance is still the biggest hurdle to be overcome. To address this, we developed a deep learning model called AnoDAN (anomalous gene detection using generative adversarial networks and graph neural networks for overcoming drug resistance) that unravels the hidden resistance mechanisms and identifies a combinatorial target to overcome the resistance. Our findings reveal that the TGF-β signaling pathway, alongside the p53 signaling pathway, mediates the resistance, with THBS1 serving as a core regulatory target in both pathways. Experimental validation in lung cancer cells confirms the effects of THBS1 on responsiveness to a p53 reactivator. We further discovered the positive feedback loop between THBS1 and the TGF-β pathway as the main source of resistance. This study enhances our understanding of p53 regulation and offers insights into overcoming drug resistance.

Germline gene fusions across species reveal the chromosomal instability regions and cancer susceptibility

The canine transmissible venereal tumor (CTVT) is a clonal cell-mediated cancer with a long evolutionary history and extensive karyotype rearrangements in its genome. However, little is known about its genetic similarity to human tumors. Here, using multi-omics data we identified 11 germline gene fusions (GGFs) in CTVT, which showed higher genetic susceptibility than others. Additionally, we illustrate a mechanism of a complex gene fusion of three gene segments (HSD17B4-DMXL1-TNFAIP8) that we refer to "greedy fusion". Our findings also provided evidence that expressions of GGFs are downregulated during the tumor regressive phase, which is associated with DNA methylation level. This study presents a comprehensive landscape of gene fusions (GFs) in CTVT, which offers a valuable genetic resource for exploring potential genetic mechanisms underlying the development of cancers in both dogs and humans.

AID-induced CXCL12 upregulation enhances castration-resistant prostate cancer cell metastasis by stabilizing β-catenin expression

Prostate cancer (PCa) is one of the most common malignant diseases of urinary system and has poor prognosis after progression to castration-resistant prostate cancer (CRPC), and increased cytosine methylation heterogeneity is associated with the more aggressive phenotype of PCa cell line. Activation-induced cytidine deaminase (AID) is a multifunctional enzyme and contributes to antibody diversification. However, the dysregulation of AID participates in the progression of multiple diseases and related with certain oncogenes through demethylation. Nevertheless, the role of AID in PCa remains elusive. We observed a significant upregulation of AID expression in PCa samples, which exhibited a negative correlation with E-cadherin expression. Furthermore, AID expression is remarkably higher in CRPC cells than that in HSPC cells, and AID induced the demethylation of CXCL12, which is required to stabilize the Wnt signaling pathway executor β-catenin and EMT procedure. Our study suggests that AID drives CRPC metastasis by demethylation and can be a potential therapeutic target for CRPC.

Functional and spatial proteomics profiling reveals intra- and intercellular signaling crosstalk in colorectal cancer

Precision oncology approaches for patients with colorectal cancer (CRC) continue to lag behind other solid cancers. Functional precision oncology-a strategy that is based on perturbing primary tumor cells from cancer patients-could provide a road forward to personalize treatment. We extend this paradigm to measuring proteome activity landscapes by acquiring quantitative phosphoproteomic data from patient-derived organoids (PDOs). We show that kinase inhibitors induce inhibitor- and patient-specific off-target effects and pathway crosstalk. Reconstruction of the kinase networks revealed that the signaling rewiring is modestly affected by mutations. We show non-genetic heterogeneity of the PDOs and upregulation of stemness and differentiation genes by kinase inhibitors. Using imaging mass-cytometry-based profiling of the primary tumors, we characterize the tumor microenvironment (TME) and determine spatial heterocellular crosstalk and tumor-immune cell interactions. Collectively, we provide a framework for inferring tumor cell intrinsic signaling and external signaling from the TME to inform precision (immuno-) oncology in CRC.

Peripheral neuropathy associated with monomethyl auristatin E-based antibody-drug conjugates

Since the successful approval of gemtuzumab ozogamicin, antibody-drug conjugates (ADCs) have emerged as a pivotal category of targeted therapies for cancer. Among these ADCs, the use of monomethyl auristatin E (MMAE) as a payload is prevalent in the development of ADC drugs, which has significantly improved overall therapeutic efficacy against various malignancies. However, increasing clinical observations have raised concerns regarding the potential nervous system toxicity associated with MMAE-based ADCs. Specifically, a higher incidence of peripheral neuropathy has been reported in ADCs incorporating MMAE as payloads. Considering the increasing global use of MMAE-based ADCs, it is imperative to provide an inclusive overview of diagnostic and management strategies for this adverse event. In this review, we examine current information and what future research directions are required to better understand and manage this type of clinical challenge.

