Pan-cancer analysis of whole genomes

Chromosome evolution screens recapitulate tissue-specific tumor aneuploidy patterns

Whole chromosome and arm-level copy number alterations occur at high frequencies in tumors, but their selective advantages, if any, are poorly understood. Here, utilizing unbiased whole chromosome genetic screens combined with in vitro evolution to generate arm- and subarm-level events, we iteratively selected the fittest karyotypes from aneuploidized human renal and mammary epithelial cells. Proliferation-based karyotype selection in these epithelial lines modeled tissue-specific tumor aneuploidy patterns in patient cohorts in the absence of driver mutations. Hi-C-based translocation mapping revealed that arm-level events usually emerged in multiples of two via centromeric translocations and occurred more frequently in tetraploids than diploids, contributing to the increased diversity in evolving tetraploid populations. Isogenic clonal lineages enabled elucidation of pro-tumorigenic mechanisms associated with common copy number alterations, revealing Notch signaling potentiation as a driver of 1q gain in breast cancer. We propose that intrinsic, tissue-specific proliferative effects underlie tumor copy number patterns in cancer.

Atherosclerosis-related biomarker PABPC1 predicts pan-cancer events

BACKGROUND

Atherosclerosis (AS) and tumours are the leading causes of death worldwide and share common risk factors, detection methods and molecular markers. Therefore, searching for serum markers shared by AS and tumours is beneficial to the early diagnosis of patients.

METHODS

The sera of 23 patients with AS-related transient ischaemic attack were screened by serological identification of antigens through recombinant cDNA expression cloning (SEREX), and cDNA clones were identified. Pathway function enrichment analysis was performed on cDNA clones to identify their biological pathways and determine whether they were related to AS or tumours. Subsequently, gene-gene and protein-protein interactions were performed and AS-associated markers would be discovered. The expression of AS biomarkers in human normal organs and pan-cancer tumour tissues were explored. Then, immune infiltration level and tumour mutation burden of various immune cells were evaluated. Survival curves analysis could show the expression of AS markers in pan-cancer.

RESULTS

AS-related sera were screened by SEREX, and 83 cDNA clones with high homology were obtained. Through functional enrichment analysis, it was found that their functions were closely related to AS and tumour functions. After multiple biological information interaction screening and the external cohort validating, poly(A) binding protein cytoplasmic 1 (PABPC1) was found to be a potential AS biomarker. To assess whether PABPC1 was related to pan-cancer, its expression in different tumour pathological stages and ages was screened. Since AS-associated proteins were closely related to cancer immune infiltration, we investigated and found that PABPC1 had the same role in pan-cancer. Finally, analysis of Kaplan-Meier survival curves revealed that high PABPC1 expression in pan-cancer was associated with high risk of death.

CONCLUSIONS

Through the findings of SEREX and bioinformatics pan-cancer analysis, we concluded that PABPC1 might serve as a potential biomarker for the prediction and diagnosis of AS and pan-cancer.

An integrated analysis revealing the angiogenic function of TP53I11 in tumor microenvironment

Despite growing evidence suggesting an important contribution of Tumor Protein P53 Inducible Protein 11 (TP53I11) in cancer progression, the role of TP53I11 remains unclear. Our first pan-cancer analysis of TP53I11 showed some tumor tissues displayed reduced TP53I11 expression compared to normal tissues, while others exhibited high TP53I11 expression. Meanwhile, TP53I11 expression carries a particular pan-cancer risk, as high TP53I11 expression levels are detrimental to survival for BRCA, KIRP, MESO, and UVM, but to beneficial survival for KIRC. We demonstrated that TP53I11 expression negatively correlates with DNA methylation in most cancers, and the S14 residue of TP53I11 is phosphorylated in several cancer types. Additionally, TP53I11 was found to be associated with endothelial cells in pan-cancer, and functional enrichment analysis provided strong evidence for its role in tumor angiogenesis. In vitro angiogenesis assays confirmed that TP53I11 can promote angiogenic function of human umbilical vein endothelial cells (HUVECs) in vitro. Mechanistic investigations reveal that TP53I11 is transcriptionally up-regulated by HIF2A under hypoxia.

Integrative multi-omics characterization of hepatocellular carcinoma in Hispanic patients

Background

The incidence and mortality rates of hepatocellular carcinoma (HCC) among Hispanics in the United States are much higher than those of non-Hispanic whites. We conducted comprehensive multi-omics analyses to understand molecular alterations in HCC among Hispanic patients.

Methods

Paired tumor and adjacent non-tumor samples were collected from 31 Hispanic HCC in South Texas (STX-Hispanic) for genomic, transcriptomic, proteomic, and metabolomic profiling. Additionally, serum lipids were profiled in 40 Hispanic and non-Hispanic patients with or without clinically diagnosed HCC.

Results

Exome sequencing revealed high mutation frequencies of AXIN2 and CTNNB1 in STX Hispanic HCCs, suggesting a predominant activation of the Wnt/β-catenin pathway. The TERT promoter mutation frequency was also remarkably high in the Hispanic cohort. Cell cycles and liver functions were identified as positively- and negatively-enriched, respectively, with gene set enrichment analysis. Gene sets representing specific liver metabolic pathways were associated with dysregulation of corresponding metabolites. Negative enrichment of liver adipogenesis and lipid metabolism corroborated with a significant reduction in most lipids in the serum samples of HCC patients. Two HCC subtypes from our Hispanic cohort were identified and validated with the TCGA liver cancer cohort. The subtype with better overall survival showed higher activity of immune and angiogenesis signatures, and lower activity of liver function-related gene signatures. It also had higher levels of immune checkpoint and immune exhaustion markers.

Conclusions

Our study revealed some specific molecular features of Hispanic HCC and potential biomarkers for therapeutic management of HCC and provides a unique resource for studying Hispanic HCC.

