Integrated Analysis of scRNA-Seq and Bulk RNA-Seq Reveals Metabolic Reprogramming of Liver Cancer and Establishes a Prognostic Risk Model.

Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients.

A transcriptomic intratumour heterogeneity-free signature overcomes sampling bias in prognostic risk classification for hepatocellular carcinoma.

Background & Aims: Intratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC). Methods: We interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients. Results: An average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality. Conclusions: We constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC. Impact and Implications: Intratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

Comprehensive analysis of intratumoural heterogeneity of somatic copy number alterations in diffuse glioma reveals clonality-dependent prognostic patterns.

AIMS: Intratumoural heterogeneity (ITH) has been implicated in tumour growth and progression as well as therapy resistance. However, the extent of ITH of somatic copy number alterations (ITH-SCNAs) as a result of tumour evolution and its influence on clinical outcomes in diffuse glioma (DG) remain poorly understood. METHODS: We used an integrated computational method to infer clonal and subclonal SCNAs in 760 untreated primary DGs from The Cancer Genome Atlas. ITH-SCNAs at the genome-wide, peak (region with recurrent SCNAs) and gene level were calculated. We used the Kaplan-Meier estimators and Cox proportional hazards models to examine the associations of ITH-SCNA with patient outcomes. An independent cohort of 243 patients with paired initial and recurrent tumours from the Glioma Longitudinal Analysis Consortium was used for validation. RESULTS: DGs showed widespread ITH-SCNA, with a median of 25.5% of SCNAs identified as subclonal. We found that clonal SCNA burden had stronger prognostic power than total SCNAs in IDH-mutant astrocytoma. Coamplifications of receptor tyrosine kinases (RTKs) tended to be subclonal, and subclonal RTK amplification was significantly associated with high tumour proliferative potential and unfavourable clinical outcomes in IDH-wild-type glioblastoma. In addition, we found that the prognostic values of the peak-level SCNAs were related to their mutated clonal architecture, from which three clonality-dependent prognostic patterns of SCNAs were proposed, including clonal-dominant, subclonal-dominant and clonality-independent schemas. CONCLUSIONS: The systematic analysis of ITH-SCNAs in large cohorts of DGs highlighted the importance of considering the clonality of SCNA in discovery of tumour prognostic markers.

Telomere maintenance mechanism dysregulation serves as an early predictor of adjuvant therapy response and a potential therapeutic target in human cancers.

Telomere maintenance mechanisms (TMMs) rescue cells from telomere crisis, endow cells immortal property, stabilize genomic integrity. However, TMM-associated molecular profiles and their clinical outcomes in cancer remain elusive. Here, we performed a pan-cancer and integrated analysis of TMM gene expression profiles from 10 107 unique samples with clinicopathological, molecular and outcome features across seven malignancies from the same microarray platform (Affymetrix GPL570 platform). This resource was divided into case-control datasets for obtaining dysregulated TMM genes and survival datasets for evaluating clinical outcomes. Multidimensional data from The Cancer Genome Atlas (TCGA) were used to elucidate associations between TMM dysregulation and survival, genomic instability. Our results demonstrated that TMMs had a consistent dysregulation spectrum across cancers, based on which we developed the TMM-dysregulation signature TMS score (TMScore) that was positively associated with various tumor adverse features. Two opposite prognostic patterns of TMScore independent of clinicopathological and molecular characteristics were identified, which might be explained by genomic instability: breast and lung cancer patients with elevated TMScore had inferior outcomes, suggesting TMScore-related genes as potential therapeutic targets, on the contrary, colon and stomach cancer patients had superior outcomes. Most important, the prognostic value of TMScore was still significant regardless of whether patients had received adjuvant therapy, which was valuable for discriminating nonresponders from responders, and could predict the effectiveness of adjuvant therapy. In summary, our resources delineate TMMs dysregulated landscape across cancers, shed light on the impact of TMMs dysregulation on patient outcomes and adjuvant therapy, and provide novel therapeutic opportunities for cancer treatment.

