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Background: High-grade DCIS with immune infiltrates may represent lesions that are able to be kept in check by the immune system, but still are at risk for progression to invasive disease. We and others have demonstrated that the presence of T cells, and in particular the spatial proximity of CD8+/PD-1+ T cells and PD-L1+ cells predicts response to chemotherapy and PD-1 inhibitors in the setting of invasive triple negative breast cancer. DCIS with high-risk features (e.g. large, palpable, high grade, HR-, or HER2+) often have T cell infiltrates. We hypothesized that it might be possible to potentiate the immune response in high-risk DCIS with immune checkpoint blockade. Since mortality for DCIS is extremely low, we proposed an intralesional pembrolizumab (pembro) treatment approach to avoid systemic adverse effects. In a phase 1 dose escalation study of single agent pembro we found 2 doses of 8 mg, administered intralesionally 2-3 wks apart, was tolerable, induced immunological changes within the DCIS lesions, but had little clinical benefit. Herein, we expanded the number of injections to 4 and also tested a combination with mRNA-2752 (Moderna), an mRNA-based therapeutic encoding a T cell co-stimulator OX40L, and pro-inflammatory cytokines IL-23 and IL-36 gamma, to determine if we could find a dose that was both tolerable and elicited anti-tumor responses. Methods Women eligible for this study had DCIS with at least 2 of the following high-risk features: age< 45; high grade, extensive comedonecrosis; palpable mass; hormone receptor negative [HR-]; HER2+; size >5 cm, or microinvasion. A dose expansion cohort was performed using 4 doses of 8 mg pembro, 2-3 wks apart (n=5). We then combined pembro with mRNA-2752 (n=8 cases), initially at 8 and 4 mg, respectively. Dose reductions were implemented based on AEs. Patients received an MRI before and after 2 injections, spaced 2-3 wks apart. A total of 4 injections were allowed. Core biopsy or surgery was conducted after the last MRI. Results We observed an increase in T cell density in the dose expansion cohort (pembro only), except when there was a paucity of T cells in the pre-treatment biopsy. However, only 1 patient in this cohort demonstrated a reduction in lesion size (clinically and on MRI). As of 22SEP22, 8 patients have received the combination of pembro and mRNA-2752; 7 are evaluable. Two patients with minimal T cell infiltrates at baseline (both HR+) failed to respond, based on imaging or pathology. 4/5 patients with moderate to high T cell infiltrates at baseline had partial (2) or complete responses (2) based on imaging (surgery pending), one of whom had absence of DCIS on post-therapy core biopsy and a clear MRI 4 months later. Correlative studies (immune cell densities, spatial proximities) will be presented at the meeting along with complete data (including post-treatment pathology) on the first 8 patients. Side effects were independent of response and included Gr1/2 fever, myalgias, and fatigue starting within 12 hours of injection (3-4 days), enlargement of regional nodes by day 2, and Gr 1/2 erythema and induration of the breast starting at 4 days and lasting 4-20 days. Earlier onset and persistence of symptoms with subsequent exposure required dose reductions for the majority of patients to maintain tolerability (avoid fevers over 39.4°C, intense erythema and swelling of the breast lasting > 4 days). No AEs were higher than grade 2. Conclusion: The combination of intralesional pembro and mRNA-2752 demonstrated modulation of the tumor immune microenvironment and robust antitumor activity in high-risk DCIS (typically HR- or HER2+) with existing T cell infiltrates. This is an ongoing study; the optimal Phase 2 dose for the combination will be based on the totality of evolving safety and translational data. Citation Format: Laura J. Esserman, Alexa Glencer, Kirithiga Ramalingam, Christopher J. Schwartz, Alexander Borowsky, Gillian L. Hirst, Rachel Woody, Nicole Schindler, Nola M. Hylton, Michael Campbell. Intralesional injection of anti-PD-1 (pembrolizumab) and OX40L/IL-23/IL-36 mRNAs (mRNA-2752) results in regression of DCIS characterized by lymphocytic infiltrates [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr OT1-20-02.

PDF file - 101KB, Supplementary Table S1 - Matching between BI-RADS and non BI-RADS mammography descriptors. Supplementary Table S2 - Matching between BI-RADS and non BI-RADS ultrasound descriptors. Supplementary Table S3 - Matching between BI-RADS and non BI-RADS MRI descriptors.

PDF file - 2985KB, Supplementary Figure S81 - Funnel and Egger's test plots of mammography feature meta-analysis: Mass lesions - Presence. Supplementary Figure S82 - Funnel and Egger's test plots of mammography feature meta-analysis: Shape - Round or oval. Supplementary Figure S83 - Funnel and Egger's test plots of mammography feature meta-analysis: Shape - Lobular. Supplementary Figure S84 - Funnel and Egger's test plots of mammography feature meta-analysis: Shape - Irregular. Supplementary Figure S85 - Funnel and Egger's test plots of mammography feature meta-analysis: Margin - Circumscribed. Supplementary Figure S86 - Funnel and Egger's test plots of mammography feature meta-analysis: Margin - Microlobulated. Supplementary Figure S87 - Funnel and Egger's test plots of mammography feature meta-analysis: Margin - Indistinct. Supplementary Figure S88 - Funnel and Egger's test plots of mammography feature meta-analysis: Margin - Obscured. Supplementary Figure S89 - Funnel and Egger's test plots of mammography feature meta-analysis: Margin - Spiculated. Supplementary Figure S90 - Funnel and Egger's test plots of mammography feature meta-analysis: Microcalcifications - Presence. Supplementary Figure S91 - Funnel and Egger's test plots of mammography feature meta-analysis: Microcalcifications - Presence without mass. Supplementary Figure S92 - Funnel and Egger's test plots of mammography feature meta-analysis: Microcalcifications - Presence with mass. Supplementary Figure S93 - Funnel and Egger's test plots of mammography feature meta-analysis: Morphology - Coarse. Supplementary Figure S94 - Funnel and Egger's test plots of mammography feature meta-analysis: Morphology - Punctate. Supplementary Figure S95 - Funnel and Egger's test plots of mammography feature meta-analysis: Morphology - Amorpheous. Supplementary Figure S96 - Funnel and Egger's test plots of mammography feature meta-analysis: Morphology - Branching or fine linear. Supplementary Figure S97 - Funnel and Egger's test plots of mammography feature meta-analysis: Morphology - Pleomorphic. Supplementary Figure S98 - Funnel and Egger's test plots of mammography feature meta-analysis: Distribution - Clustered. Supplementary Figure S99 - Funnel and Egger's test plots of mammography feature meta-analysis: Distribution - Linear. Supplementary Figure S100 - Funnel and Egger's test plots of mammography feature meta-analysis: Distribution - Segmental. Supplementary Figure S101 - Funnel and Egger's test plots of mammography feature meta-analysis: Distribution - Regional. Supplementary Figure S102 - Funnel and Egger's test plots of mammography feature meta-analysis: Distribution - Diffuse. Supplementary Figure S103 - Funnel and Egger's test plots of mammography feature meta-analysis: Breast density - BI-RADS 4. Supplementary Figure S104 - Funnel and Egger's test plots of mammography feature meta-analysis: Breast density - BI-RADS 3 and 4. Supplementary Figure S105 - Funnel and Egger's test plots of mammography feature meta-analysis: Breast density - BI-RADS 1. Supplementary Figure S106 - Funnel and Egger's test plots of mammography feature meta-analysis: Architectural distortion. Supplementary Figure S107 - Funnel and Egger's test plots of mammography feature meta-analysis: Focal asymmetry. Supplementary Figure S108 - Funnel and Egger's test plots of mammography feature meta-analysis: Skin thickening. Supplementary Figure S109 - Funnel and Egger's test plots of mammography feature meta-analysis: Level of suspicion for malignancy - Highly suspicious. Supplementary Figure S110 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Mass lesions - Presence. Supplementary Figure S111 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Shape - Round or oval. Supplementary Figure S112 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Shape - Irregular. Supplementary Figure S113 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Margin - Circumscribed. Supplementary Figure S114 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Margin - Indistinct. Supplementary Figure S115 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Margin - Angular. Supplementary Figure S116 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Margin - Microlobulated. Supplementary Figure S117 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Margin - Spiculated. Supplementary Figure S118 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Lesion boundary - Echogenic halo. Supplementary Figure S119 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Echo pattern - Complex. Supplementary Figure S120 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Echo pattern - Hypoechoic. Supplementary Figure S121 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Echo pattern - Isoechoic. Supplementary Figure S122 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Posterior acoustic feature - No distal effect. Supplementary Figure S123 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Posterior acoustic feature - Shadowing. Supplementary Figure S124 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Posterior acoustic feature - Enhancement. Supplementary Figure S125 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Orientation - Parallel. Supplementary Figure S126 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Microcalcifications - Presence. Supplementary Figure S127 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Microcalcifications - Presence with mass. Supplementary Figure S128 - Funnel and Egger's test plots of ultrasound feature meta-analysis: Level of suspicion for malignancy - Highly suspicious. Supplementary Figure S129 - Funnel and Egger's test plots of MRI feature meta-analysis: Mass lesions - Presence. Supplementary Figure S130 - Funnel and Egger's test plots of MRI feature meta-analysis: Shape - Round or oval. Supplementary Figure S131 - Funnel and Egger's test plots of MRI feature meta-analysis: Shape - Lobular. Supplementary Figure S132 - Funnel and Egger's test plots of MRI feature meta-analysis: Shape - Irregular. Supplementary Figure S133 - Funnel and Egger's test plots of MRI feature meta-analysis: Margin - Smooth. Supplementary Figure S134 - Funnel and Egger's test plots of MRI feature meta-analysis: Margin - Irregular. Supplementary Figure S135 - Funnel and Egger's test plots of MRI feature meta-analysis: Margin - Spiculated. Supplementary Figure S136 - Funnel and Egger's test plots of MRI feature meta-analysis: Internal enhancement - Homogenous. Supplementary Figure S137 - Funnel and Egger's test plots of MRI feature meta-analysis: Internal enhancement - Heterogenous. Supplementary Figure S138 - Funnel and Egger's test plots of MRI feature meta-analysis: Internal enhancement - Rim. Supplementary Figure S139 - Funnel and Egger's test plots of MRI feature meta-analysis: Kinetic curve type - Persistent. Supplementary Figure S140 - Funnel and Egger's test plots of MRI feature meta-analysis: Kinetic curve type - Plateau. Supplementary Figure S141 - Funnel and Egger's test plots of MRI feature meta-analysis: Kinetic curve type - Washout. Supplementary Figure S142 - Funnel and Egger's test plots of MRI feature meta-analysis: Time to peak enhancement - Fast initial kinetics. Supplementary Figure S143 - Funnel and Egger's test plots of MRI feature meta-analysis: Enhancement ratio - High. Supplementary Figure S144 - Funnel and Egger's test plots of MRI feature meta-analysis: Focality - Multifocal. Supplementary Figure S145 - Funnel and Egger's test plots of MRI feature meta-analysis: T2 signal intensity - High. Supplementary Figure S146 - Funnel and Egger's test plots of MRI feature meta-analysis: Fischer Score - High. Supplementary Figure S147 - Funnel and Egger's test plots of FDG PET meta-analysis: SUVmax.

