Det här examensarbetet består i ett produktutvecklingsprojekt som utfördes i samarbete med Liquid Biopsy AB. Syftet med arbetet var att utveckla ett engångskassettsbaserat produktsystem baserat på företagets patentsökta metod för isolering av cancer celler i suspension, inklusive cirkulerande tumörceller. Liquid Biopsy AB är ett svenskt utvecklingsbolag som baserat på ny och unik teknik, är oberoende av proteinmarkörer, använder cirkulerande tumörceller och andra suspenderade tumörceller för att möjliggöra bättre och billigare cancerdiagnostik. Examensarbetet har fokuserat på utvecklingen av engångskassetten, men parallellt arbete har även utförts med tillhörande basenhet. Ulrich och Eppingers produktutvecklingsprocess har utgjort grunden för den process som följts i arbetet, dock med ökat fokus på testning och utvärdering. För att få en bredare kunskapsbas inleddes arbetet med en marknads- och omvärldsanalys samt informationsinsamling om utmaningar och medicintekniska krav. För att tydligt definiera produktvisionen utfördes även undersökningar med potentiella användarna, om företagets patentsökta metod och befintliga prototyper samt framtida förbättringspotential. Det kassettkoncept som utvecklats bygger på användning av provrör av existerande standard, få tillverkningsprocesser och god användarvänlighet, något som samtliga varit av hög prioritet under arbetet. För att säkerställa att produktens flödessystem fungerar som tänkt utfördes tester under prototypframtagningen. Testningen visade att konceptet fungerar i stort sett som tänkt med avseende på flöden, dock förekom vissa toleransproblem som följd av den valda prototypframtagningsprocessen, och vissa andra viktiga egenskaper återstår att testa. Resultatet av utvecklingsprocessen är en första fysisk prototyp av engångskassetten och en funktionell partiell prototyp av basenheten, motsvarande gränssnittet mot engångkassetten, för att möjliggöra testning av engångskassetten. Slutsatsen av arbetet är att det framtagna produktsystemet har tydliga fördelar gentemot företagets befintliga prototyper: inklusive att en engångskassett framtagits, att denna kan utgöra underlag för en produkt, och att denna bland annat har väsentligt kortare processväg vilken i sin tur borde kunna leda till förkortad processtid. Utförd finansiell analys visar även att framtaget produktsystem kan säljas till konkurrenskraftiga priser och med en betydligt lägre instegskostnad än dagens konkurrerande produkter. / This thesis consists of a product development project conducted in collaboration with Liquid Biopsy AB. The purpose of this work was to develop a disposable cartridge-based product system based on the company’s patent-pending method for isolation of circulating tumor cells and other suspended tumor cells. Liquid Biopsy AB is a Swedish medical technology research company with a unique new rheological technology, that is independent of protein markers, using suspended cancer cells, including circulating tumor cells, allows better and cheaper cancer diagnostics than today. The thesis work has focused on the development of the disposable cassette, but parallel work has also been performed with the associated base unit. Ulrich and Eppingers product development process has made up the basis for the process being followed in the thesis work, with increased focus on testing and evaluation. The work began with a market analysis and information gathering on challenges and medical requirements. Several activities were also carried out in order to clearly define the product vision, including user-surveys, analysis of the company's existing prototypes, as well as potential for future improvements. The developed cartridge concept is based on the use of standard test tubes, few manufacturing processes and user-friendliness which all have been high priorities in this work. The cartridge concept consists essentially of various plastic materials and is adapted for manufacturing by injection molding. To ensure that the product’s flow system was operating as intended, tests were conducted during the prototype phase. Testing showed that the concept design flows largely as intended, yet with some tolerance problems as a result of the selected rapid prototyping process, while other essential properties remain to be tested. The result of the development process is a first physical prototype of the disposable cartridge and a partial functional prototype of the base unit to allow testing with the disposable cartridge. The conclusion of this thesis work is that the developed product system has strong advantages over the company’s existing prototypes, including a first version of a disposable cassette that has potential to form the basis of a mass-producible product, significantly shorter processing route which in turn should allow a reduction of the processing time. Financial analysis also indicates that the designed product systems can be sold at competitive prices and with a significantly lower entry cost than today's rivaling products.

