To understand genetic evolution in cancer during metastasis, we analyzed genomic profiles of 3,732 cancer patients in whom several tumor sites were longitudinally biopsied. During distant metastasis, tumors were observed to accumulate copy number alterations (CNAs) to a much greater degree than mutations. In particular, the development of whole genome duplication was a common event during metastasis, emerging de novo in 28% of patients. Loss of 9p (including CDKN2A) developed during metastasis in 11% of patients. To a lesser degree, mutations and allelic loss in human leukocyte antigen class I and other genes associated with antigen presentation also emerged. Increasing CNA, but not increasing mutational load, was associated with immune evasion in patients treated with immunotherapy. Taken together, these data suggest that CNA, rather than mutational accumulation, is enriched during cancer metastasis, perhaps due to a more favorable balance of enhanced cellular fitness versus immunogenicity.

Growing interest toward RNA modification in cancer has inspired the exploration of gene sets related to multiple RNA modifications. However, a comprehensive elucidation of the clinical value of various RNA modifications in breast cancer is still lacking.

This study aimed to provide a strategy based on RNA modification-related genes for predicting therapy response and survival outcomes in breast cancer patients.

Genes related to thirteen RNA modification patterns were integrated for establishing a nine-gene-containing signature-RMscore. Alterations of tumor immune microenvironment and therapy response featured by different RMscore levels were assessed by bulk transcriptome, single-cell transcriptome and genomics analyses. The biological function of key RMscore-related molecules was investigated by cellular experiments in vitro and in vivo, using flow cytometry, immunohistochemistry and immunofluorescence staining.

This study has raised an effective therapy strategy for breast cancer patients after a well-rounded investigation of RNA modification-related genes. With a great performance of predicting patient prognosis, high levels of the RMscore proposed in this study represented suppressive immune microenvironment and therapy resistance, including adjuvant chemotherapy and PD-L1 blockade treatment. As the key contributor of the RMscore, inhibition of WDR4 impaired breast cancer progression significantly in vitro and in vivo, as well as participated in regulating cell cycle and mTORC1 signaling pathway via m7G modification.

Briefly, this study has developed promising and effective tactics to achieve the prediction of survival probabilities and treatment response in breast cancer patients.

Regulated cell death (RCD) pathways play significant roles in tumorigenesis. However, systematic investigation into correlations between RCD and various molecular and clinical features, particularly anti-tumor immunity and immunotherapy response in pan-cancer remains lacking.

Using the single-sample gene set enrichment analysis, we quantified the activities of six RCD pathways (apoptosis, autophagy, ferroptosis, cuproptosis, necroptosis, and pyroptosis) in each cancer specimen. Then, we explored associations of these six RCD pathways with tumor immunity, genomic instability, tumor phenotypes and clinical features, and responses to immunotherapy and targeted therapies in pan-cancer by statistical analyses.

Our results showed that the RCD (except autophagy) activities were oncogenic signatures, as evidenced by their hyperactivation in late stage or metastatic cancer patients, positive correlations with tumor proliferation, stemness, genomic instability and intratumor heterogeneity, and correlation with worse survival outcomes in cancer. In contrast, autophagy was a tumor suppressive signature as its associations with molecular and clinical features in cancer shows an opposite pattern compared to the other RCD pathways. Furthermore, heightened RCD (except cuproptosis) activities were correlated with increased sensitivity to immune checkpoint inhibitors. Additionally, elevated activities of pyroptosis, autophagy, cuproptosis and necroptosis were associated with increased drug sensitivity in a broad spectrum of anti-tumor targeted therapies, while the elevated activity of ferroptosis was correlated with decreased sensitivity to numerous targeted therapies.

RCD (except autophagy) activities correlate with unfavorable cancer prognosis, while the autophagy activity correlate with favorable clinical outcomes. RCD (except cuproptosis) activities are positive biomarkers for anti-tumor immunity and immunotherapy response.

Aneuploidy is prevalent in cancer and has complicated roles in tumorigenesis. Paradoxically, artificially engineered aneuploidy in normal cells reduces cellular fitness by inducing proteotoxic and genotoxic stresses. A better molecular understanding of the multifaceted roles of aneuploidy in cancer evolution offers promising avenues for future cancer therapies. Here, we discuss the patterns and consequences of aneuploidy in human cancer. We highlight recent efforts to explore aneuploidy as a cancer vulnerability and new interventions that exploit this vulnerability for cancer treatment.

