A compendium of mutational cancer driver genes

Cell reprogramming therapy for Parkinson's disease

Parkinson's disease is typically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta. Many studies have been performed based on the supplementation of lost dopaminergic neurons to treat Parkinson's disease. The initial strategy for cell replacement therapy used human fetal ventral midbrain and human embryonic stem cells to treat Parkinson's disease, which could substantially alleviate the symptoms of Parkinson's disease in clinical practice. However, ethical issues and tumor formation were limitations of its clinical application. Induced pluripotent stem cells can be acquired without sacrificing human embryos, which eliminates the huge ethical barriers of human stem cell therapy. Another widely considered neuronal regeneration strategy is to directly reprogram fibroblasts and astrocytes into neurons, without the need for intermediate proliferation states, thus avoiding issues of immune rejection and tumor formation. Both induced pluripotent stem cells and direct reprogramming of lineage cells have shown promising results in the treatment of Parkinson's disease. However, there are also ethical concerns and the risk of tumor formation that need to be addressed. This review highlights the current application status of cell reprogramming in the treatment of Parkinson's disease, focusing on the use of induced pluripotent stem cells in cell replacement therapy, including preclinical animal models and progress in clinical research. The review also discusses the advancements in direct reprogramming of lineage cells in the treatment of Parkinson's disease, as well as the controversy surrounding in vivo reprogramming. These findings suggest that cell reprogramming may hold great promise as a potential strategy for treating Parkinson's disease.

Natural products with anti-tumorigenesis potential targeting macrophage

BACKGROUND

Inflammation is a risk factor for tumorigenesis. Macrophage, a subset of immune cells with high plasticity, plays a multifaceted role in this process. Natural products, which are bioactive compounds derived from traditional herbs or foods, have exhibited diverse effects on macrophages and tumorigenesis making them a valuable resource of drug discovery or optimization in tumor prevention.

PURPOSE

Provide a comprehensive overview of the various roles of macrophages in tumorigenesis, as well as the effects of natural products on tumorigenesis by modulating macrophage function.

METHODS

A thorough literature search spanning the past two decades was carried out using PubMed, Web of Science, Elsevier, and CNKI following the PRISMA guidelines. The search terms employed included "macrophage and tumorigenesis", "natural products, macrophages and tumorigenesis", "traditional Chinese medicine and tumorigenesis", "natural products and macrophage polarization", "macrophage and tumor related microenvironment", "macrophage and tumor signal pathway", "toxicity of natural products" and combinations thereof. Furthermore, certain articles are identified through the tracking of citations from other publications or by accessing the websites of relevant journals. Studies that meet the following criteria are excluded: (1) Articles not written in English or Chinese; (2) Full texts were not available; (3) Duplicate articles and irrelevant studies. The data collected was organized and summarized based on molecular mechanisms or compound structure.

RESULTS

This review elucidates the multifaceted effect of macrophages on tumorigenesis, encompassing process such as inflammation, angiogenesis, and tumor cell invasion by regulating metabolism, non-coding RNA, signal transduction and intercellular crosstalk. Natural products, including vitexin, ovatodiolide, ligustilide, and emodin, as well as herbal remedies, have demonstrated efficacy in modulating macrophage function, thereby attenuating tumorigenesis. These interventions mainly focus on mitigating the initial inflammatory response or modifying the inflammatory environment within the precancerous niche.

CONCLUSIONS

These mechanistic insights of macrophages in tumorigenesis offer valuable ideas for researchers. The identified natural products facilitate the selection of promising candidates for future cancer drug development.

Does the Presence of Heavy Metals Influence the Gene Expression and Oxidative Stress in Bladder Cancer?

Heavy metal toxicity is associated with cancer progression. Studies have reported the relation between some metal ions and bladder cancer (BC). Direct influence of such agents in bladder carcinogenesis is still needed. Total 49 BC patients were included in the study. Level of Pb, Cr, Hg and Cd, oxidative stress markers, and gene expression of Bcl-2, Bax, IL-6, AKT, and P38 genes were detected in cancer and non-cancerous tissues obtained from bladder cancer patients. Concentrations of Pb, Cr, and Cd were significantly elevated in cancer tissues than normal, while Hg level was significantly increased in normal tissue than cancer. MDA level was significantly higher and SOD activity was lower in the cancer tissues compared to non-cancerous. The expressions of Bcl-2, IL-6, AKT, and P38 were significantly increased in the cancer tissues than in normal tissues while Bax level was significantly increased in non-cancerous tissue than in cancer tissue. In cancer tissue, there were significant correlations between Cr level with expression of Bax, AKT, and P38 while Cd level was significantly correlate with Bax, IL-6, AKT, and P38expression. The correlation between Cr and Cd with the expression of Bax, IL-6, AKT, and P38 may indicate a carcinogenic role of these metals on progression of bladder cancer.

Immune evasion impacts the landscape of driver genes during cancer evolution

BACKGROUND

Carcinogenesis is driven by interactions between genetic mutations and the local tumor microenvironment. Recent research has identified hundreds of cancer driver genes; however, these studies often include a mixture of different molecular subtypes and ecological niches and ignore the impact of the immune system.

RESULTS

In this study, we compare the landscape of driver genes in tumors that escaped the immune system (escape +) versus those that did not (escape -). We analyze 9896 primary tumors from The Cancer Genome Atlas using the ratio of non-synonymous to synonymous mutations (dN/dS) and find 85 driver genes, including 27 and 16 novel genes, in escape - and escape + tumors, respectively. The dN/dS of driver genes in immune escaped tumors is significantly lower and closer to neutrality than in non-escaped tumors, suggesting selection buffering in driver genes fueled by immune escape. Additionally, we find that immune evasion leads to more mutated sites, a diverse array of mutational signatures and is linked to tumor prognosis.

CONCLUSIONS

Our findings highlight the need for improved patient stratification to identify new therapeutic targets for cancer treatment.