Optical assays to detect cancer-associated molecular biomarkers in biological substrates are commonly performed with antibody-targeted organic dye contrast agents but the potential for precise quantification, long-term imaging, and multiplexed readouts is limited by chemical and optical instability, non-optimal spectral characteristics, and complicated synthetic chemistry of the dyes. This dissertation tested the hypothesis that a novel class of optical contrast agents termed polymer-protected Raman-encoded nanoparticle tags (PRENTs) provides practical advantages over existing optical technologies for molecular diagnostic applications. First, PRENTs were developed through a modular design utilizing gold-nanoparticle-Raman reporter complexes protected and functionalized by polyethylene glycol derivatives. PRENTs produced optical readouts through surface enhanced Raman scattering (SERS) that were brighter and more photostable than the fluorescence of semiconductor quantum dots under identical experimental conditions. Unique spectral signatures were produced with a broader class of Raman reporters than is possible with silica coated Raman tags. Spectral signatures and colloidal stability of PRENTs were unaffected by harsh chemical conditions that cause spectral changes and aggregation of dyes, quantum dots, and protein coated Raman tags. Antibody-targeted PRENTs specifically tagged cell surface cancer biomarkers on living cells at reasonable integration times. PRENTs were non-toxic to cells under conditions exceeding those required for sensitive molecular detection. Next, PRENTs were efficiently optimized for excitation with near-infrared light through inclusion of near-infrared chromophores as Raman reporters and exploitation of the size-dependent optical enhancement of gold nanoparticles. Third, the development of a slide-based Raman-linked immunosorbent assay using antibody-conjugated PRENTs enabled quantification of protein biomarkers with a dynamic range of 3 to 4 logs. In summary, this dissertation establishes PRENTs as novel optical tags with unique features useful for biomedical applications and provides insights for further assay development.

Objective: Insulin-like growth factor-I (IGF-I) has been suggested to be a prognostic marker for the development of cancer and, more recently, cardiovascular disease. These diseases are closely linked to obesity, but reports of the association of IGF-I with measures of obesity are divergent. In this study, we assessed the association of age-dependent IGF-I standard deviation scores with body mass index (BMI) and intra-abdominal fat accumulation in a large population. Design: A cross-sectional, epidemiological study. Methods: IGF-I levels were measured with an automated chemiluminescence assay system in 6282 patients from the DETECT study. Weight, height, and waist and hip circumference were measured according to the written instructions. Standard deviation scores (SDS), correcting IGF-I levels for age, were calculated and were used for further analyses. Results: An inverse U-shaped association of IGF-I SDS with BMI, waist circumference, and the ratio of waist circumference to height was found. BMI was positively associated with IGF-I SDS in normal weight subjects, and negatively associated in obese subjects. The highest mean IGF-I SDS were seen at a BMI of 22.5–25 kg/m2 in men (+0.08), and at a BMI of 27.5–30 kg/m2 in women (+0.21). Multiple linear regression models, controlling for different diseases, medications and risk conditions, revealed a significant negative association of BMI with IGF-I SDS. BMI contributed most to the additional explained variance to the other health conditions. Conclusions: IGF-I standard deviation scores are decreased in obesity and underweight subjects. These interactions should be taken into account when analyzing the association of IGF-I with diseases and risk conditions.

