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Src family kinases (SFKs) have been implicated in the pathogenesis of kidney fibrosis. However, the specific mechanism by which SFKs contribute to the progression of diabetic kidney disease (DKD) remains unclear. Our preliminary transcriptome analysis suggested that SFK expression was increased in diabetic kidneys and that the expression of Fyn (a member of the SFKs), along with genes related to unfolded protein responses from the endoplasmic reticulum (ER) stress signaling pathway, was upregulated in the tubules of human diabetic kidneys. Thus, we examined whether SFK-induced ER stress is associated with DKD progression. Mouse proximal tubular (mProx24) cells were transfected with Fyn or Lyn siRNA and exposed to high glucose and palmitate (HG-Pal). Streptozotocin-induced diabetic rats were treated with KF-1607, a novel pan-Src kinase inhibitor (SKI) with low toxicity. The effect of KF-1607 was compared to that of losartan, a standard treatment for patients with DKD. Among the SFK family members, the Fyn and Lyn kinases were upregulated under diabetic stress. HG-Pal induced p70S6 kinase and JNK/CHOP signaling and promoted tubular injury. Fyn knockdown but not Lyn knockdown inhibited this detrimental signaling pathway. In addition, diabetic rats treated with KF-1607 showed improved kidney function and decreased ER stress, inflammation, and fibrosis compared with those treated with losartan. Collectively, these findings indicate that Fyn kinase is a specific member of the SFKs implicated in ER stress activation leading to proximal tubular injury in the diabetic milieu and that pan-SKI treatment attenuates kidney injury in diabetic rats. These data highlight Fyn kinase as a viable target for the development of therapeutic agents for DKD.

Supplementary Tables 1 and 2. Supplementary Table 1: Fold de-repression of miR-21 target genes after anti-miR-21 treatment. Supplementary Table 2: Taqman Primer and Probes

Supplementary Figure Legends. Figure legends for Supplementary Figures 1, 2, 3, and 4.

Supplementary Data 1. Excel sheet with fold change in gene expression following anti-miR-21 treatment.

Supplementary Figure 1. miR-21 mimic suppresses expression of ANKRD46, DDAH1 and RECK. SKHep1 cells were transfected with miR-21 or negative mimic. RNA was isolated and expression of ANKRD46, DDAH1 and RECK was assessed by qPCR. (Mean, {plus minus} SD, n=3).

Supplementary Figure 3. HMBG1 and LDH are induced upon miR-21 inhibition. SKHep1, HepG2 and Hep3B cells were treated with anti-miR-21 or MM control and extracellular HMBG1 and LDH activity was quantified. (Mean, {plus minus} SEM, n=3)

Supplementary Figure 4. Pathway analysis of gene expression changes following anti-miR-21 treatment. SKHep1 cells treated with anti-miR-21 were subjected to microarray gene expression analysis. Changes in cellular processes (top) and pathways (bottom) upon anti-miR-21 treatment are shown.

Supplementary Figure 2. Anti-miR-21 treatment causes limited caspase 3/7 activation in non-transformed cell lines. Caspase 3/7 activation after treatment of SKHep1, IMR-90, and WI-38 cells with MM control or anti-miR-21 for 72 hours. (Mean, {plus minus} SEM, n=3).

Supplementary Tables 1 - 15. Supp. Table 1. Sequenced reads per tumor and normal sample. Supp. Table 2. Comparison of significantly mutated genes detected using various MuTect thresholds. Supp. Table 3. Comparison of the number and rate of substitutions between never-smokers and smokers. Supp. Table 4. Comparison of various methods used to compute the background mutation rate. Supp. Table 5. Significantly mutated genes detected in our discovery cohort. Supp. Table 6. Comparison of significantly mutated genes and previously reported cancer driver genes. Supp. Table 7. Thirteen significantly altered CNV regions detected in our discovery cohort. Supp. Table 8. Druggable target candidates in the 22 significantly mutated genes and the 60 genes in the significantly altered CNV regions detected in the discovery cohort. Supp. Table 9. Pathways significantly enriched in the 22 significantly mutated genes and the 60 genes included in the significantly altered CNV regions that were detected in the discovery cohort. Supp. Table 10. Cox multivariable regression analysis for RB pathway alterations. Supp. Table 11. Cox univariable regression analysis of RB pathway alterations. Supp. Table 12. Comparison of mutations detected using whole-exome sequencing and Sequenom in selected genes. Supp. Table 13. Prevalence of known driver candidates when using two different MuTect parameters. Supp. Table 14. Number of patients included in the overall, tumor, node, and metastasis stage categories. Supp. Table 15. List of potential cancer driver genes with somatic substitutions or small indels determined in previous studies.

