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Background: Patients with EGFR-mutated non-small-cell lung cancer (NSCLC) and MET amplification as a mechanism of resistance to first-line osimertinib have few treatment options. Here, we report the primary analysis of the phase 2 INSIGHT 2 study evaluating tepotinib, a highly selective MET inhibitor, combined with osimertinib in this population., Methods: This open-label, phase 2 study was conducted at 179 academic centres and community clinics in 17 countries. Eligible patients were aged 18 years or older with an Eastern Cooperative Oncology Group performance status of 0 or 1 and advanced or metastatic EGFR-mutated NSCLC of any histology, with MET amplification by tissue biopsy fluorescence in-situ hybridisation (FISH; MET gene copy number of ≥5 or MET-to-CEP7 ratio of ≥2) or liquid biopsy next-generation sequencing (MET plasma gene copy number of ≥2·3), following progression on first-line osimertinib. Patients received oral tepotinib 500 mg plus oral osimertinib 80 mg once daily. The primary endpoint was independently assessed objective response in patients with MET amplification by central FISH treated with tepotinib plus osimertinib with at least 9 months of follow-up. Safety was analysed in patients who received at least one study drug dose. This study is registered with ClinicalTrials.gov, NCT03940703 (enrolment complete)., Findings: Between Feb 13, 2020, and Nov 4, 2022, 128 patients (74 [58%] female, 54 [42%] male) were enrolled and initiated tepotinib plus osimertinib. The primary activity analysis population included 98 patients with MET amplification confirmed by central FISH, previous first-line osimertinib and at least 9 months of follow-up (median 12·7 months [IQR 9·9-20·3]). The confirmed objective response rate was 50·0% (95% CI 39·7-60·3; 49 of 98 patients). The most common treatment-related grade 3 or worse adverse events were peripheral oedema (six [5%] of 128 patients), decreased appetite (five [4%]), prolonged electrocardiogram QT interval (five [4%]), and pneumonitis (four [3%]). Serious treatment-related adverse events were reported in 16 (13%) patients. Deaths of four (3%) patients were assessed as potentially related to either trial drug by the investigator due to pneumonitis (two [2%] patients), decreased platelet count (one [1%]), respiratory failure (one [1%]), and dyspnoea (one [1%]); one death was attributed to both pneumonitis and dyspnoea., Interpretation: Tepotinib plus osimertinib showed promising activity and acceptable safety in patients with EGFR-mutated NSCLC and MET amplification as a mechanism of resistance to first-line osimertinib, suggesting a potential chemotherapy-sparing oral targeted therapy option that should be further investigated., Funding: Merck (CrossRef Funder ID: 10.13039/100009945)., Competing Interests: Declaration of interests Y-LW reports receiving institute grants from AstraZeneca, Bristol Myers Squibb, and Pfizer; consulting fees from AstraZeneca, Boehringer Ingelheim, Merck, and Roche; and speaker fees from AstraZeneca, Eli Lilly, Hengrui, Pfizer, Roche, and Sanofi. VG reports receiving personal fees for advisory board membership from AstraZeneca, Daiichi Sankyo, Eisai, Eli Lilly, Exact Sciences, Gilead, MSD, Novartis, Pfizer, and Olema Oncology; speaker fees from Amgen, AstraZeneca, Eli Lilly, Exact Sciences, Gilead, GSK, and Novartis; fees for expert testimony for Eli Lilly; and travel support from Gilead, AstraZeneca, and PharmaMar. PJV reports receiving personal fees for advisory board membership from Amgen, AstraZeneca, BeiGene, Ipsen, MSD, Novartis, Pfizer, and Roche. MWi reports receiving personal advisory board fees, consulting fees, speaker fees, and institute grants from AstraZeneca. CKL reports receiving research grants from AstraZeneca and Boehringer Ingelheim; honoraria for lectures from AstraZeneca, Boehringer Ingelheim, Janssen, MSD, Novartis, Pfizer, Roche, and Zuellig Pharma; travel support from AstraZeneca, Boehringer Ingelheim, Merck, MSD, Novartis, Pfizer, and Roche; and personal fees for advisory board membership from AstraZeneca, Janssen, MSD, Novartis, Pfizer, and Roche. JM reports receiving advisory board fees from Roche, Bristol Myers Squibb, AstraZeneca, Pfizer, Novartis, Merck, Daiichi Sankyo, and MSD, and research funding to his institution from Roche, AstraZeneca, and Pierre Fabre. LMT reports receiving advisory board fees from AstraZeneca, Janssen, Merck, Novartis, Pfizer, and Roche; personal honoraria fees from AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Janssen, Merck, Novartis, Pfizer, and Roche; and travel support from AstraZeneca, Merck, Novartis, Pfizer, and Roche. HH reports receiving honoraria fees from AstraZeneca, Ono Pharmaceutical, Bristol Myers Squibb, Eli Lilly, Boehringer Ingelheim, Sysmex Corporation, 3H Medi Solution, Chugai Pharmaceutical, Pfizer, Novartis, Merck Biopharma Co, Tokyo, Japan, an affiliate of Merck, Amgen, Daiichi Sankyo/UCB Japan, Takeda, and MSD; research funding from IQVIA, Syneos Health Clinical, EPS Corporation, Nippon Kayaku, Takeda, MSD, Amgen, Taiho Pharmaceutical, Bristol Myers Squibb, Janssen, CMIC, Pfizer R&D, LabCorp, Kobayashi Pharmaceutical, Pfizer, Eisai, EP-CRSU, Shionogi & Co, Otsuka Pharmaceutical, GSK, Sanofi, Chugai Pharmaceutical, Boehringer Ingelheim, SRL Medisearch, PRA Health Sciences, Astellas Pharma, Ascent Development Services, and Bayer; and financial support for the present