The characterisation and manipulation of a novel subset of tumour-reactive CD4+ cytotoxic T lymphocytes in human cancer

The ability of cytotoxic CD4+ T lymphocytes (CD4+ CTLs) to exert anti-tumour effects has been demonstrated in murine models of melanoma and an increase in the number of CD4+CTLs has previously been identified in the peripheral blood of patients with Chronic Lymphocytic Leukaemia (CLL) as well as in Hepatocellular Carcinoma. The project’s objective was to further characterise CD4+ CTLs in human cancer and in particular in haematological malignancies. CD4+ CTLs were identified in the peripheral blood, lymph nodes and bone marrow trephine biopsies of patients with CLL. There was a significant increase in the percentage of CD4+ CTLs in the blood of Cytomegalovirus (CMV) seropositive patients with CLL. The immunophenotype of the cells was similar to CD8+ CTLs with respect to the expression of EOMES, CD57 and loss of CD27 and CD28. CD4+ CTLs were also identified in other haematological malignancies, including in the bone marrow aspirates of patients with Multiple Myeloma and in the lymph node biopsies of patients with Non-Hodgkin and Hodgkin Lymphoma. We were able to isolate CD4+ CTLs, rapidly expand them and show that they have the ability to secrete Interferon gamma as well as Granzyme B. We also performed an extensive phenotyping of the cells with CyTOF (mass cytometry) technology. Further study is required to examine the molecular and cellular determinants of their cytotoxic activity prior to exploring the potential to manipulate them for therapeutic benefit

Immune profiling of the tumour microenvironment in prostate cancer

Prostate cancer is the most common cancer among men in the UK and is characterised by large biological and clinical heterogeneity. There is an urgent need for better-personalised patient stratification, for example in accurately identifying patients with regional lymph node metastasis. Nodal involvement negatively impacts on patient survival outcomes and the current pre-operative staging tools to determine the need for extended pelvic lymph node dissection at time of radical prostatectomy are far from precise. The primary tumour immune microenvironment influences tumour immune editing and therefore disease progression. The primary aim of this research was to investigate the in situ phenotype of prostate cancer tumour infiltrating immune cells and determine their potential as biomarkers for regional lymph node invovlement and further explore possible underlying mechanisms for their distribution. The discovery tissue microarray comprised of index lesions from 94 patients undergoing radical prostatectomy and pelvic node dissection (50 with and 44 without histologic evidence of pelvic nodal disease respectively, referred to as LN+ and LN- thereafter). Two multiplex immunofluorescence panels were optimised to comprehensively characterise the immune microenvironment: (1) The macrophage and B cell panel includes CD68, CD163, CD20, AE1/3 (PanCK) and DAPI and (2) The T lymphocytic panel assays for CD4, CD8, FoxP3, PD-1, AE1/3 and DAPI. The macrophage (CD68, CD163+), T (CD8+, CD4+) and B (CD20+) cell immune cell subpopulations within the malignant epithelium and associated stroma were measured and correlated to the nodal status. Stromal infiltration by M1-like macrophages (CD68+CD163-) (p=0.047), CD8 effector (CD8+FoxP3-PD-1-) (p=0.008) and CD4 effector (CD4+FoxP3-PD-1-) T cells (p=0.0003, Mann Whitney test) were lower in LN+ patients. Stromal CD4 effector immune cell density remained a statistically significant independent predictor of lymph node spread in multivariate regression analysis (OR= 0.15, p=0.004). Additionally, in an independent validation cohort of 184 radical prostatectomy specimens, stromal CD4 effector immune cell density predicted the presence of nodal metastasis (OR=0.26, p=0.0004). Addition of stromal CD4 effector T cell density to currently used clinicopathological factors, namely T stage, PSA level, Gleason score and percentage of tumour positive cores, improved the predictive accuracy of current nomograms (from 63.5% to 76.8%, p<0.0001). Tumour infiltrating immune cells did not however correlate with common molecular alterations of prostate cancer such as ERG overexpression and PTEN deletion. Transcriptomic analysis (by HTG EdgeSeq) of the tumour microenvironment was performed to assay 1,041 host immune response related genes. Surprisingly, I did not observe significant differences in the expression levels of adhesion molecules or chemokines (common regulators of immune cell migration) between LN+ and LN- cases. Instead, there was significant upregulation (FC>1.5, adj p value <0.05) of extracellular matrix components (collagen I, collagen III, fibronectin 1) in LN+ tumours, suggesting increased extracellular matrix fibrosis to be associated with reduced T lymphocytic infiltration and tumour immune evasion. Increased collagen III and fibronectin 1 protein expression were confirmed in LN+ patients. Collagen I had increased density score (by second generation harmonic), but not overall abundance, in LN+ patients, eluding to a disorganised stroma with increased cross-linking and elongated fibres. B7-H3 is a newly discovered member of the B7 family of immune checkpoint molecules with both immune and non-immune functions. I investigated the relationship of B7-H3 to the tumour microenvironment as well as its non-immune functions in prostate cancer. Contrast to PD-1, high B7-H3 expression correlated with worse clinicopathological patient features: higher T stage (p<0.0001), perineural invasion (p=0.01) and lymph node spread (p=0.0006). Furthermore, there was significant decrease in migration and invasion in vitro following suppressed B7-H3 expression in multiple human prostate cancer cell lines. RNA sequencing identified extracellular space chemotactic cytokines and their receptors to be highly downregulated genes in PC3M cells with B7-H3 knocked out. Future experiments will investigate the mechanistic downstream pathways of this phenotype and further evaluate the role of B7-H3 in metastasis in vivo. Data presented in this thesis reveal differences in the immune infiltrates, particularly CD4 effector (CD4+FoxP3-PD-1-) T cells between LN+ and LN- patients. Prospective clinical studies are needed to test the predictive value of stromal CD4 effector T cell density in diagnostic prostatic biopsies for regional nodal disease. The role of increased extracellular matrix components in determining tumour immune infiltrates also warrants additional research

