PyClone: statistical inference of clonal population structure in cancer

Clonal origin and genomic diversity in Lynch syndrome-associated endometrial cancer with multiple synchronous tumors: Identification of the pathogenicity of MLH1 p.L582H

Lynch syndrome-associated endometrial cancer patients often present multiple synchronous tumors and this assessment can affect treatment strategies. We present a case of a 27-year-old woman with tumors in the uterine corpus, cervix, and ovaries who was diagnosed with endometrial cancer and exhibited cervical invasion and ovarian metastasis. Her family history suggested Lynch syndrome, and genetic testing identified a variant of uncertain significance, MLH1 p.L582H. We conducted immunohistochemical staining, microsatellite instability analysis, and Sanger sequencing for Lynch syndrome-associated cancers in three generations of the family and identified consistent MLH1 loss. Whole-exome sequencing for the corpus, cervical, and ovarian tumors of the proband identified a copy-neutral loss of heterozygosity (LOH) occurring at the MLH1 position in all tumors. This indicated that the germline variant and the copy-neutral LOH led to biallelic loss of MLH1 and was the cause of cancer initiation. All tumors shared a portion of somatic mutations with high mutant allele frequencies, suggesting a common clonal origin. There were no mutations shared only between the cervix and ovary samples. The profiles of mutant allele frequencies shared between the corpus and cervix or ovary indicated that two different subclones originating from the corpus independently metastasized to the cervix or ovary. Additionally, all tumors presented unique mutations in endometrial cancer-associated genes such as ARID1A and PIK3CA. In conclusion, we demonstrated clonal origin and genomic diversity in a Lynch syndrome-associated endometrial cancer, suggesting the importance of evaluating multiple sites in Lynch syndrome patients with synchronous tumors.

Mechanisms of action and resistance in histone methylation-targeted therapy

Epigenomes enable the rectification of disordered cancer gene expression, thereby providing new targets for pharmacological interventions. The clinical utility of targeting histone H3 lysine trimethylation (H3K27me3) as an epigenetic hallmark has been demonstrated1-7. However, in actual therapeutic settings, the mechanism by which H3K27me3-targeting therapies exert their effects and the response of tumour cells remain unclear. Here we show the potency and mechanisms of action and resistance of the EZH1-EZH2 dual inhibitor valemetostat in clinical trials of patients with adult T cell leukaemia/lymphoma. Administration of valemetostat reduced tumour size and demonstrated durable clinical response in aggressive lymphomas with multiple genetic mutations. Integrative single-cell analyses showed that valemetostat abolishes the highly condensed chromatin structure formed by the plastic H3K27me3 and neutralizes multiple gene loci, including tumour suppressor genes. Nevertheless, subsequent long-term treatment encounters the emergence of resistant clones with reconstructed aggregate chromatin that closely resemble the pre-dose state. Acquired mutations at the PRC2-compound interface result in the propagation of clones with increased H3K27me3 expression. In patients free of PRC2 mutations, TET2 mutation or elevated DNMT3A expression causes similar chromatin recondensation through de novo DNA methylation in the H3K27me3-associated regions. We identified subpopulations with distinct metabolic and gene translation characteristics implicated in primary susceptibility until the acquisition of the heritable (epi)mutations. Targeting epigenetic drivers and chromatin homeostasis may provide opportunities for further sustained epigenetic cancer therapies.

Natural History of Germline BRCA1 Mutated and BRCA Wild-type Triple-negative Breast Cancer

We report a deep next-generation sequencing analysis of 13 sequentially obtained tumor samples, eight sequentially obtained circulating tumor DNA (ctDNA) samples and three germline DNA samples over the life history of 3 patients with triple-negative breast cancer (TNBC), 2 of whom had germline pathogenic BRCA1 mutation, to unravel tumor evolution. Tumor tissue from all timepoints and germline DNA was subjected to whole-exome sequencing (WES), custom amplicon deep sequencing (30,000X) of a WES-derived somatic mutation panel, and SNP arrays for copy-number variation (CNV), while whole transcriptome sequencing (RNA-seq) was performed only on somatic tumor.There was enrichment of homologous recombination deficiency signature in all tumors and widespread CNV, which remained largely stable over time. Somatic tumor mutation numbers varied between patients and within each patient (range: 70-216, one outlier). There was minimal mutational overlap between patients with TP53 being the sole commonly mutated gene, but there was substantial overlap in sequential samples in each patient. Each patient's tumor contained a founding ("stem") clone at diagnosis, which persisted over time, from which all other clones ("subclone") were derived ("branching evolution"), which contained mutations in well-characterized cancer-related genes like PDGFRB, ARID2, TP53 (Patient_02), TP53, BRAF, BRIP1, CSF3R (Patient_04), and TP53, APC, EZH2 (Patient_07). Including stem and subclones, tumors from all patients were polyclonal at diagnosis and during disease progression. ctDNA recapitulated most tissue-derived stem clonal and subclonal mutations while detecting some additional subclonal mutations. RNA-seq revealed a stable basal-like pattern, with most highly expressed variants belonging to stem clone.

SIGNIFICANCE

In germline BRCA1 mutated and BRCA wild-type patients, TNBC shows a branching evolutionary pattern of mutations with a single founding clone, are polyclonal throughout their disease course, and have widespread copy-number aberrations. This evolutionary pattern may be associated with treatment resistance or sensitivity and could be therapeutically exploited.

Evaluation of simulation methods for tumor subclonal reconstruction

Most neoplastic tumors originate from a single cell, and their evolution can be genetically traced through lineages characterized by common alterations such as small somatic mutations (SSMs), copy number alterations (CNAs), structural variants (SVs), and aneuploidies. Due to the complexity of these alterations in most tumors and the errors introduced by sequencing protocols and calling algorithms, tumor subclonal reconstruction algorithms are necessary to recapitulate the DNA sequence composition and tumor evolution in silico. With a growing number of these algorithms available, there is a pressing need for consistent and comprehensive benchmarking, which relies on realistic tumor sequencing generated by simulation tools. Here, we examine the current simulation methods, identifying their strengths and weaknesses, and provide recommendations for their improvement. Our review also explores potential new directions for research in this area. This work aims to serve as a resource for understanding and enhancing tumor genomic simulations, contributing to the advancement of the field.

Clinical and molecular features of acquired resistance to immunotherapy in non-small cell lung cancer

Although immunotherapy with PD-(L)1 blockade is routine for lung cancer, little is known about acquired resistance. Among 1,201 patients with non-small cell lung cancer (NSCLC) treated with PD-(L)1 blockade, acquired resistance is common, occurring in >60% of initial responders. Acquired resistance shows differential expression of inflammation and interferon (IFN) signaling. Relapsed tumors can be separated by upregulated or stable expression of IFNγ response genes. Upregulation of IFNγ response genes is associated with putative routes of resistance characterized by signatures of persistent IFN signaling, immune dysfunction, and mutations in antigen presentation genes which can be recapitulated in multiple murine models of acquired resistance to PD-(L)1 blockade after in vitro IFNγ treatment. Acquired resistance to PD-(L)1 blockade in NSCLC is associated with an ongoing, but altered IFN response. The persistently inflamed, rather than excluded or deserted, tumor microenvironment of acquired resistance may inform therapeutic strategies to effectively reprogram and reverse acquired resistance.