Deep learning for real-time detection of nasopharyngeal carcinoma during nasopharyngeal endoscopy

Nasopharyngeal carcinoma (NPC) is known for high curability during early stage of the disease, and early diagnosis relies on nasopharyngeal endoscopy and subsequent pathological biopsy. To enhance the early diagnosis rate by aiding physicians in the real-time identification of NPC and directing biopsy site selection during endoscopy, we assembled a dataset comprising 2,429 nasopharyngeal endoscopy video frames from 690 patients across three medical centers. With these data, we developed a deep learning-based NPC detection model using the you only look once (YOLO) network. Our model demonstrated high performance, with precision, recall, mean average precision, and F1-score values of 0.977, 0.943, 0.977, and 0.960, respectively, for internal test set and 0.825, 0.743, 0.814, and 0.780 for external test set at 0.5 intersection over union. Remarkably, our model demonstrated a high inference speed (52.9 FPS), surpassing the average frame rate (25.0 FPS) of endoscopy videos, thus making real-time detection in endoscopy feasible.

Gene signature reveals decreased SOX10-dependent transcripts in malignant cells from immune checkpoint inhibitor-resistant cutaneous melanomas

Evidence is mounting for cross-resistance between immune checkpoint and targeted kinase inhibitor therapies in cutaneous melanoma patients. Since the loss of the transcription factor, SOX10, causes tolerance to MAPK pathway inhibitors, we used bioinformatic techniques to determine if reduced SOX10 expression/activity is associated with immune checkpoint inhibitor resistance. We integrated SOX10 ChIP-seq, knockout RNA-seq, and knockdown ATAC-seq data from melanoma cell models to develop a robust SOX10 gene signature. We used computational methods to validate this signature as a measure of SOX10-dependent activity in independent single-cell and bulk RNA-seq SOX10 knockdown, cell line panel, and MAPK inhibitor drug-resistant datasets. Evaluation of patient single-cell RNA-seq data revealed lower levels of SOX10-dependent transcripts in immune checkpoint inhibitor-resistant tumors. Our results suggest that SOX10-deficient melanoma cells are associated with cross-resistance between targeted and immune checkpoint inhibitors and highlight the need to identify therapeutic strategies that target this subpopulation.

A new staging system for postoperative prognostication in pancreatic ductal adenocarcinoma

The current TNM staging system for pancreatic ductal adenocarcinoma (PDAC) has revised the definitions of T and N categories as well as stage groups. However, studies validating these modifications have yielded inconsistent results. The existing TNM staging system in prognostic prediction remains unsatisfactory. The prognosis of PDAC is closely associated with pathological and biological factors. Herein, we propose a new staging system incorporating distant metastasis, postoperative serum levels of CA19-9 and CEA, tumor size, lymph node metastasis, lymphovascular involvement, and perineural invasion to enhance the accuracy of prognosis assessment. The proposed staging system exhibited a strong correlation with both overall survival and recurrence-free survival, effectively stratifying survival into five distinct tiers. Additionally, it had favorable discrimination and calibration. Thus, the proposed staging system demonstrates superior prognostic performance compared to the TNM staging system, and can serve as a valuable complementary tool to address the limitations of TNM staging in prognostication.

A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia

Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient's follow-up, employed to develop a reference pool of patients. Score evolution's similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient's biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution's similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL.

Integrative analysis identifies two molecular and clinical subsets in Luminal B breast cancer

Comprehensive multiplatform analysis of Luminal B breast cancer (LBBC) specimens identifies two molecularly distinct, clinically relevant subtypes: Cluster A associated with cell cycle and metabolic signaling and Cluster B with predominant epithelial mesenchymal transition (EMT) and immune response pathways. Whole-exome sequencing identified significantly mutated genes including TP53, PIK3CA, ERBB2, and GATA3 with recurrent somatic mutations. Alterations in DNA methylation or transcriptomic regulation in genes (FN1, ESR1, CCND1, and YAP1) result in tumor microenvironment reprogramming. Integrated analysis revealed enriched biological pathways and unexplored druggable targets (cancer-testis antigens, metabolic enzymes, kinases, and transcription regulators). A systematic comparison between mRNA and protein displayed emerging expression patterns of key therapeutic targets (CD274, YAP1, AKT1, and CDH1). A potential ceRNA network was developed with a significantly different prognosis between the two subtypes. This integrated analysis reveals a complex molecular landscape of LBBC and provides the utility of targets and signaling pathways for precision medicine.