αEβ7 Integrin Identifies Subsets of Pro-Inflammatory Colonic CD4+ T Lymphocytes in Ulcerative Colitis.

Background and Aims The αEβ7 integrin is crucial for retention of T lymphocytes at mucosal surfaces through its interaction with E-cadherin. Pathogenic or protective functions of these cells during human intestinal inflammation, such as ulcerative colitis [UC], have not previously been defined, with understanding largely derived from animal model data. Defining this phenotype in human samples is important for understanding UC pathogenesis and is of translational importance for therapeutic targeting of αEβ7-E-cadherin interactions. Methods αEβ7+ and αEβ7- colonic T cell localization, inflammatory cytokine production and expression of regulatory T cell-associated markers were evaluated in cohorts of control subjects and patients with active UC by immunohistochemistry, flow cytometry and real-time PCR of FACS-purified cell populations. Results CD4+αEβ7+ T lymphocytes from both healthy controls and UC patients had lower expression of regulatory T cell-associated genes, including FOXP3, IL-10, CTLA-4 and ICOS in comparison with CD4+αEβ7- T lymphocytes. In UC, CD4+αEβ7+ lymphocytes expressed higher levels of IFNγ and TNFα in comparison with CD4+αEβ7- lymphocytes. Additionally the CD4+αEβ7+ subset was enriched for Th17 cells and the recently described Th17/Th1 subset co-expressing both IL-17A and IFNγ, both of which were found at higher frequencies in UC compared to control. Conclusion αEβ7 integrin expression on human colonic CD4+ T cells was associated with increased production of pro-inflammatory Th1, Th17 and Th17/Th1 cytokines, with reduced expression of regulatory T cell-associated markers. These data suggest colonic CD4+αEβ7+ T cells are pro-inflammatory and may play a role in UC pathobiology

Cell Death Regulates Injury and Inflammation During Renal Allograft Transplantation

