Edmiston, Kirsten K.

PURPOSE Neoadjuvant immunotherapy (IO) has become the standard of care for early-stage triple-negative breast cancer (TNBC), but not yet for other subtypes. We previously developed a clinical-grade mRNA-based immune classifier (ImPrint) predicting response to IO that is now being used in I-SPY2.2 as part of the response predictive subtypes. We report the performance of ImPrint in hormone receptor–positive and human epidermal growth factor receptor 2–negative (HR+HER2–) patients from five IO arms. METHODS A total of 204 HR+HER2– (MammaPrint high-risk) patients from five IO arms (anti–PD-1, anti–PD-L1/poly [ADP-ribose] polymerase inhibitor combination, anti–PD-1/toll-like receptor 9 dual-IO combination, and anti–PD-1 ± lymphocyte activation gene 3 dual-IO combination) and 191 patients from the chemotherapy-only control arm were included in this analysis. Patients were classified as ImPrint+ (likely sensitive) versus ImPrint– (likely resistant), using pretreatment mRNA. Performance of ImPrint for predicting pathologic complete response (pCR) to IO-containing arms was characterized and compared with tumor grade (III), MammaPrint (ultra) High2 risk (MP2), and estrogen receptor (ER)-low (ER ≤ 10%). RESULTS Overall, the pCR rate across the five IO arms was 33%. 26% of HR+HER2– patients were ImPrint+, and pCR rates with IO were 75% in ImPrint+ versus 17% in ImPrint–, with the highest pCR rate >90% in a dual-IO arm. In the control arm, pCR rates were 33% in ImPrint+ and 8% in ImPrint–. Tumor grade (III), MP2, and ER-low showed pCR rates in IO of 45%, 56%, and 63%, respectively, with lower pCR odds ratios (OR < 7.5) compared with ImPrint (OR = 14.5). CONCLUSION Using an accurate selection strategy, HR+HER2– patients could achieve pCR rates similar to what is seen with best neoadjuvant therapies in TNBC and HER2+ (ie, pCR rate >65%-70%). ImPrint, an Food and Drug Administration IDE-enabled assay, may represent a way to identify HR+HER2– patients for IO that best balances likely benefit versus risk of serious immune-related adverse events.

Abstract Purpose In breast MRI, contrast enhancement of normal fibroglandular tissue is referred to as background parenchymal enhancement (BPE). Hormonal status significantly affects the degree of BPE, potentially due to the association with mammary vascularity and activity1-5. Studies have shown that BPE may be associated with breast cancer survival6, treatment response to neoadjuvant chemotherapy (NAC)7,8 and future breast cancer risk9. In most patients undergoing NAC, BPE is suppressed by the nonspecific anti-proliferative effects of chemotherapy on normal breast and/or ovary5,10. However, some patients exhibit equivalent or even stronger BPE post-NAC compared to pre-NAC. We hypothesized that non-suppressed BPE in post-NAC MRI may be associated with inferior treatment response. This study aimed to investigate the association between BPE suppression and treatment response as defined by pathologic complete response (pCR). Methods This study included patients with stage II/III breast cancer enrolled in the I-SPY 2 TRIAL being treated with standard NAC with or without investigational agents. The whole cohort was split into two subgroups based on hormone receptor status (HR+, n= 536; HR-, n=452). Patients underwent dynamic contrast enhanced MRIs at four time points during NAC: baseline (T0), after 3 weeks of the first regimen (T1), inter-regimen (T2), and pre-surgery (T3). Using in-house software, the contralateral breast parenchyma was automatically segmented for the entire breast volume. Quantitative BPE (qBPE) was calculated as the mean early (~150s post-contrast injection) percent enhancement of the central 50% of the axial slices. A breast radiologist reviewed all exams and excluded those where automated segmentation failed to accurately define tissue. For T1, T2 and T3, BPE was categorized based on the change from T0 as suppressed (qBPE &lt; qBPE[T0]) or non-suppressed (qBPE ≥ qBPE[T0]). Chi-squared test was used to examine the association between BPE suppression and pCR, with p&lt;0.05 considered statistically significant. Results HR+ cohort: pCR rates were lower for patients with non-suppressed BPE than those with suppressed BPE at every visit (T1-T3) (Table 1). The difference was statistically significant at T2 (p=0.04) and T3 (p=0.01). Table 1: HR+ cohortpCR rate (%)No. of pCR patientsNo. of non-pCR patientsTotal number of patientsP valueOverall22.8122414536BPE at T1suppressed23.6822663480.45non-suppressed20.532124156BPE at