Universal Patterns of Selection in Cancer and Somatic Tissues

Identification of cancer driver genes based on hierarchical weak consensus model

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

Exploring the tumor genomic landscape of aggressive prostate cancer by whole-genome sequencing of tissue or liquid biopsies

Treatment resistance remains a major issue in aggressive prostate cancer (PC), and novel genomic biomarkers may guide better treatment selection. Circulating tumor DNA (ctDNA) can provide minimally invasive information about tumor genomes, but the genomic landscape of aggressive PC based on whole-genome sequencing (WGS) of ctDNA remains incompletely characterized. Thus, we here performed WGS of tumor tissue (n = 31) or plasma ctDNA (n = 10) from a total of 41 aggressive PC patients, including 11 hormone-naïve, 15 hormone-sensitive, and 15 castration-resistant patients. Across all variant types, we found progressively more altered tumor genomic profiles in later stages of aggressive PC. The potential driver genes most frequently affected by single-nucleotide variants or insertions/deletions included the known PC-related genes TP53, CDK12, and PTEN and the novel genes COL13A1, KCNH3, and SENP3. Etiologically, aggressive PC was associated with age-related and DNA repair-related mutational signatures. Copy number variants most frequently affected 14q11.2 and 8p21.2, where no well-recognized PC-related genes are located, and also frequently affected regions near the known PC-related genes MYC, AR, TP53, PTEN, and BRCA1. Structural variants most frequently involved not only the known PC-related genes TMPRSS2 and ERG but also the less extensively studied gene in this context, PTPRD. Finally, clinically actionable variants were detected throughout all stages of aggressive PC and in both plasma and tissue samples, emphasizing the potential clinical applicability of WGS of minimally invasive plasma samples. Overall, our study highlights the feasibility of using liquid biopsies for comprehensive genomic characterization as an alternative to tissue biopsies in advanced/aggressive PC.

Landscape of driver mutations and their clinical effects on Down syndrome-related myeloid neoplasms

ABSTRACT

Transient abnormal myelopoiesis (TAM) is a common complication in newborns with Down syndrome (DS). It commonly progresses to myeloid leukemia (ML-DS) after spontaneous regression. In contrast to the favorable prognosis of primary ML-DS, patients with refractory/relapsed ML-DS have poor outcomes. However, the molecular basis for refractoriness and relapse and the full spectrum of driver mutations in ML-DS remain largely unknown. We conducted a genomic profiling study of 143 TAM, 204 ML-DS, and 34 non-DS acute megakaryoblastic leukemia cases, including 39 ML-DS cases analyzed by exome sequencing. Sixteen novel mutational targets were identified in ML-DS samples. Of these, inactivations of IRX1 (16.2%) and ZBTB7A (13.2%) were commonly implicated in the upregulation of the MYC pathway and were potential targets for ML-DS treatment with bromodomain-containing protein 4 inhibitors. Partial tandem duplications of RUNX1 on chromosome 21 were also found, specifically in ML-DS samples (13.7%), presenting its essential role in DS leukemia progression. Finally, in 177 patients with ML-DS treated following the same ML-DS protocol (the Japanese Pediatric Leukemia and Lymphoma Study Group acute myeloid leukemia -D05/D11), CDKN2A, TP53, ZBTB7A, and JAK2 alterations were associated with a poor prognosis. Patients with CDKN2A deletions (n = 7) or TP53 mutations (n = 4) had substantially lower 3-year event-free survival (28.6% vs 90.5%; P < .001; 25.0% vs 89.5%; P < .001) than those without these mutations. These findings considerably change the mutational landscape of ML-DS, provide new insights into the mechanisms of progression from TAM to ML-DS, and help identify new therapeutic targets and strategies for ML-DS.

Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma

In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.

Somatic mutations associate with clonal expansion of CD8+ T cells

Somatic mutations in T cells can cause cancer but also have implications for immunological diseases and cell therapies. The mutation spectrum in nonmalignant T cells is unclear. Here, we examined somatic mutations in CD4+ and CD8+ T cells from 90 patients with hematological and immunological disorders and used T cell receptor (TCR) and single-cell sequencing to link mutations with T cell expansions and phenotypes. CD8+ cells had a higher mutation burden than CD4+ cells. Notably, the biggest variant allele frequency (VAF) of non-synonymous variants was higher than synonymous variants in CD8+ T cells, indicating non-random occurrence. The non-synonymous VAF in CD8+ T cells strongly correlated with the TCR frequency, but not age. We identified mutations in pathways essential for T cell function and often affected lymphoid neoplasia. Single-cell sequencing revealed cytotoxic TEMRA phenotypes of mutated T cells. Our findings suggest that somatic mutations contribute to CD8+ T cell expansions without malignant transformation.

PIGA Mutations and Glycosylphosphatidylinositol Anchor Dysregulation in Polyposis-Associated Duodenal Tumorigenesis

The pathogenesis of duodenal tumors in the inherited tumor syndromes familial adenomatous polyposis (FAP) and MUTYH-associated polyposis (MAP) is poorly understood. This study aimed to identify genes that are significantly mutated in these tumors and to explore the effects of these mutations. Whole exome and whole transcriptome sequencing identified recurrent somatic coding variants of phosphatidylinositol N-acetylglucosaminyltransferase subunit A (PIGA) in 19/70 (27%) FAP and MAP duodenal adenomas, and further confirmed the established driver roles for APC and KRAS. PIGA catalyzes the first step in glycosylphosphatidylinositol (GPI) anchor biosynthesis. Flow cytometry of PIGA-mutant adenoma-derived and CRISPR-edited duodenal organoids confirmed loss of GPI anchors in duodenal epithelial cells and transcriptional profiling of duodenal adenomas revealed transcriptional signatures associated with loss of PIGA.

IMPLICATIONS

PIGA somatic mutation in duodenal tumors from patients with FAP and MAP and loss of membrane GPI-anchors may present new opportunities for understanding and intervention in duodenal tumorigenesis.