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PDF file - 101KB, Supplementary Table S4 - Dynamic contrast-enhanced imaging protocols of studies reporting on magnetic resonance imaging features in relation to HER2 expression in breast cancer. Supplementary Table S5 - Diffusion-weighted imaging protocols of studies reporting on apparent diffusion coefficient in relation to HER2 expression in breast cancer.

PDF file - 2800KB, Supplementary Figure S1 - Forest plot of mammography feature meta-analysis: Mass lesions - Presence. Supplementary Figure S2 - Forest plot of mammography feature meta-analysis: Shape - Round or oval. Supplementary Figure S3 - Forest plot of mammography feature meta-analysis: Shape - Lobular. Supplementary Figure S4 - Forest plot of mammography feature meta-analysis: Shape - Irregular. Supplementary Figure S5 - Forest plot of mammography feature meta-analysis: Margin - Circumscribed. Supplementary Figure S6 - Forest plot of mammography feature meta-analysis: Margin - Microlobulated. Supplementary Figure S7 - Forest plot of mammography feature meta-analysis: Margin - Indistinct. Supplementary Figure S8 - Forest plot of mammography feature meta-analysis: Margin - Obscured. Supplementary Figure S9 - Forest plot of mammography feature meta-analysis: Margin - Spiculated. Supplementary Figure S10 - Forest plot of mammography feature meta-analysis: Microcalcifications - Presence. Supplementary Figure S11 - Forest plot of mammography feature meta-analysis: Microcalcifications - Presence without mass. Supplementary Figure S12 - Forest plot of mammography feature meta-analysis: Microcalcifications - Presence with mass. Supplementary Figure S13 - Forest plot of mammography feature meta-analysis: Morphology - Coarse. Supplementary Figure S14 - Forest plot of mammography feature meta-analysis: Morphology - Punctate. Supplementary Figure S15 - Forest plot of mammography feature meta-analysis: Morphology - Amorpheous. Supplementary Figure S16 - Forest plot of mammography feature meta-analysis: Morphology - Branching or fine linear. Supplementary Figure S17 - Forest plot of mammography feature meta-analysis: Morphology - Pleomorphic. Supplementary Figure S18 - Forest plot of mammography feature meta-analysis: Distribution - Clustered. Supplementary Figure S19 - Forest plot of mammography feature meta-analysis: Distribution - Linear. Supplementary Figure S20 - Forest plot of mammography feature meta-analysis: Distribution - Segmental. Supplementary Figure S21 - Forest plot of mammography feature meta-analysis: Distribution - Regional. Supplementary Figure S22 - Forest plot of mammography feature meta-analysis: Distribution - Diffuse. Supplementary Figure S23 - Forest plot of mammography feature meta-analysis: Breast density - BI-RADS 4. Supplementary Figure S24 - Forest plot of mammography feature meta-analysis: Breast density - BI-RADS 3 and 4. Supplementary Figure S25 - Forest plot of mammography feature meta-analysis: Breast density - BI-RADS 1. Supplementary Figure S26 - Forest plot of mammography feature meta-analysis: Architectural distortion. Supplementary Figure S27 - Forest plot of mammography feature meta-analysis: Focal asymmetry. Supplementary Figure S28 - Forest plot of mammography feature meta-analysis: Skin thickening. Supplementary Figure S29 - Forest plot of mammography feature meta-analysis: Level of suspicion for malignancy - Highly suspicious. Supplementary Figure S30 - Forest plot of ultrasound feature meta-analysis: Mass lesions - Presence. Supplementary Figure S31 - Forest plot of ultrasound feature meta-analysis: Shape - Round or oval. Supplementary Figure S32 - Forest plot of ultrasound feature meta-analysis: Shape - Irregular. Supplementary Figure S33 - Forest plot of ultrasound feature meta-analysis: Margin - Circumscribed. Supplementary Figure S34 - Forest plot of ultrasound feature meta-analysis: Margin - Indistinct. Supplementary Figure S35 - Forest plot of ultrasound feature meta-analysis: Margin - Angular. Supplementary Figure S36 - Forest plot of ultrasound feature meta-analysis: Margin - Microlobulated. Supplementary Figure S37 - Forest plot of ultrasound feature meta-analysis: Margin - Spiculated. Supplementary Figure S38 - Forest plot of ultrasound feature meta-analysis: Lesion boundary - Echogenic halo. Supplementary Figure S39 - Forest plot of ultrasound feature meta-analysis: Echo pattern - Complex. Supplementary Figure S40 - Forest plot of ultrasound feature meta-analysis: Echo pattern - Hypoechoic. Supplementary Figure S41 - Forest plot of ultrasound feature meta-analysis: Echo pattern - Isoechoic. Supplementary Figure S42 - Forest plot of ultrasound feature meta-analysis: Posterior acoustic feature - No distal effect. Supplementary Figure S43 - Forest plot of ultrasound feature meta-analysis: Posterior acoustic feature - Shadowing. Supplementary Figure S44 - Forest plot of ultrasound feature meta-analysis: Posterior acoustic feature - Enhancement. Supplementary Figure S45 - Forest plot of ultrasound feature meta-analysis: Orientation - Parallel. Supplementary Figure S46 - Forest plot of ultrasound feature meta-analysis: Microcalcifications - Presence. Supplementary Figure S47 - Forest plot of ultrasound feature meta-analysis: Microcalcifications - Presence with mass. Supplementary Figure S48 - Forest plot of ultrasound feature meta-analysis: Level of suspicion for malignancy - Highly suspicious. Supplementary Figure S49 - Forest plot of MRI feature meta-analysis: Mass lesions - Presence. Supplementary Figure S50 - Forest plot of MRI feature meta-analysis: Shape - Round or oval. Supplementary Figure S51 - Forest plot of MRI feature meta-analysis: Shape - Lobular. Supplementary Figure S52 - Forest plot of MRI feature meta-analysis: Shape - Irregular. Supplementary Figure S53 - Forest plot of MRI feature meta-analysis: Margin - Smooth. Supplementary Figure S54 - Forest plot of MRI feature meta-analysis: Margin - Irregular. Supplementary Figure S55 - Forest plot of MRI feature meta-analysis: Margin - Spiculated. Supplementary Figure S56 - Forest plot of MRI feature meta-analysis: Internal enhancement - Homogenous. Supplementary Figure S57 - Forest plot of MRI feature meta-analysis: Internal enhancement - Heterogenous. Supplementary Figure S58 - Forest plot of MRI feature meta-analysis: Internal enhancement - Rim. Supplementary Figure S59 - Forest plot of MRI feature meta-analysis: Kinetic curve type - Persistent. Supplementary Figure S60 - Forest plot of MRI feature meta-analysis: Kinetic curve type - Plateau. Supplementary Figure S61 - Forest plot of MRI feature meta-analysis: Kinetic curve type - Washout. Supplementary Figure S62 - Forest plot of MRI feature meta-analysis: Time to peak enhancement - Fast initial kinetics. Supplementary Figure S63 - Forest plot of MRI feature meta-analysis: Enhancement ratio - High. Supplementary Figure S64 - Forest plot of MRI feature meta-analysis: Focality - Multifocal. Supplementary Figure S65 - Forest plot of MRI feature meta-analysis: T2 signal intensity - High. Supplementary Figure S66 - Forest plot of MRI feature meta-analysis: Fischer Score - High. Supplementary Figure S67 - Forest plot of FDG PET meta-analysis: SUVmax.