Molecular and clonal evolution in vivo reveal a common pathway of distant relapse gliomas

The evolutionary trajectories of genomic alterations underlying distant recurrence in glioma remain largely unknown. To elucidate glioma evolution, we analyzed the evolutionary trajectories of matched pairs of primary tumors and relapse tumors or tumor in situ fluid (TISF) based on deep whole-genome sequencing data (ctDNA). We found that MMR gene mutations occurred in the late stage in IDH-mutant glioma during gene evolution, which activates multiple signaling pathways and significantly increases distant recurrence potential. The proneural subtype characterized by PDGFRA amplification was likely prone to hypermutation and distant recurrence following treatment. The classical and mesenchymal subtypes tended to progress locally through subclonal reconstruction, trunk genes transformation, and convergence evolution. EGFR and NOTCH signaling pathways and CDNK2A mutation play an important role in promoting tumor local progression. Glioma subtypes displayed distinct preferred evolutionary patterns. ClinicalTrials.gov, NCT05512325.

Single-cell profiling of murine bladder cancer identifies sex-specific transcriptional signatures with prognostic relevance

Bladder cancer (BLCA) is more common in men but more aggressive in women. Sex-based differences in cancer biology are commonly studied using a murine model with BLCA generated by N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN). While tumors in the BBN model have been profiled, these profiles provide limited information on the tumor microenvironment. Here, we applied single-cell RNA sequencing to characterize cell-type specific transcriptional differences between male and female BBN-induced tumors. We found proportional and gene expression differences in epithelial and non-epithelial subpopulations between male and female tumors. Expression of several genes predicted sex-specific survival in several human BLCA datasets. We identified novel and clinically relevant sex-specific transcriptional signatures including immune cells in the tumor microenvironment and it validated the relevance of the BBN model for studying sex differences in human BLCA. This work highlights the importance of considering sex as a biological variable in the development of new and accurate cancer markers.

Machine learning reveals genetic modifiers of the immune microenvironment of cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.

Pancancer network analysis reveals key master regulators for cancer invasiveness

BACKGROUND

Tumor invasiveness reflects numerous biological changes, including tumorigenesis, progression, and metastasis. To decipher the role of transcriptional regulators (TR) involved in tumor invasiveness, we performed a systematic network-based pan-cancer assessment of master regulators of cancer invasiveness.

MATERIALS AND METHODS

We stratified patients in The Cancer Genome Atlas (TCGA) into invasiveness high (INV-H) and low (INV-L) groups using consensus clustering based on an established robust 24-gene signature to determine the prognostic association of invasiveness with overall survival (OS) across 32 different cancers. We devise a network-based protocol to identify TRs as master regulators (MRs) unique to INV-H and INV-L phenotypes. We validated the activity of MRs coherently associated with INV-H phenotype and worse OS across cancers in TCGA on a series of additional datasets in the Prediction of Clinical Outcomes from the Genomic Profiles (PRECOG) repository.

RESULTS

Based on the 24-gene signature, we defined the invasiveness score for each patient sample and stratified patients into INV-H and INV-L clusters. We observed that invasiveness was associated with worse survival outcomes in almost all cancers and had a significant association with OS in ten out of 32 cancers. Our network-based framework identified common invasiveness-associated MRs specific to INV-H and INV-L groups across the ten prognostic cancers, including COL1A1, which is also part of the 24-gene signature, thus acting as a positive control. Downstream pathway analysis of MRs specific to INV-H phenotype resulted in the identification of several enriched pathways, including Epithelial into Mesenchymal Transition, TGF-β signaling pathway, regulation of Toll-like receptors, cytokines, and inflammatory response, and selective expression of chemokine receptors during T-cell polarization. Most of these pathways have connotations of inflammatory immune response and feasibility for metastasis.

CONCLUSION

Our pan-cancer study provides a comprehensive master regulator analysis of tumor invasiveness and can suggest more precise therapeutic strategies by targeting the identified MRs and downstream enriched pathways for patients across multiple cancers.