Quantifying the seed sensitivity of cancer subclonal reconstruction algorithms

Background

Intra-tumoural heterogeneity complicates cancer prognosis and impairs treatment success. One of the ways subclonal reconstruction (SRC) quantifies intra-tumoural heterogeneity is by estimating the number of subclones present in bulk DNA sequencing data. SRC algorithms are probabilistic and need to be initialized by a random seed. However, the seeds used in bioinformatics algorithms are rarely reported in the literature. Thus, the impact of the initializing seed on SRC solutions has not been studied. To address this gap, we generated a set of ten random seeds to systematically benchmark the seed sensitivity of three probabilistic SRC algorithms: PyClone-VI, DPClust, and PhyloWGS.

Results

We characterized the seed sensitivity of three algorithms across fourteen whole-genome sequences of head and neck squamous cell carcinoma and nine SRC pipelines, each composed of a single nucleotide variant caller, a copy number aberration caller and an SRC algorithm. This led to a total of 1470 subclonal reconstructions, including 1260 single-region and 210 multi-region reconstructions. The number of subclones estimated per patient vary across SRC pipelines, but all three SRC algorithms show substantial seed sensitivity: subclone estimates vary across different seeds for the same set of input using the same SRC algorithm. No seed consistently estimated the mode number of subclones across all patients for any SRC algorithm.

Conclusions

These findings highlight the variability in quantifying intra-tumoural heterogeneity introduced by the seed sensitivity of probabilistic SRC algorithms. We recommend that authors, reviewers and editors adopt guidelines to both report and randomize seed choices. It may also be valuable to consider seed-sensitivity in the benchmarking of newly developed SRC algorithms. These findings may be of interest in other areas of bioinformatics where seeded probabilistic algorithms are used and suggest consideration of formal seed reporting standards to enhance reproducibility.

A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data

Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.

Computational validation of clonal and subclonal copy number alterations from bulk tumor sequencing using CNAqc

Copy number alterations (CNAs) are among the most important genetic events in cancer, but their detection from sequencing data is challenging because of unknown sample purity, tumor ploidy, and general intra-tumor heterogeneity. Here, we present CNAqc, an evolution-inspired method to perform the computational validation of clonal and subclonal CNAs detected from bulk DNA sequencing. CNAqc is validated using single-cell data and simulations, is applied to over 4000 TCGA and PCAWG samples, and is incorporated into the validation process for the clinically accredited bioinformatics pipeline at Genomics England. CNAqc is designed to support automated quality control procedures for tumor somatic data validation.

Integrated multi-omics profiling to dissect the spatiotemporal evolution of metastatic hepatocellular carcinoma

Comprehensive molecular analyses of metastatic hepatocellular carcinoma (HCC) are lacking. Here, we generate multi-omic profiling of 257 primary and 176 metastatic regions from 182 HCC patients. Primary tumors rich in hypoxia signatures facilitated polyclonal dissemination. Genomic divergence between primary and metastatic HCC is high, and early dissemination is prevalent. The remarkable neoantigen intratumor heterogeneity observed in metastases is associated with decreased T cell reactivity, resulting from disruptions to neoantigen presentation. We identify somatic copy number alterations as highly selected events driving metastasis. Subclones without Wnt mutations show a stronger selective advantage for metastasis than those with Wnt mutations and are characterized by a microenvironment rich in activated fibroblasts favoring a pro-metastatic phenotype. Finally, metastases without Wnt mutations exhibit higher enrichment of immunosuppressive B cells that mediate terminal exhaustion of CD8+ T cells via HLA-E:CD94-NKG2A checkpoint axis. Collectively, our results provide a multi-dimensional dissection of the complex evolutionary process of metastasis.

Transcriptomic and genomic profiling of multiple primary colorectal cancers reveals intratumor heterogeneity and a distinct immune microenvironment

This study reported on the intratumor genomic and immunological heterogeneity of different tumor lesions from a single patient with multiple primary colorectal cancer (MPCC). The goal of this study was to explore the molecular and microenvironment characteristics of tumor lesions from different primary sites in a patient with MPCC. A total of three tumor lesions located in the hepatic flexure of the transverse colon, sigmoid colon, and rectum were collected from a 72-year-old male patient with MPCC. All three tumor samples were examined by using whole-exome sequencing (WES) and single-cell RNA sequencing (scRNA-seq). The transcriptome data of The Cancer Genome Atlas (TCGA) colon cancer (COAD) dataset were explored to characterize the biological impacts of certain immune cells. Only three nonsynonymous mutations were shared by all of the tumor lesions, whereas a number of single nucleotide variant (SNV) and copy number variation (CNV) mutations were shared by tumor samples from the sigmoid colon and rectum. Transcriptomic analysis showed that tumor lesions derived from the transverse colon had decreased levels of RTK, ERK, and AKT pathway activity, thus suggesting lower oncogenic properties in the transverse lesion compared to the other two samples. Further immune landscape evaluation by using single-cell transcriptomic analysis displayed significant intratumor heterogeneity in MPCC. Specifically, more abundant mucosal-associated invariant T (MAIT) cell infiltration was found in transverse colon tumor lesions. Afterwards, we found that higher MAIT cell infiltration may correlate with a better prognosis of patients with colon cancer (immunohistochemical status was MSI-L/pMMR) by using a publicly available TCGA dataset.

Evolutionary mode and timing of dissemination of high-grade serous carcinomas

Dissemination within the peritoneal cavity is a main determinant of poor patient outcomes from high-grade serous carcinomas (HGSCs). The dissemination process is poorly understood from a cancer evolutionary perspective. We reconstructed the evolutionary trajectories across a median of 5 tumor sites and regions from each of 23 patients based on deep whole-exome sequencing. Polyclonal cancer origin was detected in 1 patient. Ovarian tumors had more complex subclonal architectures than other intraperitoneal tumors in each patient, which indicated that tumors developed earlier in the ovaries. Three common modes of dissemination were identified, including monoclonal or polyclonal dissemination of monophyletic (linear) or polyphyletic (branched) subclones. Mutation profiles of initial or disseminated clones varied greatly among cancers, but recurrent mutations were found in 7 cancer-critical genes, including TP53, BRCA1, BRCA2, and DNMT3A, and in the PI3K/AKT1 pathway. Disseminated clones developed late in the evolutionary trajectory models of most cancers, in particular in cancers with DNA damage repair deficiency. Polyclonal dissemination was predicted to occur predominantly as a single and rapid wave, but chemotherapy exposure was associated with higher genomic diversity of disseminated clones. In conclusion, we described three common evolutionary dissemination modes across HGSCs and proposed factors associated with dissemination diversity.

Genomic and Evolutionary Characterization of Concurrent Intraductal Carcinoma and Adenocarcinoma of the Prostate

Intraductal carcinoma of the prostate (IDC-P) is a lethal prostate cancer subtype that generally coexists with invasive high-grade prostate acinar adenocarcinoma (PAC) but exhibits distinct biological features compared with concomitant adenocarcinoma. In this study, we performed whole-exome, RNA, and DNA-methylation sequencing of IDC-P, concurrent invasive high-grade PAC lesions, and adjacent normal prostate tissues isolated from 22 radical prostatectomy specimens. Three evolutionary patterns of concurrent IDC-P and PAC were identified: early divergent, late divergent, and clonally distant. In contrast to those with a late divergent evolutionary pattern, tumors with clonally distant and early divergent evolutionary patterns showed higher genomic, epigenomic, transcriptional, and pathologic heterogeneity between IDC-P and PAC. Compared with coexisting PAC, IDC-P displayed increased expression of adverse prognosis-associated genes. Survival analysis based on an independent cohort of 505 patients with metastatic prostate cancer revealed that IDC-P carriers with lower risk International Society of Urological Pathology (ISUP) grade 1-4 adenocarcinoma displayed a castration-resistant free survival as poor as those with the highest risk ISUP grade 5 tumors that lacked concurrent IDC-P. Furthermore, IDC-P exhibited robust cell-cycle progression and androgen receptor activities, characterized by an enrichment of cellular proliferation-associated master regulators and genes involved in intratumoral androgen biosynthesis. Overall, this study provides a molecular groundwork for the aggressive behavior of IDC-P and could help identify potential strategies to improve treatment of IDC-P.