Machine learning identifies activation of RUNX/AP-1 as drivers of mesenchymal and fibrotic regulatory programs in gastric cancer

Gastric cancer (GC) is the fifth most common cancer worldwide and is a heterogeneous disease. Among GC subtypes, the mesenchymal phenotype (Mes-like) is more invasive than the epithelial phenotype (Epi-like). Although gene expression of the epithelial-to-mesenchymal transition (EMT) has been studied, the regulatory landscape shaping this process is not fully understood. Here we use ATAC-seq and RNA-seq data from a compendium of GC cell lines and primary tumors to detect drivers of regulatory state changes and their transcriptional responses. Using the ATAC-seq data, we developed a machine learning approach to determine the transcription factors (TFs) regulating the subtypes of GC. We identified TFs driving the mesenchymal (RUNX2, ZEB1, SNAI2, AP-1 dimer) and the epithelial (GATA4, GATA6, KLF5, HNF4A, FOXA2, GRHL2) states in GC. We identified DNA copy number alterations associated with dysregulation of these TFs, specifically deletion of GATA4 and amplification of MAPK9 Comparisons with bulk and single-cell RNA-seq data sets identified activation toward fibroblast-like epigenomic and expression signatures in Mes-like GC. The activation of this mesenchymal fibrotic program is associated with differentially accessible DNA cis-regulatory elements flanking upregulated mesenchymal genes. These findings establish a map of TF activity in GC and highlight the role of copy number driven alterations in shaping epigenomic regulatory programs as potential drivers of GC heterogeneity and progression.

VHL suppresses UBE3B-mediated breast tumor growth and metastasis

Protein homeostasis is predominantly governed through post-translational modification (PTM). UBE3B, identified as an oncoprotein, exhibits elevated protein levels in breast cancer. However, the impact of PTM on UBE3B remains unexplored. In this study, we show that VHL is a bona fide E3 ligase for UBE3B. Mechanistically, VHL directly binds to UBE3B, facilitating its lysine 48 (K48)-linked polyubiquitination at K286 and K427 in a prolyl hydroxylase (PHD)-independent manner. Consequently, this promotes the proteasomal degradation of UBE3B. The K286/427R mutation of UBE3B dramatically abolishes the inhibitory effect of VHL on breast tumor growth and lung metastasis. Additionally, the protein levels of UBE3B and VHL exhibit a negative correlation in breast cancer tissues. These findings delineate an important layer of UBE3B regulation by VHL.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

Analysis of 10,478 cancer genomes identifies candidate driver genes and opportunities for precision oncology

Tumor genomic profiling is increasingly seen as a prerequisite to guide the treatment of patients with cancer. To explore the value of whole-genome sequencing (WGS) in broadening the scope of cancers potentially amenable to a precision therapy, we analysed whole-genome sequencing data on 10,478 patients spanning 35 cancer types recruited to the UK 100,000 Genomes Project. We identified 330 candidate driver genes, including 74 that are new to any cancer. We estimate that approximately 55% of patients studied harbor at least one clinically relevant mutation, predicting either sensitivity or resistance to certain treatments or clinical trial eligibility. By performing computational chemogenomic analysis of cancer mutations we identify additional targets for compounds that represent attractive candidates for future clinical trials. This study represents one of the most comprehensive efforts thus far to identify cancer driver genes in the real world setting and assess their impact on informing precision oncology.

The tRNA Gm18 methyltransferase TARBP1 promotes hepatocellular carcinoma progression via metabolic reprogramming of glutamine

Cancer cells rely on metabolic reprogramming to sustain the prodigious energetic requirements for rapid growth and proliferation. Glutamine metabolism is frequently dysregulated in cancers and is being exploited as a potential therapeutic target. Using CRISPR/Cas9 interference (CRISPRi) screening, we identified TARBP1 (TAR (HIV-1) RNA Binding Protein 1) as a critical regulator involved in glutamine reliance of cancer cell. Consistent with this discovery, TARBP1 amplification and overexpression are frequently observed in various cancers. Knockout of TARBP1 significantly suppresses cell proliferation, colony formation and xenograft tumor growth. Mechanistically, TARBP1 selectively methylates and stabilizes a small subset of tRNAs, which promotes efficient protein synthesis of glutamine transporter-ASCT2 (also known as SLC1A5) and glutamine import to fuel the growth of cancer cell. Moreover, we found that the gene expression of TARBP1 and ASCT2 are upregulated in combination in clinical cohorts and their upregulation is associated with unfavorable prognosis of HCC (hepatocellular carcinoma). Taken together, this study reveals the unexpected role of TARBP1 in coordinating the tRNA availability and glutamine uptake during HCC progression and provides a potential target for tumor therapy.

An oncogenic enhancer promotes melanoma progression via regulating ETV4 expression

BACKGROUND

Enhancers are important gene regulatory elements that promote the expression of critical genes in development and disease. Aberrant enhancer can modulate cancer risk and activate oncogenes that lead to the occurrence of various cancers. However, the underlying mechanism of most enhancers in cancer remains unclear. Here, we aim to explore the function and mechanism of a crucial enhancer in melanoma.

METHODS

Multi-omics data were applied to identify an enhancer (enh17) involved in melanoma progression. To evaluate the function of enh17, CRISPR/Cas9 technology were applied to knockout enh17 in melanoma cell line A375. RNA-seq, ChIP-seq and Hi-C data analysis integrated with luciferase reporter assay were performed to identify the potential target gene of enh17. Functional experiments were conducted to further validate the function of the target gene ETV4. Multi-omics data integrated with CUT&Tag sequencing were performed to validate the binding profile of the inferred transcription factor STAT3.

RESULTS

An enhancer, named enh17 here, was found to be aberrantly activated and involved in melanoma progression. CRISPR/Cas9-mediated deletion of enh17 inhibited cell proliferation, migration, and tumor growth of melanoma both in vitro and in vivo. Mechanistically, we identified ETV4 as a target gene regulated by enh17, and functional experiments further support ETV4 as a target gene that is involved in cancer-associated phenotypes. In addition, STAT3 acts as a transcription factor binding with enh17 to regulate the transcription of ETV4.

CONCLUSIONS

Our findings revealed that enh17 plays an oncogenic role and promotes tumor progression in melanoma, and its transcriptional regulatory mechanisms were fully elucidated, which may open a promising window for melanoma prevention and treatment.

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

From driver genes to gene families: A computational analysis of oncogenic mutations and ubiquitination anomalies in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a widespread primary liver cancer with a high fatality rate. Despite several genes with oncogenic effects in HCC have been identified, many remain undiscovered. In this study, we conducted a comprehensive computational analysis to explore the involvement of genes within the same families as known driver genes in HCC. Specifically, we expanded the concept beyond single-gene mutations to encompass gene families sharing homologous structures, integrating various omics data to comprehensively understand gene abnormalities in cancer. Our analysis identified 74 domains with an enriched mutation burden, 404 domain mutation hotspots, and 233 dysregulated driver genes. We observed that specific low-frequency somatic mutations may contribute to HCC occurrence, potentially overlooked by single-gene algorithms. Furthermore, we systematically analyzed how abnormalities in the ubiquitinated proteasome system (UPS) impact HCC, finding that abnormal genes in E3, E2, DUB families, and Degron genes often result in HCC by affecting the stability of oncogenic or tumor suppressor proteins. In conclusion, expanding the exploration of driver genes to include gene families with homologous structures emerges as a promising strategy for uncovering additional oncogenic alterations in HCC.