CHEK2 deficiency increase the response to PD-1 inhibitors by affecting the tumor immune microenvironment

Immune checkpoint blockade (ICB) therapy has improved treatment effects in multiple cancers. Gene mutations in the DNA damage repair pathway (DDR) may cause genomic instability and may relate to the efficacy of ICB. Checkpoint kinase 2 (CHEK2) and polymerase epsilon (POLE) are important genes in the DDR. In this study, we aimed to study the impact of CHEK2 deficiency mutations on the response to ICB. We found that tumors with CHEK2 mutations had a significantly higher tumor mutational burden (TMB) compared to those with CHEK2-WT in a pancancer database. We noted that CHEK2 deficiency mutations potentiated the anti-tumor effect of anti-PD-1 therapy in MC38 and B16 tumor-bearing mice with the decrease of tumor volume and tumor weight after anti-PD-1 treatment. Mechanistically, CHEK2 deficiency tumors were with the increased cytotoxic CD8+ T-cell infiltration, especially cytotoxic CD8+ T cells, and modulated the tumor-immune microenvironment with an upregulated immune inflammatory pathway and antigen presentation pathway after anti-PD-1 treatment. Furthermore, murine models with POLE mutations confirmed that CHEK2 deficiency shaped similar mutational and immune landscapes as POLE mutations after anti-PD-1 treatment. Taken together, our results demonstrated that CHEK2 deficiency mutations may increase the response to ICB (eg. anti-PD-1) by influencing the tumor immune microenvironment. This indicated that CHEK2 deficiency mutations were a potentially predictive biomarker and CHEK2 deficiency may potentiate response to immunotherapy.

Kataegis in clinical and molecular subgroups of primary breast cancer

Kataegis is a hypermutation phenomenon characterized by localized clusters of single base pair substitution (SBS) reported in multiple cancer types. Despite a high frequency in breast cancer, large-scale analyses of kataegis patterns and associations with clinicopathological and molecular variables in established breast cancer subgroups are lacking. Therefore, WGS profiled primary breast cancers (n = 791) with associated clinical and molecular data layers, like RNA-sequencing data, were analyzed for kataegis frequency, recurrence, and associations with genomic contexts and functional elements, transcriptional patterns, driver alterations, homologous recombination deficiency (HRD), and prognosis in tumor subgroups defined by ER, PR, and HER2/ERBB2 status. Kataegis frequency was highest in the HER2-positive(p) subgroups, including both ER-negative(n)/positive(p) tumors (ERnHER2p/ERpHER2p). In TNBC, kataegis was neither associated with PAM50 nor TNBC mRNA subtypes nor with distant relapse in chemotherapy-treated patients. In ERpHER2n tumors, kataegis was associated with aggressive characteristics, including PR-negativity, molecular Luminal B subtype, higher mutational burden, higher grade, and expression of proliferation-associated genes. Recurrent kataegis loci frequently targeted regions commonly amplified in ER-positive tumors, while few recurrent loci were observed in TNBC. SBSs in kataegis loci appeared enriched in regions of open chromatin. Kataegis status was not associated with HRD in any subgroup or with distinct transcriptional patterns in unsupervised or supervised analysis. In summary, kataegis is a common hypermutation phenomenon in established breast cancer subgroups, particularly in HER2p subgroups, coinciding with an aggressive tumor phenotype in ERpHER2n disease. In TNBC, the molecular implications and associations of kataegis are less clear, including its prognostic value.

Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

Droplet Hi-C for Fast and Scalable Profiling of Chromatin Architecture in Single Cells

Comprehensive analysis of chromatin architecture is crucial for understanding the gene regulatory programs during development and in disease pathogenesis, yet current methods often inadequately address the unique challenges presented by analysis of heterogeneous tissue samples. Here, we introduce Droplet Hi-C, which employs a commercial microfluidic device for high-throughput, single-cell chromatin conformation profiling in droplets. Using Droplet Hi-C, we mapped the chromatin architecture at single-cell resolution from the mouse cortex and analyzed gene regulatory programs in major cortical cell types. Additionally, we used this technique to detect copy number variation (CNV), structural variations (SVs) and extrachromosomal DNA (ecDNA) in cancer cells, revealing clonal dynamics and other oncogenic events during treatment. We further refined this technique to allow for joint profiling of chromatin architecture and transcriptome in single cells, facilitating a more comprehensive exploration of the links between chromatin architecture and gene expression in both normal tissues and tumors. Thus, Droplet Hi-C not only addresses critical gaps in chromatin analysis of heterogeneous tissues but also emerges as a versatile tool enhancing our understanding of gene regulation in health and disease.

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.

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.

The prognostic values and immune characteristics of polo-like kinases (PLKs) family: A pan-cancer multi-omics analysis

Background

In the realm of tumor-targeted therapeutics, Polo-like kinases (PLKs) are a significant group of protein kinases that were found recently as being related to tumors. However, the significance of PLKs in pan-cancer remains systematically studied.

Methods and materials

We integrated multi-omics data to comprehensively investigate the expression patterns of the PLK family across various cancer types. Subsequently, study examined the associations between tumor mutation burden (TMB), microsatellite instability (MSI), immune subtype classification, immune infiltration, tumor microenvironment scores, immune checkpoint gene expression, and the PLKs expression profiles within various tumor types. Furthermore, using our mRNA sequencing data (TRUCE01) and four bladder cancer (BLCA) cohorts (GSE111636, GSE176307, and IMvigor210), We examined the correlation between the expression level of PLK and immunotherapy effectiveness. Next, Gene set enrichment analysis (GSEA) was evaluated to find that potentially enriched PLK signaling pathways. Utilizing TIMER 2.0, we conducted an immune infiltration analysis underlying transcriptome expression, copy number variations (CNV), or somatic mutations of PLKs in BLCA. Finally, mRNA expression validation of PLK1/3/4 by real-time PCR within 10 paired BLCA tissues, protein expression verification through the Human Protein Atlas (HPA), and PLK4 in vitro cytological studies have been employed in BLCA.

