About one in eight women in the United States will develop breast cancer over the course of her lifetime. Moreover, patient-to-patient variability in disease progression continues to complicate clinical decisions in diagnosis and treatment for breast cancer patients. Early detection of tumors is a key factor influencing patient survival, and advancements in diagnostic and imaging techniques has allowed clinicians to spot smaller sized lesions. There has also been an increase in premature treatments of non-malignant lesions because there is no clear way to predict whether these lesions will become invasive over time. Patient variability due to genetic polymorphisms has been investigated, but studies on variability at the level of cellular activity have been extremely limited. An individual’s biochemical milieu of cytokines, growth factors, and other stimuli contain a myriad of cues that pre-condition cells and induce patient variability in response to tumor progression or treatment. Circulating white blood cells called monocytes respond to these cues and enter tissues to differentiate into monocyte-derived macrophages (MDMs) and osteoclasts that produce cysteine cathepsins, powerful extracellular matrix proteases. Cathepsins have been mechanistically linked to accelerated tumor growth and metastasis. This study aims to elucidate the variability in disease progression among patients by examining the variability of protease production from tissue-remodeling macrophages and osteoclasts. Since most extracellular cues initiate multiple signaling cascades that are interconnected and dynamic, this current study uses a systems biology approach known as cue-signal-response (CSR) paradigm to capture this complexity comprehensively. The novel and significant finding of this study is that we have identified and predicted donor-to-donor variability in disease modifying cysteine cathepsin activities in macrophages and osteoclasts. This study applied this novel finding to the context of tumor invasion and showed that variability in tumor associated macrophage cathepsin activity and their inhibitor cystatin C level mediates variability in cancer cell invasion. These findings help to provide a minimally invasive way to identify individuals with particularly high remodeling capabilities. This could be used to give insight into the risk for tumor invasion and develop a personalized therapeutic regime to maximize efficacy and chance of disease free survival.

Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneous group. In this paper, we propose a new semiparametric additive single-index model for estimating individualized treatment strategy. The model assumes a flexible and nonparametric link function for the interaction between treatment and predictive covariates. We estimate the rule via monotone B-splines and establish the asymptotic properties of the estimators. Both simulations and an real data application demonstrate that the proposed method has a competitive performance.

Chimeric antigen receptors (CAR) are modular genetically modified receptors that consist of an extracellular antigen binding domain fused to intracellular T-cell signaling domains. CAR therapy broadly consists of engineering a patient’s own T-cells to express a CAR directed against a tumor cell surface antigen. This therapy has been extremely successful in treating B-cell neoplasms by targeting CD19 and is paradigm changing in developing personalized immunotherapy for oncology applications. Although impressive response rates are observed, the durability of therapeutic response remains a concern and relapse mechanisms frequently center around issues of antigen loss. In addition, heterogeneous disease and solid tumors present formidable barriers toward extending the applicability of CAR technology as a result of compounding issues of tumor microenvironment and cell trafficking. In this thesis we review the current thought on the state of CAR therapy and the challenges to therapeutic efficacy, therapeutic manufacture, and clinical safety in the context of each other with an overall emphasis on identifying the fundamental goal of making fit-for-purpose CARs for different diseases.

The subgroup identification field which sometimes is called personalized medicine, tries to group individuals such that the effects of a treatment are the most beneficial for them. One of the methods developed for this purpose is called PSICA. Currently this method works in a setting of multiple treatments and real valued response variables. In this thesis, this methodology is extended to the degree that it can also handle ordinal response variables that can take a finite number of values. It is also compared to a competitor method which results in similar performance but with the added value of a probabilistic output and a model that is interpretable and ready for policy making. This is achieved at the expense of a higher execution time. Finally, this extension is applied to a longitudinal study done in Nicaragua in the los Cuatro Santos population in which some interventions were applied in order to reduce poverty. The results showed which were the most beneficial treatments for different population subgroups.

