<div>Cancer is responsible for the second highest cause of death in the United States, and lung cancer accounts for 13% of new cancer diagnoses, with the highest rate of cancer death at 24%. Almost 85% of these cases represent non-small cell lung cancer (NSCLC), which includes lung adenocarcinoma, the most common NSCLC subtype. Traditional cancer treatments often only temporarily stop the spread of the disease, but immunotherapies, which are becoming a standard of care, are much more promising. Natural killer (NK) cells are powerful effectors of innate immunity, and genetically engineered NK cells as immunotherapies have had encouraging clinical responses in the treatment of various cancers. However, more progress is needed for solid tumor treatment, especially for lung adenocarcinoma. The activation of cancer-associated ectoenzymes, CD39 and CD73 catalyze the phosphorylation of ATP to AMP to produce extracellular adenosine (ADO), which is a highly immunosuppressive mechanism contributing to the pathogenesis of solid tumors. Understanding adenosine effects on NK cells will help develop more robust immunotherapeutic treatments to improve cytotoxicity against solid tumors. Here, we established that tumor microenvironment ADO results in impaired metabolic and anti-tumor functions of cytokine-primed NK cells. Specifically, peripheral blood-derived NK cells stimulated with IL-2, IL-15, or a combination of IL-12 and IL-15 showed suppressed anti-tumor immunity due to ADO. This was observed by the downregulation of activation receptor expression, cytotoxicity inhibition, impairment of metabolic activity, and alterations in gene expression. To target ADO-producing CD73 on cancer cells, we redirected NK cells by fusing CD73 ScFv with intracellular and transmembrane regions of NK cell specific signaling components derived from FCyRIIIa (CD16). Engineered NK cells were shown to be cytotoxic against lung adenocarcinoma <i>in vitro</i> and impede tumor growth in a lung adenocarcinoma mouse model <i>in vivo</i>. Engineered cells also had higher levels of degranulation and cytokine release, as well as more infiltration into tumors and longer survival time in mice. In summary, the microenvironment of solid tumors is highly immunosupressive, and redirecting NK cell function using a NK-specific anti-CD73 targeting construct will help to promote anti-tumor immunity and</div><div>inhibit cancer growth for a potentially powerful new immunotherapy against solid tumors.</div>

La tomodensitométrie par rayons-X (CT) est une modalité d’imagerie produisant une carte tridimensionnelle du coefficient d’atténuation des rayons-X d’un objet. En radiothérapie, le CT fournit de l’information anatomique et quantitative sur le patient afin de permettre la planification du traitement et le calcul de la dose de radiation à livrer. Le CT a plusieurs problèmes, notamment (1) une limitation au niveau de l’exactitude des paramètres physiques quantitatifs extraits du patient, et (2) une sensibilité aux biais causés par des artéfacts de durcissement du faisceau. Enfin, (3) dans le cas où le CT est fait en présence d’un agent de contraste pour améliorer la planification du traitement, il est nécessaire d’effectuer un deuxième CT sans agent de contraste à des fins de calcul de dose, ce qui augmente la dose au patient. Ces trois problèmes limitent l’efficacité du CT pour certaines modalités de traitement qui sont plus sensibles aux incertitudes comme la protonthérapie. Le CT spectral regroupe un ensemble de méthodes pour produire plusieurs cartes d’atténuation des rayons-X moyennées sur différentes plages énergétiques. L’information supplémentaire, pondérée en énergie qui est obtenue permet une meilleure caractérisation des matériaux analysés. Le potentiel de l’une de ces modalités spectrales, le CT bi-énergie (DECT), est déjà bien démontré en radiothérapie, alors qu’une approche en plein essor, le CT spectral à comptage de photons (SPCCT), promet davantage d’information spectrale à l’aide de détecteurs discriminateurs en énergie. Par contre, le SPCCT souffre d’un bruit plus important et d’un conditionnement réduit. Cette thèse investigue la question suivante : y a-t-il un bénéfice à utiliser plus d’information résolue en énergie, mais de qualité réduite pour la radiothérapie ? La question est étudiée dans le contexte des trois problèmes ci-haut. Tout d’abord, un estimateur maximum a posteriori (MAP) est introduit au niveau de la caractérisation des tissus post-reconstruction afin de débruiter les données du CT spectral. L’approche est validée expérimentalement sur un DECT. Le niveau de bruit du pouvoir d’arrêt des protons diminue en moyenne d’un facteur 3.2 à l’aide de l’estimateur MAP. Celui-ci permet