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Background A recent study has demonstrated that high blood tumor mutational burden (bTMB) was associated with significant improvements in PFS from atezolizumab in NSCLC. However, a prospective, phase II, B-F1RST study did not confirm the result. Multiple genetic mutations may result in resistance to therapy, including immunetherapy. Therefore, we speculated that low bTMB might be a favorable prognostic biomarker for immunetherapy and both high and low bTMB patients could derive benefit from atezolizumab. We thus investigated the non-linear association between bTMB and PFS, and tried to find new cut-off values. Methods This study used the clinical and bTMB data from POPLAR (n = 105, training set) and OAK (n = 324, validation set) studies. The non-linear association between bTMB and PFS was assessed using restricted cubic spline (RCS). The cut-off values for bTMB were calculated by using X-tile software. Results In training set, bTMB showed an upside down J shape curve with PFS in RCS plot, suggesting a non-linear relationship between bTMB and PFS (P for non-linear < 0.001). The cut-off values of bTMB for predicting PFS were 7 and 14 mutations/Mb, and all patients were claasified into low (≤ 7 mutations/Mb), medium (8 ≤ bTMB ≤ 13 mutations/Mb), and high bTMB (≥ 14 mutations/Mb) groups according to the cut-off values. The median PFS and OS of patients with low and high bTMB were significantly longer than those of patients with medium bTMB in multivariate analysis. Similar results were shown in the validation set and the combined set (Table). Table: 318O Prognostic value of bTMB for PFS and OS in the training, validation, and combined data sets   PFS  OS    HR  95% CI  P value  HR  95% CI  P value  Training data set (n = 105)  Low bTMB  0.367  0.212-0.637  <0.001  0.441  0.239-0.814  0.009  Medium bTMB  1  Reference    1  Reference    High bTMB  0.207  0.105-0.410  <0.001  0.336  0.161-0.704  0.004  Validation data set (n = 324)  Low bTMB  0.689  0.502-0.945  0.021  0.644  0.441-0.939  0.022  Medium bTMB  1  Reference    1  Reference    High bTMB  0.644  0.457-0.909  0.012  0.637  0.431-0.943  0.024  Combined data set (n = 429)  Low bTMB  0.596  0.456-0.779  <0.001  0.649  0.476-0.884  0.006  Medium bTMB  1  Reference    1  Reference    High bTMB  0.558  0.415-0.750  <0.001  0.689  0.496-0.958  0.027    PFS  OS    HR  95% CI  P value  HR  95% CI  P value  Training data set (n = 105)  Low bTMB  0.367  0.212-0.637  <0.001  0.441  0.239-0.814  0.009  Medium bTMB  1  Reference    1  Reference    High bTMB  0.207  0.105-0.410  <0.001  0.336  0.161-0.704  0.004  Validation data set (n = 324)  Low bTMB  0.689  0.502-0.945  0.021  0.644  0.441-0.939  0.022  Medium bTMB  1  Reference    1  Reference    High bTMB  0.644  0.457-0.909  0.012  0.637  0.431-0.943  0.024  Combined data set (n = 429)  Low bTMB  0.596  0.456-0.779  <0.001  0.649  0.476-0.884  0.006  Medium bTMB  1  Reference    1  Reference    High bTMB  0.558  0.415-0.750  <0.001  0.689  0.496-0.958  0.027  Table: 318O Prognostic value of bTMB for PFS and OS in the training, validation, and combined data sets   PFS  OS    HR  95% CI  P value  HR  95% CI  P value  Training data set (n = 105)  Low bTMB  0.367  0.212-0.637  <0.001  0.441  0.239-0.814  0.009  Medium bTMB  1  Reference    1  Reference    High bTMB  0.207  0.105-0.410  <0.001  0.336  0.161-0.704  0.004  Validation data set (n = 324)  Low bTMB  0.689  0.502-0.945  0.021  0.644  0.441-0.939  0.022  Medium bTMB  1  Reference    1  Reference    High bTMB  0.644  0.457-0.909  0.012  0.637  0.431-0.943  0.024  Combined data set (n = 429)  Low bTMB  0.596  0.456-0.779  <0.001  0.649  0.476-0.884  0.006  Medium bTMB  1  Reference    1  Reference    High bTMB  0.558  0.415-0.750  <0.001  0.689  0.496-0.958  0.027    PFS  OS    HR  95% CI  P value  HR  95% CI  P value  Training data set (n = 105)  Low bTMB  0.367  0.212-0.637  <0.001  0.441  0.239-0.814  0.009  Medium bTMB  1  Reference    1  Reference    High bTMB  0.207  0.105-0.410  <0.001  0.336  0.161-0.704  0.004  Validation data set (n = 324)  Low bTMB  0.689  0.502-0.945  0.021  0.644  0.441-0.939  0.022  Medium bTMB  1  Reference    1  Reference    High bTMB  0.644  0.457-0.909  0.012  0.637  0.431-0.943  0.024  Combined data set (n = 429)  Low bTMB  0.596  0.456-0.779  <0.001  0.649  0.476-0.884  0.006  Medium bTMB  1  Reference    1  Reference    High bTMB  0.558  0.415-0.750  <0.001  0.689  0.496-0.958  0.027  Conclusions There was a non-linear association between bTMB and survival in NSCLC patients receiving atezolizumab. Both high and low bTMB were associated with better clinical benefit with atezolizumab. Legal entity responsible for the study The authors. Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest. [ABSTRACT FROM AUTHOR]

Background: Icotinib has been previously shown to be non-inferior to gefitinib in non-selected advanced non-small-cell lung cancer patients when given as second-or further-line treatment. In this open-label, randomized, phase 3 CONVINCE trial, we assessed the efficacy and safety of first-line icotinib versus cisplatin/pemetrexed plus pemetrexed maintenance in lung adenocarcinoma patients with epidermal growth factor receptor (EGFR) mutation. Patients and methods: Eligible participants were adults with stage IIIB/IV lung adenocarcinoma and exon 19/21 EGFR mutations. Participants were randomly allocated (1 : 1) to receive oral icotinib or 3-week cycle of cisplatin plus pemetrexed for up to four cycles; non-progressive patients after four cycles were maintained with pemetrexed until disease progression or intolerable toxicity. The primary end point was progression-free survival (PFS) assessed by independent response evaluation committee. Other end points included overall survival (OS) and safety. Results: Between January 2013 and August 2014, 296 patients were randomized and 285 patients were treated (148 to icotinib, 137 to chemotherapy). Independent response evaluation committee-assessed PFS was significantly longer in the icotinib group (11.2 versus 7.9months; hazard ratio, 0.61, 95% confidence interval 0.43-0.87; P=0.006). No significant difference for OS was observed between treatments in the overall population or in EGFR-mutated subgroups (exon 19 Del/21 L858R). The most common grade 3 or 4 adverse events (AEs) in the icotinib group were rash (14.8%) and diarrhea (7.4%), compared with nausea (45.9%), vomiting (29.2%) and neutropenia (10.9%) in the chemotherapy group. AEs (79.1% versus 94.2%; P<0.001) and treatment-related AEs (54.1% versus 90.5%; P<0.001) were significantly fewer in the icotinib group than in the chemotherapy group. Conclusions: First-line icotinib significantly improves PFS of advanced lung adenocarcinoma patients with EGFR mutation with a tolerable and manageable safety profile. Icotinib should be considered as a first-line treatment for this patient population. [ABSTRACT FROM AUTHOR]

Background: Cancer initiation and development are driven by key mutations in driver genes. Applying high-throughput sequencing technologies and bioinformatic analyses, The Cancer Genome Atlas (TCGA) project has identified panels of somatic mutations that contributed to the etiology of various cancers. However, there are few studies investigating the germline genetic variations in these significantly mutated genes (SMGs) and lung cancer susceptibility. Patients and methods: We comprehensively evaluated 1655 tagged single nucleotide polymorphisms (SNPs) located in 127 SMGs identified by TCGA and test their association with lung cancer risk in large-scale case-control study. Functional effect of the validated SNPs, gene mutation frequency and pathways were analyzed. Results: We found 11 SNPs in 8 genes showed consistent association (P<0.1) and 8 SNPs significantly associated with lung cancer risk (P<0.05) in both discovery and validation phases. The most significant association was rs10412613 in PPP2R1A, with the minor G allele associated with a decreased risk of lung cancer [odds ratio=0.91, 95% confidence interval (CI): 0.87-0.96, P=2.3x10-4]. Cumulative analysis of risk score built as a weight sum of the 11 SNPs showed consistently elevated risk with increasing risk score (P for trend=9.5x10-9). In stratified analyses, the association of PPP2R1A:rs10412613 