Repurposed organoselenium tethered amidic acids as apoptosis inducers in melanoma cancer via P53, BAX, caspases-3, 6, 8, 9, BCL-2, MMP2, and MMP9 modulations

Organoselenium (OSe) agents hold promise for preventing cancer due to their potential ability to fight cancer development and protect cells from oxidative damage. Herein, OSe-based maleanilic and succinanilic acids were tested to estimate their antitumor activities against fifteen cancer cell lines. Besides, their potential safety and selectivity were further investigated against two normal cell lines, namely, human skin fibroblasts (HSF) and olfactory ensheathing cell line (OEC) using the growth inhibition percentage (GI%) assay. Moreover, the apoptotic potential of the superior anticancer candidates (8, 9, 10, and 11) was evaluated against P53, BAX, Caspase-3, Caspase-6, Caspase-8, Caspase-9, BCL-2, MMP2, and MMP9 apoptotic markers. Additionally, to enhance our understanding and predict the inhibitory potential of the examined compounds as potential anticancer agents, a thorough structure-activity relationship (SAR) analysis was conducted. On the other hand, molecular docking and ADMET studies were performed for the examined candidates as well. Overall, our findings point to significant anticancer activities of the organoselenium tethered amidic acids, suggesting their promising cytotoxic potential as effective anticancer drugs.

TRIM24 Cooperates with Ras Mutation to Drive Glioma Progression through snoRNA Recruitment of PHAX and DNA-PKcs

The factors driving glioma progression remain poorly understood. Here, the epigenetic regulator TRIM24 is identified as a driver of glioma progression, where TRIM24 overexpression promotes HRasV12 anaplastic astrocytoma (AA) progression into epithelioid GBM (Ep-GBM)-like tumors. Co-transfection of TRIM24 with HRasV12 also induces Ep-GBM-like transformation of human neural stem cells (hNSCs) with tumor protein p53 gene (TP53) knockdown. Furthermore, TRIM24 is highly expressed in clinical Ep-GBM specimens. Using single-cell RNA-sequencing (scRNA-Seq), the authors show that TRIM24 overexpression impacts both intratumoral heterogeneity and the tumor microenvironment. Mechanically, HRasV12 activates phosphorylated adaptor for RNA export (PHAX) and upregulates U3 small nucleolar RNAs (U3 snoRNAs) to recruit Ku-dependent DNA-dependent protein kinase catalytic subunit (DNA-PKcs). Overexpressed TRIM24 is also recruited by PHAX to U3 snoRNAs, thereby facilitating DNA-PKcs phosphorylation of TRIM24 at S767/768 residues. Phosphorylated TRIM24 induces epigenome and transcription factor network reprogramming and promotes Ep-GBM-like transformation. Targeting DNA-PKcs with the small molecule inhibitor NU7441 synergizes with temozolomide to reduce Ep-GBM tumorigenicity and prolong animal survival. These findings provide new insights into the epigenetic regulation of Ep-GBM-like transformation and suggest a potential therapeutic strategy for patients with Ep-GBM.

Severity of effect considerations regarding the use of mutation as a toxicological endpoint for risk assessment: A report from the 8th International Workshop on Genotoxicity Testing (IWGT)

Exposure levels without appreciable human health risk may be determined by dividing a point of departure on a dose-response curve (e.g., benchmark dose) by a composite adjustment factor (AF). An "effect severity" AF (ESAF) is employed in some regulatory contexts. An ESAF of 10 may be incorporated in the derivation of a health-based guidance value (HBGV) when a "severe" toxicological endpoint, such as teratogenicity, irreversible reproductive effects, neurotoxicity, or cancer was observed in the reference study. Although mutation data have been used historically for hazard identification, this endpoint is suitable for quantitative dose-response modeling and risk assessment. As part of the 8th International Workshops on Genotoxicity Testing, a sub-group of the Quantitative Analysis Work Group (WG) explored how the concept of effect severity could be applied to mutation. To approach this question, the WG reviewed the prevailing regulatory guidance on how an ESAF is incorporated into risk assessments, evaluated current knowledge of associations between germline or somatic mutation and severe disease risk, and mined available data on the fraction of human germline mutations expected to cause severe disease. Based on this review and given that mutations are irreversible and some cause severe human disease, in regulatory settings where an ESAF is used, a majority of the WG recommends applying an ESAF value between 2 and 10 when deriving a HBGV from mutation data. This recommendation may need to be revisited in the future if direct measurement of disease-causing mutations by error-corrected next generation sequencing clarifies selection of ESAF values.

KMT2C and KMT2D aberrations in breast cancer

KMT2C and KMT2D are histone lysine methyltransferases responsible for the monomethylation of histone 3 lysine 4 (H3K4) residues at gene enhancer sites. KMT2C/D are the most frequently mutated histone methyltransferases (HMTs) in breast cancer, occurring at frequencies of 10-20% collectively. Frequent damaging and truncating somatic mutations indicate a tumour-suppressive role of KMT2C/D in breast oncogenesis. Recent studies using cell lines and mouse models to replicate KMT2C/D loss show that these genes contribute to oestrogen receptor (ER)-driven transcription in ER+ breast cancers through the priming of gene enhancer regions. This review provides an overview of the functions of KMT2C/D and outlines the recent clinical and experimental evidence of the roles of KMT2C and KMT2D in breast cancer development.

The Many Roads from Alternative Splicing to Cancer: Molecular Mechanisms Involving Driver Genes

Cancer driver genes are either oncogenes or tumour suppressor genes that are classically activated or inactivated, respectively, by driver mutations. Alternative splicing-which produces various mature mRNAs and, eventually, protein variants from a single gene-may also result in driving neoplastic transformation because of the different and often opposed functions of the variants of driver genes. The present review analyses the different alternative splicing events that result in driving neoplastic transformation, with an emphasis on their molecular mechanisms. To do this, we collected a list of 568 gene drivers of cancer and revised the literature to select those involved in the alternative splicing of other genes as well as those in which its pre-mRNA is subject to alternative splicing, with the result, in both cases, of producing an oncogenic isoform. Thirty-one genes fall into the first category, which includes splicing factors and components of the spliceosome and splicing regulators. In the second category, namely that comprising driver genes in which alternative splicing produces the oncogenic isoform, 168 genes were found. Then, we grouped them according to the molecular mechanisms responsible for alternative splicing yielding oncogenic isoforms, namely, mutations in cis splicing-determining elements, other causes involving non-mutated cis elements, changes in splicing factors, and epigenetic and chromatin-related changes. The data given in the present review substantiate the idea that aberrant splicing may regulate the activation of proto-oncogenes or inactivation of tumour suppressor genes and details on the mechanisms involved are given for more than 40 driver genes.

