Literature DB >> 35314692

A pharmacogenetic interaction analysis of bevacizumab with paclitaxel in advanced breast cancer patients.

Luigi Coltelli1,2, Giacomo Allegrini1,2, Paola Orlandi3, Chiara Finale1,2, Andrea Fontana4, Luna Chiara Masini1,2, Marco Scalese5, Giada Arrighi1,2, Maria Teresa Barletta1,2, Ermelinda De Maio1,2, Marta Banchi3, Elisabetta Fini3, Patrizia Guidi3, Giada Frenzilli3, Sara Donati6, Simona Giovannelli7, Lucia Tanganelli7, Barbara Salvadori4, Lorenzo Livi8, Icro Meattini8, Ilaria Pazzagli9, Marco Di Lieto9, Mirco Pistelli10, Virginia Casadei11, Antonella Ferro12, Samanta Cupini1,2, Francesca Orlandi1,2, Damiana Francesca1,13, Giulia Lorenzini4, Leonardo Barellini1,14, Alfredo Falcone4,15, Alessandro Cosimi1, Guido Bocci16.   

Abstract

To investigate pharmacogenetic interactions among VEGF-A, VEGFR-2, IL-8, HIF-1α, EPAS-1, and TSP-1 SNPs and their role on progression-free survival (PFS) in metastatic breast cancer (MBC) patients treated with bevacizumab plus first-line paclitaxel or with paclitaxel alone. Analyses were performed on germline DNA, and SNPs were investigated by real-time PCR technique. The multifactor dimensionality reduction (MDR) methodology was applied to investigate the interaction between SNPs. The present study was an explorative, ambidirectional cohort study: 307 patients from 11 Oncology Units were evaluated retrospectively from 2009 to 2016, then followed prospectively (NCT01935102). Two hundred and fifteen patients were treated with paclitaxel and bevacizumab, whereas 92 patients with paclitaxel alone. In the bevacizumab plus paclitaxel group, the MDR software provided two pharmacogenetic interaction profiles consisting of the combination between specific VEGF-A rs833061 and VEGFR-2 rs1870377 genotypes. Median PFS for favorable genetic profile was 16.8 vs. the 10.6 months of unfavorable genetic profile (p = 0.0011). Cox proportional hazards model showed an adjusted hazard ratio of 0.64 (95% CI, 0.5-0.9; p = 0.004). Median OS for the favorable genetic profile was 39.6 vs. 28 months of unfavorable genetic profile (p = 0.0103). Cox proportional hazards model revealed an adjusted hazard ratio of 0.71 (95% CI, 0.5-1.01; p = 0.058). In the 92 patients treated with paclitaxel alone, the results showed no effect of the favorable genetic profile, as compared to the unfavorable genetic profile, either on the PFS (p = 0.509) and on the OS (p = 0.732). The pharmacogenetic statistical interaction between VEGF-A rs833061 and VEGFR-2 rs1870377 genotypes may identify a population of bevacizumab-treated patients with a better PFS.
© 2022. The Author(s).

Entities:  

Year:  2022        PMID: 35314692      PMCID: PMC8938486          DOI: 10.1038/s41523-022-00400-6

Source DB:  PubMed          Journal:  NPJ Breast Cancer        ISSN: 2374-4677


  27 in total

1.  Ideal discrimination of discrete clinical endpoints using multilocus genotypes.

Authors:  Lance W Hahn; Jason H Moore
Journal:  In Silico Biol       Date:  2004

2.  Detecting an overall survival benefit that is derived from progression-free survival.

Authors:  Kristine R Broglio; Donald A Berry
Journal:  J Natl Cancer Inst       Date:  2009-11-09       Impact factor: 13.506

3.  Bevacizumab pharmacogenetics in tumor treatment: still looking for the right pieces of the puzzle.

Authors:  Guido Bocci; Fotios Loupakis
Journal:  Pharmacogenomics       Date:  2011-08       Impact factor: 2.533

4.  Predictive impact of circulating vascular endothelial growth factor in four phase III trials evaluating bevacizumab.

Authors:  Priti S Hegde; Adrian M Jubb; Dafeng Chen; Nicole F Li; Y Gloria Meng; Coen Bernaards; Rebecca Elliott; Stefan J Scherer; Daniel S Chen
Journal:  Clin Cancer Res       Date:  2012-11-20       Impact factor: 12.531

5.  Pharmacogenetic interaction analysis of VEGFR-2 and IL-8 polymorphisms in advanced breast cancer patients treated with paclitaxel and bevacizumab.

Authors:  Giacomo Allegrini; Luigi Coltelli; Paola Orlandi; Andrea Fontana; Andrea Camerini; Antonella Ferro; Marina Cazzaniga; Virginia Casadei; Sara Lucchesi; Eleonora Bona; Marco Di Lieto; Ilaria Pazzagli; Federica Villa; Domenico Amoroso; Marco Scalese; Giada Arrighi; Sabrina Molinaro; Anna Fioravanti; Chiara Finale; Renza Triolo; Teresa Di Desidero; Sara Donati; Lorenzo Marcucci; Orlando Goletti; Marzia Del Re; Barbara Salvadori; Ilaria Ferrarini; Romano Danesi; Alfredo Falcone; Guido Bocci
Journal:  Pharmacogenomics       Date:  2014-12       Impact factor: 2.533

6.  Phase III study of bevacizumab plus docetaxel compared with placebo plus docetaxel for the first-line treatment of human epidermal growth factor receptor 2-negative metastatic breast cancer.

Authors:  David W Miles; Arlene Chan; Luc Y Dirix; Javier Cortés; Xavier Pivot; Piotr Tomczak; Thierry Delozier; Joo Hyuk Sohn; Louise Provencher; Fabio Puglisi; Nadia Harbeck; Guenther G Steger; Andreas Schneeweiss; Andrew M Wardley; Andreas Chlistalla; Gilles Romieu
Journal:  J Clin Oncol       Date:  2010-05-24       Impact factor: 44.544

7.  Paclitaxel plus bevacizumab versus paclitaxel alone for metastatic breast cancer.

Authors:  Kathy Miller; Molin Wang; Julie Gralow; Maura Dickler; Melody Cobleigh; Edith A Perez; Tamara Shenkier; David Cella; Nancy E Davidson
Journal:  N Engl J Med       Date:  2007-12-27       Impact factor: 91.245

8.  Bevacizumab in combination with chemotherapy as first-line therapy in advanced gastric cancer: a biomarker evaluation from the AVAGAST randomized phase III trial.

Authors:  Eric Van Cutsem; Sanne de Haas; Yoon-Koo Kang; Atsushi Ohtsu; Niall C Tebbutt; Jian Ming Xu; Wei Peng Yong; Bernd Langer; Paul Delmar; Stefan J Scherer; Manish A Shah
Journal:  J Clin Oncol       Date:  2012-05-07       Impact factor: 44.544

9.  Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development.

Authors:  Sarah A Pendergrass; Alex Frase; John Wallace; Daniel Wolfe; Neerja Katiyar; Carrie Moore; Marylyn D Ritchie
Journal:  BioData Min       Date:  2013-12-30       Impact factor: 2.522

Review 10.  Analysis pipeline for the epistasis search - statistical versus biological filtering.

Authors:  Xiangqing Sun; Qing Lu; Shubhabrata Mukherjee; Shubhabrata Mukheerjee; Paul K Crane; Robert Elston; Marylyn D Ritchie
Journal:  Front Genet       Date:  2014-04-30       Impact factor: 4.599

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