PyClone: statistical inference of clonal population structure in cancer

OUP accepted manuscript

MOTIVATION

Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference.

RESULTS

In this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration.

AVAILABILITY AND IMPLEMENTATION

https://github.com/CMUSchwartzLab/TUSV-ext.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gallbladder adenocarcinomas undergo subclonal diversification and selection from precancerous lesions to metastatic tumors

We aimed to elucidate the evolutionary trajectories of gallbladder adenocarcinoma (GBAC) using multi-regional and longitudinal tumor samples. Using whole-exome sequencing data, we constructed phylogenetic trees in each patient and analyzed mutational signatures. A total of 11 patients including 2 rapid autopsy cases were enrolled. The most frequently altered gene in primary tumors was ERBB2 and TP53 (54.5%), followed by FBXW7 (27.3%). Most mutations in frequently altered genes in primary tumors were detectable in concurrent precancerous lesions (biliary intraepithelial neoplasia [BilIN]), but a substantial proportion was subclonal. Subclonal diversity was common in BilIN (n=4). However, among subclones in BilIN, a certain subclone commonly shrank in concurrent primary tumors. In addition, selected subclones underwent linear and branching evolution, maintaining subclonal diversity. Combined analysis with metastatic tumors (n=11) identified branching evolution in nine patients (81.8%). Of these, eight patients (88.9%) had a total of 11 subclones expanded at least sevenfold during metastasis. These subclones harbored putative metastasis-driving mutations in cancer-related genes such as SMAD4, ROBO1, and DICER1. In mutational signature analysis, six mutational signatures were identified: 1, 3, 7, 13, 22, and 24 (cosine similarity >0.9). Signatures 1 (age) and 13 (APOBEC) decreased during metastasis while signatures 22 (aristolochic acid) and 24 (aflatoxin) were relatively highlighted. Subclonal diversity arose early in precancerous lesions and clonal selection was a common event during malignant transformation in GBAC. However, selected cancer clones continued to evolve and thus maintained subclonal diversity in metastatic tumors.

Genomic and Transcriptomic Correlates of Thyroid Carcinoma Evolution after BRAF Inhibitor Therapy

Targeted inhibition of BRAF V600E achieves tumor control in a subset of advanced thyroid tumors. Nearly all tumors develop resistance, and some have been observed to subsequently undergo dedifferentiation. The molecular alterations associated with thyroid cancer dedifferentiation in the setting of BRAF inhibition are unknown. We analyzed targeted next-generation sequencing data from 639 advanced, recurrent and/or metastatic thyroid carcinomas, including 15 tumors that were treated with BRAF inhibitor drugs and had tissue sampled during or posttreatment, 8 of which had matched pretherapy samples. Pre- and posttherapy tissues from one additional patient were profiled with whole-exome sequencing and RNA expression profiling. Mutations in genes comprising the SWI/SNF chromatin remodeling complex and the PI3K-AKT-mTOR, MAPK, and JAK-STAT pathways all increased in prevalence across more dedifferentiated thyroid cancer histologies. Of 7 thyroid cancers that dedifferentiated after BRAF inhibition, 6 had mutations in these pathways. These mutations were mostly absent from matched pretreatment samples and were rarely detected in tumors that did not dedifferentiate. Additional analyses in one of the vemurafenib-treated tumors before and after anaplastic transformation revealed the emergence of an oncogenic PIK3CA mutation, activation of ERK signaling, dedifferentiation, and development of an immunosuppressive tumor microenvironment. These findings validate earlier preclinical data implicating these genetic pathways in resistance to BRAF inhibitors, and suggest that genetic alterations mediating acquired drug resistance may also promote thyroid tumor dedifferentiation. IMPLICATIONS: The possibility that thyroid cancer dedifferentiation may be attributed to selective pressure applied by BRAF inhibitor-targeted therapy should be investigated further.

Multi-omics analysis of intra-tumoural and inter-tumoural heterogeneity in pancreatic ductal adenocarcinoma

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is associated with the tumour heterogeneity. To explore intra- and inter-tumoural heterogeneity in PDAC, we analysed the multi-omics profiles of 61 PDAC lesion samples, along with the matched pancreatic normal tissue samples, from 19 PDAC patients. Haematoxylin and Eosin (H&E) staining revealed that diversely differentiated lesions coexisted both within and across individual tumours. Whole exome sequencing (WES) of samples from multi-region revealed diverse types of mutations in diverse genes between cancer cells within a tumour and between tumours from different individuals. The copy number variation (CNV) analysis also showed that PDAC exhibited intra- and inter-tumoural heterogeneity in CNV and that high average CNV burden was associated poor prognosis of the patients. Phylogenetic tree analysis and clonality/timing analysis of mutations displayed diverse evolutionary pathways and spatiotemporal characteristics of genomic alterations between different lesions from the same or different tumours. Hierarchical clustering analysis illustrated higher inter-tumoural heterogeneity than intra-tumoural heterogeneity of PDAC at the transcriptional levels as lesions from the same patients are grouped into a single cluster. Immune marker genes are differentially expressed in different regions and tumour samples as shown by tumour microenvironment (TME) analysis. TME appeared to be more heterogeneous than tumour cells in the same patient. Lesion-specific differentially methylated regions (DMRs) were identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq). Furthermore, the integration analysis of multi-omics data showed that the mRNA levels of some genes, such as PLCB4, were significantly correlated with the gene copy numbers. The mRNA expressions of potential PDAC biomarkers ZNF521 and KDM6A were correlated with copy number alteration and methylation, respectively. Taken together, our results provide a comprehensive view of molecular heterogeneity and evolutionary trajectories of PDAC and may guide personalised treatment strategies in PDAC therapy.

BioVLAB-Cancer-Pharmacogenomics: tumor heterogeneity and pharmacogenomics analysis of multi-omics data from tumor on the cloud

MOTIVATION

Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline.

RESULTS

We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools.

