Pan-cancer analysis of whole genomes

The biological and clinical impact of deletions before and after large chromosomal gains in multiple myeloma

ABSTRACT

Acquisition of a hyperdiploid (HY) karyotype or immunoglobulin heavy chain (IgH) translocations are considered key initiating events in multiple myeloma (MM). To explore if other genomic events can precede these events, we analyzed whole-genome sequencing data from 1173 MM samples. By integrating molecular time and structural variants within early chromosomal duplications, we indeed identified pregain deletions in 9.4% of patients with an HY karyotype without IgH translocations, challenging acquisition of an HY karyotype as the earliest somatic event. Remarkably, these deletions affected tumor suppressor genes (TSGs) and/or oncogenes in 2.4% of patients with an HY karyotype without IgH translocations, supporting their role in MM pathogenesis. Furthermore, our study points to postgain deletions as novel driver mechanisms in MM. Using multiomics approaches to investigate their biologic impact, we found associations with poor clinical outcome in newly diagnosed patients and profound effects on both the oncogene and TSG activity despite the diploid gene status. Overall, this study provides novel insights into the temporal dynamics of genomic alterations in MM.

Implications of noncoding regulatory functions in the development of insulinomas

Insulinomas are rare neuroendocrine tumors arising from pancreatic β cells, characterized by aberrant proliferation and altered insulin secretion, leading to glucose homeostasis failure. With the aim of uncovering the role of noncoding regulatory regions and their aberrations in the development of these tumors, we coupled epigenetic and transcriptome profiling with whole-genome sequencing. As a result, we unraveled somatic mutations associated with changes in regulatory functions. Critically, these regions impact insulin secretion, tumor development, and epigenetic modifying genes, including polycomb complex components. Chromatin remodeling is apparent in insulinoma-selective domains shared across patients, containing a specific set of regulatory sequences dominated by the SOX17 binding motif. Moreover, many of these regions are H3K27me3 repressed in β cells, suggesting that tumoral transition involves derepression of polycomb-targeted domains. Our work provides a compendium of aberrant cis-regulatory elements affecting the function and fate of β cells in their progression to insulinomas and a framework to identify coding and noncoding driver mutations.

ERCC2 mutations alter the genomic distribution pattern of somatic mutations and are independently prognostic in bladder cancer

Excision repair cross-complementation group 2 (ERCC2) encodes the DNA helicase xeroderma pigmentosum group D, which functions in transcription and nucleotide excision repair. Point mutations in ERCC2 are putative drivers in around 10% of bladder cancers (BLCAs) and a potential positive biomarker for cisplatin therapy response. Nevertheless, the prognostic significance directly attributed to ERCC2 mutations and its pathogenic role in genome instability remain poorly understood. We first demonstrated that mutant ERCC2 is an independent predictor of prognosis in BLCA. We then examined its impact on the somatic mutational landscape using a cohort of ERCC2 wild-type (n = 343) and mutant (n = 39) BLCA whole genomes. The genome-wide distribution of somatic mutations is significantly altered in ERCC2 mutants, including T[C>T]N enrichment, altered replication time correlations, and CTCF-cohesin binding site mutation hotspots. We leverage these alterations to develop a machine learning model for predicting pathogenic ERCC2 mutations, which may be useful to inform treatment of patients with BLCA.

Association of genetic ancestry with molecular tumor profiles in colorectal cancer

BACKGROUND

There are known disparities in incidence and outcomes of colorectal cancer (CRC) by race and ethnicity. Some of these disparities may be mediated by molecular changes in tumors that occur at different rates across populations. Genetic ancestry is a measure complementary to race and ethnicity that can overcome missing data issues and better capture genetic similarity in admixed populations. We aimed to identify somatic mutations and tumor gene expression differences associated with both genetic ancestry and imputed race and ethnicity.

METHODS

Sequencing was performed with the Tempus xT NGS 648-gene panel and whole exome capture RNA-Seq for 8454 primarily late-stage CRC patients. Genetic ancestry proportions for five continental groups-Africa (AFR), American indigenous (AMR), East Asia (EAS), Europe (EUR), and South Asia (SAS)-were estimated using ancestry informative markers. To address data gaps, race and ethnicity categories were imputed, resulting in assignments for 952 Hispanic/Latino, 420 non-Hispanic (NH) Asian, 1061 NH Black, and 5763 NH White individuals. We assessed association of genetic ancestry proportions and imputed race and ethnicity categories with somatic mutations in relevant CRC genes and in 2608 expression profiles, as well as 1957 consensus molecular subtypes (CMS).

RESULTS

Increased AFR ancestry was associated with higher odds of somatic mutations in APC, KRAS, and PIK3CA and lower odds of BRAF mutations. Additionally, increased EAS ancestry was associated with lower odds of mutations in KRAS, EUR with higher odds in BRAF, and the Hispanic/Latino category with lower odds in BRAF. Greater AFR ancestry and the NH Black category were associated with higher rates of CMS3, while a higher proportion of Hispanic/Latino patients exhibited indeterminate CMS classifications.

CONCLUSIONS

Molecular differences in CRC tumor mutation frequencies and gene expression that may underlie observed differences by race and ethnicity were identified. The association of AFR ancestry with increased KRAS mutations aligns with higher CMS3 subtype rates in NH Black patients. The increase of indeterminate CMS in Hispanic/Latino patients suggests that subtype classification methods could benefit from enhanced patient diversity.

