An end-to-end deep learning histochemical scoring system for breast cancer TMA

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists’ decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists’ scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists

QuantISH : RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology.Peer reviewe

The use of digital pathology and image analysis in clinical trials

Digital pathology and image analysis potentially provide greater accuracy, reproducibility and standardisation of pathology‐based trial entry criteria and endpoints, alongside extracting new insights from both existing and novel features. Image analysis has great potential to identify, extract and quantify features in greater detail in comparison to pathologist assessment, which may produce improved prediction models or perform tasks beyond manual capability. In this article, we provide an overview of the utility of such technologies in clinical trials and provide a discussion of the potential applications, current challenges, limitations and remaining unanswered questions that require addressing prior to routine adoption in such studies. We reiterate the value of central review of pathology in clinical trials, and discuss inherent logistical, cost and performance advantages of using a digital approach. The current and emerging regulatory landscape is outlined. The role of digital platforms and remote learning to improve the training and performance of clinical trial pathologists is discussed. The impact of image analysis on quantitative tissue morphometrics in key areas such as standardisation of immunohistochemical stain interpretation, assessment of tumour cellularity prior to molecular analytical applications and the assessment of novel histological features is described. The standardisation of digital image production, establishment of criteria for digital pathology use in pre‐clinical and clinical studies, establishment of performance criteria for image analysis algorithms and liaison with regulatory bodies to facilitate incorporation of image analysis applications into clinical practice are key issues to be addressed to improve digital pathology incorporation into clinical trials

QuantISH: RNA in situ hybridization image analysis framework for quantifying cell type-specific target RNA expression and variability

RNA in situ hybridization (RNA-ISH) is a powerful spatial transcriptomics technology to characterize target RNA abundance and localization in individual cells. This allows analysis of tumor heterogeneity and expression localization, which are not readily obtainable through transcriptomic data analysis. RNA-ISH experiments produce large amounts of data and there is a need for automated analysis methods. Here we present QuantISH, a comprehensive open-source RNA-ISH image analysis pipeline that quantifies marker expressions in individual carcinoma, immune, and stromal cells on chromogenic or fluorescent in situ hybridization images. QuantISH is designed to be modular and can be adapted to various image and sample types and staining protocols. We show that in chromogenic RNA in situ hybridization images of high-grade serous carcinoma (HGSC) QuantISH cancer cell classification has high precision, and signal expression quantification is in line with visual assessment. We further demonstrate the power of QuantISH by showing that CCNE1 average expression and DDIT3 expression variability, as captured by the variability factor developed herein, act as candidate biomarkers in HGSC. Altogether, our results demonstrate that QuantISH can quantify RNA expression levels and their variability in carcinoma cells, and thus paves the way to utilize RNA-ISH technology

An Imaging Mass Spectrometry Investigation Into the N-linked Glycosylation Landscape of Pancreatic Ductal Adenocarcinoma and the Development of Associated Tools for Enhanced Glycan Separation and Characterization

The severity of pancreatic ductal adenocarcinoma (PDAC) is largely attributed to a failure to detect the disease before metastatic spread has occurred. CA19-9, a carbohydrate biomarker, is used clinically to surveille disease progression, but due to specificity challenges is not suitable for early discovery. As CA19-9 and other prospective markers are glycan epitopes, there is great clinical interest in understanding the glycobiology of pancreatic cancer. Unfortunately, few studies have been able to link glycosylation changes directly to pancreatic tumors and instead have focused on peripheral glycan alterations in the serum of PDAC patients. To address this gap in our understanding, we applied an imaging mass spectrometry (IMS) approach with complementary enzymatic and chemical isomer separation techniques to spatially assess the PDAC N-glycome in a cohort of pancreatic cancer patients. Orthogonally, we characterized the expression of CA19-9 and a new biomarker, sTRA, by multi-round immunofluorescence (IF) in the same cohort. These analyses revealed increased sialylation, fucosylation and branching amongst other structural themes in areas of PDAC tumor tissue. CA19-9 expressing tumors were defined by multiply branched, fucosylated bisecting N-glycans while sTRA expressing tumors favored tetraantennary N-glycans with polylactosamine extensions. IMS and IF-derived glycan and biomarker features were used to build classification models that detected PDAC tissue with an AUC of 0.939, outperforming models using either dataset individually. While studying sialylation isomers in our PDAC cohort, we saw an opportunity to enhance the chemical derivatization protocol we were using to address its shortcomings and expand its functionality. Subsequently, we developed a set of novel amidation-amidation strategies to stabilize and differentially label 2,3 and 2,6-linked sialic acids. In our alkyne-based approach, the differential mass shifts induced by the reactions allow for isomeric discrimination in imaging mass spectrometry experiments. This scheme, termed AAXL, was further characterized in clinical tissue specimens, biofluids and cultured cells. Our azide-based approach, termed AAN3, was more suitable for bioorthogonal applications, where the azide tag installed on 2,3 and 2,8-sialic acids could be reacted by click chemistry with a biotin-alkyne for subsequent streptavidin-peroxidase staining. Furthering the use of AAN3, we developed two additional techniques to fluorescently label (SAFER) and preferentially enrich (SABER) 2,3 and 2,8-linked sialic acids for more advanced glycomic applications. Initial experiments with these novel approaches have shown successful fluorescent staining and the identification of over 100 sialylated glycoproteins by LC-MS/MS. These four bioorthogonal strategies provide a new glycomic tool set for the characterization of sialic acid isomers in pancreatic and other cancers. Overall, this work furthers our collective understanding of the glycobiology underpinning pancreatic cancer and potentiates the discovery of novel carbohydrate biomarkers for the early detection of PDAC

