Yee, Douglas

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: Triple negative breast cancer (TNBC), generally considered aggressive with few options beyond standard chemotherapy, is currently experiencing a period of optimism, with multiple trials showing increased efficacy of platinum containing regimens (with and without PARP-inhibition), and more recently, immunotherapy. In I-SPY 2, veliparib/carboplatin (VC) and the immune checkpoint inhibitor pembrolizumab (Pembro) graduated in the TN subtype. As part of the I-SPY 2 biomarker program, we previously identified the signature PARPi7, a measure of DNA repair deficiency (DRD), as predicting response to VC. As well, immune signatures, including one representing dendritic cells, predicted response to Pembro. Here we investigate the overlap between these biomarkers to identify patient subgroups more likely to respond to immunotherapy or platinum-based therapy and estimate response rates to VC and Pembro in patients positive for one, both, or neither biomarker. Methods: 153 TNBC patients (Control: 85; VC: 39; Pembro: 29) were considered in this analysis. Continuous DNA repair deficiency and immune signatures assayed at the pre-treatment time point were evaluated as published (Wolf, 2017; Danaher, 2017). To identify optimal dichotomizing thresholds, 2-fold cross-validation was repeated 500 times. Bayesian logistic regression was used to estimate pCR rates by arm for patient subsets defined by biomarker combinations. Our study is exploratory with no claims for generalizability; and this analysis does not adjust for multiplicities. Results: Using the optimal dichotomizing cutpoints, 54% of TNBC patients are classified as DRD+, and 67% Immune+. As expected, DRD+ patients have a high estimated pCR rate to VC (75%); and Immune+ patients have a high estimated pCR rate to Pembro (82%). Combining these biomarkers, 40% of TNBC are positive for both biomarkers, 40% for only one biomarker (26% Immune+/DRD-, 14% Immune-/DRD+), and 20% for neither. The Immune+/DRD+ TN subset predicted sensitive to both VC and Pembro has a higher estimated pCR rate to both VC and Pembro relative to control (estimated pCR rates: VC: 75%, Pembro: 73% vs. control: 18%). In contrast, the Immune+/DRD- group, predicted to be sensitive to Pembro, has the highest pCR rate to Pembro (VC: 48%, Pembro: 83% vs. control 21%), whereas the Immune-/DRD+ group is most responsive to VC (VC: 63%, Pembro: 41% vs. control 29%). For the 20% of Immune-/DRD- TNBC patients, their estimated response rates are lower than the biomarker-positive patients (VC: 27%, Pembro: 43% vs. control: 19%). Conclusion: Molecular phenotypes capturing immune activation and DNA repair deficiency may predict response of TNBC and help prioritize selection of platinum or immunotherapy containing regimens. Citation Format: Denise M. Wolf, Christina Yau, Julia Wulfkuhle, Emanuel Petricoin, Lamorna Brown-Swigart, Gillian Hirst, Smita Asare, I-SPY 2 Consortium, Douglas Yee, Angela DeMichele, Hope Rugo, Olufunmilayo Olopade, Rita Nanda, Minetta Liu, Laura Esserman, Laura van t' Veer. Integration of DNA repair deficiency and immune biomarkers to predict which early-stage triple-negative breast cancer patients are likely to respond to platinum-containing regimens vs. immunotherapy: 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 2679.