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

Phenotypic noise and plasticity in cancer evolution

Non-genetic alterations can produce changes in a cell's phenotype. In cancer, these phenomena can influence a cell's fitness by conferring access to heritable, beneficial phenotypes. Herein, we argue that current discussions of 'phenotypic plasticity' in cancer evolution ignore a salient feature of the original definition: namely, that it occurs in response to an environmental change. We suggest 'phenotypic noise' be used to distinguish non-genetic changes in phenotype that occur independently from the environment. We discuss the conceptual and methodological techniques used to identify these phenomena during cancer evolution. We propose that the distinction will guide efforts to define mechanisms of phenotype change, accelerate translational work to manipulate phenotypes through treatment, and, ultimately, improve patient outcomes.

Analysis of somatic mutations in whole blood from 200,618 individuals identifies pervasive positive selection and novel drivers of clonal hematopoiesis

Human aging is marked by the emergence of a tapestry of clonal expansions in dividing tissues, particularly evident in blood as clonal hematopoiesis (CH). CH, linked to cancer risk and aging-related phenotypes, often stems from somatic mutations in a set of established genes. However, the majority of clones lack known drivers. Here we infer gene-level positive selection in whole blood exomes from 200,618 individuals in UK Biobank. We identify 17 additional genes, ZBTB33, ZNF318, ZNF234, SPRED2, SH2B3, SRCAP, SIK3, SRSF1, CHEK2, CCDC115, CCL22, BAX, YLPM1, MYD88, MTA2, MAGEC3 and IGLL5, under positive selection at a population level, and validate this selection pattern in 10,837 whole genomes from single-cell-derived hematopoietic colonies. Clones with mutations in these genes grow in frequency and size with age, comparable to classical CH drivers. They correlate with heightened risk of infection, death and hematological malignancy, highlighting the significance of these additional genes in the aging process.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.

Mutations, substitutions, and selection: Linking mutagenic processes to cancer using evolutionary theory

Cancers are the product of evolutionary events, where molecular variation occurs and accumulates in tissues and tumors. Sequencing of this molecular variation informs not only which variants are driving tumorigenesis, but also the mechanisms behind what is fueling mutagenesis. Both of these details are crucial for preventing premature deaths due to cancer, whether it is by targeting the variants driving the cancer phenotype or by measures to prevent exogenous mutations from contributing to somatic evolution. Here, we review tools to determine both molecular signatures and cancer drivers, and avenues by which these metrics may be linked.

Assessing the validity of driver gene identification tools for targeted genome sequencing data

Motivation

Most cancer driver gene identification tools have been developed for whole-exome sequencing data. Targeted sequencing is a popular alternative to whole-exome sequencing for large cancer studies due to its greater depth at a lower cost per tumor. Unlike whole-exome sequencing, targeted sequencing only enables mutation calling for a selected subset of genes. Whether existing driver gene identification tools remain valid in that context has not previously been studied.

Results

We evaluated the validity of seven popular driver gene identification tools when applied to targeted sequencing data. Based on whole-exome data of 14 different cancer types from TCGA, we constructed matching targeted datasets by keeping only the mutations overlapping with the pan-cancer MSK-IMPACT panel and, in the case of breast cancer, also the breast-cancer-specific B-CAST panel. We then compared the driver gene predictions obtained on whole-exome and targeted mutation data for each of the seven tools. Differences in how the tools model background mutation rates were the most important determinant of their validity on targeted sequencing data. Based on our results, we recommend OncodriveFML, OncodriveCLUSTL, 20/20+, dNdSCv, and ActiveDriver for driver gene identification in targeted sequencing data, whereas MutSigCV and DriverML are best avoided in that context.

Availability and implementation

Code for the analyses is available at https://github.com/SchmidtGroupNKI/TGSdrivergene_validity.

Pan-cancer discovery of somatic mutations from RNA sequencing data

Identification of somatic mutations (SMs) is essential for characterizing cancer genomes. While DNA-seq is the prevalent method for identifying SMs, RNA-seq provides an alternative strategy to discover tumor mutations in the transcribed genome. Here, we have developed a machine learning based pipeline to discover SMs based on RNA-seq data (designated as RNA-SMs). Subsequently, we have conducted a pan-cancer analysis to systematically identify RNA-SMs from over 8,000 tumors in The Cancer Genome Atlas (TCGA). In this way, we have identified over 105,000 novel SMs that had not been reported in previous TCGA studies. These novel SMs have significant clinical implications in designing targeted therapy for improved patient outcomes. Further, we have combined the SMs identified by both RNA-seq and DNA-seq analyses to depict an updated mutational landscape across 32 cancer types. This new online SM atlas, OncoDB ( https://oncodb.org ), offers a more complete view of gene mutations that underline the development and progression of various cancers.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

BACKGROUND

Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging.

METHODS

To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.

RESULTS

We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.

CONCLUSIONS

Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

The mutagenic forces shaping the genomic landscape of lung cancer in never smokers

Lung cancer in never smokers (LCINS) accounts for up to 25% of all lung cancers and has been associated with exposure to secondhand tobacco smoke and air pollution in observational studies. Here, we evaluate the mutagenic exposures in LCINS by examining deep whole-genome sequencing data from a large international cohort of 871 treatment-naïve LCINS recruited from 28 geographical locations within the Sherlock-Lung study. KRAS mutations were 3.8-fold more common in adenocarcinomas of never smokers from North America and Europe, while a 1.6-fold higher prevalence of EGFR and TP53 mutations was observed in adenocarcinomas from East Asia. Signature SBS40a, with unknown cause, was found in most samples and accounted for the largest proportion of single base substitutions in adenocarcinomas, being enriched in EGFR-mutated cases. Conversely, the aristolochic acid signature SBS22a was almost exclusively observed in patients from Taipei. Even though LCINS exposed to secondhand smoke had an 8.3% higher mutational burden and 5.4% shorter telomeres, passive smoking was not associated with driver mutations in cancer driver genes or the activities of individual mutational signatures. In contrast, patients from regions with high levels of air pollution were more likely to have TP53 mutations while exhibiting shorter telomeres and an increase in most types of somatic mutations, including a 3.9-fold elevation of signature SBS4 (q-value=3.1 × 10-5), previously linked mainly to tobacco smoking, and a 76% increase of clock-like signature SBS5 (q-value=5.0 × 10-5). A positive dose-response effect was observed with air pollution levels, which correlated with both a decrease in telomere length and an elevation in somatic mutations, notably attributed to signatures SBS4 and SBS5. Our results elucidate the diversity of mutational processes shaping the genomic landscape of lung cancer in never smokers.

