Ventana Medical Systems, Inc.

Cornell University

The University of Arizona

Ray B Nagle

Gary A Pestano

Michael Otter

Phillip C Miller

Karl Eric Garsha

Mark A Rubin

Francesca Demichelis

Kristie A Vanpatten

Connie Cortez

Amit M Algotar

Karl Garsha

William Kenneth Day

Jan Froehlich

Alexandra Dea Nagy

Ryan Dittamore

Gary Anthony Pestano

Ubaradka Sathyanarayana

In general, the presently disclosed technology relates to identification of cancer subtypes. More specifically, the technology relates to methods for determining molecular drivers of cancer and/or progression using a multivariate image data and statistical analysis of in-situ molecular markers and morphological characteristics in the same cells of a biological sample suspected of b cancer. This analysis takes place after a single acquisition that obtains the molecular and anatomic morphology data in parallel. The analysis compares specific morphological and molecular markers to known samples exhibiting particular genetic drivers of the cancer. This method provides statistical information that allows for an increased confidence in the identification of specific molecular drivers of the cancer.

Image acquisition methods for simultaneously detecting genetic rearrangement and nuclear morphology

The disclosure provides methods for characterizing a prostate cancer sample by detecting expression of ERG, PTEN or both, changes in which relative to a normal control are shown herein to be correlated with prostate cancer capsular penetration and more aggressive forms of prostate cancer. Such methods are useful for the prognosis of prostate cancer capsular penetration and for making treatment decisions in patients with prostate cancer that has penetrated the capsule. Also provided are kits that can be used with such methods.

Expression of ETS related gene (ERG) and phosphatase and tensin homolog (PTEN) correlates with prostate cancer capsular penetration

Spectral imaging for measurement of nuclear pathology features in cancer cells prepared for in situ analysis

Multiple modality contrast can be used to produce images that can be combined and rendered to produce images similar to those produced with wavelength absorbing stains viewed under transmitted white light illumination. Images obtained with other complementary contrast modalities can be presented using engineered color schemes based on classical contrast methods used to reveal the same anatomical structures and histochemistry, thereby providing relevance to medical training and experience. Dark-field contrast images derived from refractive index and fluorescent DAPI counterstain images are combined to produce images similar to those obtained with conventional H&E staining for pathology interpretation. Such multi-modal image data can be streamed for live navigation of histological samples, and can be combined with molecular localizations of genetic DNA probes (FISH), sites of mRNA expression (mRNA-ISH), and immunohistochemical (IHC) probes localized on the same tissue sections, used to evaluate and map tissue sections prepared for imaging mass spectrometry.

Multi-modality contrast and brightfield context rendering for enhanced pathology determination and multi-analyte detection in tissue

Growing community of inventors

Tucson, AZ, United States of America

Ray B Nagle