Chip-based digital Polymerase Chain Reaction as quantitative technique for the detection of PIK3CA mutations in breast cancer patients

• Autores: Giannoni-Luza, S, Acosta, O, Murillo Carrasco, AG, Danos, P, Cotrina Concha, JM, Guerra Miller, H, Pinto, JA, Aguilar, A, Araujo, JM, Fujita, R, Buleje, J
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Instituto Nacional de Enfermedades Neoplásicas
• Repositorio: INEN-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inen.sld.pe:20.500.14703/293
• Enlace del recurso: https://hdl.handle.net/20.500.14703/293
• Nivel de acceso: acceso abierto
• Materia: Breast neoplasms; Digital PCR; Genetic techniques; Mutation; PIK3CA; Polymerase chain reaction; https://purl.org/pe-repo/ocde/ford#3.02.21

Background: PIK3CA is a gene frequently mutated in breast cancer. With the FDA approval of alpelisib, the evaluation of PIK3CA for activating mutations is becoming routinely. Novel platforms for gene analysis as digital PCR (dPCR) are emerging as a potential replacement for the traditional Sanger sequencing. However, there are still few studies on chip-based dPCR to detect mutations in tumor samples. Thus, this cross-sectional study aimed to assess the sensibility of a chip-based dPCR to detect and quantify PIK3CA mutations and compare its performance with Sanger sequencing. Materials and Methods: Tumor samples from 57 breast cancer patients (22 pre-treatment samples, 32 tumors after neoadjuvant chemotherapy, and three lymph nodes) were collected and analyzed by Sanger sequencing and dPCR for the three PIK3CA most relevant mutations (p.E545K, p. H1047R, and p. H1047L). Digital PCR sensitivity, specificity, and overall performance were estimated by contingency tables, receptor operator characteristic (ROC), and area under the curve (AUC). Association of PIK3CA mutations with clinicopathological variables was conducted. Results: Sanger sequencing identified PIK3CA mutations in six patients (10.5%), two with p. H1047R, and four with p. E545K. Digital PCR confirmed those mutations and identified 19 additional patients with at least one mutation. Comparison between dPCR and Sanger sequencing showed a sensitivity of 100% (95% CI 53–100%), and a specificity of 84.2% (95% CI 83–84.2%). Besides, p. H1047R mutation detected by dPCR showed a significant association with breast cancer phenotype (p = 0.019) and lymphatic nodes infiltration (p = 0.046). Conclusions: Digital PCR showed a high sensitivity to detect mutations in tumor samples and it might be capable to detect low-rate mutations and tumor subpopulations not detected by Sanger sequencing. © 2022 The Author(s)