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2022

Basu A, Umashankar S, Melisko M, et al. Identification of symptoms that are associated with irAEs in the I-SPY clinical trial. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. GS5-04.

Isaacs C, Nanda R, Chien J, et al. Evaluation of anti-PD-1 Cemiplimab plus anti-LAG-3 REGN3767 in early-stage, high-risk HER2-negative breast cancer: Results from the neoadjuvant I-SPY 2 TRIAL. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. GS5-03

Zimmerman J, Carmona-Bozo J, Le NN, et al. Diffusion-weighted magnetic resonance imaging for subtype-specific prediction of pathologic complete response in neoadjuvant chemotherapy. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. P6-01-33.

Li W, Le NN, Onishi N, et al. Association of MRI morphologic phenotype from unsupervised learning with breast cancer subtypes and treatment response. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. PD16-07.

Onishi N, Jones EF, Carmona-Bozo J, et al. Early MRI and PET biomarkers for hormone receptor-positive/HER2-negative early-stage breast cancer in the setting of neoadjuvant endocrine therapy and neoadjuvant chemotherapy in the I-SPY 2 TRIAL. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. PD16-06.

Basu A, Umashankar S, Blevins K, et al. The Association Between Symptom Severity and Physical Function among Participants in I-SPY2. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. P5-07-03.

Magbanua MM, Rugo H, Brown-Swigart LA, et al. Monitoring for response and recurrence in neoadjuvant-treated hormone receptor-positive HER2-negative breast cancer by personalized circulating tumor DNA testing. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. P5-05-05

Li W, Onishi N, Wolf DM, et al. MRI models by response predictive subtype for predicting pathologic complete response. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. P4-02-10.

Stringer-Reasor E, Shatsky RA, Chien J, et al Evaluation of the PD-1 Inhibitor Cemiplimab in early-stage, high-risk HER2-negative breast cancer: Results from the neoadjuvant I-SPY 2 TRIAL. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. PD11-01.

Rosenbluth J, Bui TB, Warhadpande S, et al. Characterization of residual disease after neoadjuvant selective estrogen receptor degrader (SERD) therapy using tumor organoids in the I-SPY Endocrine Optimization Protocol (EOP). 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. P3-09-01.

Wolf DM, Yau C, Wulfkuhle J, et al. Characterizing the HER2-/Immune-/DNA repair (DRD-) response predictive breast cancer subtype: the hunt for new protein targets in a high-needs population with low response to all I-SPY2 agents. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. PD5-04.

Bui TBV, Wolf DM, Moore K,et al. An Organoid Model System to Study Resistance Mechanisms, Predictive Biomarkers, and New Strategies to Overcome Therapeutic Resistance in Early-Stage Triple-Negative Breast Cancer. 2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022; Abstract No. PD5-02.

Yee D, Shatsky RA, Yau C, et al. Improved pathologic complete response rates for triple-negative breast cancer in the I-SPY2 Trial. 2022 ASCO Annual Meeting, 3-7 Jun, 2022; Abstract No. 591.

Thomas A, Clark AS, Yau C, et al. Molecular subtype to predict pathologic complete response in HER2-positive breast cancer in the I-SPY2 trial. 2022 ASCO Annual Meeting, 3-7 Jun, 2022; Abstract No. 510.

Huppert LA, Rugo HS, Pusztai L, et al. Pathologic complete response (pCR) rates for HR+/HER2- breast cancer by molecular subtype in the I-SPY2 Trial. 2022 ASCO Annual Meeting, 3-7 Jun, 2022; Abstract No. 504.

Cha J, Warner P, Hiatt R, et al. Distribution of breast cancer molecular subtypes within receptor classifications: Lessons from the I-SPY2 Trial and FLEX Registry. 2022 ASCO Annual Meeting, 3-7 Jun, 2022; Abstract No. 592.

Mittempergher L, Kuilman MM, Barcaru A, et al. The ImPrint immune signature to identify patients with high-risk early breast cancer who may benefit from PD1 checkpoint inhibition in I-SPY2. 2022 ASCO Annual Meeting, 3-7 Jun, 2022; Abstract No. 514

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Abstract No. GS5-04
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Identification of symptoms that are associated with irAEs in the I-SPY clinical trial

Basu A, Umashankar S, Melisko M, Lu R, Yu H, Yau C, Asare S, Pitsouni M, Shatsky RA, Isaacs C, DeMichele A, Hershman D, Nanda R, Kim MO, Esserman LJ, Rugo H

Background. Immunotherapy has emerged as an important component of neoadjuvant therapy for some patients with breast cancer (BC). As a result, immune-related adverse events (irAEs) are increasing and have effects on both short and long term symptoms significantly impacting patient quality of life. BC patients may develop new conditions including arthralgias, gastrointestinal issues, endocrinopathies, and fatigue during or after cancer therapy that may be acute or long-lasting in nature. Monitoring for early onset and severity of symptoms, and adjusting treatment and symptom management could optimize therapy for a particular patient, maximizing potential efficacy while mitigating toxicity. We sought to identify patient demographic characteristics and symptom patterns associated with risk for development of irAEs in the context of a randomized trial for patients with early-stage high-risk breast cancer.

Methods. I-SPY2 is a multi-center, phase 2 trial using response-adaptive randomization for high-risk early-stage women with BC. The study population for this analysis includes enrolled patients receiving combinations of experimental immunotherapy and chemotherapy. Groups considered for statistical comparisons included those that developed an irAE versus those that did not develop an irAE up until the surgery timepoint. In I-SPY adverse events are documented through the Common Terminology Criteria for Adverse Events (CTCAEv5.0). Hypothyroidism, adrenal insufficiency, and pneumonitis were the irAEs considered in this study. A chi-square test was used to assess associations between race and ethnicity (White, Asian, Black, non-Hispanic) and irAEs. One-way ANOVA was used to evaluate the association between age (>50 vs < 50) and irAEs. 33 symptoms reported at CTCAE grade 2 or higher were included in the analyses and a symptom burden score was calculated using area under curve (AUC) which combined the duration of each symptom between baseline and week 6 of treatment, and grade of adverse event. Regularized regression using leave-one out cross validation was used to evaluate early symptoms (as quantified by the symptom burden score) as predictors, and irAEs as surrogate responses.

Results. Out of 461 patients, percentages of patients wth irAEs of interest included hypothyroidism (13%), adrenal insufficiency (9%), and pneumonitis (4%). Demographic information was available for 333 patients, of which 270 (81%) were White, 23 (7%) were Asian, 37 (11%) were African American (AA) and 278 (17%) were non-Hispanic. There were proportionately higher number of white patients that developed hypothyroidism than non-white patients (35 of 265 (13%) vs 2 of 63 (3%), P < 0.04). Pneumonitis was more common in patients over 50 years old than under 50 years old (P < 0.02). Symptoms that were most commonly reported up to week 6 of treatment among patients who developed an irAE included: diarrhea (36%), fatigue (15%), dizziness (12%) and shortness of breath (SOB) (11%). Symptoms associated with the development of hypothyroidism included fatigue (15%, mean AUC=11.8 vs 5.8 for those that did not develop irAE), SOB (11%, 4.3 vs 2.8), and blurry vision (1%, 1.0 vs 0.12). Development of adrenal insufficiency was associated with early reports of diarrhea (36%, 19.0 vs 10.5), SOB (11%, 7.8 vs 2.6), joint pain (3%, 2.29 vs 0.58), decreased appetite (3%, 3.55 vs 0.91), and constipation (1%, 3.6 vs 0.02). No significant early symptoms emerged for pneumonitis due to a limited number of events.

Conclusion. Our study utilizes an analysis framework that is aimed to determine symptom clusters that predict the development of irAEs. We describe specific symptoms presenting early with the development of hypothyroidism and adrenal insufficiency, in recognition of allowing physicians to be more diligent in active and post treatment monitoring.

