Statistical modeling of a novel clinical trial design using neoadjuvant therapy (NAT) to personalize therapy in patients (pts) with triple-negative breast cancer (TNBC).

Authors

Stacy Moulder

Stacy L. Moulder

The University of Texas MD Anderson Cancer Center, Houston, TX

Stacy L. Moulder , Roland L. Bassett Jr., Jason B White , Lei Huo , Senthil Damodaran , Bora Lim , Naoto T. Ueno , Rashmi Krishna Murthy , Banu Arun , Vicente Valero , Debu Tripathy , Gabriel N. Hortobagyi , Jennifer Keating Litton , Alastair Mark Thompson , Elizabeth A. Mittendorf , Elizabeth Ravenberg , Lumarie Santiago , Beatriz E Adrada , Rosalind P Candelaria , Gaiane M Rauch

Organizations

The University of Texas MD Anderson Cancer Center, Houston, TX, Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX, University of Texas MD Anderson Cancer Center, Houston, TX, Baylor College of Medicine, Houston, TX, Brigham and Women’s Hospital, Boston, MA

Research Funding

Other
MD Anderson Moon Shot

Background: 40-50% of pts with TNBC develop pathologic complete response (pCR) with adriamycin/cyclophosphamide (AC)àtaxane (T) NAT; thus, most pts treated in randomized trials (RCTs) adding experimental drugs (ED) to standard NAT do not benefit from trial participation. A personalized trial design that enriches for non-pCR to standard NAT would diminish toxicity from ED in pts who do not need them and enrich ED in high-risk pts that are most likely to benefit. Methods: ARTEMIS (NCT02276443) is a non-randomized trial to study personalization of NAT in TNBC. Tumor biopsies were performed pre-NAT and volumetric change by ultrasound (VCU) after 4 cycles of AC (or upon clinical progression) assessed response. Pts with sensitive TNBC (VCU >=70% after AC) had T as the second phase of NAT. Pts with <70% VCU were offered phase II trials. pCR was assessed at surgical resection. 273 pts had available pCR status and 222 had complete data to generate a model predictive of response using multivariate logistic regression with common clinical factors. Data was randomly divided into training (n=111) and validation (n=111) sets. Results: 85 pts (38%) had pCR and VCU after AC x 4 was the strongest predictor of pCR. Other factors significant on multivariate analysis and included in the model were T stage (T1-4), stromal TIL, Ki67 and PD-L1. When applied to the validation data set, the accuracy of this model for predicting pCR was 76.6%, sensitivity 78.6% and specificity 75.4%. The PPV was 66.0% and the NPV was 85.2% with a ROC curve AUC of 82.4%. Using these data, ED exposure (table) was estimated for the ARTEMIS study design vs a 1:1 or a 2:1 RCT design (with an estimated pCR in control arm=40%), with a demonstrated benefit for personalization. Conclusions: This modeling indicates that personalization of NAT trials has the potential to enrich ED exposure for non-responsive disease as well as diminish ED exposure in pts likely to achieve pCR with standard NAT. Improved prediction of pCR would further enhance personalized trial design. Clinical trial information: NCT02276443.

Estimated ED exposure in 300 pts by trial design.

No exposure to unnecessary EDFavorableUnfavorable
High risk receives EDHigh risk no EDUnnecessary exposure to ED
ARTEMIS
Predicted pCR=pCRPredicted non-pCR=non-pCRFalse positive prediction pCRFalse negative predicted pCR
901414524
RCT 1:1 Randomization
pCR on Control ArmNon-pCR enrolled in EXP armNon-pCR in Control armWould have pCR with standard NAT but enrolled in EXP arm
60909060
RCT 2:1 Randomization
401206080

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Abstract Details

Meeting

2020 ASCO Virtual Scientific Program

Session Type

Poster Session

Session Title

Breast Cancer—Local/Regional/Adjuvant

Track

Breast Cancer

Sub Track

Neoadjuvant Therapy

Clinical Trial Registration Number

NCT02276443

Citation

J Clin Oncol 38: 2020 (suppl; abstr 595)

DOI

10.1200/JCO.2020.38.15_suppl.595

Abstract #

595

Poster Bd #

87

Abstract Disclosures