également de conserver généralement le caractère quantitatif des paramètres physiques estimés, le pouvoir d’arrêt variant en moyenne de 0.9% par rapport à l’approche conventionnelle. Ensuite, l’estimateur MAP est adapté au contexte de l’imagerie avec agent de contraste. Les résultats numériques démontrent un bénéfice clair à utiliser le SPCCT pour l’imagerie virtuellement sans contraste par rapport au DECT, avec une réduction de l’erreur RMS sur le pouvoir d’arrêt des protons de 2.7 à 1.4%. Troisièmement, les outils développés ci-haut sont validés expérimentalement sur un micro-SPCCT de la compagnie MARS Bioimaging, dont le détecteur à comptage de photons est le Medipix 3, qui est utilisé pour le suivi de particules au CERN. De légers bénéfices au niveau de l’estimation des propriétés physiques à l’aide du SPCCT par rapport au DECT sont obtenus pour des matériaux substituts à des tissus humains. Finalement, une nouvelle paramétrisation du coefficient d’atténuation pour l’imagerie pré-reconstruction est proposée, dans le but ultime de corriger les artéfacts de durcissement du faisceau. La paramétrisation proposée élimine les biais au niveau de l’exactitude de la caractérisation des tissus humains par rapport aux paramétrisations existantes. Cependant, aucun avantage n’a été obtenu à l’aide du SPCCT par rapport au DECT, ce qui suggère qu’il est nécessaire d’incorporer l’estimation MAP dans l’imagerie pré-reconstruction via une approche de reconstruction itérative. / X-ray computed tomography (CT) is an imaging modality that produces a tridimensional map of the attenuation of X-rays by the scanned object. In radiation therapy, CT provides anatomical and quantitative information on the patient that is required for treatment planning. However, CT has some issues, notably (1) a limited accuracy in the estimation of quantitative physical parameters of the patient, and (2) a sensitivity to biases caused by beam hardening artifacts. Finally, (3) in the case where contrast-enhanced CT is performed to help treatment planning, a second scan with no contrast agent is required for dose calculation purposes, which increases the overall dose to the patient. Those 3 problems limit the efficiency of CT for some treatment modalities more sensitive to uncertainties, such as proton therapy. Spectral CT regroups a set of methods that allows the production of multiple X-ray attenuation maps evaluated over various energy windows. The additional energy-weighted information that is obtained allows better material characterization. The potential of one spectral CT modality, dual-energy CT (DECT), is already well demonstrated for radiation therapy, while an upcoming method, spectral photon counting CT (SPCCT), promises more spectral information with the help of energy discriminating detectors. Unfortunately, SPCCT suffers from increased noise and poor conditioning. This thesis thus investigates the following question: is there a benefit to using more, but lower quality energy-resolved information for radiotherapy? The question is studied in the context of the three problems discussed earlier. First, a maximum a posteriori (MAP) estimator is introduced for post-reconstruction tissue characterization for denoising purposes in spectral CT. The estimator is validated experimentally using a commercial DECT. The noise level on the proton stopping power is reduced, on average, by a factor of 3.2 with the MAP estimator. The estimator also generally con- serves the quantitative accuracy of estimated physical parameters. For instance, the stopping power varies on average by 0.9% with respect to the conventional approach. Then, the MAP estimation framework is adapted to the context of contrast-enhanced imaging. Numerical results show clear benefits when using SPCCT for virtual non-contrast imaging compared to DECT, with a reduction of the RMS error on the proton stopping power from 2.7 to 1.4%. Third, the developed tools are validated experimentally on a micro-SPCCT from MARS Bioimaging, which uses the Medipix 3 chip as a photon counting detector. Small benefits in the accuracy of physical parameters of tissue substitutes materials are obtained. Finally, a new parametrization of the attenuation coefficient for pre-reconstruction imaging is pro- posed, whose ultimate aim is to correct beam hardening artifacts. In a simulation study, the proposed parametrization eliminates all biases in the estimated physical parameters of human tissues, which is an improvement upon existing parametrizations. However, no ad- vantage has been obtained with SPCCT compared to DECT, which suggests the need to incorporate MAP estimation in the pre-reconstruction framework using an iterative reconstruction approach.

