6674 Background: Optimising cytotoxic dose intensity is critical to achieving the best outcomes for patients with solid tumours. Chemotherapy dose intensification can be achieved by reducing cycle length or increasing doses of cytotoxic agents. In this study we explored the feasibility of delivering carboplatin/paclitaxel at a reduced cycle interval (10–11 days) supported by pegfilgrastim and autologous whole blood infusions. Methods: Patients with solid tumours received carboplatin AUC 6 (1 hour IV infusion) and paclitaxel 175mg/m2 (3 hour IV infusion) for a total of 6 cycles. Cycle 1 was 21 days, cycles 2–6 were administered every 10–11 days. Patients were randomised to receive pegfilgrastim 6mg, 12mg or 18mg on day 2 or filgrastim 5μg/kg from day 2 to an ANC ≥10x109/L. Prior to chemotherapy on day 1 of cycles 3–6, 750ml of whole blood was collected by venesection for re-infusion on day 2. The primary endpoint of the cycle 2–6 analysis was proportion of patients receiving cycles 2–6 at full dose (i.e. >75% of planned dose) and on schedule (i.e. every 11 days ± 3 days). Results: Fifty-three patients (37 [70%] with lung or ovarian cancer) started cycle 2 chemotherapy. A total of 39 (74%) patients completed 4 cycles of chemotherapy and 27 (51%) completed 6 cycles. Of 265 planned cycles of study chemotherapy, 201 (76%) were given on schedule. Only 8 (3%) were delayed due to slow ANC or platelet recovery. The most frequently reported serious adverse events (reported by 5 patients) were thrombocytopenia, abdominal pain and nausea. Other adverse events including bone pain were similar in incidence across the groups. Conclusions: It is feasible to administer full dose carboplatin/paclitaxel every 10–11 days with pegfilgrastim and whole blood support. There was no significant difference between filgrastim and pegfilgrastim 6, 12 and 18 mg with respect to enabling full dose on schedule and no increase in SAEs or bone pain at the higher doses. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Amgen, Amgen Ltd. Amgen Amgen, Amgen Ltd. Amgen
This study compared denosumab, a fully human monoclonal anti-receptor activator of nuclear factor kappa-B ligand antibody, with zoledronic acid (ZA) for delaying or preventing skeletal-related events (SRE) in patients with advanced cancer and bone metastases (excluding breast and prostate) or myeloma.Eligible patients were randomly assigned in a double-blind, double-dummy design to receive monthly subcutaneous denosumab 120 mg (n = 886) or intravenous ZA 4 mg (dose adjusted for renal impairment; n = 890). Daily supplemental calcium and vitamin D were strongly recommended. The primary end point was time to first on-study SRE (pathologic fracture, radiation or surgery to bone, or spinal cord compression).Denosumab was noninferior to ZA in delaying time to first on-study SRE (hazard ratio, 0.84; 95% CI, 0.71 to 0.98; P = .0007). Although directionally favorable, denosumab was not statistically superior to ZA in delaying time to first on-study SRE (P = .03 unadjusted; P = .06 adjusted for multiplicity) or time to first-and-subsequent (multiple) SRE (rate ratio, 0.90; 95% CI, 0.77 to 1.04; P = .14). Overall survival and disease progression were similar between groups. Hypocalcemia occurred more frequently with denosumab. Osteonecrosis of the jaw occurred at similarly low rates in both groups. Acute-phase reactions after the first dose occurred more frequently with ZA, as did renal adverse events and elevations in serum creatinine based on National Cancer Institute Common Toxicity Criteria for Adverse Events grading.Denosumab was noninferior (trending to superiority) to ZA in preventing or delaying first on-study SRE in patients with advanced cancer metastatic to bone or myeloma. Denosumab represents a potential novel treatment option with the convenience of subcutaneous administration and no requirement for renal monitoring or dose adjustment.
