Deciphering the interplay of molecular alterations underpinning renal cell carcinoma by label-free mass spectrometry and clinical proteomics: A systems medicine approach for precision diagnosis

Renal neoplasia is the 14th most common tumor type diagnosed worldwide. With a vast heterogeneity, renal neoplasia encompasses different subtypes. 90% of the neoplasms arise from the epithelial layer of the nephron and vary from benign renal masses (renal oncocytoma, RO) to more indolent or aggressive cancers (renal cell carcinomas, RCC). As RCC subtypes, clear cell (ccRCC) subtype is the most predominant subtype, followed by papillary (pRCC) and chromophobe (chRCC). Despite the different outcomes, some overlapped histological and morphological features difficult their differentiation and diagnosis. Therefore, new approaches for a clear and accurate diagnosis are still needed. To achieve this goal, renal tissue biopsies diagnosed with ccRCC (n = 7), pRCC (n = 5), chRCC (n = 5), RO (n = 5) and normal adjacent tissue (NAT, n= 5) were enrolled in this study. As a very resourceful tool for proteome analysis and biomarker discovery, mass spectrometry (MS)-based methods were used to interrogate the proteome of each tumor in order to undisclosed differences trough which to develop faster and accurate diagnostics. The results achieved with this doctoral thesis include i) the accomplishment of an effective ultrasonic workflow to recover the proteome of optimal cutting temperature (OCT)-embedded tissues, ii) a novel analytical approach based on MALDI-MS profiling to distinguish chRCC from RO, iii) a 109-protein panel to discriminate between chRCC and RO and NAT, iv) a top 24-protein panel to diagnose ccRCC, pRCC, chRCC and RO based on absolute concentration values, v) the translation and validation of three promising biomarkers by immunohistochemical analysis, and vi) an approach for phosphopeptide enrichment. This work brings new insights into the different mechanisms underlying formation of these tumors as well as it provides valuable information to improve clinical diagnosis by opening new avenues for immunohistochemistry and mass spectrometry-based approaches

Novel epigenetic network biomarkers for early detection of esophageal cancer

BACKGROUND: Early detection of esophageal cancer is critical to improve survival. Whilst studies have identified biomarkers, their interpretation and validity is often confounded by cell-type heterogeneity. RESULTS: Here we applied systems-epigenomic and cell-type deconvolution algorithms to a discovery set encompassing RNA-Seq and DNA methylation data from esophageal adenocarcinoma (EAC) patients and matched normal-adjacent tissue, in order to identify robust biomarkers, free from the confounding effect posed by cell-type heterogeneity. We identify 12 gene-modules that are epigenetically deregulated in EAC, and are able to validate all 12 modules in 4 independent EAC cohorts. We demonstrate that the epigenetic deregulation is present in the epithelial compartment of EAC-tissue. Using single-cell RNA-Seq data we show that one of these modules, a proto-cadherin module centered around CTNND2, is inactivated in Barrett's Esophagus, a precursor lesion to EAC. By measuring DNA methylation in saliva from EAC cases and controls, we identify a chemokine module centered around CCL20, whose methylation patterns in saliva correlate with EAC status. CONCLUSIONS: Given our observations that a CCL20 chemokine network is overactivated in EAC tissue and saliva from EAC patients, and that in independent studies CCL20 has been found to be overactivated in EAC tissue infected with the bacterium F. nucleatum, a bacterium that normally inhabits the oral cavity, our results highlight the possibility of using DNAm measurements in saliva as a proxy for changes occurring in the esophageal epithelium. Both the CTNND2/CCL20 modules represent novel promising network biomarkers for EAC that merit further investigation

Molecular Understanding and Modern Application of Traditional Medicines: Triumphs and Trials

Traditional medicines provide fertile ground for modern drug development, but first they must pass along a pathway of discovery, isolation, and mechanistic studies before eventual deployment in the clinic. Here, we highlight the challenges along this route, focusing on the compounds artemisinin, triptolide, celastrol, capsaicin, and curcumin

9th Annual Postdoctoral Science Symposium

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011. The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations

Genomic instability as a predictive biomarker for the application of DNA-damaging therapies in gynecological cancer patients

[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 CommunityLópez 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/10251/19902

