Abstract 2079: Prediction of DDR and other mutation signatures using targeted panels for FFPE samples
Authors: Hyland Fiona , Chellappan Ajithavalli , Vora Chintan , Nair Shilpa , Patro Jagannath , Raj Ritika , Kanap Rushikesh 
Published: July 2021

Abstract
Mutational processes can cause driver mutations and are considered the primary cause of tumorigenesis. Cosmic signatures classify environmental or intrinsic mutational processes. Identification of signatures including DNA Damage Response (DDR) signatures enables research into the origin of cancer mutagenesis and into treatment optimization. Typically, Mutation signatures are identified using Whole Genome Sequencing or Whole Exome Sequencing. We demonstrate signature prediction of mutation signatures with two amplification-based targeted panels, which have a high sequencing success rate for FFPE samples. ~2000 FFPE Samples from a pan-solid tumor cohort were sequenced with either of two AmpliSeq panels (OCAplus and OTMLA), sizes 1.4 Mb and 1.7 Mb, to optimize a method to identify mutation signatures. First, we identified somatic SNVs by filtering out likely population germline mutations, and used these to construct the single base change substitution (SBS) matrix. The COSMIC cancer signatures use properties of the whole genome: we normalized to extend signature detection to targeted panel data. We adjusted the mutation frequencies observed using the ratio of trinucleotide counts in the genome and the ratio in the panel. Next, we measured the cosine similarity between the normalized sample and the COSMIC signatures. We selected the signatures with a strong match (&gt;0.7) to the normalized sample. We further impute the signatures using a reduced candidate set, to assess the signature fit in the sample and reduce false positives. These steps provide the optimized signatures. We detected optimized signatures in 33% of the samples. A single signature was detected in 9% of the samples, 2 signatures in 6%, and &gt;2 signatures in 17% of the samples. ~3% of samples showed at least one MMR signatures (SBS6, SBS14, SBS15, SBS20, SBS21, SBS26 and SBS44). In all 10 OCAPlus samples with MMR signatures, we also detected mutation(s) in an MMR gene. APOBEC signatures, SBS2 and SBS13, were observed in 3% of samples sequenced with OCAPlus panel and TML panel. We found 3 samples showing HRR signature SBS3. We successfully identified the putative causal DDR mutation in a number of samples. For example, of 62 samples with NTHL1 mutations, 60 had NTHL1 related signature SBS30. A sample with MUTYH mutations was assigned the SBS36 signature. We sequenced matched Tumor/Normal pairs; in most cases, the tumor sample showed a stronger mutational signature, with their corresponding normal sample showing either no signature or a weaker matching signature. We assessed reproducibility of the mutational signature. Looking at duplicate and triplicate replicates, we found that replicates consistently displayed the same mutational signatures. We show identification of mutation signatures using targeted panels designed for FFPE samples.
Citation Format: Fiona Hyland, Ajithavalli Chellappan, Chintan Vora, Shilpa Nair, Jagannath Patro, Ritika Raj, Rushikesh Kanap. Prediction of DDR and other mutation signatures using targeted panels for FFPE samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2079.