<p>  </p> <p>Metastatic breast cancer (MBC) is an extremely recalcitrant disease capable of overcoming targeted therapies and evading immune surveillance via the engagement of complicated signaling networks. Resistance to targeted therapies and therapeutic failure of immune checkpoint blockade (ICB) are two major challenges in treating MBC. To survive in the dynamic tumor microenvironment (TME) during metastatic progression, shared signaling nodes are required for MBC cells to regulate the signaling networks efficiently, which are potential multifunctional therapeutic targets. SH2 containing protein tyrosine phosphatase-2 (SHP2) is a druggable oncogenic phosphatase that is a key shared node in both tumor cells and immune cells. How tumor-cell autonomous SHP2 manages its signaling inputs and outputs to facilitate the growth of tumor cells, drug resistance, immunosuppression, and the limited response of ICB in MBC is not fully understood. Herein, we used inducible genetic depletion and two distinct types of pharmacological inhibitors to investigate anti-tumor effects with immune reprogramming during SHP2 targeting. </p> <p>We first focus on the signaling inputs and outputs of SHP2. We find that phosphorylation of SHP2 at Y542 predicts the survival rates of breast cancer patients and their immune profiles. Phosphorylation of SHP2 at Y542 is elevated with differential activation mechanisms under a growth-factor-induced and extracellular matrix (ECM)-rich culture environment. Phosphorylation of SHP2 at Y542 is also elevated in HER2 positive MBC cells upon acquired resistance to the HER2 kinase inhibitor, neratinib. The resistant cells can be targeted by SHP2 inhibitors. SHP2 inhibitors block ERK1/2 and AKT signaling and readily prevented MBC cell growth induced by multiple growth factors. Inhibition of SHP2 also blocks these signaling events generated from the ECM signaling. In fact, the inhibitory effects of SHP2 blockade are actually enhanced in the ECM-rich culture environment. We utilize the <em>in vitro</em> T-cell killing assays and demonstrate that pretreatment of tumor cells with FGF2 and PDGF reduces the cytotoxicity of CD8+ T cells in a SHP2-dependent manner. Both growth factors and ECM-rich culture environment transcriptionally induce PD-L1 via SHP2. SHP2 inhibition balances MAPK signaling and STAT1 signaling, which prevents growth factor-mediated suppression of INF-γ-induced expression of MHC class I. </p> <p>Next, we evaluate the efficacy of SHP2 inhibitors. Blockade of SHP2 in the adjuvant setting decreased pulmonary metastasis <em>in vivo</em> and extended the survival of systemic tumor-bearing mice. Tumor-cell autonomous depletion of SHP2 reduces pulmonary metastasis and relieves exhaustion markers on CD8+ and CD4+ cells. Meanwhile, both systemic SHP2 inhibition and tumor-cell autonomous SHP2 depletion reduce tumor-infiltrated CD4+ T cells and M2-polarized tumor associated macrophages. </p> <p>Finally, we investigate potential combination therapies with SHP2 inhibitors. The combination of SHP2 inhibitors and FGFR-targeted kinase inhibitors synergistically blocks the growth of MBC cells. Pharmacological inhibition SHP2 sensitizes MBC cells growing in the lung to α-PD-L1 antibody treatment via relieving T cell exhaustion induced by ICB. </p> <p>Overall, our findings support the conclusion that MBC cells are capable of simultaneously engaging several survival pathways and immune-suppressive mechanisms via SHP2 in response to multiple growth factors and ECM signaling. Inhibition of SHP2, potentially in combination with other targeted agents and ICB, holds promise for the therapeutic management of MBC.</p>

In optimization of intensity-modulated radiation therapy treatment plans, dose-volumehistogram (DVH) functions are often used as objective functions to minimize the violationof dose-volume criteria. Neither DVH functions nor dose-volume criteria, however,are ideal for gradient-based optimization as the former are not continuously differentiableand the latter are discontinuous functions of dose, apart from both beingnonconvex. In particular, DVH functions often work poorly when used in constraintsdue to their being identically zero when feasible and having vanishing gradients on theboundary of feasibility.In this work, we present a general mathematical framework allowing for direct optimizationon all DVH-based metrics. By regarding voxel doses as sample realizations ofan auxiliary random variable and using kernel density estimation to obtain explicit formulas,one arrives at formulations of volume-at-dose and dose-at-volume which are infinitelydifferentiable functions of dose. This is extended to DVH functions and so calledvolume-based DVH functions, as well as to min/max-dose functions and mean-tail-dosefunctions. Explicit expressions for evaluation of function values and corresponding gradientsare presented. The proposed framework has the advantages of depending on onlyone smoothness parameter, of approximation errors to conventional counterparts beingnegligible for practical purposes, and of a general consistency between derived functions.Numerical tests, which were performed for illustrative purposes, show that smoothdose-at-volume works better than quadratic penalties when used in constraints and thatsmooth DVH functions in certain cases have significant advantage over conventionalsuch. The results of this work have been successfully applied to lexicographic optimizationin a fluence map optimization setting. / Vid optimering av behandlingsplaner i intensitetsmodulerad strålterapi används dosvolym- histogram-funktioner (DVH-funktioner) ofta som målfunktioner för att minimera avståndet till dos-volymkriterier. Varken DVH-funktioner eller dos-volymkriterier är emellertid idealiska för gradientbaserad optimering då de förstnämnda inte är kontinuerligt deriverbara och de sistnämnda är diskontinuerliga funktioner av dos, samtidigt som båda också är ickekonvexa. Speciellt fungerar DVH-funktioner ofta dåligt i bivillkor då de är identiskt noll i tillåtna områden och har försvinnande gradienter på randen till tillåtenhet. I detta arbete presenteras ett generellt matematiskt ramverk som möjliggör direkt optimering på samtliga DVH-baserade mått. Genom att betrakta voxeldoser som stickprovsutfall från en stokastisk hjälpvariabel och använda ickeparametrisk densitetsskattning för att få explicita formler, kan måtten volume-at-dose och dose-at-volume formuleras som oändligt deriverbara funktioner av dos. Detta utökas till DVH-funktioner och så kallade volymbaserade DVH-funktioner, såväl som till mindos- och maxdosfunktioner och medelsvansdos-funktioner. Explicita uttryck för evaluering av funktionsvärden och tillhörande gradienter presenteras. Det föreslagna ramverket har fördelarna av att bero på endast en mjukhetsparameter, av att approximationsfelen till konventionella motsvarigheter är försumbara i praktiska sammanhang, och av en allmän konsistens mellan härledda funktioner. Numeriska tester genomförda i illustrativt syfte visar att slät dose-at-volume fungerar bättre än kvadratiska straff i bivillkor och att släta DVH-funktioner i vissa fall har betydlig fördel över konventionella sådana. Resultaten av detta arbete har med framgång applicerats på lexikografisk optimering inom fluensoptimering.

