Whole Genome Amplification of DNA for Genotyping Pharmacogenetics Candidate Genes

Whole genome amplification (WGA) technologies can be used to amplify genomic DNA when only small amounts of DNA are available. The Multiple Displacement Amplification Phi polymerase based amplification has been shown to accurately amplify DNA for a variety of genotyping assays; however, it has not been tested for genotyping many of the clinically relevant genes important for pharmacogenetic studies, such as the cytochrome P450 genes, that are typically difficult to genotype due to multiple pseudogenes, copy number variations, and high similarity to other related genes. We evaluated whole genome amplified samples for Taqman™ genotyping of SNPs in a variety of pharmacogenetic genes. In 24 DNA samples from the Coriell human diversity panel, the call rates, and concordance between amplified (∼200-fold amplification) and unamplified samples was 100% for two SNPs in CYP2D6 and one in ESR1. In samples from a breast cancer clinical trial (Trial 1), we compared the genotyping results in samples before and after WGA for three SNPs in CYP2D6, one SNP in CYP2C19, one SNP in CYP19A1, two SNPs in ESR1, and two SNPs in ESR2. The concordance rates were all >97%. Finally, we compared the allele frequencies of 143 SNPs determined in Trial 1 (whole genome amplified DNA) to the allele frequencies determined in unamplified DNA samples from a separate trial (Trial 2) that enrolled a similar population. The call rates and allele frequencies between the two trials were 98 and 99.7%, respectively. We conclude that the whole genome amplified DNA is suitable for Taqman™ genotyping for a wide variety of pharmacogenetically relevant SNPs

Automated lesion detection of breast cancer in [18F] FDG PET/CT using a novel AI-Based workflow

UNLABELLED: Applications based on artificial intelligence (AI) and deep learning (DL) are rapidly being developed to assist in the detection and characterization of lesions on medical images. In this study, we developed and examined an image-processing workflow that incorporates both traditional image processing with AI technology and utilizes a standards-based approach for disease identification and quantitation to segment and classify tissue within a whole-body [ METHODS: One hundred thirty baseline PET/CT studies from two multi-institutional preoperative clinical trials in early-stage breast cancer were semi-automatically segmented using techniques based on PERCIST v1.0 thresholds and the individual segmentations classified as to tissue type by an experienced nuclear medicine physician. These classifications were then used to train a convolutional neural network (CNN) to automatically accomplish the same tasks. RESULTS: Our CNN-based workflow demonstrated Sensitivity at detecting disease (either primary lesion or lymphadenopathy) of 0.96 (95% CI [0.9, 1.0], 99% CI [0.87,1.00]), Specificity of 1.00 (95% CI [1.0,1.0], 99% CI [1.0,1.0]), DICE score of 0.94 (95% CI [0.89, 0.99], 99% CI [0.86, 1.00]), and Jaccard score of 0.89 (95% CI [0.80, 0.98], 99% CI [0.74, 1.00]). CONCLUSION: This pilot work has demonstrated the ability of AI-based workflow using DL-CNNs to specifically identify breast cancer tissue as determined by

Prospective assessment of patient-reported outcomes and estradiol and drug concentrations in patients experiencing toxicity from adjuvant aromatase inhibitors

PURPOSE: Aromatase inhibitors (AI), which decrease circulating estradiol concentrations in post-menopausal women, are associated with toxicities that limit adherence. Approximately one-third of patients will tolerate a different AI after not tolerating the first. We report the effect of crossover from exemestane to letrozole or vice versa on patient-reported outcomes (PROs) and whether the success of crossover is due to lack of estrogen suppression. METHODS: Post-menopausal women enrolled on a prospective trial initiating AI therapy for early-stage breast cancer were randomized to exemestane or letrozole. Those that discontinued for intolerance were offered protocol-directed crossover to the other AI after a washout period. Changes in PROs, including pain [Visual Analog Scale (VAS)] and functional status [Health Assessment Questionnaire (HAQ)], were compared after 3 months on the first versus the second AI. Estradiol and drug concentrations were measured. RESULTS: Eighty-three patients participated in the crossover protocol, of whom 91.3% reported improvement in symptoms prior to starting the second AI. Functional status worsened less after 3 months with the second AI (HAQ mean change AI #1: 0.2 [SD 0.41] vs. AI #2: -0.05 [SD 0.36]; p = 0.001); change in pain scores was similar between the first and second AI (VAS mean change AI #1: 0.8 [SD 2.7] vs. AI #2: -0.2 [SD 2.8]; p = 0.19). No statistical differences in estradiol or drug concentrations were found between those that continued or discontinued AI after crossover. CONCLUSIONS: Although all AIs act via the same mechanism, a subset of patients intolerant to one AI report improved PROs with a different one. The mechanism of this tolerance remains unknown, but does not appear to be due to non-adherence to, or insufficient estrogen suppression by, the second AI