Mutation rate heterogeneity at the sub-gene scale due to local DNA hypomethylation

Local mutation rates in human are highly heterogeneous, with known variability at the scale of megabase-sized chromosomal domains, and, on the other extreme, at the scale of oligonucleotides. The intermediate, kilobase-scale heterogeneity in mutation risk is less well characterized. Here, by analyzing thousands of somatic genomes, we studied mutation risk gradients along gene bodies, representing a genomic scale spanning roughly 1-10 kb, hypothesizing that different mutational mechanisms are differently distributed across gene segments. The main heterogeneity concerns several kilobases at the transcription start site and further downstream into 5' ends of gene bodies; these are commonly hypomutated with several mutational signatures, most prominently the ubiquitous C > T changes at CpG dinucleotides. The width and shape of this mutational coldspot at 5' gene ends is variable across genes, and corresponds to variable interval of lowered DNA methylation depending on gene activity level and regulation. Such hypomutated loci, at 5' gene ends or elsewhere, correspond to DNA hypomethylation that can associate with various landmarks, including intragenic enhancers, Polycomb-marked regions, or chromatin loop anchor points. Tissue-specific DNA hypomethylation begets tissue-specific local hypomutation. Of note, direction of mutation risk is inverted for AID/APOBEC3 cytosine deaminase activity, whose signatures are enriched in hypomethylated regions.

The genomic landscape of Vk*MYC myeloma highlights shared pathways of transformation between mice and humans

Multiple myeloma (MM) is a heterogeneous disease characterized by frequent MYC translocations. Sporadic MYC activation in the germinal center of genetically engineered Vk*MYC mice is sufficient to induce plasma cell tumors in which a variety of secondary mutations are spontaneously acquired and selected over time. Analysis of 119 Vk*MYC myeloma reveals recurrent copy number alterations, structural variations, chromothripsis, driver mutations, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC) mutational activity, and a progressive decrease in immunoglobulin transcription that inversely correlates with proliferation. Moreover, we identify frequent insertional mutagenesis by endogenous retro-elements as a murine specific mechanism to activate NF-kB and IL6 signaling pathways shared with human MM. Despite the increased genomic complexity associated with progression, advanced tumors remain dependent on MYC. In summary, here we credential the Vk*MYC mouse as a unique resource to explore MM genomic evolution and describe a fully annotated collection of diverse and immortalized murine MM tumors.

Negative Selection Allows DNA Mismatch Repair-Deficient Mouse Fibroblasts In Vitro to Tolerate High Levels of Somatic Mutations

Substantial numbers of somatic mutations have been found to accumulate with age in different human tissues. Clonal cellular amplification of some of these mutations can cause cancer and other diseases. However, it is as yet unclear if and to what extent an increased burden of random mutations can affect cellular function without clonal amplification. We tested this in cell culture, which avoids the limitation that an increased mutation burden in vivo typically leads to cancer. We performed single-cell whole-genome sequencing of primary fibroblasts from DNA mismatch repair (MMR) deficient Msh2-/- mice and littermate control animals after long-term passaging. Apart from analyzing somatic mutation burden we analyzed clonality, mutational signatures, and hotspots in the genome, characterizing the complete landscape of somatic mutagenesis in normal and MMR-deficient mouse primary fibroblasts during passaging. While growth rate of Msh2-/- fibroblasts was not significantly different from the controls, the number of de novo single-nucleotide variants (SNVs) increased linearly up until at least 30,000 SNVs per cell, with the frequency of small insertions and deletions (INDELs) plateauing in the Msh2-/- fibroblasts to about 10,000 INDELS per cell. We provide evidence for negative selection and large-scale mutation-driven population changes, including significant clonal expansion of preexisting mutations and widespread cell-strain-specific hotspots. Overall, our results provide evidence that increased somatic mutation burden drives significant cell evolutionary changes in a dynamic cell culture system without significant effects on growth. Since similar selection processes against mutations preventing organ and tissue dysfunction during aging are difficult to envision, these results suggest that increased somatic mutation burden can play a causal role in aging and diseases other than cancer.

Non-hypermutator cancers access driver mutations through reversals in germline mutational bias

Cancer is an evolutionary disease driven by mutations in asexually-reproducing somatic cells. In asexual microbes, bias reversals in the mutation spectrum can speed adaptation by increasing access to previously undersampled beneficial mutations. By analyzing tumors from 20 tissues, along with normal tissue and the germline, we demonstrate this effect in cancer. Non-hypermutated tumors reverse the germline mutation bias and have consistent spectra across tissues. These spectra changes carry the signature of hypoxia, and they facilitate positive selection in cancer genes. Hypermutated and non-hypermutated tumors thus acquire driver mutations differently: hypermutated tumors by higher mutation rates and non-hypermutated tumors by changing the mutation spectrum to reverse the germline mutation bias.

Gastric cancer actionable genomic alterations across diverse populations worldwide and pharmacogenomics strategies based on precision oncology

