PyClone: statistical inference of clonal population structure in cancer

Scuphr: A probabilistic framework for cell lineage tree reconstruction

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method's robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.

Circulating tumor DNA: from discovery to clinical application in breast cancer

Breast cancer (BC) stands out as the cancer with the highest incidence of morbidity and mortality among women worldwide, and its incidence rate is currently trending upwards. Improving the efficiency of breast cancer diagnosis and treatment is crucial, as it can effectively reduce the disease burden. Circulating tumor DNA (ctDNA) originates from the release of tumor cells and plays a pivotal role in the occurrence, development, and metastasis of breast cancer. In recent years, the widespread application of high-throughput analytical technology has made ctDNA a promising biomarker for early cancer detection, monitoring minimal residual disease, early recurrence monitoring, and predicting treatment outcomes. ctDNA-based approaches can effectively compensate for the shortcomings of traditional screening and monitoring methods, which fail to provide real-time information and prospective guidance for breast cancer diagnosis and treatment. This review summarizes the applications of ctDNA in various aspects of breast cancer, including screening, diagnosis, prognosis, treatment, and follow-up. It highlights the current research status in this field and emphasizes the potential for future large-scale clinical applications of ctDNA-based approaches.

Integrated mutational landscape analysis of poorly differentiated high-grade neuroendocrine carcinoma of the uterine cervix

High-grade neuroendocrine cervical cancers (NETc) are exceedingly rare, highly aggressive tumors. We analyzed 64 NETc tumor samples by whole-exome sequencing (WES). Human papillomavirus DNA was detected in 65.6% (42/64) of the tumors. Recurrent mutations were identified in PIK3CA, KMT2D/MLL2, K-RAS, ARID1A, NOTCH2, and RPL10. The top mutated genes included RB1, ARID1A, PTEN, KMT2D/MLL2, and WDFY3, a gene not yet implicated in NETc. Somatic CNV analysis identified two copy number gains (3q27.1 and 19q13.12) and five copy number losses (1p36.21/5q31.3/6p22.2/9q21.11/11p15.5). Also, gene fusions affecting the ACLY-CRHR1 and PVT1-MYC genes were identified in one of the eight samples subjected to RNA sequencing. To resolve evolutionary history, multiregion WES in NETc admixed with adenocarcinoma cells was performed (i.e., mixed-NETc). Phylogenetic analysis of mixed-NETc demonstrated that adenocarcinoma and neuroendocrine elements derive from a common precursor with mutations typical of adenocarcinomas. Over one-third (22/64) of NETc demonstrated a mutator phenotype of C > T at CpG consistent with deficiencies in MBD4, a member of the base excision repair (BER) pathway. Mutations in the PI3K/AMPK pathways were identified in 49/64 samples. We used two patient-derived-xenografts (PDX) (i.e., NET19 and NET21) to evaluate the activity of pan-HER (afatinib), PIK3CA (copanlisib), and ATR (elimusertib) inhibitors, alone and in combination. PDXs harboring alterations in the ERBB2/PI3K/AKT/mTOR/ATR pathway were sensitive to afatinib, copanlisib, and elimusertib (P < 0.001 vs. controls). However, combinations of copanlisib/afatinib and copanlisib/elimusertib were significantly more effective in controlling NETc tumor growth. These findings define the genetic landscape of NETc and suggest that a large subset of these highly lethal malignancies might benefit from existing targeted therapies.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

ASCL1 Drives Tolerance to Osimertinib in EGFR Mutant Lung Cancer in Permissive Cellular Contexts

The majority of EGFR mutant lung adenocarcinomas respond well to EGFR tyrosine kinase inhibitors (TKI). However, most of these responses are partial, with drug-tolerant residual disease remaining even at the time of maximal response. This residual disease can ultimately lead to relapses, which eventually develop in most patients. To investigate the cellular and molecular properties of residual tumor cells in vivo, we leveraged patient-derived xenograft (PDX) models of EGFR mutant lung cancer. Subcutaneous EGFR mutant PDXs were treated with the third-generation TKI osimertinib until maximal tumor regression. Residual tissue inevitably harbored tumor cells that were transcriptionally distinct from bulk pretreatment tumor. Single-cell transcriptional profiling provided evidence of cells matching the profiles of drug-tolerant cells present in the pretreatment tumor. In one of the PDXs analyzed, osimertinib treatment caused dramatic transcriptomic changes that featured upregulation of the neuroendocrine lineage transcription factor ASCL1. Mechanistically, ASCL1 conferred drug tolerance by initiating an epithelial-to-mesenchymal gene-expression program in permissive cellular contexts. This study reveals fundamental insights into the biology of drug tolerance, the plasticity of cells through TKI treatment, and why specific phenotypes are observed only in certain tumors.

SIGNIFICANCE

Analysis of residual disease following tyrosine kinase inhibitor treatment identified heterogeneous and context-specific mechanisms of drug tolerance in lung cancer that could lead to the development of strategies to forestall drug resistance. See related commentary by Rumde and Burns, p. 1188.