PDF file - 3944KB, Supplementary Figure S68 - Studies focused on invasive cancer only. Supplementary Figure S69 - Studies without the exclusion of a molecular subtype in the analyses. Supplementary Figure S70 - Studies exclusively using analog mammography. Supplementary Figure S71 - Studies exclusively using digital mammography. Supplementary Figure S72 - Studies based on BI-RADS. Supplementary Figure S73 - Studies focused on invasive cancer only. Supplementary Figure S74 - Studies without the exclusion of a molecular subtype in the analyses. Supplementary Figure S75 - Studies based on BI-RADS. Supplementary Figure S76 - Studies focused on invasive cancer only. Supplementary Figure S77 - Studies exclusively using 1.5T field strength. Supplementary Figure S78 - Studies (also) using 3T field strength. Supplementary Figure S79 - Studies with high spatial resolution (i.e. sub-mm in plane resolution and smaller than 2mm slice thickness). Supplementary Figure S80 - Studies based on BI-RADS.

Breast cancer imaging phenotype is diverse and may relate to molecular alterations driving cancer behavior. We systematically reviewed and meta-analyzed relations between breast cancer imaging features and human epidermal growth factor receptor type 2 (HER2) overexpression as a marker of breast cancer aggressiveness. MEDLINE and EMBASE were searched for mammography, breast ultrasound, magnetic resonance imaging (MRI), and/or [18F]fluorodeoxyglucose positron emission tomography studies through February 2013. Of 68 imaging features that could be pooled (85 articles, 23,255 cancers; random-effects meta-analysis), 11 significantly related to HER2 overexpression. Results based on five or more studies and robustness in subgroup analyses were as follows: the presence of microcalcifications on mammography [pooled odds ratio (pOR), 3.14; 95% confidence interval (CI), 2.46–4.00] or ultrasound (mass-associated pOR, 2.95; 95% CI, 2.34–3.71), branching or fine linear microcalcifications (pOR, 2.11; 95% CI, 1.07–4.14) or extremely dense breasts on mammography (pOR, 1.37; 95% CI, 1.07–1.76), and washout (pOR, 1.57; 95% CI, 1.11–2.21) or fast initial kinetics (pOR, 2.60; 95% CI, 1.43–4.73) on MRI all increased the chance of HER2 overexpression. Maximum [18F]fluorodeoxyglucose standardized uptake value (SUVmax) was higher upon HER2 overexpression (pooled mean difference, +0.76; 95% CI, 0.10–1.42). These results show that several imaging features relate to HER2 overexpression, lending credibility to the hypothesis that imaging phenotype reflects cancer behavior. This implies prognostic relevance, which is especially relevant as imaging is readily available during diagnostic work-up. Cancer Epidemiol Biomarkers Prev; 23(8); 1464–83. ©2014 AACR.

HER2-targeted therapy dramatically improves outcomes in early breast cancer. Here we report the results of two HER2-targeted combinations in the neoadjuvant I-SPY2 phase 2 adaptive platform trial for early breast cancer at high risk of recurrence: ado-trastuzumab emtansine plus pertuzumab (T-DM1/P) and paclitaxel, trastuzumab and pertuzumab (THP). Eligible women have >2.5 cm clinical stage II/III HER2+ breast cancer, adaptively randomized to T-DM1/P, THP, or a common control arm of paclitaxel/trastuzumab (TH), followed by doxorubicin/cyclophosphamide, then surgery. Both T-DM1/P and THP arms ‘graduate’ in all subtypes: predicted pCR rates are 63%, 72% and 33% for T-DM1/P (n = 52), THP (n = 45) and TH (n = 31) respectively. Toxicity burden is similar between arms. Degree of HER2 pathway signaling and phosphorylation in pretreatment biopsy specimens are associated with response to both T-DM1/P and THP and can further identify highly responsive HER2+ tumors to HER2-directed therapy. This may help identify patients who can safely de-escalate cytotoxic chemotherapy without compromising excellent outcome., HER2-targeted therapy improves patient’s outcome in early breast cancer. Here, the authors present the efficacy and biomarker analysis of two HER2-targeted combinations (ado-trastuzumab emtansine plus pertuzumab and paclitaxel, trastuzumab and pertuzumab) in the context of the neoadjuvant I-SPY2 phase 2 adaptive platform trial for early breast cancer at high risk of recurrence.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to-patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

BACKGROUND We proposed that a test for sensitivity to the adjuvant endocrine therapy component of treatment for patients with stage II-III breast cancer (SET2,3) should measure transcription related to estrogen and progesterone receptors (SETER/PR index) adjusted for a baseline prognostic index (BPI) combining clinical tumor and nodal stage with molecular subtype by RNA4 (ESR1, PGR, ERBB2, and AURKA). PATIENTS AND METHODS Patients with clinically high-risk, hormone receptor-positive (HR+), human epidermal growth factor receptor 2 (HER2)-negative (HR+/HER2-) breast cancer received neoadjuvant taxane-anthracycline chemotherapy, surgery with measurement of residual cancer burden (RCB), and then adjuvant endocrine therapy. SET2,3 was measured from pre-treatment tumor biopsies, evaluated first in an MD Anderson Cancer Center (MDACC) cohort (n = 307, 11 years' follow-up, U133A microarrays), cut point was determined, and then independent, blinded evaluation was carried out in the I-SPY2 trial (n = 268, high-risk MammaPrint result, 3.8 years' follow-up, Agilent-44K microarrays, NCI Clinical Trials ID: NCT01042379). Primary outcome measure was distant relapse-free survival. Multivariate Cox regression models tested prognostic independence of SET2,3 relative to RCB and other molecular prognostic signatures, and whether other prognostic signatures could substitute for SETER/PR or RNA4 components of SET2,3. RESULTS SET2,3 added independent prognostic information to RCB in the MDACC cohort: SET2,3 [hazard ratio (HR) 0.23, P = 0.004] and RCB (HR 1.77, P < 0.001); and the I-SPY2 trial: SET2,3 (HR 0.27, P = 0.031) and RCB (HR 1.68, P = 0.008). SET2,3 provided similar prognostic information irrespective of whether RCB-II or RCB-III after chemotherapy, and in both luminal subtypes. Conversely, RCB was most strongly prognostic in cancers with low SET2,3 status (MDACC P < 0.001, I-SPY2 P < 0.001). Other molecular signatures were not independently prognostic; they could effectively substitute for RNA4 subtype within the BPI component of SET2,3, but they could not effectively substitute for SETER/PR index. CONCLUSIONS SET2,3 added independent prognostic information to chemotherapy response (RCB) and baseline prognostic score or subtype. Approximately 40% of patients with clinically high-risk HR+/HER2- disease had high SET2,3 and could be considered for clinical trials of neoadjuvant endocrine-based treatment.

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). Table 1. pCR rate by HR/HER2 subtype in each MRI cluster at T1 Citation Format: Nu N. Le, Natsuko Onishi, David C. Newitt, Jessica E. Gibbs, Lisa J. Wilmes, Efstathios Gennatas, Barbara LeStage, Laura J. Esserman, Nola M. Hylton, Wen Li. Association of MRI morphologic phenotype from unsupervised learning with breast cancer subtypes and treatment response [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD16-07.

This study tested the hypothesis that a change in the apparent diffusion coefficient (ADC) measured in diffusion-weighted MRI (DWI) is an independent imaging marker, and ADC performs better than functional tumor volume (FTV) for assessing treatment response in patients with locally advanced breast cancer receiving neoadjuvant immunotherapy. A total of 249 patients were randomized to standard neoadjuvant chemotherapy with pembrolizumab (pembro) or without pembrolizumab (control). DCE-MRI and DWI, performed prior to and 3 weeks after the start of treatment, were analyzed. Percent changes of tumor ADC metrics (mean, 5th to 95th percentiles of ADC histogram) and FTV were evaluated for the prediction of pathologic complete response (pCR) using a logistic regression model. The area under the ROC curve (AUC) estimated for the percent change in mean ADC was higher in the pembro cohort (0.73, 95% confidence interval [CI]: 0.52 to 0.93) than in the control cohort (0.63, 95% CI: 0.43 to 0.83). In the control cohort, the percent change of the 95th percentile ADC achieved the highest AUC, 0.69 (95% CI: 0.52 to 0.85). In the pembro cohort, the percent change of the 25th percentile ADC achieved the highest AUC, 0.75 (95% CI: 0.55 to 0.95). AUCs estimated for percent change of FTV were 0.61 (95% CI: 0.39 to 0.83) and 0.66 (95% CI: 0.47 to 0.85) for the pembro and control cohorts, respectively. Tumor ADC may perform better than FTV to predict pCR at an early treatment time-point during neoadjuvant immunotherapy.

