Citations

Abstract Purpose: This study investigated the association between tumor MYC protein expression and disease-free survival (DFS) of patients randomized to receive chemotherapy alone (Arm A) or chemotherapy with sequential (Arm B) or concurrent trastuzumab (Arm C) in the N9831 (Alliance) adjuvant HER2+ trastuzumab breast cancer trial. Experimental Design: This analysis included 1,736 patients randomized to Arms A, B, and C on N9831. Nuclear MYC protein expression was determined in tissue microarray sections containing three biopsies per patient or whole tissue sections using standard immunohistochemistry (clone 9E10). A tumor was considered positive for MYC protein overexpression (MYC+) if the nuclear 3+ staining percentage was more than 30%. Results: Five hundred and seventy-four (33%) tumors were MYC+. MYC+ was associated with hormone receptor positivity (χ2, P = 0.006), tumors 2 cm or more (χ2, P = 0.02), and a higher rate of nodal positivity (χ2, P < 0.001). HRs for DFS (median follow-up: 6.1 years) for Arm C versus A were 0.52 (P = 0.006) and 0.65 (P = 0.006) for patients with MYC+ and MYC− tumors, respectively (Pinteraction = 0.40). For Arm B versus A, HRs for patients with MYC+ and MYC− tumors were 0.79 (P = 0.21) and 0.74 (P = 0.04), respectively (Pinteraction = 0.71). For Arm C versus B, HRs for patients with MYC+ and MYC− tumors were 0.56 (P = 0.02) and 0.89 (P = 0.49), respectively (Pinteraction = 0.17). Conclusions: Our data do not support an impact of tumor MYC protein expression on differential benefit from adjuvant trastuzumab. Clin Cancer Res; 19(20); 5798–807. ©2013 AACR.

Abstract Introduction: Multivariable models that predict biochemical recurrence (BCR) can indicate patients at higher risk for developing clinical progression after prostatectomy. However, most patients that experience BCR do not metastasize or die from prostate cancer, making BCR an unreliable surrogate endpoint. In this study we compared the clinical and genomic characteristics of prostatectomy patients in order to better delineate the relationship between BCR and aggressive prostate cancer. Methods: Postoperative patients were retrospectively classified into three outcome groups: NED (no evidence of disease recurrence); BCR-only (patients that experienced BCR but didn't have progressive disease as evidenced by bone or CT scans); and CP (clinical progression with BCR plus positive CT or bone scans). Patient specimens were from the Mayo Clinic Radical Prostatectomy Registry, selected using a nested case-control design with 14 years median follow-up. RNA expression levels from FFPE tumor specimens were measured with 1.4 million feature whole-transcriptome oligonucleotide microarrays on 106 matching patients from each outcome group (total 318 patients). We analyzed the clinical covariates of the outcome groups in a pairwise manner (NED vs. BCR-only; NED vs. CP; CP vs. BCR-only), using a conditional logistic model in univariable and multivariable analyses. We performed feature reduction for the genomic data set using Kruskal-Wallis tests and regularized logistic regression. Principal Component Analysis (PCA) was used to quantify the variance of the genomic data with respect to the patient outcome groups. Results: Our initial hypothesis was that all three outcome groups would have different clinical and genomic characteristics. Univariable analysis of clinical variables failed to show statistically significant differences between NED and BCR-only groups. Multivariable analysis of the clinical variables also failed to show statistically significant differences between NED and BCR-only groups. Among the clinical variables only pathological Gleason score was statistically significant when comparing the BCR-only vs. CP and NED vs. CP groups. Genomic analysis found 363 coding and non-coding features that passed thresholds for differential expression between the three outcome groups. PCA established that the primary source of variance in the genomic data stemmed from the separation of CP from NED and BCR-only patients; whereas only a small amount of variance separates NED and BCR-only groups. The 363 features were further compared in the three outcome groups using Wilcoxon tests, adjusting for false-discovery using Benjamini-Hochberg. At a cutoff of p < 0.001 there were no features differentially expressed between NED and BCR-only, while 52 and 34 statistically significant features were found in the NED vs. CP and CP vs. BCR-only comparisons, respectively. Conclusion: Considering the clinical and genomic results together, we find that: 1) Patients that developed clinical progression were significantly different from those that did not, irrespective of whether they experienced BCR or not; 2) the NED and BCR-only groups were statistically very similar to each other. We therefore conclude that BCR may not be the most appropriate endpoint for predictive models that attempt to stratify patients at risk for progression. This abstract is also presented as Poster B44. Citation Format: Anamaria Crisan, Elai Davicion, R. Jeffery Karnes, Robert B. Jenkins, Mercedeh H. Ghadessi, Christine Buerki, Eric J. Bergstrahl, Nicholas Erho, Ismael A. Vergara, Karla V. Ballman, Peter C. Black, Timothy J. Triche. Biochemical recurrence is not a definitive surrogate endpoint for development of clinically useful predictive models for post-prostatectomy patients [abstract]. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr PR4.

BACKGROUNDData suggest that weight, and specifically body mass index (BMI), plays a role in breast cancer development and outcome. The authors hypothesized that there would be a correlation between BMI and clinical outcome in patients with early stage, human epidermal receptor 2 (HER2)‐positive breast cancer enrolled in the N9831 adjuvant trial.METHODSPatients were grouped according to baseline BMI as follows: normal (BMI <25 kg/m2), overweight (BMI ≥25 kg/m2 and <30 kg/m2), and obese (BMI ≥30 kg/m2). Disease‐free survival (DFS) was estimated using the Kaplan‐Meier method. Comparisons between treatment arms A, B, and C (chemotherapy with or without trastuzumab) were performed using a stratified Cox proportional hazards model.RESULTSAnalysis was completed on 3017 eligible patients. Obese patients were more likely to be older and postmenopausal (P < .0001 for both), to have larger tumors (P = .002), and to have positive lymph nodes (P = .004). In the pooled analysis cohort, differences in DFS among the BMI groups were statistically significant (5‐year DFS rate: 82.5%, 78.6%, and 78.5% for normal weight, overweight, and obese women, respectively; log‐rank P = .02). The adjusted hazard ratio comparing the DFS of overweight women with the DFS of normal women was 1.30 (95% confidence interval, 1.06‐1.61); and, comparing the DFS of obese women with the DFS normal women, the adjusted hazard ratio was 1.31 (95% confidence interval, 1.07‐1.59). There were no statistically significant differences in DFS by weight group for women within any trial arm.CONCLUSIONSPatients with early stage, HER2‐positive breast cancer and normal BMI had a better 5‐year DFS compared with overweight and obese women. The current results indicated that adjuvant trastuzumab improves clinical outcome regardless of BMI. Cancer 2013;119:2447‐2454. © 2013 American Cancer Society.

Abstract Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-423. doi:1538-7445.AM2012-LB-423