A pan-cancer proteomic perspective on The Cancer Genome Atlas

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

The SRC-family serves as a therapeutic target in triple negative breast cancer with acquired resistance to chemotherapy

BACKGROUND

Resistance to chemotherapy, combined with heterogeneity among resistant tumors, represents a significant challenge in the clinical management of triple negative breast cancer (TNBC). By dissecting molecular pathways associated with treatment resistance, we sought to define patient sub-groups and actionable targets for next-line treatment.

METHODS

Bulk RNA sequencing and reverse phase protein array profiling were performed on isogenic patient-derived xenografts (PDX) representing paclitaxel-sensitive and -resistant tumors. Pathways identified as upregulated in the resistant model were further explored as targets in PDX explants. Their clinical relevance was assessed in two distinct patient cohorts (NeoAva and MET500).

RESULTS

Increased activity in signaling pathways involving SRC-family kinases (SFKs)- and MAPK/ERK was found in treatment resistant PDX, with targeted inhibitors being significantly more potent in resistant tumors. Up-regulation of SFKs- and MAPK/ERK-pathways was also detected in a sub-group of chemoresistant patients after neoadjuvant treatment. Furthermore, High SFK expression (of either SRC, FYN and/or YES1) was detected in metastatic lesions of TNBC patients with fast progressing disease (median disease-free interval 27 vs 105 months).

CONCLUSIONS

Upregulation of SFK-signaling is found in a subset of chemoresistant tumors and is persistent in metastatic lesions. Based on pre-clinical results, these patients may respond favorably to treatment targeting SFKs.

Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin signature of basal-like-subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

Whole genome and reverse protein phase array landscapes of patient derived osteosarcoma xenograft models

Osteosarcoma is the most common primary bone malignancy in children and young adults, and it has few treatment options. As a result, there has been little improvement in survival outcomes in the past few decades. The need for models to test novel therapies is especially great in this disease since it is both rare and does not respond to most therapies. To address this, an NCI-funded consortium has characterized and utilized a panel of patient-derived xenograft models of osteosarcoma for drug testing. The exomes, transcriptomes, and copy number landscapes of these models have been presented previously. This study now adds whole genome sequencing and reverse-phase protein array profiling data, which can be correlated with drug testing results. In addition, four additional osteosarcoma models are described for use in the research community.

Antitumor Effects and the Potential Mechanism of 10-HDA against SU-DHL-2 Cells

10-hydroxy-2-decenoic acid (10-HDA), which is a unique bioactive fatty acid of royal jelly synthesized by nurse bees for larvae and adult queen bees, is recognized for its dual utility in medicinal and nutritional applications. Previous research has indicated that 10-HDA exerts antitumor effects on numerous tumor cell lines, including colon cancer cells, A549 human lung cancer cells, and human hepatoma cells. The present study extends this inquiry to lymphoma, specifically evaluating the impact of 10-HDA on the SU-DHL-2 cell line. Our findings revealed dose-dependent suppression of SU-DHL-2 cell survival, with an IC50 of 496.8 μg/mL at a density of 3 × 106 cells/well after 24 h. For normal liver LO2 cells and human fibroblasts (HSFs), the IC50 values were approximately 1000 μg/mL and over 1000 μg/mL, respectively. The results of label-free proteomics revealed 147 upregulated and 347 downregulated differentially expressed proteins that were significantly enriched in the complement and coagulation cascades pathway (adjusted p-value = 0.012), including the differentially expressed proteins prothrombin, plasminogen, plasminogen, carboxypeptidase B2, fibrinogen beta chain, fibrinogen gamma chain, and coagulation factor V. The top three hub proteins, ribosomal protein L5, tumor protein p53, and ribosomal protein L24, were identified via protein-protein interaction (PPI) analysis. This result showed that the complement and coagulation cascade pathways might play a key role in the antitumor process of 10-HDA, suggesting a potential therapeutic avenue for lymphoma treatment. However, the specificity of the effect of 10-HDA on SU-DHL-2 cells warrants further investigation.

MOSBY enables multi-omic inference and spatial biomarker discovery from whole slide images

The utility of deep neural nets has been demonstrated for mapping hematoxylin-and-eosin (H&E) stained image features to expression of individual genes. However, these models have not been employed to discover clinically relevant spatial biomarkers. Here we develop MOSBY (Multi-Omic translation of whole slide images for Spatial Biomarker discoverY) that leverages contrastive self-supervised pretraining to extract improved H&E whole slide images features, learns a mapping between image and bulk omic profiles (RNA, DNA, and protein), and utilizes tile-level information to discover spatial biomarkers. We validate MOSBY gene and gene set predictions with spatial transcriptomic and serially-sectioned CD8 IHC image data. We demonstrate that MOSBY-inferred colocalization features have survival-predictive power orthogonal to gene expression, and enable concordance indices highly competitive with survival-trained multimodal networks. We identify and validate (1) an ER stress-associated colocalization feature as a chemotherapy-specific risk factor in lung adenocarcinoma, and (2) the colocalization of T effector cell vs cysteine signatures as a negative prognostic factor in multiple cancer indications. The discovery of clinically relevant biologically interpretable spatial biomarkers showcases the utility of the model in unraveling novel insights in cancer biology as well as informing clinical decision-making.

Pan-cancer analyses reveal the molecular and clinical characteristics of TET family members and suggests that TET3 maybe a potential therapeutic target

The Ten-Eleven Translocation (TET) family genes are implicated in a wide array of biological functions across various human cancers. Nonetheless, there is a scarcity of studies that comprehensively analyze the correlation between TET family members and the molecular phenotypes and clinical characteristics of different cancers. Leveraging updated public databases and employing several bioinformatics analysis methods, we assessed the expression levels, somatic variations, methylation levels, and prognostic values of TET family genes. Additionally, we explored the association between the expression of TET family genes and pathway activity, tumor microenvironment (TME), stemness score, immune subtype, clinical staging, and drug sensitivity in pan-cancer. Molecular biology and cytology experiments were conducted to validate the potential role of TET3 in tumor progression. Each TET family gene displayed distinct expression patterns across at least ten detected tumors. The frequency of Single Nucleotide Variant (SNV) in TET genes was found to be 91.24%, primarily comprising missense mutation types, with the main types of copy number variant (CNV) being heterozygous amplifications and deletions. TET1 gene exhibited high methylation levels, whereas TET2 and TET3 genes displayed hypomethylation in most cancers, which correlated closely with patient prognosis. Pathway activity analysis revealed the involvement of TET family genes in multiple signaling pathways, including cell cycle, apoptosis, DNA damage response, hormone AR, PI3K/AKT, and RTK. Furthermore, the expression levels of TET family genes were shown to impact the clinical staging of tumor patients, modulate the sensitivity of chemotherapy drugs, and thereby influence patient prognosis by participating in the regulation of the tumor microenvironment, cellular stemness potential, and immune subtype. Notably, TET3 was identified to promote cancer progression across various tumors, and its silencing was found to inhibit tumor malignancy and enhance chemotherapy sensitivity. These findings shed light on the role of TET family genes in cancer progression and offer insights for further research on TET3 as a potential therapeutic target for pan-cancer.

Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cell division cycle-associated (CDCA) gene family members are essential cell proliferation regulators and play critical roles in various cancers. However, the function of the CDCA family genes in gliomas remains unclear. This study aims to elucidate the role of CDCA family members in gliomas using in vitro and in vivo experiments and bioinformatic analyses. We included eight glioma cohorts in this study. An unsupervised clustering algorithm was used to identify novel CDCA gene family clusters. Then, we utilized multi-omics data to elucidate the prognostic disparities, biological functionalities, genomic alterations, and immune microenvironment among glioma patients. Subsequently, the scRNA-seq analysis and spatial transcriptomic sequencing analysis were carried out to explore the expression distribution of CDCA2 in glioma samples. In vivo and in vitro experiments were used to investigate the effects of CDCA2 on the viability, migration, and invasion of glioma cells. Finally, based on ten machine-learning algorithms, we constructed an artificial intelligence-driven CDCA gene family signature called the machine learning-based CDCA gene family score (MLCS). Our results suggested that patients with the higher expression levels of CDCA family genes had a worse prognosis, more activated RAS signaling pathways, and more activated immunosuppressive microenvironments. CDCA2 knockdown inhibited the proliferation, migration, and invasion of glioma cells. In addition, the MLCS had robust and favorable prognostic predictive ability and could predict the response to immunotherapy and chemotherapy drug sensitivity.

Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes

Breast cancer (BC) is a leading cause of cancer-related death worldwide. The diverse nature and heterogeneous biology of BC pose challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. Clinically relevant BC intrinsic subtypes have been established through gene expression profiling and are implemented in the clinic. While these intrinsic subtypes show a significant association with clinical outcomes, their long-term survival prediction beyond 5 years often deviates from expected clinical outcomes. This study aimed to identify naturally occurring long-term prognostic subgroups of BC based on an integrated multi-omics analysis. This study incorporates a clinical cohort of 335 untreated BC patients from the Oslo2 study with long-term follow-up (>12 years). Multi-Omics Factor Analysis (MOFA+) was employed to integrate transcriptomic, proteomic, and metabolomic data obtained from the tumor tissues. Our analysis revealed three prominent multi-omics clusters of BC patients with significantly different long-term prognoses (p = 0.005). The multi-omics clusters were validated in two independent large cohorts, METABRIC and TCGA. Importantly, a lack of prognostic association to long-term follow-up above 12 years in the previously established intrinsic subtypes was shown for these cohorts. Through a systems-biology approach, we identified varying enrichment levels of cell-cycle and immune-related pathways among the prognostic clusters. Integrated multi-omics analysis of BC revealed three distinct clusters with unique clinical and biological characteristics. Notably, these multi-omics clusters displayed robust associations with long-term survival, outperforming the established intrinsic subtypes.

Multi-Omics Insights into Disulfidptosis-Related Genes Reveal RPN1 as a Therapeutic Target for Liver Cancer

Disulfidptosis, a newly identified mode of programmed cell death, is yet to be comprehensively elucidated with respect to its multi-omics characteristics in tumors, specific pathogenic mechanisms, and antitumor functions in liver cancer. This study included 10,327 tumor and normal tissue samples from 33 cancer types. In-depth analyses using various bioinformatics tools revealed widespread dysregulation of disulfidptosis-related genes (DRGs) in pan-cancer and significant associations with prognosis, genetic variations, tumor stemness, methylation levels, and drug sensitivity. Univariate and multivariate Cox regression and LASSO regression were used to screen and construct prognosis-related hub DRGs and predictive models in the context of liver cancer. Subsequently, single cell analysis was conducted to investigate the subcellular localization of RPN1, a hub DRG, in various solid tumors. Western blotting was performed to validate the expression of RPN1 at both cellular and tissue levels. Additionally, functional experiments, including CCK8, EdU, clone, and transwell assays, indicated that RPN1 knockdown promoted the proliferative and invasive capacities of liver cancer cells. Therefore, this study elucidated the multi-omics characteristics of DRGs in pan-cancer and established a prognostic model for liver cancer. Additionally, this study revealed the molecular functions of RPN1 in liver cancer, suggesting its potential as a therapeutic target for this disease.

Simultaneous screening of overexpressed genes in breast cancer for oncogenic drivers and tumor dependencies

There are hundreds of genes typically overexpressed in breast cancer cells and it's often assumed that their overexpression contributes to cancer progression. However, the precise proportion of these overexpressed genes contributing to tumorigenicity remains unclear. To address this gap, we undertook a comprehensive screening of a diverse set of seventy-two genes overexpressed in breast cancer. This systematic screening evaluated their potential for inducing malignant transformation and, concurrently, assessed their impact on breast cancer cell proliferation and viability. Select genes including ALDH3B1, CEACAM5, IL8, PYGO2, and WWTR1, exhibited pronounced activity in promoting tumor formation and establishing gene dependencies critical for tumorigenicity. Subsequent investigations revealed that CEACAM5 overexpression triggered the activation of signaling pathways involving β-catenin, Cdk4, and mTOR. Additionally, it conferred a growth advantage independent of exogenous insulin in defined medium and facilitated spheroid expansion by inducing multiple layers of epithelial cells while preserving a hollow lumen. Furthermore, the silencing of CEACAM5 expression synergized with tamoxifen-induced growth inhibition in breast cancer cells. These findings underscore the potential of screening overexpressed genes for both oncogenic drivers and tumor dependencies to expand the repertoire of therapeutic targets for breast cancer treatment.

Migration, invasion, and metastasis are mediated by P-Rex1 in neuroblastoma

Neuroblastoma accounts for approximately 15% of pediatric cancer-related deaths despite intensive multimodal therapy. This is due, in part, to high rates of metastatic disease at diagnosis and disease relapse. A better understanding of tumor biology of aggressive, pro-metastatic phenotypes is necessary to develop novel, more effective therapeutics against neuroblastoma. Phosphatidylinositol 3,4,5-trisphosphate-dependent Rac exchanger 1 (P-Rex1) has been found to stimulate migration, invasion, and metastasis in several adult malignancies. However, its role in neuroblastoma is currently unknown. In the present study, we found that P-Rex1 is upregulated in pro-metastatic murine models of neuroblastoma, as well as human neuroblastoma metastases. Correspondingly, silencing of P-Rex1 was associated with decreased migration and invasion in vitro. This was associated with decreased AKT-mTOR and ERK2 activity, dysregulation of Rac, and diminished secretion of matrix metalloproteinases. Furthermore, increased P-Rex1 expression was associated with inferior relapse-free and overall survival via tissue microarray and Kaplan-Meier survival analysis of a publicly available clinical database. Together, these findings suggest that P-Rex1 may be a novel therapeutic target and potential prognostic factor in neuroblastoma.

DDIT3 is associated with breast cancer prognosis and immune microenvironment: an integrative bioinformatic and immunohistochemical analysis

DNA damage-inducible transcript 3 (DDIT3) is a transcription factor central to apoptosis, differentiation, and stress response. DDIT3 has been extensively studied in cancer biology. However, its precise implications in breast cancer progression and its interaction with the immune microenvironment are unclear. In this study, we utilized a novel multi-omics integration strategy, combining bulk RNA sequencing, single-cell sequencing, spatial transcriptomics and immunohistochemistry, to explore the role of DDIT3 in breast cancer and establish the correlation between DDIT3 and poor prognosis in breast cancer patients. We identified a robust prognostic signature, including six genes (unc-93 homolog B1, TLR signaling regulator, anti-Mullerian hormone, DCTP pyrophosphatase 1, mitochondrial ribosomal protein L36, nuclear factor erythroid 2, and Rho GTPase activating protein 39), associated with DDIT3. This signature stratified the high-risk patient groups, characterized by increased infiltration of the regulatory T cells and M2-like macrophages and fibroblast growth factor (FGF)/FGF receptor signaling activation. Notably, the high-risk patient group demonstrated enhanced sensitivity to immunotherapy, presenting novel therapeutic opportunities. Integrating multi-omics data helped determine the spatial expression pattern of DDIT3 in the tumor microenvironment and its correlation with immune cell infiltration. This multi-dimensional analysis provided a comprehensive understanding of the intricate interplay between DDIT3 and the immune microenvironment in breast cancer. Overall, our study not only facilitates understanding the role of DDIT3 in breast cancer but also offers innovative insights for developing prognostic models and therapeutic strategies. Identifying the DDIT3-related prognostic signature and its association with the immune microenvironment provided a promising avenue for personalized breast cancer treatment.

