Histomic and transcriptomic features of MRI-visible and invisible clinically significant prostate cancers are associated with prognosis

Magnetic resonance imaging (MRI) is increasingly used to triage patients for prostate biopsy. However, 9% to 24% of clinically significant (cs) prostate cancers (PCas) are not visible in MRI. We aimed to identify histomic and transcriptomic determinants of MRI visibility and their association to metastasis, and PCa-specific death (PCSD). We studied 45 radical prostatectomy-treated patients with csPCa (grade group [GG]2-3), including 30 with MRI-visible and 15 with MRI-invisible lesions, and 18 men without PCa. First, histological composition was quantified. Next, transcriptomic profiling was performed using NanoString technology. MRI visibility-associated differentially expressed genes (DEGs) and Reactome pathways were identified. MRI visibility was classified using publicly available genes in MSK-IMPACT and Decipher, Oncotype DX, and Prolaris. Finally, DEGs and clinical parameters were used to classify metastasis and PCSD in an external cohort, which included 76 patients with metastatic GG2-4 PCa, and 84 baseline-matched controls without progression. Luminal area was lower in MRI-visible than invisible lesions and low luminal area was associated with short metastasis-free and PCa-specific survival. We identified 67 DEGs, eight of which were associated with survival. Cell division, inflammation and transcriptional regulation pathways were upregulated in MRI-visible csPCas. Genes in Decipher, Oncotype DX and MSK-IMPACT performed well in classifying MRI visibility (AUC = 0.86-0.94). DEGs improved classification of metastasis (AUC = 0.69) and PCSD (AUC = 0.68) over clinical parameters. Our data reveals that MRI-visible csPCas harbor more aggressive histomic and transcriptomic features than MRI-invisible csPCas. Thus, targeted biopsy of visible lesions may be sufficient for risk stratification in patients with a positive MRI.

A pilot study of an organised population-based testing programme for prostate cancer

OBJECTIVE

To determine the feasibility of a digitally automated population-based programme for organised prostate cancer testing (OPT) in Southern Sweden.

PATIENTS AND METHODS

A pilot project for a regional OPT was conducted between September 2020 and February 2021, inviting 999 randomly selected men aged 50, 56, or 62 years. Risk stratification was based on prostate-specific antigen (PSA) level, PSA density (PSAD), and bi-parametric prostate magnetic resonance imaging (MRI). Men with a PSA level of 3-99 ng/mL had an MRI, and men with elevated PSA level (≥3 ng/mL) had a urological check-up, including a digital rectal examination and transrectal ultrasonography (TRUS). Indications for targeted and/or systematic transrectal prostate biopsies were suspicious lesions on MRI (Prostate Imaging-Reporting and Data System [PI-RADS] 4-5) and/or PSAD > 0.15 ng/mL/mL. Additional indications for prostate biopsies were palpable tumours, PSA ratio < 0.1, or cancer suspicion on TRUS. Patient selection, mail correspondence, data collection, and algorithm processing were performed by an automated digital management system. Feasibility is reported descriptively.

RESULTS

A total of 418 men had a PSA test (42%), with increasing participation rates by age (50 years, 38%; 56 years, 44%; and 62 years, 45%). Among these, 35 men (8%) had elevated PSA levels (≥3 ng/mL: one of 139, aged 50 years; 10/143, aged 56 years; and 24/146, aged 62 years). On MRI, 16 men (48%) had a negative scan (PI-RADS < 3), seven men (21%) had PI-RADS 3, nine men (27%) had PI-RADS 4, and one man (3%) had PI-RADS 5. All men with PI-RADS 4 or 5 underwent prostate biopsies, as well as two men with PI-RADS 3 due to PSAD > 0.15 ng/mL/mL or a suspicious finding on TRUS. Prostate cancer was diagnosed in 10 men. Six men underwent active treatment, whereas four men were assigned to active surveillance.

CONCLUSION

Our OPT model is feasible from an operational point of view, but due to the limited scale of this study no conclusions can be made regarding the efficacy of the diagnostic model or outcome.