Neoadjuvant therapy in breast cancer can downstage axillary lymph nodes and reduce extent of axillary surgery. As such, accurate determination of nodal status after neoadjuvant therapy and before surgery impacts surgical management. There are scarce data on the diagnostic accuracy of breast magnetic resonance imaging (MRI) for nodal evaluation after neoadjuvant therapy in patients with invasive lobular carcinoma (ILC), a diffusely growing tumor type. We retrospectively analyzed patients with stage 1–3 ILC who underwent pre-operative breast MRI after either neoadjuvant chemotherapy or endocrine therapy at our institution between 2006 and 2019. Two breast radiologists reviewed MRIs and evaluated axillary nodes for suspicious features. All patients underwent either sentinel node biopsy or axillary dissection. We evaluated sensitivity, specificity, negative and positive predictive values, and overall accuracy of the post-treatment breast MRI in predicting pathologic nodal status. Of 79 patients, 58.2% received neoadjuvant chemotherapy and 41.8% neoadjuvant endocrine therapy. The sensitivity and negative predictive value of MRI were significantly higher in the neoadjuvant endocrine therapy cohort than in the neoadjuvant chemotherapy cohort (66.7 vs. 37.9%, p = 0.012 and 70.6 vs. 40%, p = 0.007, respectively), while overall accuracy was similar. Upstaging from clinically node negative to pathologically node positive occurred in 28.0 and 41.7%, respectively. In clinically node positive patients, those with an abnormal post-treatment MRI had a significantly higher proportion of patients with ≥4 positive nodes on pathology compared to those with a normal MRI (61.1 versus 16.7%, p = 0.034). Overall, accuracy of breast MRI for predicting nodal status after neoadjuvant therapy in ILC was low in both chemotherapy and endocrine therapy cohorts. However, post-treatment breast MRI may help identify patients with a high burden of nodal disease (≥4 positive nodes), which could impact pre-operative systemic therapy decisions. Further studies are needed to assess other imaging modalities to evaluate for nodal disease following neoadjuvant therapy and to improve clinical staging in patients with ILC.

Background: I-SPY 2 is a multicenter, phase 2 trial using response-adaptive randomization within molecular subtypes defined by receptor status and MammaPrint (MP) risk to evaluate novel agents as neoadjuvant therapy for women with high-risk stage II/III breast cancer. The primary endpoint is pathologic complete response (pCR, ypT0/Tis ypN0). SGN-LIV1A is an investigational antibody drug conjugate consisting of an antibody to LIV1A, a zinc transporter highly prevalent in breast cancer, and a highly potent microtubule inhibitor, monomethyl auristatin E. Retrospective IHC analysis of LIV1A expression levels amongst tumor samples from 100 previous ISPY-2 patients showed 88% of breast tumor samples with moderate to high expression of LIV1A (Yau, C. et al SABCS 2018).Methods: Women with tumors ≥ 2.5cm were eligible for screening. Only HER2 negative (HER2-) patients were eligible for this treatment, hormone-receptor positive (HR+) patients had to have MP high molecular profile. Treatment included SGN-LIV1A 2.5 mg/kg (max dose 250 mg) every 3 weeks x 4, followed by doxorubicin/cyclophosphamide (AC) every 2-3 weeks x 4. The control arm was weekly paclitaxel x 12 followed by AC every 2-3 weeks x 4. All patients undergo serial MR imaging where response at 3 & 12 weeks combined with accumulating pCR data are used to estimate, and continuously update, predicted pCR rate for the trial arm. Analysis set was modified intention to treat with patients who switched to non-protocol therapy counted as non-pCR and not as their pCR status at time of surgery. 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 signatures defined by HR & HER2 status & MP result. This investigational arm was eligible for graduation in 3 of 10 pre-defined signatures: HER2-, HR+HER2- and HR-HER2-. Regimens may also leave the trial for futility (< 10% probability of success), maximum sample size accrual (10% < probability of success Results: Sixty patients were randomized and evaluable to SGN-LIV1A. The study arm was stopped due to reaching the predetermined time limit for patient accrual of 2 yrs. Final estimated pCR rates are below. The estimated pCR rates were similar between the SGN-LIV1A and control arms for any tumor subtype. Preliminary safety events for SGN-LIV1A include increased rates of transaminitis and hyperglycemia and reduced rates of peripheral neuropathy compared to control. One patient was removed from the analysis as she was determined to have angiosarcoma of the breast. Notably, this patient had a dramatic early response and subsequent pCR to SGN-LIV1A treatment. Conclusion: The value of I-SPY 2 is to give insight about the performance of an investigational agent’s likelihood of achieving pCR. SGN-LIV1A delivered every 3 weeks was comparable to paclitaxel for the primary endpoint of pCR in I-SPY2 and may have a similar side effect profile, however, with less peripheral neuropathy. Clinical trials evaluating weekly dosing of SGN-LIV1A are ongoing. A trial of SGN-LIV1A in the treatment of angiosarcoma is under consideration at this time. Final Estimated pCR Rates and Predictive ProbabilitiesEstimated pCR rate(95% prob interval)SignatureSGN-LIV1AControlProbability SGNLIV1A Superior to ControlPredictive Probability of Success in Phase 3HER2-0.16 (0.08-0.24) N= 600.20 (0.16-0.25) N= 3270.180.02HR-/HER2-0.25 (0.12-0.37) N=360.28 (0.21-0.35) N=1460.310.06HR+/HER2-0.09 (0-0.18) N=240.14 (0.09-0.19) N=1810.150.03 Citation Format: Heather Beckwith, Richard Schwab, Christina Yau, Erica Stringer-Reasor, Shi Wei, A. Jo Chien, Kathy S Albain, Kevin Kalinsky, Anne Wallace, Anthony Elias, Douglas Yee, Amy S Clark, Judy C Boughey, Heather Han, Rita Nanda, Claudine Isaacs, Zahi Mitri, Julie E Lang, Alexandra Thomas, Tara Sanft, Angela DeMichele, Jane Perlmutter, Hope S Rugo, Nola M Hylton, W. Fraser Symmans, Michelle E Melisko, Laura J van't Veer, I-SPY 2 Consortium, Amy Wilson, Smita M Asare, Ashish Sanil, Donald A Berry, Laura J Esserman. Evaluation of SGN-LIV1a followed by AC in high-risk HER2 negative stage II/III breast cancer: Results from the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD1-10.

Introduction: One of the principal limiting factors to achieving cures in patients with cancer is drug resistance. Drug repositioning is the application of FDA-approved drug compounds for novel indications beyond the scope of the drug’s original intended use. This approach offers advantages over traditional drug development by reducing development costs and providing shorter paths to approval, as drug safety has already been established during the drug’s original regulatory process. One approach for computational drug repositioning involves generating a disease gene expression signature and then identifying a drug that can reverse this disease signature. In this study, we extracted drug resistance signatures from the I-SPY 2 TRIAL by comparing gene expression profiles of responder and non-responder patients stratified by treatment and molecular subtype. We then applied our drug repositioning pipeline to predict compounds that can reverse the gene expression profiles of these drug resistance signatures. We hypothesize that reversing these drug resistance signatures will resensitize tumors to treatment and improve patient outcome. Methods: We first generated the drug resistance signatures by performing differential expression between responders (RCB 0/I) and non-responders (RCB III) within treatment arms and molecular subtypes. An optimal log fold-change cutoff was selected for each signature by identifying the cutoff that best separates the responder and non-responder samples using k-means clustering. We then applied our drug repositioning pipeline to identify compounds that significantly reverse these signatures using the drug perturbation profiles generated in a breast cancer cell line in the Connectivity Map v2 dataset. Briefly, the pipeline uses a non- parametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov (KS) statistic to assess the enrichment of resistance genes in a ranked drug gene expression list. Significance of each prediction is estimated from a null distribution of scores generated from random gene signatures. Results: We found that few individual genes are shared among the resistance signatures across the treatment arms and molecular subtypes, with the most common genes present in only 5/17 of the treatment arm and molecular subtype groups. At the pathway-level, however, we found that immune-related pathways are generally enriched among the responders and estrogen-response pathways are generally enriched among the non-responders. Although most of our drug predictions are unique to treatment arms and molecular subtypes, our drug repositioning pipeline identified the selective estrogen receptor degrader (SERD) fulvestrant as a compound that can potentially reverse resistance across a majority of the treatment arms and molecular subtypes. Conclusion: We applied our drug repositioning pipeline to identify novel agents to sensitize drug-resistant tumors in the I-SPY 2+ clinical trial and identified a SERD, fulvestrant, as a potential candidate for multiple molecular subtypes and treatment arms. Citation Format: Katharine Yu, Amrita Basu, Christina Yau, Denise Wolf, Gillian Hirst, Laura Sit, Nicholas O’Grady, Thelma Brown, I-SPY 2 TRIAL Investigators, Angela DeMichele, Don Berry, Nola Hylton, Doug Yee, Laura Esserman, Laura van 't Veer, Marina Sirota. Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatment arms and molecular subtypes [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS11-04.