Die Raman-Spektroskopie ist eine optische Messtechnik, die in der Lage ist, spektroskopische Information zu liefern, welche molekülspezifisch und einzigartig in Bezug auf die Eigenschaften der untersuchten Spezies sind. Sie ist ein unverzichtbares analytisches Instrument, das Anwendung in verschiedenen Bereichen findet, wie etwa der Medizin oder der in situ Beobachtung von chemischen Prozessen. Wegen ihren Eigenschaften, wie der hohen Spezifität und der Möglichkeit von Tracer-freien Messung, hat die Raman-Spektroskopie die Tumordiagnostik stark beeinflusst. Aufgrund einer äußerst starken Beeinflussung der Raman-Spektren durch Hintergrundsignale, ist das Isolieren und Interpretieren von Raman-Spektren eine große Herausforderung. Im Rahmen dieser Arbeit wurden verschiedene Ansätze der Spektrenbearbeitung entwickelt, die benötigt werden um Raman-Spektren aus verrauschten und stark mit Hintergrundsignalen behafteten Rohspektren zu extrahieren. Diese Ansätze beinhalten im Speziellen eine auf dem Vector-Casting basierende Methode zur Rauschminimierung und eine auf dem deep neural networks basierende Methoden zur Entfernung von Rauschen und Hintergrundsignalen. Verschiedene neuronale Netze wurden mittels simulierter Spektren trainiert und an experimentell gemessenen Spektren evaluiert. Die im Rahmen dieser Arbeit vorgeschlagenen Ansätze wurden mit alternativen Methoden auf dem aktuellen Stand der Entwicklung unter Zuhilfenahme von verschiedenen Signal-Rausch-Verhältnissen, Standardabweichungen und dem Structural Similarity Index verglichen. Die hier entwickelten Ansätze zeigen gute Ergebnisse und sind bisher bekannten Methoden überlegen, vor allem für Raman-Spektren mit einem niedrigem Signal-Rausch-Verhältnis und extrem starken Fluoreszenz-Hintergrund. Zusätzlich erfordern die auf Deep Neural Networks basierten Methoden keinerlei menschliches Eingreifen. Die Motivation hinter dieser Arbeit ist die Verbesserung der Raman-Spektroskopie, vor allem der Shifted-Excitation Raman Difference Spectroscopy (SERDS) hin zu einem noch besseren Instrument in der Prozessanalytik und Tumordiagnostik. Die Integration der oben genannten Ansätze zur Spektrenbearbeitung von SERDS in Kombination mit Methoden des maschinellen Lernens ermöglichen es, physiologische Schleimhaut, nicht-maligne Läsionen und orale Plattenepithelkarzinome mit einer Genauigkeit zu unterscheiden, die bisherigen Methoden überlegen ist. Die spezifischen Merkmale in den bearbeiteten Raman-Spektren können verschiedenen chemischen Zusammensetzungen in den jeweiligen Geweben zugeordnet werden. Die Übertragbarkeit auf einen ähnlichen Ansatz zur Erkennung von Brusttumoren wurde überprüft. Die bereinigten Raman-Spektren von normalem Brustgewebe, Fibroadenoma und invasiven Mammakarzinom konnten mithilfe der spektralen Eigenschaften von Proteinen, Lipiden und Nukleinsäuren unterschieden werden. Diese Erkenntnisse lassen das Potential von SERDS in Kombination mit Ansätzen des maschinellen Lernens als universelles Werkzeug zur Tumordiagnose erkennen.:Versicherung Abstract Zusammenfassung der Ergebnisse der Dissertation Table of Contents Abbreviations and symbols 1 Introduction 2 State of the art of the purification of Raman spectra 2.1 Experimental methods for the enhancement of the signal-to-background ratio and the signal-to-noise ratio 2.2 Mathematical methods for the extraction of pure Raman spectra from raw spectra 2.3 Raman based cancer diagnostics 2.4 Neural networks for the evaluation of Raman spectra 2.5 Objective 3 Application relevant fundaments 3.1 Basics of Raman spectroscopy 3.2 Simulation of raw Raman spectra 3.3 Shifted-excitation Raman difference Spectroscopy 3.4 Raman experimental setup 3.5 Mathematical method for Raman spectra refinement 3.6 Deep neural networks 4 Summary of the published results 4.1 A shifted-excitation Raman difference spectroscopy evaluation strategy for the efficient isolation of Raman spectra from extreme fluorescence interference 4.2 Vector casting for noise reduction 4.3 Refinement of spectra using a deep neural network; fully automated removal of noise and background 4.4 Breast Tumor Analysis using Shifted Excitation Raman difference Spectroscopy 4.5 Optical diagnosis of clinically apparent lesions of oral cavity by label free Raman spectroscopy Conclusion / Raman spectroscopy is an optical measurement technique able to provide spectroscopic information that is molecule-specific and unique to the nature of the specimen under investigation. It is an invaluable analytical tool that finds application in several fields such as medicine and in situ chemical processing. Due to its high specificity and label-free features, Raman spectroscopy greatly impacted cancer diagnostics. However, retrieving and interpreting the Raman spectrum that contains the molecular information is challenging because of extreme background interference. I have developed various spectra-processing approaches required to purify Raman spectra from noisy and heavily background interfered raw Raman spectra. In detail, these are a new