Introduction: Gastric cancer is one of the most prevalent types of cancer worldwide. The World Health Organization (WHO), the International Agency for Research on Cancer (IARC), and the Global Cancer Statistics (GLOBOCAN) reported an age standardized global incidence rate of 9.2 per 100,000 individuals for gastric cancer in 2022, with a mortality rate of 6.1. Despite considerable progress in precision oncology through the efforts of international consortia, understanding the genomic features and their influence on the effectiveness of anti-cancer treatments across diverse ethnic groups remains essential. Methods: Our study aimed to address this need by conducting integrated in silico analyses to identify actionable genomic alterations in gastric cancer driver genes, assess their impact using deleteriousness scores, and determine allele frequencies across nine global populations: European Finnish, European non-Finnish, Latino, East Asian, South Asian, African, Middle Eastern, Ashkenazi Jewish, and Amish. Furthermore, our goal was to prioritize targeted therapeutic strategies based on pharmacogenomics clinical guidelines, in silico drug prescriptions, and clinical trial data. Results: Our comprehensive analysis examined 275,634 variants within 60 gastric cancer driver genes from 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, identifying 13,542 annotated and predicted oncogenic variants. We prioritized the most prevalent and deleterious oncogenic variants for subsequent pharmacogenomics testing. Additionally, we discovered actionable genomic alterations in the ARID1A, ATM, BCOR, ERBB2, ERBB3, CDKN2A, KIT, PIK3CA, PTEN, NTRK3, TP53, and CDKN2A genes that could enhance the efficacy of anti-cancer therapies, as suggested by in silico drug prescription analyses, reviews of current pharmacogenomics clinical guidelines, and evaluations of phase III and IV clinical trials targeting gastric cancer driver proteins. Discussion: These findings underline the urgency of consolidating efforts to devise effective prevention measures, invest in genomic profiling for underrepresented populations, and ensure the inclusion of ethnic minorities in future clinical trials and cancer research in developed countries.

Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK Alterations

Never-smoker lung adenocarcinoma (NSLA) is prevalent in Asian populations, particularly in women. EGFR mutations and anaplastic lymphoma kinase (ALK) fusions are major genetic alterations observed in NSLA, and NSLA with these alterations have been well studied and can be treated with targeted therapies. To provide insights into the molecular profile of NSLA without EGFR and ALK alterations (NENA), we selected 141 NSLA tissues and performed proteogenomic characterization, including whole genome sequencing (WGS), transcriptomic, methylation EPIC array, total proteomic, and phosphoproteomic analyses. Forty patients with NSLA harboring EGFR and ALK alterations and seven patients with NENA with microsatellite instability were excluded. Genome analysis revealed that TP53 (25%), KRAS (22%), and SETD2 (11%) mutations and ROS1 fusions (14%) were the most frequent genetic alterations in NENA patients. Proteogenomic impact analysis revealed that STK11 and ERBB2 somatic mutations had broad effects on cancer-associated genes in NENA. DNA copy number alteration analysis identified 22 prognostic proteins that influenced transcriptomic and proteomic changes. Gene set enrichment analysis revealed estrogen signaling as the key pathway activated in NENA. Increased estrogen signaling was associated with proteogenomic alterations, such as copy number deletions in chromosomes 14 and 21, STK11 mutation, and DNA hypomethylation of LLGL2 and ST14. Finally, saracatinib, an Src inhibitor, was identified as a potential drug for targeting activated estrogen signaling in NENA and was experimentally validated in vitro. Collectively, this study enhanced our understanding of NENA NSLA by elucidating the proteogenomic landscape and proposed saracatinib as a potential treatment for this patient population that lacks effective targeted therapies.

SIGNIFICANCE

The proteogenomic landscape in never-smoker lung cancer without known driver mutations reveals prognostic proteins and enhanced estrogen signaling that can be targeted as a potential therapeutic strategy to improve patient outcomes.

PLS-based gene subset augmentation and tumor-specific gene identification

In the study of tumor disease pathogenesis, the identification of genes specifically expressed in disease states is pivotal, yet challenges arise from high-dimensional datasets with limited samples. Conventional gene (feature) selection methods often fall short of capturing the complexity of gene-phenotype and gene-gene interactions, necessitating a more robust analysis method. To address these challenges, a gene subset augmentation strategy is proposed in this paper. Our approach introduces diverse perturbation mechanisms to generate distinct gene subsets. The partial least squares-based multiple gene measurement algorithm considers gene-phenotype and gene-gene correlations, identifying differentially expressed genes, including those with weak signals. The constructed gene networks derived from the augmented subsets unveil regulatory patterns, enabling association analysis to explore gene associations comprehensively. Our algorithm excels in identifying small-sized gene subsets with strong discriminative power, surpassing traditional methods that yield a single gene subset. Unlike conventional approaches, our algorithm reveals a spectrum of different gene subsets and their weakly differentially expressed genes. This nuanced perspective aids in unraveling the molecular characteristics and specific expression patterns of tumor genes. The versatility of our approach not only contributes to the advancement of tumor-specific gene identification but also holds promise for addressing challenges in various fields characterized by high-dimensional datasets and limited samples. The Python implementation is available at http://github.com/wenjieyou/PLSGSA.

Extrinsic and intrinsic drivers of natural killer cell clonality

Clonal expansion of antigen-specific lymphocytes is the fundamental mechanism enabling potent adaptive immune responses and the generation of immune memory. Accompanied by pronounced epigenetic remodeling, the massive proliferation of individual cells generates a critical mass of effectors for the control of acute infections, as well as a pool of memory cells protecting against future pathogen encounters. Classically associated with the adaptive immune system, recent work has demonstrated that innate immune memory to human cytomegalovirus (CMV) infection is stably maintained as large clonal expansions of natural killer (NK) cells, raising questions on the mechanisms for clonal selection and expansion in the absence of re-arranged antigen receptors. Here, we discuss clonal NK cell memory in the context of the mechanisms underlying clonal competition of adaptive lymphocytes and propose alternative selection mechanisms that might decide on the clonal success of their innate counterparts. We propose that the integration of external cues with cell-intrinsic sources of heterogeneity, such as variegated receptor expression, transcriptional states, and somatic variants, compose a bottleneck for clonal selection, contributing to the large size of memory NK cell clones.

Geographic variation of mutagenic exposures in kidney cancer genomes

International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not yet been identified by conventional epidemiology make a substantial contribution to cancer burden1. In clear cell renal cell carcinoma, obesity, hypertension and tobacco smoking are risk factors, but they do not explain the geographical variation in its incidence2. Underlying causes can be inferred by sequencing the genomes of cancers from populations with different incidence rates and detecting differences in patterns of somatic mutations. Here we sequenced 962 clear cell renal cell carcinomas from 11 countries with varying incidence. The somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures characteristic of aristolochic acid compounds were present in most cases, but these were rare elsewhere. In Japan, a mutational signature of unknown cause was found in more than 70% of cases but in less than 2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher incidence rates of kidney cancer. Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension, suggesting that non-mutagenic mechanisms of action underlie these risk factors. The results of this study indicate the existence of multiple, geographically variable, mutagenic exposures that potentially affect tens of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics.

