Expression patterns of HDAC6 in correlation to ARID1A status in different subtypes of endometriosis: A retrospective tissue microarray analysis.

Endometriosis is a disease affecting approximately 10% of reproductive age women. Loss of the tumor suppressor gene AT-rich interactive domain-containing protein 1A (ARID1A) occurs in some endometriosis cases. Histone deacetylase 6 (HDAC-6) is an enzyme with implication in several diseases including different cancer types and immunological disorders, where it is involved in protein trafficking and degradation, cell shape, and migration. In ARID1A-deficient ovarian cancer increased HDAC-6 expression lead to apoptosis-inhibiting post-translational modification of p53. It is not known if HDAC-6 expression is also altered in ARID1A-deficient endometriosis. The aim of this study was to assess HDAC-6 expression in endometriotic lesions in correlation to ARID1A-status. Two tissue-microarrays with 168 endometriotic lesions, including ovarian (64/168, 38 %), peritoneal (66/168, 39 %) and deep-infiltrating (38/168, 23 %) subtypes, and 73 endometrium of women without endometriosis were assessed. Mean ARID1A immunoreactivity score (IRS) in endometriosis group was 10.83 (±2.36) and 10.78 (±1.94) in the epithelium and stroma, respectively, while the respective mean HDAC6 IRS were 9.16 (±2.76) and 5.94 (±2.88). The comparison of the HDAC6 expression between endometriosis subtypes showed higher expression in deep-infiltrating endometriosis, in both, epithelium (p = 0.032) and stroma (p = 0.007). In ARID1A negative cases, epithelial expression of HDAC6 was higher in endometriosis compared to women without endometriosis (p = 0.031), and this was also specifically observed in the subset of ovarian endometriosis (p = 0.037). There were no significant differences in the stromal expression of HDAC6. In conclusion, our results demonstrate a complex expression pattern of HDAC6 depending on ARID1A status in different endometriosis subtypes. Further studies on HDAC6 and ARID1A are important to elucidate mechanisms involved in malignant transformation of endometriosis.

Perilipin 5 deletion protects against nonalcoholic fatty liver disease and hepatocellular carcinoma by modulating lipid metabolism and inflammatory responses.

The molecular mechanisms underlying the transition from nonalcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC) are incompletely understood. During the development of NAFLD, Perilipin 5 (PLIN5) can regulate lipid metabolism by suppressing lipolysis and preventing lipotoxicity. Other reports suggest that the lack of PLIN5 decreases hepatic injury, indicating a protective role in NAFLD pathology. To better understand the role of PLIN5 in liver disease, we established mouse models of NAFLD and NAFLD-induced HCC, in which wild-type and Plin5 null mice were exposed to a single dose of acetone or 7,12-dimethylbenz[a]anthracene (DMBA) in acetone, followed by a 30-week high-fat diet supplemented with glucose/fructose. In the NAFLD model, RNA-seq revealed significant changes in genes related to lipid metabolism and immune response. At the intermediate level, pathways such as AMP-activated protein kinase (AMPK), signal transducer and activator of transcription 3 (STAT3), c-Jun N-terminal kinase (JNK), and protein kinase B (AKT) were blunted in Plin5-deficient mice (Plin5-/-) compared to wild-type mice (WT). In the NAFLD-HCC model, only WT mice developed liver tumors, while Plin5-/- mice were resistant to tumorigenesis. Furthermore, only 32 differentially expressed genes associated with NALFD progession were identified in Plin5 null mice. The markers of mitochondrial function and immune response, such as the peroxisome proliferator-activated receptor-γ, coactivator 1-α (PGC-1α) and phosphorylated STAT3, were decreased. Lipidomic analysis revealed differential levels of some sphingomyelins between WT and Plin5-/- mice. Interestingly, these changes were not detected in the HCC model, indicating a possible shift in the metabolism of sphingomelins during carcinogenesis.

Machine learning for pan-cancer classification based on RNA sequencing data.

Despite recent improvements in cancer diagnostics, 2%-5% of all malignancies are still cancers of unknown primary (CUP), for which the tissue-of-origin (TOO) cannot be determined at the time of presentation. Since the primary site of cancer leads to the choice of optimal treatment, CUP patients pose a significant clinical challenge with limited treatment options. Data produced by large-scale cancer genomics initiatives, which aim to determine the genomic, epigenomic, and transcriptomic characteristics of a large number of individual patients of multiple cancer types, have led to the introduction of various methods that use machine learning to predict the TOO of cancer patients. In this review, we assess the reproducibility, interpretability, and robustness of results obtained by 20 recent studies that utilize different machine learning methods for TOO prediction based on RNA sequencing data, including their reported performance on independent data sets and identification of important features. Our review investigates the strengths and weaknesses of different methods, checks the correspondence of their results, and identifies potential issues with datasets used for model training and testing, assessing their potential usefulness in a clinical setting and suggesting future improvements.

The Great Majority of Homologous Recombination Repair-Deficient Tumors Are Accounted for by Established Causes.