VolcanoSV enables accurate and robust structural variant calling in diploid genomes from single-molecule long read sequencing

Structural variants (SVs) significantly contribute to human genome diversity and play a crucial role in precision medicine. Although advancements in single-molecule long-read sequencing offer a groundbreaking resource for SV detection, identifying SV breakpoints and sequences accurately and robustly remains challenging. We introduce VolcanoSV, an innovative hybrid SV detection pipeline that utilizes both a reference genome and local de novo assembly to generate a phased diploid assembly. VolcanoSV uses phased SNPs and unique k-mer similarity analysis, enabling precise haplotype-resolved SV discovery. VolcanoSV is adept at constructing comprehensive genetic maps encompassing SNPs, small indels, and all types of SVs, making it well-suited for human genomics studies. Our extensive experiments demonstrate that VolcanoSV surpasses state-of-the-art assembly-based tools in the detection of insertion and deletion SVs, exhibiting superior recall, precision, F1 scores, and genotype accuracy across a diverse range of datasets, including low-coverage (10x) datasets. VolcanoSV outperforms assembly-based tools in the identification of complex SVs, including translocations, duplications, and inversions, in both simulated and real cancer data. Moreover, VolcanoSV is robust to various evaluation parameters and accurately identifies breakpoints and SV sequences.

The cytidine deaminase APOBEC3C has unique sequence and genome feature preferences

APOBEC proteins are cytidine deaminases that restrict the replication of viruses and transposable elements. Several members of the APOBEC3 family, APOBEC3A, APOBEC3B, and APOBEC3H-I, can access the nucleus and cause what is thought to be indiscriminate deamination of the genome, resulting in mutagenesis and genome instability. Although APOBEC3C is also present in the nucleus, the full scope of its deamination target preferences is unknown. By expressing human APOBEC3C in a yeast model system, I have defined the APOBEC3C mutation signature, as well as the preferred genome features of APOBEC3C targets. The APOBEC3C mutation signature is distinct from those of the known cancer genome mutators APOBEC3A and APOBEC3B. APOBEC3C produces DNA strand-coordinated mutation clusters, and APOBEC3C mutations are enriched near the transcription start sites of active genes. Surprisingly, APOBEC3C lacks the bias for the lagging strand of DNA replication that is seen for APOBEC3A and APOBEC3B. The unique preferences of APOBEC3C constitute a mutation profile that will be useful in defining sites of APOBEC3C mutagenesis in human genomes.

An enhanced Eco1 retron editor enables precision genome engineering in human cells from a single-copy integrated lentivirus

Retrons are a retroelement class found in diverse prokaryotes that can be adapted to augment CRISPR-Cas9 genome engineering technology to efficiently rewrite short stretches of genetic information in bacteria and yeast; however, efficiency in human cells has been limited by unknown factors. We identified non-coding RNA (ncRNA) instability and impaired Cas9 activity as major contributors to poor retron editor efficiency. We re-engineered the Eco1 ncRNA to incorporate an exoribonuclease-resistant RNA pseudoknot from the Zika virus 3' UTR and devised an RNA processing strategy using Csy4 ribonuclease to liberate the sgRNA and ncRNA. These modifications yielded a ncRNA with 5'- and 3'-end protection and an sgRNA with minimal 5' extension. This strategy increased steady-state ncRNA levels and rescued Cas9 activity leading to enhanced efficiency of the Eco1 retron editor in human cells. The enhanced Eco1 retron editor enabled the insertion of missense mutations in human cells from a single integrated lentivirus, thereby ensuring genotype-phenotype linkage over multiple cell divisions. This work reveals a previously unappreciated role for ncRNA stability in retron editor efficiency in human cells. Here we present an enhanced Eco1 retron editor that enables efficient introduction of missense mutations in human cells from a single heritable genome copy.

Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone

Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

Understanding variants of unknown significance: the computational frontier

The rapid advancement of sequencing technologies has led to the identification of numerous mutations in cancer genomes, many of which are variants of unknown significance (VUS). Computational models are increasingly being used to predict the functional impact of these mutations, in both coding and noncoding regions. Integration of these models with emerging genomic datasets will refine our understanding of mutation effects and guide clinical decision making. Future advancements in modeling protein interactions and transcriptional regulation will further enhance our ability to interpret VUS. Periodic incorporation of these developments into VUS reclassification practice has the potential to significantly improve personalized cancer care.

Coordinated Targeting of S6K1/2 and AXL Disrupts Pyrimidine Biosynthesis in PTEN-Deficient Glioblastoma

Intrinsic resistance to targeted therapeutics in PTEN-deficient glioblastoma (GBM) is mediated by redundant signaling networks that sustain critical metabolic functions. Here, we demonstrate that coordinated inhibition of the ribosomal protein S6 kinase 1 (S6K1) and the receptor tyrosine kinase AXL using LY-2584702 and BMS-777607 can overcome network redundancy to reduce GBM tumor growth. This combination of S6K1 and AXL inhibition suppressed glucose flux to pyrimidine biosynthesis. Genetic inactivation studies to map the signaling network indicated that both S6K1 and S6K2 transmit growth signals in PTEN-deficient GBM. Kinome-wide ATP binding analysis in inhibitor-treated cells revealed that LY-2584702 directly inhibited S6K1, and substrate phosphorylation studies showed that BMS-777607 inactivation of upstream AXL collaborated to reduce S6K2-mediated signal transduction. Thus, combination targeting of S6K1 and AXL provides a kinase-directed therapeutic approach that circumvents signal transduction redundancy to interrupt metabolic function and reduce growth of PTEN-deficient GBM.

SIGNIFICANCE

Therapy for glioblastoma would be advanced by incorporating molecularly targeted kinase-directed agents, similar to standard of care strategies in other tumor types. Here, we identify a kinase targeting approach to inhibit the metabolism and growth of glioblastoma.

