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The main purpose of this thesis was to examine the relationship of pre-diagnostic circulating levels of sex-steroids (androgens and estrogens), sex hormone binding globuline (SHBG), insulin-like growth factor-I (IGF-I), IGF binding proteins (BP) and C-peptide (as a marker of pancreatic insulin secretion) with risk of ovarian and endometrial cancer. Additionally, the interrelationships of body mass index (BMI), sex-steroids, IGF-I and IGFBP-3 were examined. Two case-control studies were nested within 3 prospective cohort studies centered in New York (USA), Umeå (Sweden) and Milan (Italy). The ovarian study included 132 cancer cases. The endometrial study included 166 cancer cases in the IGF-I and C-peptide component and 124 postmenopausal cases in the sex-steroids component. For each case, two controls matching the case for cohort, age, menopausal status and date at recruitment were selected. In total 286 and 315 controls were included in the ovarian and endometrial cancer studies, respectively. Odds ratios (OR) and their 95% confidence intervals (CI) for cancer risk associated with increasing hormone concentrations were estimated by conditional logistic regression. The cross-sectional analysis was based on anthropometric and hormonal data from 620 controls selected for the two nested case-control studies. There was no association of prediagnostic androstenedione, testosterone, DHEAS, SHBG or estrone with ovarian cancer risk in the whole study population or in women who were pre- or postmenopausal at blood donation. In the premenopausal group, risk appeared to increase with increasing androstenedione (OR (95% CI) for the highest tertile: 2.35 (0.81-6.82), p=0.12). There was no association of IGF-I, IGFBP-1, 2, 3 or C-peptide concentrations with risk of ovarian cancer risk in the study group as a whole. In analyses restricted to subjects who had developed ovarian cancer at an early age (<55), circulating IGF-I was directly and strongly associated with risk (OR (95% CI): 4.74 (1.20-18.7), p<0.05 for the highest IGF-I tertile). In the endometrial study, previous observations were confimed that elevated circulating estrogens and androgens and decreased SHBG increase risk of developing endometrial malignancy after menopause. Multivariate ORs (95% CI) for endometrial cancer for quartiles with the highest hormone levels were: 4.13 (1.76-9.72), p<0.001 for estradiol; 3.67 (1.71-7.88), p=0.001 for estrone; 2.15 (1.05-4.40), p<0.04 for androstenedione; 1.74 (0.88-3.46), p=0.06 for testosterone; 2.90 (1.42-5.90), p<0.01 for DHEAS and 0.46 (0.20-1.05), p<0.01 for SHBG. Prediagnostic IGF-I, IGFBP-1, -2 and –3 were not related to risk of endometrial cancer in the whole study population. In postmenopausal women, levels of IGFBP-1 were inversely related to risk with an OR for the highest quartile of 0.36 (0.13-0.95), p<0.05. Endometrial cancer risk increased with increasing levels of C-peptide (p<0.01), up to an OR of 4.40 (1.65-11.7) for the highest quintile after adjustment for BMI and other confounders. The cross-sectional analyses showed that in both pre- and postmenopausal women SHBG decreased with increasing BMI. In the postmenopausal group, estrogens, testosterone and androstenedione increased with BMI, while the association with IGF-I was non-linear, the highest mean IGF-I concentration being observed in women with BMI between 24 and 25. In postmenopausal women, IGF-I was positively related to androgens, inversely correlated with SHBG, and was not correlated with estrogens. In conclusion, elevated pre-diagnostic sex-steroids, IGF-I or C-peptide increase risk of developing ovarian and endometrial cancer. BMI influences the circulating levels of these hormones, especially after menopause.

