Proteogenomic Characterization of Endometrial Carcinoma

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

The DNA Damage Response (DDR) landscape of endometrial cancer defines discrete disease subtypes and reveals therapeutic opportunities

Genome maintenance is an enabling characteristic that allows neoplastic cells to tolerate the inherent stresses of tumorigenesis and evade therapy-induced genotoxicity. Neoplastic cells also deploy many mis-expressed germ cell proteins termed Cancer Testes Antigens (CTAs) to promote genome maintenance and survival. Here, we present the first comprehensive characterization of the DNA Damage Response (DDR) and CTA transcriptional landscapes of endometrial cancer in relation to conventional histological and molecular subtypes. We show endometrial serous carcinoma (ESC), an aggressive endometrial cancer subtype, is defined by gene expression signatures comprising members of the Replication Fork Protection Complex (RFPC) and Fanconi Anemia (FA) pathway and CTAs with mitotic functions. DDR and CTA-based profiling also defines a subset of highly aggressive endometrioid endometrial carcinomas (EEC) with poor clinical outcomes that share similar profiles to ESC yet have distinct characteristics based on conventional histological and genomic features. Using an unbiased CRISPR-based genetic screen and a candidate gene approach, we confirm that DDR and CTA genes that constitute the ESC and related EEC gene signatures are required for proliferation and therapy-resistance of cultured endometrial cancer cells. Our study validates the use of DDR and CTA-based tumor classifiers and reveals new vulnerabilities of aggressive endometrial cancer where none currently exist.

SETDB1, an H3K9-specific methyltransferase: An attractive epigenetic target to combat cancer

SET domain bifurcated histone lysine methyltransferase 1 (SETDB1) is an important epigenetic regulator catalyzing histone H3 lysine 9 (H3K9) methylation, specifically di-/tri-methylation. This regulation promotes gene silencing through heterochromatin formation. Aberrant SETDB1 expression, and its oncogenic role is evident in many cancers. Thus, SETDB1 is a valid target with novel therapeutic benefits. In this review, we explore the structural and biochemical features of SETDB1, its regulatory mechanisms, and its role in various cancers. We also discuss recent discoveries in small molecules targeting SETDB1 and provide suggestions for future research.

The tumour-derived extracellular vesicle proteome varies by endometrial cancer histology and is confounded by an obesogenic environment

Endometrial cancer, the most common gynaecological cancer worldwide, is closely linked to obesity and metabolic diseases, particularly in younger women. New circulating biomarkers have the potential to improve diagnosis and treatment selections, which could significantly improve outcomes. Our approach focuses on extracellular vesicle (EV) biomarker discovery by directly profiling the proteome of EVs enriched from frozen biobanked endometrial tumours. We analysed nine tissue samples to compare three clinical subgroups-low BMI (Body Mass Index) Endometrioid, high BMI Endometrioid, and Serous (any BMI)-identifying proteins related to histological subtype, BMI, and shared secreted proteins. Using collagenase digestion and size exclusion chromatography, we successfully enriched generous quantities of EVs (range 204.8-1291.0 µg protein: 1.38 × 1011-1.10 × 1012 particles), characterised by their size (∼150 nm), expression of EV markers (CD63/81), and proposed endometrial cancer markers (L1CAM, ANXA2). Mass spectrometry-based proteomic profiling identified 2075 proteins present in at least one of the 18 samples. Compared to cell lysates, EVs were successfully depleted for mitochondrial and blood proteins and enriched for common EV markers and large secreted proteins. Further analysis highlighted significant differences in EV protein profiles between the high BMI subgroup and others, underlining the impact of comorbidities on the EV secretome. Interestingly, proteins differentially abundant in tissue subgroups were largely not also differential in matched EVs. This research identified secreted proteins known to be involved in endometrial cancer pathophysiology and proposed novel diagnostic biomarkers (EIF6, MUC16, PROM1, SLC26A2).

KinPred-RNA-kinase activity inference and cancer type classification using machine learning on RNA-seq data

Kinases as important enzymes can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates and play essential roles in various cellular processes. Existing algorithms for kinase activity from phosphorylated proteomics data are often costly, requiring valuable samples. Moreover, methods to extract kinase activities from bulk RNA sequencing data remain undeveloped. In this study, we propose a computational framework KinPred-RNA to derive kinase activities from bulk RNA-sequencing data in cancer samples. KinPred-RNA framework, using the extreme gradient boosting (XGBoost) regression model, outperforms random forest regression, multiple linear regression, and support vector machine regression models in predicting kinase activities from cancer-related RNA sequencing data. Efficient gene signatures from the LINCS-L1000 dataset were used as inputs for KinPred-RNA. The results highlight its potential to be related to biological function. In conclusion, KinPred RNA constitutes a significant advance in cancer research by potentially facilitating the identification of cancer.

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.

Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.

PAX2 is regulated by estrogen/progesterone through promoter methylation in endometrioid adenocarcinoma and has an important role in carcinogenesis via the AKT/mTOR signaling pathway

Endometrioid adenocarcinoma (EEC) is one of the most common cancers of the female reproductive system. In recent years, much emphasis has been placed on early diagnosis and treatment. PAX2 (Paired box 2) inactivation is reportedly an important biomarker for endometrioid intraepithelial neoplasia (EIN) and EEC. However, the role of PAX2 in EEC carcinogenesis remains unclear. PAX2 expression and associated clinical characteristics were analyzed via The Cancer Genome Atlas, Gene Expression Omnibus, and Cancer Cell Line Encyclopedia databases and clinical paired EIN/EEC tissue samples. Bioinformatic analysis was conducted to identify the putative molecular function and mechanism of PAX2. Cell proliferation, colony formation, cell migration, and invasion assays in vitro, and mouse xenograft models were utilized to study the biological functions of PAX2 in vivo. Pyrosequencing and the demethylating drug 5-Aza-dc were used to verify promoter methylation in clinical tissues and cell lines, respectively. The mechanism underlying the regulatory effect of estrogen (E2) and progesterone (P4) on PAX2 expression was investigated by receptor block assay and double luciferase reporter assay. PAX2 expression was found to be significantly downregulated in EIN and EEC tissues, its overexpression inhibited EEC cell malignant behaviors in vivo and in vitro and inhibited the AKT/mTOR signaling pathway. PAX2 inactivation in EEC was related to promoter methylation, and its expression was regulated by E2 and P4 through their receptors via promoter methylation. Our findings elucidated the expression and function of PAX2 in EEC and have provided hitherto undocumented evidence of the underlying molecular mechanisms. PAX2 expression is suppressed by estrogen prompting its methylation through estrogen receptor. Furthermore, PAX2 regulates the AKT/mTOR signaling pathway to influence EEC progression. © 2024 The Pathological Society of Great Britain and Ireland.