Large-scale Pan-cancer Cell Line Screening Identifies Actionable and Effective Drug Combinations

Oncology drug combinations can improve therapeutic responses and increase treatment options for patients. The number of possible combinations is vast and responses can be context-specific. Systematic screens can identify clinically relevant, actionable combinations in defined patient subtypes. We present data for 109 anticancer drug combinations from AstraZeneca's oncology small molecule portfolio screened in 755 pan-cancer cell lines. Combinations were screened in a 7 × 7 concentration matrix, with more than 4 million measurements of sensitivity, producing an exceptionally data-rich resource. We implement a new approach using combination Emax (viability effect) and highest single agent (HSA) to assess combination benefit. We designed a clinical translatability workflow to identify combinations with clearly defined patient populations, rationale for tolerability based on tumor type and combination-specific "emergent" biomarkers, and exposures relevant to clinical doses. We describe three actionable combinations in defined cancer types, confirmed in vitro and in vivo, with a focus on hematologic cancers and apoptotic targets.

SIGNIFICANCE

We present the largest cancer drug combination screen published to date with 7 × 7 concentration response matrices for 109 combinations in more than 750 cell lines, complemented by multi-omics predictors of response and identification of "emergent" combination biomarkers. We prioritize hits to optimize clinical translatability, and experimentally validate novel combination hypotheses. This article is featured in Selected Articles from This Issue, p. 695.

Multi-cohort comprehensive analysis unveiling the clinical value and therapeutic effect of GNAL in glioma

Clinical data indicates that glioma patients have poor treatment outcomes and clinical prognosis. The role of olfactory signaling pathway-related genes (OSPRGs) in glioma has not been fully elucidated. In this study, we aimed to investigate the role and relationship between OSPRGs and glioma. Univariate and multivariate Cox regression analyses were performed to assess the relationship between OSPRGs and the overall survival of glioma based on public cohorts, and the target gene (G Protein Subunit Alpha L, GNAL) was screened. The association of GNAL expression with clinicopathological characteristics, gene mutation landscape, tumor immune microenvironment (TIME), deoxyribonucleic acid (DNA) methylation, and naris-occlusion controlled genes (NOCGs) was performed. Immunohistochemistry was used to evaluate GNAL level in glioma. Further analysis was conducted to evaluate the drug sensitivity, immunotherapy response, and functional enrichment of GNAL. GNAL was an independent prognostic factor, and patients with low GNAL expression have a poor prognosis. Expression of GNAL was closely associated with clinicopathological characteristics, DNA methylation, and several immune-related pathways. Immune infiltration analysis indicated that GNAL levels were negatively correlated with immune scores. GNAL low-expression group showed efficacy with anti-PD-1 therapy. Ten compounds with significantly different half-maximal inhibitory concentration (IC50) values between the GNAL high and low-expression groups were identified. Furthermore, its expression was associated with several immune cells, immune-related genes, and NOCGs. The expression of GNAL is closely associated with clinicopathological characteristics, TIME, and the response to therapeutic interventions, highlighting its potential as a prognostic biomarker for glioma.

Stochastic modelling of single-cell gene expression adaptation reveals non-genomic contribution to evolution of tumor subclones

Cancer progression is an evolutionary process driven by the selection of cells adapted to gain growth advantage. We present the first formal study on the adaptation of gene expression in subclonal evolution. We model evolutionary changes in gene expression as stochastic Ornstein-Uhlenbeck processes, jointly leveraging the evolutionary history of subclones and single-cell expression data. Applying our model to sublines derived from single cells of a mouse melanoma revealed that sublines with distinct phenotypes are underlined by different patterns of gene expression adaptation, indicating non-genetic mechanisms of cancer evolution. Interestingly, sublines previously observed to be resistant to anti-CTLA-4 treatment showed adaptive expression of genes related to invasion and non-canonical Wnt signaling, whereas sublines that responded to treatment showed adaptive expression of genes related to proliferation and canonical Wnt signaling. Our results suggest that clonal phenotypes emerge as the result of specific adaptivity patterns of gene expression.

Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes

BACKGROUND

Aneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.

RESULTS

Here, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealing KLF5 as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research.

CONCLUSIONS

Our quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.

Contrasting somatic mutation patterns in aging human neurons and oligodendrocytes

Characterizing somatic mutations in the brain is important for disentangling the complex mechanisms of aging, yet little is known about mutational patterns in different brain cell types. Here, we performed whole-genome sequencing (WGS) of 86 single oligodendrocytes, 20 mixed glia, and 56 single neurons from neurotypical individuals spanning 0.4-104 years of age and identified >92,000 somatic single-nucleotide variants (sSNVs) and small insertions/deletions (indels). Although both cell types accumulate somatic mutations linearly with age, oligodendrocytes accumulated sSNVs 81% faster than neurons and indels 28% slower than neurons. Correlation of mutations with single-nucleus RNA profiles and chromatin accessibility from the same brains revealed that oligodendrocyte mutations are enriched in inactive genomic regions and are distributed across the genome similarly to mutations in brain cancers. In contrast, neuronal mutations are enriched in open, transcriptionally active chromatin. These stark differences suggest an assortment of active mutagenic processes in oligodendrocytes and neurons.

Lack of shared neoantigens in prevalent mutations in cancer

Tumors are mostly characterized by genetic instability, as result of mutations in surveillance mechanisms, such as DNA damage checkpoint, DNA repair machinery and mitotic checkpoint. Defect in one or more of these mechanisms causes additive accumulation of mutations. Some of these mutations are drivers of transformation and are positively selected during the evolution of the cancer, giving a growth advantage on the cancer cells. If such mutations would result in mutated neoantigens, these could be actionable targets for cancer vaccines and/or adoptive cell therapies. However, the results of the present analysis show, for the first time, that the most prevalent mutations identified in human cancers do not express mutated neoantigens. The hypothesis is that this is the result of the selection operated by the immune system in the very early stages of tumor development. At that stage, the tumor cells characterized by mutations giving rise to highly antigenic non-self-mutated neoantigens would be efficiently targeted and eliminated. Consequently, the outgrowing tumor cells cannot be controlled by the immune system, with an ultimate growth advantage to form large tumors embedded in an immunosuppressive tumor microenvironment (TME). The outcome of such a negative selection operated by the immune system is that the development of off-the-shelf vaccines, based on shared mutated neoantigens, does not seem to be at hand. This finding represents the first demonstration of the key role of the immune system on shaping the tumor antigen presentation and the implication in the development of antitumor immunological strategies.