Abstract Background: In women with breast cancer receiving neoadjuvant chemotherapy, residual cancer burden (RCB) predicts distant recurrence and survival. In those with high risk tumors, locoregional recurrence (LRR) remains a concern, and has been associated with type of local therapy received. We evaluated the impact of local therapy on LRR in the ISPY-2 TRIAL. Methods: Data were analyzed in Stata 14.2, using Chi2 test, log rank test, and a Cox proportional hazards model. RCB was considered a categorical variable (0/1 versus 2/3), as described in prior publications. Breast surgery categories were lumpectomy +/- radiotherapy, or mastectomy +/- radiotherapy. Axillary surgery was defined as sentinel lymph node (SLN) surgery (≤6 nodes removed) or axillary dissection (>6 nodes). Results: Follow up data from the I-SPY2 TRIAL were available for 630 patients (median follow up 2.76 yrs, range 0.4-7.2). Type of local therapy was significantly associated with clinical stage at presentation, with stage III patients most frequently undergoing mastectomy + radiation (p<0.001). Women with higher RCB were more likely to undergo mastectomy than those with lower RCB (61.3% vs 48.8% mastectomy rate, p=0.002), and more likely to receive adjuvant radiotherapy (62.0% vs 53.9%, p=0.048). There was no association between clinical stage, type of surgery, or radiotherapy and LRR (Table). Higher RCB was significantly associated with LRR, with 3 year locoregional recurrence free rate of 95.1% in RCB 0/1 versus 89.9% in RCB 2/3 (p=0.003). In a Cox model adjusting for clinical stage, tumor subtype, surgical therapy, RCB status, nodal radiation, and age, significant predictors for LRR were tumor subtype and RCB status. Hazard ratio (HR) for LRR in those with RCB 0/1 was 0.39 compared to those with RCB 2/3 (95% CI 0.17-0.87, p=0.021). There was no difference in LRR between breast conservation and mastectomy; within the breast conservation group, those who had lumpectomy alone had higher hazard of LRR compared to those having lumpectomy + radiation (HR 3.1, 95% CI 1.1-9.2, p=0.043). Conclusions: Extent of surgical therapy was not associated with local tumor control, regardless of advanced tumor stage at presentation. Rather, tumor biology and response to therapy were the best predictors of LRR. These data highlight the opportunity to minimize the morbidity of extensive surgical therapy for patients with excellent response to systemic therapy. LRR rates by clinical features and treatment status FrequencyLRR RateP valueClinical Stage 0.5I240 (47.5%)5.8% II185 (36.6%)8.7% III80 (15.8%)6.3% Tumor Subtype 0.014ER+PR+Her2-161 (26.4%)3.1% ER+PR-Her2-56 (9.2%)3.6% Her2+176 (28.9%)6.3% Triple negative216 (35.5%)11.1% Local therapy 0.169Lumpectomy85 (13.5%)11.8% Lumpectomy with radiation198 (31.4%)5.6% Mastectomy173 (27.5%)5.2% Mastectomy with radiation174 (27.6%)8.6% Axillary surgery 0.23None5 (0.8%)20% SLN329 (52.2%)5.8% ALND296 (47%)8.5% Axillary radiation 0.535Yes42 (6.7%)9.5% No588 (93.3%)7.0% Axillary management 0.2No surgery or radiation5 (0.8%)20.0% SLN312 (50%)5.3% SLN+Axillary radiation17 (2.7%)8.3% ALND271 (43%)10.3% ALND+Axillary radiation25 (4%)5.4% RCB 0.0020/1293 (50.1%)3.8% 2/3292 (49.9%)10.3% Citation Format: Silverstein J, Suleiman L, Yau C, Price ER, Singhrao R, Yee D, DeMichele A, Isaacs C, Albain KS, Chien AJ, Forero-Torres A, Wallace AM, Pusztai L, Ellis ED, Elias AD, Lang JE, Lu J, Han HS, Clark AS, Korde L, Nanda R, Northfelt DW, Khan QJ, Viscusi RK, Euhus DM, Edmiston KK, Chui SY, Kemmer K, Wood WC, Park JW, Liu MC, Olopade O, Leyland-Jones B, Tripathy D, Moulder SL, Rugo HS, Schwab R, Lo S, Helsten T, Beckwith H, I-SPY 2 TRIAL Consortium, Berry DA, Asare SM, Esserman LJ, Boughey JC, Mukhtar RA. The impact of local therapy on locoregional recurrence in women with high risk breast cancer in the neoadjuvant I-SPY2 TRIAL [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-14-01.