Clinical application of whole-genome sequencing of solid tumors for precision oncology

Genomic alterations in tumors play a pivotal role in determining their clinical trajectory and responsiveness to treatment. Targeted panel sequencing (TPS) has served as a key clinical tool over the past decade, but advancements in sequencing costs and bioinformatics have now made whole-genome sequencing (WGS) a feasible single-assay approach for almost all cancer genomes in clinical settings. This paper reports on the findings of a prospective, single-center study exploring the real-world clinical utility of WGS (tumor and matched normal tissues) and has two primary objectives: (1) assessing actionability for therapeutic options and (2) providing clarity for clinical questions. Of the 120 patients with various solid cancers who were enrolled, 95 (79%) successfully received genomic reports within a median of 11 working days from sampling to reporting. Analysis of these 95 WGS reports revealed that 72% (68/95) yielded clinically relevant insights, with 69% (55/79) pertaining to therapeutic actionability and 81% (13/16) pertaining to clinical clarity. These benefits include the selection of informed therapeutics and/or active clinical trials based on the identification of driver mutations, tumor mutational burden (TMB) and mutational signatures, pathogenic germline variants that warrant genetic counseling, and information helpful for inferring cancer origin. Our findings highlight the potential of WGS as a comprehensive tool in precision oncology and suggests that it should be integrated into routine clinical practice to provide a complete image of the genomic landscape to enable tailored cancer management.

Cancer takes many paths through G1/S

In the commonly accepted paradigm for control of the mammalian cell cycle, sequential cyclin-dependent kinase (CDK) and cyclin activities drive the orderly transition from G1 to S phase. However, recent studies using different technological approaches and examining a broad range of cancer cell types are challenging this established paradigm. An alternative model is evolving in which cell cycles utilize different drivers and take different trajectories through the G1/S transition. We are discovering that cancer cells in particular can adapt their drivers and trajectories, which has important implications for antiproliferative therapies. These studies have helped to refine an understanding of how CDK inhibition impinges on proliferation and have significance for understanding fundamental features of cell biology and cancer.

Mutant ARID1A: igniting cancer immunotherapy

Maxwell et al. show that ARID1A loss enhances antitumor immunity by triggering a type I IFN response through the cGAS-STING pathway, thereby promoting T cell infiltration and cytotoxicity. These findings highlight SWI/SNF inhibitors as a strategy to augment immunotherapy efficacy by potentially transforming non-responsive tumors into responders and advancing approaches to cancer treatment.

Mitochondrial DNA mosaicism in normal human somatic cells

Somatic cells accumulate genomic alterations with age; however, our understanding of mitochondrial DNA (mtDNA) mosaicism remains limited. Here we investigated the genomes of 2,096 clones derived from three cell types across 31 donors, identifying 6,451 mtDNA variants with heteroplasmy levels of ≳0.3%. While the majority of these variants were unique to individual clones, suggesting stochastic acquisition with age, 409 variants (6%) were shared across multiple embryonic lineages, indicating their origin from heteroplasmy in fertilized eggs. The mutational spectrum exhibited replication-strand bias, implicating mtDNA replication as a major mutational process. We evaluated the mtDNA mutation rate (5.0 × 10-8 per base pair) and a turnover frequency of 10-20 per year, which are fundamental components shaping the landscape of mtDNA mosaicism over a lifetime. The expansion of mtDNA-truncating mutations toward homoplasmy was substantially suppressed. Our findings provide comprehensive insights into the origins, dynamics and functional consequences of mtDNA mosaicism in human somatic cells.

Building a translational cancer dependency map for The Cancer Genome Atlas

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of 'maps' detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.

Ribosome stalling during c-myc translation presents actionable cancer cell vulnerability

Myc is a major driver of tumor initiation, progression, and maintenance. Up-regulation of Myc protein level rather than acquisition of neomorphic properties appears to underlie most Myc-driven cancers. Cellular mechanisms governing Myc expression remain incompletely defined. In this study, we show that ribosome-associated quality control (RQC) plays a critical role in maintaining Myc protein level. Ribosomes stall during the synthesis of the N-terminal portion of cMyc, generating aberrant cMyc species and necessitating deployment of the early RQC factor ZNF598 to handle translational stress and restore cMyc translation. ZNF598 expression is up-regulated in human glioblastoma (GBM), and its expression positively correlates with that of cMyc. ZNF598 knockdown inhibits human GBM neurosphere formation in cell culture and Myc-dependent tumor growth in vivo in Drosophila. Intriguingly, the SARS-COV-2-encoded translational regulator Nsp1 impinges on ZNF598 to restrain cMyc translation and consequently cMyc-dependent cancer growth. Remarkably, Nsp1 exhibits synthetic toxicity with the translation and RQC-related factor ATP-binding cassette subfamily E member 1, which, despite its normally positive correlation with cMyc in cancer cells, is co-opted by Nsp1 to down-regulate cMyc and inhibit tumor growth. Ribosome stalling during c-myc translation thus offers actionable cancer cell vulnerability.

Prognostic impact of Interleukin-8 levels in lung cancer: A meta-analysis and a bioinformatic validation

BACKGROUND

High interleukin-8 (IL-8) levels have been linked to poor prognosis in lung cancer, but conclusive data are lacking.

MATERIALS AND METHODS

A comprehensive search was conducted on April 1st, 2023, from electronic databases, focusing on studies with IL-8 expression evaluations and the availability of hazard ratio (HR) and 95% confidence intervals (CI) for overall survival (OS), progression-free survival (PFS) and disease-free survival (DFS) or adequate data for their estimation. Then, we examined IL-8 and CXCR1 RNA-seq data from The Cancer Genome Atlas (TCGA) dataset, and we correlated these data with OS.

RESULTS

Among 2655 produced records, 10 manuscripts involving both non-small cell lung cancer and small cell lung cancer, were included in the analysis. Two manuscripts and one study included two and three different cohorts, respectively, for a total of 14 cohorts of patients. Overall, 4 cohorts evaluated IL-8 levels in patients treated with chemotherapy, 3 cohorts immunotherapy, 2 cohorts surgical patients and 4 cohorts other treatments; 1 cohort was removed, as the type of treatments was lacking. The 12 cohorts included in the OS analysis revealed that patients with high IL-8 levels have a lower OS probability, as compared to patients with low IL-8 levels (HR=1.75, 95 % CI 1.36-2.26). No significant difference between patients with high and low IL-8 levels was observed in the 8 cohorts available for PFS analysis. Sensitivity analysis according to treatment revealed significant PFS and OS differences for patients treated with chemotherapy or immunotherapy. Analysis of RNA-seq data from TCGA, confirmed the correlation between high IL-8 and CXCR1 expression and worse OS in patients with resected lung cancer.