Co-occurrence of CLCN2-related leukoencephalopathy and SPG56

Family Report

Two rare autosomal recessive neurological disorders, leukoencephalopathy with ataxia and spastic paraplegia 56 (SPG56), were found in members of the same family. Two siblings presented with spastic paraplegia, cognitive impairment, bladder and bowel dysfunction and gait ataxia; their consanguineous parents were unaffected. Ophthalmological examination revealed chorioretinopathy. Brain MRI showed T2 hyperintensities and T1 hypointensities in the internal capsules, cerebral peduncles, pyramidal tracts and middle cerebellar peduncles. Both affected siblings were homozygous for CYP2U1 c.947A > T p.(Asp316Val), a known cause for SPG56. However, they were also homozygous for the novel variant CLCN2 c.607G > T, p.(Gly203Cys), classified as a variant of unknown significance. Testing of additional family members revealed homozygosity for both variants in an additional brother, whom we initially considered unaffected. Both male CLCN2 carriers were infertile, and review of the literature revealed one reported case with azoospermia, however the brother had no overt signs of SPG56. His testicular biopsy revealed incomplete maturation arrest in spermatogenesis; clinically we found mild memory impairment and hand tremor and MRI showed similar changes as his siblings. We consider CLCN2 c.607G > T pathogenic because of the neuroradiological and clinical findings, including azoospermia.

Conclusion

Considerable workup may be required to determine the pathogenicity of novel variants, and to unambiguously associate phenotype with genotype. In very rare disorders, highly specific clinical or biomarker combinations provide sufficient evidence for a variant's pathogenicity. Phenotypic variation of monogenic disorders described in the literature may be attributed to a second co-occurring monogenic disorder, especially in consanguineous families. SPG56 may have reduced penetrance.

Abstract 5171: Gene expression in multi-parametric MRI visible and invisible prostate cancers predicts progression

Prostate cancer (PCa) diagnostics are shifting towards multi-parametric magnetic resonance imaging (MRI) combined with targeted biopsies. However, some 10% of clinically significant PCa (csPCa) is missed with current MRI sequences. Evidence on biological differences between MRI visible and invisible csPCa is scarce. Our aim was to find out, whether MRI invisible lesions harbor more indolent characteristics compared to visible csPCa lesions, and to identify transcript markers of MRI visibility.

Methods: A retrospective cohort of 45 radical prostatectomy-treated men with csPCa, including 30 with MRI visible lesions, 15 with MRI invisible lesions and additional 19 benign controls. Apparent diffusion coefficient (ADC) values of the invisible lesions were measured based on their topographical locations in histological slides. The histological slides were checked for histological subtypes of PCa and the prostate epithelium and stroma surface area were measured using machine learning. mRNA was analyzed using Nanostring nCounter platform. First, hierarchical clustering of transcript copy counts was performed. Next, differentially expressed genes (DEGs) between study groups were analyzed and Reactome pathway analysis was performed to identify genes and pathways responsible for MRI visibility. Random forest models (RFMs) were trained to predict MRI visibility using DEGs, and genes in MSK-IMPACT, Decipher, Oncotype DX and Prolaris. RFMs were also trained to predict metastatic and lethal PCa using DEGs.

Results: MRI negative lesions remained invisible upon radiologist re-evaluation. Clinicopathological characteristics did not explain MRI invisibility, while ADC values were higher in invisible lesions than visible ones. Hierarchical clustering did not separate visible and invisible lesions, while benign samples formed a cluster. We identified a set of 10 and 52 DEGs and 65 enriched pathways between visible and invisible specimens. RFMs performed well in predicting MRI visibility for our 10- and 52-gene DEG sets (AUC = 0.92), for transcripts in Decipher, Oncotype DX, Prolaris (AUC = 0.69-0.86) and MSK-IMPACT (AUC = 0.93). RFMs based on 52- and 10-DEG sets showed independent value in predicting metastatic (AUC = 0.67 and 0.65) and lethal disease (AUC = 0.70 and 0.72) and the samples with predicted poor outcomes based on RFMs had significantly worse survival (p &lt; 0.0001).

Conclusions: MRI visibility is net result of many genes and might be linked to various biological processes. Invisible lesions harbor a less aggressive transcript signature than visible csPCa and might be considered for more conservative management. Further research is required to validate these findings on protein level and to find histological correlates. Promising markers altered in currently MRI invisible csPCa could be used to improve MRI sequences.

Citation Format: Timo-Pekka K. Lehto, Carolin Stürenberg, Juho Pylväläinen, Kevin Sandeman, Anu Kenttämies, Stig Nordling, Jing Tang, Tuomas Mirtti, Antti Rannikko. Gene expression in multi-parametric MRI visible and invisible prostate cancers predicts progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5171.

AI Model for Prostate Biopsies Predicts Cancer Survival

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment.

