Anatomic pathologists understand that, along with breast cancer, diagnostic testing for prostate cancer accounts for a high volume of clinical laboratory tests. Thus, a recent study indicating that a new artificial intelligence (AI)-based software tool can dramatically improve physicians’ ability to identify the extent of these cancers will be of interest.
“The study found that Unfold AI’s patient-specific encapsulation confidence score (ECS), which is generated based on multiple patient data points, including MRI scans, biopsy results, PSA [prostate-specific antigen] data, and Gleason scores, is critical for predicting treatment success,” an Avenda press release states. “These findings emphasize the importance of Unfold AI’s assessment of tumor margins in predicting treatment outcomes, surpassing the predictive capability of conventional parameters.”
“Unfold AI’s ability to identify tumor margins and provide the ECS will improve treatment recommendations and allow for less-invasive interventions,” said study co-author Wayne Brisbane, MD, a urologic oncologist and UCLA medical professor, in another press release. “This more comprehensive approach enhances our ability to predict treatment outcomes and tailor interventions effectively to individual patient needs.”
“This study is important because it shows the ability of AI to not only replicate expert physicians, but to go beyond human ability,” said study co-author Wayne Brisbane, MD (above), a urologic oncologist and UCLA medical professor, in a press release. “By increasing the accuracy of cancer identification in the prostate, more precise and effective treatment methods can be prescribed for patients.” Clinical laboratories that work with anatomic pathologists to diagnose prostate and other cancers may soon have a new AI testing tool. (Photo copyright: UCLA.)
How Unfold AI Works
To gauge the extent of prostate tumors, surgeons typically evaluate results from multiple diagnostic methods such as PSA tests and imaging scans such as MRIs, according to a UCLA press release. However some portions of a tumor may be invisible to an MRI, causing doctors to underestimate the size.
Unfold AI, originally known as iQuest, was designed to analyze data from PSA, MRI, fusion biopsy, and pathology testing, according to a company brochure. From there, it generates a 3D map of the cancer. Avenda’s website says the technology provides a more accurate representation of the tumor’s extent than conventional methods.
“Accurately determining the extent of prostate cancer is crucial for treatment planning, as different stages may require different approaches such as active surveillance, surgery, focal therapy, radiation therapy, hormone therapy, chemotherapy, or a combination of these treatments,” Brisbane said in the UCLA press release.
Putting AI to the Test
In the new study, the UCLA researchers enlisted seven urologists and three radiologists to review 50 prostate cancer cases. Each patient had undergone prostatectomy—surgical removal of all or part of the prostate—but might have been eligible for focal therapy, a less-aggressive approach that uses heat, cryotherapy, or electric shocks to attack cancer cells more selectively.
The physicians came from five hospitals and had a wide range of clinical experience from two to 23 years, the researchers noted in The Journal of Urology.
They reviewed clinical data and examined MRI scans of each patient, then “manually drew outlines around the suspected cancerous areas, aiming to encapsulate all significant disease,” the press release states. “Then, after waiting for at least four weeks, they reexamined the same cases, this time using AI software to assist them in identifying the cancerous areas.”
The researchers analyzed the physicians’ work, evaluating the accuracy of the cancer margins and the “negative margin rate,” indicating whether the clinicians had identified all of the cancerous tissue. Using conventional approaches, “doctors only achieved a negative margin 1.6% of the time,” the press release states. “When assisted by AI the number increased to 72.8%.”
The clinicians’ accuracy was 84.7% when assisted by AI versus 67.2% to 75.9% for conventional techniques.
They also found that clinicians who used the AI software were more likely to recommend focal therapy over more aggressive forms of treatment.
“We saw the use of AI assistance made doctors both more accurate and more consistent, meaning doctors tended to agree more when using AI assistance,” said Avenda Health co-founder and CEO Shyam Natarajan, PhD, who was senior author of the study.
“These results demonstrate a marked change in how physicians will be able to diagnose and recommend treatment for prostate cancer patients,” said Natarajan in a company press release. “By increasing the confidence in which we can predict a tumor’s margins, patients and their doctors will have increased certainty that their entire tumor is treated and with the appropriate intervention in correlation to the severity of their case.”
UCLA’s study found that AI can outperform doctors both in sensitivity (a higher detection rate of positive cancers) and specificity (correctly detecting the sample as negative). That’s relevant and worth watching for further developments.
Pathologists and clinical laboratory managers should consider this use of AI as one more example of how artificial intelligence can be incorporated into diagnostic tests in ways that allow medical laboratory professionals to diagnose disease earlier and more accurately. This will improve patient care because early intervention for most diseases leads to better outcomes.
