Esserman, Laura J.

Abstract Background: Pathologic complete response (pCR) is an established prognostic biomarker for aggressive HER2+ breast cancer (BC). Improving pCR rates may identify new therapies that improve survival. T-DM1 and pertuzumab have established benefits in metastatic HER2+ BC. We tested their ability when combined, without paclitaxel, to improve pCR rates (ypT0ypN0) over standard therapy in the randomized, phase 2, I-SPY 2 neoadjuvant trial. Methods: Enrolled patients (pts) had invasive breast cancer ?2.5 cm in HER2-positive subsets. Pts were adaptively randomized to 12 wkly cycles of paclitaxel+trastuzumab (TH, control) or T-DM1+pertuzumab (T-DM1+P) without T, followed by doxorubicin/cyclophosphamide (AC) x 4 and surgery. We utilized all TH control pts accrued over the course of the trial, adjusting for potential differences due to time period treated, which were informed by the several other treatment arms that have been in the trial. Adaptive assignment to the various experimental arms in the trial was based on current Bayesian probabilities of superiority vs. control. “Graduation” by signature and futility stopping were based upon Bayesian predictive probability of success in a future 2-arm, N = 300 neoadjuvant Phase 3 randomized 1:1 trial of T-DM1+P vs. control with pCR endpoint. Results: T-DM1+P met the predictive probability criterion and graduated from I-SPY 2 in 3 signatures: all HER2+, HER2+/HR+, HER2+/HR- (Table 1). Final accrual: 52 T-DM1+P and 31 TH. Safety data will be shown. Conclusions: I-SPY 2's standing platform trial mechanism efficiently evaluates agents in biomarker-defined pt subsets. T-DM1+P (w/o T) -> AC substantially improves pCR rates over standard TH -> AC in all 3 HER2+ signatures, including HR+ and HR- subsets. These findings warrant further investigation of these agents without paclitaxel in a neoadjuvant trial powered for survival endpoints. Table 1Signature*Estimated pCR Rate (95% probability interval)Probability T-DM1+P Is Superior to Control**Predictive Probability of Success in Phase 3T-DM1+PControl (TH)HER2+52% (36%-68%)22% (5%-39%)99.5%94%HER2+/HR+46% (26%-66%)17% (0% - 34%)99.1%93%HER2+/HR-64% (39%-88%)33% (6%-59%)98%90%*HR = Hormone Receptor Citation Format: Angela M. DeMichele, Stacy Moulder, Meredith Buxton, Douglas Yee, Anne Wallace, Jo Chien, Claudine Isaacs, Kathy Albain, Judy Boughey, Kathleen Kemmer, Barbara Haley, Julie Lang, Henry Kaplan, Susan Minton, Andres Forero, Anthony Elias, Rita Nanda, Larissa Korde, Richard Schwab, Michelle Melisko, Ashish Sanil, Michael Hogarth, Nola Hylton, Melissa Paoloni, Fraser Symmans, Jane Perlmutter, Julia Lyandres, Christina Yau, Don Berry, Laura Esserman, I-SPY 2 TRIAL Investigators. Efficacy of T-DM1+pertuzumab over standard therapy for HER2+ breast cancer: Results from the neoadjuvant I-SPY 2 TRIAL. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr CT042.