Clonal dispersal, resulting from the intermingling of tumor cell subpopulations, is thought to be a key driver of tumor heterogeneity. Despite advances in spatial modeling of cancer biology, quantification of clonal dispersal has been challenging. This study introduces a straightforward method, relying on fluorescent cell barcoding, to quantify clonal dispersal in various in vitro and in vivo models of colorectal cancer (CRC). Our approach allows for precise localization of clones and uncovering the degree of clonal mixing across different CRC models. Our findings suggest that clonal dispersal is correlated with the expression of genes involved in epithelial-mesenchymal transition and CMS4-related signaling pathways. We further identify a dispersal gene signature, associated with intratumor heterogeneity, which is a robust clinical predictor of poor prognosis and recurrence in CRC, highlighting its potential as a prognostic marker and a putative direction for therapeutic targeting.

Aneuploidy is a common feature of cancer that drives tumor evolution, but it also creates cellular vulnerabilities that might be exploited therapeutically. Recent advances in genomic technologies and experimental models have uncovered diverse cellular consequences of aneuploidy, revealing dependencies on mitotic regulation, DNA replication and repair, proteostasis, metabolism, and immune interactions. Harnessing aneuploidy for precision oncology requires the combination of genomic, functional, and clinical studies that will enable translation of our improved understanding of aneuploidy to targeted therapies. In this review we discuss approaches to targeting both highly aneuploid cells and cells with specific common aneuploidies, summarize the biological underpinning of these aneuploidy-induced vulnerabilities, and explore their therapeutic implications.

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy is experimentally challenging. Our approach, utilizing the Collaborative Cross mouse genetic resource, fixes the tumor genomic configuration while varying host genetics. We find that response to anti-PD-1 (aPD1) immunotherapy is significantly heritable in four distinct murine tumor models (H2: 0.18-0.40). For the MC38 colorectal carcinoma system, we map four significant ICI response quantitative trait loci (QTLs) with significant epistatic interactions. The differentially expressed genes within these QTLs that define responder genetics are highly enriched for processes involving antigen processing and presentation, allograft rejection, and graft vs. host disease (all p < 1 × 10-10). Functional blockade of two top candidate immune targets, GM-CSF and IL-2RB, completely abrogates the MC38 transcriptional response to aPD1 therapy. Thus, our in vivo experimental platform is a powerful approach for discovery of host genetic factors that establish the tumor immune microenvironment propitious for ICI response.

Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging due to complex tumor-immune interactions and the poor generalizability of current biomarkers and models. Predictors such as tumor mutational burden, PD-L1 expression, and transcriptomic signatures often fail across cancer types, therapies, and clinical settings. There is a clear need for a robust, interpretable model that captures shared immune response principles and adapts to diverse clinical contexts. We present Compass, a foundation model for predicting immunotherapy response from pan-cancer transcriptomic data using a concept bottleneck architecture. Compass encodes tumor gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interactions, and signaling pathways. Trained on 10,184 tumors across 33 cancer types, Compass outperforms 22 baseline methods in 16 independent clinical cohorts spanning seven cancers and six immune checkpoint inhibitors, increasing precision by 8.5%, Matthews correlation coefficient by 12.3%, and area under the precision-recall curve by 15.7%, with minimal or no additional training. The model generalizes to unseen cancer types and treatments, supporting indication selection and patient stratification in early-phase clinical trials. Survival analysis shows that Compass-stratified responders have significantly longer overall survival (hazard ratio = 4.7, p < 0.0001). Personalized response maps link gene expression to immune concepts, revealing distinct mechanisms of response and resistance. For example, among immune-inflamed non-responders, Compass identifies distinct resistance programs involving TGF- β signaling, endothelial exclusion, CD4+ T cell dysfunction, and B cell deficiency. By combining mechanistic interpretability with transfer learning, Compass provides mechanistic insights into treatment response variability, supports clinical decision-making, and informs trial design.

First-line nivolumab-plus-chemotherapy demonstrated superior overall survival (OS) and progression-free survival versus chemotherapy for advanced gastroesophageal adenocarcinoma with programmed death ligand 1 combined positive score ≥ 5, meeting both primary end points of the randomized phase 3 CheckMate 649 trial. Nivolumab-plus-ipilimumab provided durable responses and higher survival rates versus chemotherapy; however, the prespecified OS significance boundary was not met. To identify biomarkers predictive of differential efficacy outcomes, post hoc exploratory analyses were performed using whole-exome sequencing and RNA sequencing. Nivolumab-based therapies demonstrated improved efficacy versus chemotherapy in hypermutated and, to a lesser degree, Epstein-Barr virus-positive tumors compared with chromosomally unstable and genomically stable tumors. Within the KRAS-altered subgroup, only patients treated with nivolumab-plus-chemotherapy demonstrated improved OS benefit versus chemotherapy. Low stroma gene expression signature scores were associated with OS benefit with nivolumab-based regimens; high regulatory T cell signatures were associated with OS benefit only with nivolumab-plus-ipilimumab. Our analyses suggest that distinct and overlapping pathways contribute to the efficacy of nivolumab-based regimens in gastroesophageal adenocarcinoma.