noise reduction method based on vector casting and new deep neural networks for the efficient removal of noise and background. Several neural network models were trained on simulated spectra and then tested with experimental spectra. The here proposed approaches were compared with the state-of-the-art techniques via different signal-to-noise ratios, standard deviation, and the structural similarity index metric. The methods presented here perform well and are superior in comparison to what has been reported before, especially at small signal-to-noise ratios, and for extreme fluorescence interfered raw Raman spectra. Furthermore, the deep neural network-based methods do not rely on any human intervention. The motivation behind this study is to make Raman spectroscopy, especially the shifted-excitation Raman difference spectroscopy (SERDS), an even better tool for process analytics and cancer diagnostics. The integration of the above-mentioned spectra-processing approaches into SERDS in combination with machine learning tools enabled the differentiation between physiological mucosa, non-malignant lesions, and oral squamous cell carcinomas with high accuracy, above the state of the art. The distinguishable features obtained in the purified Raman spectra are assignable to different chemical compositions of the respective tissues. The feasibility of a similar approach for breast tumors was also investigated. The purified Raman spectra of normal breast tissue, fibroadenoma, and invasive carcinoma were discriminable with respect to the spectral features of proteins, lipids, and nucleic acid. These findings suggest the potential of SERDS combined with machine learning techniques as a universal tool for cancer diagnostics.:Versicherung Abstract Zusammenfassung der Ergebnisse der Dissertation Table of Contents Abbreviations and symbols 1 Introduction 2 State of the art of the purification of Raman spectra 2.1 Experimental methods for the enhancement of the signal-to-background ratio and the signal-to-noise ratio 2.2 Mathematical methods for the extraction of pure Raman spectra from raw spectra 2.3 Raman based cancer diagnostics 2.4 Neural networks for the evaluation of Raman spectra 2.5 Objective 3 Application relevant fundaments 3.1 Basics of Raman spectroscopy 3.2 Simulation of raw Raman spectra 3.3 Shifted-excitation Raman difference Spectroscopy 3.4 Raman experimental setup 3.5 Mathematical method for Raman spectra refinement 3.6 Deep neural networks 4 Summary of the published results 4.1 A shifted-excitation Raman difference spectroscopy evaluation strategy for the efficient isolation of Raman spectra from extreme fluorescence interference 4.2 Vector casting for noise reduction 4.3 Refinement of spectra using a deep neural network; fully automated removal of noise and background 4.4 Breast Tumor Analysis using Shifted Excitation Raman difference Spectroscopy 4.5 Optical diagnosis of clinically apparent lesions of oral cavity by label free Raman spectroscopy Conclusion

Computed x-ray tomography is one of the most common medical imaging modalities andas such ways of improving the images are of high relevance. Applying deep learningmethods to denoise CT images has been of particular interest in recent years. In thisstudy, rather than using traditional denoising metrics such as MSE or PSNR for evaluation, we use a radiomic approach combined with 3D printed phantoms as a "groundtruth" to compare with. Our approach of having a ground truth ensures that we withabsolute certainty can say what a scanned tumor is supposed to look like and compareour results to a true value. This performance metric is better suited for evaluation thanMSE since we want to maintain structures and edges in tumors and MSE-evaluationrewards over-smoothing. Here we apply U-Net networks to images of 3D printed tumors. The 4 tumors and alung phantom were printed with PLA filament and 80% fill rate with a gyroidal patternto mimic soft tissue in a CT-scan while maintaining isotropicity. CT images of the 3Dprinted phantom and tumors were taken with a GE revolution DE scanner at KarolinskaUniversity Hospital. The networks were trained on the 2016 NIH-AAPM-Mayo ClinicLow Dose CT Grand Challenge dataset, mapping Low Dose CT images to Normal DoseCT images using three different loss functions, l1, vgg16, and vgg16_l1. Evaluating the networks on RadiomicsShape features from SlicerRadiomics® we findcompetitive performance with TrueFidelityTM Deep Learning Image Reconstruction (DLIR)by GE HealthCareTM. With one of our networks (UNet_alt, vgg16_l1 loss function with32 features, and batch size 16 in training.) outperforming TrueFidelity in 63% of caseswhen evaluated by counting if a radiomic feature has a lower relative error comparedto ground truth after our own denoising for four different kind of tumors. The samenetwork outperformed FBP in 84% of cases which in combination with the majority ofour networks performing substantially better against FBP than TrueFidelity shows theviability of DLIR compared to older methods such as FBP.