Mitochondrial UQCC3 controls embryonic and tumor angiogenesis by regulating VEGF expression

Mitochondria play important roles in angiogenesis. However, the mechanisms remain elusive. In this study, we found that mitochondrial ubiquinol-cytochrome c reductase complex assembly factor 3 (UQCC3) is a key regulator of angiogenesis. TALEN-mediated knockout of Uqcc3 in mice caused embryonic lethality at 9.5-10.5 days postcoitum, and vessel density was dramatically reduced. Similarly, knockout of uqcc3 in zebrafish induced lethality post-fertilization and impaired vascular development. Knockout of UQCC3 resulted in slower tumor growth and angiogenesis. Mechanistically, UQCC3 was upregulated under hypoxia, promoted reactive oxygen species (ROS) generation, enhanced HIF-1α stability and increased VEGF expression. Finally, higher expression of UQCC3 was associated with poor prognosis in multiple types tumors, implying a role for UQCC3 in tumor progression. In conclusion, our findings highlight the important contribution of UQCC3 to angiogenesis under both physiological and pathological conditions, indicating the potential of UQCC3 as a therapeutic target for cancer.

Reinvestigating the clinical relevance of the m6A writer METTL3 in urothelial carcinoma of the bladder

METTL3 is the major writer of N6-Methyladenosine (m6A) and has been associated with controversial roles in cancer. This is best illustrated in urothelial carcinoma of the bladder (UCB), where METTL3 was described to have both oncogenic and tumor-suppressive functions. Here, we reinvestigated the role of METTL3 in UCB. METTL3 knockout reduced the oncogenic phenotype and m6A levels of UCB cell lines. However, complete depletion of METTL3/m6A was not achieved due to selection of cells expressing alternative METTL3 isoforms. Systematic vulnerability and inhibitor response analyses suggested that uroepithelial cells depend on METTL3 for viability. Furthermore, expression and survival analyses of clinical data revealed a complex role for METTL3 in UCB, with decreased m6A mRNA levels in UCB tumors. Our results suggest that METTL3 expression may be a suitable diagnostic UCB biomarker, as the enzyme promotes UCB formation. However, the suitability of the enzyme as a therapeutic target should be evaluated carefully.

MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning

Cancer is an extremely complex disease and each type of cancer usually has several different subtypes. Multi-omics data can provide more comprehensive biological information for identifying and discovering cancer subtypes. However, existing unsupervised cancer subtyping methods cannot effectively learn comprehensive shared and specific information of multi-omics data. Therefore, a novel method is proposed based on shared and specific representation learning. For each omics data, two autoencoders are applied to extract shared and specific information, respectively. To reduce redundancy and mutual interference, orthogonality constraint is introduced to separate shared and specific information. In addition, contrastive learning is applied to align the shared information and strengthen their consistency. Finally, the obtained shared and specific information for all samples are used for clustering tasks to achieve cancer subtyping. Experimental results demonstrate that the proposed method can effectively capture shared and specific information of multi-omics data and outperform other state-of-the-art methods on cancer subtyping.

Targeting Bcl-xL is a potential therapeutic strategy for extranodal NK/T cell lymphoma

Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive lymphoid malignancy with a poor prognosis and lacks standard treatment. Targeted therapies are urgently needed. Here we systematically investigated the druggable mechanisms through chemogenomic screening and identified that Bcl-xL-specific BH3 mimetics effectively induced ENKTL cell apoptosis. Notably, the specific accumulation of Bcl-xL, but not other Bcl-2 family members, was verified in ENKTL cell lines and patient tissues. Furthermore, Bcl-xL high expression was shown to be closely associated with worse patient survival. The critical role of Bcl-xL in ENKTL cell survival was demonstrated utilizing selective inhibitors, genetic silencing, and a specific degrader. Additionally, the IL2-JAK1/3-STAT5 signaling was implicated in Bcl-xL dysregulation. In vivo, Bcl-xL inhibition reduced tumor burden, increased apoptosis, and prolonged survival in ENKTL cell line xenograft and patient-derived xenograft models. Our study indicates Bcl-xL as a promising therapeutic target for ENKTL, warranting monitoring in ongoing clinical trials by targeting Bcl-xL.

