An illustration of the potential for mapping MRI/MRS parameters with genetic over-expression profiles in human prostate cancer

Longitudinal Changes and Predictive Value of Multiparametric MRI Features for Prostate Cancer Patients Treated with MRI-Guided Lattice Extreme Ablative Dose (LEAD) Boost Radiotherapy

We investigated the longitudinal changes in multiparametric MRI (mpMRI) (T2-weighted, Apparent Diffusion Coefficient (ADC), and Dynamic Contrast Enhanced (DCE-)MRI) of prostate cancer patients receiving Lattice Extreme Ablative Dose (LEAD) radiotherapy (RT) and the capability of their imaging features to predict RT outcome based on endpoint biopsies. Ninety-five mpMRI exams from 25 patients, acquired pre-RT and at 3-, 9-, and 24-months post-RT were analyzed. MRI/Ultrasound-fused biopsies were acquired pre- and at two-years post-RT (endpoint). Five regions of interest (ROIs) were analyzed: Gross tumor volume (GTV), normally-appearing tissue (NAT) and peritumoral volume in both peripheral (PZ) and transition (TZ) zones. Diffusion and perfusion radiomics features were extracted from mpMRI and compared before and after RT using two-tailed Student t-tests. Selected features at the four scan points and their differences (Δ radiomics) were used in multivariate logistic regression models to predict the endpoint biopsy positivity. Baseline ADC values were significantly different between GTV, NAT-PZ, and NAT-TZ (p-values < 0.005). Pharmaco-kinetic features changed significantly in the GTV at 3-month post-RT compared to baseline. Several radiomics features at baseline and three-months post-RT were significantly associated with endpoint biopsy positivity and were used to build models with high predictive power of this endpoint (AUC = 0.98 and 0.89, respectively). Our study characterized the RT-induced changes in perfusion and diffusion. Quantitative imaging features from mpMRI show promise as being predictive of endpoint biopsy positivity.

Histopathological features of prostate cancer conspicuity on multiparametric MRI: protocol for a systematic review and meta-analysis

INTRODUCTION

Multiparametric MRI (mpMRI) has improved risk stratification for men with suspected prostate cancer. Indeed, mpMRI-visible tumours tend to be larger and of higher pathological grade than mpMRI-invisible tumours; however, concern remains around significant cancer that is undetected by mpMRI. There has been considerable recent interest to investigate whether tumour conspicuity on mpMRI is associated with additional histopathological features (including cellular density, microvessel density and unusual prostate cancer subtypes), which may have important clinical implications in both diagnosis and prognosis. Furthermore, analysis of these features may help reveal the radiobiology that underpins the actual mechanisms of mpMRI visibility (and invisibility) of prostate tumours. Here, we describe a protocol for a systematic review of the histopathological basis of prostate cancer conspicuity on mpMRI.

METHODS AND ANALYSIS

A systematic search of the MEDLINE, PubMed, Embase and Cochrane databases will be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines will be used to guide screening, thematic reporting and conclusions drawn from all eligible studies. Included papers will be full-text, English-language articles, comparing the histopathological characteristics of mpMRI-visible lesions and mpMRI-invisible tumours. All studies published between January 1950 and January 2020 will be eligible for inclusion. Studies using confirmatory immunohistochemistry for the identification of immune subsets or structural components will be included. Study bias and quality will be assessed using a modified Newcastle-Ottawa scale. To ensure methodological rigour, this protocol is written in accordance with the PRISMA Protocol 2015 checklist. If appropriate, a meta-analysis will be conducted comparing histopathological feature frequency between mpMRI-visible and mpMRI-invisible disease.

ETHICS AND DISSEMINATION

No ethical approval will be required as this is an academic review of published literature. Findings will be disseminated through publications in peer-reviewed journals and presentations at national and international conferences.

PROSPERO REGISTRATION NUMBER

CRD42020176049.