SIGNIFICANCE

The genomic, transcriptomic, and epigenomic characterization of concurrent intraductal carcinoma and adenocarcinoma of the prostate deepens the biological understanding of this lethal disease and provides a genetic basis for developing targeted therapies.

CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction

Intratumor heterogeneity provides the fuel for the evolution and selection of subclonal tumor cell populations. However, accurate inference of tumor subclonal architecture and reconstruction of tumor evolutionary histories from bulk DNA sequencing data remains challenging. Frequently, sequencing and alignment artifacts are not fully filtered out from cancer somatic mutations, and errors in the identification of copy number alterations or complex evolutionary events (e.g., mutation losses) affect the estimated cellular prevalence of mutations. Together, such errors propagate into the analysis of mutation clustering and phylogenetic reconstruction. In this Protocol, we present a new computational framework, CONIPHER (COrrecting Noise In PHylogenetic Evaluation and Reconstruction), that accurately infers subclonal structure and phylogenetic relationships from multisample tumor sequencing, accounting for both copy number alterations and mutation errors. CONIPHER has been used to reconstruct subclonal architecture and tumor phylogeny from multisample tumors with high-depth whole-exome sequencing from the TRACERx421 dataset, as well as matched primary-metastatic cases. CONIPHER outperforms similar methods on simulated datasets, and in particular scales to a large number of tumor samples and clones, while completing in under 1.5 h on average. CONIPHER enables automated phylogenetic analysis that can be effectively applied to large sequencing datasets generated with different technologies. CONIPHER can be run with a basic knowledge of bioinformatics and R and bash scripting languages.

ZNF689 deficiency promotes intratumor heterogeneity and immunotherapy resistance in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive disease characterized by remarkable intratumor heterogeneity (ITH), which poses therapeutic challenges. However, the clinical relevance and key determinant of ITH in TNBC are poorly understood. Here, we comprehensively characterized ITH levels using multi-omics data across our center's cohort (n = 260), The Cancer Genome Atlas cohort (n = 134), and four immunotherapy-treated cohorts (n = 109). Our results revealed that high ITH was associated with poor patient survival and immunotherapy resistance. Importantly, we identified zinc finger protein 689 (ZNF689) deficiency as a crucial determinant of ITH formation. Mechanistically, the ZNF689-TRIM28 complex was found to directly bind to the promoter of long interspersed element-1 (LINE-1), inducing H3K9me3-mediated transcriptional silencing. ZNF689 deficiency reactivated LINE-1 retrotransposition to exacerbate genomic instability, which fostered ITH. Single-cell RNA sequencing, spatially resolved transcriptomics and flow cytometry analysis confirmed that ZNF689 deficiency-induced ITH inhibited antigen presentation and T-cell activation, conferring immunotherapy resistance. Pharmacological inhibition of LINE-1 significantly reduced ITH, enhanced antitumor immunity, and eventually sensitized ZNF689-deficient tumors to immunotherapy in vivo. Consistently, ZNF689 expression positively correlated with favorable prognosis and immunotherapy response in clinical samples. Altogether, our study uncovers a previously unrecognized mechanism underlying ZNF689 deficiency-induced ITH and suggests LINE-1 inhibition combined with immunotherapy as a novel treatment strategy for TNBC.

Multiregion sampling of de novo metastatic prostate cancer reveals complex polyclonality and augments clinical genotyping

De novo metastatic prostate cancer is highly aggressive, but the paucity of routinely collected tissue has hindered genomic stratification and precision oncology. Here, we leveraged a rare study of surgical intervention in 43 de novo metastatic prostate cancers to assess somatic genotypes across 607 synchronous primary and metastatic tissue regions plus circulating tumor DNA. Intra-prostate heterogeneity was pervasive and impacted clinically relevant genes, resulting in discordant genotypes between select primary restricted regions and synchronous metastases. Additional complexity was driven by polyclonal metastatic seeding from phylogenetically related primary populations. When simulating clinical practice relying on a single tissue region, genomic heterogeneity plus variable tumor fraction across samples caused inaccurate genotyping of dominant disease; however, pooling extracted DNA from multiple biopsy cores before sequencing can rescue misassigned somatic genotypes. Our results define the relationship between synchronous treatment-sensitive primary and metastatic lesions in men with de novo metastatic prostate cancer and provide a framework for implementing genomics-guided patient management.

Scrutiny of genome-wide somatic mutation profiles in centenarians identifies the key genomic regions for human longevity

Somatic mutations accumulate with age and are associated closely with human health, their characterization in longevity cohorts remains largely unknown. Here, by analyzing whole genome somatic mutation profiles in 73 centenarians and 51 younger controls in China, we found that centenarian genomes are characterized by a markedly skewed distribution of somatic mutations, with many genomic regions being specifically conserved but displaying a high function potential. This, together with the observed more efficient DNA repair ability in the long-lived individuals, supports the existence of key genomic regions for human survival during aging, with their integrity being of essential to human longevity.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Molecular evolution in different subtypes of multifocal hepatocellular carcinoma

BACKGROUND

Multifocal hepatocellular carcinoma (MF-HCC) accounts for > 40% of HCCs, exhibiting a poor prognosis than single primary HCCs. Characterizing molecular features including dynamic changes of mutational signature along with clonal evolution, intrahepatic metastatic timing, and genetic footprint in the preneoplastic stage underlying different subtypes of MF-HCC are important for understanding their molecular evolution and developing a precision management strategy.

METHODS

We conducted whole-exome sequencing in 74 tumor samples from spatially distinct regions in 35 resected lesions and adjacent noncancerous tissues from 11 patients, 15 histologically confirmed preneoplastic lesions, and six samples from peripheral blood mononuclear cells. A previously published MF-HCC cohort (n = 9) was included as an independent validation dataset. We combined well-established approaches to investigate tumor heterogeneity, intrahepatic metastatic timing, and molecular footprints in different subtypes of MF-HCCs.

RESULTS

We classified MF-HCCs patients into three subtypes, including intrahepatic metastasis, multicentric occurrence, and mixed intrahepatic metastasis and multicentric occurrence. The dynamic changes in mutational signatures between tumor subclonal expansions demonstrated varied etiologies (e.g., aristolochic acid exposure) underlying the clonal progression in different MF-HCC subtypes. Furthermore, the clonal evolution in intrahepatic metastasis exhibited an early metastatic seeding at 10-4-0.01 cm3 in primary tumor volume (below the limits of clinical detection), further validated in an independent cohort. In addition, mutational footprints in the preneoplastic lesions for multicentric occurrence patients revealed common preneoplastic arising clones, evidently being ancestors of different tumor lesions.

CONCLUSION

Our study comprehensively characterized the varied tumor clonal evolutionary history underlying different subtypes of MF-HCC and provided important implications for optimizing personalized clinical management for MF-HCC.

Whole-exome sequencing reveals the metastatic potential of hepatocellular carcinoma from the perspective of tumor and circulating tumor DNA

BACKGROUND

Relapse of hepatocellular carcinoma (HCC) due to vascular invasion is common, but the genomic mechanisms remain unclear, and molecular determinants of high-risk relapse cases are lacking. We aimed to reveal the evolutionary trajectory of microvascular invasion (MVI) and develop a predictive signature for relapse in HCC.