SCFFBXW11 Complex Targets Interleukin-17 Receptor A for Ubiquitin-Proteasome-Mediated Degradation

Interleukin-17 (IL-17) is a pro-inflammatory cytokine that participates in innate and adaptive immune responses and plays an important role in host defense, autoimmune diseases, tissue regeneration, metabolic regulation, and tumor progression. Post-translational modifications (PTMs) are crucial for protein function, stability, cellular localization, cellular transduction, and cell death. However, PTMs of IL-17 receptor A (IL-17RA) have not been investigated. Here, we show that human IL-17RA was targeted by F-box and WD repeat domain-containing 11 (FBXW11) for ubiquitination, followed by proteasome-mediated degradation. We used bioinformatics tools and biochemical techniques to determine that FBXW11 ubiquitinated IL-17RA through a lysine 27-linked polyubiquitin chain, targeting IL-17RA for proteasomal degradation. Domain 665-804 of IL-17RA was critical for interaction with FBXW11 and subsequent ubiquitination. Our study demonstrates that FBXW11 regulates IL-17 signaling pathways at the IL-17RA level.

Illuminating the Dark Cancer Phosphoproteome Through a Machine-Learned Co-Regulation Map of 26,280 Phosphosites

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, but limited knowledge about the regulation and function of most phosphosites restricts our ability to extract meaningful biological insights from phosphoproteomics data. To address this, we combine machine learning and phosphoproteomic data from 1,195 tumor specimens spanning 11 cancer types to construct CoPheeMap, a network mapping the co-regulation of 26,280 phosphosites. Integrating network features from CoPheeMap into a machine learning model, CoPheeKSA, we achieve superior performance in predicting kinase-substrate associations. CoPheeKSA reveals 24,015 associations between 9,399 phosphosites and 104 serine/threonine kinases, including many unannotated phosphosites and under-studied kinases. We validate the accuracy of these predictions using experimentally determined kinase-substrate specificities. By applying CoPheeMap and CoPheeKSA to phosphosites with high computationally predicted functional significance and cancer-associated phosphosites, we demonstrate the effectiveness of these tools in systematically illuminating phosphosites of interest, revealing dysregulated signaling processes in human cancer, and identifying under-studied kinases as putative therapeutic targets.

Widespread alteration of protein autoinhibition in human cancers

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins. Reduced autoinhibition underlies the tumorigenic effect of some known cancer drivers, but whether autoinhibition is altered generally in cancer remains elusive. Here, we demonstrate that cancer-associated missense mutations, in-frame insertions/deletions, and fusion breakpoints are enriched within inhibitory allosteric switches (IASs) across all cancer types. Selection for IASs that are recurrently mutated in cancers identifies established and unknown cancer drivers. Recurrent missense mutations in IASs of these drivers are associated with distinct, cancer-specific changes in molecular signaling. For the specific case of PPP3CA, the catalytic subunit of calcineurin, we provide insights into the molecular mechanisms of altered autoinhibition by cancer mutations using biomolecular simulations, and demonstrate that such mutations are associated with transcriptome changes consistent with increased calcineurin signaling. Our integrative study shows that autoinhibition-modulating genetic alterations are positively selected for by cancer cells.

Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides

Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.

A platform-independent AI tumor lineage and site (ATLAS) classifier

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

Integrated proteogenomic characterization of glioblastoma evolution

The evolutionary trajectory of glioblastoma (GBM) is a multifaceted biological process that extends beyond genetic alterations alone. Here, we perform an integrative proteogenomic analysis of 123 longitudinal glioblastoma pairs and identify a highly proliferative cellular state at diagnosis and replacement by activation of neuronal transition and synaptogenic pathways in recurrent tumors. Proteomic and phosphoproteomic analyses reveal that the molecular transition to neuronal state at recurrence is marked by post-translational activation of the wingless-related integration site (WNT)/ planar cell polarity (PCP) signaling pathway and BRAF protein kinase. Consistently, multi-omic analysis of patient-derived xenograft (PDX) models mirror similar patterns of evolutionary trajectory. Inhibition of B-raf proto-oncogene (BRAF) kinase impairs both neuronal transition and migration capability of recurrent tumor cells, phenotypic hallmarks of post-therapy progression. Combinatorial treatment of temozolomide (TMZ) with BRAF inhibitor, vemurafenib, significantly extends the survival of PDX models. This study provides comprehensive insights into the biological mechanisms of glioblastoma evolution and treatment resistance, highlighting promising therapeutic strategies for clinical intervention.

Whole-genome doubling is a double-edged sword: the heterogeneous role of whole-genome doubling in various cancer types

Whole-genome doubling (WGD), characterized by the duplication of an entire set of chromosomes, is commonly observed in various tumors, occurring in approximately 30-40% of patients with different cancer types. The effect of WGD on tumorigenesis varies depending on the context, either promoting or suppressing tumor progression. Recent advances in genomic technologies and large-scale clinical investigations have led to the identification of the complex patterns of genomic alterations underlying WGD and their functional consequences on tumorigenesis progression and prognosis. Our comprehensive review aims to summarize the causes and effects of WGD on tumorigenesis, highlighting its dualistic influence on cancer cells. We then introduce recent findings on WGD-associated molecular signatures and genetic aberrations and a novel subtype related to WGD. Finally, we discuss the clinical implications of WGD in cancer subtype classification and future therapeutic interventions. Overall, a comprehensive understanding of WGD in cancer biology is crucial to unraveling its complex role in tumorigenesis and identifying novel therapeutic strategies. [BMB Reports 2024; 57(3): 125-134].

