Clinical Contribution of Next-Generation Sequencing Multigene Panel Testing for BRCA Negative High-Risk Patients With Breast Cancer

The Crucial Role of Hereditary Cancer Panel Testing in Unaffected Individuals with a Strong Family History of Cancer: A Retrospective Study of a Cohort of 103 Healthy Subjects

Hereditary cancer syndromes caused by germline mutations account for 5-10% of all cancers. The finding of a genetic mutation could have far-reaching consequences for pharmaceutical therapy, personalized prevention strategies, and cascade testing. According to the National Comprehensive Cancer Network's (NCCN) and the Italian Association of Medical Oncology (AIOM) guidelines, unaffected family members should be tested only if the affected one is unavailable. This article explores whether germline genetic testing may be offered to high-risk families for hereditary cancer even if a living affected relative is missing. A retrospective study was carried out on 103 healthy subjects tested from 2017 to 2023. We enrolled all subjects with at least two first- or second-degree relatives affected by breast, ovarian, pancreatic, gastric, prostate, or colorectal cancer. All subjects were tested by Next Generation Sequencing (NGS) multi-gene panel of 27 cancer-associated genes. In the study population, 5 (about 5%) pathogenic/likely pathogenic variants (PVs/LPVs) were found, while 40 (42%) had a Variant of Uncertain Significance (VUS). This study highlights the importance of genetic testing for individuals with a strong family history of hereditary malignancies. This approach would allow women who tested positive to receive tailored treatment and prevention strategies based on their personal mutation status.

Prevention, diagnosis and clinical management of hereditary breast cancer beyond BRCA1/2 genes

The detection of germline pathogenic variants (gPVs) in BRCA1/2 and other breast cancer (BC) genes is rising exponentially thanks to the advent of multi-gene panel testing. This promising technology, coupled with the availability of specific therapies for BC BRCA-related, has increased the number of patients eligible for genetic testing. Implementing multi-gene panel testing for hereditary BC screening holds promise to maximise benefits for patients at hereditary risk of BC. These benefits range from prevention programs to antineoplastic-targeted therapies. However, the clinical management of these patients is complex and requires guidelines based on recent evidence. Furthermore, applying multi-gene panel testing into clinical practice increases the detection of variants of uncertain significance (VUSs). This augments the complexity of patients' clinical management, becoming an unmet need for medical oncologists. This review aims to collect updated evidence on the most common BC-related genes besides BRCA1/2, from their biological role in BC development to their potential impact in tailoring prevention and treatment strategies.

Frequency of germline pathogenic variants in breast cancer predisposition genes among young Turkish breast cancer patients

PURPOSE

One of the most important risk factors for hereditary breast and ovarian cancer is young age. We aim to report the frequency of pathogenic/likely pathogenic variants in breast cancer predisposing genes in young (≤ 40 years old) breast cancer patients who undergone 26-gene inherited cancer panel at our Breast Health Center.

METHODS

Medical records of breast cancer patients who were referred to genetic counseling based on NCCN criteria and were ≤ 40 years of age are reviewed. The frequency of germline pathogenic/likely pathogenic variants who undergone 26-gene inherited cancer panel was analyzed.

RESULTS

Among 414 breast cancer patients who were ≤ 40 years of age, 308 undergone 26-gene inherited cancer panel and 108 had next generation sequencing (NGS)-based BRCA 1 and 2 genetic testing. Median age was 35 (22-40), Family history in first degree relatives was present in 14% of patients. Forty-five percent of patients met one of the NCCN criteria for genetic testing, 41% of them met two criteria, and 14% of patients fulfilled ≥ 3 criteria. Seventy pathogenic/likely pathogenic variants (PV/LPV) were found in 65 (21%) patients. PV/LPs in BRCA genes and non-BRCA genes represented 53% and 44% of all PV/LPVs, accounting for 12% and 10% of patients in the study cohort respectively. Two PVs were present in 5 patients and eleven PVs were novel. The most common PVs were in BRCA 1 (n:18), BRCA 2 (n:19), ATM (n:7), CHEK2 (n:7) and TP53 (n:5) genes. Thirty-one percent of the patients with triple-negative tumors and 25% of the patients with hormone receptor-positive tumors had PV/LPVs with panel testing. Family history in first degree relatives (p = 0.029), the number of met NCCN criteria (p = 0.036) and axillary nodal involvement (p = 0.000) were more common in patients with PVs. When combined with patient group (n:106) who had only BRCA1 and 2 gene testing, 16% of Turkish breast cancer patients ≤ 40 years of age had PVs in BRCA genes.