Results

The expression of most of the PLK family members exhibits variation between cancerous tissues and adjacent normal tissues across various cancer species. Furthermore, the expression of PLKs demonstrates a significant association with immunotyping, infiltration of immune cell, tumor mutational burden (TMB), microsatellite instability (MSI), immunological checkpoint gene activity and therapeutic effectiveness in pan-tumor tissues. Additional investigation into the correlation between the PLK family and BLCA has revealed that the expression of the PLK genes holds substantial significance in the biological processes of BLCA. Furthermore, a notable association has been observed between the copy number variation, variant status, and the degree of certain immunological cell infiltration. Of note, the expression validation and in vitro phenotypic experiments have demonstrated that PLK4 has a significant function in promoting the BLCA cell proliferation, migration, and invasion.

Conclusion

Collectively, based on various databases, our results highlight the involvement of PLK gene family in the formation of different types of tumors and identify PLK-related genes that may be used for therapy.

Role of reactive oxygen species in myelodysplastic syndromes

Reactive oxygen species (ROS) serve as typical metabolic byproducts of aerobic life and play a pivotal role in redox reactions and signal transduction pathways. Contingent upon their concentration, ROS production not only initiates or stimulates tumorigenesis but also causes oxidative stress (OS) and triggers cellular apoptosis. Mounting literature supports the view that ROS are closely interwoven with the pathogenesis of a cluster of diseases, particularly those involving cell proliferation and differentiation, such as myelodysplastic syndromes (MDS) and chronic/acute myeloid leukemia (CML/AML). OS caused by excessive ROS at physiological levels is likely to affect the functions of hematopoietic stem cells, such as cell growth and self-renewal, which may contribute to defective hematopoiesis. We review herein the eminent role of ROS in the hematological niche and their profound influence on the progress of MDS. We also highlight that targeting ROS is a practical and reliable tactic for MDS therapy.

HYENA detects oncogenes activated by distal enhancers in cancer

Somatic structural variations (SVs) in cancer can shuffle DNA content in the genome, relocate regulatory elements, and alter genome organization. Enhancer hijacking occurs when SVs relocate distal enhancers to activate proto-oncogenes. However, most enhancer hijacking studies have only focused on protein-coding genes. Here, we develop a computational algorithm "HYENA" to identify candidate oncogenes (both protein-coding and non-coding) activated by enhancer hijacking based on tumor whole-genome and transcriptome sequencing data. HYENA detects genes whose elevated expression is associated with somatic SVs by using a rank-based regression model. We systematically analyze 1,146 tumors across 25 types of adult tumors and identify a total of 108 candidate oncogenes including many non-coding genes. A long non-coding RNA TOB1-AS1 is activated by various types of SVs in 10% of pancreatic cancers through altered 3-dimensional genome structure. We find that high expression of TOB1-AS1 can promote cell invasion and metastasis. Our study highlights the contribution of genetic alterations in non-coding regions to tumorigenesis and tumor progression.

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.

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.

Single-cell Landscape of Malignant Transition: Unraveling Cancer Cell-of-Origin and Heterogeneous Tissue Microenvironment

Understanding disease progression and sophisticated tumor ecosystems is imperative for investigating tumorigenesis mechanisms and developing novel prevention strategies. Here, we dissected heterogeneous microenvironments during malignant transitions by leveraging data from 1396 samples spanning 13 major tissues. Within transitional stem-like subpopulations highly enriched in precancers and cancers, we identified 30 recurring cellular states strongly linked to malignancy, including hypoxia and epithelial senescence, revealing a high degree of plasticity in epithelial stem cells. By characterizing dynamics in stem-cell crosstalk with the microenvironment along the pseudotime axis, we found differential roles of ANXA1 at different stages of tumor development. In precancerous stages, reduced ANXA1 levels promoted monocyte differentiation toward M1 macrophages and inflammatory responses, whereas during malignant progression, upregulated ANXA1 fostered M2 macrophage polarization and cancer-associated fibroblast transformation by increasing TGF-β production. Our spatiotemporal analysis further provided insights into mechanisms responsible for immunosuppression and a potential target to control evolution of precancer and mitigate the risk for cancer development.

LncRNAs exhibit subtype-specific expression, survival associations, and cancer-promoting effects in breast cancer

Long non-coding RNAs (lncRNAs) play important roles in cancer progression, influencing processes such as invasion, metastasis, and drug resistance. Their reported cell type-dependent expression patterns suggest the potential for specialized functions in specific contexts. In breast cancer, lncRNA expression has been associated with different subtypes, highlighting their relevance in disease heterogeneity. However, our understanding of lncRNA function within breast cancer subtypes remains limited, warranting further investigation. We conducted a comprehensive analysis using the TANRIC dataset derived from the TCGA-BRCA cohort, profiling the expression, patient survival associations and immune cell type correlations of 12,727 lncRNAs across subtypes. Our findings revealed subtype-specific associations of lncRNAs with patient survival, tumor infiltrating lymphocytes and other immune cells. Targeting of lncRNAs exhibiting subtype-specific survival associations and expression in a panel of breast cancer cells demonstrated a selective reduction in cell proliferation within their associated subtype, supporting subtype-specific functions of certain lncRNAs. Characterization of HER2 + -specific lncRNA LINC01269 and TNBC-specific lncRNA AL078604.2 showed nuclear localization and altered expression of hundreds of genes enriched in cancer-promoting processes, including apoptosis, cell proliferation and immune cell regulation. This work emphasizes the importance of considering the heterogeneity of breast cancer subtypes and the need for subtype-specific analyses to fully uncover the relevance and potential impact of lncRNAs. Collectively, these findings demonstrate the contribution of lncRNAs to the distinct molecular, prognostic, and cellular composition of breast cancer subtypes.

