Ricki Lewis

A diagnosis of stage IV cancer used to mark the beginning of the end. Today for many patients, it is the beginning of taking a series of drugs that specifically target the errant cells by blocking the signals that fuel their runaway cell division, while sparing healthy cells. Stage IV cancer patients can live years, even decades, sometimes succumbing to something else.

Now there's hope even for patients whose cancers become resistant to targeted drugs – using artificial intelligence to probe cancer cells and their surroundings to identify novel points of vulnerability. Researchers from Harvard Medical School describe a new ChatGPT-like model that can guide clinical decision-making to diagnose, treat, and predict survival for several types of cancer. Their report appears in Nature.

The new approach complements targeted drugs by going beyond a cancer cell's surface and biochemical pathways within, to also probe the microenvironment – the immediate surroundings – through image analysis. If deployed early, AI might identify drugs unlikely to work more effectively than can genetic and genomic testing. It is a "can't see the forest for the trees" strategy, revealing the landscape of a cancer.

A Brief History of Targeted Cancer Drugs

To continue reading, go to DNA Science at Public Library of Science, here.

I just used ChatGPT for the first time. Initially, I was concerned about my future as the chatbot near-instantaneously answered my queries on increasingly obscure terms from my field, genetics. Stumping the AI tool, however, took only about 10 minutes.

ChatGPT was released November 30, 2022, from OpenAI/Microsoft. "Chat Generative Pre-trained Transformer" is a little like Google on steroids. But after my brief encounter, I can't help but wonder whether it can handle the nuance, context, humor, and creativity of a human mind. Could it replace me as a textbook author?

My Career

I've been writing life science tomes for a long time. My favorite has always been Human Genetics: Concepts and Applications, the first edition published in 1994, at the dawn of the human genome sequencing era. The 14th edition published this week, from McGraw-Hill. A revision takes two years, one for updating and addressing reviewers' suggestions, another for "production," from copyediting through final pages. Then, a year off.

As genetics morphed into genomics, artificial intelligence stepped in, layering the combinatorial information of comparative genomics onto DNA sequences. Training on data sets and then searching for patterns could be used to deduce evolutionary trees depicting species relationships, in ancestry testing and forensics, and in identifying sequences of mutations that appear as a cancer spreads.

ChatGPT is too recent for me to have used it in revising the new edition, but I'm curious now. I could imagine it spitting out definitions, but a textbook is much more than "content." A human author adds perspective, experience, and perhaps knowledge beyond what ChatGPT can extract from the Internet.

To continue reading, go to DNA Science, where this post first appeared.