Construction of radiotherapy treatments for cancer is a laborious and time consuming task. At the same time, when presented with a treatment plan, an oncologist can quickly judge whether or not it is suitable. This means that the problem of constructing these treatment plans is well suited for automation. This thesis investigates a novel way of automatic treatment planning. The treatment planning system this pipeline is constructed for provides dose mimicking functionality with probability density functions of dose–volume histograms (DVHs) and spatial dose as inputs. Therefore this will be the output of the pipeline. The input is historically treated patient scans, segmentations and spatial doses. The approach involves three modules which are individually replaceable with little to no impact on the remaining two modules. The modules are: an autoencoder as a feature extractor to concretise important features of a patient segmentation, a distance optimisation step to learn a distance in the previously constructed feature space and, finally, a probabilistic spatial dose estimation module using sparse pseudo-input Gaussian processes trained on voxel features. Although performance evaluation in terms of clinical plan quality was beyond the scope of this thesis, numerical results show that the proposed pipeline is successful in capturing salient features of patient geometry as well as predicting reasonable probability distributions for DVH and spatial dose. Its loosely connected nature also gives hope that some parts of the pipeline can be utilised in future work. / Skapandet av strålbehandlingsplaner för cancer är en tidskrävande uppgift. Samtidigt kan en onkolog snabbt fatta beslut om en given plan är acceptabel eller ej. Detta innebär att uppgiften att skapa strålplaner är väl lämpad för automatisering. Denna uppsats undersöker en ny metod för att automatiskt generera strålbehandlingsplaner. Planeringssystemet denna metod utvecklats för innehåller funktionalitet för dosrekonstruktion som accepterar sannolikhetsfördelningar för dos–volymhistogram (DVH) och dos som input. Därför kommer detta att vara utdatan för den konstruerade metoden. Metoden är uppbyggd av tre beståndsdelar som är individuellt utbytbara med liten eller ingen påverkan på de övriga delarna. Delarna är: ett sätt att konstruera en vektor av kännetecken av en patients segmentering, en distansoptimering för att skapa en distans i den tidigare konstruerade känneteckensrymden, och slutligen en skattning av sannolikhetsfördelningar med Gaussiska processer tränade på voxelkännetecken. Trots att utvärdering av prestandan i termer av klinisk plankvalitet var bortom räckvidden för detta projekt uppnåddes positiva resultat. De estimerade sannolikhetsfördelningarna uppvisar goda karaktärer för både DVHer och doser. Den löst sammankopplade strukturen av metoden gör det dessutom möjligt att delar av projektet kan användas i framtida arbeten.

<p>  </p> <p>The phosphatases of regenerating liver are a subfamily of protein tyrosine phosphatases that consist of PRL1, PRL2 and PRL3. The overexpression of PRLs promote cell proliferation, migration and invasion and contribute to tumorigenesis and metastasis to aggravate survival outcome. Although there is increasing interest in understanding the implication of these phosphatases in tumor development, currently, limited knowledge is available about their mechanism of action and the efficacy of PRL inhibition in <em>in vivo</em> tumor models, the tumor extrinsic role of PRLs that allow them to impact tumor development, as well as <em>in vivo</em> physiological function of PRLs that could implicate them in diseases other than cancer. The work presented here aims to address these limitations.</p> <p><br></p>

科学研究費補助金 研究種目:基盤研究(C)(2) 課題番号:09680476 研究代表者:青山 隆彦 研究期間:1997-1999年度