AVAILABILITY AND IMPLEMENTATION

http://biohealth.snu.ac.kr/software/biovlab_cancer_pharmacogenomics.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clonal architecture predicts clinical outcomes and drug sensitivity in acute myeloid leukemia

The impact of clonal heterogeneity on disease behavior or drug response in acute myeloid leukemia remains poorly understood. Using a cohort of 2,829 patients, we identify features of clonality associated with clinical features and drug sensitivities. High variant allele frequency for 7 mutations (including NRAS and TET2) associate with dismal prognosis; elevated GATA2 variant allele frequency correlates with better outcomes. Clinical features such as white blood cell count and blast percentage correlate with the subclonal abundance of mutations such as TP53 and IDH1. Furthermore, patients with cohesin mutations occurring before NPM1, or transcription factor mutations occurring before splicing factor mutations, show shorter survival. Surprisingly, a branched pattern of clonal evolution is associated with superior clinical outcomes. Finally, several mutations (including NRAS and IDH1) predict drug sensitivity based on their subclonal abundance. Together, these results demonstrate the importance of assessing clonal heterogeneity with implications for prognosis and actionable biomarkers for therapy.

Intratumor heterogeneity and clonal evolution revealed in castration-resistant prostate cancer by longitudinal genomic analysis

Intratumor heterogeneity is a key driver for local relapse and treatment failure. Thus, using multifocal prostate cancer as a model to investigate tumor inter-clonal relationships and tumor evolution could aid in our understanding of drug resistance. Previous studies discovered genomic alterations by comparing hormone-sensitive prostate cancer (HSPC) with castration-resistant prostate cancer (CRPC) in large cohorts. However, most studies did not sequentially sample tumors from the same patient. In our study, we performed whole-exome sequencing (WES) on 14 specimens from five locally relapsed patients before and after androgen-deprivation therapy. We described the landscape of genomic alterations before and after treatment and identified critical driver events that could have contributed to the evolution of CRPC. In addition to confirming known cancer genes such as TP53 and CDK12, we also identified new candidate genes that may play a role in the progression of prostate cancer, including MYO15A, CHD6 and LZTR1. At copy number alteration (CNA) level, gain of 8q24.13-8q24.3 was observed in 60% of patients and was the most commonly altered locus in both HSPC and CRPC tumors. Finally, utilizing phylogenetic reconstruction, we explored the clonal progression pattern from HSPC to CRPC in each patient. Our findings highlight the complex and heterogeneous mechanisms underlying the development of drug resistance, and underscore the potential value of monitoring tumor clonal architectures during disease progression in a clinical setting.

The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conduct whole-exome sequencing and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations are shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrate similar subclonal architecture. On the other hand, transcriptomic profiling reveals shared pathways between the same histologic subtypes from different patients, which is supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data derived from mixed histologic subtypes in the setting of identical genetic background and exposure history support that the histologic fate of lung cancer cells is associated with transcriptomic features rather than the genomic profiles in most tumors.

The molecular determinants of lung cancer histologic subtypes are not well understood. Here the authors analyze lung cancers of mixed histology and find that histologic subtypes are associated with transcriptomic features rather than genomic profiles in most tumors.

Clone decomposition based on mutation signatures provides novel insights into mutational processes

Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods against artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.

Neoantigen-reactive CD8+ T cells affect clinical outcome of adoptive transfer with tumor-infiltrating lymphocytes in melanoma

BACKGROUND

Neoantigen-driven recognition and T cell-mediated killing contribute to tumor clearance following adoptive cell therapy (ACT) with Tumor-Infiltrating Lymphocytes (TILs). Yet, how diversity, frequency, and persistence of expanded neoepitope-specific CD8+ T cells derived from TIL infusion products affect patient outcome is not fully determined.

METHODS

Using barcoded pMHC multimers, we provide a comprehensive mapping of CD8+ T cells recognizing neoepitopes in TIL infusion products and blood samples from 26 metastatic mela-noma patients who received ACT.

RESULTS

We identified 106 neoepitopes within TIL infusion products corresponding to 1.8% of all predicted neoepitopes. We observed neoepitope-specific recognition to be virtually devoid in TIL infusion products given to patients with progressive disease outcome. Moreover, we found that the frequency of neoepitope-specific CD8+ T cells in TIL infusion products correlated with in-creased survival, and that detection of engrafted CD8+ T cells in post-treatment (i.e. originating from the TIL infusion product) were unique to responders of TIL-ACT. Finally, we found that a transcriptional signature for lymphocyte activity within the tumor microenvironment was associated with a higher frequency of neoepitope-specific CD8+ T cells in the infusion product.

CONCLUSIONS

These data support previous case studies of neoepitope-specific CD8+ T cells in melanoma, and indicate that successful TIL-ACT is associated with an expansion of neoepitope-specific CD8+ T cells.

FUNDING

NEYE Foundation; European Research Council; Lundbeck Foundation Fellowship; Carlsberg Foundation.

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder

Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets.

Cold and heterogeneous T cell repertoire is associated with copy number aberrations and loss of immune genes in small-cell lung cancer

Small-cell lung cancer (SCLC) is speculated to harbor complex genomic intratumor heterogeneity (ITH) associated with high recurrence rate and suboptimal response to immunotherapy. Here, using multi-region whole exome/T cell receptor (TCR) sequencing as well as immunohistochemistry, we reveal a rather homogeneous mutational landscape but extremely cold and heterogeneous TCR repertoire in limited-stage SCLC tumors (LS-SCLCs). Compared to localized non-small cell lung cancers, LS-SCLCs have similar predicted neoantigen burden and genomic ITH, but significantly colder and more heterogeneous TCR repertoire associated with higher chromosomal copy number aberration (CNA) burden. Furthermore, copy number loss of IFN-γ pathway genes is frequently observed and positively correlates with CNA burden. Higher mutational burden, higher T cell infiltration and positive PD-L1 expression are associated with longer overall survival (OS), while higher CNA burden is associated with shorter OS in patients with LS-SCLC.

A common methodological phylogenomics framework for intra-patient heteroplasmies to infer SARS-CoV-2 sublineages and tumor clones

BACKGROUND

All diseases containing genetic material undergo genetic evolution and give rise to heterogeneity including cancer and infection. Although these illnesses are biologically very different, the ability for phylogenetic retrodiction based on the genomic reads is common between them and thus tree-based principles and assumptions are shared. Just as the different frequencies of tumor genomic variants presupposes the existence of multiple tumor clones and provides a handle to computationally infer them, we postulate that the different variant frequencies in viral reads offers the means to infer multiple co-infecting sublineages.