Conspicuity of prostate cancer on multiparametric magnetic resonance imaging: A cross-disciplinary translational hypothesis

Pre-biopsy multiparametric magnetic resonance imaging (mpMRI) has transformed the risk stratification and diagnostic approach for suspected prostate cancer. The majority of clinically significant prostate cancers are visible on pre-biopsy mpMRI, however, there are a subset of significant tumors that are not detected by mpMRI. The radiobiological mechanisms underpinning mpMRI-visibility and invisibility of these cancers remain uncertain. Emerging evidence suggests that mpMRI-visible tumors are enriched with molecular features associated with increased disease aggressivity and poor clinical prognosis, which is supported by short-term endpoints, such as biochemical recurrence following surgery. Furthermore, at the histopathological level, mpMRI-visible tumors appear to exhibit increased architectural and vascular density compared to mpMRI-invisible disease. It seems probable that the genomic, pathological, radiological, and clinical features of mpMRI-visible and mpMRI-invisible prostate cancers are interrelated. Here, we propose a novel cross-disciplinary theory that links genomic and molecular evidence with cellular and histopathological appearances, elucidating both the mpMRI visibility and clinical status of significant prostate cancer.

Genetic Landscape of Prostate Cancer Conspicuity on Multiparametric Magnetic Resonance Imaging: A Systematic Review and Bioinformatic Analysis

Context

Multiparametric magnetic resonance imaging (mpMRI) detects most, but not all, clinically significant prostate cancer. The genetic basis of prostate cancer visibility and invisibility on mpMRI remains uncertain.

Objective

To systematically review the literature on differential gene expression between mpMRI-visible and mpMRI-invisible prostate cancer, and to use bioinformatic analysis to identify enriched processes or cellular components in genes validated in more than one study.

Evidence acquisition

We performed a systematic literature search of the Medline, EMBASE, PubMed, and Cochrane databases up to January 2020 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The primary endpoint was differential genetic features between mpMRI-visible and mpMRI-invisible tumours. Secondary endpoints were explanatory links between gene function and mpMRI conspicuity, and the prognostic value of differential gene enrichment.

Evidence synthesis

We retrieved 445 articles, of which 32 met the criteria for inclusion. Thematic synthesis from the included studies showed that mpMRI-visible cancer tended towards enrichment of molecular features associated with increased disease aggressivity, including phosphatase and tensin homologue (PTEN) loss and higher genomic classifier scores, such as Oncotype and Decipher. Three of the included studies had accompanying publicly available data suitable for further bioinformatic analysis. An over-representation analysis of these datasets revealed increased expression of genes associated with extracellular matrix components in mpMRI-visible tumours.

Conclusions

Prostate cancer that is visible on mpMRI is generally enriched with molecular features of tumour development and aggressivity, including activation of proliferative signalling, DNA damage, and inflammatory processes. Additionally, there appears to be concordant cellular components and biological processes associated with mpMRI conspicuity, as highlighted by bioinformatic analysis of large genetic datasets.

Patient summary

Prostate cancer that is detected by magnetic resonance imaging (MRI) tends to have genetic features that are associated with more aggressive disease. This suggests that MRI can be used to assess the likelihood of aggressive prostate cancer, based on tumour visibility.

Nitrocellulose tissue prints: an innovative approach to preparing high quality DNA and RNA from prostate biopsies without compromising the cores for pathology diagnosis

Much of the research that has been done on prostate cancer tissue biomarkers has relied on radical prostatectomies for biospecimens. However, it is well recognized that important groups of patients are under-represented or missing entirely from biorepository collections of radical prostatectomy specimens. Using prostate biopsy tissues for molecular biomarker research significantly expands the range of available patients to include men whose biopsies show no cancer as well as men who are treated non-surgically or who choose active surveillance. However, one of the challenges of biopsy-based biomarker research is the limited amount of tissue that can be obtained from each core. To address this challenge, we have developed and fully implemented innovative biopsy tissue print technologies that allow us to obtain high quality RNA and DNA from each biopsy core without compromising the specimen for a pathologic diagnosis. Prostate biopsy tissue print samples have been successfully utilized for gene expression profiling, genotyping, DNA methylation and sequencing analyses. Emerging biopsy tissue print applications include studies using viable cells to study tumor metabolism and drug response.

Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data-a preliminary study

Our objective was to identify prognostic imaging biomarkers for hepatocellular carcinoma in contrast-enhanced computed tomography (CECT) with biological interpretations by associating imaging features and gene modules. We retrospectively analyzed 371 patients who had gene expression profiles. For the 38 patients with CECT imaging data, automatic intra-tumor partitioning was performed, resulting in three spatially distinct subregions. We extracted a total of 37 quantitative imaging features describing intensity, geometry, and texture from each subregion. Imaging features were selected after robustness and redundancy analysis. Gene modules acquired from clustering were chosen for their prognostic significance. By constructing an association map between imaging features and gene modules with Spearman rank correlations, the imaging features that significantly correlated with gene modules were obtained. These features were evaluated with Cox's proportional hazard models and Kaplan-Meier estimates to determine their prognostic capabilities for overall survival (OS). Eight imaging features were significantly correlated with prognostic gene modules, and two of them were associated with OS. Among these, the geometry feature volume fraction of the subregion, which was significantly correlated with all prognostic gene modules representing cancer-related interpretation, was predictive of OS (Cox p  =  0.022, hazard ratio  =  0.24). The texture feature cluster prominence in the subregion, which was correlated with the prognostic gene module representing lipid metabolism and complement activation, also had the ability to predict OS (Cox p  =  0.021, hazard ratio  =  0.17). Imaging features depicting the volume fraction and textural heterogeneity in subregions have the potential to be predictors of OS with interpretable biological meaning.

Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases

BACKGROUND

With a wide array of multi-modal, multi-protocol, and multi-scale biomedical data being routinely acquired for disease characterization, there is a pressing need for quantitative tools to combine these varied channels of information. The goal of these integrated predictors is to combine these varied sources of information, while improving on the predictive ability of any individual modality. A number of application-specific data fusion methods have been previously proposed in the literature which have attempted to reconcile the differences in dimensionalities and length scales across different modalities. Our objective in this paper was to help identify metholodological choices that need to be made in order to build a data fusion technique, as it is not always clear which strategy is optimal for a particular problem. As a comprehensive review of all possible data fusion methods was outside the scope of this paper, we have focused on fusion approaches that employ dimensionality reduction (DR).

METHODS

In this work, we quantitatively evaluate 4 non-overlapping existing instantiations of DR-based data fusion, within 3 different biomedical applications comprising over 100 studies. These instantiations utilized different knowledge representation and knowledge fusion methods, allowing us to examine the interplay of these modules in the context of data fusion. The use cases considered in this work involve the integration of (a) radiomics features from T2w MRI with peak area features from MR spectroscopy for identification of prostate cancer in vivo, (b) histomorphometric features (quantitative features extracted from histopathology) with protein mass spectrometry features for predicting 5 year biochemical recurrence in prostate cancer patients, and (c) volumetric measurements on T1w MRI with protein expression features to discriminate between patients with and without Alzheimers' Disease.

RESULTS AND CONCLUSIONS

Our preliminary results in these specific use cases indicated that the use of kernel representations in conjunction with DR-based fusion may be most effective, as a weighted multi-kernel-based DR approach resulted in the highest area under the ROC curve of over 0.8. By contrast non-optimized DR-based representation and fusion methods yielded the worst predictive performance across all 3 applications. Our results suggest that when the individual modalities demonstrate relatively poor discriminability, many of the data fusion methods may not yield accurate, discriminatory representations either. In summary, to outperform the predictive ability of individual modalities, methodological choices for data fusion must explicitly account for the sparsity of and noise in the feature space.

Image analysis and machine learning in digital pathology: Challenges and opportunities

With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of "big data". It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine "sub-visual" image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales. The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular "omics" features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.

Neuropeptide Y (NPY) in tumor growth and progression: Lessons learned from pediatric oncology

Neuropeptide Y (NPY) is a sympathetic neurotransmitter with pleiotropic actions, many of which are highly relevant to tumor biology. Consequently, the peptide has been implicated as a factor regulating the growth of a variety of tumors. Among them, two pediatric malignancies with high endogenous NPY synthesis and release - neuroblastoma and Ewing sarcoma - became excellent models to investigate the role of NPY in tumor growth and progression. The stimulatory effect on tumor cell proliferation, survival, and migration, as well as angiogenesis in these tumors, is mediated by two NPY receptors, Y2R and Y5R, which are expressed in either a constitutive or inducible manner. Of particular importance are interactions of the NPY system with the tumor microenvironment, as hypoxic conditions commonly occurring in solid tumors strongly activate the NPY/Y2R/Y5R axis. This activation is triggered by hypoxia-induced up-regulation of Y2R/Y5R expression and stimulation of dipeptidyl peptidase IV (DPPIV), which converts NPY to a selective Y2R/Y5R agonist, NPY(3-36). While previous studies focused mainly on the effects of NPY on tumor growth and vascularization, they also provided insight into the potential role of the peptide in tumor progression into a metastatic and chemoresistant phenotype. This review summarizes our current knowledge of the role of NPY in neuroblastoma and Ewing sarcoma and its interactions with the tumor microenvironment in the context of findings in other malignancies, as well as discusses future directions and potential clinical implications of these discoveries.