Abstract Background: The explosion in new treatment options targeting immune checkpoints, HER signaling, DNA repair deficiency, AKT, and other pathways calls for updated breast cancer subtypes beyond HR and HER2 status to predict which patients will respond to which treatments. Here we leverage the I-SPY 2 TRIAL biomarker program over the past 8 years across 10 treatment arms to elucidate a minimal set of biomarkers that may improve response prediction in a modern treatment context, and to investigate which new patient phenotypes are identified by these response-predictive biomarkers. Methods: 986 patients were considered in this analysis. Treatments included paclitaxel alone (or with trastuzumab (H) in HER2+) or combined with investigational agents: veliparib/carboplatin (VC); neratinib; MK2206; ganitumab; ganetespib; AMG386; TDM1/pertuzumab (P); H/P; and pembrolizumab (Pembro). 24 prospectively defined, mechanism-of-action and pathway-based expression and phospho-protein signatures/biomarkers assayed from pre-treatment biopsies were previously found to be predictive in a particular agent/arm in pre-specified analysis. Here we evaluate these biomarkers in all patients. We assessed association between each biomarker and response in the population as a whole and within each arm and HR/HER2 subtype using a logistic model. To identify optimal dichotomizing thresholds for select biomarkers, 2-fold cross-validation was repeated 500 times. Our analysis is exploratory and does not adjust for multiplicities. Results: Our initial set of 24 predictive biomarkers reflects DNA repair deficiency (n=2), immune activation (n=7), ER signaling (n=2), HER2 signaling (n=4), proliferation (n=2), phospho-activation of AKT/mTOR (n=2), and ANG/TIE2 (n=1) pathways, among others. Biomarkers reflecting similar biology are correlated and cluster together. We make use of this correlation structure to reduce the dimensionality of the biomarker set to five predictive signals: proliferation, DNA repair deficiency (DRD), immune-engaged (Immune+), luminal/ER (lum), and HER2-activated. These biomarkers, when dichotomized, identify patient groups with differential predicted sensitivities to I-SPY 2 agents and are present at different proportions within receptor subtypes. For instance, in the HER2- subset, Immune+/DRD+ patients are predicted sensitive to both VC and Pembro, and account for 39% of TN, but only 12% of HR+HER2-. On the other end of the spectrum, only 17% of TN are Immune-/DRD-, compared to the majority (56%) of HR+HER2-. There are also subsets of patients positive for only one marker. For the HER2+ subset, 67% are HER2-activated+, and 25% lum+; of these HER2-activated+ patients are more likely to be Immune+ (44%), vs 23% in lum+. HER2-activated+/Immune+ patients have higher predicted sensitivity to HER2-targeted agents than lum+ or Immune- patients. In all, these molecular phenotypes predict sensitivity to one or more I-SPY 2 investigational agents for 75% of the ˜ 1000 patients. Conclusion: Molecular phenotypes reflecting proliferation, immune engagement, HER2-activation, luminal/ER-signaling, and DNA repair deficiency may provide a roadmap to guide treatment prioritization for emerging therapeutics. Citation Format: Wolf DM, Yau C, Wulfkuhle J, Petricoin E, Campbell M, Brown-Swigart L, Hirst G, Asare S, Zhu Z, Lee EP, Delson A, Pohlmann P, I-SPY 2 TRIAL Consortium, Hylton N, Liu MC, Symmans F, DeMichele A, Yee D, Berry D, Esserman L, van 't Veer L. Identifying breast cancer molecular phenotypes to predict response in a modern treatment landscape: Lessons from ˜1000 patients across 10 arms of the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-10-02.

