Ballman, Karla V.

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

Background Traditional clinical trial designs strive to definitively establish the superiority of an experimental treatment, which results in risk-adverse criteria and large sample sizes. Increasingly, common cancers are recognized as consisting of small subsets with specific aberrations for targeted therapy, making large trials infeasible. Purpose To compare the performance of different trial design strategies over a long-term research horizon. Methods We simulated a series of two-treatment superiority trials over 15 years using different design parameters. Trial parameters examined included the number of positive trials to establish superiority (one-trial vs. two-trial rule), α level (2.5%–50%), and the number of trials in the 15-year period, K (thus, trial sample size). The design parameters were evaluated for different disease scenarios, accrual rates, and distributions of treatment effect. Metrics used included the overall survival gain at a 15-year horizon measured by the hazard ratio (HR), year 15 versus year 0. We also computed the expected total survival benefit and the risk of selecting as new standard of care at year 15 a treatment inferior to the initial control treatment, P(detrimental effect). Results Expected survival benefits over the 15-year horizon were maximized when more (smaller) trials were conducted than recommended under traditional criteria, using the criterion of one positive trial (vs. two), and relaxing the α value from 2.5% to 20%. Reducing the sample size and relaxing the α value also increased the likelihood of selecting an inferior treatment at the end. The impact of α and K on the survival benefit depended on the specific disease scenario and accrual rate: greater gains for relaxing α in diseases with good outcome and/or low accrual rates and greater gains for increasing K for diseases with poor outcomes. Trials with smaller sample size did not perform well when using stringent (standard) level of evidence. For each disease scenario and accrual rate studied, the optimal design, defined as the design that the maximized expected total survival benefit while constraining P(detrimental effect) < 2.5%, specified α = 20% or 10%, and a sample size considerably smaller than that recommended by the traditional designs. The results were consistent under different assumed distributions for treatment effect. Limitations The simulations assumed no toxicity issues and did not consider interim analyses. Conclusions It is worthwhile to consider a design paradigm that seeks to maximize the expected survival benefit across a series of trials, over a longer research horizon. In today’s environment of constrained, biomarker-selected populations, our results indicate that smaller sample sizes and larger α values lead to greater long-term survival gains compared to traditional large trials designed to meet stringent criteria with a low efficacy bar.

Purpose The survival of patients with non–small-cell lung cancer (NSCLC), even when resectable, remains poor. Several small studies suggest that occult metastases (OMs) in pleura, bone marrow (BM), or lymph nodes (LNs) are present in early-stage NSCLC and are associated with a poor outcome. We investigated the prevalence of OMs in resectable NSCLC and their relationship with survival. Patients and Methods Eligible patients had previously untreated, potentially resectable NSCLC. Saline lavage of the pleural space, performed before and after pulmonary resection, was examined cytologically. Rib BM and all histologically negative LNs (N0) were examined for OM, diagnosed by cytokeratin immunohistochemistry (IHC). Survival probabilities were estimated using the Kaplan-Meier method. The log-rank test and Cox proportional hazards regression model were used to compare survival of groups of patients. P < .05 was considered significant. Results From July 1999 to March 2004, 1,047 eligible patients (538 men and 509 women; median age, 67.2 years) were entered onto the study, of whom 50% had adenocarcinoma and 66% had stage I NSCLC. Pleural lavage was cytologically positive in only 29 patients. OMs were identified in 66 (8.0%) of 821 BM specimens and 130 (22.4%) of 580 LN specimens. In univariate and multivariable analyses OMs in LN but not BM were associated with significantly worse disease-free survival (hazard ratio [HR], 1.50; P = .031) and overall survival (HR, 1.58; P = .009). Conclusion In early-stage NSCLC, LN OMs detected by IHC identify patients with a worse prognosis. Future clinical trials should test the role of IHC in identifying patients for adjuvant therapy.