Discovery of novel prognostic tools to stratify high risk stage II colorectal cancer patients utilising digital pathology

Colorectal cancer (CRC) patients are stratified by the Tumour, Node and Metastasis (TNM) staging system for clinical decision making. Additional genomic markers have a limited utility in some cases where precise targeted therapy may be available. Thus, classical clinical pathological staging remains the mainstay of the assessment of this disease. Surgical resection is generally considered curative for Stage II patients, however 20-30% of these patients experience disease recurrence and disease specific death. It is imperative to identify these high risk patients in order to assess if further treatment or detailed follow up could be beneficial to their overall survival. The aim of the thesis was to categorise Stage II CRC patients into high and low risk of disease specific death through novel image based analysis algorithms. Firstly, an image analysis algorithm was developed to quantify and assess the prognostic value of three histopathological features through immuno-fluorescence: lymphatic vessel density (LVD), lymphatic vessel invasion (LVI) and tumour budding (TB). Image analysis provides the ability to standardise their quantification and negates observer variability. All three histopathological features were found to be predictors of CRC specific death within the training set (n=50); TB (HR =5.7; 95% CI, 2.38-13.8), LVD (HR =5.1; 95% CI, 2.04-12.99) and LVI (HR =9.9; 95% CI, 3.57- 27.98). Only TB (HR=2.49; 95% CI, 1.03-5.99) and LVI (HR =2.46; 95%CI, 1 - 6.05), however, were significant predictors of disease specific death in the validation set (n=134). Image analysis was further employed to characterise TB and quantify intra-tumoural heterogeneity. Tumour subpopulations within CRC tissue sections were segmented for the quantification of differential biomarker expression associated with epithelial mesenchymal transition and aggressive disease. Secondly, a novel histopathological feature ‘Sum Area Large Tumour Bud’ (ALTB) was identified through immunofluorescence coupled to a novel tissue phenomics approach. The tissue phenomics approach created a complex phenotypic fingerprint consisting of multiple parameters extracted from the unbiased segmentation of all objects within a digitised image. Data mining was employed to identify the significant parameters within the phenotypic fingerprint. ALTB was found to be a more significant predictor of disease specific death than LVI or TB in both the training set (HR = 20.2; 95% CI, 4.6 – 87.9) and the validation set (HR = 4; 95% CI, 1.5 – 11.1). Finally, ALTB was combined with two parameters, ‘differentiation’ and ‘pT stage’, which were exported from the original patient pathology report to form an integrative pathology score. The integrative pathology score was highly significant at predicting disease specific death within the validation set (HR = 7.5; 95% CI, 3 – 18.5). In conclusion, image analysis allows the standardised quantification of set histopathological features and the heterogeneous expression of biomarkers. A novel image based histopathological feature combined with classical pathology allows the highly significant stratification of Stage II CRC patients into high and low risk of disease specific death