METHODS

Whole-exome sequencing was performed on tumor and peritumor tissues, portal vein tumor thrombus (PVTT), and circulating tumor DNA (ctDNA) to compare the genomic profiles between 5 HCC patients with MVI and 5 patients without MVI. We conducted an integrated analysis of exome and transcriptome to develop and validate a prognostic signature in two public cohorts and one cohort from Zhongshan Hospital, Fudan University.

RESULTS

Shared genomic landscapes and identical clonal origins among tumor, PVTT, and ctDNA were observed in MVI ( +) HCC, suggesting that genomic changes favoring metastasis occur at the primary tumor stage and are inherited in metastatic lesions and ctDNA. There was no clonal relatedness between the primary tumor and ctDNA in MVI ( - ) HCC. HCC had dynamic mutation alterations during MVI and exhibited genetic heterogeneity between primary and metastatic tumors, which can be comprehensively reflected by ctDNA. A relapse-related gene signature named RGSHCC was developed based on the significantly mutated genes associated with MVI and shown to be a robust classifier of HCC relapse.

CONCLUSIONS

We characterized the genomic alterations during HCC vascular invasion and revealed a previously undescribed evolution pattern of ctDNA in HCC. A novel multiomics-based signature was developed to identify high-risk relapse populations.

Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI

Cancer is known as a heterogeneous disease. Cancer driver genes (CDGs) need to be inferred for understanding tumor heterogeneity in cancer. However, the existing computational methods have identified many common CDGs. A key challenge exploring cancer progression is to infer cancer subtype-specific driver genes (CSDGs), which provides guidane for the diagnosis, treatment and prognosis of cancer. The significant advancements in single-cell RNA-sequencing (scRNA-seq) technologies have opened up new possibilities for studying human cancers at the individual cell level. In this study, we develop a novel unsupervised method, CSDGI (Cancer Subtype-specific Driver Gene Inference), which applies Encoder-Decoder-Framework consisting of low-rank residual neural networks to inferring driver genes corresponding to potential cancer subtypes at the single-cell level. To infer CSDGs, we apply CSDGI to the tumor single-cell transcriptomics data. To filter the redundant genes before driver gene inference, we perform the differential expression genes (DEGs). The experimental results demonstrate CSDGI is effective to infer driver genes that are cancer subtype-specific. Functional and disease enrichment analysis shows these inferred CSDGs indicate the key biological processes and disease pathways. CSDGI is the first method to explore cancer driver genes at the cancer subtype level. We believe that it can be a useful method to understand the mechanisms of cell transformation driving tumours.

Genomic profiling and pre-clinical modelling of breast cancer leptomeningeal metastasis reveals acquisition of a lobular-like phenotype

Breast cancer leptomeningeal metastasis (BCLM), where tumour cells grow along the lining of the brain and spinal cord, is a devastating development for patients. Investigating this metastatic site is hampered by difficulty in accessing tumour material. Here, we utilise cerebrospinal fluid (CSF) cell-free DNA (cfDNA) and CSF disseminated tumour cells (DTCs) to explore the clonal evolution of BCLM and heterogeneity between leptomeningeal and extracranial metastatic sites. Somatic alterations with potential therapeutic actionability were detected in 81% (17/21) of BCLM cases, with 19% detectable in CSF cfDNA only. BCLM was enriched in genomic aberrations in adherens junction and cytoskeletal genes, revealing a lobular-like breast cancer phenotype. CSF DTCs were cultured in 3D to establish BCLM patient-derived organoids, and used for the successful generation of BCLM in vivo models. These data reveal that BCLM possess a unique genomic aberration profile and highlight potential cellular dependencies in this hard-to-treat form of metastatic disease.

AAFL: automatic association feature learning for gene signature identification of cancer subtypes in single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) technologies have enabled the study of human cancers in individual cells, which explores the cellular heterogeneity and the genotypic status of tumors. Gene signature identification plays an important role in the precise classification of cancer subtypes. However, most existing gene selection methods only select the same informative genes for each subtype. In this study, we propose a novel gene selection method, automatic association feature learning (AAFL), which automatically identifies different gene signatures for different cell subpopulations (cancer subtypes) at the same time. The proposed AAFL method combines the residual network with the low-rank network, which selects genes that are most associated with the corresponding cell subpopulations. Moreover, the differential expression genes are acquired before gene selection to filter the redundant genes. We apply the proposed feature learning method to the real cancer scRNA-seq data sets (melanoma) to identify cancer subtypes and detect gene signatures of identified cancer subtypes. The experimental results demonstrate that the proposed method can automatically identify different gene signatures for identified cancer subtypes. Gene ontology enrichment analysis shows that the identified gene signatures of different subtypes reveal the key biological processes and pathways. These gene signatures are expected to bring important implications for understanding cellular heterogeneity and the complex ecosystem of tumors.

Multi-omics analyses reveal spatial heterogeneity in primary and metastatic oesophageal squamous cell carcinoma

BACKGROUND

Biopsies obtained from primary oesophageal squamous cell carcinoma (ESCC) guide diagnosis and treatment. However, spatial intra-tumoral heterogeneity (ITH) influences biopsy-derived information and patient responsiveness to therapy. Here, we aimed to elucidate the spatial ITH of ESCC and matched lymph node metastasis (LNmet ).

METHODS

Primary tumour superficial (PTsup ), deep (PTdeep ) and LNmet subregions of patients with locally advanced resectable ESCC were evaluated using whole-exome sequencing (WES), whole-transcriptome sequencing and spatially resolved digital spatial profiling (DSP). To validate the findings, immunohistochemistry was conducted and a single-cell transcriptomic dataset was analysed.

RESULTS

WES revealed 15.72%, 5.02% and 32.00% unique mutations in PTsup , PTdeep and LNmet , respectively. Copy number alterations and phylogenetic trees showed spatial ITH among subregions both within and among patients. Driver mutations had a mixed intra-tumoral clonal status among subregions. Transcriptome data showed distinct differentially expressed genes among subregions. LNmet exhibited elevated expression of immunomodulatory genes and enriched immune cells, particularly when compared with PTsup (all P < .05). DSP revealed orthogonal support of bulk transcriptome results, with differences in protein and immune cell abundance between subregions in a spatial context. The integrative analysis of multi-omics data revealed complex heterogeneity in mRNA/protein levels and immune cell abundance within each subregion.

CONCLUSIONS

This study comprehensively characterised spatial ITH in ESCC, and the findings highlight the clinical significance of unbiased molecular classification based on multi-omics data and their potential to improve the understanding and management of ESCC. The current practices for tissue sampling are insufficient for guiding precision medicine for ESCC, and routine profiling of PTdeep and/or LNmet should be systematically performed to obtain a more comprehensive understanding of ESCC and better inform treatment decisions.

Prognostic and predictive impact of molecular tumor burden index in non-small cell lung cancer patients

BACKGROUND

The biomarkers of immune checkpoint inhibitors in the treatment of non-small cell lung cancer (NSCLC) patients have limited predictive performance. In this study we aimed to investigate the feasibility of molecular tumor burden index (mTBI) in circulating tumor DNA (ctDNA) as a predictor for immunotherapy in patients with NSCLC.

METHODS

From February 2017 to November 2020, pretreatment and on-treatment (3~6 weeks after first cycle of immunotherapy) dynamic plasma ctDNA samples from NSCLC patients receiving immune monotherapy or combination therapy were analyzed by targeted capture sequencing of 1021 genes. PyClone was used to infer the mTBI. The impact of pretreatment mTBI on survival outcomes was verified in the POPLAR/OAK trials.