Abstract No. GS5-03
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Evaluation of anti-PD-1 Cemiplimab plus anti-LAG-3 REGN3767 in early-stage, high-risk HER2-negative breast cancer: Results from the neoadjuvant I-SPY 2 TRIAL

Isaacs C, Nanda R, Chien J, Trivedi MS, Stringer-Reasor E, Vaklavas C, Boughey JC, Sanford A, Wallace A, Clark AS, Thomas A, Albain KS, Kennedy LC, Sanftt TB, Kalinsky K, Han HS, Williams N, Arora M, Elias A, Falkson C, Asare S, Lu R, Pitsouni M, Wilson A, Perlmutter J, Rugo H, Schwab R, Symmans WF, Hylton NM, van ‘t Veer L, Yee D, DeMichele A, Berry D, Esserman LJ, I-SPY Investigators

Background: I-SPY2 is a multicenter, phase 2 trial using response-adaptive randomization within biomarker subtypes defined by hormone-receptor (HR), HER2, and MammaPrint (MP) status to evaluate novel agents as neoadjuvant therapy for high-risk breast cancer. The primary endpoint is pathologic complete response (pCR). Cemiplimab is an anti-PD-1 inhibitor approved for the treatment of NSCLC and cutaneous basal and squamous cell CA. Lymphocyte activation gene 3 (LAG-3) binds MHC class II leading to inhibition of T-cell proliferation and activation and is often co-expressed with PD-1. REGN3767 is a fully humanized mAb that binds to LAG-3 and blocks inhibitory T-cell signaling. Concurrent blockade of LAG-3 with an anti-PD-1 may enhance efficacy of an anti-PD-1.

‍Methods: Women with tumors ≥ 2.5cm were eligible for screening. Only HER2 negative (HER2-) patients were eligible for this treatment; HR positive (HR+) patients had to be MP high risk. Treatment included Paclitaxel 80 mg/m2 IV weekly x 12 and Cemiplimab 350 mg and REGN3767 1600 mg both given q3weeks x 4, followed by doxorubicin/cyclophosphamide (AC) every 2 weeks x 4. The control arm was weekly paclitaxel x 12 followed by AC every 2-3 weeks x 4. Cemiplimab/REGN3767 was eligible to graduate in 3 of 10 pre-defined signatures: HER2-, HR-HER2-, and HR+HER2-. The statistical methods for evaluating I-SPY 2 agents has been previously described. To adapt to changing standard of care, we constructed “dynamic controls” comprising ‘best’ alternative therapies using I-SPY 2 and external data and estimated the probability of Cemiplimab/REGN3767 being superior to the dynamic control. Response predictive subtypes (Immune+ vs Immune-) were assessed using pre-treatment gene expression data and the ImPrint signature.

‍Results: 73 HER2- patients (40 HR+ and 33 HR-) received Cemiplimab/REGN3767 treatment. The control group included [357 patients with HER2- tumors (201 HR+ and 156 HR-) enrolled since March 2010. Cemiplimab/REGN3767 graduated in both HR-/HER2- and HR+/HER2- groups; estimated pCR rates (as of June 2022) are summarized in the table. Safety events of note for Cemiplimab/REGN3767 include hypothyroidism 30.8%, adrenal insufficiency (AI) 19.2%, hyperthyroidism 14.1%, pneumonitis 1.3%, and hepatitis 3.8%. All were G1/2 except for 6 (7.7%) G3 AI and 3 (3.8%) G3 colitis. Rash occurred in 62.8%, 9% G3 and 2 pts (2.6%) had pulmonary embolism. X% of adrenal insufficiency cases required replacement therapy. 40 patients (11 HR+ and 29 HR-) in Cemiplimab/REGN3767 were predicted Immune+; 32 (29 HR+ and 3 HR-) were predicted Immune-. In the HR+ group pCR was achieved in 10/11 (91%) patients with Immune+ subtype compared with 8/29 (28%) with Immune- subtype. Additional biomarker analyses are ongoing and will be presented at the meeting.

‍Conclusion: The I-SPY 2 study aims to assess the probability that investigational regimens will be successful in a phase 3 neoadjuvant trial. Dual immune blockade with a LAG-3 inhibitor and anti-PD1 therapy resulted in a high predicted pCR rate both in HR-/HER2- (60%) and HR+/HER2- (37%) disease. The novel Imprint signature identified a group of HR+ patients most likely to benefit from this active regimen.

Abstract No. P6-01-33
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Diffusion-weighted magnetic resonance imaging for subtype-specific prediction of pathologic complete response in neoadjuvant chemotherapy

Zimmerman J, Carmona-Bozo J, Le NN, Onishi N, Wilmes LJ, Gibbs JE, Liang J, Newitt DC, Partridge S, Bolan P, Joe BN, Price ER, LeStage B, Hylton NM, Li W

Background: The apparent diffusion coefficient (ADC) presents a biomarker that is sensitive to tumor cellularity. ADC maps can be calculated from non-contrast diffusion-weighted magnetic resonance imaging (DW-MRI) measurements. ACRIN 6698, a sub-study of clinical trial I-SPY 2, investigated mean ADC – averaged over the whole tumor – as a marker to predict pathologic complete response (pCR) [1]. This work compares a group of histogram-based ADC metrics in addition to mean ADC for early prediction of pCR in patients stratified by breast cancer subtype.

‍Methods: We performed a retrospective analysis of DW-MRI, dynamic-contrast enhanced (DCE) MRI, and clinical outcome (i.e., pCR at surgery) in a cohort of 79 female patients who were diagnosed with high-risk, stage II/III breast cancer. Patients underwent neoadjuvant chemotherapy (NAC) with paclitaxel (12 weeks), followed by doxorubicin plus cyclophosphamide (12 weeks). The included population represents a subset of the I-SPY 2 cohort and comprises 48 patients with hormone receptor [HR]+/HER2-, and 31 patients with HR-/HER2-. DW- and DCE-MRI acquisitions were performed according to the I-SPY 2 protocol at pretreatment (T0) and after three weeks (T1) and were analyzed to find early treatment percentage (%) change (T0 to T1) in any metric M; where %-change = 100 × (M(T1) – M(T0))/M(T0). Histogram analysis provided nine region-of-interest (ROI)-based ADC metrics (Table 1). ROIs were manually delineated by expert observers in three-dimensional ADC maps, focusing on diffusion-restricted regions [2]. DCE-MRI was analyzed for the integral I-SPY 2 imaging marker of %-change in functional tumor volume (FTV) between T0 and T1. Statistical analysis compared the predictive power of ADC metrics and FTV, including: the receiver-operating-characteristic (ROC) curve from a logistic regression model to predict pCR as ‘positive’, area-under-the-curve (AUC) assessment, and rank-sum Wilcoxon test (p < 0.05: statistically significant).

Results: (Table 1): 16 out of 79 (20.3%) patients reached pCR at surgery, with 18.8% pCR among HR+/HER- and 22.6% among HR-/HER2- groups. For all nine computed ADC statistics (listed as median [Q1, Q3], across all patients), %-change was higher in patients who reached pCR than patients with non-pCR (highest value for metric ‘MIN’: 23.9% [-0.9%, 52.5] vs. 16.6% [0.4%, 27.6%], though without statistical significance: p=0.237). Likewise, %-change of FTV was also stronger in pCR patients than non-pCR patients (-58.8% [-80.6%, -22.5%] vs. -28.2% [54.2%, -2.7%], with statistical significance: p=0.036). For all patients combined (n=79), among the various reported ADC metrics, %-change in ‘PCTL_95’ (95th percentile of histogram) yielded the highest AUC (0.7; 95% CI = [0.56, 0.83]; p=0.012). %-change in FTV showed the second highest AUC (0.67; 95% CI = [0.52, 0.82]; p=0.036). By subtype, AUC was highest for %-change of ‘PCTL_95’ (0.69; 95% CI = [0.5, 0.87]; p=0.072) in the HR+/HER2- subgroup; and highest for both %-change of ‘MEAN’ (AUC = 0.73; 95% CI = [0.49, 0.94]; p=0.065) and ‘PCTL_75’ (AUC = 0.73; 95% CI = [0.49, 0.94]; p=0.073) triple negative (HR-/HER2-) subgroup. By comparison, %-change of FTV yielded AUCs of 0.64 (95% CI = [0.41, 0.85]; p=0.191) and 0.71 (95% CI = [0.51, 0.9]; p=0.098) in the HR+/HER2- and triple-negative subgroups, respectively.

‍Conclusion: Various tumor ADC metrics from non-contrast DW-MRI demonstrate potential biomarkers for assessing responsiveness to NAC at an early treatment timepoint. ADC may have predictive performance that is comparable to FTV, depending on the breast cancer subtype. Observations for %-change in ‘MEAN’ ADC at T1 differed from previous reports [1], which may be explained by the small sample size and single (paclitaxel) drug arm. Additional studies are warranted to include patients of experimental arms and of HER2+ subtypes.

