Body mass index, as a novel predictor of hepatocellular carcinoma patients treated with Anti-PD-1 immunotherapy.

Immunocheckpoint inhibitors have shown significant efficacy in the treatment of hepatocellular carcinoma (HCC), but there are individual differences. The aim of this study was to explore body mass index (BMI) as a predictor of anti-PD-1 efficacy in patients with HCC. We retrospectively analyzed 101 HCC patients who treated with anti-PD-1 at Sun Yat-sen University Cancer Center from July 2018 to November 2019 and divided them into overweight (BMI > 24.9) and non-overweight (BMI ≤ 24.9) groups based on baseline BMI levels. BMI > 24.9 accounted for 22 cases (21.8%) and BMI ≤ 24.9 accounted for 79 cases (78.2%) in the study cohort. Overweight patients had higher disease control rates than non-overweight patients (P = 0.019, respectively). The mean progression-free survival (PFS) in overweight patients (10.23 months) was significantly longer than that of non-overweight patients (6.85 months; P = 0.027). Among patients with immune-related adverse events (irAEs), the mean PFS was also significantly longer in overweight patients (7.72 months) than in non-overweight patients (5.31 months, P = 0.034). Multivariate analysis showed that BMI was an independent prognostic factor for PFS in HCC patients treated with anti-PD-1 (hazard ratio: 0.47, P = 0.044). Thus, higher BMI predicts a better prognosis among HCC patients treated with anti-PD-1. In clinical practice, patients' BMI can provide a useful tool for predicting the efficacy of anti-PD-1 therapy.

Pan-Cancer Analysis of PARP1 Alterations as Biomarkers in the Prediction of Immunotherapeutic Effects and the Association of Its Expression Levels and Immunotherapy Signatures.

Background: Poly (ADP-ribose) polymerases-1 (PARP1) alterations are associated with PARP1 inhibitor resistance, regulating the function of Treg cells and PDL1 expression in tumor cells, and high PARP1 expression is significantly associated with aggressive behavior and chemotherapeutic resistance in several tumors. However, a comprehensive analysis of the predictive values of PARP1 alteration for immune checkpoint inhibitor (ICI) effectiveness in tumors remains unclear, and the associations between its expression and immunotherapy signatures also needs to be explored further. Methods: We performed some analyses with the cBioPortal online database (https://www.cbioportal.org), TIMER2.0 (Tumor Immune Estimation Resource 2.0, http://timer.comp-genomics.org/) and TCGA database (https://xenabrowser.net or https://portal.gdc.cancer.gov/). Survival analysis was conducted using Kaplan-Meier method, and the associations between PARP1 transcription levels and immune checkpoint gene expression, the number of neoantigens, tumor mutation burden (TMB) levels, and microsatellite instability (MSI) event are analyzed by spearman correlation analysis and visualization of those mentioned above is performed using R, version 3.6.3 (http://www.r-project.org/). Results: We found that PARP1 was altered in 1338 (2.9%) out of 45604 patients with diverse tumors, which was associated with markedly higher TMB levels in a variety of tumors (P < 0.01). Impressively, patients with PARP1 alterations in advanced tumors showed better overall survival (OS) in the ICI-treated cohort (P = 0.016). PARP1 altered group was substantially correlated with higher immune infiltrates across most tumors, including CD8+ T cells in colorectal adenocarcinoma (P = 0.0061), endometrial carcinoma (P = 0.0033), stomach cancer (P = 0.033), and cervical cancer (P = 0.026), respectively. The PARP1 altered group showed high expression in transcription (P < 0.001), and higher expression of LAG3, PDCD1, CTLA-4, and TIGIT (P < 0.05). Higher PARP1 expression was present in 27 tumor compared the corresponding normal tissues using the GTEx and TCGA databases and it had a worse OS in several tumors (P < 0.05). Further, high PARP1 expression was significantly associated with six immune cells (B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells) in most tumors, including colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), and liver hepatocellular carcinoma (LIHC) (P < 0.05). In particular, CD8+T cell infiltration, was also positively correlated with high PARP1 expression in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), LIHC, pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), testicular germ cell tumors (TGCT), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uveal melanoma (UVM) (P < 0.05, no data shown), and PARP1 expression was significantly positively correlated with the transcription levels of some of the 47 immune checkpoint genes, such as CD274, CTLA4, and PDCD1 in several tumors, including PAAD, LIHC, KIRC, HNSC, and BLCA (P < 0.05). A significant positive association between PARP1 expression and the number of immune neoantigen was found within COAD, KIRC, lung adenocarcinoma (LUAD), PAAD and THYM (P < 0.05), and there were also significantly positive correlations between PARP1 expression and TMB in many tumors like adrenocortical carcinoma (ACC), COAD, kidney chromophobe (KICH), LGG, LUAD, READ, skin cutaneous melanoma (SKCM) and stomach adenocarcinoma (STAD) (P < 0.05). In addition, high PARP1 expression was positively associated with microsatellite instability event in COAD, KIRP, BRCA, glioblastoma multiforme (GBM), lung squamous cell carcinoma (LUSC), LGG, READ, UCEC, SKCM and LUAD (P < 0.05). Conclusions: Our results highlight the significance of PARP1 alterations as pan-cancer predictive biomarkers for ICI treatment, and its expression levels seem to be correlated with the status of immunotherapy-associated signatures, thus may be a promising biomarker for predicting ICI response in several tumors.