Supplementary Methods, Supplementary Figure Legends, and Supplementary Table Legends.

Supplementary Figures 1 - 9. Supp. Figure 1. Comparison of mutation rates between never-smokers and smokers. Supp. Figure 2. Druggable gene candidates. Supp. Figure 3. Prevalence of somatic mutations and the CNVs of the genes in the RB pathway. Supp. Figure 4. Association between RB pathway alterations and poor prognosis in early-stage LA patients who were not treated with adjuvant chemotherapy. Supp. Figure 5. Disease-free survival curve of randomly resampled early-stage LA patients included in the discovery cohort. Supp. Figure 6. Global landscape of significantly mutated genes and significantly altered CNV regions. Supp. Figure 7. Comparison of read depths for EGFR, KRAS, and BRAF. Supp. Figure 8. Mapped read depths of exome-captured regions of KEAP1 and STK11. Supp. Figure 9. Mutation rates between smokers and never-smokers.

Purpose: To better understand the complete genomic architecture of lung adenocarcinoma.Experimental Design: We used array experiments to determine copy number variations and sequenced the complete exomes of the 247 lung adenocarcinoma tumor samples along with matched normal cells obtained from the same patients. Fully annotated clinical data were also available, providing an unprecedented opportunity to assess the impact of genomic alterations on clinical outcomes.Results: We discovered that genomic alternations in the RB pathway are associated with significantly shorter disease-free survival in early-stage lung adenocarcinoma patients. This association was also observed in our independent validation cohort. The current treatment guidelines for early-stage lung adenocarcinoma patients recommend follow-up without adjuvant therapy after complete resection, except for high-risk patients. However, our findings raise the interesting possibility that additional clinical interventions might provide medical benefits to early-stage lung adenocarcinoma patients with genomic alterations in the RB pathway. When examining the association between genomic mutation and histologic subtype, we uncovered the characteristic genomic signatures of various histologic subtypes. Notably, the solid and the micropapillary subtypes demonstrated great diversity in the mutated genes, while the mucinous subtype exhibited the most unique landscape. This suggests that a more tailored therapeutic approach should be used to treat patients with lung adenocarcinoma.Conclusions: Our analysis of the genomic and clinical data for 247 lung adenocarcinomas should help provide a more comprehensive genomic portrait of lung adenocarcinoma, define molecular signatures of lung adenocarcinoma subtypes, and lead to the discovery of useful prognostic markers that could be used in personalized treatments for early-stage lung adenocarcinoma patients. Clin Cancer Res; 21(11); 2613–23. ©2014 AACR.See related commentary by Collisson, p. 2418

Anorectal malignant melanoma (ARMM) is a rare disease with poor prognosis. Determining ARMM prognosis precisely is difficult due to the lack of proper assessment techniques. Immunotherapy has proven effective against cutaneous malignant melanoma and may show efficacy in ARMM. Herein, we assessed the immune profile of ARMM to identify possible prognostic biomarkers. Twenty-two ARMM formalin-fixed and paraffin-embedded samples were evaluated using an nCounter® PanCancer Immune Profiling Panel. Validation was performed through immunohistochemical staining for CD3, CD8, Foxp3, CD68, CD163, and PD-L1. RNA analysis revealed significantly decreased scores for pathways involved in cell regulation and function, as well as chemokines, in recurrent patients compared to nonrecurrent patients. In cell-type profiling, the recurrent cases displayed significantly low tumor infiltrating lymphocyte (TIL) scores. Recurrence/death prediction models were defined using logistic regression and showed significantly lower scores in recurrent and deceased patients (all, P

