Background: Tumor mutation burden (TMB) have been served as the most prevalent biomarkers to predict immunotherapy response. LRP1B (low-density lipoprotein receptor-related protein 1B) is frequently mutated in melanoma, non-small cell lung cancer (NSCLC) and other tumors; however, its association with TMB and survival in patients with immunotherapy remains unknown. Methods: We curated somatic mutation data and clinicopathologic information from 332 melanoma immunotherapy samples for discovery and 113 NSCLC samples for further corroboration. Bayesian variants non-negative matrix factorization was used to extract tumor mutational signatures. Multivariate Cox and logistic regression models were applied to adjust confounding factors. The CIBERSORT and GSEA algorithm were separately used to infer leukocyte relative abundance and significantly enriched pathways. Results: Patients with LRP1B mutation were identified to be associated with prolonged survival in both immunotherapy cohort. Higher tumor mutation burden was found in LRP1B mutated patients, and the association remained significant after controlling for age, gender, stage, mutations in TP53 and ATR , and mutational signatures. Immune response and cell cycle regulation circuits were among the top enriched pathways in samples with LRP1B mutations. Conclusion: Our studies suggested sequencing even a single, frequently mutated gene may provide insight into genome-wide mutational burden, and may serve as a biomarker to predict immune response.

BackgroundThe Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA).MethodsThree systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive.ResultsGail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76–1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91–1.06), 1.07 (95% CI 0.66–1.74) and 2.29 (95% CI 1.95–2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31–2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification.The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53–0.56) and 0.75 (95% CI 0.63–0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59–0.63), 0.55 (95% CI 0.52–0.58) and 0.58 (95% CI 0.55–0.62), respectively.The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27–0.89), 0.91 (95% CI 0.87–0.94) and 17.38 (95% CI 2.66–113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17–0.59), 0.86 (95% CI 0.76–0.92) and 3.38 (95% CI 1.40–8.17), respectively.ConclusionsThe Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses.Trial registrationPROSPERO CRD42016047215.Electronic supplementary materialThe online version of this article (10.1186/s13058-018-0947-5) contains supplementary material, which is available to authorized users.

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Immune checkpoint blockade (ICB) therapy has achieved remarkable clinical benefit in non‐small‐cell lung cancer (NSCLC), but our understanding of biomarkers that predict the response to ICB remain obscure. Here we integrated somatic mutational profile and clinicopathologic information from 113 NSCLC patients treated by ICB (CTLA‐4/PD‐1). High tumor mutation burden (TMB) and neoantigen burden were identified significantly associated with improved efficacy in NSCLC immunotherapy. Furthermore, we identified apolipoprotein B mRNA editing enzyme, catalytic polypeptide‐like (APOBEC) mutational signature was markedly associated with responding of ICB therapy (log‐rank test, P = .001; odds ratio (OR), 0.18 [95% CI, 0.06‐0.50], P < .001). The association with progression‐free survival remained statistically significant after controlling for age, sex, histological type, smoking, PD‐L1 expression, hypermutation, smoking signature and mismatch repair (MMR) (HR, 0.30 [95% CI, 0.12‐0.75], P = .010). Combined high TMB with APOBEC signature preferably predict immunotherapy responders in NSCLC cohort. The CIBERSORT algorithm revealed that high APOBEC mutational activity samples were associated with increased infiltration of CD4 memory activated T cells, CD8+ T cells and natural killer (NK) cells, but reduced infiltration of regulatory T cells. Besides, individual genes mutation of IFNGR1 or VTCN1 were only found in responders; however, the PTEN mutation was only found in non‐responders (Fisher's exact test, all P < .05). These findings may be applicable for guiding immunotherapy for patients with NSCLC.