Modeling the cancer transcriptome: Recent initiatives such as The Cancer Genome Atlas have mapped the genome-wide effect of individual genes on tumor growth. By unraveling genomic alterations in tumors, molecular subtypes of cancers have been identified, which is improving patient diagnostics and treatment. Uhlen et al. developed a computer-based modeling approach to examine different cancer types in nearly 8000 patients. They provide an open-access resource for exploring how the expression of specific genes influences patient survival in 17 different types of cancer. More than 900,000 patient survival profiles are available, including for tumors of colon, prostate, lung, and breast origin. This interactive data set can also be used to generate personalized patient models to predict how metabolic changes can influence tumor growth. Science , this issue p. eaan2507

Christopher Amos and colleagues perform genome-wide association analysis for lung cancer using cohorts genotyped on the OncoArray and combing these with existing data. They identify 18 loci, 10 of which are new, finding heterogeneity across the different lung cancer subtypes, and explore candidate genes through eQTL analysis in lung tissue. Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer.

Non-small cell lung cancer (NSCLC) is a heterogeneous disease with unique combinations of somatic molecular alterations in individual patients, as well as significant differences in populations across the world with regard to mutation spectra and mutation frequencies. Here we aim to describe mutational patterns and linked clinical parameters in a population-based NSCLC cohort. Using targeted resequencing the mutational status of 82 genes was evaluated in a consecutive Swedish surgical NSCLC cohort, consisting of 352 patient samples from either fresh frozen or formalin fixed paraffin embedded (FFPE) tissues. The panel covers all exons of the 82 genes and utilizes reduced target fragment length and two-strand capture making it compatible with degraded FFPE samples. We obtained a uniform sequencing coverage and mutation load across the fresh frozen and FFPE samples by adaption of sequencing depth and bioinformatic pipeline, thereby avoiding a technical bias between these two sample types. At large, the mutation frequencies resembled the frequencies seen in other western populations, except for a high frequency of KRAS hotspot mutations (43%) in adenocarcinoma patients. Worse overall survival was observed for adenocarcinoma patients with a mutation in either TP53, STK11 or SMARCA4. In the adenocarcinoma KRAS-mutated group poor survival appeared to be linked to concomitant TP53 or STK11 mutations, and not to KRAS mutation as a single aberration. Similar results were seen in the analysis of publicly available data from the cBioPortal. In squamous cell carcinoma a worse prognosis could be observed for patients with MLL2 mutations, while CSMD3 mutations were linked to a better prognosis. Here we have evaluated the mutational status of a NSCLC cohort. We could not confirm any survival impact of isolated driver mutations. Instead, concurrent mutations in TP53 and STK11 were shown to confer poor survival in the KRAS-positive adenocarcinoma subgroup.