Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low-and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach

Specific immune cell and lymphatic vessel signatures identified by image analysis in renal cancer

Anti-angiogenic therapy and immune checkpoint inhibition are novel treatment strategies for patients with renal cell carcinoma. Various components and structures of the tumor microenvironment are potential predictive biomarkers and also attractive treatment targets. Macrophages, tumor infiltrating lymphocytes, vascular and lymphatic vessels represent an important part of the tumor immune environment, but their functional phenotypes and relevance for clinical outcome are yet ill defined. We applied Tissue Phenomics methods including image analysis for the standardized quantification of specific components and structures within the tumor microenvironment to profile tissue sections from 56 clear cell renal cell carcinoma patients. A characteristic composition and unique spatial relationship of CD68+ macrophages and tumor infiltrating lymphocytes correlated with overall survival. An inverse relationship was found between vascular (CD34) and lymphatic vessel (LYVE1) density. In addition, outcome was significantly better in patients with high blood vessel density in the tumors, whereas increased lymphatic vessel density in the tumors was associated with worse outcome. The Tissue Phenomics imaging analysis approach allowed visualization and simultaneous quantification of immune environment components, adding novel contextual information, and biological insights with potential applications in treatment response prediction

Qualitative and Quantitative Analysis of Cardiac Progenitor Cells in Cases of Myocarditis and Cardiomyopathy

We aimed to identify and quantify CD117+ and CD90+ endogenous cardiac progenitor cells (CPC) in human healthy and diseased hearts. We hypothesize that these cells perform a locally acting, contributing function in overcoming medical conditions of the heart by endogenous means. Human myocardium biopsies were obtained from 23 patients with the following diagnoses: Dilatative cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), myocarditis, and controls from healthy cardiac patients. High-resolution scanning microscopy of the whole slide enabled a computer-based immunohistochemical quantification of CD117 and CD90. Those signals were evaluated by Definiens Tissue Phenomics® Technology. Co-localization of CD117 and CD90 was determined by analyzing comparable serial sections. CD117+/CD90+ cardiac cells were detected in all biopsies. The highest expression of CD90 was revealed in the myocarditis group. CD117 was significantly higher in all patient groups, compared to healthy specimens (*p < 0.05). The highest co-expression was found in the myocarditis group (6.75 ± 3.25 CD90+CD117+ cells/mm2) followed by ICM (4 ± 1.89 cells/mm2), DCM (1.67 ± 0.58 cells/mm2), and healthy specimens (1 ± 0.43 cells/mm2). We conclude that the human heart comprises a fraction of local CD117+ and CD90+ cells. We hypothesize that these cells are part of local endogenous progenitor cells due to the co-expression of CD90 and CD117. With novel digital image analysis technologies, a quantification of the CD117 and CD90 signals is available. Our experiments reveal an increase of CD117 and CD90 in patients with myocarditis

DataSheet1.pdf

<p>We aimed to identify and quantify CD117⁺ and CD90⁺ endogenous cardiac progenitor cells (CPC) in human healthy and diseased hearts. We hypothesize that these cells perform a locally acting, contributing function in overcoming medical conditions of the heart by endogenous means. Human myocardium biopsies were obtained from 23 patients with the following diagnoses: Dilatative cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), myocarditis, and controls from healthy cardiac patients. High-resolution scanning microscopy of the whole slide enabled a computer-based immunohistochemical quantification of CD117 and CD90. Those signals were evaluated by Definiens Tissue Phenomics® Technology. Co-localization of CD117 and CD90 was determined by analyzing comparable serial sections. CD117⁺/CD90⁺ cardiac cells were detected in all biopsies. The highest expression of CD90 was revealed in the myocarditis group. CD117 was significantly higher in all patient groups, compared to healthy specimens (^*p < 0.05). The highest co-expression was found in the myocarditis group (6.75 ± 3.25 CD90⁺CD117⁺ cells/mm²) followed by ICM (4 ± 1.89 cells/mm²), DCM (1.67 ± 0.58 cells/mm²), and healthy specimens (1 ± 0.43 cells/mm²). We conclude that the human heart comprises a fraction of local CD117⁺ and CD90⁺ cells. We hypothesize that these cells are part of local endogenous progenitor cells due to the co-expression of CD90 and CD117. With novel digital image analysis technologies, a quantification of the CD117 and CD90 signals is available. Our experiments reveal an increase of CD117 and CD90 in patients with myocarditis.</p

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.

Rationale and objectivesTumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.Materials and methodsIndustry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.ResultsIntra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.ConclusionsNine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.

Rationale and objectivesTumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.Materials and methodsIndustry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.ResultsIntra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.ConclusionsNine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data

RATIONALE AND OBJECTIVES: Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semi-automated lung tumor volume measurement algorithms from clinical thoracic CT datasets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) CT Volumetry Profile. MATERIALS AND METHODS: Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers. RESULTS: Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility determined in three partitions and found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters above 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not just in overall volume but in detail. CONCLUSIONS: Nine of the twelve participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the current study was not designed to explicitly evaluate algorithm Profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes above 10 mm. No partition of the algorithms were able to meet the QIBA requirements for interchangeability down to 10 mm, though the partition comprised of the best performing algorithms did meet this requirement above a tumor size of approximately 40 mm