Changes in gene transcription underlying the aberrant citrate and choline metabolism in human prostate cancer samples

PURPOSE

Low concentrations of citrate and high concentrations of choline-containing compounds (ChoCC) are metabolic characteristics observed by magnetic resonance spectroscopy of prostate cancer tissue. The objective was to investigate the gene expression changes underlying these metabolic aberrations to find regulatory genes with potential for targeted therapies.

EXPERIMENTAL DESIGN

Fresh frozen samples (n = 133) from 41 patients undergoing radical prostatectomy were included. Histopathologic evaluation was carried out for each sample before a metabolic profile was obtained with high-resolution magic angle spinning (HR-MAS) spectroscopy. Following the HR-MAS, RNA was extracted from the same sample and quality controlled before carrying out microarray gene expression profiling. A partial least square statistical model was used to integrate the data sets to identify genes whose expression show significant covariance with citrate and ChoCC levels.

RESULTS

Samples were classified as benign, n = 35; cancer of low grade (Gleason score 6), n = 24; intermediate grade (Gleason score 7), n = 41; or high grade (Gleason score ≥ 8), n = 33. RNA quality was high with a mean RNA Integrity Number score of 9.1 (SD 1.2). Gene products predicting significantly a reduced citrate level were acetyl citrate lyase (ACLY, P = 0.003) and m-aconitase (ACON, P < 0.001). The two genes whose expression most closely accompanied the increase in ChoCC were those of phospholipase A2 group VII (PLA2G7, P < 0.001) and choline kinase α (CHKA, P = 0.002).

CONCLUSIONS

By integrating histologic, transcriptomic, and metabolic data, our study has contributed to an expanded understanding of the mechanisms underlying aberrant citrate and ChoCC levels in prostate cancer.

A decade in prostate cancer: from NMR to metabolomics

Over the past 30 years, continuous progress in the application of nuclear magnetic resonance (NMR) spectroscopy and magnetic resonance spectroscopic imaging (MRSI) to the detection, diagnosis and characterization of human prostate cancer has turned what began as scientific curiosity into a useful clinical option. In vivo MRSI technology has been integrated into the daily care of prostate cancer patients, and innovations in ex vivo methods have helped to establish NMR-based prostate cancer metabolomics. Metabolomic and multimodality imaging could be the future of the prostate cancer clinic--particularly given the rationale that more accurate interrogation of a disease as complex as human prostate cancer is most likely to be achieved through paradigms involving multiple, instead of single and isolated, parameters. The research and clinical results achieved through in vivo MRSI and ex vivo NMR investigations during the first 11 years of the 21st century illustrate areas where these technologies can be best translated into clinical practice.

Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data

Computer-aided prognosis (CAP) is a new and exciting complement to the field of computer-aided diagnosis (CAD) and involves developing and applying computerized image analysis and multi-modal data fusion algorithms to digitized patient data (e.g. imaging, tissue, genomic) for helping physicians predict disease outcome and patient survival. While a number of data channels, ranging from the macro (e.g. MRI) to the nano-scales (proteins, genes) are now being routinely acquired for disease characterization, one of the challenges in predicting patient outcome and treatment response has been in our inability to quantitatively fuse these disparate, heterogeneous data sources. At the Laboratory for Computational Imaging and Bioinformatics (LCIB)(1) at Rutgers University, our team has been developing computerized algorithms for high dimensional data and image analysis for predicting disease outcome from multiple modalities including MRI, digital pathology, and protein expression. Additionally, we have been developing novel data fusion algorithms based on non-linear dimensionality reduction methods (such as Graph Embedding) to quantitatively integrate information from multiple data sources and modalities with the overarching goal of optimizing meta-classifiers for making prognostic predictions. In this paper, we briefly describe 4 representative and ongoing CAP projects at LCIB. These projects include (1) an Image-based Risk Score (IbRiS) algorithm for predicting outcome of Estrogen receptor positive breast cancer patients based on quantitative image analysis of digitized breast cancer biopsy specimens alone, (2) segmenting and determining extent of lymphocytic infiltration (identified as a possible prognostic marker for outcome in human epidermal growth factor amplified breast cancers) from digitized histopathology, (3) distinguishing patients with different Gleason grades of prostate cancer (grade being known to be correlated to outcome) from digitized needle biopsy specimens, and (4) integrating protein expression measurements obtained from mass spectrometry with quantitative image features derived from digitized histopathology for distinguishing between prostate cancer patients at low and high risk of disease recurrence following radical prostatectomy.

