Mass-spectrometry-based proteomic correlates of grade and stage reveal pathways and kinases associated with aggressive human cancers

An essential gene signature of breast cancer metastasis reveals targetable pathways

BACKGROUND

The differential gene expression profile of metastatic versus primary breast tumors represents an avenue for discovering new or underappreciated pathways underscoring processes of metastasis. However, as tumor biopsy samples are a mixture of cancer and non-cancer cells, most differentially expressed genes in metastases would represent confounders involving sample biopsy site rather than cancer cell biology.

METHODS

By paired analysis, we defined a top set of differentially expressed genes in breast cancer metastasis versus primary tumors using an RNA-sequencing dataset of 152 patients from The Breast International Group Aiming to Understand the Molecular Aberrations dataset (BIG-AURORA). To filter the genes higher in metastasis for genes essential for breast cancer proliferation, we incorporated CRISPR-based data from breast cancer cell lines.

RESULTS

A significant fraction of genes with higher expression in metastasis versus paired primary were essential by CRISPR. These 264 genes represented an essential signature of breast cancer metastasis. In contrast, nonessential metastasis genes largely involved tumor biopsy site. The essential signature predicted breast cancer patient outcome based on primary tumor expression patterns. Pathways underlying the essential signature included proteasome degradation, the electron transport chain, oxidative phosphorylation, and cancer metabolic reprogramming. Transcription factors MYC, MAX, HDAC3, and HCFC1 each bound significant fractions of essential genes.

CONCLUSIONS

Associations involving the essential gene signature of breast cancer metastasis indicate true biological changes intrinsic to cancer cells, with important implications for applying existing therapies or developing alternate therapeutic approaches.

Analysis of the nischarin expression across human tumor types reveals its context-dependent role and a potential as a target for drug repurposing in oncology

Nischarin was reported to be a tumor suppressor that plays a critical role in breast cancer initiation and progression, and a positive prognostic marker in breast, ovarian and lung cancers. Our group has found that nischarin had positive prognostic value in female melanoma patients, but negative in males. This opened up a question whether nischarin has tumor type-specific and sex-dependent roles in cancer progression. In this study, we systematically examined in the public databases the prognostic value of nischarin in solid tumors, regulation of its expression and associated signaling pathways. We also tested the effects of a nischarin agonist rilmenidine on cancer cell viability in vitro. Nischarin expression was decreased in tumors compared to the respective healthy tissues, most commonly due to the deletions of the nischarin gene and promoter methylation. Unlike in healthy tissues where it was located in the cytoplasm and at the membrane, in tumor tissues nischarin could also be observed in the nuclei, implying that nuclear translocation may also account for its cancer-specific role. Surprisingly, in several cancer types high nischarin expression was a negative prognostic marker. Gene set enrichment analysis showed that in tumors in which high nischarin expression was a negative prognostic marker, signaling pathways that regulate stemness were enriched. In concordance with the findings that nischarin expression was negatively associated with pathways that control cancer growth and progression, nischarin agonist rilmenidine decreased the viability of cancer cells in vitro. Taken together, our study lays a ground for functional studies of nischarin in a context-dependent manner and, given that nischarin has several clinically approved agonists, provides rationale for their repurposing, at least in tumors in which nischarin is predicted to be a positive prognostic marker.

Rummagene: massive mining of gene sets from supporting materials of biomedical research publications

Many biomedical research publications contain gene sets in their supporting tables, and these sets are currently not available for search and reuse. By crawling PubMed Central, the Rummagene server provides access to hundreds of thousands of such mammalian gene sets. So far, we scanned 5,448,589 articles to find 121,237 articles that contain 642,389 gene sets. These sets are served for enrichment analysis, free text, and table title search. Investigating statistical patterns within the Rummagene database, we demonstrate that Rummagene can be used for transcription factor and kinase enrichment analyses, and for gene function predictions. By combining gene set similarity with abstract similarity, Rummagene can find surprising relationships between biological processes, concepts, and named entities. Overall, Rummagene brings to surface the ability to search a massive collection of published biomedical datasets that are currently buried and inaccessible. The Rummagene web application is available at https://rummagene.com .

