Diverse mechanisms of somatic structural variations in human cancer genomes.

Identification of somatic rearrangements in cancer genomes has accelerated through analysis of high-throughput sequencing data. However, characterization of complex structural alterations and their underlying mechanisms remains inadequate. Here, applying an algorithm to predict structural variations from short reads, we report a comprehensive catalog of somatic structural variations and the mechanisms generating them, using high-coverage whole-genome sequencing data from 140 patients across ten tumor types. We characterize the relative contributions of different types of rearrangements and their mutational mechanisms, find that ~20% of the somatic deletions are complex deletions formed by replication errors, and describe the differences between the mutational mechanisms in somatic and germline alterations. Importantly, we provide detailed reconstructions of the events responsible for loss of CDKN2A/B and gain of EGFR in glioblastoma, revealing that these alterations can result from multiple mechanisms even in a single genome and that both DNA double-strand breaks and replication errors drive somatic rearrangements.

CNV-BAC: Copy number Variation Detection in Bacterial Circular Genome.

MOTIVATION: Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias. RESULTS: We show that the replication bias is widespread using ∼200 bacterial WGS data. We develop CNV-BAC (CNV-Bacteria) that can properly normalize the replication bias and other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms. AVAILABILITY AND IMPLEMENTATION: CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

scRMD: imputation for single cell RNA-seq data via robust matrix decomposition.

MOTIVATION: Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant increase of noises, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values can be beneficial in scRNA-seq data analysis. RESULTS: In this article, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method called scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis. AVAILABILITY AND IMPLEMENTATION: The R package scRMD is available at https://github.com/XiDsLab/scRMD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hallmarks of pluripotency.

Stem cells self-renew and generate specialized progeny through differentiation, but vary in the range of cells and tissues they generate, a property called developmental potency. Pluripotent stem cells produce all cells of an organism, while multipotent or unipotent stem cells regenerate only specific lineages or tissues. Defining stem-cell potency relies upon functional assays and diagnostic transcriptional, epigenetic and metabolic states. Here we describe functional and molecular hallmarks of pluripotent stem cells, propose a checklist for their evaluation, and illustrate how forensic genomics can validate their provenance.

CCL15 Recruits Suppressive Monocytes to Facilitate Immune Escape and Disease Progression in Hepatocellular Carcinoma.

Chemokines play a key role in orchestrating the recruitment and positioning of myeloid cells within the tumor microenvironment. However, the tropism regulation and functions of these cells in hepatocellular carcinoma (HCC) are not completely understood. Herein, by scrutinizing the expression of all chemokines in HCC cell lines and tissues, we found that CCL15 was the most abundantly expressed chemokine in human HCC. Further analyses showed that CCL15 expression was regulated by genetic, epigenetic, and microenvironmental factors, and negatively correlated with patient clinical outcome. In addition to promoting tumor invasion in an autocrine manner, CCL15 specifically recruited CCR1+ cells toward HCC invasive margin, approximately 80% of which were CD14+ monocytes. Clinically, a high density of marginal CCR1+ CD14+ monocytes positively correlated with CCL15 expression and was an independent index for dismal survival. Functionally, these tumor-educated monocytes directly accelerated tumor invasion and metastasis through bursting various pro-tumor factors and activating signal transducer and activator of transcription 1/3, extracellular signal-regulated kinase 1/2, and v-akt murine thymoma viral oncogene homolog signaling in HCC cells. Meanwhile, tumor-derived CCR1+ CD14+ monocytes expressed significantly higher levels of programmed cell death-ligand 1, B7-H3, and T-cell immunoglobulin domain and mucin domain-3 that may lead to immune suppression. Transcriptome sequencing confirmed that tumor-infiltrating CCR1+ CD14+ monocytes were reprogrammed to upregulate immune checkpoints, immune tolerogenic metabolic enzymes (indoleamine and arginase), inflammatory/pro-angiogenic cytokines, matrix remodeling proteases, and inflammatory chemokines. Orthotopic animal models confirmed that CCL15-CCR1 axis forested an inflammatory microenvironment enriched with CCR1+ monocytes and led to increased metastatic potential of HCC cells. Conclusion: A complex tumor-promoting inflammatory microenvironment was shaped by CCL15-CCR1 axis in human HCC. Blockade of CCL15-CCR1 axis in HCC could be an effective anticancer therapy.

