nfang Yu

Prognostic and predictive value of tumor-infiltrating lymphocytes for clinical therapeutic research in patients with non-small cell lung cancer

Background Previous preclinical and clinical studies have shown that levels of tumor-infiltrating lymphocytes (TILs) significantly correlated with prognosis in non-small cell lung cancer (NSCLC), and survival after therapy; however, this finding remains controversial. We performed a meta-analysis, to evaluate, systematically, the clinical utilization of TIL subtypes in patients with NSCLC. Methods The PubMed, ISI Web of Science, EMBASE, and Cochrane Library databases were searched to identify relevant studies. We pooled estimates of treatment effects, and hazards were summarized using random or fixed effects models to evaluate survival outcomes. Results A total of 24 relevant studies involving 7,006 patients were eligible. The median percentage of lymph node positivity was 45.7% (95% confidence interval [CI], 37.1–56.4%). Pooled analysis shows that high levels of CD8+ TILs had a good prognostic effect on survival with a hazard ratio (HR) of 0.91 (P = 0.013) for death and 0.74 (P = 0.001) for recurrence, as did high levels of CD3+ and CD4+ TILs, with HRs of 0.77 (P = 0.009) and 0.78 (P = 0.005) for death, respectively. By contrast, high levels of FoxP3+ regulatory TILs had a worse prognostic effect for overall and recurrence-free survival, with HRs of 1.69 (P = 0.042) and 1.79 (P = 0.001), respectively. No individual study affected the results, and no publication bias was found. Conclusions Our findings support the hypothesis that TILs could be a prognostic marker in NSCLC. High-quality randomized studies are needed to verify statistically the effect of TILs on prognosis in future research.

Prognostic immune-related gene models for breast cancer: a pooled analysis

Breast cancer, the most common cancer among women, is a clinically and biologically heterogeneous disease. Numerous prognostic tools have been proposed, including gene signatures. Unlike proliferation-related prognostic gene signatures, many immune-related gene signatures have emerged as principal biology-driven predictors of breast cancer. Diverse statistical methods and data sets were used for building these immune-related prognostic models, making it difficult to compare or use them in clinically meaningful ways. This study evaluated successfully published immune-related prognostic gene signatures through systematic validations of publicly available data sets. Eight prognostic models that were built upon immune-related gene signatures were evaluated. The performances of these models were compared and ranked in ten publicly available data sets, comprising a total of 2,449 breast cancer cases. Predictive accuracies were measured as concordance indices (C-indices). All tests of statistical significance were two-sided. Immune-related gene models performed better in estrogen receptor-negative (ER−) and lymph node-positive (LN+) breast cancer subtypes. The three top-ranked ER− breast cancer models achieved overall C-indices of 0.62–0.63. Two models predicted better than chance for ER+ breast cancer, with C-indices of 0.53 and 0.59, respectively. For LN+ breast cancer, four models showed predictive advantage, with C-indices between 0.56 and 0.61. Predicted prognostic values were positively correlated with ER status when evaluated using univariate analyses in most of the models under investigation. Multivariate analyses indicated that prognostic values of the three models were independent of known clinical prognostic factors. Collectively, these analyses provided a comprehensive evaluation of immune-related prognostic gene signatures. By synthesizing C-indices in multiple independent data sets, immune-related gene signatures were ranked for ER+, ER−, LN+, and LN− breast cancer subtypes. Taken together, these data showed that immune-related gene signatures have good prognostic values in breast cancer, especially for ER− and LN+ tumors.