Copy Number Variation Profiles Research Articles

DNA-methylome-assisted classification of patients with poor prognostic subventricular zone associated IDH-wildtype glioblastoma

Glioblastoma (GBM) derived from the “stem cell” rich subventricular zone (SVZ) may constitute a therapy-refractory subgroup of tumors associated with poor prognosis. Risk stratification for these cases is necessary but is curtailed by error prone imaging-based evaluation. Therefore, we aimed to establish a robust DNA methylome-based classification of SVZ GBM and subsequently decipher underlying molecular characteristics. MRI assessment of SVZ association was performed in a retrospective training set of IDH-wildtype GBM patients (n = 54) uniformly treated with postoperative chemoradiotherapy. DNA isolated from FFPE samples was subject to methylome and copy number variation (CNV) analysis using Illumina Platform and cnAnalysis450k package. Deep next-generation sequencing (NGS) of a panel of 130 GBM-related genes was conducted (Agilent SureSelect/Illumina). Methylome, transcriptome, CNV, MRI, and mutational profiles of SVZ GBM were further evaluated in a confirmatory cohort of 132 patients (TCGA/TCIA). A 15 CpG SVZ methylation signature (SVZM) was discovered based on clustering and random forest analysis. One third of CpG in the SVZM were associated with MAB21L2/LRBA. There was a 14.8% (n = 8) discordance between SVZM vs. MRI classification. Re-analysis of these patients favored SVZM classification with a hazard ratio (HR) for OS of 2.48 [95% CI 1.35–4.58], p = 0.004 vs. 1.83 [1.0–3.35], p = 0.049 for MRI classification. In the validation cohort, consensus MRI based assignment was achieved in 62% of patients with an intraclass correlation (ICC) of 0.51 and non-significant HR for OS (2.03 [0.81–5.09], p = 0.133). In contrast, SVZM identified two prognostically distinct subgroups (HR 3.08 [1.24–7.66], p = 0.016). CNV alterations revealed loss of chromosome 10 in SVZM– and gains on chromosome 19 in SVZM– tumors. SVZM– tumors were also enriched for differentially mutated genes (p < 0.001). In summary, SVZM classification provides a novel means for stratifying GBM patients with poor prognosis and deciphering molecular mechanisms governing aggressive tumor phenotypes.

P-146 The first comprehensive genomic characterization of rectal squamous cell carcinoma

Rectal cancers represent 35% of colorectal cancers; 90% are adenocarcinomas while rectal squamous cell carcinomas represents 0.3% of them. Given its rarity, little is known concerning its pathogenesis, molecular profile and therapeutic management. The current treatment trend is to treat rectal squamous cell carcinoma by analogy with anal squamous cell carcinoma with definitive chemoradiotherapy setting aside surgery in case of local recurrence. We performed an in-depth genomic analysis (Next Generation Sequencing – 400 gene panel, Oncoscan, Human Papilloma Virus characterization) on 10 rectal squamous cell carcinoma samples and compared them in silico to those of anal squamous cell carcinoma and rectal adenocarcinoma. Rectal squamous cell carcinoma shows 100% HPV positivity. It has a similar mutational (PIK3CA, PTEN, TP53, ATM, BCL6, SOX2) and copy number variations profiles (3p deletion and 3q, 5p, 8q, 20p gain) to anal squamous cell carcinomas. PI3K/Akt/mTOR is the most commonly affected signaling pathway similarly to anal squamous cell carcinoma. In addition, common genomic alterations seen in rectal adenocarcinoma (FLT3, CDX2, GNAS BCL2, SMAD4, MALT1 mutations) were not found in these rectal squamous cases. This study presents the first comprehensive genomic characterization of rectal squamous cell carcinoma. We confirm the existence of this rare histology and its molecular similarity with anal squamous cell carcinoma. This molecular proximity confirms the adequacy of therapeutic management based on histology and not localization.

