blood samples and tumour tissues were obtained from 117 operated lung cancer patients admitted to oslo university hospitalrikshospitalet in the period 20062011 . the collected tumor samples consisted of 93 adenocarcinomas , 2 large cell carcinomas and 22 squamous cell carcinomas . one patient was diagnosed with metastases based on pathology report , and was omitted from survival analyses . tumour tissue was snap frozen in liquid nitrogen and stored at 80c until dna isolation . dna was extracted using maxwell16 dna purification kits and a maxwell16 instrument according to technical manual ( literature # tm284)(http://www.promega.com ) . dna from blood was isolated using the master pure dna purification kit for blood according to the dna purification protocol ( http://www.epicentre.com ) . targeted resequencing was performed using the sureselect human kinome kit ( agilent technologies ) , with capture probes targeting 3.2 mb of the human genome , including exons and untranslated regions ( utrs ) of all known kinases and selected cancerrelated genes ( 612 ) . library construction and in solution capturing was performed following agilent 's sureselectxt library construction kit and sureselect target enrichment protocol , respectively . sequencing was performed on an illumina genome analyzer using truseq sbs kit v5 generating pairedend reads of 75 bp in length . base calling , demultiplexing and quality filtering was performed using illumina 's software packages scs2.8/rta1.8 and offline basecallerv1.8 . the raw sequencing reads derived from tumour and control samples were subsequently processed in a computational pipeline to identify and functionally annotate somatic dna changes . a variant calling pipeline was established to identify somatic mutations in each tumour / control sample pair . this included several steps : ( i ) alignment of sequence reads toward the reference genome ( grch37  including unlocalised / unplaced contigs and decoy sequences ) using bwamem,9 ( ii ) marking of potential pcrderived duplicate reads using picard ( http://broadinstitute.github.io/picard/ ) , ( iii ) local realignment around indel sites using gatk realignertargetcreator and indelrealigner,10 ( iv ) base quality recalibration using gatk baserecalibrator , and ( v ) somatic single nucleotide variant ( snv ) and short insertion / deletion ( indel ) calling using strelka and mutect algorithms.11 , 12 for snvs , we only considered calls designated as somatic by both mutect and strelka ( indels were called by strelka only ) . our pipeline was recently part of a wholegenome benchmarking exercise organized through icgc , in which our performance was assessed for snv ( precision  =  0.90 , recall  =  0.73 ) and indel calling ( precision  =  0.71 , recall  = 0.70).13 
  to understand the functional role of identified variants , all variants were subject to a computational annotation workflow including a modified version of annovar ( release 2015dec14 , using ensembl as the gene model ) , cosmic ( known somatic cancer variants and cancer gene census , v76 february 2016 ) , pfam ( protein domain information , v27.0 ) , uniprot kb ( functional protein properties , release 2016_02 ) , the drug gene interaction database ( druggable targets , version 2.0 ) , database of curated mutations ( release january 2016 ) , and database of computational predictions of effects of nonsynonymous variants , v2.1.13 , 14 , 15 , 16 , 17 , 18 
  we used intogen ( integrative onco genomics ) to identify cancer driver genes in our sample cohort . considering that a frequencybased approach to discover significantly mutated genes is suboptimal in samples with low mutation counts , intogen uses an approach by which a clustering and functional bias among gene mutations ( across the sample cohort ) indicates driver candidates.19 
  we have previously reported on the survival impact of stromal cd8 expression ( cytotoxic tcells ) including most of the patients in this study ( n  =  107).7 in short , tissue microarrays were used for determination of an immunoscore by immunohistochemistry cd8 staining by ventana benchmark , xt automated slide stainer ( ventana medical system ) . by light microscopy , the tissue cores were scored for the degree of infiltration of cd8 + lymphocytes . the percentages of cd8 + lymphocytes among all nucleated cells in the stromal compartments were assessed . scoring cutoff points at 5% , 25% , or 50% were used for each core . all samples were anonymized and independently scored by two pathologists . the mean score for duplicate cores from each individual was calculated and the median stromal cd8 score was used as cutoff . time to relapse was calculated from the date of diagnosis to the date of diagnosed local relapse , distant metastasis , or lung cancer death with a maximum followup time of 80 months after surgery . the survival analyses were performed using spss ( pasw statistics for windows , version 18.0 , chicago : spss ) . survival curves and estimation of statistical significance between the survival curves utilized the kaplanmeier method and log rank