RESULTS

We found that patients without detectable baseline ctDNA had better survival outcomes (median overall survival [OS]: not reached vs. 12.8 months; hazard ratio [HR], 0.15; p = 0.035]). RB1 and SMARCA4 mutations were remarkably associated with worse survival outcomes. Furthermore, lower pretreatment mTBI was associated with superior OS (median: not reached vs. 8.1 months; HR, 0.22; p = 0.024) and PFS (median: 32.9 vs. 5.4 months; HR, 0.35; p = 0.045), but not objective response, which was validated in the POPLAR/OAK cohort, suggesting that baseline mTBI was a prognostic factor for NSCLC immunotherapy. Early dynamic changes of mTBI (ΔmTBI) significantly distinguished responsive patients, and patients with mTBI decrease to more than 68% at the final tumor evaluation had longer OS (median: 38.2 vs. 4.0 months; HR, 0.18; p = 0.017) and PFS (median: not reached vs. 2.3 months; HR, 0.24; p = 0.030).

CONCLUSION

ΔmTBI had a good sensitivity to identify potential beneficial patients based on the best effect CT scans, demonstrating that mTBI dynamics were predictive of benefit from immune checkpoint blockade.

Integrated Genomic and Transcriptomic Analysis Improves Disease Classification and Risk Stratification of MDS with Ring Sideroblasts

PURPOSE

Ring sideroblasts (RS) define the low-risk myelodysplastic neoplasm (MDS) subgroup with RS but may also reflect erythroid dysplasia in higher risk myeloid neoplasm. The benign behavior of MDS with RS (MDSRS+) is limited to SF3B1-mutated cases without additional high-risk genetic events, but one third of MDSRS+ carry no SF3B1 mutation, suggesting that different molecular mechanisms may underlie RS formation. We integrated genomic and transcriptomic analyses to evaluate whether transcriptome profiles may improve current risk stratification.

EXPERIMENTAL DESIGN

We studied a prospective cohort of MDSRS+ patients irrespective of World Health Organization (WHO) class with regard to somatic mutations, copy-number alterations, and bone marrow CD34+ cell transcriptomes to assess whether transcriptome profiles add to prognostication and provide input on disease classification.

RESULTS

SF3B1, SRSF2, or TP53 multihit mutations were found in 89% of MDSRS+ cases, and each mutation category was associated with distinct clinical outcome, gene expression, and alternative splicing profiles. Unsupervised clustering analysis identified three clusters with distinct hemopoietic stem and progenitor (HSPC) composition, which only partially overlapped with mutation groups. IPSS-M and the transcriptome-defined proportion of megakaryocyte/erythroid progenitors (MEP) independently predicted survival in multivariable analysis.

CONCLUSIONS

These results provide essential input on the molecular basis of SF3B1-unmutated MDSRS+ and propose HSPC quantification as a prognostic marker in myeloid neoplasms with RS.

Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine

BACKGROUND

Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question.

METHODS

We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones.

RESULTS

In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis' potential impact on therapy selection appears positive in these pilot cases.

CONCLUSIONS

PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value.

Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets

Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

Using patient-derived tumor organoids from common epithelial cancers to analyze personalized T-cell responses to neoantigens

Adoptive cell transfer of tumor-infiltrating lymphocytes (TIL) can mediate durable complete responses in some patients with common epithelial cancers but does so infrequently. A better understanding of T-cell responses to neoantigens and tumor-related immune evasion mechanisms requires having the autologous tumor as a reagent. We investigated the ability of patient-derived tumor organoids (PDTO) to fulfill this need and evaluated their utility as a tool for selecting T-cells for adoptive cell therapy. PDTO established from metastases from patients with colorectal, breast, pancreatic, bile duct, esophageal, lung, and kidney cancers underwent whole exomic sequencing (WES), to define mutations. Organoids were then evaluated for recognition by autologous TIL or T-cells transduced with cloned T-cell receptors recognizing defined neoantigens. PDTO were also used to identify and clone TCRs from TIL targeting private neoantigens and define those tumor-specific targets. PDTO were successfully established in 38/47 attempts. 75% were available within 2 months, a timeframe compatible with screening TIL for clinical administration. These lines exhibited good genetic fidelity with their parental tumors, especially for mutations with higher clonality. Immunologic recognition assays demonstrated instances of HLA allelic loss not found by pan-HLA immunohistochemistry and in some cases WES of fresh tumor. PDTO could also be used to show differences between TCRs recognizing the same antigen and to find and clone TCRs recognizing private neoantigens. PDTO can detect tumor-specific defects blocking T-cell recognition and may have a role as a selection tool for TCRs and TIL used in adoptive cell therapy.

Genome Doubling Shapes High-Grade Transformation and Novel EWSR1::LARP4 Fusion Shows SOX10 Immunostaining in Hyalinizing Clear Cell Carcinoma of Salivary Gland

Hyalinizing clear cell carcinoma (HCCC) is a rare indolent malignant tumor of minor salivary gland origin with EWSR1::ATF1 rearrangement. Pathologically, the tumor cells possess a clear cytoplasm in a background of hyalinized stroma. Generally, the tumor cells are positive for p63 and p40 and negative for s100 and α-smooth muscle actin, suggesting that they differentiate into squamous epithelium and not into myoepithelium. In this study, we performed a detailed histopathological and genomic analysis of 6 cases of HCCC, including 2 atypical subtypes-a case of "high-grade transformation" and 1 "possessing a novel partner gene for EWSR1." We performed a sequential analysis of the primary and recurrent tumor by whole-exome sequencing, RNA sequencing, Sanger sequencing, and fluorescence in situ hybridization to investigate the effect of genomic changes on histopathology and clinical prognosis. A fusion gene involving the EWSR1 gene was detected in all cases. Five cases, including the "high-grade transformation," harbored a known EWSR1::ATF1 fusion gene; however, 1 case harbored a novel EWSR1::LARP4 fusion gene. This novel EWSR1::LARP4-fused HCCC has a SOX10-positive staining, which is different from the EWSR1::ATF1-fused HCCC. According to whole-exome sequencing and fluorescence in situ hybridization analysis, the "whole-genome doubling" and focal deletion involving CDKN2A, CDKN2B, and PTEN were detected in HCCC with "high-grade transformation." Conclusively, we identified a novel partner gene for EWSR1, LARP4, in indolent HCCC. Importantly, "high-grade transformation" and poor prognosis were caused by whole-genome doubling and subsequent genomic aberrations.

Absence of Lugol staining indicates initiation of esophageal squamous cell carcinoma: A combined genomic and epidemiologic study

The genomic characteristics during the carcinogenic process of esophageal squamous cell carcinoma (ESCC) remain largely unknown. We report here the genomic characteristics of 106 esophageal tissues of various stages from a population-based screening cohort in China ("Endoscopic Screening for Esophageal Cancer in China" trial) and 57 ESCC tissues from a local hospital. A significant increase in somatic mutation and copy number alterations is observed in the non-dysplastic Lugol unstaining lesions (ND-LULs). Extensive clonal expansion has emerged in the ND-LULs to an extent similar to that in higher-stage lesions. The burden of genomic alterations correlates with the size of LULs in the ND-LULs. 8-year follow-up shows that ND-LULs harbor an increased risk of progression to ESCC (adjusted IRR6-10 mm vs. none = 4.66, adjusted IRR>10 mm vs. none = 40.70), and the risk is correlated with LUL size for both non-dysplastic and dysplastic lesions. Lugol unstaining can be the initial stage in the carcinogenic process of ESCC.

The heterogeneity and clonal evolution analysis of the advanced prostate cancer with castration resistance

BACKGROUND

Nowadays, the incidence rate of advanced and metastatic prostate cancer at the first time of diagnosis grows higher in China yearly. At present, androgen deprivation therapy (ADT) is the primary treatment of advanced prostate cancer. However, after several years of ADT, most patients will ultimately progress to castration-resistant prostate cancer (CRPC). Previous studies mainly focus on Caucasian and very few on East Asian patients.

