The diagnosed incidence of small intestine neuroendocrine tumors (SI-NETs) is increasing, and the underlying genomic mechanisms have not been defined for these tumors. Using exome/genome sequence analysis of SI-NETs, we identified recurrent somatic mutations and deletions in CDKN1B, the cyclin-dependent kinase inhibitor gene, which encodes p27. We observed frameshift mutations of CDKN1B in 14 of 180 SI-NETs, and we detected hemizygous deletions encompassing CDKN1B in 7 out of 50 SI-NETs, nominating p27 as a tumor suppressor and implicating cell cycle dysregulation in the etiology of SI-NET.

Small intestine neuroendocrine tumors (SI-NETs) are the most common malignancy of the small bowel. Several clinical trials target PI3K/Akt/mTOR signaling; however, it is unknown whether these or other genes are genetically altered in these tumors. To address the underlying genetics, we analyzed 48 SI-NETs by massively parallel exome sequencing. We detected an average of 0.1 somatic single nucleotide variants (SNVs) per 10 6 nucleotides (range, 0-0.59), mostly transitions (C>T and A>G), which suggests that SI-NETs are stable cancers. 197 protein-altering somatic SNVs affected a preponderance of cancer genes, including FGFR2, MEN1, HOOK3, EZH2, MLF1, CARD11, VHL, NONO, and SMAD1. Integrative analysis of SNVs and somatic copy number variations identified recurrently altered mechanisms of carcinogenesis: chromatin remodeling, DNA damage, apoptosis, RAS signaling, and axon guidance. Candidate therapeutically relevant alterations were found in 35 patients, including SRC, SMAD family genes, AURKA, EGFR, HSP90, and PDGFR. Mutually exclusive amplification of AKT1 or AKT2 was the most common event in the 16 patients with alterations of PI3K/Akt/ mTOR signaling. We conclude that sequencing-based analysis may provide provisional grouping of SI-NETs by therapeutic targets or deregulated pathways. IntroductionSmall intestine neuroendocrine neoplasms (SI-NENs) are the most common malignancy of the small bowel, represent the largest group of NENs by organ site, and are studied in clinical treatment trials targeting PI3K/Akt/mTOR signaling. Whether this or other canonical cancer pathways is recurrently mutated, however, is uncertain, because a genome-wide, unbiased sequence analysis of cancer genes has not been performed to date in SI-NENs.Massively parallel, or "nextgen," DNA sequencing is currently advancing research in other human malignancies by facilitating the collection of comprehensive, genome-wide, unbiased datasets providing a common data framework for comparing results across different tumor types and gene sets. It provides the most comprehensive technology to date to explore the potential of genomics for individualizing cancer treatment within a tumor type. To unlock and explore the potential of the technology for translational research in SI-NEN, we sequenced 48 such tumors.

BackgroundAssessing the reliability of experimental replicates (or global alterations corresponding to different experimental conditions) is a critical step in analyzing RNA-Seq data. Pearson’s correlation coefficient r has been widely used in the RNA-Seq field even though its statistical characteristics may be poorly suited to the task.ResultsHere we present a single-parameter test procedure for count data, the Simple Error Ratio Estimate (SERE), that can determine whether two RNA-Seq libraries are faithful replicates or globally different. Benchmarking shows that the interpretation of SERE is unambiguous regardless of the total read count or the range of expression differences among bins (exons or genes), a score of 1 indicating faithful replication (i.e., samples are affected only by Poisson variation of individual counts), a score of 0 indicating data duplication, and scores >1 corresponding to true global differences between RNA-Seq libraries. On the contrary the interpretation of Pearson’s r is generally ambiguous and highly dependent on sequencing depth and the range of expression levels inherent to the sample (difference between lowest and highest bin count). Cohen’s simple Kappa results are also ambiguous and are highly dependent on the choice of bins. For quantifying global sample differences SERE performs similarly to a measure based on the negative binomial distribution yet is simpler to compute.ConclusionsSERE can therefore serve as a straightforward and reliable statistical procedure for the global assessment of pairs or large groups of RNA-Seq datasets by a single statistical parameter.

Objective Mutations in Charcot-Marie-Tooth disease (CMT) genes are the cause of rare familial forms of polyneuropathy. Whether allelic variability in CMT genes is also associated with common forms of polyneuropathy—considered “acquired” in medical parlance—is unknown. Chemotherapy induced peripheral neuropathy (CIPN) occurs commonly in cancer patients and is individually unpredictable. We used CIPN as clinical model to investigate the association of non-CMT polyneuropathy with CMT genes. Methods 269 neurologically asymptomatic cancer patients were enrolled in the clinical trial Alliance N08C1 to receive the neurotoxic drug paclitaxel, while undergoing prospective assessments for polyneuropathy. 49 CMT genes were analyzed by targeted massively parallel sequencing of genomic DNA from patient blood. Results 119 (of 269) patients were identified from the two ends of the polyneuropathy phenotype distribution: patients that were most- and least susceptible to paclitaxel polyneuropathy. The CMT gene PRX was found to be deleteriously mutated in patients who were susceptible to CIPN but not in controls (p=8×10−3). Genetic variation in another CMT gene, ARHGEF10, was highly significantly associated with CIPN (p=5×10−4). Three non-synonymous recurrent single nucleotide variants contributed to the ARHGEF10 signal: rs9657362, rs2294039, and rs17683288. Of these, rs9657362 had the strongest effect (odds ratio of 4.8, p=4×10−4). Interpretation The results reveal an association of CMT gene allelic variability with susceptibility to CIPN. The findings raise the possibility that other acquired polyneuropathies may also be co-determined by genetic etiological factors, of which some may be related to genes already known to cause the phenotypically related Mendelian disorders of CMT.