Abstract Background: Patients achieving a pathologic complete response (pCR) following neoadjuvant therapy have significantly improved event-free survival relative to those who do not; and pCR is an FDA-accepted endpoint to support accelerated approval of novel agents/combinations in the neoadjuvant treatment of high risk early stage breast cancer. Previous studies have shown that recurrence risk increased with increasing burden of residual disease (as assessed by the RCB index). As well, these studies suggest that patients with minimum residual disease (RCB-I class) also have favorable outcomes (comparable to those achieving a pCR) within high risk tumor subtypes. In this study, we assess whether integrating RCB with MRI functional tumor volume (FTV), which in itself is prognostic, can improve prediction of distant recurrence free survival (DRFS); and identify a subset of patients with minimal residual disease with comparable DRFS as those who achieved a pCR. Imaging tools can then be used to identify the subset that will do well early and guide the timing of surgical therapy. Method: We performed a pooled analysis of 596 patients from the I-SPY2 TRIAL with RCB, pre-surgical MRI FTV data and known follow-up (median 2.5 years). We first assessed whether FTV predicts residual disease (pCR or pCR/RCB-I) using ROC analysis. We applied a power transformation to normalize the pre-surgical FTV distribution; and assessed its association with DRFS using a bi-variate Cox proportional hazard model adjusting for HR/HER2 subtype. We also fitted a bivariate Cox model of RCB index adjusting for subtype; and assessed whether adding pre-surgical FTV to this model further improves association with DRFS using a likelihood ratio (LR) test. For the Cox modeling, penalized splines approximation of the transformed FTV and RCB index with 2 degrees of freedom was used to allow for non-linear effects of FTV and RCB on DRFS. Result: Pre-surgical MRI FTV is significantly associated with DRFS (Wald p<0.00001), and more effective at predicting pCR/RCB-I than predicting pCR alone (AUC: 0.72 vs. 0.65). Larger pre-surgical FTV remains associated with worse DRFS adjusting for subtype (Wald p <0.00001). The RCB index is also significantly associated with DRFS adjusting for subtype (Wald p<0.00001). Adding FTV to a model containing RCB and subtype further improves association with DRFS (LR p=0.0007). RCB-I patients have excellent DRFS (94% at 3 years compared to 95% in the pCR group). Efforts are underway to identify an optimal threshold for dichotomizing pre-surgical FTV and FTV change measures for use in combination with pCR/RCB-I class to generate integrated RCB (iRCB) groups as a composite predictor of DRFS. Conclusion: Pre-surgical MRI FTV is effective at predicting minimal residual disease (RCB0/I) in the I-SPY 2 TRIAL. Despite the association between FTV and RCB, FTV appears to provide independent added prognostic value (to RCB and subtype), suggesting that integrating MRI volume measures and RCB into a composite predictor may improve DRFS prediction. Citation Format: Hylton NM, Symmans WF, Yau C, Li W, Hatzis C, Isaacs C, Albain KS, Chen Y-Y, Krings G, Wei S, Harada S, Datnow B, Fadare O, Klein M, Pambuccian S, Chen B, Adamson K, Sams S, Mhawech-Fauceglia P, Magliocco A, Feldman M, Rendi M, Sattar H, Zeck J, Ocal I, Tawfik O, Grasso LeBeau L, Sahoo S, Vinh T, Yang S, Adams A, Chien AJ, Ferero-Torres A, Stringer-Reasor E, Wallace A, Boughey JC, Ellis ED, Elias AD, Lang JE, Lu J, Han HS, Clark AS, Korde L, Nanda R, Northfelt DW, Khan QJ, Viscusi RK, Euhus DM, Edmiston KK, Chui SY, Kemmer K, Wood WC, Park JW, Liu MC, Olopade O, Tripathy D, Moulder SL, Rugo HS, Schwab R, Lo S, Helsten T, Beckwith H, Haugen PK, van't Veer LJ, Perlmutter J, Melisko ME, Wilson A, Peterson G, Asare AL, Buxton MB, Paoloni M, Clennell JL, Hirst GL, Singhrao R, Steeg K, Matthews JB, Sanil A, Berry SM, Abe H, Wolverton D, Crane EP, Ward KA, Nelson M, Niell BL, Oh K, Brandt KR, Bang DH, Ojeda-Fournier H, Eghtedari M, Sheth PA, Bernreuter WK, Umphrey H, Rosen MA, Dogan B, Yang W, Joe B, I-SPY 2 TRIAL Consortium, Yee D, Pusztai L, DeMichele A, Asare SM, Berry DA, Esserman LJ. Refining neoadjuvant predictors of three year distant metastasis free survival: Integrating volume change as measured by MRI with residual cancer burden [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-07-03.