Clonality assessment and detection of clonal diversity in classic Hodgkin lymphoma by next-generation sequencing of immunoglobulin gene rearrangements

AbstractClonality analysis in classic Hodgkin lymphoma (cHL) is of added value for correctly diagnosing patients with atypical presentation or histology reminiscent of T cell lymphoma, and for establishing the clonal relationship in patients with recurrent disease. However, such analysis has been hampered by the sparsity of malignant Hodgkin and Reed-Sternberg (HRS) cells in a background of reactive immune cells. Recently, the EuroClonality-NGS Working Group developed a novel next-generation sequencing (NGS)-based assay and bioinformatics platform (ARResT/Interrogate) to detect immunoglobulin (IG) gene rearrangements for clonality testing in B-cell lymphoproliferations. Here, we demonstrate the improved performance of IG-NGS compared to conventional BIOMED-2/EuroClonality analysis to detect clonal gene rearrangements in 16 well-characterized primary cHL cases within the IG heavy chain (IGH) and kappa light chain (IGK) loci. This was most obvious in formalin-fixed paraffin-embedded (FFPE) tissue specimens, where three times more clonal cases were detected with IG-NGS (9 cases) compared to BIOMED-2 (3 cases). In total, almost four times more clonal rearrangements were detected in FFPE with IG-NGS (N = 23) as compared to BIOMED-2/EuroClonality (N = 6) as judged on identical IGH and IGK targets. The same clonal rearrangements were also identified in paired fresh frozen cHL samples. To validate the neoplastic origin of the detected clonotypes, IG-NGS clonality analysis was performed on isolated HRS cells, demonstrating identical clonotypes as detected in cHL whole-tissue specimens. Interestingly, IG-NGS and HRS single-cell analysis after DEPArray™ digital sorting revealed rearrangement patterns and copy number variation profiles indicating clonal diversity and intratumoral heterogeneity in cHL. Our data demonstrate improved performance of NGS-based detection of IG gene rearrangements in cHL whole-tissue specimens, providing a sensitive molecular diagnostic assay for clonality assessment in Hodgkin lymphoma.

RecombineX: A generalized computational framework for automatic high-throughput gamete genotyping and tetrad-based recombination analysis.

Meiotic recombination is an essential biological process that ensures faithful chromosome segregation and promotes parental allele shuffling. Tetrad analysis is a powerful approach to quantify the genetic makeups and recombination landscapes of meiotic products. Here we present RecombineX (https://github.com/yjx1217/RecombineX), a generalized computational framework that automates the full workflow of marker identification, gamete genotyping, and tetrad-based recombination profiling based on any organism or genetic background with batch processing capability. Aside from conventional reference-based analysis, RecombineX can also perform analysis based on parental genome assemblies, which facilitates analyzing meiotic recombination landscapes in their native genomic contexts. Additional features such as copy number variation profiling and missing genotype inference further enhance downstream analysis. RecombineX also includes a dedicate module for simulating the genomes and reads of recombinant tetrads, which enables fine-tuned simulation-based hypothesis testing. This simulation module revealed the power and accuracy of RecombineX even when analyzing tetrads with very low sequencing depths (e.g., 1-2X). Tetrad sequencing data from the budding yeast Saccharomyces cerevisiae and green alga Chlamydomonas reinhardtii were further used to demonstrate the accuracy and robustness of RecombineX for organisms with both small and large genomes, manifesting RecombineX as an all-around one stop solution for future tetrad analysis. Interestingly, our re-analysis of the budding yeast tetrad sequencing data with RecombineX and Oxford Nanopore sequencing revealed two unusual structural rearrangement events that were not noticed before, which exemplify the occasional genome instability triggered by meiosis.

Detection of copy number variants and genes by chromosomal microarray in an Emirati neurodevelopmental disorders cohort.