Immune checkpoint inhibitors (ICIs) have improved outcomes of patients with many different cancers. These antibodies target molecules such as programmed cell death 1 (PD-1) or cytotoxic T lymphocyte associated protein 4 (CTLA-4) which normally function to limit immune activity. Treatment with ICIs reactivates T cells to destroy tumor cells in a highly specific manner, which in some patients, results in dramatic remissions and durable disease control. Over the last decade, much effort has been directed at characterizing factors that drive efficacy and resistance to ICI therapy. Food and Drug Administration (FDA)-approved biomarkers for ICI therapy have facilitated more judicious treatment of cancer patients and transformed the field of precision oncology. Yet, adaptive immunity against cancers is complex, and newer data have revealed the potential utility of other biomarkers. In this review, we discuss the utility of currently approved biomarkers and highlight how emerging biomarkers can further improve the identification of patients who benefit from ICIs.

Colorectal carcinoma (CRC) exhibits metastatic organotropism, primarily targeting liver, lung, and rarely the brain. Here, we study chromosomal imbalances (CIs) in cohorts of primary CRCs and metastases. Brain metastases show the highest burden of CIs, including aneuploidies and focal CIs, with enrichment of +12p encoding KRAS. Compared to liver and lung metastases, brain metastases present with increased co-occurrence of KRAS mutation and amplification. CRCs with concurrent KRAS mutation and amplification display significant metabolic reprogramming with upregulation of glycolysis, alongside upregulation of cell cycle pathways, including copy number gains of MDM2 and CDK4. Evolutionary modeling suggests early acquisition of many organotropic CIs enriched in both liver and brain metastases, while brain-enriched CIs preferentially emerge later. Collectively, this study supports a model where cytogenetic events in CRCs favor site-specific metastatic colonization. These site-enriched CI patterns may serve as biomarkers for metastatic potential in precision oncology.

Chromosomal instability (CIN) and clonal heterogeneity (CH) are fundamental hallmarks of breast cancer that drive tumor evolution, disease progression, and therapeutic resistance. Understanding the mechanisms underlying these phenomena is essential for improving cancer diagnosis, prognosis, and treatment strategies.

In this review, we provide a comprehensive overview of the biological processes contributing to CIN and CH, highlighting their molecular determinants and clinical relevance.

We discuss the latest advances in detection methods, including single-cell sequencing and other high-resolution techniques, which have enhanced our ability to characterize intratumoral heterogeneity. Additionally, we explore how CIN and CH influence treatment responses, their potential as therapeutic targets, and their role in shaping the tumor immune microenvironment, which has implications for immunotherapy effectiveness.

By integrating recent findings, this review underscores the impact of CIN and CH on breast cancer progression and their translational implications for precision medicine.

New-onset diabetes (NOD) is a common condition among patients with pancreatic adenocarcinoma (PAAD) and is related to poor clinical outcomes. The potential impact of NOD on PAAD progression and the tumor microenvironment remains unclear. Here, we revealed that NOD in PAAD was associated with metabolic disorders. Utilizing three machine-learning algorithms, an NOD-related metabolism signature (NRMS) was established. Validated in three independent cohorts, patients with a high NRMS score exhibited a worse prognosis. Moreover, an elevated NRMS score was associated with an immunosuppressive microenvironment and diminished response to immunotherapy. Further experiments demonstrated that ALDH3A1, a key feature in NRMS, was significantly upregulated in tissues from PAAD patients with NOD and played a crucial role in tumor progression and immune suppression. Our findings highlight the potential of NRMS as a prognostic biomarker and an indicator of immunotherapy response for patients with PAAD.

