Evaluation of Molecular Residual Disease by a Fixed Panel in Resectable Colorectal Cancer.

Purpose: Molecular residual disease (MRD) is a promising biomarker in colorectal cancer (CRC) for prognosis and guiding treatment, while the whole-exome sequencing (WES) based tumor-informed assay is standard for evaluating MRD based on circulating tumor DNA (ctDNA). In this study, we assessed the feasibility of a fixed-panel for evaluating MRD in CRC. Materials and Methods: 75 patients with resectable stage I-III CRC were enrolled. Tumor tissues obtained by surgery, and pre-operative and post-operative day 7 blood samples were collected. The ctDNA was evaluated using the tumor-agnostic and tumor-informed fixed assays, as well as the WES-based and panel-based personalized assays in randomly selected patients. Results: The tumor-informed fixed assay had a higher pre-operative positive rate than the tumor-agnostic assay (73.3% vs 57.3%). The pre-op ctDNA status failed to predict disease-free survival (DFS) in either of the fixed assays, while the tumor-informed fixed assay-determined post-op ctDNA positivity was significantly associated with worse DFS (HR, 20.74, 95%CI 7.19-59.83; p<0.001), which was an independent predictor by multivariable analysis (HR, 28.57, 95%CI 7.10-114.9; p<0.001). Sub-cohort analysis indicated the WES-based personalized assay had the highest pre-operative positive rate (95.1%). The two personalized assays and the tumor-informed fixed assay demonstrated same results in post-op landmark (HR, 26.34, 95%CI, 6.01-115.57; p<0.001), outperforming the tumor-agnostic fixed panel (HR, 3.04, 95%CI, 0.94-9.89; p=0.052). Conclusion: Our study confirmed the prognostic value of the ctDNA positivity at post-op day 7 by the tumor-informed fixed panel. The tumor-informed fixed panel may be a cost-effective method to evaluate MRD, which warrants further studies in future.

A 15-Inflammation-Related Gene Signature Predicts the Prognosis of Patients With Pancreatic Ductal Adenocarcinoma.

Chronic inflammation promotes the development of pancreatic ductal adenocarcinoma (PDAC) and PDAC-related inflammatory tumor microenvironment facilitates tumor growth and metastasis. Thus, we aimed to study the association between inflammatory response and prognosis in patients with PDAC. We conducted the whole transcriptomic sequencing using tissue samples collected from patients diagnosed with PDAC (n = 106) recruited from Shandong Cancer Hospital. We first constructed a prognostic signature using 15 inflammation-related genes in The Cancer Genome Atlas (TCGA) cohort (n = 177) and further validated it in an independent International Cancer Genome Consortium (ICGC) cohort (n = 90) and our in-house cohort. PDAC patients with a higher risk score had poorer overall survival (OS) (P < 0.001; HR, 3.02; 95% CI, 1.94-4.70). The association between the prognostic signature and OS remained significant in the multivariable Cox regression adjusting for age, sex, alcohol exposure, diabetes, and stage (P < 0.001; HR, 2.91; 95% CI, 1.73-4.89). This gene signature also robustly predicted prognosis in the ICGC cohort (P = 0.01; HR, 1.94; 95% CI, 1.14-3.30) and our cohort (P < 0.001; HR, 2.40; 95% CI, 1.45-3.97). Immune subtype C3 (inflammatory) was enriched and CD8+ T cells were higher in patients with a lower risk score (P < 0.05). Furthermore, PDAC patients with higher risk scores were more sensitive to chemotherapy, immunotherapy, and PARP inhibitors (P < 0.05). In sum, we identified a novel gene signature that was associated with inflammatory response for risk stratification, prognosis prediction, and therapy guidance in PDAC patients. Future studies are warranted to validate the clinical utility of the signature.

MAEL in human cancers and implications in prognostication and predicting benefit from immunotherapy over VEGFR/mTOR inhibitors in clear cell renal cell carcinoma: a bioinformatic analysis.