[ES] El cáncer es la segunda causa de muerte en el mundo y se caracteriza principalmente por la proliferación descontrolada de las células que forman el tumor. Aunque el desarrollo de un tumor es posible debido a ciertos procesos comunes desencadenados por la desregulación del equilibrio existente entre los componentes moleculares de una célula y sus elementos de control, existe una gran heterogeneidad en los mecanismos a través de los cuales ocurre dicha desregulación. Gracias al desarrollo de nuevas tecnologías de secuenciación ha sido posible observar como esta heterogeneidad no solo se observa entre los distintos tipos de tumores sino entre las propias células de un mismo tumor. La caracterización de la heterogeneidad tumoral ha tenido un gran impacto en la comprensión de la enfermedad y el desarrollo de nuevas terapias dirigidas. Por este motivo, con el fin de mejorar la caracterización de alteraciones en los distintos mecanismos regulatorios, en esta tesis se han desarrollado dos metodologías con gran potencial para su aplicación en la medicina personalizada y que permiten estudiar la heterogeneidad inter e intratumoral de los estados de activación de elementos reguladores. En primer lugar, se desarrolló una metodología que permite determinar en una muestra el estado de activación de los factores de transcripción (FTs) a partir de la expresión de los genes a los que regula. Se aplicó la metodología para realizar un análisis sistemático de varios cánceres (conocido como estudios pan-cáncer) en el que se caracterizó por primera vez el escenario regulatorio de 52 FTs en 11 tipos de cáncer distintos. Además, al poder obtener valores de activación individuales para cada muestra, fue posible observar correlaciones entre la activación de algunos FTs con la supervivencia, sugiriendo así su uso como marcadores pronósticos. En segundo lugar, se desarrolló otra metodología en la que se emplea un modelo mecanístico para determinar el estado de activación de alrededor de 1000 circuitos de señalización a partir de datos de experimentos transcriptómicos de células únicas (scRNAseq). El uso de este modelo mecanístico en datos de scRNAseq de 4 pacientes de glioblastoma, además de mostrar la heterogeneidad intratumoral presente en las muestras, ha permitido realizar intervenciones in silico para simular el efecto de distintas drogas sobre las células. De esta manera, ha sido posible describir posibles mecanismos mediante los cuales un grupo de células pueden evitar el efecto de una terapia dirigida. Las metodologías desarrolladas en esta tesis, así como los resultados obtenidos tras su aplicación supone una valiosa fuente de información para el desarrollo de marcadores de diagnóstico, pronóstico y respuesta que ayuden a entender mejor los distintos niveles de heterogeneidad presentes en cáncer, y así, poder aumentar la eficacia de las terapias dirigidas. / [CA] El càncer és la segona causa de mort al món i es caracteritza principalment per la proliferació descontrolada de les cèl·lules que formen el tumor. Encara que el desenvolupament d'un tumor és possible a causa de certs processos comuns desencadenats per la desregulació de l'equilibri existent entre els components moleculars d'una cèl·lula i els seus elements de control, hi ha una gran heterogeneïtat en els mecanismes a través dels quals s'aconseguix aquesta desregulació. Gràcies a el desenvolupament de noves tecnologies de seqüenciació ha sigut possible observar com aquesta heterogeneïtat no només s'observa entre els diferents tipus de tumors sinó entre les pròpies cèl·lules d'un mateix tumor. La caracterització de l'heterogeneïtat tumoral ha tingut un gran impacte en la comprensió de la malaltia i el desenvolupament de noves teràpies dirigides. Per aquest motiu, per tal de millorar la caracterització d'alteracions en els diferents mecanismes reguladors, en aquesta tesi s'han desenvolupat dues metodologies amb gran potencial per a la seua aplicació en la medicina personalitzada i que permeten estudiar l'heterogeneïtat inter i intratumoral dels estats de activació d'elements reguladors. En primer lloc es va desenvolupar una metodologia que permet determinar en una mostra l'estat d'activació dels factors de transcripció (FTs) a partir de l'expressió dels gens als que regula. Es va aplicar la