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The diverse expression pattern of CD36 reflects its multiple cellular functions. However, the roles of CD36 in colorectal cancer (CRC) remain unknown. Here, we discover that CD36 expression is progressively decreased from adenomas to carcinomas. CD36 loss predicts poor survival of CRC patients. In CRC cells, CD36 acts as a tumor suppressor and inhibits aerobic glycolysis in vitro and in vivo. Mechanically, CD36-Glypcian 4 (GPC4) interaction could promote the proteasome-dependent ubiquitination of GPC4, followed by inhibition of β-catenin/c-myc signaling and suppression of downstream glycolytic target genes GLUT1, HK2, PKM2 and LDHA. Moreover, disruption of CD36 in inflammation-induced CRC model as well as Apc Min/+ mice model significantly increased colorectal tumorigenesis. Our results reveal a CD36-GPC4-β-catenin-c-myc signaling axis that regulates glycolysis in CRC development and may provide an intervention strategy for CRC prevention. CD36 is a membrane glycoprotein that has been shown to have tumour promoting or suppressor function depending on tumour type. Here, the authors address CD36 function in colorectal cancer and show it acts as a tumour suppressor by inhibiting B-catenin/myc signalling, resulting in downregulation of glycolysis.

Microsatellite instability (MSI) has been approved as a pan-cancer biomarker for immune checkpoint blockade (ICB) therapy. However, current MSI identification methods are not available for all patients. We proposed an ensemble multiple instance deep learning model to predict microsatellite status based on histopathology images, and interpreted the pathomics-based model with multi-omics correlation. Methods: Two cohorts of patients were collected, including 429 from The Cancer Genome Atlas (TCGA-COAD) and 785 from an Asian colorectal cancer (CRC) cohort (Asian-CRC). We established the pathomics model, named Ensembled Patch Likelihood Aggregation (EPLA), based on two consecutive stages: patch-level prediction and WSI-level prediction. The initial model was developed and validated in TCGA-COAD, and then generalized in Asian-CRC through transfer learning. The pathological signatures extracted from the model were analyzed with genomic and transcriptomic profiles for model interpretation. Results: The EPLA model achieved an area-under-the-curve (AUC) of 0.8848 (95% CI: 0.8185-0.9512) in the TCGA-COAD test set and an AUC of 0.8504 (95% CI: 0.7591-0.9323) in the external validation set Asian-CRC after transfer learning. Notably, EPLA captured the relationship between pathological phenotype of poor differentiation and MSI (P < 0.001). Furthermore, the five pathological imaging signatures identified from the EPLA model were associated with mutation burden and DNA damage repair related genotype in the genomic profiles, and antitumor immunity activated pathway in the transcriptomic profiles. Conclusions: Our pathomics-based deep learning model can effectively predict MSI from histopathology images and is transferable to a new patient cohort. The interpretability of our model by association with pathological, genomic and transcriptomic phenotypes lays the foundation for prospective clinical trials of the application of this artificial intelligence (AI) platform in ICB therapy.

Purpose To develop and validate a nomogram model to predict complete response (CR) after concurrent chemoradiotherapy (CCRT) in esophageal squamous cell carcinoma (ESCC) patients using pretreatment CT radiomic features. Methods Data of patients diagnosed as ESCC and treated with CCRT in Shantou Central Hospital during the period from January 2013 to December 2015 were retrospectively collected. Eligible patients were included in this study and randomize divided into a training set and a validation set after successive screening. The least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomics features calculating Rad-score in the training set. The logistic regression analysis was performed to identify the predictive clinical factors for developing a nomogram model. The area under the receiver operating characteristic curves (AUC) was used to assess the performance of the predictive nomogram model and decision curve was used to analyze the impact of the nomogram model on clinical treatment decisions. Results A total of 226 patients were included and randomly divided into two groups, 160 patients in training set and 66 patients in validation set. After LASSO analysis, seven radiomics features were screened out to develop a radiomics signature Rad-score. The AUC of Rad-score was 0.812 (95% CI 0.742–0.869, p  < 0.001) in the training set and 0.744 (95% CI 0.632–0.851, p  = 0.003) in the validation set. Multivariate analysis showed that Rad-score and clinical staging were independent predictors of CR status, with p values of 0.035 and 0.023, respectively. A nomogram model incorporating Rad-socre and clinical staging was developed and validated, with an AUC of 0.844 (95% CI 0.779–0.897) in the training set and 0.807 (95% CI 0.691–0.894) in the validation set. Delong test showed that the nomogram model was significantly superior to the clinical staging, with p  < 0.001 in the training set and p  = 0.026 in the validation set. The decision curve showed that the nomogram model was superior to the clinical staging when the risk threshold was greater than 25%. Conclusion We developed and validated a nomogram model for predicting CR status of ESCC patients after CCRT. The nomogram model was combined radiomics signature Rad-score and clinical staging. This model provided us with an economical and simple method for evaluating the response of chemoradiotherapy for patients with ESCC.