Maelstrom (MAEL), a novel cancer/testis-associated gene, may facilitate the initiation and progression of human malignancies, warranting comprehensive investigations. Single-cell and tissue-bulk transcriptomic data demonstrated higher MAEL expression in testis (spermatogonia/spermatocyte), kidney (proximal tubular cell), and brain (neuron/astrocyte), and corresponding cancers, including testicular germ cell tumor, glioma, papillary renal cell carcinoma, and clear cell renal cell carcinoma (ccRCC). Of these cancers, only in ccRCC did MAEL expression exhibit associations with both recurrence-free survival and overall survival. High MAEL expression was associated with an anti-inflammatory tumor immune microenvironment and VEGFR/mTOR activation in ccRCC tissues and high sensitivities to VEGFR/PI3K-AKT-mTOR inhibitors in ccRCC cell lines. Consistent with these, low rather than high MAEL expression indicated remarkable progression-free survival benefits from immune checkpoint inhibitor (ICI)-based immunotherapies over VEGFR/mTOR inhibitors in two large phase III trials (JAVELIN Renal 101 and CheckMate-025). MAEL is a biologically and clinically significant determinant with potential for prognostication after nephrectomy and patient selection for VEGFR/mTOR inhibitors and immunotherapy-based treatments.

The prognostic significance and clinical relevance of stem cell characteristic in bladder cancer.

Background: Bladder cancer (BLCA) is a common malignant tumor of urinary system and prognostic biomarkers are needed for better clinical decision-making and patient management. Cancer stem cells (CSCs) are involved in carcinogenesis, development, metastasis and recurrence of BLCA. This study explored the prognostic and predictive value of CSCs-related genes and laid the groundwork for precision treatment development in BLCA. Methods: The mRNA data and corresponding clinical information obtained from TCGA-BLCA cohort was used to discover biomarkers and develop CSCs-related prognostic model, which was further validated in GSE32548 and GSE32894 datasets. In addition, the association between CSCs-related risk score and therapeutic efficacy was analyzed to explore the potential predictive value of the prognostic model. Results: We identified four CSCs-related subtypes and 900 differentially expressed genes (DEGs) among subtypes. Then the CSCs-related prognostic model was built based on 16 CSCs-related DEGs with the most significant prognostic value. Patients in the low-risk group had better overall survival than those in high-risk group (P < 0.001; HR, 0.42; 95 %CI, 0.31-0.57). Multivariable Cox analysis in training and test sets confirmed the independence of CSCs-related risk score as a prognostic factor (P < 0.05). The difference of survival between two risk groups were probably due to the significantly varied immune microenvironment based on the analysis of infiltrated immune cells. Additionally, the risk score was significantly associated with chemotherapy sensitivity and the response to anti-PD-L1 therapy (P < 0.05) which suggested a potential predictive value of CSCs-related risk model. Conclusion: We established a risk classifier based on 16 CSCs-related genes for predicting survival in patients with BLCA. The CSCs-related risk model has both prognostic value and potential predictive value for therapeutic efficacy, which brings us closer to understanding the important role of CSCs in BLCA and may provide guidance for clinical treatment decision-making and patient management. The clinical utility of the CSCs-related risk classifier warrants further studies.

Notch activation defines immune-suppressive subsets of ccRCCs with unfavorable benefits from immunotherapy over VEGFR/mTOR inhibitors.

The evolutionarily conserved Notch pathway, involved in cancer stem cell capacity and cancer immunity, may predict the benefit from immune checkpoint inhibitors (ICIs) in clear cell renal cell carcinoma (ccRCC). In the TCGA dataset, mRNA expression of Notch pathway genes identified three clusters with different prognoses and molecular characteristics. Based on the differentially expressed Notch pathway genes between clusters, we constructed the Notch-score, correlated with Notch activation, angiogenesis, PI3K-AKT-mTOR activity, and sensitivities to VEGFR/mTOR inhibitors. A high Notch-score was linked with more "resting"/"anti-inflammatory" rather than "activated"/"pro-inflammatory" tumor-infiltrating immune cells, inactivated immune pathways, and scarce any benefits from ICI-based therapies over VEGFR/mTOR inhibitors in the JAVELIN Renal 101 (avelumab plus axitinib vs. sunitinib) and the CheckMate-009/010/025 trials (nivolumab vs. everolimus). For the Notch-activated ccRCCs, ICIs provide limited advantages and might not be strongly recommended, by which the cost-effectiveness of treatments in ccRCCs may be potentially improved.

T proliferating cells derived autophagy signature associated with prognosis and immunotherapy resistance in a pan-cancer analysis.