Copy number variations (CNVs) are highly implicated in the etiology of neurodevelopmental disorders (NDDs), and chromosomal microarray analysis (CMA) has been recommended as a first-tier test for many NDDs. We undertook a study to identify clinically relevant CNVs and genes in an ethnically homogenous population of the United Arab Emirates. We genotyped 98 patients with NDDs using genome-wide chromosomal microarray analysis, and observed 47.1% deletion and 52.9% duplication CNVs, of which 11.8% are pathogenic, 23.5% are likely pathogenic, and 64.7% VOUS. The average size of copy number losses (3.9Mb) was generally higher than of gains (738.4kb). Analysis of VOUS CNVs for constrained genes (enrichment for brain critical exons and high pLI genes) yielded 7 unique genes. Among these 7 constrained genes, we propose FNTA and PXK as potential candidate genes for neurodevelopmental disorders, which warrants further investigation. Thirty-two overlapping CNVs (Decipher and ClinVar) containing the FNTA gene were previously identified in NDD patients and 6 overlapping CNVs (Decipher and ClinVar) containing the PXK gene were previously identified in NDD patients. Our study supports the utility of CMA for CNV profiling which aids in precise genetic diagnosis and its integration into therapeutics and management of NDD patients.

Progress in Methods for Copy Number Variation Profiling.

Copy number variations (CNVs) are the predominant class of structural genomic variations involved in the processes of evolutionary adaptation, genomic disorders, and disease progression. Compared with single-nucleotide variants, there have been challenges associated with the detection of CNVs owing to their diverse sizes. However, the field has seen significant progress in the past 20–30 years. This has been made possible due to the rapid development of molecular diagnostic methods which ensure a more detailed view of the genome structure, further complemented by recent advances in computational methods. Here, we review the major approaches that have been used to routinely detect CNVs, ranging from cytogenetics to the latest sequencing technologies, and then cover their specific features.

Comprehensive Genomic and Epigenomic Analyses on Transcriptomic Regulation in Stomach Adenocarcinoma.

Background: DNA methylation (MET)–mediated transcriptomic disturbance and copy number variations (CNVs) exert a significant influence in stimulating the heterogeneous progression of stomach adenocarcinoma (STAD). Nevertheless, the relation of DNA MET with CNVs, together with its impact on tumor occurrence, is still unclear. Methods: The messenger RNA (mRNA) expression (EXP) profiles, DNA MET, and DNA copy numbers, together with STAD mutation data, were collected from the TCGA official data portal. We employed circular binary segmentation algorithm in “DNAcopy.” library of R package for mapping DNA CNV data at genetic level for all samples based on the segmented CNV data. Stable clusters of samples were recognized using negative matrix factorization cluster analysis based on 50 iterations and the “brunet” method using the MET-correlated (METcor) and CNV-correlated (CNVcor) genes. The R package “iCluster” method was utilized to comprehensively analyze the EXP, MET, and DNA CNV profiles. Results: A total of 313 STAD samples were isolated for checking DNA copy numbers and MET and for measuring EXP. In accordance with our results, we discovered obvious co-regulation of CNVcor genes and METcor counterparts. Apart from that, these genes were subject to multi-omics integration. Meanwhile, three subtypes of STAD were detected and confirmed based on independent data. Among them, the subtype with increased aggressiveness was related to decreased mutation frequencies of ARID1A, PIK3CA, ZFHX3, SPECC1, OBSCN, KMT2D, FSIP2, ZBTB20, TTN, and RANBP2, together with the abnormal levels of JPH3, KCNB1, and PLCXD3. Conclusion: According to the results, these aforementioned genes exerted crucial roles in the development of invasive STAD. Our findings on transcriptomic regulation genomically and epigenetically facilitate the understanding of the STAD pathology from different aspects, which help to develop efficient anti-STAD therapy.

DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes.

We developed the DriverFuse package to integrate orthogonal data types such as Structural Variants (SV) and Copy Number Variations (CNV) to characterize fusion genes in cancer datasets. A fusion gene is reported as a driver or passenger fusion gene, based on mapping SV and CNV profiles. DriverFuse generates a fusion plot of fusion genes with their mapping SV, CNV profile, domain architecture and classification of its role in cancer. The analysis facilitates discrimination of driver fusions from passenger fusions. To demonstrate the utility of DriverFuse, we analyzed two datasets, one each for CCLE (Cancer Cell Line Encyclopedia) for lung cancer and HCC1395BL for breast cancer. The analysis validates the driver fusion genes that are already reported for the datasets. Thus, DriverFuse is a valuable tool for studying the driver fusion genes in cancers, enabling the identification of recurrent complex rearrangements that provide intuitive insights into disease driver events.