Purpose Cigarette smoking induces CYP1A1/1A2 and is hypothesized to alter erlotinib pharmacokinetics. This study aimed to determine the maximum tolerated dose (MTD) of erlotinib in advanced non–small-cell lung cancer (NSCLC) patients who smoke and compare the pharmacokinetics of erlotinib at the MTD in current smokers with 150 mg. Patients and Methods Cohorts of NSCLC patients currently smoking ≥ 10 cigarettes per day for ≥ 1 year received escalating doses of erlotinib for 14 days until dose-limiting toxicity (DLT). A separate cohort of patients was then randomly assigned to erlotinib at either MTD or 150 mg daily with pharmacokinetics assessed at day 14. Erlotinib was continued until progression or intolerable toxicity. Results Four dose levels were evaluated in 22 patients: 200, 250, 300, and 350 mg. DLT was observed in one of six patients at 300 mg (rash) and two of five patients at 350 mg (acneiform dermatitis and fatigue/decreased Eastern Cooperative Oncology Group performance status). Thirty-five patients were randomly assigned to 150 mg or 300 mg. Common adverse events (all grades) were: skin toxicity (150 mg, 29%; 300 mg, 67%), diarrhea (150 mg, 18%; 300 mg, 50%), and fatigue (150 mg, 12%; 300 mg, 17%). Erlotinib exposure was dose-proportional within dose range tested. Median steady-state trough erlotinib plasma concentrations were 0.375 and 1.22 μg/mL for 150 mg and 300 mg, respectively. Conclusion The MTD of erlotinib in NSCLC patients who smoke was 300 mg. Steady-state trough plasma concentrations and incidence of rash and diarrhea in smokers at 300 mg were similar to those in former or never smokers receiving 150 mg in previous studies. The potential benefit of higher erlotinib doses in current smokers warrants further evaluation.
// Yafang Li 1 , Xiangjun Xiao 1 , Yohan Bossé 2 , Olga Gorlova 3 , Ivan Gorlov 3 , Younghun Han 1 , Jinyoung Byun 1 , Natasha Leighl 4 , Jakob S. Johansen 5 , Matt Barnett 6 , Chu Chen 6 , Gary Goodman 7 , Angela Cox 8 , Fiona Taylor 8 , Penella Woll 8 , H. Erich Wichmann 9 , Judith Manz 9 , Thomas Muley 10 , Angela Risch 11,12,13 , Albert Rosenberger 14 , Jiali Han 15 , Katherine Siminovitch 16 , Susanne M. Arnold 17 , Eric B. Haura 18 , Ciprian Bolca 19 , Ivana Holcatova 20 , Vladimir Janout 20 , Milica Kontic 21 , Jolanta Lissowska 22 , Anush Mukeria 23 , Simona Ognjanovic 24 , Tadeusz M. Orlowski 25 , Ghislaine Scelo 26 , Beata Swiatkowska 27 , David Zaridze 23 , Per Bakke 28 , Vidar Skaug 29 , Shanbeh Zienolddiny 29 , Eric J. Duell 30 , Lesley M. Butler 31 , Richard Houlston 32 , María Soler Artigas 33,34 , Kjell Grankvist 35 , Mikael Johansson 36 , Frances A. Shepherd 37 , Michael W. Marcus 38 , Hans Brunnström 39 , Jonas Manjer 40 , Olle Melander 40 , David C. Muller 41 , Kim Overvad 42 , Antonia Trichopoulou 43 , Rosario Tumino 44 , Geoffrey Liu 45 , Stig E. Bojesen 46,47,48 , Xifeng Wu 49 , Loic Le Marchand 50 , Demetrios Albanes 51 , Heike Bickeböller 14 , Melinda C. Aldrich 52 , William S. Bush 53 , Adonina Tardon 54 , Gad Rennert 55 , M. Dawn Teare 56 , John K. Field 38 , Lambertus A. Kiemeney 57 , Philip Lazarus 58 , Aage Haugen 59 , Stephen Lam 60 , Matthew B. Schabath 61 , Angeline S. Andrew 62 , Pier Alberto Bertazzi 63,64 , Angela C. Pesatori 64 , David C. Christiani 65 , Neil Caporaso 51 , Mattias Johansson 45 , James D. McKay 45 , Paul Brennan 45 , Rayjean J. Hung 26 and Christopher I. Amos 66 1 Baylor College of Medicine, Houston, TX, USA 2 Laval University, Quebec, QC, Canada 3 Department of Biomedical Data Science, Dartmouth College, Hanover, NH, USA 4 University Health Network, The Princess Margaret Cancer Centre, Toronto, CA, USA 5 Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark 6 Fred Hutchinson Cancer Research Center, Seattle, WA, USA 7 Swedish Medical Group, Seattle, WA, USA 8 Department of Oncology, University of Sheffield, Sheffield, UK 9 Research Unit of Molecular Epidemiology, Institute of Epidemiology II, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany 10 Thoraxklinik at University Hospital Heidelberg, Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany 11 Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany 12 German Center for Lung Research (DKFZ), Heidelberg, Germany 13 University of Salzburg and Cancer Cluster, Salzburg, Austria 14 Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany 15 Indiana University, Bloomington, IN, USA 16 University of Toronto, Toronto, ON, Canada 17 University of Kentucky, Markey Cancer Center, Lexington, KY, USA 18 Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA 19 Institute of Pneumology "Marius Nasta", Bucharest, Romania 20 Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic 21 Clinical Center of Serbia, School of Medicine, University of Belgrade, Belgrade, Serbia 22 M. Sklodowska-Curie Cancer Center, Institute of Oncology, Warsaw, Poland 23 Department of Epidemiology and Prevention, N.N. Blokhin Russian Cancer Research Center, Moscow, Russian Federation 24 International Organization for Cancer Prevention and Research, Belgrade, Serbia 25 Department of Surgery, National Tuberculosis and Lung Diseases Research Institute, Warsaw, Poland 26 International Agency for Research on Cancer, World Health Organization, Lyon, France 27 Nofer Institute of Occupational Medicine, Department of Environmental Epidemiology, Lodz, Poland 28 Department of Clinical Science, University of Bergen, Bergen, Norway 29 National Institute of Occupational Health, Oslo, Norway 30 Unit of Nutrition and Cancer, Catalan Institute of Oncology (ICO-IDIBELL), Barcelona, Spain 31 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA 32 The Institute of Cancer Research, London, UK 33 Department of Health Sciences, Genetic Epidemiology Group, University of Leicester, Leicester, UK 34 National Institute for Health Research (NIHR) Leicester Respiratory Biomedical Research Unit, Glenfield Hospital, Leicester, UK 35 Department of Medical Biosciences, Umeå University, Umeå, Sweden 36 Department of Radiation Sciences, Umeå University, Umeå, Sweden 37 Princess Margaret Cancer Centre, Toronto, ON, Canada 38 Institute of Translational Medicine, University of Liverpool, Liverpool, UK 39 Department of Pathology, Lund University, Lund, Sweden 40 Faculty of Medicine, Lund University, Lund, Sweden 41 School of Public Health, St. Mary's Campus, Imperial College London, London, UK 42 Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark 43 Hellenic Health Foundation, Athens, Greece 44 Molecular and Nutritional Epidemiology Unit CSPO (Cancer Research and Prevention Centre), Scientific Institute of Tuscany, Florence, Italy 45 Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, University of Toronto, Toronto, Canada 46 Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Denmark 47 Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark 48 Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen, Denmark 49 Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 50 Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA 51 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 52 Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, TN, USA 53 Department of Epidemiology and Biostatistics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA 54 IUOPA, University of Oviedo and CIBERESP, Faculty of Medicine, Campus del Cristo s/n, Oviedo, Spain 55 Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel 56 School of Health and Related Research, University of Sheffield, Sheffield, UK 57 Radboud University Medical Center, Nijmegen, The Netherlands 58 Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA 59 National Institute of Occupational Health, Oslo, Norway 60 British Columbia Cancer Agency, Vancouver, Canada 61 Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA 62 Department of Epidemiology, Geisel School of Medicine, Hanover, NH, USA 63 Department of Preventive