metodologia per a realitzar una anàlisi de pan-cancer en el qual es va caracteritzar per primera vegada l'escenari regulatori de 52 FTs a 11 tipus de càncer diferents. A més, al poder obtenir valors d'activació individuals per a cada mostra, va ser possible observar correlacions entre l'activació d'alguns FTs amb la supervivència, suggerint així el seu ús com a marcadors pronòstics. En segon lloc, es va desenvolupar una altra metodologia en la qual s'empra un model mecanístic per determinar l'estat d'activació d'al voltant de 1000 circuits de senyalització a partir d'experiments transcriptòmics de cèl·lules úniques (scRNAseq). L'ús d'aquest model mecanístic en dades de scRNAseq de 4 pacients de glioblastoma, a més de mostrar l'heterogeneïtat intratumoral present en les mostres, ha permès realitzar intervencions in silico per simular l'efecte de diferents drogues sobre les cèl·lules. D'aquesta manera, ha estat possible descriure possibles mecanismes mitjançant els quals un grup de cèl·lules poden evitar l'efecte d'una teràpia dirigida. Les metodologies desenvolupades en aquesta tesi, així com els resultats obtinguts després de la seva aplicació suposa una valuosa font d'informació per al desenvolupament de marcadors de diagnòstic, pronòstic i resposta que ajudin a entendre millor els diferents nivells d'heterogeneïtat presents en càncer, i així, poder augmentar l'eficàcia de les teràpies dirigides. / [EN] Cancer is the second leading cause of death in the world and is characterized mainly by the uncontrolled proliferation of the cells that make up the tumor. Although the development of a tumor is possible due to certain common processes triggered by the dysregulation of the existing balance between the molecular components of a cell and its control elements, there is great heterogeneity in the mechanisms through which this dysregulation is achieved. Thanks to the development of new sequencing technologies, it has been possible to observe how this heterogeneity is not only observed between the different types of tumors but also between the cells of the same tumor. The characterization of tumor heterogeneity has had a great impact on the understanding of the disease and the development of new targeted therapies. For this reason, in order to improve the characterization of alterations in the different regulatory mechanisms, in this thesis two methodologies have been developed that allow studying the inter- and intratumoral heterogeneity of the activation states of regulatory elements and with great potential for their application in personalized medicine. In the first place, a methodology that allows determining in a sample the activation state of the transcription factors (FTs) from the expression of the genes that it regulates was developed. The methodology was applied to perform a pan-cancer analysis in which the regulatory scenario of 52 FTs was characterized for the first time in 11 different types of cancer. Furthermore, by being able to obtain individual activation values for each sample, it was possible to observe correlations between the activation of some FTs with survival, thus suggesting their use as prognostic markers. Second, another methodology was developed using a mechanistic model to determine the activation state of around 1000 signaling circuits in single cell transcriptomic experiments (scRNAseq). The use of this mechanistic model in scRNAseq data from 4 glioblastoma patients, in addition to showing the intratumoral heterogeneity present in the samples, has allowed in silico interventions to simulate the effect of different drugs on cells. In this way, it has been possible to describe possible mechanisms by which a group of cells can avoid the effect of a targeted therapy. The methodologies developed in this thesis, as well as the results obtained after its application, is a valuable source of information for the development of diagnostic, prognostic and response markers that help to better understand the different levels of heterogeneity present in cancer, and thus, be able increase the effectiveness of targeted therapies. / Marín Falco, M. (2021). Estudio de la heterogeneidad regulatoria en cáncer y sus implicaciones en la medicina personalizada [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/165413 / TESIS

