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Novel Assessment Method Aims to Reduce Overtreatment of Prostate Cancer

Key findings

  • Metabolic profiles of histologically benign specimens from cancerous prostates, obtained by magnetic resonance spectroscopy, differentiated tumor grade, tumor stage and identified a subgroup of patients at particularly low risk
  • In addition, metabolic profiles helped predict prostate cancer biochemical recurrence and survival time
  • Myo-inositol, an endogenous tumor suppressor and potential therapeutic target, was elevated in patients with highly aggressive cancer
  • Clinical MRS metabolomics-based evaluation may prove to more finely distinguish patients at risk and immensely improve decision-making about prostate cancer treatment

When a man has an elevated PSA test, the great challenge is to determine whether he is likely to be one of the 17% of patients who will develop aggressive prostate cancer or one of the more than 70% who will have an indolent course. Often, an analysis of a biopsy specimen cannot answer this question.

Chin-Lee Wu, MD, PhD, associate pathologist and director of Genitourinary Pathology Services, Leo Cheng, PhD, associate biophysicist in Pathology and Radiology, and colleagues at Massachusetts General Hospital are searching for cancer-specific biomarkers using a novel type of magnetic resonance spectroscopy (MRS) they developed termed high-resolution magic angle spinning. This technology is designed to detect disease-related metabolic alterations in small prostate tissue samples, comparable to core biopsy specimens.

A large body of research already supports the theory that metabolites on cancer lesions can aid cancer diagnosis when differentiated from histologically benign surrounding tissue, the researchers note in Scientific Reports.

As part of their study, they analyzed 365 prostatectomy samples from 158 patients with biopsy-proven cancer acquired between January 2002 and July 2003. Only 27 samples were found to contain cancer cells and glands, whereas 338 were histologically benign even though they came from cancerous prostates.

MRS Differentiated Tumor Grade

The researchers used 199 samples for initial analyses ("training cohort") and 166 samples for subsequent testing. They analyzed 36 spectral regions in the training cohort samples and found that two regions (integrated peaks at 3.63 and 3.60 ppm) differentiated between samples of low tumor grade (Prognostic Grade Group 1/2) and those of higher grade (Prognostic Grade Group 3/4). The Prognostic Grade Group (PGG) system is an update of Gleason scores.

In the testing cohort, the research team confirmed that statistically significant increases in the 3.60 ppm spectral region of histologically benign samples corresponded with increases in PGG scores.

MRS Differentiated Tumor Stage

Tumor grade may initially be assessed at biopsy, but pathological tumor stage (pT) can normally be determined only after radical prostatectomy. Supporting the potential clinical utility of MRS, multiple spectral regions differentiated pT IIab from pT IIc samples.

MRS Differentiated Cancer Aggressiveness

Next, the researchers divided the tissue samples into two groups: less aggressive (PGG1/2 and pT II) versus more aggressive (either PGG3 or pT III). They excluded patients with PGG 4/5 from the analysis because treatment strategies are more obvious for them.

The 3.60 ppm spectral region demonstrated statistically significant ability to distinguish between the two groups. Moreover, that region identified a particularly low-risk subgroup—patients whose low malignant potential was one standard deviation below the mean.

The most interesting metabolic finding, in the team's opinion, was that myo-inositol was elevated in histologically benign tissue from patients with more aggressive prostate cancer, suggesting that it suppresses aggressive tumor growth. Myo-inositol is known to inhibit the cancer cell cycle in the prostate and is associated with the oncogenic PI3K pathway, which has been proposed as a therapeutic target in lung cancer.

MRS Assisted in Predicting Cancer Recurrence

Using principal component analysis, the researchers derived a canonical scoring system that differentiated patients in the training cohort who developed biochemical recurrence (BCR) of prostate cancer from those who did not. Ten BCR cases were identified from the training and testing cohorts, respectively, and paired with BCR-free cases from the same cohort. Metabolomic profiles differentiating BCR from BCR-free matched cases were calculated with the training cohort, applied to the testing cohort, and yielded significant results. When applied to patients in the testing cohort with PGG2/3, the score significantly differentiated the BCR and BCR-free groups yielded a general, significant differentiation between BCR and BCR-free cases, as well as two distinct Kaplan-Meier estimator curves that emerge according to when BCR occurred after prostatectomies for the studied population.

The researchers then found that values above and below the mean of the score clearly identified patients who were at higher risk of recurrence. Indeed, even within the PGG2 group, which is pathologically indivisible, there were two distinct risk groups. The test group's BCR metabolomic discriminant canonical values were able to significantly subdivide cases of the single, clinicopathologically indivisible PGG2 group into two distinct Kaplan-Meier estimator groups for prostate cancer recurrence by using the mean value (−0.9) as a threshold. No clinical variable tested, including presurgical PSA, pT stage or prostate cancer perineural invasion, was able to discriminate BCR status within the PGG2 group.

Furthermore, when the researchers examined data on the five PGG2/3 patients who had died, they found a significant linear relationship between the score and survival times.

Metabolic thresholds are unlikely to yield a "crisp" diagnosis or prognosis for all patients, the researchers acknowledge. Still, they say, molecular evaluation of histologically benign specimens could dramatically transform the current morphology-based histology paradigm, leading to the ability to more finely distinguish patients at risk and immensely improve decision-making about prostate cancer treatment.

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