Abstract 3579: Routinely applicable, highly multiplexed triple-negative breast cancer (TNBC) genotyping

• Published in: Cancer research (Chicago, Ill.) Ill.), 2014-10, Vol.74 (19_Supplement), p.3579-3579
• Main Authors: Kotoula, Vassiliki, Papadopoulou, Kyriaki, Charalambous, Elpida, Zagouri, Flora, Lakis, Sotiris, Lymperopoulou, Angeliki, Tsolaki, Eleftheria, Pentheroudakis, George, Lilakos, Kostas, Pectasides, Dimitrios, Fountzilas, George
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2014-3579

In order to address TNBC diversity, determining the individual clonal genotypes of each tumor is urged as prerequisite. However, the applicability of whole genome approaches on routinely processed paraffin tissue material (FFPE) is still limited. Herein, we used targeted massive parallel sequencing (MPS) for genotyping FFPE tumors from patients with operable TNBC treated with adjuvant chemotherapy. Methods: Centrally assessed FFPE TNBC (n=260) were evaluated for tumor cell content (TCC) and submitted for DNA extraction. A custom panel targeting genomic regions in 43 genes previously implicated in TNBC was submitted for primer design (286 amplicons covering 21216bp). Barcoded FFPE libraries were massively processed on Ion Proton™ Sequencer PI and on Ion PGM™ Sequencer 318 chips (30 samples with both platforms, at least 2 runs each). Variants were called and annotated at higher-than-default-stringency conditions (63% of all detected variants excluded). Amplicons were checked for sequence specificity, blood DNA and FFPE performance prior to accepting results (informative: 280/286). Annotated SNVs (coding and non-coding) were assessed for allelic imbalance (AI) based on allelic frequency and TCC (AI: >0.3 unstable SNVs/tumor). The rate of failed samples was significantly higher with PGM (29.5%) than with Proton (3%). Results: In total, 238 informative TNBC were analyzed. AI was noticed in 169 tumors (71%) for multiple genes, more often TP53 (124 tumors, 52.1%), TERT (39.9%), MDM2 (35.7%), MET (24.4%), BRCA1 (20.2%) and NOTCH1 (18.9%). Coding mutations were identified in 40/43 genes but their number varied extensively per tumor (0-87). SNV functional mutations were observed in 55 tumors (23.1%), more often in PIK3CA (12 tumors, 5%). Non-SNV mutations were observed in 153 tumors (64.2%), more often TP53 (128 tumors, 53.8%), CDH1 (17.2%), MAP3K1 (8%), PTEN (6.3%), and PIK3CA (4.6%). High frequency mutations (>50% tumor cells) were found in TP53, PTEN, PIK3CA. Individually, the presence of AI (unstable tumors) and mutations predicted for worse survival. Importantly though, in comparison to patients with unstable mutated tumors, patients with unstable nonmutated tumors had significantly better outcome (DFS, HR:0.41, 95%CI:0.20-0.84, Wald p=0.014; OS, HR:0.36, 95%CI:0.15-0.86, p=0.021), similar to that observed for the favorable stable tumors, where the presence of mutations did not add any effect on survival. The same survival pattern was observed when evaluating A