Despite autophagy modulating tumor immunity in the tumor microenvironment (TME), the immunotherapeutic efficacy and potential mechanism of autophagy signature was not explicit. We manually curated an autophagy gene set and defined a pan-cancer autophagy signature by comparing malignant tissues and normal tissues in The Cancer Genome Atlas (TCGA) cohort. The pan-cancer autophagy signature was derived from T proliferating cells as demonstrated in multiple single-cell RNA sequencing (scRNA-seq) datasets. The pan-cancer autophagy signature could influence the cell-cell interactions in the TME and predict the responsiveness of immune checkpoint inhibitors (ICIs) in the metastatic renal cell carcinoma, non-small cell lung cancer, bladder cancer, and melanoma cohorts. Metabolism inactivation accompanied with dysregulation of autophagy was investigated with transcriptomic and proteomic data. The immunotherapeutic predictive role and mechanism regulation of the autophagy signature was validated in an in-house cohort. Our study provides valuable insights into the mechanisms of ICI resistance.

Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma.

Adjuvant chemotherapy benefits patients with resected pancreatic ductal adenocarcinoma (PDAC), but the compromised physical state of post-operative patients can hinder compliance. Biomarkers that identify candidates for prompt adjuvant therapy are needed. In this prospective observational study, 1,171 patients with PDAC who underwent pancreatectomy were enrolled and extensively followed-up. Proteomic profiling of 191 patient samples unveiled clinically relevant functional protein modules. A proteomics-level prognostic risk model was established for PDAC, with its utility further validated using a publicly available external cohort. More importantly, through an interaction effect regression analysis leveraging both clinical and proteomic datasets, we discovered two biomarkers (NDUFB8 and CEMIP2), indicative of the overall sensitivity of patients with PDAC to adjuvant chemotherapy. The biomarkers were validated through immunohistochemistry on an internal cohort of 386 patients. Rigorous validation extended to two external multicentic cohorts-a French multicentric cohort (230 patients) and a cohort from two grade-A tertiary hospitals in China (466 patients)-enhancing the robustness and generalizability of our findings. Moreover, experimental validation through functional assays was conducted on PDAC cell lines and patient-derived organoids. In summary, our cohort-scale integration of clinical and proteomic data demonstrates the potential of proteomics-guided prognosis and biomarker-aided adjuvant chemotherapy for PDAC.

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.

Complement and coagulation cascades pathway-related signature as a predictor of immunotherapy in metastatic urothelial cancer.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have shown efficacy in patients with metastatic urothelial cancer (mUC), however, only a small subset of patients could benefit from ICIs. Identifying predictive biomarkers of ICIs in patients with mUC is clinical meaningful for patient stratification and administration. METHODS: Clinical and transcriptomic data of mUC patients treated with ICIs from mUC cohort (IMvigor210 study) was utilized to explore the predictive biomarkers. LASSO Cox regression was performed to construct a predictive model. The predictive model was trained and tested in the mUC cohort, and then exploratively tested in clear cell renal cell carcinoma (ccRCC) and melanoma cohorts in which patients also received ICIs regimens. RESULTS: The differentially expressed genes (DEGs) in complement and coagulation cascades pathway (CCCP) were mainly enriched in non-responders of ICIs in the mUC cohort. A CCCP risk score was constructed based on the DEGs in CCCP. Patients with a low-risk score were more responsive to ICIs and had better overall survival (OS) than those with a high-risk score in the training set (HR, 0.38; 95%CI, 0.27-0.53, P<0.001) and the test set (HR, 0.34; 95%CI, 0.17-0.71, P=0.003). The association between the CCCP risk score and OS remained significant in the multivariable cox regression by adjusting PD-L1 expression and TMB (P<0.05). In addition, there was no difference for OS in the bladder cancer patients without ICIs (TCGA-BLCA cohort, HR, 0.76, 95%CI, 0.49-1.18, P=0.22), suggesting a predictive but not prognostic effect of the risk score. For the exploratory analysis, consistent results were observed that low-risk group showed superior OS in ccRCC cohort (HR, 0.52, 95%CI, 0.37-0.75, P<0.001) and melanoma cohort (HR, 0.27, 95%CI, 0.12-0.62, P=0.001). CONCLUSIONS: Our study showed that the CCCP risk score is an independent biomarker that predicts the efficacy of ICIs in mUC patients. The patients with a low-risk score tend to have a better response to ICIs and a longer life time probably due to the immune-activated TME. Further studies are needed to validate the clinical utility of the seven-gene signature.

Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell carcinoma, accounting for about 30% of all lung cancers. Yet, the evaluation of prognostic outcome and therapy response of patients with LUSC remains to be resolved. This study aimed to explore the prognostic value of cell death pathways and develop a cell death-associated signature for predicting prognosis and guiding treatment in LUSC. METHODS: Transcriptome profiles and corresponding clinical information of LUSC patients were gathered from The Cancer Genome Atlas (TCGA-LUSC, n = 493) and Gene Expression Omnibus database (GSE74777, n = 107). The cell death-related genes including autophagy (n = 348), apoptosis (n = 163), and necrosis (n = 166) were retrieved from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases. In the training cohort (TCGA-LUSC), LASSO Cox regression was used to construct four prognostic signatures of respective autophagy, apoptosis, and necrosis pathway and genes of three pathways. After comparing the four signatures, the cell death index (CDI), the signature of combined genes, was further validated in the GSE74777 dataset. We also investigated the clinical significance of the CDI signature in predicting the immunotherapeutic response of LUSC patients. RESULTS: The CDI signature was significantly associated with the overall survival of LUSC patients in the training cohort (HR, 2.13; 95% CI, 1.62‒2.82; P < 0.001) and in the validation cohort (HR, 1.94; 95% CI, 1.01‒3.72; P = 0.04). The differentially expressed genes between the high- and low-risk groups contained cell death-associated cytokines and were enriched in immune-associated pathways. We also found a higher infiltration of naive CD4+ T cells, monocytes, activated dendritic cells, neutrophils, and lower infiltration of plasma cells and resting memory CD4+ T cells in the high-risk group. Tumor stemness indices, mRNAsi and mDNAsi, were both negatively correlated with the risk score of the CDI. Moreover, LUSC patients in the low-risk group are more likely to respond to immunotherapy than those in the high-risk group (P = 0.002). CONCLUSIONS: This study revealed a reliable cell death-associated signature (CDI) that closely correlated with prognosis and the tumor microenvironment in LUSC, which may assist in predicting the prognosis and response to immunotherapy for patients with LUSC.

Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology.

Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis. Proteogenomic profiling of LICOB identified proliferative and metabolic organoid subtypes linked to patient prognosis. High-throughput drug screening revealed distinct response patterns of each subtype that were associated with specific multiomics signatures. Through integrative analyses of LICOB pharmaco-proteogenomics data, we identified the molecular features associated with drug responses and predicted potential drug combinations for personalized patient treatment. The synergistic inhibition effect of mTOR inhibitor temsirolimus and the multitargeted tyrosine kinase inhibitor lenvatinib was validated in organoids and patient-derived xenografts models. We also provide a user-friendly web portal to help serve the biomedical research community. Our study is a rich resource for investigation of liver cancer biology and pharmacological dependencies and may help enable functional precision medicine.

A prognostic and immunotherapeutic predictive model based on the cell-originated characterization of tumor microenvironment in lung adenocarcinoma.

Tumor microenvironment (TME) plays a crucial role in predicting prognosis and response to therapy in lung cancer. Our study established a prognostic and immunotherapeutic predictive model, the tumor immune cell score (TICS), by differentiating cell origins in lung adenocarcinoma (LUAD) based on the transcriptomic data of 2,510 patients in 14 independent cohorts, including 12 public datasets and two in-house cohorts. The high TICS was associated with prolonged overall survival (OS), especially in the early-stage LUAD. For the advanced-stage LUAD, high TICS predicted a superior OS in patients who were treated with immunotherapy instead of chemotherapy or TKI. The result suggested that TICS could serve as an indicator for the prognostic stratification management of patients in the early-stage LUAD, and as a potential guide for therapeutic decision-marking in the advanced-stage LUAD. Our findings provided an insight into prognosis stratification and potential guidance for treatment strategy selection.

Development and validation of an inflammatory response-related gene and clinical factor-based signature for predicting prognosis in gastric cancer.