The dual roles of circRNAs in Wnt/β-Catenin signaling and cancer progression

Cancer, a complex pathophysiological condition, arises from the abnormal proliferation and survival of cells due to genetic mutations. Dysregulation of cell cycle control, apoptosis, and genomic stability contribute to uncontrolled growth and metastasis. Tumor heterogeneity, microenvironmental influences, and immune evasion further complicate cancer dynamics. The intricate interplay between circular RNAs (circRNAs) and the Wnt/β-Catenin signalling pathway has emerged as a pivotal axis in the landscape of cancer biology. The Wnt/β-Catenin pathway, a critical regulator of cell fate and proliferation, is frequently dysregulated in various cancers. CircRNAs, a class of non-coding RNAs with closed-loop structures, have garnered increasing attention for their diverse regulatory functions. This review systematically explores the intricate crosstalk between circRNAs and the Wnt/β-Catenin pathway, shedding light on their collective impact on cancer initiation and progression. The review explores the diverse mechanisms through which circRNAs modulate the Wnt/β-Catenin pathway, including sponging microRNAs, interacting with RNA-binding proteins, and influencing the expression of key components in the pathway. Furthermore, the review highlights specific circRNAs implicated in various cancer types, elucidating their roles as either oncogenic or tumour-suppressive players in the context of Wnt/β-Catenin signaling. The intricate regulatory networks formed by circRNAs in conjunction with the Wnt/β-Catenin pathway are discussed, providing insights into potential therapeutic targets and diagnostic biomarkers. This comprehensive review delves into the multifaceted roles of circRNAs in orchestrating tumorigenesis through their regulatory influence on the Wnt/β-Catenin pathway.

It is time to implement molecular classification in endometrial cancer

A huge effort has been done in redefining endometrial cancer (EC) risk classes in the last decade. However, known prognostic factors (FIGO staging and grading, biomolecular classification and ESMO-ESGO-ESTRO risk classes stratification) are not able to predict outcomes and especially recurrences. Biomolecular classification has helped in re-classifying patients for a more appropriate adjuvant treatment and clinical studies suggest that currently used molecular classification improves the risk assessment of women with EC, however, it does not clearly explain differences in recurrence profiles. Furthermore, a lack of evidence appears in EC guidelines. Here, we summarize the main concepts why molecular classification is not enough in the management of endometrial cancer, by highlighting some promising innovative examples in scientific literature studies with a clinical potential significant impact.

Pan-cancer proteogenomics characterization of tumor immunity

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.

Detection of driver mutations and genomic signatures in endometrial cancers using artificial intelligence algorithms

Analyzed endometrial cancer (EC) genomes have allowed for the identification of molecular signatures, which enable the classification, and sometimes prognostication, of these cancers. Artificial intelligence algorithms have facilitated the partitioning of mutations into driver and passenger based on a variety of parameters, including gene function and frequency of mutation. Here, we undertook an evaluation of EC cancer genomes deposited on the Catalogue of Somatic Mutations in Cancers (COSMIC), with the goal to classify all mutations as either driver or passenger. Our analysis showed that approximately 2.5% of all mutations are driver and cause cellular transformation and immortalization. We also characterized nucleotide level mutation signatures, gross chromosomal re-arrangements, and gene expression profiles. We observed that endometrial cancers show distinct nucleotide substitution and chromosomal re-arrangement signatures compared to other cancers. We also identified high expression levels of the CLDN18 claudin gene, which is involved in growth, survival, metastasis and proliferation. We then used in silico protein structure analysis to examine the effect of certain previously uncharacterized driver mutations on protein structure. We found that certain mutations in CTNNB1 and TP53 increase protein stability, which may contribute to cellular transformation. While our analysis retrieved previously classified mutations and genomic alterations, which is to be expected, this study also identified new signatures. Additionally, we show that artificial intelligence algorithms can be effectively leveraged to accurately predict key drivers of cancer. This analysis will expand our understanding of ECs and improve the molecular toolbox for classification, diagnosis, or potential treatment of these cancers.

Systematic characterization of m6A proteomics across 12 cancer types: a multi-omics integration study

The modification patterns of N6-methyladenosine (m6A) regulators and interacting genes are deeply involved in tumors. However, the effect of m6A modification patterns on human proteomics remains largely unknown. We evaluated the molecular characteristics and clinical relevance of m6A modification proteomics patterns among 1013 pan-cancer samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). More than half of the m6A proteins were expressed at higher levels in tumor tissues and presented oncogenic characteristics. Furthermore, we performed multi-omics analyses integrating with transcriptomics data of m6A regulators and interactive coding and non-coding RNAs and developed a m6A multi-omics signature to identify potential m6A modification target proteins across global proteomics. It was significantly associated with overall survival in nine cancer types, tumor mutation burden (P = 0.01), and immune checkpoints including PD-L1 (P = 4.9 × 10-8) and PD-1 (P < 0.01). We identified 51 novel proteins associated with the multi-omics signature (PFDR < 0.05). These proteins were functional through pathway enrichment analyses. The protein with the highest hit frequency was CHORDC1, which was significantly up-regulated in tumor tissues in nine cancer types. Its higher abundance was significantly associated with a poorer prognosis in seven cancer types. The identified m6A target proteins might provide infomation for the study of molecular mechanism of cancer.

SETDB1 promotes progression through upregulation of SF3B4 expression and regulates the immunity in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is the most lethal gynecologic malignant tumour. The mechanism promoting OC initiation and progression remains unclear. SET domain bifurcated histone lysine methyltransferase 1(SETDB1) acts as an oncogene in a variety of tumours. This study aims to explore the role of SETDB1 in OC.

METHODS

GEO, TCGA, CSIOVDB and CPTAC databases jointly analysed SETDB1 mRNA and protein expression. Effect of SETDB1 expression on the clinical prognosis of OC patients was analysed through online Kaplan‒Meier plotter and CSIOVDB database. Then, the effect of SETDB1 in OC cells progression and mobility was examined using MTT, EdU, colony formation and transwell assay. Additionally, Cistrome DB database was used to visualize the binding of SETDB1 protein and splicing factor 3b subunit 4 (SF3B4) promoter, and dual-luciferase reporter gene assay was performed to confirm the interaction. Finally, bioinformatics analysis was employed to reveal the relationship between SETDB1 and the microenvironment of OC.