STAT3 is a genetic modifier of TGF-beta induced EMT in KRAS mutant pancreatic cancer

Oncogenic mutations in KRAS are among the most common in cancer. Classical models suggest that loss of epithelial characteristics and the acquisition of mesenchymal traits are associated with cancer aggressiveness and therapy resistance. However, the mechanistic link between these phenotypes and mutant KRAS biology remains to be established. Here, we identify STAT3 as a genetic modifier of TGF-beta-induced epithelial to mesenchymal transition. Gene expression profiling of pancreatic cancer cells identifies more than 200 genes commonly regulated by STAT3 and oncogenic KRAS. Functional classification of the STAT3-responsive program reveals its major role in tumor maintenance and epithelial homeostasis. The signatures of STAT3-activated cell states can be projected onto human KRAS mutant tumors, suggesting that they faithfully reflect characteristics of human disease. These observations have implications for therapeutic intervention and tumor aggressiveness.

Radical Collaboration: Reimagining Cancer Team Science

SUMMARY

Here, we define a future of cancer team science adopting "radical collaboration"-in which six "Hallmarks of Cancer Collaboration" are utilized to propel cancer teams to reach new levels of productivity and impact in the modern era. This commentary establishes a playbook for cancer team science that can be readily adopted by others.

Multifaceted roles and functions of SOX30 in human cancer

SRY-box transcription factor 30 (SOX30) participates in tumor cell apoptosis in lung cancer. The occurrence of somatic SOX30 mutations, the expression signature of SOX30 in normal and cancer tissues, the correlation of SOX30 with immune cells and immune-related genes, and the clinical significance of SOX30 in various cancers have stimulated interest in SOX30 as a potential cancer biomarker. SOX30 influences drug sensitivity and tumor immunity in specific cancer types. In this review, we have comprehensively summarized the latest research on the role of SOX30 in cancer by combining bioinformatics evidence and a literature review. We summarize recent research on SOX30 in cancer regarding somatic mutations, trials, transcriptome analysis, clinical information, and SOX30-mediated regulation of malignant phenotypes. Additionally, we report on the diagnostic value of SOX30 mRNA expression levels across different cancer types. This review on the role of SOX30 in cancer progression may provide insights into possible research directions for SOX30 in cancer and a theoretical basis for guiding future studies.

The tumor suppressor p53 is a negative regulator of the carcinoma-associated transcription factor FOXQ1

The forkhead box family transcription factor FOXQ1 is highly induced in several types of carcinomas, where it promotes epithelial-to-mesenchymal transition and tumor metastasis. The molecular mechanisms that lead to FOXQ1 deregulation in cancer are incompletely understood. Here, we used CRISPR-Cas9-based genomic locus proteomics and promoter reporter constructs to discover transcriptional regulators of FOXQ1 and identified the tumor suppressor p53 as a negative regulator of FOXQ1 expression. Chromatin immunoprecipitation followed by quantitative PCR as well as complementary gain and loss-of-function assays in model cell lines indicated that p53 binds close to the transcription start site of the FOXQ1 promoter, and that it suppresses FOXQ1 expression in various cell types. Consistently, pharmacological activation of p53 using nutlin-3 or doxorubicin reduced FOXQ1 mRNA and protein levels in cancer cell lines harboring wildtype p53. Finally, we observed that p53 mutations are associated with increased FOXQ1 expression in human cancers. Altogether, these results suggest that loss of p53 function-a hallmark feature of many types of cancer-derepresses FOXQ1, which in turn promotes tumor progression.

The role of high mobility group AT-hook 1 in viral infections: Implications for cancer pathogenesis

The crucial role of high mobility group AT-hook 1 (HMGA1) proteins in nuclear processes such as gene transcription, DNA replication, and chromatin remodeling is undeniable. Elevated levels of HMGA1 have been associated with unfavorable clinical outcomes and adverse differentiation status across various cancer types. HMGA1 regulates a diverse array of biological pathways, including tumor necrosis factor-alpha/nuclear factor-kappa B (TNF-α/NF-κB), epidermal growth factor receptor (EGFR), Hippo, Rat sarcoma/extracellular signal-regulated kinase (Ras/ERK), protein kinase B (Akt), wingless-related integration site/beta-catenin (Wnt/beta-catenin), and phosphoinositide 3-kinase/protein kinase B (PI3-K/Akt). While researchers have extensively investigated tumors in the reproductive, digestive, urinary, and hematopoietic systems, mounting evidence suggests that HMGA1 plays a critical role as a tumorigenic factor in tumors across all functional systems. Given its broad interaction network, HMGA1 is an attractive target for viral manipulation. Some viruses, including herpes simplex virus type 1, human herpesvirus 8, human papillomavirus, JC virus, hepatitis B virus, human immunodeficiency virus type 1, severe acute respiratory syndrome Coronavirus 2, and influenza viruses, utilize HMGA1 influence for infection. This interaction, particularly in oncogenesis, is crucial. Apart from the direct oncogenic effect of some of the mentioned viruses, the hit-and-run theory postulates that viruses can instigate cancer even before being completely eradicated from the host cell, implying a potentially greater impact of viruses on cancer development than previously assumed. This review explores the interplay between HMGA1, viruses, and host cellular machinery, aiming to contribute to a deeper understanding of viral-induced oncogenesis, paving the way for innovative strategies in cancer research and treatment.