Renal transplantation invariably results in tissue injury resulting from ischemia reperfusion injury (IRI), inflammation, drug toxicity, and rejection. Tubular epithelial cells (TEC) comprise the majority of renal parenchyma and are susceptible to cell death and injury during diverse forms of inflammation, which has direct and indirect effects on long term allograft function. Renal TEC have the unique ability to attenuate inflammation and alloimmune injury through the expression of various mediators of cell death and inflammatory molecules. Inhibition of cell death pathways in renal allografts may influence outcomes of alloimmune responses and graft survival. In this body of investigation, alteration of apoptosis and necroptosis forms of TEC death in vitro, were tested for their ability to extend allograft survival in vivo. Apoptotic death induced by cytotoxic cells during allograft rejection was inhibited by TEC expression of Granzyme B inhibiting serine protease inhibitor-6 (SPI-6) and prolonged graft survival and function. Apoptosis death of TEC can also be initiated during renal IRI and with rejection by pro-inflammatory cytokines through surface death receptors. However, inhibition of TNFα-induced apoptosis in TEC through caspase-8 upregulated the receptor interacting protein kinase 1 and 3 (RIPK1/3)-mediated necroptosis pathway to limit graft survival. However, inhibition of RIPK1/3 necroptotic death during renal IRI and transplantation was able to preserve renal function and promote long term graft survival. Augmented pro-inflammatory effects following necrotic cell death were related to an increased release of high mobility group box 1 (HMGB1). Use of the HMGB1 inhibitor glycyrrhizic acid (GZA) inhibited inflammatory responses in vitro and was able to ameliorate renal IRI. Collectively these studies highlight the importance of endogenous donor kidney factors in regulating inflammatory cell death and subsequently the severity and outcomes of allograft rejection. Regulators of parenchymal cell death in kidney and other solid organs may provide entirely new therapeutic targets for transplantation which will promote long term allograft survival

Use of quantitative pathology to improve grading and predict prognosis in tumours of the gastrointestinal tract

Cancer represents a formidable health burden and was the second leading cause of death globally in 2018. In Norway, almost 35000 new cancer cases were reported in 2019. For colon cancer, the incidence and mortality rates in Norway are among the highest in the world. Furthermore, the tumour-node-metastasis (TNM) system used today is not optimal for selecting which patients should receive adjuvant therapy or not. With the implementation of digital pathology in different pathology departments, there will be better opportunities for digital image analysis, a tool aimed at giving a more reproducible and objective diagnosis than subjective evaluation in a microscope. In digital image analysis, a computer programme is used for the quantification of different biomarkers. This can improve cancer diagnostics because several biases in manual evaluation can be reduced or avoided. One of the challenges in pathology is intra-and inter-observer variability of prognostic and predictive biomarkers. This especially applies for gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs), in which the proliferation marker Ki67 is important for grading (1–3), prognosis and treatment of patients. Several studies have shown interand intra-observer variations in the manual evaluation of Ki67 positivity, which can be improved with digital image analysis. This is important because the interpretation of the immunohistochemical staining of different biomarkers, such as Ki67, often influences patient prognosis and treatment. The immune system, especially the number of T-cells in and around the tumour, has been investigated as a promising biomarker for predicting prognosis and survival in colorectal cancer (CRC). The immune system is closely linked to microsatellite instability (MSI) in CRC, and MSI-high CRC has been shown to respond well to immune therapy. A TNM-immune is suggested based on scoring of the number of T-cells in the tumour centre and the invasive margin using digital image analysis. In this study, we explored the correlation between T-cells in presurgical blood samples and T-cells in the invasive margins and the tumour centres in CRC with digital image analysis in a feasibility study and found a correlation. Furthermore, we used digital image analysis to calculate the immune score in colon cancer patients based on immunohistochemical (IHC) staining of cluster of differentiation (CD)3+ and CD8+ T-cells in invasive margins and tumour centres in a prospective cohort. This immune score corresponded strongly with known clinicopathological features, such as stage and MSI status. Also, we evaluated digital image analysis as an objective assessment tool for two different proliferation markers in GEP-NENs: Ki67 and Phosphohistone 3 (PHH3). We compared manual (visual) evaluation of Ki67 from pathology reports with digital image analysis of Ki67 and found excellent agreement, but there is a tendency to upgrade cases from grade 1 to grade 2 with digital image analysis. For the digital image analysis of PHH3, the measurements were more diverging. The data presented show the use of digital image analysis in two settings: developing an immune score as a prognostic marker in colon cancer and providing an objective and reproducible evaluation of proliferation in neuroendocrine neoplasms. With the transition to digital pathology, digital image analysis can be implemented in daily diagnostics. This implementation requires more research for the validation of the different methods. With time, digital image analysis is expected to be utilized for tasks performed by pathologists today.Doktorgradsavhandlin