Abstract Background: I-SPY2 is a multicenter phase 2 trial in high risk stage II/III breast cancer (BC) using adaptive randomization within biomarker subtypes to evaluate novel agents added to standard neoadjuvant chemotherapy. The first regimen to graduate based on the predicted probability of a higher pCR rate within predefined subsets was veliparib/carboplatin + paclitaxel (VC+T→AC vs T→AC) in triple negative BC (TNBC). In TNBC the residual cancer burden (RCB) is prognostic, whether as a continuous index or grouped into classes, with pCR (RCB-0) and RCB-I classes having identical survival. Therefore, we evaluated the use of RCB to further discriminate between investigational and control arms. Methods: Site pathologists reported RCB for 99% of subjects in the primary efficacy analysis based on pCR (n=114/115). We compared the distribution of RCB reported as a continuous index in each treatment-subset combination to matched concurrently randomized controls using the Wilcoxon rank sum test for RCB index, and Fisher's Exact test for RCB classes (RCB-0/I vs RCB-II/III). The statistics are descriptive rather than inferential, and given the small sample size have no claim on generalizability. We modified the Bayesian model used to compute the estimated probability of success in a future, randomized, phase 3 trial of 300 subjects, if response were defined by either pCR or RCB-I (RCB0/I), or separately if it were defined by pCR alone. Results: VC+T→AC led to a significantly lower RCB index than T→AC in TNBC (p=0.0021), with a near-significant trend when those with pCR were excluded (p=0.06). There was no significant difference in RCB distributions in the other breast cancer subtypes treated. In TNBC, the odds ratio (OR) for achieving RCB-0/I in the VC+T→AC arm vs control was 8.2 (95% confidence interval (CI): 2.1–35), whereas the OR for achieving pCR was 4.56 (95% CI: 1.25–19.53). The simulations using response information from I-SPY2 to predict the probability of success for VC+T→AC for TNBC in a future phase 3 trial estimated this probability to be 0.99 if modeled using RCB-0/I as the response endpoint, and 0.90 if modeled using pCR as the response endpoint. Conclusions: Use of RCB index and classes provided additional insight into the effect of adding VC to T, appearing to magnify the improved treatment response that had been observed with pCR rates in TNBC. It will be important to test in randomized trials whether a decrease in the RCB index relative to controls, and/or increased rates of RCB-0/I class, are predictive of survival benefit in TNBC. Citation Format: Liu MC, Symmans WF, Yau C, Chen Y-Y, Rugo HS, Olopade OF, Datnow B, Chen B, Feldman M, Kallakury B, Hasteh F, Tickman R, Ritter J, Troxel M, Mhawech-Fauceglia P, Duan X, Berry D, Esserman L, DeMichele A. Residual cancer burden (RCB) with veliparib/carboplatin in the I-SPY2 trial. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-07-49.

Abstract Background: Further stratification of the 70-gene MammaPrintTM signature into ‘high’ and ‘ultra-high’ risk groups may help predict chemo-sensitivity. In I-SPY 2, patients were classified as MammaPrint High1 (MP1) or MammaPrint (ultra) High2 (MP2), with MP2 defined as MP_score <-0.154. MP1/MP2 classification was added to HR and HER2 to define the cancer subtypes used in the I-SPY 2 adaptive randomization engine. Neratinib (N), one of the experimental agents evaluated in I-SPY 2, graduated in the HR-HER2+ signature. All patients received at least standard chemotherapy (paclitaxel followed by doxorubicin/cyclophosphamide; T->AC). HER2- patients were randomized to receive N+T- >AC vs. T->AC. For HER2+ patients, neratinib was administered in place of trastuzumab (N+T->AC vs. H+T->AC). Here, we assess the performance of MP1/MP2 class as a specific biomarker of neratinib response. Methods: 115 patients in the neratinib arm and 76 concurrently randomized controls had Agilent 44K microarrays and pCR data available for analysis. We assess association between MP1/MP2 and response in the neratinib and control arms alone using Fisher’s exact test, and relative performance between arms (biomarker x treatment interaction, likelihood ratio p < 0.05) using a logistic model. This analysis is also performed