Background and Purpose: The therapeutic use of transcranial direct current stimulation (tDCS), an adjuvant tool for stroke, induces long-term changes in cortical excitability, for example, the secretion of activity-dependent growth factors. We assessed the proper therapeutic configuration of high-definition tDCS (HD-tDCS) in the subacute stage of ischemic stroke and its underlying expression profiling of growth factors to propose a new method for ensuring better therapeutic effects. Methods: Male C57BL/6J mice were subjected to middle cerebral artery occlusion, after which repetitive HD-tDCS (20 minutes, 55 µA/mm 2 , charge density 66 000 C/m 2 ) was applied from subacute phases of their ischemic insult. Behavioral tests assessing motor and cognitive functions were used to determine suitable conditions and HD-tDCS stimulation sites. Gene expression profiling of growth factors and their secretion and activation were analyzed to shed light on the underlying mechanisms. Results: Anodal HD-tDCS application over the contralesional cortex, especially the motor cortex, was more effective than ipsilesional stimulation in attenuating motor and cognitive deficits. In the HD-tDCS application over the contralesional motor cortex, positive changes in Bmp8b , Gdf5 , Il4 , Pdgfa , Pgf , and Vegfb were observed in the ipsilesional site. The expression of GDF5 (growth/differentiation factor 5) and PDGFA (platelet-derived growth factor subunit A) tended to similarly increase in both ipsi- and contralesional striata. However, higher expression levels of GDF5 and PDGFA and their receptors were observed in the peri-infarct regions of the striatum after HD-tDCS, especially in PDGFA expression. A higher number of proliferating or newly formed neuronal cells was detected in ipsilesional sites such as the subventricular zone. Conclusions: Application of anodal HD-tDCS over the contralesional cortex may enhance beneficial recovery through the expression of growth factors, such as GDF5 and PDGFA, in the ipsilesional site. Therefore, this therapeutic configuration may be applied in the subacute stage of ischemic stroke to ameliorate neurological impairments.

Background/Aims: Although proton pump inhibitors (PPIs) remain a mainstay for the suppression of gastric acid secretion, long-term PPI use is associated with side effects. However, the genotoxicity associated with long-term PPI use is unclear.Materials and Methods: This prospective observational pilot study enrolled patients who had been on PPIs for >1 year and healthy controls from July 2015 to August 2016. The subjects completed self-report questionnaires pertaining to their drug and medical history, and only those with no medical history and a ≥2-year wash-out period (for drugs other than PPIs) were included. We collected peripheral-blood lymphocytes from long-term PPI users and healthy controls and analyzed the genotoxicity by using the cytokinesis-block micronucleus cytome assay; we also determined the fasting serum levels of pyridoxine, folate, cobalamin, and homocysteine.Results: Ten long-term PPI users and 40 healthy control subjects were enrolled. The median serum pyridoxine, folate, cobalamin, and homocysteine levels were not significantly different between the groups. The median frequencies of micronuclei (MNi), nucleoplasmic bridges (NPBs), and nuclear buds (Nbuds) per 1,000 binucleated cells, in long-term PPI users and healthy controls, were 30.3 and 16.3 (PPPPP=0.001), and 1.3 (P=0.3), respectively.Conclusions: Long-term PPI use was significantly associated with an increased risk of genotoxicity after adjustment for age, sex, body mass index, medical history, drug history, and the serum levels of vitamins.

Peroxiredoxin 1 (Prx1) is a member of the Prx family that detoxifies various peroxide substrates through conserved catalytic cysteine residues with the use of reducing equivalents. In addition to this well-known role of Prx1, we have previously demonstrated that Prx1 also has RNA-binding properties, but its function as an RNA-binding protein (RBP) remains unknown. To characterize the role of Prx1 as an RBP, we pulled down Prx1-RNA complexes and sequenced the target RNAs of Prx1. Through sequencing and further validation studies, we revealed that Prx1 binds to a specific subset of small nucleolar RNAs (snoRNAs) and regulates these molecules at the post-transcriptional level. We also found that active cysteine residues provide a structural and functional link between these two distinct functions of Prx1 (i.e., ROS scavenging and RNA-binding activities). Prx1 functions as a snoRNA-binding protein in its reduced state, and post-transcriptionally regulates the expression of a set of snoRNAs. However, when the active cysteine residues are oxidized, Prx1 loses its activity as a snoRNA-binding protein. This study is the first report describing the novel role of Prx1 as a post-transcriptional regulator of snoRNAs.

