Jasreet Hundal and Elaine R. Mardis | Jul 15, 2019 | 10+ min read
The field is young, but predicting antigens produced by patients’ malignant cells could yield successful treatments for individuals with a range of cancer types.
Research in mice and humans is beginning to establish a link between the composition of microbes in the gut and immune responses to tumor cells, but the mechanisms are not yet clear.
An artificial intelligence program called a neural network exceeds radiologists’ ability to detect malignancies, but more testing is needed before using the program clinically.
In determining that the illnesses came about from exposure to glyphosate in Roundup, a California jury delivers the biggest loss so far to the herbicide manufacturer in lawsuits about the product.
Machine learning can analyze photographs of cancer, tumor pathology slides, and genomes. Now, scientists are poised to integrate that information into cancer uber-models.
The latest machine learning models can identify many visual and molecular features of a particular cancer. If the technology advances to the clinic, it could help diagnose patients and predict survival.
An early-stage clinical study finds that none of the 25 patients treated developed neurotoxicity or cytokine release syndrome, common hazards of the cancer immunotherapy.
Mouse fathers whose sperm lacks the gene Kdm6a pass down altered methylation patterns to male offspring, along with a better chance of developing tumors and dying.
Breast cancer researcher and oncologist Nancy Davidson discusses what we’ve learned from the first wave of epigenetic trials for breast cancer, and what challenges lie ahead before such therapies reach the clinic.