Programmed cell death (PCD) is a significant factor in the progression of hepatocellular carcinoma (HCC) and might serve as a crucial marker for predicting HCC prognosis and therapy response. However, the classification of HCC based on diverse PCD patterns requires further investigation. This study identified a novel molecular classification named PCD subtype (C1, C2, and C3) based on the genes associated with 19 PCD patterns, distinguished by clinical, biological functional pathways, mutations, immune characteristics, and drug sensitivity. Validated in 4 independent datasets, diverse cell death pathways were enriched in the C3 subtype, including apoptosis, pyroptosis, and autophagy, it also exhibited a highly infiltrative immunosuppressive microenvironment and demonstrated higher sensitivity to compounds such as Paclitaxel, Bortezomib, and YK-4-279, while C1 subtype was significantly enriched in cuproptosis and metabolism-related pathways, suggesting that it may be more suitable for cuproptosis-inducing agent therapy. Subsequently, utilizing the machine learning algorithms, we constructed a cell death-related index (CDRI) with 22 gene features and constructed prognostic nomograms with high predictive performance by combining CDRI with clinical features. Notably, we found that CDRI effectively predicted the response of HCC patients to therapeutic strategies, where patients with high CDRI were more suitable for sorafenib drug therapy and patients with low CDRI were more ideal for transarterial chemoembolization (TACE). In conclusion, the PCD subtype and CDRI demonstrate significant efficacy in predicting the prognosis and therapeutic outcomes for patients with HCC. These findings offer valuable insights for the development of precise, individualized treatment strategies.

Kinesins are a family of motor proteins essential for intracellular transport and cellular dynamics, with kinesin family member C1 (KIFC1) emerging as a key regulator of cancer progression. Recent studies highlight KIFC1's crucial role in mitotic spindle assembly, chromosome segregation, and cell migration-processes frequently dysregulated in cancer. Its involvement in promoting malignant cell proliferation and metastasis underscores its significance in tumor biology. In various cancer types, aberrant KIFC1 expression correlates with poor prognosis and aggressive phenotypes, suggesting its potential as a biomarker for disease severity. Mechanistically, KIFC1 influences signaling pathways linked to cell cycle regulation and programmed cell death, reinforcing its role in oncogenesis. Given its pivotal function in cancer cell dynamics, KIFC1 represents a promising therapeutic target. Strategies aimed at modulating its activity, including small molecules or RNA interference, could disrupt cancer cell viability and proliferation. The current review article highlights KIFC1's importance in cancer biology, advocating for further investigation into its mechanisms and the development of KIFC1-targeted therapies to enhance treatment efficacy and improve patient outcomes across various malignancies.

To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi-omics data integration. Our research demonstrates that iMLGAM scores exhibit superior predictive performance across independent cohorts, with lower scores correlating significantly with enhanced therapeutic responses and outperforming existing clinical biomarkers. Detailed analysis revealed that tumors with low iMLGAM scores display distinctive immune microenvironment characteristics, including increased immune cell infiltration and amplified antitumor immune responses. Critically, through clustered regularly interspaced short palindromic repeats screening, we identified Centrosomal Protein 55 (CEP55) as a key molecule modulating tumor immune evasion, mechanistically confirming its role in regulating T cell-mediated antitumor immune responses. These findings not only validate iMLGAM as a powerful prognostic tool but also propose CEP55 as a promising therapeutic target, offering novel strategies to enhance ICB treatment efficacy. The iMLGAM package is freely available on GitHub (https://github.com/Yelab1994/iMLGAM), providing researchers with an innovative approach to personalized cancer immunotherapy prediction.

Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. Neoadjuvant therapy (NAT), administered prior to surgery, is integral to breast cancer treatment strategies. It aims to downsize tumors, optimize surgical outcomes, and evaluate tumor responsiveness to treatment. However, accurately predicting NAT efficacy remains challenging due to the disease's complexity and the diverse responses across different molecular subtypes.

In this study, we harnessed multimodal data, including proteomic, genomic, MRI imaging, and clinical information, sourced from multiple cohorts such as I-SPY2, TCGA-BRCA, GSE161529, and METABRIC. Post data preprocessing, Lasso regression was utilized for feature extraction and selection. Five machine learning algorithms were employed to construct diagnostic models, with pathological complete response (pCR) as the predictive endpoint.