Stromal FAP Expression is Associated with MRI Visibility and Patient Survival in Prostate Cancer

Some clinically significant prostate cancers are missed by MRI. We asked whether the tumor stroma in surgically treated localized prostate cancer lesions positive or negative with MRI are different in their cellular and molecular properties, and whether the differences are reflected to the clinical course of the disease. We profiled the stromal and immune cell composition of MRI-classified tumor lesions by applying multiplexed fluorescence IHC (mfIHC) and automated image analysis in a clinical cohort of 343 patients (cohort I). We compared stromal variables between MRI-visible lesions, invisible lesions, and benign tissue and assessed the predictive significance for biochemical recurrence (BCR) and disease-specific survival (DSS) using Cox regression and log-rank analysis. Subsequently, we carried out a prognostic validation of the identified biomarkers in a population-based cohort of 319 patients (cohort II). MRI true-positive lesions are different from benign tissue and MRI false-negative lesions in their stromal composition. CD163⁺ cells (macrophages) and fibroblast activation protein (FAP)⁺ cells were more abundant in MRI true-positive than in MRI false-negative lesions or benign areas. In MRI true-visible lesions, a high proportion of stromal FAP⁺ cells was associated with PTEN status and increased immune infiltration (CD8⁺, CD163⁺), and predicted elevated risk for BCR. High FAP phenotype was confirmed to be a strong indicator of poor prognosis in two independent patient cohorts using also conventional IHC. The molecular composition of the tumor stroma may determine whether early prostate lesions are detectable by MRI and associates with survival after surgical treatment.

Significance

These findings may have a significant impact on clinical decision making as more radical treatments may be recommended for men with a combination of MRI-visible primary tumors and FAP⁺ tumor stroma.

Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy

Background

To determine the added value of preoperative prostate multiparametric MRI (mpMRI) supplementary to clinical variables and their role in predicting post prostatectomy adverse findings and biochemically recurrent cancer (BCR).

Methods

All consecutive patients treated at HUS Helsinki University Hospital with robot assisted radical prostatectomy (RALP) between 2014 and 2015 were included in the analysis. The mpMRI data, clinical variables, histopathological characteristics, and follow-up information were collected. Study end-points were adverse RALP findings: extraprostatic extension, seminal vesicle invasion, lymph node involvement, and BCR. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, Cancer of the Prostate Risk Assessment (CAPRA) score and the Partin score were combined with any adverse findings at mpMRI. Predictive accuracy for adverse RALP findings by the regression models was estimated before and after the addition of MRI results. Logistic regression, area under curve (AUC), decision curve analyses, Kaplan-Meier survival curves and Cox proportional hazard models were used.

Results

Preoperative mpMRI data from 387 patients were available for analysis. Clinical variables alone, MSKCC nomogram or Partin tables were outperformed by models with mpMRI for the prediction of any adverse finding at RP. AUC for clinical parameters versus clinical parameters and mpMRI variables were 0.77 versus 0.82 for any adverse finding. For MSKCC nomogram versus MSKCC nomogram and mpMRI variables the AUCs were 0.71 and 0.78 for any adverse finding. For Partin tables versus Partin tables and mpMRI variables the AUCs were 0.62 and 0.73 for any adverse finding. In survival analysis, mpMRI-projected adverse RP findings stratify CAPRA and MSKCC high-risk patients into groups with distinct probability for BCR.

Conclusions

Preoperative mpMRI improves the predictive value of commonly used clinical variables for pathological stage at RP and time to BCR. mpMRI is available for risk stratification prebiopsy, and should be considered as additional source of information to the standard predictive nomograms.

Associations of PTEN and ERG with Magnetic Resonance Imaging Visibility and Assessment of Non-organ-confined Pathology and Biochemical Recurrence After Radical Prostatectomy

BACKGROUND

Diagnosing clinically significant prostate cancer (PCa) is challenging, but may be facilitated by biomarkers and multiparametric magnetic resonance imaging (MRI).

OBJECTIVE

To determine the association between biomarkers phosphatase and tensin homolog (PTEN) and ETS-related gene (ERG) with visible and invisible PCa lesions in MRI, and to predict biochemical recurrence (BCR) and non-organ-confined (non-OC) PCa by integrating clinical, MRI, and biomarker-related data.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective analysis of a population-based cohort of men with PCa, who underwent preoperative MRI followed by radical prostatectomy (RP) during 2014-2015 in Helsinki University Hospital (n = 346), was conducted. A tissue microarray corresponding to the MRI-visible and MRI-invisible lesions in RP specimens was constructed and stained for PTEN and ERG.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Associations of PTEN and ERG with MRI-visible and MRI-invisible lesions were examined (Pearson's χ² test), and predictions of non-OC disease together with clinical and MRI parameters were determined (area under the receiver operating characteristic curve and logistic regression analyses). BCR prediction was analyzed by Kaplan-Meier and Cox proportional hazard analyses.

