Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence

Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel glioma treatment. However, not all patients experience long-lasting benefits, underscoring the necessity to discover reliable biomarkers for predicting treatment outcomes. This study applied a range of advanced artificial intelligence methods to identify a new biomarker linked to the effectiveness of anti-PD-1 immunotherapy in glioma patients. Through an extensive analysis of single-cell RNA sequencing and bulk transcriptomic data from over 3000 patients, the gene SLFN12 emerged as a significant and independent predictor of immunotherapy response. Our results indicate that elevated SLFN12 expression is associated with worse overall survival across various glioma cohorts. Notably, we found that patients with high SLFN12 levels are less likely to respond favourably to anti-PD-1 treatment, positioning SLFN12 as a clinically valuable biomarker for personalised treatment decisions. Functional studies revealed that SLFN12 is involved in key immune-related pathways, shedding light on its potential role in altering the tumour microenvironment and impacting immunotherapy outcomes. Additional laboratory experiments confirmed the role of SLFN12 in promoting glioma cell proliferation, migration and macrophage recruitment. In summary, this study identifies SLFN12 as a novel biomarker for predicting immunotherapy response in glioma patients, offering new insights for precision immunotherapy approaches.

PD-1/PD-L1 immune checkpoint blockade in breast cancer: research insights and sensitization strategies

Immunotherapy targeting programmed cell death-1 (PD-1) and PD-L1 immune checkpoints has reshaped treatment paradigms across several cancers, including breast cancer. Combining PD-1/PD-L1 immune checkpoint blockade (ICB) with chemotherapy has shown promising efficacy in both early and metastatic triple-negative breast cancer, although only a subset of patients experiences durable responses. Identifying responders and optimizing immune drug selection are therefore critical. The effectiveness of PD-1/PD-L1 immunotherapy depends on both tumor-intrinsic factors and the extrinsic cell-cell interactions within the tumor microenvironment (TME). This review systematically summarizes the key findings from clinical trials of ICBs in breast cancer and examines the mechanisms underlying PD-L1 expression regulation. We also highlight recent advances in identifying potential biomarkers for PD-1/PD-L1 therapy and emerging evidence of TME alterations following treatment. Among these, the quantity, immunophenotype, and spatial distribution of tumor-infiltrating lymphocytes stand out as promising biomarkers. Additionally, we explore strategies to enhance the effectiveness of ICBs in breast cancer, aiming to support the development of personalized treatment approaches tailored to the unique characteristics of each patient's tumor.

High level of aneuploidy and recurrent loss of chromosome 11 as relevant features of somatotroph pituitary tumors

BACKGROUND

Somatotroph neuroendocrine pituitary tumors (sPitNET) are a subtype of pituitary tumors that commonly cause acromegaly. Our study aimed to determine the spectrum of DNA copy number abnormalities (CNAs) in sPitNETs and their relevance.

METHODS

A landscape of CNAs in sPitNETs was determined using combined whole-genome approaches involving low-pass whole genome sequencing and SNP microarrays. Fluorescent in situ hybridization (FISH) was used for microscopic validation of CNAs. The tumors were also subjected to transcriptome and DNA methylation analyses with RNAseq and microarrays, respectively.

RESULTS

We observed a wide spectrum of cytogenetic changes ranging from multiple deletions, recurrent chromosome 11 loss, stable genomes, to duplication of the majority of the chromosomes. The identified CNAs were confirmed with FISH. sPitNETs with multiple duplications were characterized by intratumoral heterogeneity in chromosome number variation in individual tumor cells, as determined with FISH. These tumors were separate CNA-related sPitNET subtype in clustering analyses with CNA signature specific for whole genome doubling-related etiology. This subtype encompassed GNAS-wild type, mostly densely granulated tumors with favorable expression level of known prognosis-related genes, notably enriched with POUF1/NR5A1-double positive PitNETs. Chromosomal deletions in sPitNETs are functionally relevant. They occurred in gene-dense DNA regions and were related to genes downregulation and increased DNA methylation in the CpG island and promoter regions in the affected regions. Recurrent loss of chromosome 11 was reflected by lowered MEN1 and AIP. No such unequivocal relevance was found for chromosomal gains. Comparisons of transcriptomes of selected most cytogenetically stable sPitNETs with tumors with recurrent loss of chromosome 11 showed upregulation of processes related to gene dosage compensation mechanism in tumors with deletion. Comparison of stable tumors with those with multiple duplications showed upregulation of processes related to mitotic spindle, DNA repair, and chromatin organization. Both comparisons showed upregulation of the processes related to immune infiltration in cytogenetically stable tumors and deconvolution of DNA methylation data indicated a higher content of specified immune cells and lower tumor purity in these tumors.

CONCLUSIONS

sPitNETs fall into three relevant cytogenetic groups: highly aneuploid tumors characterized by known prognostically favorable features and low aneuploidy tumors including specific subtype with chromosome 11 loss.

Chromosome instability functions as a potential therapeutic reference by enhancing chemosensitivity to BCL-XL inhibitors in colorectal carcinoma

Chromosome instability (CIN) and subsequent aneuploidy are prevalent in various human malignancies, influencing tumor progression such as metastases and relapses. Extensive studies demonstrate the development of chemoresistance in high-CIN tumors, which poses significant therapeutic challenges. Given the association of CIN with poorer prognosis and suppressed immune microenvironment observed in colorectal carcinoma (CRC), here we aimed to discover chemotherapeutic drugs exhibiting increased inhibition against high-CIN CRC cells. By using machine learning methods, we screened out two BCL-XL inhibitors Navitoclax and WEHI-539 as CIN-sensitive reagents in CRC. Subsequent analyses using a CIN-aneuploidy cell model confirmed the vulnerability of high-CIN CRC cells to these drugs. We further revealed the critical role of BCL-XL in the viability of high-CIN CRC cells. In addition, to ease the evaluation of CIN levels in clinic, we developed a three-gene signature as a CIN surrogate to predict prognosis, chemotherapeutic and immune responses in CRC samples. Our results demonstrate the potential value of CIN as a therapeutic target in CRC treatment and the importance of BCL-XL in regulating survival of high-CIN CRC cells, therefore representing a valuable attempt to translate a common trait of heterogeneous tumor cells into an effective therapeutic target.

The immunotherapy-based combination associated score as a robust predictor for outcome and response to combination of immunotherapy and VEGF inhibitors in renal cell carcinoma

BACKGROUND

Over the past decade, the realm of immunotherapy-based combination therapy has witnessed rapid growth for renal cell carcinoma (RCC), however, success has been constrained thus far. This limitation primarily stems from the absence of biomarkers essential for identifying patients likely to derive benefits from such treatments.

METHODS

In this study, the immunotherapy-based combination associated score (IBCS) was established using single-sample gene set enrichment analysis (ssGSEA) based on the genes identified in the key modules extracted by weighted correlation network analysis (WGCNA) in the IMmotion151 dataset, a randomized, global phase III trial.