IDH clonal heterogeneity segregates a subgroup of non-1p/19q codeleted gliomas with unfavourable clinical outcome.

AIMS: Diffuse gliomas (DGs) are classified into three major molecular subgroups following the revised World Health Organisation (WHO) classification criteria based on their IDH mutation and 1p/19q codeletion status. However, substantial biological heterogeneity and differences in the clinical course are apparent within each subgroup, which remain to be resolved. We sought to assess the clonal status of somatic mutations and explore whether additional molecular subgroups exist within DG. METHODS: A computational framework that integrates the variant allele frequency, local copy number and tumour purity was used to infer the clonality of somatic mutations in 876 DGs from The Cancer Genome Atlas (TCGA). We performed an unsupervised cluster analysis to identify molecular subgroups and characterised their clinical and biological significance. RESULTS: DGs showed widespread genetic intratumoural heterogeneity (ITH), with nearly all driver genes harbouring subclonal mutations, even for known glioma initiating event IDH1 (17.1%). Gliomas with subclonal IDH mutation and without 1p/19q codeletion showed shorter overall and disease-specific survival, higher ITH and exhibited differences in genomic patterns, transcript levels and proliferative potential, when compared with IDH clonal mutation and no 1p/19q codeletion gliomas. We defined a refined stratification system based on the current WHO glioma molecular classification, which showed close correlations with patients' clinical outcomes. CONCLUSIONS: For the first time, we integrated the clonal status of somatic mutations into cancer genomic classification and highlighted the necessity of considering IDH clonal architectures in glioma precision stratification.

Clonal tumor mutations in homologous recombination genes predict favorable clinical outcome in ovarian cancer treated with platinum-based chemotherapy.

OBJECTIVE: Platinum-based chemotherapy remains the first-line treatment for ovarian carcinoma by inducing DNA damage. The therapeutic impact of clonal and subclonal somatic mutations in DNA damage repair (DDR) pathways remains unexplored. METHODS: We performed an integrated analysis to infer the clonality of somatic deleterious mutations in 385 ovarian carcinomas treated with platinum-based chemotherapy. The Kaplan-Meier method was performed for visualization and the differences between survival curves were calculated by log-rank test. Proportional hazards models were used to estimate relative hazards for platinum-free interval (PFI), progression-free survival (PFS) and overall survival (OS). RESULTS: We found that somatic deleterious mutations in DDR pathways exhibited widespread clonal heterogeneity, and that patients with DDR clonal mutations exhibited a "hypermutator phenotype". Clonal somatic mutations in homologous recombination repair (HRR) pathway were significantly associated with better OS (HR = 0.19 (95% CI, 0.06-0.59), P = 0.0044) and PFS (HR = 0.20 (95% CI, 0.08-0.49), P = 0.0005) than HRR wild-type, while HRR subclonal mutations were not associated with prognosis. Moreover, HRR clonal mutations were associated with significantly higher chemotherapy sensitive rate (P = 0.0027) and longer PFI (HR = 0.20 (95% CI, 0.08-0.49), P = 0.0005) than HRR wild-type, while HRR subclonal mutations were not. We validated our findings using an independent cohort of 93 ovarian cancer patients that received platinum-based chemotherapy. CONCLUSIONS: HRR clonal mutations, but not subclonal mutations, were associated with improved survival, chemotherapy response, and genome instability compared with HRR wild-type.

Revealing clonality and subclonality of driver genes for clinical survival benefits in breast cancer.

