Publications

Publications

Quantitative Analysis of human Cancer Cell Extravasation Using Intravital Imaging

Methods Mol Biol. 2016;1458:27-37

Willetts L, Bond D, Stoletov 1, Lewis JD

Abstract

Metastasis, or the spread of cancer cells from a primary tumor to distant sites, is the leading cause of cancer-associated death. Metastasis is a complex multi-step process comprised of invasion, intravasation, survival in circulation, extravasation, and formation of metastatic colonies. Currently, in vitro assays are limited in their ability to investigate these intricate processes and do not faithfully reflect metastasis as it occurs in vivo. Traditional in vivo models of metastasis are limited by their ability to visualize the seemingly sporadic behavior of where and when cancer cells spread (Reymond et al., Nat Rev Cancer 13:858-870, 2013). The avian embryo model of metastasis is a powerful platform to study many of the critical steps in the metastatic cascade including the migration, extravasation, and invasion of human cancer cells in vivo (Sung et al., Nat Commun 6:7164, 2015; Leong et al., Cell Rep 8, 1558-1570, 2014; Kain et al., Dev Dyn 243:216-28, 2014; Leong et al., Nat Protoc 5:1406-17, 2010; Zijlstra et al., Cancer Cell 13:221-234, 2008; Palmer et al., J Vis Exp 51:2815, 2011). The chicken chorioallantoic membrane (CAM) is a readily accessible and well-vascularized tissue that surrounds the developing embryo. When the chicken embryo is grown in a shell-less, ex ovo environment, the nearly transparent CAM provides an ideal environment for high-resolution fluorescent microcopy approaches. In this model, the embryonic chicken vasculature and labeled cancer cells can be visualized simultaneously to investigate specific steps in the metastatic cascade including extravasation. When combined with the proper image analysis tools, the ex ovo chicken embryo model offers a cost-effective and high-throughput platform for the quantitative analysis of tumor cell metastasis in a physiologically relevant in vivo setting. Here we discuss detailed procedures to quantify cancer cell extravasation in the shell-less chicken embryo model with advanced fluorescence microscopy techniques.

PubMed

goes to…APCaRI member Russ Greiner

Image of DREAM challenge winners, Russ Greiner pictured on far left.

Dr. Russ Greiner, Canada CIFAR AI Chair, Fellow-in-Residence at Amii, University of Alberta Professor, and APCaRI member, received the CAIAC Lifetime Achievement Award announced at the Canadian AI Conference on May 27, 2021. This the highest honour bestowed by CAIAC, given in recognition to researchers who have distinguished themselves through outstanding research excellence in AI during the course of their academic career. APCaRI congratulates Russ Greiner for his well-deserved CAIAC Lifetime Achievement Award!

“Using machine learning techniques to produce effective, evidence-based personalized treatment”

The main foci of Russ Greiner’s current work are (1) bioinformatics and medical informatics; (2) learning and using effective probabilistic models and (3) formal foundations of learnability. He has published over 200 refereed papers and patents, most in the areas of machine learning and knowledge representation, including 4 that have been awarded Best Paper prizes.

One of these four papers was an entry into an international machine learning competition hosted by DREAM, an open-science effort dedicated to improving health and health care through crowdsourcing problem-solving. DREAM’s challenge was to develop an algorithm to predict which prostate cancer patients would respond to certain treatments and which would follow the medication regimen. The algorithm could be used by clinicians to help chose the best treatment plans for the patient.

Greiner and a team of students tied for the top place in the competition against over 50 teams from around the world. Then the winners collaborated to create an even better solution to the problem!

 

 

 

 

 

 

- Perrin Beatty