RESULTS

High IBCS patients showed better responses to immunotherapy-based combinations and had longer progression-free survival (PFS). Further transcriptomic analysis revealed that IBCS was negatively correlated to TIDE score, identifying a subset of RCC patients characterized by enrichment of T-effector and moderate cell-cycle/angiogenesis gene expression. Our analysis of hub genes unveiled a novel molecule that could potentially serve as a target antigen in RCC. Validation through multiplex immunofluorescence assays on tissue microarrays (TMAs) containing 180 samples confirmed the pivotal role of this hub gene in immunoregulation. Furthermore, we developed an independent risk score model, which is significant for prognostic evaluation and patient stratification. Notably, we devised a forecasting nomogram using this risk score model, surpassing the IMDC score (a widely accepted risk score for predicting survival in patients undergoing VEGF-targeted therapy) in prognostic accuracy for patients treated with immunotherapy-based combinations.

CONCLUSION

This study has collectively developed an immunotherapy-based combination associated score, pinpointed effective biomarkers for prognostic and responsiveness of kidney cancer patients to immunotherapy-based combinations, and delved into their potential biological mechanisms, offering promising targets for further exploration.

A deep profile of gene expression across 18 human cancers

Clinically and biologically valuable information may reside untapped in large cancer gene expression data sets. Deep unsupervised learning has the potential to extract this information with unprecedented efficacy but has thus far been hampered by a lack of biological interpretability and robustness. Here, we present DeepProfile, a comprehensive framework that addresses current challenges in applying unsupervised deep learning to gene expression profiles. We use DeepProfile to learn low-dimensional latent spaces for 18 human cancers from 50,211 transcriptomes. DeepProfile outperforms existing dimensionality reduction methods with respect to biological interpretability. Using DeepProfile interpretability methods, we show that genes that are universally important in defining the latent spaces across all cancer types control immune cell activation, while cancer type-specific genes and pathways define molecular disease subtypes. By linking DeepProfile latent variables to secondary tumor characteristics, we discover that tumor mutation burden is closely associated with the expression of cell cycle-related genes. DNA mismatch repair and MHC class II antigen presentation pathway expression, on the other hand, are consistently associated with patient survival. We validate these results through Kaplan-Meier analyses and nominate tumor-associated macrophages as an important source of survival-correlated MHC class II transcripts. Our results illustrate the power of unsupervised deep learning for discovery of cancer biology from existing gene expression data.

Hallmarks of a genomically distinct subclass of head and neck cancer

Cancer is caused by an accumulation of somatic mutations and copy number alterations (CNAs). Besides mutations, these copy number changes are key characteristics of cancer development. Nonetheless, some tumors show hardly any CNAs, a remarkable phenomenon in oncogenesis. Head and neck squamous cell carcinomas (HNSCCs) arise by either exposure to carcinogens, or infection with the human papillomavirus (HPV). HPV-negative HNSCCs are generally characterized by many CNAs and frequent mutations in CDKN2A, TP53, FAT1, and NOTCH1. Here, we present the hallmarks of the distinct subgroup of HPV-negative HNSCC with no or few CNAs (CNA-quiet) by genetic profiling of 802 oral cavity squamous cell carcinomas (OCSCCs). In total, 73 OCSCC (9.1%) are classified as CNA-quiet and 729 as CNA-other. The CNA-quiet group is characterized by wild-type TP53, frequent CASP8 and HRAS mutations, and a less immunosuppressed tumor immune microenvironment with lower density of regulatory T cells. Patients with CNA-quiet OCSCC are older, more often women, less frequently current smokers, and have a better 5-year overall survival compared to CNA-other OCSCC. This study demonstrates that CNA-quiet OCSCC should be considered as a distinct, clinically relevant subclass. Given the clinical characteristics, the patient group with these tumors will rapidly increase in the aging population.

The integrated molecular and histological analysis defines subtypes of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is highly heterogeneous. Our understanding of full molecular and immune landscape of ESCC remains limited, hindering the development of personalised therapeutic strategies. To address this, we perform genomic-transcriptomic characterizations and AI-aided histopathological image analysis of 120 Chinese ESCC patients. Here we show that ESCC can be categorized into differentiated, metabolic, immunogenic and stemness subtypes based on bulk and single-cell RNA-seq, each exhibiting specific molecular and histopathological features based on an amalgamated deep-learning model. The stemness subgroup with signature genes, such as WFDC2, SFRP1, LGR6 and VWA2, has the poorest prognosis and is associated with downregulated immune activities, a high frequency of EP300 mutation/activation, functional mutation enrichment in Wnt signalling and the highest level of intratumoural heterogeneity. The immune profiling by transcriptomics and immunohistochemistry reveals ESCC cells overexpress natural killer cell markers XCL1 and CD160 as immune evasion. Strikingly, XCL1 expression also affects the sensitivity of ESCC cells to common chemotherapy drugs. This study opens avenues for ESCC treatment and provides a valuable public resource to better understand ESCC.

A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), a machine learning model aimed at identifying cell-type-specific tumor microenvironment ligand-receptor interactions relevant to ICB resistance. Applying IRIS to deconvolved transcriptomics data of the five largest melanoma ICB cohorts, we identify specific downregulated interactions, termed resistance downregulated interactions (RDI), as tumors develop resistance. These RDIs often involve chemokine signaling and offer a stronger predictive signal for ICB response compared to upregulated interactions or the state-of-the-art published transcriptomics biomarkers. Validation across multiple independent melanoma patient cohorts and modalities confirms that RDI activity is associated with CD8 + T cell infiltration and highly manifested in hot/brisk tumors. This study presents a strongly predictive ICB response biomarker, highlighting the key role of downregulating chemotaxis-associated ligand-receptor interactions in inhibiting lymphocyte infiltration in resistant tumors.

Genomic instability as a driver and suppressor of anti-tumor immunity

Genomic instability is a driver and accelerator of tumorigenesis and influences disease outcomes across cancer types. Although genomic instability has been associated with immune evasion and worsened disease prognosis, emerging evidence shows that genomic instability instigates pro-inflammatory signaling and enhances the immunogenicity of tumor cells, making them more susceptible to immune recognition. While this paradoxical role of genomic instability in cancer is complex and likely context-dependent, understanding it is essential for improving the success rates of cancer immunotherapy. In this review, we provide an overview of the underlying mechanisms that link genomic instability to pro-inflammatory signaling and increased immune surveillance in the context of cancer, as well as discuss how genomically unstable tumors evade the immune system. A better understanding of the molecular crosstalk between genomic instability, inflammatory signaling, and immune surveillance could guide the exploitation of immunotherapeutic vulnerabilities in cancer.

The baseline hemoglobin level is a positive biomarker for immunotherapy response and can improve the predictability of tumor mutation burden for immunotherapy response in cancer

Purpose

Because only a subset of cancer patients can benefit from immunotherapy, identifying predictive biomarkers of ICI therapy response is of utmost importance.