Current data and future perspectives on DNA methylation in ovarian cancer (Review)

Ovarian cancer (OC) represents the most prevalent malignancy of the female reproductive system. Its distinguishing features include a high aggressiveness, substantial morbidity and mortality, and a lack of apparent symptoms, which collectively pose significant challenges for early detection. Given that aberrant DNA methylation events leading to altered gene expression are characteristic of numerous tumor types, there has been extensive research into epigenetic mechanisms, particularly DNA methylation, in human cancers. In the context of OC, DNA methylation is often associated with the regulation of critical genes, such as BRCA1/2 and Ras‑association domain family 1A. Methylation modifications within the promoter regions of these genes not only contribute to the pathogenesis of OC, but also induce medication resistance and influence the prognosis of patients with OC. As such, a more in‑depth understanding of DNA methylation underpinning carcinogenesis could potentially facilitate the development of more effective therapeutic approaches for this intricate disease. The present review focuses on classical tumor suppressor genes, oncogenes, signaling pathways and associated microRNAs in an aim to elucidate the influence of DNA methylation on the development and progression of OC. The advantages and limitations of employing DNA methylation in the diagnosis, treatment and prevention of OC are also discussed. On the whole, the present literature review indicates that the DNA methylation of specific genes could potentially serve as a prognostic biomarker for OC and a therapeutic target for personalized treatment strategies. Further investigations in this field may yield more efficacious diagnostic and therapeutic alternatives for patients with OC.

Stem-cell states converge in multistage cutaneous squamous cell carcinoma development

Stem cells play a critical role in cancer development by contributing to cell heterogeneity, lineage plasticity, and drug resistance. We created gene expression networks from hundreds of mouse tissue samples (both normal and tumor) and integrated these with lineage tracing and single-cell RNA-seq, to identify convergence of cell states in premalignant tumor cells expressing markers of lineage plasticity and drug resistance. Two of these cell states representing multilineage plasticity or proliferation were inversely correlated, suggesting a mutually exclusive relationship. Treatment of carcinomas in vivo with chemotherapy repressed the proliferative state and activated multilineage plasticity whereas inhibition of differentiation repressed plasticity and potentiated responses to cell cycle inhibitors. Manipulation of this cell state transition point may provide a source of potential combinatorial targets for cancer therapy.

The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model

Gene expression is temporally and spatially regulated by the interaction of transcription factors (TFs) and cis-regulatory elements (CREs). The uneven distribution of TF binding sites across the genome poses challenges in understanding how this distribution evolves to regulate spatio-temporal gene expression and consequent heritable phenotypic variation. In this study, chromatin accessibility profiles and gene expression profiles were collected from several species including mammals (human, mouse, bovine), fish (zebrafish and medaka), and chicken. Transcription factor binding sites clustered regions (TFCRs) at different embryonic stages were characterized to investigate regulatory evolution. The study revealed dynamic changes in TFCR distribution during embryonic development and species evolution. The synchronization between TFCR complexity and gene expression was assessed across species using RegulatoryScore. Additionally, an explainable machine learning model highlighted the importance of the distance between TFCR and promoter in the coordinated regulation of TFCRs on gene expression. Our results revealed the developmental and evolutionary dynamics of TFCRs during embryonic development from fish, chicken to mammals. These data provide valuable resources for exploring the relationship between transcriptional regulation and phenotypic differences during embryonic development.

Genetic landscape of interval and screen detected breast cancer

Interval breast cancers (IBCs) are cancers diagnosed between screening episodes. Understanding the biological differences between IBCs and screen-detected breast-cancers (SDBCs) has the potential to improve mammographic screening and patient management. We analysed and compared the genomic landscape of 288 IBCs and 473 SDBCs by whole genome sequencing of paired tumour-normal patient samples collected as part of the UK 100,000 Genomes Project. Compared to SDBCs, IBCs were more likely to be lobular, higher grade, and triple negative. A more aggressive clinical phenotype was reflected in IBCs displaying features of genomic instability including a higher mutation rate and number of chromosomal structural abnormalities, defective homologous recombination and TP53 mutations. We did not however, find evidence to indicate that IBCs are associated with a significantly different immune response. While IBCs do not represent a unique molecular class of invasive breast cancer they exhibit a more aggressive phenotype, which is likely to be a consequence of the timing of tumour initiation. This information is relevant both with respect to treatment as well as informing the screening interval for mammography.

Developing an Approach for Integrating Chemical Analysis and Transcriptional Changes to Assess Contaminants in Water, Sediment, and Fish

Pharmaceuticals in aquatic environments pose threats to aquatic organisms because of their continuous release and potential accumulation. Monitoring methods for these contaminants are inadequate, with targeted analyses falling short in assessing water quality's impact on biota. The present study advocates for integrated strategies combining suspect and targeted chemical analyses with molecular biomarker approaches to better understand the risks posed by complex chemical mixtures to nontarget organisms. The research aimed to integrate chemical analysis and transcriptome changes in fathead minnows to prioritize contaminants, assess their effects, and apply this strategy in Wascana Creek, Canada. Analysis revealed higher pharmaceutical concentrations downstream of a wastewater-treatment plant, with clozapine being the most abundant in fathead minnows, showing notable bioavailability from water and sediment sources. Considering the importance of bioaccumulation factor and biota-sediment accumulation factor in risk assessment, these coefficients were calculated based on field data collected during spring, summer, and fall seasons in 2021. Bioaccumulation was classified as very bioaccumulative with values >5000 L kg-1, suggesting the ability of pharmaceuticals to accumulate in aquatic organisms. The study highlighted the intricate relationship between nutrient availability, water quality, and key pathways affected by pharmaceuticals, personal care products, and rubber components. Prioritization of these chemicals was done through suspect analysis, supported by identifying perturbed pathways (specifically signaling and cellular processes) using transcriptomic analysis in exposed fish. This strategy not only aids in environmental risk assessment but also serves as a practical model for other watersheds, streamlining risk-assessment processes to identify environmental hazards and work toward reducing risks from contaminants of emerging concern. Environ Toxicol Chem 2024;00:1-22. © 2024 SETAC.