METHODS

In this study, the pre- and post-ADT tumor samples were collected from five Chinese patients with advanced prostate cancer. The whole-exome sequencing, tumor heterogeneity, and clonal evolution pattern were analyzed.

RESULTS

The results showed that the gene mutation pattern and heterogeneity changed significantly after androgen deprivation therapy. Tumor Mutational Burden (TMB) and Copy Number Alteration (CNA) were substantially reduced in the post-treatment group, but the Mutant-allele tumor heterogeneity (MATH), Socio-Demographic Index (SDI), Intratumor heterogeneity (ITH), and weighted Genome Instability Index (wGII) had no significant difference. According to the clone types and characteristics, the presence of main clones in five pre-and post-treatment samples, the clonal evolution pattern can be further classified into two sub-groups (the Homogeneous origin clonal model or the Heterogeneous origin clonal model). The Progression-free survival (PFS) of the patients with the "Homogeneous origin clonal model" was shorter than the "Heterogeneous origin clonal model". The longer PFS might relate to MUC7 and MUC5B mutations repaired. ZNF91 mutation might be responsible for resistance to ADT resistance.

CONCLUSION

Our findings revealed potential genetic regulators to predict the castration resistance and provide insights into the castration resistance processes in advanced prostate cancer. The crosstalk between clonal evolution patterns and tumor microenvironment may also play a role in castration resistance. A multicenter-research including larger populations with different background are needed to confirm our conclusion in the future.

Deep learning applications in single-cell genomics and transcriptomics data analysis

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

Whole Slide Imaging-Based Prediction of TP53 Mutations Identifies an Aggressive Disease Phenotype in Prostate Cancer

In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify the metastatic potential of a tumor at an early stage. While recent analyses indicated TP53 mutations as candidate biomarkers, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in whole slide images (WSI) offer the potential to mitigate this issue. To assess the potential of WSIs as proxies for spatially resolved profiling and as biomarkers for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. In an independent multifocal cohort, the model showed successful generalization at both the patient and lesion level. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions, suggesting that some FP carry an alteration that leads to the same histological phenotype as TP53 mutations. Comparative expression and histologic cell type analyses identified a TP53-like cellular phenotype triggered by expression of pathways affecting stromal composition. Together, these findings indicate that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations but they have the potential to elucidate the prognosis of a tumor by depicting a downstream phenotype associated with aggressive disease biomarkers.

SIGNIFICANCE

Deep learning models predicting TP53 mutations from whole slide images of prostate cancer capture histologic phenotypes associated with stromal composition, lymph node metastasis, and biochemical recurrence, indicating their potential as in silico prognostic biomarkers. See related commentary by Bordeleau, p. 2809.

Genomic and evolutionary characteristics of metastatic gastric cancer by routes

BACKGROUND

In gastric cancer (GC) patients, metastatic progression through the lymphatic, hematogenous, peritoneal, and ovarian routes, is the ultimate cause of death. However, the genomic and evolutionary characteristics of metastatic GC have not been widely evaluated.

METHODS

Whole-exome sequencing data were analyzed for 99 primary and paired metastatic gastric cancers from 15 patients who underwent gastrectomy and metastasectomy.

RESULTS

Hematogenous metastatic tumors were associated with increased chromosomal instability and de novo gain/amplification in cancer driver genes, whereas peritoneal/ovarian metastasis was linked to sustained chromosomal stability and de novo somatic mutations in driver genes. The genomic distance of the hematogenous and peritoneal metastatic tumors was found to be closer to the primary tumors than lymph node (LN) metastasis, while ovarian metastasis was closer to LN and peritoneal metastasis than the primary tumor. Two migration patterns for metastatic GCs were identified; branched and diaspora. Both molecular subtypes of the metastatic tumors, rather than the primary tumor, and their migration patterns were related to patient survival.

CONCLUSIONS

Genomic characteristics of metastatic gastric cancer is distinctive by routes and associated with patients' prognosis along with genomic evolution pattenrs, indicating that both primary and metastatic gastric cancers require genomic evaluation.

Multi-level functional genomics reveals molecular and cellular oncogenicity of patient-based 3' untranslated region mutations

3' untranslated region (3' UTR) somatic mutations represent a largely unexplored avenue of alternative oncogenic gene dysregulation. To determine the significance of 3' UTR mutations in disease, we identify 3' UTR somatic variants across 185 advanced prostate tumors, discovering 14,497 single-nucleotide mutations enriched in oncogenic pathways and 3' UTR regulatory elements. By developing two complementary massively parallel reporter assays, we measure how thousands of patient-based mutations affect mRNA translation and stability and identify hundreds of functional variants that allow us to define determinants of mutation significance. We demonstrate the clinical relevance of these mutations, observing that CRISPR-Cas9 endogenous editing of distinct variants increases cellular stress resistance and that patients harboring oncogenic 3' UTR mutations have a particularly poor prognosis. This work represents an expansive view of the extent to which disease-relevant 3' UTR mutations affect mRNA stability, translation, and cancer progression, uncovering principles of regulatory functionality and potential therapeutic targets in previously unexplored regulatory regions.

Clonal dynamics and Stereo-seq resolve origin and phenotypic plasticity of adenosquamous carcinoma

The genomic origin and development of the biphasic lung adenosquamous carcinoma (ASC) remain inconclusive. Here, we derived potential evolutionary trajectory of ASC through whole-exome sequencing, Stereo-seq, and patient-derived xenografts. We showed that EGFR and MET activating mutations were the main drivers in ASCs. Phylogenetically, these drivers and passenger mutations found in both components were trunk clonal events, confirming monoclonal origination. Comparison of multiple lesions also revealed closer genomic distance between lymph node metastases and the ASC component with the same phenotype. However, as mutational signatures of EGFR-positive lung squamous carcinomas (LUSCs) were more comparable to EGFR-positive ASCs than to wild-type LUSCs, we postulated different origination of these LUSCs, with ASC being the potential intermediate state of driver-positive LUSCs. Spatial transcriptomic profiling inferred transformation from adenocarcinoma to squamous cell carcinoma, which was then histologically captured in vivo. Together, our results explained the development of ASC and provided insights into future clinical decisions.

Phylogenetic inference from single-cell RNA-seq data

Tumors are comprised of subpopulations of cancer cells that harbor distinct genetic profiles and phenotypes that evolve over time and during treatment. By reconstructing the course of cancer evolution, we can understand the acquisition of the malignant properties that drive tumor progression. Unfortunately, recovering the evolutionary relationships of individual cancer cells linked to their phenotypes remains a difficult challenge. To address this need, we have developed PhylinSic, a method that reconstructs the phylogenetic relationships among cells linked to their gene expression profiles from single cell RNA-sequencing (scRNA-Seq) data. This method calls nucleotide bases using a probabilistic smoothing approach and then estimates a phylogenetic tree using a Bayesian modeling algorithm. We showed that PhylinSic identified evolutionary relationships underpinning drug selection and metastasis and was sensitive enough to identify subclones from genetic drift. We found that breast cancer tumors resistant to chemotherapies harbored multiple genetic lineages that independently acquired high K-Ras and β-catenin, suggesting that therapeutic strategies may need to control multiple lineages to be durable. These results demonstrated that PhylinSic can reconstruct evolution and link the genotypes and phenotypes of cells across monophyletic tumors using scRNA-Seq.