Study may lead to clinical laboratory involvement in repurposing hormonal treatments to prevent cancer treatment resistance
Diagnosing prostate cancer and identifying which patients have aggressive forms of the cancer has been a challenge. But new insights into how aggressive cancers become resistant to drug therapies—and the discovery of a way to repurpose hormonal treatment to block or slow aggressive prostate cancer—may lead to clinical laboratories monitoring the progress of patients’ being treated with this new type of therapy.
Instead of treating tumors directly, the new approach developed by an international team of scientists would target proteins that typically regulate a cell’s circadian rhythm, but which have been found to be helping cancerous cells become resistant to treatment therapies.
“Our discovery has shown us that we will need to start thinking outside the box when it comes to new drugs to treat prostate cancer and test medicines that affect the circadian clock proteins in order to increase sensitivity to hormonal therapy in prostate cancer,” said Wilbert Zwart, PhD (above), Lead Researcher and Senior Group Leader Oncogenomics Division at NKI, in a news release. This discovery could give clinical laboratories and anatomic pathology groups an effective way to monitor new forms of cancer hormonal treatments. (Photo copyright: Netherlands Cancer Institute.)
Breakthrough Could Mean New Treatment for Aggressive Cancer
The aim of prostate cancer hormone therapy (AKA, androgen suppression therapy) is to halt signals by male hormones (usually testosterone) that stimulate tumor growth. This approach works until cancer becomes resistant to the drug therapy.
So, the challenge in metastatic prostate cancer treatment is finding a drug that prevents resistance to hormonal therapy.
In addressing the challenge, the researchers made a surprising discovery about what exactly dilutes anti-hormonal therapy’s effectiveness. Proteins that regulate the body’s sleep-wake cycle, or circadian rhythm, were found to also “dampen the effects of the anti-hormonal therapy,” according to the study.
“Prostate cancer cells no longer have a circadian rhythm. But these ‘circadian clock’ proteins acquire an entirely new function in the tumor cells upon hormonal therapy: they keep these cancer cells alive, despite treatment. This has never been seen before,” said Wilbert Zwart, PhD, Lead Researcher and Senior Group Leader Oncogenomics Division, NKI, in the news release.
The research suggests treatment for metastatic prostate cancer requires drugs “which influence the day-and-night rhythm of a cell,” and not necessarily medications that fight cancer, Technology Networks noted.
“Fortunately, there are already several therapies that affect circadian proteins, and those can be combined with anti-hormonal therapies. This lead, which allows for a form of drug repurposing, could save a decade of research,” Zwart added.
Questioning Hormonal Therapy Resistance
In their paper, the Dutch researchers acknowledged that androgen receptor (AR)-targeting agents are effective in prostate disease stages. What they wanted to learn was how tumor cells bypass AR suppression.
For the study, the scientists enrolled 56 patients with high-risk prostate cancer in a neoadjuvant clinical trial. Unlike adjuvant therapy, which works to lower the risk that cancer will return following treatment, the purpose of neoadjuvant therapy is to reduce the size of a tumor prior to surgery or radiation therapy, according to the National Institute of Health (NIH) National Cancer Institute (NCI).
The researchers performed DNA analysis of tissue samples from patients who had three months of anti-hormonal therapy before surgery. They observed that “genes keeping tumor cells alive were controlled by a protein that normally regulates the circadian (body) clock,” said Simon Linder, PhD student and researcher at NKI, in the news release.
“We performed integrative multi-omics analyses on tissues isolated before and after three months of AR-targeting enzalutamide monotherapy from patients with high-risk prostate cancer enrolled in a neoadjuvant clinical trial. Transcriptomic analyses demonstrated that AR inhibition drove tumors toward a neuroendocrine-like disease state,” the researchers wrote in Cancer Discovery.
“Understanding how prostate cancers adapt to AR-targeted interventions is critical for identifying novel drug targets to improve the clinical management of treatment-resistant disease. Our study revealed an enzalutamide-induced epigenomic plasticity toward pro-survival signaling and uncovered the circadian regulator ARNTL [Aryl hydrocarbon receptor nuclear translocator-like protein 1] as an acquired vulnerability after AR inhibition, presenting a novel lead for therapeutic development,” the scientists concluded.
More Research Planned
The scientists expressed intent to follow-up with Oncode to develop a drug therapy that would increase anti-hormonal therapy’s effectiveness in prostate cancer patients.