Indiana University-Purdue University Indianapolis (IUPUI) / The marker strategy design (MSGD) has been proposed to assess and validate predictive markers for targeted therapies and immunotherapies. Under this design, patients are randomized into two strategies: the marker-based strategy, which treats patients based on their marker status, and the non-marker-based strategy, which randomizes patients into treatments independent of their marker status in the same way as in a standard randomized clinical trial. The strategy effect is then tested by comparing the response rate between the two strategies and this strategy effect is commonly used to evaluate the predictive capability of the markers. We show that this commonly used between-strategy test is flawed, which may cause investigators to miss the opportunity to discover important predictive markers or falsely claim an irrelevant marker as predictive. Then we propose new procedures to improve the power of the MSGD to detect the predictive marker effect. One is based on a binary response endpoint; the second is based on survival endpoints. We conduct simulation studies to compare the performance of the MSGD with the widely used marker stratified design (MSFD). Numerical studies show that the MSGD and MSFD has comparable performance. Hence, contrary to popular belief that the MSGD is an inferior design compared with the MSFD, we conclude that using the MSGD with the proposed tests is an efficient and ethical way to find predictive markers for targeted therapies.

<div>The advent of Patient-Centric therapy demands technologies capable of producing multiple versions of a given product, each tailored for specific segments of the population/individual, but in a time- and cost-effective manner. Prevailing manufacturing methods for oral dosage forms do not easily lend themselves for the transition to the Patient-Centric area. The purpose of this research was to develop a formulation/manufacturing platform technology meeting the flexibility requirements for Patient-Centric formulation and product development for oral dosage forms. The approach is based on the molecular designing and manufacturing of the dosage form. The dosage form consists of a 3D assembly of prefabricated functional modules, each with a specific pharmaceutical performance function. </div><div>The characterization of individual modules showed that solvent casting produced API-loaded HPMC films with homogeneous content distribution. The release profile of 3D assemblies was significantly influenced by the physicochemical properties of single modules. API-loading, thickness, and diameter had a significant effect on the release kinetics. In contrast, the hydrophobicity of the casting substrate did not affect the release kinetics. The initial geometry of the final 3D assembly given by the number of modules and their diameter was proved to have a significant impact on the release kinetics as well. </div><div>The 3D assemblies were used to produce dosage forms with customizable release profiles. Two API-loaded thin HPMC-based films with fast (FRA) and slow (SRB) release rates were produced by the solvent casting method. Accurate dose control (API loading) was accomplished by varying the number of individual modules in the 3D assemblies, whereas control of release kinetics was achieved by combining different ratios FRA and SRB film modules in the assembled dosage form. </div><div>The modular design was also tested for its ability to generate a dosage form of a weak-base API. This part was accomplished using a module containing citric acid (CA) interspaced between weak-base loaded FRA modules. Characterization of the 3D assemblies that were devoid of CA modules showed that the API release rate from modular assemblies containing weekly basic API exhibited strong pH-dependence. The 3D assemblies featuring CA modules in their design exhibited nearly pH-independent release kinetics. </div><div>Electrospinning was used as an enabling technology to produce HPMC-based fibrous films. HPMC films were able to encapsulate a wide variety of APIs with different aqueous solubility. All fibers produced were in the range of a few hundred nanometers to a few microns. X-ray diffraction and differential scanning calorimetry exhibited the amorphous or crystalline state of the API dispersed. Disintegration and release tests showed the fast dissolution of the fibrous system. </div><div><br></div>

With new treatments and novel technology available, personalized medicine has become a key topic in the new era of healthcare. Traditional statistical methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials (RCTs). With restricted inclusion and exclusion criteria, data from RCTs may not reflect real world treatment effectiveness. However, electronic medical records (EMR) offers an alternative venue. In this paper, we propose a general framework to identify individualized treatment rule (ITR), which connects the subgroup identification methods and ITR. It is applicable to both RCT and EMR data. Given the large scale of EMR datasets, we develop a recursive partitioning algorithm to solve the problem (ITR-Tree). A variable importance measure is also developed for personalized medicine using random forest. We demonstrate our method through simulations, and apply ITR-Tree to datasets from diabetes studies using both RCT and EMR data. Software package is available at https://github.com/jinjinzhou/ITR.Tree.

Advanced information technologies promise a massive influx of individual-specific medical data. These rich sources offer great potential for an increased understanding of disease mechanisms and for providing evidence-based and personalized clinical decision support. However, the size, complexity, and biases of the data pose new challenges, which make it difficult to transform the data to useful and actionable knowledge using conventional statistical analysis. The so-called “Big Data” era has created an emerging and urgent need for scalable, computer-based data mining methods that can turn data into useful, personalized decision support knowledge in a flexible, cost-effective, and productive way. The goal of my Ph.D. research is to address some key challenges in current clinical deci-sion support, including (1) the lack of a flexible, evidence-based, and personalized data mining tool, (2) the need for interactive interfaces and visualization to deliver the decision support knowledge in an accurate and effective way, (3) the ability to generate temporal rules based on patient-centric chronological events, and (4) the need for quantitative and progressive clinical predictions to investigate the causality of targeted clinical outcomes. The problem statement of this dissertation is that the size, complexity, and biases of the current clinical data make it very difficult for current informatics technologies to extract individual-specific knowledge for clinical decision support. This dissertation addresses these challenges with four overall specific aims: Evidence-Based and Personalized Decision Support: To develop clinical decision support systems that can generate evidence-based rules based on personalized clinical conditions. The systems should also show flexibility by using data from different clinical settings. Interactive Knowledge Delivery: To develop an interactive graphical user interface that expedites the delivery of discovered decision support knowledge and to propose a new visualiza-tion technique to improve the accuracy and efficiency of knowledge search. Temporal Knowledge Discovery: To improve conventional rule mining techniques for the discovery of relationships among temporal clinical events and to use case-based reasoning to evaluate the quality of discovered rules. Clinical Casual Analysis: To expand temporal rules with casual and time-after-cause analyses to provide progressive clinical prognostications without prediction time constraints. The research of this dissertation was conducted with frequent collaboration with Children’s Healthcare of Atlanta, Emory Hospital, and Georgia Institute of Technology. It resulted in the development and adoption of concrete application deliverables in different medical settings, including: the neuroARM system in pediatric neuropsychology, the PHARM system in predictive health, and the icuARM, icuARM-II, and icuARM-KM systems in intensive care. The case studies for the evaluation of these systems and the discovered knowledge demonstrate the scope of this research and its potential for future evidence-based and personalized clinical decision support.

Follicular lymphoma (FL) is the second most common non-Hodgkin lymphoma (NHL). FL is clinically designated as an indolent disease with a long median survival of 8-10 years. However, the clinical and biological behavior of FL shows considerable variability, with some patients showing aggressive disease progression and very short survival. Because defects in the regulation of apoptotic cell death are fundamental in FL pathogenesis, we hypothesized that deregulated expression of components of the pRb signaling pathway may promote cell proliferation, thereby complementing antecedent anti-apoptotic mutations and producing more aggressive disease. In the present study we undertook an immunohistochemical (IHC) evaluation of expression of key cell-cycle regulatory proteins in diagnostic biopsies from 127 cases of FL using formalin-fixed, paraffin-embedded tissues (FFPE) in tissue microarray (TMA) sections immunostained for p53, pRb, p16INK4A and cyclin D3. Data analysis revealed that increased abundance of p53 or p16INK4A is associated with reduced overall survival (OS) (p=0.005 and p=0.014 respectively), and with conventional pathological markers of tumour aggressiveness including high histologic grade. Encouraged by this remarkable finding of a counterintuitive association between p16INK4A expression and clinical outcome, we analyzed CDKN2A gene deletion and methylation, as these are the most frequent mechanisms of the CDKN2A gene inactivation in NHL including FL. We determined the deletion and methylation status of CDKN2A in 105 FL cases. Laser-capture microdissection was used to enrich the samples for lymphoma cells. CDKN2A was deleted in 9 cases and methylated in 22 cases. The 29 cases (28%) with CDKN2A deletion or methylation had decreased overall survival (OS) (p=0.046) in all cases and in cases treated with rituximab (p<0.001). Our findings indicate that deleterious alterations of CDKN2A are relatively prevalent in FL at diagnosis and can predict poor clinical outcome. In summary, our data reveal novel insights into the pathogenesis of FL and suggest a relationship between increased p16INK4A expression and CDKN2A deletion or methylation and unfavorable clinical outcome in FL. We hope that the work presented herein will provide a useful prognostic tool for predicting the prognosis and choosing optimal treatment approaches to help patients suffering from FL. / Thesis (Ph.D, Pathology & Molecular Medicine) -- Queen's University, 2013-03-12 23:49:44.541