Για τις Ευρωπαίες γυναίκες ο καρκίνος του σώματος της μήτρας αποτελεί το τέταρτο συχνότερο νεόπλασμα και την δέκατη σε σειρά αιτία θανάτου από καρκίνο. Ανεξάρτητα από το εάν η διακολπική υπερηχογραφία (TVS) αποτελεί δόκιμο μέσο διαλογής (screening) για την ανίχνευση ενδομητρικού καρκίνου σε ασυμπτωματικές μετεμμηνοπαυσιακές γυναίκες, εντούτοις κυριαρχεί στους διαγνωστικούς αλγόριθμους διερεύνησης κάθε μητρορραγίας προς αποκλεισμό του καρκίνου αυτού. Παράλληλα με τα πιθανά οφέλη από την ενσωμάτωση τεχνικών Υπερηχοϋστερογραφίας (SIS) και Doppler στην ενδομητρική απεικόνιση, η δισδιά- στατη “gray scale” διακολπική υπερηχογραφία οφείλει μεγάλο μέρος της προόδου της στην ώθηση από τις εξελίξεις της τεχνολογίας. Μετά την εισαγωγή των διακολπικών ηχοβολέων πολλαπλών συχνοτήτων (multifrequency) και της “αρμονικής” (harmonic) απεικόνισης, τα σύγχρονα υπερηχογραφικά μηχανήματα διαθέτουν επιλογές λογισμι- κού για ενίσχυση της ανάλυσης της αντίθεσης δομών, λεπτών ρυθμίσεων για εξέταση διαφορετικών τύπων ιστών, πολλαπλού εύρους εστίασης, μετάδοσης της δέσμης σε πλάγια διεύθυνση ως προς το ακουστικό παράθυρο, κ.α. Τα παραπάνω, καθώς και φίλτρα μείωσης του θορύβου βελτιστοποιούν την απεικόνιση του ενδομητρίου διευκολύνοντας την αποτίμησή του, ακόμη και στα χέρια άπειρων εξεταστών. Το πάχος της διπλής ενδομητρικής στιβάδας αποτελεί ιστορικά τον πλέον αδιαμφισβήτητο ποσοτικό δείκτη ενδομητρικού καρκίνου, ειδικά στην παρουσία μετεμμηνοπαυσιακής μητρορραγίας. Η συνδυασμένη μελέτη της ενδομητρικής μορφο- λογίας και πάχους παρέχει περισσότερες πληροφορίες, ειδικά στην αποτίμηση της “γκρίζας ζώνης” των 4-10 χιλιοστών ενδομητρικού πάχους, αν και τα ευρήματα των “μορφολογικών” αυτών μελετών δεν υπήρξαν πάντα σταθερά. Με δεδομένη τη σημασία της μορφολογίας στην αποτίμηση του ενδομητρικού ιστού, και αποσκοπώντας στην υπέρβαση του υποκειμενικού χαρακτήρα της ποιοτικής εκτίμησης της υπερηχογραφικής εικόνας, θα ήταν χρήσιμη η εφαρμογή αυτοματοποιημένων τεχνικών που αξιολογούν αντικειμενικά μορφολογικά χαρακτη- ριστικά, όπως η υποβοηθούμενη από υπολογιστή ανάλυση υφής, (“computerized texture analysis”). Στις ψηφιακές εικόνες, η υφή αντικατοπτρίζει τονικές (ένταση των εικονο- στοιχείων) και δομικές (χωρική κατανομή της έντασης των εικονοστοιχείων) ιδιότητες. Η “ανάλυση υφής” αναφέρεται σε αλγόριθμους που ποσοτικοποιούν περιεχόμενο και στοιχεία υφής που πιθανόν, ή όχι, να γίνονται αντιληπτά με το γυμνό μάτι. Δεδομένου ότι στην ιατρική απεικόνιση οι εικόνες περιλαμβάνουν πολλαπλές ιδιότητες των βιολογικών δομών, η ανάλυση υφής των εικόνων αυτών παρέχει ποσοτικές πληροφο- ρίες σχετικές με τα χαρακτηριστικά, τη μορφολογία και τις ιδιότητες των δομών αυτών. Σχήματα ταξινόμησης στηριζόμενα στην υφή έχουν χρησιμοποιηθεί με επιτυχία σε ποικιλία υπερηχογραφικών εφαρμογών. Η βασισμένη σε υπολογιστή αποτίμηση εικόνων του ενδομητρίου έχει βρει κυρίως εφαρμογή στη Υποβοηθούμενη Αναπαραγωγή, αλλά δεν έχει χρησιμοποιηθεί για τη διάγνωση ενδομητρικών κακοηθειών στην δισδιάστατη υπερηχογραφία. Σκοπός της διδακτορικής αυτής διατριβής είναι η αξιολόγηση του εφικτού της υποβοηθούμενης από υπολογιστή ανάλυσης υφής του ενδομητρικού ιστού όπως απεικονίζεται σε δισδιάστατες “gray scale” υπερηχογραφικές εικόνες. Περαιτέρω, διερευνήθηκε το αποτέλεσμα μιας τεχνικής επεξεργασίας