High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types

A novel glycogene-related signature for prognostic prediction and immune microenvironment assessment in kidney renal clear cell carcinoma

BACKGROUND

Kidney Renal Clear Cell Carcinoma (KIRC) is a prevalent urinary malignancies worldwide. Glycosylation is a key post-translational modification that is essential in cancer progression. However, its relationship with prognosis, tumour microenvironment (TME), and treatment response in KIRC remains unclear.

METHOD

Expression profiles and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering, Cox regression, and LASSO regression analyses were conducted to develop an optimal glycogene-related signature. The prognostic relevance of this molecular signature was rigorously analyzed, along with its connections to tumour microenvironment (TME), tumour mutation burden, immune checkpoint activity, cancer-immunity cycle regulation, immunomodulatory gene expression patterns, and therapeutic response profiles. Validation was performed using real-world clinical specimens, quantitative PCR (qPCR), and immunohistochemistry (IHC), supported by cohort analyses from the Human Protein Atlas (HPA) database.

RESULTS

A glycogene-associated prognostic scoring system was established to categorize patients into risk-stratified subgroups. Patients in the high-risk cohort exhibited significantly poorer survival outcomes (p < 0.001). By incorporating clinicopathological variables into this framework, we established a predictive nomogram demonstrating strong calibration and a concordance index (C-index) of 0.78. The high-risk subgroup displayed elevated immune infiltration scores (p < 0.001), upregulated expression of immune checkpoint-related genes (p < 0.05), and an increased frequency of somatic mutations (p = 0.043). The risk score positively correlated with cancer-immunity cycle activation and immunotherapy-related signals. The high-risk groups also showed associations with T cell exhaustion, immune-activating genes, chemokines, and receptors. Drug sensitivity analysis revealed that low-risk patients were more sensitive to sorafenib, pazopanib, and erlotinib, whereas high-risk individuals responded better to temsirolimus (p < 0.01). qPCR and IHC analyses consistently revealed distinct expression patterns of MX2 and other key genes across the risk groups, further corroborated by the HPA findings.

CONCLUSION

This glycogene-based signature provides a robust tool for predicting prognosis, TME characteristics, and therapeutic responses in KIRC, offering potential clinical utility in patient management.

Biomarkers in metastatic castration-resistant prostate cancer for efficiency of immune checkpoint inhibitors

Almost all patients with prostate cancer progress to metastatic castration-resistant prostate cancer (mCRPC) despite initial responses. In cases where traditional first-line treatments prove ineffective, the potential of immune checkpoint blockade (ICB) therapy emerges as a promising approach for managing mCRPC. However, while immune checkpoint inhibitor monotherapy or combination therapy targeting cytotoxic T lymphocyte antigen 4 (CTLA-4) and/or programmed cell death-1 (PD-1)/PD-1 ligand 1 (PD-L1) axis has been regarded as the standard therapy in many solid tumours, mCRPC as 'cold' tumours are considered to be relatively resistant to ICB treatment. Encouragingly, recent evidence suggests that ICB therapy may be particularly beneficial in specific subgroups of patients with high PD-L1 tumour expression, high tumour mutational burden or high tumour microsatellite instability/mismatch repair deficiency. Better understanding of these predictive biomarkers could screen which patients are most likely to benefit. This review article examines biomarkers for screening patients potentially effective in immune checkpoint inhibitor therapy.

Neurogranin facilitates maintaining the immunosuppressive state of hepatocellular carcinoma by promoting TGF-β1 secretion

Immunotherapy has revolutionized cancer treatment, but its effectiveness is limited due to the complexity of the tumor immune microenvironment. Identifying reliable biomarkers that can predict immunotherapy response is essential for enhancing treatment strategies. This study evaluated the potential of Neurogranin (NRGN) as a biomarker for prognosis and immunotherapy response across multiple cancers. Through pan-cancer bioinformatics analyses, coupled with in vitro and in vivo experiments, we explored NRGN's differential expression across various cancer types and its role in the immune microenvironment. Our approach involved database mining, immune genomic feature correlation analyses, and functional validation through NRGN knockdown and overexpression studies. The results revealed differential NRGN expression across cancers, particularly hepatocellular carcinoma (HCC), where elevated levels correlated with immune evasion, poor prognosis, and upregulation of checkpoint genes like TGFB1. NRGN modulated T cell activity and macrophage polarization by regulating the TGF-β pathway through interaction with TCF4 and promoting its nuclear localization, driving tumor progression. Targeting TGF-β with anti-TGF-β and anti-PD-1 antibodies additively inhibited HCC in an Nrgn-dependent manner in mice. These findings indicate that NRGN may serve as a promising immunotherapeutic target, as its overexpression predicts poor prognosis and immune evasion, thereby offering insights for improving immunotherapy and developing new treatments.

Deep learning radiomics analysis for prediction of survival in patients with unresectable gastric cancer receiving immunotherapy

Objective

Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.

Method

Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.

Result

A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10-0.37, P < 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.

Conclusion

The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.

Identification of CXCL13 as a Promising Biomarker for Immune Checkpoint Blockade Therapy and PARP Inhibitor Therapy in Ovarian Cancer

Ovarian cancer has poor response rates to immune checkpoint blockade (ICB) therapy, despite the use of genomic sequencing to identify molecular targets. Homologous recombination deficiency (HRD) is a conventional indicator of genomic instability (GI) and has been used as a marker for targeted therapies. Indicators reflecting HRD status have shown potential in predicting the efficacy of ICB treatment. Public databases, including TCGA, ICGC, and GEO, were used to obtain data. HRD scores, neoantigen load, and TMB were obtained from the TCGA cohort. Candidate biomarkers were validated in multiple databases, such as the Imvigor210 immunotherapy cohort and the open-source single-cell sequencing database. Immunohistochemistry was performed to further validate the results in independent cohorts. CXCL10, CXCL11, and CXCL13 were found to be significantly upregulated in HRD tumors and exhibited prognostic value. A comprehensive analysis of the tumor immune microenvironment (TIME) revealed that CXCL13 expression positively correlated with neoantigen load and immune cell infiltration. In addition, single-cell sequencing data and clinical trial results supported the utility of CXCL13 as a biomarker for ICB therapy. Not only does CXCL13 serve as a biomarker reflecting HRD status, but it also introduces a potentially novel perspective on prognostic biomarkers for ICB in ovarian cancer.

Immunotherapy in breast cancer: current landscape and emerging trends

Breast cancer remains one of the most prevalent malignancies worldwide, underscoring an urgent need for innovative therapeutic strategies. Immunotherapy has emerged as a transformative frontier in this context. In triple-negative breast cancer (TNBC), the combination of immunotherapy based on PD-1/PD-L1 immune checkpoint inhibitors (ICIs) with chemotherapy has proven efficacious in both early and advanced clinical trials. These encouraging results have led to the approval of ICIs for TNBC, opening up new therapeutic avenues for challenging-to-treat patient populations. Furthermore, a multitude of ongoing trials are actively investigating the efficacy of immunotherapy-based combinations, including ICIs in conjunction with chemotherapy, targeted therapy and radiation therapy, as well as other novel strategies such as bispecific antibodies, CAR-T cells and cancer vaccines across all breast cancer subtypes, including HR-positive/HER2-negative and HER2-positive disease. This review provides a comprehensive overview of current immunotherapeutic approaches in breast cancer, highlighting pivotal findings from recent clinical trials and the potential impact of these advancements on patient outcomes.

Comprehensive analysis Neddylation-related genes identified UBB as a prognostic biomarker for clear cell renal cell carcinoma

Neddylation, as a type of post-translational modification, plays a key role in cancer development. However, the biological characteristics and clinical prognosis value of Neddylation-related genes (NRGs) signatures in clear cell renal cell carcinoma (ccRCC) remain undetermined. Here, we identified two subtypes of NRGs in ccRCC based on TCGA data and constructed a NRGs risk signature (NRGS). Survival analysis, ROC curves, and nomograms showed that NRGS was an important predictor of prognosis in patients with clear cell renal cell carcinoma. We further revealed important correlations between NRGS and clinicopathological features, gene mutations, drug sensitivity, and immune cell infiltration. High NRGS indicates a poorer prognosis for kidney cancer, but higher remission rates with immunotherapy. Drug sensitivity also varies across risk groups. UBB was identified as a hub gene for NRGS and was downregulated in ccRCC, which is associated with poor prognosis. In conclusion, this study provides strategies for predicting prognosis and individualizing treatment for ccRCC.

State-of-the-art in Metastatic Uveal Melanoma Treatment: A 2025 Update : How to treat Metastatic Uveal Melanoma in 2025

PURPOSE OF REVIEW

Uveal melanoma (UM) is the most common intraocular malignancy in adults, representing a rare but aggressive melanoma subtype with a distinct molecular landscape, unique metastatic behavior and limited therapeutic options in the metastatic setting. This review provides an in-depth analysis of the latest evidence on the evolving treatment landscape of metastatic UM.