Results of statistical analysis of ADC-based and FTV markers for predicting treatment response at 3 weeks into NAC. Median [Q1, Q3] values represent the median and interquartile range over the respective patient population regarding the %-change of the respective ADC (FTV) metric from T0 to T1. T0: pretreatment, T1: 3-week timepoint, ADC: apparent diffusion coefficient, MRI: magnetic resonance imaging, DW: diffusion-weighted, DCE: dynamic-contrast enhanced, pCR: pathologic complete response, AUC: area under the ROC curve, 95% CI [LL, UL]: confidence interval lower and upper limit, PCTL_x: xth-percentile of tumor ADC histogram, MEAN: mean of ADC within ROI, MIN: minimum of ADC within ROI, MAX: maximum of ADC within ROI, FTV: functional tumor volume, **: statistically significant.

[1] Partridge et al., Radiology 289(3):618-27 (2018)

[2] Nu et al., Tomography 8: 1208-20 (2022)

Abstract No. PD16-07
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Association of MRI morphologic phenotype from unsupervised learning with breast cancer subtypes and treatment response

Li W, Le NN, Onishi N, Newitt DC, Gibbs JE, Wilmes LJ, Gennatas E, LeStage B, Esserman LJ, Hylton NM

Background: Breast cancer is a heterogeneous disease and can be categorized into clinically or biologically meaningful subtypes. Predictive models built by MRI biomarkers performed better when they are optimized by breast cancer subtype than models optimized in the full cohort [1]. Functional tumor volume (FTV) measured from breast MRI has been used to assess tumor response to neoadjuvant therapy longitudinally in the I-SPY 2 TRIAL. Tumors show distinct morphological patterns, or phenotypes, on MRI. Previous studies demonstrated that either qualitative or quantitative measurements characterizing these phenotypes may provide additional information about treatment response [2,3]. In this study, we investigated if MRI morphologic phenotypes defined by unsupervised clustering is associated with breast cancer subtype and pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC).

‍Methods: A cohort of 990 patients enrolled in the I-SPY 2 TRIAL were included in this retrospective analysis. Patients were randomized to one of nine experimental drug arms or standard NAC, and pCR was assessed at surgery. DCE-MRI data acquired at pretreatment (T0) and early treatment (T1) were analyzed. Four subtypes of breast cancer were defined by immunohistochemistry (IHC) based on hormone receptor (HR) and HER2 status.

Radiomic features were extracted by PyRadiomics [4] using FTV masks from DCE-MRI. MRI morphologic phenotypes were determined based on unsupervised hierarchical clustering approach on extracted radiomic shape features plus FTV using Pearson correlation with agglomerative ward linkage. The associations between the unsupervised clusters of radiomic features and FTV with four IHC subtypes and pCR were evaluated using χ2 test of independence. Cramer’s V [5] were computed to measure the strength of association (higher Cramer’s V means stronger association). P-value < 0.05 was considered statistically significant.

Results: Three clusters were generated by unsupervised hierarchical clustering in a population of 910 patients included in our analysis (80 patients excluded due to missing pCR or DCE-MRIs). At T0, the unsupervised clusters showed statistically significant but weak association with pCR (Cramer’s V = 0.088, p = 0.029), but the association between the clusters and HR/HER2 subtypes did not reach significance (Cramer’s V = 0.055, p = 0.48). The unsupervised clusters based on T1 shape radiomic features showed statistically significant association with both pCR and HR/HER2 subtypes (p < 0.001 for both) with Cramer’s V of 0.231 and 0.154, respectively. Our results showed stronger association between pCR and cancer subtypes with MRI shape radiomic features at T1 than at T0.

Various pCR rates were observed in MRI clusters at T1. They were 56%, 36%, and 23% in Cluster 1, 2, 3, respectively. Table 1 shows pCR rates by HR/HER2 subtype in each cluster. In all sub-cohorts, pCR rate was highest in Cluster 1 and lowest in Cluster 3. In HR+/HER2-, the pCR rate in Cluster 1 was 2-fold of the pCR rates in Clusters 2 and 3-fold of Cluster 3. pCR rate was statistically significantly different depending on the MRI clusters in the sub-cohorts except for the HR/HER2+ sub-cohort: HR+/HER2-, p< 0.001; HR+/HER2+, p=0.021; HR-/HER2+, p=0.083; HR-/HER2-, p< 0.001.

Conclusion: MRI phenotype generated by unsupervised clustering using radiomic shape features at both pretreatment and early-treatment time points was associated with pCR outcome. Stronger association was observed at early-treatment time point. The association differed by subtype, with the strongest observed in HR+/HER2- and triple negative subtypes. Our results suggest that radiomic shape features derived from DCE-MRI may be helpful for early prediction of tumor response to NAC.

Citations

1. npj Breast Cancer 6, (2020).

2. Tomography 6, (2020).

3. Annals of Surgical Oncology 20, 3823–3830 (2013).

4. Cancer Research 77, e104–e107 (2017).

5. Korean Stat Soc 42, 323–328 (2013).

Abstract No. PD16-06
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Early MRI and PET biomarkers for hormone receptor-positive/HER2-negative early-stage breast cancer in the setting of neoadjuvant endocrine therapy and neoadjuvant chemotherapy in the I-SPY 2 TRIAL

Onishi N, Jones EF, Carmona-Bozo J, Gibbs JE, Bareng TJ, Molina-Vega J, Ray KM, Heath CL, Joe BN, Li W, Liang J, Newitt DC, Heditsian D, Brain S, Wolf DM, Yau C, Giridhar KV, Olopade OI, Kalinsky K, Mukhtar R, I-SPY2 Imaging Working Group, I-SPY2 Consortium, Esserman LJ, Chien J, Hylton NM

Purpose: Neoadjuvant endocrine therapy (NET) is increasingly used for patients with hormone receptor-positive (HR+) breast cancer. Dynamic contract-enhanced breast MRI is the most accurate modality to monitor tumor response during neoadjuvant chemotherapy (NAC)1, but there is limited research on response to NET.The Endocrine Optimization Protocol (EOP) is a sub-study of the ongoing I-SPY 2 TRIAL testing amcenestrant (an oral selective estrogen receptor degrader [SERD]), with or without addition of abemaciclib (a CDK4/6 inhibitor) or letrozole (an aromatase inhibitor) in patients with stage 2/3, MammaPrint (MP) low-risk (index 0 to 1) or high-risk 1 (index -0.57 to 0), HR+/HER2-negative breast cancer. All I-SPY2 (including EOP) patients undergo MRI at baseline (T0), 3 weeks (T1), 12 weeks (T2), and 6 months, prior to surgery (T3). Functional tumor volume (FTV)2,3 is derived as a quantitative measure of tumor burden from each MRI. A subset of EOP patients also have 3 dedicated breast PET (dbPET) exams with 18F-fluoroestradiol (an estrogen receptor-targeted tracer, FES) at T0, T1, and T3. FES uptake on dbPET indicates the presence of functional estrogen receptor.This study evaluates changes in FTV and FES uptake in patients receiving NET in the ongoing EOP trial. FTV changes in EOP were compared with those in a cohort of patients who received NAC in I-SPY 2.

Methods: The breast MRI and FES-dbPET images from patients in the EOP trial as of June 2022 were evaluated by a blinded central radiology team at a single institution. FTV was measured using standard procedure in I-SPY 2. Percent FTV change (ΔFTV) at Tn (n = 1, 2, or 3) was calculated by 100x(FTVTn-FTVT0)/FTVT0. FES uptake was quantified as standardized uptake value (SUV). Maximum SUV over the tumor volume (SUVmax) was measured using Osirix MD (Pixmeo SARL) and percent change (ΔSUVmax) was similarly defined. For comparison, FTV was evaluated using curated imaging data of I-SPY 2 patients with stage 2/3, MP high-risk 1, HR+/HER2-negative cancer who completed standard NAC between 2010–2016.

Results: We included 55 EOP patients (NET cohort) and 68 I-SPY 2 patients (NAC cohort). At T0, median FTV was 9.8cc for the NET cohort and 10.1cc for the NAC cohort. Table 1 shows the longitudinal FTV change in the two cohorts. At T1, median FTV change was similar in the NET cohort (-33.8%) and NAC cohort (-33.9%). The NET cohort showed a dynamic range of FTV change from -65.4% (1st quartile) to -11.0% (3rd quartile), which covered the 1st to 3rd quartile ranges for the NAC cohort. At T2 and T3, FTV change was more gradual in the NET cohort compared to the NAC cohort.Seven patients in the NET cohort underwent FES-dbPET. At T0, tumor FES uptake exceeded background uptake in all 7 patients with a median SUVmax of 8.2. At T1 and T3, tumor uptake decreased in all patients. Tumor uptake was indistinguishable from background for 3 patients (43%) at T1 and 5 patients (71%) at T3, despite evidence of residual tumor on MRI. The median change of SUVmax was -45.9% at T1 and -74.7% for T3 (Table 2).

