Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma.

While mutations in the KRAS oncogene are among the most prevalent in human cancer, there are few successful treatments to target these tumors. It is also likely that heterogeneity in KRAS-mutant tumor biology significantly contributes to the response to therapy. We hypothesized that the presence of commonly co-occurring mutations in STK11 and TP53 tumor suppressors may represent a significant source of heterogeneity in KRAS-mutant tumors. To address this, we utilized a large cohort of resected tumors from 442 lung adenocarcinoma patients with data including annotation of prevalent driver mutations (KRAS and EGFR) and tumor suppressor mutations (STK11 and TP53), microarray-based gene expression and clinical covariates, including overall survival (OS). Specifically, we determined impact of STK11 and TP53 mutations on a new KRAS mutation-associated gene expression signature as well as previously defined signatures of tumor cell proliferation and immune surveillance responses. Interestingly, STK11, but not TP53 mutations, were associated with highly elevated expression of KRAS mutation-associated genes. Mutations in TP53 and STK11 also impacted tumor biology regardless of KRAS status, with TP53 strongly associated with enhanced proliferation and STK11 with suppression of immune surveillance. These findings illustrate the remarkably distinct ways through which tumor suppressor mutations may contribute to heterogeneity in KRAS-mutant tumor biology. In addition, these studies point to novel associations between gene mutations and immune surveillance that could impact the response to immunotherapy.

Prognostic factors for survival after complete resections of synchronous lung cancers in multiple lobes: pooled analysis based on individual patient data.

BACKGROUND: Some reports suggest that patients with synchronous multiple foci of nonsmall-cell lung cancers (NSCLC) distributed in multiple lobes have a poor prognosis, even when there is no extrathoracic metastasis. The vast majority of such patients do not receive surgical treatment. For those who undergo surgery, prognostic factors are unclear. PATIENTS AND METHODS: We systematically reviewed the literature on surgery for synchronous NSCLC in multiple lobes published between 1990 and 2011. Individual patient data were used to obtain adjusted hazard ratios (HRs) in each dataset and pooled analyses were carried out. RESULTS: Six studies contributed 467 eligible patients for analysis. The median overall survival was 52.0 months [95% confidence interval 45.6-63.7]. Male gender and advanced age were associated with a decreased survival: HRs 1.64 (1.22, 2.22) and 1.40 (1.20, 1.80) per 20-year increment, respectively. Patients with cancers distributed in one lung had a higher mortality risk than those with bilateral disease: HRs 1.45 (1.06, 2.00). N1 or N2 had a decreased survival compared with N0: HRs 1.68 (1.12, 2.51) and 1.94 (1.33, 2.82), respectively. There was a trend toward increased mortality among patients with different histology: HRs 1.29 (0.96, 1.75). CONCLUSION: Advanced age, male gender, nodal involvement, and unilateral tumor location were poor prognostic factors.

Analysis of overdispersed count data: application to the Human Papillomavirus Infection in Men (HIM) Study.

The Poisson model can be applied to the count of events occurring within a specific time period. The main feature of the Poisson model is the assumption that the mean and variance of the count data are equal. However, this equal mean-variance relationship rarely occurs in observational data. In most cases, the observed variance is larger than the assumed variance, which is called overdispersion. Further, when the observed data involve excessive zero counts, the problem of overdispersion results in underestimating the variance of the estimated parameter, and thus produces a misleading conclusion. We illustrated the use of four models for overdispersed count data that may be attributed to excessive zeros. These are Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial models. The example data in this article deal with the number of incidents involving human papillomavirus infection. The four models resulted in differing statistical inferences. The Poisson model, which is widely used in epidemiology research, underestimated the standard errors and overstated the significance of some covariates.