A New HPV score System Predicts the Survival of Patients With Cervical Cancers.

Persistent high-risk human papillomavirus (hrHPV) infection is confirmed as the major cause of cervical cancer. According to the HPV infection status, cervical cancer could be generalized as following three subgroups: HPV-negative, pure HPV-infection, and HPV-integration. Currently, the impact of HPV status on cervical cancer prognosis remains under dispute. Therefore, we explored the potential correlation between HPV status and the clinical outcome of cervical cancer, by establishing a robust prognostic predicting model based on a cervical cancer cohort using The Cancer Genome Atlas (TCGA) database. We performed an iCluster algorithm incorporating DNA copy number variation, SNP, DNA methylation, mRNA expression, and miRNA expression profile together and classified the cohort into three clusters. According to defined clusters, we established an HPV score system by weighing resultant gene alterations through random forest and COX models. This prediction tool could help to identify cervical cancer prognosis through evaluating individual HPV infection status and subsequent genetic modification, which might provide insights into HPV-related gene driven cervical cancer treatment strategies, yet its predictive power and robustness need to be further verified with independent cohorts.

PATH-34. MOLECULAR AND CLINICAL HETEROGENEITY WITHIN SPINAL EPENDYMOMAS

Abstract Ependymomas encompass multiple clinically relevant tumor types based on localization, genetic alterations, as well as epigenetic and transcriptomic profiles. Distinct global DNA methylation signatures serve as the most powerful diagnostic tool to distinguish these types. The methylation class of spinal ependymomas (SP-EPN) comprises mostly WHO°II tumors with slow progression and incomplete surgical resection rate. Molecular data of SP-EPN are scarce and clear treatment recommendations are lacking although these neoplasms represent the most common intramedullary tumors in children and adults. The only known recurrent genetic events in SP-EPN are the loss of chromosome 22q and mutations of the NF2 gene. However, data on the frequency of NF2 mutations range from 16 % to 71 % and originate from small series that lack epigenetic or transcriptomic characterization. Furthermore, it remains unclear whether SP-EPN with germline or sporadic NF2 mutation or with NF2 wild type status display clinical and other molecular differences. Finally, the underlying genomic and transcriptomic changes of SP-EPN without NF2 mutations are fully unclear. To provide a comprehensive molecular profile of SP-EPN, we integrated genomic and epigenetic analyses and clinical data of 170 cases. Unsupervised hierarchical clustering and t-SNE analyses of methylation data revealed three distinct molecular SP-EPN subtypes. Of the three subtypes, only subtype 1 and subtype 2 contained tumors with NF2 mutations, either as previously known germline mutations or as sporadic mutations without evidence for a syndromic disease (p&amp;lt; 0.0001). Besides the lack of NF2 mutations, subtype 3 tumors showed a higher frequency of MGMT promoter methylation (p= 0.0015) and occurred in significantly older patients compared to tumors of subtypes 1 and 2 (p= 0.0038). Further investigations such as whole-exome sequencing, copy number variation profiling, gene expression analysis, and histological evaluation are ongoing and will add to the picture of molecular and clinical heterogeneity within SP-EPN.

Somatic variant analysis suite: copy number variation clonal visualization online platform for large-scale single-cell genomics.

The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

Whole-genome copy number and immunohistochemical analyses on surgically resected intracholecystic papillary neoplasms.