DoSurvive: A webtool for investigating the prognostic power of a single or combined cancer biomarker

We present DoSurvive, a user-friendly survival analysis web tool and a cancer prognostic biomarker centered database. DoSurvive is the first database that allows users to perform multivariant survival analysis for cancers with customized gene/patient list. DoSurvive offers three survival analysis methods, Log rank test, Cox regression and accelerated failure time model (AFT), for users to analyze five types of quantitative features (mRNA, miRNA, lncRNA, protein and methylation of CpG islands) with four survival types, i.e. overall survival, disease-specific survival, disease-free interval, and progression-free interval, in 33 cancer types. Notably, the implemented AFT model provides an alternative method for genes/features which failed the proportional hazard assumption in Cox regression. With the unprecedented number of survival models implemented and high flexibility in analysis, DoSurvive is a unique platform for the identification of clinically relevant targets for cancer researcher and practitioners. DoSurvive is freely available at http://dosurvive.lab.nycu.edu.tw/.

TSC22D3 as an immune-related prognostic biomarker for acute myeloid leukemia

Acute myeloid leukemia (AML) is the type of hematologic neoplasm most common in adults. Glucocorticoid-induced gene TSC22D3 regulates cell proliferation through its function as a transcription factor. However, there is no consensus on the prognostic and immunoregulatory significance of TSC22D3 in AML. In the present study, we evaluated the correlation between TSC22D3 expression, immunoinfiltration, and prognostic significance in AML. Knockdown of TSC22D3 significantly attenuated the proliferation of Hel cells and increased sensitivity to cytarabine (Ara-c) drugs. Furthermore, TSC22D3 reduced the release of interleukin-1β (IL-1β) by inhibiting the NF-κB/NLRP3 signaling pathway, thereby inhibiting macrophage polarization to M1 subtype, and attenuating the pro-inflammatory tumor microenvironment. In conclusion, this study identified TSC22D3 as an immune-related prognostic biomarker for AML patients and suggested that therapeutic targeting of TSC22D3 may be a potential treatment option for AML through tumor immune escape.

ICAM1 antibody drug conjugates exert potent antitumor activity in papillary and anaplastic thyroid carcinoma

Treatment options for anaplastic thyroid cancer (ATC) and refractory papillary thyroid carcinoma (PTC) are limited and outcomes remain poor. In this study, we determined via bioinformatic expression analyses and immunohistochemistry staining that intercellular adhesion molecule-1(ICAM1) is an attractive target for ATC and PTC. We designed and engineered two ICAM1-directed antibody-drug conjugate (I1-MMAE and I1-DXd), both of which potently and selectively ablate multiple human ATC and PTC cell lines without affecting non-plastic cells in vitro. Furthermore, I1-MMAE and I1-DXd mediated a potent tumor regression in ATC and PTC xenograft models. To develop a precision medicine, we also explored magnetic resonance imaging (MRI) as a non-invasive biomarker detection method to quantitatively map ICAM1 antigen expression in heterogeneous thyroid tumors. Taken together, this study provides a strong rationale for the further development of I1-MMAE and I1-DXd as promising therapeutic candidates to treat advanced PTC and ATC.

Genetic ablation of ketohexokinase C isoform impairs pancreatic cancer development

Although dietary fructose is associated with an elevated risk for pancreatic cancer, the underlying mechanisms remain elusive. Here, we report that ketohexokinase (KHK), the rate-limiting enzyme of fructose metabolism, is a driver of PDAC development. We demonstrate that fructose triggers KHK and induces fructolytic gene expression in mouse and human PDAC. Genetic inactivation of KhkC enhances the survival of KPC-driven PDAC even in the absence of high fructose diet. Furthermore, it decreases the viability, migratory capability, and growth of KPC cells in a cell autonomous manner. Mechanistically, we demonstrate that genetic ablation of KHKC strongly impairs the activation of KRAS-MAPK pathway and of rpS6, a downstream target of mTORC signaling. Moreover, overexpression of KHKC in KPC cells enhances the downstream KRAS pathway and cell viability. Our data provide new insights into the role of KHK in PDAC progression and imply that inhibiting KHK could have profound implications for pancreatic cancer therapy.

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFβ in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

An EZH2-NF-κB regulatory axis drives expression of pro-oncogenic gene signatures in triple negative breast cancer

The histone methyltransferase EZH2 has been studied most extensively in the context of PRC2-dependent gene repression. Accumulating evidence indicates non-canonical functions for EZH2 in cancer contexts including promoting paradoxical gene expression through interactions with transcription factors, including NF-κB in triple negative breast cancer (TNBC). We profile EZH2 and NF-κB factor co-localization and positive gene regulation genome-wide, and define a subset of NF-κB targets and genes associated with oncogenic functions in TNBC that is enriched in patient datasets. We demonstrate interaction between EZH2 and RelA requiring the recently identified transactivation domain (TAD) which mediates EZH2 recruitment to, and activation of certain NF-κB-dependent genes, and supports downstream migration and stemness phenotypes in TNBC cells. Interestingly, EZH2-NF-κB positive regulation of genes and stemness does not require PRC2. This study provides new insight into pro-oncogenic regulatory functions for EZH2 in breast cancer through PRC2-independent, and NF-κB-dependent regulatory mechanisms.