Analysis of genomic and immune intratumor heterogeneity in linitis plastica via multiregional exome and T-cell receptor sequencing

The molecular landscape and the intratumor heterogeneity (ITH) architecture of gastric linitis plastica (LP) are poorly understood. We performed whole-exome sequencing (WES) and T-cell receptor (TCR) sequencing on 40 tumor regions from four LP patients. The landscape and ITH at the genomic and immunological levels in LP tumors were compared with multiple cancers that have previously been reported. The lymphocyte infiltration was further assessed by immunohistochemistry (IHC) in LP tumors. In total, we identified 6339 non-silent mutations from multi-samples, with a median tumor mutation burden (TMB) of 3.30 mutations per Mb, comparable to gastric adenocarcinoma from the Cancer Genome Atlas (TCGA) cohort (P = 0.53). An extremely high level of genomic ITH was observed, with only 12.42%, 5.37%, 5.35%, and 30.67% of mutations detectable across 10 regions within the same tumors of each patient, respectively. TCR sequencing revealed that TCR clonality was substantially lower in LP than in multi-cancers. IHC using antibodies against CD4, CD8, and PD-L1 demonstrated scant T-cell infiltration in the four LP tumors. Furthermore, profound TCR ITH was observed in all LP tumors, with no T-cell clones shared across tumor regions in any of the patients, while over 94% of T-cell clones were restricted to individual tumor regions. The Morisita overlap index (MOI) ranged from 0.21 to 0.66 among multi-regions within the same tumors, significantly lower than that of lung cancer (P = 0.002). Our results show that LP harbored extremely high genomic and TCR ITH and suppressed T-cell infiltration, suggesting a potential contribution to the frequent recurrence and poor therapeutic response of this adenocarcinoma.

Mutational profiling of Chinese patients with thyroid cancer

Background

The incidence of thyroid cancer in China has rapidly increased in recent decades. As the genetic profiles of thyroid cancer vary dramatically between different geographical regions, a comprehensive genetic landscape of thyroid cancer in the Chinese population is urgently needed.

Methods

We retrospectively included thyroid cancer patients from three Chinese medical centers between February 2015 and August 2020. To dissect the genomic profiling of these patients, we performed targeted next-generation sequencing on their tumor tissues using a 1,021-gene panel.

Results

A total of 458 Chinese patients with thyroid cancer were enrolled, including four malignant histological subtypes arising from follicular epithelial thyroid cells. BRAF driver mutations were identified in 76.0% of patients, followed by RET rearrangements (7.6%) and RAS driver mutations (4.1%). Tumors with more somatic mutations correlated with worse clinical characteristics, including older age at diagnosis, less differentiation of tumor, larger tumor size, lymph node metastasis and distal metastasis. Subclonal BRAF mutations occurred in 20% (6/30) of patients and were frequent in poorly differentiated or anaplastic tumors (33.3% [2/6] vs. 4.2% [1/24], P = 0.09) and those with distal metastasis (50.0% [2/4] vs. 8.7% [2/23], P = 0.09). Tumors with TERT promoter mutations had significantly more somatic mutations (average: 6.5 vs. 1.8, P < 0.001). Moreover, TERT promoter mutations were not associated with lymph node metastasis but significantly associated with older age at diagnosis and poorly differentiated or anaplastic tumors, regardless of their clonal architecture.

Conclusion

Our results shed light on the molecular pathogenesis and clinical characteristics of thyroid cancer in the Chinese population. The number of somatic mutations, TERT promoter mutations, and the clonal architecture of BRAF mutations should be considered in the risk stratification of thyroid cancer.

The clonal heterogeneity of colon cancer with liver metastases

BACKGROUND

Colon cancer with liver metastases (CCLM) characterized by genetic heterogeneity is an evolutionary process leading to variations in response to selective pressure, but the underlying evolutionary models still remains unclear.

METHODS

Total of 30 samples, including primary tumor and two to four matched liver metastases from 8 treatment-naïve patients with CCLM were collected, and subjected to whole-exome DNA sequencing. PyClone was used to calculate intra and inter-tumor heterogeneity, LICHeE was used to reconstruct the cancer phylogeny trees and investigate the subclonal composition.

RESULTS

The genetic differences were observed between primary and metastatic lesions, as well as among multiple metastases in all patients. The natural history models of colorectal cancer in each case were identified, including parallel, linear, and branching evolution. Liver metastases could originate from primary lesions or other metastases. Pathway and process enrichment analysis also showed obvious heterogeneity and enhancement of several molecular functions.

CONCLUSIONS

Our data reveal the genetic and heterogeneity between primary and metastatic lesions, as well as among multiple metastases and provide genomic evidence for clonal heterogeneity for CCLM.

Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and with more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.

Precise microdissection of gastric mixed adeno-neuroendocrine carcinoma dissects its genomic landscape and evolutionary clonal origins

Gastric mixed adenoneuroendocrine carcinoma (MANEC) is a clinically aggressive and heterogeneous tumor composed of adenocarcinoma (ACA) and neuroendocrine carcinoma (NEC). The genomic properties and evolutionary clonal origins of MANEC remain unclear. We conduct whole-exome and multiregional sequencing on 101 samples from 33 patients to elucidate their evolutionary paths. We identify four significantly mutated genes, TP53, RB1, APC, and CTNNB1. MANEC resembles chromosomal instability stomach adenocarcinoma in that whole-genome doubling in MANEC is predominant and occurs earlier than most copy-number losses. All tumors are of monoclonal origin, and NEC components show more aggressive genomic properties than their ACA counterparts. The phylogenetic trees show two tumor divergence patterns, including sequential and parallel divergence. Furthermore, ACA-to-NEC rather than NEC-to-ACA transition is confirmed by immunohistochemistry on 6 biomarkers in ACA- and NEC-dominant regions. These results provide insights into the clonal origin and tumor differentiation of MANEC.

Late-Stage Metastatic Melanoma Emerges through a Diversity of Evolutionary Pathways

Understanding the evolutionary pathways to metastasis and resistance to immune-checkpoint inhibitors (ICI) in melanoma is critical for improving outcomes. Here, we present the most comprehensive intrapatient metastatic melanoma dataset assembled to date as part of the Posthumous Evaluation of Advanced Cancer Environment (PEACE) research autopsy program, including 222 exome sequencing, 493 panel-sequenced, 161 RNA sequencing, and 22 single-cell whole-genome sequencing samples from 14 ICI-treated patients. We observed frequent whole-genome doubling and widespread loss of heterozygosity, often involving antigen-presentation machinery. We found KIT extrachromosomal DNA may have contributed to the lack of response to KIT inhibitors of a KIT-driven melanoma. At the lesion-level, MYC amplifications were enriched in ICI nonresponders. Single-cell sequencing revealed polyclonal seeding of metastases originating from clones with different ploidy in one patient. Finally, we observed that brain metastases that diverged early in molecular evolution emerge late in disease. Overall, our study illustrates the diverse evolutionary landscape of advanced melanoma.

SIGNIFICANCE

Despite treatment advances, melanoma remains a deadly disease at stage IV. Through research autopsy and dense sampling of metastases combined with extensive multiomic profiling, our study elucidates the many mechanisms that melanomas use to evade treatment and the immune system, whether through mutations, widespread copy-number alterations, or extrachromosomal DNA. See related commentary by Shain, p. 1294. This article is highlighted in the In This Issue feature, p. 1275.