Spatial and temporal clonal evolution of intrahepatic cholangiocarcinoma.

BACKGROUND & AIMS: Intrahepatic cholangiocarcinoma (ICC) is the second-most lethal primary liver cancer. Little is known about intratumoral heterogeneity (ITH) and its impact on ICC progression. We aimed to investigate the ITH of ICC in the hope of helping to develop new therapeutic strategies. METHODS: We obtained 69 spatially distinct regions from six operable ICCs. Patient-derived primary cancer cells (PDPCs) were established for each region, followed by whole-exome sequencing (WES) and multi-level validation. RESULTS: We observed widespread ITH for both somatic mutations and clonal architecture, shaped by multiple mechanisms, like clonal "illusion", parallel evolution and chromosome instability. A median of 60.3% of mutations were heterogeneous, among which 85% of the driver mutations were located on the branches of tumor phylogenetic trees. Many truncal and clonal driver mutations occurred in tumor suppressor genes, such as TP53, SMARCB1 and PBRM1 that are involved in DNA repair and chromatin-remodeling. Genome doubling occurred in most cases (5/6) after the accumulation of truncal mutations and was shared by all intratumoral sub-regions. In all cases, ongoing chromosomal instability is evident throughout the evolutionary trajectory of ICC. The recurrence of ICC1239 provided evidence to support the polyclonal metastatic seeding in ICC. The change of mutation landscape and internal diversity among subclones during metastasis, such as the loss of chemoresistance mediator, can be used for new treatment strategies. Targeted therapy against truncal alterations, such as IDH1, JAK1, and KRAS mutations and EGFR amplification, was developed in 5/6 patients. CONCLUSIONS: Integrated investigations of spatial ITH and clonal evolution may provide an important molecular foundation for enhanced understanding of tumorigenesis and progression in ICC. LAY SUMMARY: We applied multiregional whole-exome sequencing to investigate the evolution of intrahepatic cholangiocarcinoma (ICC). The results revealed that many factors, such as parallel evolution and chromosome instability, may participate and promote the branch diversity of ICC. Interestingly, in one patient with primary and recurrent metastatic tumors, we found evidence of polyclonal metastatic seeding, indicating that symbiotic communities of multiple clones existed and were maintained during metastasis. More realistically, some truncal alterations, such as IDH1, JAK1, and KRAS mutations and EGFR amplification, could be promising treatment targets in patients with ICC.

Cell Culture System for Analysis of Genetic Heterogeneity Within Hepatocellular Carcinomas and Response to Pharmacologic Agents.