CONCLUSION

To the best of our knowledge, this study represents the first meta-analysis demonstrating a negative prognostic impact of high IL-8 level in lung cancer, particularly in patients treated with chemotherapy and/or immunotherapy.

Complement C3d enables protective immunity capable of distinguishing spontaneously transformed from non-transformed cells

Immune-surveillance depends in part on the recognition of peptide variants by T cell antigen receptors. Given that both normal B cells and malignant B cells accumulate mutations we chose a murine model of multiple myeloma to test conditions to induce cell-mediated immunity targeting malignant plasma cell (PC) clones but sparing of normal PCs. Revealing a novel function for intracellular C3d, we discovered that C3d engaged T cell responses against malignant plasma cells in the bone marrow of mice that had developed multiple myeloma spontaneously. Our results show that C3d internalized by cells augments immune surveillance by several mechanisms. In one, C3d induces a master transcription regulator, E2f1, to increase the expression of long non-coding (lnc) RNAs, to generate peptides for MHC-I presentation and increase MHC-I expression. In another, C3d increases expression of RNAs encoding ribosomal proteins linked to processing of defective ribosomal products (DRiPs) that arise from non-canonical translation and known to promote immunosurveillance. Cancer cells are uniquely susceptible to increased expression and presentation of mutant peptides given the extent of protein misfolding and accumulation of somatic mutations. Accordingly, although C3d can be internalized by any cell, C3d preferentially targets malignant clones by evoking specific T cell mediated immunity (CMI) and sparing most non-transformed polyclonal B cells and plasma cells with lower mutation loads. Malignant plasma cell deletion was blocked by cyclosporin or by CD8 depletion confirming that endogenous T cells mediated malignant clone clearance. Besides the potential for therapeutic application our results highlight how intracellular C3d modifies cellular metabolism to augment immune surveillance.

One Sentence Summary

We show that intracellular soluble fragment 3d of complement (C3d) induces regression of spontaneous multiple myeloma in mice reducing tumor burden by 10 fold, after 8 weeks. C3d enables cell-mediated immunity to target multiple myeloma clones sparing non-transformed polyclonal B cells and plasma cells with lower mutation loads. We show that C3d increases the expression of ribosomal subunits associated with the translation of defective ribosomal products (DRiPs). C3d also decreases expression of protein arginine methyl transferase (PRMT) 5 which in turn relieves E2f1 repression increasing the expression of Lnc RNAs and derived peptides that evoke anti-tumor cellular immunity. The approach increases MHC-I expression by tumor cells and generates a CMI response that overcomes tumor immune-evasion strategies.

Significance

Tumors are immunogenic in part because of somatic mutations that originate novel peptides that once presented on MHC engage cell-mediated immunity (CMI). However, in spite of the higher mutation load most tumors evade immunity. We discovered that a component of the complement system (C3d) overcomes tumor immune evasion by augmenting expression of ribosomal proteins and lncRNAs linked to the presentation of novel peptides by tumor cells. C3d induced CMI targets cancer cells sparing non transformed cells uncovering a novel function for complement in immune surveillance.

Co-Expression Network Analysis and Molecular Docking Demonstrate That Diosgenin Inhibits Gastric Cancer Progression via SLC1A5/mTORC1 Pathway

Background

Tumor-Node-Metastasis (TNM) stage of gastric cancer (GC) is one of the main factors affecting clinical outcome. The aim of this study was to explore the targets related to TNM stage of GC, and screening natural bioactive drug.

Methods

RNA sequencing data of the TCGA-STAD cohort were downloaded from UCSC database. Genes associated with TNM staging were identified by weighted gene co-expression network analysis (WGCNA). Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (Xgboost), random forest (RF) and cytohubba plug-in of cytoscope were applied to screen hub genes. Natural bioactive ingredients were available from the HERB database. Molecular docking was used to evaluate the binding activity of active ingredients to the hub protein. CCK-8, flow cytometry, transwell and Western blot assays were used to analyze the effects of diosgenin on GC cells.

Results

898 TNM-related genes were screened out through WGCNA. Three genes associated with GC progression/prognosis were identified, including nuclear receptor subfamily 3 group C member 2 (NR3C2), solute carrier family 1 member 5 (SLC1A5) and FAT atypical cadherin 1 (FAT1) based on the machine learning algorithms and hub co-expression network analysis. Diosgenin had good binding activity with SLC1A5. SLC1A5 was highly expressed in GC and was closely associated with tumor stage, overall survival and immune infiltration of GC patients. Diosgenin could inhibit cell viability and invasive ability, promote apoptosis and induce cell cycle arrest in G0/G1 phase. In addition, diosgenin promoted cleaved caspase 3 expression and inhibited Ki67, cyclin D1, p-S6K1, and SLC1A5 expression levels, while the mTORC1 activator (MHY1485) reversed this phenomenon.

Conclusion

For the first time, this work reports diosgenin may inhibit the activation of mTORC1 signaling through targeting SLC1A5, thereby inhibiting the malignant behaviors of GC cells.

The genetic evolution of acral melanoma

Acral melanoma is an aggressive type of melanoma with unknown origins. It is the most common type of melanoma in individuals with dark skin and is notoriously challenging to treat. We examine exome sequencing data of 139 tissue samples, spanning different progression stages, from 37 patients. We find that 78.4% of the melanomas display clustered copy number transitions with focal amplifications, recurring predominantly on chromosomes 5, 11, 12, and 22. These complex genomic aberrations are typically shared across all progression stages of individual patients. TERT activating alterations also arise early, whereas MAP-kinase pathway mutations appear later, an inverted order compared to the canonical evolution. The punctuated formation of complex aberrations and early TERT activation suggest a unique mutational mechanism that initiates acral melanoma. The marked intratumoral heterogeneity, especially concerning MAP-kinase pathway mutations, may partly explain the limited success of therapies for this melanoma subtype.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors

Background

Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME.