The two sides of chromosomal instability: drivers and brakes in cancer

Chromosomal instability (CIN) is a hallmark of cancer and is associated with tumor cell malignancy. CIN triggers a chain reaction in cells leading to chromosomal abnormalities, including deviations from the normal chromosome number or structural changes in chromosomes. CIN arises from errors in DNA replication and chromosome segregation during cell division, leading to the formation of cells with abnormal number and/or structure of chromosomes. Errors in DNA replication result from abnormal replication licensing as well as replication stress, such as double-strand breaks and stalled replication forks; meanwhile, errors in chromosome segregation stem from defects in chromosome segregation machinery, including centrosome amplification, erroneous microtubule-kinetochore attachments, spindle assembly checkpoint, or defective sister chromatids cohesion. In normal cells, CIN is deleterious and is associated with DNA damage, proteotoxic stress, metabolic alteration, cell cycle arrest, and senescence. Paradoxically, despite these negative consequences, CIN is one of the hallmarks of cancer found in over 90% of solid tumors and in blood cancers. Furthermore, CIN could endow tumors with enhanced adaptation capabilities due to increased intratumor heterogeneity, thereby facilitating adaptive resistance to therapies; however, excessive CIN could induce tumor cells death, leading to the "just-right" model for CIN in tumors. Elucidating the complex nature of CIN is crucial for understanding the dynamics of tumorigenesis and for developing effective anti-tumor treatments. This review provides an overview of causes and consequences of CIN, as well as the paradox of CIN, a phenomenon that continues to perplex researchers. Finally, this review explores the potential of CIN-based anti-tumor therapy.

CRISPR/Cas9 targeting of passenger single nucleotide variants in haploinsufficient or essential genes expands cancer therapy prospects

CRISPR/Cas9 technology has effectively targeted cancer-specific oncogenic hotspot mutations or insertion-deletions. However, their limited prevalence in tumors restricts their application. We propose a novel approach targeting passenger single nucleotide variants (SNVs) in haploinsufficient or essential genes to broaden therapeutic options. By disrupting haploinsufficient or essential genes through the cleavage of DNA in the SNV region using CRISPR/Cas9, we achieved the selective elimination of cancer cells without affecting normal cells. We found that, on average, 44.8% of solid cancer patients are eligible for our approach, a substantial increase compared to the 14.4% of patients with CRISPR/Cas9-applicable oncogenic hotspot mutations. Through in vitro and in vivo experiments, we validated our strategy by targeting a passenger mutation in the essential ribosomal gene RRP9 and haploinsufficient gene SMG6. This demonstrates the potential of our strategy to selectively eliminate cancer cells and expand therapeutic opportunities.

Comprehensive mutational scanning of EGFR reveals TKI sensitivities of extracellular domain mutants

The epidermal growth factor receptor, EGFR, is frequently activated in lung cancer and glioblastoma by genomic alterations including missense mutations. The different mutation spectra in these diseases are reflected in divergent responses to EGFR inhibition: significant patient benefit in lung cancer, but limited in glioblastoma. Here, we report a comprehensive mutational analysis of EGFR function. We perform saturation mutagenesis of EGFR and assess function of ~22,500 variants in a human EGFR-dependent lung cancer cell line. This approach reveals enrichment of erlotinib-insensitive variants of known and unknown significance in the dimerization, transmembrane, and kinase domains. Multiple EGFR extracellular domain variants, not associated with approved targeted therapies, are sensitive to afatinib and dacomitinib in vitro. Two glioblastoma patients with somatic EGFR G598V dimerization domain mutations show responses to dacomitinib treatment followed by within-pathway resistance mutation in one case. In summary, this comprehensive screen expands the landscape of functional EGFR variants and suggests broader clinical investigation of EGFR inhibition for cancers harboring extracellular domain mutations.

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.

The genomic and evolutionary landscapes of anaplastic thyroid carcinoma

Anaplastic thyroid carcinoma is arguably the most lethal human malignancy. It often co-occurs with differentiated thyroid cancers, yet the molecular origins of its aggressivity are unknown. We sequenced tumor DNA from 329 regions of thyroid cancer, including 213 from patients with primary anaplastic thyroid carcinomas. We also whole genome sequenced 9 patients using multi-region sequencing of both differentiated and anaplastic thyroid cancer components. Using these data, we demonstrate thatanaplastic thyroid carcinomas have a higher burden of mutations than other thyroid cancers, with distinct mutational signatures and molecular subtypes. Further, different cancer driver genes are mutated in anaplastic and differentiated thyroid carcinomas, even those arising in a single patient. Finally, we unambiguously demonstrate that anaplastic thyroid carcinomas share a genomic origin with co-occurring differentiated carcinomas and emerge from a common malignant field through acquisition of characteristic clonal driver mutations.

Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads

Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.

Anchored-fusion enables targeted fusion search in bulk and single-cell RNA sequencing data

Here, we present Anchored-fusion, a highly sensitive fusion gene detection tool. It anchors a gene of interest, which often involves driver fusion events, and recovers non-unique matches of short-read sequences that are typically filtered out by conventional algorithms. In addition, Anchored-fusion contains a module based on a deep learning hierarchical structure that incorporates self-distillation learning (hierarchical view learning and distillation [HVLD]), which effectively filters out false positive chimeric fragments generated during sequencing while maintaining true fusion genes. Anchored-fusion enables highly sensitive detection of fusion genes, thus allowing for application in cases with low sequencing depths. We benchmark Anchored-fusion under various conditions and found it outperformed other tools in detecting fusion events in simulated data, bulk RNA sequencing (bRNA-seq) data, and single-cell RNA sequencing (scRNA-seq) data. Our results demonstrate that Anchored-fusion can be a useful tool for fusion detection tasks in clinically relevant RNA-seq data and can be applied to investigate intratumor heterogeneity in scRNA-seq data.

GPX8+ cancer-associated fibroblast, as a cancer-promoting factor in lung adenocarcinoma, is related to the immunosuppressive microenvironment

BACKGROUND

Cancer-associated fibroblasts (CAFs) play a crucial role in the tumor microenvironment of lung adenocarcinoma (LUAD) and are often associated with poorer clinical outcomes. This study aimed to screen for CAF-specific genes that could serve as promising therapeutic targets for LUAD.

