Applied Materials, Inc.

Lam Research Corporation

Ventana Medical Systems, Inc.

International Business Machines Corporation

Other

Roche Molecular Systems, Inc.

Novellus Systems Incorporated

Intevac, Inc.

Oregon Health & Science University

Brooks Automation Gmbh

Leland Stanford Junior University

Providence Health & Services - Oregon

Roche Diagnostics Gmbh

F. Hoffmann-la Roche Ag

Michael S Barnes

John Patrick Holland

Srinivas Chukka

John H Keller

Farhad K Moghadam

Tetsuya Ishikawa

Christophe Chefd'hotel

Shin-Puu Jeng

Yung-Cheng Lu

David Wingto Cheung

Wai-Fan Yau

Terry Bluck

Robert Parkash Mandal

Tze Wing Poon

Ralf B Willecke

Kuo-Wei Liu

John C Forster

Brian K McMillin

Chi-I Lang

Valentin Nikolov Todorow

Joerg Bredno

Judy H Huang

Sunil Shanker

Ting Chen

Kevin P Fairbairn

Thomas M Grogan

Paul K Shufflebotham

Robert P Westerfield, Jr

Dennis K Coultas

Huong Thanh Nguyen

Joseph S Logan

Joachim Schmid

Raashina Humayun

Brett C Richardson

Valentin N Todorov

Vishal Gauri

Li-Qun Xia

Yan Ye

Daniel John Hoffman

Christopher F Lane

Joe W Gray

Tuqiang Q Ni

Alexander Miller Paterson

Alan M Schoepp

Anindya Sarkar

David Chafin

James Anthony O'Neill

Benjamin C Stevens

Karl Eric Garsha

Mehul B Naik

Michael Santiago Cox

Matthew L Miller

Canfeng Lai

Jon McChesney

Jim F Martin

David Palagashvili

Thomas E Wicker

Hongqing Shan

Jonathan David Mohn

Wenli Z Collison

Kien Nguyen

Kartik Ramaswamy

Kenneth S Collins

Andrew Nguyen

Ellie Y Yieh

Wei Liu

Hiroji Hanawa

Christopher Dennis Bencher

Alex Paterson

Dan Maydan

Dennis Michael Hausmann

Hichem M'Saad

Ashok K Sinha

Timothy Weidman

Neil M Benjamin

George D Papasouliotis

Diana Xiaobing Ma

Bryan Y Pu

Michael D Armacost

Steven C Shannon

Mihai A Buretea

Hongching Shan

William Kenneth Day

Seon-Mee Cho

Christopher S Ngai

John P Parks

Thanh Ngoc Pham

Jonathan Daniel Chapple-Sokol

Claes H Bjorkman

Yaxin Wang

Ron Rulkens

Robert A Maraschin

Kenny Linh Doan

Yao Nie

Lily L Pang

Yunfeng Lu

Jingbao Liu

David S L Mui

Robert G Veltrop

Wendy H Yeh

Automated systems and methods are presented for retrospectively analyzing clinical trial data. A plurality of image derived from biological samples of patients in a cohort population are accessed. Image features are computed based on the plurality of images. A diagnostic feature metric is derived based on the computed image features. A cut point value is determined by applying a statistical minimization method using the derived diagnostic feature metric and patient outcome data from the cohort population, in which the cut point value identifies a patient in the cohort population as positive or negative for a diagnostic test.

Computational pathology approach for retrospective analysis of tissue-based companion diagnostic driven clinical trial studies

Immune context scores are calculated for tumor tissue samples using continuous scoring functions. Feature metrics for at least one immune cell marker are calculated for a region or regions of interest, the feature metrics including at least a quantitative measure of human CD3 or total lymphocyte counts. A continuous scoring function is then applied to a feature vector including the feature metric and at least one additional metric related to an immunological biomarker, the output of which is an immune context score. The immune context score may then be plotted as a function of a diagnostic or treatment metric, such as a prognostic metric (e.g. overall survival, disease-specific survival, progression-free survival) or a predictive metric (e.g. likelihood of response to a particular treatment course). The immune context score may then be incorporated into diagnostic and/or treatment decisions.