manuscript from Guardant Health. PLC reports receiving advisory board fees from AstraZeneca, Merck, and Pfizer, and honoraria fees from AstraZeneca. JR reports receiving personal honoraria from Bristol Myers Squibb, speaker fees (to their institution) from Merck, and advisory board fees (to their institution) from Merck. GF reports receiving personal fees for speaker engagements with AstraZeneca, Bristol Myers Squibb, and MSD, and travel support from Roche. TK reports receiving honoraria from Amgen, AstraZeneca, BeiGene, Boehringer Ingelheim, Chugai Pharmaceutical, Daiichi Sankyo, Eli Lilly, GlaxoSmithKline, Janssen, Merck, MSD, Novartis, Ono Pharmaceutical, Pfizer, Taiho, and Takeda; receiving research funding for their institution from AbbVie, Amgen, AstraZeneca, BeiGene, BluePrint, Chugai Pharmaceutical, Daiichi Sankyo, Eli Lilly, Haihe Biopharma, Merck, MSD, Novartis, Pfizer, Regeneron, Takeda, and TurningPoint; receiving advisory board fees from AstraZeneca, BeiGene, Daiichi Sankyo, Janssen, Merck, MSD, Novartis, and Pfizer; and their spouse as an employee of Eli Lilly. EN reports receiving research funding from Roche, Pfizer, EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, and Bristol Myers Squibb; consulting fees from Roche, Bristol Myers Squibb, MSD, EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, Sanofi, Pfizer, Eli Lilly, Amgen, Janssen, Daiichi Sankyo, Boehringer Ingelheim, AstraZeneca, Takeda, Sanofi, Janssen, Pierre Fabre, Qiagen, and Bayer; personal honoraria for lectures from Roche, Bristol Myers Squibb, MSD, Sanofi, Pfizer, Eli Lilly, Amgen, Janssen, Boehringer Ingelheim, AstraZeneca, Takeda, Sanofi, Janssen, Qiagen, Mirati, and Bayer; travel support from Takeda, MSD, AstraZeneca, and Roche; and fees for participation in data safety meetings from Apollomics. HSH reports receiving research grants for their institution from AstraZeneca, Merck, Janssen, Novartis, and Boehringer Ingelheim; honoraria and fees for lectures and advisory board meetings from AstraZeneca, MSD, Novartis, Pfizer, Roche, and Janssen; and travel support from AstraZeneca, Boehringer Ingelheim, MSD, Novartis, Pfizer, and Roche. EFS reports receiving advisory or consultancy fees to their institution from Eli Lilly, AstraZeneca, MSD, Boehringer Ingelheim, Bristol Myers Squibb, Takeda, and Daiichi Sankyo; personal speaker fees from Boehringer Ingelheim; and advisory board fees from Merck and Daiichi Sankyo. MWe reports holding a consulting or advisory role for Bristol Myers Squibb, Novartis, Eli Lilly, Boehringer Ingelheim, ISA Pharmaceuticals, Amgen, Immatics, Bayer, and ImCheck therapeutics; honoraria fees from Eli Lilly, Boehringer Ingelheim, SYNLAB, Janssen, EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, GWT, Amgen, and Novartis; travel support from Pfizer, Bristol Myers Squibb, AstraZeneca, Amgen, GEMoaB, Sanofi/Aventis, Immatics, and EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck; and research funding to his institution from Roche. DT reports receiving consulting fees for their institution from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, DKSH, GSK, Merck, Novartis, Roche, Pfizer, and Takeda; research funding from ACM Biolabs, Amgen, AstraZeneca, Bayer, and Pfizer; and speaker fees for their institution from Amgen, Bayer, Merck, Pfizer, Novartis, Boehringer Ingelheim, Roche, and Takeda. MM reports receiving research funding from Boehringer Ingelheim and Eli Lilly; personal honoraria fees from Boehringer Ingelheim, Daiichi Sankyo, AstraZeneca, Pfizer, Eli Lilly, Chugai Pharmaceutical, MSD, Ono Pharmaceutical, and Taiho Pharmaceutical; and other clinical trial support from Chugai Pharmaceutical, AstraZeneca, Ono Pharmaceutical, Pfizer, EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, Kissei, Taiho, and Novartis. AO’B, SA, BMP, CS, DJ, RS, BE-L, and NK report being employees of Merck. KB reports being an employee of Merck at the time of the study and manuscript preparation. KG reports being an employee of EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck. XL reports receiving consulting fees from EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, AstraZeneca, Spectrum Pharmaceuticals, Novartis, Eli Lilly, Boehringer Ingelheim, Hengrui Therapeutics, Janssen Oncology, Daiichi Sankyo, Blueprint Medicines, Sensei Biotherapeutics, AbbVie, Arrivent, and Regeneron; travel support from Spectrum Pharmaceuticals and EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, and research funding to their institution from Eli Lilly, Boehringer Ingelheim, EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, and Regeneron. TMK reports receiving grants for their institution from AstraZeneca; consulting fees from AstraZeneca, Janssen, Regeneron, Samsung Bioepis, Takeda, and Yuhan; honoraria for lectures from AstraZeneca, IMBDx, Janssen, Takeda, and Yuhan; advisory board fees from AstraZeneca, Janssen, Regeneron, and Takeda; and clinical trial funding to their institution from AbbVie, Amgen, AstraZeneca/Medimmune, Bayer, Black Diamond Therapeutics, Blueprint Medicines, Boryung, Bristol Myers Squibb, Celgene, Dizal, EMD Serono Research & Development Institute, Billerica, MA, USA, an affiliate of Merck, Roche/Genentech, Hanmi, Genmab, Janssen, Novartis, RAPT Therapeutics, Regeneron, Sanofi, Takeda, and Yuhan. All other authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved, including those for text and data mining, AI training, and similar technologies.)