Predicting Response To Anti-Pd-1 Immunotherapy In Metastatic Melanoma

Predictive biomarkers for antibodies against programmed death 1 (PD-1) remain a major unmet need in metastatic melanoma. Thus, we evaluated three alternative tissue- and blood based markers biomarkers for response to anti-PD-1 therapy. First, pre-treatment melanoma samples were assayed for expression of: 1) IRF-1, a PD-L1 transcription factor, as a proxy for a tumor’s capacity to express PD-L1, and 2) an immune activation panel consisting of CD3, Ki67, and Granzyme B to distinguish immune-active and immune-quiescent tumors. Additionally, we conducted pilot studies to determine the feasibility of measuring soluble PD-L1 in the plasma of cancer patients. For tissue-based assays, samples from melanoma patients that received nivolumab, pembrolizumab, or combination ipilimumab/nivolumab at Yale New Haven Hospital from May 2013 to March 2016 were collected. Expression of IRF-1 and PD-L1 in archival pre-treatment formalin-fixed, paraffin-embedded tumor samples were assessed by the AQUA method of quantitative immunofluorescence. Objective radiographic response (ORR) and progression-free survival (PFS) were assessed using modified RECIST v1.1 criteria. For pilot studies of sPD-L1, plasma from 62 patients with non-small cell lung cancer and 10 cancer-free controls were accessed from pre-existing de-identified tissue banks at Yale School of Medicine. Nuclear IRF-1 expression was higher in patients with partial or complete response (PR/CR) than in patients with stable or progressive disease (SD/PD) (p = 0.044). There was an insignificant trend toward higher PD-L1 expression in patients with PR/CR (p = 0.085). PFS was higher in the IRF-1-high group than the IRF-1-low group (p = 0.017), while PD-L1 expression had no effect on PFS (p = 0.83). In a subset analysis, a strong association between IRF-1 and PFS is seen in patients treated with combination ipilimumab and nivolumab (p = 0.0051). Higher CD3 infiltrates were more likely to be associated with PR/CR (p = 0.0067) and with improved PFS (p = 0.017). Conversely, higher expression of Granzyme B within CD3+ cells was associated with SD/PD (p = 0.023) and a trend toward inferior PFS (p = 0.066). Soluble PD-L1 in human plasma was detected by ELISA, and was elevated in NSCLC cases compared to controls (p \u3c 0.0001). As a measure of PD-L1 expression capability, IRF-1 expression may be a more valuable predictive biomarker for anti-PD-1 therapy than PD-L1 itself. Additionally, patients with quiescent immune infiltrates may benefit more from anti-PD-1 therapy than those with immune-active tumors. The viability of plasma-based predictive biomarkers for immunotherapies warrants additional investigation

A Colour Wheel to Rule them All: Analysing Colour & Geometry in Medical Microscopy

