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Background: Biomarkers for chemotherapy efficacy in non–small cell lung cancer (NSCLC) are lacking. This retrospective study assesses the association between blood-based tumor mutational burden (bTMB) and clinical benefit of chemotherapy. Methods: Clinical and targeted next-generation sequencing data from the OAK trial (training set; n=318) and POPLAR trial (validation set; n=106) in the docetaxel arm were analyzed. The cutoff value of bTMB for outcome prediction was determined based on a time-dependent receiver operating characteristic curve in the training set, and propensity score matching (PSM) was conducted. The primary outcome was overall survival (OS). Durable clinical benefit (DCB) was defined as OS lasting >12 months. Interaction between treatment and bTMB was assessed in the combined set. Results: A lower bTMB was observed in patients with DCB compared with no durable benefit, and in those with a partial response and stable disease compared with progressive disease. The optimized cutoff value of bTMB for predicting OS was 7 single-nucleotide variants per megabase. In the training set, a low bTMB was significantly associated with longer OS and progression-free survival (PFS). The prognostic value of bTMB was confirmed in the validation set and PSM set. The interaction between bTMB and treatment was significant for PFS (interaction P=.043) in the combined set. Mutations in KEAP1 were associated with high bTMB and a lack of benefit from chemotherapy. Conclusions: Low bTMB is associated with a survival advantage in patients with NSCLC treated with docetaxel, suggesting the prognostic and predictive potential of bTMB for determining chemotherapy efficacy.

Background: Differences between the features of primary cancer and matched metastatic cancer have recently drawn attention in research. This study investigated the concordance in microsatellite instability (MSI) and mismatch repair (MMR) status between primary and corresponding metastatic colorectal cancer (CRC). Methods: Consecutive patients with metastatic CRC who had both primary and metastatic tumors diagnosed at our institution in January 2008 through December 2016 were identified. Immunohistochemistry was used to test the MMR status of both primary and matched metastatic tumors, and PCR analysis was performed to test MSI in patients with deficient MMR (dMMR) status. Results: A total of 369 patients were included. Of the 46 patients with MSI-high primary tumors, 37 (80.4%) also had MSI-high metastatic tumors, whereas 9 (19.6%) had microsatellite stable (MSS) metastatic tumors. A high concordance was found in patients with liver, lung, or distant lymph node metastases. Interestingly, the discrepancy was more likely to be limited to peritoneal (5/20) or ovarian (4/4) metastasis (chi-square test, P<.001). These organ-specific features were also found in the pooled analysis. Along with the change of MSI-high in primary cancer to MSS in metastatic cancer, lymphocyte infiltration decreased significantly (P=.008). However, the change did not influence survival; the median overall survival of MSI-high and MSS metastatic tumors was 21.3 and 21.6 months, respectively (P=.774). The discrepancy rate was 1.6% for patients with proficient MMR primary tumors. Conclusions: For patients with dMMR primary tumors, the concordance of MSI and MMR status in primary CRC and corresponding metastatic cancer is potentially organ-specific. High concordance is found in liver, lung, and distant lymph node metastases, whereas discrepancy is more likely to occur in peritoneal or ovarian metastasis. Rebiopsy to evaluate MSI-high/dMMR status might be needed during the course of anti–PD-1 therapy in cases of peritoneal or ovarian metastasis.

Background: Early relapse after hepatectomy presents a significant challenge in the treatment of hepatocellular carcinoma (HCC). The aim of this study was to construct and validate a novel nomogram model for predicting early relapse and survival after hepatectomy for HCC. Patients and Methods: We conducted a large-scale, multicenter retrospective analysis of 1,505 patients with surgically treated HCC from 4 medical centers. All patients were randomly divided into either the training cohort (n=1,053) or the validation cohort (n=452) in a 7:3 ratio. A machine learning–based nomogram model for prediction of HCC was established by integrating multiple risk factors that influence early relapse and survival, which were identified from preoperative clinical data and postoperative pathologic characteristics of the patients. Results: The median time to early relapse was 7 months, whereas the median time from early relapse to death was only 19 months. The concordance indexes of the postoperative nomogram for predicting disease-free survival and overall survival were 0.741 and 0.739, respectively, with well-calibrated curves demonstrating good consistency between predicted and observed outcomes. Moreover, the accuracy and predictive performance of the postoperative nomograms were significantly superior to those of the preoperative nomogram and the other 7 HCC staging systems. The patients in the intermediate- and high-risk groups of the model had significantly higher probabilities of early and critical recurrence (P<.001), whereas those in the low-risk group had higher probabilities of late and local recurrence (P<.001). Conclusions: This postoperative nomogram model can better predict early recurrence and survival and can serve as a useful tool to guide clinical treatment decisions for patients with HCC.