Genomic Classification and Individualized Prognosis in Multiple Myeloma

PURPOSE

Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years.

METHODS

To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data.

RESULTS

Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression-based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited.

CONCLUSION

Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

Whole-genome mapping of APOBEC mutagenesis in metastatic urothelial carcinoma identifies driver hotspot mutations and a novel mutational signature

Apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like (APOBEC) enzymes mutate specific DNA sequences and hairpin-loop structures, challenging the distinction between passenger and driver hotspot mutations. Here, we characterized 115 whole genomes of metastatic urothelial carcinoma (mUC) to identify APOBEC mutagenic hotspot drivers. APOBEC-associated mutations were detected in 92% of mUCs and were equally distributed across the genome, while APOBEC hotspot mutations (ApoHMs) were enriched in open chromatin. Hairpin loops were frequent targets of didymi (twins in Greek), two hotspot mutations characterized by the APOBEC SBS2 signature, in conjunction with an uncharacterized mutational context (Ap[C>T]). Next, we developed a statistical framework that identified ApoHMs as drivers in coding and non-coding genomic regions of mUCs. Our results and statistical framework were validated in independent cohorts of 23 non-metastatic UCs and 3,744 samples of 17 metastatic cancers, identifying cancer-type-specific drivers. Our study highlights the role of APOBEC in cancer development and may contribute to developing novel targeted therapy options for APOBEC-driven cancers.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Evidence for negative selection in human primary fibroblasts to tolerate high somatic mutation loads upon treatment with multiple low doses of N-ethyl-N-nitrosourea

High-throughput sequencing at the single-cell and single-molecule level has shown that mutation rate is much higher in somatic cells than in the germline, with thousands of mutations accumulating with age in most human tissues. While there is now ample evidence that some of these mutations can clonally amplify and lead to disease, most notably cancer, the total burden of mutations a cell can tolerate without functional decline remains unknown. Here we addressed this question by exposing human primary fibroblasts multiple times to low doses of N-ethyl-N-nitrosourea (ENU) and quantitatively analyzing somatic mutation burden using single-cell whole genome sequencing. The results indicate that individual cells can sustain ∼60,000 single-nucleotide variants (SNVs) with only a slight adverse effect on growth rate. We found evidence for selection against potentially deleterious variants in gene coding regions as well as depletion of mutations in sequences associated with genetic pathways expressed in these human fibroblasts, most notably those relevant for maintaining basic cellular function and growth. However, no evidence of negative selection was found for variants in non-coding regions. We conclude that actively proliferating fibroblasts can tolerate very high levels of somatic mutations without major adverse effects on growth rate via negative selection against damaging coding mutations. Since most tissues in adult organisms have very limited capacity to select against mutations based on a growth disadvantage, these results suggest that a causal effect of somatic mutations in aging and disease cannot be ruled out.

Somatic mutations in human ageing: New insights from DNA sequencing and inherited mutations

The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear. Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic mutations in ageing tissues. These studies have revealed a gradual accumulation of mutations in normal tissues with age as well as a substantial clonal expansion driven mostly by cancer-related mutations. Nevertheless, it is difficult to envision how the burden and stochastic nature of age-related somatic mutations identified so far can explain most ageing phenotypes that develop gradually. Studies across species have also found that longer-lived species have lower somatic mutation rates, though these could be due to selective pressures acting on other phenotypes such as perhaps cancer. Recent studies in patients with higher somatic mutation burden and no signs of accelerated ageing further question the role of somatic mutations in ageing. Overall, with a few exceptions like cancer, recent DNA sequencing studies and inherited mutations do not support the idea that somatic mutations accumulating with age drive ageing phenotypes, and the phenotypic role, if any, of somatic mutations in ageing remains unclear.

The molecular map of CLL and Richter's syndrome

Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.

What can we learn about acid-base transporters in cancer from studying somatic mutations in their genes?

Acidosis is a chemical signature of the tumour microenvironment that challenges intracellular pH homeostasis. The orchestrated activity of acid-base transporters of the solute-linked carrier (SLC) family is critical for removing the end-products of fermentative metabolism (lactate/H+) and maintaining a favourably alkaline cytoplasm. Given the critical role of pH homeostasis in enabling cellular activities, mutations in relevant SLC genes may impact the oncogenic process, emerging as negatively or positively selected, or as driver or passenger mutations. To address this, we performed a pan-cancer analysis of The Cancer Genome Atlas simple nucleotide variation data for acid/base-transporting SLCs (ABT-SLCs). Somatic mutation patterns of monocarboxylate transporters (MCTs) were consistent with their proposed essentiality in facilitating lactate/H+ efflux. Among all cancers, tumours of uterine corpus endometrial cancer carried more ABT-SLC somatic mutations than expected from median tumour mutation burden. Among these, somatic mutations in SLC4A3 had features consistent with meaningful consequences on cellular fitness. Definitive evidence for ABT-SLCs as 'cancer essential' or 'driver genes' will have to consider microenvironmental context in genomic sequencing because bulk approaches are insensitive to pH heterogeneity within tumours. Moreover, genomic analyses must be validated with phenotypic outcomes (i.e. SLC-carried flux) to appreciate the opportunities for targeting acid-base transport in cancers.