Discussion: After 3 weeks of NET, we observed a large dynamic range of FTV change similar to that seen in NAC and a robust decrease in FES uptake. These results suggest the potential for combined use of early MRI change and FES-dbPET to provide scalable biomarkers to stratify response-based NET strategies.

References:

1. Radiology 285: 358–375, 2017

2. Radiology 263:663–672, 2012

3. Radiology 279:44–55, 2016

Abstract No. P5-07-03
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

The Association Between Symptom Severity and Physical Function among Participants in I-SPY2

Basu A, Umashankar S, Blevins K, Northrop A, Christofferson A, Olunuga E, Cha J, Mittal A, Molina-Vega, Sit L, Brown T, Parker B, Heditsian D, Brain S, Simmons C, Taboada A, Hielen TJ, Ruddy K, Salvador C, Mainor C, Afghahi A, Tevis S, Blaes A, Kang IM, Perlmutter J, Rugo H, Kanaparthi S, Peterson G, Weiss LT, Asare A, Esserman LJ, Melisko M, Hershman D

Background. Patient-reported outcomes (PROs) are increasingly recognized as a valuable component to understand treatment tolerability and toxicity among patients on clinical trials. We have implemented a system for monitoring patient reported outcomes (PROs), symptoms, and quality of life (QOL) using electronic PRO (ePRO) instruments for patients enrolled in the I-SPY2 trial. I-SPY2 is a phase II multi-site clinical trial evaluating the effect of novel neoadjuvant therapies for locally advanced breast cancer. We correlated patient demographic factors with symptoms, investigated the trajectory of symptoms throughout treatment, and sought to characterize symptoms associated with decline in physical function (PF).

Methods. Our study population included 259 I-SPY2 patients that completed surveys on one of 9 study arms (including novel oral taxane/immunotherapy combinations, IV paclitaxel, checkpoint inhibitor+/- LAG3 inhibitor, and control IV paclitaxel +/- trastuzumab/pertuzumab). After the 12 week period of investigational agents, most patients received standard adriamycin and cyclophosphamide (AC). Symptom severity, frequency, and interference was assessed weekly using 33 items from the PRO-CTCAE item bank. PF was assessed using the NIH PROMIS instrument and was evaluated at baseline, inter-regimen (after 12 weeks of treatment), pre-surgery, and 1 and 6 months at follow-up. An odds ratio was used to assess univariate associations between age and race, and symptoms. Regularized multi-variate regression was used to evaluate early symptoms (prior to week 6) predictive of a clinically significant (>5 point T-score) decline in PF from baseline to post-treatment follow-up among all races and age groups.

Results. Of 259 patients (mean age (SD) = 46.8 (13.6)), 160 (58%) were White, 13 (5%) were Asian, 26 (10%) were African American (AA), 25 (9.3%) were Hispanic, and 35 (13.5%) self-reported “Other”. At baseline, AA patients had a higher severity of joint pain than White patients (OR = 14.9, P < 0.05). During study treatment with paclitaxel and/or novel agent within the first 12 weeks of treatment, AA patients and non-white (NW) patients were more likely to report severe vomiting than White patients (OR =13.22 and 12.72, P< 0.05 and P< 0.03 respectively). During treatment with AC, NW patients were more likely to report higher severity of neuropathy than White patients (OR = 5.43, P< 0.03). Among all patients, in analysis of early symptoms predictive of a clinically significant decline in PF between baseline and 1 month post treatment, predictors included high frequency of diarrhea, severity of itching, and severity of joint pain. Further analysis of symptom trajectories revealed that frequency of diarrhea reported rose sharply between baseline and Cycle 2 with 9 patients (7%) reporting occasional or frequent diarrhea to 39 patients (28%) reporting occasional to almost constant diarrhea and remained stable at that proportion for the remainder of treatment. Frequency of diarrhea declined slightly during AC (17%) and dropped to baseline levels by follow-up. In contrast, severity of joint pain persisted post-treatment, rising consistently from baseline through follow- up with 3 patients (2%) reporting moderate to severe joint pain at baseline to 18 patients (35%) reporting moderate to severe joint pain at follow-up.

Conclusion. Among I-SPY2 participants, when higher grade of diarrhea is persistent (or uncontrolled), it impacts physical function even after end of therapy. In some cases, race was also a determinant in symptom trajectory, although a higher enrollment of AA and NW patients will enable more robust estimates to be computed. While some of these early symptom predictors are transient and resolve by the time of follow-up, others persist long-term and contribute more directly towards impaired physical function at follow-up.

Abstract No. P5-05-05
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Monitoring for response and recurrence in neoadjuvant-treated hormone receptor-positive HER2-negative breast cancer by personalized circulating tumor DNA testing

Magbanua MM, Rugo H, Brown-Swigart LA, Ahmed Z, Hirst GL, Wolf DM, Lu R, Kalashnikova E, Renner D, Rodriguez A, Liu MC, Yau C, Esserman LJ, van ‘t Veer L, DeMichele A

Background: The detection of circulating tumor DNA (ctDNA) may serve as an early predictor of response and recurrence. In this study, we used a tumor-informed ctDNA test to monitor clinical outcomes in patients with high-risk hormone receptor-positive HER2-negative (HR+HER2-) tumors who received neoadjuvant chemotherapy (NAC) on the I-SPY 2 trial (NCT01042379).

‍Methods: We collected blood samples at pretreatment, during (at 3 and 12 weeks after initiation of paclitaxel-based treatment with or without an investigational drug), after NAC prior to surgery, 4 weeks after surgery, and annually until clinical diagnosis of recurrence. Cell-free DNA was isolated from plasma (N=329 samples) and ctDNA was detected using a personalized, tumor-informed multiplex polymerase chain reaction next generation sequencing-based test (SignateraTM). All patients were at high risk for recurrence by MammaPrint. The response endpoints were pathologic complete response (pCR) and residual cancer burden (RCB), and the survival endpoint was event-free survival (EFS).

‍Results: This analysis included 66 patients with HR+HER2- breast cancer who had blood samples collected before, during, after NAC and had at least one blood sample after surgery with sufficient plasma for analysis. 57.1% (32/56) had grade III disease; 72.4% (42/58) were node-positive; 36.2% (21/58) had T3/T4 disease; and 33.3% (22/66) were MammaPrint High 2. The percent ctDNA positivity rates at pretreatment, after NAC prior to surgery, and 4 weeks after surgery were 79.7% (47/59), 6.5% (4/62), and 2% (1/50), respectively. Significantly higher ctDNA positivity rates at pretreatment were observed in patients with larger tumors (95% in T3/T4 vs. 69% in T1/T2, Fisher’s exact p=0.0387), higher grade tumors (94% in Grade III vs. 67% in Grade I/II, p=0.0147) and by MammaPrint score (100% in High 2 vs. 71% in High 1, p=0.0052). In this high-risk HR+/HER2- cohort, 10/66 (15.2%) achieved pCR/RCB 0, who were all ctDNA-negative at surgery. 56/66 (84.8%) had no-PCR, with RCB I (limited residual cancer), II (moderate) and III (extensive) in 7 (10.6%), 31 (47.0%) and 18 (27.3%), respectively. ctDNA-positivity after paclitaxel-based treatment was significantly associated with RCB II/III status (Fisher’s exact p=0.01). All patients in this cohort with persistent ctDNA subsequently had RCB II or III at surgery. 47 patients had paired samples collected after NAC prior to surgery and at 4 weeks after surgery. Of the 47, 91.5% (43/47) were ctDNA-negative at both time points and 8.5% (4/47) were discordant; 1 was ctDNA-negative and later tested ctDNA-positive, while 3 were ctDNA-positive and later tested ctDNA-negative. 61/66 patients had EFS data with a median of 1.6 years of follow up (range: 0.6 to 5.6). 5 tested ctDNA-positive in at least one time point after surgery. Of these, 2 experienced a recurrence (one local relapse and one distant metastasis) and both tested positive at the time of recurrence. For the patient who developed a distant recurrence it was the only blood sample available at a follow-up time point; for the patient who developed a local recurrence, blood from two earlier follow-up time points had tested negative. To date, no recurrences have been observed in those whose test(s) after surgery were negative for ctDNA.