Introduction: Dedicated breast positron emission tomography (dbPET) is an emerging imaging technique with the spatial resolution needed to assess functionality and intra-tumor heterogeneity in primary breast lesions. Breast cancer patients may benefit from dbPET imaging combined with molecularly targeted agents to non-invasively assess and predict response to targeted therapy in the neoadjuvant treatment setting. We have previously observed that [18F]-fluorodeoxyglucose (FDG) PET provides tumor metabolic information complementary to the angiogenic properties reflected by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for characterizing triple-negative breast cancers (TNBC) (1). In this study, we examined the relationship between FDG-dbPET and MRI features in a cohort of breast cancer patients receiving neoadjuvant chemotherapy (NAC). Methods: With institutional review board approval, patients with biopsy-proven locally-advanced breast cancer were imaged with breast MRI and dbPET before (T0) and after three weeks (T1) of NAC. Standard DCE-MRI was obtained using a dedicated breast coil. Patients also underwent dbPET with 5 mCi of FDG at 45 minutes post-injection. Functional tumor volumes (FTV) were calculated from DCE-MRI by summing all voxels with an early percent enhancement (PE) exceeding 70% within a manually defined volume of interest (VOI). Maximum and mean PE (PEMax, PEMean) values within the VOI were also computed for analyses. Tumors were segmented in dbPET images using semi-automated threshold-driven methods. Body weight-corrected maximum and mean standardized uptake values (SUVMax, SUVMean), total lesion glycolysis (TLG), and metabolic tumor volume (MTV) were calculated for FDG-dbPET. Percent change relative to T0 (Δ = 100*(T1 - T0)/T0) was calculated for each feature. Spearman’s correlation coefficient was used to evaluate the relationship between MRI and dbPET features. Results: Of the 16 patients enrolled in this study, 13 patients (N = 15 unique tumors) with MRI and dbPET at T0 and T1 were included in the analysis. 46% (6/13) of the patients had TNBC. Our initial findings indicated that ΔPEMax and ΔSUVMax had the highest correlation (ρ = 0.59, p = 0.022). FTV and TLG at T1 were also correlated (ρ = 0.56, p = 0.032). Among all imaging features, ΔMTV showed the largest post-treatment difference between TNBC (-54.5%) and non-TNBC (-6.06%) groups. Among MRI features, ΔFTV exhibited the largest difference between the groups: -70.4% in TNBC and -43.1% in non-TNBC. ΔSUVMax and ΔTLG were additional dbPET features with large differences between TNBC and non-TNBC patients (Table 1). Conclusion: This exploratory study suggests that post-treatment ΔSUVMax and TLG provide complementary metabolic information to angiogenic properties (ΔPEMax and FTV, respectively) reflected by MRI. Other dbPET features may provide independent information adjunct to MRI for describing primary breast tumors. Patients with TNBC exhibited larger reductions in FDG uptake values and metabolic volume than non-TNBC patients. These observed reductions may improve early treatment response in patients with TNBC, enabling more precise treatment guidance. Further studies in larger cohorts are needed to validate these initial observations. 1. Bolouri MS, et al. Triple-Negative and Non-Triple-Negative Invasive Breast Cancer: Association between MR and Fluorine 18 Fluorodeoxyglucose PET Imaging. Radiology 2013;269:354-61 Comparison of ΔMRI and ΔFDG-dbPET in TNBC vs non-TNBC patientsAll Tumors (N = 15 tumors) Median(IQR)TNBC (N = 6 tumors) Median(IQR)non-TNBC (N = 9 tumors) Median(IQR)DCE-MRIΔFTV (%)-66.1 (-77.6, -19.1)-70.4 (-79.0, -62.1)-43.1 (-72.5, -2.95)ΔPEMax (%)-9.91 (-31.5, 19.5)-10.3 (-26.3, 24.2)-9.91 (-31.8, 8.42)ΔPEMean (%)-10.7 (-22.4, 2.77)-9.57 (-12.8, 0.29)-17.0 (-26.0, 2.33)FDG-dbPETΔSUVMax (%)-31.6 (-53.9, -20.6)-47.3 (-55.7, -41.1)-23.1 (-31.6, 1.13)ΔSUVMean (%)-34.1 (-65.4, -14.2)-48.5 (-74.6, -9.74)-34.1 (-44.5, -14.8)ΔMTV (%)-6.18 (-57.0, 38.5)-54.5 (-75.4, 15.3)-6.06 (-47.2, 38.9)ΔTLG (%)-64.9 (-75.2, 23.3)-75.2 (-84.0, -60.0)-47.9 (-65.2, 31.1) Citation Format: Deep K Hathi, Ella F Jones, Wen Li, David C Newitt, Ruby Guo, Youngho Seo, Robert R Flavell, Bonnie N Joe, Diane Heditsian, Susie Brain, ISPY-2 Imaging Working Group, ISPY-2 Consortium, Laura J Esserman, Nola M Hylton. Relationship of dedicated breast PET and MRI features in breast cancer patients receiving neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS13-50.

I-SPY2 is a neoadjuvant trial evaluating experimental therapies in combination with cytotoxic chemotherapy compared to chemotherapy alone with the primary endpoint of pathologic complete response (pCR). Abundant preclinical evidence suggested the type I insulin-like growth factor receptor (IGF-1R) regulated breast cancer growth, although multiple clinical trials did not show benefit. We were the first to report the results of a monoclonal IGF-1R antibody ganitumab (G) in combination with chemotherapy. PGM followed by doxorubicin/cyclophosphamide (AC) did not result in substantial increases in pCR when compared to P followed by AC. In this report, we examined several potential predictive biomarkers. IGF-1R inhibitors induce hyperglycemia and we examined hemoglobin A1C (HgbA1c) as a measure of glucose control in patients before and after PGM therapy. 106 patients received PGM and 104 patients had baseline HgbA1c with a median of 5.4%. However, 27% (28/104) had levels greater than 5.7% the upper limit of normal as defined by the NIDDK. 4 of 104 had HgbA1c greater than 6.5%, a level associated with type 2 diabetes. pCR rates are similar between patients with baseline HgbA1c ≤5.7% (21%) vs. >5.7% (25%) (Fisher test p=0.79). 72 of these patients had an additional HgbA1c during the course of PGM therapy. For patients with HgbA1c ≤5.7%, 27% (14/52) had subsequent elevation above 5.7% after PGM. For patients with a baseline HgbA1C >5.7%, all 20 patients continued to have elevated levels through PGM. We also examined pre-treatment tumor gene expression profiles derived from custom Agilent 44K full-genome microarrays. We studied 11 genes associated with the IGF-1R signaling (IGF1, IGF2, IGF1R, INSR, IGFBP2, IRS1, IRS2, IGFBP3, IGFBP4, IGFBP5, CDH1), the IGFBP5/IGFBP4 ratio, and two IGFR expression signatures (Creighton, et al. J Clin Oncol 26:4078 2008 PMID: 18757322; Mu, et al. Breast Cancer Res Treat 133:321 2012 PMID: 22297468). The 2 signatures evaluated: the IGF1 ligand score and the IGF1-R signature are anti-correlated (Rp= - 0.79). In the population as a whole, lower levels of IRS1 and IGFBP5 significantly associated with response to PGM (likelihood ratio test (LR) p< 0.05), as do lower levels of the IGF1 ligand score and higher levels of the IGF-1R signature. However, levels of IRS1 and the two expression signatures also trend toward or are significantly associated with response in the control arm; and treatment interactions for all four biomarkers are non-significant (LR p>0.05). Therefore, none of these biomarkers qualify as specific predictors of response to PGM. Similarly, high MammaPrint scores (MP2) were associated with higher pCR scores in both PGM and Control arms. Previous gene expression profiles were divided into tertiles (low, intermediate, high). Similar to the continuous case, IGF1Rsig-class associates with pCR in both the PGM and control arms (Fisher test p=0.033 and 0.044, respectively), and thus also fails as a specific predictor of response to PGM. We conclude that PGM therapy results in worsening of glucose control and likely increases serum insulin levels. While IGF gene expression profiling associated with treatment response, they were not specific for PGM. Further, biomarker analysis and strategies to control glucose will be needed to optimize anti-IGF-1R therapies. Citation Format: Douglas Yee, Paul Haluska, Denise M Wolf, Christina Yau, Amy Wilson, I-SPY2 TRIAL Consortium, Angela DeMichele, Claudine Isaacs, Jane Perlmutter, Joan Venticinque, Hope S Rugo, Richard Schwab, Nola M Hylton, W Fraser Symmans, Michelle E Melisko, Teresa L Helsten, Laura J van't Veer, Donald A Berry, Laura J Esserman. Biomarker analysis of paclitaxel, ganitumab, and metformin (PGM) therapy in the I-SPY2 neoadjuvant clinical trial [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-08.

