The ReIMAGINE Multimodal Warehouse: Using Artificial Intelligence for Accurate Risk Stratification of Prostate Cancer

Introduction. Prostate cancer (PCa) is the most frequent cancer diagnosis in men worldwide. Our ability to identify those men whose cancer will decrease their lifespan and/or quality of life remains poor. The ReIMAGINE Consortium has been established to improve PCa diagnosis. Materials and methods. MRI will likely become the future cornerstone of the risk-stratification process for men at risk of early prostate cancer. We will, for the first time, be able to combine the underlying molecular changes in PCa with the state-of-the-art imaging. ReIMAGINE Screening invites men for MRI and PSA evaluation. ReIMAGINE Risk includes men at risk of prostate cancer based on MRI, and includes biomarker testing. Results. Baseline clinical information, genomics, blood, urine, fresh prostate tissue samples, digital pathology and radiomics data will be analysed. Data will be de-identified, stored with correlated mpMRI disease endotypes and linked with long term follow-up outcomes in an instance of the Philips Clinical Data Lake, consisting of cloud-based software. The ReIMAGINE platform includes application programming interfaces and a user interface that allows users to browse data, select cohorts, manage users and access rights, query data, and more. Connection to analytics tools such as Python allows statistical and stratification method pipelines to run profiling regression analyses. Discussion. The ReIMAGINE Multimodal Warehouse comprises a unique data source for PCa research, to improve risk stratification for PCa and inform clinical practice. The de-identified dataset characterized by clinical, imaging, genomics and digital pathology PCa patient phenotypes will be a valuable resource for the scientific and medical community.

Localising occult prostate cancer metastasis with advanced imaging techniques (LOCATE trial): a prospective cohort, observational diagnostic accuracy trial investigating whole-body magnetic resonance imaging in radio-recurrent prostate cancer

BACKGROUND

Accurate whole-body staging following biochemical relapse in prostate cancer is vital in determining the optimum disease management. Current imaging guidelines recommend various imaging platforms such as computed tomography (CT), Technetium 99 m (99mTc) bone scan and 18F-choline and recently 68Ga-PSMA positron emission tomography (PET) for the evaluation of the extent of disease. Such approach requires multiple hospital attendances and can be time and resource intensive. Recently, whole-body magnetic resonance imaging (WB-MRI) has been used in a single visit scanning session for several malignancies, including prostate cancer, with promising results, providing similar accuracy compared to the combined conventional imaging techniques. The LOCATE trial aims to investigate the application of WB-MRI for re-staging of patients with biochemical relapse (BCR) following external beam radiotherapy and brachytherapy in patients with prostate cancer.

METHODS/DESIGN

The LOCATE trial is a prospective cohort, multi-centre, non-randomised, diagnostic accuracy study comparing WB-MRI and conventional imaging. Eligible patients will undergo WB-MRI in addition to conventional imaging investigations at the time of BCR and will be asked to attend a second WB-MRI exam, 12-months following the initial scan. WB-MRI results will be compared to an enhanced reference standard comprising all the initial, follow-up imaging and non-imaging investigations. The diagnostic performance (sensitivity and specificity analysis) of WB-MRI for re-staging of BCR will be investigated against the enhanced reference standard on a per-patient basis. An economic analysis of WB-MRI compared to conventional imaging pathways will be performed to inform the cost-effectiveness of the WB-MRI imaging pathway. Additionally, an exploratory sub-study will be performed on blood samples and exosome-derived human epidermal growth factor receptor (HER) dimer measurements will be taken to investigate its significance in this cohort.

DISCUSSION

The LOCATE trial will compare WB-MRI versus the conventional imaging pathway including its cost-effectiveness, therefore informing the most accurate and efficient imaging pathway.

TRIAL REGISTRATION

LOCATE trial was registered on ClinicalTrial.gov on 18th of October 2016 with registration reference number NCT02935816.

Abstract P5-07-06: A competing risks analysis of the association between prediagnostic serum glucose and lipids and breast cancer survival

Background: Abnormal levels of glucose and lipids may be linked to survival after breast cancer (BC) diagnosis, but their association to other causes of mortality such as cardiovascular (CV) disease may result in a competing risk problem and invalidate conventional analyses.