‍Conclusions: The persistence of ctDNA during neoadjuvant therapy is associated with the extent of residual disease in a cohort of patients with HR+HER2- breast cancer in the I-SPY 2 trial and thus may be useful in identifying patients who are not having an optimal response to therapy. I-SPY 2.2 will test whether ctDNA has utility in redirecting therapy to improve surgical outcome and subsequent prognosis.

Abstract No. P4-02-10
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

MRI models by response predictive subtype for predicting pathologic complete response

Li W, Onishi N, Wolf DM, Newitt DC, Yau C, Wilmes LJ, Gibbs JE, Price ER, Joe BN, Kornak J, LeStage B, I–SPY2 Imaging Working Group, I-SPY2 Consortium, Esserman LJ, van ‘t Veer L, Hylton NM

Background: MRI predictive modeling is used in the I-SPY 2 neoadjuvant clinical trial as a key component of the pre-RCB (Predicted Residual Cancer Burden) clinical workflow for re-directing “good responders” to skip AC (anthracycline) and proceed to surgery early. The current MRI model is hormone receptor (HR)- and human epidermal growth factor receptor 2 (HER2)-specific, and was trained retrospectively using data from 990 patients in I-SPY 2. Recently, new breast cancer subtypes based on gene expression and pathologic response were proposed by Wolf et al [1]. Their study predicted that drug allocation by the new response-predictive subtype (RPS) would lead to a higher pathologic complete response (pCR) rate than allocation based on HR/HER2 subtypes. In this project, we evaluated the MRI model optimized by RPS and compared it with the HR/HER2 optimized model.

‍Methods: A total of 990 patients enrolled in I-SPY 2 and randomized to one of 9 drug arms or control were evaluated in this analysis. Functional tumor volume (FTV) was calculated from dynamic-contrast enhanced MRI [2] performed pretreatment (T0), after 3 weeks of treatment (T1), and between sequential drug regimens (T2). pCR was assessed at surgery after treatment was completed. HR/HER2 subtype was defined by HR and HER2 +/-, which resulted in four subtypes: HR+/HER2-, HR+/HER2+, HR-/HER2+, and HR-/HER2- (triple negative). RPS subtype was defined by immune, DNA repair deficiency (DRD), HER2, and BluePrint (BP) subtype (Agendia) biomarkers to define five subtypes: HER2-/Immune-/DRD-, HER2-/Immune+, HER2-/Immune-/DRD+, HER2+/BP-HER2_or_Basal, and HER2+/BP-Luminal. A logistic regression model using at least 1 FTV variable (value at T0, percent change at T1 or T2) was analyzed for predicting pCR. AUC (area under the receiver operating characteristic curve) was used to identify the optimal logistic regression model (highest AUC) in each biomarker-defined subset. For multi-predictor analysis, 10-fold cross validation was used.

‍Results: 854 patients (301 pCRs, 35%) with FTV evaluations at T0, T1, and T2, HR/HER2 and RPS subtypes, and pCR outcomes were included. Numbers of patients and pCR rates in individual subtypes are listed in Table 1. Of FTV variables, percent change at T2 was selected for inclusion in almost all subtype specific optimal models except HR+/HER2+. FTV at T0 (pretreatment tumor volume) was included in triple negative, HER2-/Immune+, and HER2+/ BP-HER2_or_Basal models. Using the current HR/HER2-specific model, the highest AUC (0.74) was found in triple negatives and the lowest AUC (0.68) was in HR+/HER2+. Using the proposed RPS-specific model, the highest AUC (0.84) was found in HER2-/Immune-/DRD+ and the lowest AUC (0.59) was found in HER2+/BP-Luminal cohorts. Table 1 shows AUCs estimated using predictions generated by HR/HER2- versus RPS-specific models, in the full cohort and in individual HR/HER2 sub-cohorts. AUCs were improved when RPS-specific models were used in full and in HR+/HER2-, HR+/HER2+, and triple negative cohorts. No improvement was observed in the HR-/HER2+ cohort where 97% (72/74) were HER2+/BP-HER2_or_Basal.

‍Conclusion: Improved prediction of pCR was observed using the RPS-specific MRI model compared to the current HR/HER2-specific model. A new preRCB workflow is being developed to combine MRI-based prediction with core biopsy assessment to re-direct “good responders” to surgery earlier and more precisely based on a patient’s biological subtype.

Abstract No. PD11-01
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Evaluation of the PD-1 Inhibitor Cemiplimab in early-stage, high-risk HER2-negative breast cancer: Results from the neoadjuvant I-SPY 2 TRIAL

Stringer-Reasor E, Shatsky RA, Chien J, Wallace A, Boughey JC, Albain KS, Han HS, Nanda R, Isaacs C, Kalinsky K, Mitri Z, Clark AS, Vaklavas C, Thomas A, Trivedi MS, Lu J, Asare S, Lu R, PItsouni M, Wilson A, Perlmutter J, Rugo H, Schwab R, Symmans WF, Hylton NM, van ‘t Veer L, Yee D, DeMichele A, Berry D, Esserman LJ, I-SPY Investigators

‍Methods: Women with tumors ≥ 2.5cm were eligible for screening. Only HER2 negative (HER2-) patients were eligible for this treatment; HR positive (HR+) patients had to be MP high risk. Treatment included paclitaxel 80 mg/m2 IV weekly x 12 and Cemi 350 mg IV given q3weeks x 4, followed by doxorubicin/cyclophosphamide (AC) every 2 weeks x 4. The control arm was weekly paclitaxel x 12 followed by AC every 2-3 weeks x 4. All patients undergo serial MRI imaging; and imaging response (at 3 weeks, 12 weeks and prior to surgery) were used along with accumulating pCR data to continuously update and estimate pCR rates for trial arms. Analysis was modified intent to treat. Patients who switched to non-protocol therapy count as non-pCR. The goal is to identify (graduate) regimens with ≥85% Bayesian predictive probability of success (i.e. demonstrating superiority to control) in a future 300-patient phase 3 neoadjuvant trial with a pCR endpoint within responsive signatures. Cemi was eligible to graduate in 3 pre-defined signatures: HER2-, HR-HER2-, and HR+HER2-. To adapt to changing standard of care, we constructed “dynamic controls” comprising ‘best’ alternative therapies using I-SPY 2 and external data and estimated the probability of Cemi being superior to the dynamic control.

‍Results: 60 HER2- patients (28 HR+ and 32 HR-) received Cemi arm treatment. The control group included 357 patients with HER2- tumors (201 HR+ and 156 HR-) enrolled since March 2010. Cemi graduated in HR-/HER2- signature. Estimated pCR rates (as of June 2022) are summarized in the table.Immune-related endocrine disorders include: hypothyroid (14.5%), adrenal insufficiency (10%), hyperthyroid (4.8%),) and thyroiditis (3.2%). Only one grade 3 adrenal insufficiency was observed. All immune related AE’s were manageable. Additional biomarker analyses are ongoing and will be presented at the meeting. Response predictive subtypes (Immune+ vs Immune-) and additional predictive biomarkers were assessed. Associations with pCR will be presented at SABCS.

‍Conclusion: The I-SPY 2 study aims to assess the probability that investigational regimens will be successful in a phase 3 neoadjuvant trial. Anti-PD-1 therapy with Cemi resulted in a higher predicted pCR rate in HR-/HER2- 55 rate% disease compared to control at 29%. Immune-mediated AE’s were observed. This data is consistent with previously published data using check point inhibitors in early-stage HR-/HER2- breast cancer.

Abstract No. P3-09-01
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Characterization of residual disease after neoadjuvant selective estrogen receptor degrader (SERD) therapy using tumor organoids in the I-SPY Endocrine Optimization Protocol (EOP)

Rosenbluth J, Bui TB, Warhadpande S, Phadatare P, Eini S, Bruck M, Molina-Vega J, Pullakhandam K, Schindler N, Brown-Swigart LA, Yau C, Hirst G, Mukhtar R, Giridhar KV, Olopade OI, Kalinsky K, Ewing CA, Wong JM, Alvarado MD, van ‘t Veer L, Esserman LJ, Chien J

Background: Treatment of estrogen receptor (ER)-positive breast cancer with selective estrogen receptor degraders (SERDs) frequently results in the loss or reduction of ER expression. Whether these changes are due to on-target effects of SERDs degrading ER or arise as a mechanism of tumor resistance with associated changes in cellular phenotypes remains unknown. It is critical to distinguish between these possibilities to accurately assess treatment response and determine the most appropriate subsequent therapy. To this end, we created and conducted molecular analyses on patient-derived organoid cultures from post-treatment tissue in patients receiving neoadjuvant SERD therapy for early-stage ER+ breast cancer in the I-SPY2 Endocrine Optimization Protocol (EOP).