T2suppressed25.7972803770.04*non-suppressed16.01789106BPE at T3suppressed25.7982833810.01*non-suppressed12.5128496 HR- cohort: pCR rates were slightly lower for the non-suppressed BPE group, but no statistically significant association was found (Table 2). Table 2: HR- cohortpCR rate (%)No. of pCR patientsNo. of non-pCR patientsTotal number of patientsP valueOverall44.7202250452BPE at T1suppressed46.81411603010.66non-suppressed44.45265117BPE at T2suppressed48.81441512950.79non-suppressed47.3434891BPE at T3suppressed49.31461502960.94non-suppressed48.9434588 Conclusion In HR+ breast cancer, lack of BPE suppression may indicate inferior treatment response. The contrasting results in HR+ and HR- cohorts are noteworthy in terms of the possible relationship between suppression of normal mammary and ovarian activity and treatment response in HR+ cancer. Reference Radiographics 2014; 34: 234-47. Radiology 1997; 203: 137-44. Radiology 1997; 203: 145-9. Breast J 2005; 11: 236-41. AJR Am J Roentgenol 2015; 204: 669-73. Eur Radiol 2018; 28: 4705-16. Eur Radiol 2016; 26: 1590-6. Transl Oncol 2015; 8: 204-9. J Clin Oncol 2019; : JCO1800378. Radiology 2015; 277: 687-96. Citation Format: Natsuko Onishi, Wen Li, David C. Newitt, Roy Harnish, Jessica Gibbs, Ella F. Jones, Alex Nguyen, Lisa Wilmes, Bonnie N. Joe, Michael J. Campbell, Amrita Basu, Laura J. van’t Veer, Angela DiMichele, Douglas Yee, Donald A. Berry, Kathy S. Albain, Judy C. Boughey, A. Jo Chien, Amy S. Clark, Kirsten K. Edmiston, Anthony D. Elias, Erin D. Ellis, David M. Euhus, Heather S. Han, Claudine Isaacs, Qamar J. Khan, Julie E. Lang, Janice Lu, Jane L. Meisel, Zaha Mitri, Rita Nanda, Donald W. Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K. Viscusi, Anne M. Wallace, Rachel Yung, Michelle E. Melisko, Jane Perlmutter, Hope S. Rugo, Richard Schwab, W. Fraser Symmans, Smita M. Asare, Julie E. Yau, Christina Yau, Laura J. Esserman, Nola M. Hylton. Lack of background parenchymal enhancement suppression in breast MRI during neoadjuvant chemotherapy may be associated with inferior treatment response in hormone receptor positive breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-05.

Abstract Background: In an adaptive randomized trial, when new treatment combinations are being tested, it is important to be able to identify patients who are progressing on treatment so that they can be changed to a different therapeutic regimen. We know that even within the molecularly high risk patients in I-SPY 2, there is considerable variation in biology. In this study, we will present results of using MRI-calculated functional tumor volume (FTV) to identify tumor progression for each breast cancer subtype. Methods: Patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Four MRI exams were performed for each patient: pre-NAC (T0), after 3 weeks of NAC (T1), between regimens (T2), and post-NAC (T3). Functional tumor volume (FTV) was calculated at each exam by summing voxels meeting enhancement thresholds. Tumor progression at T1, T2 or T3 was identified by a positive FTV change relative to T0. Visual inspection was used to exclude false progression due to strong background parenchymal enhancement post-contrast, prominent vessels, motion, or insufficient image quality. pCR was defined as no invasive disease in the breast and lymph nodes. Negative predictive value for pCR was defined as:NPV=number of true non-pCRs / number of patients with MRI assessed tumor progressions, where “true non-pCRs” referred to patients who were non-pCRs at surgery and were assessed as progressors by MRI. The analysis was performed in the full cohort and in sub-cohorts defined by HR and HER2 statuses. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Total and non-pCR numbers for each subtype, number of patients with tumor progression assessed by MRI at T1, T2, and T3, and NPVs, are shown in Table 1. In the full cohort, the NPV increased consistently over treatment, from T1 (NPV=83%) to T2 (93%), and to T3 (100%). The HER2+ cancer subtypes showed fewer MRI-assessed tumor progressions than HER2- subtypes: e.g. 10/209 (5%) vs. 108/669 (16%) at T1. NPV was 100% for HER2+ subtypes at T1 and T2 except for a single misclassification of a HR- tumor at T1. Only 6 tumor progressors, all HER2- were identified at T3, and all were confirmed at surgery as non-pCRs (NPV=100%). For HR+/HER2-, the NPV increased slightly from 89% at T1 to 91% at T2, while triple negative subtype had a more substantial increase, from 78% to 92%. Conclusions: Our study showed strong association between tumor progressors assessed by MRI