RECENT FINDINGS

For liver-only metastatic disease, locoregional therapies provide significant benefit compared to systemic therapies. The recent approval of tebentafusp-tebn, a bispecific gp100 peptide-HLA-directed CD3 T-cell engager, marks a pivotal advancement for HLA-A*02:01-positive patients with unresectable/metastatic UM, demonstrating a clinically significant survival benefit. Several clinical studies are currently active, examining emerging locoregional and systemic treatments for metastatic UM, with promising early data. Despite effective local disease control through radiotherapy and enucleation, approximately 50% of patients develop metastatic disease, predominantly in the liver, with a median survival of less than one year. The approval of tebentafusp represents a landmark achievement in UM treatment, while promising experimental combinations have demonstrated clinical utility in late phase clinical trials, offering hope for further improvement in patient survival.

Efficacy of nivolumab + ipilimumab ± chemotherapy versus pembrolizumab + chemotherapy in patients with PD-L1-negative non-small cell lung cancer (START001 PART-B): a multicenter retrospective observational study

BACKGROUND

Programmed death ligand 1 (PD-L1) serves as a crucial biomarker for predicting the efficacy of immune checkpoint inhibitors in patients with non-small cell lung cancer (NSCLC). This study aimed to identify the most suitable first-line treatment regimen for patients with PD-L1 expression <1% (PD-L1-negative) NSCLC by comparing nivolumab plus ipilimumab (NI), NI combined with chemotherapy (NICT), and pembrolizumab and chemotherapy (PCT).

METHODS

We analyzed data from 141 patients with PD-L1-negative NSCLC treated with NI, NICT, or PCT at 14 Japanese institutions between December 2020 and November 2022. Propensity score analysis was employed to minimize selection bias, and Kaplan-Meier analysis and Cox proportional hazards regression were used to evaluate progression-free survival (PFS) and overall survival (OS).

RESULTS

Neither NI nor NICT demonstrated superior PFS or OS than PCT. Subgroup analyses revealed no significant differences between treatment groups across age, histological subtypes, or clinical features. Results from propensity score matching and inverse probability of treatment weighting were consistent with those observed in the overall cohort. Moreover, safety profiles showed that PCT was associated with the lowest rates of treatment discontinuation and immune-related adverse events requiring systemic corticosteroid therapy.

CONCLUSIONS

In patients with PD-L1-negative NSCLC, the efficacy of NI and NICT was not superior to that of PCT. Thus, we concluded that PCT could be a favorable treatment option for this patient population.

A redefined InDel taxonomy provides insights into mutational signatures

Despite their deleterious effects, small insertions and deletions (InDels) have received far less attention than substitutions. Here we generated isogenic CRISPR-edited human cellular models of postreplicative repair dysfunction (PRRd), including individual and combined gene edits of DNA mismatch repair (MMR) and replicative polymerases (Pol ε and Pol δ). Unique, diverse InDel mutational footprints were revealed. However, the prevailing InDel classification framework was unable to discriminate these InDel signatures from background mutagenesis and from each other. To address this, we developed an alternative InDel classification system that considers flanking sequences and informative motifs (for example, longer homopolymers), enabling unambiguous InDel classification into 89 subtypes. Through focused characterization of seven tumor types from the 100,000 Genomes Project, we uncovered 37 InDel signatures; 27 were new. In addition to unveiling previously hidden biological insights, we also developed PRRDetect-a highly specific classifier of PRRd status in tumors, with potential implications for immunotherapies.

Artificial intelligence-driven microsatellite instability profiling reveals distinctive genetic features in patients with lung cancer

BACKGROUND

Microsatellite instability (MSI) has emerged as a predictive biomarker for immunotherapy response in various cancers, but its role in non-small cell lung cancer (NSCLC) is not fully understood.

METHODS

The authors used the bioinformatics tool MIAmS to assess microsatellite status from next-generation sequencing (NGS) data using a tailored microsatellite score. Immunohistochemistry (IHC) assays were also performed to evaluate the correspondence between MSI and deficient mismatch repair (dMMR) status. A retrospective analysis of 1547 lung cancer patients was conducted, focusing on those with an MSI phenotype. Clinical characteristics, co-occurring molecular alterations, tumor mutation burden (TMB), and homologous recombination deficiency (HRD) status were evaluated in this subset.

RESULTS

Of the 1547 patients analyzed, eight (0.52%) were identified as having MSI through MIAmS, with six (0.39%) of these cases also being dMMR on IHC. All patients with dMMR had an MS score ≥2 and a history of smoking. Most patients showed loss of MLH1 and PMS2 staining on IHC. No correlation was found between MSI status and programmed death-ligand 1 expression, although all MSI patients exhibited high TMB, averaging 21.4 ± 5.6 mutations per megabase.

DISCUSSION

MSI/dMMR in lung cancer is exceedingly rare, affecting less than 1% of cases. NGS-based analysis combined with bioinformatics tools provides a robust method to identify MSI/dMMR patients, potentially guiding immunotherapy decisions. This comprehensive approach integrates molecular genotyping and MSI detection, offering personalized treatment options for lung cancer patients. NGS-based MSI testing is emerging as the preferred method for detecting microsatellite instability in various tumor types, including rare cancers.

Advancing precision oncology with AI-powered genomic analysis

Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.

Whole-exome tumor-agnostic ctDNA analysis enhances minimal residual disease detection and reveals relapse mechanisms in localized colon cancer

In stage 2-3 colon cancer (CC), postsurgery circulating tumor DNA (ctDNA) assessment is crucial for guiding adjuvant chemotherapy (ACT) decisions. While existing assays detect ctDNA and help identify high-risk persons with CC for recurrence, their limited sensitivity after surgery poses challenges in deciding on ACT. Additionally, a substantial portion of persons with CC fail to clear ctDNA after ACT, leading to recurrence. In this study, we performed whole-exome sequencing (WES) of ctDNA at different time points in participants with relapsed CC in two independent cohorts, alongside transcriptomic and proteomic analyses of metastases, to enhance comprehension of progression mechanisms. A plasma WES-based tumor-agnostic assay demonstrated higher sensitivity in detecting minimal residual disease (MRD) compared to current assays. Immune evasion appears to be the primary driver of progression in the localized CC setting, indicating the potential efficacy of immunotherapy for microsatellite stability in persons with CC. Organoid modeling further supports the promising potential of targeted therapy in eradicating MRD, surpassing conventional treatments.

Latest Advances in Perioperative care for Resectable Non-small lung cancer

Resectable non-small cell lung cancer (NSCLC) has a relatively poor prognosis owing to the risk of developing local or distant metastatic recurrence, even at stage I. To overcome the high recurrence rate, perioperative therapies have been rapidly developed through the combination of existing cytotoxic chemotherapies with immune checkpoint inhibitors (ICIs) and molecular targeted therapies. These new therapeutic strategies have significantly improved the prognosis of patients with stage II-III NSCLC and have been approved for clinical use. However, new challenges have emerged in the selection of the optimal perioperative treatment in clinical practice. First, it is currently difficult to determine which perioperative treatment is superior, preoperative or postoperative. Additionally, since surgery alone is curative in some patients, the addition of anticancer agents such as ICIs raises concerns regarding toxicity, as serious side effects during preoperative treatment may lead to an inability to perform the surgery itself. Moreover, because various perioperative treatments are still being developed, treatment options for perioperative care are expected to increase soon. To summarize the increasingly complex perioperative treatment of resectable NSCLC, this review provides a comprehensive summary of the clinical efficacies of current perioperative therapies and future directions based on basic background, patient selection, ongoing trials, and enhancing immunotherapy.

Tumor-derived erythropoietin acts as an immunosuppressive switch in cancer immunity

Successful cancer immunotherapy requires a patient to mount an effective immune response against tumors; however, many cancers evade the body's immune system. To investigate the basis for treatment failure, we examined spontaneous mouse models of hepatocellular carcinoma (HCC) with either an inflamed T cell-rich or a noninflamed T cell-deprived tumor microenvironment (TME). Our studies reveal that erythropoietin (EPO) secreted by tumor cells determines tumor immunotype. Tumor-derived EPO autonomously generates a noninflamed TME by interacting with its cognate receptor EPOR on tumor-associated macrophages (TAMs). EPO signaling prompts TAMs to become immunoregulatory through NRF2-mediated heme depletion. Removing either tumor-derived EPO or EPOR on TAMs leads to an inflamed TME and tumor regression independent of genotype, owing to augmented antitumor T cell immunity. Thus, the EPO/EPOR axis functions as an immunosuppressive switch for antitumor immunity.