Comprehensive pan-cancer analysis of inflammatory age-clock-related genes as prognostic and immunity markers based on multi-omics data

Inflammatory age (iAge) is a vital concept for understanding the intricate interplay between chronic inflammation and aging in the context of cancer. However, the importance of iAge-clock-related genes (iAge-CRGs) across cancers remains unexplored. This study aimed to explore the mechanisms and applications of these genes across diverse cancer types. We analyzed profiling data from over 10,000 individuals, covering 33 cancer types, 750 small molecule drugs, and 24 immune cell types. We focused on DCBLD2's function at the single-cell level and computed an iAge-CRG score using GSVA. This score was correlated with cancer pathways, immune infiltration, and survival. A signature was then derived using univariate Cox and LASSO regression, followed by ROC curve analysis, nomogram construction, decision curve analysis, and immunocytochemistry. Our comprehensive analysis revealed epigenetic, genomic, and immunogenomic alterations in iAge-CRGs, especially DCBLD2, leading to abnormal expression. Aberrant DCBLD2 expression strongly correlated with cancer-associated fibroblast infiltration and prognosis in multiple cancers. Based on GSVA results, we developed a risk model using five iAge-CRGs, which proved to be an independent prognostic index for uveal melanoma (UVM) patients. We also systematically evaluated the correlation between the iAge-related signature risk score and immune cell infiltration. iAge-CRGs, particularly DCBLD2, emerge as potential targets for enhancing immunotherapy outcomes. The strong correlation between abnormal DCBLD2 expression, cancer-associated fibroblast infiltration, and patient survival across various cancers underscores their significance. Our five-gene risk signature offers an independent prognostic tool for UVM patients, highlighting the crucial role of these genes in suppressing the immune response in UVM.Kindly check and confirm whether the corresponding affiliation is correctly identified.I identified the affiliation is correctly.thank you.Per style, a structured abstract is not allowed so we have changed the structured abstract to an unstructured abstract. Please check and confirm.I confirm the abstract is correctly ,thank you.

Pan-cancer analysis of polo-like kinase family genes reveals polo-like kinase 1 as a novel oncogene in kidney renal papillary cell carcinoma

Background

Polo-like kinases (PLKs) are a kinase class of serine/threonine with five members that play crucial roles in cell cycle regulation. However, their biological functions, regulation, and expression remain unclear. This study revealed the molecular properties, oncogenic role, and clinical significance of PLK genes in pan-cancers, particularly in kidney renal papillary cell carcinoma (KIRP).

Methods

We evaluated the mutation landscape, expression level, and prognostic values of PLK genes using bioinformatics analyses and explored the association between the expression level of PLK genes and tumor microenvironment (TME), immune subtype, cancer immunotherapy, tumor stemness, and drug sensitivity. Finally, we verified the prognostic value in patients with KIRP through univariate and multivariate analyses and nomogram construction.

Results

PLK genes are extensively altered in pan-cancer, which may contribute to tumorigenesis. These genes are aberrantly expressed in some types of cancer, with PLK1 being overexpressed in 31 cancers. PLK expression is closely associated with the prognosis of various cancers. The expression level of PLK genes is related with sensitivity to diverse drugs and cancer immunity as well as cancer immunotherapy. Importantly, we verified that PLK1 was overexpressed in KIRP tissues and could be an unfavorable prognostic biomarker in patients with KIRP. Hence, PLK1 may serve as an oncogenic gene in KIRP and should be explored in future studies.

Conclusions

Our study comprehensively reports the molecular characteristics and biological functions of PLK family gens across human cancers and recommends further investigation of these genes as potential biomarkers and therapeutic targets, especially in KIRP.

Multiomic-Based Molecular Landscape of FaDu Xenograft Tumors in Mice after a Combinatorial Treatment with Radiation and an HSP90 Inhibitor Identifies Adaptation-Induced Targets of Resistance and Therapeutic Intervention

Treatments involving radiation and chemotherapy alone or in combination have improved patient survival and quality of life. However, cancers frequently evade these therapies due to adaptation and tumor evolution. Given the complexity of predicting response based solely on the initial genetic profile of a patient, a predetermined treatment course may miss critical adaptation that can cause resistance or induce new targets for drug and immunotherapy. To address the timescale for these evasive mechanisms, using a mouse xenograft tumor model, we investigated the rapidity of gene expression (mRNA), molecular pathway, and phosphoproteome changes after radiation, an HSP90 inhibitor, or combination. Animals received radiation, drug, or combination treatment for 1 or 2 weeks and were then euthanized along with a time-matched untreated group for comparison. Changes in gene expression occur as early as 1 week after treatment initiation. Apoptosis and cell death pathways were activated in irradiated tumor samples. For the HSP90 inhibitor and combination treatment at weeks 1 and 2 compared with Control Day 1, gene-expression changes induced inhibition of pathways including invasion of cells, vasculogenesis, and viral infection among others. The combination group included both drug-alone and radiation-alone changes. Our data demonstrate the rapidity of gene expression and functional pathway changes in the evolving tumor as it responds to treatment. Discovering these phenotypic adaptations may help elucidate the challenges in using sustained treatment regimens and could also define evolving targets for therapeutic efficacy.

Diagnostic, prognostic, and therapeutic potential of exosomal microRNAs in renal cancer

Renal cell carcinoma (RCC) arises from the tubular epithelial cells of the nephron. It has the highest mortality rate among urological cancers. There are no effective therapeutic approaches and no non-invasive biomarkers for diagnosis and follow-up. Thus, suitable novel biomarkers and therapeutic targets are essential for improving RCC diagnosis/prognosis and treatment. Circulating exosomes such as exosomal microRNAs (Exo-miRs) provide non-invasive prognostic/diagnostic biomarkers and valuable therapeutic targets, as they can be easily isolated and quantified and show high sensitivity and specificity. Exosomes secreted by an RCC can exhibit alterations in the miRs' profile that may reflect the cellular origin and (patho)physiological state, as a ''signature'' or ''fingerprint'' of the donor cell. It has been shown that the transportation of renal-specific miRs in exosomes can be rapidly detected and measured, holding great potential as biomarkers in RCC. The present review highlights the studies reporting tumor microenvironment-derived Exo-miRs with therapeutic potential as well as circulating Exo-miRs as potential diagnostic/prognostic biomarkers in patients with RCC.

Proteogenomic insights into early-onset endometrioid endometrial carcinoma: predictors for fertility-sparing therapy response

Endometrial carcinoma remains a public health concern with a growing incidence, particularly in younger women. Preserving fertility is a crucial consideration in the management of early-onset endometrioid endometrial carcinoma (EEEC), particularly in patients under 40 who maintain both reproductive desire and capacity. To illuminate the molecular characteristics of EEEC, we undertook a large-scale multi-omics study of 215 patients with endometrial carcinoma, including 81 with EEEC. We reveal an unexpected association between exposome-related mutational signature and EEEC, characterized by specific CTNNB1 and SIGLEC10 hotspot mutations and disruption of downstream pathways. Interestingly, SIGLEC10Q144K mutation in EEECs resulted in aberrant SIGLEC-10 protein expression and promoted progestin resistance by interacting with estrogen receptor alpha. We also identified potential protein biomarkers for progestin response in fertility-sparing treatment for EEEC. Collectively, our study establishes a proteogenomic resource of EEECs, uncovering the interactions between exposome and genomic susceptibilities that contribute to the development of primary prevention and early detection strategies for EEECs.

