Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas

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Abstract

To compare lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SqCC) and to identify new drivers of lung carcinogenesis, we examined exome sequences and copy number profiles of 660 lung ADC and 484 lung SqCC tumor/normal pairs. Recurrent alterations in lung SqCCs were more similar to other squamous carcinomas than to lung ADCs. Novel significantly mutated genes included PPP3CA, DOT1L, and FTSJD1 in lung ADC, RASA1 in lung SqCC, and KLF5, EP300, and CREBBP in both tumor types. Novel amplification peaks encompassed MIR21 in lung ADC, MIR205 in lung SqCC, and MAPK1 in both. Lung ADCs lacking receptor tyrosine kinase/Ras/Raf alterations revealed mutations in SOS1, VAV1, RASA1, and ARHGAP35. Regarding neoantigens, 47% of the lung ADC and 53% of the lung SqCC tumors had at least 5 predicted neoepitopes. While targeted therapies for lung ADC and lung SqCC are largely distinct, immunotherapies may aid in treatment for both subtypes.

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Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

Katherine A. Hoadley, Christina Yau, Denise M Wolf … (2014)

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

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Morten L. Nielsen, Claus Lundegaard, Peder Worning … (2003)

In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

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New driver mutations in non-small-cell lung cancer.

William Pao, Nicolas Girard (2011)

Treatment decisions for patients with lung cancer have historically been based on tumour histology. Some understanding of the molecular composition of tumours has led to the development of targeted agents, for which initial findings are promising. Clearer understanding of mutations in relevant genes and their effects on cancer cell proliferation and survival, is, therefore, of substantial interest. We review current knowledge about molecular subsets in non-small-cell lung cancer that have been identified as potentially having clinical relevance to targeted therapies. Since mutations in EGFR and KRAS have been extensively reviewed elsewhere, here, we discuss subsets defined by so-called driver mutations in ALK, HER2 (also known as ERBB2), BRAF, PIK3CA, AKT1, MAP2K1, and MET. The adoption of treatment tailored according to the genetic make-up of individual tumours would involve a paradigm shift, but might lead to substantial therapeutic improvements. Copyright © 2011 Elsevier Ltd. All rights reserved.