RESULTS

In the present study, we found that SETDB1 was obviously upregulated in OC and its overexpression predicted poor prognosis of OC patients. Then, we verified that SETDB1 promoted the progression and motility of OC cells in vitro. Knockdown of SETDB1 had the opposite effect. Further research showed that SETDB1 acted as a transcription factor to activate SF3B4 expression. SF3B4 knockdown impaired the effect of SETDB1 to promote the proliferative capacity and motility of OC cells. Finally, the results of bioinformatics analysis confirmed that SETDB1 regulated the immune microenvironment of ovarian cancer.

CONCLUSION

SETDB1 promoted ovarian cancer progression by upregulating the expression of SF3B4 and inhibiting the tumour immunity. SETDB1 may be a promising prognostic and therapeutic marker for OC.

Transgelin Promotes Glioblastoma Stem Cell Hypoxic Responses and Maintenance Through p53 Acetylation

Glioblastoma (GBM) is a lethal cancer characterized by hypervascularity and necrosis associated with hypoxia. Here, it is found that hypoxia preferentially induces the actin-binding protein, Transgelin (TAGLN), in GBM stem cells (GSCs). Mechanistically, TAGLN regulates HIF1α transcription and stabilizes HDAC2 to deacetylate p53 and maintain GSC self-renewal. To translate these findings into preclinical therapeutic paradigm, it is found that sodium valproate (VPA) is a specific inhibitor of TAGLN/HDAC2 function, with augmented efficacy when combined with natural borneol (NB) in vivo. Thus, TAGLN promotes cancer stem cell survival in hypoxia and informs a novel therapeutic paradigm.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Molecular subtypes of endometrial cancer: Implications for adjuvant treatment strategies

BACKGROUND

When determining adjuvant treatment for endometrial cancer, the decision typically relies on factors such as cancer stage, histologic grade, subtype, and a few histopathologic markers. The Cancer Genome Atlas revealed molecular subtyping of endometrial cancer, which can provide more accurate prognostic information and guide personalized treatment plans.

OBJECTIVE

To summarize the expression and molecular basis of the main biomarkers of endometrial cancer.

SEARCH STRATEGY

PubMed was searched from January 2000 to March 2023.

SELECTION CRITERIA

Studies evaluating molecular subtypes of endometrial cancer and implications for adjuvant treatment strategies.

DATA COLLECTION AND ANALYSIS

Three authors independently performed a comprehensive literature search, collected and extracted data, and assessed the methodological quality of the included studies.

MAIN RESULTS

We summarized the molecular subtyping of endometrial cancer, including mismatch repair deficient, high microsatellite instability, polymerase epsilon (POLE) exonuclease domain mutated, TP53 gene mutation, and non-specific molecular spectrum. We also summarized planned and ongoing clinical trials and common therapy methods in endometrial cancer. POLE mutated endometrial cancer consistently exhibits favorable patient outcomes, regardless of adjuvant therapy. Genomic similarities between p53 abnormality endometrial cancer and high-grade serous ovarian cancer suggested possible overlapping treatment strategies. High levels of immune checkpoint molecules, such as programmed cell death 1 and programmed cell death 1 ligand 1 can counterbalance mismatch repair deficient endometrial cancer immune phenotype. Hormonal treatment is an appealing option for high-risk non-specific molecular spectrum endometrial cancers, which are typically endometrioid and hormone receptor positive. Combining clinical and pathologic characteristics to guide treatment decisions for patients, including concurrent radiochemotherapy, chemotherapy, inhibitor therapy, endocrine therapy, and immunotherapy, might improve the management of endometrial cancer and provide more effective treatment options for patients.

CONCLUSIONS

We have characterized the molecular subtypes of endometrial cancer and discuss their value in terms of a patient-tailored therapy in order to prevent significant under- or overtreatment.

VISTA promotes the metabolism and differentiation of myeloid-derived suppressor cells by STAT3 and polyamine-dependent mechanisms

Myeloid-derived suppressor cells (MDSCs) impair antitumor immune responses. Identifying regulatory circuits during MDSC development may bring new opportunities for therapeutic interventions. We report that the V-domain suppressor of T cell activation (VISTA) functions as a key enabler of MDSC differentiation. VISTA deficiency reduced STAT3 activation and STAT3-dependent production of polyamines, which causally impaired mitochondrial respiration and MDSC expansion. In both mixed bone marrow (BM) chimera mice and myeloid-specific VISTA conditional knockout mice, VISTA deficiency significantly reduced tumor-associated MDSCs but expanded monocyte-derived dendritic cells (DCs) and enhanced T cell-mediated tumor control. Correlated expression of VISTA and arginase-1 (ARG1), a key enzyme supporting polyamine biosynthesis, was observed in multiple human cancer types. In human endometrial cancer, co-expression of VISTA and ARG1 on tumor-associated myeloid cells is associated with poor survival. Taken together, these findings unveil the VISTA/polyamine axis as a central regulator of MDSC differentiation and warrant therapeutically targeting this axis for cancer immunotherapy.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Mapping three-dimensional intratumor proteomic heterogeneity in uterine serous carcinoma by multiregion microsampling

BACKGROUND

Although uterine serous carcinoma (USC) represents a small proportion of all uterine cancer cases, patients with this aggressive subtype typically have high rates of chemotherapy resistance and disease recurrence that collectively result in a disproportionately high death rate. The goal of this study was to provide a deeper view of the tumor microenvironment of this poorly characterized uterine cancer variant through multi-region microsampling and quantitative proteomics.

METHODS

Tumor epithelium, tumor-involved stroma, and whole "bulk" tissue were harvested by laser microdissection (LMD) from spatially resolved levels from nine USC patient tumor specimens and underwent proteomic analysis by mass spectrometry and reverse phase protein arrays, as well as transcriptomic analysis by RNA-sequencing for one patient's tumor.

