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      Neoantigen-directed immune escape in lung cancer evolution

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          Abstract

          The interplay between an evolving cancer and the dynamic immune-microenvironment remains unclear. Here, we analyze 258 regions from 88 early-stage untreated non-small cell lung cancers (NSCLCs) using RNAseq and pathology tumor infiltrating lymphocyte estimates. The immune-microenvironment was variable both between and within patients’ tumors. Diverse immune selection pressures were associated with different mechanisms of neoantigen presentation dysfunction restricted to distinct microenvironments. Sparsely infiltrated tumors exhibited evidence for historical immunoediting, with a waning of neoantigen-editing during tumor evolution, or copy number loss of historically clonal neoantigens. Immune-infiltrated tumor regions exhibited ongoing immunoediting, with either HLA LOH or depletion of expressed neoantigens. Promoter hypermethylation of genes harboring neoantigens was identified as an epigenetic mechanism of immunoediting. Our results suggest the immune-microenvironment exerts a strong selection pressure in early stage, untreated NSCLCs, producing multiple routes to immune evasion, which are clinically relevant, forecasting poor disease-free survival in multivariate analysis.

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          Most cited references35

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

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              Robust enumeration of cell subsets from tissue expression profiles

              We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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                Author and article information

                Journal
                Nature
                Nature
                Springer Science and Business Media LLC
                0028-0836
                1476-4687
                March 2019
                March 20 2019
                March 2019
                : 567
                : 7749
                : 479-485
                Article
                10.1038/s41586-019-1032-7
                6954100
                30894752
                cfcea1e9-bae5-491e-86ca-9e7505a6bc20
                © 2019

                http://www.springer.com/tdm

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