METHODS

We established a single-cell transcriptional profile of LUAD, focusing on genetic changes in fibroblasts. Next, we identified key genes associated with fibroblasts through weighted gene co-expression network analysis (WGCNA) and univariate Cox analysis. Then, we evaluated the relationship between glutathione peroxidase 8 (GPX8) and clinical features in multiple independent LUAD cohorts. Furthermore, we analyzed immune infiltration to shed light on the relationship between GPX8 immune microenvironment remodeling. For clinical treatment, we used the tumor immune dysfunction and exclusion (TIDE) algorithm to assess the immunotherapy prediction efficiency of GPX8. After that, we screened potential therapeutic drugs for LUAD by the connectivity map (cMAP). Finally, we conducted a cell trajectory analysis of GPX8+ CAFs to show their unique function.

RESULTS

Fibroblasts were found to be enriched in tumor tissues. Then we identified GPX8 as a key gene associated with CAFs through comprehensive bioinformatics analysis. Further analysis across multiple LUAD cohorts demonstrated the relationship between GPX8 and poor prognosis. Additionally, we found that GPX8 played a role in inducing the formation of an immunosuppressive microenvironment. The TIDE method indicated that patients with low GPX8 expression were more likely to be responsive to immunotherapy. Using the cMAP, we identified beta-CCP as a potential drug-related to GPX8. Finally, cell trajectory analysis provided insights into the dynamic process of GPX8+ CAFs formation.

CONCLUSIONS

This study elucidates the association between GPX8+ CAFs and poor prognosis, as well as the induction of immunosuppressive formation in LUAD. These findings suggest that targeting GPX8+ CAFs could potentially serve as a therapeutic strategy for the treatment of LUAD.

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.

Germline structural variation globally impacts the cancer transcriptome including disease-relevant genes

Germline variation and somatic alterations contribute to the molecular profile of cancers. We combine RNA with whole genome sequencing across 1,218 cancer patients to determine the extent germline structural variants (SVs) impact expression of nearby genes. For hundreds of genes, recurrent and common germline SV breakpoints within 100 kb associate with increased or decreased expression in tumors spanning various tissues of origin. A significant fraction of germline SV expression associations involves duplication of intergenic enhancers or 3' UTR disruption. Genes altered by both somatic and germline SVs include ATRX and CEBPA. Genes essential in cancer cell lines include BARD1 and IRS2. Genes with both expression and germline SV breakpoint patterns associated with patient survival include GCLM. Our results capture a class of phenotypic variation at work in the disease setting, including genes with cancer roles. Specific germline SVs represent potential cancer risk variants for genetic testing, including those involving genes with targeting implications.

Distinguishing preferences of human APOBEC3A and APOBEC3B for cytosines in hairpin loops, and reflection of these preferences in APOBEC-signature cancer genome mutations

The APOBEC3 enzymes convert cytosines in single-stranded DNA to uracils to protect against viruses and retrotransposons but can contribute to mutations that diversify tumors. To understand the mechanism of mutagenesis, we map the uracils resulting from expression of APOBEC3B or its catalytic carboxy-terminal domain (CTD) in Escherichia coli. Like APOBEC3A, the uracilomes of A3B and A3B-CTD show a preference to deaminate cytosines near transcription start sites and the lagging-strand replication templates and in hairpin loops. Both biochemical activities of the enzymes and genomic uracil distribution show that A3A prefers 3 nt loops the best, while A3B prefers 4 nt loops. Reanalysis of hairpin loop mutations in human tumors finds intrinsic characteristics of both the enzymes, with a much stronger contribution from A3A. We apply Hairpin Signatures 1 and 2, which define A3A and A3B preferences respectively and are orthogonal to published methods, to evaluate their contribution to human tumor mutations.

Oncolytic alphavirus replicons mediated recruitment and activation of T cells

Oncolytic viruses show promise in enhancing tumor immunogenicity by releasing immunogenic signals during tumor cell infection and lysis. In this study, we improved the virus-induced tumor immunogenicity of recombinant Semliki Forest virus (rSFV)-based replicon particles by encoding immunogenic cytokines such as C-X-C motif chemokine ligand 10 (CXCL10), FMS-like tyrosine kinase 3 ligand (Flt3L), or interferon-gamma (IFN-ƴ). Real-time imaging and flow cytometry of human cancer cell-based monolayer and spheroid cultures, using LNCaP or PANC-1 cells, revealed effective infection and transgene expression in both models. LNCaP cells exhibited higher and earlier rSFV infection compared to PANC-1 cells. While infected LNCaP cells effectively triggered immune recruitment and T cell activation even without encoding cytokines, PANC-1 cells demonstrated improved immune responses only when infected with replicons encoding cytokines, particularly IFN-ƴ, which enhanced tumor immunogenicity irrespective of cancer cell susceptibility to infection. Our study demonstrates that despite innate phenotypic disparities in cancer cells, rSFV-based replicons encoding cytokines can potentially generate effective immune responses in the tumor.

Fork coupling directs DNA replication elongation and termination

DNA replication is initiated at multiple loci to ensure timely duplication of eukaryotic genomes. Sister replication forks progress bidirectionally, and replication terminates when two convergent forks encounter one another. To investigate the coordination of replication forks, we developed a replication-associated in situ HiC method to capture chromatin interactions involving nascent DNA. We identify more than 2000 fountain-like structures of chromatin contacts in human and mouse genomes, indicative of coupling of DNA replication forks. Replication fork interaction not only occurs between sister forks but also involves forks from two distinct origins to predetermine replication termination. Termination-associated chromatin fountains are sensitive to replication stress and lead to coupled forks-associated genomic deletions in cancers. These findings reveal the spatial organization of DNA replication forks within the chromatin context.

