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