βασισμένης σε μετασχη- ματισμό κυματίου (wavelet) στη διαδικασία τμηματοποίησης και χαρακτηρισμού του ενδομητρικού ιστού. / Cancer of the corpus uteri represents the fourth commonest neoplasm among European women and the tenth most common cause of death attributed to cancer. Irrespective whether the use of transvaginal ultrasonography (TVS) as a screening tool for detecting endometrial cancer in asymptomatic postmenopausal women is warranted, TVS dominates most diagnostic algorithms in assessing metrorrhagias to exclude this cancer. Alongside the potential benefits stemming from the integration of Saline Infusion Sonography) and Doppler modalities in endometrial imaging, gray scale TVS showed remarkable advances in the previous decades, largely attributed to the evolution in computer sciences. Following the introduction of multifrequency transvaginal probes and harmonic imaging, modern scanners are equipped with software options that enhance the resolution or the contrast between different structures, fine tune while assessing different types of tissue, implement different depth of focusing, transmit the ultrasonic beam in oblique directions to the acoustic window; all these features, in addition to de-speckle filters optimize the endometrial depiction, facilitating its assessment, even in the hands of moderately skilled operators. Double stripe endometrial thickness has illustrated a remarkable robustness over time as a quantitative indicator of endometrial cancer, especially in the presence of postmenopausal bleeding. The combined consideration of endometrial morphology and thickness has proven particularly beneficial, especially in the assessment of the 4-10 mm endometrial thickness “grey zone”, although the findings of the “morphologic” studies haven’t always been consistent. Given the importance of morphology in assessing endometrial tissue, and aiming to overcome the inherent subjectivity of the qualitative consideration of ultrasonic images, implementation of automated techniques assessing objective morphologic features such as “computerized texture analysis” would be beneficial. In digital images, texture reflects tonal (intensities of image pixels) and structural (spatial distribution of pixel intensities) properties. Texture analysis refers to algorithms that quantify texture content that may, or may not, be visually perceived. Since medical images capture various properties of biological structures, texture analysis of medical images can provide quantitative metrics relevant to structure, morphology and status of biological tissues. Texture based classification schemes have been successfully implemented in a variety of ultrasound applications. Computerized TVS assessment of endometrial morphology, has been applied mainly in assisted reproduction techniques; however, computerized texture analysis has not been implemented for diagnosing endometrial malignancies in grey scale TVS. The aim of this study is to investigate the feasibility of computerized texture analysis in characterizing endometrial tissue as depicted in 2D grey scale TVS images. Furthermore, we assess the effect of a wavelet-based image processing technique in the segmentation and subsequent characterization tasks of endometrial tissue.