RESULTS

We present a common methodological framework to infer the phylogenomics from genomic data, be it reads of SARS-CoV-2 of multiple COVID-19 patients or bulk DNAseq of the tumor of a cancer patient. We describe the Concerti computational framework for inferring phylogenies in each of the two scenarios.To demonstrate the accuracy of the method, we reproduce some known results in both scenarios. We also make some additional discoveries.

CONCLUSIONS

Concerti successfully extracts and integrates information from multi-point samples, enabling the discovery of clinically plausible phylogenetic trees that capture the heterogeneity known to exist both spatially and temporally. These models can have direct therapeutic implications by highlighting "birth" of clones that may harbor resistance mechanisms to treatment, "death" of subclones with drug targets, and acquisition of functionally pertinent mutations in clones that may have seemed clinically irrelevant. Specifically in this paper we uncover new potential parallel mutations in the evolution of the SARS-CoV-2 virus. In the context of cancer, we identify new clones harboring resistant mutations to therapy.

Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST

Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent à priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth (~50×). Given at least one pure tumor sample (>70% purity), we could normalize and deconvolve paired samples down to a purity of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumors, which are often aneuploid and contaminated by non-cancer cells.

Benchmarking pipelines for subclonal deconvolution of bulk tumour sequencing data

Intratumour heterogeneity provides tumours with the ability to adapt and acquire treatment resistance. The development of more effective and personalised treatments for cancers, therefore, requires accurate characterisation of the clonal architecture of tumours, enabling evolutionary dynamics to be tracked. Many methods exist for achieving this from bulk tumour sequencing data, involving identifying mutations and performing subclonal deconvolution, but there is a lack of systematic benchmarking to inform researchers on which are most accurate, and how dataset characteristics impact performance. To address this, we use the most comprehensive tumour genome simulation tool available for such purposes to create 80 bulk tumour whole exome sequencing datasets of differing depths, tumour complexities, and purities, and use these to benchmark subclonal deconvolution pipelines. We conclude that i) tumour complexity does not impact accuracy, ii) increasing either purity or purity-corrected sequencing depth improves accuracy, and iii) the optimal pipeline consists of Mutect2, FACETS and PyClone-VI. We have made our benchmarking datasets publicly available for future use.

Characterizing Intra-Tumor Heterogeneity From Somatic Mutations Without Copy-Neutral Assumption

Bulk samples of the same patient are heterogeneous in nature, comprising of different subpopulations (subclones) of cancer cells. Cells in a tumor subclone are characterized by unique mutational genotype profile. Resolving tumor heterogeneity by estimating the genotypes, cellular proportions and the number of subclones present in the tumor can help in understanding cancer progression and treatment. We present a novel method, ChaClone2, to efficiently deconvolve the observed variant allele fractions (VAFs), with consideration for possible effects from copy number aberrations at the mutation loci. Our method describes a state-space formulation of the feature allocation model, deconvolving the observed VAFs from samples of the same patient into three matrices: subclonal total and variant copy numbers for mutated genes, and proportions of subclones in each sample. We describe an efficient sequential Monte Carlo (SMC) algorithm to estimate these matrices. Extensive simulation shows that the ChaClone2 yields better accuracy when compared with other state-of-the-art methods for addressing similar problem and it offers scalability to large datasets. Also, ChaClone2 features that the model parameter estimates can be refined whenever new mutation data of freshly sequenced genomic locations are available. MATLAB code and datasets are available to download at: https://github.com/moyanre/method2.

Dynamic phenotypic heterogeneity and the evolution of multiple RNA subtypes in hepatocellular carcinoma: the PLANET study

Intra-tumor heterogeneity (ITH) is a key challenge in cancer treatment, but previous studies have focused mainly on the genomic alterations without exploring phenotypic (transcriptomic and immune) heterogeneity. Using one of the largest prospective surgical cohorts for hepatocellular carcinoma (HCC) with multi-region sampling, we sequenced whole genomes and paired transcriptomes from 67 HCC patients (331 samples). We found that while genomic ITH was rather constant across stages, phenotypic ITH had a very different trajectory and quickly diversified in stage II patients. Most strikingly, 30% of patients were found to contain more than one transcriptomic subtype within a single tumor. Such phenotypic ITH was found to be much more informative in predicting patient survival than genomic ITH and explains the poor efficacy of single-target systemic therapies in HCC. Taken together, we not only revealed an unprecedentedly dynamic landscape of phenotypic heterogeneity in HCC, but also highlighted the importance of studying phenotypic evolution across cancer types.

The Mutagenic Impact of Environmental Exposures in Human Cells and Cancer: Imprints Through Time

During life, the DNA of our cells is continuously exposed to external damaging processes. Despite the activity of various repair mechanisms, DNA damage eventually results in the accumulation of mutations in the genomes of our cells. Oncogenic mutations are at the root of carcinogenesis, and carcinogenic agents are often highly mutagenic. Over the past decade, whole genome sequencing data of healthy and tumor tissues have revealed how cells in our body gradually accumulate mutations because of exposure to various mutagenic processes. Dissection of mutation profiles based on the type and context specificities of the altered bases has revealed a variety of signatures that reflect past exposure to environmental mutagens, ranging from chemotherapeutic drugs to genotoxic gut bacteria. In this review, we discuss the latest knowledge on somatic mutation accumulation in human cells, and how environmental mutagenic factors further shape the mutation landscapes of tissues. In addition, not all carcinogenic agents induce mutations, which may point to alternative tumor-promoting mechanisms, such as altered clonal selection dynamics. In short, we provide an overview of how environmental factors induce mutations in the DNA of our healthy cells and how this contributes to carcinogenesis. A better understanding of how environmental mutagens shape the genomes of our cells can help to identify potential preventable causes of cancer.

DeCiFering the subclonal composition of tumors

Accurately identifying the subclones that make up tumors is critical for understanding cancer biology. In an article in this issue of Cell Systems, Satas et al. examine mutations with an evolutionary perspective to decipher the composition of tumors.

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples.

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.

