Berry, Don

With rapid advances in our understanding of cancer, there is an expanding number of potential novel combination therapies, including novel–novel combinations. Identifying which combinations are appropriate and in which subpopulations are among the most difficult questions in medical research. We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review of trials of novel–novel combination therapies involving immunotherapies or molecular targeted therapies in advanced solid tumors. A MEDLINE search was conducted using a modified Cochrane Highly Sensitive Search Strategy for published clinical trials between July 1, 2017, and June 30, 2020, in the top-ranked medical and oncology journals. Trials were evaluated according to a criterion adapted from previously published Food and Drug Administration guidance and other key considerations in designing trials of combinations. This included the presence of a strong biological rationale, the use of a new established or emerging predictive biomarker prospectively incorporated into the clinical trial design, appropriate comparator arms of monotherapy or supportive external data sources and a primary endpoint demonstrating a clinically meaningful benefit. Of 32 identified trials, there were 11 (34%) trials of the novel–novel combination of anti-programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) and anti-cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) therapy, and 10 (31%) trials of anti-PD-1/PD-L1 and anti-vascular endothelial growth factor (VEGF) combination therapy. 20 (62.5%) trials were phase II trials, while 12 (37.5%) were phase III trials. Most (72%) trials lacked significant preclinical evidence supporting the development of the combination in the given indication. A majority of trials (69%) were conducted in biomarker unselected populations or used pre-existing biomarkers within the given indication for patient selection. Most studies (66%) were considered to have appropriate comparator arms or had supportive external data sources such as prior studies of monotherapy. All studies were evaluated as selecting a clinically meaningful primary endpoint. In conclusion, designing trials to evaluate novel–novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design. Designing trials to evaluate novel–novel combination therapies presents numerous challenges to demonstrate efficacy in a comprehensive manner. A greater understanding of biological rationale for combinations and incorporating predictive biomarkers may improve effective evaluation of combination therapies. Innovative statistical methods and increasing use of external data to support combination approaches are potential strategies that may improve the efficiency of trial design.

Abstract Background: I-SPY2 is a multicenter, Phase II trial using response-adaptive randomization within biomarker subtypes to evaluate novel agents as neoadjuvant therapy for high-risk at least T2N0 breast cancer. The primary endpoint is pathologic complete response (pCR) at surgery. The goal is to identify regimens that have ≥ 85% Bayesian predictive probability of success in a 300-patient phase 3 neoadjuvant trial defined by hormone-receptor (HR) and HER2 status, and MammaPrint (MP). Regimens may leave the trial for futility (< 10% probability of success), maximum sample size accrual (with probability of success ≥ 10% and < 85%), or as recommended by the independent DSMB. For HER2- subjects the control arm is weekly paclitaxel x12 then doxorubicin & cyclophosphamide (AC) q2-3 weeks x4. For this arm, paclitaxel was omitted and replaced with maximum tolerated dose PARPi talazoparib with synergy dosed irinotecan (25mg/m2). Paclitaxel could be given adjuvantly for these subjects and non-responding subjects could be taken off of experimental therapy. Methods: Women with tumors ≥ 2.5cm were eligible for screening. MP low/HR+ tumors were ineligible. MRI scans (baseline, 3 cycles after start of therapy, prior to AC, and prior to surgery) were used in a longitudinal statistical model to predict pCR for individual patients. Talazoparib was given at 1mg daily with 25mg/m2 irinotecan q2wks. Analysis was intention to treat. Subjects who switched to non-protocol therapy count as non-pCR. Subjects on experimental therapy at time of arm closure are non-evaluable. Talazoparib/irinotecan (TI) was open only to HER2- tumors and eligible for graduation in 3 of 10 pre-defined signatures: HER2-, HR+HER2- and HR-/HER2-. Results: TI did not meet criteria for graduation and was stopped at the recommendation of the DSMB based on expectations of limited activity beyond that seen with standard treatment. Maximum sample size had been reached at the time of this recommendation and subjects currently receiving TI were allowed to continue or change to standard therapy. Exploratory “as treated” analysis for response in gBRCA mutation carriers showed 6/10 gBRCA carriers attained a pCR in the TI arm. Except for 1 patient these gBRCA pCR subjects had >90% tumor reduction by MRI after TI and prior to AC (range: 68-96%). In the TI arm pCR rates were also higher in subjects with a PARPi7-High/MP2 gene expression signature (0.344 vs 0.146). Expected differences in toxicity were seen between arms including g3/4 peripheral neuropathy on control therapy which included paclitaxel (2.6% vs none) and g3/4 neutropenia with TI (30.2% vs 8.2%). Notably gBRCA mutation carriers receiving TI had higher rates of g3/4 neutropenia (60% vs 25.9%). Conclusion: The I-SPY2 study finds the probability that investigational regimens will be successful in a Phase III neoadjuvant trial; TI did not reach the efficacy threshold of 85% probability of success in Phase III in any of the 3 signatures. However by adding talazoparib with synergy dosed irinotecan we were able to omit paclitaxel and observe similar estimated pCR rates. This informs current work to evolve the I-SPY2 trial design to reduce toxicity without compromising outcomes and develop successful combinations targeted to biology, including DNA repair deficiency. Citation Format: Richard Schwab, Amy S. Clark, Christina Yau, Nola Hylton, Wen Li, Denise Wolfe, A Jo Chien, Anne M. Wallace, Andres Forero-Torres, Erica Stringer-Reasor, Rita Nanda, Nora Jaskowiak, Judy Boughey, Tufia Haddad, Heather S. Han, Catherine Lee, Kathy Albain, Claudine Isaacs, Anthony D. Elias, Erin D. Ellis, Payal Shah, Julie E. Lang, Janice Lu, Debasish Tripathy, Kathleen Kemmer, Douglas Yee, Barbara Haley, Melanie Majure, Erin Roesch, Christos Vaklavas, Cheryl Ewing, Teresa Helsten, W Fraser Symmans, Jane Perlmutter, Hope S. Rugo, Michelle Melisko, Amy Wilson, Ruby Singhrao, Laura van 't Veer, Angela DeMichele, Smita Asare, Don Berry, Laura J. Esserman. Evaluation of talazoparib in combination with irinotecan in early stage, high-risk HER2 negative breast cancer: Results from the I-SPY 2 TRIAL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT136.