Molecular basis of sex differences in cancer: Perspective from Asia

Cancer is a leading cause of mortality and morbidity globally. Sex differences in cancer are evident in death rates and treatment responses in several cancers. Asian patients have unique cancer epidemiology influenced by their genetic ancestry and sociocultural factors in the region. In this review, we show molecular associations that potentially mediate sex disparities observed in cancer in Asian populations. Differences in sex characteristics are evident at the cytogenetic, genetic, and epigenetic levels mediating processes that include cell cycle, oncogenesis, and metastasis. Larger clinical and in vitro studies that explore mechanisms can confirm the associations of these molecular markers. In-depth studies of these markers can reveal their importance as diagnostics, prognostics, and therapeutic efficacy markers. Sex differences should be considered in designing novel cancer therapeutics in this era of precision medicine.

RUNX1 colludes with NOTCH1 to reprogram chromatin in T cell acute lymphoblastic leukemia

Runt-related transcription factor 1 (RUNX1) is oncogenic in diverse types of leukemia and epithelial cancers where its expression is associated with poor prognosis. Current models suggest that RUNX1 cooperates with other oncogenic factors (e.g., NOTCH1, TAL1) to drive the expression of proto-oncogenes in T cell acute lymphoblastic leukemia (T-ALL) but the molecular mechanisms controlled by RUNX1 and its cooperation with other factors remain unclear. Integrative chromatin and transcriptional analysis following inhibition of RUNX1 and NOTCH1 revealed a surprisingly widespread role of RUNX1 in the establishment of global H3K27ac levels and that RUNX1 is required by NOTCH1 for cooperative transcription activation of key NOTCH1 target genes including MYC, DTX1, HES4, IL7R, and NOTCH3. Super-enhancers were preferentially sensitive to RUNX1 knockdown and RUNX1-dependent super-enhancers were disrupted following the treatment of a pan-BET inhibitor, I-BET151.

Stratification of non-small cell lung adenocarcinoma patients with EGFR actionable mutations based on drug-resistant stem cell genes

EGFR-TKIs were used in NSCLC patients with actionable EGFR mutations and prolong prognosis. However, most patients treated with EGFR-TKIs developed resistance within around one year. This suggests that residual EGFR-TKIs resistant cells may eventually lead to relapse. Predicting resistance risk in patients will facilitate individualized management. Herein, we built an EGFR-TKIs resistance prediction (R-index) model and validate in cell line, mice, and cohort. We found significantly higher R-index value in resistant cell lines, mice models and relapsed patients. Patients with an elevated R-index had significantly shorter relapse time. We also found that the glycolysis pathway and the KRAS upregulation pathway were related to EGFR-TKIs resistance. MDSC is a significant immunosuppression factor in the resistant microenvironment. Our model provides an executable method for assessing patient resistance status based on transcriptional reprogramming and may contribute to the clinical translation of patient individual management and the study of unclear resistance mechanisms.

Comprehensive multiomics analyses reveal pervasive involvement of aberrant cohesin binding in transcriptional and chromosomal disorder of cancer cells

Chromatin organization, whose malfunction causes various diseases including cancer, is fundamentally controlled by cohesin. While cancer cells have been found with mutated or misexpressed cohesin genes, there is no comprehensive survey about the presence and role of abnormal cohesin binding in cancer cells. Here, we systematically identified ∼1% of cohesin-binding sites (701-2,633) as cancer-aberrant binding sites of cohesin (CASs). We integrated CASs with large-scale transcriptomics, epigenomics, 3D genomics, and clinical information. CASs represent tissue-specific epigenomic signatures enriched for cancer-dysregulated genes with functional and clinical significance. CASs exhibited alterations in chromatin compartments, loops within topologically associated domains, and cis-regulatory elements, indicating that CASs induce dysregulated genes through misguided chromatin structure. Cohesin depletion data suggested that cohesin binding at CASs actively regulates cancer-dysregulated genes. Overall, our comprehensive investigation suggests that aberrant cohesin binding is an essential epigenomic signature responsible for dysregulated chromatin structure and transcription in cancer cells.