Methods

We analyzed the association between hemoglobin (HGB) levels and clinical outcomes in 1,479 ICIs-treated patients across 16 cancer types. We explored the dose-dependent associations between HGB levels and survival and immunotherapy response using the spline-based cox regression analysis. Furthermore, we investigated the associations across subgroups of patients with different clinicopathological characteristics, treatment programs and cancer types using the bootstrap resampling method.

Results

HGB levels correlated positively with clinical outcomes in cancer patients receiving immunotherapy but not in those without immunotherapy. Moreover, this association was independent of other clinicopathological characteristics (such as sex, age, tumor stage and tumor mutation burden (TMB)), treatment program and cancer type. Also, this association was independent of the established biomarkers of immunotherapy response, including TMB, PD-L1 expression and microsatellite instability. The combination of TMB and HGB level are more powerful in predicting immunotherapy response than TMB alone. Multi-omics analysis showed that HGB levels correlated positively with antitumor immune signatures and negatively with tumor properties directing antitumor immunosuppression, such as homologous recombination defect, stemness and intratumor heterogeneity.

Conclusion

The HGB measure has the potential clinical value as a novel biomarker of immunotherapy response that is easily accessible from clinically routine examination. The combination of TMB and HGB measures have better predictive performance for immunotherapy response than TMB.

The Innate Immune Landscape of dMMR/MSI Cancers Predicts the Outcome of Nivolumab Treatment: Results from the Drug Rediscovery Protocol

PURPOSE

The treatment efficacy of nivolumab was evaluated in patients with advanced, treatment-refractory solid mismatch repair deficiency/microsatellite-instable (dMMR/MSI) tumors, and in-depth biomarker analyses were performed to inform precision immunotherapy approaches.

PATIENTS AND METHODS

Patients with dMMR/MSI tumors who exhausted standard-of-care treatment options were enrolled in the Drug Rediscovery Protocol, a pan-cancer clinical trial that treats patients with cancer based on their tumor molecular profile with off-label anticancer drugs (NCT02925234). Patients received nivolumab (four cycles of 240 mg every 2 weeks, thereafter 480 mg every 4 weeks). The primary endpoint was clinical benefit (CB: objective response or stable disease ≥16 weeks). Whole-genome sequencing and RNA sequencing were performed on pretreatment tumor biopsies.

RESULTS

A total of 130 evaluable patients were enrolled with 16 different cancer types. CB was observed in 62% [95% confidence interval (CI), 53-70], with an objective response in 45% (95% CI, 36-54). After a median follow-up of 14.5 months (95% CI, 13-19), the median progression-free survival was 18 months (95% CI, 9-not reached), and the median overall survival was not reached. Whereas CB was not, or only weakly, associated with markers of adaptive immune cell infiltration, CB was strongly associated with expression of a broad set of innate immune receptors/ligands. This clearly contrasted findings in melanoma, in which markers of adaptive immunity dominated the biomarker landscape.

CONCLUSIONS

Nivolumab proved highly effective in advanced dMMR/MSI tumors. Expression of key innate immune receptors/ligands was the main predictor of a good treatment outcome, contrasting findings in melanoma and strengthening the rationale for tumor type-specific biomarkers for guiding immunotherapy.

Mechanism of immune activation mediated by genomic instability and its implication in radiotherapy combined with immune checkpoint inhibitors

Various genetic and epigenetic changes associated with genomic instability (GI), including DNA damage repair defects, chromosomal instability, and mitochondrial GI, contribute to development and progression of cancer. These alterations not only result in DNA leakage into the cytoplasm, either directly or through micronuclei, but also trigger downstream inflammatory signals, such as the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway. Apart from directly inducing DNA damage to eliminate cancer cells, radiotherapy (RT) exerts its antitumor effects through intracellular DNA damage sensing mechanisms, leading to the activation of downstream inflammatory signaling pathways. This not only enables local tumor control but also reshapes the immune microenvironment, triggering systemic immune responses. The combination of RT and immunotherapy has emerged as a promising approach to increase the probability of abscopal effects, where distant tumors respond to treatment due to the systemic immunomodulatory effects. This review emphasizes the importance of GI in cancer biology and elucidates the mechanisms by which RT induces GI remodeling of the immune microenvironment. By elucidating the mechanisms of GI and RT-induced immune responses, we aim to emphasize the crucial importance of this approach in modern oncology. Understanding the impact of GI on tumor biological behavior and therapeutic response, as well as the possibility of activating systemic anti-tumor immunity through RT, will pave the way for the development of new treatment strategies and improve prognosis for patients.

A Predictive Network-Based Immune Checkpoint Blockade Immunotherapeutic Signature Optimizing Patient Selection and Treatment Strategies

Immune checkpoint blockade (ICB) therapy has brought significant advancements to the field of oncology. However, the diverse responses among patients highlight the need for more accurate predictive tools. In this study, insights are drawn from tumor-immunology pathways, and a novel network-based ICB immunotherapeutic signature, termed ICBnetIS, is constructed. The signature is derived from advanced biological network-based computational strategies involving co-expression networks and molecular interactions networks. The efficacy of ICBnetIS is established through its association with enhanced patient survival and a robust immune response characterized by diverse immune cell infiltration and active anti-tumor immune pathways. The validation process positions ICBnetIS as an effective tool in predicting responses to ICB therapy, analyzing ICB data from a broad collection of over 700 samples from multiple cancer types of more than 15 datasets. It achieves an aggregated prediction AUC of 0.784, which outperforms the other nine renowned immunotherapeutic signatures, indicating the superior predictive capability of ICBnetIS. To sum up, the findings suggest ICBnetIS as a potent tool in predicting ICB therapy responses, offering significant implications for patient selection and treatment optimization in oncology. The study highlights the role of ICBnetIS in advancing personalized treatment strategies, potentially transforming the clinical landscape of ICB therapy.

A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies.

METHODS

Through the Asia-Pacific Hepatocellular Carcinoma trials group (NCT03267641), we recruited one of the largest prospective cohorts of patients with HCC, with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients.

RESULTS

Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival.

CONCLUSIONS

Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provides a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories.

IMPACT AND IMPLICATIONS

This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected hepatocellular carcinoma (HCC), reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of HCC. These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for personalized treatment strategies tailored to specific tumor evolutionary and transcriptomic profiles. The coexistence of multiple subtypes within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making.

CLINICAL TRIAL NUMBER

NCT03267641 (Observational cohort).