<div> Advancements in cancer treatments have increased rapidly in recent years, but cures remain elusive. Surgical tumor resection is a central treatment for many solid malignancies. Residual tumor at surgical margins leads to tumor recurrence. Novel tools for assessing residual tumor at surgical margins could improve surgical outcomes by helping to maximize the extent of resection. Ambient ionization-mass spectrometry (MS) methods generate and analyze ions from minimally prepared samples in near-real-time (e.g. seconds to minutes). These methods leverage the high sensitivity and specificity of mass spectrometry for analyzing gas phase ions and generating those ions quickly and with minimal sample preparation. Recent work has shown that differential profiles of ions, corresponding to phospholipids and small metabolites, are detected from cancerous and their respective normal tissue with ambient ionization-MS methods. When properly implemented, ambient ionization-MS could be used to assess for tumor at surgical margins and provide a molecular diagnosis during surgery. </div><div><br></div><div>The research herein reports efforts in developing rapid intraoperative ambient ionization-MS methods for the molecular assessment of cancerous tissues. Touch spray (TS) ionization and desorption electrospray ionization (DESI) were utilized to analyze kidney cancer and brain cancer.</div><div><br></div><div> As a demonstration of the applicability of TS-MS to provide diagnostic information from fresh surgical tissues, TS-MS was used to rapidly analyze renal cell carcinoma and healthy renal tissue biopsies obtained from human subjects undergoing nephrectomy surgery. Differential phospholipid profiles were identified using principal component analysis (PCA), and the significant ions were characterized using multiple stages of mass spectrometry and high resolution/exact mass MS. The same TS-MS analyzed renal tissues were subsequently analyzed with DESI-MS imaging to corroborate the TS-MS results, and the significant DESI-MS ions were also characterized with MS.</div><div><br></div><div>Significant efforts were made in developing and evaluating a standalone intraoperative DESI-MS system for analyzing brain tissue biopsies during brain tumor surgery. The intraoperative DESI-MS system consists of a linear trap quadrupole mass spectrometer placed on a custom-machined cart that contains all hardware for operating the mass spectrometer. This instrument was operated in the neurosurgical suites at Indiana University School of Medicine to rapidly analyze brain tissue biopsies obtained from glioma resection surgeries. A DESI-MS library of normal brain tissue and glioma was used to statistically classify the brain tissue biopsies collected in the operating room. Multivariate statistical methodologies were employed to predict the disease state and tumor cell percentage of the samples. A DESI-MS assay for detecting 2-hydroxyglutarate (2HG), the oncometabolic product of the isocitrate dehydrogenase (IDH) mutation (a key glioma prognostic marker), was developed and applied to determine the IDH mutation status during the surgical resection. The strengths, weaknesses, and areas of future work in this field are discussed. </div><div><br></div>

<p>My research has focused on the development and translation of ambient ionization mass spectrometry (MS)-based platforms in clinical and surgical settings, specifically in the area of brain cancer diagnostics and surgical decision making. Ambient ionization MS methods, such as those described herein, generate and analyze gas phase ions with high sensitivity and specificity from minimally prepared samples in near-real-time, on the order of seconds to minutes, rendering them well suited to point-of-care applications. We used ambient ionization MS methods, specifically desorption electrospray ionization mass spectrometry (DESI-MS) and extraction nanoelectrospray ionization mass spectrometry (nESI-MS) to molecularly characterize brain cancer biopsies. The characterization was made using diagnostic compounds identified as markers of disease state, tissue composition, tumor type, and genotype in human brain tissue. Methods were developed and validated offline in the laboratory and translated to clinical and surgical settings, thereby generating chemical information on prognostic features intraoperatively and providing valuable information that would be otherwise unavailable. We believe that, with approval, the methodologies described can assist physicians and improve patient outcomes by providing analytical tools and molecular information that can inform surgical decision making and adjuvant treatment strategies, complementing and not interfering with standard of care protocols.