Comprehensive Characterization of Cancer Driver Genes and Mutations

Establishment and validation of a novel risk model based on PANoptosis-related genes to predict prognosis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is a common cancer worldwide with poor prognosis. Current treatment methods have limited effect on improving the prognosis of patients with HNSC. Differentially expressed PANoptosis-related genes in HNSC were identified from the TCGA using limma and WGCNA. A prognostic model was established using univariate and multivariate Cox regression analyses and machine learning, and its performance was evaluated using Kaplan-Meier and receiver operating characteristic curves. SNP data was analyzed using maftools package. Immune analysis was performed using IOBR package and TIDE website. The scRNA data was analyzed using Seurat and cellchat package. The expression of hub genes was validated in vitro. The prognostic model comprising 5 hub PANoptosis-related genes (AIFM1, AKT3, CDKN2A, EGFR, IL1RAP) accurately predicted patient outcomes, with the high-risk group exhibiting poorer survival. mRNA expression levels of all 5 hub genes were elevated in the tumor cells, but only AIFM1, AKT3 and IL1RAP's protein expression were higher in tumor tissues. Additionally, high expression of AIFM1, AKT3, EGFR, IL1RAP and low expression of CDKN2A indicated poor prognosis of HNSC patients. The decreasing levels of CD4 T cells, CD8 T cells and M1 macrophages were observed in high-risk groups. There was a significant difference of 5-fluorouracil in low and high-risk groups. scRNA analysis exhibited that TNF pathway was important in the interaction between macrophages and T cells. We identified 5 hub genes and constructed a great risk model for the prognosis of HNSC. The immune cells may influence the HNSC malignant through TNF signal pathway.

The Germline and Somatic Origins of Prostate Cancer Heterogeneity

SIGNIFICANCE

This study uncovered 223 recurrently mutated driver regions using the largest cohort of prostate tumors to date. It reveals associations between germline SNPs, somatic drivers, and tumor aggression, offering significant insights into how prostate tumor evolution is shaped by germline factors and the timing of somatic mutations.

Papillary Thyroid Carcinoma With 5 Unique Point Mutations and Typical Behavior

The frequency and impact of multiple driver mutations have not been extensively explored in papillary thyroid carcinoma (PTC), in which driver mutations are most commonly solitary. We present a case of a 62-year-old female who was found to have a 2.6-cm classical, nonaggressive-appearing PTC. A next-generation sequencing panel assessed the tumor for mutations. Five unique single nucleotide sequence variants, none of which was seen in The Cancer Genome Atlas study on PTC, were found: BRAF D594N, NRAS Q61H, PIK3CA G1007R, PTEN R335*, and PTEN Y225*. We believe that 5 pathogenic variants are the highest reported number for a primary PTC resection specimen to date. The observed typical PTC behavior may be due to a weaker strength of the individual pathogenic variants to drive oncogenic processes. In this case, the high number of genetic alterations did not translate into aggressive histopathology or clinical course.

Systems Biology of the Cancer Cell

Questions in cancer have engaged systems biologists for decades. During that time, the quantity of molecular data has exploded, but the need for abstractions, formal models, and simplifying insights has remained the same. This review brings together classic breakthroughs and recent findings in the field of cancer systems biology, focusing on cancer cell pathways for tumorigenesis and therapeutic response. Cancer cells mutate and transduce information from their environment to alter gene expression, metabolism, and phenotypic states. Understanding the molecular architectures that make each of these steps possible is a long-term goal of cancer systems biology pursued by iterating between quantitative models and experiments. We argue that such iteration is the best path to deploying targeted therapies intelligently so that each patient receives the maximum benefit for their cancer.

Precision proteogenomics reveals pan-cancer impact of germline variants

We investigate the impact of germline variants on cancer patients' proteomes, encompassing 1,064 individuals across 10 cancer types. We introduced an approach, "precision peptidomics," mapping 337,469 coding germline variants onto peptides from patients' mass spectrometry data, revealing their potential impact on post-translational modifications, protein stability, allele-specific expression, and protein structure by leveraging the relevant protein databases. We identified rare pathogenic and common germline variants in cancer genes potentially affecting proteomic features, including variants altering protein abundance and structure and variants in kinases (ERBB2 and MAP2K2) impacting phosphorylation. Precision peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively. Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins. Polygenic risk scores correlated with distal effects from risk variants. Our findings emphasize the contribution of germline genetics to cancer heterogeneity and high-throughput precision peptidomics.

Refining prognostic tools for luminal breast cancer: genetic insights and comprehensive analysis

BACKGROUND

Luminal breast cancer (BC) is generally associated with a lower risk of recurrence compared with other subtypes. However, patients with luminal BC can still experience recurrence, which remains a significant concern and contributes to BC-related mortality. Current clinical practice for recurrence risk prognosis relies on prognostic tests based on tumor gene expression profiles.

MATERIALS AND METHODS

In this study, we aimed to investigate the association between different genetic alterations with the likelihood of recurrence and gene expression prognostic prediction (Oncotype DX®, MammaPrint®, and PAM50-ROR) in luminal BC patients. We constructed three transcriptome-based predictive models, based on these widely used clinical tests, to evaluate the recurrence risk of patients with luminal BC, using RNA-seq data from 1527 samples across 11 datasets. We further classified 1780 patients from the TCGA and METABRIC datasets into risk groups and detected distinct recurrence risk patterns.

RESULTS

Our analysis revealed that low-risk groups had higher frequencies of mutations in PIK3CA, MAP3K1, CDH1, KMT2C, and CBFB, as well as co-mutations in PIK3CA-MAP3K1, PIK3CA-CBFB, and KMT2C-MAP3K1. In contrast, high-risk groups showed enrichment of TP53, RB1, and PTPN22 mutations compared with the whole cohort, with notable co-mutations in TP53-PIK3CA and TP53-KMT2C. Furthermore, mutations in TP53 and BRCA2, and deletions in the 7p22.3 region were at least threefold more frequent in high-risk patients compared with low-risk patients. Using an independent dataset, we validated our finding of higher frequency of BRCA2 mutations in Oncotype DX® high-risk patients. Notably, PIK3CA mutations had an unexpected negative impact on recurrence and survival among high-risk patients.

CONCLUSION

Our study reveals key genetic factors associated with recurrence risk in luminal BC. Identifying these mutations and copy number alterations provides a basis for refined prognostic models and suggests avenues for further research, potentially improving treatment strategies and follow-up care for patients with luminal BC.

The Landscape of Prostate Tumour Methylation

Prostate cancer is characterized by profound clinical and molecular heterogeneity. While its genomic heterogeneity is well-characterized, its epigenomic heterogeneity remains less understood. We therefore created a compendium of 3,001 multi-ancestry prostate methylomes spanning normal tissue through localized disease of all grades to poly-metastatic disease. A subset of 884 samples had multi-omic DNA and/or RNA characterization. We identify four epigenomic subtypes that risk-stratify patients and reflect distinct evolutionary trajectories. We demonstrate extensive regulatory interplay between DNA ploidy and DNA methylation, with transcriptional consequences that vary across genes and disease stages. We define the epigenetic dysregulation signatures of the 15 most important clinico-molecular features, creating predictive models for each. For example, we identify specific epigenetic features that predict patient outcome and that are synergistic with clinico-genomic prognostic features. These results define a complex interplay between tumour genetics and epigenetics that converges to modify gene-expression programs and clinical presentation.

Discovery and validation of frameshift-derived neopeptides in Lynch syndrome: paving the way for novel cancer prevention strategies

BACKGROUND

Lynch syndrome (LS), caused by germline pathogenic variants in the mismatch repair genes, leads to high rates of frameshift-derived neopeptide (FSDN) expression due to microsatellite instability (MSI). While colorectal cancer (CRC) prevention is effective, most LS-related tumors lack such strategies. Cancer vaccines targeting FSDNs offer a promising approach for immune interception in LS. This study aimed to identify and validate LS-related FSDNs to develop vaccines for cancer prevention.