Establishment of a near-contiguous genome sequence of the citric acid producing yeast Yarrowia lipolytica DSM 3286 with resolution of rDNA clusters and telomeres

Yarrowia lipolytica is an oleaginous yeast that is particularly suitable for the sustainable production of secondary metabolites. The genome of this yeast is characterized by its relatively large size and its high number of different rDNA clusters located in its telomeric regions. However, due to the presence of long repetitive elements in the sub-telomeric regions, rDNA clusters and telomeres are missing in current genome assemblies of Y. lipolytica. Here, we present the near-contiguous genome sequence of the biotechnologically relevant strain DSM 3286. We employed a hybrid assembly strategy combining Illumina and nanopore sequencing reads to integrate all six rDNA clusters as well as telomeric repeats into the genome sequence. By fine-tuning of DNA isolation and library preparation protocols, we were able to create ultra-long reads that not only contained multiples of mitochondrial genomes but also shed light on the inter- and intra-chromosomal diversity of rDNA cluster types. We show that there are ten different rDNA units present in this strain that additionally appear in a predefined order in a cluster. Based on single reads, we also demonstrate that the number of rDNA repeats in a specific cluster varies from cell to cell within a population.

Case Report: Systemic Treatment and Serial Genomic Sequencing of Metastatic Prostate Adenocarcinoma Progressing to Small Cell Carcinoma

Small cell carcinoma (SCC)/neuroendocrine prostate cancer (NEPC) is a rare and highly aggressive subtype of prostate cancer associated with an AR(androgen receptor)-null phenotype and visceral metastases. This study presents a 44-year-old man originally diagnosed with metastatic hormone-sensitive prostatic adenocarcinoma. After 6-month androgen deprivation therapy (ADT) combined with docetaxel, the patient developed paraplegia. Laminectomy was performed, and a thoracic vertebral biopsy revealed neuroendocrine differentiation and mixed adenocarcinoma. The patient developed liver metastases and experienced stable disease for 4 months following etoposide combined with cisplatin and pembrolizumab. Seminal vesicle biopsy after chemotherapy revealed small-cell cancer. The prostate biopsy specimen also indicated pure SCC. We witnessed the dynamic evolution from pure adenocarcinoma to fully differentiated SCC, leading to obstruction and death. In addition, whole-exome sequencing was performed on both biopsy specimens of the thoracic vertebra at the beginning of castration resistance and that of seminal vesicle after multiple lines of treatment failure. Utilizing phylogenetic reconstruction, we observed that both samples shared a common ancestor clone harboring aberrations in the TP53, RB1, and NF2 genes. We also discovered that driver events in the private subclones of both samples, such as alterations in CDC27 and RUNX1, might have played a significant role in tumor progression or even neuroendocrine differentiation. Tumor biopsy and IHC assessment must be repeated at different stages of progression, because of intrapatient spatial and temporal heterogeneity of adenocarcinoma versus SCC/NEPC. Although, typical treatments including ADT, docetaxel, etoposide, cisplatin, and pembrolizumab provided temporary response, the patient still had a poor prognosis.

Genomic comparison between cerebrospinal fluid and primary tumor revealed the genetic events associated with brain metastasis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is most common pathological type of lung cancer. LUAD with brain metastases (BMs) usually have poor prognosis. To identify the potential genetic factors associated with BM, a genomic comparison for BM cerebrospinal fluid (CSF) and primary lung tumor samples obtained from 1082 early- and late-stage LUAD patients was performed. We found that single nucleotide variation (SNV) of EGFR was highly enriched in CSF (87% of samples). Compared with the other primary lung tissues, copy number gain of EGFR (27%), CDK4 (11%), PMS2 (11%), MET (10%), IL7R (8%), RICTOR (7%), FLT4 (5%), and FGFR4 (4%), and copy number loss of CDKN2A (28%) and CDKN2B (18%) were remarkably more frequent in CSF samples. CSF had significantly lower tumor mutation burden (TMB) level but more abundant copy number variant. It was also found that the relationships among co-occurrent and mutually exclusive genes were dynamically changing with LUAD development. Additionally, CSF (97% of samples) harbored more abundant targeted drugs related driver and fusion genes. The signature 15 associated with defective DNA mismatch repair (dMMR) was only identified in the CSF group. Cancer associated pathway analysis further revealed that ErbB (95%) and cell cycle (84%) were unique pathways in CSF samples. The tumor evolution analysis showed that CSF carried significantly fewer clusters, but subclonal proportion of EGFR was remarkably increased with tumor progression. Collectively, CSF sequencing showed unique genomic characteristics and the intense copy number instability associated with cell cycle disorder and dMMR might be the crucial genetic factors in BM of LUAD.

Integrated analysis reveals prognostic value of HLA-I LOH in triple-negative breast cancer

Background

Triple-negative breast cancers (TNBCs), especially those non-immune-inflamed tumors, have a poor prognosis and limited therapies. Human leukocyte antigen (HLA)-I not only contributes to antitumor immune response and the phenotype of the tumor microenvironment, but also is a negative predictor of outcomes after immunotherapy. However, the importance of HLA functional status in TNBCs remains poorly understood.

Methods

Using the largest original multiomics datasets on TNBCs, we systematically characterized the HLA-Ⅰ status of TNBCs from the perspective of HLA-Ⅰ homogeneity and loss of heterozygosity (LOH). The prognostic significance of HLA-I status was measured. To explain the potential mechanism of prognostic value in HLA-Ⅰ status, the mutational signature, copy number alteration, neoantigen and intratumoral heterogeneity were measured. Furthermore, the correlation between HLA-Ⅰ functional status and the tumor immune microenvironment was analyzed.

Results

LOH and homogeneity in HLA-I accounted for 18% and 21% of TNBCs, respectively. HLA-I LOH instead of HLA-I homogeneity was an independent prognostic biomarker in TNBCs. In particular, for patients with non-immune-inflamed tumors, HLA-I LOH indicated a worse prognosis than HLA-I non-LOH. Furthermore, integrated genomic and transcriptomic analysis showed that HLA-I LOH was accompanied by upregulated scores of mutational signature 3 and homologous recombination deficiency scores, which implied the failure of DNA double-strand break repair. Moreover, HLA-I LOH had higher mutation and neoantigen loads and more subclones than HLA-I non-LOH. These results indicated that although HLA-I LOH tumors with failure of DNA double-strand break repair were prone to produce neoantigens, their limited capacity for antigen presentation finally contributed to poor immune selection pressure.

