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.

Antithetical impacts of deleterious LRP1B mutations in non-squamous and squamous NSCLCs on predicting benefits from immune checkpoint inhibitor alone or with chemotherapy over chemotherapy alone: retrospective analyses of the POPLAR/OAK and CHOICE-01 trials.

In non-small cell lung cancers, the non-squamous and squamous subtypes (nsqNSCLC and sqNSCLC) exhibit disparities in pathophysiology, tumor immunology, and potential genomic correlates affecting responses to immune checkpoint inhibitor (ICI)-based treatments. In our in-house training cohort (n=85), the presence of the LRP1B deleterious mutation (LRP1B-del) was associated with longer and shorter progression-free survival (PFS) on ICIs alone in nsqNSCLCs and sqNSCLCs, respectively (Pinteraction=0.008). These results were validated using a larger public ICI cohort (n=208, Pinteraction<0.001). Multiplex immunofluorescence staining revealed an association between LRP1B-del and increased and decreased numbers of tumor-infiltrating CD8+ T cells in nsqNSCLCs (P=0.040) and sqNSCLCs (P=0.014), respectively. In the POPLAR/OAK cohort, nsqNSCLCs with LRP1B-del demonstrated improved PFS benefits from atezolizumab over docetaxel (hazard ratio (HR) =0.70, P=0.046), whereas this benefit was negligible in those without LRP1B-del (HR=1.05, P=0.64). Conversely, sqNSCLCs without LRP1B-del benefited more from atezolizumab (HR=0.60, P=0.002) than those with LRP1B-del (HR=1.30, P=0.31). Consistent results were observed in the in-house CHOICE-01 cohort, in which nsqNSCLCs with LRP1B-del and sqNSCLCs without LRP1B-del benefited more from toripalimab plus chemotherapy than from chemotherapy alone (Pinteraction=0.008). This multi-cohort study delineates the antithetical impacts of LRP1B-del in nsqNSCLCs and sqNSCLCs on predicting the benefits from ICI alone or with chemotherapy over chemotherapy alone. Our findings highlight the distinct clinical utility of LRP1B-del in guiding treatment choices for nsqNSCLCs and sqNSCLCs, emphasizing the necessity for a detailed analysis based on pathological subtypes when investigating biomarkers for cancer therapeutics.

Deciphering the predictive value of senescence-related signature in lung adenocarcinoma: Implications for antitumor immunity and immunotherapy efficacy.

Objective: The senescence process is pivotal in both the onset and advancement of lung adenocarcinoma (LUAD), influencing cell growth, immune evasion, the potential for metastasis, and resistance to treatments. Senescent cells' dual nature, both harmful and advantageous, adds complexity to understanding their expression patterns and clinical relevance in LUAD. In this study, we sought to evaluate the predictive value of the senescence-related signature in survival outcomes and immunotherapy efficacy in patients with LUAD. Materials and methods: We integrated data from 1449 LUAD cases sourced from different publicly accessible datasets and a clinical cohort of Chinese LUAD patients. The Cox regression analysis employing the least absolute shrinkage and selection operator (LASSO) was performed on 156 senescence-associated genes to develop the senescence-related signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic curves were utilizaed to assess the prognostic significance of the senescence-related signature. Functional annotation, immune infiltration analysis, and gene set variation analysis were applied to investigate the association of the senescence-related signature with anti-tumor immunity in LUAD. Immunotherapy cohorts of non-small cell lung cancer, urothelial carcinoma, skin cutaneous melanoma, and glioblastoma patients were included to assess the senescence-related signature in predicting immunotherapy efficacy. Results: The senescence-related signature, which encompasses seven senescence-related genes, namely, FOXM1, VDAC1, PPP3CA, MAPK13, PIK3CD, RRAS, and CCND3, was identified to have predictive significance across multiple LUAD cohorts and demonstrated a negative association with antitumor immunity and tumor-infiltrating neutrophils. Patients exhibiting low expression levels of the senescence-related signature responded more favorably to immune checkpoint inhibitors in various solid tumors, including LUAD. Inhibiting FOXM1 pharmacologically with thiostrepton produced tumor-suppressive effects and improved immunotherapy responses in a Lewis lung carcinoma mouse model. Conclusions: The senescence-related signature demonstrates potential in predicting patient prognosis and immunotherapy efficacy in LUAD.

Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model. METHODS: We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles. FINDINGS: Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality. CONCLUSIONS: The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables. FUNDING: This study was supported by the National Key R&D Program of China.

Evaluation of first-line and salvage therapies for unresectable malignant mesothelioma: A systematic review and network meta-analysis.