Vitamin B deficiency in patients with inflammatory bowel disease (IBD) is well-documented; however, few studies have explored genomic damage in patients with IBD using the cytokinesis-block micronucleus cytome (CBMN-Cyt) assay. This study investigated the frequency of micronuclei (MNi) using the CBMN-Cyt assay and the level of vitamin B in patients with IBD. This prospective study was conducted in 15 patients with ulcerative colitis, 15 patients with Crohn’s disease, and 30 healthy controls from one tertiary hospital. Serum vitamin B and homocysteine levels were measured, and the MNi status was analyzed using the CBMN-Cyt assay. The patients with IBD showed significantly lower serum pyridoxine levels and significantly higher homocysteine levels than controls. The frequencies of binucleated cells (BNCs) with MNi, nucleoplasmic bridges (NPBs), and nuclear buds (Nbuds) were 8.5 [5.8–13.5], 1.0 [0.0–1.9], and 5.4 [4.3–7.4] for the IBD group, and 5.9 [4.8–7.7], 0.2 [0.0–1.0], and 3.5 [2.9–5.4] for the control group ( P = 0.011, P = 0.010, and P = 0.002), respectively. This study suggests that patients with IBD have increased frequencies of MNi and decreased levels of pyridoxine than healthy controls.

Background Bladder cancer has numerous genomic features that are potentially actionable by targeted agents. Nevertheless, both pre-clinical and clinical research using molecular targeted agents have been very limited in bladder cancer. Results We created the Genomics of Drug Sensitivity in Bladder Cancer (GDBC) database, an integrated database (DB) to facilitate the genomic understanding of bladder cancer in relation to drug sensitivity, in order to promote potential therapeutic applications of targeted agents in bladder cancer treatment. The GDBC database contains two separate datasets: 1) in-house drug sensitivity data, in which 13 targeted agents were tested against 10 bladder cancer cell lines; 2) data extracted and integrated from public databases, including the Cancer Therapeutics Research Portal, Cancer Cell Line Encyclopedia, Genomics of Drug Sensitivity in Cancer, Kyoto Encyclopedia of Genes and Genomes, and the Cancer Gene Census databases, as well as bladder cancer genomics data and synthetic lethality/synthetic dosage lethality connections. Conclusions GDBC is an integrated DB of genomics and drug sensitivity data with a specific focus on bladder cancer. With a user-friendly web-interface, GDBC helps users generate genomics-based hypotheses that can be tested experimentally using drugs and cell lines included in GDBC. Electronic supplementary material The online version of this article (10.1186/s12920-018-0406-2) contains supplementary material, which is available to authorized users.

Background Hepatocytes usually express fibroblast growth factor receptor 4 (FGFR4), but not its ligand, fibroblast growth factor 19 (FGF19). A subtype of hepatocellular carcinoma (HCC) expresses FGF19, which activates the FGFR4 signaling pathway that induces cell proliferation. FGFR4 inhibitors that target this mechanism are under clinical development for the treatment of HCCs with FGF19 amplification or FGFR4 overexpression. Src plays an essential role in the FGFR1 and FGFR2 signaling pathways. However, it is yet to be understood whether Src has any role in the FGF19-FGFR4 pathway in HCCs. In this study, we aimed to elucidate the role of Src in the FGF19-FGFR4 axis in HCC. Methods 3 HCC cell lines expressing both FGF19 and FGFR4 were selected. The expression of each protein was suppressed by siRNA treatment, and the activity-regulating relationship between FGFR4 and Src was investigated by westernblot. Co-immunoprecipitation was performed using the FGFR4 antibody to identify the endosomal complex formation and receptor endocytosis. The intracellular migration pathways of the endosomal complex were observed by immuno-fluorescence and nuclear co-immunoprecipitation. Dasatinib and BLU9931 were used for cytotoxicity comparison. Results FGFR4 modulates the activity of Src and Src modulates the expression of FGFR4, showing a mutual regulatory relationship. FGFR4 activated by FGF19 formed an endosomal complex with Src and STAT3 and moved to the nucleus. However, when Src was suppressed, the formation of the endosomal complex was not observed. FGFR4 was released from the complex transferred into the nucleus and the binding of Src and STAT3 was maintained. Dasatinib showed cytotoxic results comparable to BLU9931. The results of our study demonstrated that Src is essential for the nuclear transport of STAT3, as it induces the endosomal delivery of FGFR4 in FGF19-expressing HCC cell lines. Conclusions We found that Src is essential for the endosomal delivery of the FGFR4 signaling complex in HCC. Our findings provide a scientific rationale for repurposing Src inhibitors for the treatment of HCCs in which the FGFR4 pathway is activated.