Our results revealed that the multi-omics Ridge regression model achieved the optimal performance in predicting pCR, with an AUC of 0.917. Through unsupervised clustering using the R package MOVICS and nine clustering algorithms, we identified four distinct multimodal breast cancer subtypes associated with NAT. These subtypes exhibited significant differences in proteomic profiles, hallmark cancer gene sets, pathway activities, tumor immune microenvironments, transcription factor activities, and clinical characteristics. For instance, CS1 subtype, predominantly ER-positive, had a low pCR rate and poor response to chemotherapy drugs, while CS4 subtype, characterized by high immune infiltration, showed a better response to immunotherapy. At the single-cell level, we detected significant heterogeneity in the tumor microenvironment among the four subtypes. Malignant cells in different subtypes displayed distinct copy number variations, differentiation levels, and evolutionary trajectories. Cell-cell communication analysis further highlighted differential interaction patterns among the subtypes, with implications for tumor progression and treatment response.

Our multimodal diagnostic model and subtype analysis provide novel insights into predicting NAT efficacy in breast cancer. These findings hold promise for guiding personalized treatment strategies. Future research should focus on experimental validation, in-depth exploration of the underlying mechanisms, and extension of these methods to other cancers and treatment modalities.

Colorectal cancer (CRC) is a malignant tumor of the digestive tract that significantly impacts human health. LDL receptor-related protein 1B (LRP1B) may play a crucial role in tumorigenesis and disease progression.

We performed a comparative analysis of differential gene expression, mutation patterns, drug sensitivity, and cellular phenotypes across different subgroups with varying LRP1B expression levels. Cellular and molecular experiments were conducted to validate our findings.

Our analysis implicated LRP1B as a tumor suppressor gene. Experimental results confirmed that LRP1B expression was reduced in CRC and its knockdown was associated with poor prognosis. Molecular mechanism studies revealed that LRP1B negatively regulated the Hedgehog (Hh) signaling pathway, influencing cell cycle and apoptosis processes. Single-cell analysis showed significant differences in the infiltration of T cells, B cells, epithelial cells, and myeloid cells between high and low LRP1B expression groups. Immune cell infiltration and drug sensitivity analyses demonstrated that LRP1B plays a crucial role in immunotherapy and targeted therapy, suggesting that restoring LRP1B function could be a promising treatment strategy for CRC.

Our results indicate that LRP1B may function as a tumor suppressor factor in CRC, playing a significant role in mutation, therapy, and immune infiltration. Knockdown of LRP1B activates the Hh pathway in tumor cells, leading to the inhibition of several malignant biological behaviors.

Mutations in the exonuclease domains of the replicative nuclear DNA polymerases POLD1 and POLE are associated with increased cancer incidence, elevated tumor mutation burden (TMB), and enhanced response to immune checkpoint blockade (ICB). Although ICB is approved for treatment of several cancers, not all tumors with elevated TMB respond, highlighting the need for a better understanding of how TMB affects tumor biology and subsequently immunotherapy response. To address this, we generated mice with germline and conditional mutations in the exonuclease domains of Pold1 and Pole. Engineered mice with Pold1 and Pole mutator alleles presented with spontaneous cancers, primarily lymphomas, lung cancer, and intestinal tumors, whereas Pold1 mutant mice also developed tail skin carcinomas. These cancers had highly variable tissue type-dependent increased TMB with mutational signatures associated with POLD1 and POLE mutations found in human cancers. The Pold1 mutant tail tumors displayed increased TMB; however, only a subset of established tumors responded to ICB. Similarly, introducing the mutator alleles into mice with lung cancer driven by mutant Kras and Trp53 deletion did not improve survival, whereas passaging these tumor cells in vitro without immune editing and subsequently implanting them into immunocompetent mice caused tumor rejection in vivo. These results demonstrated the efficiency by which cells with antigenic mutations are eliminated in vivo. Finally, ICB treatment of mutator mice earlier, before observable tumors had developed delayed cancer onset, improved survival and selected for tumors without aneuploidy, suggesting the potential of ICB in high-risk individuals for cancer prevention. Significance: Treating high-mutation burden mice with immunotherapy prior to cancer onset significantly improves survival, raising the possibility of utilizing immune checkpoint blockade for cancer prevention, especially in individuals with increased risk.

Intratumoral heterogeneity drives therapy resistance and relapses in advanced stage cancers, such as ovarian cancer. Here, we present a live cell imaging assay using patient-derived ovarian cancer organoids for real time capture and quantification of natural killer cell-mediated apoptotic events in >500 organoids simultaneously. Our assay revealed significant inter- and intratumor response heterogeneity and identified a rare resistant organoid population, opening avenues to test immunomodulatory strategies that overcome resistance.