CONCLUSION

One fifth of Turkish breast cancer patients ≤ 40 years of age had at least one PV/LPV in breast cancer predisposing genes with 26-gene inherited cancer panel. The frequency of PV/LPVs was higher in triple-negative young-onset patients compared to hormone receptor and Her-2 positive subtypes. Our findings regarding to frequency PV/LPVs in BRCA 1/2 and non-BRCA genes in young-onset breast cancer patients are in line with the literature.

Mutational signatures for breast cancer diagnosis using artificial intelligence

BACKGROUND

Breast cancer is the most common female cancer worldwide. Its diagnosis and prognosis remain scanty, imprecise, and poorly documented. Previous studies have indicated that some genetic mutational signatures are suspected to lead to progression of various breast cancer scenarios. There is paucity of data on the role of AI tools in delineating breast cancer mutational signatures. This study sought to investigate the relationship between breast cancer genetic mutational profiles using artificial intelligence models with a view to developing an accurate prognostic prediction based on breast cancer genetic signatures. Prior research on breast cancer has been based on symptoms, origin, and tumor size. It has not been investigated whether diagnosis of breast cancer can be made utilizing AI platforms like Cytoscape, Phenolyzer, and Geneshot with potential for better prognostic power. This is the first ever attempt for a combinatorial approach to breast cancer diagnosis using different AI platforms.

METHOD

Artificial intelligence (AI) are mathematical algorithms that simulate human cognitive abilities and solve difficult healthcare issues such as complicated biological abnormalities like those experienced in breast cancer scenarios. The current models aimed to predict outcomes and prognosis by correlating imaging phenotypes with genetic mutations, tumor profiles, and hormone receptor status and development of imaging biomarkers that combine tumor and patient-specific features. Geneshotsav 2021, Cytoscape 3.9.1, and Phenolyzer Nature Methods, 12:841-843 (2015) tools, were used to mine breast cancer-associated mutational signatures and provided useful alternative computational tools for discerning pathways and enriched networks of genes of similarity with the overall goal of providing a systematic view of the variety of mutational processes that lead to breast cancer development. The development of novel-tailored pharmaceuticals, as well as the distribution of prospective treatment alternatives, would be aided by the collection of massive datasets and the use of such tools as diagnostic markers.

RESULTS

Specific DNA-maintenance defects, endogenous or environmental exposures, and cancer genomic signatures are connected. The PubMed database (Geneshot) search for the keywords yielded a total of 21,921 genes associated with breast cancer. Then, based on their propensity to result in gene mutations, the genes were screened using the Phenolyzer software. These platforms lend credence to the fact that breast cancer diagnosis using Cytoscape 3.9.1, Phenolyzer, and Geneshot 2021 reveals high profile of the following mutational signatures: BRCA1, BRCA2, TP53, CHEK2, PTEN, CDH1, BRIP1, RAD51C, CASP3, CREBBP, and SMAD3.

The role of next-generation sequencing in the examination of signaling genes in Brca1/2-negative breast cancer cases

INTRODUCTION

Breast cancer is the most prevalent malignancy in women worldwide. Although pathogenic variants in the BRCA1/2 genes are responsible for the majority of hereditary breast cancer cases, a substantial proportion of patients are negative for pathogenic variations in these genes. In cancers, the signal transduction pathways of the cell are usually affected first. Therefore, this study aimed to detect and classified genetic variations in non-BRCA signaling genes and investigate the underlying genetic causes of susceptibility to breast cancer.

METHODS

Ninety-six patients without pathogenic variants in the BRCA1/2 genes who met the inclusion criteria were enrolled in the study, and 34 genes were analyzed using next-generation sequencing (NGS) for genetic analysis.

RESULTS

Based on the ClinVar database or American College of Medical Genetics criteria, a total of 55 variants of 16 genes were detected in 43 (44.8%) of the 96 patients included in the study. The pathogenic variants were found in the TP53, CHEK2, and RET genes, whereas the likely pathogenic variants were found in the FGFR1, FGFR3, EGFR, and NOTCH1 genes.

