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.

A 52-year-old woman is at her annual physical exam. The physician assistant mentions he'll need two extra vials of blood for new cancer screening tests, one just FDA-approved, the other available as part of a clinical trial.

 

"But I already get mammograms and colonoscopies based on family history, and my husband gets his PSA screen for prostate cancer every year. So far, so good. Why do I need these new tests?" the patient asks.

 

"They can catch cancers much earlier, from DNA and proteins in your blood plasma, the liquid part. Including cancers much rarer than breast, colon, and prostate."

 

"Sure," says the patient, rolling up a sleeve. She'd be one of the first to have "multi-cancer early detection" – MCED – blood tests that zero in on clues that cancer cells shed into the bloodstream. A treatment begun early is more likely to work. An MCED blood test could be a gamechanger for people who haven't had cancer.

"If you're an adult with newly diagnosed non-small cell lung cancer that's spread and tests positive for PDL1 without an abnormal EGFR your first option could be …" announces a TV ad for a pair of targeted cancer drugs, flying by so fast that I doubt many patients can grasp anything.

 

According to the FDA, the wording of the ads comes from a "research team of social psychologists." Science journalists might better communicate drug mechanisms to consumers.

 

Another way to fathom the info in cancer drug ads is to go back to high school biology and consider the cell cycle – the molecular choreography that tells a cell whether, when, and how often to divide. The cycle has offshoots, called checkpoints, which enable a cell to die by apoptosis (aka programmed cell death) or pause for a time-out. Many targeted cancer drugs interrogate cell cycle enzymes and proteins that oversee checkpoints, stopping runaway cell division.

 

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

Five years ago today, I learned that I had breast cancer.

 

I didn't find out in the usual way, an alarmingly ambiguous phone call and then a sit-down with my doctor. The radiologist knew I saw patients in the office for genetic counseling, so while I was getting dressed after my annual mammogram, she beckoned me to her nearby office.

"Take a look at the two screens, Ricki. The left one is last year's image."

 

It didn't take training in radiology to see that something had happened since last year's mammogram. On the right screen, a small mass blocked a narrow passageway, a milk duct.

 

When the radiologist enlarged the image, the clump of cells was not only blocking the duct, but pushing against one wall. I realized instantly that if I had skipped my mammogram that year, the next year's scan would have shown invasive cancer.

 

 

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

Discovery of a new gene behind autism cleverly combines genetic techniques new and classic.

Autism has been difficult to characterize genetically. It is probably a common endpoint for many genotypes, and is a multifactorial (“complex”) trait. That is, hundreds of genes contribute risk to different degrees, as do environmental factors. Research reports implicate either dozens of genes in genomewide sweeps, or focus on a few genes that encode proteins that act at synapses, such as the < href="https://www.autismspeaks.org/science/grants/neuroligins-and-neurexins-autism-candidate-genes-study-their-association-synaptic-con">neuroligins and neurexins. Read More