Abstract:Neoadjuvant chemotherapy (NAC) for breast cancer is widely utilized, and we performed genome-wide association studies (GWAS) to determine whether germ-line genetic variability was associated with benefit in terms of pathological complete response (pCR), disease-free survival, and overall survival in patients entered on the NSABP B-40 NAC trial, wherein patients were randomized to receive, or not, bevacizumab in addition to chemotherapy. Patient DNA samples were genotyped with the Illumina OmniExpress BeadChip.… Show more
“…In addition, the identification of biomarkers that can indicate which patients would benefit or not from treatment with bevacizumab regardless of tumor type is even more challenging. Many studies have attempted to identify single nucleotide polymorphisms (SNPs) associated with overall survival (OS) in patients treated with bevacizumab, including a genome‐wide association study (GWAS) 11 and studies focused on VEGF‐pathway genes (for a review 12,13 ). However, for approved indications, there is no molecular marker that can identify the patients who are more or less likely to benefit from treatment with bevacizumab.…”
Germline variants might predict cancer progression. Bevacizumab improves overall survival (OS) in patients with advanced cancers. No biomarkers are available to identify patients that benefit from bevacizumab. A meta‐analysis of genome‐wide association studies (GWAS) was conducted in 1,520 patients from Phase III trials (CALGB 80303, 40503, 80405 and ICON7), where bevacizumab was randomized to treatment without bevacizumab. We aimed to identify genes and single nucleotide polymorphisms (SNPs) associated with survival independently of bevacizumab treatment or through interaction with bevacizumab. A cause‐specific Cox model was used to test the SNP‐OS association in both arms combined (prognostic), and the effect of SNPs‐bevacizumab interaction on OS (predictive) in each study. The SNP effects across studies were combined using inverse variance. Findings were tested for replication in advanced colorectal and ovarian cancer patients from The Cancer Genome Atlas (TGCA). In the GWAS meta‐analysis, patients with rs680949 in PRUNE2 experienced shorter OS compared to patients without it (P = 1.02 × 10−7, hazard ratio [HR] = 1.57, 95% confidence interval [CI] 1.33‐1.86), as well as in TCGA (P = .0219, HR = 1.58, 95% CI 1.07‐2.35). In the GWAS meta‐analysis, patients with rs16852804 in BARD1 experienced shorter OS compared to patients without it (P = 1.40 × 10−5, HR = 1.51, 95% CI 1.25‐1.82) as well as in TCGA (P = 1.39 × 10−4, HR = 3.09, 95% CI 1.73‐5.51). Patients with rs3795897 in AGAP1 experienced shorter OS in the bevacizumab arm compared to the nonbevacizumab arm (P = 1.43 × 10−5). The largest GWAS meta‐analysis of bevacizumab treated patients identified PRUNE2 and BARD1 (tumor suppressor genes) as prognostic genes of colorectal and ovarian cancer, respectively, and AGAP1 as a potentially predictive gene that interacts with bevacizumab with respect to patient survival.
“…In addition, the identification of biomarkers that can indicate which patients would benefit or not from treatment with bevacizumab regardless of tumor type is even more challenging. Many studies have attempted to identify single nucleotide polymorphisms (SNPs) associated with overall survival (OS) in patients treated with bevacizumab, including a genome‐wide association study (GWAS) 11 and studies focused on VEGF‐pathway genes (for a review 12,13 ). However, for approved indications, there is no molecular marker that can identify the patients who are more or less likely to benefit from treatment with bevacizumab.…”
Germline variants might predict cancer progression. Bevacizumab improves overall survival (OS) in patients with advanced cancers. No biomarkers are available to identify patients that benefit from bevacizumab. A meta‐analysis of genome‐wide association studies (GWAS) was conducted in 1,520 patients from Phase III trials (CALGB 80303, 40503, 80405 and ICON7), where bevacizumab was randomized to treatment without bevacizumab. We aimed to identify genes and single nucleotide polymorphisms (SNPs) associated with survival independently of bevacizumab treatment or through interaction with bevacizumab. A cause‐specific Cox model was used to test the SNP‐OS association in both arms combined (prognostic), and the effect of SNPs‐bevacizumab interaction on OS (predictive) in each study. The SNP effects across studies were combined using inverse variance. Findings were tested for replication in advanced colorectal and ovarian cancer patients from The Cancer Genome Atlas (TGCA). In the GWAS meta‐analysis, patients with rs680949 in PRUNE2 experienced shorter OS compared to patients without it (P = 1.02 × 10−7, hazard ratio [HR] = 1.57, 95% confidence interval [CI] 1.33‐1.86), as well as in TCGA (P = .0219, HR = 1.58, 95% CI 1.07‐2.35). In the GWAS meta‐analysis, patients with rs16852804 in BARD1 experienced shorter OS compared to patients without it (P = 1.40 × 10−5, HR = 1.51, 95% CI 1.25‐1.82) as well as in TCGA (P = 1.39 × 10−4, HR = 3.09, 95% CI 1.73‐5.51). Patients with rs3795897 in AGAP1 experienced shorter OS in the bevacizumab arm compared to the nonbevacizumab arm (P = 1.43 × 10−5). The largest GWAS meta‐analysis of bevacizumab treated patients identified PRUNE2 and BARD1 (tumor suppressor genes) as prognostic genes of colorectal and ovarian cancer, respectively, and AGAP1 as a potentially predictive gene that interacts with bevacizumab with respect to patient survival.
Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual’s genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host–microbiome genome-wide association studies.
In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.
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