RESULTS AND LIMITATIONS

Patients with MRI-invisible lesions (n = 35) had less PTEN loss and ERG-positive expression compared with patients (n = 90) with MRI-visible lesions (17.2% vs 43.3% [p = 0.006]; 8.6% vs 20.0% [p = 0.125]). Patients with invisible lesions had better, but not statistically significantly improved, BCR-free survival probability in Kaplan-Meier analyses (p = 0.055). Rates of BCR (5.7% vs 21.1%; p = 0.039), extraprostatic extension (11.4% vs 44.6%; p < 0.001), seminal vesicle invasion (0% vs 21.1%; p = 0.003), and lymph node metastasis (0% vs 12.2%; p = 0.033) differed between the groups in favor of patients with MRI-invisible lesions. Biomarkers had no independent role in predicting non-OC disease or BCR. The short follow-up period was a limitation.

CONCLUSIONS

PTEN loss, BCR, and non-OC RP findings were more often encountered with MRI-visible lesions.

PATIENT SUMMARY

Magnetic resonance imaging (MRI) of the prostate misses some cancer lesions. MRI-invisible lesions seem to be less aggressive than MRI-visible lesions.

Abstract 1396: Detection and local histological staging of prostate cancer foci in H&E whole slide images using convolutional neural networks

A pathologist’s histological diagnosis is the gold standard of prostate cancer diagnostic measures. In addition to cancer grade, the stage of cancer determines the follow-up and possible adjuvant therapies after surgery. Not only glandular pattern recognition but also the assessment of three-dimensional extent and size of cancer, in relation to the whole organ, are parts of the subjective diagnosis, which may vary among pathologists. Furthermore, evaluation of lymph nodes for any possible metastases is time-consuming and missing cancer in lymph nodes can lead to undertreatment of the patient. For the abovementioned reasons, our team is developing an algorithm that calculates the amount of cancer tissue objectively, assisting the pathologist in the diagnostic procedure. In addition, we are creating an assistant for cancer detection in lymph nodes, which are removed for histological evaluation. All in all, we aim to create a tool that will help pathologists to a better, faster, more secure and more accurate diagnosis. Our material consists of full sets of scanned whole slide images from 302 prostates that were retrieved by radical prostatectomy. After supervised learning procedure, convolutional neural networks were employed for the classification of cancerous and non-cancerous regions in the images. A tiling based approach was used in which a slide was divided into square shaped small tiles. Millions of cancerous and non-cancerous tiles were sampled from the dataset for training and validation. During the convolutional neural network training, several different tile sizes were used, i.e., 256x256, 512x512, 1024x1024 pixels. Four different types of architectures were fine-tuned and trained for the task of tile-wise binary classification, namely InceptionV3, Xception, ResNet50 and a custom convolutional neural network architecture. In our preliminary assays for cancer detection, in both pixel-wise and tile-wise evaluation, InceptionV3 performed outstandingly well with an AUC score of 0.97 and 0.951, respectively. In conclusion, our algorithm has developed very well thus far with an accuracy in cancer detection of 97%. It is not only a versatile assisting tool, aiding pathologists to a more objective, standardized and accurate diagnosis, but also serves as a second opinion in difficult and challenging diagnostic cases.

Citation Format: Carolin Stürenberg, Umair Khan, Kevin Sandeman, Oguzhan Gencoglu, Adrian Malen, Andrew Erickson, Timo Heikkinen, Antti Rannikko, Tuomas Mirtti. Detection and local histological staging of prostate cancer foci in H&E whole slide images using convolutional neural networks [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1396.

Patient Experience of Systematic Versus Fusion Prostate Biopsies

BACKGROUND

The magnetic resonance imaging/ultrasound fusion-guided biopsy (FBx) technique has gained popularity in prostate cancer (PCa) diagnostics, but little is known about its effect on patient experience.

OBJECTIVE

To evaluate pain, discomfort and other non-infectious complications in PCa patients undergoing either systematic 12-core transrectal ultrasound-guided biopsy (SBx) or FBx and patient willingness to undergo rebiopsy.