Most therapeutic candidates for treating attention-deficit hyperactivity disorder (ADHD) have focused on modulating the dopaminergic neurotransmission system with neurotrophic factors. Regulation of this system by transcranial direct current stimulation (tDCS) could contribute to the recovery of cognitive symptoms observed in patients with ADHD. Here, male spontaneously hypertensive rats (SHR) were subjected to consecutive high-definition tDCS (HD-tDCS) (20 min, 50 μA, current density 63.7 A/m2, charge density 76.4 kC/m2) over the prefrontal cortex. This treatment alleviated cognitive deficits, with an increase in tyrosine hydroxylase and vesicular monoamine transporter two and significantly decreased plasma membrane reuptake transporter (DAT). HD-tDCS application increased the expression of several neurotrophic factors, particularly brain-derived neurotrophic factor (BDNF), and activated hippocampal neurogenesis. Our results suggest that anodal HD-tDCS over the prefrontal cortex may ameliorate cognitive dysfunction via regulation of DAT and BDNF in the mesocorticolimbic dopaminergic pathways, and therefore represents a potential adjuvant therapy for ADHD.

Src family kinases (SFKs), an important group of non-receptor tyrosine kinases, are suggested to be excessively activated during various types of tissue fibrosis. The present study investigated the effect of KF-1607, an orally active and a newly synthesized Src kinase inhibitor (SKI) with proposed low toxicity, in preventing the progression of renal interstitial fibrosis. Unilateral ureteral obstruction (UUO) surgery was performed in 6-week-old male C57BL/6 mice to induce renal interstitial fibrosis. Either KF-1607 (30 mg/kg, oral gavage) or PP2 (2 mg/kg, intraperitoneal injection), a common experimental SKI, was administered to mice for seven days, started one day prior to surgery. UUO injury-induced SFK expression, including Src, Fyn, and Lyn kinase. SFK inhibition by KF-1607 prevented the progression of tubular injury in UUO mice, as indicated by decreases in albuminuria, urinary KIM-1 excretion, and kidney NGAL protein expression. Renal tubulointerstitial fibrosis was attenuated in response to KF-1607, as shown by decreases in α-SMA, collagen I and IV protein expression, along with reduced Masson’s trichrome and collagen-I staining in kidneys. KF-1607 also inhibited inflammation in the UUO kidney, as exhibited by reductions in F4/80 positive-staining and protein expression of p-NFκB and ICAM. Importantly, the observed effects of KF-1607 were similar to those of PP2. A new pan Src kinase inhibitor, KF-1607, is a potential pharmaceutical agent to prevent the progression of renal interstitial fibrosis.

Preoperative chemoradiotherapy (PCRT) and subsequent surgery is the standard multimodal treatment for locally advanced rectal cancer (LARC), albeit PCRT response varies among the individuals. This creates a dire necessity to identify a predictive model to forecast treatment response outcomes and identify patients who would benefit from PCRT. In this study, we performed a gene expression study using formalin-fixed paraffin-embedded (FFPE) tumor biopsy samples from 156 LARC patients (training cohort n = 60, validation cohort n = 96), we identified the nine-gene signature (FGFR3, GNA11, H3F3A, IL12A, IL1R1, IL2RB, NKD1, SGK2, and SPRY2) that distinctively differentiated responders from non-responders in the training cohort (accuracy = 86.9%, specificity = 84.8%, sensitivity = 81.5%) as well as in an independent validation cohort (accuracy = 81.0%, specificity = 79.4%, sensitivity = 82.3%). The signature was independent of all pathological and clinical features and was robust in predicting PCRT response. It is readily applicable to the clinical setting using FFPE samples and Food and Drug Administration (FDA) approved hardware and reagents. Predicting the response to PCRT may aid in tailored therapies for respective responders to PCRT and improve the oncologic outcomes for LARC patients.