Conclusion

Our study illustrates the genomic landscape of HLA-I functional status and stresses the prognostic significance of HLA-I LOH in TNBCs. For “cold” tumors in TNBCs, HLA-I LOH indicated a worse prognosis than HLA-I non-LOH.

The Feasibility of Using Biomarkers Derived from Circulating Tumor DNA Sequencing as Predictive Classifiers in Patients with Small-Cell Lung Cancer

Purpose

To investigate the feasibility of biomarkers based on dynamic circulating tumor DNA (ctDNA) to classify small cell lung cancer (SCLC) into different subtypes.

Materials and Methods

Tumor and longitudinal plasma ctDNA samples were analyzed by next-generation sequencing of 1,021 genes. PyClone was used to infer the molecular tumor burden index (mTBI). Pre-treatment tumor tissues [T1] and serial plasma samples were collected (pre-treatment [B1], after two [B2], six [B3] cycles of chemotherapy and at progression [B4]).

Results

Overall concordance between T1 and B1 sequencing (n=30) was 66.5%, and 89.5% in the gene of RB1. A classification method was designed according to the changes of RB1 mutation, named as subtype Ⅰ (both positive at B1 and B2), subtype Ⅱ (positive at B1 but negative at B2), and subtype Ⅲ (both negative at B1 and B2). The median progressive-free survival for subtype Ⅰ patients (4.5 months [95%CI: 2.6-5.8]) was inferior to subtype Ⅱ (not reached, p<0.0001) and subtype Ⅲ (10.8 months [95%CI: 6.0-14.4], p=0.002). The median overall survival for subtype Ⅰ patients (16.3 months [95%CI: 5.3-22.9]) was inferior to subtype Ⅱ (not reached, p=0.01) and subtype Ⅲ (not reached, p=0.02). Patients with a mTBI dropped to zero at B2 had longer median overall survival (not reached vs. 19.5 months, p=0.01). The changes of mTBI from B4 to B1 were sensitive to predict new metastases, with a sensitivity of 100% and a specificity of 85.7%.

Conclusion

Monitoring ctDNA based RB1 mutation and mTBI provided a feasible tool to predict the prognosis of SCLC.

Indication-specific tumor evolution and its impact on neoantigen targeting and biomarkers for individualized cancer immunotherapies

Background

Individualized neoantigen-specific immunotherapy (iNeST) requires robustly expressed clonal neoantigens for efficacy, but tumor mutational heterogeneity, loss of neoantigen expression, and variable tissue sampling present challenges. It is assumed that clonal neoantigens are preferred targets for immunotherapy, but the distributions of clonal neoantigens are not well characterized across cancer types.

Methods

We combined multiregion sequencing (MR-seq) analysis of five untreated, synchronously sampled metastatic solid tumors with re-analysis of published MR-seq data from 103 patients in order to characterize their globally clonal neoantigen content and factors that would impact neoantigen targeting.

Results

Branching evolution in colorectal cancer and renal cell carcinoma led to fewer clonal neoantigens and to clade-specific neoantigens (those shared across a subset of tumor regions but not fully clonal), with the latter not being readily distinguishable in single tumor samples. In colorectal, renal, and bladder cancer, most tumors had few globally clonal neoantigens. Prioritizing mutations with higher purity-adjusted and ploidy-adjusted variant allele frequency enriched for globally clonal neoantigens (those found in all tumor regions), whereas estimated cancer cell fraction derived from clustering-based tools, surprisingly, did not. Neoantigen quality was associated with loss of neoantigen expression in the bladder cancer case, and HLA-allele loss was observed in the renal and non-small cell lung cancer cases.

Conclusions

We show that tumor type, multilesion sampling, neoantigen expression, and HLA allele retention are important factors for iNeST targeting and patient selection, and may also be important factors to consider in the development of biomarker strategies.

Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts

BACKGROUND & AIMS

Colorectal cancer (CRC) shows variable response to immune checkpoint blockade, which can only partially be explained by high tumor mutational burden (TMB). We conducted an integrated study of the cancer tissue and associated tumor microenvironment (TME) from patients treated with pembrolizumab (KEYNOTE 177 clinical trial) or nivolumab to dissect the cellular and molecular determinants of response to anti- programmed cell death 1 (PD1) immunotherapy.

METHODS

We selected multiple regions per tumor showing variable T-cell infiltration for a total of 738 regions from 29 patients, divided into discovery and validation cohorts. We performed multiregional whole-exome and RNA sequencing of the tumor cells and integrated these with T-cell receptor sequencing, high-dimensional imaging mass cytometry, detection of programmed death-ligand 1 (PDL1) interaction in situ, multiplexed immunofluorescence, and computational spatial analysis of the TME.

RESULTS

In hypermutated CRCs, response to anti-PD1 immunotherapy was not associated with TMB but with high clonality of immunogenic mutations, clonally expanded T cells, low activation of Wnt signaling, deregulation of the interferon gamma pathway, and active immune escape mechanisms. Responsive hypermutated CRCs were also rich in cytotoxic and proliferating PD1+CD8 T cells interacting with PDL1+ antigen-presenting macrophages.

CONCLUSIONS

Our study clarified the limits of TMB as a predictor of response of CRC to anti-PD1 immunotherapy. It identified a population of antigen-presenting macrophages interacting with CD8 T cells that consistently segregate with response. We therefore concluded that anti-PD1 agents release the PD1-PDL1 interaction between CD8 T cells and macrophages to promote cytotoxic antitumor activity.

Clonal Architecture and Evolutionary Dynamics in Acute Myeloid Leukemias

Acute myeloid leukemias (AML) results from the accumulation of genetic and epigenetic alterations, often in the context of an aging hematopoietic environment. The development of high-throughput sequencing-and more recently, of single-cell technologies-has shed light on the intratumoral diversity of leukemic cells. Taking AML as a model disease, we review the multiple sources of genetic, epigenetic, and functional heterogeneity of leukemic cells and discuss the definition of a leukemic clone extending its definition beyond genetics. After introducing the two dimensions contributing to clonal diversity, namely, richness (number of leukemic clones) and evenness (distribution of clone sizes), we discuss the mechanisms at the origin of clonal emergence (mutation rate, number of generations, and effective size of the leukemic population) and the causes of clonal dynamics. We discuss the possible role of neutral drift, but also of cell-intrinsic and -extrinsic influences on clonal fitness. After reviewing available data on the prognostic role of genetic and epigenetic diversity of leukemic cells on patients' outcome, we discuss how a better understanding of AML as an evolutionary process could lead to the design of novel therapeutic strategies in this disease.