CONCLUSION

The examination of signaling genes in patients who met the established criteria for hereditary breast cancer but were negative for BRCA1/2 pathogenic variants provided additional information for approximately 8% of the families. The results of the present study suggest that NGS is a powerful tool for investigating the underlying genetic causes of occurrence and progression of breast cancer.

The performance of multi-gene panels for breast/ovarian cancer predisposition

BRCA1 and BRCA2 are the most mutated genes in breast cancer. We analyzed 48 breast cancer subjects using two methods that differ in terms of number of genes investigated and strategy used (primers: Panel A - 12 genes - vs probes: Panel B - 48 genes). Both the panels and procedures identified "pathogenic" or "likely pathogenic" variants in TP53, ATM, CHEK2 and BARD1 besides BRCA1 and BRCA2. Panel B identified two other putatively pathogenic variants in RNASEL and in RAD50. Identification of variants other than the BRCA genes can be useful in patient management. A total of 121 variants were distributed within the 12 genes and were correctly detected by both panels. However, the number of calls without divergence, namely ± 0.10 difference of allelic frequency, was 78.3%, while calls with a divergence below 0.10 was 16.7%, thus indicating that only 5% (n = 275) of 5,412 calls had a divergence above 0.10. Although these panels differ from each other, both are useful in different situations, particularly when patients should be tested for genes other than BRCA1/2 (as occurs in patients affected by a so called hereditary syndrome) or for therapeutic purposes.

Germline PALB2 Mutation in High-Risk Chinese Breast and/or Ovarian Cancer Patients

The prevalence of the PALB2 mutation in breast cancer varies across different ethnic groups; hence, it is of intense interest to evaluate the cancer risk and clinical association of the PALB2 mutation in Chinese breast and/or ovarian cancer patients. We performed sequencing with a 6-gene test panel (BRCA1, BRCA2, TP53, PTEN, PALB2, and CDH1) to identify the prevalence of the PALB2 germline mutation among 2631 patients with breast and/or ovarian cancer. In this cohort, 39 mutations were identified with 24 types of mutation variants, where the majority of the mutations were frame-shift mutations and resulted in early termination. We also identified seven novel PALB2 mutations. Most of the PALB2 mutation carriers had breast cancer (36, 92.3%) and were more likely to have family history of breast cancer (19, 48.7%). The majority of the breast tumors were invasive ductal carcinoma (NOS type) (34, 81.0%) and hormonal positive (ER: 32, 84.2%; PR: 23, 60.5%). Pathogenic mutations of PALB2 were found in 39 probands with a mutation frequency of 1.6% and 1% in breast cancer and ovarian cancer patients, respectively. PALB2 mutation carriers were more likely have hormonal positive tumors and were likely to have familial aggregation of breast cancer.

Recent Advances in Enhancing the Therapeutic Index of PARP Inhibitors in Breast Cancer

Simple Summary

Two to three percent of breast cancer patients harbor germline mutation of either BRCA1 or BRCA2 genes. Their tumor cells are deficient in homologous recombination, a BRCA-dependent DNA repair machinery. These deficient cells survive thanks to the PARP-mediated alternative pathway. Therefore, PARP inhibitors have already shown some level of efficiency in the treatment of metastatic breast cancer patients. Unfortunately, some tumor cells inevitably resist PARP inhibitors by different mechanisms. In this review, we (i) present the notion of homologous recombination deficiency and its evaluation methods, (ii) detail the PARP inhibitor clinical trials in breast cancer, (iii) briefly describe the mechanisms to PARP inhibitors resistance, and (iv) discuss some strategies currently under evaluation to enhance the therapeutic index of PARP inhibitors in breast cancer.

Abstract

As poly-(ADP)-ribose polymerase (PARP) inhibition is synthetic lethal with the deficiency of DNA double-strand (DSB) break repair by homologous recombination (HR), PARP inhibitors (PARPi) are currently used to treat breast cancers with mutated BRCA1/2 HR factors. Unfortunately, the increasingly high rate of PARPi resistance in clinical practice has dented initial hopes. Multiple resistance mechanisms and acquired vulnerabilities revealed in vitro might explain this setback. We describe the mechanisms and vulnerabilities involved, including newly identified modes of regulation of DSB repair that are now being tested in large cohorts of patients and discuss how they could lead to novel treatment strategies to improve the therapeutic index of PARPi.