Combining the antianginal drug perhexiline with chemotherapy induces complete pancreatic cancer regression in vivo

Pancreatic ductal adenocarcinoma (PDAC) remains one of the human cancers with the poorest prognosis. Interestingly, we found that mitochondrial respiration in primary human PDAC cells depends mainly on the fatty acid oxidation (FAO) to meet basic energy requirements. Therefore, we treated PDAC cells with perhexiline, a well-recognized FAO inhibitor used in cardiac diseases. Some PDAC cells respond efficiently to perhexiline, which acts synergistically with chemotherapy (gemcitabine) in vitro and in two xenografts in vivo. Importantly, perhexiline in combination with gemcitabine induces complete tumor regression in one PDAC xenograft. Mechanistically, this co-treatment causes energy and oxidative stress promoting apoptosis but does not exert inhibition of FAO. Yet, our molecular analysis indicates that the carnitine palmitoyltransferase 1C (CPT1C) isoform is a key player in the response to perhexiline and that patients with high CPT1C expression have better prognosis. Our study reveals that repurposing perhexiline in combination with chemotherapy is a promising approach to treat PDAC.

Supplementing a specific synbiotic suppressed the incidence of AOM/DSS-induced colorectal cancer in mice

In this study, we evaluated the effect of a specific synbiotic on CAC (AOM/DSS-induced colitis-associated cancer). We confirmed that the synbiotic intervention was able to protect the intestinal barrier and inhibit CAC occurrence via upregulating tight junction proteins and anti-inflammatory cytokines, and downregulating pro-inflammatory cytokines. Moreover, the synbiotic significantly improved the disorder of the colonic microbiota of CAC mice, promoted the formation of SCFAs and the production of secondary bile acids, and alleviated the accumulation of primary bile acids in the CAC mice. Meanwhile, the synbiotic could significantly inhibit the abnormal activation of the intestinal Wnt/β-catenin signaling pathway significantly related to IL-23. In a word, the synbiotic can inhibit the occurrence and development of colorectal tumors and it may be a functional food to prevent inflammation-related colon tumors, and the research also provided a theoretical basis for improving the intestinal microecological environment through diet therapy.

LAPTM4B-YAP loop feedback amplification enhances the stemness of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is highly heterogeneous, and stemness signatures are frequently elevated in HCC tumor cells to generate heterogeneous subtypes via multidirectional differentiation. However, the mechanisms affecting the regulation of stemness in HCC remain unclear. In this study, we identified that lysosome-associated protein transmembrane-4β (LAPTM4B) was significantly overexpressed in stem-like tumor cell populations with multidirectional differentiation potential at the single cell level, and verified that LAPTM4B was closely related to stemness of HCC using in vitro and in vivo experiments. Mechanistically, elevated LAPTM4B suppresses Yes-associated protein (YAP) phosphorylation and ubiquitination degradation. In turn, stabilized YAP localizes to the nucleus and binds to cAMP responsive element binding protein-1 (CREB1), which promotes transcription of LAPTM4B. Overall, our findings suggest that LAPTM4B forms a positive feedback loop with YAP, which maintains the stemness of HCC tumor cells and leads to an unfavorable prognosis for HCC patients.

Dissecting molecular, pathological, and clinical features associated with tumor neural/neuroendocrine heterogeneity

Lineage plasticity, especially transdifferentiation between neural/neuroendocrine (NE) and non-NE lineage, has been observed in multiple cancer types and linked to increased tumor aggressiveness. However, existing NE/non-NE subtype classifications in various cancer types were established through ad hoc approaches in different studies, making it difficult to align findings across cancer types and extend investigations to new datasets. To address this issue, we developed a generalized strategy to generate quantitative NE scores and a web application to facilitate its implementation. We applied this method to nine datasets covering seven cancer types, including two neural cancers, two neuroendocrine cancers, and three non-NE cancers. Our analysis revealed significant NE inter-tumoral heterogeneity and identified strong associations between NE scores and molecular, histological, and clinical features, including prognosis in different cancer types. These results support the translational utility of NE scores. Overall, our work demonstrated a broadly applicable strategy for determining the NE properties of tumors.