Single-cell RNA sequencing analysis reveals the distinct features of colorectal cancer with or without Fusobacterium nucleatum infection in PD-L1 blockade therapy

MSS/pMMR patients are unresponsive to PD-1/PD-L1 blockade in colorectal cancer (CRC), but the mechanisms are unclear. A better understanding of immunotherapy resistance in CRC may lead to more precise treatment and expand the benefit of immunotherapy to patients. In this study, we constructed mouse model of subcutaneous CRC tumor received anti-PD-L1 treatment with or without fusobacterium nucleatum (F. nucleatum) infection. Then we used single-cell RNA sequencing (scRNA-seq) to explore the comprehensive landscape of the tumor microenvironment (TME). Our data delineated the composition, subclonal diversity and putative function of distinct cells, tracked the developmental trajectory of tumor cells and highlighted cell-cell interactions. We found different compositions and functions of both tumor cells and immune cells. Single anti-PD-L1 monoclonal antibody (mAb) treated tumor exhibited two specific clusters which might be resistant to PD-L1 blockade. The accumulation of immune cells, including T cell, NK cell and pro-inflammatory macrophage subset in tumors infected with F. nucleatum may be one of the reasons for the increased sensitivity to PD-L1 blockade. Thus, targeting F. nucleatum to change the composition of tumor cell subclusters and enliven the immune response might help to overcome immune checkpoint blockade (ICB) resistance.

AI-powered genomic mutation signature for predicting immune checkpoint inhibitor therapy outcomes in gastroesophageal cancer: a multi-cohort analysis

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly transformed the treatment of gastroesophageal cancer (GEC). However, the lack of reliable prognostic biomarkers hinders the ability to predict patient response to ICI therapy.

METHODS

In this study, we engineered and validated a genomic mutation signature (GMS) utilizing an innovative artificial intelligence (AI) algorithm to forecast ICI therapy outcomes in GEC patients. We further explored immune profiles across subtypes through comprehensive multiomics analysis. Our investigation of drug sensitivity data from the Genomics of Drug Sensitivity in Cancer (GDSC) database led to the identification of trametinib as a potential therapeutic agent. We subsequently evaluated trametinib's efficacy in AGS and MKN45 cell lines using Cell Counting Kit-8 (CCK8) assays and clonogenic experiments.

RESULTS

We developed a GMS by integrating 297 algorithms, enabling autonomous prognosis prediction for GEC patients. The GMS demonstrated consistent performance across three public cohorts, exhibiting high sensitivity and specificity for overall survival (OS) at 6, 12, and 18 months, as shown by Receiver Operator Characteristic Curve (ROC) analysis. Notably, the GMS surpassed traditional clinical and molecular features, including tumor mutational burden (TMB), programmed death-ligand 1 (PD-L1) expression, and microsatellite instability (MSI), in predictive accuracy. Low-risk samples exhibited elevated levels of cytolytic immune cells and heightened immunogenic potential compared to high-risk samples. Our investigation identified trametinib as a potential therapeutic agent. An inverse correlation was observed between GMS and trametinib IC50. Moreover, the high-risk-derived AGS cell line showed increased sensitivity to trametinib compared to the low-risk-derived MKN45 cell line.

CONCLUSION

The GMS utilized in this study successfully demonstrated the ability to reliably predict the survival advantage for patients with GECs undergoing ICI therapy.

Proteogenomic characterization of skull-base chordoma

Skull-base chordoma is a rare, aggressive bone cancer with a high recurrence rate. Despite advances in genomic studies, its molecular characteristics and effective therapies remain unknown. Here, we conduct integrative genomics, transcriptomics, proteomics, and phosphoproteomics analyses of 187 skull-base chordoma tumors. In our study, chromosome instability is identified as a prognostic predictor and potential therapeutic target. Multi-omics data reveals downstream effects of chromosome instability, with RPRD1B as a putative target for radiotherapy-resistant patients. Chromosome 1q gain, associated with chromosome instability and upregulated mitochondrial functions, lead to poorer clinical outcomes. Immune subtyping identify an immune cold subtype linked to chromosome 9p/10q loss and immune evasion. Proteomics-based classification reveals subtypes (P-II and P-III) with high chromosome instability and immune cold features, with P-II tumors showing increased invasiveness. These findings, confirmed in 17 paired samples, provide insights into the biology and treatment of skull-base chordoma.

Selective loss of Y chromosomes in lung adenocarcinoma modulates the tumor immune environment through cancer/testis antigens

There is increasing recognition that the sex chromosomes, X and Y, play an important role in health and disease that goes beyond the determination of biological sex. Loss of the Y chromosome (LOY) in blood, which occurs naturally in aging men, has been found to be a driver of cardiac fibrosis and heart failure mortality. LOY also occurs in most solid tumors in males and is often associated with worse survival, suggesting that LOY may give tumor cells a growth or survival advantage. We analyzed LOY in lung adenocarcinoma (LUAD) using both bulk and single-cell expression data and found evidence suggesting that LOY affects the tumor immune environment by altering cancer/testis antigen expression and consequently facilitating tumor immune evasion. Analyzing immunotherapy data, we show that LOY and changes in expression of particular cancer/testis antigens are associated with response to pembrolizumab treatment and outcome, providing a new and powerful biomarker for predicting immunotherapy response in LUAD tumors in males.

Prediction of immunotherapy response using mutations to cancer protein assemblies

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.

An integrative pan-cancer analysis of MASP1 and the potential clinical implications for the tumor immune microenvironment

Mannose-binding lectin-associated serine protease 1 (MASP1) plays a crucial role in the complement lectin pathway and the mediation of immune responses. However, comprehensive research on MASP1 across various cancer types has not been performed to date. This study aimed to evaluate the significance of MASP1 in pan-cancer. The Cancer Genome Atlas (TCGA), UCSC Xena and Genotype Tissue Expression (GTEx) databases were used to evaluate the expression profiles, genomic features, prognostic relevance, and immune microenvironment associations of MASP1 across 33 cancer types. We observed significant dysregulation of MASP1 expression in multiple cancers, with strong associations between MASP1 expression levels and diagnostic value as well as patient prognosis. Mechanistic insights revealed significant correlations between MASP1 levels and various immunological and genomic factors, including tumor-infiltrating immune cells (TIICs), immune-related genes, mismatch repair (MMR), tumor mutation burden (TMB), and microsatellite instability (MSI), highlighting a critical regulatory function of MASP1 within the tumor immune microenvironment (TIME). In vitro and in vivo experiments demonstrated that MASP1 expression was markedly decreased in liver hepatocellular carcinoma (LIHC). Moreover, the overexpression of MASP1 in hepatocellular carcinoma (HCC) cell lines significantly inhibited their proliferation, invasion and migration. In conclusion, MASP1 exhibits differential expression in the pan-cancer analyses and might play an important role in TIME. MASP1 is a promising prognostic biomarker and a potential target for immunological research, particularly in LIHC.