BACKGROUND & AIMS: No targeted therapies have been found to be effective against hepatocellular carcinoma (HCC), possibly due to the large degree of intratumor heterogeneity. We performed genetic analyses of different regions of HCCs to evaluate levels of intratumor heterogeneity and associate alterations with responses to different pharmacologic agents. METHODS: We obtained samples of HCCs (associated with hepatitis B virus infection) from 10 patients undergoing curative resection, before adjuvant therapy, at hospitals in China. We collected 4-9 spatially distinct samples from each tumor (55 regions total), performed histologic analyses, isolated cancer cells, and carried them low-passage culture. We performed whole-exome sequencing, copy-number analysis, and high-throughput screening of the cultured primary cancer cells. We tested responses of an additional 105 liver cancer cell lines to a fibroblast growth factor receptor (FGFR) 4 inhibitor. RESULTS: We identified a total of 3670 non-silent mutations (3192 missense, 94 splice-site variants, and 222 insertions or deletions) in the tumor samples. We observed considerable intratumor heterogeneity and branched evolution in all 10 tumors; the mean percentage of heterogeneous mutations in each tumor was 39.7% (range, 12.9%-68.5%). We found significant mutation shifts toward C>T and C>G substitutions in branches of phylogenetic trees among samples from each tumor (P < .0001). Of note, 14 of the 26 oncogenic alterations (53.8%) varied among subclones that mapped to different branches. Genetic alterations that can be targeted by existing pharmacologic agents (such as those in FGF19, DDR2, PDGFRA, and TOP1) were identified in intratumor subregions from 4 HCCs and were associated with sensitivity to these agents. However, cells from the remaining subregions, which did not have these alterations, were not sensitive to these drugs. High-throughput screening identified pharmacologic agents to which these cells were sensitive, however. Overexpression of FGF19 correlated with sensitivity of cells to an inhibitor of FGFR 4; this observation was validated in 105 liver cancer cell lines (P = .0024). CONCLUSIONS: By analyzing genetic alterations in different tumor regions of 10 HCCs, we observed extensive intratumor heterogeneity. Our patient-derived cell line-based model, integrating genetic and pharmacologic data from multiregional cancer samples, provides a platform to elucidate how intratumor heterogeneity affects sensitivity to different therapeutic agents.

Evaluation of somatic copy number estimation tools for whole-exome sequencing data.

Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed.

SVmine improves structural variation detection by integrative mining of predictions from multiple algorithms.

MOTIVATION: Structural variation (SV) is an important class of genomic variations in human genomes. A number of SV detection algorithms based on high-throughput sequencing data have been developed, but they have various and often limited level of sensitivity, specificity and breakpoint resolution. Furthermore, since overlaps between predictions of algorithms are low, SV detection based on multiple algorithms, an often-used strategy in real applications, has little effect in improving the performance of SV detection. RESULTS: We develop a computational tool called SVmine for further mining of SV predictions from multiple tools to improve the performance of SV detection. SVmine refines SV predictions by performing local realignment and assess quality of SV predictions based on likelihoods of the realignments. The local realignment is performed against a set of sequences constructed from the reference sequence near the candidate SV by incorporating nearby single nucleotide variations, insertions and deletions. A sandwich alignment algorithm is further used to improve the accuracy of breakpoint positions. We evaluate SVmine on a set of simulated data and real data and find that SVmine has superior sensitivity, specificity and breakpoint estimation accuracy. We also find that SVmine can significantly improve overlaps of SV predictions from other algorithms. AVAILABILITY AND IMPLEMENTATION: SVmine is available at https://github.com/xyc0813/SVmine. CONTACT: ruibinxi@math.pku.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants.

Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately. Analysis of simulation data showed that this method outperforms existing methods. We apply this algorithm to low coverage whole-genome sequencing data from peripheral blood of nearly a thousand patients across eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions. We confirm known regions and discover new ones including those covering KMT2C, GOLPH3, ERBB2 and PLAG1 Analysis of colorectal cancer genomes in particular reveals novel recurrent CNVs including deletions at two chromatin-remodeling genes RERE and NPM2 This method will be useful to many researchers interested in profiling CNVs from whole-genome sequencing data.

Pysim-sv: a package for simulating structural variation data with GC-biases.

BACKGROUND: Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking. RESULTS: Pysim-sv is a package for simulating HTS data to evaluate performance of SV detection algorithms. Pysim-sv can introduce a wide spectrum of germline and somatic genomic variations. The package contains functionalities to simulate tumor data with aneuploidy and heterogeneous subclones, which is very useful in assessing algorithm performance in tumor studies. Furthermore, Pysim-sv can introduce GC-bias, the most important and prevalent bias in HTS data, in the simulated HTS data. CONCLUSIONS: Pysim-sv provides an unbiased toolkit for evaluating HTS-based SV detection algorithms.

