Tissue Phenomics Blog

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Topics:  companion diagnostics, precision medicine, IVD, LDT, CDx strategy

Does it always take an IVD to be a CDx

Jul 10, 2017 7:30:00 AM

From the dawn of the Precision Medicine Age, pathologists have relied upon in vitro diagnostics (IVDs) to inform treating physicians of possible therapy options for cancer patients. From tests for HER2, EGFR, KRAS, and other biomarkers, IVDs have been more than sufficient to generate the necessary information to enable physicians to make educated treatment decisions. However, in the last few years, two pharmaceutical companies have successfully launched a drug with a sole-source lab commercializing a laboratory developed test (LDT). The first was AstraZeneca launching Lynparza™ requiring the use of Myriad Genetics’ BRACAnalysis CDx™ in December 2014, and the second was Clovis Oncology launching Rubraca™ requiring the use of Foundation Medicine’s FoundationFocus™ CDxBRCA in December 2016. It is still too early to know whether or not launching a CDx as a LDT is a new and sustainable trend, but it begs the question: Why would a pharma company select a lab as a CDx partner over an IVD manufacturer?

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Topics:  personalized medicine, companion diagnostics, reimbursement, market access, quantitative IHC

Enabling Market Access for Quantitative IHC-Based Companion Diagnostics

Jun 5, 2017 6:00:00 AM

The complexity of the tumor microenvironment represents a significant challenge to the personalized medicine movement in the field of oncology. While genetic and genomic analyses enable an understanding of the patient’s genotype, this does not necessarily translate to active tumor microenvironment expression – phenomics – through which pharmacodynamic pathways are modulated by targeted therapies. Similarly, while single biomarker immunohistochemistry (IHC) can effectively quantify expression of a particular biomarker, it fails to evaluate the entire picture of the tumor microenvironment, where a myriad of potentially confounding factors exists to distort the causal relationship between expression of a single marker and treatment response. 

Enter context-based analysis of biomarker expression – quantitative, multiplex IHC technology leveraging “big data” capabilities to overcome the inherent limitations of existing diagnostic techniques and more precisely assess the tumor microenvironment. Applied as a companion diagnostic (CDx), quantitative IHC or IF (qIHC/IF) using image analysis (IA) technology represents a potentially paradigm-shifting approach to predict patient response to oncologics and thus bring the movement of personalized medicine to fruition. Read More

Topics:  predictive test, image analysis, clinical trials, Immunotherapy, oncology, tumor microenvironment

Evaluation of the Tumor Microenvironment Using Image Analysis for Clinical Trials

May 8, 2017 7:30:00 AM

Advances in immunotherapy have dramatically changed the landscape for cancer researchers. With significant improvements in overall survival and recurrent free survival in some of the deadliest cancers, lives have been extended by years for some patients while other patients have not experienced benefit from immunotherapy treatment. For some cancer indications, such as non-small cell lung cancer, the FDA approved companion diagnostic test for PD-L1 by immunohistochemistry has predictive value on patient response. However, the test is not a perfect indicator of response as some PD-L1 negative patients will respond to therapy and, conversely, some PD-L1 positive patients will not. At the same time, the number of clinical trials examining immunotherapy and combination therapies with immunotherapy as a backbone has exceeded 1000 in 2017. The possibility of combination therapy brings many questions to light for the researcher and ultimately the physician on which therapy to choose, for how long, in what order and what potential combination. With these questions, comes the increasing need for predictive tests that can lead the physician to the right therapy at the right time.

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Topics:  image analysis, big data, digital pathology, machine learning, deep learning

Machine Learning in Digital Pathology: A Journey from Handcrafted Feature Descriptors to Deep Learning Approaches

Apr 3, 2017 5:00:00 AM

As outlined in the recent blog article by Dr. Ralf Huss, CMO of Definiens, we believe in big data and machine learning to significantly influence decision making in medicine. Already in digital histopathology, machine learning is a key component, for example, for the detection of regions of interest (e.g., tumor metastasis regions, stroma regions) or for the detection, segmentation and classification of objects of interest (e.g., nuclei, cells, mitosis, glomeruli and glands). The use of machine learning in digital pathology is motivated by the complexity and variety of the problems, and it has been enabled only recently by the availability of large amounts of raw data (e.g., the public TCGA database from the NIH) and of efficient algorithms or increased computing power. However, while big data is getting even bigger by the minute, we cannot accept algorithms without a medical plausibility check and robust clinical validation.

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Topics:  big data, artificial intelligence, machine learning

AI Makes Good Doctors Even Better Doctors

Mar 6, 2017 8:00:00 AM

Highly-paid doctors will not be obsolete and radiologists or anatomical pathologists will not be obsolete in the age of AI in medical practice. In a recent New England Journal of Medicine Paper (http://www.nejm.org/doi/full/10.1056/NEJMp1606181) Drs. Obermeyer and Emanuel (1) rightly suggest that big data will transform daily medical practice and computer-based algorithms will guide clinical decision making.

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