Pan-Cancer Molecular Classes Transcending Tumor Lineage Across 32 Cancer Types, Multiple Data Platforms, and over 10,000 Cases

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.

Identification ATP5F1D as a Biomarker Linked to Diagnosis, Prognosis, and Immune Infiltration in Endometrial Cancer Based on Data-Independent Acquisition (DIA) Analysis

In developed countries, endometrial cancer (EC) is the most prevalent gynecological cancer. ATP5F1D is a subunit of ATP synthase, as well as an important component of the mitochondrial electron transport chain (ETC). ETC plays a compelling role in carcinogenesis. To date, little is known about the role of ATP5F1D in EC. We undertook data-independent acquisition mass spectrometry (DIA-MS) of 20 EC patients, comprising 10 high-grade and 10 low-grade cancer tissues. Biological functions of differentially expressed genes (DEGs) were analyzed by GO and KEGG. The expression level, clinicopathological features, diagnostic potency, prognostic value, RNA modifications, immune characteristics, and therapy response of ATP5F1D were investigated. In total, 77 DEGs were acquired by DIA analysis, which were closely related to regulating immune response and metabolic pathways. Among the five genes (NDUFB8, SLC26A2, RAF1, ATP5F1D, and GSTM5) involving in reactive oxygen species pathway, ATP5F1D showed the most significant differential expression (2.903-fold change). We found ATP5F1D had a high diagnostic value and was associated with a favorable prognosis in EC patients. After analyzing the RNA modifications of ATP5F1D, revealing a negative regulation between them. Additionally, ATP5F1D was closely related to tumor immune infiltration. Our results suggested T-cell dysfunction and TAM-M2 polarization might be the important mechanisms of ATP5F1D to facilitate tumor immune escape. Noticeably, EC patients with ATP5F1D-high expression had better immune treatment responses and were more sensitive to chemotherapy drugs. ATP5F1D can be used as a biomarker for diagnosis, prognosis, and immune infiltration of EC, and offers a crucial reference for personalized treatment of EC patients.

Genomic Landscape of Pancreatic Neuroendocrine Tumors and Implications for Clinical Practice

Pancreatic neuroendocrine tumors (pNETs) are the second most prevalent neoplasms of the pancreas with variable prognosis and clinical course. Our knowledge of the genetic alterations in patients with pNETs has expanded in the past decade with the availability of whole-genome sequencing and germline testing. This review will focus on potential clinical applications of the genetic testing in patients with pNETs. For somatic testing, we discuss the commonly prevalent somatic mutations and their impact on prognosis and treatment of patients with pNET. We also highlight the relevant genomic biomarkers that predict response to specific treatments. Previously, germline testing was only recommended for high-risk patients with syndromic features (MEN1, VHL, TSC, and NF1), we review the evolving paradigm of germline testing in pNETs as recent studies have now shown that sporadic-appearing pNETs can also harbor germline variants.

The DNA methylome of pediatric brain tumors appears shaped by structural variation and predicts survival

Structural variation heavily influences the molecular landscape of cancer, in part by impacting DNA methylation-mediated transcriptional regulation. Here, using multi-omic datasets involving >2400 pediatric brain and central nervous system tumors of diverse histologies from the Children's Brain Tumor Network, we report hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of somatic structural variant (SV) breakpoints is recurrently associated with altered expression or DNA methylation, respectively, including tumor suppressor genes ATRX and CDKN2A. Altered DNA methylation near enhancers associates with nearby somatic SV breakpoints, including MYC and MYCN. A subset of genes with SV-CGI methylation associations also have expression associations with patient survival, including BCOR, TERT, RCOR2, and PDLIM4. DNA methylation changes in recurrent or progressive tumors compared to the initial tumor within the same patient can predict survival in pediatric and adult cancers. Our comprehensive and pan-histology genomic analyses reveal mechanisms of noncoding alterations impacting cancer genes.

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.

Germline structural variation globally impacts the cancer transcriptome including disease-relevant genes

Germline variation and somatic alterations contribute to the molecular profile of cancers. We combine RNA with whole genome sequencing across 1,218 cancer patients to determine the extent germline structural variants (SVs) impact expression of nearby genes. For hundreds of genes, recurrent and common germline SV breakpoints within 100 kb associate with increased or decreased expression in tumors spanning various tissues of origin. A significant fraction of germline SV expression associations involves duplication of intergenic enhancers or 3' UTR disruption. Genes altered by both somatic and germline SVs include ATRX and CEBPA. Genes essential in cancer cell lines include BARD1 and IRS2. Genes with both expression and germline SV breakpoint patterns associated with patient survival include GCLM. Our results capture a class of phenotypic variation at work in the disease setting, including genes with cancer roles. Specific germline SVs represent potential cancer risk variants for genetic testing, including those involving genes with targeting implications.

Multi-omics analysis reveals the involvement of origin recognition complex subunit 6 in tumor immune regulation and malignant progression

Background

Origin recognition complex 6 (ORC6) is one of the six highly conserved subunit proteins required for DNA replication and is essential for maintaining genome stability during cell division. Recent research shows that ORC6 regulates the advancement of multiple cancers; however, it remains unclear what regulatory impact it has on the tumor immune microenvironment.

Methods

Unpaired Wilcoxon rank sum and signed rank tests were used to analyze the differences in the expression of ORC6 in normal tissues and corresponding tumor tissues. Multiple online databases have evaluated the genetic alterations, protein expression and localization, and clinical relevance of ORC6. To evaluate the potential prognostic impact and diagnostic significance of ORC6 expression, we carried out log-rank, univariate Cox regression, and receiver operating characteristic curve analysis. The ICGC-LIRI-JP cohort, CGGA-301 cohort, CGGA-325 cohort, CGGA-693 cohort, and GSE13041 cohort were used for external validation of the study findings. The associations between ORC6 expression and immune cell infiltration, immune checkpoint expression, and immunotherapy cohorts was further analyzed. To explore the functional and signaling pathways related to ORC6 expression, gene set enrichment analysis was performed. To clarify the expression and function of ORC6 in hepatocellular carcinoma (LIHC) and glioma, we conducted in vitro experiments.

Results

Expression of ORC6 is upregulated in the majority of cancer types and is associated with poor patient prognosis, notably in cases of LIHC and gliomas. In addition, ORC6 may be involved in multiple signaling pathways related to cancer progression and immune regulation. High expression of ORC6 correlates with an immunosuppressive state in the tumor microenvironment. The results of further immunotherapy cohort analysis suggested that patients in the ORC6 high-expression group benefited from immunotherapy. Inhibiting ORC6 expression suppressed the proliferative and migratory abilities of LIHC and glioma cells.

Conclusion

High expression of ORC6 may be used as a biomarker to predict the poor prognosis of most tumor patients. The high expression of ORC6 may be involved in the regulation of the tumor immunosuppressive environment, and it is expected to become a molecular target for inhibiting tumor progression.

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.