Multi-omics Analysis Reveals Immune Features Associated with Immunotherapy Benefit in Patients with Squamous Cell Lung Cancer from Phase III Lung-MAP S1400I Trial

PURPOSE

Identifying molecular and immune features to guide immune checkpoint inhibitor (ICI)-based regimens remains an unmet clinical need.

EXPERIMENTAL DESIGN

Tissue and longitudinal blood specimens from phase III trial S1400I in patients with metastatic squamous non-small cell carcinoma (SqNSCLC) treated with nivolumab monotherapy (nivo) or nivolumab plus ipilimumab (nivo+ipi) were subjected to multi-omics analyses including multiplex immunofluorescence (mIF), nCounter PanCancer Immune Profiling Panel, whole-exome sequencing, and Olink.

RESULTS

Higher immune scores from immune gene expression profiling or immune cell infiltration by mIF were associated with response to ICIs and improved survival, except regulatory T cells, which were associated with worse overall survival (OS) for patients receiving nivo+ipi. Immune cell density and closer proximity of CD8+GZB+ T cells to malignant cells were associated with superior progression-free survival and OS. The cold immune landscape of NSCLC was associated with a higher level of chromosomal copy-number variation (CNV) burden. Patients with LRP1B-mutant tumors had a shorter survival than patients with LRP1B-wild-type tumors. Olink assays revealed soluble proteins such as LAMP3 increased in responders while IL6 and CXCL13 increased in nonresponders. Upregulation of serum CXCL13, MMP12, CSF-1, and IL8 were associated with worse survival before radiologic progression.

CONCLUSIONS

The frequency, distribution, and clustering of immune cells relative to malignant ones can impact ICI efficacy in patients with SqNSCLC. High CNV burden may contribute to the cold immune microenvironment. Soluble inflammation/immune-related proteins in the blood have the potential to monitor therapeutic benefit from ICI treatment in patients with SqNSCLC.

Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma

Solid tumors are complex ecosystems with heterogeneous 3D structures, but the spatial intra-tumor heterogeneity (sITH) at the macroscopic (i.e., whole tumor) level is under-explored. Using a phylogeographic approach, we sequence genomes and transcriptomes from 235 spatially informed sectors across 13 hepatocellular carcinomas (HCC), generating one of the largest datasets for studying sITH. We find that tumor heterogeneity in HCC segregates into spatially variegated blocks with large genotypic and phenotypic differences. By dissecting the transcriptomic heterogeneity, we discover that 30% of patients had a "spatially competing distribution" (SCD), where different spatial blocks have distinct transcriptomic subtypes co-existing within a tumor, capturing the critical transition period in disease progression. Interestingly, the tumor regions with more advanced transcriptomic subtypes (e.g., higher cell cycle) often take clonal dominance with a wider geographic range, rejecting neutral evolution for SCD patients. Extending the statistical tests for detecting natural selection to many non-SCD patients reveal varying levels of selective signal across different tumors, implying that many evolutionary forces including natural selection and geographic isolation can influence the overall pattern of sITH. Taken together, tumor phylogeography unravels a dynamic landscape of sITH, pinpointing important evolutionary and clinical consequences of spatial heterogeneity in cancer.

Understanding the malignant potential of gastric metaplasia of the oesophagus and its relevance to Barrett's oesophagus surveillance: individual-level data analysis

OBJECTIVE

Whether gastric metaplasia (GM) of the oesophagus should be considered as Barrett's oesophagus (BO) is controversial. Given concern intestinal metaplasia (IM) may be missed due to sampling, the UK guidelines include GM as a type of BO. Here, we investigated whether the risk of misdiagnosis and the malignant potential of GM warrant its place in the UK surveillance.

DESIGN

We performed a thorough pathology and endoscopy review to follow clinical outcomes in a novel UK cohort of 244 patients, covering 1854 person years of follow-up. We complemented this with a comparative genomic analysis of 160 GM and IM specimens, focused on early molecular hallmarks of BO and oesophageal adenocarcinoma (OAC).

RESULTS

We found that 58 of 77 short-segment (<3 cm) GM (SS-GM) cases (75%) continued to be observed as GM-only across a median of 4.4 years of follow-up. We observed that disease progression in GM-only cases and GM+IM cases (cases with reported GM on some occasions, IM on others) was significantly lower than in the IM-only cases (Kaplan-Meier, p=0.03). Genomic analysis revealed that the mutation burden in GM is significantly lower than in IM (p<0.01). Moreover, GM does not bear the mutational hallmarks of OAC, with an absence of associated signatures and driver gene mutations. Finally, we established that GM found adjacent to OAC is evolutionarily distant from cancer.

CONCLUSION

SS-GM is a distinct entity from SS-IM and the malignant potential of GM is lower than IM. It is questionable whether SS-GM warrants inclusion in BO surveillance.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Background

Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.

Method

To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.

Results

We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.

Conclusion

Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.

Deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial sections

Tumors may contain billions of cells including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that is consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

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.