A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction

A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studied to elucidate the relationship between cellular features and treatment response. Due to the high dimensionality of these datasets, machine learning (ML) is commonly used for their analysis. However, choosing a suitable algorithm and set of input features can be challenging. We performed a comprehensive benchmarking of ML methods and dimension reduction (DR) techniques for predicting drug response metrics. Using the Genomics of Drug Sensitivity in Cancer cell line panel, we trained random forests, neural networks, boosting trees and elastic nets for 179 anti-cancer compounds with feature sets derived from nine DR approaches. We compare the results regarding statistical performance, runtime and interpretability. Additionally, we provide strategies for assessing model performance compared with a simple baseline model and measuring the trade-off between models of different complexity. Lastly, we show that complex ML models benefit from using an optimized DR strategy, and that standard models-even when using considerably fewer features-can still be superior in performance.

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.

Divorce and physical health: A three-level meta-analysis

Divorce is often considered a major and stressful life transition. Given that divorcees are overrepresented in primary care and there is a association between individuals' physical health and psychological adjustment, potential post-divorce health problems are of concern. Yet, empirical evidence is lacking on the magnitude of the overall physical health risk after divorce, on possible differences across specific pathologies, and on factors that may increase or reduce this risk. The current meta-analysis addresses these issues. We identified 94 studies including u = 248 relevant effect sizes, based on N = 1,384,507 participants. Generally, compared to married individuals, divorcees showed significantly worse self-reported health (OR = 1.20, [1.08-1.33]), experienced more physical symptoms (OR = 1.34, [1.17-1.53]), and had a higher risk for diabetes (OR = 1.18 [1.05-1.33]), joint pathologies (OR = 1.24, [1.14-1.34]), cardiovascular (OR = 1.24, [1.09-1.41]) and cerebrovascular conditions (OR = 1.31, [1.14-1.51]), and sexually transmitted diseases (OR = 2.48, [1.32-4.64]). However, they had no increased risk of hypertension, hypercholesterolemia, cancer and cancer development, disabilities or limitations, or cognitive pathologies. Nor did divorcees significantly differ from married individuals when aggregating all pathologies to measure overall physical health problems (OR = 1.14, [0.85 to 1.54]). Yet, moderation analyses revealed that being female, unemployed, childless, or having a lower education constitutes a higher risk for overall physical health problems after divorce. The same applied to having a heavy alcohol consumption, lack of exercise, and being overweight. Our meta-analysis shows that divorcees are at heightened risk of certain pathologies, with sexually transmitted diseases as a particular post-divorce hazard. These findings call for more awareness among counsellors and physicians on divorcees' health conditions and the characteristics that make divorcees even more vulnerable to health problems.

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.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

In silico analysis of several frequent SLX4 mutations appearing in human cancers

SLX4 is an interactor and activator of structure-specific exonuclease that helps resolve tangled recombination intermediates arising at stalled replication forks. It is one of the many factors that assist with homologous recombination, the major mechanism for restarting replication. SLX4 mutations have been reported in many cancers but a pan cancer map of all the mutations has not been undertaken. Here, using data from the Catalogue of Somatic Mutations in Cancers (COSMIC), we show that mutations occur in almost every cancer and many of them truncate the protein which should severely alter the function of the enzyme. We identified a frequent R1779W point mutation that occurs in the SLX4 domain required for heterodimerization with its partner, SLX1. In silico protein structure analysis of this mutation shows that it significantly alters the protein structure and is likely to destabilize the interaction with SLX1. Although this brief communication is limited to only in silico analysis, it identifies certain high frequency SLX4 mutations in human cancers that would warrant further in vivo studies. Additionally, these mutations may be potentially actionable for drug therapies.

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.

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Treatment strategy for HER2-negative advanced gastric cancer: salvage-line strategy for advanced gastric cancer

After immune checkpoint inhibitor (ICI) comes into third-line treatment of advanced gastric cancer, the therapeutic strategy has been dramatically changed. Recent first-line regimen, which consists of ICI and chemotherapeutic agents, prolonged progression-free survival, and subsequent treatment options enabled continuous treatment beyond second-line therapy. Moreover, the advent of vascular endothelial growth factor (VEGF)-targeted agents including angiogenesis inhibitors and TKIs provides an opportunity of considering the interaction between ICI and anti-VEGF agents, and facilitating novel treatment proposal. Although clinical benefit of prolonged VEGF blockade after disease progression has not been confirmed in gastric cancer, combination therapy of cytotoxic agents and anti-VEGF agent, such as irinotecan plus ramucirumab demonstrated favorable objective response rate and progression-free survival in third- or later-line setting. In this review, we discuss recent progress and future directions of later-line treatments of HER2-negative advancer gastric cancer.

Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.

P. S, C. E. A, Rajarajan S, Sahoo S, Mohan G, Jaikumar VS, Ramesh RS, B. S. S, Jolly MK, Maliekal TT, Prabhu JS. Elucidating the Role of MicroRNA-18a in Propelling a Hybrid Epithelial-Mesenchymal Phenotype and Driving Malignant Progression in ER-Negative Breast Cancer

Epigenetic alterations that lead to differential expression of microRNAs (miRNAs/miR) are known to regulate tumour cell states, epithelial-mesenchymal transition (EMT) and the progression to metastasis in breast cancer. This study explores the key contribution of miRNA-18a in mediating a hybrid E/M cell state that is pivotal to the malignant transformation and tumour progression in the aggressive ER-negative subtype of breast cancer. The expression status and associated effects of miR-18a were evaluated in patient-derived breast tumour samples in combination with gene expression data from public datasets, and further validated in in vitro and in vivo breast cancer model systems. The clinical relevance of the study findings was corroborated against human breast tumour specimens (n = 446 patients). The down-regulated expression of miR-18a observed in ER-negative tumours was found to drive the enrichment of hybrid epithelial/mesenchymal (E/M) cells with luminal attributes, enhanced traits of migration, stemness, drug-resistance and immunosuppression. Further analysis of the miR-18a targets highlighted possible hypoxia-inducible factor 1-alpha (HIF-1α)-mediated signalling in these tumours. This is a foremost report that validates the dual role of miR-18a in breast cancer that is subtype-specific based on hormone receptor expression. The study also features a novel association of low miR-18a levels and subsequent enrichment of hybrid E/M cells, increased migration and stemness in a subgroup of ER-negative tumours that may be attributed to HIF-1α mediated signalling. The results highlight the possibility of stratifying the ER-negative disease into clinically relevant groups by analysing miRNA signatures.

