is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that (KL) or (KP) comutations define distinct subgroups of -mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups ( < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with -mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free ( < 0.001) and overall ( = 0.0015) survival compared with ; LUAC. Among 924 LUACs, alterations were the only marker significantly associated with PD-L1 negativity in TMB LUAC. The impact of alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In-mutant murine LUAC models, loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify alterations as a major driver of primary resistance to PD-1 blockade in -mutant LUAC. This work identifies alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. .

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR] = 2.4; 95% CI = 1.4–3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1–2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4–2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04–1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n ¼ 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kB pathway association with high-risk group. NF-kB inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kB targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kB activity by inhibiting AKT through a p53-PTENdependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kB-activated highrisk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kB pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. Cancer Res; 73(22); 6563-73. Ó2013 AACR.

Outcomes for patients with melanoma have improved over the past decade as a result of the development and FDA approval of immunotherapies targeting cytotoxic T lymphocyte antigen-4 (CTLA-4), programmed death-1 (PD-1), and programmed death ligand 1 (PD-L1). However, these therapies do not benefit all patients, and an area of intensive research investigation is identifying biomarkers that can predict which patients are most likely to benefit from them. Here, we report exploratory analyses of the associations of tumor mutational burden (TMB), a 4-gene inflammatory gene expression signature, and BRAF mutation status with tumor response, progression-free survival, and overall survival in patients with advanced melanoma treated as part of the CheckMate 066 and 067 phase III clinical trials evaluating immuno-oncology therapies. In patients enrolled in CheckMate 067 receiving the anti–PD-1 inhibitor nivolumab (NIVO) alone or in combination with the anti–CTLA-4 inhibitor ipilimumab (IPI) or IPI alone, longer survival appeared to associate with high (&gt;median) versus low (≤median) TMB and with high versus low inflammatory signature scores. For NIVO-treated patients, the results regarding TMB association were confirmed in CheckMate 066. In addition, improved survival was observed with high TMB and absence of BRAF mutation. Weak correlations were observed between PD-L1, TMB, and the inflammatory signature. Combined assessment of TMB, inflammatory gene expression signature, and BRAF mutation status may be predictive for response to immune checkpoint blockade in advanced melanoma.