Designing a high-throughput somatic mutation profiling panel specifically for gynaecological cancers

Abstract

Somatic mutations play a major role in tumour initiation and progression. The mutation status of a tumour may predict prognosis and guide targeted therapies. The majority of techniques to study oncogenic mutations require high quality and quantity DNA or are analytically challenging. Mass-spectrometry based mutation analysis however is a relatively simple and high-throughput method suitable for formalin-fixed, paraffin-embedded (FFPE) tumour material. Targeted gene panels using this technique have been developed for several types of cancer. These current cancer hotspot panels are not focussed on the genes that are most relevant in gynaecological cancers. In this study, we report the design and validation of a novel, mass-spectrometry based panel specifically for gynaecological malignancies and present the frequencies of detected mutations. Using frequency data from the online Catalogue of Somatic Mutations in Cancer, we selected 171 somatic hotspot mutations in the 13 most important genes for gynaecological cancers, being BRAF, CDKN2A, CTNNB1, FBXW7, FGFR2, FGFR3, FOXL2, HRAS, KRAS, NRAS, PIK3CA, PPP2R1A and PTEN. A total of 546 tumours (205 cervical, 227 endometrial, 89 ovarian, and 25 vulvar carcinomas) were used to test and validate our panel, and to study the prevalence and spectrum of somatic mutations in these types of cancer. The results were validated by testing duplicate samples and by allele-specific qPCR. The panel presented here using mass-spectrometry shows to be reproducible and high-throughput, and is usefull in FFPE material of low quality and quantity. It provides new possibilities for studying large numbers of gynaecological tumour samples in daily practice, and could be useful in guided therapy selection.

Figure 1. Concordance between MALDI-TOF mutation genotyping and allele-specific qPCR results.

The concordance between MALDI-TOF mutation genotyping (GynCarta, Sequenom, Hamburg, Germany) and allele-specific qPCR for 3 PIK3CA and 7 KRAS mutations was determined for 164 (30% of the total cohort of 546 carcinomas) samples to validate the results. Concordance was calculated for all wild type-wild type matches (1546 in total) and all mutation-mutation matches (45 in total) in all reactions (164*10, 1640 in total). Failed reactions were excluded because comparison was not possible (4*3 for PIK3CA and 4*7 for KRAS; 40 in total). This lead to a concordance of (1546+45)/(1640−40)  = 0.994. WT  =  Wild type; MUT  =  mutant.

Figure 2. Mutation Spectrum.

The spectrum and frequencies of mutations identified using MALDI-TOF in 546 gynaecological carcinomas. The mutation spectrum is shown (from top to bottom) for cervical (N = 205), endometrial (N = 227), ovarian (N = 89), and vulvar carcinomas (N = 25). From left to right, N is the number of samples with the mutation, ‘%’ is the percentage of mutated samples within the cohort, and bars represent the percentages graphically: blue, 4 mutations per sample (N = 6); red, 3 mutations per sample (N = 29); green, 2 mutations per sample (N = 65); and yellow, 1 mutation per sample (N = 189).