Methods

We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset.

Results

Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes.

Conclusion

Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4. Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC, on the same complex amplicons such as ecDNA. We characterized a MYC-ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC's enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations

Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Comprehensive pan-cancer analysis of the C2ORF40 expression: Infiltration associations and prognostic implications

In recent years, C2ORF40 has been identified as a tumor suppressor gene with multiple functions, including roles in cell proliferation, migration, and senescence. To explore the role of the C2ORF40 gene in different tumors, we used multiple databases for analysis. Compared to adjacent normal tissues, C2ORF40 is downregulated in a variety of malignant tumors, including tumors such as breast cancer, colorectal cancer, bladder cancer, hepatocellular carcinoma and prostate cancer. Notably, low expression of the gene is significantly associated with poor overall survival and relapse-free survival rates. In specific cancers including colon cancer and prostate cancer, the expression of C2ORF40 is correlated with the infiltration of CAFs. C2ORF40 is also involved in biological processes such as cell apoptosis and regulation of protein stability. In conclusion, C2ORF40 can hold promise as a prognostic marker for pan-cancer analysis.

Biosensors for Public Health and Environmental Monitoring: The Case for Sustainable Biosensing

Climate change is a profound crisis that affects every aspect of life, including public health. Changes in environmental conditions can promote the spread of pathogens and the development of new mutants and strains. Early detection is essential in managing and controlling this spread and improving overall health outcomes. This perspective article introduces basic biosensing concepts and various biosensors, including electrochemical, optical, mass-based, nano biosensors, and single-molecule biosensors, as important sustainability and public health preventive tools. The discussion also includes how the sustainability of a biosensor is crucial to minimizing environmental impacts and ensuring the long-term availability of vital technologies and resources for healthcare, environmental monitoring, and beyond. One promising avenue for pathogen screening could be the electrical detection of biomolecules at the single-molecule level, and some recent developments based on single-molecule bioelectronics using the Scanning Tunneling Microscopy-assisted break junctions (STM-BJ) technique are shown here. Using this technique, biomolecules can be detected with high sensitivity, eliminating the need for amplification and cell culture steps, thereby enhancing speed and efficiency. Furthermore, the STM-BJ technique demonstrates exceptional specificity, accurately detects single-base mismatches, and exhibits a detection limit essentially at the level of individual biomolecules. Finally, a case is made here for sustainable biosensors, how they can help, the paradigm shift needed to achieve them, and some potential applications.

GIHP: Graph convolutional neural network based interpretable pan-specific HLA-peptide binding affinity prediction

Accurately predicting the binding affinities between Human Leukocyte Antigen (HLA) molecules and peptides is a crucial step in understanding the adaptive immune response. This knowledge can have important implications for the development of effective vaccines and the design of targeted immunotherapies. Existing sequence-based methods are insufficient to capture the structure information. Besides, the current methods lack model interpretability, which hinder revealing the key binding amino acids between the two molecules. To address these limitations, we proposed an interpretable graph convolutional neural network (GCNN) based prediction method named GIHP. Considering the size differences between HLA and short peptides, GIHP represent HLA structure as amino acid-level graph while represent peptide SMILE string as atom-level graph. For interpretation, we design a novel visual explanation method, gradient weighted activation mapping (Grad-WAM), for identifying key binding residues. GIHP achieved better prediction accuracy than state-of-the-art methods across various datasets. According to current research findings, key HLA-peptide binding residues mutations directly impact immunotherapy efficacy. Therefore, we verified those highlighted key residues to see whether they can significantly distinguish immunotherapy patient groups. We have verified that the identified functional residues can successfully separate patient survival groups across breast, bladder, and pan-cancer datasets. Results demonstrate that GIHP improves the accuracy and interpretation capabilities of HLA-peptide prediction, and the findings of this study can be used to guide personalized cancer immunotherapy treatment. Codes and datasets are publicly accessible at: https://github.com/sdustSu/GIHP.

Neotelomeres and telomere-spanning chromosomal arm fusions in cancer genomes revealed by long-read sequencing

Alterations in the structure and location of telomeres are pivotal in cancer genome evolution. Here, we applied both long-read and short-read genome sequencing to assess telomere repeat-containing structures in cancers and cancer cell lines. Using long-read genome sequences that span telomeric repeats, we defined four types of telomere repeat variations in cancer cells: neotelomeres where telomere addition heals chromosome breaks, chromosomal arm fusions spanning telomere repeats, fusions of neotelomeres, and peri-centromeric fusions with adjoined telomere and centromere repeats. These results provide a framework for the systematic study of telomeric repeats in cancer genomes, which could serve as a model for understanding the somatic evolution of other repetitive genomic elements.