Background: Gastric cancer (GC) is an aggressive disease that requires prognostic tools to aid in clinical management. The prognostic power of clinical features is unsatisfactory, which might be improved by combining mRNA-based signatures. Inflammatory response is widely associated with cancer development and treatment response. It is worth exploring the prognostic performance of inflammatory-related genes plus clinical factors in GC. Methods: An 11-gene signature was trained using the least absolute shrinkage and selection operator (LASSO) based on the messenger RNA (mRNA) and overall survival (OS) data of The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort. A nomogram was established using the signature and clinical factors with a significant linkage with OS and was validated in 3 independent cohorts (GSE15419, GSE13861, and GSE66229) via calculating the area under the receiver operator characteristic curve (AUC). The association between the signature and immunotherapy efficacy was explored in the ERP107734 cohort. Results: A high risk score was associated with shorter OS in both the training and the validation sets (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.691, 0.644, and 0.707; GSE15459: 0.602, 0.602, and 0.650; GSE13861: 0.648, 0.611, and 0.647; GSE66229: 0.661, 0.630, and 0.610). Its prognostic power was improved by combining clinical factors including age, sex, and tumor stage (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.759, 0.706, and 0.742; GSE15459: 0.773, 0.786, and 0.803; GSE13861: 0.749, 0.881, and 0.795; GSE66229: 0.773, 0.735, and 0.722). Moreover, a low-risk score was associated with a favorable response to pembrolizumab monotherapy in the advanced setting (AUC =0.755, P=0.010). Conclusions: In GCs, the inflammatory response-related gene-based signature was related to immunotherapy efficacy, and its risk score plus clinical features yielded robust prognostic power. With prospective validation, this model may improve the management of GC by enabling risk stratification and the prediction of response to immunotherapy.

A pyroptosis-related gene signature for prognosis prediction in hepatocellular carcinoma.

Introduction: Hepatocellular carcinoma (HCC) is one of the most invasive cancers with a low 5-year survival rate. Pyroptosis, a specialized form of cell death, has shown its association with cancer progression. However, its role in the prognosis of HCC has not been fully understood. Methods: In our study, clinical information and mRNA expression for 1076 patients with HCC were obtained from the five public cohorts. Pyroptotic clusters were generated by unsupervised clustering based on 40 pyroptosis-related genes (PRGs) in the TCGA and ICGC cohort. A pyroptosis-related signature was constructed using least absolute shrinkage and selection operator (LASSO) regression according to differentially expressed genes (DEGs) of pyroptotic clusters. The signature was then tested in the validation cohorts (GES10142 and GSE14520) and subsequently validated in the CPTAC cohort (n=159) at both mRNA and protein levels. Response to sorafenib was explored in GSE109211. Results: Three clusters were identified based on the 40 PRGs in the TCGA cohort. A total of 24 genes were selected based on DEGs of the above three pyroptotic clusters to construct the pyroptotic risk score. Patients with the high-risk score showed shorter overall survival (OS) compared to those with the low-risk score in the training set (P<0.001; HR, 3.06; 95% CI, 2.22-4.24) and the test set (P=0.008; HR, 1.61; 95% CI, 1.13-2.28). The predictive ability of the risk score was further confirmed in the CPTAC cohort at both mRNAs (P<0.001; HR, 2.99; 95% CI, 1.67-5.36) and protein levels (P<0.001; HR, 2.97; 95% CI 1.66-5.31). The expression of the model genes was correlated with immune cell infiltration, angiogenesis-related genes, and sensitivity to antiangiogenic therapy (P<0.05). Discussion: In conclusion, we established a prognostic signature of 24 genes based on pyroptosis clusters for HCC patients, providing insight into the risk stratification of HCC.

Senescence-related genes define prognosis, immune contexture, and pharmacological response in gastric cancer.

