Immune outposts inside tumors predict post-surgery outcomes

The immune system establishes “forward operating bases”, or lymph node-like structures, inside the tumors of some patients with kidney and other urologic Read more

Hedgehog pathway outside cilia (with CBD bonus)

The Hedgehog pathway has roles in both specifying what embryonic cells will become and in guiding growing neural Read more

Tracking how steroid hormone receptor proteins evolved

When thinking about the evolution of female and male, consider that the first steroid receptor proteins, which emerged about 550 million years ago, were responsive to estrogen. The ancestor of other steroid hormone receptors, responsive to hormones such as testosterone, progesterone and cortisol, emerged many millions of years later. Biochemist Eric Ortlund and colleagues have a new paper in Structure that reconstructs how interactions of steroid receptor proteins evolved over time. This is a complex Read more

Lee Cooper

CAPTCHA some cancer cells

Humans are good at deciphering complex images, compared to computers. Until recently, internet users often needed to verify that they were human by completing a CAPTCHA security check. A familiar variety asked the user to check all the boxes that contain a car, or a street sign.

If we asked random people off the street to look at pathology slides and “quick, check all the boxes that contain tumor cells,” what would happen? The accuracy, compared to a trained pathologist, wouldn’t be very good.

Not as easy as labeling which boxes contain street signs!

This challenge of expertise – crowdsourcing and pathology are not immediately compatible – is what Lee Cooper and colleagues sought to overcome in a recent paper published in Bioinformatics. So they put together something they called “structured crowdsourcing.”

“We are interested in describing how the immune system behaves in breast cancers, and so we built an artificial intelligence system to look at pathology slides and identify the tissue components,” Cooper says.

His group was particularly interested in the aggressive form of breast cancer: triple negative. They used pathology slide images from the Cancer Genome Atlas, a National Cancer Institute resource. The goal was to mark up the slides and label which sections contained tumor, stroma, white blood cells, dead cells etc.

They used social media to recruit 25 volunteers — medical students and pathologists from around the world (Egypt, Bangladesh, Saudi Arabia, United Arab Emirates, Syria, USA). Participants underwent training and used Slack to communicate and learn about how to classify images. They collaborated using the Digital Slide Archive, a tool developed at Emory. Read more

Posted on by Quinn Eastman in Cancer Leave a comment