APOB is a potential prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is significantly associated with adverse prognostic outcomes. The development and progression of different types of human tumors are significantly influenced by APOB. Nevertheless, the significance and pathomechanisms of APOB in HCC have not been conclusively determined. We assessed APOB expression levels in HCC using three publicly available databases of TIMER2.0, UALCAN and Human Protein Atlas. To identify the biological function of APOB, we conducted enrichment analysis via LinkedOmics. Moreover, UALCAN was employed to assess the relationship between APOB expression and clinicopathological features among HCC patients. Additionally, the Kaplan-Meier plotter was utilized to investigate the prognostic relevance of APOB in HCC. To explore potential regulatory ncRNAs that could bind to APOB, we utilized StarBase and GEPIA. Furthermore, the correlation between APOB expression and immune cell infiltration, as well as immune checkpoint genes, was investigated using Spearman's correlation analysis in TISIDB, GEPIA, and TIMER2.0. The findings of our investigation showed a notable decrease in the expression levels of APOB among individuals diagnosed with HCC. Moreover, a noteworthy correlation was observed between the expression of APOB and immune checkpoint genes, alongside the occurrence of immune cell infiltration. The levels of APOB expression in HCC tissues also showed correlations with various clinicopathological features. According to Cox regression analysis, decreased APOB expression emerged as a potential autonomous predictor for OS, RFS, DSS, and PFS among HCC patients. Furthermore, we identified six potential pathways associated with non-coding RNA (ncRNA) as the most promising pathway for APOB in HCC. Our results illuminate the possible involvement of APOB in HCC and offer understanding into its governing mechanisms and medical importance.

Global impact of somatic structural variation on the cancer proteome

Both proteome and transcriptome data can help assess the relevance of non-coding somatic mutations in cancer. Here, we combine mass spectrometry-based proteomics data with whole genome sequencing data across 1307 human tumors spanning various tissues to determine the extent somatic structural variant (SV) breakpoint patterns impact protein expression of nearby genes. We find that about 25% of the hundreds of genes with SV-associated cis-regulatory alterations at the mRNA level are similarly associated at the protein level. SVs associated with enhancer hijacking, retrotransposon translocation, altered DNA methylation, or fusion transcripts are implicated in protein over-expression. SVs combined with altered protein levels considerably extend the numbers of patients with tumors somatically altered for critical pathways. We catalog both SV breakpoint patterns involving patient survival and genes with nearby SV breakpoints associated with increased cell dependency in cancer cell lines. Pan-cancer proteogenomics identifies targetable non-coding alterations, by virtue of the associated deregulated genes.

Inhibition of CDK9 exhibits anticancer activity in hepatocellular carcinoma cells via targeting ribonucleotide reductase

Cyclin-dependent kinase 9 (CDK9) inhibitors are a novel category of anticancer treatment for cancers. However, their effects on hepatocellular carcinoma (HCC) are rarely investigated. Human ribonucleotide reductase (RR, which consists of RRM1 and RRM2 subunits) catalyzes the conversion of ribonucleoside diphosphate into 2'-deoxyribonucleoside diphosphate to maintain the homeostasis of nucleotide pools, which play essential roles in DNA synthesis and DNA repair. In this study, we identified that CDK9 protein expression in adjacent non-tumor tissues predicted HCC patients' overall and progression-free survivals. The anticancer activity of a CDK9-selective inhibitor, LDC000067, on HCC cells was positively associated with its ability to inhibit the expression of RRM1 and RRM2. LDC000067 downregulated RRM1 and RRM2 expression through post-transcriptional pathway. Specifically, LDC000067 triggered RRM2 protein degradation via multiple pathways, including proteasome-, lysosome-, and calcium-dependent pathways. Furthermore, CDK9 positively correlates with RRM1 or RRM2 expression in HCC patients, and the expressions of these three genes were associated with the higher infiltration of immune cells in HCC. Taken together, this study identified the prognostic relevance of CDK9 in HCC and the molecular mechanism for the anticancer effect of CDK9 inhibitors on HCC.

A comprehensive analysis of different types of databases reveals that CDH1 mRNA and E-cadherin protein are not downregulated in most carcinoma tissues and carcinoma cell lines

BACKGROUND

The CDH1 gene codes for the epithelial-cadherin (E-cad) protein, which is embedded in the plasma membrane of epithelial cells to form adherens junctions. E-cad is known to be essential for maintaining the integrity of epithelial tissues, and the loss of E-cad has been widely considered a hallmark of metastatic cancers enabling carcinoma cells to acquire the ability to migrate and invade nearby tissues. However, this conclusion has come under scrutiny.