Intracholecystic papillary neoplasms are newly defined precancerous lesions. According to Classification of the World Health Organization, they have four histological morphologies, which are biliary, gastric, intestinal, and oncocytic. This study evaluated 17 patients with resected intracholecystic papillary neoplasms in terms of histological, immunohistochemical, and copy number variation (CNV). The histological subtypes included 5 cases of low-grade (5 gastric) and 12 cases of high-grade (6 gastric and 6 biliary) neoplasms. Most cases showed high expression of MUC1, MUC5AC, and CK7, moderate expression of MUC6 and Ki-67, and low expression of CK20, MUC2, and CDX2. The CNV profile identified gain of 7q in 12%, and loss of 1p (18%), 5q (29%), 9p (35%), 12p (17%), 17p (24%), and 19p (18%). No CNVs were observed in low-grade neoplasms, whereas high-grade ones had increasing abnormalities. β-catenin was often expressed in the nucleus of neoplasms with gastric morphology, suggesting the involvement of the Wnt/β-catenin pathway. However, it was not expressed among those with biliary morphology, which instead exhibited high p53 expression. Neoplasms with biliary morphology showed more CNV changes (9p, 17p, 19p losses). Distinct immunological and CNV patterns were seen in both morphologies, suggesting differences in their pathogenesis. More CNVs accumulated with tumor progression.

Copy number variation profile-based genomic typing of premenstrual dysphoric disorder in Chinese

Premenstrual dysphoric disorder (PMDD) affects nearly 5% of women of reproductive age. Symptomatic heterogeneity, together with largely unknown genetics, has greatly hindered its effective treatment. In the present study, analysis of genomic sequencing-based copy number variations (CNVs) called from 100 kb white blood cell DNA sequence windows by means of semisupervized clustering led to the segregation of patient genomes into the D and V groups, which correlated with the depression and invasion clinical types, respectively, with 89.0% consistency. Application of diagnostic CNV features selected using the correlation-based machine learning method enabled the classification of the CNVs obtained into the D group, V group, total patient group, and control group with an average accuracy of 83.0%. The power of the diagnostic CNV features was 0.98 on average, suggesting that these CNV features could be used for the molecular diagnosis of the major clinical types of PMDD. This demonstrated concordance between the CNV profiles and clinical types of PMDD supported the validity of symptom-based diagnosis of PMDD for differentiating between its two major clinical types, as well as the predominantly genetic nature of PMDD with a host of overlaps between multiple susceptibility genes/pathways and the diagnostic CNV features as indicators of involvement in PMDD etiology.

Coexpression of ΔNp63/p40 and TTF1 Within Most of the Same Individual Cells Identifies Life-Threatening NSCLC Featuring Squamous and Glandular Biphenotypic Differentiation: Clinicopathologic Correlations

IntroductionDouble occurrence of TTF1 and ΔNp63/p40 (henceforth, p40) within the same individual cells is exceedingly rare in lung cancer. Little is known on their biological and clinical implications. MethodsTwo index cases immunoreactive for both p40 and TTF1 and nine tumors selected from The Cancer Genome Atlas (TCGA) according to the mRNA levels of the two relevant genes entered the study. ResultsThe two index cases were peripherally located, poorly differentiated, and behaviorally unfavorable carcinomas, which shared widespread p40 and TTF1 decoration within the same individual tumor cells. They also retained SMARCA2 and SMARCA4 expression, while variably stained for p53, cytokeratin 5, and programmed death-ligand 1. A subset of basal cells p40+/TTF1+ could be found in normal distal airways. Biphenotypic glandular and squamous differentiation was unveiled by electron microscopy, along with EGFR, RAD51B, CCND3, or NF1 mutations and IGF1R, MYC, CCND1, or CDK2 copy number variations on next-generation sequencing analysis. The nine tumors from TCGA (0.88% of 1018 tumors) shared the same poor prognosis, clinical presentation, and challenging histology and had activated pathways of enhanced angiogenesis and epithelial-mesenchymal transition. Mutation and copy number variation profiles did not differ from the other TCGA tumors. ConclusionsDouble p40+/TTF1+ lung carcinomas are aggressive and likely underrecognized non–small cell carcinomas, whose origin could reside in double-positive distal airway stem-like basal cells through either de novo-basal-like or differentiating cell mechanisms according to a model of epithelial renewal.

Immune-Omics Networks of CD27, PD1, and PDL1 in Non-Small Cell Lung Cancer.