METHODS

We identified LS-related coding MS mutations and predicted FSDN with high coverage on common Human Leukocyte Antigen (HLA)-I and II alleles. We validated FSDN-associated mutations in colorectal adenomas (CrAD), endometrial cancers (EC), and CRC samples from patients with LS, non-LS tumors, and cell lines. Immunogenicity was assessed through interferon (IFN)-γ enzyme-linked immunospot and flow cytometry analysis of tissue-infiltrating lymphocytes (TILs) from LS carriers.

RESULTS

We prioritized 53 HLA-I and 45 HLA-II FSDNs in MSI tumors using in silico predictions. Validation revealed 86.7% of FSDN-associated mutations present in LS-CRC samples, with a median of 7.67 (6.5-9) mutations in CrADs and 6.02 (2-10) in CRCs. Sequencing of CrAD and EC samples showed 95% and 77.5% of predicted FSDN-associated mutations, respectively. MSI cancer cell lines transcribed 69.8% of FSDNs. Immunogenicity assays showed that 71% of potential FSDNs elicited IFN-γ responses, with a median of 7.37 (1-10.75) HLA-I and 6 (2-5.75) HLA-II FSDNs per patient. After prioritizing 24 FSDN, in a cohort of 19 LS-derived samples (4 CrAD and 15 normal mucosa), 52% (10/19) demonstrated T-cell reactivity to an HLA-I neoantigen pool. CD8+CD137+ activation markers increased significantly (p=0.037) over time and peptide-specific cells were detected by pentamer staining.

CONCLUSIONS

Our predicted FSDN set has optimal coverage among LS carriers and can induce IFN-γ inflammatory responses in LS-derived TILs, offering an opportunity for vaccine development.

Molecular features of T and N stage progression in laryngeal cancer

Laryngeal squamous cell cancer (LSCC) is a common type of head and neck cancer that is typically unrelated to human papilloma virus (HPV) infection. Late-stage laryngeal cancers are associated with greater morbidity due to obstructive symptoms, and poorer overall survival. Using data from The Cancer Genome Atlas (TCGA), we analyzed 112 patient LSCC samples, comparing patient proteome, transcriptome and genome between early and late T and N samples. We observed significant differences in SNV frequency for various genes between the early and late-stage groups. Most notably we observed that NOTCH1 mutation, which was more frequent in late N-stage supraglottic cancers, was also associated with poorer patient survival in LSCCs. Methylation analysis also revealed changes in JUN gene methylation in late N glottic cancers. Transcriptomic analysis revealed differential expression in c-JUN, HOXB7 and HOXB9 transcript levels, suggesting potential involvement of these pathways in progression and nodal involvement. Our findings illustrate that LSCC undergoes distinct molecular changes associated with different stages and subsites. We observed multiple potential markers for progression, metastases and survival, including NOTCH1 mutation, which may aid as prognostic indicators in future studies.

Intersection of rare pathogenic variants from TCGA in the All of Us Research Program v6

Using rare cancer predisposition alleles derived from The Cancer Genome Atlas (TCGA) and high cancer prevalence (14% of participants) in All of Us (version 6), we assessed the impact of these rare alleles on cancer occurrence in six broad groups of genetic similarity provided by All of Us: African/African American (AFR), Admixed American/Latino (AMR), East Asian (EAS), European (EUR), Middle Eastern (MID), or South Asian (SAS). We observed that germline susceptibility to cancer consistently replicates in EUR-like participants but less so in other participants. We found that All of Us participants from the EUR (p = 1.8 × 10-7), AFR (p = 0.018), and MID (p = 0.0083) genetic similarity groups who carry a rare pathogenic mutation are more likely to have cancer than those without a rare pathogenic mutation. With the advent of combining medical records and genetic mutations, we also performed a phenome-wide association study (PheWAS) to assess the effect of pathogenic variants on additional phenotypes. This analysis again showed several associations between predisposition variants and cancer in EUR-like participants, but fewer in those of the other genetic similarity groups. As All of Us grows to 1 million participants, our projections suggest sufficient power (>99%) to detect cancer-associated variants that are common, but limited power (∼28%) to detect rare mutations when using the entire cohort. This study provides preliminary insights into genetic predispositions to cancer across a diverse cohort and demonstrates the value of All of Us as a resource for cancer research.

Multimodal cell maps as a foundation for structural and functional genomics

Human cells consist of a complex hierarchy of components, many of which remain unexplored1,2. Here we construct a global map of human subcellular architecture through joint measurement of biophysical interactions and immunofluorescence images for over 5,100 proteins in U2OS osteosarcoma cells. Self-supervised multimodal data integration resolves 275 molecular assemblies spanning the range of 10-8 to 10-5 m, which we validate systematically using whole-cell size-exclusion chromatography and annotate using large language models3. We explore key applications in structural biology, yielding structures for 111 heterodimeric complexes and an expanded Rag-Ragulator assembly. The map assigns unexpected functions to 975 proteins, including roles for C18orf21 in RNA processing and DPP9 in interferon signalling, and identifies assemblies with multiple localizations or cell type specificity. It decodes paediatric cancer genomes4, identifying 21 recurrently mutated assemblies and implicating 102 validated new cancer proteins. The associated Cell Visualization Portal and Mapping Toolkit provide a reference platform for structural and functional cell biology.

Transcriptome-wide association study of alternative polyadenylation identifies susceptibility genes in non-small cell lung cancer

Alternative polyadenylation (APA) plays a crucial role in cancer development and prognosis. However, the molecular characteristics of APA related to non-small cell lung cancer (NSCLC) susceptibility remain understudied, especially in East Asian populations. In this study, we constructed an atlas of APA-regulated 3' untranslated region (3'UTR) and profiled its genetic regulation in 747 lung tissue samples (including tumors and paired normal tissues) from 417 NSCLC Chinese patients. We verified a significant global shortening of 3'UTRs in tumor samples compared to normal samples and underscored the value of APA-regulation as a prognostic marker. The 3'UTR APA quantitative trait loci (3'aQTL) was identified by regressing the percentage of distal poly(A) site usage index (PDUI) value on genetic variants. We found that a significant proportion 3'aQTLs are independent of genetic regulation of expression and are specific in Chinese. We also conducted a 3'UTR APA transcriptome-wide association study (3'aTWAS) by integrating the APA regulation atlas with a genome-wide association study (GWAS) for NSCLC involving 7035 cases and 185,413 cancer-free controls. We identified NSCLC-associated genes, highlighting TUBB, TEAD3, and PPP1R10. Combining the consistent results from colocalization analysis, differential APA analysis, and survival analysis, we provide novel evidence for the role TUBB APA regulation in NSCLC and identified potential upstream regulators. Overall, our study profiled the APA regulation and highlighted the substantial role of APA in NSCLC carcinogenesis and prognosis in East Asian populations.

Proteome-wide assessment of differential missense variant clustering in neurodevelopmental disorders and cancer

Prior studies examining genomic variants suggest that some proteins contribute to both neurodevelopmental disorders (NDDs) and cancer. While there are several potential etiologies, here, we hypothesize that missense variation in proteins occurs in different clustering patterns, resulting in distinct phenotypic outcomes. This concept was first explored in 1D protein space and expanded using 3D protein structure models. Missense de novo variants were examined from 39,883 families with NDDs and missense somatic variants from 10,543 sequenced tumors covering five The Cancer Genome Atlas (TCGA) cancer types and two Catalog of Somatic Mutations in Cancer (COSMIC) pan-cancer aggregates of tissue types. We find 18 proteins with differential missense variation clustering in NDDs compared to cancers and 19 in cancers relative to NDDs. These proteins may be important for detailed assessments in thinking of future prognostic and therapeutic applications. We establish a framework for interpreting missense patterns in NDDs and cancer, using advances in 3D protein structure prediction.

