Investigation of BRCA1 methylation and FAT3 mutation as a potential biomarker in ovarian cancer samples

Singh, Manish Pratap; Suyal, Shradha; Rai, Suresh; Yadav, Saumya; Singh, Alka; Sachan, Manisha; Singh, Nand Kumar; Srivastava, Sameer

doi:10.1016/j.humgen.2022.201032

Cited by 3 publications

(1 citation statement)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another group of cancer driver gene mutations commonly shared among OC patient samples was the family of FAT genes ( FAT3 and FAT4 ). These genes are members of a large family of protocadherins and mutations in both of these genes have been previously implicated in a variety of cancers [ 53 , 54 ], including OC [ 55 , 56 ]. We found that mutations in the FAT genes are shared among 26% of early-stage OC patients ( FAT3 : 13%; FAT4 : 13%).…”

Section: Discussionmentioning

confidence: 99%

The Clinical Significance of Genetic Variation in Ovarian Cancer

Ban

Housley

McDonald

2023

IJMS

View full text Add to dashboard Cite

Genetic variation is a well-known contributor to the onset and progression of cancer. The goal of this study is to provide a comprehensive examination of the nucleotide and chromosomal variation associated with the onset and progression of serous ovarian cancer. Using a variety of computational and statistical methods, we examine the exome sequence profiles of genetic variants present in the primary tumors of 432 ovarian cancer patient samples to compute: (1) the tumor mutational burden for all genes and (2) the chromosomal copy number alterations associated with the onset/progression of ovarian cancer. Tumor mutational burden is reduced in the late vs. early stages, with the highest levels being associated with loss-of-function mutations in DNA-repair genes. Nucleotide variation and copy number alterations associated with known cancer driver genes are selectively favored over ovarian cancer development. The results indicate that genetic variation is a significant contributor to the onset and progression of ovarian cancer. The measurement of the relative levels of genetic variation associated with individual ovarian cancer patient tumors may be a clinically valuable predictor of potential tumor aggressiveness and resistance to chemotherapy. Tumors found to be associated with high levels of genetic variation may help in the clinical identification of high-risk ovarian cancer patients who could benefit from more frequent monitoring.

show abstract

Section: Discussionmentioning

confidence: 99%

The Clinical Significance of Genetic Variation in Ovarian Cancer

Ban

Housley

McDonald

2023

IJMS

View full text Add to dashboard Cite

show abstract

Evolving landscape of detection and targeting miRNA/epigenetics for therapeutic strategies in ovarian cancer

Dey Bhowmik,

Shaw,

Gopinatha Pillai

et al. 2025

Cancer Letters

View full text Add to dashboard Cite

Optimal Prescriptive Treatments for Ovarian Cancer with Genetic Data

Mertzios,

Gjika,

et al. 2024

Preprint

View full text Add to dashboard Cite

Among the cancers affecting the female population, Ovarian Cancer (OC), while being relatively rare, is the leading cause of gynecological cancer-related deaths, with overall 5-year survival rates of approximately 50% for all stages combined. This is because of the challenges associated with the diagnosis, resulting in detection at advanced stages of OC, coupled with the slow progress in effective treatment options since the approval of platinum-based chemotherapy in the late 1970s. There has been a relative lack of sophisticated methods based on Machine Learning (ML) models that use genetic data for better prediction of Ovarian Cancer outcomes and result in more effective treatment recommendations. Therefore, there is an unmet clinical need to create models that allow physicians to make informed decisions based on all available data, including patient demographic, social, health, and genomic data. Hence, we develop new techniques for leveraging genetic information in prescribing optimal treatments for patients with OC, using a publicly available dataset from the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) trial. Our approach is able to transform genotype sequencing information into a simple tabular form that can then be used as the input to any ML model. Coupled with the recorded treatment regimen and clinical parameters of matched patients from the genetic dataset, we estimate the treatment effect in terms of mortality prediction and use it to prescribe the optimal treatment for any given patient. By including the genetic features engineered through our proposed method, our models have a higher accuracy than the models without genetic information embedded. The increase in predictive accuracy demonstrates the improved efficacy of our method in the predictive setting. Furthermore, in the prescriptive setting, the models including genetic features output different treatment choices for patients, showing the impact of their inclusion. This is further highlighted by the feature importance of the genetic features such as mutations in the FAT3, BRCA1, BRCA2, and NF1 genes, where they rank highly with a tighter aggregation of the top features, relative to the sharp drop-off in feature importance after the top feature in the models without genetic data. Taken together, in summary, our models will allow oncologists to make more informed and accurate decisions, incorporating a patient's genetic data with all other available clinical information, which has the potential for improved prognosis and better long-term survival outcomes for Ovarian Cancer patients.

show abstract

Investigation of BRCA1 methylation and FAT3 mutation as a potential biomarker in ovarian cancer samples

Cited by 3 publications

References 39 publications

The Clinical Significance of Genetic Variation in Ovarian Cancer

The Clinical Significance of Genetic Variation in Ovarian Cancer

Evolving landscape of detection and targeting miRNA/epigenetics for therapeutic strategies in ovarian cancer

Optimal Prescriptive Treatments for Ovarian Cancer with Genetic Data

Contact Info

Product

Resources

About