Pathogenic NF1 truncating mutation and copy number alterations in a dedifferentiated liposarcoma with multiple lung metastasis: a case report.

BACKGROUND: Dedifferentiated liposarcoma (DDLPS), which accounts for an estimated 15-20% of liposarcomas, is a high-grade and aggressive malignant neoplasm, exhibiting a poor response to available therapeutic agents. However, genetic alteration profiles of DDLPS as well as the role of NF1 mutations have not been studied extensively. CASE PRESENTATION: The current study reports a patient presenting with rapidly growing DDLPS accompanied by multiple lung and pleural metastases, in whom whole-exome sequencing revealed a NF1 truncating mutation of the known pathogenic variant, c.C7486T, p.R2496X, as well as multiple copy number alterations (CNAs), including the well-known 12q13-15 amplification, and multiple chromothripsis events encompassing potential cancer-related genes. CONCLUSIONS: Our results suggest that, in addition to the 12q13-15 amplification, NF1 inactivation mutation and other CNAs may contribute to DDLPS tumorigenesis accompanied by aggressive clinical features.

Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring.

BACKGROUND: Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias. RESULTS: We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal's superiority to exsiting GC-bias correction methods at any level of subclonal population. CONCLUSIONS: Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.

CSMD1 Shows Complex Patterns of Somatic Copy Number Alterations and Expressions of mRNAs and Target Micro RNAs in Esophageal Squamous Cell Carcinoma.

BACKGROUND: Human Cub and Sushi Multiple Domains 1 (CSMD1) is a novel candidate tumor-suppressor gene that codes for multiple domains, including complement regulatory and adhesion proteins, and has recently been shown to have alterations in multiple cancers. We investigated CSMD1 in esophageal squamous cell carcinoma (ESCC) by performing an integrated analysis on somatic copy number alterations (CNAs), including copy-number gain or loss, allelic imbalance (AI), loss of heterozygosity (LOH), and the expressions of mRNA and its target miRNAs on specimens from the same patients with ESCC. RESULTS: (i) Two-thirds of ESCC patients had all three types of alterations studied-somatic DNA alterations in 70%, and abnormal expressions of CSMD1 RNA in 69% and in target miRNAs in 66%; patterns among these alterations were complex. (ii) In total, 97% of 888 CSMD1 SNPs studied showed somatic DNA alterations, with most located near exons 4-11, 24-25, 39-40, 55-56, and 69-70. (iii) In total, 68% of SNPs with a CNA were correlated with expression of CSMD1. (iv) A total of 33 correlations between non-coding SNPs and expression of CSMD1 target miRs were found. CONCLUSIONS: Our results indicate that the CSMD1 gene may play a role in ESCC through complex patterns of DNA alterations and RNA and miRNA expressions. Alterations in some somatic SNPs in non-coding regions of CSMD1 appear to influence expression of this gene and its target miRNAs.

Comprehensive analyses unveil novel genomic and immunological characteristics of micropapillary pattern in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) usually contains heterogeneous histological subtypes, among which the micropapillary (MIP) subtype was associated with poor prognosis while the lepidic (LEP) subtype possessed the most favorable outcome. However, the genomic features of the MIP subtype responsible for its malignant behaviors are substantially unknown. In this study, eight FFPE samples from LUAD patients were micro-dissected to isolate MIP and LEP components, then sequenced by whole-exome sequencing. More comprehensive analyses involving our samples and public validation cohorts on the two subtypes were performed to better decipher the key biological and evolutionary mechanisms. As expected, the LEP and MIP subtypes exhibited the largest disease-free survival (DFS) differences in our patients. EGFR was found with the highest mutation frequency. Additionally, shared mutations were observed between paired LEP and MIP components from single patients, and recurrent mutations were verified in the Lung-Broad, Lung-OncoSG, and TCGA-LUAD cohorts. Distinct biological processes or pathways were involved in the evolution of the two components. Besides, analyses of copy number variation (CNV) and intratumor heterogeneity (ITH) further discovered the possible immunosurveillance escape, the discrepancy between mutation and CNV level, ITH, and the pervasive DNA damage response and WNT pathway gene alternations in the MIP component. Phylogenetic analysis of five pairs of LEP and MIP components further confirmed the presence of ancestral EGFR mutations. Through comprehensive analyses combining our samples and public cohorts, PTP4A3, NAPRT, and RECQL4 were identified to be co-amplified. Multi-omics data also demonstrated the immunosuppression prevalence in the MIP component. Our results uncovered the evolutionary pattern of the concomitant LEP and MIP components from the same patient that they were derived from the same initiation cells and the pathway-specific mutations acquired after EGFR clonal mutation could shape the subtype-specificity. We also confirmed the immunosuppression prevalence in the MIP subtype by multi-omics data analyses, which may have resulted in its unfavorable prognosis.

A Pipeline for Reconstructing Somatic Copy Number Alternation's Subclonal Population-Based Next-Generation Sequencing Data.

State-of-the-art next-generation sequencing (NGS)-based subclonal reconstruction methods perform poorly on somatic copy number alternations (SCNAs), due to not only it needs to simultaneously estimate the subclonal population frequency and the absolute copy number for each SCNA, but also there exist complex bias and noise in the tumor and its paired normal sequencing data. Both existing NGS-based SCNA detection methods and SCNA's subclonal population frequency inferring tools use the read count on radio (RCR) of tumor to its paired normal as the key feature of tumor sequencing data; however, the sequencing error and bias have great impact on RCR, which leads to a large number of redundant SCNA segments that make the subsequent process of SCNA's subclonal population frequency inferring and subclonal reconstruction time-consuming and inaccurate. We perform a mathematical analysis of the solution number of SCNA's subclonal frequency, and we propose a computational algorithm to reduce the impact of false breakpoints based on it. We construct a new probability model that incorporates the RCR bias correction algorithm, and by stringing it with the false breakpoint filtering algorithm, we construct a whole SCNA's subclonal population reconstruction pipeline. The experimental result shows that our pipeline outperforms the existing subclonal reconstruction programs both on simulated data and TCGA data. Source code is publicly available as a Python package at https://github.com/dustincys/msphy-SCNAClonal.