Lipidomic Profiling Reveals Biological Differences between Tumors of Self-Identified African Americans and Non-Hispanic Whites with Cancer

In the US, the incidence and mortality of many cancers are disproportionately higher in African Americans (AA). Yet, AA remain poorly represented in molecular studies investigating the roles that biological factors might play in the development, progression, and outcomes of many cancers. Given that sphingolipids, key components of mammalian cellular membranes, have well-established roles in the etiology of cancer progression, malignancy, and responses to therapy, we conducted a robust mass spectrometry analysis of sphingolipids in normal adjacent uninvolved tissues and tumors of self-identified AA and non-Hispanic White (NHW) males with cancers of the lung, colon, liver, and head and neck and of self-identified AA and NHW females with endometrial cancer. In these cancers, AA have worse outcomes than NHW. The goal of our study was to identify biological candidates to be evaluated in future preclinical studies targeting race-specific alterations in the cancers of AA. We have identified that various sphingolipids are altered in race-specific patterns, but more importantly, the ratios of 24- to 16-carbon fatty acyl chain-length ceramides and glucosylceramides are higher in the tumors of AA. As there is evidence that ceramides with 24-carbon fatty acid chain length promote cellular survival and proliferation, whereas 16-carbon chain length promote apoptosis, these results provide important support for future studies tailored to evaluate the potential roles these differences may play in the outcomes of AA with cancer.

Interim Analysis of a Prospective Validation of 2 Blood-Based Genomic Assessments (PPQ and NETest) to Determine the Clinical Efficacy of 177Lu-DOTATATE in Neuroendocrine Tumors

Reliable biomarkers for neuroendocrine tumor (NET) management during peptide receptor radionuclide therapy (PRRT) are lacking. We validated the role of 2 circulating biomarkers: the PRRT prediction quotient (PPQ) as a predictive marker for response and the NETest as a monitoring biomarker. Furthermore, we evaluated whether tissue-based genetic alterations are effective in predicting progression-free survival (PFS). Methods: Data were prospectively collected on patients at the Memorial Sloan Kettering Cancer Center with 177Lu-DOTATATE-treated somatostatin receptor (SSTR)-positive gastroenteropancreatic and lung NETs (n = 67; median age, 66 y; 52% female; 42% pancreatic, 39% small-bowel; 78% grade 1 or 2). All cases were metastatic (89% liver) and had received 1-8 prior treatments (median, 3), including somatostatin analogs (91%), surgery (55%), or chemotherapy (49%). Treatment response included PFS. According to RECIST, version 1.1, responders had stable disease or a partial response (disease-control rate) and nonresponders had progression. Blood was collected before each cycle and at follow-up. Samples were deidentified and assayed and underwent masked analyses. The gene expression assays included RNA isolation, real-time quantitative polymerase chain reaction, and multialgorithm analyses. The PPQ (positive predicts a responder; negative predicts a nonresponder) at baseline was determined. The NETest (0-100 score) was performed. Statistics were analyzed using Mann-Whitney U testing (2-tailed) or Kaplan-Meier survival testing (PFS). In patients with archival tumor tissue, next-generation sequencing was performed through an institutional platform (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets). Results: Forty-one patients (61%) were responders. PPQ accurately predicted 96% (64/67). The hazard ratio for prediction was 24.4 (95% CI, 8.2-72.5). Twelve-month disease control was 97% for PPQ-positive patients versus 26% for PPQ-negative patients (P < 0.0001). Median progression-free survival was not reached in those predicted to respond (PPQ-positive, n = 40) but was 8 mo in those predicted not to respond (PPQ-negative, n = 27). The NETest result in responders was 67 ± 25 at baseline and significantly (P < 0.05) decreased (-37 ± 44%) at follow-up. The NETest result in nonresponders was 44 ± 23 at baseline and significantly (P < 0.05) increased (+76% ± 56%) at progression. Overall, the NETest changes (increases or decreases) were 90% accurate. Thirty patients underwent next-generation sequencing. Tumors were microsatellite-stable, and the median mutational burden was 1.8. Alterations involved mainly the mTOR/PTEN/TSC pathway (30%). No relationship was associated with PRRT response. Conclusion: Our interim analysis confirmed that PPQ is an accurate predictor of 177Lu-DOTATATE responsiveness (radiosensitivity) and that NETest changes accurately correlated with treatment response. Tissue-based molecular genetic information had little value in PRRT prediction. Blood-based gene signatures may improve the management of patients undergoing 177Lu-DOTATATE by providing information on tumor radiosensitivity and disease course, thus allowing individualized strategies.

Pan-cancer molecular subtypes of metastasis reveal distinct and evolving transcriptional programs

Molecular mechanisms underlying cancer metastasis span diverse tissues of origin. Here, we synthesize and collate the transcriptomes of patient-derived xenografts and patient tumor metastases, and these data collectively represent 38 studies and over 3,000 patients and 4,000 tumors. We identify four expression-based subtypes of metastasis transcending tumor lineage. The first subtype has extensive copy alterations, higher expression of MYC transcriptional targets and DNA repair genes, and bromodomain inhibitor response association. The second subtype has higher expression of genes involving metabolism and prostaglandin synthesis and regulation. The third subtype has evidence of neuronal differentiation, higher expression of DNA and histone methylation genes and EZH2 transcriptional targets, and BCL2 inhibitor response association. The fourth subtype has higher expression of immune checkpoint and Notch pathway genes. The metastasis subtypes reflect expression differences from paired primaries, with subtype switching being common. These subtypes facilitate understanding of the molecular underpinnings of metastases beyond tissue-oriented domains, with therapeutic implications.

Searching for DNA methylation in patients triple-negative breast cancer: a liquid biopsy approach

INTRODUCTION

Liquid biopsies are proving to have diagnostic and prognostic value in many different cancers, and in breast cancer they have the potential to improve outcomes by providing valuable information throughout a patient's cancer journey. However, patients with triple negative breast cancer (TNBC) have received little benefit from such liquid biopsies due to underlying limitations in the discovery and utility of robust biomarkers. Here, we examine the development of DNA methylation-based liquid biopsy assays for breast cancer and how they pertain to TNBC.

AREAS COVERED

We conducted a systematic review of liquid biopsy assays for breast cancer and analyzed their relevance in TNBC. We show that the utility of DNA mutation-based assays is poor for TNBC due to the low mutational frequencies across the genome in this subtype. We offer a detailed review of mDETECT - a liquid biopsy specifically designed for assessing tumor burden in TNBC patients.

EXPERT OPINION

DNA methylation are foundational and robust events that occur in cancer evolution and may differentiate almost all forms of cancer, including TNBC. Longitudinal patient monitoring using DNA methylation-based liquid biopsies offers great potential for improving the detection and management of TNBC.

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.