Unlocking the tumor-immune microenvironment in osteosarcoma: insights into the immune landscape and mechanisms

Osteosarcoma has a unique tumor microenvironment (TME), which is characterized as a complex microenvironment comprising of bone cells, immune cells, stromal cells, and heterogeneous vascular structures. These elements are intricately embedded in a mineralized extracellular matrix, setting it apart from other primary TMEs. In a state of normal physiological function, these cell types collaborate in a coordinated manner to maintain the homeostasis of the bone and hematopoietic systems. However, in the pathological condition, i.e., neoplastic malignancies, the tumor-immune microenvironment (TIME) has been shown to promote cancer cells proliferation, migration, apoptosis and drug resistance, as well as immune escape. The intricate and dynamic system of the TIME in osteosarcoma involves crucial roles played by various infiltrating cells, the complement system, and exosomes. This complexity is closely associated with tumor cells evading immune surveillance, experiencing uncontrolled proliferation, and facilitating metastasis. In this review, we elucidate the intricate interplay between diverse cell populations in the osteosarcoma TIME, each contributing uniquely to tumor progression. From chondroblastic and osteoblastic osteosarcoma cells to osteoclasts, stromal cells, and various myeloid and lymphoid cell subsets, the comprehensive single-cell analysis provides a detailed roadmap of the complex osteosarcoma ecosystem. Furthermore, we summarize the mutations, epigenetic mechanisms, and extracellular vesicles that dictate the immunologic landscape and modulate the TIME of osteosarcoma. The perspectives of the clinical implementation of immunotherapy and therapeutic approaches for targeting immune cells are also intensively discussed.

High ratio of resident to exhausted CD4 + T cells predicts favorable prognosis and potentially better immunotherapeutic efficacy in hepatocellular carcinoma

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) are significantly implicated in regulating the tumor immune microenvironment (TIME) and immunotherapeutic response. However, little is known about the impact of the resident and exhausted status of TILs in hepatocellular carcinoma (HCC).

METHODS

Single-cell RNA sequencing data was applied to discover resident and exhausted signatures of TILs. Survival outcomes, biological function, immune infiltration, genomic variation, immunotherapeutic efficacy, and sorafenib response were further explored the clinical significance and molecular association of TILs in HCC. Moreover, a candidate gene with predictive capability for the dismal subtype was identified through univariate Cox regression analysis, survival analysis, and the BEST website.

RESULTS

Single-cell analysis revealed that CD8 + T, CD4 + T, and NK cells were strongly associated with resident and exhausted patterns. Specific resident and exhausted signatures for each subpopulation were extracted in HCC. Further multivariate Cox analysis revealed that the ratio of resident to exhausted CD4 + T cells in TIME was an independent prognostic factor. After incorporating tumor purity with the ratio of resident to exhausted CD4 + T cells, we stratified HCC patients into three subtypes and found that (i) CD4 residencyhighexhaustionlow subtype was endowed with favorable prognosis, immune activation, and sensitivity to immunotherapy; (ii) CD4 exhaustionhighresidencylow subtype was characterized by genome instability and sensitivity to sorafenib; (iii) Immune-desert subtype was associated with malignant-related pathways and poor prognosis. Furthermore, spindle assembly abnormal protein 6 homolog (SASS6) was identified as a key gene, which accurately predicted the immune-desert subtype. Prognostic analysis as well as in vitro and in vivo experiments further demonstrated that SASS6 was closely associated with tumor prognosis, proliferation, and migration.

CONCLUSIONS

The ratio of resident to exhausted CD4 + T cells shows promise as a potential biomarker for HCC prognosis and immunotherapy response and SASS6 may serve as a biomarker and therapeutic target for prognostic assessment of HCC.

CBioProfiler: A Web and Standalone Pipeline for Cancer Biomarker and Subtype Characterization

Cancer is a leading cause of death worldwide, and the identification of biomarkers and subtypes that can predict the long-term survival of cancer patients is essential for their risk stratification, treatment, and prognosis. However, there are currently no standardized tools for exploring cancer biomarkers or subtypes. In this study, we introduced Cancer Biomarker and subtype Profiler (CBioProfiler), a web server and standalone application that includes two pipelines for analyzing cancer biomarkers and subtypes. The cancer biomarker pipeline consists of five modules for identifying and annotating cancer survival-related biomarkers using multiple survival-related machine learning algorithms. The cancer subtype pipeline includes three modules for data preprocessing, subtype identification using multiple unsupervised machine learning methods, and subtype evaluation and validation. CBioProfiler also includes CuratedCancerPrognosisData, a novel R package that integrates reviewed and curated gene expression and clinical data from 268 studies. These studies cover 43 common blood and solid tumors and draw upon 47,686 clinical samples. The web server is available at https://www.cbioprofiler.com/ and https://cbioprofiler.znhospital.cn/CBioProfiler/, and the standalone app and source code can be found at https://github.com/liuxiaoping2020/CBioProfiler.

Chromosomal instability as an architect of the cancer stemness landscape

Despite a critical role for tumor-initiating cancer stem cells (CSCs) in breast cancer progression, major questions remain about the properties and signaling pathways essential for their function. Recent discoveries highlighting mechanisms of CSC-resistance to the stress caused by chromosomal instability (CIN) may provide valuable new insight into the underlying forces driving stemness properties. While stress tolerance is a well-known attribute of CSCs, CIN-induced stress is distinctive since levels appear to increase during tumor initiation and metastasis. These dynamic changes in CIN levels may serve as a barrier constraining the effects of non-CSCs and shaping the stemness landscape during the early stages of disease progression. In contrast to most other stresses, CIN can also paradoxically activate pro-tumorigenic antiviral signaling. Though seemingly contradictory, this may indicate that mechanisms of CIN tolerance and pro-tumorigenic inflammatory signaling closely collaborate to define the CSC state. Together, these unique features may form the basis for a critical relationship between CIN and stemness properties.

Circulating tumor DNA-based stratification strategy for chemotherapy plus PD-1 inhibitor in advanced non-small-cell lung cancer

Stratification strategies for chemotherapy plus PD-1 inhibitors in advanced non-small-cell lung cancer (NSCLC) are critically demanded. We performed high-throughput panel-based deep next-generation sequencing and low-pass whole genome sequencing on prospectively collected circulating tumor DNA (ctDNA) specimens from 460 patients in the phase 3 CHOICE-01 study at different time points. We identified predictive markers for chemotherapy plus PD-1 inhibitor, including ctDNA status and genomic features such as blood-based tumor mutational burden, intratumor heterogeneity, and chromosomal instability. Furthermore, we established an integrated ctDNA-based stratification strategy, blood-based genomic immune subtypes (bGIS) scheme, to distinguish patients who benefit from the addition of PD-1 inhibitor to first-line chemotherapy. Moreover, we demonstrated potential applications for the dynamic monitoring of ctDNA. Overall, we proposed a potential therapeutic algorithm based on the ctDNA-based stratification strategy, shedding light on the individualized management of immune-chemotherapies for patients with advanced NSCLC.