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Parkinson’s disease (PD) is characterized by dopaminergic neuron loss in the substantia nigra. However, specific sensory stimulation via electroacupuncture (EA) therapy may attenuate this loss by promoting the expression of endogenous neurotrophic factors in a manner similar to physical therapy. We investigated the potential protective effects of EA on dopaminergic neurons in a mouse model of PD and whether these effects are associated with the promotion of endogenous brain-derived neurotrophic factor (BDNF) and glial cell line-derived neurotrophic factor (GDNF). Mouse models of PD were generated using 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and 6-hydroxydopamine. Motor performance was assessed using behavioral tests, and Western blot experiments, enzyme-linked immunosorbent assays, and immunohistochemical assays were performed. In both mouse models, EA treatment ameliorated motor impairments and dopaminergic neuron loss; these changes were accompanied by increases in BDNF and GDNF. In the MPTP group, EA treatment improved motor dysfunction by attenuating dopaminergic neuron loss in the substantia nigra, similar to the effects of levodopa. EA treatment significantly upregulated BDNF and GDNF expression in both the substantia nigra and striatum. Moreover, EA treatment induced the expression of cAMP response element binding protein (CREB) as well as Akt and Pitx3 in dopaminergic neurons in the substantia nigra. However, levodopa treatment did not induce BDNF/GDNF activation or related signaling factors. Thus, EA therapy may exert protective effects on dopaminergic neurons by upregulating the expression of BDNF, GDNF, and related signaling factors, thereby improving motor function. Hence, EA may represent an effective adjuvant therapy for motor deficits in patients with PD.

Background: FGF19 amplification is a relatively novel type of genetic aberration that has been proposed to be a driver of hepatocarcinogenesis. Selective inhibitors of FGFR4, a receptor of FGF19, have been developed as targeted therapies for hepatocellular carcinoma (HCC). Despite the role of FGF19 in mediating HCC progression, the clinicopathological characterization of patients exhibiting FGF19 amplification remains unclear. Immunohistochemical staining is the simplest and most widely used method of identifying aberrations in the FGF19 gene, although its specificity is very low. Methods: This study investigated the prognostic significance of FGF19 amplification in a large cohort of 989 HCC patients using fluorescence in situ hybridization (FISH), which has a high degree of specificity. In addition, FISH data from formalin-fixed, paraffin-embedded sections were compared with copy number variation (CNV) data obtained from fresh frozen sections to validate the use of FISH as a diagnostic tool. Results: FGF19 amplifications were detected by FISH in 51 (5.15%) of the 989 patients, and were independently associated with poor survival and a higher risk of tumor recurrence, as well as with poor prognostic factors such as a high α-fetoprotein level, hepatitis B or C virus infection, a large tumor size, microvascular invasion, and necrosis. In addition, FGF19 amplification was associated with TP53 mutation, and was mutually exclusive with CTNNB1 mutation. The results of the FISH and CNV analyses exhibited a significant concordance rate of 96% (κ = 0.618, p < 0.001). Conclusions: These data indicate that FGF19 amplification represents a unique molecular subtype associated with poor prognostic characteristics, which supports the hypothesis that the FGF19-FGFR4 signaling pathway plays an important role in hepatocarcinogenesis. We have also demonstrated that FISH is a viable alternative to CNV analysis, offering a number of advantages in the clinical setting.

We prepared and grafted tropomyosin receptor kinase B (TrkB) gene-transfected mesenchymal stem cells (TrkB-MSCs) into the ischemic penumbra and investigated whether electroacupuncture (EA) treatment could promote functional recovery from ischemic stroke. For the behavioral test, TrkB-MSCs+EA resulted in significantly improved motor function compared to that obtained with MSCs+EA or TrkB-MSCs alone. At 30 days after middle cerebral artery occlusion (MCAO), the largest number of grafted MSCs was detected in the TrkB-MSC+EA group. Some differentiation into immature neuroblasts and astrocytes was detected; however, only a few mature neuron-like cells were found. Compared to other treatments, TrkB-MSCs+EA upregulated the expression of mature brain-derived neurotrophic factor (BDNF) and neurotrophin-4/5 (NT4) and induced the activation of TrkB receptor and its transcription factor cAMP response element-binding protein (CREB). At 60 days after MCAO, EA highly promoted the differentiation of TrkB-MSCs into mature neuron-like cells compared to the effect in MSCs. A selective TrkB antagonist, ANA-12, reverted the effect of TrkB-MSCs+EA in motor function recovery and survival of grafted MSCs. Our results suggest that EA combined with grafted TrkB-MSCs promotes the expression of BDNF and NT4, induces the differentiation of TrkB-MSCs, and improves motor function. TrkB-MSCs could serve as effective therapeutic agents for ischemic stroke if used in combination with BDNF/NT4-inducing therapeutic approaches.

