Highly sensitive detection of the PIK3CA (H1047R) mutation in colorectal cancer using a novel PCR-RFLP method.
BMC Cancer
; 16: 454, 2016 07 12.
Article
en En
| MEDLINE
| ID: mdl-27405731
ABSTRACT
BACKGROUND:
The PIK3CA (H1047R) mutation is considered to be a potential predictive biomarker for EGFR-targeted therapies. In this study, we developed a novel PCR-PFLP approach to detect the PIK3CA (H1047R) mutation in high effectiveness.METHODS:
A 126-bp fragment of PIK3CA exon-20 was amplified by PCR, digested with FspI restriction endonuclease and separated by 3 % agarose gel electrophoresis for the PCR-RFLP analysis. The mutant sequence of the PIK3CA (H1047R) was spiked into the corresponding wild-type sequence in decreasing ratios for sensitivity analysis. Eight-six cases of formalin-fixed paraffin-embedded colorectal cancer (CRC) specimens were subjected to PCR-RFLP to evaluate the applicability of the method.RESULTS:
The PCR-RFLP method had a capability to detect as litter as 0.4 % of mutation, and revealed 16.3 % of the PIK3CA (H1047R) mutation in 86 CRC tissues, which was significantly higher than that discovered by DNA sequencing (9.3 %). A positive association between the PIK3CA (H1047R) mutation and the patients' age was first found, except for the negative relationship with the degree of tumor differentiation. In addition, the highly sensitive detection of a combinatorial mutation of PIK3CA, KRAS and BRAF was achieved using individual PCR-RFLP methods.CONCLUSIONS:
We developed a sensitive, simple and rapid approach to detect the low-abundance PIK3CA (H1047R) mutation in real CRC specimens, providing an effective tool for guiding cancer targeted therapy.Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Polimorfismo de Longitud del Fragmento de Restricción
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Neoplasias Colorrectales
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Reacción en Cadena de la Polimerasa
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Proteínas Proto-Oncogénicas p21(ras)
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Proteínas Proto-Oncogénicas B-raf
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Fosfatidilinositol 3-Quinasa Clase I
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
Límite:
Female
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Humans
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Male
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Middle aged
Idioma:
En
Año:
2016
Tipo del documento:
Article