test , respectively . a cox regression model was applied for the multivariate survival analysis using spss ( v. 18 ) . probes that targeted the human kinome were captured and sequenced at 5060 coverage in 117 tumourcontrol sample pairs . the medians for allelic fraction and read depth at confident variant sites were 0.2 and 84 , respectively . the best covered variant sites ( read depth of 150 ) enabled detection of variants with allelic fraction as low as 34% . in addition to coding exonic sequences , the targeted regions included canonical splice sites in introns , as well as utrs . each tumour sample had an accompanying control sample ( blood ) , which allowed the detection of point mutations and insertions / deletions of somatic origin . our pipeline for somatic mutation detection relied on a consensus between two different algorithms ( mutect and strelka ) , a strategy that performed well in a previous benchmarking exercise.20 only mutations called by both the algorithms were thus included in the further analysis . the number of coding mutations varied extensively between tumours , ranging from 0 to 50 ( mean  =  11 , median  =  9 ) . calculation of coding mutations in the same genes ( i.e. , kinome ) of tcga lung adenocarcinoma samples revealed similar numbers ( mean 13.2 , median 9).21 there was no significant difference in number of mutations between men and women or between tumours with different histological subtypes . number of mutations was significantly associated with packyears of tobacco smoking ( p  =  0.009 ) . the tp53 gene and the ttn gene were the most frequently mutated genes , the latter being a known artefact due to its large size ( fig . 1 
 a ) . the tp53 mutations were all validated by sanger sequencing where the sequences were aligned and analysed using seqscape v.2.5 according to the project template [ tp53 accession nr . ( a ) recurrence of somatically mutated genes . shown are all mutated genes with a recurrence frequency > = 4% among all 117 samples . ad  =  adenocarcinoma , scc  =  squamous cell carcinoma , lcc  =  large cell carcinoma . genes defined by intogen as significant cancer drivers are indicated in red . through use of intogen , kras , egfr , tp53 and kras was mutated in 28 samples ( 27 adenocarcinomas , one large cell carcinoma ) . eight samples ( seven adenocarcinomas ) were mutated in the stk11 gene . only adenocarcinomas were egfrmutated , with mainly l858r mutations and exon 19 deletions . twentyseven samples had mutations in known dna repair genes,1 including palb2 , atm , atr , brca1 , brca2 , fancl , chek1 , chek2 and clk2 . there was a significant correlation between mutations in one of these genes and the total number of mutations ( p  <  0.001 ) . tumours with a mutation in tp53 , atm or atr had an increased number of mutations when analysed separately ( fig . 2 ) , whereas tumours with mutations in the other repair genes did not show a significant increase by themselves ; however , the number of samples in these groups were small . mutations in one of the dna repair genes were significantly associated with a higher number of mutations . mutations in tp53 , atr and atm alone were associated with a higher number of mutations . the tumours were divided in three separate groups based on an immunoscore reflecting the number of infiltrating cd8 + tlymphocytes , as defined by donnem et al.7 there was no significant association between immunoscore and the number of point mutations . however , when using the median stromal cd8score as cutoff ( high score n  =  58 , low score n  =  49 ) , there was a significant inverse correlation between the number of infiltrating stromal cd8 + lymphocytes and presence of egfr mutations ( r  =  0.21 , p  = 0.029 ) ( pearson  ) . patients with more than the median number of mutations in their tumours had shorter relapsefree survival than patients with submedian mutation counts ( fig . multivariate analysis of relapsefree survival was performed using cox regression to study the influence of known prognostic factors ( histology , stage , sex ) . the number of mutations was not significantly associated with survival in a coxanalysis with number of mutations as a continuous variable ( p  =  0.60 ) . however , the number of mutations ( over median ) was found to be significantly associated with time to progression after adjusting for clinical variables ( p  =  0.021 ) . there was a tendency toward increased relapsefree survival for patients with high cd8score ( p  =  0.10 ) . twentyseven samples had mutations in known dna repair genes,1 including palb2 , atm , atr , brca1 , brca2 , fancl , chek1 , chek2 and clk2 . there was a significant correlation between mutations in one of these genes and the total number of mutations ( p  <  0.001 ) . tumours with a mutation in tp53 , atm or atr had an increased number of mutations when analysed separately ( fig . 