Mutational signatures define immune and Wnt-associated subtypes of ampullary carcinoma

BACKGROUND AND OBJECTIVE

Ampullary carcinoma (AMPAC) taxonomy is based on morphology and immunohistochemistry. This classification lacks prognostic reliability and unique genetic associations. We applied an approach of integrative genomics characterising patients with AMPAC exploring molecular subtypes that may guide personalised treatments.

DESIGN

We analysed the mutational landscapes of 170 patients with AMPAC. The discovery included 110 tumour/normal pairs and the validation comprised 60 patients. In a tumour subset, we interrogated the transcriptomes and DNA methylomes. Patients were stratified based on mutational signatures and associated with molecular and clinical features. To evaluate tumour and immune cellularity, 22 tumours were independently assessed histomorphologically and by digital pathology.

RESULTS

We defined three patient clusters by mutational signatures independent of histomorphology. Cluster 1 (C1) was defined by spontaneous deamination of DNA 5-methylcytosine and defective mismatch repair. C2 and C3 were related to the activity of transcription-coupled nucleotide excision repair but C3 was further defined by the polymerase eta mutational process. C1-2 showed enrichment of Wnt pathway alterations, aberrant DNA methylation profiles, immune cell exclusion and patients with poor prognosis. These features were associated with a hypermutator phenotype caused by C>T alterations at CpGs. C3 patients with improved overall survival were associated with activation of immune-related pathways, immune infiltration and elevated expression of immunoinhibitory checkpoint genes.

CONCLUSION

Immunogenicity and Wnt pathway associations, emphasised by the mutational signatures, defined patients with prospective sensitivity to either immunotherapy or Wnt pathway inhibitors. This emphasises a novel mutational signature-based AMPAC classification with prognostic potential, suggesting prospective implications for subgroup-specific management of patients with AMPAC.

Synonymous mutations promote tumorigenesis by disrupting m6A-dependent mRNA metabolism

Cancer cells acquire numerous mutations during tumorigenesis, including synonymous mutations that do not change the amino acid sequence of a protein. RNA N6-methyladenosine (m6A) is a post-transcriptional modification that plays critical roles in oncogenesis. Herein, we identified 12,849 mutations in the cancer genome with the potential to perturb m6A modification patterns, which we refer to as "m6A disruption mutations (m6A-DMs)." These are either synonymous m6A-DMs (sm6A-DMs) or missense m6A-DMs (mm6A-DMs) mutations, and the former is enriched within tumor suppressor genes, such as CDKN2A and BRCA2. Using epitranscriptomic editing, we demonstrate that manipulating m6A levels at specific sm6A-DM sites influences mRNA stability. Furthermore, introducing CDKN2A sm6A-DMs into cancer cells promotes tumor growth while BRCA2 sm6A-DMs sensitize tumors to the poly (ADP-ribose) polymerase inhibitor (PARPi) treatment. Our findings demonstrate sm6A-DMs as potential oncogenic drivers, unveiling implications for synonymous mutations in tumorigenesis and beyond.

Profiling the Activity of the Potent and Highly Selective CDK2 Inhibitor BLU-222 Reveals Determinants of Response in CCNE1-Aberrant Ovarian and Endometrial Tumors

BLU-222 is an investigational, potent, highly selective, orally bioavailable cyclin-dependent kinase 2 (CDK2) inhibitor in clinical development. BLU-222 demonstrated robust antitumor activity in select CCNE1-high ovarian and endometrial cancer models. We used a combination of CRISPR whole-genome screens coupled with targeted genetic and pharmacologic approaches in ovarian and endometrial cell lines to identify biological determinants to predict BLU-222 monotherapy activity. Rb and p16 expression were biomarkers that enriched for CDK2-dependency/BLU-222 sensitivity in CCNE1-overexpressed, nonamplified cells. Furthermore, intact Rb and low p16 expression predicted a BLU-222 and CDK4/6 inhibitor combination response. BLU-222 demonstrated robust activity in combination with carboplatin or paclitaxel in CCNE1-aberrant models, rendering chemotherapy-resistant tumors strongly sensitive to the combination. These findings demonstrate that response to CDK2 inhibition by BLU-222 can be further predicted using a combinatorial biomarker signature that could refine patient selection criteria in CCNE1-high patients and support clinical development. Significance: The identification of biomarkers of response to the CDK2-selective inhibitor BLU-222 and effective combinations with CDK4/6 inhibitors or chemotherapy could enable precision medicine strategies for CDK2 inhibition in ovarian and endometrial cancer. See related article by Dommer and colleagues, p. 1310.

The strength of organ, tissue, and body field effects determines the frequency of all neoplasia

In 1953, Danely Slaughter proposed the concept of field cancerization, or field effect, to explain the development of additional neoplasia of similar type. A recent theory (de Groen, 2022) states that all DNA is exposed to a constant source of damage, resulting in a constant rate of germline and somatic DNA mutations. If the field effect and constant mutation theories are correct and a single somatic mutation causes the transition from non-neoplastic to neoplastic phenotype, then all rates of neoplasia formation can be modeled by exponential equations containing a single variable that determines the chance of phenotype transition. In this perspective, studies from 1953 till 2021 originating from America, Europe, and Asia about head, chest, abdomen, pelvic, and skin neoplasia were reviewed and showed consistent field effects that are modeled by tapering exponential equations containing a single variable defining field effect strength; Pearson and linear correlation coefficients for observed and modeled data range from 0.994 to 1. Thus, existing data are compatible with a constant rate of DNA damage. Organ-specific, tissue-specific, or body-wide mutagenesis conditions determine the rate of neoplasia development and explain the co-occurrence of seemingly unrelated neoplasia at predictable frequencies. Shared risk factors explain increased risk for additional neoplasia in persons with one neoplastic lesion.

Advances in Palmitoylation: A key Regulator of liver cancer development and therapeutic targets

Liver cancer ranks as the second leading cause of cancer-related deaths globally, which remains a significant public health concern. The development of liver cancer is associated with several signaling pathways, particularly metabolic reprogramming. Protein S-palmitoylation, a type of lipid post-translational modification (PTM), involves the reversible attachment of palmitic acid to a cysteine residue through a thioester bond. This modification is found in a wide range of proteins, including enzymes, cancer promoters, tumor suppressors, and transcription factors. The palmitoylation process is catalyzed by the zinc finger DHHC-type containing (ZDHHC) protein family, while the reverse process, depalmitoylation, is facilitated by palmitoyl-protein thioesterases (PPTs). Dysregulation of palmitoylation has been linked to various cancer hallmark functions, cancer metabolism, and tumor microenvironment regulation. Currently, membrane palmitoylated protein (MPP) and PPT1 have been identified as prognostic markers and potential therapeutic targets in liver cancer. In this review, we summarize recent advances in understanding the effects of palmitoylation on liver cancer development, metastasis, and prognosis prediction, and explore potential therapeutic strategies for managing liver cancer.

M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis

Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers. However, existing MIL methods predominantly focus on single-task learning, resulting in not only overall low efficiency but also the overlook of inter-task relatedness. To address these issues, we proposed an adapted architecture of Multi-gate Mixture-of-experts with Multi-proxy for Multiple instance learning (M4), and applied this framework for simultaneous prediction of multiple genetic mutations from WSIs. The proposed M4 model has two main innovations: (1) adopting a multi-gate mixture-of-experts strategy for multiple genetic mutation simultaneous prediction on a single WSI; (2) introducing a multi-proxy CNN construction on the expert and gate networks to effectively and efficiently capture patch-patch interactions from WSI. Our model achieved significant improvements across five tested TCGA datasets in comparison to current state-of-the-art single-task methods. The code is available at: https://github.com/Bigyehahaha/M4.