As one of the prevalent tumors worldwide, gastric cancer (GC) has obtained sufficient attention in its clinical management and prognostic stratification. Senescence-related genes are involved in the tumorigenesis and progression of GC. A machine learning algorithm-based prognostic signature was developed from six senescence-related genes including SERPINE1, FEN1, PDGFRB, SNCG, TCF3, and APOC3. The TCGA-STAD cohort was utilized as a training set while the GSE84437 and GSE13861 cohorts were analyzed for validation. Immune cell infiltration and immunotherapy efficacy were investigated in the PRJEB25780 cohort. Data from the genomics of drug sensitivity in cancer (GDSC) database revealed pharmacological response. The GSE13861 and GSE54129 cohorts, single-cell dataset GSE134520, and The Human Protein Atlas (THPA) database were utilized for localization of the key senescence-related genes. Association of a higher risk-score with worse overall survival (OS) was identified in the training cohort (TCGA-STAD, P<0.001; HR = 2.03, 95% CI, 1.45-2.84) and the validation cohorts (GSE84437, P = 0.005; HR = 1.48, 95% CI, 1.16-1.95; GSE13861, P = 0.03; HR = 2.23, 95% CI, 1.07-4.62). The risk-score was positively correlated with densities of tumor-infiltrating immunosuppressive cells (P < 0.05) and was lower in patients who responded to pembrolizumab monotherapy (P = 0.03). Besides, patients with a high risk-score had higher sensitivities to the inhibitors against the PI3K-mTOR and angiogenesis (P < 0.05). Expression analysis verified the promoting roles of FEN1, PDGFRB, SERPINE1, and TCF3, and the suppressing roles of APOC3 and SNCG in GC, respectively. Immunohistochemistry staining and single-cell analysis revealed their location and potential origins. Taken together, the senescence gene-based model may potentially change the management of GC by enabling risk stratification and predicting response to systemic therapy.

bITH, a blood-based metric of intratumor heterogeneity, is associated with clinical response to immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Intratumor heterogeneity (ITH) has been associated with poor prognosis in advanced non-small cell cancer (NSCLC) patients receiving immune checkpoint blockade (ICB) therapies. However, there is currently no evidence supporting an ITH metric as a predictor of clinical benefit from ICB. The unique advantages of blood make it a promising material for ITH estimation and relevant applications. This study aims to develop and validate a blood-based ITH index for predicting ICB response. METHODS: NSCLC patients from the OAK and POPLAR clinical trials were used as the training cohorts for algorithm development. Survival analyses with overall survival (OS) and progression-free survival (PFS) as endpoints were performed to assess clinical response. The predictive value of bITH was subsequently validated with an independent cohort of 42 NSCLC patients treated with PD-1 blockade. FINDINGS: bITH was significantly associated with the differential OS and PFS elicited by atezolizumab vs. docetaxel in both univariable and multivariable analyses in the OAK patients, suggesting bITH as an independent predictor for response to ICB. Moreover, compared with blood tumor mutation burden (bTMB), bITH enabled greater OS segregation and comparable PFS segregation, and obtained a predictive role regardless of bTMB status. Moreover, the association between bITH and PFS was validated with an independent cohort. INTERPRETATION: Patients with low blood-based ITH metric manifest significant OS and PFS benefit from immunotherapy versus chemotherapy. Future research is awaited to corroborate our findings and to enrich the clinical utility of ITH. FUNDING: This study was supported by the National Natural Science Foundation of China (Nos. 81972718 and 81572321), the Natural Scientific Foundation of Zhejiang Province, China (No. LY19H160007), the Science and Technology Program for Health and Medicine in Zhejiang Province, China (No. 2021KY541), the Scientific Research Project, Science and Technology Department of Sichuan Province (No. 21YYJC1616), the Scientific Research Project, Sichuan Medical Association (No. S20002), Wu Jieping Medical Foundation (No. 320.6750), and 2018 Entrepreneurial Leading Talent of Guangzhou Huangpu District and Guangzhou Development District (No. 2022-L023).

BAP1-related signature predicts benefits from immunotherapy over VEGFR/mTOR inhibitors in ccRCC: a retrospective analysis of JAVELIN Renal 101 and checkmate-009/010/025 trials.