Comprehensive analysis of the chromatin landscape in Drosophila melanogaster.

Chromatin is composed of DNA and a variety of modified histones and non-histone proteins, which have an impact on cell differentiation, gene regulation and other key cellular processes. Here we present a genome-wide chromatin landscape for Drosophila melanogaster based on eighteen histone modifications, summarized by nine prevalent combinatorial patterns. Integrative analysis with other data (non-histone chromatin proteins, DNase I hypersensitivity, GRO-Seq reads produced by engaged polymerase, short/long RNA products) reveals discrete characteristics of chromosomes, genes, regulatory elements and other functional domains. We find that active genes display distinct chromatin signatures that are correlated with disparate gene lengths, exon patterns, regulatory functions and genomic contexts. We also demonstrate a diversity of signatures among Polycomb targets that include a subset with paused polymerase. This systematic profiling and integrative analysis of chromatin signatures provides insights into how genomic elements are regulated, and will serve as a resource for future experimental investigations of genome structure and function.

Functional genomic analysis of chromosomal aberrations in a compendium of 8000 cancer genomes.

A large database of copy number profiles from cancer genomes can facilitate the identification of recurrent chromosomal alterations that often contain key cancer-related genes. It can also be used to explore low-prevalence genomic events such as chromothripsis. In this study, we report an analysis of 8227 human cancer copy number profiles obtained from 107 array comparative genomic hybridization (CGH) studies. Our analysis reveals similarity of chromosomal arm-level alterations among developmentally related tumor types as well as a number of co-occurring pairs of arm-level alterations. Recurrent ("pan-lineage") focal alterations identified across diverse tumor types show an enrichment of known cancer-related genes and genes with relevant functions in cancer-associated phenotypes (e.g., kinase and cell cycle). Tumor type-specific ("lineage-restricted") alterations and their enriched functional categories were also identified. Furthermore, we developed an algorithm for detecting regions in which the copy number oscillates rapidly between fixed levels, indicative of chromothripsis. We observed these massive genomic rearrangements in 1%-2% of the samples with variable tumor type-specific incidence rates. Taken together, our comprehensive view of copy number alterations provides a framework for understanding the functional significance of various genomic alterations in cancer genomes.

Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion.

DNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders. Array comparative genomic hybridization has been used widely to identify CNVs genome wide, but the next-generation sequencing technology provides an opportunity to characterize CNVs genome wide with unprecedented resolution. In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control. This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion. Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10× coverage, whereas we could only detect large CNVs (> 15 kb) in the array comparative genomic hybridization profiles for the same genome. Eighty percent (14/16) of the small variants tested (110 bp to 14 kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm. We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach. We propose this statistical approach as a principled yet practical and efficient method to estimate CNVs in whole-genome sequencing data.

Directly selecting cell-type marker genes for single-cell clustering analyses.

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes.

Identification of eight genes associated with recurrent patellar dislocation.

The inheritance of recurrent patellar dislocation (RPD) is known, but the susceptible gene remains unidentified. Here, we performed the first whole exome sequencing (WES) cohort study to identify the susceptible genes. The results showed eight genes were associated with this disease. Notably, the carboxypeptidase D (CPD) gene showed the highest relevance based on its gene function and tissue expression. Single-cell sequencing results indicate that the CPD gene is involved in the pathophysiological process of RPD through granulocytes. Implicated pathways include nuclear factor kappa B (NF-κB), mitogen-activated protein kinase (MAPK), and Wnt/ß-catenin signaling, potentially influencing CPD's role in RPD pathogenesis. This study identified the susceptible gene and investigates the potential pathogenesis of RPD, which provided a new prospect for the understanding of RPD. Besides, it would offer the theoretical basis for disease prevention and genetic counseling.