Network analysis of driver genes in human cancers

Cancer is a heterogeneous disease that results from genetic alteration of cell cycle and proliferation controls. Identifying mutations that drive cancer, understanding cancer type specificities, and delineating how driver mutations interact with each other to establish disease is vital for identifying therapeutic vulnerabilities. Such cancer specific patterns and gene co-occurrences can be identified by studying tumor genome sequences, and networks have proven effective in uncovering relationships between sequences. We present two network-based approaches to identify driver gene patterns among tumor samples. The first approach relies on analysis using the Directed Weighted All Nearest Neighbors (DiWANN) model, which is a variant of sequence similarity network, and the second approach uses bipartite network analysis. A data reduction framework was implemented to extract the minimal relevant information for the sequence similarity network analysis, where a transformed reference sequence is generated for constructing the driver gene network. This data reduction process combined with the efficiency of the DiWANN network model, greatly lowered the computational cost (in terms of execution time and memory usage) of generating the networks enabling us to work at a much larger scale than previously possible. The DiWANN network helped us identify cancer types in which samples were more closely connected to each other suggesting they are less heterogeneous and potentially susceptible to a common drug. The bipartite network analysis provided insight into gene associations and co-occurrences. We identified genes that were broadly mutated in multiple cancer types and mutations exclusive to only a few. Additionally, weighted one-mode gene projections of the bipartite networks revealed a pattern of occurrence of driver genes in different cancers. Our study demonstrates that network-based approaches can be an effective tool in cancer genomics. The analysis identifies co-occurring and exclusive driver genes and mutations for specific cancer types, providing a better understanding of the driver genes that lead to tumor initiation and evolution.

Low coverage whole genome sequencing of low-grade dysplasia strongly predicts colorectal cancer risk in ulcerative colitis

Patients with inflammatory bowel disease (IBD) are at increased risk of colorectal cancer (CRC), and this risk increases dramatically in those who develop low-grade dysplasia (LGD). However, there is currently no accurate way to risk-stratify patients with LGD, leading to both over- and under-treatment of cancer risk. Here we show that the burden of somatic copy number alterations (CNAs) within resected LGD lesions strongly predicts future cancer development. We performed a retrospective multi-centre validated case-control study of n=122 patients (40 progressors, 82 non-progressors, 270 LGD regions). Low coverage whole genome sequencing revealed CNA burden was significantly higher in progressors than non-progressors (p=2×10-6 in discovery cohort) and was a very significant predictor of CRC risk in univariate analysis (odds ratio = 36; p=9×10-7), outperforming existing clinical risk factors such as lesion size, shape and focality. Optimal risk prediction was achieved with a multivariate model combining CNA burden with the known clinical risk factor of incomplete LGD resection. The measurement of CNAs in LGD lesions is a robust, low-cost and rapidly translatable predictor of CRC risk in IBD that can be used to direct management and so prevent CRC in high-risk individuals whilst sparing those at low-risk from unnecessary intervention.

Hypomorphic mutation in the large subunit of replication protein A affects mutagenesis by human APOBEC cytidine deaminases in yeast

Human APOBEC single-strand (ss) specific DNA and RNA cytidine deaminases change cytosines to uracils and function in antiviral innate immunity, RNA editing, and can cause hypermutation in chromosomes. The resulting uracils can be directly replicated, resulting in C to T mutations, or uracil-DNA glycosylase can convert the uracils to abasic (AP) sites which are then fixed as C to T or C to G mutations by translesion DNA polymerases. We noticed that in yeast and in human cancers, contributions of C to T and C to G mutations depends on the origin of ssDNA mutagenized by APOBECs. Since ssDNA in eukaryotic genomes readily binds to replication protein A (RPA) we asked if RPA could affect APOBEC-induced mutation spectrum in yeast. For that purpose, we expressed human APOBECs in the wild-type yeast and in strains carrying a hypomorph mutation rfa1-t33 in the large RPA subunit. We confirmed that the rfa1-t33 allele can facilitate mutagenesis by APOBECs. We also found that the rfa1-t33 mutation changed the ratio of APOBEC3A-induced T to C and T to G mutations in replicating yeast to resemble a ratio observed in long-persistent ssDNA in yeast and in cancers. We present the data suggesting that RPA may shield APOBEC formed uracils in ssDNA from Ung1, thereby facilitating C to T mutagenesis through the accurate copying of uracils by replicative DNA polymerases. Unexpectedly, we also found that for uracils shielded from Ung1 by wild-type RPA the mutagenic outcome is reduced in the presence of translesion DNA polymerase zeta.

Fusion Challenges in Solid Tumors: Shaping the Landscape of Cancer Care in Precision Medicine

Targeting actionable fusions has emerged as a promising approach to cancer treatment. Next-generation sequencing (NGS)-based techniques have unveiled the landscape of actionable fusions in cancer. However, these approaches remain insufficient to provide optimal treatment options for patients with cancer. This article provides a comprehensive overview of the actionability and clinical development of targeted agents aimed at driver fusions. It also highlights the challenges associated with fusion testing, including the evaluation of patients with cancer who could potentially benefit from testing and devising an effective strategy. The implementation of DNA NGS for all tumor types, combined with RNA sequencing, has the potential to maximize detection while considering cost effectiveness. Herein, we also present a fusion testing strategy aimed at improving outcomes in patients with cancer.

Clonal expansion of cancer driver gene mutants investigated using advanced sequencing technologies

Advanced sequencing technologies (ASTs) have revolutionized the quantitation of cancer driver mutations (CDMs) as rare events, which has utility in clinical oncology, cancer research, and cancer risk assessment. This review focuses on studies that have used ASTs to characterize clonal expansion (CE) of cells carrying CDMs and to explicate the selective pressures that shape CE. Importantly, high-sensitivity ASTs have made possible the characterization of mutant clones and CE in histologically normal tissue samples, providing the means to investigate nascent tumor development. Some ASTs can identify mutant clones in a spatially defined context; others enable integration of mutant data with analyses of gene expression, thereby elaborating immune, inflammatory, metabolic, and/or stromal microenvironmental impacts on CE. As a whole, these studies make it clear that a startlingly large fraction of cells in histologically normal tissues carry CDMs, CDMs may confer a context-specific selective advantage leading to CE, and only a small fraction of cells carrying CDMs eventually result in neoplasia. These observations were integrated with available literature regarding the mechanisms underlying clonal selection to interpret how measurements of CDMs and CE can be interpreted as biomarkers of cancer risk. Given the stochastic nature of carcinogenesis, the potential functional latency of driver mutations, the complexity of potential mutational and microenvironmental interactions, and involvement of other types of genetic and epigenetic changes, it is concluded that CDM-based measurements should be viewed as probabilistic rather than deterministic biomarkers. Increasing inter-sample variability in CDM levels (as a consequence of CE) may be interpretable as a shift away from normal tissue homeostasis and an indication of increased future cancer risk, a process that may reflect normal aging or carcinogen exposure. Consequently, analyses of variability in levels of CDMs have the potential to bolster existing approaches for carcinogenicity testing.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Discovering mechanisms of human genetic variation and controlling cell states at scale