Background: Gene-agnostic genomic biomarkers were recently developed to identify homologous recombination deficiency (HRD) tumors that are likely to respond to treatment with PARP inhibitors. Two machine-learning algorithms that predict HRD status, CHORD, and HRDetect, utilize various HRD-associated features extracted from whole-genome sequencing (WGS) data and show high sensitivity in detecting patients with BRCA1/2 bi-allelic inactivation in all cancer types. When using only DNA mutation data for the detection of potential causes of HRD, both HRDetect and CHORD find that 30-40% of cases that have been classified as HRD are due to unknown causes. Here, we examined the impact of tumor-specific thresholds and measurement of promoter methylation of BRCA1 and RAD51C on unexplained proportions of HRD cases across various tumor types. Methods: We gathered published CHORD and HRDetect probability scores for 828 samples from breast, ovarian, and pancreatic cancer from previous studies, as well as evidence of their biallelic inactivation (by either DNA alterations or promoter methylation) in HR-related genes. ROC curve analysis evaluated the performance of each classifier in specific cancer. Tenfold nested cross-validation was used to find the optimal threshold values of HRDetect and CHORD for classifying HR-deficient samples within each cancer type. Results: With the universal threshold, HRDetect has higher sensitivity in the detection of biallelic inactivation in BRCA1/2 than CHORD and resulted in a higher proportion of unexplained cases. When promoter methylation was excluded, in ovarian carcinoma, the proportion of unexplained cases increased from 26.8 to 48.8% for HRDetect and from 14.7 to 41.2% for CHORD. A similar increase was observed in breast cancer. Applying cancer-type-specific thresholds led to similar sensitivity and specificity for both methods. The cancer-type-specific thresholds for HRDetect reduced the number of unexplained cases from 21 to 12.3% without reducing the 96% sensitivity to known events. For CHORD, unexplained cases were reduced from 10 to 9% while sensitivity increased from 85.3 to 93.9%. Conclusion: These results suggest that WGS-based HRD classifiers should be adjusted for tumor types. When applied, only ∼10% of breast, ovarian, and pancreas cancer cases are not explained by known events in our dataset.

High endogenous CCL2 expression promotes the aggressive phenotype of human inflammatory breast cancer.

Inflammatory Breast Cancer (IBC) is a highly aggressive malignancy with distinct clinical and histopathological features whose molecular basis is unresolved. Here we describe a human IBC cell line, A3250, that recapitulates key IBC features in a mouse xenograft model, including skin erythema, diffuse tumor growth, dermal lymphatic invasion, and extensive metastases. A3250 cells express very high levels of the CCL2 chemokine and induce tumors enriched in macrophages. CCL2 knockdown leads to a striking reduction in macrophage densities, tumor proliferation, skin erythema, and metastasis. These results establish IBC-derived CCL2 as a key factor driving macrophage expansion, and indirectly tumor growth, with transcriptomic analysis demonstrating the activation of multiple inflammatory pathways. Finally, primary human IBCs exhibit macrophage infiltration and an enriched macrophage RNA signature. Thus, this human IBC model provides insight into the distinctive biology of IBC, and highlights potential therapeutic approaches to this deadly disease.

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.

Somatic mutation landscape reveals differential variability of cell-of-origin for primary liver cancer.

Primary liver tissue cancer types are renowned to display a consistent increase in global disease burden and mortality, thus needing more effective diagnostics and treatments. Yet, integrative research efforts to identify cell-of-origin for these cancers by utilizing human specimen data were poorly established. To this end, we analyzed previously published whole-genome sequencing data for 384 tumor and progenitor tissues along with 423 publicly available normal tissue epigenomic features and single cell RNA-seq data from human livers to assess correlation patterns and extended this information to conduct in-silico prediction of the cell-of-origin for primary liver cancer subtypes. Despite mixed histological features, the cell-of-origin for mixed hepatocellular carcinoma/intrahepatic cholangiocarcinoma subtype was predominantly predicted to be hepatocytic origin. Individual sample-level predictions also revealed hepatocytes as one of the major predicted cell-of-origin for intrahepatic cholangiocarcinoma, thus implying trans-differentiation process during cancer progression. Additional analyses on the whole genome sequencing data of hepatic progenitor cells suggest these cells may not be a direct cell-of-origin for liver cancers. These results provide novel insights on the nature and potential contributors of cell-of-origins for primary liver cancers.

Genomic characterization of biliary tract cancers identifies driver genes and predisposing mutations.