PD-1/PD-L1 blockade therapy in hepatocellular carcinoma: Current status and potential biomarkers

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related death and the sixth most prevalent cancer worldwide. However, most patients with HCC are at an advanced stage at the time of clinical diagnosis, making surgery impossible. In the past, targeted therapeutic drugs such as sorafenib and lenvatinib were the main treatments. With recent breakthroughs in medicine, immunotherapy, particularly immune checkpoint inhibitors (ICIs), has garnered interest and has been extensively studied for clinical treatment. In addition to single-agent therapies, combination regimens involving ICIs have also been developed. Despite this progress, not all patients with HCC benefit from immunotherapy. Therefore, to improve the treatment response rates, it is crucial to identify patients with HCC who are suitable for immunotherapy. The exploration and validation of markers to predict the outcomes of immunotherapeutic treatments in patients with HCC are of clinical importance. In this article, we provide a comprehensive review of research progress in immunotherapy, particularly ICIs and combination therapies, for HCC. Furthermore, we summarize the clinical indicators and tumor markers discovered in recent years to forecast immunotherapy outcomes in patients with HCC. We also outline predictive markers for the occurrence of immune-related adverse events in patients with HCC receiving immunotherapy and discuss future research directions in the immunotherapeutic treatment landscape.

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

Transforming treatment paradigms: Focus on personalized medicine for high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of ovarian cancer, accounting for approximately 70% of all ovarian cancer cases and contributing significantly to the high mortality rates associated with this disease. Because of the asymptomatic nature of early stage disease, most patients are diagnosed at advanced stages when the cancer has already spread into the abdominal cavity, requiring complex and intensive surgical and chemotherapeutic interventions followed by maintenance therapies. Although a minority of cases are associated with well defined genetic syndromes, specific risk factors and a clear etiology in many cases remain elusive. HGSOC tumors are characterized by a high frequency of somatic gene copy number alterations, often associated with defects in homologous recombination repair of DNA. All attempts to introduce an effective screening for HGSOC to date have been unsuccessful. This review elucidates the complexities surrounding HGSOC and encompasses its etiology, epidemiology, classification, pathogenesis, and the current array of treatment strategies. Understanding molecular underpinnings is crucial for the development of targeted therapies and personalized multimodal treatment approaches in centralized therapeutic structures. This review also examines the importance of the tumor microenvironment. In addition, the authors' objective is to underscore the critical importance of placing the patient's perspective and diversity at the forefront of therapeutic strategies, thereby fostering a genuinely participatory decision-making process and ultimately improving patient quality of life.

Predicting the Efficacy of Immune Checkpoint Inhibitors in Esophageal Cancer: Changes in Peripheral Blood Lymphocyte Subsets Before and After Immunotherapy

Background

Immunotherapy has demonstrated potential in the treatment of esophageal cancer (EC); however, the overall response rate (ORR) remains below 30% among EC patients. Herein, the use of peripheral blood lymphocyte subsets as biomarkers was explored to evaluate the efficacy of immunotherapy in this patient population.

Methods

Sixty-three patients were enrolled. The patients were diagnosed with EC and treated with immune checkpoint inhibitors (ICIs) at The Fourth Hospital of Hebei Medical University from December 2019 to June 2023. Kaplan-Meier (KM) survival curves were used to reflect differences in survival benefit. The prognostic factors of survival were investigated using the Cox proportional hazards regression model for both univariate and multivariate analyses. Two-tailed P values were reported and statistical significance was defined as P < 0.05.

Results

The results of univariate and multifactorial Cox regression analysis for progression-free survival (PFS) revealed that only CD8+ T lymphocytes demonstrated a significant association with PFS (P = 0.034, P = 0.020). Additionally, the multifactorial Cox regression analysis results for overall survival (OS) revealed a significant association between natural killer (NK) cells and OS (P=0.049). Further, a systematic analysis was conducted on the CD8+ T cell biomarker. The KM survival curves indicated that the group with low CD8+ T cell levels experienced a significantly greater PFS benefit compared to the high CD8+ T cell group (P = 0.030).

Conclusion

The present study reveals that the reduction of both CD8+ T lymphocytes and NK cells in peripheral blood lymphocyte subsets after immunotherapy can serve as superior predictors for the effectiveness of ICIs in patients diagnosed with EC.

Association of heightened host and tumor immunity with prolonged duration of response to checkpoint inhibition across solid tumors

Cancer immunotherapy is a beneficial therapy for many cancer types, but predictive pan-tumor biomarkers for clinical benefit are suboptimal. Our study, employing DNA and RNA based analysis, investigated the role of predicted neoantigens in the benefits of immunotherapy within a cohort of 88 patients of European descent with advanced solid tumors. Patients who had a prolonged (> 12 months) duration of immunotherapy exhibited heightened immune responses, characterized by increased levels of predicted neoantigens with strong HLA binding potential, elevated cytotoxic marker levels, and enhanced T cell activity. Furthermore, our analysis revealed associations between prolonged duration of therapy and rare variants, notably within the EPHA8 gene. These variants, exclusive to patients with a prolonged (> 12 months) duration of immunotherapy, suggest potential implications for immunotherapy response. In addition, the evolutionary conservation of these variants across vertebrate species underscores their functional importance in tumor biology and ultimately, treatment outcomes. Despite limitations in sample size and patient homogeneity, our findings emphasize the potential utility of understanding the molecular and immunological mechanisms underlying immunotherapy responses to further refine personalized treatment strategies.

Asparagine drives immune evasion in bladder cancer via RIG-I stability and type I IFN signaling

Tumor cells often employ many ways to restrain type I IFN signaling to evade immune surveillance. However, whether cellular amino acid metabolism regulates this process remains unclear, and its effects on antitumor immunity are relatively unexplored. Here, we found that asparagine inhibited IFN-I signaling and promoted immune escape in bladder cancer. Depletion of asparagine synthetase (ASNS) strongly limited in vivo tumor growth in a CD8+ T cell-dependent manner and boosted immunotherapy efficacy. Moreover, clinically approved L-asparaginase (ASNase),synergized with anti-PD-1 therapy in suppressing tumor growth. Mechanistically, asparagine can directly bind to RIG-I and facilitate CBL-mediated RIG-I degradation, thereby suppressing IFN signaling and antitumor immune responses. Clinically, tumors with higher ASNS expression show decreased responsiveness to immune checkpoint inhibitor therapy. Together, our findings uncover asparagine as a natural metabolite to modulate RIG-I-mediated IFN-I signaling, providing the basis for developing the combinatorial use of ASNase and anti-PD-1 for bladder cancer.

Progress of PD-1/PD-L1 immune checkpoint inhibitors in the treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous cancer with substantial recurrence potential. Currently, surgery and chemotherapy are the main treatments for this disease. However, chemotherapy is often limited by several factors, including low bioavailability, significant systemic toxicity, inadequate targeting, and multidrug resistance. Immune checkpoint inhibitors (ICIs), including those targeting programmed death protein-1 (PD-1) and its ligand (PD-L1), have been proven effective in the treatment of various tumours. In particular, in the treatment of TNBC with PD-1/PD-L1 inhibitors, both monotherapy and combination chemotherapy, as well as targeted drugs and other therapeutic strategies, have broad therapeutic prospects. In addition, these inhibitors can participate in the tumour immune microenvironment (TIME) through blocking PD-1/PD-L1 binding, which can improve immune efficacy. This article provides an overview of the use of PD-1/PD-L1 inhibitors in the treatment of TNBC and the progress of multiple therapeutic studies. To increase the survival of TNBC patients, relevant biomarkers for predicting the efficacy of PD-1/PD-L1 inhibitor therapy have been explored to identify new strategies for the treatment of TNBC.

CD8+ T Cell Subsets as Biomarkers for Predicting Checkpoint Therapy Outcomes in Cancer Immunotherapy

The advent of immune checkpoint blockade (ICB) has transformed cancer immunotherapy, enabling remarkable long-term outcomes and improved survival, particularly with ICB combination treatments. However, clinical benefits remain confined to a subset of patients, and life-threatening immune-related adverse effects pose a significant challenge. This limited efficacy is attributed to cancer heterogeneity, which is mediated by ligand-receptor interactions, exosomes, secreted factors, and key transcription factors. Oncogenic regulators like E2F1 and MYC drive metastatic tumor environments and intertwine with immunoregulatory pathways, impairing T cell function and reducing immunotherapy effectiveness. To address these challenges, FDA-approved biomarkers, such as tumor mutational burden (TMB) and programmed cell death-ligand 1 (PD-L1) expression, help to identify patients most likely to benefit from ICB. Yet, current biomarkers have limitations, making treatment decisions difficult. Recently, T cells-the primary target of ICB-have emerged as promising biomarkers. This review explores the relationship between cancer drivers and immune response, and emphasizes the role of CD8+ T cells in predicting and monitoring ICB efficacy. Tumor-infiltrating CD8+ T cells correlate with positive clinical outcomes in many cancers, yet obtaining tumor tissue remains complex, limiting its practical use. Conversely, circulating T cell subsets are more accessible and have shown promise as predictive biomarkers. Specifically, memory and progenitor exhausted T cells are associated with favorable immunotherapy responses, while terminally exhausted T cells negatively correlate with ICB efficacy. Ultimately, combining biomarkers enhances predictive accuracy, as demonstrated by integrating TMB/PD-L1 expression with CD8+ T cell frequency. Computational models incorporating cancer and immune signatures could further refine patient stratification, advancing personalized immunotherapy.