506 Background: Pembro is an anti-PD-1 antibody with single agent activity in HER2– metastatic BC. I-SPY 2 is a multicenter, phase 2 platform trial which evaluates novel neoadjuvant therapies; the primary endpoint is pathological complete response (pCR, ypT0/Tis ypN0). We report current efficacy results, with final results at ASCO. Methods: Patients (pts) with invasive BC ≥2.5 cm by exam or ≥2 cm by imaging are assigned weekly paclitaxel x 12 (control) +/- an experimental agent, followed by doxorubicin/cyclophosphamide x 4. Combinations of hormone-receptor (HR), HER2, & MammaPrint (MP) status define the 8 signatures studied. MP low HR+ BC is excluded. Adaptive randomization is based on each arm’s Bayesian probability of superiority over control. Graduation by signature is based on an arm’s Bayesian predictive probability of a successful 1:1 randomized phase 3 trial with a pCR endpoint. We provide raw & Bayesian estimated pCR rates adjusted for covariates, time effects over the course of the trial, & serial MRI modeling for pts not yet assessed for pCR surgically. Results: 69 pts were randomized to pembro (HER2- subsets only) from Dec 2015 until it graduated in Nov 2016. 46 pts have undergone surgery (table); the other 23 have on-therapy MRI assessments. In 29 HR–/HER2– (TNBC) pts, pembro increased raw & estimated pCR rates by >50% & 40%, respectively; in 40 HR+/HER– pts, it did so by 13% and 21%. 5 pts had immune-related grade 3 adverse events (AEs); 1 hypophysitis & 4 adrenal insufficiency. 4 pts presented after completion of AC (149-179 d after starting pembro); 1 presented prior to AC (37 d after starting pembro). 7 pts had grade 1-2 thyroid abnormalities. Conclusion: Pembro added to standard therapy improved pCR rates in all HER2- BCs that meet I-SPY 2 eligibility, especially in TNBC. Immune-mediated AEs were observed; pt follow up is ongoing. Clinical trial information: NCT01042379. [Table: see text]

Abstract Background: Pembrolizumab (P), an anti-PD-1 immune checkpoint inhibitor, has been approved for treatment of microsatellite instability-high and mismatch repair deficient cancers. In I-SPY 2, patients were randomized to receive standard chemotherapy alone or in combination with an experimental agent. P was one of the experimental agents evaluated in HER2- patients in I-SPY 2 and graduated in the TN, HR+HER2-, and HER2- signatures. We hypothesize that a combination of two signatures predicting response to veliparib/carboplatin therapy in I-SPY 2 [MammaPrint High2 (MP2)/PARPi7-high] and reflecting DNA damage repair deficiency, may also predict response to P. In addition, we also tested 9 gene expression signatures reflecting different aspects of DNA damage and repair: FA, MMR, BER, HR, TLS, NER, NHEJ, DR, and DNA damage sensing (DDS) pathways. Methods: Data from 249 patients (P: 69 and controls: 180) were available. Pre-treatment biopsies were assayed using Agilent gene expression arrays. All I-SPY 2 qualifying biomarker analyses follow a pre-specified analysis plan. We used logistic modeling to assess biomarker performance. A biomarker is considered a specific predictor of P response if it associates with response in the P arm but not the control arm, and if the biomarker x treatment interaction is significant (likelihood ratio test, p<0.05). This analysis is also performed adjusting for HR status as a covariate, and within receptor subsets, sample size permitting. For successful biomarkers, we use Bayesian modeling to estimate the pCR rates of 'predicted sensitive' patients in each arm. Our statistics are descriptive rather than inferential and do not adjust for multiplicities of other biomarkers outside this study. Results: MP2 status associates with pCR in P (OR=7.7; p=0.00021), but also to a lesser extent in the control arm (OR=2.4:p=0.045), with an OR ratio of 3.3 which trends toward significance, even after adjusting for HR status (LR p=0.083). A majority of TN patients are MP2; and TN/MP2 patients have an estimated pCR rate of 67% in P (vs. 23% in control). Although only ~30% of HR+HER2- patients were MP2, their estimated pCR rate in P is 61%, compared to 29% in unselected HR+/HER2- patients. PARPi7 predicted response in the P arm only in the HR+HER2- group (LR p= 0.025), but not in the population as a whole or the TN subtype. Combining MP2 and PARPi7 into MP2/PARPi7-high did not improve performance over MP2 as a single biomarker. Of the 9 DDR pathway signatures tested, both BER and DDS associate with pCR in P, but only DDS (which includes ATM, ATR, CHEK1-2) associates with pCR in the P arm (LR p=0.00029), and not the control arm (LR p=0.53), with a significant interaction with treatment (LR p=0.0064) that retains significance in a model adjusting for HR status. When dichotomized to optimize the biomarker x treatment interaction, the estimated pCR rate is 75% in P vs 18% in control, in the DDS+ subset. Conclusion: In this small study, MP2 status and a DNA damage sensing pathway but not the PARPi7 or other repair pathways show promise as predictive biomarkers for immune checkpoint inhibition therapy in breast cancer. Citation Format: Yau C, Wolf D, Brown-Swigart L, Hirst G, Sanil A, Singhrao R, I-SPY 2 TRIAL Investigators, Asare S, DeMichele A, Berry D, Esserman L, van 't Veer L, Nanda R, Liu M, Yee D. Analysis of DNA repair deficiency biomarkers as predictors of response to the PD1 inhibitor pembrolizumab: Results from the neoadjuvant I-SPY 2 trial for stage II-III high-risk breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr PD6-14.