Background: The I-SPY 2 TRIAL, open to patients with locally advanced, molecular high-risk breast cancer, aims to bring each patient to pathologic complete response (pCR) with a minimum of toxicity. Here we test the hypothesis that imaging (MR volume predictors) combined with core biopsy may be used to accurately select candidates who show early response and provide an option of treatment de-escalation at mid-therapy (12 weeks). Methods: Of 100 I-SPY 2 patients with pathologist-assessed core biopsies at the inter-regimen time point (~12 weeks through treatment) and pCR data, 87 also had serial MR images and were considered in this study. Eleven I-SPY 2 TRIAL pathologists independently provided a digital assessment of the presence or absence of residual invasive cancer from H&E stained, and any requested ancillary IHC, images from imaging-guided core biopsies. Pathology predicts pCR if there is a consensus of no invasive residual disease. We generated predictions for all (55) unique pairs over the 11 pathologists, where pCR is predicted if both pathologists find no invasive cells. MRI pCR prediction models were previously developed on an independent dataset of ~990 I-SPY 2 patients, and applied to this cohort. Volume-based prediction models were previously optimized within each subtype and predicted probability thresholds were selected over a range of positive predictive value (PPV). In this study, MR predicts pCR (positive test) if the predicted probability is above a threshold that yields a given PPV value. For each pathologist pair, we combined pathology-based and MR-based predictors into a predictive-RCB (pre-RCB); and pre-RCB predicts a patient as pCR (RCB0) if both MR and pathology predicts pCR. Predictive performance is assessed by calculating the mean and range of PPV and sensitivity.Results: 39% (34/87) of the patients in this study achieved pCR. Over all pairs of pathologists, on average 80% of pathology-only predicted pCRs were true pCRs (mean PPV = 80% [range: 69-92%]), and 74% of patients who achieved pCR were predicted pCR by pathology alone (mean sensitivity = 74% [65-82%]). We assessed combinations with MR probability thresholds at PPV levels 50%-70%; and observed the best balance of PPV and sensitivity for the pre-RCB when MR thresholds were set at 50% PPV level. At this threshold setting, the pre-RCB achieved a PPV = 92% [83-100%], meaning on average 92% of predicted pCRs were true pCRs, and this improvement in positive predictive performance over pathology alone is achieved with a lower but still-reasonable 53% sensitivity [33-62%]. Conclusion: Pre-RCB, which predicts a patient as pCR if both MR and inter-regimen pathology predicts pCR, provides clinically actionable accuracy for treatment de-escalation for early responders (PPV>90%). Adding a final MR review at the time of early surgery may further improve performance. Resulting from data presented in this abstract, the pre-RCB algorithm, including the final MR review, has been operationalized and will be used prospectively to identify patients who are highly likely to have already achieved pCR by the inter-regimen timepoint. Citation Format: Sara J Venters, Wen Li, Denise M Wolf, Jodi M Carter, Molly E Klein, Kamaljeet Singh, Kimmie Rabe, I Tolgay Ocal, David Newitt, Christina Yau, Natsuko Onishi, Jessica Gibbs, Sunati Sahoo, Shuko Harada, Laila Khazai, Malini Harigopal, Alexander D Borowsky, Gregor Krings, Ronald Balassanian, Yunn-Yi Chen, Kimberley Cole, Sonal Shad, Barbara LeStage, Amy Delson, Sandra Finestone, Lamorna Brown-Swigart, I-SPY 2 Imaging Working Group, I-SPY 2 TRIAL Consortium, Laura Esserman, Laura van ‘t Veer, W Fraser Symmans, Nola M Hylton. Serial MRI and pathology combined to select candidates for therapy de-escalation in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-10.

Background: The I-SPY 2 TRIAL randomizes patients with breast cancer to neoadjuvant chemotherapy (NAC) with or without experimental agents followed by anthracycline-cyclophosphamide (AC). As part of a de-escalation strategy to avoid overtreatment in good responders, combined information from serial MRI and mid-treatment core biopsy will be used to identify patients who may be candidates to skip AC. Presented are results of a pilot study to assess the location of biopsy clip in relation to tumor enhancement in MRI before and during treatment. Methods: 40 patients enrolled in I-SPY 2 who underwent mid-treatment core biopsy were reviewed. Patients had MRIs at four time points: pre-treatment (T0), early treatment (T1, 3 weeks after the start of first regimen), inter-regimen (T2, 12 weeks after completing the first regimen), and pre-surgery (T3). Clip visibility and location were assessed by a trained researcher on T1 weighted, fat suppressed dynamic contrast enhanced MR images at three time points: T0, T1, and T2. If clip was visible, location was scored 1 (inside), 2 (edge), or 3 (outside) in relation to tumor enhancement seen on a signal enhancement ratio (SER) map. Clips inside (score 1) were fully embedded and surrounded by a clear margin of tumor enhancement. Clips on the edge (score 2) were not fully embedded, with a portion of the clip touching tumor enhancement. Clips outside (score 3) had no portion of clip touching tumor enhancement. For patients with a focal tumor but with multiple clips visible in MRI, the clip most embedded within tumor enhancement was designated as the primary clip for evaluation. In cases of multifocal disease, the clip visualized in the largest area of tumor enhancement was assessed. Clips touching tumor cavity edge and enhancement were scored as 3. Results: Among 40 patients, two patients had no clips visualized in MRI at all three time points. One patient had no clip visualized at T0, but a clip was observed at T1 and T2. In addition, one T1 MRI was rejected due to incomplete exam and one T2 MRI was rejected due to different scanner from baseline. At T0, 51% (19/37) of clips were inside tumor enhancement and no clips were assessed outside tumor enhancement. 59% (22/37) of clips at T1 were on the edge or outside. At T2, 73% (27/37) of clips were on the edge and 19% (7/37) of clips were outside. While no clips were identified outside of tumor at baseline, tumor shrinkage with treatment resulted in clips outside of tumor in approximately 20 percent of cases at inter-regimen, and higher rates of clips identified at tumor edge. Conclusions: Clips were visible and location could be assessed in MRI in a majority of cases. Clip evaluation can be challenging and attention to clip placement is essential for patients with multifocal disease or diffuse tumors. The results of this study may have implications for assisting the mid-treatment core biopsy and selecting candidates for de-escalation of neoadjuvant chemotherapy. Table 1: Clips observed at three MR time pointsT0T12T231 (inside)191532 (edge)1821273 (outside)01171 No clips were seen outside tumor enhancement 2 One T1 MR was rejected due to incomplete exam 3 One T2 MR was rejected due to different scanner from baseline Citation Format: Teffany Joy Bareng, Jessica Gibbs, Wen Li, Natsuko Onishi, Bonnie N Joe, Elissa Price, Barbara LeStage, Coleen Crespo, I-SPY 2 Imaging Working Group, I-SPY 2 Consortium, Nola M Hylton. Assessment of clip locations on breast MRI during NAC to guide tumor bed biopsy at mid-treatment [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-14.

Background: The I-SPY 2 TRIAL is a multi-site response adaptive clinical trial evaluating novel drug combinations for neoadjuvant treatment of breast cancer. Patients receive four or more MRI studies during treatment, and serial measurement of functional tumor volume (FTV) is used to assess response. Under FDA IDE approval, FTV plays an integral role in adjusting patient randomization and evaluating treatment efficacy. FTV is a quantitative measure that requires stringent standards for image quality and protocol adherence. The I-SPY 2 TRIAL consistently reports a high level of data quality and data acceptance for FTV measurements. We present an overview of MRI operational performance and share lessons learned about maintaining high quality MRI data in a multi-site clinical trial. Methods: Over the 10-year course of the I-SPY 2 TRIAL, workflow has been improved to optimize communication between the Imaging Core Lab (ICL) and sites and to collect details about the MRI that are needed for accurate FTV measurement. A standardized imaging acquisition protocol is distributed to all sites, and new sites submit two test cases for review at site initiation. A scan verification form is required for each MRI study completed at sites to document critical information. Sites submit studies using TRIAD image transfer and de-identification software (American College of Radiology), and data is archived and processed at the ICL. All MRI studies are reviewed by the ICL for protocol adherence as soon as they are submitted, and feedback is provided to sites. Image quality factors including motion, fat suppression, and signal-to-noise ratio are qualitatively assessed. The ICL communicates with sites through a centralized email account, regular Coordinator Calls, and Imaging Working Group meetings to discuss emerging issues and offer ongoing training. The ICL contributes to revisions of study protocols and standard operating procedures. Results: As of June 2020, 3020 patients had been registered in I-SPY 2, 1741 patients randomized to treatment with one of 18 experimental drugs or standard therapy (controls), and a total of 7527 MRI studies were performed. FTV could be calculated for 97% (7317/7527) of MRI studies. Of the 7317 studies where FTV could be calculated, relatively minor issues with image quality or imaging protocol adherence were documented for 28% (2030/7317). These issues included motion artifacts (32%, 659/2030), off-protocol scan duration (21%, 433/2030), off-protocol contrast injection rate (14%, 281/2030), and off-protocol imaging field of view (9%, 191/2030). Conclusion: Breast MRI studies using a variety of scan protocols are well-suited for diagnostic evaluation, including BIRADS categorization, measurement of longest diameter, and assessment of lesion washout. The quantitative measures used in the I-SPY 2 trial require adherence to a specific imaging protocol that is kept consistent for all MRI studies for a single patient. Operational standardization, clear communication with sites, and streamlined workflow yield high quality MRI data across multiple sites and scanners. As a result, FTV is a robust biomarker of response to treatment, and is being used to predict patient response and guide treatment planning. We are actively investigating strategies that will improve FTV accuracy for predicting response and informing guidelines for treatment de-escalation. This will allow the ICL to further standardize and improve image quality and will provide the foundation for testing a variety of imaging biomarkers in the I-SPY 2 TRIAL. Citation Format: Jessica Gibbs, David C Newitt, Margarita Watkins, Wen Li, Lisa Cimino, Clifton Li, Natsuko Onishi, Lisa J Wilmes, Teffany Joy Bareng, Evelyn Proctor, Barbara LeStage, Bev Parker, the I-SPY 2 Coodinators, the I-SPY 2 Imaging Working Group, Nola M Hylton. Operational standardization and quality assurance yield high acceptance rate for breast MRI in the I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS11-08.