Methods: The I-SPY2 EOP study is a prospective, randomized substudy within the I-SPY TRIAL testing the oral SERD amcenestrant alone or in combination with letrozole or abemaciclib in stage 2/3 ER+ Her2-negative breast cancer. Enrollment is ongoing, with patients receiving amcenestrant neoadjuvantly for 6 months until the day before surgery. Tumor tissue is collected at baseline, 3 weeks, and at surgery. Organoids were generated from post-treatment surgical samples. Organoid cultures were optimized based on established methods (Dekkers et al., Nature Protocols, 2021) to assess ER levels and activity. Pre- and post-treatment tissue samples were also assessed for ER, PR, Ki67, and GATA3, a luminal marker and transcription factor that is functionally linked with ER, via immunohistochemistry.

‍Results: In 7 patients with both pre- and post-treatment tissue samples including fresh surgical samples for organoid generation, the ER in baseline tumor tissue was >=90% in all patients, PR ranged from 40-90%, and Ki67 ranged from 5-30%. In post-treatment surgical tissue from these cases, ER ranged from 0-30%, PR from 0-50%, Ki67 from < 1%-10%, and GATA3 was positive in 5 of 5 cases tested to-date. The creation of organoids from residual disease at surgery was attempted for these 7 patients, with organoids successfully propagated in 5 cases thus far. 3 of 5 organoid cultures were ready for analysis and in all cases strong ER and PR expression in organoids was observed after culture for > 1 month in the absence of amcenestrant. Detailed gene expression profiling (including Mammaprint/Blueprint) and gene set enrichment analyses (GSEA) to assess for intrinsic breast cancer subtype and ER activity in each sample and corresponding organoid culture are in progress and will be reported with the full dataset.

‍Conclusion: Patient-derived organoid culturing of residual disease after neoadjuvant endocrine therapy is feasible. Neoadjuvant treatment with a SERD can render ER and PR low or absent at the time of surgical resection, which does not necessarily imply the presence of endocrine therapy resistant disease. The use of organoids and additional IHC markers (GATA3) demonstrate that receptor negativity may be an indicator of the drug hitting its target, suggesting ER signaling is still intact. In general, patient-derived tumor organoid cultures modeling residual disease states can be a useful adjunct to existing methods used to monitor the effects of neoadjuvant endocrine therapy and is being explored in the I-SPY EOP trial.

Abstract No. PD5-04
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

Characterizing the HER2-/Immune-/DNA repair (DRD-) response predictive breast cancer subtype: the hunt for new protein targets in a high-needs population with low response to all I-SPY2 agents

Wolf DM, Yau C, Wulfkuhle J, Gallagher RI, Brown-Swigart LA, Hirst GL, Coppe JP, Magbanua MJM, Sayaman R, I-SPY2 Investigators, Sit L, Hylton NM, DeMichele A, Berry DA, Pusztai L, Yee D, Esserman LJ, Petricoin EF, van ‘t Veer L

Background: In previous work we leveraged the I-SPY2 trial to create treatment response predictive subtypes (RPS) incorporating tumor biology beyond clinical HR/HER2, to better predict drug responses in an expanded treatment landscape that includes platinum agents, dual HER2-targeting regimens and immunotherapy [1]. We showed that best performing schemas incorporate Immune, DRD and HER2/Luminal phenotypes, and that treatment allocation based on these would increase the overall pCR rate to 63% from 51% using HR/HER2-based treatment selection. The RPS schema has been selected for prospective evaluation in I-SPY2. Using the RPS, one would prioritize platinum-based therapy for HER2-/Immune-/DRD+, immunotherapy for HER2-/Immune+, and dual-anti-HER2 for HER2+ that are not luminal. HER2+/Luminal patients have low response rates to dual-anti-HER2 therapy but may respond better to anti-AKT. However, there is still a considerable ‘biomarker-negative’ group of resistant cancers (HER2-/Immune-/DRD-) with very low pCR rates to all tested agents, that require a new therapeutic approach. Here we characterize the protein signaling architecture of these tumors to identify new target candidates.

‍Methods: 987 I-SPY 2 patients from 10 arms of the trial were considered for this analysis. All have gene expression, pCR and RPS; 944 have distant recurrence free survival (DRFS) data; and 736 have reverse phase protein array (RPPA) data from laser capture microdissected tumor epithelium. These data – known collectively as the I-SPY2-990 mRNA/RPPA Data Resource – were recently made public on NCBI’s Gene Expression Omnibus [GEO: GSE196096]. We focus on HER2-/Immune-/DRD- tumors, applying Wilcoxon and t-tests to identify phosphoproteins that differ between HR+HER2-/Immune-/DRD- and other HR+HER2- tumors; and between TN/Immune-/DRD- and other TNs. The Benjamini-Hochberg (BH) method is used to adjust p-values for multiple hypothesis testing. In addition, the Kaplan-Meier method is used to estimate DRFS.

‍Results: 201/736 I-SPY 2 patients with RPPA data are classified HER2-/Immune-/DRD- (HR+HER2-: n=138; TN: n=63). Of these, 8.5% (17/201) achieved pCR. Non-responding HER2-/Immune-DRD- had worse outcomes than responders (~75% vs. ~95% DRFS at 5 years). 60/139 phospho-proteins differ significantly between HR+HER2-/Immune-/DRD- and other HR+HER2- tumors (n=122). These tumors are relatively ‘cold’, in that 90% (54/60) of the phosphoprotein activities characterizing this group are at lower levels than in the overall HR+HER2- population; including immune (e.g. pPDL1, pJAK/STAT) and proliferation (e.g., Ki67, CyclinB1, pAURK) endpoints. Phosphoproteins showing higher levels in this subset include ERBB2 (BH p=1.7E-06), Cyclin D1 (BH p=1.4E-05), pAR (BH p=1.4E-05), and ER (BH p=3E-04). Within the TN subset, only 3/139 phospho-proteins differed significantly between TN/Immune-/DRD- and other TN tumors (n=189). These were all immune-related (pPDL1, pSTAT1, and HLA DR), with lower expression in the TN/Immune-/DRD- group.

‍Conclusion: HR+HER2- and TN patients who are Immune-Low and DRD-Low have very low pCR rates to all tested therapeutics in I-SPY2 including standard chemotherapy, platinum, and immunotherapy. Senolytics (possibly targeting Cyclin D1), HER2low agents, and AR modulators may overcome resistance in HR+HER2-/Immune-/DRD-, whereas an immune activator beyond checkpoint inhibition is suggested for TN/Immune-/DRD- patients.

[1] Wolf et. al., Redefining Breast Cancer Subtypes to Guide Treatment Prioritization and Maximize Response: Predictive Biomarkers across 10 Cancer Therapies. Cancer Cell 2022

Abstract No. PD5-02
2022 San Antonio Breast Cancer Symposium, 8-11 Dec, 2022

An Organoid Model System to Study Resistance Mechanisms, Predictive Biomarkers, and New Strategies to Overcome Therapeutic Resistance in Early-Stage Triple-Negative Breast Cancer

Bui TBV, Wolf DM, Moore K, Harris IS, Phadatare P, Yau C, Brown-Swigart LA, Esserman LJ, Coppe JP, Wulfkuhle J, Petricoin EF, Campbell M, Selfors LM, Dillon DA, Overmoyer B, Lynce F, van ‘t Veer L, Rosenbluth J

Background: While new treatments and improved subtyping schemas are anticipated to improve treatment response in triple-negative breast cancer (TNBC) patients, therapeutic resistance remains a significant challenge. Moreover, there is an urgent need for additional research model systems to study resistance and residual disease in breast cancer, including aggressive subtypes of breast cancer. Organoid culture is a promising technology used for growing breast cancer cells with high efficiency; however, the extent to which treatment resistance can be modeled using this system is unknown. This research used patient-derived organoid cultures in the context of computational analyses of large molecular and clinical datasets to study resistance mechanisms, biomarkers, and alternative treatment strategies to overcome drug resistance in early-stage TNBC.

