Abstract: Omics patient data are analyzed using sequences or diff objects of tumor and matched normal tissue to identify patient and disease specific mutations, using transcriptomic data to identify expression levels of the mutated genes, and pathway analysis based on the so obtained omic data to identify specific pathway characteristics for the diseased tissue. Most notably, many different tumors have shared pathway characteristics, and identification of a pathway characteristic of a tumor may thus indicate effective treatment options ordinarily not considered when tumor analysis is based on anatomical tumor type only.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: Various compounds, compositions, and methods for inhibition of Rit1 are presented. In especially preferred aspects, contemplated compounds and compositions are suitable for treatment of cancers and other diseases associated with Rit1 signaling.

Abstract: Contemplated cancer treatments comprise recursive analysis of patient-, cancer-, and location-specific neoepitopes from various biopsy sites of a patient after treatment or between successive rounds of immunotherapy and/or chemotherapy to inform further immunotherapy. Recursive analysis preferably includes various neoepitope attributes to so identify treatment relevant neoepitopes.

Abstract: Various compounds, compositions, and methods for inhibition of Rit1 are presented. In especially preferred aspects, contemplated compounds and compositions are suitable for treatment of cancers and other diseases associated with Rit1 signaling.

Abstract: Techniques are provided for predicting DNA accessibility. DNase-seq data files and RNA-seq data files for a plurality of cell types are paired by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype. A neural network is configured to be trained using batches of the paired data files, where configuring the neural network comprises configuring convolutional layers to process a first input comprising DNA sequence data from a paired data file to generate a convolved output, and fully connected layers following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the paired data file and process the concatenation to generate a DNA accessibility prediction output. The trained neural network is used to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type.

Abstract: Compounds and compositions are presented that inhibit K-ras, and especially mutant K-ras. Certain compounds preferentially or even selectively inhibit specific forms of mutant K-Ras, and particularly the G12D mutant form.

Abstract: Methods are provided for identifying whether a tumor will be responsive to treatment with an anti-EGFR agent. Specific protein fragment peptides are precisely detected and quantitated by SRM-mass spectrometry directly in tumor cells collected from tumor tissue that was obtained from a cancer patient and compared to reference levels in order to determine if the lung cancer patient will positively respond to treatment with an anti-EGFR agent such as, for example, pamitumumab and/or erbitux.

Abstract: Specific mutations of FGFR3 (S249C) and of TP53 (V272M) are identified as being characteristic of breast cancer, and of having utility in diagnosis and prognosis of an individual with breast cancer. Systems and methods useful for identification of such mutations are also presented.

Abstract: This disclosure provides a technology for users to gain first-hand knowledge and experience with interpreting whole genomes. The technology graphically depicts variations in genome sequences in an expandable display, and provides a platform whereby the user may find and research the biological significance of such variants. The technology also provides a unique collaborative environment designed to capture and improve the collective knowledge of the participating community.

Abstract: Contemplated antiviral/cancer treatments comprise analysis of neoepitopes from viral DNA that has integrated into the host genome, and design of immunotherapeutic agents against such neoepitopes.

Abstract: Specific mutations of FGFR3 (S249C) and of TP53 (V272M) are identified as being characteristic of breast cancer, and of having utility in diagnosis and prognosis of an individual with breast cancer. Systems and methods useful for identification of such mutations are also presented.

Abstract: Systems and methods for more accurate prediction of the treatment outcome for immune therapy using checkpoint inhibitors are presented in which omics data of a patient tumor sample are used. In one aspect, a pathway signature is identified as being associated with immune suppression and as being responsive to treatment with immune checkpoint inhibitors.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: Various devices, systems, structures and methods are disclosed related to securely authorizing a transaction by synchronizing digital genomic data with associated synthetic genomic variants. An embodiment of the present invention utilizes digital genomic data associated with an entity, such as a person, who may utilize a genome-based security device to complete a transaction. In one embodiment, a person may use a genome-based security device to communicate with an external device over a wireless or other communication interface, synchronize digital genomic data and an associated synthetic variant received from the external device with digital genomic data and associated synthetic variant stored on the genome-based security device.

Abstract: Various protein markers can be used as post-treatment relapse predictors in HER2 positive breast cancer. Notably, these markers appear to be independent of the size of the tumor, metastasis status, grade, and hormone receptor status. In addition, HER2 quantities were in large part not correlated with likelihood of relapse.

Abstract: Various devices, systems, structures and methods are disclosed related to securely authorizing a transaction by synchronizing digital genomic data with associated synthetic genomic variants. An embodiment of the present invention utilizes digital genomic data associated with an entity, such as a person, who may utilize a genome-based security device to complete a transaction. In one embodiment, a person may use a genome-based security device to communicate with an external device over a wireless or other communication interface, synchronize digital genomic data and an associated synthetic variant received from the external device with digital genomic data and associated synthetic variant stored on the genome-based security device.

Abstract: Techniques are provided for predicting DNA accessibility. DNase-seq data files and RNA-seq data files for a plurality of cell types are paired by assigning DNase-seq data files to RNA-seq data files that are at least within a same biotype. A neural network is configured to be trained using batches of the paired data files, where configuring the neural network comprises configuring convolutional layers to process a first input comprising DNA sequence data from a paired data file to generate a convolved output, and fully connected layers following the convolutional layers to concatenate the convolved output with a second input comprising gene expression levels derived from RNA-seq data from the paired data file and process the concatenation to generate a DNA accessibility prediction output. The trained neural network is used to predict DNA accessibility in a genomic sample input comprising RNA-seq data and whole genome sequencing for a new cell type.

Abstract: A computer implemented method of generating at least one shape of a region of interest in a digital image is provided.

Abstract: Contemplated systems and methods allow for prediction of chemotherapy outcome for patients diagnosed with high-grade bladder cancer. In particularly preferred aspects, the prediction is performed using a model based on machine learning wherein the model has a minimum predetermined accuracy gain and wherein a thusly identified model provides the identity and weight factors for omics data used in the outcome prediction.