Tag Archives: UCSF

MEDICINE: NEW ‘SMART CELL’ THERAPIES TO TREAT CANCER

Finding medicines that can kill cancer cells while leaving normal tissue unscathed is a Holy Grail of oncology research. In two new papers, scientists at UC San Francisco and Princeton University present complementary strategies to crack this problem with “smart” cell therapies—living medicines that remain inert unless triggered by combinations of proteins that only ever appear together in cancer cells.

Biological aspects of this general approach have been explored for several years in the laboratory of Wendell Lim, PhD, and colleagues in the UCSF Cell Design Initiative and National Cancer Institute– sponsored Center for Synthetic Immunology. But the new work adds a powerful new dimension to this work by combining cutting-edge therapeutic cell engineering with advanced computational methods.

For one paper, published September 23, 2020 in Cell Systems, members of Lim’s lab joined forces with the research group of computer scientist Olga G. Troyanskaya, PhD, of Princeton’s Lewis-Sigler Institute for Integrative Genomics and the Simons Foundation’s Flatiron Institute. Using a machine learning approach, the team analyzed massive databases of thousands of proteins found in both cancer and normal cells. They then combed through millions of possible protein combinations to assemble a catalog of combinations that could be used to precisely target only cancer cells while leaving normal ones alone.

In another paper, published in Science on November 27, 2020, Lim and colleagues then showed how this computationally derived protein data could be put to use to drive the design of effective and highly selective cell therapies for cancer. “Currently, most cancer treatments, including CAR T cells, are told ‘block this,’ or ‘kill this,’” said Lim, also professor and chair of cellular and molecular pharmacology and a member of the UCSF Helen Diller Family Comprehensive Cancer Center.

“We want to increase the nuance and sophistication of the decisions that a therapeutic cell makes.” Over the past decade, chimeric antigen receptor (CAR) T cells have been in the spotlight as a powerful way to treat cancer.

In CAR T cell therapy, immune system cells are taken from a patient’s blood, and manipulated in the laboratory to express a specific receptor that will recognize a very particular marker, or antigen, on cancer cells. While scientists have shown that CAR T cells can be quite effective, and sometimes curative, in blood cancers such as leukemia and lymphoma, so far the method hasn’t worked well in solid tumors, such as cancers of the breast, lung, or liver.

Cells in these solid cancers often share antigens with normal cells found in other tissues, which poses the risk that CAR T cells could have off-target effects by targeting healthy organs. Also, solid tumors also often create suppressive microenvironments that limit the efficacy of CAR T cells. For Lim, cells are akin to molecular computers that can sense their environment and then integrate that information to make decisions. Since solid tumors are more complex than blood cancers, “you have to make a more complex product” to fight them, he said.

Technology: SCIENTISTS DEVELOP SMARTPHONE APP TO DETECT “DIABETES” (UCSF)

Overall, the algorithm correctly identified the presence of diabetes in up to 81 percent of patients in two separate datasets. When the algorithm was tested in an additional dataset of patients enrolled from in-person clinics, it correctly identified 82 percent of patients with diabetes.  

In the Nature Medicine study, UCSF researchers obtained nearly 3 million PPG recordings from 53,870 patients in the Health eHeart Study who used the Azumio Instant Heart Rate app on the iPhone and reported having been diagnosed with diabetes by a health care provider. This data was used to both develop and validate a deep-learning algorithm to detect the presence of diabetes using smartphone-measured PPG signals.  

Among the patients that the algorithm predicted did not have diabetes, 92 to 97 percent indeed did not have the disease across the validation datasets. When this PPG-derived prediction was combined with other easily obtainable patient information, such as age, gender, body mass index and race/ethnicity, predictive performance improved further.

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ORTHOPAEDICS: “TOTAL JOINT REPLACEMENT” (UCSF)

Hip and knee replacement are two of the most commonly performed operations in orthopedic surgery. UCSF orthopedic surgeon Dr. Paul Toogood takes a closer look at what Total Joint Replacement actually is, how it improves your quality of life, and what some of the considerations are when having this surgical procedure.