BACKGROUND: Randomized controlled trials (RCTs) of systemic therapies for unresectable malignant mesothelioma have reported conflicting results. It is crucial and urgent to find optimal treatment options for this malignancy, which currently has a poor prognosis. METHODS: Databases PubMed, EMBASE, Cochrane Library, ClinicalTrials.gov, and major international conferences were searched until February 29, 2024. The main outcomes of interest were overall survival (OS), progression-free survival (PFS), overall response rate (ORR), and grade ≥3 treatment-related adverse events (TRAEs). RESULTS: We analyzed 16 RCTs with a total of 5018 patients. Among first-line therapies, nivolumab and ipilimumab significantly increased OS and resulted in fewer grade ≥3 TRAEs. Bevacizumab plus chemotherapy significantly increased PFS. Among salvage therapies, ramucirumab and chemotherapy was associated with the best OS and PFS, but resulted in more grade ≥3 TRAEs. Subgroup analysis by histologic types suggested that in first-line settings, bevacizumab and chemotherapy increase OS the most for epithelioid type, while the nivolumab plus ipilimumab treatment increases OS the most for non-epithelioid type. In salvage therapies, ramucirumab and chemotherapy increase OS for both epithelioid and non-epithelioid types. CONCLUSION: Nivolumab plus ipilimumab was associated with the best OS among first-line treatments. Ramucirumab and chemotherapy was associated with the best clinical outcomes in salvage settings. Treatment for malignant mesothelioma should be tailored based on different clinicopathological characteristics.

A single-cell characterised signature integrating heterogeneity and microenvironment of lung adenocarcinoma for prognostic stratification.

BACKGROUND: The high heterogeneity of tumour and the complexity of tumour microenvironment (TME) greatly impacted the tumour development and the prognosis of cancer in the era of immunotherapy. In this study, we aimed to portray the single cell-characterised landscape of lung adenocarcinoma (LUAD), and develop an integrated signature incorporating both tumour heterogeneity and TME for prognosis stratification. METHODS: Single-cell tagged reverse transcription sequencing (STRT-seq) was performed on tumour tissues and matched normal tissues from 14 patients with LUAD for immune landscape depiction and candidate key genes selection for signature construction. Kaplan-Meier survival analyses and in-vitro cell experiments were conducted to confirm the gene functions. The transcriptomic profile of 1949 patients from 11 independent cohorts including nine public datasets and two in-house cohorts were obtained for validation. FINDINGS: We selected 11 key genes closely related to cell-to-cell interaction, tumour development, T cell phenotype transformation, and Ma/Mo cell distribution, including HLA-DPB1, FAM83A, ITGB4, OAS1, FHL2, S100P, FSCN1, SFTPD, SPP1, DBH-AS1, CST3, and established an integrated 11-gene signature, stratifying patients to High-Score or Low-Score group for better or worse prognosis. Moreover, the prognostically-predictive potency of the signature was validated by 11 independent cohorts, and the immunotherapeutic predictive potency was also validated by our in-house cohort treated by immunotherapy. Additionally, the in-vitro cell experiments and drug sensitivity prediction further confirmed the gene function and generalizability of this signature across the entire RNA profile spectrum. INTERPRETATION: This single cell-characterised 11-gene signature might offer insights for prognosis stratification and potential guidance for treatment selection. FUNDING: Support for the study was provided by National key research and development project (2022YFC2505004, 2022YFC2505000 to Z.W. and J.W.), Beijing Natural Science Foundation (7242114 to J.X.), National Natural Science Foundation of China of China (82102886 to J.X., 81871889 and 82072586 to Z.W.), Beijing Nova Program (20220484119 to J.X.), NSFC general program (82272796 to J.W.), NSFC special program (82241229 to J.W.), CAMS Innovation Fund for Medical Sciences (2021-1-I2M-012, 2022-I2M-1-009 to Z.W. and J.W.), Beijing Natural Science Foundation (7212084 to Z.W.), CAMS Key lab of translational research on lung cancer (2018PT31035 to J.W.), Aiyou Foundation (KY201701 to J.W.). Medical Oncology Key Foundation of Cancer Hospital Chinese Academy of Medical Sciences (CICAMS-MOCP2022003 to J.X.).

Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer.

BACKGROUND: The aim of the present study was to compare the predictive accuracy of PD-L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non-small cell lung cancer (NSCLC). METHODS: In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)-treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured. RESULTS: In part 1, the AUC of combined biomarkers (0.75) was higher than PD-L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD-L1 plus TMB (PD-L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD-L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD-L1, tTMB-high, wild-type EGFR, and positive PD-L1 plus TMB were associated with prolonged progression-free survival (PFS). CONCLUSION: Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings.