and lung cancer risk appeared stronger among population of younger age at diagnosis and never smokers. The expression quantitative trait loci analysis indicated that rs10412613, rs10804682, rs635469 and rs6742399 genotypes significantly correlated with the expression of PPP2R1A, ATR, SETBP1 and ERBB4, respectively. From TCGA data, expression of the identified genes was significantly different in lung tumors compared with normal tissues and the gene's highest mutation frequency was found in lung cancers. Integrative pathway analysis indicated the identified genes were mainly involved in AKT/NF-kB regulatory pathway suggesting the underlying biological processes. Conclusion: This study revealed novel genetic variants in SMGs associated with lung cancer risk, which might contribute to elucidating the biological network involved in lung cancer development. [ABSTRACT FROM AUTHOR]

Background: The phase III, randomized, open-label ENSURE study (NCT01342965) evaluated first-line erlotinib versus gemcitabine/cisplatin (GP) in patients from China, Malaysia and the Philippines with epidermal growth factor receptor (EGFR) mutation-positive non-small-cell lung cancer (NSCLC). Patients and methods: Patients ≥18 years old with histologically/cytologically confirmed stage IIIB/IV EGFR mutation-positive NSCLC and Eastern Cooperative Oncology Group performance status 0-2 were randomized 1:1 to receive erlotinib (oral; 150 mg once daily until progression/unacceptable toxicity) or GP [G 1250 mg/m² i.v. days 1 and 8 (3-weekly cycle); P 75 mg/m² i.v. day 1, (3-weekly cycle) for up to four cycles]. Primary end point: investigator-assessed progression-free survival (PFS). Other end points include objective response rate (ORR), overall survival (OS), and safety. Results: A total of 217 patients were randomized: 110 to erlotinib and 107 to GP. Investigator-assessed median PFS was 11.0 months versus 5.5 months, erlotinib versus GP, respectively [hazard ratio (HR), 0.34, 95% confidence interval (CI) 0.22-0.51; log-rank P < 0.0001]. Independent Review Committee-assessed median PFS was consistent (HR, 0.42). Median OS was 26.3 versus 25.5 months, erlotinib versus GP, respectively (HR, 0.91, 95% CI 0.63-1.31; log-rank P = .607). ORR was 62.7% for erlotinib and 33.6% for GP. Treatment-related serious adverse events (AEs) occurred in 2.7% versus 10.6% of erlotinib and GP patients, respectively. The most common grade ≥3 AEs were rash (6.4%) with erlotinib, and neutropenia (25.0%), leukopenia (14.4%), and anemia (12.5%) with GP. Conclusion: These analyses demonstrate that first-line erlotinib provides a statistically significant improvement in PFS versus GP in Asian patients with EGFR mutation-positive NSCLC (NCT01342965). [ABSTRACT FROM AUTHOR]

Background: There has been reported that the association between nodal spread and tumor size was disrupted in triple-negative breast cancer (TNBC) and it showed characteristically early relapse. The TNM (tumor–node–metastasis) staging system might not be equally effective as a prognostic indicator for all subtypes. The aim of our study was to evaluate the usefulness of the staging according to subtypes.Patients and methods: We conducted a retrospective analysis of invasive breast cancer patients who received curative surgery at Samsung Medical Center from 2000 to 2004. Relapse-free survivals (RFS) by stage were analyzed.Results: Thousand eight hundred and seventy-nine patients who were available clinicopathologic data were included. These patients were divided into three subtypes: hormone receptor (HR)+, human epidermal growth factor receptor 2+, and triple negative groups. As the stage became more advanced, the slope of each stage of the RFS curves of patients with HR+ and HER2+ steadily increased. In contrast, RFS curves intermingled and showed overlap from stage 1 to 3A in TNBC patients. There was only wide separation of RFS curves between stage 1-3A and 3B-3C in TNBC.Conclusions: The current TNM staging system might not be enough for encompassing the tumor biology and for predicting outcomes to make therapeutic decisions for all BCs, especially for TNBC patients. [ABSTRACT FROM AUTHOR]