KRAS driven expression signature has prognostic power superior to mutation status in non-small cell lung cancer

KRAS is the most frequently mutated oncogene in non-small cell lung cancer (NSCLC). However, the prognostic role of KRAS mutation status in NSCLC still remains controversial. We hypothesize that the expression changes of genes affected by KRAS mutation status will have the most prominent effect and could be used as a prognostic signature in lung cancer. We divided NSCLC patients with mutation and RNA-seq data into KRAS mutated and wild type groups. Mann-Whitney test was used to identify genes showing altered expression between these cohorts. Mean expression of the top five genes was designated as a "transcriptomic fingerprint" of the mutation. We evaluated the effect of this signature on clinical outcome in 2,437 NSCLC patients using univariate and multivariate Cox regression analysis. Mutation of KRAS was most common in adenocarcinoma. Mutation status and KRAS expression were not correlated to prognosis. The transcriptomic fingerprint of KRAS include FOXRED2, KRAS, TOP1, PEX3 and ABL2. The KRAS signature had a high prognostic power. Similar results were achieved when using the second and third set of strongest genes. Moreover, all cutoff values delivered significant prognostic power (p < 0.01). The KRAS signature also remained significant (p < 0.01) in a multivariate analysis including age, gender, smoking history and tumor stage. We generated a "surrogate signature" of KRAS mutation status in NSCLC patients by computationally linking genotype and gene expression. We show that secondary effects of a mutation can have a higher prognostic relevance than the primary genetic alteration itself

BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking.

Here, we present an update of Breast Cancer Now Tissue Bank bioinformatics, a rich platform for the sharing, mining, integration and analysis of breast cancer data. Its modalities provide researchers with access to a centralised information gateway from which they can access a network of bioinformatic resources to query findings from publicly available, in-house and experimental data generated using samples supplied from the Breast Cancer Now Tissue Bank. This in silico environment aims to help researchers use breast cancer data to their full potential, irrespective of any bioinformatics barriers. For this new release, a complete overhaul of the IT and bioinformatic infrastructure underlying the portal has been conducted and a host of novel analytical modules established. We developed and adopted an automated data selection and prioritisation system, expanded the data content and included tissue and cell line data generated from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia, designed a host of novel analytical modalities and enhanced the query building process. Furthermore, the results are presented in an interactive format, providing researchers with greater control over the information on which they want to focus. Breast Cancer Now Tissue Bank bioinformatics can be accessed at http://bioinformatics.breastcancertissuebank.org/.Breast Cancer Campaign [TB2016BIF]; Pancreatic Cancer Research Fund (PCRFTB) [Tissue Bank grant, to J.M and A.Z.D.U.]. Funding for open access charge: Breast Cancer Campaign [TB2016BIF]

In epithelial cancers, aberrant COL17A1 promoter methylation predicts its misexpression and increased invasion

Background: Metastasis is a leading cause of death among cancer patients. In the tumor microenvironment, altered levels of extracellular matrix proteins, such as collagens, can facilitate the first steps of cancer cell metastasis, including invasion into surrounding tissue and intravasation into the blood stream. However, the degree of misexpression of collagen genes in tumors remains understudied, even though this knowledge could greatly facilitate the development of cancer treatment options aimed at preventing metastasis. Methods: We systematically evaluate the expression of all 44 collagen genes in breast cancer and assess whether their misexpression provides clinical prognostic significance. We use immunohistochemistry on 150 ductal breast cancers and 361 cervical cancers and study DNA methylation in various epithelial cancers. Results: In breast cancer, various tests show that COL4A1 and COL4A2 overexpression and COL17A1 (BP180, BPAG2) underexpression provide independent prognostic strength (HR = 1.25, 95% CI = 1.17–1.34, p = 3.03 × 10; HR = 1.18, 95% CI = 1.11–1.25, p = 8.11 × 10; HR = 0.86, 95% CI = 0.81–0.92, p = 4.57 × 10; respectively). Immunohistochemistry on ductal breast cancers confirmed that the COL17A1 protein product, collagen XVII, is underexpressed. This strongly correlates with advanced stage, increased invasion, and postmenopausal status. In contrast, immunohistochemistry on cervical tumors showed that collagen XVII is overexpressed in cervical cancer and this is associated with increased local dissemination. Interestingly, consistent with the opposed direction of misexpression in these cancers, the COL17A1 promoter is hypermethylated in breast cancer and hypomethylated in cervical cancer. We also find that the COL17A1 promoter is hypomethylated in head and neck squamous cell carcinoma, lung squamous cell carcinoma, and lung adenocarcinoma, in all of which collagen XVII overexpression has previously been shown. Conclusions: Paradoxically, collagen XVII is underexpressed in breast cancer and overexpressed in cervical and other epithelial cancers. However, the COL17A1 promoter methylation status accurately predicts both the direction of misexpression and the increased invasive nature for five out of five epithelial cancers. This implies that aberrant epigenetic control is a key driver of COL17A1 gene misexpression and tumor cell invasion. These findings have significant clinical implications, suggesting that the COL17A1 promoter methylation status can be used to predict patient outcome. Moreover, epigenetic targeting of COL17A1 could represent a novel strategy to prevent metastasis in patients

A pan-cancer analysis of enhancer expression in nearly 9000 patient samples

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on “chromatin-state” to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers

Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors

Availability of lung cancer models that closely mimic human tumors remains a significant gap in cancer research, as tumor cell lines and mouse models may not recapitulate the spectrum of lung cancer heterogeneity seen in patients. We aimed to establish a patient-derived tumor xenograft (PDX) resource from surgically resected non-small cell lung cancer (NSCLC). Fresh tumor tissue from surgical resection was implanted and grown in the subcutaneous pocket of non-obese severe combined immune deficient (NOD SCID) gamma mice. Subsequent passages were in NOD SCID mice. A subset of matched patient and PDX tumors and non-neoplastic lung tissues were profiled by whole exome sequencing, single nucleotide polymorphism (SNP) and methylation arrays, and phosphotyrosine (pY)-proteome by mass spectrometry. The data were compared to published NSCLC datasets of NSCLC primary and cell lines. 127 stable PDXs were established from 441 lung carcinomas representing all major histological subtypes: 52 adenocarcinomas, 62 squamous cell carcinomas, one adeno-squamous carcinoma, five sarcomatoid carcinomas, five large cell neuroendocrine carcinomas, and two small cell lung cancers. Somatic mutations, gene copy number and expression profiles, and pY-proteome landscape of 36 PDXs showed greater similarity with patient tumors than with established cell lines. Novel somatic mutations on cancer associated genes were identified but only in PDXs, likely due to selective clonal growth in the PDXs that allows detection of these low allelic frequency mutations. The results provide the strongest evidence yet that PDXs established from lung cancers closely mimic the characteristics of patient primary tumors