ERK pathway agonism for cancer therapy: evidence, insights, and a target discovery framework

At least 40% of human cancers are associated with aberrant ERK pathway activity (ERKp). Inhibitors targeting various effectors within the ERKp have been developed and explored for over two decades. Conversely, a substantial body of evidence suggests that both normal human cells and, notably to a greater extent, cancer cells exhibit susceptibility to hyperactivation of ERKp. However, this vulnerability of cancer cells remains relatively unexplored. In this review, we reexamine the evidence on the selective lethality of highly elevated ERKp activity in human cancer cells of varying backgrounds. We synthesize the insights proposed for harnessing this vulnerability of ERK-associated cancers for therapeutical approaches and contextualize these insights within established pharmacological cancer-targeting models. Moreover, we compile the intriguing preclinical findings of ERK pathway agonism in diverse cancer models. Lastly, we present a conceptual framework for target discovery regarding ERKp agonism, emphasizing the utilization of mutual exclusivity among oncogenes to develop novel targeted therapies for precision oncology.

Genome-wide repeat landscapes in cancer and cell-free DNA

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

The Mutographs biorepository: A unique genomic resource to study cancer around the world

Large-scale biorepositories and databases are essential to generate equitable, effective, and sustainable advances in cancer prevention, early detection, cancer therapy, cancer care, and surveillance. The Mutographs project has created a large genomic dataset and biorepository of over 7,800 cancer cases from 30 countries across five continents with extensive demographic, lifestyle, environmental, and clinical information. Whole-genome sequencing is being finalized for over 4,000 cases, with the primary goal of understanding the causes of cancer at eight anatomic sites. Genomic, exposure, and clinical data will be publicly available through the International Cancer Genome Consortium Accelerating Research in Genomic Oncology platform. The Mutographs sample and metadata biorepository constitutes a legacy resource for new projects and collaborations aiming to increase our current research efforts in cancer genomic epidemiology globally.

Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data

BACKGROUND

Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development.

RESULTS

Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed.

CONCLUSIONS

Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites and also suggest cancer treatment strategies.

Combinatorial in vivo genome editing identifies widespread epistasis during lung tumorigenesis

Lung adenocarcinoma, the most common subtype of lung cancer, is genomically complex, with tumors containing tens to hundreds of non-synonymous mutations. However, little is understood about how genes interact with each other to enable tumorigenesis in vivo , largely due to a lack of methods for investigating genetic interactions in a high-throughput and multiplexed manner. Here, we employed a novel platform to generate tumors with all pairwise inactivation of ten tumor suppressor genes within an autochthonous mouse model of oncogenic KRAS-driven lung cancer. By quantifying the fitness of tumors with every single and double mutant genotype, we show that most tumor suppressor genetic interactions exhibited negative epistasis, with diminishing returns on tumor fitness. In contrast, Apc inactivation showed positive epistasis with the inactivation of several other genes, including dramatically synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. This approach has the potential to expand the scope of genetic interactions that may be functionally characterized in vivo , which could lead to a better understanding of how complex tumor genotypes impact each step of carcinogenesis.

A long-read sequencing strategy with overlapping linkers on adjacent fragments (OLAF-Seq) for targeted resequencing and enrichment

In this report, we present OLAF-Seq, a novel strategy to construct a long-read sequencing library such that adjacent fragments are linked with end-terminal duplications. We use the CRISPR-Cas9 nickase enzyme and a pool of multiple sgRNAs to perform non-random fragmentation of targeted long DNA molecules (> 300kb) into smaller library-sized fragments (about 20 kbp) in a manner so as to retain physical linkage information (up to 1000 bp) between adjacent fragments. DNA molecules targeted for fragmentation are preferentially ligated with adaptors for sequencing, so this method can enrich targeted regions while taking advantage of the long-read sequencing platforms. This enables the sequencing of target regions with significantly lower total coverage, and the genome sequence within linker regions provides information for assembly and phasing. We demonstrated the validity and efficacy of the method first using phage and then by sequencing a panel of 100 full-length cancer-related genes (including both exons and introns) in the human genome. When the designed linkers contained heterozygous genetic variants, long haplotypes could be established. This sequencing strategy can be readily applied in both PacBio and Oxford Nanopore platforms for both long and short genes with an easy protocol. This economically viable approach is useful for targeted enrichment of hundreds of target genomic regions and where long no-gap contigs need deep sequencing.

Barriers and Unequal Access to Timely Molecular Testing Results: Addressing the Inequities in Cancer Care Delays across Canada

Genomic medicine is a powerful tool to improve diagnosis and outcomes for cancer patients by facilitating the delivery of the right drug at the right dose at the right time for the right patient. In 2023, a Canadian conference brought together leaders with expertise in different tumor types. The objective was to identify challenges and opportunities for change in terms of equitable and timely access to biomarker testing and reporting at the education, delivery, laboratory, patient, and health-system levels in Canada. Challenges identified included: limited patient and clinician awareness of genomic medicine options with need for formal education strategies; failure by clinicians to discuss genomic medicine with patients; delays in or no access to hereditary testing; lack of timely reporting of results; intra- and inter-provincial disparities in access; lack of funding for patients to access testing and for laboratories to provide testing; lack of standardized testing; and impact of social determinants of health. Canada must standardize its approach to biomarker testing across the country, with a view to addressing current inequities, and prioritize access to advanced molecular testing to ensure systems are in place to quickly bring innovation and evidence-based treatments to Canadian cancer patients, regardless of their place of residence or socioeconomic status.

Evolving copy number gains promote tumor expansion and bolster mutational diversification

The timing and fitness effect of somatic copy number alterations (SCNA) in cancer evolution remains poorly understood. Here we present a framework to determine the timing of a clonal SCNA that encompasses multiple gains. This involves calculating the proportion of time from its last gain to the onset of population expansion (lead time) as well as the proportion of time prior to its first gain (initiation time). Our method capitalizes on the observation that a genomic segment, while in a specific copy number (CN) state, accumulates point mutations proportionally to its CN. Analyzing 184 whole genome sequenced samples from 75 patients across five tumor types, we commonly observe late gains following early initiating events, occurring just before the clonal expansion relevant to the sampling. These include gains acquired after genome doubling in more than 60% of cases. Notably, mathematical modeling suggests that late clonal gains may contain final-expansion drivers. Lastly, SCNAs bolster mutational diversification between subpopulations, exacerbating the circle of proliferation and increasing heterogeneity.

