Jun Yasuda

Sequential Plasma Metabolome and Proteome Analyses to Develop a Novel Monitoring Strategy for Patients with Epithelial Ovarian Cancer: A Pilot Study

Epithelial ovarian cancer (EOC) is diagnosed at an advanced stage in over half of the patients and its prognosis remains unfavorable. CA125, one of the most frequent positive tumor markers in patients with EOC, has certain limitations. Therefore, more accurate clinical biomarkers are needed. Liquid biopsy with cancer related molecules, such as circulating tumor DNA, is a new option for cancer diagnosis and prognosis. We explored the potential of plasma metabolomic and proteomic analyses as novel monitoring methods for the patients with EOC. Of seven patients, six experienced disease recurrence or progression. CA125 plasma measurements were conducted for disease monitoring. Plasma metabolome and proteome analyses were performed using liquid chromatography–tandem mass spectrometry. Ten and four metabolome indicators were significantly increased and decreased in association with chemotherapeutic resistance, respectively. In addition, thirty-seven and nine proteins displayed high and low levels associated with chemotherapeutic resistance, respectively. Several metabolome pathways and protein concentrations corresponded to the clinical course of each patient. This pilot study suggested the potential of the assessment of metabolome and proteome analysis as a useful tool for developing novel monitoring biomarkers for patients with recurrent EOC.

Novel candidates of pathogenic variants of the BRCA1 and BRCA2 genes from a dataset of 3,552 Japanese whole genomes (3.5KJPNv2)

Identification of the population frequencies of definitely pathogenic germline variants in two major hereditary breast and ovarian cancer syndrome (HBOC) genes, BRCA1/2 , is essential to estimate the number of HBOC patients. In addition, the identification of moderately penetrant HBOC gene variants that contribute to increasing the risk of breast and ovarian cancers in a population is critical to establish personalized health care. A prospective cohort subjected to genome analysis can provide both sets of information. Computational scoring and prospective cohort studies may help to identify such likely pathogenic variants in the general population. We annotated the variants in the BRCA1 and BRCA2 genes from a dataset of 3,552 whole-genome sequences obtained from members of a prospective cohorts with genome data in the Tohoku Medical Megabank Project (TMM) with InterVar software. Computational impact scores (CADD_phred and Eigen_raw) and minor allele frequencies (MAFs) of pathogenic (P) and likely pathogenic (LP) variants in ClinVar were used for filtration criteria. Familial predispositions to cancers among the 35,000 TMM genome cohort participants were analyzed to verify the identified pathogenicity. Seven potentially pathogenic variants were newly identified. The sisters of carriers of these moderately deleterious variants and definite P and LP variants among members of the TMM prospective cohort showed a statistically significant preponderance for cancer onset, from the self-reported cancer history. Filtering by computational scoring and MAF is useful to identify potentially pathogenic variants in BRCA genes in the Japanese population. These results should help to follow up the carriers of variants of uncertain significance in the HBOC genes in the longitudinal prospective cohort study.