Michele Dal Bo

Novel De Novo BRCA2 Variant in an Early-Onset Ovarian Cancer Reveals a Unique Tumor Evolution Pathway

Ovarian cancer (OC) is a highly heterogeneous malignancy, often characterized by complex genomic alterations that drive tumor progression and therapy resistance. In this paper, we report a novel de novo BRCA2 germline variant NM_000059.3:c.(8693_8695delinsGT) associated with early-onset OC that featured two regions with differential MMR (Mismatch Repair) gene expression. To date, only six cases of de novo BRCA2 variants have been reported, none of which were associated with early-onset high-grade serous OC. The immunohistochemical analysis of MMR genes revealed two distinct tumor areas, separated by a clear topographic boundary, with the heterogeneous expression of MLH1 and PMS2 proteins. Seventy-five percent of the tumor tissue showed positivity, while the remaining 25% exhibited a complete absence of expression, underscoring the spatial variability in MMR gene expression within the tumor. Integrated comparative spatial genomic profiling identified several tumor features associated with the genetic variant as regions of loss of heterozygosity (LOH) that involved BRCA2 and MLH1 genes, along with a significantly higher mutational tumor burden in the tumor area that lacked MLH1 and PMS2 expression, indicating its further molecular evolution. The following variants were acquired: c.6572C>T in NOTCH2, c.1852C>T in BCL6, c.191A>T in INHBA, c.749C>T in CUX1, c.898C>A in FANCG, and c.1712G>C in KDM6A. Integrated comparative spatial proteomic profiles revealed defects in the DNA repair pathways, as well as significant alterations in the extracellular matrix (ECM). The differential expression of proteins involved in DNA repair, particularly those associated with MMR and Base Excision Repair (BER), highlights the critical role of defective repair mechanisms in driving genomic instability. Furthermore, ECM components, such as collagen isoforms, Fibrillin-1, EMILIN-1, Prolargin, and Lumican, were found to be highly expressed in the MLH1/PMS2-deficient tumor area, suggesting a connection between DNA repair deficiencies, ECM remodeling, and tumor progression. Thus, the identification of the BRCA2 variant sheds light on the poorly understood interplay between DNA repair deficiencies and ECM remodeling in OC, providing new insights into their dual role in shaping tumor evolution and suggesting potential targets for novel therapeutic strategies.

Machine Learning Application Identifies Germline Markers of Hypertension in Patients With Ovarian Cancer Treated With Carboplatin, Taxane, and Bevacizumab

Pharmacogenomics studies how genes influence a person's response to treatment. When complex phenotypes are influenced by multiple genetic variations with little effect, a single piece of genetic information is often insufficient to explain this variability. The application of machine learning (ML) in pharmacogenomics holds great potential — namely, it can be used to unravel complicated genetic relationships that could explain response to therapy. In this study, ML techniques were used to investigate the relationship between genetic variations affecting more than 60 candidate genes and carboplatin‐induced, taxane‐induced, and bevacizumab‐induced toxicities in 171 patients with ovarian cancer enrolled in the MITO‐16A/MaNGO‐OV2A trial. Single‐nucleotide variation (SNV, formerly SNP) profiles were examined using ML to find and prioritize those associated with drug‐induced toxicities, specifically hypertension, hematological toxicity, nonhematological toxicity, and proteinuria. The Boruta algorithm was used in cross‐validation to determine the significance of SNVs in predicting toxicities. Important SNVs were then used to train eXtreme gradient boosting models. During cross‐validation, the models achieved reliable performance with a Matthews correlation coefficient ranging from 0.375 to 0.410. A total of 43 SNVs critical for predicting toxicity were identified. For each toxicity, key SNVs were used to create a polygenic toxicity risk score that effectively divided individuals into high‐risk and low‐risk categories. In particular, compared with low‐risk individuals, high‐risk patients were 28‐fold more likely to develop hypertension. The proposed method provided insightful data to improve precision medicine for patients with ovarian cancer, which may be useful for reducing toxicities and improving toxicity management.