Abstract The androgen receptor (AR) is the principal target for treatment of non–organ-confined prostate cancer (PCa). Androgen deprivation therapies (ADT) directed against the AR ligand–binding domain do not fully inhibit androgen-dependent signaling critical for PCa progression. Thus, information that could direct the development of more effective ADTs is desired. Systems and bioinformatics approaches suggest that considerable variation exists in the mechanisms by which AR regulates expression of effector genes, pointing to a role for secondary transcription factors. A combination of microarray and in silico analyses led us to identify a 158-gene signature that relies on AR along with the transcription factor SRF (serum response factor), representing less than 6% of androgen-dependent genes. This AR-SRF signature is sufficient to distinguish microdissected benign and malignant prostate samples, and it correlates with the presence of aggressive disease and poor outcome. The AR-SRF signature described here associates more strongly with biochemical failure than other AR target gene signatures of similar size. Furthermore, it is enriched in malignant versus benign prostate tissues, compared with other signatures. To our knowledge, this profile represents the first demonstration of a distinct mechanism of androgen action with clinical relevance in PCa, offering a possible rationale to develop novel and more effective forms of ADT. Cancer Res; 71(5); 1978–88. ©2011 AACR.

Background Data collection and review were identified as major contributors to the cost of randomized clinical trials (RCTs). Purpose We proposed and assessed a novel alternative for long-term clinical trial follow-up based on the data captured through an accredited Cancer Registry (CR) that is part of the National Cancer Database (NCDB). Methods Patients from Mayo Clinic, Rochester, enrolled in the North Central Cancer Treatment Group N934653 (COST) trial (98 patients) and the American College of Surgeons Oncology Group Z0030 trial (55 patients) were included in the study. Demographic, treatment, and long-term outcome data were compared between the hospital-based CR and the RCTs’ databases. Concordances were used to estimate the agreement between two databases. Kaplan—Meier curves were plotted to examine the consistency of time-to-event long-term outcomes of the CR and RCT databases. Results High concordances (>95%) were observed for most demographic and treatment variables between the CR data and RCT data. The vital status concordances were 100% and 94.5% between the CR and COST and Z0030 databases, respectively. Three discrepant death dates were observed, one in the COST trial and two in the Z0030 trial. The concordances of disease-free status between the CR and RCT databases were 99.0% and 87.3%, and 15 discrepant disease recurrence cases were identified: 4 for COST and 11 for Z0030. Limitations The analysis has been focused on patients from a single site, Mayo Clinic, Rochester, enrolled in two large RCT evaluating surgical treatments. The findings herein need to be confirmed in a broader setting, such as multi-center, multi-registry including nonsurgical trials. Conclusions CR data were nearly identical to data from two randomized phase III trials in different disease types and conducted by two different cooperative groups. The NCDB Cancer Registries represent a feasible alternative for obtaining long-term follow-up data for large clinical trials. Clinical Trials 2010; 7: 686—695. http:// ctj.sagepub.com

Purpose In March 1998, Common Toxicity Criteria (CTC) version 2.0 introduced the collection of attribution of adverse events (AEs) to study drug. We investigate whether attribution adds value to the interpretation of AE data. Patients and Methods Patients in the placebo arm of two phase III trials—North Central Cancer Treatment Group Trial 97-24-51 (carboxyamino-triazole v placebo in advanced non–small-cell lung cancer) and American College of Surgeons Oncology Group Trial Z9001 (imatinib mesylate v placebo after resection of primary gastrointestinal stromal tumors)—were studied. Attribution was categorized as unrelated (not related or unlikely) and related (possible, probable, or definite). Results In total, 398 patients (84 from Trial 97-24-51 and 314 from Trial Z9001) and 7,736 AEs were included; 47% and 50% of the placebo-arm AEs, respectively, were reported as related. When the same AE was reported in the same patient on multiple visits, the attribution category changed at least once 36% and 31% of the time. AE type and sex (Trial Z9001) and AE type and performance status (Trial 97-24-51) were associated with a higher likelihood of AEs being deemed related. Conclusion Nearly 50% of AEs were reported as attributed to study drug on the placebo arm of two randomized clinical trials. These data provide strong evidence that AE attribution is difficult to determine, unreliable, and of questionable value in interpreting AE data in randomized clinical trials.