A Response Prediction Scheme Was Created Using Data from the I-SPY2 Trial

Although outcomes for breast cancer have vastly improved, therapies to enhance the number of patients achieving a complete response are still needed. To address this, Wolf, Yau, and colleagues utilized data from the ongoing phase II I-SPY2 trial to predict biomarkers of drug response across 987 patients from 10 arms of this trial. The 10 arms included in this analysis were control, veliparib/carboplatin, neratinib, MK2206, ganitumab, ganetespib, trebananib, TDM1/pertuzumab, pertuzumab, and pembrolizumab with patients in each group spanning disease subtypes. Twenty-seven qualifying biomarker signatures were selected and included DNA repair deficiency, immune activation, estrogen signaling, HER2 signaling, proliferation, activation of AKT/mTOR, and angiopoietin/Tie-2 pathways, with none showing exclusivity to the arm in which they were first proposed, suggesting a broad predictive function. Those with the broadest prediction included immune, proliferation, and estrogen signaling; however, the specific biomarkers differed depending on receptor subtype and drug/drug class. Conversely, the most specific biomarker, pMTOR for MK2206, was not the most predictive. Combining predictive biology to determine a classification scheme led to the creation of the RPS-5 response-predictive subtypes, which include HER2⁻/Immune⁻/DRD⁻, HER2⁻/Immune⁻/DRD⁺, HER2⁻/Immune⁺, HER2⁺/BP-HER2_or_Basal, and HER2⁺/BP-Luminal. In silico analysis of this schema was performed to evaluate changes to the pathologic complete response (pCR) rate if treatments had been assigned based upon RPS-5 revealed an estimated overall pCR rate of 58% as compared to the actual observed rate of 35%. This gain in pCR rate was not the same among all subtypes, with the HR⁻ HER2⁺ subtype showing no increase in pCR, while the HER2⁺/BP-HER2_or_Basal subtype showed a 16% increase in the pCR rate. Moreover, addition of new drug classes likely will lead to revisions to the classification schema. In summary, this study utilized gene expression, protein/phosphoprotein, and clinical response data obtained from the I-SPY2 trial to create a resource that tests for predictive biomarkers of response across multiple drug classes, with the developed schema allowing for individual treatment adaptations to improve overall patient outcome.

Wolf DM, Yau C, Wulfkuhle J, Brown-Swigart L, Gallagher IR, Lee PRE, et al. Redefining breast cancer subtypes to guide treatment prioritization and maximize response: predictive biomarkers across 10 cancer therapies. Cancer Cell 2022 May 26 [Epub ahead of print].

Note:Research Watch is written by Cancer Discovery editorial staff. Readers are encouraged to consult the original articles for full details. For more Research Watch, visit Cancer Discovery online at http://cancerdiscovery.aacrjournals.org/CDNews.