Abstract Background: Pathological complete response (pCR) is accepted by FDA as a surrogate endpoint for accelerated approval of targeted agents in combination with chemotherapy based on better long-term outcomes compared to residual disease (Cortazar 2014). Methods: The multi-center, adaptively-randomized I-SPY2 platform trial uses pCR as the primary endpoint to identify investigational agents that will improve outcomes in women with stage 2/3 breast cancer with high risk of early recurrence, across all signatures, based on hormone receptor (HR), HER2, and 70-gene (MammaPrint) status. For patients with HR+ HER2- tumors, only 70-gene (Mammaprint) high-risk patients are enrolled. To date, 1200+ patients have been randomized to one of 14 arms: control (paclitaxel followed by AC); veliparib/carboplatin; neratinib; MK2206; trebananib; trastuzumab/pertuzumab; ado-trastuzumab emtansine/pertuzumab; pembrolizumabx4; ganitumab/metformin; ganetespib; PLX-3397. 7 agents graduated in at least one signature (> 85% probability of success in a 300-patient phase III confirmatory trial); 2 did not graduate; 1 stopped for toxicity, and 3 are enrolling (patritumab/trastuzumab, talazoparib/irinotecan, pembrolizumabx8). Local pathologists were centrally trained using the Residual Cancer Burden (RCB) assessment to ensure uniform evaluation and response classification; RCB 0 = pCR. Results: We evaluated the relationship between pCR and event free (EFS) and distant disease free survival (DDFS) in the first 522 pts (median follow-up:2.5 years). 180 pts achieved pCR (36%) while 338 did not (RCB=1-3). There were 82 EFS and 65 DRFS events. Over the entire group (including all arms), pCR was highly associated with 3-year EFS (p<0.001 for both). Pts achieving pCR had a 3% recurrence risk (RR) at 3 years; those with non-pCR had 24% RR over this time period. For distant recurrence, the 3-year RR with pCR was 2%, compared to 20% in pts with non-pCR. As expected, pCR rates varied by breast cancer subtype (HR+/HER2: 18% (35/196), HR+/HER2+: 40% (33/82), HR-/HER2+:68% (34/50), HR-/HER2-:41% (76/188)). The relationship between pCR and EFS was significant and clinically impactful within each subtype. 3-year survival (pCR group)Hazard Ratio OverallOverallHR+/HER2-HER2+TNBCEFS97%0.08 (0.03-0.23)0.14 (0.02-1.04)0 (NA)0.11 (0.03-0.37)DDFS98%0.08 (0.03-0.26)0.17 (0.01-1.23)0 (NA)0.09 (0.02-0.40) Conclusions: The first long-term efficacy results from the I-SPY2 TRIAL demonstrate that achieving pCR is a very strong surrogate endpoint for improved EFS and DDFS in a high-risk population, across all treatment arms, regardless of subtype. I-SPY2 shows substantially lower estimated EFS hazards for patients achieving pCR, compared to the 5 yr EFS hazard ratio for pCR vs not in Cortazar (hazard ratio 0.49), demonstrating important differences between a metaanalysis compared to a platform trial with uniform high-risk eligibility, standardized pathology assessment, and multiple targeted therapies. Our data support the use of pCR as a primary endpoint for accelerated approval of new drugs if EFS is evaluated in the same population. Based on these findings, the I-SPY2 TRIAL will test whether therapy can be deescalated or escalated for individual patients with the goal of achieving pCR for all. Citation Format: Yee D, DeMichele A, Isaacs C, Symmans F, Yau C, Albain KS, Hylton NM, Forero-Torres A, van't Veer LJ, Perlmutter J, Rugo HS, Melisko M, Chen Y-Y, Balassanian R, Krings G, Datnow B, Hasteh F, Tipps A, Weidner N, Zhang H, Tickman R, Thornton S, Ritter J, Amin K, Klein M, Chen B, Keeney G, Ocal T, Feldman M, Klipfel N, Sattar H, Mueller J, Gwin K, Baker G, Kallakury B, Zeck J, Duan X, Ersahin C, Gamez R, Troxell M, Mansoor A, Grasso LeBeau L, Sams S, Wisell J, Wei S, Harada S, Vinh T, Stamatakos MD, Tawfik O, Fan F, Adams A, Rendi M, Minton S, Magliocco A, Sahoo S, Fang Y, Hirst G, Singhrao R, Asare SM, Wallace AM, Chien AJ, Ellis ED, Han HS, Clark AS, Boughey JC, Elias AD, Nanda R, Korde L, Murthy R, Lang J, Northfelt D, Khan Q, Edmiston KK, Viscusi R, Haley B, Kemmer K, Zelnak A, Berry DA, Esserman LJ. Pathological complete response predicts event-free and distant disease-free survival in the I-SPY2 TRIAL [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr GS3-08.