BACKGROUND: In patients with advanced clear cell renal cell carcinoma, despite the undoubted benefits from immune checkpoint inhibitor (ICI)-based therapies over monotherapies of angiogenic/mTOR inhibitors in the intention-to-treat population, approximately a quarter of the patients can scarcely gain advantage from ICIs, prompting the search for predictive biomarkers for patient selection. METHODS: Clinical and multi-omic data of 2428 ccRCC patients were obtained from The Cancer Genome Atlas (TCGA, n = 537), JAVELIN Renal 101 (avelumab plus axitinib vs. sunitinib, n = 885), and CheckMate-009/010/025 (nivolumab vs. everolimus, n = 1006). RESULTS: BAP1 mutations were associated with large progression-free survival (PFS) benefits from ICI-based immunotherapies over sunitinib/everolimus (pooled estimate of interaction HR = 0.71, 95% CI 0.51-0.99, P = 0.045). Using the top 20 BAP1 mutation-associated differentially expressed genes (DEGs) generated from the TCGA cohort, we developed the BAP1-score, negatively correlated with angiogenesis and positively correlated with multiple immune-related signatures concerning immune cell infiltration, antigen presentation, B/T cell receptor, interleukin, programmed death-1, and interferon. A high BAP1-score indicated remarkable PFS benefits from ICI-based immunotherapies over angiogenic/mTOR inhibitors (avelumab plus axitinib vs. sunitinib: HR = 0.55, 95% CI 0.43-0.70, P < 0.001; nivolumab vs. everolimus: HR = 0.72, 95% CI 0.52-1.00, P = 0.045), while these benefits were negligible in the low BAP1-score subgroup (HR = 1.16 and 1.02, respectively). CONCLUSION: In advanced ccRCCs, the BAP1-score is a biologically and clinically significant predictor of immune microenvironment and the clinical benefits from ICI-based immunotherapies over angiogenic/mTOR inhibitors, demonstrating its potential utility in optimizing the personalized therapeutic strategies in patients with advanced ccRCC.

Tumor immune contexture predicts recurrence after prostatectomy and efficacy of androgen deprivation and immunotherapy in prostate cancer.

BACKGROUND: Prostate cancer is one of the most common cancers in men with notable interpatient heterogeneity. Implications of the immune microenvironment in predicting the biochemical recurrence-free survival (BCRFS) after radical prostatectomy and the efficacy of systemic therapies in prostate cancer remain ambiguous. METHODS: The tumor immune contexture score (TICS) involving eight immune contexture-related signatures was developed using seven cohorts of 1120 patients treated with radical prostatectomy (training: GSE46602, GSE54460, GSE70769, and GSE94767; validation: GSE70768, DKFZ2018, and TCGA). The association between the TICS and treatment efficacy was investigated in GSE111177 (androgen deprivation therapy [ADT]) and EGAS00001004050 (ipilimumab). RESULTS: A high TICS was associated with prolonged BCRFS after radical prostatectomy in the training (HR = 0.32, 95% CI 0.24-0.45, P < 0.001) and the validation cohorts (HR = 0.45, 95% CI 0.32-0.62, P < 0.001). The TICS showed stable prognostic power independent of tumor stage, surgical margin, pre-treatment prostatic specific antigen (PSA), and Gleason score (multivariable HR = 0.50, 95% CI 0.39-0.63, P < 0.001). Adding the TICS into the prognostic model constructed using clinicopathological features significantly improved its 1/2/3/4/5-year area under curve (P < 0.05). A low TICS was associated with high homologous recombination deficiency scores, abnormally activated pathways concerning DNA replication, cell cycle, steroid hormone biosynthesis, and drug metabolism, and fewer tumor-infiltrating immune cells (P < 0.05). The patients with a high TICS had favorable BCRFS with ADT (HR = 0.25, 95% CI 0.06-0.99, P = 0.034) or ipilimumab monotherapy (HR = 0.23, 95% CI 0.06-0.81, P = 0.012). CONCLUSIONS: Our study delineates the associations of tumor immune contexture with molecular features, recurrence after radical prostatectomy, and the efficacy of ADT and immunotherapy. The TICS may improve the existing risk stratification systems and serve as a patient-selection tool for ADT and immunotherapy in prostate cancer.

A prognostic 15-gene model based on differentially expressed genes among metabolic subtypes in diffuse large B-cell lymphoma.

The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04-4.01, p < 0.001; PFS: HR 2.42, 95% CI 1.77-3.31, p < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, p = 0.002; GSE53786: HR 2.05, p = 0.02; GSE87371: HR 1.85, p = 0.027; GSE23051: HR 6.16, p = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, p = 0.033; GSE23051: HR 2.74, p = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors (p < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP (p = 0.0042), PI3K inhibitor (p < 0.05), and proteasome inhibitor (p < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients.