[2020] Predicting likelihood and site of metastasis from patient records
Systems and methods are provided for predicting metastasis of a cancer in a subject. A plurality of data elements for the subject's cancer is obtained, including sequence features comprising relative abundance values for gene expression in a cancer biopsy of the subject, optional personal characteristics about the subject, and optional clinical features related to the stage, histopathological grade, diagnosis, symptom, comorbidity, and/or treatment of the cancer in the subject, and/or a temporal element associated therewith. One or more models are applied to the plurality of data elements, determining one or more indications of whether the cancer will metastasize. A clinical report comprising the one or more indications is generated
[2020] Ecg based future atrial fibrillation predictor systems and methods
A method and system for predicting the likelihood that a patient will suffer from atrial fibrillation is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from atrial fibrillation within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method
[2020] Methods and systems for detecting microsatellite instability of a cancer in a liquid biopsy assay
Methods, systems, and software are provided for determining a microsatellite instability (MSI) status of a subject. Nucleotide sequences are obtained for cell-free DNA molecules from a liquid biopsy sample of the subject. The nucleotide sequences are used to determine, for each respective microsatellite locus in a plurality of predetermined microsatellite loci, one or more independent corresponding metrics, where each metric in the one or more metrics is determined at least in part by the distribution of the number of repeat units at the respective microsatellite locus. The one or more metrics are input into a classifier trained to distinguish between stable and unstable microsatellite loci, in order to classify the MSI status of the subject. In certain aspects, microsatellite stability metrics are compared against metrics from solid tumor samples and/or normal tissues. In certain aspects, the microsatellite stability metrics are determined relative to a subject-specific standard for microsatellite stability.
[2019] Method and process for predicting and analyzing patient cohort response, progression, and survival
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
[2019] Evaluating effect of event on condition using propensity scoring
Systems and methods are provided for implementing a tool for evaluating an effect on an event, such as a medication or treatment, on a subject's condition, using a propensity model that identifies matched treatment and control cohorts within a base population of subjects. A propensity value threshold, which can be obtained based on user input, can be used to adjust the selection of subjects for treatment and control cohorts. The tool allows analyzing features of the subjects in the treatment and control groups, and further allows for evaluation and comparison of survival objectives of subjects in the treatment and control groups.
[2019] Methods of normalizing and correcting rna expression data
A platform to perform normalization and correction on gene expression datasets and combines different datasets into a standard dataset using a framework configured to continuously incorporate new gene expression data. The framework determines a series of conversion factors that are used to on-board new gene expression datasets, such as unpaired datasets, where these conversion factors are able to correct for variations in data type, variations in gene expressions, and variations in collection systems.
[2020 - Clinical Breast Cancer]
Louis E. Fernandes*, Caroline G. Epstein*, Alexandria M. Bobe*, Joshua S.K. Bell*, Martin C. Stumpe, Michael E. Salazar, Ameen A. Salahudeen, Ruth A. Pe Benito, Calvin McCarter, Benjamin D. Leibowitz, Matthew Kase, Catherine Igartua, Robert Huether , Ashraf Hafez, Nike Beaubier , Michael D. Axelson, Mark D. Pegram, Sarah L. Sammons, Joyce A. O'Shaughnessy, and Gary A. Palmer
We performed a retrospective analysis of longitudinal real-world data (RWD) from breast cancer patients to replicate results from clinical studies and demonstrate the feasibility of generating real-world evidence. We also assessed the value of transcriptome profiling as a complementary tool for determining molecular subtypes.We performed a retrospective analysis of longitudinal real-world data (RWD) from breast cancer patients to replicate results from clinical studies and demonstrate the feasibility of generating real-world evidence. We also assessed the value of transcriptome profiling as a complementary tool for determining molecular subtypes.
[2020 - Circulation]
John Pfeifer, Sushravya M Raghunath, Alvaro Ulloa, Arun Nemani, Tanner Carbonati, Linyuan Jing, David vanMaanen, Dustin Hartzel, Jeffrey Ruhl, Nathan J Stoudt, Kipp W Johnson, Noah Zimmerman, Joseph Leader, H Lester Kirchner, Christoph Griessenauer, Ashraf Hafez, Christopher Good, Brandon Fornwalt, Christopher M Haggerty
Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke
[2020 - Nature Medicine]
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Sushravya Raghunath, Alvaro E. Ulloa Cerna, Linyuan Jing, David P. vanMaanen, Joshua Stough, Dustin N. Hartzel, Joseph B. Leader, H. Lester Kirchner, Martin C. Stumpe, Ashraf Hafez, Arun Nemani, Tanner Carbonati, Kipp W. Johnson, Katelyn Young, Christopher W. Good, John M. Pfeifer, Aalpen A. Patel, Brian P. Delisle, Amro Alsaid, Dominik Beer, Christopher M. Haggerty & Brandon K. Fornwalt
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart1. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage–time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (n = 45,285) with ECGs interpreted as ‘normal’ by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (P < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians.
[2019 - Tempus.com]
Microsatellite instability detection with cell-free DNA next-generation sequencing
Ariane Lozac’hmeur, Ashraf Hafez, Jason Perera, Denise Lau, and Aly A. Khan
Microsatellite instability is a clinically actionable genomic indication for cancer immunotherapy. In microsatellite instability-high (MSI-H) tumors, defects in DNA mismatch repair (MMR) can cause a hypermutated phenotype where alterations accumulate in the repetitive microsatellite regions of DNA. MSI detection is typically performed by subjecting tumor tissue (“solid biopsy”) to clinical next-generation sequencing or specific assays, such as MMR IHC or MSI PCR. Circulating cellfree tumor DNA (cfDNA) testing (“liquid biopsy”) is rapidly emerging as a less invasive method for detecting cancer and monitoring disease progression. Here, we explore the possibility of detecting MSI in cfDNA using the Tempus xF cfDNA liquid biopsy assay