Activity of Wnt/PCP Regulation Pathway Classifies Patients of Low-Grade Glioma Into Molecularly Distinct Subgroups With Prognostic Difference

Wingless/Int-1 (Wnt) signaling is one of the most well-known oncogenic pathways. Numerous studies have uncovered an aberrant expression of Wnt in cancer and its association with multiple oncogenic processes, such as cell proliferation, epithelial–mesenchymal transition (EMT), and invasiveness. Most previous studies mainly focused on the canonical branch of Wnt signaling pathway, i.e., Wnt/β-catenin signaling. The Wnt/planar cell polarity (PCP) signaling pathway, as the most recently described branch of Wnt signaling, was much less investigated in oncology research. In this study, we thoroughly characterized the activity of the Wnt/PCP regulation pathway in low-grade glioma (LGG) patients. Subtyping based on the expression pattern of the Wnt/PCP regulation pathway revealed three (C1–C3) subgroups with significant survival differences. Each group displayed distinct genomic characteristics. For instance, C1 was enriched with capicua transcriptional repressor (CIC) truncating mutations and 1p19q codel. C2 was characterized with tumor protein p53 (TP53) and ATRX chromatin remodeler (ATRX) inactivating mutations but depletion of telomerase reverse transcriptase (TERT) promoter mutations. C3 showed elevated malignancy reflected from several oncogenic characteristics, such as tumor heterogeneity and cell stemness, and demonstrated the worst survival outcome. In addition, C3 showed elevated macrophage segregation via induction of cytokines that are able to enhance the permeability of the brain–blood barrier (BBB). Lastly, we developed a prognostic model based on the risk score system. Validation indicated that our model can independently predict the prognosis of LGG patients.

DEVOLUTION-A method for phylogenetic reconstruction of aneuploid cancers based on multiregional genotyping data

Phylogenetic reconstruction of cancer cell populations remains challenging. There is a particular lack of tools that deconvolve clones based on copy number aberration analyses of multiple tumor biopsies separated in time and space from the same patient. This has hampered investigations of tumors rich in aneuploidy but few point mutations, as in many childhood cancers and high-risk adult cancer. Here, we present DEVOLUTION, an algorithm for subclonal deconvolution followed by phylogenetic reconstruction from bulk genotyping data. It integrates copy number and sequencing information across multiple tumor regions throughout the inference process, provided that the mutated clone fraction for each mutation is known. We validate DEVOLUTION on data from 56 pediatric tumors comprising 253 tumor biopsies and show a robust performance on simulations of bulk genotyping data. We also benchmark DEVOLUTION to similar bioinformatic tools using an external dataset. DEVOLUTION holds the potential to facilitate insights into the development, progression, and response to treatment, particularly in tumors with high burden of chromosomal copy number alterations.

CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data

Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.

Genomic and epigenomic evolution of acquired resistance to combination therapy in esophageal squamous cell carcinoma

BACKGROUNDTargeted arterial infusion of verapamil combined with chemotherapy (TVCC) is an effective clinical interventional therapy for esophageal squamous cell carcinoma (ESCC), but multidrug resistance (MDR) remains the major cause of relapse or poor prognosis, and the underlying molecular mechanisms of MDR, temporal intratumoral heterogeneity, and clonal evolutionary processes of resistance have not been determined.METHODSTo elucidate the roles of genetic and epigenetic alterations in the evolution of acquired resistance during therapies, we performed whole-exome sequencing on 16 serial specimens from 7 patients with ESCC at every cycle of therapeutic intervention from 3 groups, complete response, partial response, and progressive disease, and we performed whole-genome bisulfite sequencing for 3 of these 7 patients, 1 patient from each group.RESULTSPatients with progressive disease exhibited a substantially higher genomic and epigenomic temporal heterogeneity. Subclonal expansions driven by the beneficial new mutations were observed during combined therapies, which explained the emergence of MDR. Notably, SLC7A8 was identified as a potentially novel MDR gene, and functional assays demonstrated that mutant SLC7A8 promoted the resistance phenotypes of ESCC cell lines. Promoter methylation dynamics during treatments revealed 8 drug resistance protein-coding genes characterized by hypomethylation in promoter regions. Intriguingly, promoter hypomethylation of SLC8A3 and mutant SLC7A8 were enriched in an identical pathway, protein digestion and absorption, indicating a potentially novel MDR mechanism during treatments.CONCLUSIONOur integrated multiomics investigations revealed the dynamics of temporal genetic and epigenetic inter- and intratumoral heterogeneity, clonal evolutionary processes, and epigenomic changes, providing potential MDR therapeutic targets in treatment-resistant patients with ESCC during combined therapies.FUNDINGNational Natural Science Foundation of China, Science Foundation of Peking University Cancer Hospital, CAMS Innovation Fund for Medical Sciences, Major Program of Shenzhen Bay Laboratory, Guangdong Basic and Applied Basic Research Foundation, and the third round of public welfare development and reform pilot projects of Beijing Municipal Medical Research Institutes.

The genomic architecture of EBV and infected gastric tissue from precursor lesions to carcinoma

BACKGROUND

Epstein-Barr virus (EBV)-associated gastric carcinomas (EBVaGCs) present unique molecular signatures, but the tumorigenesis of EBVaGCs and the role EBV plays during this process remain poorly understood.

METHODS

We applied whole-exome sequencing, EBV genome sequencing, and whole-genome bisulfite sequencing to multiple samples (n = 123) derived from the same patients (n = 25), which covered saliva samples and different histological stages from morphologically normal epithelial tissues to dysplasia and EBVaGCs. We compared the genomic landscape between EBVaGCs and their precursor lesions and traced the clonal evolution for each patient. We also analyzed genome sequences of EBV from samples of different histological types. Finally, the key molecular events promoting the tumor evolution were demonstrated by MTT, IC50, and colony formation assay in vitro experiments and in vivo xenograft experiments.

