Nant Holdingsip, LLC

Nantomics, LLC

Nantcell, Inc.

Nantbio, Inc.

Easel Biotechnologies LLC

Nanthealth, Inc.

Five3 Genomics, LLC

Immunitybio, Inc.

The Buck Institute for Age Research

Buck Institute

Chan Soon-shiong Nanthealth Foundation

Nantbioscience, Inc.

Nanthealth Labs, Inc.

The Buck Institute for Age Reasearch

Shahrooz Rabizadeh

Kayvan R Niazi

Patrick Soon-Shiong

Stephen Charles Benz

Oleksandr V Buzko

Justin Golovato

Charles Joseph Vaske

Andrew Nguyen

Bing Song

John Zachary Sanborn

Liudmila A Beziaeva

Anne-Laure Le Ny

Hans G Klingemann

Dale E Bredesen

Christopher W Szeto

Yi-Xin Huo

Nicholas James Witchey

Kamil A Wnuk

Kwang Myung Cho

Laurent H Boissel

Mustafa Jaber

Paul Weingarten

Wendy M Higashide

Barry J Simon

Heather McFarlane

Lars Erik Ulf Rohlin

Anne-Laure LeNy

Nicole Hensley

Jeremi M Sudol

Chunlin Tao

Peter Sieling

Annie Shin

Hermes J Garban

Jeffrey Safrit

Elizabeth Gabitzsch

Adrian Rice

Lise Geissert

Kathleen Danenberg

Chad Garner

Leonard Sender

Kevin B Givechian

Connie Tsai

Benjamin Schilling

Hing C Wong

Wenxin Xu

David T Ho

Anna B Juncker-Jensen

Chrissie Lee

Jay Gardner Nelson

Jay Nelson

Heather Mcfarlane

Niazi Kayvan

Anna Juncker-Jensen

Systems and methods for predicting an immune response against a tumor in a patient having the tumor are provided. The relative mass or changes of mass of tumor cells or immune cell in the tumor can be ex vivo observed, and an immune status of the tumor can be determined based on the mass of tumor cells or immune cell. The immune status can provide a guidance to predict the immune response against the tumor in the patient.

Live cell imaging systems and methods to validate triggering of immune response

An immune gene expression signature is associated with favorable clinical features in Treg-enriched tumor samples and can be used to predict immunogenicity of a tumor, overall survival, and/or chemosensitivity.

Method of characterizing a tumor based on an immune gene expression signature in regulatory T cell (Treg)-enriched tumor samples

Techniques are provided for determining a cell count within a whole slide pathology image. The image is segmented using a global threshold value to define a tissue area. A plurality of patches comprising the tissue area are selected. Stain intensity vectors are determined within the plurality of patches to generate a stain intensity image. The stain intensity image is iteratively segmented to generate a cell mask using a local threshold value that is and gradually reduced after each iteration. A chamfer distance transform is applied to the cell mask to generate a distance map. Cell seeds are determined on the distance map. Cell segments are determined using a watershed transformation, and a whole cell count is calculated for the plurality of patches based on the cell segments. A client device may be configured for real-time cell counting based on the whole cell count.

Real-time whole slide pathology image cell counting

Contemplated systems and methods employ chimeric reference sequences that include a plurality of viral genome sequences to identify/quantify integration and co-amplification events. Most typically, the viral genome sequences are organized in the chimeric reference sequences as single chromosomes and the chimeric reference sequences are in BAM format.

Systems, methods, and compositions for viral-associated tumors

Disclosed herein are methods for inducing immunity against a virus such as a coronavirus in the mucosal tissue of a patient, include administering a vaccine composition to the patient by oral administration (e.g., nasal injection, nasal inhalation, oral inhalation, and/or oral ingestion). Also disclosed are compositions for assaying the presence of anti-viral antibodies induced by the administered vaccine or the presence of viral proteins in a saliva sample include a stabilizing solution and may also include the use of aragonite particle beads. Compositions and methods are presented for prevention and/or treatment of a coronavirus disease wherein the composition comprises a recombinant entity. The recombinant entity is bivalent, comprising a nucleic acid encoding a coronavirus 2 nucleocapsid protein CoV2 nucleocapsid protein fused to an endosomal targeting sequence, and a nucleic acid encoding a CoV2 spike protein sequence optimized for cell surface expression.

SARS-CoV-2 vaccines comprising human adenovirus vectors encoding spike and nucleocapsid-ETSD immunogens

A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.

Deep learning models for region of interest determination

Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.

Weakly supervised learning with whole slide images

Circulating free RNA (cfRNA) is used for monitoring status and/or treatment response for neural tumors, and especially glioma, glioblastoma, and neuroblastoma. Particularly preferred cfRNAs include those that encode a marker that is specific to a neural tumor, but also markers that are specific to DNA repair status and/or immune status.

Dynamic changes in circulating free RNA of neural tumors

Systems and methods for validating immune therapy that targets patient- and tumor-specific neoepitopes are presented in which cellular components are identified and/or used that present patient- and tumor-specific neoepitopes. Presence of these neoepitopes confirms the suitability of the neoepitopes. Advantageously, synthetic antibodies are created based on in silico analysis of the tumor genome and RNA or phage display, and can be used to isolate cellular components, and especially circulating tumor cells, metastatic cells, and/or exosomes and microvesicles.

Circulating tumor cell enrichment using neoepitopes

The invention provides a method of validating the therapeutic composition that is prepared for immunotherapy of a tumor or cancer. The method includes, triggering of an immune response to a neoepitope of a subject's tumor by: a) obtaining neoepitope sequence data from the tumor of a subject; b) obtaining immune competent cells; c) using the neoepitope sequence data to generate a neoepitope presentation system; d) triggering an immune response by contacting the immune competent cells with the neoepitope presentation system; and e) quantifying the triggering of the immune response from the contacted immune competent cells.

Method of validating the triggering of an immune response to a neoepitope of a tumor with t-cells

RNA from a biological fluid is stabilized during isolation and/or storage using DNA. In especially preferred aspects, the RNA is cfRNA and/or ctRNA, and the biological fluid is blood.

DNA stabilization of RNA

Deep learning models for region-of-interest determination

Systems and methods for prediction of the treatment outcome for immune therapy are presented in which omics data of a patient tumor sample are used. Most typically, the omics data are processed to identify mutational signatures (especially APOBEC/POLE signatures), immune checkpoint expression, and MSI status as leading indicators to predict the treatment outcome for immune therapy. Such prediction advantageously integrates various parameters that would otherwise, when individually considered, skew prediction outcome.

Immunotherapy markers and uses therefor

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Los Angeles, CA, United States of America

Shahrooz Rabizadeh