Undetectable circulating tumor DNA levels correlate with low risk of recurrence/metastasis in postoperative pathologic stage I lung adenocarcinoma patients.

OBJECTIVES: The application of circulating tumor DNA (ctDNA) monitoring after resection in pathologic(p) stage I lung adenocarcinoma (LUAD) patients remains controversial and it is of great clinical interest to decipher the difference of genetic features between ground-glass opacity (GGO) and solid nodules (non-GGO) subgroups. We aim to assess the utility of ctDNA in tracking early recurrence or metastasis following surgery and reveal the genetic differences between GGO and non-GGO. MATERIALS AND METHODS: Tumor tissues and matched postoperative plasma samples were collected from a total of 82 (p)stage I LUAD patients. Comprehensive genomic profiling was performed using capture-based hybrid next generation sequencing by targeting 422 cancer relevant genes. RESULTS: EGFR and TP53 represent commonly mutated genes in this cohort of (p)stage I lung adenocarcinoma, followed by alterations in ALK, PIK3CA, STK11 and MYC. For a median follow-up period of 22.83 months after surgery, 65 out of 67 ctDNA-negative patients remained progression-free, while 3 out of 15 ctDNA-positive patients progressed [P = 0.040; positive predictive value = 0.20, 95 % confidence interval (CI), 0.04-0.48; negative predictive value = 0.97, 95 % CI, 0.9-1]. With time-dependent Cox regression analysis, we observed that ctDNA positivity significantly correlated with increased probability of early tumor recurrence or metastasis (P = 0.02, HR=8.5). Further comparison between GGO and non-GGO subgroups indicated the frequency of TP53 mutations in non-GGO was markedly higher than that in GGO (47 % vs 21 %, P < 0.05). Pathway analysis showed the epigenetic regulation pathway was more frequently affected in GGO subgroup, while impaired apoptosis/cell cycle pathway was more enriched in non-GGO LUADs. CONCLUSIONS: Our longitudinal ctDNA monitoring data showed that undetectable ctDNA may predict low risk of tumor recurrence or metastasis in postoperative (p)stage I LUAD patients, while it requires further investigation on how robust the positive ctDNA results could predict tumor relapse in these patients. CLINICAL REGISTRATION NUMBER: NCT03172156.

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.