Methods and systems for evaluation of immune cell infiltrate in tumor samples

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

Assessing risk of breast cancer recurrence

An informatics platform provides an architecture to integrate information from relevant patient information systems. The informatics platform may include: a workflow tool that can be used to prepare and review information at multi-disciplinary board meetings; a visual timeline of patient events; a search engine to search for patients with specific attributes; a graphing tool that can display disparate clinical variables in a single chart; a virtual PinBoard for users to identify relevant patient information for board meetings; an image viewing application that can provide for comparison of images from different information systems; structured reporting functionality that incorporates system aggregated patient information and board recommendations; an application interface that integrates clinically relevant tools to provide patient specific references; a collaboration interface that facilitates communication of patient specific information and documents the discussion threads as independent reference points; and a default display of relevant patient information customized for each clinical specialty.

Informatics platform for integrated clinical care

The subject disclosure presents systems and methods for improved meso-dissection of biological specimens and tissue slides including importing one or more reference slides with annotations, using inter-marker registration algorithms to automatically map the annotations to an image of a milling slide, and dissecting the annotated tissue from the selected regions in the milling slide for analysis, while concurrently tracking the data and analysis using unique identifiers such as bar codes.

Systems and methods for meso-dissection

The subject disclosure provides systems, computer-implemented methods, and clinical workflows for meso-dissection of biological specimens and tissue slides by incorporating annotation and inter-marker registration modules within digital pathology imaging and meso-dissection (or milling) systems. Images of a reference slide a milling slide may be acquired using the same imaging system, with the annotations on the image associated with the milling slide being based on the inter-marker registration. Each image along with its respective annotations and meta-data may be associated with a project or a case, and stored in an image management system. A same-marker registration may be used to map annotations from the annotated image of the milling slide to a live image of the milling slide. The milling slide may be milled based on the annotations, with milled tissue output into a contained that is labeled in association with the labeled input slides.

Methods, systems, and apparatuses for detecting and describing heterogeneity in a cell sample are disclosed herein. A plurality of fields of view (FOV) are generated for one or more areas of interest (AOI) within an image of the cell sample are generated. Hyperspectral or multispectral data from each FOV is organized into an image stack containing one or more z-layers, with each z-layer containing intensity data for a single marker at each pixel in the FOV. A cluster analysis is applied to the image stacks, wherein the clustering algorithm groups pixels having a similar ratio of detectable marker intensity across layers of the z-axis, thereby generating a plurality of clusters having similar expression patterns.

Methods, systems, and apparatuses for quantitative analysis of heterogeneous biomarker distribution

Automated systems and methods for determining the variability between derived expression scores for a series of biomarkers between different identified cell clusters in a whole slide image are presented. The variability between derived expression scores may be a derived inter-marker heterogeneity metric.

Method of computing tumor spatial and inter-marker heterogeneity

Disclosed herein are methods for identifying a subject as having NSCLC that is predicted or is likely to respond to treatment with an ALK inhibitor, for example crizotinib. The methods include identifying a sample including NSCLC tumor cells as ALK-positive or ALK-negative using immunohistochemistry (IHC) and scoring methods disclosed herein. A subject is identified as having NSCLC likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-positive and is identified as having NSCLC not likely to respond to treatment with an ALK inhibitor if the sample is identified as ALK-negative. According to certain embodiments of the methods, subjects predicted to respond to an ALK inhibitor may then be treated with an ALK inhibitor such as crizotinib.

Method of identifying treatment responsive non-small cell lung cancer using anaplastic lymphoma kinase (ALK) as a marker

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

Cancer risk stratification based on histopathological tissue slide analysis

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San Francisco, CA, United States of America

Michael S Barnes