METHODS

To assess how CDH1 and E-cad expression changes during cancer progression, we analyzed multiple large transcriptomics, proteomics, and immunohistochemistry datasets on clinical cancer samples and cancer cell lines to determine the CDH1 mRNA and E-cad protein expression profiles in tumor and normal cells.

RESULTS

In contrast to the textbook knowledge of the loss of E-cad during tumor progression and metastasis, the levels of CDH1 mRNA and E-cad protein are either upregulated or remain unchanged in most carcinoma cells compared to normal cells. In addition, the CDH1 mRNA upregulation occurs in the early stages of tumor development and the levels remain elevated as tumors progress to later stages across most carcinoma types. Furthermore, E-cad protein levels are not downregulated in most metastatic tumor cells compared to primary tumor cells. The CDH1 mRNA and E-cad protein levels are positively correlated, and the CDH1 mRNA levels are positively correlated to cancer patient's survival. We have discussed potential mechanisms underlying the observed expression changes in CDH1 and E-cad during tumor progression.

CONCLUSIONS

CDH1 mRNA and E-cadherin protein are not downregulated in most tumor tissues and cell lines derived from commonly occurring carcinomas. The role of E-cad in tumor progression and metastasis may have previously been oversimplified. CDH1 mRNA levels may serve as a reliable biomarker for the diagnosis of some tumors (such as colon and endometrial carcinomas) due to the marked upregulation of CDH1 mRNA in the early stages of tumor development of these carcinomas.

Using cancer proteomics data to identify gene candidates for therapeutic targeting

Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

How to Design Peptides

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

Gene Expression Profiles in Cancers and Their Therapeutic Implications

ABSTRACT

The vast amount of gene expression profiling data of bulk tumors and cell lines available in the public domain represents a tremendous resource. For any major cancer type, expression data can identify molecular subtypes, predict patient outcome, identify markers of therapeutic response, determine the functional consequences of somatic mutation, and elucidate the biology of metastatic and advanced cancers. This review provides a broad overview of gene expression profiling in cancer (which may include transcriptome and proteome levels) and the types of findings made using these data. This review also provides specific examples of accessing public cancer gene expression data sets and generating unique views of the data and the resulting genes of interest. These examples involve pan-cancer molecular subtyping, metabolism-associated expression correlates of patient survival involving multiple cancer types, and gene expression correlates of chemotherapy response in breast tumors.

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.

Identifying the genes impacted by cell proliferation in proteomics and transcriptomics studies

Hypothesis-free high-throughput profiling allows relative quantification of thousands of proteins or transcripts across samples and thereby identification of differentially expressed genes. It is used in many biological contexts to characterize differences between cell lines and tissues, identify drug mode of action or drivers of drug resistance, among others. Changes in gene expression can also be due to confounding factors that were not accounted for in the experimental plan, such as change in cell proliferation. We combined the analysis of 1,076 and 1,040 cell lines in five proteomics and three transcriptomics data sets to identify 157 genes that correlate with cell proliferation rates. These include actors in DNA replication and mitosis, and genes periodically expressed during the cell cycle. This signature of cell proliferation is a valuable resource when analyzing high-throughput data showing changes in proliferation across conditions. We show how to use this resource to help in interpretation of in vitro drug screens and tumor samples. It informs on differences of cell proliferation rates between conditions where such information is not directly available. The signature genes also highlight which hits in a screen may be due to proliferation changes; this can either contribute to biological interpretation or help focus on experiment-specific regulation events otherwise buried in the statistical analysis.