PURPOSE: Genomic studies have revealed that genomic aberrations play important roles in the progression of this disease. The aim of this study was to evaluate the associations between clinical survival outcomes of the clonality and subclonality status of driver genes in breast cancer. METHODS: We performed an integrated analysis to infer the clonal status of 55 driver genes in breast cancer data from TCGA. We used the chi-squared test to assess the relations between clonality of driver gene mutations and clinicopathological factors. The Kaplan-Meier method was performed for the visualization and the differences between survival curves were calculated by log-rank test. Univariate and multivariate Cox proportional hazards regression models were used to adjust for clinicopathological factors. RESULTS: We identified a high proportion of clonal mutations in these driver genes. Among them, there were 17 genes showing significant associations between their clonality and multiple clinicopathologic factors. Performing survival analysis on BRCA patients with clonal or subclonal driver gene mutations, we found that clonal ERBB2, FOXA1, and KMT2C mutations and subclonal GATA3 and RB1 mutations predicted shorter overall survival compared with those with wild type. Furthermore, clonal ERBB2 and FOXA1 mutations and subclonal GATA3 and RB1 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. By longitudinal analysis, the clonality of ERBB2, FOXA1, GATA3, and RB1 significantly predicted patients' outcome within some specific BRCA tumor stages and histological subtypes. CONCLUSIONS: In summary, these clonal or subclonal mutations of driver genes have implications for diagnosis, prognosis, and treatment with BRCA patients.

Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability.

Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.

Sex difference of mutation clonality in diffuse glioma evolution.

BACKGROUND: Sex differences in glioma incidence and outcome have been previously reported but remain poorly understood. Many sex differences that affect the cancer risk were thought to be associated with cancer evolution. METHODS: In this study, we used an integrated framework to infer the timing and clonal status of mutations in ~600 diffuse gliomas from The Cancer Genome Atlas (TCGA) including glioblastomas (GBMs) and low-grade gliomas (LGGs), and investigated the sex difference of mutation clonality. RESULTS: We observed higher overall and subclonal mutation burden in female patients with different grades of gliomas, which could be largely explained by the mutations of the X chromosome. Some well-established drivers were identified showing sex-biased clonality, such as CDH18 and ATRX. Focusing on glioma subtypes, we further found a higher subclonal mutation burden in females than males in the majority of glioma subtypes, and observed opposite clonal tendency of several drivers between male and female patients in a specific subtype. Moreover, analysis of clinically actionable genes revealed that mutations in genes of the mitogen-activated protein kinase (MAPK) signaling pathway were more likely to be clonal in female patients with GBM, whereas mutations in genes involved in the receptor tyrosine kinase signaling pathway were more likely to be clonal in male patients with LGG. CONCLUSIONS: The patients with diffuse glioma showed sex-biased mutation clonality (eg, different subclonal mutation number and different clonal tendency of cancer genes), highlighting the need to consider sex as an important variable for improving glioma therapy and clinical care.

A pan-cancer atlas of cancer hallmark-associated candidate driver lncRNAs.

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non-coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well-known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan-cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in 'Tissue Invasion and Metastasis' in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA-based target therapy.

SEECancer: a resource for somatic events in evolution of cancer genome.

Cancer cells progressively evolve from a premalignant to a malignant state, which is driven by accumulating somatic alterations that confer normal cells a fitness advantage. Improvements in high-throughput sequencing techniques have led to an increase in construction of tumor phylogenetics and identification of somatic driver events that specifically occurred in different tumor progression stages. Here, we developed the SEECancer database (http://biocc.hrbmu.edu.cn/SEECancer), which aims to present the comprehensive cancer evolutionary stage-specific somatic events (including early-specific, late-specific, relapse-specific, metastasis-specific, drug-resistant and drug-induced genomic events) and their temporal orders. By manually curating over 10 000 published articles, 1231 evolutionary stage-specific genomic events and 5772 temporal orders involving 82 human cancers and 23 tissue origins were collected and deposited in the SEECancer database. Each entry contains the somatic event, evolutionary stage, cancer type, detection approach and relevant evidence. SEECancer provides a user-friendly interface for browsing, searching and downloading evolutionary stage-specific somatic events and temporal relationships in various cancers. With increasing attention on cancer genome evolution, the necessary information in SEECancer will facilitate understanding of cancer etiology and development of evolutionary therapeutics, and help clinicians to discover biomarkers for monitoring tumor progression.