Personalized medicine is a rapidly growing field in healthcare that aims to customize medical treatments and preventive measures based on each patient’s unique characteristics, such as their genes, environment, and lifestyle factors. This approach acknowledges that people with the same medical condition may respond differently to therapies and seeks to optimize patient outcomes while minimizing the risk of adverse effects. To achieve these goals, personalized medicine relies on advanced technologies, such as genomics, proteomics, metabolomics, and medical imaging. Digital histopathology, a crucial aspect of medical imaging, provides clinicians with valuable insights into tissue structure and function at the cellular and molecular levels. By analyzing small tissue samples obtained through minimally invasive techniques, such as biopsy or aspirate, doctors can gather extensive data to evaluate potential diagnoses and clinical decisions. However, digital analysis of histology images presents unique challenges, including the loss of 3D information and stain variability, which is further complicated by sample variability. Limited access to data exacerbates these challenges, making it difficult to develop accurate computational models for research and clinical use in digital histology. Deep learning (DL) algorithms have shown significant potential for improving the accuracy of Computer-Aided Diagnosis (CAD) and personalized treatment models, particularly in medical microscopy. However, factors such as limited generability, lack of interpretability, and bias sometimes hinder their clinical impact. Furthermore, the inherent variability of histology images complicates the development of robust DL methods. Thus, this thesis focuses on developing new tools to address these issues. Our essential objective is to create transparent, accessible, and efficient methods based on classical principles from various disciplines, including histology, medical imaging, mathematics, and art, to tackle microscopy image registration and colour analysis successfully. These methods can contribute significantly to the advancement of personalized medicine, particularly in studying the tumour microenvironment for diagnosis and therapy research. First, we introduce a novel automatic method for colour analysis and non-rigid histology registration, enabling the study of heterogeneity morphology in tumour biopsies. This method achieves accurate tissue cut registration, drastically reducing landmark distance and excellent border overlap. Second, we introduce ABANICCO, a novel colour analysis method that combines geometric analysis, colour theory, fuzzy colour spaces, and multi-label systems for automatically classifying pixels into a set of conventional colour categories. ABANICCO outperforms benchmark methods in accuracy and simplicity. It is computationally straightforward, making it useful in scenarios involving changing objects, limited data, unclear boundaries, or when users lack prior knowledge of the image or colour theory. Moreover, results can be modified to match each particular task. Third, we apply the acquired knowledge to create a novel pipeline of rigid histology registration and ABANICCO colour analysis for the in-depth study of triple-negative breast cancer biopsies. The resulting heterogeneity map and tumour score provide valuable insights into the composition and behaviour of the tumour, informing clinical decision-making and guiding treatment strategies. Finally, we consolidate the developed ideas into an efficient pipeline for tissue reconstruction and multi-modality data integration on Tuberculosis infection data. This enables accurate element distribution analysis to understand better interactions between bacteria, host cells, and the immune system during the course of infection. The methods proposed in this thesis represent a transparent approach to computational pathology, addressing the needs of medical microscopy registration and colour analysis while bridging the gap between clinical practice and computational research. Moreover, our contributions can help develop and train better, more robust DL methods.En una época en la que la medicina personalizada está revolucionando la asistencia sanitaria, cada vez es más importante adaptar los tratamientos y las medidas preventivas a la composición genética, el entorno y el estilo de vida de cada paciente. Mediante el empleo de tecnologías avanzadas, como la genómica, la proteómica, la metabolómica y la imagen médica, la medicina personalizada se esfuerza por racionalizar el tratamiento para mejorar los resultados y reducir los efectos secundarios. La microscopía médica, un aspecto crucial de la medicina personalizada, permite a los médicos recopilar y analizar grandes cantidades de datos a partir de pequeñas muestras de tejido. Esto es especialmente relevante en oncología, donde las terapias contra el cáncer se pueden optimizar en función de la apariencia tisular específica de cada tumor. La patología computacional, un subcampo de la visión por ordenador, trata de crear algoritmos para el análisis digital de biopsias. Sin embargo, antes de que un ordenador pueda analizar imágenes de microscopía médica, hay que seguir varios pasos para conseguir las imágenes de las muestras. La primera etapa consiste en recoger y preparar una muestra de tejido del paciente. Para que esta pueda observarse fácilmente al microscopio, se corta en secciones ultrafinas. Sin embargo, este delicado procedimiento no está exento de