SILAC kinase screen identifies potential MASTL substrates

Microtubule-associated serine/threonine kinase-like (MASTL) has emerged as a critical regulator of mitosis and as a potential oncogene in a variety of cancer types. To date, Arpp-19/ENSA are the only known substrates of MASTL. However, with the roles of MASTL expanding and increased interest in development of MASTL inhibitors, it has become critical to determine if there are additional substrates and what the optimal consensus motif for MASTL is. Here we utilized a whole cell lysate in vitro kinase screen combined with stable isotope labelling of amino acids in cell culture (SILAC) to identify potential substrates and the residue preference of MASTL. Using the related AGC kinase family members AKT1/2, the kinase screen identified several known and new substrates highly enriched for the validated consensus motif of AKT. Applying this method to MASTL identified 59 phospho-sites on 67 proteins that increased in the presence of active MASTL. Subsequent in vitro kinase assays suggested that MASTL may phosphorylate hnRNPM, YB1 and TUBA1C under certain in vitro conditions. Taken together, these data suggest that MASTL may phosphorylate several additional substrates, providing insight into the ever-increasing biological functions and roles MASTL plays in driving cancer progression and therapy resistance.

A Novel Biomarker, FKBP10, for Poor Prognosis Prediction in Patients with Clear Cell Renal Cell Carcinoma

Objective

To screen genes associated with poor prognosis of clear cell renal cell carcinoma (CcRCC) from the public databases HPA (Human Protein Atlas), UALCAN, and GEPIA (Gene Expression Profiling Interactive Analysis) and to investigate the expression of FKBP10 in CcRCC and the effect on prognosis of the patients and the biological behavior of CcRCC cells.

Methods

The tumor tissues and adjacent noncancerous tissues of 42 patients with CcRCC diagnosed and treated in our hospital were collected, and the general information of the patients was recorded. FKBP10 expression in the tissues was determined by qRT-PCR and western blot, and its relationship with general information and prognosis of patients was analyzed. Knockdown or overexpression experiments were carried out with the human proximal tubule epithelial cell line HK-2 and CcRCC cell lines Caki-1, 786-O, ACHN, and A498 to verify the relationship between FKBP10 expression and cell proliferation and adhesion ability using MTT assay and fibronectin adhesion assay, respectively. Western blot was utilized to examine the protein expression level of c-Myc, cyclin D1, and Bcl-2 in the cells.

Results

FKBP10 was highly expressed in CcRCC tissues and cells and was correlated with poor prognosis. In addition, FKBP10 expression was positively correlated with CcRCC tumor size and staging and negatively correlated with tumor differentiation. Moreover, knockdown of FKBP10 significantly inhibited the proliferation of CcRCC cells, notably declined the protein expression of c-Myc, cyclin D1, and Bcl-2, and promoted cell adhesion.

Conclusion

FKBP10 is highly expressed in CcRCC tissues and cells and is associated with poor prognosis in patients. FKBP10 participated in the occurrence and development of CcRCC by promoting cell proliferation and inhibiting apoptosis and adhesion.

Expression of GOT2 Is Epigenetically Regulated by DNA Methylation and Correlates with Immune Infiltrates in Clear-Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (KIRC) is the most common and highly malignant pathological type of kidney cancer, characterized by a profound metabolism dysregulation. As part of aspartate biosynthesis, mitochondrial GOT2 (glutamic-oxaloacetic transaminase 2) is essential for regulating cellular energy production and biosynthesis, linking multiple pathways. Nevertheless, the expression profile and prognostic significance of GOT2 in KIRC remain unclear. This study comprehensively analyzed the transcriptional levels, epigenetic regulation, correlation with immune infiltration, and prognosis of GOT2 in KIRC using rigorous bioinformatics analysis. We discovered that the expression levels of both mRNA and protein of GOT2 were remarkably decreased in KIRC tissues in comparison with normal tissues and were also significantly related to the clinical features and prognosis of KIRC. Remarkably, low GOT2 expression was positively associated with poorer overall survival (OS) and disease-free survival (DFS). Further analysis revealed that GOT2 downregulation is driven by DNA methylation in the promoter-related CpG islands. Finally, we also shed light on the influence of GOT2 expression in immune cell infiltration, suggesting that GOT2 may be a potential prognostic marker and therapeutic target for KIRC patients.