Histopathology-selective spatial oncogenic phenotypes in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) constitutes over 85% of lung cancer. Histologically, NSCLC can be broadly classified into adenocarcinoma (AC), squamous cell carcinoma (SCC), large cell carcinoma (LCC), and adenosquamous carcinoma (ASC). AC represents about 65% of all NSCLC cases, and it can be further subdivided based on tumor size and primary growth patterns, such as papillary, acinar, and mucinous. The formation of NSCLC histotypes is orchestrated by cells of origin, genetic alterations, and microenvironmental properties. Although NSCLC carries significant heterogeneity, some genetic mutations, functional phenotypes, and therapeutic responses are associated with specific NSCLC histotypes. Therefore, understanding histotype-selective etiology becomes essential for mechanistic studies and therapeutic applications in the NSCLC research field. Image-based tissue phenotyping has been commonly used for histological classification. It also allows the direct visualization of the distribution and expression of functional molecules. Quantifying such in situ phenotypes can be applied to hypothesis-based functional studies or data-driven correlative analyses. The first part of this thesis developed a spatial image analysis tool package. The making of Spa-RQ, an open-source tool package for image registration and quantification, reflected on the need to perform spatial phenotyping using serial tissue sections in a standardized laboratory workflow. Subsequently, we applied Spa-RQ to identify the histotype-selective, rather than genetically defined activation of MAPK, AKT, and mTOR signaling pathways in murine and human NSCLC samples. The diverse co-activation patterns between these pathways in different tissue compartments, measured by marker expression overlapping using Spa-RQ, may associate with heterogeneous responses towards combinatorial targeted therapies. The second part of this thesis work investigated the histotype-selective functions of a potential therapeutic target. The lung developmental transcription factor SOX9 is silenced in normal adult lung epithelia while it is re-expressed in NSCLC tissues. Its oncogenicity is widely acknowledged but has thus far not been confirmed in NSCLC subtypes. Analyzing the correlation between SOX9 expression and histotype-specific clinical staging, survival, and invasiveness revealed a clinical significance for increased SOX9 expression only in non-mucinous ACs, despite its broad expression in ASC, SCC, and mucinous AC. Supporting this, by comparing the histotype spectra in mouse models following Sox9 loss, we identified a critical role of SOX9 in promoting lung papillary AC progression. On the other hand, its expression was not required for developing squamous and mucinous structure tissues. Finally, using spatial phenotyping, we explained such opposing roles of SOX9 in NSCLC subtypes by the different cells of origin and microenvironmental properties: SOX9 expression was required to form advanced AC from the lung alveolar progenitor cells; on the contrary, its expression was dispensable for SCC development and even interfered with squamous metastasis. Therefore, this work exposed SOX9 as a potential drug target specific to a subgroup of lung AC. In summary, the identification of histotype-selective functional oncogenic phenotypes, as achieved in this thesis, contributes to understanding the heterogeneous nature of tumorigenesis, cancer progression, and drug sensitivities.Non-small cell lung cancer (NSCLC) constitutes over 85% of lung cancer. Histologically, NSCLC can be broadly classified into adenocarcinoma (AC), squamous cell carcinoma (SCC), large cell carcinoma (LCC), and adenosquamous carcinoma (ASC). AC represents about 65% of all NSCLC cases, and it can be further subdivided based on tumor size and primary growth patterns, such as papillary, acinar, and mucinous. The formation of NSCLC histotypes is orchestrated by cells of origin, genetic alterations, and microenvironmental properties. Although NSCLC carries significant heterogeneity, some genetic mutations, functional phenotypes, and therapeutic responses are associated with specific NSCLC histotypes. Therefore, understanding histotype-selective etiology becomes essential for mechanistic studies and therapeutic applications in the NSCLC research field. Image-based tissue phenotyping has been commonly used for histological classification. It also allows the direct visualization of the distribution and expression of functional molecules. Quantifying such in situ phenotypes can be applied to hypothesis-based functional studies or data-driven correlative analyses. The first part of this thesis developed a spatial image analysis tool package. The making of Spa-RQ, an open-source tool package for image registration and quantification, reflected on the need to perform spatial phenotyping using serial tissue sections in a standardized laboratory workflow. Subsequently, we applied Spa-RQ to identify the histotype-selective, rather than genetically defined activation of MAPK, AKT, and mTOR signaling pathways in murine and human NSCLC samples. The diverse co-activation patterns between these pathways in different tissue compartments, measured by marker expression overlapping using Spa-RQ, may associate with heterogeneous responses towards combinatorial targeted therapies. The second part of this thesis work investigated the histotype-selective functions of a potential therapeutic target. The lung developmental transcription factor SOX9 is silenced in normal adult lung epithelia while it is re-expressed in NSCLC tissues. Its oncogenicity is widely acknowledged but has thus far not been confirmed in NSCLC subtypes. Analyzing the correlation between SOX9 expression and histotype-specific clinical staging, survival, and invasiveness revealed a clinical significance for increased SOX9 expression only in non-mucinous ACs, despite its broad expression in ASC, SCC, and mucinous AC. Supporting this, by comparing the histotype spectra in mouse models following Sox9 loss, we identified a critical role of SOX9 in promoting lung papillary AC progression. On the other hand, its expression was not required for developing squamous and mucinous structure tissues. Finally, using spatial phenotyping, we explained such opposing roles of SOX9 in NSCLC subtypes by the different cells of origin and microenvironmental properties: SOX9 expression was required to form advanced AC from the lung alveolar progenitor cells; on the contrary, its expression was dispensable for SCC development and even interfered with squamous metastasis. Therefore, this work exposed SOX9 as a potential drug target specific to a subgroup of lung AC. In summary, the identification of histotype-selective functional oncogenic phenotypes, as achieved in this thesis, contributes to understanding the heterogeneous nature of tumorigenesis, cancer progression, and drug sensitivity