Methods: We assessed serum glucose, triglycerides (TG) and total cholesterol (TC) measured prospectively three months to three years before diagnosis in 1,798 women with BC in the Swedish Apolipoprotein Mortality Risk Study (AMORIS). In addition to using multivariable Cox proportional hazards regression, we employed latent class proportional hazards models to capture any heterogeneity of associations between these markers and BC death. The latter method was extended to include the primary outcome (BC death) and competing outcomes (CV death and death from other causes), allowing latent class-specific hazard estimation for cause-specific deaths.

Results: No association between prediagnostic glucose, TG or TC with BC death was observed with Cox regression. With latent class proportional hazards model, two latent classes (Class I and II) were identified in the cohort. Class I, comprising the majority (81.5%) of BC patients, had an increased risk of BC death following higher TG levels (HR: 1.87, 95% CI: 1.01-3.45 for every log TG increase). Lower overall survival was observed in Class II, but no association for BC death was found. On the other hand, TC positively corresponded to CV death in Class II, and similarly, glucose to death from other causes.

Conclusion: Higher TG was associated with an increased risk of BC death in the majority of BC patients. Our study also identified a subgroup of BC patients at higher risk of early death likely driven by other metabolic-related diseases, which adds to our understanding into BC survival in presence of competing outcomes.

Citation Format: Wulaningsih W, Vahdaninia M, Rowley M, Holmberg L, Garmo H, Malmstrom H, Lambe M, Hammar N, Walldius G, Jungner I, Coolen A, Van Hemelrijck M. A competing risks analysis of the association between prediagnostic serum glucose and lipids and breast cancer survival. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-07-06.

Abstract P2-10-29: Time dependent breast cancer metastasis prediction using novel biological imaging, clinico-pathological and genomic data combined with Bayesian modeling to reduce over-fitting and improve on inter-cohort reproducibility

Background: Breast cancer heterogeneity demands that prognostic models must be biologically driven and recent clinical evidence indicates that future prognostic signatures need evaluation in the context of early versus late metastatic risk prediction. The aim of our work was to identify biologically validated quantitative imaging parameters with improved correlation to clinical outcome, and to address some of the remaining obstacles for a truly robust prognostic model in clinical use.

Method: We identified 4 seed proteins (ezrin/radixin/moesin-cofilin), along with several kinases as biologically relevant subnetwork of proteins that control tumor cell motility and metastasis. Patient-derived breast cancer tumour samples were used to perform a combination of imaging methods such as Fluoresecence lifetime imaging microscopy, automated segmentation and co-localisation intensity analysis. A complexity optimized Bayesian proportional hazard regression model was performed on a total of 419 breast cancer patients to validate time dependent predictions using traditional clinicopathological, genomic and our novel optical imaging-derived parameters. An independent dataset of 300 patient samples from the Leeds Institute of Molecular Medicine is currently being evaluated, representing a large cross centre validation of our integrated model.

Results: We demonstrate that the traditional gold standard clinico-pathological variables are poor predictors for patients that survive long periods, and that their predictive significance (in terms of hazard ratios) varies significantly between two temporal cohorts where the adjuvant treatments are vastly different. Moreover, we investigate the predictive accuracy of a combined imaging/clinicopathological model compared with genomic/clinicopathological models. We demonstrate how to reduce over-fitting to help improve the performance of prognostic models. Results of an integrated model combining genomic and imaging parameters are still awaited.

Discussion: We have produced the first optical imaging-derived multivariate tumour metastatic signature, which measures underlying key biological variables involved in regulating cancer cell motility. Using Bayesian proportional hazards regression in a time-dependent manner, we highlight the inadequacies of existing prediction tools and present a model combining the clinicopathological parameters with our imaging-based metastatic signature, as an integrative reproducible prognostic tool across different temporal cohorts.

Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P2-10-29.

False-negative tests in breast cancer management

We review the sensitivity of different diagnostic tests for breast cancer management based on recent experience in a 34-year-old patient. False-negative tests at diagnosis of early disease and of relapse resulted in diagnostic and therapeutic delays. Initial mammography and breast ultrasonography were falsely negative despite a palpable breast lump. Clinical examination and axillary ultrasound missed macroscopically involved lymph nodes. At relapse, metastatic lesions were missed despite symptoms, three years after primary treatment. CA 15-3 was normal; bone and liver metastases were missed by standard and more advanced imaging techniques including liver ultrasonography, nuclear bone scan and PET -CT scan. Worsening of clinical symptoms, lab results and abnormal tissue biopsies finally led to the diagnosis of extensive metastatic disease. Genetic screening showed an abnormality within the BRCA-1 region of unknown clinical importance. This review highlights 1) that diagnostic tests managing symptomatic breast cancer patients may have a low sensitivity, 2) the importance of clinical findings and other markers for disease, such as lactate dehydrogenase and 3) the need for diagnostic biopsies for clinically suspect symptoms despite normal imaging and biochemistry.