adjusting for HR status as a covariate, and in receptor subsets. Our study is exploratory with no claims for generalizability of the data. Statistical calculations are descriptive (e.g. p-values are measures of distance with no inferential content). Our analyses do not adjust for multiplicities of other biomarkers in the trial but outside this study. Results: There are 133 MP1 patients (neratinib: 74, Control: 59) and 58 MP2 patients (neratinib: 41, Control: 17), 84% (49) of which are Her2-. The distribution of pCR rates among MP1/MP2 dichotomized groups are summarized in Table 1. Neratinib (n=115)Control (n=76)MP1 (n=74)MP2 (n=41)MP1 (n=59)MP2 (n=17)HER2- (n=105)0 / 1715 / 337 / 395 / 16HER2+ (n=86)22 / 574 / 85 / 200 / 1 MP2, one of the 10 eligible signatures, did not meet the graduation threshold; and MP1/MP2 did not show a significant biomarker x treatment interaction (OR in neratinib relative to control arm = 1.25). The MP1/MP2 x treatment interaction remains non-significant after adjustment for HR and HER2 status (p=0.54). In HER2- patients receiving neratinib, 45% (15/33) of MP2 patients achieved a pCR, compared to 0% (0/17) of MP1 patients. In the HER2- controls, there is a 31% pCR rate in MP2 (5/16) vs. 18% in MP1 (7/39) patients (OR=2.14). This difference in performance between treatment arms appears significant (p=0.041). 90% of HER2+ patients are MP1, thus MP1/MP2 status x treatment interaction within the HER2+ subtype cannot be evaluated. Conclusion: Within the I-SPY 2 population as a whole, MP1/MP2 stratification does not appear to be a specific biomarker of response to neratinib relative to the control arm. The number of HER2- patients is small and precludes any definitive conclusion, but these data motivate further investigation of the biological mechanisms distinguishing MP1 from MP2 to better understand chemotherapy and/or neratanib responsiveness. Citation Format: Christina Yau, Denise M Wolf, Ashish Sanil, Jo Chien, Anne Wallace, Judy Boughey, Doug Yee, Debu Tripathy, Angela DeMichele, Rita Nanda, Steven Chiu, Claudine Isaacs, Kathy Albain, Hank Kaplan, Stacey Moulder, Rebecca Viscusi, Donald Northfelt, Kirsten Edmiston, Anthony Elias, Toncred Styblo, Barbara Haley, Lamorna Brown-Swigart, Susan Flynn, Gillian L Hirst, Meredith Buxton, Nola Hylton, Melissa Paoloni, W Fraser Symmans, Laura Esserman, Don Berry, Minetta C Liu, John W Park, Laura van 't Veer. MammaPrint High1/High2 risk class as a biomarker of response to neratinib plus standard neoadjuvant therapy for breast cancer in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-06-29.

Abstract Background: Further stratification of the 70-gene MammaPrintTM signature into ‘high’ and ‘ultra-high’ risk groups may help predict chemo-sensitivity. In I-SPY 2, patients were classified as MammaPrint High1 (MP1) or MammaPrint (ultra) High2 (MP2), with MP2 defined as MP_score <-0.154. MP1/MP2 classification was added to HR and Her2 to define the cancer subtypes used in the I-SPY 2 adaptive randomization engine. HER2- patients were randomized to receive standard chemotherapy or the oral PARP inhibitor veliparib in combination with carboplatin (V/C) and chemotherapy. V/C graduated in the triple-negative (TN) signature, where MP2 was not an eligible signature for graduation. Here, we assess the performance of MP1/MP2 class as a specific biomarker of response to V/C. Methods:115 HER2- patients (V/C: 71 and concurrent controls: 44) were considered in this analysis. We assess association between MP1/MP2 and response in the V/C and control arms alone using Fisher’s exact test, and relative performance between arms (biomarker x treatment interaction, likelihood ratio p < 0.05) using a logistic model. This analysis is also performed adjusting for HR status as a covariate. To assess MP1/MP2 in the context of the graduating signature, we added the MP2 patients to the graduating TN subset and evaluated the treatment effect in this ‘biomarker-positive’ group. Our study is exploratory with no claims for generalizability of the data. Statistical calculations are descriptive (e.g. p-values are measures of distance with no inferential content). This analysis does not adjust for multiplicities of other biomarkers in the trial but outside