3D Models of Sarcomas: The Next-generation Tool for Personalized Medicine

Sarcoma is a complex and heterogeneous cancer that has been difficult to study in vitro. While two-dimensional (2D) cell cultures and mouse models have been the dominant research tools, three-dimensional (3D) culture systems such as organoids have emerged as promising alternatives. In this review, we discuss recent developments in sarcoma organoid culture, with a focus on their potential as tools for drug screening and biobanking. We also highlight the ways in which sarcoma organoids have been used to investigate the mechanisms of gene regulation, drug resistance, metastasis, and immune interactions. Sarcoma organoids have shown to retain characteristics of in vivo biology within an in vitro system, making them a more representative model for sarcoma research. Our review suggests that sarcoma organoids offer a potential path forward for translational research in this field and may provide a platform for developing personalized therapies for sarcoma patients.

Loss-of-function mutations are main drivers of adaptations during short-term evolution

We noticed that during short-term experimental evolution and carcinogenesis, mutations causing gene inactivation (i.e., nonsense mutations or frameshifts) are frequent. Our meta-analysis of 65 experiments using modified dN/dS statistics indicated that nonsense mutations are adaptive in different experimental conditions and we empirically confirmed this prediction. Using yeast S. cerevisiae as a model we show that fixed or highly frequent gene loss-of-function mutations are almost exclusively adaptive in the majority of experiments.

Widespread alteration of protein autoinhibition in human cancers

Autoinhibition is a prevalent allosteric regulatory mechanism in signaling proteins. Reduced autoinhibition underlies the tumorigenic effect of some known cancer drivers, but whether autoinhibition is altered generally in cancer remains elusive. Here, we demonstrate that cancer-associated missense mutations, in-frame insertions/deletions, and fusion breakpoints are enriched within inhibitory allosteric switches (IASs) across all cancer types. Selection for IASs that are recurrently mutated in cancers identifies established and unknown cancer drivers. Recurrent missense mutations in IASs of these drivers are associated with distinct, cancer-specific changes in molecular signaling. For the specific case of PPP3CA, the catalytic subunit of calcineurin, we provide insights into the molecular mechanisms of altered autoinhibition by cancer mutations using biomolecular simulations, and demonstrate that such mutations are associated with transcriptome changes consistent with increased calcineurin signaling. Our integrative study shows that autoinhibition-modulating genetic alterations are positively selected for by cancer cells.

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.

Optimizing Design of Genomics Studies for Clonal Evolution Analysis

Genomic biotechnologies have seen rapid development over the past two decades, allowing for both the inference and modification of genetic and epigenetic information at the single cell level. While these tools present enormous potential for basic research, diagnostics, and treatment, they also raise difficult issues of how to design research studies to deploy these tools most effectively. In designing a study at the population or individual level, a researcher might combine several different sequencing modalities and sampling protocols, each with different utility, costs, and other tradeoffs. The central problem this paper attempts to address is then how one might create an optimal study design for a genomic analysis, with particular focus on studies involving somatic variation, typically for applications in cancer genomics. We pose the study design problem as a stochastic constrained nonlinear optimization problem and introduce a simulation-centered optimization procedure that iteratively optimizes the objective function using surrogate modeling combined with pattern and gradient search. Finally, we demonstrate the use of our procedure on diverse test cases to derive resource and study design allocations optimized for various objectives for the study of somatic cell populations.

Multi-omics analysis of TLCD1 as a promising biomarker in pan-cancer

Background: The TLC Domain Containing 1 (TLCD1) protein, a key regulator of phosphatidylethanolamine (PE) composition, is distributed across several cellular membranes, including mitochondrial plasma membranes. Existing research has revealed the impact of TLCD1 on the development of non-alcoholic fatty liver disease. However, there remains a gap in comprehensive pan-cancer analyses of TLCD1, and the precise role of TLCD1 in cancer patient prognosis and immunological responses remains elusive. This study aims to provide a comprehensive visualization of the prognostic landscape associated with TLCD1 across a spectrum of cancers, while shedding light on the potential links between TLCD1 expression within the tumor microenvironment and immune infiltration characteristics. Methods: TLCD1 expression data were obtained from GTEx, TCGA, and HPA data repositories. Multiple databases including TIMER, HPA, TISIDB, cBioPortal, GEPIA2, STRING, KEGG, GO, and CancerSEA were used to investigate the expression pattern, diagnostic and prognostic significance, mutation status, functional analysis, and functional status of TLCD1. In addition, we evaluated the relationship between TLCD1 expression and immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and immune-related genes in pan-cancer. Furthermore, the association of TLCD1 with drug sensitivity was analyzed using the CellMiner database. Results: We found that TLCD1 is generally highly expressed in pan-cancers and is significantly associated with the staging and prognosis of various cancers. Furthermore, our results also showed that TLCD1 was significantly associated with immune cell infiltration and immune regulatory factor expression. Using CellMiner database analysis, we then found a strong correlation between TLCD1 expression and sensitivity to anticancer drugs, indicating its potential as a therapeutic target. The most exciting finding is that high TLCD1 expression is associated with worse survival and prognosis in GBM and SKCM patients receiving anti-PD1 therapy. These findings highlight the potential of TLCD1 as a predictive biomarker for response to immunotherapy. Conclusion: TLCD1 plays a role in the regulation of immune infiltration and affects the prognosis of patients with various cancers. It serves as both a prognostic and immunologic biomarker in human cancer.

Somatic mutations in aging and disease

Time always leaves its mark, and our genome is no exception. Mutations in the genome of somatic cells were first hypothesized to be the cause of aging in the 1950s, shortly after the molecular structure of DNA had been described. Somatic mutation theories of aging are based on the fact that mutations in DNA as the ultimate template for all cellular functions are irreversible. However, it took until the 1990s to develop the methods to test if DNA mutations accumulate with age in different organs and tissues and estimate the severity of the problem. By now, numerous studies have documented the accumulation of somatic mutations with age in normal cells and tissues of mice, humans, and other animals, showing clock-like mutational signatures that provide information on the underlying causes of the mutations. In this review, we will first briefly discuss the recent advances in next-generation sequencing that now allow quantitative analysis of somatic mutations. Second, we will provide evidence that the mutation rate differs between cell types, with a focus on differences between germline and somatic mutation rate. Third, we will discuss somatic mutational signatures as measures of aging, environmental exposure, and activities of DNA repair processes. Fourth, we will explain the concept of clonally amplified somatic mutations, with a focus on clonal hematopoiesis. Fifth, we will briefly discuss somatic mutations in the transcriptome and in our other genome, i.e., the genome of mitochondria. We will end with a brief discussion of a possible causal contribution of somatic mutations to the aging process.