Proteogenomic characterization of 2002 human cancers reveals pan-cancer molecular subtypes and associated pathways

Mass-spectrometry-based proteomic data on human tumors—combined with corresponding multi-omics data—present opportunities for systematic and pan-cancer proteogenomic analyses. Here, we assemble a compendium dataset of proteomics data of 2002 primary tumors from 14 cancer types and 17 studies. Protein expression of genes broadly correlates with corresponding mRNA levels or copy number alterations (CNAs) across tumors, but with notable exceptions. Based on unsupervised clustering, tumors separate into 11 distinct proteome-based subtypes spanning multiple tissue-based cancer types. Two subtypes are enriched for brain tumors, one subtype associating with MYC, Wnt, and Hippo pathways and high CNA burden, and another subtype associating with metabolic pathways and low CNA burden. Somatic alteration of genes in a pathway associates with higher pathway activity as inferred by proteome or transcriptome data. A substantial fraction of cancers shows high MYC pathway activity without MYC copy gain but with mutations in genes with noncanonical roles in MYC. Our proteogenomics survey reveals the interplay between genome and proteome across tumor lineages.

Pan-cancer proteomics analysis enables the analysis of protein expression across multiple cancer types. Here, the authors compare proteomics from 14 cancer types and show 11 distinct subtypes across multiple cancer types. Proteome data could link higher pathway activity levels with somatic alteration of specific genes in the pathway.

The N6-methyladenosine modification of circALG1 promotes the metastasis of colorectal cancer mediated by the miR-342-5p/PGF signalling pathway

BACKGROUND

Previous studies have shown that the N⁶-methyladenosine (m⁶A) modification enhances the binding ability of mRNAs/long noncoding RNAs (lncRNAs) to microRNAs (miRNAs), but the impact of this modification on the competitive endogenous RNA (ceRNA) function of circular RNAs (circRNAs) is unclear.

METHODS

We used a human circRNA microarray to detect the expression profiles of circRNAs in 3 pairs of cancer and paracancerous tissues from patients with colorectal cancer (CRC) and 3 pairs of peripheral blood specimens from patients with CRC and healthy individuals. The circRNAs highly expressed in both peripheral blood and tumour tissues of patients with CRC, including circALG1, were screened. A quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis of an expanded sample size was performed to detect the expression level of circALG1 in peripheral blood and tumour tissues of patients with CRC and determine its correlation with clinicopathological features, and circRNA loop-forming validation and stability assays were then conducted. Transwell assays and a nude mouse cancer metastasis model were used to study the function of circALG1 in CRC and the role of altered m⁶A modification levels on the regulation of circALG1 function. qRT-PCR, western blot (WB), Transwell, RNA-binding protein immunoprecipitation (RIP), RNA antisense purification (RAP), and dual-luciferase reporter gene assays were performed to analyse the ceRNA mechanism of circALG1 and the effect of the m⁶A modification of circALG1 on the ceRNA function of this circRNA.

RESULTS

CircALG1 was highly expressed in both the peripheral blood and tumour tissues of patients with CRC and was closely associated with CRC metastasis. CircALG1 overexpression promoted the migration and invasion of CRC cells, and circALG1 silencing and reduction of the circALG1 m⁶A modification level inhibited CRC cell migration and invasion. In vivo experiments further confirmed the prometastatic role of circALG1 in CRC. Further mechanistic studies showed that circALG1 upregulated the expression of placental growth factor (PGF) by binding to miR-342-5p and that m⁶A modification enhanced the binding of circALG1 to miR-342-5p and promoted its ceRNA function.

CONCLUSION

M⁶A modification enhances the binding ability of circALG1 to miR-342-5p to promote the ceRNA function of circALG1, and circALG1 could be a potential therapeutic target in and a prognostic marker for CRC.

UALCAN: An update to the integrated cancer data analysis platform

Cancer genomic, transcriptomic, and proteomic profiling has generated extensive data that necessitate the development of tools for its analysis and dissemination. We developed UALCAN to provide a portal for easy exploring, analyzing, and visualizing these data, allowing users to integrate the data to better understand the gene, proteins, and pathways perturbed in cancer and make discoveries. UALCAN web portal enables analyzing and delivering cancer transcriptome, proteomics, and patient survival data to the cancer research community. With data obtained from The Cancer Genome Atlas (TCGA) project, UALCAN has enabled users to evaluate protein-coding gene expression and its impact on patient survival across 33 types of cancers. The web portal has been used extensively since its release and received immense popularity, underlined by its usage from cancer researchers in more than 100 countries. The present manuscript highlights the task we have undertaken and updates that we have made to UALCAN since its release in 2017. Extensive user feedback motivated us to expand the resource by including data on a) microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and promoter DNA methylation from TCGA and b) mass spectrometry-based proteomics from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). UALCAN provides easy access to pre-computed, tumor subgroup-based gene/protein expression, promoter DNA methylation status, and Kaplan-Meier survival analyses. It also provides new visualization features to comprehend and integrate observations and aids in generating hypotheses for testing. UALCAN is accessible at http://ualcan.path.uab.edu