DDX54 downregulation enhances anti-PD1 therapy in immune-desert lung tumors with high tumor mutational burden

High tumor mutational burden (TMB-H) is a predictive biomarker for the responsiveness of cancer to immune checkpoint inhibitor (ICI) therapy that indicates whether immune cells can sufficiently recognize cancer cells as nonself. However, about 30% of all cancers from The Cancer Genome Atlas (TCGA) are classified as immune-desert tumors lacking T cell infiltration despite TMB-H. Since the underlying mechanism of these immune-desert tumors has yet to be unraveled, there is a pressing need to transform such immune-desert tumors into immune-inflamed tumors and thereby enhance their responsiveness to anti-PD1 therapy. Here, we present a systems framework for identifying immuno-oncotargets, based on analysis of gene regulatory networks, and validating the effect of these targets in transforming immune-desert into immune-inflamed tumors. In particular, we identify DEAD-box helicases 54 (DDX54) as a master regulator of immune escape in immune-desert lung cancer with TMB-H and show that knockdown of DDX54 can increase immune cell infiltration and lead to improved sensitivity to anti-PD1 therapy.

Identifying predictors of overall survival among patients with TMB-low metastatic cancer treated with immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly advanced cancer therapy, yet their efficacy in tumors with low tumor mutational burden (TMB) remains suboptimal. In this study, we aimed to elucidate the impact of somatic mutations on overall survival (OS) in TMB-low patients treated with ICIs and to explore the potential for personalized treatment selection through machine learning.

METHODS

We conducted a comprehensive analysis of 1172 TMB-low (TMB < 10 mutations per megabase) patients with cancer receiving ICIs, examining the association between specific gene mutations and OS. Additionally, we developed a decision tree model (DTM) to predict OS based on clinical features and tumor mutational profiles.

RESULTS

Our findings reveal that mutations in DAXX, HLA-A, H3C2, IGF1R, CTNNB1, SMARCA4, KMT2D, and TP53 are significantly associated with poorer survival outcomes in the multivariate analysis. Remarkably, for renal cell carcinoma (RCC) patients, VHL mutations predicted improved OS following ICI even when adjusted for age, sex, and microsatellite instability (MSI) status in both multivariate analysis and the DTM model.

CONCLUSIONS

These results reinforce the prevailing notion that TMB alone does not predict ICI response, highlighting the critical role of individual gene mutations in TMB-low tumors under ICI therapy. Furthermore, our study demonstrates the promise of machine learning models in optimizing ICI treatment decisions, paving the way for more precise and effective therapeutic strategies in this patient population.

Combined Chemotherapy-Immunotherapy for Advanced Biliary Tract Cancer (BTC): A Clinical, Genomic, and Biomarker Analysis

BACKGROUND

Biliary tract cancer (BTC) represents a heterogeneous disease spectrum associated with an unfavorable prognosis. A combination of immunotherapy and chemotherapy has become a new standard strategy for advanced BTC. However, understanding the association between genomic alterations and outcomes of immunotherapy in BTC is crucial for further improving clinical benefits.

METHOD

Patients with metastatic BTC were included in this study retrospectively, who received PD-1/PD-L1 (ICI) antibodies combined with chemotherapy. The primary endpoint was progression-free survival (PFS), and the secondary endpoints included overall response rate (ORR) and disease control rate (DCR). Additionally, we conducted exploratory analysis of genomic alterations and biomarkers.

RESULTS

Ninety-one patients were enrolled in this study. The patients were divided into two groups: albumin paclitaxel + S1 (AS) + PD-1 (n = 56) group and GC + ICI (n = 35) group. There were no significant differences in terms of PFS, ORR, and DCR between the two groups. Regarding biomarker analysis, 44 patients had positive PD-L1 expression, with a mPFS of 4.8 months and an ORR of 15.9%. Surprisingly, 29 patients had negative PD-L1 expression, with a mPFS of 9.9 months and an ORR of 27.6%. The average tumor mutational burden (TMB) was 4.5 mutations per megabase (mut/MB) for patients with microsatellite-stable (MSS) tumors. There was no significant difference in PFS between patients with TMB high and low (cutoff = 4.5 mut/MB). Genomic analysis revealed TP53 (n = 13, 43.3%), KRAS (n = 8, 26.7%), NTRK1/2/3 (n = 8, 26.7%), isocitrate dehydrogenase (IDH) 1/2 (n = 6, 20.0%), PIK3CA (n = 6, 20.0%), BRCA2 (n = 5, 16.7%), MDM2/4 (n = 5, 16.7%), and BRAF (n = 4, 13.3%) as the most common gene alterations. MDM2/4 mutations were associated with shorter survival (p < 0.05).

CONCLUSION

GC plus immunotherapy is still the standard of care for late stage BTC. PD-L1 expression and TMB were not good predictors for selecting patients who would benefit more from immunotherapy plus chemotherapy.

High tumor mutation burden mitigates the negative impact of chemotherapy history on immune checkpoint blockade therapy

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small-cell lung cancer (NSCLC) accounting for the majority of cases. Immune checkpoint inhibitor (ICI) therapy, particularly with PD-1 inhibitors like nivolumab, has become a critical treatment option for advanced NSCLC. ICI therapy has revolutionized treatment, but prior chemotherapy may diminish ICI treatment efficacy. Tumor mutation burden (TMB) has emerged as a crucial predictor of ICI response, yet its interaction with chemotherapy history in ICI therapy is not fully understood. In this study, I investigate the impact of chemotherapy history on ICI treatment outcomes, focusing on TMB as a potential mitigating factor. Analyzing data from 512 patients with advanced NSCLC treated with PD-1/PD-L1 or CTLA-4 inhibitors, this sudy found that prior chemotherapy significantly reduced objective response rates (ORR) to ICI therapy, particularly in patients with low TMB (<15 mut/Mb). However, in patients with high TMB (≥15 mut/Mb), the negative impact of chemotherapy history on ICI treatment efficacy is minimal, suggesting that high TMB mitigates chemotherapy-induced resistance to ICI therapy. Furthermore, while chemotherapy history is associated with worse overall survival (OS) and progression-free survival (PFS) following ICI therapy in low-TMB patients, no such association is observed in high-TMB patients. These findings highlight the importance of TMB as a predictive biomarker, emphasizing the need for optimal treatment sequencing and personalized therapeutic strategies to overcome chemotherapy-induced immune resistance and maximize ICI treatment efficacy. These results suggest that ICI therapy may be more beneficial as a first-line treatment, particularly for patients with low TMB.

Exploring the impact of RPN1 on tumorigenesis and immune response in cancer

The ribophorin family, including RPN1, has been associated with tumor progression, but its specific role in pan-cancer dynamics remains unclear. Using data from TCGA, GTEx, and Ualcan databases, we investigated the relationship of RPN1 with prognosis, genomic alterations, and epigenetic modifications across various cancers. Differential analysis revealed elevated RPN1 expression in multiple cancer types, indicating a potential prognostic value. Amplification was the predominant mutation type of RPN1 in pan-cancer, with notable correlations with DNA methylation and copy number variation. Gene set variation analysis identified RPN1's involvement in cancer development, immunity, and metabolism. Additionally, RPN1 expression correlated with the tumor microenvironment, immune response factors, and response to anti-tumor therapies. Functional validation in triple-negative breast cancer, glioblastoma, and bladder cancer cell lines demonstrated the role of RPN1 in tumor cell proliferation and migration. Our findings highlight RPN1 as a potential biomarker for cancer diagnosis and treatment response in pan-cancer therapy.