dificultades. Los frágiles tejidos pueden distorsionarse, desgarrarse o agujerearse, poniendo en peligro la integridad general de la muestra. Una vez que el tejido está debidamente preparado, suele tratarse con tintes de colores característicos. Estos tintes acentúan diferentes tipos de células y tejidos con colores específicos, lo que facilita a los profesionales médicos la identificación de características particulares. Sin embargo, esta mejora en visualización tiene un alto coste. En ocasiones, los tintes pueden dificultar el análisis informático de las imágenes al mezclarse de forma inadecuada, traspasarse al fondo o alterar el contraste entre los distintos elementos. El último paso del proceso consiste en digitalizar la muestra. Se toman imágenes de alta resolución del tejido con distintos aumentos, lo que permite su análisis por ordenador. Esta etapa también tiene sus obstáculos. Factores como una calibración incorrecta de la cámara o unas condiciones de iluminación inadecuadas pueden distorsionar o hacer borrosas las imágenes. Además, las imágenes de porta completo obtenidas so de tamaño considerable, complicando aún más el análisis. En general, si bien la preparación, la tinción y la digitalización de las muestras de microscopía médica son fundamentales para el análisis digital, cada uno de estos pasos puede introducir retos adicionales que deben abordarse para garantizar un análisis preciso. Además, convertir un volumen de tejido completo en unas pocas secciones teñidas reduce drásticamente la información 3D disponible e introduce una gran incertidumbre. Las soluciones de aprendizaje profundo (deep learning, DL) son muy prometedoras en el ámbito de la medicina personalizada, pero su impacto clínico a veces se ve obstaculizado por factores como la limitada generalizabilidad, el sobreajuste, la opacidad y la falta de interpretabilidad, además de las preocupaciones éticas y en algunos casos, los incentivos privados. Por otro lado, la variabilidad de las imágenes histológicas complica el desarrollo de métodos robustos de DL. Para superar estos retos, esta tesis presenta una serie de métodos altamente robustos e interpretables basados en principios clásicos de histología, imagen médica, matemáticas y arte, para alinear secciones de microscopía y analizar sus colores. Nuestra primera contribución es ABANICCO, un innovador método de análisis de color que ofrece una segmentación de colores objectiva y no supervisada y permite su posterior refinamiento mediante herramientas fáciles de usar. Se ha demostrado que la precisión y la eficacia de ABANICCO son superiores a las de los métodos existentes de clasificación y segmentación del color, e incluso destaca en la detección y segmentación de objetos completos. ABANICCO puede aplicarse a imágenes de microscopía para detectar áreas teñidas para la cuantificación de biopsias, un aspecto crucial de la investigación de cáncer. La segunda contribución es un método automático y no supervisado de segmentación de tejidos que identifica y elimina el fondo y los artefactos de las imágenes de microscopía, mejorando así el rendimiento de técnicas más sofisticadas de análisis de imagen. Este método es robusto frente a diversas imágenes, tinciones y protocolos de adquisición, y no requiere entrenamiento. La tercera contribución consiste en el desarrollo de métodos novedosos para registrar imágenes histopatológicas de forma eficaz, logrando el equilibrio adecuado entre un registro preciso y la preservación de la morfología local, en función de la aplicación prevista. Como cuarta contribución, los tres métodos mencionados se combinan para crear procedimientos eficientes para la integración completa de datos volumétricos, creando visualizaciones altamente interpretables de toda la información presente en secciones consecutivas de biopsia de tejidos. Esta integración de datos puede tener una gran repercusión en el diagnóstico y el tratamiento de diversas enfermedades, en particular el cáncer de mama, al permitir la detección precoz, la realización de pruebas clínicas precisas, la selección eficaz de tratamientos y la mejora en la comunicación el compromiso con los pacientes. Por último, aplicamos nuestros hallazgos a la integración multimodal de datos y la reconstrucción de tejidos para el análisis preciso de la distribución de elementos químicos en tuberculosis, lo que arroja luz sobre las complejas interacciones entre las bacterias, las células huésped y el sistema inmunitario durante la infección tuberculosa. Este método también aborda problemas como el daño por adquisición, típico de muchas modalidades de imagen. En resumen, esta tesis muestra la aplicación de métodos clásicos de visión por ordenador en el registro de microscopía médica y el análisis de color para abordar los retos únicos de este campo, haciendo hincapié en la visualización eficaz y fácil de datos complejos. Aspiramos a seguir perfeccionando nuestro trabajo con una amplia validación técnica y un mejor análisis de los datos. Los métodos presentados en esta tesis se caracterizan por su claridad, accesibilidad, visualización eficaz de los datos, objetividad y transparencia. Estas características los hacen perfectos para tender puentes robustos entre los investigadores de inteligencia artificial y los clínicos e impulsar así la patología computacional en la práctica y la investigación médicas.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidenta: María Jesús Ledesma Carbayo.- Secretario: Gonzalo Ricardo Ríos Muñoz.- Vocal: Estíbaliz Gómez de Marisca