Methods: Organoid cultures were derived from breast tumor samples (taken from lumpectomy, mastectomy, or core biopsy samples), digested to the organoid level using collagenase, and grown in three dimensional cultures using a basement membrane extract and a fully-defined organoid medium (Dekkers et al. Nat Protoc 2021). An evaluation of all available I-SPY2 biomarker data (Wolf et al. Cancer Cell 2022) was performed focusing on genes, proteins, and pathways associated with resistance. These were then used to study whether resistance biomarkers identified in patient tumors are also present in organoids propagated from breast cancer post-treatment residual disease. To this end, bulk RNA sequencing data of organoids were normalized and merged with the TCGA dataset (Hoadley et al. Cell 2018) to enable analysis in a larger context, and immunofluorescence staining of organoids was performed. A high-throughput 386 anti-cancer drug compound screen and subsequent synergy testing with the most promising compounds were performed to identify and predict alternative treatment strategies. Additional assays to explore kinome activity in this organoid model are in progress.

Results: A TNBC organoid biobank was established (n=31), which was enriched for inflammatory breast cancer (IBC; n=15), an aggressive form of breast cancer. Most organoids were derived from residual disease after neoadjuvant therapy. Bulk RNA sequencing analysis performed on 10 TNBC organoids revealed 3 subsets that were characterized predominantly by either normal-like/luminal androgen receptor or basal-like features or expressed distinct gene expression profiles, with IBC cases present in all 3 subsets. Intriguingly, the IBC organoids were characterized by higher expression of a number of immune-related signatures, despite an absence of clear immune cells in culture. A residual disease IBC/TNBC organoid resistant to chemotherapy was used to perform the 386-drug compound screen. The organoid model showed resistance to veliparib-cisplatin, consistent with the expression of gene/protein biomarkers predictive of drug resistance found in I-SPY2 (low PARPi7 levels and high pFOXO1 and pMEK1/2 expression). In addition, the screen identified multiple classes of inhibitors as promising synergistic/additive candidates for overcoming resistance to cisplatin.

Conclusion: In this proof-of-principle study, we demonstrated the utility of matching I-SPY2 resistance biomarkers and signatures to residual disease tumor organoid cultures. We show that tumor organoid cultures modeling drug resistance states are a useful complement to existing research models of breast cancer and can be used for compound testing. We have developed a pipeline to propagate residual tumors from patients enrolled in I-SPY2 into organoid cultures to create a broader platform for preclinical drug testing informed by tumor biology with the ultimate goal of enabling faster, more successful translational studies and increased treatment options for resistant disease.

Abstract No. 591
2022 ASCO Annual Meeting, 3-7 Jun, 2022

Improved pathologic complete response rates for triple-negative breast cancer in the I-SPY2 Trial

Yee D, Shatsky RA, Yau C, Wolf DM, Nanda R, van ‘t Veer L, Berry DA, DeMichele A, Esserman L, I-SPY2 Consortium

Background: The I-SPY2 Trial evaluates multiple investigative agents in neoadjuvant breast cancer therapy with the primary endpoint of estimated pathologic complete response (pCR) rate. As a platform phase 2 trial it utilizes an adaptive design to compare new regimens with control chemotherapy (weekly paclitaxel followed by AC).

‍Methods: Specific regimens are assigned based on clinically relevant signatures, including triple negative breast cancer (TNBC). Drug regimens graduate from the trial when the predicted pCR rate in any signature meets the pre-specified threshold of 85% probability of success in a hypothetical 300-patient, 1:1 randomized, phase 3 trial. The strong correlation between pCR rate and event free survival has been reported. To establish the benefit of administering investigational agents in combination with control weekly paclitaxel x 12 in TNBC, we report estimated pCR rates for the first 7 investigational agents.

‍Results:TNBC accounted for 37% (363/987) of enrolled patients. Only veliparib and carboplatin (VC) and pembrolizumab (Pembro) met the graduation criteria for TNBC. However, compared to control chemotherapy, each drug tested in TNBC resulted in a numerically superior pCR rate compared to control. These findings imply that stratification of TNBC by response-predictive biomarkers may lead to improved pCR rates. For example, we have used gene expression profiling to further refine TNBC classification into Immune enhanced (Immune+), Immune-/DNA Repair Deficient (DRD)+, and Immune-/DRD- classes. TNBC identified as immune enhanced (63%) have an 89% pCR rate to pembrolizumab, while VC is less effective with pCR rate of 71%. Similarly, Immune-/DRD+ (11%) identifies TNBCs with a 80% pCR rate to VC, while pembrolizumab’s pCR rate in this group is only 33%. For tumors that are neither immune enhanced or DRD-positive (Immune-/DRD-; 25%) show numerically improved pCR rates for neratinib (20%), MK2206 (25%), ganitumab (24%), and ganetespib (22%) compared to control (12%). pCR rates for VC (10%) and pembrolizumab (20%) in this group were similar to drugs that did not graduate. For TNBC, many agents in I-SPY2 showed numerically improved pCR rates compared to conventional chemotherapy even when they did not meet our specified definition of graduation.

‍Conclusions: Further refinement of TNBC signatures should yield improved therapeutic strategies while also sparing women unnecessary systemic therapy.

‍Clinical trial information: NCT01042379.

Abstract No. 510
2022 ASCO Annual Meeting, 3-7 Jun, 2022

Molecular subtype to predict pathologic complete response in HER2-positive breast cancer in the I-SPY2 trial

Thomas A, Clark AS, Yau C, Wolf DM, van ‘t Veer L, Douglas EH, Chien AJ, Huppert LA, Rugo HS, Shatsky RA, Isaacs C, Berry DA, Yee D, DeMichele A, Esserman L, I-SPY2 Consortium

Background: HER2-positive breast cancer (bc) is a very heterogenous disease. We hypothesized that molecular subtype may predict disease response to investigational agents in HER2+ bc. Here, we report the pathologic complete response (pCR) rate in the first six agents tested in HER2+ bc in the I-SPY 2 trial for the full HER2+ cohort, by molecular subtype, and by disease receptor status.

‍Methods: Women with HER2+ tumors which were > 2.5 cm were eligible. The I-SPY2 platform trial tests novel agents given neoadjuvantly with a backbone of taxol (T) and trastuzumab (H) followed by doxorubicin and cyclophosphamide. Agents investigated in HER2+ bc were TH (control), MK2206, AMG386, pertuzumab (P), neratinib (N (given in place of H), and TDM1+P (given in place of TH). An investigational arm graduated if there was >85% chance of success compared to control in a 300-person phase 3 neoadjuvant trial. Further details of the I-SPY2 methods have been previously published. Molecular subtyping based on gene expression was utilized to categorize tumors into 5 response predictive subtypes (RPS) (HER2-/Immune-/DRD (DNA repair deficiency)-, HER2-/Immune+, HER2-/Immune-/DRD+, HER2+/Her2_or_Basal and HER2+/Luminal).

‍Results: For the full HER2+ cohort (N=245) pCR rate was higher in all investigational arms than control (Table). By tumor receptor status, HER2+/HR- tumors (N=89) had a higher pCR rate than HER2+/HR+ tumors (N=156; 63% vs 37%, p = 0.0001). In HER2+/HR- tumors N, MK2206, P and TDM1/P graduated. In HER2+/HR+ tumors P and TDM1/P graduated. 76% (185/245) of I-SPY 2 HER2+ patients were classified as HER2+/Her2_or_Basal and 24% (60/245) were HER2+Luminal. pCR rate was significantly higher in the HER2+/Her2_or_Basal group than in the HER2+/Luminal group (57% vs 15%, p < 0.0001). All agents, except for MK2206, where numbers were small, showed greater efficacy in the HER2+/Her2_or_Basal group than in the HER2+/Luminal group. HER2+/Luminal appeared to be more sensitive to the AKT inhibitor MK2206 than to targeted HER2 agents, though numbers are small.

‍Conclusions: pCR rates for patients with HER2+ bc treated with investigational agents, particularly dual HER2-blockade, were promising. Molecular response predictive subtype classification provides insight on how to better target therapy. The HER2+/Luminal group had low pCR rates with dual HER2-blockade but may have higher pCR rate with the addition of an AKT inhibitor and identifies a subgroup of HER2+ tumors in need of novel approaches. AKT inhibition for HER2/Luminal is being tested in I-SPY 2.2.

‍Clinical trial information: NCT01042379.