Pan-cancer analysis shapes the understanding of cancer biology and medicine

Advances in multi-omics datasets and analytical methods have revolutionized cancer research, offering a comprehensive, pan-cancer perspective. Pan-cancer studies identify shared mechanisms and unique traits across different cancer types, which are reshaping diagnostic and treatment strategies. However, continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine. This review summarized key findings from pan-cancer research and  explored their potential to drive future advancements in oncology.

Deciphering lung adenocarcinoma evolution and the role of LINE-1 retrotransposition

Understanding lung cancer evolution can identify tools for intercepting its growth. In a landscape analysis of 1024 lung adenocarcinomas (LUAD) with deep whole-genome sequencing integrated with multiomic data, we identified 542 LUAD that displayed diverse clonal architecture. In this group, we observed an interplay between mobile elements, endogenous and exogenous mutational processes, distinct driver genes, and epidemiological features. Our results revealed divergent evolutionary trajectories based on tobacco smoking exposure, ancestry, and sex. LUAD from smokers showed an abundance of tobacco-related C:G>A:T driver mutations in KRAS plus short subclonal diversification. LUAD in never smokers showed early occurrence of copy number alterations and EGFR mutations associated with SBS5 and SBS40a mutational signatures. Tumors harboring EGFR mutations exhibited long latency, particularly in females of European-ancestry (EU_N). In EU_N, EGFR mutations preceded the occurrence of other driver genes, including TP53 and RBM10. Tumors from Asian never smokers showed a short clonal evolution and presented with heterogeneous repetitive patterns for the inferred mutational order. Importantly, we found that the mutational signature ID2 is a marker of a previously unrecognized mechanism for LUAD evolution. Tumors with ID2 showed short latency and high L1 retrotransposon activity linked to L1 promoter demethylation. These tumors exhibited an aggressive phenotype, characterized by increased genomic instability, elevated hypoxia scores, low burden of neoantigens, propensity to develop metastasis, and poor overall survival. Reactivated L1 retrotransposition-induced mutagenesis can contribute to the origin of the mutational signature ID2, including through the regulation of the transcriptional factor ZNF695, a member of the KZFP family. The complex nature of LUAD evolution creates both challenges and opportunities for screening and treatment plans.

High resolution profiling of cell cycle-dependent protein and phosphorylation abundance changes in non-transformed cells

The cell cycle governs a precise series of molecular events, regulated by coordinated changes in protein and phosphorylation abundance, that culminates in the generation of two daughter cells. Here, we present a proteomic and phosphoproteomic analysis of the human cell cycle in hTERT-RPE-1 cells using deep quantitative mass spectrometry by isobaric labelling. By analysing non-transformed cells and improving the temporal resolution and coverage of key cell cycle regulators, we present a dataset of cell cycle-dependent protein and phosphorylation site oscillation that offers a foundational reference for investigating cell cycle regulation. These data reveal regulatory intricacies including proteins and phosphorylation sites exhibiting cell cycle-dependent oscillation, and proteins targeted for degradation during mitotic exit. Integrated with complementary resources, our data link cycle-dependent abundance dynamics to functional changes and are accessible through the Cell Cycle database (CCdb), an interactive web-based resource for the cell cycle community.

Proteogenomic characterization reveals tumorigenesis and progression of lung cancer manifested as subsolid nodules

Lung adenocarcinoma (LUAD) radiologically displayed as subsolid nodules (SSNs) is prevalent. Nevertheless, the precise clinical management of SSNs necessitates a profound understanding of their tumorigenesis and progression. Here, we analyze 66 LUAD displayed as SSNs covering 3 histological stages including adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) by incorporating genomics, proteomics, phosphoproteomics and glycoproteomics. Intriguingly, cholesterol metabolism is aberrantly regulated in the preneoplastic AIS stage. Importantly, target ablation of proprotein convertase subtilisin/kexin type 9 (PCSK9) promotes the initiation of LUAD. Furthermore, sustained endoplasmic reticulum stress is demonstrated to be a hallmark and a reliable biomarker of AIS progression to IAC. Consistently, target promotion of ER stress profoundly retards LUAD progression. Our study provides comprehensive proteogenomic landscape of SSNs, sheds lights on the tumorigenesis and progression of SSNs and suggests preventive and therapeutic strategies for LUAD.

Integrating epidemiology and genomics data to estimate the prevalence of acquired cysteine drug targets in the U.S. cancer patient population

Reliable estimates for the number of cancer patients with a specific mutation can help quantify the size of the population that could potentially benefit from a targeted therapy. We adapt our previously developed approach for estimating gene-level mutation abundances to estimate mutation-specific (e.g., KRAS G12C) abundances by combining United States cancer epidemiology and genomic data. We demonstrate the approach by obtaining population-level estimates for all acquired somatic missense mutations that create a de novo cysteine residue. We find that approximately 14% of non-epidemiological informed estimates are more than twice the epidemiological informed estimate. Non-epidemiologically informed pan-cancer estimation of mutation rates may not be representative of the number of cancer patients with a specific mutation. Our study suggests that epidemiological and genomic information should be combined when estimating the population level abundance of specific pathogenic mutations.

CENPF (+) cancer cells promote malignant progression of early-stage TP53 mutant lung adenocarcinoma

The prevention and precise treatment of early-stage lung adenocarcinoma (LUAD) characterized by small nodules (stage IA) remains a significant challenge for clinicians, which is due largely to the limited understanding of the oncogenic mechanisms spanning from preneoplasia to invasive adenocarcinoma. Our study highlights the pivotal role of cancer cells exhibiting high expression of centromere protein F (CENPF), driven by TP53 mutations, which become increasingly prevalent during the transition from preneoplasia to invasive LUAD. Biologically, cancer cells (CENPF+) exhibited robust proliferative and stem-like capabilities, thereby propelling the malignant progression of early-stage LUAD. Clinically, autoantibodies against CENPF in the serum and elevated cancer cells (CENPF+) in tissue correlated positively with the progression of early-stage LUAD, especially those in stage IA. Our findings suggest that cancer cells (CENPF+) play a central role in orchestrating the malignant evolution of LUAD and hold potential as a novel biomarker for early-stage detection and management of the disease.

Decoding the functional impact of the cancer genome through protein-protein interactions

Acquisition of genomic mutations enables cancer cells to gain fitness advantages under selective pressure and, ultimately, leads to oncogenic transformation. Interestingly, driver mutations, even within the same gene, can yield distinct phenotypes and clinical outcomes, necessitating a mutation-focused approach. Conversely, cellular functions are governed by molecular machines and signalling networks that are mostly controlled by protein-protein interactions (PPIs). The functional impact of individual genomic alterations could be transmitted through regulated nodes and hubs of PPIs. Oncogenic mutations may lead to modified residues of proteins, enabling interactions with other proteins that the wild-type protein does not typically interact with, or preventing interactions with proteins that the wild-type protein usually interacts with. This can result in the rewiring of molecular signalling cascades and the acquisition of an oncogenic phenotype. Here, we review the altered PPIs driven by oncogenic mutations, discuss technologies for monitoring PPIs and provide a functional analysis of mutation-directed PPIs. These driver mutation-enabled PPIs and mutation-perturbed PPIs present a new paradigm for the development of tumour-specific therapeutics. The intersection of cancer variants and altered PPI interfaces represents a new frontier for understanding oncogenic rewiring and developing tumour-selective therapeutic strategies.

