Prostate cancer multiparametric magnetic resonance imaging visibility is a tumor-intrinsic phenomena

Molecular Correlates of Prostate Cancer Visibility on Multiparametric Magnetic Resonance Imaging: A Systematic Review

BACKGROUND AND OBJECTIVE

Although prostate magnetic resonance imaging (MRI) is increasingly used to diagnose and stage prostate cancer (PCa), the biologic and clinical significance of MRI visibility of the disease is unclear. Our aim was to examine the existing knowledge regarding the molecular correlates of MRI visibility of PCa.

METHODS

The PubMed, Scopus, and Web of Science databases were queried through November 2023. We defined MRI-visible and MRI-invisible lesions based on the Prostate Imaging Reporting and Data System (PI-RADS) score, and compared these based on the genomic, transcriptomic, and proteomic characteristics.

KEY FINDINGS AND LIMITATIONS

From 2015 individual records, 25 were selected for qualitative data synthesis. Current evidence supports the polygenic nature of MRI visibility, primarily influenced by genes related to stroma, adhesion, and cellular organization. Several gene signatures related to MRI visibility were associated with oncologic outcomes, which support that tumors appearing as PI-RADS 4-5 lesions harbor lethal disease. Accordingly, MRI-invisible tumors detected by systematic biopsies were, generally, less aggressive and had a more favorable prognosis; however, some MRI-invisible tumors harbored molecular features of biologically aggressive PCa. Among the commercially available prognostic gene panels, only Decipher was strongly associated with MRI visibility.

CONCLUSIONS AND CLINICAL IMPLICATIONS

High PI-RADS score is associated with biologically and clinically aggressive PCa molecular phenotypes, and could potentially be used as a biomarker. However, MRI-invisible lesions can harbor adverse features, advocating the continued use of systemic biopsies. Further research to refine the integration of imaging data to prognostic assessment is warranted.

PATIENT SUMMARY

Magnetic resonance imaging visibility of prostate cancer is a polygenic trait. Higher Prostate Imaging Reporting and Data System scores are associated with features of biologically and clinically aggressive cancer.

Editorial for Special Topics: Imaging-Based Diagnosis for Prostate Cancer-State of the Art

This Special Topics Issue, "Imaging-based Diagnosis of Prostate Cancer-State of the Art", of Diagnostics compiles 10 select articles [...].

The Proteogenomics of Prostate Cancer Radioresistance

Prostate cancer is frequently treated with radiotherapy. Unfortunately, aggressive radioresistant relapses can arise, and the molecular underpinnings of radioresistance are unknown. Modern clinical radiotherapy is evolving to deliver higher doses of radiation in fewer fractions (hypofractionation). We therefore analyzed genomic, transcriptomic, and proteomic data to characterize prostate cancer radioresistance in cells treated with both conventionally fractionated and hypofractionated radiotherapy. Independent of fractionation schedule, resistance to radiotherapy involved massive genomic instability and abrogation of DNA mismatch repair. Specific prostate cancer driver genes were modulated at the RNA and protein levels, with distinct protein subcellular responses to radiotherapy. Conventional fractionation led to a far more aggressive biomolecular response than hypofractionation. Testing preclinical candidates identified in cell lines, we revealed POLQ (DNA Polymerase Theta) as a radiosensitizer. POLQ-modulated radioresistance in model systems and was predictive of it in large patient cohorts. The molecular response to radiation is highly multimodal and sheds light on prostate cancer lethality.

SIGNIFICANCE

Radiation is standard of care in prostate cancer. Yet, we have little understanding of its failure. We demonstrate a new paradigm that radioresistance is fractionation specific and identified POLQ as a radioresistance modulator.

Should systematic prostatic biopsies be discontinued?

INTRODUCTION

The use of systematic biopsies in addition to targeted biopsies is based on multiple studies showing that 15-20% of "clinically significant" cancers are missed on targeted biopsies. Concern about these 'missed' cancers drives many interventions. This includes systematic biopsies in men with negative imaging and in men having targeted biopsies, and drives a preference for total gland treatment in men who may be candidates for partial gland ablation. This article summarizes recent genomic and clinical data indicating that, despite "clinically significant" histology, MRI invisible lesions are genomically and clinically favorable. These studies have demonstrated that the genetic aberrations associated with cancer visibility are the same aberrations that drive cancer invasiveness and metastasis. Thus invisible cancers, even if undiagnosed at baseline, are in most cases indolent and pose little threat to the patient. The implications are that patients should be monitored with imaging rather than systematic biopsy, and subject to repeat targeted biopsy for evidence of MR progression. Patients prefer this strategy. It has many advantages in terms of reduced burden of care, cost, psychological benefits, and less diagnosis of insignificant cancer.

CONCLUSION

It is now appropriate to abandon systematic biopsies in most patients.

Prostate cancer reshapes the secreted and extracellular vesicle urinary proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Recent advances and future perspectives in the therapeutics of prostate cancer

Prostate cancer (PC) is one of the most common cancers in males and the fifth leading reason of death. Age, ethnicity, family history, and genetic defects are major factors that determine the aggressiveness and lethality of PC. The African population is at the highest risk of developing high-grade PC. It can be challenging to distinguish between low-risk and high-risk patients due to the slow progression of PC. Prostate-specific antigen (PSA) is a revolutionary discovery for the identification of PC. However, it has led to an increase in over diagnosis and over treatment of PC in the past few decades. Even if modifications are made to the standard PSA testing, the specificity has not been found to be significant. Our understanding of PC genetics and proteomics has improved due to advances in different fields. New serum, urine, and tissue biomarkers, such as PC antigen 3 (PCA3), have led to various new diagnostic tests, such as the prostate health index, 4K score, and PCA3. These tests significantly reduce the number of unnecessary and repeat biopsies performed. Chemotherapy, radiotherapy, and prostatectomy are standard treatment options. However, newer novel hormone therapy drugs with a better response have been identified. Androgen deprivation and hormonal therapy are evolving as new and better options for managing hormone-sensitive and castration-resistant PC. This review aimed to highlight and discuss epidemiology, various risk factors, and developments in PC diagnosis and treatment regimens.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Multimodal Prediction of Five-Year Breast Cancer Recurrence in Women Who Receive Neoadjuvant Chemotherapy

In current clinical practice, it is difficult to predict whether a patient receiving neoadjuvant chemotherapy (NAC) for breast cancer is likely to encounter recurrence after treatment and have the cancer recur locally in the breast or in other areas of the body. We explore the use of clinical history, immunohistochemical markers, and multiparametric magnetic resonance imaging (DCE, ADC, Dixon) to predict the risk of post-treatment recurrence within five years. We performed a retrospective study on a cohort of 1738 patients from Institut Curie and analyzed the data using classical machine learning, image processing, and deep learning. Our results demonstrate the ability to predict recurrence prior to NAC treatment initiation using each modality alone, and the possible improvement achieved by combining the modalities. When evaluated on holdout data, the multimodal model achieved an AUC of 0.75 (CI: 0.70, 0.80) and 0.57 specificity at 0.90 sensitivity. We then stratified the data based on known prognostic biomarkers. We found that our models can provide accurate recurrence predictions (AUC > 0.89) for specific groups of women under 50 years old with poor prognoses. A version of our method won second place at the BMMR2 Challenge, with a very small margin from being first, and was a standout from the other challenge entries.