Simple SummaryThere are currently no effective biomarkers to select chemotherapy, immunotherapy, and radiotherapy for treating lung cancer patients. This study identified genetic networks containing major immune-checkpoint inhibitors CD27, PD1, and PDL1, and their associated prognostic genes and proliferation genes in lung cancer tumors. A 5-gene prognostic model was developed and validated in extensive cohorts to select patients at a high risk for developing metastasis. CRISPR-Cas9 and RNA interference screening data were used in the selection of proliferation genes. These genes were associated with chemoresponse and radiotherapy response in lung cancer cell lines and patient tumors. This immune-omics network led to the discovery of repositioning drugs for improving lung cancer treatment.To date, there are no prognostic/predictive biomarkers to select chemotherapy, immunotherapy, and radiotherapy in individual non-small cell lung cancer (NSCLC) patients. Major immune-checkpoint inhibitors (ICIs) have more DNA copy number variations (CNV) than mutations in The Cancer Genome Atlas (TCGA) NSCLC tumors. Nevertheless, CNV-mediated dysregulated gene expression in NSCLC is not well understood. Integrated CNV and transcriptional profiles in NSCLC tumors (n = 371) were analyzed using Boolean implication networks for the identification of a multi-omics CD27, PD1, and PDL1 network, containing novel prognostic genes and proliferation genes. A 5-gene (EIF2AK3, F2RL3, FOSL1, SLC25A26, and SPP1) prognostic model was developed and validated for patient stratification (p < 0.02, Kaplan–Meier analyses) in NSCLC tumors (n = 1163). A total of 13 genes (COPA, CSE1L, EIF2B3, LSM3, MCM5, PMPCB, POLR1B, POLR2F, PSMC3, PSMD11, RPL32, RPS18, and SNRPE) had a significant impact on proliferation in 100% of the NSCLC cell lines in both CRISPR-Cas9 (n = 78) and RNA interference (RNAi) assays (n = 92). Multiple identified genes were associated with chemoresponse and radiotherapy response in NSCLC cell lines (n = 117) and patient tumors (n = 966). Repurposing drugs were discovered based on this immune-omics network to improve NSCLC treatment.

Genetic alternations and immune characteristics in patients with small cell lung cancer

Genetic alternations and immune characteristics in patients with small cell lung cancer

Size matters: Large copy number losses in Hirschsprung disease patients reveal genes involved in enteric nervous system development.

Hirschsprung disease (HSCR) is a complex genetic disease characterized by absence of ganglia in the intestine. HSCR etiology can be explained by a unique combination of genetic alterations: rare coding variants, predisposing haplotypes and Copy Number Variation (CNV). Approximately 18% of patients have additional anatomical malformations or neurological symptoms (HSCR-AAM). Pinpointing the responsible culprits within a CNV is challenging as often many genes are affected. Therefore, we selected candidate genes based on gene enrichment strategies using mouse enteric nervous system transcriptomes and constraint metrics. Next, we used a zebrafish model to investigate whether loss of these genes affects enteric neuron development in vivo. This study included three groups of patients, two groups without coding variants in disease associated genes: HSCR-AAM and HSCR patients without associated anomalies (HSCR-isolated). The third group consisted of all HSCR patients in which a confirmed pathogenic rare coding variant was identified. We compared these patient groups to unaffected controls. Predisposing haplotypes were determined, confirming that every HSCR subgroup had increased contributions of predisposing haplotypes, but their contribution was highest in isolated HSCR patients without RET coding variants. CNV profiling proved that specifically HSCR-AAM patients had larger Copy Number (CN) losses. Gene enrichment strategies using mouse enteric nervous system transcriptomes and constraint metrics were used to determine plausible candidate genes located within CN losses. Validation in zebrafish using CRISPR/Cas9 targeting confirmed the contribution of UFD1L, TBX2, SLC8A1, and MAPK8 to ENS development. In addition, we revealed epistasis between reduced Ret and Gnl1 expression and between reduced Ret and Tubb5 expression in vivo. Rare large CN losses-often de novo-contribute to HSCR in HSCR-AAM patients. We proved the involvement of six genes in enteric nervous system development and Hirschsprung disease.

The Progenetix oncogenomic resource in 2021.

