Veliparib combined with carboplatin (VC) was an experimental regimen evaluated in the biomarker-rich neoadjuvant I-SPY 2 trial for breast cancer. VC showed improved efficacy in the triple negative signature. However, not all triple negative patients achieved pathologic complete response and some HR+HER2- patients responded. Pre-specified analysis of five DNA repair deficiency biomarkers (BRCA1/2 germline mutation; PARPi-7, BRCA1ness, and CIN70 expression signatures; and PARP1 protein) was performed on 116 HER2- patients (VC: 72 and concurrent controls: 44). We also evaluated the 70-gene ultra-high risk signature (MP1/2), one of the biomarkers used to define subtype in the trial. We used logistic modeling to assess biomarker performance. Successful biomarkers were combined using a simple voting scheme to refine the 'predicted sensitive' group and Bayesian modeling used to estimate the pathologic complete response rates. BRCA1/2 germline mutation status associated with VC response, but its low prevalence precluded further evaluation. PARPi-7, BRCA1ness, and MP1/2 specifically associated with response in the VC arm but not the control arm. Neither CIN70 nor PARP1 protein specifically predicted VC response. When we combined the PARPi-7 and MP1/2 classifications, the 42% of triple negative patients who were PARPi7-high and MP2 had an estimated pCR rate of 75% in the VC arm. Only 11% of HR+/HER2- patients were PARPi7-high and MP2; but these patients were also more responsive to VC with estimated pathologic complete response rates of 41%. PARPi-7, BRCA1ness and MP1/2 signatures may help refine predictions of VC response, thereby improving patient care.

Gene expression data obtained in large studies hold great promises for discovering disease signatures or subtypes through data analysis. It is also prone to technical variation, whose removal is essential to avoid spurious discoveries. Because this variation is not always known and can be confounded with biological signals, its removal is a challenging task. Here we provide a step-wise procedure and comprehensive analysis of the MINDACT microarray dataset. The MINDACT trial enrolled 6693 breast cancer patients and prospectively validated the gene expression signature MammaPrint for outcome prediction. The study also yielded a full-transcriptome microarray for each tumor. We show for the first time in such a large dataset how technical variation can be removed while retaining expected biological signals. Because of its unprecedented size, we hope the resulting adjusted dataset will be an invaluable tool to discover or test gene expression signatures and to advance our understanding of breast cancer.

Background

Patients with BRCA1-like tumors correlate with improved response to DNA double-strand break-inducing therapy. A gene expression-based classifier was developed to distinguish between BRCA1-like and non-BRCA1-like tumors. We hypothesized that these tumors may also be more sensitive to PARP inhibitors than standard treatments.

Methods

A diagnostic gene expression signature (BRCA1ness) was developed using a centroid model with 128 triple-negative breast cancer samples from the EU FP7 RATHER project. This BRCA1ness signature was then tested in HER2-negative patients (n = 116) from the I-SPY 2 TRIAL who received an oral PARP inhibitor veliparib in combination with carboplatin (V-C), or standard chemotherapy alone. We assessed the association between BRCA1ness and pathologic complete response in the V-C and control arms alone using Fisher's exact test, and the relative performance between arms (biomarker × treatment interaction, likelihood ratio p < 0.05) using a logistic model and adjusting for hormone receptor status (HR).

Results

We developed a gene expression signature to identify BRCA1-like status. In the I-SPY 2 neoadjuvant setting the BRCA1ness signature associated significantly with response to V-C (p = 0.03), but not in the control arm (p = 0.45). We identified a significant interaction between BRCA1ness and V-C (p = 0.023) after correcting for HR.

Conclusions

A genomic-based BRCA1-like signature was successfully translated to an expression-based signature (BRC1Aness). In the I-SPY 2 neoadjuvant setting, we determined that the BRCA1ness signature is capable of predicting benefit of V-C added to standard chemotherapy compared to standard chemotherapy alone.

Trial registration

I-SPY 2 TRIAL beginning December 31, 2009: Neoadjuvant and Personalized Adaptive Novel Agents to Treat Breast Cancer (I-SPY 2), NCT01042379 .