Multiparametric imaging of tumor response to therapy

There is an increasing opportunity to perform multifunctional imaging at a variety of organ sites with relatively short examination times. Each technique yields quantitative parameters that reflect specific aspects of the underlying tumor or tissue biology. Many biomarkers have emerged that provide unique information on tumor behavior, including response to treatment. The multiparametric approach combines the information from different functional imaging techniques; this goes beyond what can be achieved by using any single functional technique, thus allowing an improved understanding of biologic processes and of responses to therapeutic interventions. Multiparametric imaging has many potential clinical roles; it is useful for pharmaceutical drug development and for predicting therapeutic efficacy.

Choline PET for monitoring early tumor response to photodynamic therapy

Photodynamic therapy (PDT) is a relatively new therapy that has shown promise for treating various cancers in both preclinical and clinical studies. The present study evaluated the potential use of PET with radiolabeled choline to monitor early tumor response to PDT in animal models.

METHODS

Two human prostate cancer models (PC-3 and CWR22) were studied in athymic nude mice. A second-generation photosensitizer, phthalocyanine 4 (Pc 4), was delivered to each animal by a tail vein injection 48 h before laser illumination. Small-animal PET images with (11)C-choline were acquired before PDT and at 1, 24, and 48 h after PDT. Time-activity curves of (11)C-choline uptake were analyzed before and after PDT. The percentage of the injected dose per gram of tissue was quantified for both treated and control tumors at each time point. In addition, Pc 4-PDT was performed in cell cultures. Cell viability and (11)C-choline uptake in PDT-treated and control cells were measured.

RESULTS

For treated tumors, normalized (11)C-choline uptake decreased significantly 24 and 48 h after PDT, compared with the same tumors before PDT (P < 0.001). For the control tumors, normalized (11)C-choline uptake increased significantly. For mice with CWR22 tumors, the prostate-specific antigen level decreased 24 and 48 h after PDT. Pc 4-PDT in cell culture showed that the treated tumor cells, compared with the control cells, had less than 50% (11)C-choline activity at 5, 30, and 45 min after PDT, whereas the cell viability test showed that the treated cells were viable longer than 7 h after PDT.

CONCLUSION

PET with (11)C-choline is sensitive for detecting early changes associated with Pc 4-PDT in mouse models of human prostate cancer. Choline PET has the potential to determine whether a PDT-treated tumor responds to treatment within 48 h after therapy.

Dynamic contrast-enhanced MR imaging in the evaluation of patients with prostate cancer

Prostate cancer is a common tumor among men, with increasing diagnosis at an earlier stage and a lower volume of disease because of screening with prostate-specific antigen (PSA). The need for imaging of the prostate stems from a desire to optimize treatment strategy on a patient and tumor-specific level. The major goals of prostate imaging are (1) staging of known cancer, (2) determination of tumor aggressiveness, (3) diagnosis of cancer in patients who have elevated PSA but a negative biopsy, (4) treatment planning, and (5) the evaluation of therapy response. This article concentrates on the role of dynamic contrast-enhanced MR imaging in the evaluation of patients who have prostate cancer and how it might be used to help achieve the above goals. Various dynamic contrast enhancement approaches (quantitative/semiquantitative/qualitative, high temporal versus high spatial resolution) are summarized with reference to the relevant strengths and compromises of each approach.

Molecular imaging of prostate cancer: a concise synopsis

Prostate cancer is the most common malignancy in men and continues to be a major public health problem. Imaging of prostate cancer remains particularly challenging owing to disease heterogeneity. Molecular imaging can provide unprecedented opportunities for deciphering the molecular mechanisms that are involved in the development and natural progression of prostate cancer from a localized process to the hormone-refractory metastatic disease. Such understanding will be the key for targeted imaging and therapy and for predicting and evaluating treatment response and prognosis. In this article, we review briefly the contribution of multimodality molecular imaging methods for the in vivo characterization of the pathophysiology of prostate cancer.