Modeling breast cancer proliferation, drug synergies, and alternating therapies

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Toward a systems-level probing of tumor clonality

Cancer has been described as a genetic disease that clonally evolves in the face of selective pressures imposed by cell-intrinsic and extrinsic factors. Although classical models based on genetic data predominantly propose Darwinian mechanisms of cancer evolution, recent single-cell profiling of cancers has described unprecedented heterogeneity in tumors providing support for alternative models of branched and neutral evolution through both genetic and non-genetic mechanisms. Emerging evidence points to a complex interplay between genetic, non-genetic, and extrinsic environmental factors in shaping the evolution of tumors. In this perspective, we briefly discuss the role of cell-intrinsic and extrinsic factors that shape clonal behaviors during tumor progression, metastasis, and drug resistance. Taking examples of pre-malignant states associated with hematological malignancies and esophageal cancer, we discuss recent paradigms of tumor evolution and prospective approaches to further enhance our understanding of this spatiotemporally regulated process.

Development and validation of a collagen signature to predict the prognosis of patients with stage II/III colorectal cancer

The tumor, nodes and metastasis (TNM) classification system provides useful but incomplete prognostic information and lacks the assessment of the tumor microenvironment (TME). Collagen, the main component of the TME extracellular matrix, plays a nonnegligible role in tumor invasion and metastasis. In this cohort study, we aimed to develop and validate a TME collagen signature (CS_TME) for prognostic prediction of stage II/III colorectal cancer (CRC) and to compare the prognostic values of "TNM stage + CS_TME" with that of TNM stage alone. Results indicated that the CS_TME was an independent prognostic risk factor for stage II/III CRC (hazard ratio: 2.939, 95% CI: 2.180-3.962, p < 0.0001), and the integration of the TNM stage and CS_TME had a better prognostic value than that of the TNM stage alone (AUC_(TNM+CSTME) = 0.772, AUC _TNM = 0.687, p < 0.0001). This study provided an application of "seed and soil" strategy for prognosis prediction and individualized therapy.

Identifying signatures of EV secretion in metastatic breast cancer through functional single-cell profiling

Extracellular vesicles (EVs) regulate the tumor microenvironment by facilitating transport of biomolecules. Despite extensive investigation, heterogeneity in EV secretion among cancer cells and the mechanisms that support EV secretion are not well characterized. We developed an integrated method to identify individual cells with differences in EV secretion and performed linked single-cell RNA-sequencing on cloned single cells from the metastatic breast cancer cells. Differential gene expression analyses identified a four-gene signature of breast cancer EV secretion: HSP90AA1, HSPH1, EIF5, and DIAPH3. We functionally validated this gene signature by testing it across cell lines with different metastatic potential in vitro. Analysis of the TCGA and METABRIC datasets showed that this signature is associated with poor survival, invasive breast cancer types, and poor CD8⁺ T cell infiltration in human tumors. We anticipate that our method for directly identifying the molecular determinants of EV secretion will have broad applications across cell types and diseases.

A mechanistic view of the use of cold temperature in the treatment of cancer

In their latest article, Seki and colleagues investigate the potential role of cold as a therapeutical option to treat various cancer types, including even clinically untreatable cancers such as pancreatic cancers. The authors suggest that cold exposure may have a tumor-suppressive effect mediated by the activation of brown adipose tissue (BAT), in charge of dissipating heat through non-shivering thermogenesis. In this regard, circulating blood glucose is decreased, restricting the tumor glucose uptake, which is redistributed, favoring BAT uptake to fuel thermogenesis.¹.

Systematic analysis of the aberrances and functional implications of cuproptosis in cancer

Cuproptosis is a novel form of cell death driven by a copper-dependent proteotoxic stress response whose comprehensive landscape in tumors remains unclear. Here, we comprehensively characterized cuproptosis-related genes (CRGs) across 33 cancers using multi-omic data from The Cancer Genome Atlas (TCGA), showing complicated and diverse results in different cancers. We also explored the relationships between CRGs and cancer metabolic patterns, pathway activity, and tumor microenvironment (TME), suggesting that they played critical roles in tumor progression and TME cell infiltration. We further established the cuproptosis potential index (CPI) to reveal the functional roles of cuproptosis, and characterized multi-omic molecular features associated with cuproptosis. In clinical applications, we performed a combined analysis of the sensitivity of CRGs and CPI to drug response and immunotherapy. This study provides a rich resource for understanding cuproptosis, offering a broad molecular perspective for future functional and therapeutic studies of multiple cancer pathways mediated by cuproptosis.