Effects of exemestane and letrozole therapy on plasma concentrations of estrogens in a randomized trial of postmenopausal women with breast cancer

PURPOSE: Inter-individual differences in estrogen concentrations during treatment with aromatase inhibitors (AIs) may contribute to therapeutic response and toxicity. The aim of this study was to determine plasma concentrations of estradiol (E2), estrone (E1), and estrone sulfate (E1S) in a large cohort of AI-treated breast cancer patients. METHODS: In a randomized, multicenter trial of postmenopausal women with early-stage breast cancer starting treatment with letrozole (n = 241) or exemestane (n = 228), plasma estrogen concentrations at baseline and after 3 months were quantitated using a sensitive mass spectrometry-based assay. Concentrations and suppression below the lower limit of quantification (LLOQ) were compared between estrogens and between drugs. RESULTS: The ranges of baseline estrogen concentrations were <LLOQ-361 pg/mL for E2, <LLOQ-190 pg/mL for E1, and 8.3-4060 pg/mL for E1S. For E2, the frequency of suppression below the LLOQ was not statistically significantly different between AIs (exemestane: 89.0%, letrozole: 86.9%, p = 0.51). However, patients on letrozole were more likely to achieve suppression below the LLOQ of both E1 (exemestane: 80.1%, letrozole: 90.1%, p = 0.005) and E1S (exemestane: 17.4%, letrozole: 54.9%, p = 4.34e-15). After 3 months of AI therapy, the ranges of estrogen concentrations were <LLOQ-63.8 pg/mL, <LLOQ-36.7 pg/mL, and <LLOQ-1090 pg/mL for E2, E1, and E1S, respectively. During treatment, 16 patients had an increased concentration compared to the baseline concentration of at least one estrogen. CONCLUSIONS: Letrozole had greater suppression of plasma E1 and E1S than exemestane, though the response was highly variable among patients. Additional research is required to examine the clinical relevance of differential estrogen suppression

Tumor and serum DNA methylation in women receiving preoperative chemotherapy with or without vorinostat in TBCRC008

BACKGROUND: Methylated gene markers have shown promise in predicting breast cancer outcomes and treatment response. We evaluated whether baseline and changes in tissue and serum methylation levels would predict pathological complete response (pCR) in patients with HER2-negative early breast cancer undergoing preoperative chemotherapy. METHODS: The TBCRC008 trial investigated pCR following 12 weeks of preoperative carboplatin and albumin-bound paclitaxel + vorinostat/placebo (n = 62). We measured methylation of a 10-gene panel by quantitative multiplex methylation-specific polymerase chain reaction and expressed results as cumulative methylation index (CMI). We evaluated association between CMI level [baseline, day 15 (D15), and change] and pCR using univariate and multivariable logistic regression models controlling for treatment and hormone receptor (HR) status, and performed exploratory subgroup analyses. RESULTS: In univariate analysis, one log unit increase in tissue CMI levels at D15 was associated with 40% lower chance of obtaining pCR (odds ratio, OR 0.60, 95% CI 0.37-0.97; p = 0.037). Subgroup analyses suggested a significant association between tissue D15 CMI levels and pCR in vorinostat-treated [OR 0.44 (0.20, 0.93), p = 0.03], but not placebo-treated patients. CONCLUSION: In this study investigating the predictive roles of tissue and serum CMI levels in patients with early breast cancer for the first time, we demonstrate that high D15 tissue CMI levels may predict poor response. Larger studies and improved analytical procedures to detect methylated gene markers in early stage breast cancer are needed. TBCRC008 is registered on ClinicalTrials.gov (NCT00616967)

Genome-wide association study of letrozole plasma concentrations identifies non-exonic variants that may affect CYP2A6 metabolic activity.

OBJECTIVES: Letrozole is a nonsteroidal aromatase inhibitor used to treat hormone-receptor-positive breast cancer. Variability in letrozole efficacy and toxicity may be partially attributable to variable systemic drug exposure, which may be influenced by germline variants in the enzymes responsible for letrozole metabolism, including cytochrome P450 2A6 (CYP2A6). The objective of this genome-wide association study (GWAS) was to identify polymorphisms associated with steady-state letrozole concentrations. METHODS: The Exemestane and Letrozole Pharmacogenetics (ELPh) Study randomized postmenopausal patients with hormone-receptor-positive nonmetastatic breast cancer to letrozole or exemestane treatment. Germline DNA was collected pretreatment and blood samples were collected after 1 or 3 months of treatment to measure steady-state letrozole (and exemestane) plasma concentrations via HPLC/MS. Genome-wide genotyping was conducted on the Infinium Global Screening Array (>650 000 variants) followed by imputation. The association of each germline variant with age- and BMI-adjusted letrozole concentrations was tested in self-reported white patients via linear regression assuming an additive genetic model. RESULTS: There were 228 patients who met the study-specific inclusion criteria and had both DNA and letrozole concentration data for this GWAS. The association for one genotyped polymorphism (rs7937) with letrozole concentration surpassed genome-wide significance (P = 5.26 × 10-10), explaining 13% of the variability in untransformed steady-state letrozole concentrations. Imputation around rs7937 and in silico analyses identified rs56113850, a variant in the CYP2A6 intron that may affect CYP2A6 expression and activity. rs7937 was associated with age- and BMI-adjusted letrozole levels even after adjusting for genotype-predicted CYP2A6 metabolic phenotype (P = 3.86 × 10-10). CONCLUSION: Our GWAS findings confirm that steady-state letrozole plasma concentrations are partially determined by germline polymorphisms that affect CYP2A6 activity, including variants near rs7937 such as the intronic rs56113850 variant. Further research is needed to confirm whether rs56113850 directly affects CYP2A6 activity and to integrate nonexonic variants into CYP2A6 phenotypic activity prediction systems