Abstract Background: Pembrolizumab (Pembro), an anti-PD-1 immune checkpoint inhibitor, has been approved for the treatment of a variety of cancers. Pembro was recently evaluated in HER2- breast cancer patients in the neoadjuvant I-SPY 2 TRIAL and graduated in the triple negative (TN), HR+HER2-, and HER2- signatures. TN breast cancers tend to have high numbers of immune infiltrates, including T cells and tumor associated macrophages (TAMs). We utilized multiplex multispectral imaging to evaluate the presence and spatial proximity of various immune cell populations as specific predictors of response to Pembro. Methods: Pre-treatment biopsies were available from 54 patients. FFPE sections were immunostained using Opal fluorescence reagent kits on a fully automated Ventana Discovery platform and imaged with a Vectra automated imaging system. Image analyses were performed with a variety of software packages. Immune biomarkers included CD3, CD8, FoxP3, CD68, PD-1, PD-L1, and cytokeratins. Cell densities were determined per area of tissue. Spatial analyses were performed to quantitate proximity of cell types. We used logistic regression with the likelihood ratio test to evaluate associations between pathologic complete response (pCR) with immune cell counts, ratios, and spatial proximity measurements. This analysis was also performed adjusting for HR status, and within receptor subsets, sample size permitting. Our statistics are descriptive rather than inferential and do not adjust for multiple hypothesis testing or multiplicities of other biomarkers outside this study. Results: Densities of CD3+ T cells ranged from 193-5155 cells/mm2, with CD8+ cytotoxic T cells (Tc) ranging from 103 to 3774 cells/mm2 and FoxP3+ Tregs from 0-416 cells/mm2. Densities of PD-1+ T cells and PD-1+ Tc cells ranged from 0-1341 cells/mm2 and 0-698 cells/mm2, respectively. Macrophage cell densities ranged from 84-2644 cells/mm2. PD-L1+ tumor cell densities ranged from 0-2981 cells/mm2. High PD-L1 expression (>=10% of tumor cells positive for PD-L1) was observed in 12% of the cases. Overall T cell density was positively associated with pathological complete response (pCR), as were Treg cell density and PD-1+ T cell density. The ratio of macrophages to Tc cells was negatively associated with response. Finally, the spatial distribution of CD3+ T cells in proximity to cancer cells correlated positively with pCR. Conclusion: Cell density, immune cell ratios and spatial resolution provide insight to response and resistance. We confirmed the observation that T cell density predicts pCR, but more specifically that Treg and PD1+ T cell densities increase the chance of pCR. An increase in macrophages relative to cytotoxic T cells predicts resistance. The closer the T cells are to the tumor, the better the response. These results demonstrate the utility of characterizing the immune microenvironment, in the context of therapy with immuno-oncology agents, not only to predict response, but to gain insight and learn now to potentiate agents and combinations. Citation Format: Michael J. Campbell, Christina Yau, Jennifer Bolen, Scott Vandenberg, Clifford Hoyt, Lamorna Brown-Swigart, Gillian Hirst, Rita Nanda, Minetta Liu, Smita Asare, Laura van'tVeer, Douglas Yee, Angie DeMichele, Don Berry, Laura Esserman, I SPY 2 TRIAL Investigators. Analysis of immune cell infiltrates as predictors of response to the checkpoint inhibitor pembrolizumab in the neoadjuvant I-SPY 2 TRIAL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT003.