High-level MET amplification (METamp) is a primary driver in ∼1%-2% of non-small cell lung cancers (NSCLCs). Cohort B of the phase 2 VISION trial evaluates tepotinib, an oral MET inhibitor, in patients with advanced NSCLC with high-level METamp who were enrolled by liquid biopsy. While the study was halted before the enrollment of the planned 60 patients, the results of 24 enrolled patients are presented here. The objective response rate (ORR) is 41.7% (95% confidence interval [CI], 22.1-63.4), and the median duration of response is 14.3 months (95% CI, 2.8-not estimable). In exploratory biomarker analyses, focal METamp, RB1 wild-type, MYC diploidy, low circulating tumor DNA (ctDNA) burden at baseline, and early molecular response are associated with better outcomes. Adverse events include edema (composite term; any grade: 58.3%; grade 3: 12.5%) and constipation (any grade: 41.7%; grade 3: 4.2%). Tepotinib provides antitumor activity in high-level METamp NSCLC (ClinicalTrials.gov: NCT02864992)., Competing Interests: Declaration of interests X.L. reported personal/consulting fees from EMD Serono during the conduct of the study; personal or consulting fees from AstraZeneca, Spectrum Pharmaceuticals, Novartis, Eli Lilly, Boehringer Ingelheim, Janssen, Blueprint Medicines, Bayer, and Albion; grants from ArriVent, Eli Lilly, Boehringer Ingelheim, and Regeneron; and personal fees from AbbVie outside the submitted work. L.G.P.-A. reported consulting roles with AstraZeneca, Lilly, EMD Serono, Spectrum Pharmaceuticals, and Daiichi Sankyo/Eli Lilly; research funding from Lilly and Boehringer Ingelheim; leadership roles from Genomica and ALTUM Sequencing; speakers bureau from Merck & Co., Kenilworth, NJ, Bristol-Myers Squibb, Roche, Pfizer, Lilly, AstraZeneca, and the healthcare business of Merck KGaA, Darmstadt, Germany; travel/accommodations/expenses from Roche, AstraZeneca, Merck & Co., Kenilworth, NJ, Bristol-Myers Squibb, Pfizer, and Takeda; and honoraria from Roche, Lilly, Pfizer, Bristol-Myers Squibb, Merck & Co., Kenilworth, NJ, AstraZeneca, the healthcare business of Merck KGaA, Darmstadt, Germany, PharmaMar, Novartis, Celgene, Amgen, Sanofi, Ipsen, Servier, Bayer, Blueprint Medicines, Mirati Therapeutics, and Takeda outside the submitted work. J.V.M. reported an advisory role with Amgen outside the submitted work. S.V. reported consulting or advisory role from the healthcare business of Merck KGaA, Darmstadt, Germany, AbbVie, Bristol-Myers Squibb, AstraZeneca, Merck & Co., Kenilworth, NJ, and Roche; non-financial support from OSE Immunotherapeutics; and personal fees from Janssen and Puma Biotechnology outside the submitted work. C.C.G. reported a consulting/advisory role with Boehringer Ingelheim and travel/accommodations/expenses from Roche and Merck & Co., Kenilworth, NJ, outside the submitted work. E.F.S. reported an advisory/consultancy role (institution) with Eli Lilly, AstraZeneca, Boehringer Ingelheim, Roche/Genentech, Bristol-Myers Squibb, the healthcare business of Merck KGaA, Darmstadt, Germany, Merck & Co., Kenilworth, NJ, Takeda, Bayer, Regeneron, Novartis, Daiichi Sankyo, and Seattle Genetics; and research funding (institution) from Boehringer Ingelheim, Bayer, Roche/Genentech, AstraZeneca, and Bristol-Myers Squibb outside the submitted work. M.G. reported personal fees from the healthcare business of Merck KGaA, Darmstadt, Germany, during the conduct of the study; grants and personal fees from AstraZeneca; and personal fees from the healthcare business of Merck KGaA, Darmstadt, Germany, Bayer, Bristol-Myers Squibb, AbbVie, Takeda, Janssen, Roche, Sanofi, Boehringer Ingelheim, Daiichi Sankyo, Eli Lilly, Novartis, and Blueprint outside the submitted work. R.V. reported research funding from the healthcare business of Merck KGaA, Darmstadt, Germany, during the conduct of the study; personal consulting fees from Janssen; personal speaker fees from Bristol-Myers Squibb and Takeda; personal speaker bureau fees from Amgen, Sanofi, Roche, and AstraZeneca; and travel fees from Pfizer Travel and Janssen outside the submitted work. D.V.B. reported advisory/consultancy honoraria from Roche, the healthcare business of Merck KGaA, Darmstadt, Germany, Bristol-Myers Squibb, AstraZeneca, Pfizer, Boehringer Ingelheim, and Takeda; and speaker honoraria from Roche, the healthcare business of Merck, KGaA, Darmstadt, Germany, Bristol-Myers Squibb, AstraZeneca, Pfizer, and Boehringer Ingelheim outside the submitted work. J.F.P. reported consulting/advisory roles with Roche, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Merck & Co., Kenilworth, NJ, and GlaxoSmithKline outside the submitted work. M.S. reported an advisory/consulting role with Roche, AstraZeneca, Bristol-Myers Squibb, and Merck & Co., Kenilworth, NJ, outside the submitted work. T.K. reported grants and personal fees from Chugai Pharmaceutical Co., AstraZeneca, Eli Lilly Japan, Taiho Pharmaceutical Co., Bristol-Myers Squibb, Merck & Co., Kenilworth, NJ, and Kyowa Hakko Kirin; personal fees from Ono Pharmaceutical, Pfizer Japan, Nippon Boehringer Ingelheim, Nippon Kayaku, Novartis, and Daiichi-Sankyo; and grants from the healthcare business of Merck KGaA, Darmstadt, Germany, outside the submitted work. Y.-C.K. reported honoraria from AstraZeneca, Roche, Boehringer Ingelheim, Merck & Co., Kenilworth, NJ, Pfizer, Ono, Bristol-Myers Squibb, Daiichi Sankyo, and Yuhan; and research funding from AstraZeneca, Roche, and Boehringer Ingelheim outside the submitted work. S.S.Y. reported honoraria from AstraZeneca, Roche, Boehringer Ingelheim, Merck & Co., Kenilworth, NJ, Pfizer, Ono Pharmaceutical, Bristol-Myers Squibb, Daiichi Sankyo, and Yuhan; and research funding from AstraZeneca, Roche, and Boehringer Ingelheim outside the submitted work. J.-Y.H. reported research funding from Hoffmann-La Roche, Ltd., Ono Pharmaceutical, Pfizer, and Takeda outside the submitted work. J.-H.K. reported honoraria from Roche, Boehringer Ingelheim, Merck & Co., Kenilworth, NJ, and Bristol-Myers Squibb; consulting roles with Roche, Boehringer Ingelheim, Merck & Co., Kenilworth, NJ, AstraZeneca, and Yuhan; speakers bureau for Pfizer, Merck & Co., Kenilworth, NJ, and Roche; and research funding from Boehringer Ingelheim, AstraZeneca, Daiichi Sankyo, and Yuhan outside the submitted work. Y.J.C. reported consulting/advisory role with Astella, Yuhan, Merck & Co., Kenilworth, NJ, Roche, and Chong Kun Dang outside the submitted work. C.S. is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany. D.J. is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany, and holds stock in Merck KGaA, Darmstadt, Germany. H.V. is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany. R.B. is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany, and holds stock in Merck KGaA, Darmstadt, Germany. G.O. was an employee of the healthcare business of Merck KGaA, Darmstadt, Germany, at the time of the study and holds stock in Novartis. A.J. is an employee of the healthcare business of Merck KGaA, Darmstadt, Germany, and holds stock in Merck KGaA, Darmstadt, Germany. P.K.P. reported an advisory/consulting role from Takeda, Xencor, Janssen, CrownBio, Bicara, Mirati, and EMD Serono; and research funding (institution) from Bicara, Boehringer Ingelheim, and EMD Serono outside the submitted work., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Copy number alterations constitute important phenomena in tumor evolution. Whole genome single-cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient, regularized MCMC procedure to search the space of possible model structures and parameters. We introduce a range of model priors and penalties for efficient regularization. CONET reveals copy number evolution in two breast cancer samples, and outperforms other methods in tree reconstruction, breakpoint identification and copy number calling., (© 2022. The Author(s).)

Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Molecular drivers of PCNSL have not been fully elucidated. Here, we profile and compare the whole-genome and transcriptome landscape of 51 CNS lymphomas (CNSL) to 39 follicular lymphoma and 36 DLBCL cases outside the CNS. We find recurrent mutations in JAK-STAT, NFkB, and B-cell receptor signaling pathways, including hallmark mutations in MYD88 L265P (67%) and CD79B (63%), and CDKN2A deletions (83%). PCNSLs exhibit significantly more focal deletions of HLA-D (6p21) locus as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis are significantly enriched in PCNSL. TERT gene expression is significantly higher in PCNSL compared to activated B-cell (ABC)-DLBCL. Transcriptome analysis clearly distinguishes PCNSL and systemic DLBCL into distinct molecular subtypes. Epstein-Barr virus (EBV)+ CNSL cases lack recurrent mutational hotspots apart from IG and HLA-DRB loci. We show that PCNSL can be clearly distinguished from DLBCL, having distinct expression profiles, IG expression and translocation patterns, as well as specific combinations of genetic alterations., (© 2022. The Author(s).)

MET exon 14 skipping alterations ( MET ex14) comprise a diverse set of actionable oncogene drivers in non-small-cell lung cancer (NSCLC). Recent studies have established the efficacy of tyrosine kinase inhibitors for this patient population. The landscape of co-occurring genetic alterations in MET ex14 NSCLC and their potential impact to therapeutic sensitivities has not yet been fully described., Materials and Methods: MET ex14 NSCLC cases were collected from three cohorts: the VISION trial, and data sets from Guardant360 and GenePlus. Clinicopathologic characteristics and MET ex14 mutation sites were analyzed and compared across data sets. Co-occurring genetic alterations and the clonality relationships to MET ex14 were evaluated., Results: Of 40,824 NSCLCs, 692 MET ex14 cases (1.7%) were identified, including 332 in Guardant360, 188 in VISION, and 172 in GenePlus. The demographics and mutation type and/or sites were similar in the Asian versus Western cohorts. MET amplification, which were found to be associated with sensitivity to MET kinase inhibitors, co-occurs in 7.6%-13.8% of cases, whereas kinase domain secondary mutation of MET co-occurs in 5%-6%. When co-occurring with MET ex14, EGFR mutations were often identified as the dominant clone (78%, 7 of 9), whereas when co-occurring, MET ex14 (39%, 7 of 18) and KRAS (44%, 8 of 18) had similar rates of clonal dominance. PIK3CA and PTEN mutations were almost always subclones (89%, 16 of 18) to MET ex14. Moreover, RET-CCDC6 fusion and EGFR mutation were detected following crizotinib treatment in two patients, suggesting novel mechanisms of resistance., Conclusion: MET ex14 mutations frequently co-occur with other potential driver oncogenes with differing patterns of clonal dominance observed among the drivers. This cellular context can provide insights into whether MET ex14 is acting as a primary oncogenic driver or resistance mechanism and help guide treatment choices., Competing Interests: John V. Heymach Stock and Other Ownership Interests: Cardinal Spine, Bio-Tree Consulting or Advisory Role: AstraZeneca, Bristol Myers Squibb, Spectrum Pharmaceuticals, Guardant Health, Hengrui Pharmaceutical, GlaxoSmithKline, EMD Serono, Lilly, Takeda, Sanofi/Aventis, Genentech/Roche, Boehringer Ingelheim, Catalyst Biotech, Foundation Medicine, Novartis, Mirati Therapeutics, BrightPath Biotheraputics, Janssen, Nexus Health Systems, Pneuma Respiratory, Kairos Ventures, Roche, Leads Biolabs Research Funding: AstraZeneca (Inst), Spectrum Pharmaceuticals, GlaxoSmithKline Patents, Royalties, Other Intellectual Property: Licensing agreement between Spectrum and MD Anderson (including myself) regarding intellectual property for treatment of EGFR and HER2 exon 20 mutations No other potential conflicts of interest were reported. John V. Heymach Stock and Other Ownership Interests: Cardinal Spine, Bio-Tree Consulting or Advisory Role: AstraZeneca, Bristol Myers Squibb, Spectrum Pharmaceuticals, Guardant Health, Hengrui Pharmaceutical, GlaxoSmithKline, EMD Serono, Lilly, Takeda, Sanofi/Aventis, Genentech/Roche, Boehringer Ingelheim, Catalyst Biotech, Foundation Medicine, Novartis, Mirati Therapeutics, BrightPath Biotheraputics, Janssen, Nexus Health Systems, Pneuma Respiratory, Kairos Ventures, Roche, Leads Biolabs Research Funding: AstraZeneca (Inst), Spectrum Pharmaceuticals, GlaxoSmithKline Patents, Royalties, Other Intellectual Property: Licensing agreement between Spectrum and MD Anderson (including myself) regarding intellectual property for treatment of EGFR and HER2 exon 20 mutations No other potential conflicts of interest were reported., (© 2021 by American Society of Clinical Oncology.)

Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R [Formula: see text] 0.82 correlation between true and predicted response for the unseen cell lines. The interpretability analysis identifies 67 biological processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib., (© 2021. The Author(s).)

Background: A splice-site mutation that results in a loss of transcription of exon 14 in the oncogenic driver MET occurs in 3 to 4% of patients with non-small-cell lung cancer (NSCLC). We evaluated the efficacy and safety of tepotinib, a highly selective MET inhibitor, in this patient population., Methods: In this open-label, phase 2 study, we administered tepotinib (at a dose of 500 mg) once daily in patients with advanced or metastatic NSCLC with a confirmed MET exon 14 skipping mutation. The primary end point was the objective response by independent review among patients who had undergone at least 9 months of follow-up. The response was also analyzed according to whether the presence of a MET exon 14 skipping mutation was detected on liquid biopsy or tissue biopsy., Results: As of January 1, 2020, a total of 152 patients had received tepotinib, and 99 patients had been followed for at least 9 months. The response rate by independent review was 46% (95% confidence interval [CI], 36 to 57), with a median duration of response of 11.1 months (95% CI, 7.2 to could not be estimated) in the combined-biopsy group. The response rate was 48% (95% CI, 36 to 61) among 66 patients in the liquid-biopsy group and 50% (95% CI, 37 to 63) among 60 patients in the tissue-biopsy group; 27 patients had positive results according to both methods. The investigator-assessed response rate was 56% (95% CI, 45 to 66) and was similar regardless of the previous therapy received for advanced or metastatic disease. Adverse events of grade 3 or higher that were considered by investigators to be related to tepotinib therapy were reported in 28% of the patients, including peripheral edema in 7%. Adverse events led to permanent discontinuation of tepotinib in 11% of the patients. A molecular response, as measured in circulating free DNA, was observed in 67% of the patients with matched liquid-biopsy samples at baseline and during treatment., Conclusions: Among patients with advanced NSCLC with a confirmed MET exon 14 skipping mutation, the use of tepotinib was associated with a partial response in approximately half the patients. Peripheral edema was the main toxic effect of grade 3 or higher. (Funded by Merck [Darmstadt, Germany]; VISION ClinicalTrials.gov number, NCT02864992.)., (Copyright © 2020 Massachusetts Medical Society.)

Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. Critically, the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Here, we compare standard, data-driven feature selection approaches to feature selection driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, evaluating 2484 unique models. For 23 drugs, better predictive performance is achieved when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r = 0.75). Extending the drug-dependent features with gene expression signatures yields the most predictive models for 60 drugs, with the best performing example of Dabrafenib. For many compounds, even a very small subset of drug-related features is highly predictive of drug sensitivity. Small feature sets selected using prior knowledge are more predictive for drugs targeting specific genes and pathways, while models with wider feature sets perform better for drugs affecting general cellular mechanisms. Appropriate feature selection strategies facilitate the development of interpretable models that are indicative for therapy design.

CD8 + T cells are considered prototypical cells of adaptive immunity. Here, we uncovered a distinct CD8 + T cell population expressing the activating natural killer (NK) receptor NKp30 in the peripheral blood of healthy individuals. We revealed that IL-15 could de novo induce NKp30 expression in a population of CD8 + T cells and drive their differentiation toward a broad innate transcriptional landscape. The adaptor FcεRIγ was concomitantly induced and was shown to be crucial to enable NKp30 cell-surface expression and function in CD8 + T cells. FcεRIγ de novo expression required promoter demethylation and was accompanied by acquisition of the signaling molecule Syk and the "innate" transcription factor PLZF. IL-15-induced NKp30 + CD8 + T cells exhibited high NK-like antitumor activity in vitro and were able to synergize with T cell receptor signaling. Importantly, this population potently controlled tumor growth in a preclinical xenograft mouse model. Our study, while blurring the borders between innate and adaptive immunity, reveals a unique NKp30 + FcεRIγ + CD8 + T cell population with high antitumor therapeutic potential., Competing Interests: The authors declare no conflict of interest.

