P1-06-22: Identification of Biomarkers in Breast Cancer for Prediction of Response to PARP Inhibitor Olaparib.
Authors: Daemen A , Wolf DM , Korkola JE , Griffith OL , Frankum JR , Jakkula LR , Wang NJ , Natrajan R , Reis-Filho JS , Lord CJ , Ashworth A , Gray JW , Spellman PT , van't Veer L 
Published: June 2012

Background: Poly (ADP-ribose) polymerase (PARP) is an enzyme involved in DNA repair. PARP inhibitors operate on the principle of synthetic lethality in conjunction with DNA damaging agents, and are likely to be useful for treatment of BRCA-mutated cancers and triple negative breast cancers exhibiting ‘BRCA-ness’ or other signs of DNA repair deficiency. Multiple PARP inhibitors have been developed, such as Olaparib (AstraZeneca), BSI-201 (Sanofi-Aventis) and ABT-888 (Abbott Laboratories). Though some clinical trials have shown drugs in this class to be promising, not all results have been positive. As PARP inhibitors differ in mechanism of action, dosing interval and toxicities, trial results seem to depend on the specific combination of PARP inhibitor and patient population. To understand why some studies succeeded and others failed and to guide new clinical trials in patient selection, there is an urgent need for biomarker identification, both for PARP inhibitors in general and for the specific idiosyncratic mechanisms of each drug.

Material and Methods: Thirty-three in vitro breast cancer cell lines were administered the PARP inhibitor Olaparib, with sensitivity to the compound summarized as the dose necessary to kill 50% of each culture. mRNA expression (Affymetrix U133A, Exon 1.0ST array) and transcriptome sequence (Illumina GAII) were available for 22/33 cell lines, among which 9 were sensitive and 13 resistant. To obtain robust predictive markers that are minimally dependent on the specific PARP inhibitor and expression platform, a bottom-up approach was opted for, restricted to genes in the major DNA repair pathways. Logistic regression with forward selection was used to determine the most important markers, further reduced based on consistency across platforms. The weighted voting algorithm was used to build the final predictor. Eight U133A data sets with number of tumor samples varying from 61 to 289 were subsequently used to verify prevalence and to identify the subpopulations that are likely to respond according to the predictor. To verify cross-platform generalizability, the signature was additionally tested in 430 TCGA samples with custom Agilent 244K gene expression.

Results: For the development of a genomic signature that might work for multiple PARP inhibitors and expression platforms, prior knowledge of DNA repair pathways was incorporated and stringent criteria for marker inclusion were applied using three different platforms. Eight genes fulfilled the criteria, of which 5 were resistance markers and 3 sensitivity markers. When testing the 8-gene signature in eight U133A data sets, 40–48% of patients were predicted to be responsive to Olaparib. In addition, well-known markers (ER, PR, ERBB2, KRT5/17) were examined in patients expressing the 8-gene sensitivity signature. A higher percentage of these patients were ERBB2-negative and did overexpress KRT5/17, indicative for the basal subtype. Prevalence and relationship with these markers were confirmed in 430 samples on a distinct platform (Agilent).

Conclusion: Cell line exposure to Olaparib has yielded an 8-gene predictor of sensitivity. This signature was observed in a substantial fraction of primary breast tumors predicted to benefit from Olaparib.

Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-06-22.