Objective The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance. Methods The A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping. Results Thirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p Conclusion Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.

This retrospective study examined magnetic resonance imaging (MRI)–derived tumor sphericity (SPH) as a quantitative measure of breast tumor morphology, and investigated the association between SPH and reader-assessed morphological pattern (MP). In addition, association of SPH with pathologic complete response was evaluated in patients enrolled in an adaptively randomized clinical trial designed to rapidly identify new agents for breast cancer. All patients underwent MRI examinations at multiple time points during the treatment. SPH values from pretreatment (T0) and early-treatment (T1) were investigated in this study. MP on T0 dynamic contrast-enhanced MRI was ranked from 1 to 5 in 220 patients. Mean SPH values decreased with the increased order of MP. SPH was higher in patients with pathologic complete response than in patients without (difference at T0: 0.04, 95% confidence interval [CI]: 0.02–0.05, P &lt, 001, difference at T1: 0.03, 95% CI: 0.02–0.04, P &lt, 001). The area under the receiver operating characteristic curve was estimated as 0.61 (95% CI, 0.57–0.65) at T0 and 0.58 (95% CI, 0.55–0.62) at T1. When the analysis was performed by cancer subtype defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status, highest area under the receiver operating characteristic curve were observed in HR−/HER2+: 0.67 (95% CI, 0.54–0.80) at T0, and 0.63 (95% CI, 0.51–0.76) at T1. Tumor SPH showed promise to quantify MRI MPs and as a biomarker for predicting treatment outcome at pre- or early-treatment time points.

In diffusion-weighted MRI (DW-MRI), choice of

PURPOSETo examine the ability of 5 artificial intelligence (AI)-based computer vision algorithms, most trained to detect visible breast cancer on mammograms, to predict future risk relative to the Breast Cancer Surveillance Consortium clinical risk prediction model (BCSC v2).PATIENTS AND METHODSIn this case-cohort study, women who had a screening mammogram in 2016 at Kaiser Permanente Northern California with no evidence of cancer on final imaging assessment were followed through September 2021. Women with prior breast cancer or a known highly penetrant gene mutation were excluded. From the 329,814 total eligible women, a random subcohort of 13,881 women (4.2%) were selected, of whom 197 had incident cancer. All 4,475 additional incident cancers were also included. Continuous AI-predicted scores were generated from the index 2016 mammogram. Risk estimates were generated with the Kaplan-Meier method and time-varying area under the curve [AUC(t)].RESULTSFor incident cancers at 0-1 year (interval cancer risk), BCSC demonstrated an AUC(t) of 0.62 (95% CI, 0.58-0.66), and the AI algorithms had AUC(t)s ranging from 0.66-0.71, all significantly higher than BCSC (P < .05). For incident cancers at 1 to 5 years (5-year future cancer risk), BCSC demonstrated an AUC(t) of 0.61 (95% CI, 0.60-0.62), and the AI algorithms had AUC(t)s ranging from 0.63 to 0.67, all significantly higher than BCSC. Combined BCSC and AI models demonstrated AUC(t)s for interval cancer risk of 0.67-0.73 and for 5-year future cancer risk of 0.66-0.68.CONCLUSIONThe AI mammography algorithms we evaluated had significantly higher discrimination than the BCSC clinical risk model for interval and 5-year future cancer risk. Combined AI and BCSC models had slightly higher discrimination than AI alone.

Background: Previous studies have independently validated the prognostic relevance of residual cancer burden (RCB) after neoadjuvant chemotherapy. We used results from several independent cohorts in a pooled patient-level analysis to evaluate the relationship of RCB with long-term prognosis across different phenotypic subtypes of breast cancer, to assess generalisability in a broad range of practice settings. Methods: In this pooled analysis, 12 institutes and trials in Europe and the USA were identified by personal communications with site investigators. We obtained participant-level RCB results, and data on clinical and pathological stage, tumour subtype and grade, and treatment and follow-up in November, 2019, from patients (aged ≥18 years) with primary stage I–III breast cancer treated with neoadjuvant chemotherapy followed by surgery. We assessed the association between the continuous RCB score and the primary study outcome, event-free survival, using mixed-effects Cox models with the incorporation of random RCB and cohort effects to account for between-study heterogeneity, and stratification to account for differences in baseline hazard across cancer subtypes defined by hormone receptor status and HER2 status. The association was further evaluated within each breast cancer subtype in multivariable analyses incorporating random RCB and cohort effects and adjustments for age and pretreatment clinical T category, nodal status, and tumour grade. Kaplan-Meier estimates of event-free survival at 3, 5, and 10 years were computed for each RCB class within each subtype. Findings: We analysed participant-level data from 5161 patients treated with neoadjuvant chemotherapy between Sept 12, 1994, and Feb 11, 2019. Median age was 49 years (IQR 20–80). 1164 event-free survival events occurred during follow-up (median follow-up 56 months [IQR 0–186]). RCB score was prognostic within each breast cancer subtype, with higher RCB score significantly associated with worse event-free survival. The univariable hazard ratio (HR) associated with one unit increase in RCB ranged from 1·55 (95% CI 1·41–1·71) for hormone receptor-positive, HER2-negative patients to 2·16 (1·79–2·61) for the hormone receptor-negative, HER2-positive group (with or without HER2-targeted therapy; p

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. Table 1: Estimated pCR rates Citation Format: Claudine Isaacs, Rita Nanda, Jo Chien, Meghna S. Trivedi, Erica Stringer-Reasor, Christos Vaklavas, Judy C. Boughey, Amy Sanford, Anne Wallace, Amy S. Clark, Alexandra Thomas, Kathy S. Albain, Laura C. Kennedy, Tara B. Sanft, Kevin Kalinsky, Hyo S. Han, Nicole Williams, Mili Arora, Anthony Elias, Carla Falkson, Smita Asare, Ruixiao Lu, Maria Pitsouni, Amy Wilson, Jane Perlmutter, Hope Rugo, Richard Schwab, W. Fraser Symmans, Nola M. Hylton, Laura Van’t Veer, Douglas Yee, Angela DeMichele, Donald Berry, Laura J. Esserman, I-SPY Investigators. 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 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr GS5-03.

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. [1] Partridge et al., Radiology 289(3):618-27 (2018) [2] Nu et al., Tomography 8: 1208-20 (2022) 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. Citation Format: Judith Zimmermann, Julia Carmona-Bozo, Nu N. Le, Natsuko Onishi, Lisa J. Wilmes, Jessica E. Gibbs, Jiachao Liang, David C. Newitt, Savannah Partridge, Patrick Bolan, Bonnie N. Joe, Elissa R. Price, Barbara LeStage, Nola M. Hylton, Wen Li. Diffusion-weighted magnetic resonance imaging for subtype-specific prediction of pathologic complete response in neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-33.

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 (Cemi) is a PD-1 inhibitor approved for the treatment of NSCLC, cutaneous basal, and squamous cell cancer. Here, we report current efficacy rates of Cemi in combination with paclitaxel followed by AC. 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. Estimated pCR rates Citation Format: Erica Stringer-Reasor, Rebecca A. Shatsky, Jo Chien, Anne Wallace, Judy C. Boughey, Kathy S. Albain, Hyo S. Han, Rita Nanda, Claudine Isaacs, Kevin Kalinsky, Zahi Mitri, Amy S. Clark, Christos Vaklavas, Alexandra Thomas, Meghna S. Trivedi, Janice Lu, Smita Asare, Ruixiao Lu, Maria Pitsouni, Amy Wilson, Jane Perlmutter, Hope Rugo, Richard Schwab, W. Fraser Symmans, Nola M. Hylton, Laura Van ’t Veer, Douglas Yee, Angela DeMichele, Donald Berry, Laura J. Esserman, I-SPY Investigators. PD11-01 Evaluation of the PD-1 Inhibitor Cemiplimab in early-stage, high-risk HER2-negative breast cancer: Results from the neoadjuvant I-SPY 2 TRIAL [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD11-01.

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 Citation Format: Denise M. Wolf, Christina Yau, Julia Wulfkuhle, Rosa I. Gallagher, Lamorna A. Brown Swigart, Gillian L. Hirst, Jean-Philippe Coppe, Mark Jesus M. Magbanua, Rosalyn Sayaman, I-SPY2 Investigators, Laura Sit, Nola M. Hylton, Angela DeMichele, Donald A. Berry, Lajos Pusztai, Douglas Yee, Laura J. Esserman, Emanuel F. Petricoin, Laura Van ’t Veer. PD5-04 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 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD5-04.

Quantitative imaging (QI) plays a vital role in identifying biomarkers describing the genetic and phenotypic heterogeneity underpinning breast cancer through screening, diagnosis, and treatment assessment in patients receiving neoadjuvant chemotherapy. Changes to the structural biology of the breast tumor microenvironment are reflected in morphologic and functional imaging. They may serve as surrogate markers for identifying disease burden or therapeutic effect, which may provide valuable information on subsequent clinical decision making. In breast cancer, mammography is the primary tool for screening and initial evaluation, while magnetic resonance imaging (MRI) and positron emission tomography (PET) are used in diagnosis, staging, and treatment response assessment. In the past decade, advances in ultrafast sequence design in MRI, targeted PET tracers, and the adoption of new mammographic technology hold promise for rapid screening, more accurate diagnosis, and personalized treatment. In this chapter, the application of current QI technologies at each stage of breast cancer management is summarized, and advances in each modality for informing early diagnosis and therapy interventions to improve patient outcomes are highlighted.