Liver kinase B1 ( LKB1 ) mutation is prevalent and a driver of resistance to immune checkpoint blockade (ICB) therapy for lung adenocarcinoma. Here leveraging single cell RNA sequencing data, we demonstrate that trafficking and adhesion process of activated T cells are defected in genetically engineered Kras -driven mouse model with Lkb1 conditional knockout. LKB1 mutant cancer cells result in marked suppression of intercellular adhesion molecule-1 (ICAM1). Ectopic expression of Icam1 in Lkb1- deficient tumor increases homing and activation of adoptively transferred SIINFEKL-specific CD8 + T cells, reactivates tumor-effector cell interactions and re-sensitises tumors to ICB. Further discovery proves that CDK4/6 inhibitors upregulate ICAM1 transcription by inhibiting phosphorylation of retinoblastoma protein RB in LKB1 deficient cancer cells. Finally, a tailored combination strategy using CDK4/6 inhibitors and anti-PD-1 antibodies promotes ICAM1-triggered immune response in multiple Lkb1 -deficient murine models. Our findings renovate that ICAM1 on tumor cells orchestrates anti-tumor immune response, especially for adaptive immunity. LKB1 mutations have been associated with primary resistance to immune checkpoint inhibitors in patients with lung cancer. Here the authors show that Lkb1 -deficient lung tumors are characterized by defective trafficking and adhesion of T cells and that, by upregulating ICAM1 expression, CDK4/6 inhibitors sensitize LKB1 mutant lung cancer to anti-PD1 blockade.

Background Dysregulation of long non-coding RNAs (lncRNAs) is responsible for cancer initiation and development, positioning lncRNAs as not only biomarkers but also promising therapeutic targets for cancer treatment. A growing number of lncRNAs have been reported in hepatocellular carcinoma (HCC), but their functional and mechanistic roles remain unclear. Methods Gene Set Enrichment Analysis was used to investigate the molecular mechanism of UPK1A antisense RNA 1 (UPK1A-AS1). Cell Counting Kit-8 assays, EdU assays, flow cytometry, western blotting, and xenograft assays were used to confirm the role of UPK1A-AS1 in the proliferation of HCC cells in vitro and in vivo. Bioinformatics analyses and quantitative polymerase chain reaction (qRT-PCR) were performed to explore the interplay between UPK1A-AS1 and enhancer of zeste homologue 2 (EZH2). RNA immunoprecipitation (RIP), RNA pull-down assays, western blotting, and qRT-PCR were conducted to confirm the interaction between UPK1A-AS1 and EZH2. The interaction between UPK1A-AS1 and miR-138-5p was examined by luciferase reporter and RIP assays. Finally, the expression level and prognosis value of UPK1A-AS1 in HCC were analyzed using RNA sequencing data from The Cancer Genome Atlas datasets. Results We showed that UPK1A-AS1, a newly identified lncRNA, promoted cellular proliferation and tumor growth by accelerating cell cycle progression. Cell cycle-related genes, including CCND1, CDK2, CDK4, CCNB1, and CCNB2, were significantly upregulated in HCC cells overexpressing UPK1A-AS1. Furthermore, overexpression of UPK1A-AS1 could protect HCC cells from cis-platinum toxicity. Mechanistically, UPK1A-AS1 interacted with EZH2 to mediate its nuclear translocation and reinforce its binding to SUZ12, leading to increased H27K3 trimethylation. Targeting EZH2 with specific small interfering RNA impaired the UPK1A-AS1-mediated upregulation of proliferation and cell cycle progression-related genes. Moreover, miR-138-5p was identified as a direct target of UPK1A-AS1. Additionally, UPK1A-AS1 was significantly upregulated in HCC, and the upregulation of UPK1A-AS1 predicted poor prognosis for patients with HCC. Conclusions Our study revealed that UPK1A-AS1 promotes HCC development by accelerating cell cycle progression through interaction with EZH2 and sponging of miR-138-5p, suggesting that UPK1A-AS1 possesses substantial potential as a novel biomarker for HCC prognosis and therapy.