Ku70 Binding to YAP Alters PARP1 Ubiquitination to Regulate Genome Stability and Tumorigenesis

Yes-associated protein (YAP) is a central player in cancer development, with functions extending beyond its recognized role in cell growth regulation. Recent work has identified a link between YAP/transcriptional coactivator with PDZ-binding motif (TAZ) and the DNA damage response. Here, we investigated the mechanistic underpinnings of the cross-talk between DNA damage repair and YAP activity. Ku70, a key component of the nonhomologous end joining pathway to repair DNA damage, engaged in a dynamic competition with TEAD4 for binding to YAP, limiting the transcriptional activity of YAP. Depletion of Ku70 enhanced interaction between YAP and TEAD4 and boosted YAP transcriptional capacity. Consequently, Ku70 loss enhanced tumorigenesis in colon cancer and hepatocellular carcinoma (HCC) in vivo. YAP impeded DNA damage repair and elevated genome instability by inducing PARP1 degradation through the SMURF2-mediated ubiquitin-proteasome pathway. Analysis of samples from patients with HCC substantiated the link between Ku70 expression, YAP activity, PARP1 levels, and genome instability. In conclusion, this research provides insight into the mechanistic interactions between YAP and key regulators of DNA damage repair, highlighting the role of a Ku70-YAP-PARP1 axis in preserving genome stability. Significance: Increased yes-associated protein transcriptional activity stimulated by loss of Ku70 induces PARP1 degradation by upregulating SMURF2 to inhibit DNA damage, driving genome instability and tumorigenesis.

CD24 affects the immunosuppressive effect of tumor-infiltrating cells and tumor resistance in a variety of cancers

BACKGROUND

Cluster of differentiation 24 (CD24) is a highly glycosylated glycosylphosphatidylinositol (GPI)-anchored surface protein, expressed in various tumor cells, as a "don't eat me" signaling molecule in tumor immune. This study aimed to investigate the potential features of CD24 in pan-cancer.

METHODS

The correlations between 22 immune cells and CD24 expression were using TIMER analysis. R package "ESTIMATE" was used to predict the proportion of immune and stromal cells in pan-cancer. Spearman's correlation analysis was performed to evaluate the relationships between CD24 expression and immune checkpoints, chemokines, mismatch repair, tumor mutation burden and microsatellite instability, and qPCR and western blot were conducted to assess CD24 expression levels in liver hepatocellular carcinoma (LIHC). In addition, loss of function was performed for the biological evaluation of CD24 in LIHC.

RESULTS

CD24 expression was positively correlated with myeloid cells, including neutrophils and myeloid-derived suppressor cells, in various tumors, such as BLCA, HNSC-HPV, HNSC, KICH, KIRC, KIRP, TGCT, THCA, THYM, and UCEC. In contrast, anti-tumor NK cells and NKT cells showed a negative association with CD24 expression in BRCA-Her2, ESCA, HNSC-HPV, KIRC, THCA, and THYM. The top three tumors with the highest correlation between CD24 and ImmuneScore were TGCT, THCA, and SKCM. Functional enrichment analysis revealed CD24 expression was negatively associated with various immune-related pathways. Immune checkpoints and chemokines also exhibited inverse correlations with CD24 in CESC, CHOL, COAD, ESCA, READ, TGCT, and THCA. Additionally, CD24 was overexpressed in most tumors, with high CD24 expression in BRCA, LIHC, and CESC correlating with poor prognosis. The TIDE database indicated tumors with high CD24 expression, particularly melanoma, were less responsive to PD1/PD-L1 immunotherapy. Finally, CD24 knockdown resulted in impaired proliferation and cell cycle progression in LIHC.

CONCLUSION

CD24 participates in regulation of immune infiltration, influences patient prognosis and serves as a potential tumor marker.

Current development of molecular classifications of gastric cancer based on omics (Review)

Gastric cancer (GC) is a complex and heterogeneous disease with significant phenotypic and genetic variation. Traditional classification systems rely mainly on the evaluation of clinical pathological features and conventional biomarkers and might not capture the diverse clinical processes of individual GCs. The latest discoveries in omics technologies such as next‑generation sequencing, proteomics and metabolomics have provided crucial insights into potential genetic alterations and biological events in GC. Clustering strategies for identifying subtypes of GC might offer new tools for improving GC treatment and clinical trial outcomes by enabling the development of therapies tailored to specific subtypes. However, the feasibility and therapeutic significance of implementing molecular classifications of GC in clinical practice need to addressed. The present review examines the current molecular classifications, delineates the prevailing landscape of clinically relevant molecular features, analyzes their correlations with traditional GC classifications, and discusses potential clinical applications.

Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer

Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34-7.3; HR = 2.24 and 95% CI = 1.28-3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11-4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution.

Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package

Understanding cancer mechanisms, defining subtypes, predicting prognosis and assessing therapy efficacy are crucial aspects of cancer research. Gene-expression signatures derived from bulk gene expression data have played a significant role in these endeavors over the past decade. However, recent advancements in high-resolution transcriptomic technologies, such as single-cell RNA sequencing and spatial transcriptomics, have revealed the complex cellular heterogeneity within tumors, necessitating the development of computational tools to characterize tumor mass heterogeneity accurately. Thus we implemented signifinder, a novel R Bioconductor package designed to streamline the collection and use of cancer transcriptional signatures across bulk, single-cell, and spatial transcriptomics data. Leveraging publicly available signatures curated by signifinder, users can assess a wide range of tumor characteristics, including hallmark processes, therapy responses, and tumor microenvironment peculiarities. Through three case studies, we demonstrate the utility of transcriptional signatures in bulk, single-cell, and spatial transcriptomic data analyses, providing insights into cell-resolution transcriptional signatures in oncology. Signifinder represents a significant advancement in cancer transcriptomic data analysis, offering a comprehensive framework for interpreting high-resolution data and addressing tumor complexity.

Histone lactylation dynamics: Unlocking the triad of metabolism, epigenetics, and immune regulation in metastatic cascade of pancreatic cancer

Cancer cells rewire metabolism to sculpt the immune tumor microenvironment (TME) and propel tumor advancement, which intricately tied to post-translational modifications. Histone lactylation has emerged as a novel player in modulating protein functions, whereas little is known about its pathological role in pancreatic ductal adenocarcinoma (PDAC) progression. Employing a multi-omics approach encompassing bulk and single-cell RNA sequencing, metabolomics, ATAC-seq, and CUT&Tag methodologies, we unveiled the potential of histone lactylation in prognostic prediction, patient stratification and TME characterization. Notably, "LDHA-H4K12la-immuno-genes" axis has introduced a novel node into the regulatory framework of "metabolism-epigenetics-immunity," shedding new light on the landscape of PDAC progression. Furthermore, the heightened interplay between cancer cells and immune counterparts via Nectin-2 in liver metastasis with elevated HLS unraveled a positive feedback loop in driving immune evasion. Simultaneously, immune cells exhibited altered HLS and autonomous functionality across the metastatic cascade. Consequently, the exploration of innovative combination strategies targeting the metabolism-epigenetics-immunity axis holds promise in curbing distant metastasis and improving survival prospects for individuals grappling with challenges of PDAC.