Advances in Tumor Organoids for the Evaluation of Drugs: A Bibliographic Review

Tumor organoids are defined as self-organized three-dimensional assemblies of heterogeneous cell types derived from patient samples that mimic the key histopathological, genetic, and phenotypic characteristics of the original tumor. This technology is proposed as an ideal candidate for the evaluation of possible therapies against cancer, presenting advantages over other models which are currently used. However, there are no reports in the literature that relate the techniques and material development of tumor organoids or that emphasize in the physicochemical and biological properties of materials that intent to biomimicry the tumor extracellular matrix. There is also little information regarding the tools to identify the correspondence of native tumors and tumoral organoids (tumoroids). Moreover, this paper relates the advantages of organoids compared to other models for drug evaluation. A growing interest in tumoral organoids has arisen from 2009 to the present, aimed at standardizing the process of obtaining organoids, which more accurately resemble patient-derived tumor tissue. Likewise, it was found that the characteristics to consider for the development of organoids, and therapeutic responses of them, are cell morphology, physiology, the interaction between cells, the composition of the cellular matrix, and the genetic, phenotypic, and epigenetic characteristics. Currently, organoids have been used for the evaluation of drugs for brain, lung, and colon tumors, among others. In the future, tumor organoids will become closer to being considered a better model for studying cancer in clinical practice, as they can accurately mimic the characteristics of tumors, in turn ensuring that the therapeutic response aligns with the clinical response of patients.

Ectopic expression of meiotic cohesin generates chromosome instability in cancer cell line

This work originated from mining of cancer genome data and proceeded to analyze the effects of ectopic expression of meiotic cohesins in mitotic cells in culture. In the process, apart from conclusively answering the question on mechanisms for RAD21L toxicity and its underrepresentation in tumor transcriptomes, we found an association of meiotic cohesin binding with BORIS/CTCFL sites in the normal testis. We also elucidated the patterns and outcomes of meiotic cohesin binding to chromosomes in model cell lines. Furthermore, we uncovered that RAD21L-based meiotic cohesin possesses a self-contained chromosome restructuring activity able to trigger sustainable but imperfect mitotic arrest leading to chromosomal instability. The discovered epigenomic and genetic mechanisms can be relevant to chromosome instability in cancer.

Many tumors express meiotic genes that could potentially drive somatic chromosome instability. While germline cohesin subunits SMC1B, STAG3, and REC8 are widely expressed in many cancers, messenger RNA and protein for RAD21L subunit are expressed at very low levels. To elucidate the potential of meiotic cohesins to contribute to genome instability, their expression was investigated in human cell lines, predominately in DLD-1. While the induction of the REC8 complex resulted in a mild mitotic phenotype, the expression of the RAD21L complex produced an arrested but viable cell pool, thus providing a source of DNA damage, mitotic chromosome missegregation, sporadic polyteny, and altered gene expression. We also found that genomic binding profiles of ectopically expressed meiotic cohesin complexes were reminiscent of their corresponding specific binding patterns in testis. Furthermore, meiotic cohesins were found to localize to the same sites as BORIS/CTCFL, rather than CTCF sites normally associated with the somatic cohesin complex. These findings highlight the existence of a germline epigenomic memory that is conserved in cells that normally do not express meiotic genes. Our results reveal a mechanism of action by unduly expressed meiotic cohesins that potentially links them to aneuploidy and chromosomal mutations in affected cells.

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.

Analysis of cancer omics data: a selective review of statistical techniques

Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data-which has higher dimensionality, weaker signals and more complex distributional properties-is much more challenging. Developments in the literature are often 'scattered', with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the 'overall framework' of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss 'special topics' including interaction analysis, multi-datasets analysis and multi-omics analysis.

Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

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.

A novel liquid biopsy (NETest) identifies paragangliomas and pheochromocytomas with high accuracy

Pheochromocytomas and paragangliomas (PHEOs/PGLs) represent diagnostically challenging and complex neuroendocrine tumors (NETs). Current biomarker tests for PHEOs/PGLs are technically complex or limited. We assessed the diagnostic utility of a NET-specific 51-marker gene blood assay (NETest) in patients with PHEOs/PGLs (n = 81), including ten pediatric patients, and age-/gender-matched controls (n = 142) using a prospective case:control (1:2) analysis. mRNA was measured (qPCR), and results were scaled from 0 to 100 (upper limit of normal < 20). Receiver operating curve (ROC) and non-parametric (Mann-Whitney) tests were used for analyses (two-tailed). All data are presented as mean ± s.e.m. NETest accuracy for PHEO/PGL diagnosis was 100%. PHEO/PGL scores were 70 ± 3 vs 8.5 ± 1 in controls (P < 0.0001), and ROC analysis was 0.99 ± 0.004 (P < 0.0001). Diagnostic metrics were 94% accurate, 100% sensitive, and 92% specific. Imaging correlation with 68Ga-PET-SSA was 100%. NETest levels in PHEOs (n = 26) were significantly (P < 0.0001) elevated (83 ± 4) vs 66 ± 4 in PGLs (n = 40) and mixed PHEOs/PGLs (n = 5: 37 ± 3). Adrenal-derived tumors (n = 30) exhibited higher scores (76 ± 5) than extra-adrenal-derived tumors (66 ± 4, P < 0.05). Cluster 2 tumors exhibited significantly (P = 0.034) elevated NETest levels (n = 4: 92 ± 2) vs cluster 1 tumors (n = 35: 69 ± 4). Regulatory pathway analysis identified elevated RAS-RAF, metastatic, pluripotential, neural and secretory gene cluster levels (P < 0.05) in PHEOs compared to PGLs. Cluster 2 PPGLs exhibited elevated (P = 0.046) levels of growth factor signaling genes compared to cluster 1. The PHEOs/PGLs in the pediatric cohort (n = 10) were all NETest-positive (81 ± 8) and exhibited a gene expression profile spectrum analogous to adults. Circulating NET transcript analysis identifies PHEOs/PGLs with 100% efficacy and is likely to have clinical utility in the diagnosis and management of PHEO/PGL patients.

Simplifying cancer: binary pan-cancer superclasses stratified by opposite YAP/TEAD effects

The inherent complexity of cancer complicates treatment. Identifying higher-order principles that govern cancer biology can circumvent this problem and pinpoint broadly applicable treatment options. We recently found that opposite expression and pro- versus anti-cancer activity of a single transcriptional complex functionally stratifies cancer into binary superclasses.

A protumorigenic secretory pathway activated by p53 deficiency in lung adenocarcinoma

Therapeutic strategies designed to target TP53-deficient cancer cells remain elusive. Here, we showed that TP53 loss initiated a pharmacologically actionable secretory process that drove lung adenocarcinoma (LUAD) progression. Molecular, biochemical, and cell biological studies showed that TP53 loss increased the expression of Golgi reassembly and stacking protein 55 kDa (G55), a Golgi stacking protein that maintains Golgi organelle integrity and is part of a GOLGIN45 (G45)-myosin IIA-containing protein complex that activates secretory vesicle biogenesis in the Golgi. TP53 loss activated G55-dependent secretion by relieving G55 and myosin IIA from miR-34a-dependent silencing. G55-dependent secreted proteins enhanced the proliferative and invasive activities of TP53-deficient LUAD cells and promoted angiogenesis and CD8+ T cell exhaustion in the tumor microenvironment. A small molecule that blocks G55-G45 interactions impaired secretion and reduced TP53-deficient LUAD growth and metastasis. These results identified a targetable secretory vulnerability in TP53-deficient LUAD cells.