Patient-derived organoid elucidates the identical clonal origin of bilateral breast cancer with diverse molecular subtypes

Bilateral breast cancer (BBC), an infrequent breast cancer subtype, has primarily been studied in terms of incidence, prognosis, and through comparative analysis of synchronous (SBBC) and metachronous (MBBC) manifestations. The advent and application of organoid technology hold profound implications for tumor research and clinical management. This study represents the pioneering use of organoid models in BBC research. We established organoid lines from two surgical tumor specimens of a BBC patient, with one line undergoing detailed pathological and genomic analysis. The BBC organoid from the right breast demonstrated a marker expression profile of ER (-), PR (-), HER-2 (0), and Ki67 index 10%, indicating that it may derived from the TNBC tissue. Whole Exome Sequencing (WES) displayed consistent set of Top10 cancer driver genes affected by missense mutations, frameshift mutation, or splice site mutations in three tumor tissues and the organoid samples. The organoids' single nucleotide polymorphisms (SNPs) were more closely aligned with the TNBC tissue than other tumor tissues. Evolutionary analysis suggested that different tumor regions might evolve from a common ancestral layer. In this case, the development of BBC organoids indicated that simultaneous lesions with diverse molecular profiles shared a high degree of consistency in key tumor-driving mutations. These findings suggest the feasibility of generating BBC organoids representing various molecular types, accurately replicating significant markers and driver mutations of the originating tumor. Consequently, organoids serve as a valuable in vitro model for exploring treatment strategies and elucidating the underlying mechanisms of BBC.

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.

Analysis of Tumor-Associated AXIN1 Missense Mutations Identifies Variants That Activate β-Catenin Signaling

AXIN1 is a major component of the β-catenin destruction complex and is frequently mutated in various cancer types, particularly liver cancers. Truncating AXIN1 mutations are recognized to encode a defective protein that leads to β-catenin stabilization, but the functional consequences of missense mutations are not well characterized. Here, we first identified the GSK3β, β-catenin, and RGS/APC interaction domains of AXIN1 that are the most critical for proper β-catenin regulation. Analysis of 80 tumor-associated variants in these domains identified 18 that significantly affected β-catenin signaling. Coimmunoprecipitation experiments revealed that most of them lost binding to the binding partner corresponding to the mutated domain. A comprehensive protein structure analysis predicted the consequences of these mutations, which largely overlapped with the observed effects on β-catenin signaling in functional experiments. The structure analysis also predicted that loss-of-function mutations within the RGS/APC interaction domain either directly affected the interface for APC binding or were located within the hydrophobic core and destabilized the entire structure. In addition, truncated AXIN1 length inversely correlated with the β-catenin regulatory function, with longer proteins retaining more functionality. These analyses suggest that all AXIN1-truncating mutations at least partially affect β-catenin regulation, whereas this is only the case for a subset of missense mutations. Consistently, most colorectal and liver cancers carrying missense variants acquire mutations in other β-catenin regulatory genes such as APC and CTNNB1. These results will aid the functional annotation of AXIN1 mutations identified in large-scale sequencing efforts or in individual patients.

SIGNIFICANCE

Characterization of 80 tumor-associated missense variants of AXIN1 reveals a subset of 18 mutations that disrupt its β-catenin regulatory function, whereas the majority are passenger mutations.

Circulating Tumor DNA Profiling Approach Based on In Silico Background Elimination Guides Chemotherapy in Nasopharyngeal Carcinoma

Circulating tumor DNA (ctDNA) analysis increasingly provides a promising minimally invasive alternative to tissue biopsies in precision oncology. However, there are no ctDNA analysis approaches available in nasopharyngeal carcinoma (NPC) and current methods of ctDNA mutation profiling have limited resolution because of the high background noise and false-positive rate caused by benign variants in plasma cell-free DNA (cfDNA), majorly generated during clonal hematopoiesis. Although personalized parallel white blood cell genome sequencing suppresses the noise of clonal hematopoiesis variances, the system cost and complexity restrict its extensive application in clinical settings. We developed Matched WBC Genome sequencing Independent CtDNA profiling (MaGIC) approaches, which synergically integrated a ctDNA capturing panel for a hybrid capture cfDNA deep sequencing, in silico background elimination, and a reliable readout measurement. We profiled the ctDNAs of 80 plasma samples from 40 patients with NPC before and during chemotherapy by MaGICs. In addition, the public cfDNA sequencing data and The Cancer Genome Atlas project data were analyzed by MaGICs to evaluate their application in other scenarios of patient classification. The MaGIC version-2 has the ability to predict the chemosensitivity of patients with NPC with high accuracy by utilizing a single sample of liquid biopsy from each patient prior to a standardized treatment regimen. Moreover, both versions of MaGICs are of ideal performance in the diagnosis of patients with prostate cancer by liquid biopsy and prognosis prediction of multiple cancers by tissue biopsy. This study has the potential to enhance the sensitivity and expand the application scope of ctDNA detection, independently of other paired genome sequencing methods. As a result, it might further increase the clinical utilization of liquid biopsy based on ctDNA.

Clinical relevance of somatic mutations in Chinese lung adenocarcinoma and their prognostic implications for survival

BACKGROUND

To comprehensively elucidate the genomic and mutational features of lung cancer cases, and lung adenocarcinoma (LUAD), it is imperative to conduct ongoing investigations into the genomic landscape. In this study, we aim to analyze the somatic mutation profile and assessed the significance of these informative genes utilizing a retrospective LUAD cohort.

METHODS

A total of 247 Chinese samples were analyzed to exhibit the tumor somatic genomic alterations in patients with LUAD. The Cox regression analysis was employed to identify prognosis-related genes and establish a predictive model for stratifying patients with LUAD.

