An artificial intelligence approach for investigating multifactorial pain-related features of endometriosis.

Endometriosis is a debilitating, chronic disease that is estimated to affect 11% of reproductive-age women. Diagnosis of endometriosis is difficult with diagnostic delays of up to 12 years reported. These delays can negatively impact health and quality of life. Vague, nonspecific symptoms, like pain, with multiple differential diagnoses contribute to the difficulty of diagnosis. By investigating previously imprecise symptoms of pain, we sought to clarify distinct pain symptoms indicative of endometriosis, using an artificial intelligence-based approach. We used data from 473 women undergoing laparoscopy or laparotomy for a variety of surgical indications. Multiple anatomical pain locations were clustered based on the associations across samples to increase the power in the probability calculations. A Bayesian network was developed using pain-related features, subfertility, and diagnoses. Univariable and multivariable analyses were performed by querying the network for the relative risk of a postoperative diagnosis, given the presence of different symptoms. Performance and sensitivity analyses demonstrated the advantages of Bayesian network analysis over traditional statistical techniques. Clustering grouped the 155 anatomical sites of pain into 15 pain locations. After pruning, the final Bayesian network included 18 nodes. The presence of any pain-related feature increased the relative risk of endometriosis (p-value < 0.001). The constellation of chronic pelvic pain, subfertility, and dyspareunia resulted in the greatest increase in the relative risk of endometriosis. The performance and sensitivity analyses demonstrated that the Bayesian network could identify and analyze more significant associations with endometriosis than traditional statistical techniques. Pelvic pain, frequently associated with endometriosis, is a common and vague symptom. Our Bayesian network for the study of pain-related features of endometriosis revealed specific pain locations and pain types that potentially forecast the diagnosis of endometriosis.

Automated prioritization of sick newborns for whole genome sequencing using clinical natural language processing and machine learning.

BACKGROUND: Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NICU. There is a dire need for automated means to prioritize patients for WGS. METHODS: Institutional databases of electronic health records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. We have developed automated means to prioritize patients for rapid and whole genome sequencing (rWGS and WGS) directly from clinical notes. Our approach combines a clinical natural language processing (CNLP) workflow with a machine learning-based prioritization tool named Mendelian Phenotype Search Engine (MPSE). RESULTS: MPSE accurately and robustly identified NICU patients selected for WGS by clinical experts from Rady Children's Hospital in San Diego (AUC 0.86) and the University of Utah (AUC 0.85). In addition to effectively identifying patients for WGS, MPSE scores also strongly prioritize diagnostic cases over non-diagnostic cases, with projected diagnostic yields exceeding 50% throughout the first and second quartiles of score-ranked patients. CONCLUSIONS: Our results indicate that an automated pipeline for selecting acutely ill infants in neonatal intensive care units (NICU) for WGS can meet or exceed diagnostic yields obtained through current selection procedures, which require time-consuming manual review of clinical notes and histories by specialized personnel.

Genomic landscape of advanced prostate cancer patients with BRCA1 versus BRCA2 mutations as detected by comprehensive genomic profiling of cell-free DNA.

BRCA1-mutated prostate cancer has been shown to be less responsive to poly (ADP-ribose) polymerase (PARP) inhibitors as compared to BRCA2-mutated prostate cancer. The reason for this differential response is not clear. We hypothesized this differential sensitivity to PARP inhibitors may be explained by distinct genomic landscapes of BRCA1 versus BRCA2 co-segregating genes. In a large dataset of 7,707 men with advanced prostate cancer undergoing comprehensive genomic profiling (CGP) of cell-free DNA (cfDNA), 614 men harbored BRCA1 and/or BRCA2 alterations. Differences in the genomic landscape of co-segregating genes was investigated by Fisher's exact test and probabilistic graphical models (PGMs). Results demonstrated that BRCA1 was significantly associated with six other genes, while BRCA2 was not significantly associated with any gene. These findings suggest BRCA2 may be the main driver mutation, while BRCA1 mutations tend to co-segregate with mutations in other molecular pathways contributing to prostate cancer progression. These hypothesis-generating data may explain the differential response to PARP inhibition and guide towards the development of combinatorial drug regimens in those with BRCA1 mutation.