Background: Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables., Methods: A cohort of 695 neuroblastoma patients was divided into a discovery set (n=75) for multigene predictor generation, a training set (n=411) for risk score development, and a validation set (n=209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score., Results: The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9±3.4 vs 63.6±14.5 vs 31.0±5.4; P<.001), and its prognostic value was validated by multivariable analysis., Conclusion: We here propose a translational strategy for developing risk assessment systems based on hazard ratios of relevant prognostic variables. Our final neuroblastoma risk score comprised two multigene predictors only, supporting the notion that molecular properties of the tumor cells strongly impact clinical courses of neuroblastoma patients., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

The present study investigated the genetic contribution to alcohol dependence (AD) using genome-wide association data from three German samples. These comprised patients with: (i) AD; (ii) chronic alcoholic pancreatitis (ACP); and (iii) alcohol-related liver cirrhosis (ALC). Single marker, gene-based, and pathway analyses were conducted. A significant association was detected for the ADH1B locus in a gene-based approach ( p uncorrected = 1.2 × 10 -6 ; p corrected = 0.020). This was driven by the AD subsample. No association with ADH1B was found in the combined ACP + ALC sample. On first inspection, this seems surprising, since ADH1B is a robustly replicated risk gene for AD and may therefore be expected to be associated also with subgroups of AD patients. The negative finding in the ACP + ALC sample, however, may reflect genetic stratification as well as random fluctuation of allele frequencies in the cases and controls, demonstrating the importance of large samples in which the phenotype is well assessed., Competing Interests: Norbert Scherbaum has received honoraria from Sanofi-Aventis, Reckitt-Benckiser/Indivior, Lundbeck, and Janssen-Cilag for advisory board participation, lectures, and the preparation of manuscripts and educational materials. In the past three years, he has participated in clinical trials financed by the pharmaceutical industry (Reckitt & Benckiser/Indivior). These funding sources had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or and the decision to publish the results.

Background: Pathological gambling is a behavioural addiction with negative economic, social, and psychological consequences. Identification of contributing genes and pathways may improve understanding of aetiology and facilitate therapy and prevention. Here, we report the first genome-wide association study of pathological gambling. Our aims were to identify pathways involved in pathological gambling, and examine whether there is a genetic overlap between pathological gambling and alcohol dependence., Methods: Four hundred and forty-five individuals with a diagnosis of pathological gambling according to the Diagnostic and Statistical Manual of Mental Disorders were recruited in Germany, and 986 controls were drawn from a German general population sample. A genome-wide association study of pathological gambling comprising single marker, gene-based, and pathway analyses, was performed. Polygenic risk scores were generated using data from a German genome-wide association study of alcohol dependence., Results: No genome-wide significant association with pathological gambling was found for single markers or genes. Pathways for Huntington's disease (P-value=6.63×10(-3)); 5'-adenosine monophosphate-activated protein kinase signalling (P-value=9.57×10(-3)); and apoptosis (P-value=1.75×10(-2)) were significant. Polygenic risk score analysis of the alcohol dependence dataset yielded a one-sided nominal significant P-value in subjects with pathological gambling, irrespective of comorbid alcohol dependence status., Conclusions: The present results accord with previous quantitative formal genetic studies which showed genetic overlap between non-substance- and substance-related addictions. Furthermore, pathway analysis suggests shared pathology between Huntington's disease and pathological gambling. This finding is consistent with previous imaging studies., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)

The microRNA (miRNA) landscape changes during the progression of cancer. We defined a metastasis-associated miRNA landscape using a systematic approach. We profiled and validated miRNA and mRNA expression in a unique series of human colorectal metastasis tissues together with their matched primary tumors and corresponding normal tissues. We identified an exclusive miRNA signature that is differentially expressed in metastases. Three of these miRNAs were identified as key drivers of an EMT-regulating network acting though a number of novel targets. These targets include SIAH1, SETD2, ZEB2, and especially FOXN3, which we demonstrated for the first time as a direct transcriptional suppressor of N-cadherin. The modulation of N-cadherin expression had significant impact on migration, invasion, and metastasis in two different in vivo models. The significant deregulation of the miRNAs defining the network was confirmed in an independent patient set as well as in a database of diverse malignancies derived from more than 6,000 patients. Our data define a novel metastasis-orchestrating network based on systematic hypothesis generation from metastasis tissues., (©2015 American Association for Cancer Research.)

MYCN amplification occurs in 20% of neuroblastomas and is strongly related to poor clinical outcome. We have identified folate-mediated one-carbon metabolism as highly upregulated in neuroblastoma tumors with MYCN amplification and have validated this finding experimentally by showing that MYCN amplified neuroblastoma cell lines have a higher requirement for folate and are significantly more sensitive to the antifolate methotrexate than cell lines without MYCN amplification. We have demonstrated that methotrexate uptake in neuroblastoma cells is mediated principally by the reduced folate carrier (RFC; SLC19A1), that SLC19A1 and MYCN expression are highly correlated in both patient tumors and cell lines, and that SLC19A1 is a direct transcriptional target of N-Myc. Finally, we assessed the relationship between SLC19A1 expression and patient survival in two independent primary tumor cohorts and found that SLC19A1 expression was associated with increased risk of relapse or death, and that SLC19A1 expression retained prognostic significance independent of age, disease stage and MYCN amplification. This study adds upregulation of folate-mediated one-carbon metabolism to the known consequences of MYCN amplification, and suggests that this pathway might be targeted in poor outcome tumors with MYCN amplification and high SLC19A1 expression.

Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers., Experimental Design: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4 × 44 K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n = 634) by Kaplan-Meier estimates and Cox regression analyses., Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity, 0.93; specificity, 0.97) in the validation cohort. The highest potential clinical value of this predictor was observed for current low-risk patients [5-year event-free survival (EFS), 0.84 ± 0.02 vs. 0.29 ± 0.10; 5-year overall survival (OS), 0.99 ± 0.01 vs. 0.76 ± 0.11; both P < 0.001] and intermediate-risk patients (5-year EFS, 0.88 ± 0.06 vs. 0.41 ± 0.10; 5-year OS, 1.0 vs. 0.70 ± 0.09; both P < 0.001). In multivariate Cox regression models for low-risk/intermediate-risk patients, the classifier outperformed risk assessment of the current German trial NB2004 [EFS: hazard ratio (HR), 5.07; 95% confidence interval (CI), 3.20-8.02; OS: HR, 25.54; 95% CI, 8.40-77.66; both P < 0.001]. On the basis of these findings, we propose to integrate the classifier into a revised risk stratification system for low-risk/intermediate-risk patients. According to this system, we identified novel subgroups with poor outcome (5-year EFS, 0.19 ± 0.08; 5-year OS, 0.59 ± 0.1), for whom we propose intensified treatment, and with beneficial outcome (5-year EFS, 0.87 ± 0.05; 5-year OS, 1.0), who may benefit from treatment de-escalation., Conclusions: Combination of gene expression-based classification and established prognostic markers improves risk estimation of patients with low-risk/intermediate-risk neuroblastoma. We propose to implement our revised treatment stratification system in a prospective clinical trial., (©2014 American Association for Cancer Research.)

Genetic factors have as large role as environmental factors in the etiology of alcohol dependence (AD). Although genome-wide association studies (GWAS) enable systematic searches for loci not hitherto implicated in the etiology of AD, many true findings may be missed owing to correction for multiple testing. The aim of the present study was to circumvent this limitation by searching for biological system-level differences, and then following up these findings in humans and animals. Gene-set-based analysis of GWAS data from 1333 cases and 2168 controls identified 19 significantly associated gene-sets, of which 5 could be replicated in an independent sample. Clustered in these gene-sets were novel and previously identified susceptibility genes. The most frequently present gene, ie in 6 out of 19 gene-sets, was X-ray repair complementing defective repair in Chinese hamster cells 5 (XRCC5). Previous human and animal studies have implicated XRCC5 in alcohol sensitivity. This phenotype is inversely correlated with the development of AD, presumably as more alcohol is required to achieve the desired effects. In the present study, the functional role of XRCC5 in AD was further validated in animals and humans. Drosophila mutants with reduced function of Ku80-the homolog of mammalian XRCC5-due to RNAi silencing showed reduced sensitivity to ethanol. In humans with free access to intravenous ethanol self-administration in the laboratory, the maximum achieved blood alcohol concentration was influenced in an allele-dose-dependent manner by genetic variation in XRCC5. In conclusion, our convergent approach identified new candidates and generated independent evidence for the involvement of XRCC5 in alcohol dependence.

Bipolar disorder (BD) is a highly heritable psychiatric disease characterized by recurrent episodes of mania and depression. To identify new BD genes and pathways, the present study employed a three-step approach. First, gene-expression profiles of BD patients were assessed during both a manic and an euthymic phase. These profiles were compared intra-individually and with the gene-expression profiles of controls. Second, those differentially expressed genes that were considered potential trait markers of BD were validated using data from the Psychiatric Genomics Consortiums' genome-wide association study (GWAS) of BD. Third, the implicated molecular mechanisms were investigated using pathway analytical methods. In the present patients, this novel approach identified: (i) sets of differentially expressed genes specific to mania and euthymia; and (ii) a set of differentially expressed genes that were common to both mood states. In the GWAS data integration analysis, one gene (STAB1) remained significant (P=1.9 × 10(-4)) after adjustment for multiple testing. STAB1 is located in close proximity to PBMR1 and the NEK4-ITIH1-ITIH3-ITIH4 region, which are the top findings from GWAS meta-analyses of mood disorder, and a combined BD and schizophrenia data set. Pathway analyses in the mania versus control comparison revealed three distinct clusters of pathways tagging molecular mechanisms implicated in BD, for example, energy metabolism, inflammation and the ubiquitin proteasome system. The present findings suggest that STAB1 is a new and highly promising candidate gene in this region. The combining of gene expression and GWAS data may provide valuable insights into the biological mechanisms of BD.

In the present study, an integrated hierarchical approach was applied to: (1) identify pathways associated with susceptibility to schizophrenia; (2) detect genes that may be potentially affected in these pathways since they contain an associated polymorphism; and (3) annotate the functional consequences of such single-nucleotide polymorphisms (SNPs) in the affected genes or their regulatory regions. The Global Test was applied to detect schizophrenia-associated pathways using discovery and replication datasets comprising 5,040 and 5,082 individuals of European ancestry, respectively. Information concerning functional gene-sets was retrieved from the Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and the Molecular Signatures Database. Fourteen of the gene-sets or pathways identified in the discovery dataset were confirmed in the replication dataset. These include functional processes involved in transcriptional regulation and gene expression, synapse organization, cell adhesion, and apoptosis. For two genes, i.e. CTCF and CACNB2, evidence for association with schizophrenia was available (at the gene-level) in both the discovery study and published data from the Psychiatric Genomics Consortium schizophrenia study. Furthermore, these genes mapped to four of the 14 presently identified pathways. Several of the SNPs assigned to CTCF and CACNB2 have potential functional consequences, and a gene in close proximity to CACNB2, i.e. ARL5B, was identified as a potential gene of interest. Application of the present hierarchical approach thus allowed: (1) identification of novel biological gene-sets or pathways with potential involvement in the etiology of schizophrenia, as well as replication of these findings in an independent cohort; (2) detection of genes of interest for future follow-up studies; and (3) the highlighting of novel genes in previously reported candidate regions for schizophrenia.

Cell competition is an emerging principle underlying selection for cellular fitness during development and disease. Competition may be relevant for cancer, but an experimental link between defects in competition and tumorigenesis is elusive. In the thymus, T lymphocytes develop from precursors that are constantly replaced by bone-marrow-derived progenitors. Here we show that in mice this turnover is regulated by natural cell competition between 'young' bone-marrow-derived and 'old' thymus-resident progenitors that, although genetically identical, execute differential gene expression programs. Disruption of cell competition leads to progenitor self-renewal, upregulation of Hmga1, transformation, and T-cell acute lymphoblastic leukaemia (T-ALL) resembling the human disease in pathology, genomic lesions, leukaemia-associated transcripts, and activating mutations in Notch1. Hence, cell competition is a tumour suppressor mechanism in the thymus. Failure to select fit progenitors through cell competition may explain leukaemia in X-linked severe combined immune deficiency patients who showed thymus-autonomous T-cell development after therapy with gene-corrected autologous progenitors.