Variable Aromatase Inhibitor Plasma Concentrations Do Not Correlate with Circulating Estrogen Concentrations in Post-Menopausal Breast Cancer Patients

Purpose: The aromatase inhibitors (AI) exemestane (EXE), letrozole (LET), and anastrozole suppress estrogen biosynthesis, and are effective treatments for estrogen receptor (ER)-positive breast cancer. Prior work suggests that anastrozole blood concentrations are associated with the magnitude of estrogen suppression. The objective of this study was to determine whether the magnitude of estrogen suppression, as determined by plasma estradiol (E2) concentrations, in EXE or LET treated patients is associated with plasma AI concentrations. Methods: Five hundred post-menopausal women with ER-positive breast cancer were enrolled in the prospective Exemestane and Letrozole Pharmacogenetic (ELPh) Study conducted by the COnsortium on BReast cancer phArmacogomics (COBRA) and randomly assigned to either drug. Estrogen concentrations were measured at baseline and after 3 months of AI treatment and drug concentrations were measured after 1 or 3 months. EXE or LET concentrations were compared with 3-month E2 concentration or the change from baseline to 3 months using several complementary statistical procedures. Results: Four-hundred patients with on-treatment E2 and AI concentrations were evaluable (EXE n = 200, LET n = 200). Thirty (7.6%) patients (EXE n = 13, LET n = 17) had 3-month E2 concentrations above the lower limit of quantification (LLOQ) (median: 4.75; range: 1.42-63.8 pg/mL). EXE and LET concentrations were not associated with on-treatment E2 concentrations or changes in E2 concentrations from baseline (all p > 0.05). Conclusions: Steady-state plasma AI concentrations do not explain variability in E2 suppression in post-menopausal women receiving EXE or LET therapy, in contrast with prior evidence in anastrozole treated patients

Genome-wide association study of aromatase inhibitor discontinuation due to musculoskeletal symptoms.

OBJECTIVE: Aromatase inhibitors (AIs) are commonly used to treat hormone receptor positive (HR +) breast cancer. AI-induced musculoskeletal syndrome (AIMSS) is a common toxicity that causes AI treatment discontinuation. The objective of this genome-wide association study (GWAS) was to identify genetic variants associated with discontinuation of AI therapy due to AIMSS and attempt to replicate previously reported associations. METHODS: In the Exemestane and Letrozole Pharmacogenetics (ELPh) study, postmenopausal patients with HR + non-metastatic breast cancer were randomized to letrozole or exemestane. Genome-wide genotyping of germline DNA was conducted followed by imputation. Each imputed variant was tested for association with time-to-treatment discontinuation due to AIMSS using a Cox proportional hazards model assuming additive genetic effects and adjusting for age, baseline pain score, prior taxane treatment, and AI arm. Secondary analyses were conducted within each AI arm and analyses of candidate variants previously reported to be associated with AIMSS risk. RESULTS: Four hundred ELPh participants were included in the combined analysis. Two variants surpassed the genome-wide significance level in the primary analysis (p value < 5 × 10(-8)), an intronic variant (rs79048288) within CCDC148 (HR = 4.42, 95% CI: 2.67-7.33) and an intergenic variant (rs912571) upstream of PPP1R14C (HR = 0.30, 95% CI: 0.20-0.47). In the secondary analysis, rs74418677, which is known to be associated with expression of SUPT20H, was significantly associated with discontinuation of letrozole therapy due to AIMSS (HR = 5.91, 95% CI: 3.16-11.06). We were able to replicate associations for candidate variants previously reported to be associated with AIMSS in this cohort, but were not able to replicate associations for any other variants previously reported in other patient cohorts. CONCLUSIONS: Our GWAS findings identify several candidate variants that may be associated with AIMSS risk from AI generally or letrozole specifically. Validation of these associations in independent cohorts is needed before translating these findings into clinical practice to improve treatment outcomes in patients with HR + breast cancer