A dominant negative ADIPOQ mutation in a diabetic family with renal disease, hypoadiponectinemia, and hyperceramidemia.

Adiponectin, encoded by ADIPOQ, is an insulin-sensitizing, anti-inflammatory, and renoprotective adipokine that activates receptors with intrinsic ceramidase activity. We identified a family harboring a 10-nucleotide deletion mutation in ADIPOQ that cosegregates with diabetes and end-stage renal disease. This mutation introduces a frameshift in exon 3, resulting in a premature termination codon that disrupts translation of adiponectin's globular domain. Subjects with the mutation had dramatically reduced circulating adiponectin and increased long-chain ceramides levels. Functional studies suggest that the mutated protein acts as a dominant negative through its interaction with non-mutated adiponectin, decreasing circulating adiponectin levels, and correlating with metabolic disease.

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

BACKGROUND: Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. METHODS: We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. RESULTS: GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. CONCLUSIONS: GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool.

BACKGROUND: Prioritization of sequence variants for diagnosis and discovery of Mendelian diseases is challenging, especially in large collections of whole genome sequences (WGS). Fast, scalable solutions are needed for discovery research, for clinical applications, and for curation of massive public variant repositories such as dbSNP and gnomAD. In response, we have developed VVP, the VAAST Variant Prioritizer. VVP is ultrafast, scales to even the largest variant repositories and genome collections, and its outputs are designed to simplify clinical interpretation of variants of uncertain significance. RESULTS: We show that scoring the entire contents of dbSNP (> 155 million variants) requires only 95 min using a machine with 4 cpus and 16 GB of RAM, and that a 60X WGS can be processed in less than 5 min. We also demonstrate that VVP can score variants anywhere in the genome, regardless of type, effect, or location. It does so by integrating sequence conservation, the type of sequence change, allele frequencies, variant burden, and zygosity. Finally, we also show that VVP scores are consistently accurate, and easily interpreted, traits not shared by many commonly used tools such as SIFT and CADD. CONCLUSIONS: VVP provides rapid and scalable means to prioritize any sequence variant, anywhere in the genome, and its scores are designed to facilitate variant interpretation using ACMG and NHS guidelines. These traits make it well suited for operation on very large collections of WGS sequences.

Quantitative differences in nourishment affect caste-related physiology and development in the paper wasp Polistes metricus.

The distinction between worker and reproductive castes of social insects is receiving increased attention from a developmental rather than adaptive perspective. In the wasp genus Polistes, colonies are founded by one or more females, and the female offspring that emerge in that colony are either non-reproducing workers or future reproductives of the following generation (gynes). A growing number of studies now indicate that workers emerge with activated reproductive physiology, whereas the future reproductive gynes do not. Low nourishment levels for larvae during the worker-rearing phase of the colony cycle and higher nourishment levels for larvae when gynes are reared are now strongly suspected of playing a major role in this difference. Here, we present the results of a laboratory rearing experiment in which Polistes metricus single foundresses were held in environmental conditions with a higher level of control than in any previously published study, and the amount of protein nourishment made available to feed larvae was the only input variable. Three experimental feeding treatments were tested: restricted, unrestricted, and hand-supplemented. Analysis of multiple response variables shows that wasps reared on restricted protein nourishment, which would be the case for wasps reared in field conditions that subsequently become workers, tend toward trait values that characterize active reproductive physiology. Wasps reared on unrestricted and hand-supplemented protein, which replicates higher feeding levels for larvae in field conditions that subsequently become gynes, tend toward trait values that characterize inactive reproductive physiology. Although the experiment was not designed to test for worker behavior per se, our results further implicate activated reproductive physiology as a developmental response to low larval nourishment as a fundamental aspect of worker behavior in Polistes.