RESULTS

In the Dianjiang People's Hospital (DPH) cohort, the top five frequent mutated genes were (Epidermal growth factor receptor) EGFR (68%), TP53 (30%), RBM10 (13%), LRP1B (9%), and KRAS (9%). Of which, EGFR is a mostly altered driver gene, and most mutation sites are located in tyrosine kinase regions. Oncogene pathway alteration and mutation signature analysis demonstrated the RTK-RAS pathway alteration, and smoking was the main carcinogenic factor of the DPH cohort. Furthermore, we identified 34 driver genes in the DPH cohort, including EGFR (68%), TP53 (30.4%), RBM10 (12.6%), KRAS (8.5%), LRP1B (8.5%), and so on, and 45 Clinical Characteristic-Related Genes (CCRGs) were found to closely related to the clinical high-risk factors. We developed a Multiple Parameter Gene Mutation (MPGM) risk model by integrating critical genes and oncogenic pathway alterations in LUAD patients from the DPH cohort. Based on publicly available LUAD datasets, we identified five genes, including BRCA2, Anaplastic lymphoma kinase (ALK), BRAF, EGFR, and Platelet-Derived Growth Factor Receptor Alpha (PDGFRA), according to the multivariable Cox regression analysis. The MPGM-low group showed significantly better overall survival (OS) compared to the MPGM-high group (p < 0.0001, area under the curve (AUC) = 0.754). The robust performance was validated in 55 LUAD patients from the DPH cohort and another LUAD dataset. Immune characteristics analysis revealed a higher proportion of primarily DCs and mononuclear cells in the MPGM-low risk group, while the MPGM-high risk group showed lower immune cells and higher tumor cell infiltration.

CONCLUSION

This study provides a comprehensive genomic landscape of Chinese LUAD patients and develops an MPGM risk model for LUAD prognosis stratification. Further follow-up will be performed for the patients in the DPH cohort consistently to explore the resistance and prognosis genetic features.

Advancing cancer driver gene detection via Schur complement graph augmentation and independent subspace feature extraction

Accurately identifying cancer driver genes (CDGs) is crucial for guiding cancer treatment and has recently received great attention from researchers. However, the high complexity and heterogeneity of cancer gene regulatory networks limit the precition accuracy of existing deep learning models. To address this, we introduce a model called SCIS-CDG that utilizes Schur complement graph augmentation and independent subspace feature extraction techniques to effectively predict potential CDGs. Firstly, a random Schur complement strategy is adopted to generate two augmented views of gene network within a graph contrastive learning framework. Rapid randomization of the random Schur complement strategy enhances the model's generalization and its ability to handle complex networks effectively. Upholding the Schur complement principle in expectations promotes the preservation of the original gene network's vital structure in the augmented views. Subsequently, we employ feature extraction technology using multiple independent subspaces, each trained with independent weights to reduce inter-subspace dependence and improve the model's expressiveness. Concurrently, we introduced a feature expansion component based on the structure of the gene network to address issues arising from the limited dimensionality of node features. Moreover, it can alleviate the challenges posed by the heterogeneity of cancer gene networks to some extent. Finally, we integrate a learnable attention weight mechanism into the graph neural network (GNN) encoder, utilizing feature expansion technology to optimize the significance of various feature levels in the prediction task. Following extensive experimental validation, the SCIS-CDG model has exhibited high efficiency in identifying known CDGs and uncovering potential unknown CDGs in external datasets. Particularly when compared to previous conventional GNN models, its performance has seen significant improved. The code and data are publicly available at: https://github.com/mxqmxqmxq/SCIS-CDG.

Long-term Multimodal Recording Reveals Epigenetic Adaptation Routes in Dormant Breast Cancer Cells

Patients with estrogen receptor-positive breast cancer receive adjuvant endocrine therapies (ET) that delay relapse by targeting clinically undetectable micrometastatic deposits. Yet, up to 50% of patients relapse even decades after surgery through unknown mechanisms likely involving dormancy. To investigate genetic and transcriptional changes underlying tumor awakening, we analyzed late relapse patients and longitudinally profiled a rare cohort treated with long-term neoadjuvant ETs until progression. Next, we developed an in vitro evolutionary study to record the adaptive strategies of individual lineages in unperturbed parallel experiments. Our data demonstrate that ETs induce nongenetic cell state transitions into dormancy in a stochastic subset of cells via epigenetic reprogramming. Single lineages with divergent phenotypes awaken unpredictably in the absence of recurrent genetic alterations. Targeting the dormant epigenome shows promising activity against adapting cancer cells. Overall, this study uncovers the contribution of epigenetic adaptation to the evolution of resistance to ETs.

SIGNIFICANCE

This study advances the understanding of therapy-induced dormancy with potential clinical implications for breast cancer. Estrogen receptor-positive breast cancer cells adapt to endocrine treatment by entering a dormant state characterized by strong heterochromatinization with no recurrent genetic changes. Targeting the epigenetic rewiring impairs the adaptation of cancer cells to ETs. See related commentary by Llinas-Bertran et al., p. 704. This article is featured in Selected Articles from This Issue, p. 695.

Improvement of assessment in surrogate endpoint and safety outcome of single-arm trials for anticancer drugs

INTRODUCTION

Single-arm trials (SATs) and surrogate endpoints were adopted as pivotal evidence for accelerated approval of anticancer drugs for more than 30 years. However, concerns regarding clinical evidence quality in trials, particularly in the SATs of anticancer drugs have increasingly been raised. SAT may not always provide strong evidence due to the lack of control and endpoint of overall survival that is typically present in randomized controlled trials.

AREAS COVERED

Clinical trial endpoint adjudication is a crucial factor in surrogate outcome measurement to ensure the data quality of the clinical trial of anticancer drugs. In this review, we systematically discuss the characteristics of adjudications in assessments in surrogate endpoint and safety outcome respectively, which are essential for ensuring reliable and transparent outcomes. Endpoint adjudication effectively reduces potential bias and mitigates variance that may be introduced by investigators when analyzing the medical records for the surrogate endpoints. We analyze the advantages and disadvantages of each type of adjudicator and provide a summary of the roles of adjudicators.

EXPERT OPINION

By suggestion of improving data reliability and transparency in pivotal trials, this review aims to supply a strategy for better clinical investigation for anticancer drugs, ultimately leading to better patient outcomes.

Human-mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model

Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human-mouse difference in radiation-related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age-related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per-lifespan basis (0.72- and 0.71-fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species-independent radiation-induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation-associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.

A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer

A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).

Massively parallel screen uncovers many rare 3' UTR variants regulating mRNA abundance of cancer driver genes

Understanding the function of rare non-coding variants represents a significant challenge. Using MapUTR, a screening method, we studied the function of rare 3' UTR variants affecting mRNA abundance post-transcriptionally. Among 17,301 rare gnomAD variants, an average of 24.5% were functional, with 70% in cancer-related genes, many in critical cancer pathways. This observation motivated an interrogation of 11,929 somatic mutations, uncovering 3928 (33%) functional mutations in 155 cancer driver genes. Functional MapUTR variants were enriched in microRNA- or protein-binding sites and may underlie outlier gene expression in tumors. Further, we introduce untranslated tumor mutational burden (uTMB), a metric reflecting the amount of somatic functional MapUTR variants of a tumor and show its potential in predicting patient survival. Through prime editing, we characterized three variants in cancer-relevant genes (MFN2, FOSL2, and IRAK1), demonstrating their cancer-driving potential. Our study elucidates the function of tens of thousands of non-coding variants, nominates non-coding cancer driver mutations, and demonstrates their potential contributions to cancer.