CRISPR/Cas9 model of prostate cancer identifies Kmt2c deficiency as a metastatic driver by Odam/Cabs1 gene cluster expression

Metastatic prostate cancer (PCa) poses a significant therapeutic challenge with high mortality rates. Utilizing CRISPR-Cas9 in vivo, we target five potential tumor suppressor genes (Pten, Trp53, Rb1, Stk11, and RnaseL) in the mouse prostate, reaching humane endpoint after eight weeks without metastasis. By further depleting three epigenetic factors (Kmt2c, Kmt2d, and Zbtb16), lung metastases are present in all mice. While whole genome sequencing reveals few mutations in coding sequence, RNA sequencing shows significant dysregulation, especially in a conserved genomic region at chr5qE1 regulated by KMT2C. Depleting Odam and Cabs1 in this region prevents metastasis. Notably, the gene expression signatures, resulting from our study, predict progression-free and overall survival and distinguish primary and metastatic human prostate cancer. This study emphasizes positive genetic interactions between classical tumor suppressor genes and epigenetic modulators in metastatic PCa progression, offering insights into potential treatments.

Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact

BACKGROUND

Cancer, a disease with high morbidity and mortality rates, poses a significant threat to human health. Driver genes, which harbor mutations accountable for the initiation and progression of tumors, play a crucial role in cancer development. Identifying driver genes stands as a paramount objective in cancer research and precision medicine.

RESULTS

In the present work, we propose a method for identifying driver genes using a Generalized Linear Regression Model (GLM) with Shrinkage and double-Weighted strategies based on Functional Impact, which is named GSW-FI. Firstly, an estimating model is proposed for assessing the background functional impacts of genes based on GLM, utilizing gene features as predictors. Secondly, the shrinkage and double-weighted strategies as two revising approaches are integrated to ensure the rationality of the identified driver genes. Lastly, a statistical method of hypothesis testing is designed to identify driver genes by leveraging the estimated background function impacts. Experimental results conducted on 31 The Cancer Genome Altas datasets demonstrate that GSW-FI outperforms ten other prediction methods in terms of the overlap fraction with well-known databases and consensus predictions among different methods.

CONCLUSIONS

GSW-FI presents a novel approach that efficiently identifies driver genes with functional impact mutations using computational methods, thereby advancing the development of precision medicine for cancer.

Tissue mosaicism following stem cell aging: blood as an exemplar

Loss of stem cell regenerative potential underlies aging of all tissues. Somatic mosaicism, the emergence of cellular patchworks within tissues, increases with age and has been observed in every organ yet examined. In the hematopoietic system, as in most tissues, stem cell aging through a variety of mechanisms occurs in lockstep with the emergence of somatic mosaicism. Here, we draw on insights from aging hematopoiesis to illustrate fundamental principles of stem cell aging and somatic mosaicism. We describe the generalizable changes intrinsic to aged stem cells and their milieu that provide the backdrop for somatic mosaicism to emerge. We discuss genetic and nongenetic mechanisms that can result in tissue somatic mosaicism and existing methodologies to detect such clonal outgrowths. Finally, we propose potential avenues to modify mosaicism during aging, with the ultimate aim of increasing tissue resiliency.

Deep whole-genome analysis of 494 hepatocellular carcinomas

Over half of hepatocellular carcinoma (HCC) cases diagnosed worldwide are in China1-3. However, whole-genome analysis of hepatitis B virus (HBV)-associated HCC in Chinese individuals is limited4-8, with current analyses of HCC mainly from non-HBV-enriched populations9,10. Here we initiated the Chinese Liver Cancer Atlas (CLCA) project and performed deep whole-genome sequencing (average depth, 120×) of 494 HCC tumours. We identified 6 coding and 28 non-coding previously undescribed driver candidates. Five previously undescribed mutational signatures were found, including aristolochic-acid-associated indel and doublet base signatures, and a single-base-substitution signature that we termed SBS_H8. Pentanucleotide context analysis and experimental validation confirmed that SBS_H8 was distinct to the aristolochic-acid-associated SBS22. Notably, HBV integrations could take the form of extrachromosomal circular DNA, resulting in elevated copy numbers and gene expression. Our high-depth data also enabled us to characterize subclonal clustered alterations, including chromothripsis, chromoplexy and kataegis, suggesting that these catastrophic events could also occur in late stages of hepatocarcinogenesis. Pathway analysis of all classes of alterations further linked non-coding mutations to dysregulation of liver metabolism. Finally, we performed in vitro and in vivo assays to show that fibrinogen alpha chain (FGA), determined as both a candidate coding and non-coding driver, regulates HCC progression and metastasis. Our CLCA study depicts a detailed genomic landscape and evolutionary history of HCC in Chinese individuals, providing important clinical implications.

Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32 917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA (The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

Clonal haematopoiesis, ageing and kidney disease

Clonal haematopoiesis of indeterminate potential (CHIP) is a preclinical condition wherein a sizeable proportion of an individual's circulating blood cells are derived from a single mutated haematopoietic stem cell. CHIP occurs frequently with ageing - more than 10% of individuals over 65 years of age are affected - and is associated with an increased risk of disease across several organ systems and premature death. Emerging evidence suggests that CHIP has a role in kidney health, including associations with predisposition to acute kidney injury, impaired recovery from acute kidney injury and kidney function decline, both in the general population and among those with chronic kidney disease. Beyond its direct effect on the kidney, CHIP elevates the susceptibility of individuals to various conditions that can detrimentally affect the kidneys, including cardiovascular disease, obesity and insulin resistance, liver disease, gout, osteoporosis and certain autoimmune diseases. Aberrant pro-inflammatory signalling, telomere attrition and epigenetic ageing are potential causal pathophysiological pathways and mediators that underlie CHIP-related disease risk. Experimental animal models have shown that inhibition of inflammatory cytokine signalling can ameliorate many of the pathological effects of CHIP, and assessment of the efficacy and safety of this class of medications for human CHIP-associated pathology is ongoing.