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[ES] El curso natural de los tumores va acompañado de la acumulación progresiva de alteraciones genómicas, propiciando una cadena de eventos que resultan en inestabilidad genómica (IG). Éste fenómeno, caracterizado por alteraciones en el número de copias, constituye un hallmark genómico con impacto pronóstico más allá de la histología y otras características moleculares del tumor. En el ámbito de la investigación en oncología ginecológica, la IG ha ganado fuerza en los últimos años, permitiendo la estratificación de pacientes de acuerdo al pronóstico y la respuesta a agentes que dañan el ADN, como las terapias basadas en platinos y los inhibidores de PARP. En el cáncer de ovario, en particular, se ha descrito un subgrupo molecular caracterizado por alta incidencia de alteraciones en el número de copias relacionado con un mejor pronóstico y respuesta a quimioterapia. Esta correlación presenta la IG como un buen marcador predictivo y pronóstico. Así, un modelo basado en la IG trasladable a la práctica clínica constituirá una herramienta útil para la optimización de la toma de decisiones. La era de la medicina personalizada llegó de la mano de los estudios integrativos, donde las técnicas de alto rendimiento se aplican de manera combinada para obtener una visión molecular global de los tumores, completando y complementando la caracterización clásica a nivel anatómico e histológico. Esta tesis propone un estudio global de la IG como biomarcador pronóstico y predictivo de respuesta en cáncer ginecológico, haciendo hincapié en el cáncer de ovario seroso de alto grado y cáncer de endometrio. A través de la aplicación de estrategias basadas en NGS con la adaptación de pipelines de análisis disponibles obtuvimos los perfiles de IG de muestras de tejido fijadas en formol y embebidas en parafina, de una manera fiable, portable y coste efectiva, combinando herramientas de machine learning para ajustar modelos predictivos y pronósticos. Partiendo de esta premisa, ajustamos y validamos, en cohortes clínicas bien caracterizadas, tres modelos a partir de los datos ómicos individuales y un modelo integrativo (Scarface Score) que demostró la capacidad de predecir la respuesta a agentes que dañan el ADN en un escenario clínico concreto de pacientes con cáncer de ovario seroso de alto grado. Paralelamente, desarrollamos y validamos un algoritmo basado en el perfil de mutaciones, con impacto pronóstico, en cáncer de endometrio. Este algoritmo consiguió una estratificación que respondía al perfil de IG de los pacientes. Finalmente, se caracterizó un panel de líneas celulares de cáncer de ovario a nivel de respuesta, genético y genómico. Se interrogó el estatus de la vía de recombinación homóloga y su asociación a patrones de IG, completando el perfil molecular y estableciendo las bases para futuros estudios preclínicos y clínicos. Los resultados obtenidos en esta tesis doctoral presentan herramientas de gran valor para el manejo clínico en cuanto a la búsqueda de una medicina personalizada. Adicionalmente, diferentes estudios para trasladar el modelo predictivo a otros escenarios clínicos pueden ser explorados, usando como base el planteado, pero restableciendo puntos de corte nuevos y específicos. / [CA] El curs natural dels tumors va acompanyat de l'acumulació progressiva d'alteracions genòmiques, propiciant una cadena d'esdeveniments que resulten en inestabilitat genòmica (IG). Aquest fenomen, caracteritzat per la presencia de alteracions en el nombre de cópies, constitueix un hallmark genòmic amb impacte pronòstic més enllà de la histologia i altres característiques moleculars del tumor. En l'àmbit de la recerca en oncologia ginecològica, la IG ha guanyat força en els últims anys, permetent l'estratificació de pacients d'acord amb el pronòstic i la resposta d'agents que danyen l'ADN, com les teràpies basades en platins i els inhibidors de PARP. En el càncer d'ovari en particular, s'ha descrit un subgrup molecular caracteritzat per una alta incidència d'alteracions en el nombre de còpies relacionat amb un millor pronòstic i resposta a quimioteràpia. Aquesta correlació presenta la IG com un marcador predictiu i pronòstic adeqüat. Així, un model basat en la IG traslladable a la pràctica clínica constituirà una eina útil per a l'optimització de la presa de decisions. L'era de la medicina personalitzada va arribar de la mà dels estudis integratius, on les tècniques d'alt rendiment s'apliquen de manera combinada per a obtenir una visió molecular global dels tumors, completant i complementant la caracterització clàssica a nivell anatòmic i histològic. Aquesta tesi proposa un estudi global de la IG com a biomarcador pronòstic i predictiu de resposta en càncer ginecològic, posant l'accent en el càncer d'ovari serós d'alt grau i càncer d'endometri. A través de la aplicación d'estratègies basades en NGS amb l'adaptació de pipelines d'anàlisis disponibles, vam obtenir els perfils de IG de mostres de teixit fixades en formol i embegudes en parafina d'una manera fiable, portable i cost efectiva, combinant eines de machine learning per a ajustar models