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN may result from chromosome mis-segregation errors and excessive chromatin is frequently packaged in micronuclei (MN), which can be enumerated to quantify CIN. The assessment of CIN remains a predominantly manual and time-consuming task. Here, we present micronuclAI, a pipeline for automated and reliable quantification of MN of varying size and morphology in cells stained only for DNA. micronuclAI can achieve close to human-level performance on various human and murine cancer cell line datasets. The pipeline achieved a Pearson's correlation of 0.9278 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and on several publicly available image datasets where we achieved a Pearson's correlation of 0.9620. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on images that are routinely obtained for research purposes. We release a GUI-implementation for easy access and utilization of the pipeline.

Midnolin (MIDN) is a protein coding gene that promotes the destruction of transcription factors encoded by immediate-early genes. Previous research has found that those immediate-early genes are involved in tumour progression. However, the role of MIDN is still not clearly identified in human cancers. With the help of the TCGA, GTEx, and HPA databases, we revealed that the expression of MIDN was disordered in cancers. MIDN is a potential prognostic biomarker in liver cancer and bladder cancer. Prognostic analysis indicates that the expression level of MIDN gains survival benefits or promotes progression in multiple tumours. After analysing the sequencing results of TCGA via Gene Set Enrichment Analysis (GSEA), results suggested the regulative role of MIDN in cell proliferation and tumour immunity. Single cell sequencing results revealed that MIDN is highly expressed in several tumour tissues and also expressed in immune cells. With the help of the ESTIMATE, TIMER, and CIBERSORT databases, we analysed the immune score, immune cell infiltration, and anti-cancer immunity cycle depending on the expression of MIDN. Results showed that low MIDN levels are tightly associated with high CD4 + T and NK cell infiltration. Furthermore, mutations of MIDN in cancers were significantly associated with immune cell infiltration. This study presents a robust link between the expression of MIDN and tumour progression across multiple cancer types. The MIDN/CTNNB1/MMP9 axis promotes liver cancer progression via inducing a suppressive tumour immune microenvironment.

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.

Immune-checkpoint inhibitors (ICIs) have transformed the treatment paradigm for advanced-stage squamous non-small-cell lung cancer (LUSC), a histological subtype associated with inferior outcomes compared with lung adenocarcinoma. However, only a subset of patients derive durable clinical benefit. In the first-line setting, multiple ICI regimens are available, including anti-PD-(L)1 antibodies as monotherapy, in combination with chemotherapy, or with an anti-CTLA4 antibody with or without chemotherapy. Several important questions persist regarding the optimal regimen for individual patients, particularly how to identify patients who might benefit from adding chemotherapy and/or anti-CTLA4 antibodies to anti-PD-(L)1 antibodies. An urgent need exists for predictive biomarkers beyond PD-L1 to better guide precision oncology approaches. Deeper knowledge of the underlying molecular biology of LUSC and its implications for response to ICIs will be important in this regard. Integration of this knowledge into multi-omics methods coupled with artificial intelligence might enable the development of more robust biomarkers. Finally, several novel therapeutic strategies, including novel ICIs, bispecific antibodies and personalized cancer vaccines, are emerging. Addressing these unresolved questions through innovative clinical trials and translational research will be crucial to further improving the outcomes of patients with LUSC. In this Review, we provide a comprehensive overview of current immunotherapeutic approaches, unresolved challenges and emerging strategies for patients with LUSC.

Aneuploidy is prevalent in cancer and associates with fitness advantage and poor patient prognosis. Yet, experimentally induced aneuploidy initially leads to adverse effects and impaired proliferation, suggesting that cancer cells must adapt to aneuploidy. We performed in vitro evolution of cells with extra chromosomes and obtained cell lines with improved proliferation and gene expression changes congruent with changes in aneuploid cancers. Integrated analysis of cancer multi-omics data and model cells revealed increased expression of DNA replicative and repair factors, reduced genomic instability, and reduced lysosomal degradation. We identified E2F4 and FOXM1 as transcription factors strongly associated with adaptation to aneuploidy in vitro and in cancers and validated this finding. The adaptation to aneuploidy also coincided with specific copy number aberrations that correlate with poor patient prognosis. Chromosomal engineering mimicking these aberrations improved aneuploid cell proliferation, while loss of previously present extra chromosomes impaired it. The identified common adaptation strategies suggest replication stress, genomic instability, and lysosomal stress as common liabilities of aneuploid cancers.