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Benefits for children with suspected cancer from routine whole-genome sequencing

Clinical whole-genome sequencing (WGS) has been shown to deliver potential benefits to children with cancer and to alter treatment in high-risk patient groups. It remains unknown whether offering WGS to every child with suspected cancer can change patient management. We collected WGS variant calls and clinical and diagnostic information from 281 children (282 tumors) across two English units (n = 152 from a hematology center, n = 130 from a solid tumor center) where WGS had become a routine test. Our key finding was that variants uniquely attributable to WGS changed the management in ~7% (20 out of 282) of cases while providing additional disease-relevant findings, beyond standard-of-care molecular tests, in 108 instances for 83 (29%) cases. Furthermore, WGS faithfully reproduced every standard-of-care molecular test (n = 738) and revealed several previously unknown genomic features of childhood tumors. We show that WGS can be delivered as part of routine clinical care to children with suspected cancer and can change clinical management by delivering unexpected genomic insights. Our experience portrays WGS as a clinically impactful assay for routine practice, providing opportunities for assay consolidation and for delivery of molecularly informed patient care.

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks

MOTIVATION

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.

RESULTS

Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes.

AVAILABILITY AND IMPLEMENTATION

DISHyper is freely available for download at https://github.com/genemine/DISHyper.

Kataegis associated mutational processes linked to adverse prostate cancer presentation in African men

Kataegis, the focal hypermutation of single base substitutions (SBS) in tumour genomes, has received little attention with respect to prostate cancer (PCa) associated molecular and clinical features. Most notably, data is lacking with regards to this tumour evolutionary phenomenon and PCa racial disparities, with African men disproportionately impacted. Here through comparison between African (n = 109) and non-African (n = 79) whole genome sequenced treatment naïve primary tumours, using a single analytical workflow we assessed for shared and unique features of kataegis. Linking kataegis to aggressive presentation, structural variant burden and copy number loss, we attributed APOBEC3 activity through higher rates of SBS2 to high-risk African tumours. While kataegis positive African patients presented with elevated prostate specific antigen levels, their tumours showed evolutionary unique trajectories marked by increased subclonal and structural variant-independent kataegis. The potential to exacerbate tumour heterogeneity emphases the significance of continued exploration of biological behaviours and environmental exposures for African patients.

Selection forces underlying aneuploidy patterns in cancer

Aneuploidy, the presence of an aberrant number of chromosomes, has been associated with tumorigenesis for over a century. More recently, advances in karyotyping techniques have revealed its high prevalence in cancer: About 90% of solid tumors and 50-70% of hematopoietic cancers exhibit chromosome gains or losses. When analyzed at the level of specific chromosomes, there are strong patterns that are observed in cancer karyotypes both pan-cancer and for specific cancer types. These specific aneuploidy patterns correlate strongly with outcomes for tumor initiation, progression, metastasis formation, immune evasion and resistance to therapeutic treatment. Despite their prominence, understanding the basis underlying aneuploidy patterns in cancer has been challenging. Advances in genetic engineering and bioinformatic analyses now offer insights into the genetic determinants of aneuploidy pattern selection. Overall, there is substantial evidence that expression changes of particular genes can act as the positive selective forces for adaptation through aneuploidy. Recent findings suggest that multiple genes contribute to the selection of specific aneuploid chromosomes in cancer; however, further research is necessary to identify the most impactful driver genes. Determining the genetic basis and accompanying vulnerabilities of specific aneuploidy patterns is an essential step in selectively targeting these hallmarks of tumors.

Pan-cancer analysis of the interplay between mutational signatures and cellular signaling

Cancer is a multi-faceted disease with intricate relationships between mutagenic processes, alterations in cellular signaling, and the tissue microenvironment. To date, these processes have been largely studied in isolation. A systematic understanding of how they interact and influence each other is lacking. Here, we present a framework for systematically characterizing the interaction between pairs of mutational signatures and between signatures and signaling pathway alterations. We applied this framework to large-scale data from TCGA and PCAWG and identified multiple positive and negative interactions, both cross֊tissue and tissue֊specific, that provide new insights into the molecular routes observed in tumorigenesis and their respective drivers. This framework allows for a more fine-grained dissection of common and distinct etiology of mutational signatures. We further identified several interactions with both positive and negative impacts on patient survival, demonstrating their clinical relevance and potential for improving personalized cancer care.

AVERON notebook to discover actionable cancer vulnerabilities enabled by neomorph protein-protein interactions

Genomic alterations, such as missense mutations, often lead to the activation of oncogenic pathways and cell transformation by rewiring protein-protein interaction (PPI) networks. Understanding how mutant-directed neomorph PPIs (neoPPIs) drive cancer is vital to developing new personalized clinical strategies. However, the experimental interrogation of neoPPI functions in patients with cancer is highly challenging. To address this challenge, we developed a computational platform, termed AVERON for discovering actionable vulnerabilities enabled by rewired oncogenic networks. AVERON enables rapid systematic profiling of the clinical significance of neomorph PPIs across different cancer types, informing molecular mechanisms of neoPPI-driven tumorigenesis, and revealing therapeutically actionable neoPPI-regulated genes. We demonstrated the application of the AVERON platform by evaluating the biological functions and clinical significance of 130 neomorph interactions, experimentally determined for oncogenic BRAFV600E. The AVERON application to broad sets of mutant-directed PPIs may inform new testable biological models and clinical strategies in cancer.