Pan-cancer and single-cell analysis of actin cytoskeleton genes related to disulfidptosis

Disulfidptosis was recently reported to be caused by abnormal disulfide accumulation in cells with high SLC7A11 levels subjected to glucose starvation, suggesting that targeting disulfidptosis was a potential strategy for cancer treatment. We analyzed the relationships between gene expression and mutations and prognoses of patients. In addition, the correlation between gene expression and immune cell infiltration was explored. The potential regulatory mechanisms of these genes were assessed by investigating their related signaling pathways involved in cancer, their expression patterns, and their cellular localization. Most cancer types showed a negative correlation between the gene-set variation analysis (GSVA) scores and infiltration of B cells and neutrophils, and a positive correlation between GSVA scores and infiltration of natural killer T and induced regulatory T cells. Single-cell analysis revealed that ACTB, DSTN, and MYL6 were highly expressed in different bladder urothelial carcinoma subtypes, but MYH10 showed a low expression. Immunofluorescence staining showed that actin cytoskeleton proteins were mainly localized in the actin filaments and plasma membrane. Notably, IQGAP1 was localized in the cell junctions. In conclusion, this study provided an overview of disulfidptosis-related actin cytoskeleton genes in pan-cancer. These genes were associated with the survival of patients and might be involved in cancer-related pathways.

Pan-cancer analyses suggest kindlin-associated global mechanochemical alterations

Kindlins serve as mechanosensitive adapters, transducing extracellular mechanical cues to intracellular biochemical signals and thus, their perturbations potentially lead to cancer progressions. Despite the kindlin involvement in tumor development, understanding their genetic and mechanochemical characteristics across different cancers remains elusive. Here, we thoroughly examined genetic alterations in kindlins across more than 10,000 patients with 33 cancer types. Our findings reveal cancer-specific alterations, particularly prevalent in advanced tumor stage and during metastatic onset. We observed a significant co-alteration between kindlins and mechanochemical proteome in various tumors through the activation of cancer-related pathways and adverse survival outcomes. Leveraging normal mode analysis, we predicted structural consequences of cancer-specific kindlin mutations, highlighting potential impacts on stability and downstream signaling pathways. Our study unraveled alterations in epithelial-mesenchymal transition markers associated with kindlin activity. This comprehensive analysis provides a resource for guiding future mechanistic investigations and therapeutic strategies targeting the roles of kindlins in cancer treatment.

Tutorial on survival modeling with applications to omics data

MOTIVATION

Identification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.

RESULTS

We present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally.

AVAILABILITY AND IMPLEMENTATION

A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.

LASS2 enhances chemosensitivity to cisplatin by inhibiting PP2A-mediated β-catenin dephosphorylation in a subset of stem-like bladder cancer cells

BACKGROUND

The benefits of first-line, cisplatin-based chemotherapy for muscle-invasive bladder cancer are limited due to intrinsic or acquired resistance to cisplatin. Increasing evidence has revealed the implication of cancer stem cells in the development of chemoresistance. However, the underlying molecular mechanisms remain to be elucidated. This study investigates the role of LASS2, a ceramide synthase, in regulating Wnt/β-catenin signaling in a subset of stem-like bladder cancer cells and explores strategies to sensitize bladder cancer to cisplatin treatment.

METHODS

Data from cohorts of our center and published datasets were used to evaluate the clinical characteristics of LASS2. Flow cytometry was used to sort and analyze bladder cancer stem cells (BCSCs). Tumor sphere formation, soft agar colony formation assay, EdU assay, apoptosis analysis, cell viability, and cisplatin sensitivity assay were used to investigate the functional roles of LASS2. Immunofluorescence, immunoblotting, coimmunoprecipitation, LC-MS, PCR array, luciferase reporter assays, pathway reporter array, chromatin immunoprecipitation, gain-of-function, and loss-of-function approaches were used to investigate the underlying mechanisms. Cell- and patient-derived xenograft models were used to investigate the effect of LASS2 overexpression and a combination of XAV939 on cisplatin sensitization and tumor growth.

RESULTS

Patients with low expression of LASS2 have a poorer response to cisplatin-based chemotherapy. Loss of LASS2 confers a stem-like phenotype and contributes to cisplatin resistance. Overexpression of LASS2 results in inhibition of self-renewal ability of BCSCs and increased their sensitivity to cisplatin. Mechanistically, LASS2 inhibits PP2A activity and dissociates PP2A from β-catenin, preventing the dephosphorylation of β-catenin and leading to the accumulation of cytosolic phospho-β-catenin, which decreases the transcription of the downstream genes ABCC2 and CD44 in BCSCs. Overexpression of LASS2 combined with a tankyrase inhibitor (XAV939) synergistically inhibits tumor growth and restores cisplatin sensitivity.

CONCLUSIONS

Targeting the LASS2 and β-catenin pathways may be an effective strategy to overcome cisplatin resistance and inhibit tumor growth in bladder cancer patients.

DriverDBv4: a multi-omics integration database for cancer driver gene research

Advancements in high-throughput technology offer researchers an extensive range of multi-omics data that provide deep insights into the complex landscape of cancer biology. However, traditional statistical models and databases are inadequate to interpret these high-dimensional data within a multi-omics framework. To address this limitation, we introduce DriverDBv4, an updated iteration of the DriverDB cancer driver gene database (http://driverdb.bioinfomics.org/). This updated version offers several significant enhancements: (i) an increase in the number of cohorts from 33 to 70, encompassing approximately 24 000 samples; (ii) inclusion of proteomics data, augmenting the existing types of omics data and thus expanding the analytical scope; (iii) implementation of multiple multi-omics algorithms for identification of cancer drivers; (iv) new visualization features designed to succinctly summarize high-context data and redesigned existing sections to accommodate the increased volume of datasets and (v) two new functions in Customized Analysis, specifically designed for multi-omics driver identification and subgroup expression analysis. DriverDBv4 facilitates comprehensive interpretation of multi-omics data across diverse cancer types, thereby enriching the understanding of cancer heterogeneity and aiding in the development of personalized clinical approaches. The database is designed to foster a more nuanced understanding of the multi-faceted nature of cancer.

Current advances in comprehensive omics data mining for oncology and cancer research

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research.

StabJGL: a stability approach to sparsity and similarity selection in multiple-network reconstruction

Motivation

In recent years, network models have gained prominence for their ability to capture complex associations. In statistical omics, networks can be used to model and study the functional relationships between genes, proteins, and other types of omics data. If a Gaussian graphical model is assumed, a gene association network can be determined from the non-zero entries of the inverse covariance matrix of the data. Due to the high-dimensional nature of such problems, integrative methods that leverage similarities between multiple graphical structures have become increasingly popular. The joint graphical lasso is a powerful tool for this purpose, however, the current AIC-based selection criterion used to tune the network sparsities and similarities leads to poor performance in high-dimensional settings.