Osteosarcoma (OS) is the most frequent primary solid malignancy of bone, whose course is usually dismal without efficient treatments. The aim of the study was to discover novel risk models to more accurately predict and improve the prognosis of patients with osteosarcoma. The single-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of OS were obtained from the TARGET and GEO databases respectively. A clustering tree was plotted to classify all cells into different clusters. The "cellchat" R package was used to establish and visualise cell-cell interaction networks. Then Univariate COX regression analysis was used to determine the prognostic CAF-related genes, followed by the Lasso-Cox regression analysis to build a risk on the prognostic CAF-related genes. Finally, from multiple perspectives, the signature was validated as an accurate and dependable tool in predicting the prognosis and guiding treatment therapies in OS patients. From the single-cell dataset, six OS patients and 46,544 cells were enrolled. All cells were classified into 22 clusters, and the clusters were annotated to 14 types of cells. Subsequently, CAFs were observed as a vital TME components. In cell-cell interaction networks in OS cells, CAFs had a profound impact as four roles. Via the Univariate COX regression analysis, 14 CAF-related genes were screened out. By the Lasso-Cox regression analyses, 11 key CAF-related genes were obtained, based on which an 11-gene signature that could predict the prognosis of osteosarcoma patients was constructed. According to the median of risk scores, all patients were grouped in to the high- and low-risk group, and their overall survival, activated pathways, immune cell infiltrations, and drug sensitivity were significantly differential, which may have important implications for the clinical treatment of patients with osteosarcoma. Our study, a systematic analysis of gene and regulatory genes, has proven that CAF-related genes had excellent diagnostic and prognostic capabilities in OS, and it may reshape the TME in OS. The novel CAF-related risk signature can effectively predict the prognosis of OS and provide new strategies for cancer treatment.

Recognition and elimination of pathogens and cancer cells depend on the adaptive immune system. Thus, accurate quantification of immune subsets is vital for precision medicine. We present immune lymphocyte estimation from nucleotide sequencing (ImmuneLENS), which estimates T cell and B cell fractions, class switching and clonotype diversity from whole-genome sequencing data at depths as low as 5× coverage. By applying ImmuneLENS to the 100,000 Genomes Project, we identify genes enriched with somatic mutations in T cell-rich tumors, significant sex-based differences in circulating T cell fraction and demonstrated that the circulating T cell fraction in patients with cancer is significantly lower than in healthy individuals. Low circulating B cell fraction was linked to increased cancer incidence. Finally, circulating T cell abundance was more prognostic of 5-year cancer survival than infiltrating T cells.

The introduction of immune checkpoint inhibitors (ICIs) has significantly improved the outcomes of patients with advanced non-small cell lung cancer (NSCLC). However, ICIs only benefit a subset of patients. The study aimed to identify genomic biomarkers and construct models to predict the response to second-line ICI therapy.

We retrospectively collected clinical data and genetic testing results from patients with NSCLC treated with second-line ICI at a single medical center between August 2018 and June 2021. We reanalyzed the raw sequence data of clinical genetic testing and defined the common detection region among the different testing panels. Immunotherapy sensitivity was evaluated using the immune-based Response Evaluation Criteria in Solid Tumors.

We included 102 patients as a training cohort and 46 as a test cohort. In the training cohort, we examined the relationship between ICI response and the mutation status of 343 genes. Mutations in the EGFR gene were significantly more common in the resistant group than in the sensitive group (41.0% vs. 20.6%; P=0.04), while mutations in the EP300 gene were associated with greater sensitivity to ICIs (39.7% vs. 15.4%; P=0.01). A nomogram was built based on clinical variables, genomic data, and programmed death-ligand 1 (PD-L1) expression. The total nomogram points were significantly higher in the sensitive group than in the resistance group in both cohorts, and the areas under the receiver operating characteristic curve were 0.780 in the training cohort and 0.720 in the test cohort. The higher nomogram points also indicated better progression-free survival.

Based on real-world clinical settings, the clinical genomic nomogram, which involved limited input variables that were economical and easy to obtain, demonstrated a good ability to predict the response to second-line ICI treatment in advanced NSCLC.