RESULTS

LMD enriched cell subpopulations demonstrated varying degrees of relatedness, indicating substantial intratumor heterogeneity emphasizing the necessity for enrichment of cellular subpopulations prior to molecular analysis. Known prognostic biomarkers were quantified with stable levels in both LMD enriched tumor and stroma, which were shown to be highly variable in bulk tissue. These USC data were further used in a comparative analysis with a data generated from another serous gynecologic malignancy, high grade serous ovarian carcinoma, and have been added to our publicly available data analysis tool, the Heterogeneity Analysis Portal ( https://lmdomics.org/ ).

CONCLUSIONS

Here we identified extensive three-dimensional heterogeneity within the USC tumor microenvironment, with disease-relevant biomarkers present in both the tumor and the stroma. These data underscore the critical need for upfront enrichment of cellular subpopulations from tissue specimens for spatial proteogenomic analysis.

T proliferating cells derived autophagy signature associated with prognosis and immunotherapy resistance in a pan-cancer analysis

Despite autophagy modulating tumor immunity in the tumor microenvironment (TME), the immunotherapeutic efficacy and potential mechanism of autophagy signature was not explicit. We manually curated an autophagy gene set and defined a pan-cancer autophagy signature by comparing malignant tissues and normal tissues in The Cancer Genome Atlas (TCGA) cohort. The pan-cancer autophagy signature was derived from T proliferating cells as demonstrated in multiple single-cell RNA sequencing (scRNA-seq) datasets. The pan-cancer autophagy signature could influence the cell-cell interactions in the TME and predict the responsiveness of immune checkpoint inhibitors (ICIs) in the metastatic renal cell carcinoma, non-small cell lung cancer, bladder cancer, and melanoma cohorts. Metabolism inactivation accompanied with dysregulation of autophagy was investigated with transcriptomic and proteomic data. The immunotherapeutic predictive role and mechanism regulation of the autophagy signature was validated in an in-house cohort. Our study provides valuable insights into the mechanisms of ICI resistance.

Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia

Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.

MMP12 is a Potential Predictive and Prognostic Biomarker of Various Cancers Including Lung Adenocarcinoma

OBJECTIVE

This study sought to explore the clinical value of matrix metalloproteinases 12 (MMP12) in multiple cancers, including lung adenocarcinoma (LUAD).

METHODS

Using >10,000 samples, this retrospective study demonstrated the first pan-cancer analysis of MMP12. The expression of MMP12 between cancer groups and their control groups was analyzed using Wilcoxon rank-sum tests. The clinical significance of MMP12 expression in multiple cancers was assessed using receiver operating characteristic curves, Kaplan-Meier curves, and univariate Cox analysis. A further LUAD-related analysis based on 4565 multi-center and in-house samples was performed to verify the findings regarding MMP12 in pan-cancer analysis partly.

RESULTS

MMP12 mRNA is highly expressed in 13 cancers compared to their controls, and the MMP12 protein level is elevated in some of these cancers (e.g., colon adenocarcinoma) (P < .05). MMP12 expression makes it feasible to distinguish 21 cancer tissues from normal tissues (AUC = 0.86). A high MMP12 expression is a prognosis risk factor in eight cancers, such as adrenocortical carcinoma (hazard ratio >1, P < .05). The elevated MMP12 expression is also a prognosis protective factor in breast-invasive carcinoma and colon adenocarcinoma (hazard ratio <1, P < .05). Some pan-cancer findings regarding MMP12 are verified in LUAD-MMP12 expression is upregulated in LUAD at both the mRNA and protein levels (P < .05), has the potential to distinguish LUAD with considerable accuracy (AUC = .91), and plays a risk prognosis factor for patients with the disease (P < .05).

CONCLUSIONS

MMP12 is highly expressed in most cancers and may serve as a novel biomarker for the prediction and prognosis of numerous cancers.

p120 RasGAP and ZO-2 are essential for Hippo signaling and tumor-suppressor function mediated by p190A RhoGAP

ARHGAP35, which encodes p190A RhoGAP (p190A), is a major cancer gene. p190A is a tumor suppressor that activates the Hippo pathway. p190A was originally cloned via direct binding to p120 RasGAP (RasGAP). Here, we determine that interaction of p190A with the tight-junction-associated protein ZO-2 is dependent on RasGAP. We establish that both RasGAP and ZO-2 are necessary for p190A to activate large tumor-suppressor (LATS) kinases, elicit mesenchymal-to-epithelial transition, promote contact inhibition of cell proliferation, and suppress tumorigenesis. Moreover, RasGAP and ZO-2 are required for transcriptional modulation by p190A. Finally, we demonstrate that low ARHGAP35 expression is associated with shorter survival in patients with high, but not low, transcript levels of TJP2 encoding ZO-2. Hence, we define a tumor-suppressor interactome of p190A that includes ZO-2, an established constituent of the Hippo pathway, and RasGAP, which, despite strong association with Ras signaling, is essential for p190A to activate LATS kinases.

Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy

Patients with hepatocellular carcinoma (HCC) at the same clinical stage can have extremely different prognoses, and molecular subtyping provides an opportunity for individualized precision treatment. In this study, genomic, transcriptomic, proteomic, and phosphoproteomic profiling of primary tumor tissues and paired para-tumor tissues from HCC patients (N = 160) are integrated. Proteomic profiling identifies three HCC subtypes with different clinical prognosis, which are validated in three publicly available external validation sets. A simplified panel of nine proteins associated with metabolic reprogramming is further identified as a potential subtype-specific biomarker for clinical application. Multi-omics analysis further reveals that three proteomic subtypes have significant differences in genetic alterations, microenvironment dysregulation, kinase-substrate regulatory networks, and therapeutic responses. Patient-derived cell-based drug tests (N = 26) show personalized responses for sorafenib in three proteomic subtypes, which can be predicted by a machine-learning response prediction model. Overall, this study provides a valuable resource for better understanding of HCC subtypes for precision clinical therapy.

Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants

Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.

Explore the alterations of downstream molecular pathways caused by ARID1A mutation/knockout in human endometrial cancer cells

PURPOSE

As one of the most common gynecologic malignancies, endometrial cancer (EC) is driven by multiple genetic alterations that may be targeted for treatments. AT-rich interaction domain 1A (ARID1A) gene mutations were reported as early events in endometrial carcinogenesis.