Deep profiling of gene expression across 18 human cancers

Clinical and biological information in large datasets of gene expression across cancers could be tapped with unsupervised deep learning. However, difficulties associated with biological interpretability and methodological robustness have made this impractical. Here we describe an unsupervised deep-learning framework for the generation of low-dimensional latent spaces for gene-expression data from 50,211 transcriptomes across 18 human cancers. The framework, which we named DeepProfile, outperformed dimensionality-reduction methods with respect to biological interpretability and allowed us to unveil that genes that are universally important in defining latent spaces across cancer types control immune cell activation, whereas cancer-type-specific genes and pathways define molecular disease subtypes. By linking latent variables in DeepProfile to secondary characteristics of tumours, we discovered that mutation burden is closely associated with the expression of cell-cycle-related genes, and that the activity of biological pathways for DNA-mismatch repair and MHC class II antigen presentation are consistently associated with patient survival. We also found that tumour-associated macrophages are a source of survival-correlated MHC class II transcripts. Unsupervised learning can facilitate the discovery of biological insight from gene-expression data.

iModEst: disentangling -omic impacts on gene expression variation across genes and tissues

Many regulatory factors impact the expression of individual genes including, but not limited, to microRNA, long non-coding RNA (lncRNA), transcription factors (TFs), cis-methylation, copy number variation (CNV), and single-nucleotide polymorphisms (SNPs). While each mechanism can influence gene expression substantially, the relative importance of each mechanism at the level of individual genes and tissues is poorly understood. Here, we present the integrative Models of Estimated gene expression (iModEst), which details the relative contribution of different regulators to the gene expression of 16,000 genes and 21 tissues within The Cancer Genome Atlas (TCGA). Specifically, we derive predictive models of gene expression using tumour data and test their predictive accuracy in cancerous and tumour-adjacent tissues. Our models can explain up to 70% of the variance in gene expression across 43% of the genes within both tumour and tumour-adjacent tissues. We confirm that TF expression best predicts gene expression in both tumour and tumour-adjacent tissue whereas methylation predictive models in tumour tissues does not transfer well to tumour adjacent tissues. We find new patterns and recapitulate previously reported relationships between regulator and gene-expression, such as CNV-predicted FGFR2 expression and SNP-predicted TP63 expression. Together, iModEst offers an interactive, comprehensive atlas of individual regulator-gene-tissue expression relationships as well as relationships between regulators.

Causal Inference and Annotation of Phosphoproteomics Data in Multiomics Cancer Studies

Protein phosphorylation plays a crucial role in regulating diverse biological processes. Perturbations in protein phosphorylation are closely associated with downstream pathway dysfunctions, whereas alterations in protein expression could serve as sensitive indicators of pathological status. However, there are currently few methods that can accurately identify the regulatory links between protein phosphorylation and expression, given issues like reverse causation and confounders. Here, we present Phoslink, a causal inference model to infer causal effects between protein phosphorylation and expression, integrating prior evidence and multiomics data. We demonstrated the feasibility and advantages of our method under various simulation scenarios. Phoslink exhibited more robust estimates and lower false discovery rate than commonly used Pearson and Spearman correlations, with better performance than canonical instrumental variable selection methods for Mendelian randomization. Applying this approach, we identified 345 causal links involving 109 phosphosites and 310 proteins in 79 lung adenocarcinoma (LUAD) samples. Based on these links, we constructed a causal regulatory network and identified 26 key regulatory phosphosites as regulators strongly associated with LUAD. Notably, 16 of these regulators were exclusively identified through phosphosite-protein causal regulatory relationships, highlighting the significance of causal inference. We explored potentially druggable phosphoproteins and provided critical clues for drug repurposing in LUAD. We also identified significant mediation between protein phosphorylation and LUAD through protein expression. In summary, our study introduces a new approach for causal inference in phosphoproteomics studies. Phoslink demonstrates its utility in potential drug target identification, thereby accelerating the clinical translation of cancer proteomics and phosphoproteomic data.

Discovery of Cis-Regulatory Mechanisms via Non-Coding Mutations in Acute Lymphoblastic Leukemia

The non-coding genome, constituting 98% of human DNA, remains largely unexplored, yet holds potential for identifying new biomarkers and therapeutic targets in acute lymphoblastic leukemia (ALL). In this study, we conducted a systematic analysis of recurrent somatic non-coding single nucleotide variants (SNVs) in pediatric B-cell precursor (BCP) ALL. We leveraged whole genome sequencing (WGS) data from 345 pediatric BCP ALL cases, representing all major genetic subtypes and identified 346 mutational hotspots that harbored somatic SNVs in at least three cases. Through the integration of paired RNA sequencing along with published ChIP-seq and ATAC-seq data, we found 128 non-coding hotspots associated with differentially expressed genes nearby, which were enriched for cis-regulatory elements, demonstrating the effectiveness of multi-omics integration in distinguishing pathogenic mutations from passengers. We identified one mutational hotspot that was associated with increased expression of the leukemia-associated gene NRAS in three primary ALLs. Micro-C analysis in the leukemia cell line demonstrated interactions between the hotspot region and NRAS regulatory elements. Dual luciferase assays indicated that the mutations disrupted regulatory interactions and CRISPR-mediated deletion of the region significantly upregulated NRAS, confirming the hypothesized regulatory link. Altogether, we provide new insights into the functional roles of non-coding mutations in leukemia.

Advancements in proteogenomics for preclinical targeted cancer therapy research

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

Tumor microenvironment and drug resistance in lung adenocarcinoma: molecular mechanisms, prognostic implications, and therapeutic strategies

The fight against lung adenocarcinoma (LUAD) is challenged by tumor microenvironment (TME)-mediated drug resistance, which limits effective treatment. This study examines the LUAD TME and identifies four distinct subtypes through multi-omics profiling: immune-rich, immune-exhausted, stromal-dominant, and TME-desert. Each subtype has unique molecular features, tumor diversity, and links to clinical outcomes. Immune-rich subtypes respond better to immune checkpoint inhibitors, while stromal-dominant and TME-desert subtypes show resistance to treatment and poor prognosis. Molecular analysis uncovers subtype-specific mutations, chromosomal instability, and altered signaling pathways, pointing to potential therapeutic targets. In silico drug screening identifies promising treatments for resistant subtypes. These findings, validated in independent cohorts, highlight the critical role of the TME in drug resistance and treatment response, providing insights for personalized treatment strategies in LUAD.

The spatial landscape of cancer hallmarks reveals patterns of tumor ecological dynamics and drug sensitivity

Tumors are complex ecosystems of interacting cell types. The concept of cancer hallmarks distills this complexity into underlying principles that govern tumor growth. Here, we explore the spatial distribution of cancer hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that hallmark activity is spatially organized, with the cancer compartment contributing to the activity of seven out of 13 hallmarks, while the tumor microenvironment (TME) contributes to the activity of the rest. Additionally, we discover that genomic distance between tumor subclones correlates with differences in hallmark activity, even leading to clone-hallmark specialization. Finally, we demonstrate interdependent relationships between hallmarks at the junctions of TME and cancer compartments and how they relate to sensitivity to different neoadjuvant treatments in 33 bladder cancer patients from the DUTRENEO trial. In conclusion, our findings may improve our understanding of tumor ecology and help identify new drug biomarkers.

Understanding the conformational dynamics of PI3Kα due to helical domain mutations: insights from Markov state model analysis

Phosphoinositide 3-kinases (PI3Ks) phosphorylate phosphoinositides on the membrane, which act as secondary signals for various cellular processes. PI3Kα, a heterodimer of the p110α catalytic subunit and the p85α regulatory subunit, is activated by growth factor receptors or mutations. Among these mutations, E545K present in the helical domain is strongly associated with cancer, and is known to disrupt interactions between the regulatory and catalytic subunits, leading to its constitutive activation. However, while the mutation's role in disrupting autoinhibition is well documented, the molecular mechanisms linking this mutation in the helical domain to the structural changes in the kinase domain remain poorly understood. This study aims to understand the conformational events triggered by the E545K mutation, elucidate how these changes propagate from the helical domain to the kinase domain, and identify crucial residues involved in the activation process. Molecular dynamics (MD) simulations combined with Markov state modeling (MSM) were employed to explore the conformational landscapes of both the wild-type and mutant systems. Structural and energetic analyses, including Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations, revealed that the E545K mutation significantly reduces the binding affinity between the regulatory and catalytic subunits. The mutation was found to induce a sliding motion of the regulatory subunit along the catalytic subunit, leading to the disruption of key salt-bridges between these domains. This disruption releases the inhibitory effect of the regulatory subunit, resulting in increased domain motion, particularly in the adaptor-binding domain (ABD). Enhanced flexibility in the ABD, helical, and C2 domains facilitates the rearrangement of the two lobes of kinase domain, thereby promoting activation. Additionally, the mutation appears to enhance PI3Kα's membrane affinity via the Ras-binding domain (RBD). Network analysis helped to identify key residues that may involve in allosteric signaling pathways, providing insights into the communication between domains. Druggable pockets in the metastable states were predicted followed by its docking with a PI3K inhibitor library. Docking studies revealed the crucial residues that may be participating in inhibitor binding. The identification of residues and regions involved in activation mechanisms using MSM helped to reveal the conformational events and the knowledge on probable allosteric pockets, which may be helpful in designing better therapeutics.