Identification of immune-related gene signatures to evaluate immunotherapeutic response in cancer patients using exploratory subgroup discovery

Phenotypic and genotypic heterogeneity are characteristic features of cancer patients. To tackle patients[trademark] heterogeneity, immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches. However, approximately 50 percent of cancer patients that are eligible for treatment with ICIs will not respond well, which motivates the exploration of immunotherapy in combination with either targeted treatments or chemotherapy. Over the years, multiple patient stratification techniques have been developed to identify homogenous patient subgroups, although, matching patient subgroup to treatment option that can improve patients[trademark] health outcome remains a challenging task. We extend our exploratory subgroup discovery algorithm to identify patient subpopulations that can potentially benefit from immuno-targeted combination therapies or chemoimmunotherapy in five cancer types: Head and Neck Squamous Carcinoma (HNSC), Lung Adenocarcinoma (LUAD), Lung Squamous Carcinoma (LUSC), Skin Cutaneous Melanoma (SKCM) and Triple-Negative Breast Cancer (TNBC). We employ various regression models to identify immune-related gene signatures and drug targets that increase the likelihood of partial remission on combination therapies, either immunotargeted regimen or chemoimmunotherapy. Moreover, our pipelines can pinpoint adverse drug effects associated with predicted drug combinations. In addition, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients that differentiate patients with partial remission from patients with progressive disease after chemoimmunotherapy. Finally, we incorporate our methodological developments on Mutational Forks Formalism that enable an assessment of patient-specific flow by leveraging information from multiple single-nucleotide alterations to adjust the transitional likelihoods that are solely based on the canonical view of a disease. Our suit of methods can help to better select responders for combination therapies and improve health outcome for cancer patients with limited treatment options.Includes bibliographical references

Experimental modelling of Alzheimer’s disease for therapeutic screening.

Neurodegenerative diseases, including Alzheimer’s disease (AD), pose a significant and urgent challenge to healthcare systems worldwide. With an increasing life expectancy, these progressive age-related disorders are expected to rise exponentially. No cure currently exists for AD, and the aetiology remains poorly understood. Furthermore, AD drug development faces one of the highest failure rates. Thus, a review of the experimental modelling of the disease is crucial to understanding how the current disease models can be applied to gain useful results while also considering their limitations. Disease models include in vitro, in vivo, ex vivo, and in silico systems as well as clinical trials. These systems are important for testing potential therapeutics to advance drug development, in addition to modelling the pathology of the disease to gain a greater understanding of the cause and progression. This review will discuss the current experimental models employed for the study of AD with the aim of providing an overview of how they are used and discuss their benefits and drawbacks as model systems, as well as highlighting the potential future of the experimental modelling of AD

Melanoma Models for the Next Generation of Therapies

There is a lack of appropriate melanoma models that can be used to evaluate the efficacy of novel therapeutic modalities. Here, we discuss the current state of the art of melanoma models including genetically engineered mouse, patient-derived xenograft, zebrafish, and ex vivo and in vitro models. We also identify five major challenges that can be addressed using such models, including metastasis and tumor dormancy, drug resistance, the melanoma immune response, and the impact of aging and environmental exposures on melanoma progression and drug resistance. Additionally, we discuss the opportunity for building models for rare subtypes of melanomas, which represent an unmet critical need. Finally, we identify key recommendations for melanoma models that may improve accuracy of preclinical testing and predict efficacy in clinical trials, to help usher in the next generation of melanoma therapies

Plsi: A Computational Software Pipeline For Pathway Level Disease Subtype Identification

It is accepted that many complex diseases, like cancer, consist in collections of distinct genetic diseases. Clinical advances in treatments are attributed to molecular treatments aimed at specific genes resulting in greater ecacy and fewer debilitating side effects. This proves that it is important to identify and appropriately treat each individual disease subtype. Our current understanding of subtypes is limited: despite targeted treatment advances, targeted therapies often fail for some patients. The main limitation of current methods for subtype identification is that they focus on gene expression, and they are subject to its intrinsic noise. Signaling pathways describe biological processes that are carried out by networks of genes interacting with each other. We developed PLSI, a software that allows to identify the specific pathways impacted in individual patients, subgroups of patients, or a given subtype of disease. The expected impact includes a better understanding of disease and resistance to treatment