A major challenge in cancer genomics is identifying driver mutations from the large number of neutral 'passenger' mutations within a given tumor. Here, we utilize motifs critical for kinase activity to functionally filter genomic data to identify driver mutations that would otherwise be lost within mutational noise.In the first step of our screen, we define a putative tumor suppressing kinome by identifying kinases with truncation mutations occurring within or before the kinase domain. We aligned these kinase sequences and, utilizing data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas databases, identified amino acids that represent predicted hotspots for loss--of--function mutations. The functional consequences of new LOF mutations were validated and the top 15 hotspot LOF residues were used in a pan--cancer analysis to define the tumor--suppressing kinome. A ranked list revealed MAP2K7 as a candidate tumor suppressor in gastric cancer, despite the mutational frequency of MAP2K7 falling within the mutational noise for this cancer type. The majority of mutations in MAP2K7 abolished catalytic activity compared to the wild type kinase, consistent with a tumor suppressive role for MAP2K7 in gastric cancer.Furthermore, reactivation of the JNK pathway in gastric cancer cells harboring LOF mutations in MAP2K7 or JNK1 suppresses clonogenicity and growth in soft agar, demonstrating the functional importance of inactivating the JNK pathway in gastric cancer. In summary, our data highlights a broadly applicable strategy to identify functional cancer driver mutations leading us to define the JNK pathway as tumor suppressive in gastric cancer. 3It was estimated that by the end of 2017 more than 1.6 million cancer samples would have been sequenced by next generation sequencing (NGS) [1]. The greatest challenge now lies in interpreting this data to dissect tumorigenic mechanisms and identify therapeutic targets. A major problem is that the data is often noisy with many inconsequential 'passenger' mutations obscuring the detection of driver mutations [2--3]. Now that most cancer subtypes have been characterized by large--scale sequencing studies, the common drivers have been identified [4]. However, the fact that many of the samples in these studies do not have an identifiable common driver suggests there are a multitude of lower frequency drivers that we struggle to detect above the noise [5--6]. The best method to discover more cancer drivers is under debate [7--9]. Should we continue sequencing more and more samples, or do we focus on functional studies? Currently, in silico methods are already widely used to attempt functional analysis of large genomic data sets [2,10], however these assessors are limited and may miss functional driver mutations [11--14]. Therefore, there is a need to improve genomic analysis to assist in unlocking the potential of these huge public datasets. By better linking existing knowledge of a protein's function to the associated structural features we can begin to functi...

not peer-reviewed) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is alsoThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/254813 doi: bioRxiv preprint first posted online Jan. 27, 2018; 2 ABSTRACTA major challenge in cancer genomics is identifying driver mutations from the large number of neutral 'passenger' mutations within a given tumor. Here, we utilize motifs critical for kinase activity to functionally filter genomic data to identify driver mutations that would otherwise be lost within mutational noise.In the first step of our screen, we define a putative tumor suppressing kinome by identifying kinases with truncation mutations occurring within or before the kinase domain. We aligned these kinase sequences and, utilizing data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas databases, identified amino acids that represent predicted hotspots for loss--of--function mutations. The functional consequences of new LOF mutations were validated and the top 15 hotspot LOF residues were used in a pan--cancer analysis to define the tumor--suppressing kinome. A ranked list revealed MAP2K7 as a candidate tumor suppressor in gastric cancer, despite the mutational frequency of MAP2K7 falling within the mutational noise for this cancer type. The majority of mutations in MAP2K7 abolished catalytic activity compared to the wild type kinase, consistent with a tumor suppressive role for MAP2K7 in gastric cancer.Furthermore, reactivation of the JNK pathway in gastric cancer cells harboring LOF mutations in MAP2K7 or JNK1 suppresses clonogenicity and growth in soft agar, demonstrating the functional importance of inactivating the JNK pathway in gastric cancer. In summary, our data highlights a broadly applicable strategy to identify functional cancer driver mutations leading us to define the JNK pathway as tumor suppressive in gastric cancer.made available for use under a CC0 license. not peer-reviewed) is the author/funder. This article is a US Government work. It is not subject to copyright under 17 USC 105 and is also The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/254813 doi: bioRxiv preprint first posted online 3 It was estimated that by the end of 2017 more than 1.6 million cancer samples would have been sequenced by next generation sequencing (NGS) [1]. The greatest challenge now lies in interpreting this data to dissect tumorigenic mechanisms and identify therapeutic targets. A major problem is that the data is often noisy with many inconsequential 'passenger' mutations obscuring the detection of driver mutations [2--3]. Now that most cancer subtypes have been characterized by large--scale sequencing studies, the common drivers have been identified [4]. However, the fact that many of the samples in these studies do not have an identifiable common driver suggests there are a multitude of lower frequency drivers that we struggle to detect above the noise [5--6]. The best method t...