Multi-model analysis of gallbladder cancer reveals the role of OxLDL-absorbing neutrophils in promoting liver invasion.

BACKGROUND: Gallbladder cancer (GBC) is the most common and lethal malignancy of the biliary tract that lacks effective therapy. In many GBC cases, infiltration into adjacent organs or distant metastasis happened long before the diagnosis, especially the direct liver invasion, which is the most common and unfavorable way of spreading. METHODS: Single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), proteomics, and multiplexed immunohistochemistry (mIHC) were performed on GBC across multiple tumor stages to characterize the tumor microenvironment (TME), focusing specifically on the preferential enrichment of neutrophils in GBC liver invasion (GBC-LI). RESULTS: Multi-model Analysis reveals the immunosuppressive TME of GBC-LI that was characterized by the enrichment of neutrophils at the invasive front. We identified the context-dependent transcriptional states of neutrophils, with the Tumor-Modifying state being associated with oxidized low-density lipoprotein (oxLDL) metabolism. In vitro assays showed that the direct cell-cell contact between GBC cells and neutrophils led to the drastic increase in oxLDL uptake of neutrophils, which was primarily mediated by the elevated OLR1 on neutrophils. The oxLDL-absorbing neutrophils displayed a higher potential to promote tumor invasion while demonstrating lower cancer cytotoxicity. Finally, we identified a neutrophil-promoting niche at the invasive front of GBC-LI that constituted of KRT17+ GBC cells, neutrophils, and surrounding fibroblasts, which may help cultivate the oxLDL-absorbing neutrophils. CONCLUSIONS: Our study reveals the existence of a subset of pro-tumoral neutrophils with a unique ability to absorb oxLDL via OLR1, a phenomenon induced through cell-cell contact with KRT17+ GBC cells in GBC-LI.

High-resolution detection of copy number alterations in single cells with HiScanner.

Improvements in single-cell whole-genome sequencing (scWGS) assays have enabled detailed characterization of somatic copy number alterations (CNAs) at the single-cell level. Yet, current computational methods are mostly designed for detecting chromosome-scale changes in cancer samples with low sequencing coverage. Here, we introduce HiScanner (High-resolution Single-Cell Allelic copy Number callER), which combines read depth, B-allele frequency, and haplotype phasing to identify CNAs with high resolution. In simulated data, HiScanner consistently outperforms state-of-the-art methods across various CNA types and sizes. When applied to high-coverage scWGS data from human brain cells, HiScanner shows a superior ability to detect smaller CNAs, uncovering distinct CNA patterns between neurons and oligodendrocytes. For 179 cells we sequenced from longitudinal meningioma samples, integration of CNAs with point mutations revealed evolutionary trajectories of tumor cells. These findings show that HiScanner enables accurate characterization of frequency, clonality, and distribution of CNAs at the single-cell level in both non-neoplastic and neoplastic cells.

Patient-derived tumor-like cell clusters for personalized chemo- and immunotherapies in non-small cell lung cancer.

Many patient-derived tumor models have emerged recently. However, their potential to guide personalized drug selection remains unclear. Here, we report patient-derived tumor-like cell clusters (PTCs) for non-small cell lung cancer (NSCLC), capable of conducting 100-5,000 drug tests within 10 days. We have established 283 PTC models with an 81% success rate. PTCs contain primary tumor epithelium self-assembled with endogenous stromal and immune cells and show a high degree of similarity to the original tumors in phenotypic and genotypic features. Utilizing standardized culture and drug-response assessment protocols, PTC drug-testing assays reveal 89% overall consistency in prospectively predicting clinical outcomes, with 98.1% accuracy distinguishing complete/partial response from progressive disease. Notably, PTCs enable accurate prediction of clinical outcomes for patients undergoing anti-PD1 therapy by combining cell viability and IFN-γ value assessments. These findings suggest that PTCs could serve as a valuable preclinical model for personalized medicine and basic research in NSCLC.