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1 , ETV1 , CRKL , and ID4 . Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC , on the same complex amplicons such as ecDNA. We characterized a MYC - ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC 's enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

Hidden secrets of the cancer genome: unlocking the impact of non-coding mutations in gene regulatory elements

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely "junk DNA", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

ARID1A suppresses R-loop-mediated STING-type I interferon pathway activation of anti-tumor immunity

Clinical trials have identified ARID1A mutations as enriched among patients who respond favorably to immune checkpoint blockade (ICB) in several solid tumor types independent of microsatellite instability. We show that ARID1A loss in murine models is sufficient to induce anti-tumor immune phenotypes observed in ARID1A mutant human cancers, including increased CD8+ T cell infiltration and cytolytic activity. ARID1A-deficient cancers upregulated an interferon (IFN) gene expression signature, the ARID1A-IFN signature, associated with increased R-loops and cytosolic single-stranded DNA (ssDNA). Overexpression of the R-loop resolving enzyme, RNASEH2B, or cytosolic DNase, TREX1, in ARID1A-deficient cells prevented cytosolic ssDNA accumulation and ARID1A-IFN gene upregulation. Further, the ARID1A-IFN signature and anti-tumor immunity were driven by STING-dependent type I IFN signaling, which was required for improved responsiveness of ARID1A mutant tumors to ICB treatment. These findings define a molecular mechanism underlying anti-tumor immunity in ARID1A mutant cancers.

Loss of GABARAP mediates resistance to immunogenic chemotherapy in multiple myeloma

ABSTRACT

Immunogenic cell death (ICD) is a form of cell death by which cancer treatments can induce a clinically relevant antitumor immune response in a broad range of cancers. In multiple myeloma (MM), the proteasome inhibitor bortezomib is an ICD inducer and creates durable therapeutic responses in patients. However, eventual relapse and resistance to bortezomib appear inevitable. Here, by integrating patient transcriptomic data with an analysis of calreticulin (CRT) protein interactors, we found that GABA type A receptor-associated protein (GABARAP) is a key player whose loss prevented tumor cell death from being perceived as immunogenic after bortezomib treatment. GABARAP is located on chromosome 17p, which is commonly deleted in patients with high risk MM. GABARAP deletion impaired the exposure of the eat-me signal CRT on the surface of dying MM cells in vitro and in vivo, thus reducing tumor cell phagocytosis by dendritic cells and the subsequent antitumor T-cell response. Low GABARAP was independently associated with shorter survival in patients with MM and reduced tumor immune infiltration. Mechanistically, we found that GABARAP deletion blocked ICD signaling by decreasing autophagy and altering Golgi apparatus morphology, with consequent defects in the downstream vesicular transport of CRT. Conversely, upregulating autophagy using rapamycin restored Golgi morphology, CRT exposure, and ICD signaling in GABARAPKO cells undergoing bortezomib treatment. Therefore, coupling an ICD inducer, such as bortezomib, with an autophagy inducer, such as rapamycin, may improve patient outcomes in MM, in which low GABARAP in the form of del(17p) is common and leads to worse outcomes.

Exploring the Role of Clustered Mutations in Carcinogenesis and Their Potential Clinical Implications in Cancer

Abnormal cell proliferation and growth leading to cancer primarily result from cumulative genome mutations. Single gene mutations alone do not fully explain cancer onset and progression; instead, clustered mutations-simultaneous occurrences of multiple mutations-are considered to be pivotal in cancer development and advancement. These mutations can affect different genes and pathways, resulting in cells undergoing malignant transformation with multiple functional abnormalities. Clustered mutations influence cancer growth rates, metastatic potential, and drug treatment sensitivity. This summary highlights the various types and characteristics of clustered mutations to understand their associations with carcinogenesis and discusses their potential clinical significance in cancer. As a unique mutation type, clustered mutations may involve genomic instability, DNA repair mechanism defects, and environmental exposures, potentially correlating with responsiveness to immunotherapy. Understanding the characteristics and underlying processes of clustered mutations enhances our comprehension of carcinogenesis and cancer progression, providing new diagnostic and therapeutic approaches for cancer.

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.

Cancer mutational signatures identification in clinical assays using neural embedding-based representations

While mutational signatures provide a plethora of prognostic and therapeutic insights, their application in clinical-setting, targeted gene panels is extremely limited. We develop a mutational representation model (which learns and embeds specific mutation signature connections) that enables prediction of dominant signatures with only a few mutations. We predict the dominant signatures across more than 60,000 tumors with gene panels, delineating their landscape across different cancers. Dominant signature predictions in gene panels are of clinical importance. These included UV, tobacco, and apolipoprotein B mRNA editing enzyme, catalytic polypeptide (APOBEC) signatures that are associated with better survival, independently from mutational burden. Further analyses reveal gene and mutation associations with signatures, such as SBS5 with TP53 and APOBEC with FGFR3S249C. In a clinical use case, APOBEC signature is a robust and specific predictor for resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs). Our model provides an easy-to-use way to detect signatures in clinical setting assays with many possible clinical implications for an unprecedented number of cancer patients.

Comparison of germline and somatic structural variants in cancers reveal systematic differences in variant generating and selection processes

Although several recent studies have characterized structural variants (SVs) in germline and cancer genomes, the features of SVs in these different contexts have not been directly compared. We examined similarities and differences between 2 million germline and 115 thousand tumor SVs from a cohort of 963 patients from The Cancer Genome Atlas (TCGA). We found significant differences in features related to their genomic sequences and localization that suggest differences between SV-generating processes and selective pressures. For example, we found that transposon-mediated processes shape germline much more than somatic SVs, while somatic SVs more frequently show features characteristic of chromoanagenesis. These differences were extensive enough to enable us to develop a classifier - "the great GaTSV" - that accurately distinguishes between germline and cancer SVs in tumor samples that lack a matched normal sample.