The Spiral Model of Evolution: Stable Life Forms of Organisms and Unstable Life Forms of Cancers

If one must prioritize among the vast array of contributing factors to cancer evolution, environmental-stress-mediated chromosome instability (CIN) should easily surpass individual gene mutations. CIN leads to the emergence of genomically unstable life forms, enabling them to grow dominantly within the stable life form of the host. In contrast, stochastic gene mutations play a role in aiding the growth of the cancer population, with their importance depending on the initial emergence of the new system. Furthermore, many specific gene mutations among the many available can perform this function, decreasing the clinical value of any specific gene mutation. Since these unstable life forms can respond to treatment differently than stable ones, cancer often escapes from drug treatment by forming new systems, which leads to problems during the treatment for patients. To understand how diverse factors impact CIN-mediated macroevolution and genome integrity-ensured microevolution, the concept of two-phased cancer evolution is used to reconcile some major characteristics of cancer, such as bioenergetic, unicellular, and multicellular evolution. Specifically, the spiral of life function model is proposed, which integrates major historical evolutionary innovations and conservation with information management. Unlike normal organismal evolution in the microevolutionary phase, where a given species occupies a specific location within the spiral, cancer populations are highly heterogenous at multiple levels, including epigenetic levels. Individual cells occupy different levels and positions within the spiral, leading to supersystems of mixed cellular populations that exhibit both macro and microevolution. This analysis, utilizing karyotype to define the genetic networks of the cellular system and CIN to determine the instability of the system, as well as considering gene mutation and epigenetics as modifiers of the system for information amplification and usage, explores the high evolutionary potential of cancer. It provides a new, unified understanding of cancer as a supersystem, encouraging efforts to leverage the dynamics of CIN to develop improved treatment options. Moreover, it offers a historically contingent model for organismal evolution that reconciles the roles of both evolutionary innovation and conservation through macroevolution and microevolution, respectively.

Clinical and molecular correlates of tumor aneuploidy in metastatic non-small cell lung cancer

Recent studies have linked elevated tumor aneuploidy to anti-tumor immune suppression and adverse survival following immunotherapy. Herein, we provide supportive evidence for tumor aneuploidy as a biomarker of response to immunotherapy in patients with non-small cell lung cancer (NSCLC). We identify a dose-response relationship between aneuploidy score and patient outcomes. In two independent NSCLC cohorts (n = 659 patients), we demonstrate a novel association between elevated aneuploidy and non-smoking-associated oncogenic driver mutations. Lastly, we report enrichment of TERT amplification and immune-suppressive phenotypes of highly aneuploid NSCLC. Taken together, our findings emphasize a potentially critical role for tumor aneuploidy in guiding immunotherapy treatment strategies.

Chromosomal copy number based stratification of gastric cancer has added prognostic value to Lauren's histological classification

BACKGROUND

The Cancer Genome Atlas (TCGA) recognizes four molecular subgroups of gastric cancer: Epstein-Barr virus (EBV) positive, microsatellite instable (MSI), genomically stable (GS), and chromosomal instable (CIN). Since a GS/CIN classifier is lacking, alternative markers such as Lauren's histopathology or CDH1/p53 immunohistochemistry are commonly applied. Here we compared survival of gastric cancer subgroups determined by four methods.

METHODS

309 EBV negative and microsatellite stable tumors were included from the Dutch D1/D2 trial and assigned to subgroups by: (i) TCGA's specific chromosomal copy number aberrations, (ii) genome instability index (GII), (iii) Lauren's classification, and (iv) CDH1/p53 immunohistochemistry. Subgroups were associated with cancer-related survival (CRS).

RESULTS

Five-year CRS was 42.0% for diffuse and 49.5% for patients with intestinal type tumors, and 57.8% for GS and 41.6% for patients with CIN tumors. Classification by GII or CDH1/p53 IHC did not correlate with CRS. The combination of TCGA and Lauren classifications resulted in four distinct subgroups. Five-year CRS for GS-intestinal (n = 24), GS-diffuse (n = 57), CIN-intestinal (n = 142) and CIN-diffuse (n = 86) was 61.4%, 56.5%, 47.6%, and 31.5%, respectively.

CONCLUSIONS

TCGA's GS and CIN subgroups have additional prognostic value to Lauren's classification in resectable gastric cancer. GS-intestinal, GS-diffuse, CIN-intestinal and CIN-diffuse are suggested stratification variables for future studies.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Patterns of Aneuploidy and Signaling Consequences in Cancer

Aneuploidy, or a change in the number of whole chromosomes or chromosome arms, is a near-universal feature of cancer. Chromosomes affected by aneuploidy are not random, with observed cancer-specific and tissue-specific patterns. Recent advances in genome engineering methods have allowed the creation of models with targeted aneuploidy events. These models can be used to uncover the downstream effects of individual aneuploidies on cancer phenotypes including proliferation, apoptosis, metabolism, and immune signaling. Here, we review the current state of research into the patterns of aneuploidy in cancer and their impact on signaling pathways and biological processes.

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

A major goal of cancer biology is to understand the mechanisms underlying tumorigenesis driven by somatically acquired mutations. Two distinct types of computational methodologies have emerged: one focuses on analyzing clustering of mutations within protein sequences and 3D structures, while the other characterizes mutations by leveraging the topology of protein-protein interaction network. Their insights are largely non-overlapping, offering complementary strengths. Here, we established a unified, end-to-end 3D structurally-informed protein interaction network propagation framework, NetFlow3D, that systematically maps the multiscale mechanistic effects of somatic mutations in cancer. The establishment of NetFlow3D hinges upon the Human Protein Structurome, a comprehensive repository we compiled that incorporates the 3D structures of every single protein as well as the binding interfaces of all known protein interactions in humans. NetFlow3D leverages the Structurome to integrate information across atomic, residue, protein and network levels: It conducts 3D clustering of mutations across atomic and residue levels on protein structures to identify potential driver mutations. It then anisotropically propagates their impacts across the protein interaction network, with propagation guided by the specific 3D structural interfaces involved, to identify significantly interconnected network "modules", thereby uncovering key biological processes underlying disease etiology. Applied to 1,038,899 somatic protein-altering mutations in 9,946 TCGA tumors across 33 cancer types, NetFlow3D identified 1,4444 significant 3D clusters throughout the Human Protein Structurome, of which ~55% would not have been found if using only experimentally-determined structures. It then identified 26 significantly interconnected modules that encompass ~8-fold more proteins than applying standard network analyses. NetFlow3D and our pan-cancer results can be accessed from http://netflow3d.yulab.org.

