Background Although TP53 co-mutation with KRAS/ATM/EGFR/STK11 have been proved to have predictive value for response to immune checkpoint inhibitors (ICIs), not all TP53 mutations are equal in this context. As the main part of TP53 mutant types, Missense and Nonsense alternations in TP53 as independent factors to predict the response to ICIs within Lung Adenocarcinoma (LUAD) patients have not yet been reported. Methods An integrated analysis based on multiple-dimensional data types including genomic, transcriptomic, proteomic and clinical data from published lung adenocarcinoma data and local database of LUAD taking immune checkpoint inhibitors. Gene set enrichment analysis (GSEA) was used to determine potentially relevant gene expression signatures between specific subgroups. Single-sample GSEA (GSVA) is conducted to calculate the score for enrichment of a set of genes regulating DNA damage repair (DDR) pathway. Findings The TP53-missense-mutation group showed increased PD-L1 (CD274) level and enriched IFN-γ signatures compared with the TP53-wild-type subgroup, but no differences were noted in patients with nonsense-mutant vs wild-type p53. Furthermore, a group of suppressor Immune cells like M2 Macrophage and Neutrophils are found enriched in nonsense group. On the other-side, both TP53 missense and nonsense mutations are associated with elevated TMB and neoantigen levels and contribute equally to DNA damage repair deficiency. The distribution regarding to multi-dimensional factors determining the efficacy of ICIs finally transformed into diverse clinical benefits for LUAD. TP53 missense but not -nonsense Mutants are associated with better clinical benefits taking antiPD-1/1L. However, all such TP53 subgroups responds well to nivolumab (antiPD-L1) plus ipilimumab (antiCTLA-4) therapy. Interpretation Our study demonstrated that not all TP53 mutations are equal in predicting efficacy in patients with LUAD treated with ICIs. Multi-center data showed that TP53 missense and nonsense mutations were significantly different in terms of associations with PD-L1 expression, IFN-γ signatures and TME composition. Special attention should be paid to potential TP53 mutation heterogeneity when evaluating TP53 status as biomarker for ICIs. Funding The study was supported by Key Lab System Project of Guangdong Science and Technology Department – Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer (Grant No. 2017B030314120, to Yi-Long WU)

Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumor microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analyzing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-γ signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 (n = 54) and anti-CTLA-4 (n = 42), as well as in patients with gastric cancer treated with anti-PD-1 (n = 45), demonstrating the potential utility of IMS as a predictive biomarker that complements existing GEP signatures for immunotherapy.

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Fairness has emerged as a critical problem in federated learning (FL). In this work, we identify a cause of unfairness in FL -- conflicting gradients with large differences in the magnitudes. To address this issue, we propose the federated fair averaging (FedFV) algorithm to mitigate potential conflicts among clients before averaging their gradients. We first use the cosine similarity to detect gradient conflicts, and then iteratively eliminate such conflicts by modifying both the direction and the magnitude of the gradients. We further show the theoretical foundation of FedFV to mitigate the issue conflicting gradients and converge to Pareto stationary solutions. Extensive experiments on a suite of federated datasets confirm that FedFV compares favorably against state-of-the-art methods in terms of fairness, accuracy and efficiency. The source code is available at https://github.com/WwZzz/easyFL.