The beneficial effects of mesenchymal stem cells (MSCs) and electroacupuncture (EA) on neurogenesis and related trophic factors remain unclear. Bone marrow MSCs (mBMSC) were transplanted into the striatum of mice with middle cerebral artery occlusion (MCAO), and EA stimulation was applied at two acupoints, Baihui and Dazhui. EA treatment significantly improved motor function, and a synergistic effect of combined mBMSC and EA treatment was observed. Combined mBMSC and EA treatment reduced prominent atrophic changes in the striatum and led to proliferation of neural progenitor cells in the subventricular zone (SVZ) and the surrounding areas of the striatum (SVZ + striatum) of MCAO mice. The mBMSC and EA treatment markedly enhanced mature brain-derived neurotrophic factor (mBDNF) expression in the SVZ + striatum and hippocampus of mice with MCAO, and combined treatment enhanced neurotrophin-4 (NT4) expression. The number of mBDNF- and NT4-positive neurons in the SVZ + striatum and hippocampus increased following EA treatment. Combined treatment led to an increase in the expression levels of phosphorylated cAMP response element binding protein in the neuroblasts of the striatum. Our results indicate that combined MSC and EA treatment may lead to a better therapeutic effect via co-regulation of neurotrophic factors in the brain, by regulating neurogenesis more than single therapy.

Accurate and physiologically meaningful biomarkers for human aging are key to assessing antiaging therapies. Given ethnic differences in health, diet, lifestyle, behavior, environmental exposures, and even average rate of biological aging, it stands to reason that aging clocks trained on datasets obtained from specific ethnic populations are more likely to account for these potential confounding factors, resulting in an enhanced capacity to predict chronological age and quantify biological age. Here, we present a deep learning-based hematological aging clock modeled using the large combined dataset of Canadian, South Korean, and Eastern European population blood samples that show increased predictive accuracy in individual populations compared to population specific hematologic aging clocks. The performance of models was also evaluated on publicly available samples of the American population from the National Health and Nutrition Examination Survey (NHANES). In addition, we explored the association between age predicted by both population specific and combined hematological clocks and all-cause mortality. Overall, this study suggests (a) the population specificity of aging patterns and (b) hematologic clocks predicts all-cause mortality. The proposed models were added to the freely-available Aging.AI system expanding the range of tools for analysis of human aging.

Human leukocyte antigen (HLA) class I has more than 18,000 alleles, each of which binds to a set of unique peptides from the cellular degradome. Deciphering the interaction between antigenic peptides and HLA proteins is crucial for understanding immune responses in autoimmune diseases and cancer. In this study, we aimed to characterize the peptidome that binds to HLA-A*33:03, which is one of the most prevalent HLA-A alleles in the Northeast Asian population, but poorly studied. For this purpose, we analyzed the HLA-A*33:03 monoallelic B cell line using immunoprecipitation of HLA-A and peptide complexes, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). In this study, we identified 5731 unique peptides that were associated with HLA A*33:03, and experimentally validated the affinity of 40 peptides for HLA-A*33:03 and their stability in HLA A*33:03-peptides complexes. To our knowledge, this study represents the largest dataset of peptides associated with HLA-A*33:03. Also, this is the first study in which HLA A*33:03-associated peptides were experimentally validated.

Targeting Bcr-Abl with tyrosine kinase inhibitors (TKIs) has revolutionized the treatment of chronic myeloid leukemia (CML) by significantly improving patients' survival. However, the gatekeeper mutation T315I confers resistance to TKIs targeting Bcr-Abl, presenting a formidable task in CML's clinical management. Ponatinib is the only approved drug for Bcr-AblT315I, but often accompanies severe, life-threatening side effects. Herein, we report KF1601, a novel orally bioavailable TKI: 1) KF1601 inhibited kinase functions of both Bcr-AblWT and Bcr-AblT315I with nanomolar IC50 values; 2) In xenograft models using Ba/F3 Bcr-AblT315I cell line, it showed promising in vivo antitumor efficacy, comparable to that of ponatinib; 3) In animal models, it did not cause thrombotic microangiopathy, a major mechanism of ponatinib's toxicity. In summary, KF1601 is a promising drug candidate for safely treating CML patients with drug-resistant Bcr-AblT315I mutation. Citation Format: HyunJin Kwon, Sung-Min Ahn, Kiho Chang, WooSik Kim, Amir Khan, Doohyun LEE, Seungyeon Lee, Ye Ri Han, Chun Young Im. KF1601, a novel orally bioavailable inhibitor of Bcr-Abl T315I, without inducing thrombotic microangiopathy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1462.