METHODS

To explore the alterations of downstream molecular pathways caused by ARID1A mutations and the associated therapeutic implications, we edited ARID1A gene in human endometrial cancer cell line Ishikawa using the Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-Associated Proteins (CRISPR/Cas9) technology. We successfully constructed a stable Ishikawa cell line with a confirmed 10 bp deletion on the ARID1A gene, which resulted in a code-shift mutation and gene knockout.

RESULTS

Compared with unedited wild-type cells, ARID1A knockout (KO) led to reduced apoptosis, accelerated transformation from G0/G1 to S phase, and enhanced cell proliferation. ARID1A deficiency would reduce the protein levels of p21, caspase 7, and caspase 9 in Ishikawa endometrial cancer cells compared with the wild-type cells. In addition, ARID1A KO resulted in high levels of microsatellite instability (MSI-H). Moreover, transcriptomic analyses showed that ARID1A KO can lead to activated phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) signaling. Furthermore, experimental analyses demonstrated that ARID1A KO cells had reduced expression of genetic instability-associated markers mutL homologue 1 (MLH1) and progesterone receptor B (PR) and increased p-Akt expression.

CONCLUSION

These findings support further exploration of ARID1A as a therapeutic target for EC and provide insight into developing more effective treatments in EC, such as the combinatory use of immune checkpoint inhibitors.

Comparison of clinical characteristics and prognosis between type I and type II endometrial cancer: a single-center retrospective study

OBJECTIVES

To explore the differences in clinical characteristics, prognosis, and risk factors between type I and type II endometrial cancer (EC).

MATERIALS AND METHODS

We retrospectively collected EC patients diagnosed with type I or type II EC from 2009 to 2021 in the First Affiliated Hospital of Zhengzhou University.

RESULTS

In total, 606 eligible EC patients (396 type I, and 210 type II) were included. Baseline analyses revealed that type II patients were older, had more advanced clinical stage, were more likely to receive chemoradiotherapy, and had higher incidence of myometrial infiltration, cervix involvement, lymph node metastasis and positive ascites cytology. Type II significantly favored poorer overall survival (OS) (HR = 9.10, 95%CI 4.79-17.28, P < 0.001) and progression-free survival (PFS) (HR = 6.07, 95%CI 2.75-13.37, P < 0.001) compared to type I. For all included EC, univariate and multivariate COX analyses revealed age, myometrial infiltration and pathological type were independent risk factors for OS and PFS. Subgroup analyses identified age, menopause, clinical stage, and lymph node metastasis as independent risk factors for type I regarding OS. While age, myometrial infiltration and chemoradiotherapy were identified as risk and protective factors for type II regrading OS. Age and cervix involvement were identified as independent risk factors for type I regarding PFS. Myometrial infiltration was identified as independent risk factor for type II regarding PFS.

CONCLUSION

Type II patients shared different clinical characteristics and worse prognosis compared to type I, and their independent risk and protective factors also varied.

VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis

Disrupted protein phosphorylation due to genetic variation is a widespread phenomenon that triggers oncogenic transformation of healthy cells. However, few relevant phosphorylation disruption events have been verified due to limited biological experimental methods. Because of the lack of reliable benchmark datasets, current bioinformatics methods primarily use sequence-based traits to study variant impact on phosphorylation (VIP). Here, we increased the number of experimentally supported VIP events from less than 30 to 740 by manually curating and reanalyzing multi-omics data from 916 patients provided by the Clinical Proteomic Tumor Analysis Consortium. To predict VIP events in cancer cells, we developed VIPpred, a machine learning method characterized by multidimensional features that exhibits robust performance across different cancer types. Our method provided a pan-cancer landscape of VIP events, which are enriched in cancer-related pathways and cancer driver genes. We found that variant-induced increases in phosphorylation events tend to inhibit the protein degradation of oncogenes and promote tumor suppressor protein degradation. Our work provides new insights into phosphorylation-related cancer biology as well as novel avenues for precision therapy.

The DNA Damage Response (DDR) landscape of endometrial cancer defines discrete disease subtypes and reveals therapeutic opportunities

Genome maintenance is an enabling characteristic that allows neoplastic cells to tolerate the inherent stresses of tumorigenesis and evade therapy-induced genotoxicity. Neoplastic cells also deploy mis-expressed germ cell proteins termed Cancer Testes Antigens (CTAs) to promote genome maintenance and survival. Here, we present the first comprehensive characterization of the DNA Damage Response (DDR) and CTA transcriptional landscapes of endometrial cancer in relation to conventional histological and molecular subtypes. We show endometrial serous carcinoma (ESC), an aggressive endometrial cancer subtype, is defined by gene expression signatures comprising members of the Replication Fork Protection Complex (RFPC) and Fanconi Anemia (FA) pathway and CTAs with mitotic functions. DDR and CTA- based profiling also defines a subset of highly aggressive endometrioid endometrial carcinomas (EEC) with poor clinical outcomes that share similar profiles to ESC yet have distinct characteristics based on conventional histological and genomic features. Using an unbiased CRISPR-based genetic screen and a candidate gene approach, we confirm that DDR and CTA genes that constitute the ESC and related EEC gene signatures are required for proliferation and therapy-resistance of cultured endometrial cancer cells. Our study validates the use of DDR and CTA-based tumor classifiers and reveals new vulnerabilities of aggressive endometrial cancer where none currently exist.

Specific intracellular retention of circSKA3 promotes colorectal cancer metastasis by attenuating ubiquitination and degradation of SLUG

Our previous study demonstrated that tumor-suppressor circular RNAs (circRNAs) can be specifically secreted outside of colorectal cancer (CRC) cells within exosomes to maintain tumor cell fitness. However, whether tumor-driving circRNAs can be specifically retained in cells to facilitate tumor progression remains unknown. In this study, circRNA-seq showed that circSKA3 was significantly upregulated in CRC tissues but downregulated in serum samples from CRC patients. In addition, circSKA3 promoted CRC progression in vitro and in vivo and was retained in CRC cells via a specific cellmotif element. Interestingly, the cellmotif element was also the site of interaction of circSKA3 with SLUG, which inhibited SLUG ubiquitination degradation and promoted CRC epithelial-mesenchymal transition (EMT). Moreover, FUS was identified as a key circularization regulator of circSKA3 that bound to the key element. Finally, we designed and synthesized specific antisense oligonucleotides (ASOs) targeting circularization and cellmotif elements, which repressed circSKA3 expression, abolished the SLUG-circSKA3 interaction, and further inhibited CRC EMT and metastasis in vitro and in vivo.

Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities

Gene regulation plays a critical role in the cellular processes that underlie human health and disease. The regulatory relationship between transcription factors (TFs), key regulators of gene expression, and their target genes, the so called TF regulons, can be coupled with computational algorithms to estimate the activity of TFs. However, to interpret these findings accurately, regulons of high reliability and coverage are needed. In this study, we present and evaluate a collection of regulons created using the CollecTRI meta-resource containing signed TF-gene interactions for 1186 TFs. In this context, we introduce a workflow to integrate information from multiple resources and assign the sign of regulation to TF-gene interactions that could be applied to other comprehensive knowledge bases. We find that the signed CollecTRI-derived regulons outperform other public collections of regulatory interactions in accurately inferring changes in TF activities in perturbation experiments. Furthermore, we showcase the value of the regulons by examining TF activity profiles in three different cancer types and exploring TF activities at the level of single-cells. Overall, the CollecTRI-derived TF regulons enable the accurate and comprehensive estimation of TF activities and thereby help to interpret transcriptomics data.

Role of steroid receptor-associated and regulated protein in tumor progression and progesterone receptor signaling in endometrial cancer

BACKGROUND

Steroid receptor-associated and regulated protein (SRARP) suppresses tumor progression and modulates steroid receptor signaling by interacting with estrogen receptors and androgen receptors in breast cancer. In endometrial cancer (EC), progesterone receptor (PR) signaling is crucial for responsiveness to progestin therapy. The aim of this study was to investigate the role of SRARP in tumor progression and PR signaling in EC.

METHODS

Ribonucleic acid sequencing data from the Cancer Genome Atlas, Clinical Proteomic Tumor Analysis Consortium, and Gene Expression Omnibus were used to analyze the clinical significance of SRARP and its correlation with PR expression in EC. The correlation between SRARP and PR expression was validated in EC samples obtained from Peking University People's Hospital. SRARP function was investigated by lentivirus-mediated overexpression in Ishikawa and HEC-50B cells. Cell Counting Kit-8 assays, cell cycle analyses, wound healing assays, and Transwell assays were used to evaluate cell proliferation, migration, and invasion. Western blotting and quantitative real-time polymerase chain reaction were used to evaluate gene expression. The effects of SRARP on the regulation of PR signaling were determined by co-immunoprecipitation, PR response element (PRE) luciferase reporter assay, and PR downstream gene detection.

RESULTS

Higher SRARP expression was significantly associated with better overall survival and disease-free survival and less aggressive EC types. SRARP overexpression suppressed growth, migration, and invasion in EC cells, increased E-cadherin expression, and decreased N-cadherin and Wnt family member 7A ( WNT7A ) expression. SRARP expression was positively correlated with PR expression in EC tissues. In SRARP -overexpressing cells, PR isoform B (PRB) was upregulated and SRARP bound to PRB. Significant increases in PRE-based luciferase activity and expression levels of PR target genes were observed in response to medroxyprogesterone acetate.

CONCLUSIONS

This study illustrates that SRARP exerts a tumor-suppressive effect by inhibiting the epithelial-mesenchymal transition via Wnt signaling in EC. In addition, SRARP positively modulates PR expression and interacts with PR to regulate PR downstream target genes.

Proteogenomics Reveal the Overexpression of HLA-I in Cancer

An accurate quantification of HLA class I gene expression is important in understanding the interplay with the tumor microenvironment of antitumor cytotoxic T cell activities. Because HLA-I sequences are highly variable, standard RNAseq and mass spectrometry-based quantification workflows using common genome and protein sequence references do not provide HLA-I allele specific quantifications. Here, we used personalized HLA-I nucleotide and protein reference sequences based on the subjects' HLA-I genotypes and surveyed tumor and adjacent normal samples from patients across nine cancer types. Mass spectrometry using data dependent acquisition data was validated to be sufficient to estimate HLA-A protein expression at the allele level. We found that HLA-I proteins were present in significantly higher levels in tumors compared to adjacent normal tissues from 41 to 63% of head and neck squamous cell carcinoma, uterine corpus endometrial carcinoma, and clear cell renal cell carcinoma patients, and this was driven by increased levels of HLA-I gene transcripts. Most immune cell types are universally enriched in HLA-I high tumors, while endothelial and neuronal cells showed divergent relationships with HLA-I. Pathway analysis revealed that tumor senescence and autophagy activity influence the level of HLA-I proteins in glioblastoma. Genes correlated to HLA-I protein expression are mostly the ones directly involved in HLA-I function in immune response and cell death, while glycosylation genes are exclusively co-expressed with HLA-I at the protein level.

Epigenetic regulation during cancer transitions across 11 tumour types

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.

The combined signatures of G protein-coupled receptor family and immune landscape provide a prognostic and therapeutic biomarker in endometrial carcinoma

G protein-coupled receptors (GPRs) are one of the largest surface receptor superfamilies, and many of them play essential roles in biological processes, including immune responses. In this study, we aim to construct a GPR- and tumor immune environment (TME-i)-associated risk signature to predict the prognosis of patients with endometrial carcinoma (EC). The GPR score was generated by applying univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression in succession. This involved identifying the differentially expressed genes (DEGs) in the Cancer Genome Atlas-Uterine Corpus Endometrioid Carcinoma (TCGA-UCEC) cohort. Simultaneously, the CIBERSORT algorithm was applied to identify the protective immune cells for TME score construction. Subsequently, we combined the GPR and TME scores to establish a GPR-TME classifier for conducting clinical prognosis assessments. Various functional annotation algorithms were used to conduct biological process analysis distinguished by GPR-TME subgroups. Furthermore, weighted correlation network analysis (WGCNA) was applied to depict the tumor somatic mutations landscapes. Finally, we compared the immune-related molecules between GPR-TME subgroups and resorted to the Tumor Immune Dysfunction and Exclusion (TIDE) for immunotherapy response prediction. The mRNA and protein expression of GPR-related gene P2RY14 were, respectively, validated by RT-PCR in clinical samples and HPA database. To conclude, our GPR-TME classifier may aid in predicting the EC patients' prognosis and immunotherapy responses.