Global molecular alterations involving recurrence or progression of pediatric brain tumors

BACKGROUND

We aimed to identify molecular changes in recurrent or progressive pediatric brain tumors, as compared to the corresponding initial tumors from the same patients, using genomic, transcriptomic, and proteomic data from a unique and large cohort of 55 patients and 63 recurrent or progressive tumors from the Children's Brain Tumor Tissue Consortium, representing various histologic types.

METHODS

We carried out paired analyses for each gene between recurrent/progressive and initial tumor groups, using RNA-sequencing and mass spectrometry-based proteomic data. By whole-genome sequencing (WGS) analysis, we also examined somatic DNA events for a set of cancer-associated genes.

RESULTS

Of 44 patients examined by WGS, 35 involved at least one cancer-associated gene with a somatic alteration event in a recurrent or progressive tumor that was not present in the initial tumor, including genes NF1, CDKN2A, CCND2, EGFR, and MYCN. By paired analysis, 68 mRNA transcripts were differentially expressed in recurrent/progressive tumors with p<0.001, and these genes could predict patient outcomes in an independent set of pediatric brain tumors. Gene transcript-level associations with recurrence or progression were enriched for protein-level associations. There was a significant overlap in results from pediatric brain tumors and results from adult brain tumors from The Cancer Genome Atlas. Unsupervised analysis defined five subsets of recurrent or progressive tumors, with differences in gene expression and overall patient survival.

CONCLUSIONS

Our study uncovers genes showing consistent expression differences in recurrent or progressive tumors. These genes may provide molecular clues as to processes or pathways underlying more aggressive pediatric brain tumors.

Rearrangement-mediated cis-regulatory alterations in advanced patient tumors reveal interactions with therapy

The global impact of somatic structural variants (SVs) on gene regulation in advanced tumors with complex treatment histories has been mostly uncharacterized. Here, using whole-genome and RNA sequencing from 570 recurrent or metastatic tumors, we report the altered expression of hundreds of genes in association with nearby SV breakpoints, including oncogenes and G-protein-coupled receptor-related genes such as PLEKHG2. A significant fraction of genes with SV-expression associations correlate with worse patient survival in primary and advanced cancers, including SRD5A1. In many instances, SV-expression associations involve retrotransposons being translocated near genes. High overall SV burden is associated with treatment with DNA alkylating agents or taxanes and altered expression of metabolism-associated genes. SV-expression associations within tumors from topoisomerase I inhibitor-treated patients include chromatin-related genes. Within anthracycline-treated tumors, SV breakpoints near chromosome 1p genes include PDE4B. Patient treatment and history can help understand the widespread SV-mediated cis-regulatory alterations found in cancer.

Multi-omics integration of methyltransferase-like protein family reveals clinical outcomes and functional signatures in human cancer

Human methyltransferase-like (METTL) proteins transfer methyl groups to nucleic acids, proteins, lipids, and other small molecules, subsequently playing important roles in various cellular processes. In this study, we performed integrated genomic, transcriptomic, proteomic, and clinicopathological analyses of 34 METTLs in a large cohort of primary tumor and cell line data. We identified a subset of METTL genes, notably METTL1, METTL7B, and NTMT1, with high frequencies of genomic amplification and/or up-regulation at both the mRNA and protein levels in a spectrum of human cancers. Higher METTL1 expression was associated with high-grade tumors and poor disease prognosis. Loss-of-function analysis in tumor cell lines indicated the biological importance of METTL1, an m⁷G methyltransferase, in cancer cell growth and survival. Furthermore, functional annotation and pathway analysis of METTL1-associated proteins revealed that, in addition to the METTL1 cofactor WDR4, RNA regulators and DNA packaging complexes may be functionally interconnected with METTL1 in human cancer. Finally, we generated a crystal structure model of the METTL1–WDR4 heterodimeric complex that might aid in understanding the key functional residues. Our results provide new information for further functional study of some METTL alterations in human cancer and might lead to the development of small inhibitors that target cancer-promoting METTLs.