A multigenomic liquid biopsy biomarker for neuroendocrine tumor disease outperforms CgA and has surgical and clinical utility

BACKGROUND

Biomarkers are key tools in cancer management. In neuroendocrine tumors (NETs), Chromogranin A (CgA) was considered acceptable as a biomarker. We compared the clinical efficacy of a multigenomic blood biomarker (NETest) to CgA over a 5-year period.

PATIENTS AND METHODS

An observational, prospective, cross-sectional, multicenter, multinational, comparative cohort assessment. Cohort 1: NETest evaluation in NETs (n = 1684) and cancers, benign diseases, controls (n = 731). Cohort 2: (n = 1270): matched analysis of NETest/CgA in a sub-cohort of NETs (n = 922) versus other diseases and controls (n = 348). Disease status was assessed by response evaluation criteria in solid tumors (RECIST). NETest measurement: qPCR [upper limit of normal (ULN: 20)], CgA (EuroDiagnostica, ULN: 108 ng/ml).

STATISTICS

Mann-Whitney U-test, AUROC, chi-square and McNemar' test.

RESULTS

Cohort 1: NETest diagnostic accuracy was 91% (P < 0.0001) and identified pheochromocytomas (98%), small intestine (94%), pancreas (91%), lung (88%), gastric (80%) and appendix (79%). NETest reflected grading: G1: 40 ± 1, G2 (50 ± 1) and G3 (52 ± 1). Locoregional disease levels were lower (38 ± 1) than metastatic (52 ± 1, P < 0.0001). NETest accurately stratified RECIST-assessed disease extent: no disease (21 ± 1), stable (43 ± 2), progressive (62 ± 2) (P < 0.0001). NETest concordance with imaging (CT/MRI/⁶⁸Ga-SSA-PET) 91%. Presurgery, all NETs (n = 153) were positive (100%). After palliative R1/R2 surgery (n = 51) all (100%) remained elevated. After curative R0-surgery (n = 102), NETest levels were normal in 81 (70%) with no recurrence at 2 years. In the 31 (30%) with elevated levels, 25 (81%) recurred within 2 years. Cohort #2: NETest diagnostic accuracy was 87% and CgA 54% (P < 0.0001). NETest was more accurate than CgA for grading (chi-square = 7.7, OR = 18.5) and metastatic identification (chi-square = 180, OR = 8.4). NETest identified progressive disease (95%) versus CgA (57%, P < 0.0001). Imaging concordance for NETest was 91% versus CgA (46%) (P < 0.0001). Recurrence prediction after surgery was NETest-positive in >94% versus CgA 11%.

CONCLUSION

NETest accurately diagnoses NETs and is an effective surrogate marker for imaging, grade, metastases and disease status compared to CgA. A multigenomic liquid biopsy is an accurate biomarker of NET disease.

Binary pan-cancer classes with distinct vulnerabilities defined by pro- or anti-cancer YAP/TEAD activity

Cancer heterogeneity impacts therapeutic response, driving efforts to discover over-arching rules that supersede variability. Here, we define pan-cancer binary classes based on distinct expression of YAP and YAP-responsive adhesion regulators. Combining informatics with in vivo and in vitro gain- and loss-of-function studies across multiple murine and human tumor types, we show that opposite pro- or anti-cancer YAP activity functionally defines binary YAP^on or YAP^off cancer classes that express or silence YAP, respectively. YAP^off solid cancers are neural/neuroendocrine and frequently RB1^-/-, such as retinoblastoma, small cell lung cancer, and neuroendocrine prostate cancer. YAP silencing is intrinsic to the cell of origin, or acquired with lineage switching and drug resistance. The binary cancer groups exhibit distinct YAP-dependent adhesive behavior and pharmaceutical vulnerabilities, underscoring clinical relevance. Mechanistically, distinct YAP/TEAD enhancers in YAP^off or YAP^on cancers deploy anti-cancer integrin or pro-cancer proliferative programs, respectively. YAP is thus pivotal across cancer, but in opposite ways, with therapeutic implications.

A DNA methylation-based liquid biopsy for triple-negative breast cancer

Here, we present a next-generation sequencing (NGS) methylation-based blood test called methylation DETEction of Circulating Tumour DNA (mDETECT) designed for the optimal detection and monitoring of metastatic triple-negative breast cancer (TNBC). Based on a highly multiplexed targeted sequencing approach, this assay incorporates features that offer superior performance and included 53 amplicons from 47 regions. Analysis of a previously characterised cohort of women with metastatic TNBC with limited quantities of plasma (<2 ml) produced an AUC of 0.92 for detection of a tumour with a sensitivity of 76% for a specificity of 100%. mDETECT_TNBC was quantitative and showed superior performance to an NGS TP53 mutation-based test carried out on the same patients and to the conventional CA15-3 biomarker. mDETECT also functioned well in serum samples from metastatic TNBC patients where it produced an AUC of 0.97 for detection of a tumour with a sensitivity of 93% for a specificity of 100%. An assay for BRCA1 promoter methylation was also incorporated into the mDETECT assay and functioned well but its clinical significance is currently unclear. Clonal Hematopoiesis of Indeterminate Potential was investigated as a source of background in control subjects but was not seen to be significant, though a link to adiposity may be relevant. The mDETECT_TNBC assay is a liquid biopsy able to quantitatively detect all TNBC cancers and has the potential to improve the management of patients with this disease.

p53 loss activates prometastatic secretory vesicle biogenesis in the Golgi

Cancer cells exhibit hyperactive secretory states that maintain cancer cell viability and remodel the tumor microenvironment. However, the oncogenic signals that heighten secretion remain unclear. Here, we show that p53 loss activates prometastatic secretory vesicle biogenesis in the Golgi. p53 loss up-regulates the expression of a Golgi scaffolding protein, progestin and adipoQ receptor 11 (PAQR11), which recruits an adenosine diphosphate ribosylation factor 1-containing protein complex that loads cargos into secretory vesicles. PAQR11-dependent secretion of a protease, PLAU, prevents anoikis and initiates autocrine activation of a PLAU receptor/signal transducer and activator of transcription-3-dependent pathway that up-regulates PAQR11 expression, thereby completing a feedforward loop that amplifies prometastatic effector protein secretion. Pharmacologic inhibition of PLAU receptor impairs the growth and metastasis of p53-deficient cancers. Blockade of PAQR11-dependent secretion inhibits immunosuppressive processes in the tumor microenvironment. Thus, Golgi reprogramming by p53 loss is a key driver of hypersecretion in cancer.

Multisite Tumor Sampling Reveals Extensive Heterogeneity of Tumor and Host Immune Response in Ovarian Cancer

BACKGROUND/AIM

Ovarian cancer (OVCA) is characterized by genomic/molecular intra-patient heterogeneity (IPH). Tissue histology and morphological features are surrogates of the underlying genomic/molecular contexture. We assessed the morphological IPH of OVCA tumor compartments and of lymphocytic infiltrates in multiple matched samples per patient.

MATERIALS AND METHODS

We examined 294 hematoxylin & eosin (H&E) OVCA tumor whole sections from 70 treatment-naïve patients who had undergone cytoreductive surgery. We assessed morphological subtypes as immunoreactive (IR), solid - proliferative (SD), papilloglandular (PG), and mesenchymal transition (MT); subtype load per patient; stromal tumor-infiltrating lymphocyte (sTIL) density as average per sample; and, as maximal sTIL values (max-TILs) among all samples per patient, ovaries and implants.