Abstract Background: I-SPY 2 is a multicenter, phase 2 trial using response-adaptive randomization within biomarker subtypes to evaluate novel agents when added to standard neoadjuvant therapy for women with high-risk stage II/III breast cancer - investigational agent(I) +paclitaxel(T) qwk, doxorubicin & cyclophosphamide(AC) q2-3 wk x 4 vs. T/AC (control arm). The primary endpoint is pathologic complete response (pCR) at surgery. The goal is to identify/graduate regimens that have ≥85% Bayesian predictive probability of success (statistical significance) in a 300-patient phase 3 neoadjuvant trial defined by hormone-receptor (HR) & HER2 status & MammaPrint (MP). Regimens may also leave the trial for futility (< 10% probability of success) or following accrual of maximum sample size (10%< probability of success <85%). We report the results for experimental arm Ganitumab, a type I insulin-like growth factor receptor (IGF1R) inhibitor. IGF1R inhibitors are known to induce insulin resistance and all patients assigned to Ganitumab received metformin. Methods: Women with tumors ≥2.5cm were eligible for screening. MP low/HR+ and HER2+ tumors were ineligible for randomization. Hemoglobin A1C≥ 8.0% were ineligible. MRI scans (baseline, 3 cycles after start of therapy, at completion of weekly T and prior to surgery) were used in a longitudinal statistical model to improve the efficiency of adaptive randomization. Ganitumab was given at 12mg/kg q2 weeks and metformin at 850mg PO BID, while receiving ganitumab. Analysis was intention to treat with patients who switched to non-protocol therapy counted as non-pCRs. Ganitumab/metformin was open only to HER2- patients, and eligible for graduation in 3 of 10 pre-defined signatures: HER2-, HR+HER2- and HR-HER2-. Results: Ganitumab/metformin did not meet the criteria for graduation in the 3 signatures tested. When the maximum sample size was reached, accrual to this arm stopped. Ganitumab/metformin was assigned to 106 patients; there were 128 controls. We report probabilities of superiority for Ganitumab/metformin over control and Bayesian predictive probabilities of success in a neoadjuvant phase 3 trial equally randomized between Ganitumab/metformin and control, for each of the 3 biomarker signatures, using the final pathological response data from all patients. Safety data will be presented. SignatureEstimated pCR Rate (95% probability interval)Probability Ganitumab/ Metformin Is Superior to ControlPredictive Probability of Success in Phase 3 Ganitumab/ Metformin N = 106Control N = 128 All HER2-22% (13%-31%)16% (10%-23%)89%33%HR+/HER2-14% (4%-24%)12% (4%-19%)66%21%HR-/HER2-32% (17%-46%)21% (11%-32%)91%51% Conclusion: The I-SPY 2 adaptive randomization study estimates the probability that investigational regimens will be successful in a phase 3 neoadjuvant trial. The value of I-SPY 2 is to give insight about the performance of an investigational agent's likelihood of achieving pCR. For Ganitumab/metformin, no subtype came close to the efficacy threshold of 85% likelihood of success in phase 3, and this regimen does not appear to impact upfront reduction of tumor burden. Our data do not support its continued development for the neoadjuvant treatment of breast cancer. Citation Format: Yee D, Paoloni M, van't Veer L, Sanil A, Yau C, Forero A, Chien AJ, Wallace AM, Moulder S, Albain KS, Kaplan HG, Elias AD, Haley BB, Boughey JC, Kemmer KA, Korde LA, Isaacs C, Minton S, Nanda R, DeMichele A, Lang JE, Buxton MB, Hylton NM, Symmans WF, Lyandres J, Hogarth M, Perlmutter J, Esserman LJ, Berry DA. The evaluation of ganitumab/metformin plus standard neoadjuvant therapy in high-risk breast cancer: Results from the I-SPY 2 trial [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-11-04.