Given the molecular processes involved in the researchers’ discovery, there may be a supportive role for clinical laboratories and anatomic pathology groups in the future. But that can only happen after more studies and a US Food and Drug Administration (FDA) review of any potential new therapy to combat hormonal treatment resistance in prostate cancer patients.
In the same way that BRCA1 and BRCA2 mutations helped pathologists identify women with increased breast cancer risks in the late 1990s, this new study isolates an additional 72 mutations medical laboratories may soon use to diagnose breast cancer and assess risk factors
For 20 years genetic scientists, anatomic pathologists, and medical laboratories have employed the BRCA1/BRCA2 genes to identify women at higher risk for breast cancer. And, because pathologists receive a high number of breast biopsies to diagnose, physicians and clinical laboratories already have collaborative experience working with genetic mutations supported by ample published evidence outlining their relationship with cancer.
Now, a global research study is adding 72 more mutations to the list of mutations already known to be associated with breast cancer.
In coming years, physicians and anatomic pathologists can expect to use the knowledge of these 72 genetic mutations when diagnosing breast cancer and possibly other types of cancers in which these mutations may be involved.
New Precision Medicine Tools to Improve Breast Cancer Survival
Combining the efforts of more than 550 researchers across 300 institutions and six continents, the OncoArray Consortium analyzed the DNA of nearly 300,000 blood samples. The analysis included samples of both estrogen receptor (ER-positive and ER-negative) cases.
Taken from a study published in the British Journal of Cancer, the graph above illustrates “proportions of familial risk of breast cancer explained by hereditary variants.” It is expected that anatomic pathologists will eventually incorporate these genetic variants into diagnostic test for breast and other cancers. (Graphic copyright: British Journal of Cancer.)
The results of their research were published in two separate studies: one in the scientific journal Nature and the other in Nature Genetics. The studies outlined 72 newly isolated genetic mutations that might help quantify the risk of a woman developing breast cancer in her lifetime.
Among the 72 mutations, seven genes were specifically associated with ER-negative cases. ER-negative breast cancer often fails to respond to hormone therapy. Thus, this discovery could be crucial to developing and administering precision medicine therapies tailored to specific patients’ physiologies and conditions. Treatments that improve patient outcomes and overall survival rates in ER-negative and ER-positive breast cancers.
Genetics Could Help Clinical Laboratories Wage War on All Cancers
According to data published by the Centers for Disease Control and Prevention (CDC), breast cancer is the most common form of cancer among women of all races. It’s the second-leading cause of all cancer deaths among most races and first among Hispanic women.
In the past, it was estimated that 5-10% of breast cancers were inherited through the passing of abnormal genes. However, Lisa Schlager, Vice President of community affairs and public policy for FORCE (Facing Our Risk of Cancer Empowered), told CNN, “This new information may mean that that estimate is low.” FORCE is a national nonprofit organization dedicated to fighting hereditary breast, ovarian, and related cancers.
Schlager calls upon health systems to “embrace the ability to use genetic information to tailor healthcare by providing affordable access to the needed screening and preventive interventions.” As precision therapy and genetic analysis continue to shape the way patients are treated, medical laboratories will play a significant role in providing the information powering these innovative approaches.
Identifying Women at Increased Risk for Breast Cancer
Peter Kraft, PhD, Professor of Epidemiology at Harvard’s T.H. Chan School of Public Health, and a study author, told CNN, “Taken together, these risk variants may identify a small proportion of women who are at three-times increased risk of breast cancer.”
Kraft notes that samples were sourced from women of primarily European ancestry. Further study of other ethnic populations could lead to yet more mutations and indicators for cancers more common outside of the European region.
Research authors also highlight the importance of continued standard screening, such as mammograms. However, they suggest that genetic mutations, such as those found in the OncoArray study, might be used to highlight high-risk individuals and screen sooner, or conduct more in-depth genetic analyses, to catch potential cancer cases earlier and improve outcomes.
“Many women are offered mammogram screening when they are middle-aged,” Georgia Chenevix-Trench, PhD, co-author of the Nature Genetics study and researcher at the QIMR Berghofer Medical Research Institute in Australia, told LabRoots. “But if we know a woman has genetic markers that place her at higher risk of breast cancer, we can recommend more intensive screening at a younger age.”
Anatomic pathologists and clinical laboratories can use these new insights to offer increased options for oncologists and physicians on the front lines of the battle against cancer. While the list of genetic mutations related to cancer is far from complete, each added mutation holds the potential to power a new treatment, improve early detection rates, and improve survival rates of this global killer.