Geographic and age-related variations in mutational processes in colorectal cancer

Colorectal cancer incidence rates vary geographically and have changed over time. Notably, in the past two decades, the incidence of early-onset colorectal cancer, affecting individuals under the age of 50 years, has doubled in many countries. The reasons for this increase are unknown. Here, we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown etiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia, and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals diagnosed before age 40 than in those over 70, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked to APC driver mutations, with ID18 responsible for about 25% of APC driver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that early-life mutagenic exposure to colibactin-producing bacteria may contribute to the rising incidence of early-onset colorectal cancer.

The mutational landscape and functional effects of noncoding ultraconserved elements in human cancers

The mutational landscape of phylogenetically ultraconserved elements (UCEs), especially those in noncoding DNAs (ncUCEs), and their functional relevance in cancers remain poorly characterized. Here, we perform a systematic analysis of whole-genome and in-house targeted UCE sequencing datasets from more than 3000 patients with cancer of 13,736 UCEs and demonstrate that ncUCE somatic alterations are common. Using a multiplexed CRISPR knockout screen in colorectal cancer cells, we show that the loss of several altered ncUCEs significantly affects cell proliferation. In-depth functional studies in vitro and in vivo further reveal that specific ncUCEs can be enhancers of tumor suppressors (such as ARID1B) and silencers of oncogenic proteins (such as RPS13). Moreover, several miRNAs located in ncUCEs are recurrently mutated. Mutations in miR-142 locus can affect the Drosha-mediated processing of precursor miRNAs, resulting in the down-regulation of the mature transcript. These results provide systematic evidence that specific ncUCEs play diverse regulatory roles in cancer.

Tracking the Evolution of Cutaneous Melanoma by Multiparameter Flow Sorting and Genomic Profiling

Intratumoral heterogeneity and clonal evolution are pivotal in the progression and metastasis of melanoma. However, when combined with variable tumor cellularity, intratumoral heterogeneity limits the sensitivity and accuracy of uncovering a cancer's clonal evolution. In this study, we combined fluorescence-activated cell sorting (FACS) with whole-exome sequencing (WES) to investigate the clonal composition and evolutionary patterns in seven melanoma biopsies obtained from three patients, each having both primary site and metastatic samples. We employed a multiparameter ploidy sorting approach to isolate tumor populations based on DNA ploidy and melanoma biomarkers (SOX10 or S100), enabling us to investigate clonal evolution with high resolution. Our approach increased the mean tumor purity from 70% (range 19-88%) in unsorted material to 91% (range 87-96%) post-sorting. Our findings revealed significant inter- and intratumor heterogeneity, with one patient exhibiting two genomically distinct clonal tumor populations within a single primary site biopsy, each giving rise to different metastases. Our findings highlight the critical role of intratumoral heterogeneity and clonal evolution in melanoma, especially when analyzing tumor trajectories. The unique combination of multiparameter FACS and WES provides a powerful method for identifying clonal populations and reconstructing clonal evolution. This study provides valuable insights into the clonal architecture of melanoma and lays the groundwork for future research with larger patient groups.

Nonstop mutations cause loss of renal tumor suppressor proteins VHL and BAP1 and affect multiple stages of protein translation

Nonstop extension or stop-loss mutations lead to the extension of a protein at its carboxyl terminus. Recently, nonstop mutations in the tumor suppressor SMAD Family Member 4 (SMAD4) have been discovered to lead to proteasomal SMAD4 degradation. However, this mutation type has not been studied in other cancer genes. Here, we explore somatic nonstop mutations in the tumor suppressor genes BRCA1 Associated Protein 1 (BAP1) and Von Hippel-Lindau (VHL) enriched in renal cell carcinoma. For BAP1, nonstop mutations generate an extremely long extension. Instead of proteasomal degradation, the extension decreases translation and depletes BAP1 messenger RNA from heavy polysomes. For VHL, the short extension leads to proteasomal degradation. Unexpectedly, the mutation alters the selection of the translational start site shifting VHL isoforms. We identify germline VHL nonstop mutations in patients leading to the early onset of severe disease manifestations. In summary, nonstop extension mutations inhibit the expression of renal tumor suppressor genes with pleiotropic effects on translation and protein stability.

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic. We validate select classifiers using external datasets. Predictive performance and classifier-selected features yield insight into the different machine-learning approaches and genomic data platforms. For each cancer and data type we provide containerized versions of the top-performing models as a public resource.

Immunogenomic determinants of exceptional response to immune checkpoint inhibition in renal cell carcinoma

Immune checkpoint inhibitors can lead to 'exceptional', durable responses in a subset of persons. However, the molecular basis of exceptional response (ER) to immunotherapy in metastatic clear cell renal cell carcinoma (mccRCC) has not been well characterized. Here we analyzed pretherapy genomic and transcriptomic data in treatment-naive persons with mccRCC treated with standard-of-care immunotherapies: (1) combination of programmed cell death protein and ligand 1 (PD1/PDL1) and cytotoxic T lymphocyte-associated protein 4 inhibitors (IO/IO) or (2) combination of PD1/PDL1 and vascular endothelial growth factor (VEGF) receptor inhibitors (IO/VEGF). In the IO/IO cohort, clonal neoantigen load was significantly higher in persons with ER. In the IO/VEGF cohort, ER participants displayed strong enrichment of B cell receptor signaling-related pathways, tertiary lymphoid structure (TLS) signatures and evidence of increased metabolic activity. Our results suggest that ER may be related to clonal neoantigen-driven cytotoxic T cell responses and TLS formation in tumor microenvironments. Therapeutic combinations that elicit both T cell-directed and B cell-directed antitumor immunity may be important to achieve exceptional benefit to IO-based treatment in ccRCC.

Microproteins in cancer: identification, biological functions, and clinical implications

Cancer continues to be a major global health challenge, accounting for 10 million deaths annually worldwide. Since the inception of genome-wide cancer sequencing studies 20 years ago, a core set of ~700 oncogenes and tumor suppressor genes has become the basis for cancer research. However, this research has been based largely on an understanding that the human genome encodes ~19 500 protein-coding genes. Complementing this genomic landscape, recent advances have described numerous microproteins which are now poised to redefine our understanding of oncogenic processes and open new avenues for therapeutic intervention. This review explores the emerging evidence for microprotein involvement in cancer mechanisms and discusses potential therapeutic applications, with an emphasis on highlighting recent advances in the field.

Loss of SGK1 supports metastatic colonization in hepatocellular carcinoma by promoting resistance to T cell-mediated immunity

BACKGROUND & AIMS

Immune evasion by tumor cells is a principal obstacle to effectively targeting metastasis in hepatocellular carcinoma (HCC). However, the specific molecular mechanisms facilitating immune escape during metastatic seeding are not fully elucidated.

METHODS

Utilizing in vivo CRISPR library screening in murine HCC metastasis models under conditions of both intact and depleted T-cell immunity, we identified genes critical to tumor immune evasion during metastatic colonization and investigated intrinsic mechanisms using several experimental approaches.