Abstract No. 504
2022 ASCO Annual Meeting, 3-7 Jun, 2022

Pathologic complete response (pCR) rates for HR+/HER2- breast cancer by molecular subtype in the I-SPY2 Trial

Huppert LA, Rugo HS, Pusztai L, Mukhtar RA, Chien AJ, Yau C, Wolf DM, Berry DA, van ‘t Veer L, Yee D, DeMichele A, Esserman L, I-SPY2 Consortium

Background: Hormone receptor positive (HR+), HER2- breast cancer (BC) is a heterogenous disease. We hypothesized that molecular subtypes capturing luminal, basal, and immune biology could predict response for patients (pts) with HR+/HER2- disease in the I-SPY2 trial.

‍Methods: I-SPY2 trial is a phase II, randomized, adaptive study evaluating multiple investigational agents as neoadjuvant BC therapy; the primary endpoint is estimated pCR rate. Investigational agents are given with control weekly paclitaxel x 12, followed by AC x 4. Regimens graduate when the predicted pCR rate in any signature meets the pre-specified threshold of 85% probability of success in a hypothetical 300 pt randomized, phase 3 trial. We analyzed estimated pCR rates for the 1st 7 investigational agents in the HR+/HER- subset, analyzed by clinical/molecular features: BluePrint (BP) Luminal vs. Basal, Mammaprint High1 [MP1] vs. Mammaprint High2 [MP2], MP2 is < -0.57, Responsive Predictive Subtype-5 (RPS-5) (classification based on HR, HER2, immune, DNA-repair, and basal/luminal markers), histology, and stage/nodal status.

‍Results: 38% (379/987) of pts had HR+/HER2- disease. Only pembrolizumab met the pre-specified graduation criteria for HR+/HER2- BC. pCR rates by treatment arm and molecular subtype are described in the Table. 28% were MP2; 72% were MP1. Overall, pCR rates were higher in pts with MP2 vs MP1 disease (30% vs 11%) including with pembrolizumab (55% vs. 21%). 29% were BP Basal, 71% were BP Luminal; BP Basal was more likely to be MP2 than BP Luminal (77% vs 8%). In all arms except MK2206, HR+/HER2- BP Basal pts were more likely to achieve pCR than BP Luminal pts. For MK2206, BP Luminal pts were more likely to achieve pCR. Immune+ by RPS-5 (39% of HR+/HER2-) predicted pCR to pembrolizumab irrespective of BP Basal or Luminal status (11 pCR/16 pts). Results by histology and stage/nodal status will also be reported.

‍Conclusions: Our data suggest that MP2 and BP Basal signatures identify a subset of HR+/HER2- BC more likely to respond to neoadjuvant therapy; and that an immune signature can identify pts more likely to respond to pembrolizumab. These findings will aid in guiding prioritization of targeted agents with the goal to optimize pCR for all pts.

Clinical trial information: NCT01042379

Abstract No. 592
2022 ASCO Annual Meeting, 3-7 Jun, 2022

Distribution of breast cancer molecular subtypes within receptor classifications: Lessons from the I-SPY2 Trial and FLEX Registry

Cha J, Warner P, Hiatt R, Gomez SL, van ‘tVeer L, Stover-Fiscalini A, Borowsky A, Symmans WF, Wolf DM, Yau C, Yee D, DeMichele A, Berry DA, Esserman L, Audeh W, Modh S, I-SPY2 Consortium

Background: Expression-based molecular subtypes of breast cancer (BC) predict tumor behavior and therapeutic response. Subtype distributions by age and sociodemographics can inform strategies for BC screening, treatment, and prognosis. The conventional approach, adopted by NCI’s Surveillance, Epidemiology, and End Results (SEER) Program, uses HR and HER2 to label: “triple negative” (HR-HER2-), “HER2-enriched” (HR-HER2+), “luminal A” (HR+HER2-), and “luminal B” (HR+HER2+). However, immunohistochemical (IHC)-based receptor labels may not reflect clinically and epidemiologically relevant molecular subtypes that share the same nomenclature, e.g., luminal B.

‍Methods: We compared IHC labels by HR/HER2 to molecular subtypes by MammaPrint (MP) and BluePrint (BP) for patients in the phase II neoadjuvant I-SPY2 TRIAL for high-risk, stage II-III BC (NCT01042379, n = 981) and in the multicenter, prospective FLEX Registry for stage I-III BC (NCT03053193, n = 5,679).

‍Results: IHC labels were discordant with MP/BP in 52% of I-SPY2 and 43% of FLEX cases (Table 1). HR-HER2- had the highest concordance with basal-type (99% in I-SPY2, 88% in FLEX). HR+ labels had the least agreement with MP/BP: HR+HER2- tumors were molecularly luminal B and basal in 71% and 29% of I-SPY2 and 40% and 4% of FLEX cases, respectively. HR+HER2+ tumors were molecularly luminal A and HER2-type in 10% and 60% of I-SPY2 and 15% and 36% of FLEX cases, respectively. Of molecularly luminal B cases, only 14% in I-SPY2 and 7% in FLEX were HR+HER2+.

‍Conclusions: IHC markers collected by population-based registries (SEER) enable BC surveillance. However, IHC labels cannot be used as surrogates for molecular subtypes by MP/BP, especially for luminal B tumors. Given the unmet need to improve management of luminal B BC, we anticipate the growing importance of molecular subtyping to inform treatment and epidemiological research. We propose that the BC research community work with SEER to update its IHC labels to avoid overlap with molecular subtype nomenclature and incorporate such modern classifications when available.

Abstract No. 514
2022 ASCO Annual Meeting, 3-7 Jun, 2022

The ImPrint immune signature to identify patients with high-risk early breast cancer who may benefit from PD1 checkpoint inhibition in I-SPY2

Mittempergher L, Kuilman MM, Barcaru A, Nota B, Delahaye JMJ, Audeh W, Wolf DM, Yau C, Brown-Swigart L, Hirst G, Symmans WF, Lu R, Liu MC, Nanda R, Esserman L, van ‘t Veer L, Glas Annuska, I-SPY2 Investigators

Background: The remarkable increase of novel Immuno-Oncology drugs in many malignancies has led to the need for biomarkers to identify who would benefit. Various predictive biomarkers have been developed (PD-1/PD-L1 expression, mutations in mismatch repair genes and microsatellite instability, tumor mutational burden and immune infiltration), none have consistently predicted efficacy. The I-SPY2 consortium qualified several expression-based immune biology related signatures that predict response to PD1 checkpoint inhibition. Here we assessed whole transcriptome data of high-risk early-breast cancer (EBC) patients who received Pembrolizumab within the neoadjuvant biomarker-rich I-SPY2 trial (NCT01042379), aiming to migrate the I-SPY2 research findings to a robust clinical grade platform signature to predict sensitivity to PD1 checkpoint inhibition.

‍Methods: Whole transcriptome microarray data were available from pre-treatment biopsies of 69 HER2- patients enrolled in the Pembrolizumab (4 cycles) arm of the I-SPY2 trial. All patients had a High-Risk 70-gene MammaPrint profile. Pathologic complete response (pCR) was defined as no residual invasive cancer in breast or nodes at the time of surgery. Of the 69 patients, 31 had a pCR (12 HR (hormonal receptor)+HER2-, 19 Triple Negative (TN)), while 38 (28 HR+HER2-, 10 TN) had residual disease (RD). To identify the most predictive genes associated with pCR, gene selection was performed comparing pCR and RD groups by iteratively splitting the dataset in training and test, balancing for HR status. Due to limited sample size, leave one out cross validation was used for performance assessment. Genes with effect size > 0.45 were considered significant.

‍Results: A signature of 53 genes, named ImPrint, was identified with overall sensitivity and specificity > 90% and > 80% for predicting pCR to pembrolizumab in all patients. Sensitivity and specificity in TN were > 95% and ≥70%, and in HR+HER2- > 80% and > 85%, respectively. The Positive Predictive Value (PPV) is 77% for the HR+HER2- subgroup. Biological annotation of the 53 genes showed that over 90% of the genes have known immune system related functions, of which 63% were previously known to be involved in immune response (including genes coding PD-L1 and PD-1, as well as those identified in I-SPY2).

‍Conclusions: In the signature development phase, ImPrint predicts pCR to Pembrolizumab in a set of 69 high risk EBC with high sensitivity and specificity. The signature features genes with immune-related functions known to be involved in immune response indicating that it might aid identifying patients with an immune-active phenotype. Importantly, ImPrint appears effective in identifying a subset of HR+HER2- patients who could benefit from immunotherapy. External validation in independent dataset(s) is ongoing and will be presented at the time of the meeting.