this study. Results: In the V/C arm vs. concurrent controls, there were 66 MP1 (V/C: 32, Control: 34) and 49 MP2 patients (V/C: 39, Control: 10), 78% of which are TN. The distribution of pCR rates among MP1/MP2 dichotomized groups are summarized in Table 1. V/C (n=71)Control (n=44)MP1 (n=32)MP2 (n=39)MP1 (n=34)MP2 (n=10)TN (n=59)3 / 819 / 303 / 132 / 8HR+HER2- (n=56)1 / 244 / 94 / 210 / 2 The OR between MP1/MP2 risk groups for predicting pCR is 9.71 in the V/C arm (p=6.63E-05), in comparison to an OR of 0.97 in the control arm (p=1). There is a significant biomarker x treatment interaction (p=0.023), which remains upon adjusting for HR status (p= 0.028). Based on the I-SPY 2 Bayesian model, a Phase III trial with 300 MP2 patients has a 95% predictive probability of success. When the MP2 patients are added to the graduating TN subset, the OR associated with V/C is 4.36, which is comparable to that of the TN signature (OR: 4.29), while increasing the prevalence of biomarker-positive patients by ∼10%. Conclusion: In our exploratory analysis, MP2 suggests higher sensitivity to V/C combination therapy relative to controls. This observation has prompted an investigation into the biological mechanisms distinguishing the MP1/MP2 subtype that may account for this specificity. Citation Format: Denise M Wolf, Christina Yau, Ashish Sanil, Jo Chien, Anne Wallace, Angela DeMichele, Hank Kaplan, Doug Yee, Claudine Isaacs, Kathy Albain, Rebecca Viscuzi, Judy Boughey, Stacey Moulder, Steven Chui, Qamar Khan, Toncred Styblo, Kirsten Edmiston, Donald Northfelt, Anthony Elias, Barbara Haley, Debu Tripathy, Lamorna Brown-Swigart, Susan Flynn, Gillian Hirst, Meredith Buxton, Nola Hylton, Melissa Paoloni, Fraser Symmans, Laura Esserman, Don Berry, Hope Rugo, Olufunmilayo I. Olopade, Laura van 't Veer. MammaPrint High1/High2 risk class as a biomarker of response to veliparib/carboplatin plus standard neoadjuvant therapy for breast cancer in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-06-25.

Abstract Background: We hypothesize that response to the pan-ERBB inhibitor, neratinib (N), may be predicted by pre-treatment HER2-EGFR signaling. In the I-SPY 2 TRIAL, N graduated in the HR-/HER2+ signature. All patients received at least standard chemotherapy. For HER2+ patients, N was administered in place of trastuzumab. We evaluated 18 HER family signaling proteins as biomarkers of N response using reverse phase protein microarray (RPMA) data from pre-treatment LCM purified tumor epithelium. Methods: 168 patients (N: 106, concurrent controls: 62) had RPMA and pCR data. 18 biomarkers relating to HER family signaling were evaluated: AKT S473, AKT T308, EGFR, EGFR Y1068, EGFR Y1148, EGFR Y1173, EGFR Y992, ERBB2, ERBB2 Y1248, ERBB3 total, ERBB3 Y1289, ERK1/2 T202/Y204, Heregulin, mTOR, mTOR S2448, PI3K p85 Y458/p55 Y199, PTEN S380, and SHC Y317. We assessed association between biomarker and response in the N and control arms alone (likelihood ratio test), and relative performance between arms (biomarker x treatment interaction) using a logistic model. Analysis was also performed adjusting for HR/HER2 status. In an exploratory analysis, we selected the marker with the greatest interaction (phosphorylated EGFR (Y1173)) to dichotomize patients optimally based on the data and assessed it in the context of the graduating signature by adding the EGFR Y1173-High patients to the HR-/HER2+ subtype and evaluating the treatment effect in this ‘biomarker-positive’ group. Our study is exploratory with no claims for generalizability of the data and does not account for multiplicities. Statistical calculations are descriptive (e.g. p-values are measures of distance with no inferential content). Results: 7 HER pathway markers (EGFR Y1068, EGFR Y1173, EGFR Y992, ERBB2 total, ERBB2 Y1248, ERBB3 Y1289, SHC Y317) are associated with response in the N but not the control arm. However, the difference in performance between arms did not reach significance by permutation testing. Adjusting for HR/HER2 status, EGFR Y1173 shows a significant biomarker x treatment interaction (p = 0.049). In an exploratory analysis, we dichotomized patients by their EGFR Y1173 levels and evaluated the distribution of pCR rates (Table 1). Neratinib (n=106)Control (n=62)EGFR Y1173 Low (n=31)EGFR Y1173 High (n=75)EGFR Y1173 Low (n=29)EGFR Y1173 High (n=33)HR-/HER2+ (n=28)0 / 412 / 181 / 11 / 5Not HR-/HER2+ (n=140)3 / 2724 / 575 / 285 / 28Table 1 OR between EGFR Y1173 groups in the N relative to control arm is 10.1. When EGFR Y1173 High patients are added to the graduating HR-/HER2+ subset, the OR associated with treatment is 3.2 and is comparable to that in the HR-/HER2+ signature (OR: 2.1), while increasing the prevalence of biomarker-positive patients by ∼50%. Evaluation of EGFR Y1173 under the I-SPY 2 Bayesian model is pending. Conclusion: Our sample size is too small to draw definitive conclusions. Our exploratory analysis reveals that HER family phosphoproteins associate with response to N, but only phosphorylated EGFR Y1173 appears to add value to the graduating signature. Given that this biomarker would expand the patient population that may benefit, it merits evaluation in other ongoing trials of neratinib. Citation Format: Julia D Wulfkuhle, Christina Yau, Denise M Wolf, Rosa I Gallagher, Ashish Sanil, Lamorna Brown-Swigart, Susan Flynn, Gillian Hirst, I-SPY 2 TRIAL Investigators, Meredith Buxton, Angela DeMichele, Nola Hylton, William F Symmans, Laura van't Veer, Douglas Yee, Melissa Paoloni, Laura Esserman, Donald Berry, Minetta C Liu, John W Park, Emanuel F Petricoin III. Evaluation of HER family protein signaling network as a predictive biomarker for pCR for breast cancer patients treated with neratinib in the I-SPY 2 trial [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-06-15.

Abstract Background: I-SPY 2 is a multicenter, phase II neoadjuvant trial in women with high-risk stage II/III breast cancer using adaptive randomization within biomarker subtypes to evaluate novel agents added to standard chemotherapy. Primary endpoint is pathologic complete response (pCR). Goal is to identify regimens that meet a high Bayesian predictive probability of statistical significance in a neoadjuvant 300-patient phase III trial defined by hormone-receptor (HR), HER2 status, and MammaPrint (MP). Experimental regimens may “graduate” in 1 of 10 signatures, with a maximum of 120 patients. We report efficacy results for neratinib (N). Methods: Tumors ≥2.5cm by clinical exam & ≥2cm by imaging are eligible for screening. MP low risk/HR+/HER2- tumors are ineligible for randomization. Patients receive chemotherapy (paclitaxel qwk x 12, doxorubicin and cyclophosphamide q2-3 wk x 4, T->AC). HER2- pts were randomized to N+T->AC vs. T->AC and HER2+ pts to N+T->AC vs. trastuzumab+T->AC. Analysis is intent-to-treat with pts who switch to non-protocol therapy regarded as non-pCRs. We provide estimated pCR rates (95% Bayesian probability intervals), probabilities of superiority of neratinib over control, and Bayesian predictive probabilities of success in an equally randomized phase III trial. Results: Neratinib met the predictive probability criterion in HR-/HER2+, “graduated”, and accrual ceased [115 N patients (65 HER2+), 78 concurrently randomized controls (22 HER2+)]. The table shows results for all 10 signatures. Two patients (1 N and 1 control) withdrew consent and are not included. Conclusion: I-SPY 2's standing trial mechanism efficiently evaluates agents in biomarker-defined patient subsets. In a modest number of patients, adaptive randomization successfully identified a biomarker signature (HR-/HER2+) for neratinib's further development. All HER2+ and MP+ tumors may also benefit from this regimen, consistent with preclinical data. Evaluation in I-SPY 3, a phase III registration trial, is planned. SignatureEstimated pCR Rate (95% probability interval)Probability NeratinibIs Superior to ControlPredictive Probability of Success in Phase IIINeratinibControlALL32% (28-36%)23% (19%-28%)92%44%HR+23% (18%-28%)17% (11%-22%)81%40%HR-43% (37%-49%)31% (24%-38%)89%53%HER2+39% (33%-45%)23% (15%-30%)95%73%HER2-26% (21%-32%)24% (18%-29%)63%20%MP+*45% (38%-53%)29% (20%-39%)91%66%HR-/HER2-36% (29%-43%)30% (23%-38%)72%34%HR-/HER2+55% (46%-64%)32% (22%-43%)94%78%HR+/HER2+31% (24%-37%)17% (10%-24%)91%65%HR+/HER2-14% (8%-19%)16% (10%-21%)39%12%* MammaPrint 44K Array Low/High score was adjusted to MammaPrint High1 (MP-) or High2 (MP+), using a pre-defined median