Neuroblastoma is an embryonal malignancy of the sympathetic nervous system. Spontaneous regression and differentiation of neuroblastoma is observed in a subset of patients, and has been suggested to represent delayed activation of physiologic molecular programs of fetal neuroblasts. Homeobox genes constitute an important family of transcription factors, which play a fundamental role in morphogenesis and cell differentiation during embryogenesis. In this study, we demonstrate that expression of the majority of the human HOX class I homeobox genes is significantly associated with clinical covariates in neuroblastoma using microarray expression data of 649 primary tumors. Moreover, a HOX gene expression-based classifier predicted neuroblastoma patient outcome independently of age, stage and MYCN amplification status. Among all HOX genes, HOXC9 expression was most prominently associated with favorable prognostic markers. Most notably, elevated HOXC9 expression was significantly associated with spontaneous regression in infant neuroblastoma. Re-expression of HOXC9 in three neuroblastoma cell lines led to a significant reduction in cell viability, and abrogated tumor growth almost completely in neuroblastoma xenografts. Neuroblastoma growth arrest was related to the induction of programmed cell death, as indicated by an increase in the sub-G1 fraction and translocation of phosphatidylserine to the outer membrane. Programmed cell death was associated with the release of cytochrome c from the mitochondria into the cytosol and activation of the intrinsic cascade of caspases, indicating that HOXC9 re-expression triggers the intrinsic apoptotic pathway. Collectively, our results show a strong prognostic impact of HOX gene expression in neuroblastoma, and may point towards a role of Hox-C9 in neuroblastoma spontaneous regression.

Background: Genome-wide association studies are a powerful tool for unravelling the genetic background of complex disorders such as major depression., Methods: We conducted a genome-wide association study of 604 patients with major depression and 1364 population based control subjects. The top hundred findings were followed up in a replication sample of 409 patients and 541 control subjects., Results: Two SNPs showed nominally significant association in both the genome-wide association study and the replication samples: 1) rs9943849 (p(combined) = 3.24E-6) located upstream of the carboxypeptidase M (CPM) gene and 2) rs7713917 (p(combined) = 1.48E-6), located in a putative regulatory region of HOMER1. Further evidence for HOMER1 was obtained through gene-wide analysis while conditioning on the genotypes of rs7713917 (p(combined) = 4.12E-3). Homer1 knockout mice display behavioral traits that are paradigmatic of depression, and transcriptional variants of Homer1 result in the dysregulation of cortical-limbic circuitry. This is consistent with the findings of our subsequent human imaging genetics study, which revealed that variation in single nucleotide polymorphism rs7713917 had a significant influence on prefrontal activity during executive cognition and anticipation of reward., Conclusion: Our findings, combined with evidence from preclinical and animal studies, suggest that HOMER1 plays a role in the etiology of major depression and that the genetic variation affects depression via the dysregulation of cognitive and motivational processes., (2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Microarray-based prediction of clinical endpoints may be performed using either a one-color approach reflecting mRNA abundance in absolute intensity values or a two-color approach yielding ratios of fluorescent intensities. In this study, as part of the MAQC-II project, we systematically compared the classification performance resulting from one- and two-color gene-expression profiles of 478 neuroblastoma samples. In total, 196 classification models were applied to these measurements to predict four clinical endpoints, and classification performances were compared in terms of accuracy, area under the curve, Matthews correlation coefficient and root mean-squared error. Whereas prediction performance varied with distinct clinical endpoints and classification models, equivalent performance metrics were observed for one- and two-color measurements in both internal and external validation. Furthermore, overlap of selected signature genes correlated inversely with endpoint prediction difficulty. In summary, our data strongly substantiate that the choice of platform is not a primary factor for successful gene expression based-prediction of clinical endpoints.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Purpose: To evaluate the impact of a predefined gene expression-based classifier for clinical risk estimation and cytotoxic treatment decision making in neuroblastoma patients., Patients and Methods: Gene expression profiles of 440 internationally collected neuroblastoma specimens were investigated by microarray analysis, 125 of which were examined prospectively. Patients were classified as either favorable or unfavorable by a 144-gene prediction analysis for microarrays (PAM) classifier established previously on a separate set of 77 patients. PAM classification results were compared with those of current prognostic markers and risk estimation strategies., Results: The PAM classifier reliably distinguished patients with contrasting clinical courses (favorable [n = 249] and unfavorable [n = 191]; 5-year event free survival [EFS] 0.84 +/- 0.03 v 0.38 +/- 0.04; 5-year overall survival [OS] 0.98 +/- 0.01 v 0.56 +/- 0.05, respectively; both P < .001). Moreover, patients with divergent outcome were robustly discriminated in both German and international cohorts and in prospectively analyzed samples (P

A real-time polymerase chain reaction (PCR) method was applied to quantify the nifH gene pool in cucumber shoot and root and to evaluate how nitrogen (N) supply and plant age affect the nifH gene pool. In shoots, the relative abundance of the nifH gene was affected neither by different stages of plant growth nor by N supply. In roots, higher numbers of diazotrophic bacteria were found compared with that in the shoot. The nifH gene pool in roots significantly increased with plant age, and unexpectedly, the pool size was positively correlated with N supply. The relative abundance of nifH gene copy numbers in roots was also positively correlated (r = 0.96) with total N uptake of the plant. The data suggest that real-time PCR-based nifH gene quantification in combination with N-content analysis can be used as an efficient way to perform further studies to evaluate the direct contribution of the N2-fixing plant-colonizing plant growth promoting bacteria to plant N nutrition.