Oncogene amplification is a key driver of cancer pathogenesis. Both breakage fusion bridge (BFB) cycles and extrachromosomal DNA (ecDNA) can lead to high oncogene copy numbers, but the impact of BFB amplifications on intratumoral heterogeneity, treatment response, and patient survival remains poorly understood due to detection challenges with DNA sequencing. We introduce an algorithm, OM2BFB, designed to detect and reconstruct BFB amplifications using optical genome mapping (OGM). OM2BFB demonstrates high precision (>93%) and recall (92%) in identifying BFB amplifications across cancer cell lines, patient-derived xenograft models, and primary tumors. Comparisons using OGM reveal that BFB detection with our AmpliconSuite toolkit for short-read sequencing also achieves high precision, though with reduced sensitivity. We identify 371 BFB events through whole genome sequencing of 2557 primary tumors and cancer cell lines. BFB amplifications are prevalent in cervical, head and neck, lung, and esophageal cancers, but rare in brain cancers. Genes amplified through BFB exhibit lower expression variance, with limited potential for regulatory adaptation compared to ecDNA-amplified genes. Tumors with BFB amplifications (BFB(+)) show reduced structural heterogeneity in amplicons and delayed resistance onset relative to ecDNA(+) tumors. These findings highlight ecDNA and BFB amplifications as distinct oncogene amplification mechanisms with differing biological characteristics, suggesting distinct avenues for therapeutic intervention.

The cellular proteome determines the functional state of cells and is often skewed to direct pathological conditions. Autophagy shapes cellular proteomes primarily through lysosomal degradation of either damaged or unnecessary proteins. Here, we show that autophagy directs the senescence-specific translatome to fuel inflammation by coupling selective protein degradation with alternative splicing. RNA splicing is significantly altered during senescence, some of which surprisingly depend on autophagy, including exon 5 skipping of the translation regulator EIF4H. Systematic translatome profiling indicates that this event is key to the translational bias toward inflammation in senescence. Autophagy promotes these changes by selectively degrading the splicing regulator splicing factor proline and glutamine rich (SFPQ) via the autophagy receptor NBR1. These autophagy-centric inflammatory controls appear to be conserved during human tissue aging and cancer. Our work highlights the role of autophagy in the on-demand functional remodeling of cellular proteomes as well as the crosstalk between autophagy, alternative splicing, and inflammatory translation.

The advent of immune-based combinations, primarily leveraging immune checkpoint inhibitors, has revolutionized the therapeutic landscape of hepatocellular carcinoma (HCC). The current scenario features multiple therapies that have shown superiority over tyrosine kinase inhibitors; however, the absence of direct comparisons and validated prognostic biomarkers complicates therapeutic decision-making. Additionally, a significant proportion of patients still exhibit primary or secondary resistance to existing immunotherapies, underscoring the ongoing need for novel therapeutic strategies.

This narrative review discusses current strategies aimed at improving the efficacy of immunotherapy for HCC, focusing on the following aspects: available therapeutic options, identification of prognostic biomarkers, approaches to overcoming resistance (including the development of neoantigen vaccines), and the exploration of adjuvant and neoadjuvant strategies.

The future of systemic therapies for HCC is likely to be driven by advancements in immunotherapy. Key areas of exploration for the coming years include the discovery of novel checkpoint inhibitors or complementary agents to enhance tumor response when combined with existing treatments, a shift toward neoadjuvant/perioperative trials instead of traditional adjuvant approaches, and the development of personalized neoantigen vaccines.

Single-cell assay of transposase-accessible chromatin sequencing (scATAC-seq) unbiasedly profiles genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often relies on copy-number alterations (CNAs). However, CNA detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNA, a computational algorithm that accurately detects high-resolution CNAs from scATAC-seq data. We benchmark AtaCNA using simulation and real data and find AtaCNA's superior performance. Analyses of 10 scATAC-seq datasets show that AtaCNA could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial, and ovarian cancer samples, AtaCNA identifies subclones at distinct cellular states, suggesting an important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale (10-20 Mb) CNAs, demonstrating the importance of high-resolution CNA detection. These data show that AtaCNA can aid in integrative analysis to understand the complex heterogeneity in cancer.

A major goal of cancer biology is to understand the mechanisms driven by somatically acquired mutations. Two distinct methodologies-one analyzing mutation clustering within protein sequences and 3D structures, the other leveraging protein-protein interaction network topology-offer complementary strengths. We present NetFlow3D, a unified, end-to-end 3D structurally-informed protein interaction network propagation framework that maps the multiscale mechanistic effects of mutations. Built upon the Human Protein Structurome, which incorporates the 3D structures of every protein and the binding interfaces of all known protein interactions, NetFlow3D integrates atomic, residue, protein and network-level information: It clusters mutations on 3D protein structures to identify driver mutations and propagates their impacts anisotropically across the protein interaction network, guided by the involved interaction interfaces, to reveal systems-level impacts. Applied to 33 cancer types, NetFlow3D identifies 2 times more 3D clusters and incorporates 8 times more proteins in significantly interconnected network modules compared to traditional methods.