Landscape of somatic mutated genes and inherited susceptibility genes in gynecological cancer

Traditionally, gynecological cancers have been classified based on histology. Since remarkable advancements in next-generation sequencing technology have enabled the exploration of somatic mutations in various cancer types, comprehensive sequencing efforts have revealed the genomic landscapes of some common forms of human cancer. The genomic features of various gynecological malignancies have been reported by several studies of large-scale genomic cohorts, including The Cancer Genome Atlas. Although recent comprehensive genomic profiling tests, which can detect hundreds of genetic mutations at a time from cancer tissues or blood samples, have been increasingly used as diagnostic clinical biomarkers and in therapeutic management decisions, germline pathogenic variants associated with hereditary cancers can also be detected using this test. Gynecological cancers are closely related to genetic factors, with approximately 5% of endometrial cancer cases and 20% of ovarian cancer cases being caused by germline pathogenic variants. Hereditary breast and ovarian cancer syndrome and Lynch syndrome are the two major cancer susceptibility syndromes among gynecological cancers. In addition, several other hereditary syndromes have been reported to be associated with gynecological cancers. In this review, we highlight the genes for somatic mutation and germline pathogenic variants commonly seen in gynecological cancers. We first describe the relationship between clinicopathological attributes and somatic mutated genes. Subsequently, we discuss the characteristics and clinical management of inherited cancer syndromes resulting from pathogenic germline variants in gynecological malignancies.

Exploring the pan-cancer landscape of posttranscriptional regulation

Understanding the mechanisms underlying cancer gene expression is critical for precision oncology. Posttranscriptional regulation is a key determinant of protein abundance and cancer cell behavior. However, to what extent posttranscriptional regulatory mechanisms impact protein levels and cancer progression is an ongoing question. Here, we exploit cancer proteogenomics data to systematically compare mRNA-protein correlations across 14 different human cancer types. We identify two clusters of genes with particularly low mRNA-protein correlations across all cancer types, shed light on the role of posttranscriptional regulation of cancer driver genes and drug targets, and unveil a cohort of 55 mutations that alter systems-wide posttranscriptional regulation. Surprisingly, we find that decreased levels of posttranscriptional control in patients correlate with shorter overall survival across multiple cancer types, prompting further mechanistic studies into how posttranscriptional regulation affects patient outcomes. Our findings underscore the importance of a comprehensive understanding of the posttranscriptional regulatory landscape for predicting cancer progression.

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Prospective Clinical Prognostication of Endometrial Carcinomas Based on Next-Generation Sequencing and Immunohistochemistry-Real-World Implementation and Results at a Tertiary Care Center

Based on findings from The Cancer Genome Atlas and the Proactive Molecular Risk Classifier for Endometrial Cancer algorithm, endometrial carcinoma can now be stratified into 4 prognostically distinct subgroups based on molecular alterations and immunohistochemical (IHC) aberrations. In this study, we describe the de novo adoption and clinical reporting of prognostic subgroup classification based on next-generation sequencing (NGS) and IHC analyses of all endometrial carcinoma resections at a single institution, framed by the Exploration, Preparation, Implementation, and Sustainment model. Results from the first 13 months show 188 tumors underwent analysis by a combination of IHC and a medium-sized (56 analyzed genes) NGS-based assay. All cases were assigned as either POLE (POLE-mutated) (5.3%), mismatch repair deficient (27.7%), no specific molecular profile (45.7%), or p53 abnormal (21.3%) inclusive of multiple-classifier cases. NGS-based analysis revealed additional distinctions among the subgroups, including reduced levels of PI3K pathway activation in the p53 abnormal subgroup, an increased rate of CTNNB1 activating mutation in the no specific molecular profile subgroup, and lower TP53 mutation variant allele frequencies in POLE and mismatch repair deficient subgroups compared with the p53 abnormal subgroup. Overall, we describe the testing protocol, reporting, and results of a combination of NGS and IHC to prospectively prognosticate endometrial carcinomas at a single tertiary care center.

NLRC5 potentiates anti-tumor CD8+ T cells responses by activating interferon-β in endometrial cancer

OBJECTIVES

NLR family CARD domain containing 5 (NLRC5) could promote major histocompatibility complex class I (MHC-I)-dependent CD8+ T cell-mediated anticancer immunity. In this study, the immunosurveillance role and underlying mechanisms of NLRC5 in endometrial cancer (EC) were characterized.

METHODS

CD8+ T cells were separated from healthy women's peripheral blood by using magnetic beads. The effect of NLRC5 and interferon-β (IFN-β) on immunosurveillance of EC were examined through a mouse tumor model and a CD8+ T cell-EC cell coculture system after NLRC5 overexpression and IFN-β overexpression or depletion. The effect of NLRC5 on IFN-β expression was examined with gain- and loss-of-function experiments.

RESULTS

NLRC5 overexpression in the EC cell and CD8+ T cell coculture system inhibited EC cell proliferation and migration and promoted EC cell apoptosis and CD8+ T cell proliferation. In vivo, NLRC5 overexpression increased the proportion of CD8+ T cells and inhibited EC progression. Furthermore, IFN-β overexpression in the EC cell and CD8+ T cell coculture system activated CD8+ T cell proliferation; however, genetic depletion of IFN-β exerted the opposite effects. In addition, NLRC5 could negatively regulate IFN-β expression in EC cells. Mechanistically, NLRC5 potentiated the antitumor responses of CD8+ T cells to EC by activating IFN-β.

CONCLUSIONS

Taken together, our findings demonstrated that NLRC5 potentiates anti-tumor CD8+ T cells responses by activating interferon-β in EC, suggesting that genetically escalated NLRC5 and IFN-β may act as potential candidates for the clinical translation of adjuvant immunotherapies to patients with EC.