Results

We propose stabJGL, which equips the joint graphical lasso with a stable and well-performing penalty parameter selection approach that combines the notion of model stability with likelihood-based similarity selection. The resulting method makes the powerful joint graphical lasso available for use in omics settings, and outperforms the standard joint graphical lasso, as well as state-of-the-art joint methods, in terms of all performance measures we consider. Applying stabJGL to proteomic data from a pan-cancer study, we demonstrate the potential for novel discoveries the method brings.

Availability and implementation

A user-friendly R package for stabJGL with tutorials is available on Github https://github.com/Camiling/stabJGL.

Genome-Wide CRISPR Screens Identify Multiple Synthetic Lethal Targets That Enhance KRASG12C Inhibitor Efficacy

Non-small lung cancers (NSCLC) frequently (∼30%) harbor KRAS driver mutations, half of which are KRASG12C. KRAS-mutant NSCLC with comutated STK11 and/or KEAP1 is particularly refractory to conventional, targeted, and immune therapy. Development of KRASG12C inhibitors (G12Ci) provided a major therapeutic advance, but resistance still limits their efficacy. To identify genes whose deletion augments efficacy of the G12Cis adagrasib (MRTX-849) or adagrasib plus TNO155 (SHP2i), we performed genome-wide CRISPR/Cas9 screens on KRAS/STK11-mutant NSCLC lines. Recurrent, potentially targetable, synthetic lethal (SL) genes were identified, including serine-threonine kinases, tRNA-modifying and proteoglycan synthesis enzymes, and YAP/TAZ/TEAD pathway components. Several SL genes were confirmed by siRNA/shRNA experiments, and the YAP/TAZ/TEAD pathway was extensively validated in vitro and in mice. Mechanistic studies showed that G12Ci treatment induced gene expression of RHO paralogs and activators, increased RHOA activation, and evoked ROCK-dependent nuclear translocation of YAP. Mice and patients with acquired G12Ci- or G12Ci/SHP2i-resistant tumors showed strong overlap with SL pathways, arguing for the relevance of the screen results. These findings provide a landscape of potential targets for future combination strategies, some of which can be tested rapidly in the clinic.

SIGNIFICANCE

Identification of synthetic lethal genes with KRASG12C using genome-wide CRISPR/Cas9 screening and credentialing of the ability of TEAD inhibition to enhance KRASG12C efficacy provides a roadmap for combination strategies. See related commentary by Johnson and Haigis, p. 4005.

Pan-cancer analyses of senescence-related genes in extracellular matrix characterization in cancer

PURPOSE

The aged microenvironment plays a crucial role in tumor onset and progression. However, it remains unclear whether and how the aging of the extracellular matrix (ECM) influences cancer onset and progression. Furthermore, the mechanisms and implications of extracellular matrix senescence-related genes (ECM-SRGs) in pan-cancer have not been investigated.

METHODS

We collected profiling data from over 10,000 individuals, covering 33 cancer types, 750 small molecule drugs, and 24 immune cell types, for a thorough and systematic analysis of ECM-SRGs in cancer.

RESULTS

We observed a significant correlation between immune cell infiltrates and Gene Set Variation Analysis enrichment scores of ECM-SRGs in 33 cancer types. Moreover, our results revealed significant differences in immune cell infiltration among patients with copy number variations (CNV) and single nucleotide variations (SNV) in ECM-SRGs across various malignancies. Aberrant hypomethylation led to increased ECM-SRGs expression, and in specific malignancies, a connection between ECM-SRGs hypomethylation and adverse patient survival was established. The frequency of CNV and SNV in ECM-SRGs was elevated. We observed a positive correlation between CNV, SNV, and ECM-SRGs expression. Furthermore, a correlation was found between the high frequency of CNV and SNV in ECM-SRGs and poor patient survival in several cancer types. Additionally, the results demonstrated that ECM-SRGs expression could serve as a predictor of patient survival in diverse cancers. Pathway analysis unveiled the role of ECM-SRGs in activating EMT, apoptosis, and the RAS/MAPK signaling pathway while suppressing the cell cycle, hormone AR, and the response to DNA damage signaling pathway. Finally, we conducted searches in the "Genomics of Drug Sensitivity in Cancer" and "Genomics of Therapeutics Response Portal" databases, identifying several drugs that target ECM-SRGs.

CONCLUSIONS

We conducted a comprehensive evaluation of the genomes and immunogenomics of ECM-SRGs, along with their clinical features in 33 solid tumors. This may provide insights into the relationship between ECM-SRGs and tumorigenesis. Consequently, targeting these ECM-SRGs holds promise as a clinical approach for cancer treatment.

Ephexin3/ARHGEF5 Together with Cell Migration Signaling Partners within the Tumor Microenvironment Define Prognostic Transcriptional Signatures in Multiple Cancer Types

Cancer cell migration involves a repertoire of signaling proteins that lead cytoskeleton reorganization as a critical step in metastatic dissemination. RhoGEFs are multidomain effectors that integrate signaling inputs to activate the molecular switches that orchestrate actin cytoskeleton reorganization. Ephexins, a group of five RhoGEFs, play oncogenic roles in invasive and metastatic cancer, leading to a mechanistic hypothesis about their function as signaling nodes assembling functional complexes that guide cancer cell migration. To identify clinically significant Ephexin signaling partners, we applied three systematic data mining strategies, based on the screening of essential Ephexins in multiple cancer cell lines and the identification of coexpressed signaling partners in the TCGA cancer patient datasets. Based on the domain architecture of encoded proteins and gene ontology criteria, we selected Ephexin signaling partners with a role in cytoskeletal reorganization and cell migration. We focused on Ephexin3/ARHGEF5, identified as an essential gene in multiple cancer cell types. Based on significant coexpression data and coessentiality, the signaling repertoire that accompanies Ephexin3 corresponded to three groups: pan-cancer, cancer-specific and coessential. To further select the Ephexin3 signaling partners likely to be relevant in clinical settings, we first identified those whose high expression was statistical linked to shorter patient survival. The resulting Ephexin3 transcriptional signatures represent significant accumulated risk, predictive of shorter survival, in 17 cancer types, including PAAD, LUAD, LGG, OSC, AML, KIRC, THYM, BLCA, LIHC and UCEC. The signaling landscape that accompanies Ephexin3 in various cancer types included the tyrosine kinase receptor MET and the tyrosine phosphatase receptor PTPRF, the serine/threonine kinases MARK2 and PAK6, the Rho GTPases RHOD, RHOF and RAC1, and the cytoskeletal regulator DIAHP1. Our findings set the basis to further explore the role of Ephexin3/ARHGEF5 as an essential effector and signaling hub in cancer cell migration.

Palmitoylation landscapes across human cancers reveal a role of palmitoylation in tumorigenesis

BACKGROUND

Protein palmitoylation, which is catalyzed by palmitoyl-transferase and de-palmitoyl-transferase, plays a crucial role in various biological processes. However, the landscape and dynamics of protein palmitoylation in human cancers are not well understood.

METHODS

We utilized 23 palmitoyl-acyltransferases and seven de-palmitoyl-acyltransferases as palmitoylation-related genes for protein palmitoylation analysis. Multiple publicly available datasets were employed to conduct pan-cancer analysis, examining the transcriptome, genomic alterations, clinical outcomes, and correlation with c-Myc (Myc) for palmitoylation-related genes. Real-time quantitative PCR and immunoblotting were performed to assess the expression of palmitoylation-related genes and global protein palmitoylation levels in cancer cells treated with Myc depletion or small molecule inhibitors. Protein docking and drug sensitivity analyses were employed to predict small molecules that target palmitoylation-related genes.