DESIGN, SETTING, AND PARTICIPANTS

A prospective trial of 262 male patients, 203 of whom underwent transrectal SBx and 59 FBx at Helsinki University Hospital in 2015-2016. Patients completed two questionnaires immediately after and at 30 d after biopsy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES

Patients reported pain and discomfort on a numeric rating scale (NRS; 0-10) immediately after biopsy. At 30 d, discomfort was measured on a scale ranging from 1 (no inconvenience) to 4 (maximal inconvenience). Other symptoms were reported dichotomously (yes/no) in both questionnaires. Mann-Whitney U, Pearson's χ², and logistic regression tests were used.

RESULTS AND LIMITATIONS

For the SBx and FBx groups the median number of cores per patient was 12 and three, respectively. At 30 d, a higher proportion of patients in the SBx group had experienced pain than in the FBx group (70/203 [34%] vs 12/59 [20%]; p=0.043), whereas there was no difference in the median discomfort scores. Hematuria was less common in the FBx group (26/59 [44%] vs 140/203 [69%]; p<0.001). Patients willing to undergo rebiopsy immediately post-biopsy reported lower median NRS (3.0 [interquartile range 2.0-5.0] vs 5.0 [4.3-6.0]; p<0.001) and discomfort scores (4.0 [2.0-6.0] vs 7.0 [5.0-8.0]; p<0.001) than those unwilling. At 30 d, less discomfort (2.0 [interquartile range 1.0-2.0] vs 2.0 [2.0-3.0]; p=0.008) and fever (6/195 [3.1%] vs 6/28 [22%]; p=0.001) were experienced by patients willing to undergo rebiopsy. The nonrandomized design was a limitation.

CONCLUSIONS

FBx is associated with less pain and hematuria than SBx during the 30-d interval after biopsy.

PATIENT SUMMARY

Magnetic resonance imaging (MRI)-targeted prostate biopsy is associated with less pain, discomfort, and blood in the urine compared to the standard ultrasound-guided procedure. Performing MRI-targeted procedures may reduce biopsy-related complications and promote adherence to recommended repeat biopsy for patients on active surveillance for prostate cancer.

Repeat multiparametric MRI in prostate cancer patients on active surveillance

Introduction

This study was conducted to describe the changes in repeat multiparametric MRI (mpMRI) occurring in prostate cancer (PCa) patients during active surveillance (AS), and to study possible associations between mpMRI-related parameters in predicting prostate biopsy (Bx) Gleason score (GS) upgrading >3+3 and protocol-based treatment change (TC).

Materials and methods

The study cohort consisted of 76 AS patients with GS 3+3 PCa and at least two consecutive mpMRIs of the prostate performed between 2006–2015. Patients were followed according to the Prostate Cancer Research International Active Surveillance (PRIAS) protocol and an additional mpMRI. The primary end points were GS upgrading (GU) (>3+3) in protocol-based Bxs and protocol-based TC.

Results

Out of 76 patients, 53 (69%) had progression (PIRADS upgrade, size increase or new lesion[s]), while 18 (24%) had radiologically stable disease, and 5 (7%) had regression (PIRADS or size decrease, disappearance of lesion[s]) in repeat mpMRIs during AS. PIRADS scores of 4–5 in the initial mpMRI were associated with GU (p = 0.008) and protocol-based TC (p = 0.009). Tumour progression on repeat mpMRIs was associated with TC (p = 0.045) but not with GU (p = 1.00). PIRADS scores of 4–5 predict GU (sensitivity 0.80 [95% confidence interval (CI); 0.51–0.95, specificity 0.62 [95% CI; 0.52–0.77]) with PPV and NPV values of 0.34 (95% CI; 0.21–0.55) and 0.93 (95% CI; 0.80–0.98), respectively.

Conclusion

mpMRI is a useful tool not only to select but also to monitor PCa patients on AS.

The diagnostics of prostate cancer is changing

The alternative to immediate radical treatment of prostate cancer is active surveillance, to which an estimated 30% of new patients with prostate cancer could be directed on the basis of risk classification. Active surveillance is based on repeated PSA measurements, digital rectal examination, repeat biopsies, and increasingly also on MRI. The most important prognostic factor in prostate cancer is still the Gleason score, forming the basis for the new grade group classification with the purpose of rendering active surveillance of prostate cancer a form of treatment that is more easily acceptable for the patients. MRI enables a more reliable selection of low-risk prostate cancer patients for active surveillance. The significance of MRI as a surveillance tool remains unclear.