Screening for depression and anxiety in lung cancer patients: A real-world study using GAD-7 and HADS.

BACKGROUND: The psychological well-being of lung cancer patients is critical in-patient care but frequently overlooked. METHODS: This study, employing a cross-sectional, questionnaire-based design, aimed to elucidate the prevalence of depressive and anxiety symptoms among lung cancer patients and identify associated risk factors. Participants' demographic, medical history, disease stage, and pathology were systematically collected. Psychological assessment was conducted using the general anxiety disorder-7 (GAD-7), patient health questionnaire-9 (PHQ-9), and hospital anxiety and depression scale (HADS). Statistical analyses were performed using SPSS software (version 25.0). RESULTS: Out of 294 distributed questionnaires, 247 lung cancer patients were included in the final analysis, with an average completion time of 9.08 min. Notably, 32.4% exhibited depressive symptoms, while 30% displayed signs of anxiety. A significant correlation was found between both depressive and anxiety symptoms and a history of tobacco and alcohol consumption. Specifically, increased nicotine dependence and greater cumulative tobacco use were linked to higher rates of depressive symptoms, whereas cumulative alcohol consumption was associated with increased risks of anxiety symptoms. CONCLUSION: The study affirms the feasibility of GAD-7, PHQ-9, and HADS as screening tools for depressive and anxiety symptoms in lung cancer patients. It further highlights tobacco and alcohol consumption as significant risk factors for poor psychological health in this population.

Real-world outcomes of chemoimmunotherapy and selective RET inhibitors in Chinese patients with RET fusion-positive non-small cell lung cancer.

Background: Rearranged during transfection (RET) gene fusion is a target for non-small cell lung cancer (NSCLC) treatment, and RET inhibitors are approved for advanced NSCLC. The role of immune checkpoint inhibitors (ICIs) in RET fusion-positive NSCLC remains controversial. This retrospective study analyzed the efficacy of ICIs and RET inhibitors in Chinese patients with RET fusion-positive NSCLC. Methods: Data from patients diagnosed with advanced NSCLC harboring RET fusion from Jan 2017 to Sep 2021 were analyzed. Clinicopathological characteristics and outcomes of ICIs and RET inhibitors treatments were collected. Results: Seventy-five patients with RET fusion-positive advanced NSCLC were identified. The median age of patients was 57 years, half of the patients were female (50.3%), and most were non-smokers or light smokers (72%). Of the cancer types diagnosed in study patients, the KIF5B-RET fusion subtype accounted for 73.3% (55/75), twelve patients (16%) had CCDC6-RET fusion, and three (4%) had NCOA4-RET fusion. Sixteen patients were treated with ICIs. In previously untreated patients, we observed an objective response rate (ORR) of 71.4% and median progression free survival (PFS) of 7.5 months in seven assessable patients. Of four patients with PD-L1 overexpression (>50%) one received pembrolizumab and the other three patients received pemetrexed, carboplatin, and pembrolizumab or camrelizumab. In these patients, the ORR was 75% and disease control rate was 100%. Fifteen patients received selective RET inhibitors (pralsetinib and selpercatinib), resulting in an ORR of 53.3% (8/15) and median PFS of 10.0 months (95% CI 5.2-14.9). Conclusions: ICIs for PD-L overexpression and treatment naive patients offer comparable benefits for RET fusion-positive NSCLC, warranting further investigation.

Assessment of human leukocyte antigen-based neoantigen presentation to determine pan-cancer response to immunotherapy.

Despite the central role of human leukocyte antigen class I (HLA-I) in tumor neoantigen presentation, quantitative determination of presentation capacity remains elusive. Based on a pooled pan-cancer genomic dataset of 885 patients treated with immune checkpoint inhibitors (ICIs), we developed a score integrating the binding affinity of neoantigens to HLA-I, as well as HLA-I allele divergence, termed the HLA tumor-Antigen Presentation Score (HAPS). Patients with a high HAPS were more likely to experience survival benefit following ICI treatment. Analysis of the tumor microenvironment indicated that the antigen presentation pathway was enriched in patients with a high HAPS. Finally, we built a neural network incorporating factors associated with neoantigen production, presentation, and recognition, which exhibited potential for differentiating cancer patients likely to benefit from ICIs. Our findings highlight the clinical utility of evaluating HLA-I tumor antigen presentation capacity and describe how ICI response may depend on HLA-mediated immunity.

