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Splicing mutation analysis reveals previously unrecognized pathways in lymph node-invasive breast cancer.

ABSTRACT: Somatic mutations reported in large-scale breast cancer (BC) sequencing studies primarily consist of protein coding mutations. mRNA splicing mutation analyses have been limited in scope, despite their prevalence in Mendelian genetic disorders. We predicted splicing mutations in 442 BC tumour and matched normal exomes from The Cancer Genome Atlas Consortium (TCGA). These splicing defects were validated by abnormal expression changes in these tumours. Of the 5,206 putative mutations identified, exon skipping, leaky or cryptic splicing was confirmed for 988 variants. Pathway enrichment analysis of the mutated genes revealed mutations in 9 NCAM1-related pathways, which were significantly increased in samples with evidence of lymph node metastasis, but not in lymph node-negative tumours. We suggest that comprehensive reporting of DNA sequencing data should include non-trivial splicing analyses to avoid missing clinically-significant deleterious splicing mutations, which may reveal novel mutated pathways present in genetic disorders.

SUBMITTER: Dorman SN

PROVIDER: S-EPMC4231324 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Splicing mutation analysis reveals previously unrecognized pathways in lymph node-invasive breast cancer.

Dorman Stephanie N SN Viner Coby C Rogan Peter K PK

Scientific reports 20141114

Somatic mutations reported in large-scale breast cancer (BC) sequencing studies primarily consist of protein coding mutations. mRNA splicing mutation analyses have been limited in scope, despite their prevalence in Mendelian genetic disorders. We predicted splicing mutations in 442 BC tumour and matched normal exomes from The Cancer Genome Atlas Consortium (TCGA). These splicing defects were validated by abnormal expression changes in these tumours. Of the 5,206 putative mutations identified, ex ...[more]

PMID: 25394353

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Project description:Breast cancer has become the malignant tumor with the highest incidence in women. Axillary lymph node dissection (ALND) is an effective method of maintaining regional control; however, it is associated with a significant risk of complications. Meanwhile, whether the patients need ALND or not is according to sentinel lymph node biopsy (SLNB). However, the false-negative results of SLNB had been reported. Automated breast volume scanning (ABVS) is a routine examination in breast cancer. A real-world cohort consisting of 245 breast cancer patients who underwent ABVS examination were enrolled, including 251 tumor lesions. The ABVS manifestations were analyzed with the SLNB results, and the ALND results for selecting the lymph node metastasis were related to ABVS features. Finally, a nomogram was used to construct a breast cancer axillary lymph node tumor burden prediction model. Breast cancer patients with a molecular subtype of luminal B type, a maximum lesion diameter of ≥5 cm, tumor invasion of the Cooper's ligament, and tumor invasion of the nipple had heavy lymph node tumor burden. Molecular classification, tumor size, and Cooper's ligament status were used to construct a clinical prediction model of axillary lymph node tumor burden. The consistency indexes (or AUC) of the training cohort and the validation cohort were 0.743 and 0.711, respectively, which was close to SLNB (0.768). The best cutoff value of the ABVS nomogram was 81.146 points. After combination with ABVS features and SLNB, the AUC of the prediction model was 0.889, and the best cutoff value was 178.965 points. The calibration curve showed that the constructed nomogram clinical prediction model and the real results were highly consistent. The clinical prediction model constructed using molecular classification, tumor size, and Cooper's ligament status can effectively predict the probability of heavy axillary lymph node tumor burden, which can be the significant supplement to the SLNB. Therefore, this model may be used for individual decision-making in the diagnosis and treatments of breast cancer.

Dataset Information

Splicing mutation analysis reveals previously unrecognized pathways in lymph node-invasive breast cancer.

Publications

Splicing mutation analysis reveals previously unrecognized pathways in lymph node-invasive breast cancer.

Similar Datasets

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