Construction of CDKN2A-related competitive endogenous RNA network and identification of GAS5 as a prognostic indicator for hepatocellular carcinoma

BACKGROUND

Competitive endogenous RNA (ceRNA) is an innovative way of gene expression modulation, which plays a crucial part in neoplasia. However, the intricacy and behavioral characteristics of the ceRNA network in hepatocellular carcinoma (HCC) remain dismal.

AIM

To establish a cyclin dependent kinase inhibitor 2A (CDKN2A)-related ceRNA network and recognize potential prognostic indicators for HCC.

METHODS

The mutation landscape of CDKN2A in HCC was first explored using the cBioPortal database. Differential expression analysis was implemented between CDKN2Ahigh and CDKN2Alow expression HCC samples. The targeted microRNAs were predicted by lncBasev3.0, and the targeted mRNAs were predicted by miRDB, and Targetscan database. The univariate and multivariate analysis were utilized to identify independent prognostic indicators.

RESULTS

CDKN2A was frequently mutated and deleted in HCC. The single-cell RNA-sequencing analysis revealed that CDKN2A participated in cell cycle pathways. The CDKN2A-related ceRNA network-growth arrest specific 5 (GAS5)/miR-25-3p/SRY-box transcription factor 11 (SOX11) was successfully established. GAS5 was recognized as an independent prognostic biomarker, whose overexpression was correlated with a poor prognosis in HCC patients. The association between GAS5 expression and methylation, immune infiltration was explored. Besides, traditional Chinese medicine effective components targeting GAS5 were obtained.

CONCLUSION

This CDKN2A-related ceRNA network provides innovative insights into the molecular mechanism of HCC formation and progression. Moreover, GAS5 might be a significant prognostic biomarker and therapeutic target in HCC.

Pathogenic role of super-enhancers as potential therapeutic targets in lung cancer

Lung cancer is still one of the deadliest malignancies today, and most patients with advanced lung cancer pass away from disease progression that is uncontrollable by medications. Super-enhancers (SEs) are large clusters of enhancers in the genome's non-coding sequences that actively trigger transcription. Although SEs have just been identified over the past 10 years, their intricate structure and crucial role in determining cell identity and promoting tumorigenesis and progression are increasingly coming to light. Here, we review the structural composition of SEs, the auto-regulatory circuits, the control mechanisms of downstream genes and pathways, and the characterization of subgroups classified according to SEs in lung cancer. Additionally, we discuss the therapeutic targets, several small-molecule inhibitors, and available treatment options for SEs in lung cancer. Combination therapies have demonstrated considerable advantages in preclinical models, and we anticipate that these drugs will soon enter clinical studies and benefit patients.

Mutational Signature Changes in Patients With Metastatic Squamous Cell Carcinoma of the Anal Canal

PURPOSE

We examined the concordance of genetic mutations between pretreatment tumor tissue and posttreatment circulating tumor DNA (ctDNA) in patients with metastatic squamous cell carcinoma of the anal canal (SCCA) and assessed the impact of therapy on this concordance.

METHODS

We analyzed next-generation sequencing reports from pretreatment tumor tissue and posttreatment ctDNA in 11 patients with metastatic SCCA treated at Vanderbilt University Medical Center between 2017 and 2021.

RESULTS

Among the mutations identified in posttreatment ctDNA, 34.5% were also found in pretreatment tumor tissue, while 47.6% of pretreatment tumor tissue mutations were found in posttreatment ctDNA. Four patients had preservation of potentially actionable mutations in both pretreatment tissue and posttreatment ctDNA, while 7 patients had newly identified mutations in posttreatment ctDNA that were not present in pretreatment tumor tissue.

CONCLUSION

Patients with SCCA demonstrate a high degree of temporal mutational heterogeneity. This supports the hypothesis that ctDNA can serve as a real-time tracking mechanism for solid tumors' molecular evolution in response to therapy. Our findings highlight the potential of ctDNA in identifying emerging actionable mutations, supplementing information from tissue-based genomic assessments. Further research, ideally with larger and multi-institutional cohorts, is needed to validate our findings in this relatively rare tumor type.

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues1,2. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B)3-6. However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence7, apoptosis8, and cell regeneration9, as indicated by high expression of pulmonary healing signaling pathway, stemness markers and distal cell-of-origin in HAS. The expected association of tobacco smoking variables (e.g., time to first cigarette) with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS have more neoantigens, slower clonal expansion, and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells and frequent immuno-editing. These findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development, with important clinical implications.

CINner: modeling and simulation of chromosomal instability in cancer at single-cell resolution

Cancer development is characterized by chromosomal instability, manifesting in frequent occurrences of different genomic alteration mechanisms ranging in extent and impact. Mathematical modeling can help evaluate the role of each mutational process during tumor progression, however existing frameworks can only capture certain aspects of chromosomal instability (CIN). We present CINner, a mathematical framework for modeling genomic diversity and selection during tumor evolution. The main advantage of CINner is its flexibility to incorporate many genomic events that directly impact cellular fitness, from driver gene mutations to copy number alterations (CNAs), including focal amplifications and deletions, missegregations and whole-genome duplication (WGD). We apply CINner to find chromosome-arm selection parameters that drive tumorigenesis in the absence of WGD in chromosomally stable cancer types. We found that the selection parameters predict WGD prevalence among different chromosomally unstable tumors, hinting that the selective advantage of WGD cells hinges on their tolerance for aneuploidy and escape from nullisomy. Direct application of CINner to model the WGD proportion and fraction of genome altered (FGA) further uncovers the increase in CNA probabilities associated with WGD in each cancer type. CINner can also be utilized to study chromosomally stable cancer types, by applying a selection model based on driver gene mutations and focal amplifications or deletions. Finally, we used CINner to analyze the impact of CNA probabilities, chromosome selection parameters, tumor growth dynamics and population size on cancer fitness and heterogeneity. We expect that CINner will provide a powerful modeling tool for the oncology community to quantify the impact of newly uncovered genomic alteration mechanisms on shaping tumor progression and adaptation.