In cancer, copy number aberrations (CNAs) represent a type of nearly ubiquitous and frequently extensive structural genome variations. To disentangle the molecular mechanisms underlying tumorigenesis as well as identify and characterize molecular subtypes, the comparative and meta-analysis of large genomic variant collections can be of immense importance. Over the last decades, cancer genomic profiling projects have resulted in a large amount of somatic genome variation profiles, however segregated in a multitude of individual studies and datasets. The Progenetix project, initiated in 2001, curates individual cancer CNA profiles and associated metadata from published oncogenomic studies and data repositories with the aim to empower integrative analyses spanning all different cancer biologies. During the last few years, the fields of genomics and cancer research have seen significant advancement in terms of molecular genetics technology, disease concepts, data standard harmonization as well as data availability, in an increasingly structured and systematic manner. For the Progenetix resource, continuous data integration, curation and maintenance have resulted in the most comprehensive representation of cancer genome CNA profiling data with 138 663 (including 115 357 tumor) copy number variation (CNV) profiles. In this article, we report a 4.5-fold increase in sample number since 2013, improvements in data quality, ontology representation with a CNV landscape summary over 51 distinctive National Cancer Institute Thesaurus cancer terms as well as updates in database schemas, and data access including new web front-end and programmatic data access. Database URL: progenetix.org

Whole-genome sequencing of single circulating tumor cells from neuroendocrine neoplasms.

Single-cell profiling of circulating tumor cells (CTCs) as part of a minimally invasive liquid biopsy presents an opportunity to characterize and monitor tumor heterogeneity and evolution in individual patients. In this study, we aimed to compare single-cell copy number variation (CNV) data with tissue and define the degree of intra- and inter-patient genomic heterogeneity. We performed next-generation sequencing (NGS) whole-genome CNV analysis of 125 single CTCs derived from seven patients with neuroendocrine neoplasms (NEN) alongside matched white blood cells (WBC), formalin-fixed paraffin-embedded (FFPE), and fresh frozen (FF) samples. CTC CNV profiling demonstrated recurrent chromosomal alterations in previously reported NEN copy number hotspots, including the prognostically relevant loss of chromosome 18. Unsupervised hierarchical clustering revealed CTCs with distinct clonal lineages as well as significant intra- and inter-patient genomic heterogeneity, including subclonal alterations not detectable by bulk analysis and previously unreported in NEN. Notably, we also demonstrated the presence of genomically distinct CTCs according to the enrichment strategy utilized (EpCAM-dependent vs size-based). This work has significant implications for the identification of therapeutic targets, tracking of evolutionary change, and the implementation of CTC-biomarkers in cancer.

Transcriptome and genome evolution during HER2-amplified breast neoplasia

BackgroundThe acquisition of oncogenic drivers is a critical feature of cancer progression. For some carcinomas, it is clear that certain genetic drivers occur early in neoplasia and others late. Why these drivers are selected and how these changes alter the neoplasia’s fitness is less understood.MethodsHere we use spatially oriented genomic approaches to identify transcriptomic and genetic changes at the single-duct level within precursor neoplasia associated with invasive breast cancer. We study HER2 amplification in ductal carcinoma in situ (DCIS) as an event that can be both quantified and spatially located via fluorescence in situ hybridization (FISH) and immunohistochemistry on fixed paraffin-embedded tissue.ResultsBy combining the HER2-FISH with the laser capture microdissection (LCM) Smart-3SEQ method, we found that HER2 amplification in DCIS alters the transcriptomic profiles and increases diversity of copy number variations (CNVs). Particularly, interferon signaling pathway is activated by HER2 amplification in DCIS, which may provide a prolonged interferon signaling activation in HER2-positive breast cancer. Multiple subclones of HER2-amplified DCIS with distinct CNV profiles are observed, suggesting that multiple events occurred for the acquisition of HER2 amplification. Notably, DCIS acquires key transcriptomic changes and CNV events prior to HER2 amplification, suggesting that pre-amplified DCIS may create a cellular state primed to gain HER2 amplification for growth advantage.ConclusionBy using genomic methods that are spatially oriented, this study identifies several features that appear to generate insights into neoplastic progression in precancer lesions at a single-duct level.