RESULTS

Among all 294 tumor sections, the most frequent primary morphological subtype was PG (n=150, 51.0%), followed by MT (71, 24.1%), SD (48, 16.3%) and IR (15, 5.1%). Subtype combinations were observed in 67/294 sections (22.8%) and IPH in 48/70 patients (68.6%). PG prevailed in ovaries (p<0.001), SD and MT in implants (p=0.023 and p<0.001, respectively). sTILs were higher in SD compared to non-SD (p=0.019) and lower in PG, respectively (p<0.001). sTIL density was higher in implants than in ovaries (p<0.001). Higher max-TILs were associated with stage IV disease (p=0.043), upper abdominal dissemination (p=0.024), endometrioid histology (p=0.013), and grade 3 tumors (p=0.021). Favorable prognosticators were higher max-TILs per patient (PFS, OS) and higher SD-load (PFS).

CONCLUSION

Clinically relevant morphological and host immune-response IPH appear to be the norm in OVCA. This may complicate efforts to decipher sensitivity of the tumor to certain treatment modalities from a single pre-operative biopsy.

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

Proteomic signatures associated with clinical measures of more aggressive cancers could yield molecular clues as to disease drivers. Here, utilizing the Clinical Proteomic Tumor Analysis Consortium (CPTAC) mass-spectrometry-based proteomics datasets, we defined differentially expressed proteins and mRNAs associated with higher grade or higher stage, for each of seven cancer types (breast, colon, lung adenocarcinoma, clear cell renal, ovarian, uterine, and pediatric glioma), representing 794 patients. Widespread differential patterns of total proteins and phosphoproteins involved some common patterns shared between different cancer types. More proteins were associated with higher grade than higher stage. Most proteomic signatures predicted patient survival in independent transcriptomic datasets. The proteomic grade signatures, in particular, involved DNA copy number alterations. Pathways of interest were enriched within the grade-associated proteins across multiple cancer types, including pathways of altered metabolism, Warburg-like effects, and translation factors. Proteomic grade correlations identified protein kinases having functional impact in vitro in uterine endometrial cancer cells, including MAP3K2, MASTL, and TTK. The protein-level grade and stage associations for all proteins profiled-along with corresponding information on phosphorylation, pathways, mRNA expression, and copy alterations-represent a resource for identifying new potential targets. Proteomic analyses are often concordant with corresponding transcriptomic analyses, but with notable exceptions.

A pediatric brain tumor atlas of genes deregulated by somatic genomic rearrangement

The global impact of somatic structural variants (SSVs) on gene expression in pediatric brain tumors has not been thoroughly characterised. Here, using whole-genome and RNA sequencing from 854 tumors of more than 30 different types from the Children's Brain Tumor Tissue Consortium, we report the altered expression of hundreds of genes in association with the presence of nearby SSV breakpoints. SSV-mediated expression changes involve gene fusions, altered cis-regulation, or gene disruption. SSVs considerably extend the numbers of patients with tumors somatically altered for critical pathways, including receptor tyrosine kinases (KRAS, MET, EGFR, NF1), Rb pathway (CDK4), TERT, MYC family (MYC, MYCN, MYB), and HIPPO (NF2). Compared to initial tumors, progressive or recurrent tumors involve a distinct set of SSV-gene associations. High overall SSV burden associates with TP53 mutations, histone H3.3 gene H3F3C mutations, and the transcription of DNA damage response genes. Compared to adult cancers, pediatric brain tumors would involve a different set of genes with SSV-altered cis-regulation. Our comprehensive and pan-histology genomic analyses reveal SSVs to play a major role in shaping the transcriptome of pediatric brain tumors.

PI4KIIIβ is a therapeutic target in chromosome 1q-amplified lung adenocarcinoma

Heightened secretion of protumorigenic effector proteins is a feature of malignant cells. Yet, the molecular underpinnings and therapeutic implications of this feature remain unclear. Here, we identify a chromosome 1q region that is frequently amplified in diverse cancer types and encodes multiple regulators of secretory vesicle biogenesis and trafficking, including the Golgi-dedicated enzyme phosphatidylinositol (PI)-4-kinase IIIβ (PI4KIIIβ). Molecular, biochemical, and cell biological studies show that PI4KIIIβ-derived PI-4-phosphate (PI4P) synthesis enhances secretion and accelerates lung adenocarcinoma progression by activating Golgi phosphoprotein 3 (GOLPH3)-dependent vesicular release from the Golgi. PI4KIIIβ-dependent secreted factors maintain 1q-amplified cancer cell survival and influence prometastatic processes in the tumor microenvironment. Disruption of this functional circuitry in 1q-amplified cancer cells with selective PI4KIIIβ antagonists induces apoptosis and suppresses tumor growth and metastasis. These results support a model in which chromosome 1q amplifications create a dependency on PI4KIIIβ-dependent secretion for cancer cell survival and tumor progression.

Meta-analysis of host transcriptional responses to SARS-CoV-2 infection reveals their manifestation in human tumors

A deeper understanding of the molecular biology of SARS-CoV-2 infection, including the host response to the virus, is urgently needed. Commonalities exist between the host immune response to viral infections and cancer. Here, we defined transcriptional signatures of SARS-CoV-2 infection involving hundreds of genes common across lung adenocarcinoma cell lines (A549, Calu-3) and normal human bronchial epithelial cells (NHBE), with additional signatures being specific to one or both adenocarcinoma lines. Cross-examining eight transcriptomic databases, we found that host transcriptional responses of lung adenocarcinoma cells to SARS-CoV-2 infection shared broad similarities with host responses to multiple viruses across different model systems and patient samples. Furthermore, these SARS-CoV-2 transcriptional signatures were manifested within specific subsets of human cancer, involving ~ 20% of cases across a wide range of histopathological types. These cancer subsets show immune cell infiltration and inflammation and involve pathways linked to the SARS-CoV-2 response, such as immune checkpoint, IL-6, type II interferon signaling, and NF-κB. The cell line data represented immune responses activated specifically within the cancer cells of the tumor. Common genes and pathways implicated as part of the viral host response point to therapeutic strategies that may apply to both SARS-CoV-2 and cancer.

Neuroendocrine Tumor Omic Gene Cluster Analysis Amplifies the Prognostic Accuracy of the NETest

BACKGROUND

The NETest is a multigene assay comprising 51 circulating neuroendocrine tumor (NET)-specific transcripts. The quotient of the 51-gene assay is based upon an ensemble of machine learning algorithms. Eight cancer hallmarks or "omes" (apoptome, epigenome, growth factor signalome, metabolome, proliferome, plurome, secretome, SSTRome) represent 29 genes. The NETest is an accurate diagnostic (>90%) test, but its prognostic utility has not been assessed. In this study, we describe the expansion of the NETest omic cluster components and demonstrate that integration amplifies NETest prognostic accuracy.