Overview on biomarkers for immune oncology drugs

Although immune checkpoint inhibitors (ICIs) are widely used in clinical oncology, less than half of treated cancer patients derive benefit from this therapy. Both tumor- and host-related variables are implicated in response to ICIs. The predictive value of PD-L1 expression is confined only to several cancer types, so this molecule is not an agnostic biomarker. Highly elevated tumor mutation burden (TMB) caused either by excessive carcinogenic exposure or by a deficiency in DNA repair is a reliable indicator for ICI efficacy, as exemplified by tumors with high-level microsatellite instability (MSI-H). Other potentially relevant tumor-related characteristics include gene expression signatures, pattern of tumor infiltration by immune cells, and, perhaps, some immune-response modifying somatic mutations. Host-related factors have not yet been comprehensively considered in relevant clinical trials. Microbiome composition, markers of systemic inflammation [e.g., neutrophil-to-lymphocyte ratio (NLR)], and human leucocyte antigen (HLA) diversity may influence the efficacy of ICIs. Studies on ICI biomarkers are likely to reveal modifiable tumor or host characteristics, which can be utilized to direct the antitumor immune defense. Examples of the latter approach include tumor priming to immune therapy by cytotoxic drugs and elevation of ICI efficacy by microbiome modification.

Immunotherapy for Advanced Conjunctival Squamous Cell Carcinoma: Treatment Failures

PURPOSE

To review the treatment response of advanced conjunctival squamous cell carcinoma (SCC) to systemic immune checkpoint inhibitor (ICI) therapy at a single institution.

METHODS

A retrospective review of patients treated at a single institution from 2015 to 2024 was conducted to identify those with advanced conjunctival SCC who had been treated with ICI therapy. Advanced disease included patients with orbital invasion of tumors, unresectable disease, or metastatic disease. Computed tomography imaging and tumor mutational burden data were evaluated for all patients.

RESULTS

Five patients with advanced conjunctival SCC were treated with ICIs. All patients had the American Joint Committee on Cancer stage cT3N0M0. All patients had best corrected visual acuity in the affected eye of 20/30 or better at presentation. All patients progressed while on ICIs, with 3 ultimately requiring exenteration at a median time of 6 months from initial diagnosis. One patient had progressive metastatic disease, and one had direct intracranial extension. All patients had low tumor mutational burden.

CONCLUSIONS

Unlike prior reports demonstrating complete treatment response while on ICI therapy in patients with advanced conjunctival SCC, the current study demonstrates that 5 of 5 patients had disease progression while on ICI therapy. All patients also had low tumor mutational burden. Tumor mutational burden may be important in predicting disease response to ICI in patients with conjunctival SCC; however, given the small number of patients with conjunctival SCC treated with ICI to date, more data are needed to understand the role of ICIs in conjunctival SCC.

Pan-Cancer Molecular Biomarkers: Practical Considerations for the Surgical Pathologist

Traditional anatomic pathologic classification of cancer is based on tissue of origin and morphologic and immunohistochemical characterization of the malignant cells. With the technological improvements of massively parallel or next-generation sequencing, oncogenic drivers that are shared across different tumor types are increasingly being identified and used as pan-cancer biomarkers. This approach is reflected in the growing list of Food and Drug Administration-approved tumor-agnostic therapies, including pembrolizumab in the setting of microsatellite instability and high tumor mutational burden, larotrectinib and entrectinib for solid tumors with NTRK fusions, and combined dabrafenib-trametinib for BRAF V600E-mutated neoplasms. Several other biomarkers are currently under investigation, including fibroblast growth factor receptor (FGFR), RET, and ROS1 fusions; ERBB2 amplification; and mutations in the AKT1/2/3, NF1, RAS pathway and (mitogen-activated protein kinase (MAPK) pathway. As molecular assays are increasingly incorporated into routine tumor workup, the emergence of additional pan-cancer biomarkers is likely to be a matter more of "when" than "if." In this review, we first explore some of the conceptual and technical considerations at the intersection of surgical and molecular pathology, followed by a brief overview of both established and emerging molecular pan-cancer biomarkers and their diagnostic and clinical applications.

Characterization of SUSD3 as a novel prognostic biomarker and therapeutic target for breast cancer

BACKGROUND

Breast cancer (BC) remains a significant global health challenge, contributing substantially to cancer-related deaths worldwide. Its prevalence and associated death rates remain alarmingly high, highlighting the persistent public health burden. The objective of this study was to systematically examine the involvement of SUSD3 (Sushi Domain-Containing 3) in BC, highlighting its crucial role in the pathogenesis and progression of this disease.

METHODS

BC-related gene microarray data, along with corresponding clinicopathological information, were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Leveraging TIMER and HPA databases, we conducted comparative analyses to evaluate SUSD3 expression in BC. We then analyzed the association between SUSD3 and clinical traits, as well as the prognostic value of SUSD3. SUSD3-related differential expression genes (DEGs) were sent for analysis utilizing GO, KEGG, and GSEA. We utilized SUSD3 mRNA expression to assess immune cells' scores in BC tissues calculated by single-sample enrichment analyses based on "CIBERSORT" R package. Drug sensitivity analysis was used to screen potential drugs sensitive to SUSD3. R software was used for statistical analyses and graphical representation of the data.

RESULTS

Our findings confirmed a significant upregulation of SUSD3 expression in BC, which correlated with a favorable prognosis. Clinical correlation analysis further emphasized the strong association between SUSD3 expression and key clinical parameters like estrogen receptor (ER) status, progesterone receptor (PR) status, stage, and T classification in breast cancer. Univariate and multivariate Cox regression analyses showed that SUSD3 could be used as an independent prognostic factor for BC. Differentially expressed genes (DEGs) co-expressed with SUSD3 were significantly associated with various biological processes, such as the cell cycle, DNA replication, p53 signaling pathway, cancer-related pathways, and Wnt signaling pathway, as indicated by gene set enrichment analysis (GSEA). Furthermore, our analysis demonstrated that SUSD3 generally exhibited negative associations with immune modulators. Drug sensitivity analysis revealed positive correlations between SUSD3 and the efficacy of Fulvestrant, Raloxifene, and Fluphenazine.

CONCLUSION

The research emphasizes the significance of SUSD3 as a potential marker for BC, providing insights into the underlying molecular mechanisms implicated in tumorigenesis. SUSD3 holds promise in helping the classification of breast cancer pathological groups, predicting prognosis, and facilitating targeted therapy.

An integrated machine learning framework for developing and validating a prognostic risk model of gastric cancer based on endoplasmic reticulum stress-associated genes

Background

Gastric cancer (GC), a prevalent and deadly malignancy, demonstrates poor survival outcomes. Evidence has emerged indicating that disruptions in endoplasmic reticulum homeostasis are significantly implicated in the onset and progression of various oncological conditions. This study was designed to construct a prognostic model based on genes related to endoplasmic reticulum stress(ERS) to predict survival outcomes in patients with GC.

Methods

Expression profiling data for GC samples were extracted and analyzed from TCGA-STAD, revealing 214 genes related to endoplasmic reticulum stress that show differential expression when compared with normal gastric tissue. Building on these insights, a prognostic model was formulated using data from TCGA-STAD and validated through subsequent analyses of GEO datasets. The tumor immune dysfunction and exclusion(TIDE) algorithm was applied to determine the susceptibility of individuals in high- and low-risk categories to immunotherapy. The presence of immune and stromal cells within the tumor microenvironment was assessed with the aid of the ESTIMATE algorithm. Sensitivity variations to prevalent anticancer drugs between the risk groups were evaluated using the Genomics of Drug Sensitivity in Cancer(GDSC) database, and prospective therapeutic agents were confirmed through molecular docking techniques.

Results

Thirty-one endoplasmic reticulum stress (ERS)-related differentially expressed genes (DEGs) crucial for prognosis in GC were pinpointed. These DEGs were then used to construct a prognostic model and were considered as independent prognostic factors for GC patients. This risk model proved to have a good predictive performance for estimating the overall survival of these patients. The patients placed into the high-risk group showed worse results and lower sensitivity to immunotherapy. Moreover, five specific targeted therapy drugs, namely BMS-754807, Dasatinib, JQ1, AZD8055 and SB505124, produced better results in the treatment of the high-risk group of patients.

Conclusions

A new molecular prognostic model associated with ERS was established and validated for GC and showed relatively good discriminative and predictive ability. This model greatly expands the collection of weapons in the armoury of prognostic analysis in GC.

Tumor and blood B-cell abundance outperforms established immune checkpoint blockade response prediction signatures in head and neck cancer

BACKGROUND

Immunotherapy has improved the outcomes for some patients with head and neck squamous-cell carcinoma (HNSCC). However, the low and variable response rates observed highlight the need for robust response biomarkers to select patients for treatment.

PATIENTS AND METHODS

We assembled and analyzed a large HNSCC dataset, encompassing 11 clinical cohorts including 1232 patient samples, spanning a variety of disease subtypes and immune checkpoint blockade (ICB) treatment types, tissue sources, data modalities, and timing of measurements. We conducted a comprehensive evaluation of the predictive power of various cell types, traditional biomarkers, and emerging predictors in both blood and tumor tissues of HNSCC patients.

