Popescu's team develops new screening method for prostate cancer recurrence
- Researchers used spatial light interference microscopy (SLIM), a label-free method, to perform localized measurements of light scattering in prostatectomy tissue microarrays.
- The researchers found that the higher value of anisotropy indicated that the tissue is more organized, while a lower value indicated that the various components within the tissue are fragmented and disorganized.
- "We found that for patients who had bad outcomes, the connective tissue around the glands (stroma) is more disorganized than in the case of patients who have better outcomes," said Shamira Sridharan, a graduate research assistant in the QLI Lab, and the lead author of the study.
The American Cancer Society estimatesthat 220,800 new cases of prostate cancer will be diagnosed in the United States in 2015. About 27,500 men will die of the disease, accounting for 5 percent of all cancer deaths.
A common treatment for prostate cancer is a prostatectomy, in which all or part of the prostate gland is removed. Recent studies have shown that this procedure is often over-prescribed. As early as 2010, the New England Journal of Medicine reported that such a procedure extended the lives of just one patient in 48. Side effects from the surgery, including urinary incontinence and impotence, can affect the quality of life of the patient.
“For every 20 surgery procedures to take out the prostate, it is estimated that only one life is saved,” said Associate Professor Gabriel Popescu, director of the Quantitative Light Imaging Laboratory (QLI) at the Beckman Institute for Advanced Science and Technology and senior author on the study. “For the other 19 people, they would be better left alone, because with removing the prostate, the quality of life goes down dramatically. So if you had a tool that could tell which patient will actually be more likely to have a bad outcome, then you could more aggressively treat that case.”
The researchers recently used spatial light interference microscopy (SLIM) in order to identify patients at higher risk for prostate cancer recurrence. The National Science Foundation and Agilent Technologies funded the research, and the results can be found in an article “Prediction of Prostate Cancer Recurrence using Quantitative Phase Imaging,” published in Scientific Reports in May.
“Among individuals who undergo prostatectomy, there are a few statistical tools that take various clinical parameters into consideration and then predict the risk for recurrence,” said Shamira Sridharan, a graduate research assistant in the QLI Lab, and the lead author of the study. “But among people who are in the intermediate risk for recurrence, those methods often fail, so this might lead to under- or overtreatment. Clearly, more accurate tools are necessary for predicting recurrence among that cohort.”
Researchers used spatial light interference microscopy (SLIM), a label-free method, to perform localized measurements of light scattering in prostatectomy tissue microarrays. The quantitative phase imaging (QPI) performed by the SLIM examines the anisotropy, or the difference in a material’s physical properties, as light is scattered through the stroma, the tissue surrounding the prostate glands.
The researchers found that the higher value of anisotropy indicated that the tissue is more organized. A lower value indicated that the various components within the tissue are fragmented and disorganized.
“We found that for patients who had bad outcomes, the connective tissue around the glands (stroma) is more disorganized than in the case of patients who have better outcomes,” Sridharan said.
For example, after a prostatectomy is performed, the tumor is graded by the pathologist and, in combination with other surgical parameters such as the surgical margin positivity, whether the cancer has invaded into the lymph nodes, extra-prostatic extensions, and PSA levels, a recurrence risk is assigned.
However, some of this information is only available post-surgery. By examining the quality of the tissue surrounding the cancerous glands, the researchers believe they can determine progression of the disease at the pre-surgical, or biopsy stage.
The study of 181 tissue samples obtained from the National Cancer Institute-sponsored Cooperative Prostate Tissue Resource (CPCTR) were from individuals who had already undergone a prostatectomy, about half who had no recurrence and half who did. SLIM was able to identify those in which the cancer would reappear.
The study is the result of collaborative work between the QLI Lab and three board-certified pathologists: Drs. Andre Balla and Virgilia Macias from the University of Illinois at Chicago, and Dr. Krishnarao Tangella from Presence Covenant Medical Center in Urbana.
“It is rather remarkable that the difference between cancers with bad outcomes and good outcomes is found not in the malignant cells, but in the tissue adjacent to the cancer. Possibly, this is because the body can recognize which tumors are more aggressive and react to them,” Balla said.
An established method of screening for prostate cancer is the prostate-specific antigen (PSA) test.
“PSA is a very good tool in terms of predicting the recurrence of prostate cancer in an individual who’s undergone a prostatectomy,” Sridharan said. “But when PSA screening first started, there was a huge spike in the number of prostate cancer cases diagnosed. So if a screening tool is indeed good, you would see an initial spike, but after that the cases would level off. With PSA that leveling off never happened. The number of cases diagnosed remained high, so now the United States Preventative Task Force no longer recommends routine screening for PSA.”
Popescu said PSA levels are helpful following a prostatectomy to predict recurrence.
"After prostatectomy, serum PSA levels go to nearly zero because it is produced almost exclusively in the prostate," Popescu said. "So PSA is great tool after prostatectomy in terms of predicting recurrence if the level starts to climb up again, indicating that the cancer has spread to other sites in the body. But in that pre-diagnosis stage, it’s not particularly great because it can lead to over-diagnosis."
His team's method is designed to predict recurrence, possibly before a patient goes through radical surgery.
“What SLIM is very good at is to make invisible objects visible with nanoscale sensitivity,” Popescu said. “So we pick these structural details without the need for staining, which can introduce new variables into the specimen."
His dream: one day, all labs will have SLIM capabilities.
"One can imagine that a SLIM-based tissue imager will scan biopsies in a clinic and, paired with software that is intelligent enough to look for these specific markers, will provide the pathologist with valuable new information," he said. "This additional information will translate into more accurate diagnosis and prognosis.”
To further that goal, the QLI is working with students based in the lab of Professor Minh N Do, who also works in image formation and processing at the Beckman, to build software that will find patterns in the tissue that are relevant for diagnosis and prognosis, with the goal of helping patients make decisions about their care, Sridharan said.
“The next step is trying to help with patient treatment decisions and translating this to the biopsy, pre-surgery stage,” she said. “This method is very promising and demonstrates the potential to help with determining who should undergo active surveillance versus surgical treatment."