3 Cutting-Edge Cancer-Fighting Tools
Last fall, providers from across the country gathered at the University of Vermont Cancer Center’s Women’s Health and Cancer Conference to hear from a panel of experts on the newest groundbreaking techniques in cancer diagnosis and treatment.
These innovative approaches, based on molecular engineering and artificial intelligence, offer providers and patients new tools to treat and prevent cancer. Hope is on the horizon for those who have had cancer in the past or have been recently diagnosed, as well as those facing an increased risk of cancer.
Here are the top three advances in cancer treatment everyone should know about:
1. T-cell Therapy: Turns cells into ‘personalized cancer-killing machines’
Starting in February, the UVM Cancer Center will offer a novel, highly effective form of cancer treatment called CAR T-cell therapy. UVM Cancer Center will be the only health care institution in Vermont and northern New York to provide this treatment.
CAR (chimeric antigen receptor) T-cell therapy, or CART as it is known, is a promising treatment that harnesses the power of the body’s own immune system to target cancer cells. Normally, our T cells attack foreign invaders, from cold viruses to cancer, fighting off infections that could make us sick. With the help of our T cells, the body is able to kill off many kinds of cancers on its own before they become established; the diagnosable cancers are the ones that have evaded the immune system.
But maybe not for much longer.
CAR T-cell therapy re-engineers a patient’s own T cells to recognize a specific cancer, turning the re-programmed T cells into personalized cancer-killing machines.
Currently used to treat lymphomas, leukemia, and myelomas, this leading-edge therapy gives patients another option when chemotherapy or other current treatments fail. The therapy offers great promise for someday treating patients with breast, prostate and other types of cancers.
“It’s unlike any other therapy that’s ever been given before,” says James Gerson, MD, a hematologist and medical oncologist at the UVM Medical Center who joined the UVM Cancer Center in September and will launch a CAR T-Cell program for patients in 2023. “This therapy is a living, breathing treatment that goes back into the patient and lives with them for years. It’s exciting to be starting a new CAR T-cell program here at UVM, and to offer this therapy to patients in the area.”
2. AI-Powered Breast Cancer Diagnosis
For every 1,000 women who receive a mammogram, typically 100 women are called back for further imaging. Then, of those 100 women, approximately 25 undergo a biopsy, five of whom are ultimately diagnosed with invasive cancer.
The question is, how can we improve upon that ‘narrowing-down’ process to better serve women who are at highest risk and avoid unnecessary biopsies?
The answer may be found in AI, or artificial intelligence.
According to John Shepherd, PhD, interim deputy director and chief scientific officer of the University of Hawaii Cancer Center, AI is a “learning algorithm” that is capable of processing far more information than humans. For example, the human eye can distinguish 256 shades of gray under the best conditions, while an AI-powered algorithm can see 65,000 shades of gray. “So, if you’re looking at a medical image, the algorithm brings 65,000 levels of discernment, while we have, at most, 256,” Shepherd explains.
Then, from all that data, AI-powered algorithms can recognize patterns that we humans would never guess – such as the subtle characteristics within a mammogram image that make it more likely to identify cancer.
Diagnostic teams have proposed two main roles for AI to improve the accuracy and efficiency of reading mammograms:
- A stand-in for a radiologist: At most imaging centers two radiologists read each image. If an AI-powered algorithm were to act as one of those radiologists, providers would be able to offer screening to many more patients. According to 2019 data from the CDC, less than 70% of American women over age 40 had a mammogram within the previous two years. Even as providers and public education campaigns strive to increase that to 100%, a shortage of radiologists has strained the system – so incorporating AI into the process is a promising development.
- Acting as a ‘first read.’ AI might also serve as a helpful “first reading” to quickly identify the images that are most suspicious, so radiologists can look at them first when their eyes are freshest, and devote more time to analyzing them. Studies have shown that this strategy increases reading accuracy across the board.
3. A Blood Test for Cancer
Imagine a five-minute outpatient test that can identify cancer before it’s even visible on a mammogram or an MRI. It sounds like something out of Star Trek, but it could be reality within our lifetimes.
Enter the liquid biopsy. From the patient side, it’s nothing more than a routine blood draw, but when it undergoes a sophisticated analysis back at the laboratory, it can pick up most kinds of cancer DNA.
Like every other bodily system, tumors are constantly shedding their DNA into our bloodstreams, says Nikoletta Sidiropoulos, MD, associate professor of pathology and laboratory medicine at UVM’s Larner College of Medicine. So, certain biomarkers found in the blood could indeed warn of cancer.
The U.S. Food and Drug Administration approved molecular biomarker tests in 2016 for certain types of lung cancer, and in 2020 for some types of tumors. While liquid biopsies have proven very effective in detecting the recurrence of cancer after treatment or remission, the technology isn’t yet as accurate as the traditional tissue biopsy in cases of initial cancer diagnosis. But Dr. Sidiropoulos expects that to change as the technology matures.
In fact, she thinks the technique could someday be used for multi-cancer early detection, a “one-stop shop” that looks for many biomarkers across many genes simultaneously. Such a test would tell a patient, “You may have cancer, and it’s probably cancer X, but we don’t know where it is,” Dr. Sidiropoulos explains. That patient could then be referred right away for further imaging.
Considering that approximately 70% of cancer deaths are caused by variations that do not have any effective early-stage screening options — most notably pancreatic and ovarian cancer – this could be a future life-saver.