RESULTS

Tumor B-cell infiltration emerged as a strong and robust predictor of both patient survival and ICB response. It outperformed all other established biomarkers of response to ICB, including the tertiary lymphoid structure signature and numerous T-cell-based signatures. B-cell infiltration was associated with a 'hot' antitumor microenvironment that promotes tumor eradication. Furthermore, B-cell levels in peripheral blood mononuclear cells (PBMCs) correlated strongly with tumor B-cell levels and demonstrated high predictive value for ICB response, with high odds ratios (≥7.8) in two independent clinical cohorts.

CONCLUSION

B-cell abundance, whether assessed in PBMCs or tumor tissues, is one of the strongest predictors of ICB response in HNSCC. For translation to patient care, measuring B-cell abundance in PBMCs via cytometry offers a practical and accessible tool for clinical decision making.

A real-world experience of pembrolizumab monotherapy in microsatellite instability-high and/or tumor mutation burden-high metastatic castration-resistant prostate cancer: outcome analysis

BACKGROUND

The efficacy of pembrolizumab monotherapy in metastatic castration-resistant prostate cancer patients (mCRPC) when stratified by MSI-H and/or TMB-H is poorly defined. Additionally, outcomes based on sequencing source (i.e., tissue or liquid biopsy) have not been well described. We sought to assess outcomes of pembrolizumab monotherapy in patients with mCRPC and compare efficacy based on MSI-H and/or TMB-H when identified by tissue or liquid biopsy.

METHODS

A retrospective analysis was performed of mCRPC patients treated at Mayo Clinic with pembrolizumab monotherapy between 2018 and 2023. Objective response rates (ORR), median progression-free survival (mPFS), and overall survival (mOS), were determined by RECIST v1.1 criteria.

RESULTS

Twenty-two patients with mCRPC received pembrolizumab monotherapy for at least 3 cycles for a MSI-H or TMB-H indication. All patients had next generation sequencing (NGS) performed via tissue (n = 11) or liquid (n = 10) biopsy source. The ORR was 50% (27.3% complete response and 22.7% had partial response). The mPFS for TMB 10-14.9 mut/Mb (n = 4), TMB 15-24.9 mut/Mb (n = 6), and TMB ≥ 25 mut/Mb (n = 10) was 2.1, not reached (NR), and NR, respectively (p = 0.0003). The mOS for these same groups was 5.1 months, 20.5 months, and not reached, respectively. Among patients with TMB-H without co-occurring MSI-H or CDK12 (n = 6), none experienced a response and only one patient had stable disease compared to patients with MSI-H (n = 12) for whom the ORR was 75%. Immunotherapy responsive alterations such as ATRX and PTCH1 mutations were frequently noticed among patients who had complete response (CR).

CONCLUSIONS

Our hypothesis-generating study suggests that MSI-H drives the efficacy of pembrolizumab in mCRPC with better survival outcomes as TMB increases. Clinicians should consider alternative treatment strategies for advanced prostate cancer when TMB-H is present without co-occurring MSI-H or CDK12.

Clinical-genomic nomogram for predicting sensitivity to second-line immunotherapy for advanced non-small cell lung cancer

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Integration of multiple machine learning approaches develops a gene mutation-based classifier for accurate immunotherapy outcomes

In addition to traditional biomarkers like PD-(L)1 expression and tumor mutation burden (TMB), more reliable methods for predicting immune checkpoint blockade (ICB) response in cancer patients are urgently needed. This study utilized multiple machine learning approaches on nonsynonymous mutations to identify key mutations that are most significantly correlated to ICB response. We proposed a classifier, Gene mutation-based Predictive Signature (GPS), to categorize patients based on their predicted response and clinical outcomes post-ICB therapy. GPS outperformed conventional predictors when validated in independent cohorts. Multi-omics analysis and multiplex immunohistochemistry (mIHC) revealed insights into tumor immunogenicity, immune responses, and the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) across different GPS groups. Finally, we validated distinct responses of different GPS samples to ICB in an ex-vivo tumor organoid-PBMC co-culture model. Overall, our findings highlight a simple, robust classifier for accurate ICB response prediction, which could reduce costs, shorten testing times, and facilitate clinical implementation.

Proteogenomic Profiling of Treatment-Naïve Metastatic Malignant Melanoma

BACKGROUND

Melanoma is a highly heterogeneous disease, and a deeper molecular classification is essential for improving patient stratification and treatment approaches. Here, we describe the histopathology-driven proteogenomic landscape of 142 treatment-naïve metastatic melanoma samples to uncover molecular subtypes and clinically relevant biomarkers.

METHODS

We performed an integrative proteogenomic analysis to identify proteomic subtypes, assess the impact of BRAF V600 mutations, and study the molecular profiles and cellular composition of the tumor microenvironment. Clinical and histopathological data were used to support findings related to tissue morphology, disease progression, and patient outcomes.

RESULTS

Our analysis revealed five distinct proteomic subtypes that integrate immune and stromal microenvironment components and correlate with clinical and histopathological parameters. We demonstrated that BRAF V600-mutated melanomas exhibit biological heterogeneity, where an oncogene-induced senescence-like phenotype is associated with improved survival. This led to a proposed mortality risk-based stratification that may contribute to more personalized treatment strategies. Furthermore, tumor microenvironment composition strongly correlated with disease progression and patient outcomes, highlighting a histopathological connective tissue-to-tumor ratio assessment as a potential decision-making tool. We identified a melanoma-associated SAAV signature linked to extracellular matrix remodeling and SAAV-derived neoantigens as potential targets for anti-tumor immune responses.

CONCLUSIONS

This study provides a comprehensive stratification of metastatic melanoma, integrating proteogenomic insights with histopathological features. The findings may aid in the development of tailored diagnostic and therapeutic strategies, improving patient management and outcomes.

Comprehensive genetic variant analysis reveals combination of KRAS and LRP1B as a predictive biomarker of response to immunotherapy in patients with non-small cell lung cancer

BACKGROUND

In non-small cell lung cancer (NSCLC), the rapid advancement of predictive genetic testing of tumors by identifying specific pathogenic driver variants has significantly improved treatment guidance. However, immune checkpoint blockade (ICB) is typically administered to patients with tumors in the absence of such driver variants. Since only about 30% of patients will respond to ICB treatment, identifying novel genetic biomarkers of clinical response is crucial and will improve treatment decisions. This prospective clinical study aims to combine molecular biology, advanced bioinformatics and clinical data on response to treatment with ICB from a prospective cohort of NSCLC patients to identify single or combination of genetic variants in the tumor that can serve as predictive biomarkers of clinical response.

METHODS

In this prospective bi-center clinical study, we performed next-generation sequencing (NGS) of 597 cancer-associated genes in a prospective cohort of 49 patients as the final cohort analyzed, with stage III or IV NSCLC, followed by establishment of an in-house developed bioinformatics-based molecular classification method that integrates, interprets and evaluates data from multiple databases and variant prediction tools. Overall survival (OS) and progression-free survival (PFS) were analyzed for selected candidate genes and variants identified using our novel methodology including molecular tools, databases and clinical information.

RESULTS

Our novel molecular interpretation and classification method identified high impact variants in frequently altered genes KRAS, LRP1B, and TP53. Analysis of these genes as single predictive biomarkers in ICB-treated patients revealed that the presence of likely pathogenic variants and variants of unclear significance in LRP1B was associated with improved OS (p = 0.041). Importantly, further analysis of variant combinations in the tumor showed that co-occurrence of KRAS and LRP1B variants significantly improved OS (p = 0.003) and merged PFS (p = 0.008). Notably, the triple combination of variants in KRAS, LRP1B, and TP53 positively impacted both OS (p = 0.026) and merged PFS (p = 0.003).

CONCLUSIONS

This study suggests that combination of the LRP1B and KRAS variants identified through our novel molecular classification scheme leads to better outcomes following ICB treatment in NSCLC. The addition of TP53 improves the outcome even further. To our knowledge, this is the first report indicating that harboring a combination of KRAS, LRP1B, and TP53 variants can significantly enhance the response to ICB, suggesting a novel predictive biomarker combination for NSCLC patients.

A microRNA-regulated transcriptional state defines intratumoral CD8+ T cells that respond to immunotherapy

The rising incidence of advanced-stage colorectal cancer (CRC) and poor survival outcomes necessitate new and effective therapies. Immune checkpoint inhibitors (ICIs), specifically anti-PD-1 therapy, show promise, yet clinical determinants of a positive response are suboptimal. Here, we identify microRNA-155 (miR-155) as necessary for CD8+ T cell-infiltrated tumors through an unbiased in vivo CRISPR-Cas9 screen identifying functional tumor antigen-specific CD8+ T cell-expressed microRNAs. T cell miR-155 is required for anti-PD-1 responses and for a vital intratumor CD8+ T cell differentiation cascade by repressing Ship-1, inhibiting Tcf-1 and stemness, and subsequently enhancing Cxcr6 expression, anti-tumor immunity, and effector functions. Based on an underlying miR-155-dependent CD8+ T cell transcriptional profile, we identify a gene signature that predicts ICI responses across 12 diverse cancers. Together, our findings support a model whereby miR-155 serves as a central regulator of CD8+ T cell-dependent cancer immunity and ICI responses that may be leveraged for future therapeutics.