</p> <p><br></p> <p>We have successfully demonstrated the use of desorption electrospray ionization mass spectrometry (DESI-MS) for the expedient molecular assessment of human glioma tissue biopsies based on lipid profiles and prognostic metabolites, both at the tumor core and near surgical margins, in two small-scale, clinical studies. Maximal surgical resection of gliomas that avoids non-infiltrated tissue is associated with survival benefit in patients with glioma. The infiltrative nature of gliomas, as well as their morphological and genetic diversity, renders treatment difficult and demands an integrated imaging and diagnostic approach during surgery to guide clinicians in achieving maximal tumor resection. Further, the estimation of tumor cell percentage (TCP), a measure of tumor infiltration at surgical margins, is not routinely assessed intraoperatively. </p> <p>We have previously shown that rapid, offline molecular assessment of tumor infiltration in tissue biopsies is possible and believe that the same assessment performed intraoperatively in biopsied tissue near surgical margins could improve resection and better inform patient management strategies, including postoperative radiotherapy. Using a DESI-MS spectral library of normal brain tissue and glioma biopsies to generate a statistical model to classify brain tissue biopsies intraoperatively, multivariate statistical approaches were used to predict the disease state and tumor cell percentage (TCP) of each biopsy, thereby providing an measure of tumor infiltration at surgical margins via molecular indicators. In addition to assessment of tumor infiltration, we have developed DESI-MS assays for detecting the oncometabolite 2-Hydroxyglutarate (2HG) to detect isocitrate dehydrogenase (IDH) mutations in gliomas intraoperatively. Knowledge of IDH genotypes at the time of surgical resection could improve patient outcomes, as more aggressive tumor resection of IDH-mutated gliomas is associated with increased survival. While assessments of IDH genotype are typically not available until days after surgery, we have demonstrated the ability to provide this information is less than five minutes. An intraoperative DESI-MS system has successfully been used in a proof-of-concept clinical study and intraoperative performance validation of this platform is ongoing. The findings of these two studies as well as strengths, weaknesses, and areas of improvement for upcoming future iterations of the research are discussed.</p> <p><br></p> <p>Point-of-care applications necessitate the adaptation of MS methodologies to smaller devices. Miniature mass spectrometers (Mini MS) boast small footprints, simple operation, and low power consumption, noise levels, and cost, making them attractive candidates for point-of-care use. In a small-scale clinical study, we demonstrated the first application of a Mini MS for determination of IDH mutation status in gliomas intraoperatively. This study paves a path forward for the application of Mini MS in the OR. With its small footprint and low power consumption and noise level, this application of miniature mass spectrometers represents a simple and cost-effective platform for an important intraoperative measurement. </p> <p><br></p> <p>While MS-based methods of tissue analysis can detect molecular features of interest and rapidly produce large quantities of data, their inherent speed is rarely utilized because they are traditionally coupled with time-consuming separation techniques (e.g., chromatography). Ambient ionization MS, specifically DESI-MS, is well suited for high-throughput applications due to its lack of sample preparation and purification techniques. In an attempt to rapidly characterize microarrays of tissue biopsies, we developed a high-throughput DESI-MS (HT-DESI-MS) method for the rapid characterization of disease state, human brain tumor type, glioma classification, and detection of IDH mutations in tissue microarrays (TMA) of banked and fresh human brain tissue biopsies. We anticipate that HT-DESI-MS analysis of TMAs could become a standard tool for the generation of spectral libraries for sample classification, the identification of biomarkers through large-scale studies, the correlation of molecular features with anatomical features when coupled to digital pathology, and the assessment of drug efficacy. </p>