with true non-pCRs after NAC. For HER2+ tumors, although MRI progressors are rare, they strongly indicate non-pCR at all treatment time points, while HER2- subtypes show more accurate results later in treatment. We are evaluating MRI change at 6 weeks to determine if that time point is sufficient to predict progressors. Table 1 MRI assessed tumor progression at different treatment time pointN/non-pCRs/%non-pCRMRI assessed tumor progressionT1 (after 3 weeks)T2 (inter-regimen)T3 (post-NAC)NNPV (%)NNPV (%)NNPV (%)Full cohort878/572/65%11883.14192.76100%HR+/HER2-344/280/81%4588.91190.93100%HR+/HER2+134/85/63%610021000N/AHR-/HER2+75/23/31%47521000N/Atriple negative325/184/57%6377.82692.33100% Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Jessica Gibbs, Lisa J Wilmes, Ella F Jones, Bonnie N Joe, Laura S Sit, Christina Yau, A. Jo Chien, Elissa Price, Kathy S Albain, Theresa Kuritza, Kevin Morley, Judy C Boughey, Kathy Brandt, Sadia Choudhery, Amy S Clark, Mark Rosen, Elizabeth S McDonald, Anthony D Elias, Dulcy Wolverton, Kelly Fountain, David M Euhus, Heather S Han, Bethany Niell, Jennifer Drukteinis, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Smita M Asare, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Donald A Berry, Angela DeMichele, Hiroyuki Abe, Deepa Sheth, Kirsten K Edmiston, Erin D Ellis, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Michael Nelson, An Church, Claudine Isaacs, Qamar J Khan, Karen Y Oh, Neda Jafarian, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Pulin Sheth, Linda Hovanessian-Larsen, Mohammad Eghtedari, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Sally Goudreau, Thelma Brown, Laura J Esserman, Nola M Hylton. Breast cancer subtype specific association of pCR with MRI assessed tumor volume progression during NAC in the I-SPY 2 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-04.

Abstract Background: Strong background parenchymal enhancement (BPE) may cause overestimation in tumor volume measured from dynamic contrast-enhanced (DCE) MRI, which may adversely affect the ability of MR tumor volume to predict treatment outcome for patients undergoing neoadjuvant chemotherapy (NAC). Specifically, an overestimation of tumor volume can result in misclassification of patients with complete pathologic response (pCR) as non-responders, leading to less confidence in MRI prediction. As well, overestimation of extent of disease might lead to more aggressive surgical therapy than necessary. This study investigated whether high BPE in the contralateral breast influences the predictive performance of MRI-measured functional tumor volume (FTV) for patients with locally advanced breast cancer undergoing NAC. Methods: patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Each patient had 4 MRI exams: pre-NAC (T0), after 3 weeks of NAC (T1), between NAC regimens (T2), and post-NAC (T3). FTV was calculated at each MRI exam by summing voxels meeting enhancement thresholds. Background parenchymal enhancement (BPE) in the contralateral breast was calculated automatically as mean percentage enhancement on the early (nominal 150sec post-contrast) image in the fibroglandular tissue segmented from 5 continuous axial slices centered in the inferior-to-superior stack. For each treatment time point, patients having both FTV and BPE measurements were included in the analysis. The area under the ROC curve (AUC) was estimated as the association between FTV and pCR at T1, T2, and T3. The analysis was conducted in the full patient cohort and in sub-cohorts defined by hormone receptor (HR) and HER2 status. In each patient cohort, a cut-off BPE value was selected to classify patients with high vs. low BPE by testing AUCs estimated with low-BPE patients reached maximum when the cut-off value varied from median to maximum in steps of 10%. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Table 1 shows the number of patients, pCR rate, and AUC of FTV for predicting pCR using all patients available vs. a subset patients with low BPE (&lt; BPE cut-off). In the full cohort, AUC increased slightly across all time points after patients with high BPE were removed. In the HR+/HER2- subtype, AUC increased at T1 after removal of cases with high BPE (0.65 vs. 0.71). For HR-/HER2+, AUC increased substantially after removal of high BPE cases (0.65 to 0.86 at T1, 0.71 to 0.87 at T2, and 0.71 to 0.89 at T3), with greater improvement at the early time point (T1) compared to later time points (T2 and T3). Only a slight improvement in the AUC was observed in the HR+/HER2+ and HR-/HER2- subtypes