Rechallenge of immune checkpoint inhibitors in advanced non-small cell lung cancer.

Immune checkpoint inhibitor (ICI) rechallenge in non-small cell lung cancer (NSCLC) is a promising therapeutic strategy. The situation for ICI rechallenge can be divided into three categories: adverse events (AEs); resistance to ICIs, and rechallenge becomes compulsive because of tumor relapse while the patients had completed a 2 year course of immunotherapy. However, these categories are still controversial and should be explored further. Through voting at the 6th Straits Summit Forum on Lung Cancer, in this study we summarize the consensus of 147 experts in ICI rechallenges. A total of 97.74% experts agreed to rechallenge; 48.87% experts rechallenge with the original drug, and the others rechallenge with a different drug; 40.3% agreed to rechallenge directly after progression; 88.06% experts agreed to ICI rechallenge with a combination regimen; and factors such as previous performance status score, PD-1 expression, and age should also be considered. Understanding the the clinical studies in ICI rechallenge could bring us one step closer to understanding the consensus. In patients with advanced NSCLC who have suffered recurrent or distant metastasis after immunotherapy, the option of rechallenge with ICIs is a promising treatment option.

Safety, Tolerability, Pharmacokinetics, and Preliminary Efficacy of YK-029A in Treatment-Naive Patients With Advanced NSCLC Harboring EGFR Exon 20 Insertion Mutations: A Phase 1 Trial.

INTRODUCTION: Treatment options for treatment-naive patients with advanced NSCLC harboring EGFR exon 20 insertion (ex20ins) mutations are limited. This study evaluated the safety, tolerability, and pharmacokinetics of YK-029A, a third-generation EGFR tyrosine kinase inhibitor, and the preliminary efficacy of YK-029A in treatment-naive patients with EGFR ex20ins mutation. METHODS: This multicenter, dose-escalation, and dose-expansion phase 1 clinical trial enrolled patients with NSCLC harboring EGFR mutations. During the dose-escalation phase, YK-029A was orally administered using the traditional 3+3 principle at 50, 100, 150, 200, and 250 mg/d. In the dose-expansion phase, treatment-naive patients with EGFR ex20ins mutations were enrolled and administered YK-029A 200 mg/d. The primary end point was safety and tolerability. RESULTS: The safety analysis included 108 patients. No dose-limiting toxicity was observed, and the maximum tolerated dose was not reached. The most common treatment-emergent adverse events were anemia (50.9%), diarrhea (49.1%), and rash (34.3%). There was minimal drug accumulation after multiple doses. A total of 28 treatment-naive patients with EGFR ex20ins mutations were enrolled in the dose-expansion and 26 were included in the efficacy analysis. According to the independent review committee evaluation, the objective response rate was 73.1% (95% confidence interval: 52.21%-88.43%), and the disease control rate was 92.3% (95% confidence interval: 74.87%-99.05%). CONCLUSIONS: YK-029A was found to have manageable safety and be tolerable in patients with NSCLC harboring EGFR mutations and have promising antitumor activity in untreated patients with EGFR ex20ins mutations.

Genetic Intratumor Heterogeneity Remodels the Immune Microenvironment and Induces Immune Evasion in Brain Metastasis of Lung Cancer.

INTRODUCTION: Brain metastasis, with the highest incidence in patients with lung cancer, significantly worsens prognosis and poses challenges to clinical management. To date, how brain metastasis evades immune elimination remains unknown. METHODS: Whole-exome sequencing and RNA sequencing were performed on 30 matched brain metastasis, primary lung adenocarcinoma, and normal tissues. Data from The Cancer Genome Atlas primary lung adenocarcinoma cohort, including multiplex immunofluorescence, were used to support the findings of bioinformatics analysis. RESULTS: Our study highlights the key role of intratumor heterogeneity of genomic alterations in the metastasis process, mainly caused by homologous recombination deficiency or other somatic copy number alteration-associated mutation mechanisms, leading to increased genomic instability and genomic complexity. We further proposed a selection model of brain metastatic evolution in which intratumor heterogeneity drives immune remodeling, leading to immune escape through different mechanisms under local immune pressure. CONCLUSIONS: Our findings provide novel insights into the metastatic process and immune escape mechanisms of brain metastasis and pave the way for precise immunotherapeutic strategies for patients with lung cancer with brain metastasis.

Tumor-immune microenvironment and NRF2 associate with clinical efficacy of PD-1 blockade combined with chemotherapy in lung squamous cell carcinoma.