Exploration of the clonal evolution and construction of the tumor clonal evolution rate as a prognostic indicator in metastatic breast cancer

BACKGROUND

Tumor heterogeneity and clonal evolution are related to the treatment resistance and disease progression in metastatic breast cancer (MBC). However, the process of clonal evolution and their relationship to prognosis remain unclear. This study aimed to elucidate the evolution of MBC through circulating tumor DNA (ctDNA) analysis and to develop a novel indicator for predicting treatment efficacy and prognosis.

METHODS

This multicenter retrospective study enrolled MBC patients who underwent next-generation sequencing between April 2016 and October 2022. The clonal evolution of tumors was inferred using PyClone and CITUP software.

RESULTS

The study included 406 MBC patients. A cohort of 139 patients from the National Cancer Center served as the training cohort, while 267 patients from other centers comprised the validation cohort. In the training cohort, clonal analysis revealed that most MBCs exhibited branched clonal evolution, while a minority showed linear evolution. The branched evolution pattern was associated with slower disease progression (HR, 0.53; 95% CI, 0.32-0.87; P = 0.012). We introduced tumor clonal evolution rate (TER) as a novel concept to reflect the speed of clonal evolution. Survival analysis demonstrated that compared to the TER-high group, patients in the TER-low group had better progression-free survival (PFS) (HR, 0.62; 95% CI, 0.40-0.96; P = 0.033) and overall survival (OS) (HR, 0.45; 95% CI, 0.24-0.85; P = 0.013). Similarly, in the validation cohort, although the median OS was not reached, patients in the TER-low group had better prognosis compared to those in the TER-high group (HR, 0.41; 95% CI, 0.21-0.83; P < 0.001).

CONCLUSIONS

Patients with branched evolution have better treatment efficacy than those with linear evolution. The TER shows potential as a biomarker for treatment efficacy and prognosis, providing new evidence that ctDNA is a valuable molecular indicator for predicting treatment outcomes in metastatic breast cancer.

Clinical metric of tumor mutational burden depicts colorectal cancer patients at the extremes

PURPOSE

Rare cases of colorectal cancer patients with exceptionally good or poor prognosis often remain overlooked, limiting insights into prognostic factors and underlying mechanisms.

METHODS

This study developed an analytical framework to investigate cancer patients at the extremes using tumor mutational burden (TMB). By analyzing data from 1277 colorectal cancer patients who did not receive immunotherapy, this analysis assessed how patient survival varies with a broad range of TMB levels.

RESULTS

Among patients with TMB ≤ 10 mutations per megabase (mut/Mb), increasing TMB was associated with worse survival outcomes. In contrast, patients with TMB > 10 mut/Mb showed  increasingly improved survival. Notably, a small subgroup (3.83%) with TMB > 60 mut/Mb had significantly better survival outcomes.

CONCLUSIONS

These findings highlight TMB's dual role in colorectal cancer progression. This study suggests that atypical patients can coexist within the same "disease continuum" with typical patients, under the universal context unified by a shared cancer hallmark. TMB provides a useful biomarker for identifying these extremes, offering a clinical metric to better predict patient outcomes and personalize treatment strategies.

Predicting immunotherapy response of advanced bladder cancer through a meta-analysis of six independent cohorts

Advanced bladder cancer patients show very variable responses to immune checkpoint inhibitors (ICIs) and effective strategies to predict response are still lacking. Here we integrate mutation and gene expression data from 707 advanced bladder cancer patients treated with anti-PD-1/anti-PD-L1 to build highly accurate predictive models. We find that, in addition to tumor mutational burden (TMB), enrichment in the APOBEC mutational signature, and the abundance of pro-inflammatory macrophages, are major factors associated with the response. Paradoxically, patients with high immune infiltration do not show an overall better response. We show that this can be explained by the activation of immune suppressive mechanisms in a large portion of these patients. In the case of non-immune-infiltrated cancer subtypes, we uncover specific variables likely to be involved in the response. Our findings provide information for advancing precision medicine in patients with advanced bladder cancer treated with immunotherapy.

Risk factors for immunoresistance in advanced non-small cell lung cancer and the advantages of targeted therapy in improving prognosis

OBJECTIVES

The advent of immunotherapy has transformed the therapeutic landscape for advanced non-small cell lung cancer (NSCLC); nonetheless, the emergence of resistance to immunotherapy poses a considerable obstacle. Our research sought to identify factors contributing to immunotherapy resistance and to assess the effectiveness of subsequent treatments in patients with advanced NSCLC who have been exposed to immune checkpoint inhibitors (ICIs).

METHODS

This retrospective study analyzed data from 232 individuals with advanced NSCLC who were treated with ICIs during January 2020 to December 2023. Based on their response to ICIs, these patients were classified into two groups: immunoresistance group (IM group) and non-immunoresistance group (NIM group). Data collected included demographics, clinical parameters, cytokine profiles, tumor mutational burden (TMB), PD-L1 expression, overall survival (OS), progression-free survival (PFS), and adverse events. The association between risk factors and immunoresistance were assessed, and second-line treatment outcomes were evaluated.

RESULTS

Key risk factors for immunoresistance included lower TMB, higher levels of interleukin-10 (IL-10), and PD-L1 expression ≥ 50%. TMB was inversely correlated with immunoresistance (rho = -0.838, P < 0.001). In multivariate analysis, IL-10 remained a significant risk factor (OR = 33.654, P = 0.021), whereas TMB was protective (OR = 0.786, P < 0.001). Second-line targeted therapy significantly improved OS (8.72 ± 2.02 months) and PFS (5.37 ± 2.15 months) compared to chemotherapy (OS: 7.93 ± 2.13 months; PFS: 4.86 ± 1.68 months) (P < 0.05). The targeted therapy group experienced distinct side effects, notably increased hypertension and hand-foot syndrome, while chemotherapy group had higher rates of fatigue (P < 0.05).

CONCLUSION

Immunoresistance in advanced NSCLC is influenced by IL-10, TMB, and PD-L1 expression. Targeted therapies offer superior outcomes than chemotherapy, though side effect management remains crucial.

B-cell signatures characterize the immune landscape and predict LUAD prognosis via the integration of scRNA-seq and bulk RNA-seq

Lung adenocarcinoma (LUAD) is the most common type of lung cancer, accounting for approximately 35-40% of lung cancers, and the overall survival time of patients with LUAD is still very poor. B cells are important effector cells of adaptive immunity, and B-cell infiltration increases in various tumors. The role of B cells in LUAD is still largely unknown. Therefore, it is particularly important to clarify the role of B cells in LUAD. GSE164983, GSE50081, GSE37745 and GSE30219 were obtained from the GEO database. The TCGA-LUAD dataset was obtained from the TCGA database. UMAP was used to perform clustering descending and subgroup identification on single-cell RNA-sequencing (scRNA-seq) data to obtain B-cell markers. The TCGA cohort was used to obtain differentially expressed genes (DEGs). B-cell-related differentially expressed genes (BRGs) were identified through the intersection of B-cell markers and DEGs. The LASSO method was used to identify characteristic genes of BRGs and construct a prognostic risk model. LUAD patients were divided into high-risk and low-risk groups based on risk scores, and the immune landscape of the two groups was evaluated. We also analyzed the differences in clinical characteristics, mutations, immunotherapy, and drug sensitivity between the two groups. Thirty BRGs were obtained, and 6 characteristic genes were identified. Based on the characteristic genes, a prognostic risk model was constructed. According to the prognostic risk model, LUAD patients were divided into two groups: high-risk group and low-risk group. Patients in the high-risk group had worse outcomes and shorter survival times. Low-risk patients had better survival, while patients with high TNM stage accounted for a greater proportion of patients in the high-risk group. In addition, high-risk patients had a greater probability of mutation and worse immunotherapy response. Finally, we found different susceptibility profiles between the high-risk and low-risk groups. The prognostic risk model built based on the BRGs had good predictive performance, providing a new perspective on the prognosis and immunotherapy of LUAD patients and a new reference for LUAD research.

