Tissue Phenomics Blog

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Topics:  image analysis, clinical trials, tissue phenomics, machine learning

Benefits of Automated Slide Reading as a Way to Decrease Human Error in Clinical Trials and thus in Patient Care

Sep 14, 2017 8:28:20 AM

Human error is natural. It is a result on how the human brain works and its biological limitations. Though human error is inevitable and also normal in highly specialized experts such as pathologists, it should not end in failure and affecting the quality of diagnosis and the selection of the adequate treatment. However, mistakes also help us and any system to learn. The error rate for complex logic errors (which applies to reading and interpreting tissue slides) is about 5% overall. Despite extensive training and education, it does not seem to drop below this natural threshold.

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Topics:  Immunotherapy, personalized medicine, cancer diagnostics, precision medicine, cancer vaccines

Promising results with personalized cancer vaccines highlight the need for novel diagnostics to personalize cancer treatments

Aug 9, 2017 7:00:00 AM

Personalized medicine aims at bringing the right drug to the right patient at the right time. Getting many things right at the same time is challenging. There are many potential treatments for cancer patients and many of these can be combined with each other resulting in a lot of potential combination treatments. This growing number of treatment options makes it increasingly difficult to determine the right one for an individual patient. Biomarkers that help to predict response to a treatment are highly needed to guide treatment selection to maximize efficiency for both patients and payers.

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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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