The RATIONALE-307 study (ClinicalTrials.gov: NCT03594747) demonstrates prolonged progression-free survival (PFS) with first-line tislelizumab plus chemotherapy versus chemotherapy in advanced lung squamous cell carcinoma (LUSC; N = 360). Here we describe an immune-related gene expression signature (GES), composed of genes involved in both innate and adaptive immunity, that appears to differentiate tislelizumab plus chemotherapy PFS benefit versus chemotherapy. In contrast, a tislelizumab plus chemotherapy PFS benefit is observed regardless of programmed death ligand 1 (PD-L1) expression or tumor mutational burden (TMB). Genetic analysis reveals that NRF2 pathway activation is enriched in PD-L1positive and TMBhigh patients. NRF2 pathway activation is negatively associated with PFS, which affects efficacy outcomes associated with PD-L1 and TMB status, impairing their predictive potential. Mechanistic studies demonstrate that NRF2 directly mediates PD-L1 constitutive expression independent of adaptive PD-L1 regulation in LUSC. In summary, the GES is an immune signature that might identify LUSC patients likely to benefit from first-line tislelizumab plus chemotherapy.

Clinical definition of secondary resistance to immunotherapy in non-small cell lung cancer.

Immune checkpoint inhibitors (PD-1/PD-L1 and CTLA-4 blockade) have revolutionized the treatment landscape in non-small cell lung cancer (NSCLC). Secondary resistance to immunotherapy (IO), which poses a substantial challenge in clinical settings, occurs in several initial responders. Currently, new treatment approaches have been extensively evaluated in investigational studies for these patients to tackle this difficult problem; however, the lack of consistency in clinical definition, uniform criteria for enrollment in clinical trials, and interpretation of results remain significant hurdles to progress. Thus, our expert panel comprehensively synthesized data from current studies to propose a practical clinical definition of secondary resistance to immunotherapy in NSCLC in metastatic and neoadjuvant settings. In addition to patients who received IO alone (including IO-IO combinations), we also generated a definition for patients treated with chemotherapy plus IO. This consensus aimed to provide guidance for clinical trial design and facilitate future discussions with investigators. It should be noted that additional updates in this consensus are required when new data is available.

Biomarkers for Immunotherapy in Driver-Gene-Negative Advanced NSCLC.

Outcome improvement in patients with driver-gene-negative advanced non-small cell lung cancer (NSCLC) has been significantly enhanced through targeting the immune system, specifically the PD-L1/PD-1 axis. Nevertheless, only a subset of patients with advanced NSCLC may derive benefits from immuno-monotherapy or immunotherapy combined with chemotherapy. Hence, in order to identify patients who will gain the maximum advantage from immunotherapy, it is crucial to investigate predictive biomarkers. This review provides a summary of the currently identified biomarkers associated with the extent of benefit from immuno-monotherapy or immunotherapy combined with chemotherapy in patients with advanced NSCLC. These biomarkers can be categorized into three groups: tumor-related, tumor-microenvironment-related, and host-factor-related.Tumor-related factors include PD-L1 expression, tumor mutational burden and specific genetic mutations, while tumor-microenvironment-related factors include extracellular vesicles and T-cell receptors, and host-related factors include systemic inflammation, circulating fatty acid profile, and the microbiome.

Ablation of ERO1A induces lethal endoplasmic reticulum stress responses and immunogenic cell death to activate anti-tumor immunity.

Immunophenotyping of the tumor microenvironment (TME) is essential for enhancing immunotherapy efficacy. However, strategies for characterizing the TME exhibit significant heterogeneity. Here, we show that endoplasmic reticular oxidoreductase-1α (ERO1A) mediates an immune-suppressive TME and attenuates the response to PD-1 blockade. Ablation of ERO1A in tumor cells substantially incites anti-tumor T cell immunity and promotes the efficacy of aPD-1 in therapeutic models. Single-cell RNA-sequencing analyses confirm that ERO1A correlates with immunosuppression and dysfunction of CD8+ T cells along anti-PD-1 treatment. In human lung cancer, high ERO1A expression is associated with a higher risk of recurrence following neoadjuvant immunotherapy. Mechanistically, ERO1A ablation impairs the balance between IRE1α and PERK signaling activities and induces lethal unfolded protein responses in tumor cells undergoing endoplasmic reticulum stress, thereby enhancing anti-tumor immunity via immunogenic cell death. These findings reveal how tumor ERO1A induces immunosuppression, highlighting its potential as a therapeutic target for cancer immunotherapy.

Multimodal analysis of cell-free DNA whole-methylome sequencing for cancer detection and localization.

Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy.

Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.