A constitutive interferon-high immunophenotype defines response to immunotherapy in colorectal cancer

Fewer than 50% of metastatic deficient mismatch repair (dMMR) colorectal cancer (CRC) patients respond to immune checkpoint inhibition (ICI). Identifying and expanding this patient population remains a pressing clinical need. Here, we report that an interferon-high immunophenotype locally enriched in cytotoxic lymphocytes and antigen-presenting macrophages is required for response. This immunophenotype is not exclusive to dMMR CRCs but comprises a subset of MMR proficient (pMMR) CRCs. Single-cell spatial analysis and in vitro cell co-cultures indicate that interferon-producing cytotoxic T cells induce overexpression of antigen presentation in adjacent macrophages and tumor cells, including MHC class II invariant chain CD74. dMMR CRCs expressing high levels of CD74 respond to ICI and a subset of CD74 high pMMR CRC patients show better progression free survival when treated with ICI. Therefore, CD74 abundance can identify the constitutive interferon-high immunophenotype determining clinical benefit in CRC, independently of tumor mutational burden or MMR status.

Tumor mutational burden and survival on immune checkpoint inhibition in >8000 patients across 24 cancer types

BACKGROUND

There is uncertainty around clinical applicability of tumor mutational burden (TMB) across cancer types, in part because of inconsistency between TMB measurements from different platforms. The KEYNOTE 158 trial supported United States Food and Drug Administration (FDA) approval of the Foundation Medicine test (FoundationOneCDx) at TMB≥10 mut/Mb as a companion diagnostic (CDx) for single-agent pembrolizumab in second+line. Using a large real-world dataset with validated survival endpoint data, we evaluated clinical validity of TMB measurement by the test in over 8000 patients across 24 cancer types who received single-agent immune checkpoint inhibitor (ICI).

METHODS

Patients with advanced-stage cancers from 24 cancer types treated with single-agent anti-PD(L)1 therapy in standard-of-care settings were included. Deidentified data from electronic health records from approximately 280 cancer treatment facilities were captured into a clinico-genomic database. This study used the TMB algorithm from the FDA-approved test supporting solid tumor CDx and composite mortality variable validated against the national death index: real-world overall survival (rwOS). Following a prespecified analysis plan, rwOS by TMB level was assessed using Cox PH models adjusted for Eastern Cooperative Oncology Group performance status, prior treatment, microsatellite instability, sex, age, opioid rx pretherapy, and socioeconomic assessment.

RESULTS

8440 patients met inclusion criteria. Adjusting for aforementioned factors, increasing TMB was significantly associated with rwOS across tumor types; HRs (95% CIs) relative to TMB<5: TMB 5 to <10: 0.95 (0.89 to 1.02), TMB 10 to <20: 0.79 (0.73 to 0.85), TMB≥20: 0.52 (0.47 to 0.58). For individual cancer types with prespecified statistical power, adjusted rwOS comparing TMB≥10 vs TMB<10 significantly favored TMB≥10 in 9 of 10 cancer types. In microsatellite stable subcohorts (except colorectal cancer), TMB≥10 remained associated with enriched ICI benefit. Exploratory assessments of patients receiving ICI+chemotherapy (n=4369) observed more favorable rwOS only in TMB≥20.

CONCLUSIONS

Across >8000 patients treated with single-agent ICI, and within individual cancer types with sufficient power, elevated TMB based on the FDA-approved CDx was associated with more favorable rwOS compared with similar patients with lower TMB levels. This biomarker deserves further clinical investigation to potentially guide the use of immunotherapy in expanded clinical contexts.

Prognostic value and immune landscapes of disulfidptosis‑related lncRNAs in bladder cancer

Disulfidptosis, which was recently identified, has shown promise as a potential cancer treatment. Nonetheless, the precise role of long non-coding RNAs (lncRNAs) in this phenomenon is currently unclear. To elucidate their significance in bladder cancer (BLCA), a signature of disulfidptosis-related lncRNAs (DRlncRNAs) was developed and their potential prognostic significance was explored. BLCA sample data were sourced from The Cancer Genome Atlas. A predictive signature comprising DRlncRNAs was formulated and subsequently validated. The combination of this signature with clinical characteristics facilitated the development of a nomogram with practical clinical utility. Additionally, enrichment analysis was conducted, the tumor microenvironment (TME) was assessed, the tumor mutational burden (TMB) was analyzed, and drug sensitivity was explored. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to quantify lncRNA expression. The results revealed an eight-gene signature based on DRlncRNAs was established, and the predictive accuracy of the nomogram that incorporated the risk score [area under the curve (AUC)=0.733] outperformed the nomogram without it (AUC=0.703). High-risk groups were associated with pathways such as WNT signaling, focal adhesion and cell cycle pathways. The TME study revealed that high-risk patients had increased immune infiltration, whereas the TMB and tumor immune dysfunction and exclusion scores in low-risk patients indicated a potentially robust immune response. Drug sensitivity analysis identified appropriate antitumor drugs for each group. RT-qPCR experiments validated significant differences in DRlncRNAs expression between normal and BLCA cell lines. In conclusion, the prognostic risk signature, which includes the eight identified DRlncRNAs, demonstrates promise for predicting prognosis of patients with BLCA and guiding the selection of suitable immunotherapy and chemotherapy strategies.

Liposomes-enabled cancer chemoimmunotherapy

Chemoimmunotherapy is an emerging paradigm in the clinic for treating several malignant diseases, such as non-small cell lung cancer, breast cancer, and large B-cell lymphoma. However, the efficacy of this strategy is still restricted by serious adverse events and a high therapeutic termination rate, presumably due to the lack of tumor-targeted distribution of both chemotherapeutic and immunotherapeutic agents. Targeted drug delivery has the potential to address this issue. Among the most promising nanocarriers in clinical translation, liposomes have drawn great attention in cancer chemoimmunotherapy in recent years. Liposomes-enabled cancer chemoimmunotherapy has made significant progress in clinics, with impressive therapeutic outcomes. This review summarizes the latest preclinical and clinical progress in liposome-enabled cancer chemoimmunotherapy and discusses the challenges and future directions of this field.

Deep Learning Model for Predicting Immunotherapy Response in Advanced Non-Small Cell Lung Cancer

Importance

Only a small fraction of patients with advanced non-small cell lung cancer (NSCLC) respond to immune checkpoint inhibitor (ICI) treatment. For optimal personalized NSCLC care, it is imperative to identify patients who are most likely to benefit from immunotherapy.

Objective

To develop a supervised deep learning-based ICI response prediction method; evaluate its performance alongside other known predictive biomarkers; and assess its association with clinical outcomes in patients with advanced NSCLC.

Design, Setting, and Participants

This multicenter cohort study developed and independently validated a deep learning-based response stratification model for predicting ICI treatment outcome in patients with advanced NSCLC from whole slide hematoxylin and eosin-stained images. Images for model development and validation were obtained from 1 participating center in the US and 3 in the European Union (EU) from August 2014 to December 2022. Data analyses were performed from September 2022 to May 2024.

Exposure

Monotherapy with ICIs.

Main Outcomes and Measures

Model performance measured by clinical end points and objective response rate (ORR) differentiation power vs other predictive biomarkers, ie, programmed death-ligand 1 (PD-L1), tumor mutational burden (TMB), and tumor-infiltrating lymphocytes (TILs).

Results

A total of 295 581 image tiles from 958 patients (mean [SD] age, 66.0 [10.6] years; 456 [48%] females and 502 [52%] males) treated with ICI for NSCLC were included in the analysis. The US-based development cohort consisted of 614 patients with median (IQR) follow-up time of 54.5 (38.2-68.1) months, and the EU-based validation cohort, 344 patients with 43.3 (27.4-53.9) months of follow-up. The ORR to ICI was 26% in the developmental cohort and 28% in the validation cohort. The deep learning model's area under the receiver operating characteristic curve (AUC) for ORR was 0.75 (95% CI, 0.64-0.85) in the internal test set and 0.66 (95% CI, 0.60-0.72) in the validation cohort. In a multivariable analysis, the deep learning model's score was an independent predictor of ICI response in the validation cohort for both progression-free (hazard ratio, 0.56; 95% CI, 0.42-0.76; P < .001) and overall survival (hazard ratio, 0.53; 95% CI, 0.39-0.73; P < .001). The tuned deep learning model achieved a higher AUC than TMB, TILs, and PD-L1 in the internal set; in the validation cohort, it was superior to TILs and comparable with PD-L1 (AUC, 0.67; 95% CI, 0.60-0.74), with a 10-percentage point improvement in specificity. In the validation cohort, combining the deep learning model with PD-L1 scores achieved an AUC of 0.70 (95% CI, 0.63-0.76), outperforming either marker alone, with a response rate of 51% compared to 41% for PD-L1 (≥50%) alone.

Conclusions and Relevance

The findings of this cohort study demonstrate a strong and independent deep learning-based feature associated with ICI response in patients with NSCLC across various cohorts. Clinical use of this deep learning model could refine treatment precision and better identify patients who are likely to benefit from ICI for treatment of advanced NSCLC.