Background With current imaging technologies, assessing response to neoadjuvant chemotherapy (NAC) or neoadjuvant endocrine therapy (NET) remains a challenge for patients with invasive lobular carcinoma (ILC). Therefore, we evaluated imaging features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before and after neoadjuvant therapy in a cohort of patients with ILC, including the longest tumor diameter (LD), functional tumor volume (FTV), and peak signal enhancement ratio (SER). FTV is the sum of all image voxels enhancing above a set threshold within a defined region, and has been validated as a predictor of recurrence-free survival (RFS) for breast cancer. SER is a measure of contrast wash-in and wash-out and reflects neovascularity of tumors. We determined whether baseline and post-treatment imaging features differed by type of neoadjuvant therapy and if they were associated with RFS in neoadjuvantly treated ILC regardless of treatment type. Methods With institutional review board approval, a retrospective analysis of pre- and post-treatment breast DCE-MRI was performed on a cohort of ILC patients receiving neoadjuvant therapy between 1998 and 2017. We compared pre-treatment, post-treatment, and percent reduction (α) in LD, FTV, and peak SER by neoadjuvant treatment type (i.e. NAC or NET) using the Wilcoxon rank-sum test. Univariate t-tests, Chi-squared tests, analysis of variance, and Spearmen’s correlation were used to evaluate associations of clinicopathologic features and treatment type. Univariate and multivariate associations with RFS in the entire neoadjuvantly treated cohort adjusting for treatment type were evaluated using the log-rank test and Cox proportional hazards models. Results A cohort of 76 patients with pre- and post-treatment MRI were included in this study, of whom 42 (55.3%) received NAC and 34 (44.7%) received NET. The NAC group was significantly younger (55 vs. 60 years, p=0.013), less likely to have stage 1 disease (26.2% vs. 73.5%, p ConclusionPre-treatment peak SER measured by MRI may provide prognostic information beyond standard clinicopathologic variables in patients with ILC receiving either neoadjuvant chemotherapy or endocrine therapy. Further evaluation in a larger ILC cohort is needed to validate our initial findings. Table 1. Comparison of MR imaging features of ILC patients receiving neoadjuvant chemotherapy and neoadjuvant endocrine therapy.Overall(n=76)NAC(n=42)NET(n=34)P-valuePre-treatment (median, IQR)LD (cm)2.7, 1.6-5.44.6, 2.5-71.8, 1.4-3.20.0006FTV (cc)5.8, 2.2-16.48.7, 4.1-24.23.0, 0.9-80.0004SER0.9, 0.8-1.00.9, 0.9-1.00.9, 0.8-1.10.8335Post-treatment (median, IQR)LD (cm)1.1, 0-1.91.0, 0-1.81.3, 0-1.90.6071FTV (cc)0.5, 0.1-1.80.3, 0.1-1.80.7, 0.3-1.80.2196SER0.9, 0.8-1.00.8, 0.8-1.00.9, 0.8-1.10.2321Percent reduction (%)LD44.6, 15.1-10079.6, 33.8-10033.8, 10.1-1000.0958FTV93, 72.5-97.495.8, 90.7-99.172.6, 43.1-94.5 Citation Format: Geraldine Tran, Ella Jones, Wen Li, Kelly Fahrner-Scott, David Newitt, Bonnie Joe, Laura Esserman, Nola Hylton, Rita Mukhtar. DCE-MRI derived imaging features for characterizing invasive lobular breast cancer and predicting recurrence-free survival after neoadjuvant therapy [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-08.

Introduction: Standard treatment for ductal carcinoma in situ (DCIS) involves surgical excision with radiation and often endocrine therapy. However, not all DCIS progresses to invasive ductal carcinoma (IDC); thus, surgical intervention may constitute overtreatment for DCIS that has low risk for progressing to IDC. A period of active surveillance (AS) with magnetic resonance imaging (MRI) monitoring may offer the opportunity to stratify lesions with high and low risk for invasion and to avoid overtreatment of DCIS. The purpose of this study was to characterize the outcomes of a cohort of women who elected to go on AS to avoid surgical intervention and to identify whether clinical and imaging features would identify patients who should convert to operative management. Methods: The clinicopathologic variables and outcomes of patients with DCIS who prospectively enrolled in MRI monitoring studies between 2002 and 2019 were retrospectively analyzed with IRB approval. We included 64 cases among 63 women who declined standard operative management for DCIS and had at least two breast MRIs. Clinicopathologic data and physician recommendations regarding continuing surveillance versus converting to operative management were recorded from the medical record. Those who had surgical excision showing IDC were considered to have progressed; those with only DCIS at excision or no operative intervention were considered to have stable disease. Results: Women in the cohort were an average of 53.6 years (29.8 - 78.9) old, and nearly all cases of DCIS with estrogen receptor (ER) status were ER+ (98.3%). Of the 64 cases in the cohort, 57 received endocrine therapy (89.1%). Average length of time on AS was 2.7 years (.2 – 11.9) with a median of 3 (2 – 18) MRIs performed and mean follow-up time 5.6 years (0.9 – 15.3). A total of 31 cases (48.4%) eventually had surgical excision while 33 (51.6%) remained on AS. There were 17 cases with IDC at surgery (26.6%). The IDC was an average of 1.5 cm (0.1 – 9.0) and most commonly ER + (88.2%), HER2 + (53.3%), grade 2 (58.9%), and node negative (82.4%). Only 7 women did not take endocrine therapy, but 3 of those women had IDC (42.9%). In 15 of the 17 cases with IDC (88.2%), the physician noted concern for progression in their clinic note and recommended surgical excision. This occurred a mean of 1.9 years (0.2 – 6.5) from the start of AS, with 47%, 67%, and 87% of cases identified within 1, 2 and 3 years from the start of AS. Suspicion of progression was based on an increase in lesion size or prominence on MRI and/or increase in calcifications on mammography. Of those that were identified as good candidates to continue AS with no concern for progression (n = 49), 16 chose to undergo surgical excision (32.7%) and 2 had IDC (4.1%). Conclusion: After over a decade of following women who seek alternatives to surgery for DCIS, we have identified clinicopathologic and imaging features that discriminate good candidates for AS and endocrine risk reducing therapy from those best treated with surgical excision. In our cohort of mostly ER+ patients receiving endocrine therapy on AS, over half of the cohort (51.6%) avoided surgical intervention. Her2 status, Oncotype DCIS, and Mammaprint scores of IDC is in process and will be presented. Our data support the study of AS as a method to stratify the risk of IDC and avoid overtreatment. We will present a personalized AS algorithm (based on biology and imaging) that we intend to prospectively test in the ATHENA network and NCI funded MCL consortium. Table 1: Cohort CharacteristicsFull Cohort (n=64)No evidence of IDC (n=47)IDC at surgery (n=17)Age at Diagnosis (years)53.6 (29.8 - 78.9)52.9 (29.8 - 74.5)55.5 (41.9 - 78.9)Time on AS (years)2.7 (.2 - 11.9)2.8 (.2 - 11.9)2.2 (.3 -5.9)Follow-Up (years)5.6 (0.9 - 15.3)Menopausal StatusPremenopausal26 (40.6%)22 (46.8%)4 (23.5%)Postmenopausal35 (54.7%)24 (51.1%)11 (64.7%)Unknown3 (4.7%)1 (2.1%)2 (11.8%)Breast CompositionFatty4 (6.3%)3 (6.4%)1 (5.9%)Scattered14 (21.9%)9 (19.1%)5 (29.4%)Heterogeneous27 (42.2%)19 (40.4%)8 (47.1%)Extreme17 (26.5%)14 (29.8%)3 (17.6%)Unknown2 (3.1%)2 (4.3%)0 (0.0%)ER StatusPositive57 (89.1%)41 (87.2%)16 (94.1%)Negative1 (1.6%)0 (0.0%)1 (5.9%)Unknown6 (9.3%)6 (12.8%)0 (0.0%)PR StatusPositive50 (78.1%)36 (76.6%)14 (82.3%)Negative5 (7.8%)3 (6.4%)2 (11.8%)Unknown9 (14.1%)8 (17.0%)1 (5.9%)GradeHigh20 (31.3%)15 (31.8%)5 (29.4%)Intermediate32 (50.0%)21 (44.6%)11 (64.7%)Low10 (15.6%)9 (19.1%)1 (5.9%)Unknown2 (3.1%)2 (28.5%)0 (0.0%Hormone TherapyYes57 (89.1%)43 (91.5%)14 (82.4%)No7 (10.9%)4 (8.5%)3 (17.6%) Citation Format: Case Brabham, Rita Mukhtar, April Liang, Paul Kim, Gillian Hirst, Amrita Basu, Heather Greenwood, Rita Freimanis, Alexander Borowsky, Shelley Hwang, Rick Baehner, Gregor Krings, Nola Hylton, Laura Esserman. Active surveillance for DCIS: Clinical outcomes at 5.6 years mean follow-up [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS1-03.