across all time points. Conclusions: High background parenchymal enhancement adversely affected the predictive performance of functional tumor volume measured by DCE-MRI, at early treatment time point for HR+/HER2- and across all time points for HR-/HER2+ cancer subtype. The adverse effect might be offset using subtype-optimized enhancement threshold in calculating functional tumor volume. Table 1 Effect of BPE on the prediction of pCR using FTV at various treatment time pointsT1T2T3npCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offFullAll64734%0.662762334%0.701761134%0.6925Subset45334%0.6831133%0.7230534%0.72HR+/HER2-All26218%0.651924918%0.718225518%0.7519Subset13118%0.7124818%0.7120419%0.76HR+/HER2+All10636%0.642110538%0.62269634%0.7120Subset5332%0.668438%0.665740%0.73HR-/HER2+All5175%0.65204774%0.71204973%0.7116Subset3073%0.862871%0.872475%0.89HR-/HER2-All22842%0.682822243%0.751821143%0.6916Subset15940%0.7111137%0.7810540%0.75 Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Roy Harnish, Ella F Jones, Lisa J Wilmes, Jessica Gibbs, Elissa Price, Bonnie N Joe, A. Jo Chien, Donald A Berry, Judy C Boughey, Kathy S Albain, Amy S Clark, Kirsten K Edmiston, Anthony D Elias, Erin D Ellis, David M Euhus, Heather S Han, Claudine Isaacs, Qamar J Khan, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Christina Yau, Smita M Asare, Angela DeMichele, Sally Goudreau, Hiroyuki Abe, Deepa Sheth, Dulcy Wolverton, Kelly Fountain, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Theresa Kuritza, Kevin Morley, Michael Nelson, An Church, Bethany Niell, Jennifer Drukteinis, Karen Y Oh, Neda Jafarian, Kathy Brandt, Sadia Choudhery, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Mohammad Eghedari, Pulin Sheth, Linda Hovanessian-Larsen, Mark Rosen, Elizabeth S McDonald, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Laura J Esserman, Nola M Hylton. The effect of background parenchymal enhancement on the predictive performance of functional tumor volume measured in MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-01.

Abstract Background: Achieving a pathologic complete response (pCR) has been shown on the patient level to predict excellent long-term event-free survival outcomes. Residual cancer burden (RCB) quantifies the extent of residual disease for patients who did not achieve pCR. A high proportion of metastatic events to the central nervous system (CNS), a known chemotherapy sanctuary site, was previously observed among the small number of relapses in patients achieving a pCR (Symmans et al 2017), raising the possibility that these CNS events may be independent of response in the breast. I-SPY2 is an adaptively randomized, phase II, platform trial that evaluates new drugs and combinations in the neoadjuvant setting for women with high-risk primary breast cancer. In this study, we evaluated the type and sites of recurrences by RCB classes in the I-SPY 2 TRIAL. Methods: I-SPY 2 patients enrolled prior to 11/2016 across 9 experimental and control arms, with available RCB and event-free survival (EFS) data were included in this analysis. The median follow-up is 3.8 years. We summarized the EFS event type, further sub-dividing the distant recurrence events by their site of relapse (CNS-only, CNS and other sites, Non-CNS). We estimated the overall and site-specific distant recurrence incidence in each RCB class at 3 years using a competing risk (Fine-Gray) model. In addition, we assessed the association between RCB and distant recurrence free survival including all distant recurrences (DRFS), as well as excluding the CNS-only recurrences (non-CNS DRFS) using a Cox model. Our statistics do not adjust for multiplicities beyond variables evaluated in this study. Results: Among 938 subjects, there were 180 EFS events, including 28 (16%) local recurrences (without distant recurrence and/or death) and 152 DRFS events. Among the DRFS events, 25 patients died without a distant recurrence. 