RESULTS

Our analysis revealed increasing mutational burden and EBV load from normal tissues and low-grade dysplasia (LD) to high-grade dysplasia (HD) and EBVaGCs, and oncogenic amplifications occurred late in EBVaGCs. Interestingly, within each patient, EBVaGCs and HDs were monoclonal and harbored single-strain-originated EBV, but saliva or normal tissues/LDs had different EBV strains from that in EBVaGCs. Compared with precursor lesions, tumor cells showed incremental methylation in promotor regions, whereas EBV presented consistent hypermethylation. Dominant alterations targeting the PI3K-Akt and Wnt pathways were found in EBV-infected cells. The combinational inhibition of these two pathways in EBV-positive tumor cells confirmed their synergistic function.

CONCLUSIONS

We portrayed the (epi) genomic evolution process of EBVaGCs, revealed the extensive genomic diversity of EBV between tumors and normal tissue sites, and demonstrated the synergistic activation of the PI3K and Wnt pathways in EBVaGCs, offering a new potential treatment strategy for this disease.

Profiling diverse sequence tandem repeats in colorectal cancer reveals co-occurrence of microsatellite and chromosomal instability involving Chromosome 8

We developed a sensitive sequencing approach that simultaneously profiles microsatellite instability, chromosomal instability, and subclonal structure in cancer. We assessed diverse repeat motifs across 225 microsatellites on colorectal carcinomas. Our study identified elevated alterations at both selected tetranucleotide and conventional mononucleotide repeats. Many colorectal carcinomas had a mix of genomic instability states that are normally considered exclusive. An MSH3 mutation may have contributed to the mixed states. Increased copy number of chromosome arm 8q was most prevalent among tumors with microsatellite instability, including a case of translocation involving 8q. Subclonal analysis identified co-occurring driver mutations previously known to be exclusive.

Assessing tumor heterogeneity: integrating tissue and circulating tumor DNA (ctDNA) analysis in the era of immuno-oncology - blood TMB is not the same as tissue TMB

Tissue tumor mutational burden (tTMB) is calculated to aid in cancer treatment selection. High tTMB predicts a favorable response to immunotherapy in patients with non-small cell lung cancer. Blood TMB (bTMB) from circulating tumor DNA is reported to have similar predictive power and has been proposed as an alternative to tTMB. Across many studies not only are tTMB and bTMB not concordant but also as reported previously by our group predict conflicting outcomes. This implies that bTMB is not a substitute for tTMB, but rather a composite index that may encompass tumor heterogeneity. Here, we provide a thorough overview of the predictive power of TMB, discuss the use of tumor heterogeneity alongside TMB to predict treatment response and review several methods of tumor heterogeneity assessment. Furthermore, we propose a hypothetical method of estimating tumor heterogeneity and touch on its clinical implications.

IDH Mutation Subgroup Status Associates with Intratumor Heterogeneity and the Tumor Microenvironment in Intrahepatic Cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is highly heterogeneous. Here, the authors perform exome sequencing and bulk RNA sequencing on 73 tumor regions from 14 ICC patients to portray the multi-faceted intratumor heterogeneity (ITH) landscape of ICC. The authors show that ITH is highly concordant across genomic, transcriptomic, and immune levels. Comparison of these data to 8 published datasets reveals significantly higher degrees of ITH in ICC than hepatocellular carcinoma. Remarkably, the authors find that high-ITH tumors highly overlap with the IDH (isocitrate dehydrogenase)-mutant subgroup (IDH-SG), comprising of IDH-mutated tumors and IDH-like tumors, that is, those IDH-wildtype tumors that exhibit similar molecular profiles to the IDH-mutated ones. Furthermore, IDH-SG exhibits less T cell infiltration and lower T cell cytotoxicity, indicating a colder tumor microenvironment (TME). The higher ITH and colder TME of IDH-SG are successfully validated by single-cell RNA sequencing on 17 503 cells from 4 patients. Collectively, the study shows that IDH mutant subgroup status, rather than IDH mutation alone, is associated with ITH and the TME of ICC tumors. The results highlight that IDH-like patients may also benefit from IDH targeted therapies and provide important implications for the diagnosis and treatment of ICC.

Decoding human cancer with whole genome sequencing: a review of PCAWG Project studies published in February 2020

Cancer is underlined by genetic changes. In an unprecedented international effort, the Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) sequenced the tumors of over two thousand five hundred patients across 38 different cancer types, as well as the corresponding healthy tissue, with the aim of identifying genome-wide mutations exclusively found in cancer and uncovering new genetic changes that drive tumor formation. What set this project apart from earlier efforts is the use of whole genome sequencing (WGS) that enabled to explore alterations beyond the coding DNA, into cancer's non-coding genome. WGS of the entire cohort allowed to tease apart driving mutations that initiate and support carcinogenesis from passenger mutations that do not play an overt role in the disease. At least one causative mutation was found in 95% of all cancers, with many tumors showing an average of 5 driver mutations. The PCAWG Project also assessed the transcriptional output altered in cancer and rebuilt the evolutionary history of each tumor showing that initial driver mutations can occur years if not decades prior to a diagnosis. Here, I provide a concise review of the Pan-Cancer Project papers published on February 2020, along with key computational tools and the digital framework generated as part of the project. This represents an historic effort by hundreds of international collaborators, which provides a comprehensive understanding of cancer genetics, with publicly available data and resources representing a treasure trove of information to advance cancer research for years to come.

Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data

BACKGROUND

Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities.

RESULT

In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data.

CONCLUSIONS

In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.