METHODS

Group 1: n = 222; including stable disease (SD, n = 146), progressive disease (PD, n = 76), and controls (n = 139). Group 2: NET Registry NCT02270567; n = 88; prospective samples (SD, n = 54; PD, n = 34) with up to 24 months follow-up. We used PubMed literature review, interactomic analysis, nonparametric testing, Kaplan-Meier survival curves, and χ2 analyses to inform and define the prognostic significance of NET genomic "hallmarks."

RESULTS

2020 analyses: In-depth analyses of 47 -NETest genes identified a further six omes: fibrosome, inflammasome, metastasome, NEDome, neurome, and TFome. Group 1 analysis: Twelve omes, excluding the inflammasome and apoptome, were significantly (p < 0.05, 2.1- to 8.2-fold) elevated compared to controls. In the PD group, seven omes (proliferome, NEDome, epigenome, SSTRome, neurome, metastasome, and fibrosome) were elevated (both expression levels and fold change >2) versus SD. Group 2 analysis: All these seven omes were upregulated. In PD, they were significantly more elevated (p < 0.02) than in SD. The septet omic expression exhibited a 69% prognostic accuracy. The NETest alone was 70.5% accurate. A low NETest (≤40) integrated with epigenome/metastasome levels was an accurate prognostic for PD (90%). A high NETest (>40) including the fibrosome/NEDome predicted PD development within 3 months (100%). Using decision tree analysis to integrate the four omes (epigenome, metastasome, fibrosome, and NEDome) with the NETest score generated an overall prognostic accuracy of 93%.

CONCLUSIONS

Examination of NETest omic gene cluster analysis identified five additional clinically relevant cancer hallmarks. Identification of seven omic clusters (septet) provides a molecular pathological signature of disease progression. The integration of the quartet (epigenome, fibrosome, metastasome, NEDome) and the NETest score yielded a 93% accuracy in the prediction of future disease status.

The Challenge of Evaluating Response to Peptide Receptor Radionuclide Therapy in Gastroenteropancreatic Neuroendocrine Tumors: The Present and the Future

The NETTER-1 study has proven peptide receptor radionuclide therapy (PRRT) to be one of the most effective therapeutic options for metastatic neuroendocrine tumors (NETs), improving progression-free survival and overall survival. However, PRRT response assessment is challenging and no consensus on methods and timing has yet been reached among experts in the field. This issue is owed to the suboptimal sensitivity and specificity of clinical biomarkers, limitations of morphological response criteria in slowly growing tumors and necrotic changes after therapy, a lack of standardized parameters and timing of functional imaging and the heterogeneity of PRRT protocols in the literature. The aim of this article is to review the most relevant current approaches for PRRT efficacy prediction and response assessment criteria in order to provide an overview of suitable tools for safe and efficacious PRRT.

Renal Carcinoma Is Associated With Increased Risk of Coronavirus Infections

Background: The current COVID-19 pandemic has affected most severely people with old age, or with comorbidities like hypertension, diabetes mellitus, and cancer. Cancer patients are twice more likely to contract the disease because of the malignancy or treatment-related immunosuppression; hence identification of the vulnerable population among these patients is essential. Method: We took a bioinformatics approach to analyze the gene and protein expression data of these coronavirus receptors (DPP4, ANPEP, ENPEP, TMPRSS2) in human normal and cancer tissues of multiple organs including the brain, liver, kidney, heart, lung, skin, GI tract, pancreas, endocrine tissues, and the reproductive organs. RNA-Seq data from The Cancer Genome Atlas (TCGA) and GTeX databases were used for extensive profiling analysis of these receptors across 9,736 tumors and 8,587 normal tissues comparing coronavirus receptors. Protein expression from immunohistochemistry data was assessed from The Human Protein Atlas database including 144 samples, corresponding to 48 different normal human tissue types, and 432 tumor samples from 216 different cancer patients. The correlations between immune cell infiltration, chemokine, and cytokines were investigated via Tumor Immune Estimation Resource (TIMER) and TCGA. Result: We found that among all, renal tumor and normal tissues exhibited increased levels of ACE2, DPP4, ANPEP, and ENPEP. Our results revealed that TMPRSS2 may not be the co-receptor for coronavirus infection in renal carcinoma patients. The other receptors DPP4, ANPEP, and ENPEP may act as the compensatory receptor proteins to help ACE2. The receptors' expression levels were variable in different tumor stage, molecular, and immune subtypes of renal carcinoma. Intriguingly, in clear cell renal cell carcinomas, coronavirus receptors were associated with high immune infiltration, markers of immunosuppression, and T cell exhaustion. Conclusion: Our study indicates that CoV receptors may play an important role in modulating the immune infiltrate and hence cellular immunity in renal carcinoma. As our current knowledge of pathogenic mechanisms will improve, it may help us in designing focused therapeutic approaches.

ERK3/MAPK6 is required for KRAS-mediated NSCLC tumorigenesis

KRAS is one of the most frequently mutated oncogenes, especially in lung cancers. Targeting of KRAS directly or the downstream effector signaling machinery is of prime interest in treating lung cancers. Here, we uncover that ERK3, a ubiquitously expressed atypical MAPK, is required for KRAS-mediated NSCLC tumors. ERK3 is highly expressed in lung cancers, and oncogenic KRAS led to the activation and stabilization of the ERK3 protein. In particular, phosphorylation of serine 189 in the activation motif of ERK3 is significantly increased in lung adenocarcinomas in comparison to adjacent normal controls in patients. Loss of ERK3 prevents the anchorage-independent growth of KRAS G12C-transformed human bronchial epithelial cells. We further find that loss of ERK3 reduces the oncogenic growth of KRAS G12C-driven NSCLC tumors in vivo and that the kinase activity of ERK3 is required for KRAS-driven oncogenesis in vitro. Our results demonstrate an obligatory role for ERK3 in NSCLC tumor progression and suggest that ERK3 kinase inhibitors can be pursued for treating KRAS G12C-driven tumors.

Molecular profiling of neuroendocrine tumours to predict response and toxicity to peptide receptor radionuclide therapy

Peptide receptor radionuclide therapy (PRRT) is a type of radiotherapy that targets peptide receptors and is typically used for neuroendocrine tumours (NETs). Some of the key challenges in its use are the prediction of efficacy and toxicity, patient selection, and response optimisation. In this Review, we assess current knowledge on the molecular profile of NETs and the strategies and tools used to predict, monitor, and assess the toxicity of PRRT. The few mutations in tumour genes that can be evaluated (eg, ATM and DAXX) are limited to pancreatic NETs and are most likely not informative. Assays that are transcriptomic or based on genes are effective in the prediction of radiotherapy response in other cancers. A blood-based assay for eight genes (the PRRT prediction quotient [PPQ]) has an overall accuracy of 95% for predicting responses to PRRT in NETs. No molecular markers exist that can predict the toxicity of PRRT. Candidate molecular targets include seven single nucleotide polymorphisms (SNPs) that are susceptible to radiation. Transcriptomic evaluations of blood and a combination of gene expression and specific SNPs, assessed by machine learning with algorithms that are tumour-specific, might yield molecular tools to enhance the efficacy and safety of PRRT.