2 ) , whereas tumours with mutations in the other repair genes did not show a significant increase by themselves ; however , the number of samples in these groups were small . mutations in one of the dna repair genes were significantly associated with a higher number of mutations . mutations in tp53 , atr and atm alone were associated with a higher number of mutations . the tumours were divided in three separate groups based on an immunoscore reflecting the number of infiltrating cd8 + tlymphocytes , as defined by donnem et al.7 there was no significant association between immunoscore and the number of point mutations . however , when using the median stromal cd8score as cutoff ( high score n  =  58 , low score n  =  49 ) , there was a significant inverse correlation between the number of infiltrating stromal cd8 + lymphocytes and presence of egfr mutations ( r  =  0.21 , p  = 0.029 ) ( pearson  ) . patients with more than the median number of mutations in their tumours had shorter relapsefree survival than patients with submedian mutation counts ( fig . multivariate analysis of relapsefree survival was performed using cox regression to study the influence of known prognostic factors ( histology , stage , sex ) . the number of mutations was not significantly associated with survival in a coxanalysis with number of mutations as a continuous variable ( p  =  0.60 ) . however , the number of mutations ( over median ) was found to be significantly associated with time to progression after adjusting for clinical variables ( p  =  0.021 ) . there was a tendency toward increased relapsefree survival for patients with high cd8score ( p  =  0.10 ) . by secondgeneration sequencing , we have confirmed that lung cancers are highly heterogeneous in terms of somatic mutation profiles , and that the number of mutations is associated with patient survival . an improved understanding of tumour emergence relies on the identification of genes that drive carcinogenesis . several bioinformatics approaches have been proposed to identify driver genes , all with shortcomings and particular biases.19 given the limited number of mutations per tumour kinome , we chose an approach ( intogen ) that emphasizes enrichment of clustered and functionally biased mutations . this algorithm has previously demonstrated its capability of detecting cancer drivers.19 
  when applying the intogen algorithm on our dataset , tp53 , egfr , kras , and stk11 were identified as significant driver genes , as previously described.21 despite extensive efforts , no effective treatments targeting tp53 and kras have been presented up to now . conversely , targeted treatment of mutated egfr has changed the prospects for the subset of patients with lung egfrmutationpositive lung cancers . stk11 is a serine / threonine kinase and a tumour suppressor and has been ranked as the third most frequently mutated gene in lung cancers.22 it is implicated in cell polarity , energy metabolism , apoptosis , cell cycle arrest and proliferation . the mutations observed were in the protein kinase domain , but are probably inactivating as stk11 is a tumour suppressor . other known genes involved in lung carcinogenesis , like alk and ros1 , were also mutated in a small subset of the tumours . however , these genes are usually changed by inversions and translocations , not detected by our sequencing analysis . they hamper tumour development , but unfortunately tumours can prevent sustained tcell responses via immune checkpoints . unfortunately , good predictive biomarkers for response to checkpoint inhibitors are hitherto lacking.23 
  there have been several reports indicating that the effect of induced immuneresponse correlates with the number of mutations , with the novel immunotherapeutic checkpoint inhibitors having better efficacy on tumours with a high number of mutations.8 
 egfrmutated tumours generally have lower number of mutations.24 in our study , we found significantly more infiltrating stromal cd8 + tcells in tumours without egfr mutations . this would be in accordance with a higher number of immunogenic antigens in tumours with a high number of mutations . in addition , this is in accordance with the lack of cd8 + lymphocyte response in egfrmutant mice models and human tumours.25 
  mutations in dna repair genes would most often lead to repair defects , which would in turn allow certain types of mutations to accumulate . in our material , we find a significant association between number of mutations and mutated dna repair genes . whether the significantly higher number of mutations is a consequence of dysfunctional dna repair genes in these cases has not been determined . clearly , our study is limited in size , so ruling out which genes in the dnarepair pathway are the most important should be studied more extensively , perhaps revealing a correlation of mutation signature to specific defects . a total of only 28 samples were mutated in the dna repair genes . despite the modest number of samples in our cohort , we report a correlation between number of mutations and course of disease . in the present era of evolving immune therapies , we believe this to be an important insight , which needs more attention in larger cohorts , and possibly with a further potential in the clinical setting .