Eckel-Passow, JE

Abstract Background: Adding adjuvant trastuzumab to chemotherapy for patients (pts) with resected HER2+ breast cancer (BC) improves disease free and overall survival (Perez EA, et al. J Clin Oncol 2011). Understanding which pts are most likely to benefit from these therapies is a goal of our team. Methods: Baseline 1639 annotated tumor specimens from pts enrolled in the phase III N9831 adjuvant trastuzumab trial (NCT00005970) were processed via the >29,000 gene probe DASL technology (Illumina) to identify genomic predictors of pt outcome to Arm A chemotherapy or Arm C chemotherapy plus concurrent trastuzumab. Samples were spotted in 96 well plates, with appropriate replicates. Extensive quality control and internal validation were performed. The association of each gene with recurrence-free survival (RFS) was determined for each treatment arm separately using CoxPH models adjusted for clinical and tumor risk factors. A p < 0.0001 was used to define significance. A geneset analysis (using 408 Kegg/BioCarta genesets) was performed to determine pathways associated with RFS. Results: PRPF4B, EWSR1, BMI1, CNNM1, FOXO4, CLIP2, MCCC1, CHRM2 were found to be significantly associated with RFS for Arm A and AAK1, DAZAP2, TP53INP1, MACF1 and ADHFE1 were associated with RFS for Arm C. The most significant pathway associated with RFS in Arm A was the KEGG hypertrophic cardiomyopathy pathway and for Arm C, the BioCarta pertussis toxin-insensitive CCR5 signaling in macrophage pathway. Conclusions: We have identified 8 genes associated with RFS for pts treated with adjuvant chemotherapy, and 5 genes associated with RFS for pts treated with concurrent trastuzumab and chemotherapy in pts with HER2 early stage BC. In addition, geneset analysis identified BioCarta and Kegg pathways associated with RFS for each arm. Validation in an independent cohort is expected to be completed by the end of 2012. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr PD10-04.

Abstract Background: Based upon the association between pathologic response and disease free survival, the neoadjuvant setting is increasingly being used for drug development. NGS has identified unique and recurrent genetic alterations in breast cancer (BC) that are potentially targetable; however, the clinical implications are mostly unknown. We developed a prospective neoadjuvant study (BEAUTY) in high risk BC patients (pts) using weekly T followed by anthracycline-based chemo wherein percutaneous tumor biopsies (PTB) are obtained before/during/after chemo for NGS and PDX. Our goal is to identify novel biomarkers/pathways and develop PDX to test new therapeutic approaches. Methods: Pts underwent PTB at baseline and after 12 wks of T. Response to T was defined based upon 12 week Ki-67: responder (<15%) vs non-responder (≥15%). Pts with histologic response and absence of invasive BC at 12 wks were classified as responders. NGS was performed using PTB/blood DNA (exome) and PTB (RNA seq). MRI response was classified using RECIST criteria. NGS data were used to identify somatic copy number variants (cnvs) and expressed single nucleotide variants (eSNVs). Non-SCID mice (estrogen supplemented) were implanted ≤ 30 minutes with PTB samples. Results: Of the first 78 pts, 44 have completed T. Here we focus on 18 pts with either triple negative or luminal B BC. Clinical characteristics according to Ki-67 response are shown in Table 1. Comparison of genomic alterations in BEAUTY pts with TCGA identified a greater overlap with copy number gains (73%) compared to deletions (40%), along with similar observations of mutations in TP53, PTEN, RYR2, and AKT1 genes. Association analysis of CNVs and eSNVs between responders/non-responders identified 33 genes (predominantly located in chromosomes 1, 8, 13) and 580 eSNVs (corresponding to 497 genes) with a p < 0.05. Differential gene expression (DGE) analysis of responders/non-responders identified 198 genes with a p-value < 0.05. Integrated analysis of 539 genes (CNVs, eSNVs and DGE) identified pathways such as TGF-beta, Jak-Stat, WNT and NOTCH signalling. PDX take rate was 44% [triple negative (6/10); Luminal B (2/8)]. PDX growth rate was significantly associated with clinical baseline Ki-67 (p = 0.00014). Conclusion: This is the first prospective study to demonstrate the feasibility of using PTB to obtain both NGS data and PDX in the neoadjuvant setting. PDX take rate is associated with BC subtype and baseline Ki-67. Studies are ongoing to 1) validate genes/pathways associated with treatment response in subsequent BEAUTY pts; 2) genomically characterize and assess PDX in vivo response to T and 3) Use NGS data to prioritize new drugs/drug combinations in PDX. Funded by Mayo Clinic Center for Individualized Medicine and MC Cancer Center. Clinical CharacteristicsOverallResponders: 12 week Ki-67 < 15% (n = 9)Non-Responders: 12 week Ki-67 ≥ 15% (n = 9)Median Age495345T stage T2/T314 (78%)7 (78%)7 (78%)Node Positive8 (44%)4 (44%)4 (44%)Triple negative10 (56%)6 (67%)4 (44%)Luminal B8 (44%)3 (33%)5 (56%)Ki-67 after 12 Weeks of T Median 5% (0-11%)Median 35% (17-60%)Complete/Partial MRI Response after T 6 (67%)2 (22%) Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-10.