BACKGROUND & AIMS: Biliary tract cancers (BTCs) are clinically and pathologically heterogeneous and respond poorly to treatment. Genomic profiling can offer a clearer understanding of their carcinogenesis, classification and treatment strategy. We performed large-scale genome sequencing analyses on BTCs to investigate their somatic and germline driver events and characterize their genomic landscape. METHODS: We analyzed 412 BTC samples from Japanese and Italian populations, 107 by whole-exome sequencing (WES), 39 by whole-genome sequencing (WGS), and a further 266 samples by targeted sequencing. The subtypes were 136 intrahepatic cholangiocarcinomas (ICCs), 101 distal cholangiocarcinomas (DCCs), 109 peri-hilar type cholangiocarcinomas (PHCs), and 66 gallbladder or cystic duct cancers (GBCs/CDCs). We identified somatic alterations and searched for driver genes in BTCs, finding pathogenic germline variants of cancer-predisposing genes. We predicted cell-of-origin for BTCs by combining somatic mutation patterns and epigenetic features. RESULTS: We identified 32 significantly and commonly mutated genes including TP53, KRAS, SMAD4, NF1, ARID1A, PBRM1, and ATR, some of which negatively affected patient prognosis. A novel deletion of MUC17 at 7q22.1 affected patient prognosis. Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes such as BRCA1, BRCA2, RAD51D, MLH1, or MSH2 were detected in 11% (16/146) of BTC patients. CONCLUSIONS: BTCs have distinct genetic features including somatic events and germline predisposition. These findings could be useful to establish treatment and diagnostic strategies for BTCs based on genetic information. LAY SUMMARY: We here analyzed genomic features of 412 BTC samples from Japanese and Italian populations. A total of 32 significantly and commonly mutated genes were identified, some of which negatively affected patient prognosis, including a novel deletion of MUC17 at 7q22.1. Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes were detected in 11% of patients with BTC. BTCs have distinct genetic features including somatic events and germline predisposition.

Analysis of somatic microsatellite indels identifies driver events in human tumors.

Microsatellites (MSs) are tracts of variable-length repeats of short DNA motifs that exhibit high rates of mutation in the form of insertions or deletions (indels) of the repeated motif. Despite their prevalence, the contribution of somatic MS indels to cancer has been largely unexplored, owing to difficulties in detecting them in short-read sequencing data. Here we present two tools: MSMuTect, for accurate detection of somatic MS indels, and MSMutSig, for identification of genes containing MS indels at a higher frequency than expected by chance. Applying MSMuTect to whole-exome data from 6,747 human tumors representing 20 tumor types, we identified >1,000 previously undescribed MS indels in cancer genes. Additionally, we demonstrate that the number and pattern of MS indels can accurately distinguish microsatellite-stable tumors from tumors with microsatellite instability, thus potentially improving classification of clinically relevant subgroups. Finally, we identified seven MS indel driver hotspots: four in known cancer genes (ACVR2A, RNF43, JAK1, and MSH3) and three in genes not previously implicated as cancer drivers (ESRP1, PRDM2, and DOCK3).

A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer.

Biallelic inactivation of BRCA1 or BRCA2 is associated with a pattern of genome-wide mutations known as signature 3. By analyzing ∼1,000 breast cancer samples, we confirmed this association and established that germline nonsense and frameshift variants in PALB2, but not in ATM or CHEK2, can also give rise to the same signature. We were able to accurately classify missense BRCA1 or BRCA2 variants known to impair homologous recombination (HR) on the basis of this signature. Finally, we show that epigenetic silencing of RAD51C and BRCA1 by promoter methylation is strongly associated with signature 3 and, in our data set, was highly enriched in basal-like breast cancers in young individuals of African descent.

Cell-of-origin chromatin organization shapes the mutational landscape of cancer.

Cancer is a disease potentiated by mutations in somatic cells. Cancer mutations are not distributed uniformly along the human genome. Instead, different human genomic regions vary by up to fivefold in the local density of cancer somatic mutations, posing a fundamental problem for statistical methods used in cancer genomics. Epigenomic organization has been proposed as a major determinant of the cancer mutational landscape. However, both somatic mutagenesis and epigenomic features are highly cell-type-specific. We investigated the distribution of mutations in multiple independent samples of diverse cancer types and compared them to cell-type-specific epigenomic features. Here we show that chromatin accessibility and modification, together with replication timing, explain up to 86% of the variance in mutation rates along cancer genomes. The best predictors of local somatic mutation density are epigenomic features derived from the most likely cell type of origin of the corresponding malignancy. Moreover, we find that cell-of-origin chromatin features are much stronger determinants of cancer mutation profiles than chromatin features of matched cancer cell lines. Furthermore, we show that the cell type of origin of a cancer can be accurately determined based on the distribution of mutations along its genome. Thus, the DNA sequence of a cancer genome encompasses a wealth of information about the identity and epigenomic features of its cell of origin.

Genetic variation in human DNA replication timing.

Genomic DNA replicates in a choreographed temporal order that impacts the distribution of mutations along the genome. We show here that DNA replication timing is shaped by genetic polymorphisms that act in cis upon megabase-scale DNA segments. In genome sequences from proliferating cells, read depth along chromosomes reflected DNA replication activity in those cells. We used this relationship to analyze variation in replication timing among 161 individuals sequenced by the 1000 Genomes Project. Genome-wide association of replication timing with genetic variation identified 16 loci at which inherited alleles associate with replication timing. We call these "replication timing quantitative trait loci" (rtQTLs). rtQTLs involved the differential use of replication origins, exhibited allele-specific effects on replication timing, and associated with gene expression variation at megabase scales. Our results show replication timing to be shaped by genetic polymorphism and identify a means by which inherited polymorphism regulates the mutability of nearby sequences.