RESULTS

Our screens identified Sgk1 as an essential suppressor of metastatic colonization under T-cell immunosurveillance. Sgk1-deficient tumor cells displayed significantly enhanced metastatic capacity in the presence of CD8+ T cells, underscoring the role of Sgk1 in regulating immune escape. Clinical analyses corroborated these findings, showing markedly lower SGK1 expression in circulating tumor cells and metastatic lesions relative to matched primary tumors in patients with HCC, with low SGK1 expression associating with compromised T-cell function and poorer clinical outcomes. Mechanistically, Sgk1 inactivation in tumor cells attenuated CD8+ T cell-mediated, RIPK1-dependent necroptosis - a cell death pathway essential for cytotoxic T cell-mediated restriction of metastasis. Loss of Sgk1 consequently enabled tumor cells to circumvent T cell-induced cytotoxicity, thereby promoting metastatic colonization. Furthermore, the outgrowth of Sgk1-deficient metastatic cells induced a microenvironmental shift toward terminal T-cell exhaustion, establishing conditions conducive to sustained immune evasion.

CONCLUSIONS

These findings establish SGK1 as a crucial regulator of immune-mediated control over metastatic growth in HCC. SGK1 expression in metastatic lesions may serve as a predictive biomarker for response to immune checkpoint inhibitors, presenting new avenues for therapeutic intervention to overcome immune resistance in metastatic HCC.

IMPACT AND IMPLICATIONS

Despite metastasis being a common occurrence and lethal determinant in cancers, the mechanism underlying tumor immune evasion during metastatic seeding is unclear. Our study reveals that loss of Sgk1 confers metastatic tumor cells with a survival advantage by abrogating CD8+ T cell-induced RIPK1-dependent necroptosis. Growth of Sgk1-silenced metastasis led to infiltration of terminally exhausted CD8+ T cells, which could be reversed by immune checkpoint inhibitors administered at an early stage of metastatic seeding. These findings provide valuable insights into potential therapeutic strategies targeting resistance to T-cell immunity in cancer metastasis.

Uridine-cytidine kinase 2 is correlated with immune, DNA damage repair and promotion of cancer stemness in pan-cancer

Background

UCK2 (Uridine-Cytidine Kinase 2) is a promising prognostic marker for malignant tumors, but its association with immune infiltration and cancer stemness in pan-cancer remains to be fully understood. we find that gene UCK2 is closed related to RNA stemness scores (RNAss) and DNA stemness scores (DNAss), which is measured the tumor stemness. We also discover an association between UCK2 expression and immune cells by CIBERSORT algorithm, ESTIMATE algorithm and ssGSEA algorithm, especially, related to T cell, monocytes, mast cells, and macrophages. This study aims to shed light on the role and possible mechanism of UCK2 in pan-cancer.

Methods

We used the R programming language for pan-cancer bulk sequencing data analysis, which were obtained from the University of California, Santa Cruz (UCSC) datasets. UCSC database is a very useful for explore data from TCGA and other cancer genomics datasets, The data we explored at the UCK2 transcriptome level came from TCGA data in the UCSC database. We explored differential UCK2 expression between tumor and normal samples. Immunohistochemistry (IHC) was utilized to validate the expression of UCK2 in different types cancers using tumor tissue chips. The correlations of UCK2 with prognosis, genetic instability, DNA repair, cancer stem cell characteristics, and immune cell infiltration were investigated. Furthermore, single-cell datasets, acquired from the Gene Expression Omnibus (GEO) database, were used to validate the relationship between UCK2 and immune cells. GEO is a famous public genomics database supporting freely disseminates microarray data. Finally, we analyzed the correlation between UCK2 and drug sensitivity.

Results

UCK2 expression was observed to be high in most cancers and was remarkably related to the prognosis of pan-cancers. We found that the increased UCK2 expression was associated with higher genetic instability. Additionally, positive relationships were observed between UCK2 expression and mismatch repair genes, homologous recombination repair genes, and cancer stemness across different cancer types. There were significant correlations between UCK2 and T cells, monocytes, mast cells, and macrophages. Moreover, as expected, the immune checkpoint human leucocyte antigen (HLA) was found to be negatively related to UCK2. Similarly, UCK2 was also observed to have a negative association with major histocompatibility complex (MHC) genes. We noted that UCK2 had significant correlations with the sensitivity to various anti-cancer drug.

Conclusion

We have observed that UCK2 plays pivotal roles in prognosis and tumor immunity, and it is associated with DNA repair and cancer stemness. The UCK2 gene exhibits a strong correlation with the immune checkpoints HLA. This study highlights its potential impact on drug sensitivity.

Preventive effects of kefir on colon tumor development in Wistar rats: gut microbiota critical role

To clarify the effects of kefir in critical periods of development in adult diseases, we study the effects of kefir intake during early life on gut microbiota and prevention of colorectal carcinogenesis in adulthood. Lactating Wistar rats were divided into three groups: control (C), kefir lactation (KL), and kefir puberty (KP) groups. The C and KP groups received 1 mL of water/day; KL dams received kefir milk daily (108 CFU/mL) during lactation. After weaning (postnatal day 21), KP pups received kefir treatment until 60 days. At 67 days old, colorectal carcinogenesis was induced through intraperitoneal injection of 1, 2-dimethylhydrazine. The gut microbiota composition were analyzed by 16S rRNA gene sequencing and DESeq2 (differential abundance method), revealing significant differences in bacterial abundances between the kefir consumption periods. Maternal kefir intake strong anticancer power, suppressed tumors in adult offspring and reduced the relative risk of offspring tumor development. The gut microbiota in cecal samples of the KL group was enriched with Lactobacillus, Romboutsia, and Blautia. In contrast, control animals were enriched with Acinetobacter. The administration of kefir during critical periods of development, with emphasis on lactation, affected the gut microbial community structure to promote host benefits. Pearson analysis indicated positive correlation between tumor number with IL-1 levels. Therefore, the probiotic fermented food intake in early life may be effective as chemopreventive potential against colon tumor development, especially in lactation period.

Tobacco smoke carcinogens exacerbate APOBEC mutagenesis and carcinogenesis

Mutations in somatic cells are inflicted by both extrinsic and intrinsic sources and contribute over time to cancer. Tobacco smoke contains chemical carcinogens that have been causatively implicated with cancers of the lung and head & neck1,2. APOBEC family DNA cytosine deaminases have emerged as endogenous sources of mutation in cancer, with hallmark mutational signatures (SBS2/SBS13) that often co-occur in tumors of tobacco smokers with an equally diagnostic mutational signature (SBS4)3,4. Here we challenge the dogma that mutational processes are thought to occur independently and with additive impact by showing that 4-nitroquinoline 1-oxide (NQO), a model carcinogen for tobacco exposure, sensitizes cells to APOBEC3B (A3B) mutagenesis and leads to synergistic increases in both SBS2 mutation loads and oral carcinomas in vivo. NQO-exposed/A3B-expressing animals exhibit twice as many head & neck lesions as carcinogen-exposed wildtype animals. This increase in carcinogenesis is accompanied by a synergistic increase in mutations from APOBEC signature SBS2, but not from NQO signature SBS4. Interestingly, a large proportion of A3B-catalyzed SBS2 mutations occurs as strand-coordinated pairs within 32 nucleotides of each other in transcribed regions, suggesting a mechanism in which removal of NQO-DNA adducts by nucleotide excision repair exposes short single-stranded DNA tracts to enzymatic deamination. These highly enriched pairs of APOBEC signature mutations are termed didyma (Greek for twins) and are mechanistically distinct from other types of clustered mutation (omikli and kataegis). Computational analyses of lung and head & neck tumor genomes show that both APOBEC mutagenesis and didyma are elevated in cancers from smokers compared to non-smokers. APOBEC signature mutations and didyma are also elevated in normal lung tissues in smokers prior to cancer initiation. Collectively, these results indicate that DNA adducting mutagens in tobacco smoke can amplify DNA damage and mutagenesis by endogenous APOBEC enzymes and, more broadly, suggest that mutational mechanisms can interact synergistically in both cancer initiation and promotion.