Inherited Mutations in DNA Damage Repair Genes in Italian Men with Metastatic Prostate Cancer: Results from the Meet-URO 10 Study

Background

The prevalence of pathogenic germline mutations in DNA damage repair (gDDR) genes in the Italian population is unknown.

Objective

In this prospective multicenter cohort study, we evaluated the prevalence of gDDR alterations in the Italian population affected by metastatic prostate cancer (mPCa) and analyzed the impact on response to therapy, survival, and time to castration resistance.

Design setting and participants

In an observational prospective trial, 300 consecutive Italian mPCa patients, enrolled in the Meet-Uro-10 trial from three academic Italian centers, were recruited between 2017 and 2019 and were screened for gDDR mutations in 107 genes.

Outcome measurements and statistical analysis

The primary endpoint was to assess the prevalence of gDDR mutations in the Italian population of patients with mPCa. The secondary endpoints included the association of gDDR subgroups with metastatic onset, Gleason score, and time to castration resistance.

Results and limitations

We identified 297 valuable patients. Forty-six patients had a pathogenic/likely pathogenic variant (15.5%, 95% confidence interval: 11.4-19.6): the more frequent was gBRCA2 found in nine cases (3%), followed by gATM in five cases (1.7%). In patients without mutations, longer median overall survival was observed with the sequence docetaxel-androgen receptor signaling inhibitor (ARSI) than with the sequence ARSI-docetaxel (87.9 vs 42 mo, p = 0.0001). In a univariate analysis, the median time to castration resistance in gDDR mutated patients was 19.8 mo, versus 23.7 mo in no mutated patients (p = 0.024). There were no associations of gDDR subgroups with metastatic onset and Gleason score ≥8. In our cohort, variants of unknown significance in gDDR genes were found in 80 patients and might have a prognostic relevance.

Conclusions

The study reported the prevalence of gDDR in the Italian population. The presence of gBRCA2 mutations correlates with a shorter time to the onset of castration resistance disease.

Patient summary

The prevalence of gBRCA2 in the Italian population is 3%, which is similar to that in the Spanish population, identifying similarities between people of the Western Mediterranean area.

Prognostic analysis of mutated genes and insight into effects of DNA damage and repair on mutational strand asymmetries in gastric cancer

Gastric cancer (GACA) is a complex and multifaceted disease influenced by a variety of environmental and genetic factors. Somatic mutations play a major role in its development, and their characteristics, including the asymmetry between two DNA strands, are of great interest and appear as a signal of information and guidance, revealing mechanisms of DNA damage and repair. Here, we analyzed the impact of High-frequency mutated genes on patient prognosis and found that the effect of expression levels of tumor protein p53 (TP53) and lysine methyltransferase 2C (KMT2C) genes remained high throughout the development of GACA, with similar expression patterns. After investigating mutation asymmetry across mutagenic processes, we found that transcriptional asymmetry was dominated by T > G mutations under the influence of transcription couples repair and damage. The apolipoprotein B mRNA editing enzyme catalytic polypeptide like (APOBEC) enzyme that induces mutations during DNA replication has been identified here and we identified a replicative asymmetry, which was dominated by C > A mutations in left-replicating. Strand bias in different mutation classes at transcription factor binding sites and enhancer regions were also confirmed, which implies the important role of non-coding regulatory elements in the occurrence of mutations. This work systematically describes mutational strand asymmetries in specific genomic regions, shedding light on the DNA damage and repair mechanisms underlying somatic mutations in cohorts of GACA patients with gastric cancer.

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.

Uncovering dark matter in cancer by identifying epigenetic drivers

The complex relationship between chromatin accessibility, transcriptional regulation, and cancer transitions presents a daunting puzzle. Terekhanova et al. created a pan-cancer epigenetic and transcriptomic atlas at single-cell resolution, yielding important insights into the underlying chromatin architecture of cancer transitions and novel discoveries with the potential to advance precision medicine.

BMP-ACVR1 Axis is Critical for Efficacy of PRC2 Inhibitors in B-Cell Lymphoma

EZH2 is the catalytic subunit of the histone methyltransferase Polycomb Repressive Complex 2 (PRC2), and its somatic activating mutations drive lymphoma, particularly the germinal center B-cell type. Although PRC2 inhibitors, such as tazemetostat, have demonstrated anti-lymphoma activity in patients, the clinical efficacy is not limited to EZH2-mutant lymphoma. In this study, Activin A Receptor Type 1 (ACVR1), a type I Bone Morphogenetic Protein (BMP) receptor, is identified as critical for the anti-lymphoma efficacy of PRC2 inhibitors through a whole-genome CRISPR screen. BMP6, BMP7, and ACVR1 are repressed by PRC2-mediated H3K27me3, and PRC2 inhibition upregulates their expression and signaling in cell and patient-derived xenograft models. Through BMP-ACVR1 signaling, PRC2 inhibitors robustly induced cell cycle arrest and B cell lineage differentiation in vivo. Remarkably, blocking ACVR1 signaling using an inhibitor or genetic depletion significantly compromised the in vitro and in vivo efficacy of PRC2 inhibitors. Furthermore, high levels of BMP6 and BMP7, along with ACVR1, are associated with longer survival in lymphoma patients, underscoring the clinical relevance of this study. Altogether, BMP-ACVR1 exhibits anti-lymphoma function and represents a critical PRC2-repressed pathway contributing to the efficacy of PRC2 inhibitors.

A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes

Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.