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. Pertuzumab (P) has established survival benefit in the metastatic setting, and received accelerated approval in the neoadjuvant setting when combined with trastuzumab (H) and docetaxel(D) as part of a complete treatment regimen for early breast cancer. We tested its ability, when combined with standard therapy (paclitaxel, T, and H) to improve pCR (ypT0ypN0) over TH in the adaptively randomized, phase II, I-SPY 2 neoadjuvant trial. Methods: Enrolled patients (pts) had invasive BC ?2.5 cm in HER2-positive subsets. Pts were adaptively randomized to control (TH, qwk x 12) or THP (P, q3wk x 4) followed by doxorubicin/cyclophosphamide (AC) x 4 and surgery. To compare THP to TH we utilized all 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 experimental arms was based on current Bayesian probabilities of superiority over control. “Graduation” by signature and futility stopping were based upon Bayesian predictive probability of success in a 2-arm, N = 300 phase III randomized 1:1 trial of THP vs. TH with pCR endpoint. Results: THP met the predictive probability criterion and graduated in 3 signatures: all HER2+, HER2+/HR+, and HER2+/HR- (See Table 1). Final accrual: 44 THP and 31 TH. Safety data will be shown. Conclusions: I-SPY 2's standing platform trial efficiently evaluates agents in biomarker-defined pt subsets. THP -> AC substantially improves pCR rates over standard TH -> AC in all 3 HER2+ signatures, including HR+ and HR- subsets. APHINITY, a trial of adjuvant pertuzumab with a primary outcome of invasive disease-free survival, is ongoing. Table 1Signature*Estimated pCR Rate (95% probability interval)Signature*THPControl (TH)Probability THP Is Superior to ControlPredictive Probability of Success in Phase IIIHER2+54% (38%-70%)22% (5%-39%)99.8%96%HER2+/HR+44% (24%-63%)17% (0%-34%)99%91%HER2+/HR-74% (53%-95%)33% (6%-59%)99.8%98%*HR = Hormone Receptor Citation Format: Meredith Buxton, Angela M. DeMichele, Stephen Chia, Laura van't Veer, Jo Chien, Anne Wallace, Henry Kaplan, Julie Lang, Douglas Yee, Claudine Isaacs, Stacy Moulder, Kathy Albain, Judy Boughey, Kathleen Kemmer, Barbara Haley, Susan Minton, Andres Forero, Rita Nanda, Anthony Elias, Larissa Korde, Rebecca Viscuzi, Hope Rugo, Richard Schwab, Fraser Symmans, Melissa Paoloni, Nola Hylton, Michael Hogarth, Julia Lyandres, Jane Perlmutter, Ashish Sanil, Christina Yau, Laura Esserman, Don Berry, I-SPY 2 TRIAL Investigators. Efficacy of pertuzumab/trastuzumab/paclitaxel over standard trastuzumab/paclitaxel 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 CT106.

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: 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.