A Deep Learning-Based Tumor Classifier Directly Using MS Raw Data

Since the launch of Chinese Human Proteome Project (CNHPP) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), large-scale mass spectrometry (MS) based proteomic profiling of different kinds of human tumor samples have provided huge amount of valuable data for both basic and clinical researchers. Accurate prediction for tumor and non-tumor samples, as well as the tumor types has become a key step for biological and medical research, such as biomarker discovery, diagnosis, and monitoring of diseases. The traditional MS-based classification strategy mainly depends on the identification and quantification results of MS data, which has some inherent limitations, such as the low identification rate of MS data. Here, a deep learning-based tumor classifier directly using MS raw data is proposed, which is independent of the identification and quantification results of MS data. The potential precursors with intensities and retention times from MS data as input is first detected and extracted. Then, a deep learning-based classifier is trained, which can accurately distinguish between the tumor and non-tumor samples. Finally, it is demonstrated the deep learning-based classifier has a good performance compared with other machine learning methods and may help researchers find the potential biomarkers which are likely to be missed by the traditional strategy.

Recursive Consensus Clustering for novel subtype discovery from transcriptome data

Large-scale transcriptomic data is used by biologists for the discovery of new molecular patterns or cell subpopulations. Clustering is one of the most popular methods for dimensionality reduction and data analysis for large scale datasets. The major problem while clustering the data is the selection of the optimal number of clusters (k) for each dataset and to discover new insights from it. We have developed Recursive Consensus Clustering (RCC), an unsupervised clustering algorithm for novel subtype discovery from both bulk and single-cell datasets. RCC is available as an R package and facilitates the generation of new biological insights through intuitive visualization of clustering results.

Comprehensive Molecular Characterization Identifies Distinct Genomic and Immune Hallmarks of Renal Medullary Carcinoma

Renal medullary carcinoma (RMC) is a highly lethal malignancy that mainly afflicts young individuals of African descent and is resistant to all targeted agents used to treat other renal cell carcinomas. Comprehensive genomic and transcriptomic profiling of untreated primary RMC tissues was performed to elucidate the molecular landscape of these tumors. We found that RMC was characterized by high replication stress and an abundance of focal copy-number alterations associated with activation of the stimulator of the cyclic GMP-AMP synthase interferon genes (cGAS-STING) innate immune pathway. Replication stress conferred a therapeutic vulnerability to drugs targeting DNA-damage repair pathways. Elucidation of these previously unknown RMC hallmarks paves the way to new clinical trials for this rare but highly lethal malignancy.

The clinical applications of a multigene liquid biopsy (NETest) in neuroendocrine tumors

PURPOSE

There are few effective biomarkers for neuroendocrine tumors. Precision oncology strategies have provided liquid biopsies for real-time and tailored decision-making. This has led to the development of the first neuroendocrine tumor liquid biopsy (the NETest). The NETest represents a transcriptomic signature of neuroendocrine tumor (NETs) that captures tumor biology and disease activity. The data have direct clinical application in terms of identifying residual disease, disease progress and the efficacy of treatment. In this overview we assess the available published information on the metrics and clinical efficacy of the NETest.

MATERIAL AND METHODS

Published data on the NETest have been collated and analyzed to understand the clinical application of this multianalyte biomarker in NETs.

RESULTS

NETest assay has been validated as a standardized and reproducible clinical laboratory measurement. It is not affected by demographic characteristics, or acid suppressive medication. Clinical utility of the NETest has been documented in gastroenteropancreatic, bronchopulmonary NETs, in paragangliomas and pheochromocytomas. The test facilitates accurate diagnosis of a NET disease, and real-time monitoring of the disease status (stable/progressive disease). It predicts aggressive tumor behavior, identifies operative tumor resection, and efficacy of the medical treatment (e.g. somatostatin analogues), or peptide receptor radionuclide therapy (PRRT). NETest metrics and clinical applications out-perform standard biomarkers like chromogranin A.

CONCLUSIONS

The NETest exhibits clinically competent metrics as an effective biomarker for neuroendocrine tumors. Measurement of NET transcripts in blood is a significant advance in neuroendocrine tumor management and demonstrates that blood provides a viable source to identify and monitor tumor status.

High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations

The impact of somatic structural variants (SVs) on gene expression in cancer is largely unknown. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data and RNA sequencing from a common set of 1220 cancer cases, we report hundreds of genes for which the presence within 100 kb of an SV breakpoint associates with altered expression. For the majority of these genes, expression increases rather than decreases with corresponding breakpoint events. Up-regulated cancer-associated genes impacted by this phenomenon include TERT, MDM2, CDK4, ERBB2, CD274, PDCD1LG2, and IGF2. TERT-associated breakpoints involve ~3% of cases, most frequently in liver biliary, melanoma, sarcoma, stomach, and kidney cancers. SVs associated with up-regulation of PD1 and PDL1 genes involve ~1% of non-amplified cases. For many genes, SVs are significantly associated with increased numbers or greater proximity of enhancer regulatory elements near the gene. DNA methylation near the promoter is often increased with nearby SV breakpoint, which may involve inactivation of repressor elements.

Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures

For many years, head and neck squamous cell carcinoma (HNSCC) has been considered as a single entity. However, in the last decades HNSCC complexity and heterogeneity have been recognized. In parallel, high-throughput omics techniques had allowed picturing a larger spectrum of the behavior and characteristics of molecules in cancer and a large set of omics web-based tools and informative repository databases have been developed. The objective of the present review is to provide an overview on biological, prognostic and predictive molecular signatures in HNSCC. To contextualize the selected data, our literature survey includes a short summary of the main characteristics of omics data repositories and web-tools for data analyses. The timeframe of our analysis was fixed, encompassing papers published between January 2015 and January 2019. From more than 1000 papers evaluated, 61 omics studies were selected: 33 investigating mRNA signatures, 11 and 13 related to miRNA and other non-coding-RNA signatures and 4 analyzing DNA methylation signatures. More than half of identified signatures (36) had a prognostic value but only in 10 studies selection of a specific anatomical sub-site (8 oral cavity, 1 oropharynx and 1 both oral cavity and oropharynx) was performed. Noteworthy, although the sample size included in many studies was limited, about one-half of the retrieved studies reported an external validation on independent dataset(s), strengthening the relevance of the obtained data. Finally, we highlighted the development and exploitation of three gene-expression signatures, whose clinical impact on prognosis/prediction of treatment response could be high. Based on this overview on omics-related literature in HNSCC, we identified some limits and strengths. The major limits are represented by the low number of signatures associated to DNA methylation and to non-coding RNA (miRNA, lncRNA and piRNAs) and the availability of a single dataset with multiple omics on more than 500 HNSCC (i.e. TCGA). The major strengths rely on the integration of multiple datasets through meta-analysis approaches and on the growing integration among omics data obtained on the same cohort of patients. Moreover, new approaches based on artificial intelligence and informatic analyses are expected to be available in the next future.