127 experienced distant recurrences, including 22 (17.3%) with CNS-only, 16 (12.6%) with CNS and other sites, 87 (68.5%) with non-CNS distant recurrence; 2 (1.6%) patients had missing recurrence site information. Incidence of CNS-only recurrences are low and are similar across RCB classes (pCR/RCB-0 (n=338): 1%, RCB-I (n=129): 3%, RCB-II (n=328): 2%, RCB-III (n=143): 2% at 3 years). In contrast, the incidence of non-CNS recurrences increase with increasing RCB (RCB-0: 2%, RCB-I: 4%, RCB-II: 11%, RCB-III: 19% at 3 years). DRFS of RCB-I patients do not significantly differ from those achieving a pCR/RCB-0 (DRFS at 3 years: 92% vs. 95%, hazard ratio: 1.77 (0.87-3.63)); the small numerical difference is further reduced when the CNS-only recurrences are excluded (non-CNS DRFS at 3 years: 95% vs. 96%, hazard ratio: 1.48 (0.61-3.58)). CNS recurrences among DRFS events are proportionally higher within the pCR (5/16 (31%)) and RCB-I (5/12 (42%)) than in the RCB-II (8/57 (14%)) and RCB-III (4/42 (9%)) groups largely because of the relative low frequency of non-CNS recurrence events. Conclusions: In our high-risk I-SPY 2 cohort, CNS-only recurrences are uncommon but appear similar across RCB groups, independent of response, suggesting that the CNS is a treatment sanctuary site. In contrast, non-CNS recurrence rates increase as RCB increases. These findings, if confirmed, support the use of RCB to identify patients with excellent outcomes beyond those achieving a pCR; and suggest that inclusion of CNS only recurrences as an outcome event may impact the association between neoadjuvant therapy response and long-term outcome. Citation Format: Christina Yau, Angela DeMichele, W. Fraser Symmans, Lajos Pusztai, Douglas Yee, Amy S. Clark, Christos Hatzis, Jeffrey B. Matthews, Jodi Carter, Yunn-Yi Chen, Kimberly Cole, Laila Khazai, Molly Klein, Dina Kokh, Gregor Krings, Sunati Sahoo, Kathy S. Albain, A. Jo Chien, Kirsten K. Edmiston, Anthony D. Elias, Erin D. Ellis, David M. Euhus, Heather S. Han, Claudine Isaacs, Qamar J. Khan, Julie E. Lang, Janice Lu, Jane L. Meisel, Zaha Mitri, Rita Nanda, Donald W. Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K. Viscusi, Anne M. Wallace, Rachel Yung, Nola M. Hylton, Judy C. Boughey, Michelle E. Melisko, Jane Perlmutter, Hope S. Rugo, Richard Schwab, Laura J. van' t Veer, Donald A. Berry, Laura J. Esserman. Site of recurrence after neoadjuvant therapy: Clues to biology and impact on endpoints [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-20-02.

PURPOSEThe phosphatidylinositol 3-kinase/Akt/mammalian target of rapamycin is a key pathway of survival and therapeutic resistance in breast cancer. We evaluated the pan-Akt inhibitor MK-2206 in combination with standard therapy in patients with high-risk early-stage breast cancer.PATIENTS AND METHODSI-SPY 2 is a multicenter, phase II, open-label, adaptively randomized neoadjuvant platform trial that screens experimental therapies and efficiently identifies potential predictive biomarker signatures. Patients are categorized by human epidermal growth factor receptor 2 (HER2), hormone receptor (HR), and MammaPrint statuses in a 2 × 2 × 2 layout. Patients within each of these 8 biomarker subtypes are adaptively randomly assigned to one of several experimental therapies, including MK-2206, or control. Therapies are evaluated for 10 biomarker signatures, each of which is a combination of these subtypes. The primary end point is pathologic complete response (pCR). A therapy graduates with one or more of these signatures if and when it has an 85% Bayesian predictive probability of success in a hypothetical phase III trial, adjusting for biomarker covariates. Patients in the current report received standard taxane- and anthracycline-based neoadjuvant therapy without (control) or with oral MK-2206 135 mg/week.RESULTSMK-2206 graduated with 94 patients and 57 concurrently randomly assigned controls in 3 graduation signatures: HR-negative/HER2-positive, HR-negative, and HER2-positive. Respective Bayesian mean covariate-adjusted pCR rates and percentage probability that MK-2206 is superior to control were 0.48:0.29 (97%), 0.62:0.36 (99%), and 0.46:0.26 (94%). In exploratory analyses, MK-2206 evinced a numerical improvement in event-free survival in its graduating signatures. The most significant grade 3-4 toxicity was rash (14% maculopapular, 8.6% acneiform).CONCLUSIONThe Akt inhibitor MK-2206 combined with standard neoadjuvant therapy resulted in higher estimated pCR rates in HR-negative and HER2-positive breast cancer. Although MK-2206 is not being further developed at this time, this class of agents remains of clinical interest.

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 &lt;-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-&gt;AC). HER2- patients were randomized to receive N+T- &gt;AC vs. T-&gt;AC. For HER2+ patients, neratinib was administered in place of trastuzumab (N+T-&gt;AC vs. H+T-&gt;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 &lt; 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 &lt;-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 &lt; 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.