Whole exome and transcriptome sequencing reveal clonal evolution and exhibit immune-related features in metastatic colorectal tumors

Liver is the most common site where metastatic lesions of colorectal cancer (CRC) arise. Although researches have shown mutations in driver genes, copy number variations (CNV) and alterations in relevant signaling pathways promoted the tumor evolution and immune escape during colorectal liver metastasis (CLM), the underlying mechanism remains largely elusive. Tumor and matched metastatic tissues were collected from 16 patients diagnosed with colorectal cancer and subjected to whole-exome sequencing (WES) and RNA sequencing (RNA-seq) for studying colorectal cancer clonal evolution and immune escape during CLM. Shared somatic mutations between primary and metastatic tissues with a commonly observed subclonal-clonal (S-C) changing pattern indicated a common clonal origin between two lesions. The recurrent mutations with S-C changing pattern included those in KRAS, SYNE1, CACNA1H, PCLO, FBXL2, and DNAH11. The main CNV events underwent clonal-clonal evolution (20q amplification (amp), 17p deletion (del), 18q del and 8p del), subclonal-clonal evolution (8q amp, 13q amp, 8p del) and metastasis-specific evolution (8q amp) during the process of CLM. In addition, we revealed a potential mechanism of tumor cell immune escape by analyzing human leukocytes antigens (HLA) related clonal neoantigens and immune cell components in CLM. Our study proposed a novel liver metastasis-related evolutionary process in colorectal cancer and emphasized the theory of neo-immune escape in colorectal liver metastasis.

Genomic Analysis Reveals Heterogeneity Between Lesions in Synchronous Primary Right-Sided and Left-Sided Colon Cancer

Background: The synchronous primary right-sided and left-sided colon cancer (sRL-CC) is a peculiar subtype of colorectal cancer. However, the genomic landscape of sRL-CC remains elusive. Methods: Twenty-eight paired tumor samples and their corresponding normal mucosa samples from 14 patients were collected from the Second Affiliated Hospital of Harbin Medical University from 2011 to 2018. The clinical-pathological data were obtained, and whole-exome sequencing was performed based on formalin-fixed and paraffin-embedded samples of these patients, and then, comprehensive bioinformatic analyses were conducted. Results: Both the lesions of sRL-CC presented dissimilar histological grade and differentiation. Based on sequencing data, few overlapping SNV signatures, onco-driver gene mutations, and SMGs were identified. Moreover, the paired lesions harbored a different distribution of copy number variants (CNVs) and loss of heterozygosity. The clonal architecture analysis demonstrated the polyclonal origin of sRL-CC and inter-cancerous heterogeneity between two lesions. Conclusion: Our work provides evidence that lesions of sRL-CC share few overlapping mutational signatures and CNVs, and may originate from different clones.

Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples

Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

Predictive biomarkers for 5-fluorouracil and oxaliplatin-based chemotherapy in gastric cancers via profiling of patient-derived xenografts

Gastric cancer (GC) is commonly treated by chemotherapy using 5-fluorouracil (5-FU) derivatives and platinum combination, but predictive biomarker remains lacking. We develop patient-derived xenografts (PDXs) from 31 GC patients and treat with a combination of 5-FU and oxaliplatin, to determine biomarkers associated with responsiveness. When the PDXs are defined as either responders or non-responders according to tumor volume change after treatment, the responsiveness of PDXs is significantly consistent with the respective clinical outcomes of the patients. An integrative genomic and transcriptomic analysis of PDXs reveals that pathways associated with cell-to-cell and cell-to-extracellular matrix interactions enriched among the non-responders in both cancer cells and the tumor microenvironment (TME). We develop a 30-gene prediction model to determine the responsiveness to 5-FU and oxaliplatin-based chemotherapy and confirm the significant poor survival outcomes among cases classified as non-responder-like in three independent GC cohorts. Our study may inform clinical decision-making when designing treatment strategies.

Chronological genome and single-cell transcriptome integration characterizes the evolutionary process of adult T cell leukemia-lymphoma

Subclonal genetic heterogeneity and their diverse gene expression impose serious problems in understanding the behavior of cancers and contemplating therapeutic strategies. Here we develop and utilize a capture-based sequencing panel, which covers host hotspot genes and the full-length genome of human T-cell leukemia virus type-1 (HTLV-1), to investigate the clonal architecture of adult T-cell leukemia-lymphoma (ATL). For chronologically collected specimens from patients with ATL or pre-onset individuals, we integrate deep DNA sequencing and single-cell RNA sequencing to detect the somatic mutations and virus directly and characterize the transcriptional readouts in respective subclones. Characteristic genomic and transcriptomic patterns are associated with subclonal expansion and switches during the clinical timeline. Multistep mutations in the T-cell receptor (TCR), STAT3, and NOTCH pathways establish clone-specific transcriptomic abnormalities and further accelerate their proliferative potential to develop highly malignant clones, leading to disease onset and progression. Early detection and characterization of newly expanded subclones through the integrative analytical platform will be valuable for the development of an in-depth understanding of this disease.

Characterising the clonal architecture of Adult T-cell leukemia-lymphoma (ATL) remains crucial. Here, the authors develop a capture-based sequencing panel and use deep DNA and single cell RNA sequencing and report distinct genomic and transcriptomic features associated with subclonal evolution.

Mutational patterns and clonal evolution from diagnosis to relapse in pediatric acute lymphoblastic leukemia

The mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients.

Genomic characterization of co-existing neoplasia and carcinoma lesions reveals distinct evolutionary paths of gallbladder cancer

Gallbladder carcinoma is the most common cancer of the biliary tract with dismal survival largely due to delayed diagnosis. Biliary tract intraepithelial neoplasia (BilIN) is the common benign tumor that is suspected to be precancerous lesions. However, the genetic and evolutionary relationships between BilIN and carcinoma remain unclear. Here we perform whole-exome sequencing of coexisting low-grade BilIN (adenoma), high-grade BilIN, and carcinoma lesions, and normal tissues from the same patients. We identify aging as a major factor contributing to accumulated mutations and a critical role of CTNNB1 mutations in these tumors. We reveal two distinct carcinoma evolutionary paths: carcinoma can either diverge earlier and evolve more independently or form through the classic adenoma/dysplasia-carcinoma sequence model. Our analysis suggests that extensive loss-of-heterozygosity and mutation events in the initial stage tend to result in a cancerous niche, leading to the subsequent BilIN-independent path. These results reframes our understanding of tumor transformation and the evolutionary trajectory of carcinogenesis in the gallbladder, laying a foundation for the early diagnosis and effective treatment of gallbladder cancer.

The progression from biliary tract intraepithelial neoplasia (BilIN) to gallbladder carcinoma (GBC) remains unclear. Here the authors use genomics to analyze coexisting GBC lesions, low-grade and high-grade BilINs, revealing two distinct evolutionary paths for GBC development.