predictius i pronòstics. Partint d'aquesta premissa, vam ajustar i validar, en cohortes clíniques ben caracteritzades, tres models a partir de les dades omiques individuals i un model integratiu (Scarface Score) que va demostrar la capacitat de predir la resposta a agents que danyen l'ADN en un escenari clínic concret de pacients amb càncer d'ovari serós d'alt grau. Paral·lelament, desenvoluparem i validarem un algoritme basat en el perfil de mutacions amb impacte pronòstic en càncer d'endometri. Aquest algoritme va aconseguir una estratificació que responia al perfil de IG dels pacients. Finalment, es va caracteritzar un panell de línies cel·lulars de càncer d'ovari a nivell de resposta, genètic i genòmic. Es varen interrogar l'estatus de la via de recombinació homòloga i la seua associació a patrons de IG, completant el perfil molecular i establint les bases per a futurs estudis preclínics i clínics. Els resultats obtinguts en aquesta tesi doctoral presenten eines de gran valor per al maneig clínic en quant a la cerca d'una medicina personalitzada. Addicionalment, diferents estudis per a traslladar el model predictiu a altres escenaris clínics poden ser plantejats, usant com a base el propost però restablint punts de tall nous i específics. / [EN] The natural course of tumors matches the progressive accumulation of genomic alterations, triggering a cascade of events that results in genomic instability (GI). This phenomenon includes copy number alterations and constitutes a genomic hallmark that defines specific outcomes beyond histology and other molecular features of the tumor. In the context of gynaecologic oncology research, GI has gained strength in the last years allowing the stratification of patients according to prognosis and response to certain DNA-damaging agents, such as platinum-based therapies and PARP inhibitors. Particularly in ovarian and endometrial cancers, it has been described a molecular subgroup characterized by high copy number alterations (CNA) related to good prognosis and better response to chemotherapy. This relationship highlights GI as a predictive and prognostic biomarker. Hence, a GI-based model translated into clinical practice would constitute a tool for optimizing clinical decision-making. The era of personalised medicine arrived together with the coming of integrative studies, where results of high-throughput techniques are combined to obtain a comprehensive molecular landscape of the diseases, bringing a new paradigm to characterize the tumors beyond classical anatomic and histological characteristics. This thesis proposes a global study of the phenomenon of GI as a prognostic and predictive biomarker of treatment response in gynaecological cancers, mainly focused on high-grade ovarian cancer and endometrial cancer. Through the development of an NGS-based strategy with the adaptation of available pipelines of analysis, we obtained GI profiles on formalin-fixed paraffin-embedded samples in a reliable, portable, and cost-effective approach, with the combination of Machine Learning tools to fit prognostic and predictive models based on the integration of omic data. Based on that premise, we fit and validated, in well-characterized clinical cohorts, three single-source models and an integrative ensemble model (Scarface Score) that proved to be able to predict response to DNA-damaging agents in a clinical scenario of High-Grade Serous Ovarian Cancer. In addition, a mutational-based algorithm (12g algorithm) with prognostic impact was developed and validated for endometrial cancer patients. This algorithm achieved a GI-based stratification of patients. Finally, a panel of ovarian cancer cell lines was characterized at the response, genetic and genomic level, interrogating homologous recombination repair pathway status and its associated GI profiles, completing the molecular landscape, and establishing the basis and breeding ground of future preclinical and clinical studies. The results reported in this Doctoral Thesis provide valuable clinical management tools in the accomplishment of a reliable tailored therapy. Additionally, future studies in different tumor types and drugs for implementation of the predictive model can be planned, using as a base the defined one but re-establishing new and specific cut-offs. / The present doctoral thesis was partially funded by GVA Grants “Subvencions per a la realització de projectes d’i+d+i desenvolupats per grups d’investigació emergents (GV/2020/158)” and “Ayudas para la contratación de personal investigador en formación de carácter predoctoral” (ACIF/2016/008), “Beca de investigación traslacional Andrés Poveda 2020” from GEICO group and Phase II clinical trial (POLA: NCT02684318, EudraCT 2015-001141-08, 03.10.2015). This study was awarded the Prize “Antonio Llombart Rodriguez-FINCIVO 2020” from the Royal Academy of Medicine of the Valencian Community / López Reig, R. (2023). Genomic instability as a predictive biomarker for the application of DNA-damaging therapies in gynecological cancer patients [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/199026

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