Multiomics analysis of serial PARP inhibitor treated metastatic TNBC inform on rational combination therapies

Abstract

In a pilot study, we evaluated the feasibility of real-time deep analysis of serial tumor samples from triple negative breast cancer patients to identify mechanisms of resistance and treatment opportunities as they emerge under therapeutic stress engendered by poly-ADP-ribose polymerase (PARP) inhibitors (PARPi). In a BRCA-mutant basal breast cancer exceptional long-term survivor, a striking tumor destruction was accompanied by a marked infiltration of immune cells containing CD8 effector cells, consistent with pre-clinical evidence for association between STING mediated immune activation and benefit from PARPi and immunotherapy. Tumor cells in the exceptional responder underwent extensive protein network rewiring in response to PARP inhibition. In contrast, there were minimal changes in the ecosystem of a luminal androgen receptor rapid progressor, likely due to indifference to the effects of PARP inhibition. Together, identification of PARPi-induced emergent changes could be used to select patient specific combination therapies, based on tumor and immune state changes.

Fig. 1. Schematic of study design and patient’s response to olaparib and durvalumab combination.

a After consent, mTNBC patients were subjected to a pre-treatment biopsy (#1) followed by one cycle of olaparib (28 days) and an on-treatment biopsy (#2). Patents were then treated with a combination of olaparib and durvalumab for up to 12 additional cycles. b (Upper panel) Blood biomarkers, (lower) lesion size. Green stars represent the time of pre and on-treatment biopsies collection and the blue bar represent the first cycle of olaparib monotherapy, which started on day 0.

Fig. 2. Tumors histopathological phenotype and immune composition.

a An H&E and b CLIA IHC staining of estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR) and human epidermal growth factor receptor 2 (HER2) was performed on both pre and on-treatment biopsies for each patient. mIHC was used to C reconstruct a region of each tumor showing epithelial cells (pan-CK) and various immune cells subtypes as indicated, d identification of immune cell population subset and density, and e determination of CD8⁺ T cell population characteristics based on expression of PD-1 and EOMES expression.

Fig. 3. Protein network rewiring and single-cell proteomics analysis of the tumor composition.

RPPA analysis was used to analyze protein changes following olaparib treatment. a Heat map representing the protein expression in each sample as well as the fold change between on and pre-treatment samples from individual patients. The protein was ordered from the most downregulated to the most upregulated in the on-treatment samples compared to pre-treatment. b Distribution plot showing the fold change of each protein during the course of treatment (on-treatment/pre-treatment). c Pathway score analysis showing the score distribution of each TCGA breast cancer subtype and the pathway score of each sample from individual patients. d Chart constructed using the elbow method to determine the optimal number of clusters for e a K-mean clustering analysis identifying different cancer cell populations. Rows represent individual cells and columns represent individual proteins. The first and second columns identifies each patient samples and cell cluster, respectively. f Distribution plot showing the percentage of cells from each K-mean cluster within each sample. g Table showing the median expression of each protein within each cluster. The table was colored based on expression intensity, with red being high and blue being low expression values.