An Assessment of Circulating Chromogranin A as a Biomarker of Bronchopulmonary Neuroendocrine Neoplasia: A Systematic Review and Meta-Analysis

BACKGROUND

Management of bronchopulmonary neuroendocrine neoplasia (NEN; pulmonary carcinoids [PCs], small-cell lung cancer [SCLC], and large cell neuroendocrine carcinoma) is hampered by the paucity of biomarkers. Chromogranin A (CgA), the default neuroendocrine tumor biomarker, has undergone wide assessment in gastroenteropancreatic neuroendocrine tumors.

OBJECTIVES

To evaluate CgA in lung NEN, define its clinical utility as a biomarker, assess its diagnostic, prognostic, and predictive efficacy, as well as its accuracy in the identification of disease recurrence.

METHODS

A systematic review of PubMed was undertaken using the preferred reporting items for systematic reviews and meta-analyses guidelines. No language restrictions were applied. Overall, 33 original scientific papers and 3 case reports, which met inclusion criteria, were included in qualitative analysis, and meta-analysis thereafter. All studies, except 2, were retrospective. Meta-analysis statistical assessment by generic inverse variance methodology.

RESULTS

Ten different CgA assay types were reported, without consistency in the upper limit of normal (ULN). For PCs (n = 16 studies; median patient inclusion 21 [range 1-200, total: 591 patients]), the CgA diagnostic sensitivity was 34.5 ± 2.7% with a specificity of 93.8 ± 4.7. CgA metrics were not available separately for typical or atypical carcinoids. CgA >100 ng/mL (2.7 × ULN) and >600 ng/mL (ULN unspecified) were anecdotally prognostic for overall survival (n = 2 retrospective studies). No evidence was presented for predicting treatment response or identifying post-surgery residual disease. For SCLC (n = 19 studies; median patient inclusion 23 [range 5-251, total: 1,241 patients]), the mean diagnostic sensitivity was 59.9 ± 6.8% and specificity 79.4 ± 3.1. Extensive disease typically exhibited higher CgA levels (diagnostic accuracy: 61 ± 2.5%). An elevated CgA was prognostic for overall survival (n = 4 retrospective studies). No prospective studies evaluating predictive benefit or prognostic utility were identified.

CONCLUSION

The available data are scarce. An assessment of all published data showed that CgA exhibits major limitations as an effective and accurate biomarker for either PC or SCLC. Its utility especially for localized PC/limited SCLC (when surgery is potentially curative), is limited. The clinical value of CgA remains to be determined. This requires validated, well-constructed, multicenter, prospective, randomized studies. An assessment of all published data indicates that CgA does not exhibit the minimum required metrics to function as a clinically useful biomarker for lung NENs.

PRRT neuroendocrine tumor response monitored using circulating transcript analysis: the NETest

PURPOSE

Peptide receptor radionuclide therapy (PRRT) is effective for metastatic/inoperable neuroendocrine tumors (NETs). Imaging response assessment is usually efficient subsequent to treatment completion. Blood biomarkers such as PRRT Predictive Quotient (PPQ) and NETest are effective in real-time. PPQ predicts PRRT efficacy; NETest monitors disease. We prospectively evaluated: (1) NETest as a surrogate biomarker for RECIST; (2) the correlation of NETest levels with PPQ prediction.

METHODS

Three independent ¹⁷⁷Lu-PRRT-treated GEP-NET and lung cohorts (Meldola, Italy: n = 72; Bad-Berka, Germany: n = 44; Rotterdam, Netherlands: n = 41). Treatment response: RECIST1.1 (responder (stable, partial, and complete response) vs non-responder). Blood sampling: pre-PRRT, before each cycle and follow-up (2-12 months). PPQ (positive/negative) and NETest (0-100 score) by PCR. Stable < 40; progressive > 40). CgA (ELISA) as comparator. Samples de-identified, measurement and analyses blinded. Kaplan-Meier survival and standard statistics.

RESULTS

One hundred twenty-two of the 157 were evaluable. RECIST stabilization or response in 67%; 33% progressed. NETest significantly (p < 0.0001) decreased in RECIST "responders" (- 47 ± 3%); in "non-responders," it remained increased (+ 79 ± 19%) (p < 0.0005). NETest monitoring accuracy was 98% (119/122). Follow-up levels > 40 (progressive) vs stable (< 40) significantly correlated with mPFS (not reached vs. 10 months; HR 0.04 (95%CI, 0.02-0.07). PPQ response prediction was accurate in 118 (97%) with a 99% accurate positive and 93% accurate negative prediction. NETest significantly (p < 0.0001) decreased in PPQ-predicted responders (- 46 ± 3%) and remained elevated or increased in PPQ-predicted non-responders (+ 75 ± 19%). Follow-up NETest categories stable vs progressive significantly correlated with PPQ prediction and mPFS (not reached vs. 10 months; HR 0.06 (95%CI, 0.03-0.12). CgA did not reflect PRRT treatment: in RECIST responders decrease in 38% and in non-responders 56% (p = NS).

CONCLUSIONS

PPQ predicts PRRT response in 97%. NETest accurately monitors PRRT response and is an effective surrogate marker of PRRT radiological response. NETest decrease identified responders and correlated (> 97%) with the pretreatment PPQ response predictor. CgA was non-informative.

Integrated Multi-omic Analysis of Esthesioneuroblastomas Identifies Two Subgroups Linked to Cell Ontogeny

Esthesioneuroblastoma (ENB) is a rare cancer of the olfactory mucosa, with no established molecular stratification to date. We report similarities of ENB with tumors arising in the neural crest and perform integrative analysis of these tumors. We propose a molecular-based subtype classification of ENB as basal or neural, both of which have distinct pathological, transcriptomic, proteomic, and immune features. Among the basal subtype, we uncovered an IDH2 R172 mutant-enriched subgroup (∼35%) harboring a CpG island methylator phenotype reminiscent of IDH2 mutant gliomas. Compared with the basal ENB methylome, the neural ENB methylome shows genome-wide reprogramming with loss of DNA methylation at the enhancers of axonal guidance genes. Our study reveals insights into the molecular pathogenesis of ENB and provides classification information of potential therapeutic relevance.

Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers

Mass-spectrometry-based proteomic profiling of human cancers has the potential for pan-cancer analyses to identify molecular subtypes and associated pathway features that might be otherwise missed using transcriptomics. Here, we classify 532 cancers, representing six tissue-based types (breast, colon, ovarian, renal, uterine), into ten proteome-based, pan-cancer subtypes that cut across tumor lineages. The proteome-based subtypes are observable in external cancer proteomic datasets surveyed. Gene signatures of oncogenic or metabolic pathways can further distinguish between the subtypes. Two distinct subtypes both involve the immune system, one associated with the adaptive immune response and T-cell activation, and the other associated with the humoral immune response. Two additional subtypes each involve the tumor stroma, one of these including the collagen VI interacting network. Three additional proteome-based subtypes—respectively involving proteins related to Golgi apparatus, hemoglobin complex, and endoplasmic reticulum—were not reflected in previous transcriptomics analyses. A data portal is available at UALCAN website.

Mass-spectrometry-based profiling can be used to stratify tumours into molecular subtypes. Here, by classifying over 500 tumours, the authors show that this approach reveals proteomic subgroups which cut across tumour types.