High-throughput sequencing: a breakthrough in molecular diagnosis for precision medicine

High-resolution insights into the nucleotide arrangement within an organism's genome are pivotal for deciphering its genetic composition, function, and evolutionary trajectory. Over the years, nucleic acid sequencing has been instrumental in driving significant advancements in genomics and molecular biology. The advent of high-throughput or next-generation sequencing (NGS) technologies has revolutionized whole genome sequencing, revealing novel and intriguing features of genomes, such as single nucleotide polymorphisms and lethal mutations in both coding and non-coding regions. These platforms provide a practical approach to comprehensively identifying and analyzing whole genomes with remarkable throughput, accuracy, and scalability within a short time frame. The resulting data holds immense potential for enhancing healthcare systems, developing novel and personalized therapies, and preparing for future pandemics and outbreaks. Given the wide array of available high-throughput sequencing platforms, selecting the appropriate technology based on specific needs is crucial. However, there is limited information regarding sample preparation, sequencing principles, and output data to facilitate a comparative evaluation of these platforms. This review details various NGS technologies and approaches, examining their advantages, limitations, and future potential. Despite being in their early stages and facing challenges, ongoing advancements in NGS are expected to yield significant future benefits.

An ensemble machine learning-based performance evaluation identifies top In-Silico pathogenicity prediction methods that best classify driver mutations in cancer

BACKGROUND AND OBJECTIVE

Accurate identification and prioritization of driver-mutations in cancer is critical for effective patient management. Despite the presence of numerous bioinformatic algorithms for estimating mutation pathogenicity, there is significant variation in their assessments. This inconsistency is evident even for well-established cancer driver mutations. This study aims to develop an ensemble machine learning approach to evaluate the performance (rank) of pathogenic and conservation scoring algorithms (PCSAs) based on their ability to distinguish pathogenic driver mutations from benign passenger (non-driver) mutations in head and neck squamous cell carcinoma (HNSC).

METHODS

The study used a dataset from 502 HNSC patients, classifying mutations based on 299 known high-confidence cancer driver genes. Missense somatic mutations in driver genes were treated as driver mutations, while non-driver mutations were randomly selected from other genes. Each mutation was annotated with 41 PCSAs. Three machine learning algorithms-logistic regression, random forest, and support vector machine-along with recursive feature elimination, were used to rank these PCSAs. The final ranking of the PCSAs was determined using rank-average-sort and rank-sum-sort methods.

RESULTS

The random forest algorithm emerged as the top performer among the three tested ML algorithms, with an AUC-ROC of 0.89, compared to 0.83 for the other two, in distinguishing pathogenic driver mutations from benign passenger mutations using all 41 PCSAs. The top 11 PCSAs were selected based on the first quintile cut-off from the final rank-sum distribution. Classifiers built using these top 11 PCSAs (DEOGEN2, Integrated_fitCons, MVP, etc.) demonstrated significantly higher performance (p-value < 2.22e-16) compared to those using the remaining 30 PCSAs across all three ML algorithms, in separating pathogenic driver from benign passenger mutations. The top PCSAs demonstrated strong performance on a validation cohort including independent HNSC and other cancer types: breast, lung, and colorectal - reflecting its consistency, robustness and generalizability.

CONCLUSIONS

The ensemble machine learning approach effectively evaluates the performance of PCSAs based on their ability to differentiate pathogenic drivers from benign passenger mutations in HNSC and other cancer types. Notably, some well-known PCSAs performed poorly, underscoring the importance of data-driven selection over relying solely on popularity.

Identifying somatic driver mutations in cancer with a language model of the human genome

Somatic driver mutations play important roles in cancer and must be precisely identified to advance our understanding of tumorigenesis and its promotion and progression. However, identifying somatic driver mutations remains challenging in Homo sapiens genomics due to the random nature of mutations and the high cost of qualitative experiments. Building on the powerful sequence interpretation capabilities of language models, we propose a self-attention-based contextualized pretrained language model for somatic driver mutation identification. We pretrained the model with the Homo sapiens reference genome to equip it with the ability to understand genome sequences and then fine-tuned it for oncogene and tumor suppressor gene prediction tasks, enabling it to extract features related to driver genes from the original genome sequence. The fine-tuned model was used to obtain the mutations' carcinogenic effect characteristics to further identify whether the mutation is a driver or a passenger. Compared with other computational algorithms, our method achieved excellent somatic driver mutation identification performance on the test set, with an absolute improvement of 4.31% in AUROC over the best comparison method. The strong performance of our method indicates that it can provide new insights into the discovery of cancer drivers.

Molecular characterization of EBV-associated primary pulmonary lymphoepithelial carcinoma by multiomics analysis

BACKGROUND

Primary pulmonary lymphoepithelial carcinoma (pLEC) is a subtype of non-small cell lung cancer (NSCLC) characterized by Epstein-Barr virus (EBV) infection. However, the molecular pathogenesis of pLEC remains poorly understood.

METHODS

In this study, we explored pLEC using whole-exome sequencing (WES) and RNA-whole-transcriptome sequencing (RNA-seq) technologies. Datasets of normal lung tissue, other types of NSCLC, and EBV-positive nasopharyngeal carcinoma (EBV+-NPC) were obtained from public databases. Furthermore, we described the gene signatures, viral integration, cell quantification, cell death and immune infiltration of pLEC.

RESULTS

Compared with other types of NSCLC and EBV+-NPC, pLEC patients exhibited a lower somatic mutation burden and extensive copy number deletions, including 1p36.23, 3p21.1, 7q11.23, and 11q23.3. Integration of EBV associated dysregulation of gene expression, with CNV-altered regions coinciding with EBV integration sites. Specifically, ZBTB16 and ERRFI1 were downregulated by CNV loss, and the FOXD family genes were overexpressed with CNV gain. Decreased expression of the FOXD family might be associated with a favorable prognosis in pLEC patients, and these patients exhibited enhanced cytotoxicity.

CONCLUSION

Compared with other types of NSCLC and NPC, pLEC has distinct molecular characteristics. EBV integration, the aberrant expression of genes, as well as the loss of CNVs, may play a crucial role in the pathogenesis of pLEC. However, further research is needed to assess the potential role of the FOXD gene family as a biomarker.

Systematically developing a registry of splice-site creating variants utilizing massive publicly available transcriptome sequence data

Genomic variants causing abnormal splicing play important roles in genetic disorders and cancer development. Among them, variants that cause the formation of novel splice-sites (splice-site creating variants, SSCVs) are particularly difficult to identify and often overlooked in genomic studies. Additionally, these SSCVs are frequently considered promising candidates for treatment with splice-switching antisense oligonucleotides (ASOs). To leverage massive transcriptome sequence data such as those available from the Sequence Read Archive, we develop a novel framework to screen for SSCVs solely using transcriptome data. We apply it to 322,072 publicly available transcriptomes and identify 30,130 SSCVs. Among them, 5121 SSCVs affect disease-causing variants. By utilizing this extensive collection of SSCVs, we reveal the characteristics of Alu exonization via SSCVs, especially the hotspots of SSCVs within Alu sequences and their evolutionary relationships. We discover novel gain-of-function SSCVs in the deep intronic region of the NOTCH1 gene and demonstrate that their activation can be suppressed using splice-switching ASOs. Collectively, we provide a systematic approach for automatically acquiring a registry of SSCVs, which facilitates the elucidation of novel biological mechanisms underlying splicing and serves as a valuable resource for drug discovery. The catalogs of SSCVs identified in this study are accessible on the SSCV DB ( https://sscvdb.io ).