The potential of optical proteomic technologies to individualize prognosis and guide rational treatment for cancer patients

Genomics and proteomics will improve outcome prediction in cancer and have great potential to help in the discovery of unknown mechanisms of metastasis, ripe for therapeutic exploitation. Current methods of prognosis estimation rely on clinical data, anatomical staging and histopathological features. It is hoped that translational genomic and proteomic research will discriminate more accurately than is possible at present between patients with a good prognosis and those who carry a high risk of recurrence. Rational treatments, targeted to the specific molecular pathways of an individual's high-risk tumor, are at the core of tailored therapy. The aim of targeted oncology is to select the right patient for the right drug at precisely the right point in their cancer journey. Optical proteomics uses advanced optical imaging technologies to quantify the activity states of and associations between signaling proteins by measuring energy transfer between fluorophores attached to specific proteins. Förster resonance energy transfer (FRET) and fluorescence lifetime imaging microscopy (FLIM) assays are suitable for use in cell line models of cancer, fresh human tissues and formalin-fixed paraffin-embedded tissue (FFPE). In animal models, dynamic deep tissue FLIM/FRET imaging of cancer cells in vivo is now also feasible. Analysis of protein expression and post-translational modifications such as phosphorylation and ubiquitination can be performed in cell lines and are remarkably efficiently in cancer tissue samples using tissue microarrays (TMAs). FRET assays can be performed to quantify protein-protein interactions within FFPE tissue, far beyond the spatial resolution conventionally associated with light or confocal laser microscopy. Multivariate optical parameters can be correlated with disease relapse for individual patients. FRET-FLIM assays allow rapid screening of target modifiers using high content drug screens. Specific protein-protein interactions conferring a poor prognosis identified by high content tissue screening will be perturbed with targeted therapeutics. Future targeted drugs will be identified using high content/throughput drug screens that are based on multivariate proteomic assays. Response to therapy at a molecular level can be monitored using these assays while the patient receives treatment: utilizing re-biopsy tumor tissue samples in the neoadjuvant setting or by examining surrogate tissues. These technologies will prove to be both prognostic of risk for individuals when applied to tumor tissue at first diagnosis and predictive of response to specifically selected targeted anticancer drugs. Advanced optical assays have great potential to be translated into real-life benefit for cancer patients.

Use of novel optical proteomics to profile breast cancer patients leading to individualised prognosis and tailored treatment

e22090

Background: Optical proteomics quantifies interactions between proteins and post-translational modifications by measuring Förster resonance energy transfer (FRET) quantified by fluorescence lifetime imaging microscopy (FLIM). This project aims to derive multiple high throughput optical proteomic markers, to predict metastatic risk at first diagnosis, and to perturb ‘high risk' protein-protein interactions using targeted therapeutics. This initial step develops robust FRET/FLIM assays, suitable for use in formalin fixed paraffin embedded (FFPE) tissue to be correlated with patient outcome. Methods: Fluorophore-conjugated antibodies to proteins involved in cell migration and survival, were applied to tissue microarrays (TMA), created from archived FFPE invasive ductal breast carcinoma samples. Where fluorophores are located within nanometer proximity, FRET occurs, thus allowing quantification of protein-protein interaction. Ezrin and PKCα phosphorylation, distribution, and interaction were imaged on four TMAs (patients diagnosed with early breast cancer 1984 -1987: 20 years follow-up data). Results: 71 patient samples were optically imaged. Patients were clustered based on the pairwise distances between 18 optical variables ‘input data'. Data are represented on self organising maps and dendrograms and correlated with clinical outcome ‘output data', displaying a heatmap distribution. Conclusions: Ezrin and PKCα phosphorylation, distribution, and interaction imaged optically within FFPE contain prognostic information regarding metastatic outcome in breast cancer, thus stepping ever closer to individualising prognosis. These advanced optics-based parameters informing on metastatic potential will be validated in prospective studies in conjunction with FRET/FLIM assays measuring HER2/HER3 dimerisation, and EGFR and HER2 ubiquitination in order to improve patient selection for targeted therapy.

No significant financial relationships to disclose.