The impact of CLDN18.2 expression on effector cells mediating antibody-dependent cellular cytotoxicity in gastric cancer

Activating antibody-dependent cellular cytotoxicity (ADCC) by targeting claudin-18 isoform 2 (CLDN18.2) using zolbetuximab, a monoclonal antibody against CLDN18.2, has been considered a promising novel therapeutic strategy for gastric cancer (GC). However, the impact of CLDN18.2 expression on natural killer (NK) cells and monocytes/macrophages-crucial effector cells of ADCC-in GC has not been fully investigated. In the present study, we assessed the impact of CLDN18.2 expression on clinical outcomes, molecular features, and the frequencies of tumor-infiltrating NK cells and macrophages, as well as peripheral blood NK cells and monocytes, in GC by analyzing our own GC cohorts. The expression of CLDN18.2 did not significantly impact clinical outcomes of GC patients, while it was significantly and positively associated with Epstein-Barr virus (EBV) status and PD-L1 expression. The frequencies of tumor-infiltrating NK cells and macrophages, as well as peripheral blood NK cells and monocytes, were comparable between CLDN18.2-positive and CLDN18.2-negative GCs. Importantly, both CLDN18.2 expression and the number of tumor-infiltrating NK cells were significantly higher in EBV-associated GC compared to other molecular subtypes. Our findings support the effectiveness of zolbetuximab in CLDN18.2-positive GC, and offer a novel insight into the treatment of this cancer type, highlighting its potential effectiveness for CLDN18.2-positive/EBV-associated GC.

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

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

The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

SIGNIFICANCE

We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/ .

Antitumor immunity and prognosis value elicited by FAT3 and LRP1B co-mutation in endometrial cancer

OBJECTIVE

FAT3 and LRP1B are two tumor suppressor genes with high mutation frequency in multiple cancer types, we sought to investigate the prognostic and immunological significance of these two genes in EC.

METHODS

Based on a cohort of 502 EC samples, we conducted a comprehensive analysis of its multidimensional data types including genomic, transcriptomic, and clinical information, the potential impact of FAT3 and LRP1B co-mutation on antitumor immune response and prognosis were systematically discussed.

RESULTS

We observed that FAT3 and LRP1B co-mutation was not only defined a dataset with prominently increased TMB, decreased tumor aneuploidy, and specially enriched in MSI-H subtype, but also manifested increased expression of immune-related markers, especially exclusive upregulation of PD-L1 levels and higher PD-L1+/CD8A+ proportion. Further analysis focused on lymphocyte infiltration and pathway enrichment explored the immune cell composition of the microenvironment and underlying molecular mechanisms affecting tumor development. Furthermore, EC patients with FAT3 and LRP1B co-mutation possessed significantly prolonged PFS and OS, and the co-mutation status was proved to be an independent prognostic factor. And a nomogram with high predictive performance was constructed by incorporating co-mutation with clinical features. More strikingly, the prognosis of MSI-H patients in EC with co-mutation was significantly improved, and their survival reached a level consistent with the POLE subtype.

CONCLUSIONS

In endometrial cancer, co-mutation of FAT3 and LRP1B not only leads to activation of the immune state, but also represents a subgroup with an improved prognosis, particularly in the MSI-H subtype.

Cancer-wide in silico analyses using differentially expressed genes demonstrate the functions and clinical relevance of JAG, DLL, and NOTCH

Notch ligands [jagged (JAG) and, delta-like (DLL) families] and receptors [NOTCH family] are key regulators of Notch signaling. NOTCH signaling contributes to vascular development, tissue homeostasis, angiogenesis, and cancer progression. To elucidate the universal functions of the JAG, DLL, and NOTCH families and their connections with various biological functions, we examined 15 types of cancer using The Cancer Genome Atlas clinical database. We selected the differentially expressed genes (DEGs), which were positively correlated to the JAG, DLL, and NOTCH families in each cancer. We selected positive and negative hallmark signatures across cancer types. These indicated biological features associated with angiogenesis, hypoxia, KRAS signaling, cell cycle, and MYC targets by gene ontology and gene set enrichment analyses using DEGs. Furthermore, we analyzed single-cell RNA sequencing data to examine the expression of JAG, DLL, and NOTCH families and enrichment of hallmark signatures. Positive signatures identified using DEGs, such as KRAS signaling and hypoxia, were enriched in clusters with high expression of JAG, DLL, and NOTCH families. We subsequently validated the correlation between the JAG, DLL, and NOTCH families and clinical stages, including treatment response, metastasis, and recurrence. In addition, we performed survival analysis to identify hallmark signatures that critically affect patient survival when combining the expression of JAG, DLL, and NOTCH families. By combining the DEG enrichment and hallmark signature enrichment in survival analysis, we suggested unexplored regulatory functions and synergistic effects causing synthetic lethality. Taken together, our observations demonstrate the functions of JAG, DLL, and NOTCH families in cancer malignancy and provide insights into their molecular regulatory mechanisms.

The genomic landscape of lung cancer in never-smokers from the Women's Health Initiative

Over 200,000 individuals are diagnosed with lung cancer in the United States every year, with a growing proportion of cases, especially lung adenocarcinoma, occurring in individuals who have never smoked. Women over the age of 50 comprise the largest affected demographic. To understand the genomic drivers of lung adenocarcinoma and therapeutic response in this population, we performed whole genome and/or whole exome sequencing on 73 matched lung tumor/normal pairs from postmenopausal women who participated in the Women's Health Initiative. Somatic copy number alterations showed little variation by smoking status, suggesting that aneuploidy may be a general characteristic of lung cancer regardless of smoke exposure. Similarly, clock-like and APOBEC mutation signatures were prevalent but did not differ in tumors from smokers and never-smokers. However, mutations in both EGFR and KRAS showed unique allelic differences determined by smoking status that are known to alter tumor response to targeted therapy. Mutations in the MYC-network member MGA were more prevalent in tumors from smokers. Fusion events in ALK, RET, and ROS1 were absent, likely due to age-related differences in fusion prevalence. Our work underscores the profound effect of smoking status, age, and sex on the tumor mutational landscape and identifies areas of unmet medical need.

Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer

Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.

Timing of whole genome duplication is associated with tumor-specific MHC-II depletion in serous ovarian cancer

Whole genome duplication is frequently observed in cancer, and its prevalence in our prior analysis of end-stage, homologous recombination deficient high grade serous ovarian cancer (almost 80% of samples) supports the notion that whole genome duplication provides a fitness advantage under the selection pressure of therapy. Here, we therefore aim to identify potential therapeutic vulnerabilities in primary high grade serous ovarian cancer with whole genome duplication by assessing differentially expressed genes and pathways in 79 samples. We observe that MHC-II expression is lowest in tumors which have acquired whole genome duplication early in tumor evolution, and further demonstrate that reduced MHC-II expression occurs in subsets of tumor cells rather than in canonical antigen-presenting cells. Early whole genome duplication is also associated with worse patient survival outcomes. Our results suggest an association between the timing of whole genome duplication, MHC-II expression and clinical outcome in high grade serous ovarian cancer that warrants further investigation for therapeutic targeting.

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.