cut-point of I-SPY 1 participants, who fit the eligibility criteria of I-SPY 2. Citation Format: John W. Park, Minetta C. Liu, Douglas Yee, Angela DeMichele, Laura van 't Veer, Nola Hylton, Fraser Symmans, Meredith B. Buxton, A. Jo Chien, Amy Wallace, Michelle Melisko, Richard Schwab, Judy Boughey, Debashish Tripathy, Hank Kaplan, Rita Nanda, Stephen Chui, Kathy S. Albain, Stacy Moulder, Anthony Elias, Julie E. Lang, Kirsten Edminston, Donald Northfelt, David Euhus, Qamar Khan, Julia Lyandres, Sarah E. Davis, Christina Yau, Ashish Sanil, Laura J. Esserman, Donald A. Berry. Neratinib plus standard neoadjuvant therapy for high-risk breast cancer: Efficacy results from the I-SPY 2 TRIAL. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr CT227. doi:10.1158/1538-7445.AM2014-CT227

AbstractThe many improvements in breast cancer therapy in recent years have so lowered rates of recurrence that it is now difficult or impossible to conduct adequately powered adjuvant clinical trials. Given the many new drugs and potential synergistic combinations, the neoadjuvant approach has been used to test benefit of drug combinations in clinical trials of primary breast cancer. A recent FDA-led meta-analysis showed that pathologic complete response (pCR) predicts disease-free survival (DFS) within patients who have specific breast cancer subtypes. This meta-analysis motivated the FDA's draft guidance for using pCR as a surrogate endpoint in accelerated drug approval. Using pCR as a registration endpoint was challenged at ASCO 2014 Annual Meeting with the presentation of ALTTO, an adjuvant trial in HER2-positive breast cancer that showed a nonsignificant reduction in DFS hazard rate for adding lapatinib, a HER-family tyrosine kinase inhibitor, to trastuzumab and chemotherapy. This conclusion seemed to be inconsistent with the results of NeoALTTO, a neoadjuvant trial that found a statistical improvement in pCR rate for the identical lapatinib-containing regimen. We address differences in the two trials that may account for discordant conclusions. However, we use the FDA meta-analysis to show that there is no discordance at all between the observed pCR difference in NeoALTTO and the observed HR in ALTTO. This underscores the importance of appropriately modeling the two endpoints when designing clinical trials. The I-SPY 2/3 neoadjuvant trials exemplify this approach. Clin Cancer Res; 21(13); 2911–5. ©2015 AACR.

Abstract New approaches to drug development are critically needed to lessen the time, cost, and resources necessary to identify and optimize active agents. Strategies to accelerate drug development include testing drugs earlier in the disease process, such as the neoadjuvant setting. The U.S. Food and Drug Administration (FDA) has issued guidance designed to accelerate drug approval through the use of neoadjuvant studies in which the surrogate short-term endpoint, pathologic response, can be used to identify active agents and shorten the time to approval of both efficacious drugs and biomarkers identifying patients most likely to respond. However, this approach has unique challenges. In particular, issues of patient safety are paramount, given the exposure of potentially curable patients to investigational agents with limited safety experience. Key components to safe drug development in the neoadjuvant setting include defining a study population at sufficiently poor prognosis with standard therapy to justify exposure to investigational agents, defining the extent and adequacy of safety data from phase I, detecting potentially harmful interactions between investigational and standard therapies, improving study designs, such as adaptive strategies, that limit patient exposure to ineffective agents, and intensifying safety monitoring in the course of the trial. The I-SPY2 trial is an example of a phase II neoadjuvant trial of novel agents for breast cancer in which these issues have been addressed, both in the design and conduct of the trial. These adaptations of phase II design enable acceleration of drug development by reducing time and cost to screen novel therapies for activity without compromising safety. Clin Cancer Res; 19(11); 2817–23. ©2013 AACR.

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