Acquired DNA damage repairs deficiency-driven immune evolution and involved immune factors of local versus distant metastases in non-small cell lung cancer

The evolution of immune profile from primary tumors to distant and local metastases in non-small cell lung cancer (NSCLC), as well as the impact of the immune background of primary tumors on metastatic potential, remains unclear. To address this, we performed whole-exome sequencing and immunohistochemistry for 73 paired primary and metastatic tumor samples from 41 NSCLC patients, and analyzed the change of immune profile from primary tumors to metastases and involved genetic factors. We found that distant metastases tended to have a decreased CD8+ T cell level along with an increased chromosomal instability (CIN) compared with primary tumors, which was partially ascribed to acquired DNA damage repair (DDR) deficiency. Distant metastases were characterized by immunosuppression (low CD8+ T cell level) and immune evasion (high PD-L1 level) whereas local metastases (pleura) were immune-competent with high CD8+ T cell, low CD4+ T cell and low PD-L1 level. Primary tumors with high levels of CD4+ T cells were associated with distant metastases rather than local metastases. Analysis of TCGA data and a single-cell RNA-sequencing dataset revealed a decreasing trend of major immune cells, such as CD8+ T cells, and an increasing trend of CD4 T helper cells (Th2 and Th1) in primary tumors with metastases from local to distant sites. Our study indicates that there are differences in the immune evolution between distant and local metastases, and that acquired DDR deficiency contributes to the immunosuppression in distant metastases of NSCLC. Moreover, the immune background of primary tumors may affect their metastatic potential.

Quantification of subtype purity in Luminal A breast cancer predicts clinical characteristics and survival

PURPOSE

PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples.

METHODS

We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes-pLumA, pLumB, pHER2, and pBasal-for each case and measured associations with tumor characteristics, molecular features, and survival.

RESULTS

Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival.

CONCLUSION

Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.

Bootstrap confidence for molecular evolutionary estimates from tumor bulk sequencing data

Bulk sequencing is commonly used to characterize the genetic diversity of cancer cell populations in tumors and the evolutionary relationships of cancer clones. However, bulk sequencing produces aggregate information on nucleotide variants and their sample frequencies, necessitating computational methods to predict distinct clone sequences and their frequencies within a sample. Interestingly, no methods are available to measure the statistical confidence in the variants assigned to inferred clones. We introduce a bootstrap resampling approach that combines clone prediction and statistical confidence calculation for every variant assignment. Analysis of computer-simulated datasets showed the bootstrap approach to work well in assessing the reliability of predicted clones as well downstream inferences using the predicted clones (e.g., mapping metastatic migration paths). We found that only a fraction of inferences have good bootstrap support, which means that many inferences are tentative for real data. Using the bootstrap approach, we analyzed empirical datasets from metastatic cancers and placed bootstrap confidence on the estimated number of mutations involved in cell migration events. We found that the numbers of driver mutations involved in metastatic cell migration events sourced from primary tumors are similar to those where metastatic tumors are the source of new metastases. So, mutations with driver potential seem to keep arising during metastasis. The bootstrap approach developed in this study is implemented in software available at https://github.com/SayakaMiura/CloneFinderPlus.

Landscape of chromatin remodeling gene alterations in endometrial carcinoma

OBJECTIVE

Chromatin remodeling genes (CRGs) encode components of epigenetic regulatory mechanisms and alterations in these genes have been identified in several tumor types, including gynecologic cancers. In this study, we sought to investigate the prevalence and clinicopathological associations of CRG alterations in endometrial carcinoma (EC).

METHODS

We performed a retrospective analysis of 660 ECs sequenced using a clinical massively parallel sequencing assay targeting up to 468 genes, including 25 CRGs, and defined the presence of somatic CRG alterations. Clinicopathologic features were obtained for all cases. Immunohistochemical interrogation of ARID1A and PTEN proteins was performed in a subset of samples.

RESULTS

Of the 660 ECs sequenced, 438 (66.4%) harbored CRG alterations covered by our panel. The most commonly altered CRG was ARID1A (46%), followed by CTCF (21%), KMT2D (18%), KMT2B (17%), BCOR (16%), ARID1B (12%) and SMARCA4 (11%). We found that ARID1A genetic alterations were preferentially bi-allelic and often corresponded to altered ARID1A protein expression in ECs. We further observed that ARID1A alterations were often subclonal when compared to PTEN alterations, which were primarily clonal in ECs harboring both mutations. Finally, CRG alterations were associated with an increased likelihood of myometrial and lymphovascular invasion in endometrioid ECs.

CONCLUSION

CRG alterations are common in EC and are associated with clinicopathologic features and likely play a crucial role in EC.

Distinct genomic features between osteosarcomas firstly metastasing to bone and to lung

Background

Osteosarcoma initially metastasing to bone only shows distinct biological features compared to osteosarcoma that firstly metastasizes to the lung, which suggests us underlying different genomic pathogenetic mechanism.

Methods

We analyzed whole-exome sequencing (WES) data for 38 osteosarcoma with paired samples in different relapse patterns. We also sought to redefine disease subclassifications for osteosarcoma based on genetic alterations and correlate these genetic profiles with clinical treatment courses to elucidate potential evolving cladograms.

Results

We investigated WES of 12/38 patients with high-grade osteosarcoma (31.6%) with initial bone metastasis (group A) and 26/38 (68.4%) with initial pulmonary metastasis (group B), of whom 15/38 (39.5%) had paired samples of primary lesions and metastatic lesions. We found that osteosarcoma in group A mainly carries single-nucleotide variations displaying higher tumor mutation burden and neoantigen load and more tertiary lymphoid structures, while those in group B mainly exhibits structural variants. High conservation of reported genetic sequencing over time in their evolving cladograms.

Conclusions

Osteosarcoma with mainly single-nucleotide variations other than structural variants might exhibit biological behavior predisposing toward bone metastases as well as better immunogenicity in tumor microenvironment.

ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA

BACKGROUND

Liquid biopsies and the dynamic tracking of somatic mutations within circulating tumour DNA (ctDNA) can provide insight into the dynamics of cancer evolution and the intra-tumour heterogeneity that fuels treatment resistance. However, identifying and tracking dynamic changes in somatic copy number alterations (SCNAs), which have been associated with poor outcome and metastasis, using ctDNA is challenging. Pancreatic adenocarcinoma is a disease which has been considered to harbour early punctuated events in its evolution, leading to an early fitness peak, with minimal further subclonal evolution.

METHODS

To interrogate the role of SCNAs in pancreatic adenocarcinoma cancer evolution, we applied whole-exome sequencing of 55 longitudinal cell-free DNA (cfDNA) samples taken from 24 patients (including 8 from whom a patient-derived xenograft (PDX) was derived) with metastatic disease prospectively recruited into a clinical trial. We developed a method, Aneuploidy in Circulating Tumour DNA (ACT-Discover), that leverages haplotype phasing of paired tumour biopsies or PDXs to identify SCNAs in cfDNA with greater sensitivity.

RESULTS

SCNAs were observed within 28 of 47 evaluable cfDNA samples. Of these events, 30% could only be identified by harnessing the haplotype-aware approach leveraged in ACT-Discover. The exceptional purity of PDX tumours enabled near-complete phasing of genomic regions in allelic imbalance, highlighting an important auxiliary function of PDXs. Finally, although the classical model of pancreatic cancer evolution emphasises the importance of early, homogenous somatic events as a key requirement for cancer development, ACT-Discover identified substantial heterogeneity of SCNAs, including parallel focal and arm-level events, affecting different parental alleles within individual tumours. Indeed, ongoing acquisition of SCNAs was identified within tumours throughout the disease course, including within an untreated metastatic tumour.

CONCLUSIONS

This work demonstrates the power of haplotype phasing to study genomic variation in cfDNA samples and reveals undiscovered intra-tumour heterogeneity with important scientific and clinical implications. Implementation of ACT-Discover could lead to important insights from existing cohorts or underpin future prospective studies seeking to characterise the landscape of tumour evolution through liquid biopsy.