Abstract: Methods, systems, and software are provided for estimating a circulating tumor fraction for a test subject. Sequence reads are obtained from a panel-enriched sequencing reaction, including sequences for a first plurality of cfDNA fragments corresponding to probe sequences and a second plurality of cfDNA fragments not corresponding to probe sequences. Bin-level coverage ratios are determined from the sequences. Segments are formed by grouping adjacent bins based on similar coverage ratios and segment-level coverage ratios are determined based on bin-level coverage ratios for bins in the segment. For each simulated circulating tumor fraction in a plurality of circulating tumor fractions, segments are fitted to an integer copy state by identifying the integer copy state that best matches the segment-level coverage ratio.

Abstract: Methods, systems, and software are provided for validating a copy number variation in a test subject. A first dataset is obtained comprising bin-level sequence ratios, segment-level sequence ratios and segment-level measures of dispersion. Bins representing regions of a human reference genome are determined from sequencing cell-free nucleic acids in a liquid biopsy sample and reference samples. Segments encompass subsets of adjacent bins, where segment-level sequence ratios and measures of dispersion are determined using bin-level sequence ratios. A copy number status annotation for a segment is validated by applying the first dataset to a plurality of filters comprising a measure of central tendency bin-level sequence ratio filter, a confidence filter, and a measure of central tendency-plus-deviation bin-level sequence ratio filter. When a filter is fired, the copy number status annotation of the segment is rejected; and when no filter is fired, the copy number status annotation of the segment is validated.

Abstract: A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.

Abstract: Methods, systems, and software are provided for an ensemble model trained to distinguish between cancers with homologous recombination pathway deficiencies (HRD positive cancers) and cancers without homologous recombination pathway deficiencies (HRD negative cancers) based on nucleic acid sequencing data, e.g., both RNA and DNA sequencing data, generated from a cancerous tissue sample of the subject.

Abstract: 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.

Abstract: A method and system of audibly broadcasting responses to a user based on user queries about a specific patient molecular report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the molecular report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.

Abstract: Processes are provided for detecting loss of heterozygosity of Human Leukocyte Antigen (HLA) in a subject using analysis of next generation sequencing (NGS) data. The processes include aligning NGS data and identifying unmapped and mapped reads, updating reference data, and feeding one or more sequence reads to an HLA typing process for identifying candidate HLA alleles and feeding HLA type data to a loss of heterozygosity (LOH) modeling process for determining a LOH status for each HLA allele. A report may be generated of the LOH statuses for each of HLA allele.

Abstract: Systems and methods are provided for performing quality control analysis. The method obtains, in electronic form, a batch dataset comprising, for each respective sample in a batch of samples, a corresponding plurality of sequence reads derived from the respective sample by targeted or whole transcriptome RNA sequencing and corresponding metadata for the respective sample. The method determines for the batch dataset a cohort-matched reference batch, where the cohort-matched reference batch is balanced for tissue site, tumor purity, cancer type, sequencer identity, or date sequenced. The method performs one or more global batch quality control tests on the batch dataset using at least the cohort-matched reference batch. The method removes respective samples from the batch dataset that fail any one of the one or more global batch quality control tests or flagging for manual inspection respective samples that fail any one of the one or more global batch quality control tests.

Abstract: Systems and methods are provided for determining an optimized probe set. The method proceeds by obtaining a set of probes, where each probe has a respective concentration. The set of probes is assayed against a sample library, and at least i) a respective recovery rate for each probe in the set of probes, and ii) a median recovery rate for the set of probes are obtained. Modify the respective concentration of each probe that does not satisfy predetermined recovery rate threshold. Reevaluate the set of probes against the sample library. Repeat the modifying and reevaluation until the respective updated recovery rate for each probe in the updated set of probes satisfies the predetermined recovery rate threshold, thereby providing the optimized set of probes for the sample library.

Abstract: Systems and methods are provided for training a classifier to discriminate between a first cancer condition associated with an oncogenic pathogenic infection a second cancer condition that is not associated with an oncogenic pathogenic infection. Systems and methods are provided for distinguishing cancers associated with oncogenic pathogenic infections that contribute to the cancer pathology and cancers that are not associated with oncogenic pathogenic infections. Systems and methods are provided for treating cancer based on whether the cancer is associated with an oncogenic pathogenic infection.

Abstract: 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.

Abstract: Techniques for generating an overlay map on a digital medical image of a slide are provided, and include cell detection and tissue classification processes. Techniques include receiving a medical image, separating the image into tiles, and performing tile classifications and tissue classifications based on a multi-tile analysis. Techniques additionally include identifying cell objects in the image, separating the image into and displaying polygons identifying the cell objects and cell classifications. Generated displays may be overlays over the initial digital image.

Abstract: A system for personalized depression disorder treatment is disclosed herein. The system includes a server configured to communicate with existing healthcare resources and to receive patient data corresponding to a patient, the server including an analytics module. The system further includes a first database configured to store empirical patient outcomes, and further configured to communicate with the analytics module. Additionally, the system includes a user device having a graphical user interface (GUI) configured to communicate with the server and to display at least one output generated by the analytics module. The analytics module is configured to determine at least one of a personalized depression treatment and a personalized depression state prediction based on the empirical patient outcomes and the patient data.

Abstract: Methods, systems, and software are provided for determining a homologous recombination pathway status of a cancer in a test subject, e.g., to improve cancer treatment predictions and outcomes. In some embodiments, classifiers using one or more of (i) a heterozygosity status for DNA damage repair genes in a cancerous tissue, (ii) a measure of the loss of heterozygosity across the genome of the cancerous tissue, (iii) a measure of variant alleles detected in a second plurality of DNA damage repair genes in the genome of the cancerous tissue, (iv) a measure of variant alleles detected in the second plurality of DNA damage repair genes in the genome of a non-cancerous tissue, and (v) tumor sample purity are provided.

Abstract: A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.

Abstract: A method includes the steps of determining a first concept from a text of a medical record from an electronic health record system, the first concept relating to a patient, identifying a match to the first concept in a first list of concepts, wherein the first list of concepts is not a predetermined authority, referencing the first concept with an entity in a database of related concepts, identifying a match to a second concept in a second list of concepts, the second list of concepts not directly linked to the first list of concepts except by a relationship to the entity, wherein the second list of concepts is the predetermined authority, and providing the second concept as an identifier of the patient's medical record.

Abstract: A system, method, and mobile device application are configured to capture, with a mobile device, a document such as a next generation sequencing (NGS) report that includes NGS medical information about a genetically sequenced patient. At least some of the information is extracted from the document using an entity linking engine, and the extracted information is provided into a structured data repository where it is accessible to provide information regarding the patient specifically as well as collectively as part of a cohort of patients with similar genetic variants, medical histories, or other commonalities. In one aspect, the document is matched to a template model, and the document is processed using one or more masks segregating the template model, and therefore the document, into a series of distinct subregions.