Background Up to 17% of cancer survivors have been reported to develop second primary cancers (SPC), which cause significant physical and economic distress and often complicate clinical decision-making. However, understanding of SPC remains limited and superficial. Human leukocyte antigen (HLA) is characterized by its polymorphism and has been associated with various diseases. This study aims to explore the role of HLA diversity in SPC incidence. Methods We analyzed a cohort of 47,550 cancer patients from the UK Biobank. SNP-derived HLA alleles were used and SPC-related HLA alleles were identified using logistic regression, followed by stepwise filtering based on the Akaike information criterion (AIC) and permutation tests. Additionally, we examined the association between extragenetic factors and the risk of SPC in patients carrying hazardous HLA alleles. Results During a median follow-up of 3.11 years, a total of 2894 (6.09%) participants developed SPC. We identified three protective HLA alleles (DRB1*04:03 and DPA1*02:02 for males and DRB5*01:01 for females) and two hazardous alleles (A*26:01 for males and DPB1*11:01 for females) about SPC. The presence of the protective alleles was associated with a reduced SPC risk (males: hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.59–0.89; females: HR 0.81, 95% CI 0.70–0.93), while the hazardous alleles were linked to an increased risk (males: HR 1.27, 95% CI 1.03–1.56; females: HR 1.35, 95% CI 1.07–1.70). The hazardous allele A*26:01 indicated skin-lung organ-specific SPC occurrence in males. Animal fat and vitamin C were associated with SPC risk in males carrying the hazardous alleles, while free sugar and vegetable fat were linked to SPC risk in females. Conclusions These results suggest that HLA alleles may serve as biomarkers for the susceptibility and organ-specific occurrence of SPC, while dietary modulation may mitigate hazardous alleles-related SPC risk, potentially aiding in the early prediction and prevention of SPC.

Persistent Müllerian duct syndrome (PMDS) is a rare clinically and genetically overlapping disorder caused by mutations in the anti-Müllerian hormone (AMH) gene or the anti-Müllerian hormone receptor type 2 (AMHR2) gene. Affected individuals present uterus and tubes in normally virilized males and are discovered unexpectedly during other surgeries. Since it is rare and complex, a definitive clinical diagnosis can be missed, and there are no guidelines regarding how to deal with the uterus. In the present study, exome sequencing and Sanger verification were performed for causal variants in 12 PMDS patients. Preoperative diagnoses were made by positive exome sequencing in 8 patients. Of them, 7 patients evoked on the basis of ultrasound indicating bilateral testes on the same side of the body. Twelve different AMH variants (2 frameshift/nonsense, 1 deletion, 8 missense, and 1 in-frame) in 9 patients and 6 different AMHR2 variants (5 missense and 1 splicing) in 3 patients were identified. Seven variants were classified as \"pathogenic\" or \"likely pathogenic\", and 4 of them were novel. All but two patients with AMH defects showed low serum AMH concentrations, but all patients with AMHR2 defects showed elevated AMH levels. During surgery, an abnormal vas deferens was observed in half of the patients. Eight patients underwent orchidopexy with uterine preservation. Of them, 2 patients presented complications including irreducible cryptorchidism, and 3 patients developed Müllerian remnant cysts. Three patients underwent subtotal hysterectomy. Of them, one patient had complication of injury to the vas deferens, and one had hemorrhage after operation. This is the first report of PMDS involving a large Chinese population. The present study not only expands the variation spectrum but also provides clinical experience about the management of the uterus.

Background Immune checkpoint inhibitor (ICI) therapy combined with conventional therapies is being broadly applied in non-small cell lung cancer (NSCLC) patients. However, the risk of interstitial pneumonitis (IP) following a combined regimen is incompletely characterized. Methods A total of 46,127 NSCLC patients were extracted for disproportionality analyses of IP from the Food and Drug Administration’s Adverse Event Reporting System (FAERS) database. A total of 1108 NSCLC patients who received ICI treatment at Nanfang Hospital of Southern Medical University were collected and utilized for real-world validation. Results Of the 46,127 patients with NSCLC, 3830 cases (8.3%; 95% confidence interval [CI], 8.05–8.56) developed IP. Multivariable logistic regression analyses revealed that the adjusted ROR of ICI combined with radiation (RT) was the highest (121.69; 95% CI, 83.60–184.96; P < 0.0001) among all therapies, while that of ICI combined with chemotherapy (CHEMO) or targeted therapy (TARGET) was 0.90 (95% CI, 0.78–1.04; P = 0.160) and 1.49 (95% CI, 0.95–2.23; P = 0.065), respectively, using ICI monotherapy as reference. Furthermore, analyses from our validation cohort of 1108 cases showed that the adjusted odds ratio of ICI combined with RT was the highest (12.25; 95% CI, 3.34–50.22; P < 0.01) among all the therapies, while that of ICI combined with CHEMO or TARGET was 2.32 (95% CI, 0.89–7.92; P = 0.12) and 0.66 (95% CI, 0.03–4.55; P = 0.71), respectively, using ICI monotherapy as reference. Conclusions Compared with ICI monotherapy, ICI combined with RT, rather than with CHEMO or TARGET, is associated with a higher risk of IP in NSCLC patients. Hence, patients receiving these treatments should be carefully monitored for IP.

BackgroundGenetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis to develop a genomic mutation signature (GMS) and predict the response to anti-PD-(L)1 therapy.MethodsIn this multicohort analysis, 316 patients with non-squamous NSCLC treated with anti-PD-(L)1 from three independent cohorts were included in our study. Tumor samples from the patients were molecularly profiled by MSK-IMPACT or whole exome sequencing. We developed a risk model named GMS based on the MSK training cohort (n=123). The predictive model was first validated in the separate internal MSK cohort (n=82) and then validated in an external cohort containing 111 patients from previously published clinical trials.ResultsA GMS risk model consisting of eight genes (TP53, KRAS, STK11, EGFR, PTPRD, KMT2C, SMAD4, and HGF) was generated to classify patients into high and low GMS groups in the training cohort. Patients with high GMS in the training cohort had longer progression-free survival (hazard ratio (HR) 0.41, 0.28–0.61, p<0.0001) and overall survival (HR 0.53, 0.32–0.89, p=0.0275) compared with low GMS. We noted equivalent findings in the internal validation cohort and in the external validation cohort. The GMS was demonstrated as an independent predictive factor for anti-PD-(L)1 therapy comparing with tumor mutational burden. Meanwhile, GMS showed undifferentiated predictive value in patients with different clinicopathological features. Notably, both GMS and PD-L1 were independent predictors and demonstrated poorly correlated; inclusion of PD-L1 with GMS further improved the predictive capacity for PD-1 blockade immunotherapy.ConclusionsOur study highlights the potential predictive value of GMS for immunotherapeutic benefit in non-squamous NSCLC. Besides, the combination of GMS and PD-L1 may serve as an optimal partner in guiding treatment decisions for anti-PD-(L)1 based therapy.

Background Organ-specific metastatic context has not been incorporated into the clinical practice of guiding programmed death-(ligand) 1 [PD-(L)1] blockade, due to a lack of understanding of its predictive versus prognostic value. We aim at delineating and then incorporating both the predictive and prognostic effects of the metastatic-organ landscape to dissect PD-(L)1 blockade efficacy in non-small cell lung cancer (NSCLC). Methods A total of 2062 NSCLC patients from a double-arm randomized trial (OAK), two immunotherapy trials (FIR, BIRCH), and a real-world cohort (NFyy) were included. The metastatic organs were stratified into two categories based on their treatment-dependent predictive significance versus treatment-independent prognosis. A metastasis-based scoring system (METscore) was developed and validated for guiding PD-(L)1 blockade in clinical trials and real-world practice. Results Patients harboring various organ-specific metastases presented significantly different responses to immunotherapy, and those with brain and adrenal gland metastases survived longer than others [overall survival (OS), p = 0.0105; progression-free survival (PFS), p = 0.0167]. In contrast, survival outcomes were similar in chemotherapy-treated patients regardless of metastatic sites (OS, p = 0.3742; PFS, p = 0.8242). Intriguingly, the immunotherapeutic predictive significance of the metastatic-organ landscape was specifically presented in PD-L1-positive populations (PD-L1 > 1%). Among them, a paradoxical coexistence of a favorable predictive effect coupled with an unfavorable prognostic effect was observed in metastases to adrenal glands, brain, and liver (category I organs), whereas metastases to bone, pleura, pleural effusion, and mediastinum yielded consistent unfavorable predictive and prognostic effects (category II organs). METscore was capable of integrating both predictive and prognostic effects of the entire landscape and dissected OS outcome of NSCLC patients received PD-(L)1 blockade ( p < 0.0001) but not chemotherapy ( p = 0.0805) in the OAK training cohort. Meanwhile, general performance of METscore was first validated in FIR ( p = 0.0350) and BIRCH ( p < 0.0001), and then in the real-world NFyy cohort ( p = 0.0181). Notably, METscore was also applicable to patients received PD-(L)1 blockade as first-line treatment both in the clinical trials (OS, p = 0.0087; PFS, p = 0.0290) and in the real-world practice (OS, p = 0.0182; PFS, p = 0.0045). Conclusions Organ-specific metastatic landscape served as a potential predictor of immunotherapy, and METscore might enable noninvasive forecast of PD-(L)1 blockade efficacy using baseline radiologic assessments in advanced NSCLC.