RESULTS

We identified associations between palmitoylation and cancer subtype, stage, and patient survival. We discovered that abnormal DNA methylation and oncogenic Myc-driven transcriptional regulation synergistically contribute to the dysregulation of palmitoylation-related genes. This dysregulation of palmitoylation was closely correlated with immune infiltration in the tumor microenvironment and the response to immunotherapy. Importantly, dysregulated palmitoylation was found to modulate canonical cancer-related pathways, thus influencing tumorigenesis. To support our findings, we performed a proof-of-concept experiment showing that depletion of Myc led to reduced expression of most palmitoylation-related genes, resulting in decreased global protein palmitoylation levels. Through mass spectrometry and enrichment analyses, we also identified palmitoyl-acyltransferases ZDHHC7 and ZDHHC23 as significant contributors to mTOR signaling, DNA repair, and immune pathways, highlighting their potential roles in tumorigenesis. Additionally, our study explored the potential of three small molecular (BI-2531, etoposide, and piperlongumine) to modulate palmitoylation by targeting the expression or activity of palmitoylation-related genes or enzymes.

CONCLUSIONS

Overall, our findings underscore the critical role of dysregulated palmitoylation in tumorigenesis and the response to immunotherapy, mediated through classical cancer-related pathways and immune cell infiltration. Additionally, we propose that the aforementioned three small molecule hold promise as potential therapeutics for modulating palmitoylation, thereby offering novel avenues for cancer therapy.

The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers

Proteomics is a powerful approach that can rapidly enhance our understanding of cancer development. Detailed characterization of the genetic, pharmacogenomic, and immune landscape in relation to protein expression in patients with cancer could provide new insights into the functional roles of proteins in cancer. By taking advantage of the genotype data from The Cancer Genome Atlas and protein expression data from The Cancer Proteome Atlas, we characterized the effects of genetic variants on protein expression across 31 cancer types and identified approximately 100,000 protein quantitative trait loci (pQTL). Among these, over 8000 pQTLs were associated with patient overall survival. Furthermore, characterization of the impact of protein expression on more than 350 imputed anticancer drug responses in patients revealed nearly 230,000 significant associations. In addition, approximately 21,000 significant associations were identified between protein expression and immune cell abundance. Finally, a user-friendly data portal, GPIP (https://hanlaboratory.com/GPIP), was developed featuring multiple modules that enable researchers to explore, visualize, and browse multidimensional data. This detailed analysis reveals the associations between the proteomic landscape and genetic variation, patient outcome, the immune microenvironment, and drug response across cancer types, providing a resource that may offer valuable clinical insights and encourage further functional investigations of proteins in cancer.

SIGNIFICANCE

Comprehensive characterization of the relationship between protein expression and the genetic, pharmacogenomic, and immune landscape of tumors across cancer types provides a foundation for investigating the role of protein expression in cancer development and treatment.

Rational therapeutic targets with biomolecular liquid-liquid phase separation regulating synergy: A pan-cancer analysis

Liquid-liquid phase separation (LLPS) is characterized as an ubiquitous framework for diverse biological processes including carcinogenesis and cancer progression. While targeting cancer from perspective of LLPS offers an opportunity to drug the conventionally undruggables with cancer-driving potential, the therapeutic value of cancer associated LLPS (CAL) proteins remains elusive. Here, we report the genomic landscape, prognostic relevance, immune-infiltration association, down-stream pathway alteration and small molecular responsiveness of CAL protein-coding gene signatures based on protein-coding associated mutations and transcriptional abundance in pan-cancer. Correlations of CAL protein-coding associated mutations and transcriptional abundances to overall survival and progression-free survival were observed in an array of cancers and further characterized by differential survival outcomes between patients with intrinsic disordered region (IDR) enriched and non-IDR enriched mutations in endometrial cancer. Altered signaling pathways and universal pattern of immune infiltrates on account of CAL protein-coding associated gene-set mutations involved key components of oncogenesis in various cancer types and well established therapeutic targets including MAPK signaling pathway and implied an inflamed tumor immunity that might be highly responsive to immunotherapy. LLPS inhibitor enhanced cytotoxicity of cisplatin/paclitaxel in selective cancer cell lines. These findings provide preliminary evidences for rational chemo-, targeted- and immuno-therapeutic innovation with LLPS regulating synergy.

Pan-cancer genetic analysis of disulfidptosis-related gene set

BACKGROUND

A recent study has identified a novel programmed cell death pathway, termed disulfidptosis, which is based on disulfide proteins. This discovery provides new insight into the mechanisms of cell death and may have implications for therapeutic strategies targeting cell death pathways. This study aimed to evaluate the pan-cancer genomics and clinical association of disulfidptosis and disulfidptosis-related cell death genes, including SLC7A11, INF2, CD2AP, PDLIM1, ACTN4, MYH9, MYH10, IQGAP1, FLNA, FLNB, TLN1, MYL6, ACTB, DSTN, and CAPZB.

METHODS

Using multi-omics profiling data, this study provides a comprehensive and systematic characterization of disulfidptosis genes across more than 9000 samples of over 30 types of cancer.

RESULTS

FLNA and FLNB were the two most frequently mutated disulfidptosis cell death genes in cancer. UCEC and SKCM were the two cancer types that have the highest mutation rates while the mutation of ACTN4 was associated with worse survival of CESC and ESCA. Breast cancer was potentially affected by disulfidptosis because its subtypes are different in disulfidptosis gene expression. Similarly, KIRC might also be associated with disulfidptosis. Additionally, the association of disulfidptosis-related cell death genes with survival was analyzed, with MESO and LGG as the top cancer types with survival associated with disulfidptosis cell death genes. The correlation between CNV and survival across multiple cancer types found that UCEC, KIRP, LGG, and KIRC were the top cancer types where the CNV level was associated with survival. There was a negative correlation between expression and methylation for most of the genes and there was only a slight correlation between methylation levels and survival of cancer in LGG. About half of the disulfidptosis-related cell death proteins were associated with the activation of EMT. Disulfidptosis genes were correlated to immune cell infiltration levels in cancers. Multiple compounds were identified as potential drugs that might be affected by disulfidptosis-related cell death for future study.

CONCLUSION

Disulfidptosis cell death genes are potentially involved in many cancer types and can be developed as candidates for cancer diagnosis, prognosis, and therapeutic biomarkers.

Valosin-containing protein (VCP/p97) is prognostically unfavorable in pediatric AML, and negatively correlates with unfolded protein response proteins IRE1 and GRP78: A report from the Children's Oncology Group

PURPOSE

The endoplasmic reticulum (ER) is the major site of protein synthesis and folding in the cell. ER-associated degradation (ERAD) and unfolded protein response (UPR) are the main mechanisms of ER-mediated cell stress adaptation. Targeting the cell stress response is a promising therapeutic approach in acute myeloid leukemia (AML).

EXPERIMENTAL DESIGN

Protein expression levels of valosin-containing protein (VCP), a chief element of ERAD, were measured in peripheral blood samples from in 483 pediatric AML patients using reverse phase protein array methodology. Patients participated in the Children's Oncology Group AAML1031 phase 3 clinical trial that randomized patients to standard chemotherapy (cytarabine (Ara-C), daunorubicin, and etoposide [ADE]) versus ADE plus bortezomib (ADE+BTZ).

RESULTS

Low-VCP expression was significantly associated with favorable 5-year overall survival (OS) rate compared to middle-high-VCP expression (81% versus 63%, p < 0.001), independent of additional bortezomib treatment. Multivariable Cox regression analysis identified VCP as independent predictor of clinical outcome. UPR proteins IRE1 and GRP78 had significant negative correlation with VCP. Five-year OS in patients characterized by low-VCP, moderately high-IRE1 and high-GRP78 improved after treatment with ADE+BTZ versus ADE (66% versus 88%, p = 0.026).

CONCLUSION AND CLINICAL RELEVANCE

Our findings suggest the potential of the protein VCP as biomarker in prognostication prediction in pediatric AML.

The E3 ubiquitin ligase RBCK1: Implications in the tumor immune microenvironment and antiangiogenic therapy of glioma

E3 ubiquitin ligases (E3s) play a pivotal role in regulating the specificity of protein ubiquitination, and their significant functions as regulators of immune responses against tumors are attracting considerable interest. RBCK1-an RBR E3 ligase-is involved in immune regulation and tumor development. However, the potential effect of RBCK1 on glioma remains enigmatic. In the present study, we performed comprehensive analyses of multilevel data, which disclosed distribution characteristics of RBCK1 in pan-cancer, especially in glioma. Functional roles of RBCK1 were further confirmed using immunohistochemistry, cell biological assays, and xenograft experiments. Aberrant ascending of RBCK1 in multiple types of cancer was found to remodel the immunosuppressive microenvironment of glioma by regulating immunomodulators, cancer immunity cycles, and immune cell infiltration. Notably, the MES-like/RBCK1High cell population, a unique subset of cells in the microenvironment, suppressed T cell-mediated cell killing in glioma. Elevated expression levels of RBCK1 suggested a glioma subtype characterized by immunosuppression and hypo-responsiveness to immunotherapy but manifesting surprisingly increased responses to anti-angiogenic therapy. In conclusion, anti-RBCK1 target therapy might be beneficial for glioma treatment. Moreover, RBCK1 assisted in predicting molecular subtypes of glioma and response rates of patients to different clinical treatments, which could guide personalized therapy.

Cross-Talks between RKIP and YY1 through a Multilevel Bioinformatics Pan-Cancer Analysis

Recent studies suggest that PEBP1 (also known as RKIP) and YY1, despite having distinct molecular functions, may interact and mutually influence each other's activity. They exhibit reciprocal control over each other's expression through regulatory loops, prompting the hypothesis that their interplay could be pivotal in cancer advancement and resistance to drugs. To delve into this interplay's functional characteristics, we conducted a comprehensive analysis using bioinformatics tools across a range of cancers. Our results confirm the association between elevated YY1 mRNA levels and varying survival outcomes in diverse tumors. Furthermore, we observed differing degrees of inhibitory or activating effects of these two genes in apoptosis, cell cycle, DNA damage, and other cancer pathways, along with correlations between their mRNA expression and immune infiltration. Additionally, YY1/PEBP1 expression and methylation displayed connections with genomic alterations across different cancer types. Notably, we uncovered links between the two genes and different indicators of immunosuppression, such as immune checkpoint blockade response and T-cell dysfunction/exclusion levels, across different patient groups. Overall, our findings underscore the significant role of the interplay between YY1 and PEBP1 in cancer progression, influencing genomic changes, tumor immunity, or the tumor microenvironment. Additionally, these two gene products appear to impact the sensitivity of anticancer drugs, opening new avenues for cancer therapy.

The Breast Cancer Proteome and Precision Oncology

The goal of precision oncology is to translate the molecular features of cancer into predictive and prognostic tests that can be used to individualize treatment leading to improved outcomes and decreased toxicity. Success for this strategy in breast cancer is exemplified by efficacy of trastuzumab in tumors overexpressing ERBB2 and endocrine therapy for tumors that are estrogen receptor positive. However, other effective treatments, including chemotherapy, immune checkpoint inhibitors, and CDK4/6 inhibitors are not associated with strong predictive biomarkers. Proteomics promises another tier of information that, when added to genomic and transcriptomic features (proteogenomics), may create new opportunities to improve both treatment precision and therapeutic hypotheses. Here, we review both mass spectrometry-based and antibody-dependent proteomics as complementary approaches. We highlight how these methods have contributed toward a more complete understanding of breast cancer and describe the potential to guide diagnosis and treatment more accurately.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.

Global proteomic identifies multiple cancer-related signaling pathways altered by a gut pathobiont associated with colorectal cancer

In this work, we investigated the oncogenic role of Streptococcus gallolyticus subsp. gallolyticus (SGG), a gut bacterium associated with colorectal cancer (CRC). We showed that SGG UCN34 accelerates colon tumor development in a chemically induced CRC murine model. Full proteome and phosphoproteome analysis of murine colons chronically colonized by SGG UCN34 revealed that 164 proteins and 725 phosphorylation sites were differentially regulated. Ingenuity Pathway Analysis (IPA) indicates a pro-tumoral shift specifically induced by SGG UCN34, as ~ 90% of proteins and phosphoproteins identified were associated with digestive cancer. Comprehensive analysis of the altered phosphoproteins using ROMA software revealed up-regulation of several cancer hallmark pathways such as MAPK, mTOR and integrin/ILK/actin, affecting epithelial and stromal colonic cells. Importantly, an independent analysis of protein arrays of human colon tumors colonized with SGG showed up-regulation of PI3K/Akt/mTOR and MAPK pathways, providing clinical relevance to our findings. To test SGG's capacity to induce pre-cancerous transformation of the murine colonic epithelium, we grew ex vivo organoids which revealed unusual structures with compact morphology. Taken together, our results demonstrate the oncogenic role of SGG UCN34 in a murine model of CRC associated with activation of multiple cancer-related signaling pathways.

Dimension reduction for integrative survival analysis

We propose a constrained maximum partial likelihood estimator for dimension reduction in integrative (e.g., pan-cancer) survival analysis with high-dimensional predictors. We assume that for each population in the study, the hazard function follows a distinct Cox proportional hazards model. To borrow information across populations, we assume that each of the hazard functions depend only on a small number of linear combinations of the predictors (i.e., "factors"). We estimate these linear combinations using an algorithm based on "distance-to-set" penalties. This allows us to impose both low-rankness and sparsity on the regression coefficient matrix estimator. We derive asymptotic results that reveal that our estimator is more efficient than fitting a separate proportional hazards model for each population. Numerical experiments suggest that our method outperforms competitors under various data generating models. We use our method to perform a pan-cancer survival analysis relating protein expression to survival across 18 distinct cancer types. Our approach identifies six linear combinations, depending on only 20 proteins, which explain survival across the cancer types. Finally, to validate our fitted model, we show that our estimated factors can lead to better prediction than competitors on four external datasets.

Clinical evidence for a role of E2F1-induced replication stress in modulating tumor mutational burden and immune microenvironment

DNA replication stress (RS) is frequently induced by oncogene activation and is believed to promote tumorigenesis. However, clinical evidence for the role of oncogene-induced RS in tumorigenesis remains scarce, and the mechanisms by which RS promotes cancer development remain incompletely understood. By performing a series of bioinformatic analyses on the oncogene E2F1, other RS-inducing factors, and replication fork processing factors in TCGA cancer database using previously established tools, we show that hyperactivity of E2F1 likely promotes the expression of several of these factors in virtually all types of cancer to induce RS and cytosolic self-DNA production. In addition, the expression of these factors positively correlates with that of ATR and Chk1 that govern the cellular response to RS, the tumor mutational load, and tumor infiltration of immune-suppressive CD4+Th2 cells and myeloid-derived suppressor cells (MDSCs). Consistently, high expression of these factors is associated with poor patient survival. Our study provides new insights into the role of E2F1-induced RS in tumorigenesis and suggests therapeutic approaches for E2F1-overexpressing cancers by targeting genomic instability, cytosolic self-DNA and the tumor immune microenvironment.

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

Single-cell trajectory analysis reveals a CD9 positive state to contribute to exit from stem cell-like and embryonic diapause states and transit to drug-resistant states

Bromo- and extra-terminal domain (BET) inhibitors (BETi) have been shown to decrease tumor growth in preclinical models and clinical trials. However, toxicity and rapid emergence of resistance have limited their clinical implementation. To identify state changes underlying acquisition of resistance to the JQ1 BETi, we reanalyzed single-cell RNAseq data from JQ1 sensitive and resistant SUM149 and SUM159 triple-negative breast cancer cell lines. Parental and JQ1-resistant SUM149 and SUM159 exhibited a stem cell-like and embryonic diapause (SCLED) cell state as well as a transitional cell state between the SCLED state that is present in both treatment naïve and JQ1 treated cells, and a number of JQ1 resistant cell states. A transitional cell state transcriptional signature but not a SCLED state transcriptional signature predicted worsened outcomes in basal-like breast cancer patients suggesting that transit from the SCLED state to drug-resistant states contributes to patient outcomes. Entry of SUM149 and SUM159 into the transitional cell state was characterized by elevated expression of the CD9 tetraspanin. Knockdown or inhibition of CD9-sensitized cells to multiple targeted and cytotoxic drugs in vitro. Importantly, CD9 knockdown or blockade sensitized SUM149 to JQ1 in vivo by trapping cells in the SCLED state and limiting transit to resistant cell states. Thus, CD9 appears to be critical for the transition from a SCLED state into treatment-resistant cell states and warrants exploration as a therapeutic target in basal-like breast cancer.

A Tale of Three Proteomes: Visualizing Protein and Transcript Abundance Relationships in the Breast Cancer Proteome Portal

Molecular characterization is transforming research on novel therapeutics in breast cancer. High-throughput methodologies are unbiased to hypotheses; thus, data produced are relevant to address unlimited questions and provide resources for the experimental design process. However, the opportunity is often overlooked because data are not readily accessed or analyzed. Herein, the Breast Cancer Proteome Portal, the only online tool for analyzing protein and transcript abundances across the three breast cancer proteomics studies, is presented. The tool is applied to demonstrate that cofunctioning protein abundances are highly correlated and, conversely, high abundance correlation may be an indicator of cofunction. Furthermore, the cofunction-correlation relationship is less resolved at the transcript level. By applying analysis and visualization tools within the Breast Cancer Proteome Portal, insights are garnered about serine synthesis and the compartmentalization of one-carbon metabolism in breast cancer, and a transcription factor tumorigenic regulatory network of glutamine deamination and oxidation is proposed, illustrating that the Breast Cancer Proteome Portal provides an interface for garnering insights from the information-rich studies of the breast cancer proteome.

Antibody reliability influences observed mRNA-protein correlations in tumour samples

Reverse phase protein arrays (RPPA) have been used to quantify the abundance of hundreds of proteins across thousands of tumour samples in the Cancer Genome Atlas. By number of samples, this is the largest tumour proteomic dataset available and it provides an opportunity to systematically assess the correlation between mRNA and protein abundances. However, the RPPA approach is highly dependent on antibody reliability and approximately one-quarter of the antibodies used in the the Cancer Genome Atlas are deemed to be somewhat less reliable. Here, we assess the impact of antibody reliability on observed mRNA-protein correlations. We find that, in general, proteins measured with less reliable antibodies have lower observed mRNA-protein correlations. This is not true of the same proteins when measured using mass spectrometry. Furthermore, in cell lines, we find that when the same protein is quantified by both mass spectrometry and RPPA, the overall correlation between the two measurements is lower for proteins measured with less reliable antibodies. Overall our results reinforce the need for caution in using RPPA measurements from less reliable antibodies.

Pan-cancer structurome reveals overrepresentation of beta sandwiches and underrepresentation of alpha helical domains

The recent progress in the prediction of protein structures marked a historical milestone. AlphaFold predicted 200 million protein models with an accuracy comparable to experimental methods. Protein structures are widely used to understand evolution and to identify potential drug targets for the treatment of various diseases, including cancer. Thus, these recently predicted structures might convey previously unavailable information about cancer biology. Evolutionary classification of protein domains is challenging and different approaches exist. Recently our team presented a classification of domains from human protein models released by AlphaFold. Here we evaluated the pan-cancer structurome, domains from over and under expressed proteins in 21 cancer types, using the broadest levels of the ECOD classification: the architecture (A-groups) and possible homology (X-groups) levels. Our analysis reveals that AlphaFold has greatly increased the three-dimensional structural landscape for proteins that are differentially expressed in these 21 cancer types. We show that beta sandwich domains are significantly overrepresented and alpha helical domains are significantly underrepresented in the majority of cancer types. Our data suggest that the prevalence of the beta sandwiches is due to the high levels of immunoglobulins and immunoglobulin-like domains that arise during tumor development-related inflammation. On the other hand, proteins with exclusively alpha domains are important elements of homeostasis, apoptosis and transmembrane transport. Therefore cancer cells tend to reduce representation of these proteins to promote successful oncogeneses.

Next generation pan-cancer blood proteome profiling using proximity extension assay

A comprehensive characterization of blood proteome profiles in cancer patients can contribute to a better understanding of the disease etiology, resulting in earlier diagnosis, risk stratification and better monitoring of the different cancer subtypes. Here, we describe the use of next generation protein profiling to explore the proteome signature in blood across patients representing many of the major cancer types. Plasma profiles of 1463 proteins from more than 1400 cancer patients are measured in minute amounts of blood collected at the time of diagnosis and before treatment. An open access Disease Blood Atlas resource allows the exploration of the individual protein profiles in blood collected from the individual cancer patients. We also present studies in which classification models based on machine learning have been used for the identification of a set of proteins associated with each of the analyzed cancers. The implication for cancer precision medicine of next generation plasma profiling is discussed.

Impact of Cuproptosis-related markers on clinical status, tumor immune microenvironment and immunotherapy in colorectal cancer: A multi-omic analysis

Background

Cuproptosis, a novel identified cell death form induced by copper, is characterized by aggregation of lipoylated mitochondrial enzymes and the destabilization of Fe-S cluster proteins. However, the function and potential clinical value of cuproptosis and cuproptosis-related biomarkers in colorectal cancer (CRC) remain largely unknown.

Methods

A comprehensive multi-omics (transcriptomics, genomics, and single-cell transcriptome) analysis was performed for identifying the influence of 16 cuproptosis-related markers on clinical status, molecular functions and tumor microenvironment (TME) in CRC. A novel cuproptosis-related scoring system (CuproScore) based on cuproptosis-related markers was also constructed to predict the prognosis of CRC individuals, TME and the response to immunotherapy. In addition, our transcriptome cohort of 15 paired CRC tissue, tissue-array, and various assays in 4 kinds of CRC cell lines in vitro were applied for verification.

Results

Cuproptosis-related markers were closely associated with both clinical prognosis and molecular functions. And the cuproptosis-related molecular phenotypes and scoring system (CuproScore) could distinguish and predict the prognosis of CRC patients, TME, and the response to immunotherapy in both public and our transcriptome cohorts. Besides, the expression, function and clinical significance of these markers were also checked and analyzed in CRC cell lines and CRC tissues in our own cohorts.

Conclusions

In conclusion, we indicated that cuproptosis and CPRMs played a significant role in CRC progression and in modeling the TME. Inducing cuproptosis may be a useful tool for tumor therapy in the future.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.