Medicine, IRCCS Foundation Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy 64 Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy 65 Department of Epidemiology, Program in Molecular and Genetic Epidemiology Harvard School of Public Health, Boston, MA, USA 66 Biomedical Data Science Department, Dartmouth College, Hanover, NH, USA Correspondence to: Christopher I. Amos, email: Christopher.i.amos@dartmouth.edu Keywords : epistasis; lung cancer; oncogenesis; functional annotation Received: October 27, 2018 Accepted: January 22, 2019 Published: March 05, 2019 Abstract The development of cancer is driven by the accumulation of many oncogenesis-related genetic alterationsand tumorigenesis is triggered by complex networks of involved genes rather than independent actions. To explore the epistasis existing among oncogenesis-related genes in lung cancer development, we conducted pairwise genetic interaction analyses among 35,031 SNPs from 2027 oncogenesis-related genes. The genotypes from three independent genome-wide association studies including a total of 24,037 lung cancer patients and 20,401 healthy controls with Caucasian ancestry were analyzed in the study. Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in RGL1:RAD51B (OR=0.44, p value=3.27x10 -11 in overall lung cancer and OR=0.41, p value=9.71x10 -11 in non-small cell lung cancer), SYNE1:RNF43 (OR=0.73, p value=1.01x10 -12 in adenocarcinoma) and FHIT:TSPAN8 (OR=1.82, p value=7.62x10 -11 in squamous cell carcinoma) in our analysis. None of these genes have been identified from previous main effect association studies in lung cancer. Further eQTL gene expression analysis in lung tissues provided information supporting the functional role of the identified epistasis in lung tumorigenesis. Gene set enrichment analysis revealed potential pathways and gene networks underlying molecular mechanisms in overall lung cancer as well as histology subtypes development. Our results provide evidence that genetic interactions between oncogenesis-related genes play an important role in lung tumorigenesis and epistasis analysis, combined with functional annotation, provides a valuable tool for uncovering functional novel susceptibility genes that contribute to lung cancer development by interacting with other modifier genes.
Genome‐wide association studies (GWAS) have identified 45 susceptibility loci associated with lung cancer. Only less than SNPs, small insertions and deletions (INDELs) are the second most abundant genetic polymorphisms in the human genome. INDELs are highly associated with multiple human diseases, including lung cancer. However, limited studies with large‐scale samples have been available to systematically evaluate the effects of INDELs on lung cancer risk. Here, we performed a large‐scale meta‐analysis to evaluate INDELs and their risk for lung cancer in 23,202 cases and 19,048 controls. Functional annotations were performed to further explore the potential function of lung cancer risk INDELs. Conditional analysis was used to clarify the relationship between INDELs and SNPs. Four new risk loci were identified in genome‐wide INDEL analysis (1p13.2: rs5777156, Insertion, OR = 0.92, p = 9.10 × 10 −8 ; 4q28.2: rs58404727, Deletion, OR = 1.19, p = 5.25 × 10 −7 ; 12p13.31: rs71450133, Deletion, OR = 1.09, p = 8.83 × 10 −7 ; and 14q22.3: rs34057993, Deletion, OR = 0.90, p = 7.64 × 10 −8 ). The eQTL analysis and functional annotation suggested that INDELs might affect lung cancer susceptibility by regulating the expression of target genes. After conducting conditional analysis on potential causal SNPs, the INDELs in the new loci were still nominally significant. Our findings indicate that INDELs could be potentially functional genetic variants for lung cancer risk. Further functional experiments are needed to better understand INDEL mechanisms in carcinogenesis.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
