F-Box Protein-Mediated Resistance to PARP Inhibitor Therapy.

PARP inhibitor (PARPi) therapy targets BRCA1/2 mutant tumor cells, but acquired resistance limits its effectiveness. In this issue of Molecular Cell, Marzio et al. (2019) identify a novel mechanism of resistance to PARPi through regulation of RAD51 protein stability via an SCF ubiquitin ligase dependent on EMI1.

Pharmacogenomics: Driving Personalized Medicine.

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Germline Variants Impact Somatic Events during Tumorigenesis.

Cancer is characterized by diverse genetic alterations in both germline and somatic genomes that disrupt normal biology and provide a selective advantage to cells during tumorigenesis. Germline and somatic genomes have been extensively studied independently, leading to numerous biological insights. Analyses integrating data from both genomes have identified genetic variants impacting somatic events in tumors, including hotspot driver mutations. Interactions among specific germline variants and somatic events influence cancer subtypes, treatment response, and clinical outcomes. Investigation of these complex interactions is increasing our understanding of aberrant pathways in tumors that may uncover novel therapeutic targets. Here, we review the literature describing the role of germline genetic variants in promoting the selection and generation of specific mutations during tumorigenesis.

Differences in somatic TP53 mutation type in breast tumors by race and receptor status.

PURPOSE: Somatic driver mutations in TP53 are associated with triple-negative breast cancer (TNBC) and poorer outcomes. Breast cancers in women of African ancestry (AA) are more likely to be TNBC and have somatic TP53 mutations than cancers in non-Hispanic White (NHW) women. Missense driver mutations in TP53 have varied functional impact including loss-of-function (LOF) or gain-of-function (GOF) activity, and dominant negative (DNE) effects. We aimed to determine if there were differences in somatic TP53 mutation types by patient ancestry or TNBC status. METHODS: We identified breast cancer datasets with somatic TP53 mutation data, ancestry, age, and hormone receptor status. Mutations were classified for functional impact using published data and type of mutation. We assessed differences using Fisher's exact test. RESULTS: From 96 breast cancer studies, we identified 2964 women with somatic TP53 mutations: 715 (24.1%) Asian, 258 (8.7%) AA, 1931 (65.2%) NHW, and 60 (2%) Latina. The distribution of TP53 mutation type was similar by ancestry. However, 35.8% of tumors from NHW individuals had GOF mutations compared to 29% from AA individuals (p = 0.04). Mutations with DNE activity were positively associated with TNBC (OR 1.37, p = 0.03) and estrogen receptor (ER) negative status (OR 1.38; p = 0.005). CONCLUSIONS: Somatic TP53 mutation types did not differ by ancestry overall, but GOF mutations were more common in NHW women than AA women. ER-negative and TNBC tumors are less likely to have DNE+ TP53 mutations which could reflect biological processes. Larger cohorts and functional studies are needed to further elucidate these findings.

Genetic analysis of a malignant meningioma and associated metastases.

To identify genes altered in a highly aggressive metastatic meningioma primary as well as its metastases. Exome sequencing of a primary anaplastic meningioma and metastatic lesions in which DNA could be extracted and compared to germline DNA. Genetic analysis of the metastatic sites found 31 common mutations among the primary tumor and two metastatic sites. Additionally, genetic mutations were identified which were either infrequently (MUC3A, ALDH1A3, HOXA1) or not at all previously described in meningiomas (CASS4, CMKLR1). Exome sequencing of a metastatic meningioma and its distant metastases outside the CNS identified mutations that were not previously well described.

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in Phox2b-derived astrocytes ablated mice.

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community.NEW & NOTEWORTHY ML/AI classification workflows are capable of providing insight into differences between genotypes for neurophysiology research. Analytical techniques utilized in the neurophysiology community can be augmented by implementing ML/AI workflows. Random forest is a robust classification algorithm for respiratory plethysmography data. Utilization of ML/AI workflows in neurophysiology research requires heightened transparency and improved community research standards.

The BRCA1 c. 5096G>A p.Arg1699Gln (R1699Q) intermediate risk variant: breast and ovarian cancer risk estimation and recommendations for clinical management from the ENIGMA consortium.

BACKGROUND: We previously showed that the BRCA1 variant c.5096G>A p.Arg1699Gln (R1699Q) was associated with an intermediate risk of breast cancer (BC) and ovarian cancer (OC). This study aimed to assess these cancer risks for R1699Q carriers in a larger cohort, including follow-up of previously studied families, to further define cancer risks and to propose adjusted clinical management of female BRCA1*R1699Q carriers. METHODS: Data were collected from 129 BRCA1*R1699Q families ascertained internationally by ENIGMA (Evidence-based Network for the Interpretation of Germline Mutant Alleles) consortium members. A modified segregation analysis was used to calculate BC and OC risks. Relative risks were calculated under both monogenic model and major gene plus polygenic model assumptions. RESULTS: In this cohort the cumulative risk of BC and OC by age 70 years was 20% and 6%, respectively. The relative risk for developing cancer was higher when using a model that included the effects of both the R1699Q variant and a residual polygenic component compared with monogenic model (for BC 3.67 vs 2.83, and for OC 6.41 vs 5.83). CONCLUSION: Our results confirm that BRCA1*R1699Q confers an intermediate risk for BC and OC. Breast surveillance for female carriers based on mammogram annually from age 40 is advised. Bilateral salpingo-oophorectomy should be considered based on family history.

Polygenic risk scores for prostate cancer: testing considerations.

Genome-wide association studies (GWAS) have identified more than 170 single nucleotide variants (SNVs) associated with prostate cancer risk. Each variant is associated with only small increases in risk and is not predictive of an individuals' overall risk of developing prostate cancer. Polygenic risk scores (PRS) combining these variants are now clinically available and may improve predictive value of other factors such as PSA. This overview reviews the current state of PRS for prostate cancer including testing considerations.

Exploring genetic counselors' perceptions of usefulness and intentions to use refined risk models in clinical care based on the Technology Acceptance Model (TAM).

Pathogenic germline mutations in the BRCA1 or BRCA2 genes are associated with an elevated lifetime risk for breast (50%-85% risk) and ovarian cancer (20%-40% risk). Genome-wide association studies have identified over 100 genetic variants associated with modified breast and/or ovarian cancer risk in BRCA1 and BRCA2 carriers. Risk models generated based on these variants have shown that these genetic modifiers strongly influence absolute risk of developing breast or ovarian cancer in BRCA mutation carriers. There is a lack of understanding, however, about the clinical applicability and utility of these risk models. To investigate this gap, we collected survey data from 274 cancer genetic counselors (GCs) through the National Society of Genetic Counselors Cancer Special Interest Group. Questions assessed perceptions of usefulness and intentions of genetic counselors to use these refined risk models in clinical care based on the Technology Acceptance Model (TAM). We found that GCs' reactions to the estimates were largely positive, though they thought the possibility of changing management based on results was unlikely. Additionally, we found that more experienced GCs were more likely to consider refined risk estimates in clinic. Support also was provided for core predictions within the TAM, whereby the perceived usefulness (indirect effect est. = 0.08, 95% CI: [0.04, 0.13]) and perceived ease of use (indirect effect est. = 0.078, 95% CI: [0.04, 0.13]) of refined risk estimates were indirectly associated with intentions to use via attitudes.