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Masspektrometri (MS) är en analysmetod som indikerar provers kemiska sammansättning. Provernas innehåll fragmenteras och joniseras, varefter jonernas förhållande mellan massa och laddning (m/z) och sammanlagda intensiteter mäts i form av ett masspektrum. Tandem-masspektrometri (MS/MS eller MS2) innebär att prover utsätts för MS i två omgångar, där den första resulterar i s.k precursor-joner med skilda m/z och den följande MS omgången analyserar masspektrum från varje precursor-jon. MS2 leder till masspektrum med hög upplösning vilket är användbart vid analys av komplexa prover. Proteinsammansättning i biologiska prover är ett exempel på en typ av vetenskapligt- och kliniskt viktig provtyp samt en mycket komplex sådan. Analysmetoder där MS2 används för sådan analys kallas shotgun-proteomik, vilka tar hänsyn till det extremt stora antalet möjliga peptider genom att använda specifika algoritmer för databehandling. Målet är att identifiera peptid-spektrum matchningar (PSMs), d.v.s att uppskatta vilka peptider som gett upphov till de observerade MS2-spektrumen. I detta syfte används sökmotorer som uppskattar vilka peptider som bäst matchar de precursor-jonerna som för varje MS-spektrum, och bibliotekssökning där MS2-spektrum jämförs med dokumenterade MS2-spektrum som härstammar från diverse peptider för att hitta bäst matchning. I detta projekt utnyttjas en nyligen utvecklad algoritm, en spektrumprediktor, för att skapa en workflow där peptiders masspektrum predikeras utifrån PSMs som hittats av en sökmotor. Därefter jämförs predikerade spektrum med de experimentella spektrumen som användes av sökmotorn, och likheten mellan paren av spektrum räknas ut. Projektet har som mål att kombinera fördelarna med sökmotorer och med bibliotekssökning genom att använda de uträknade likheterna mellan spektrum för att öka antalet PSMs som kan identifieras för den experimentella datan. Genom att använda PSM post-processorn Percolator så kan den uträknade likheten mellan par av spektrum leda till fler PSM-identifikationer då likheten implementeras som features i Percolator. Resultaten av detta visar att Percolator kan identifiera PSMs utifrån features baserade på likhet mellan par av spektrum, varav vissa har q-värden under 0.01 och vissa inte kunde identifieras då Percolator användes i kombination med sökmotorn Crux. Om metoden kan förbättras genom att öka den genomsnittliga likheten mellan par av spektrum, samt om fler mått på likhet implementeras, så kan metoden som beskrivs i projektet bidra till att öka antalet PSM-identifikationer utifrån sökmotorresultat. Mass spectrometry (MS) is an analysis method revealing chemical composition of samples by fragmenting and ionizing the sample contents and measuring the mass-to-charge ratio (m/z) and cumulative intensity of each produced ion as an ion mass spectrum. Tandem mass spectrometry (MS/MS or MS2) uses two round of MS, the first to produce a set of precursor ions with distinct m/z, and then sequentially analyzing the ionization pattern of each precursor ion with a second round of MS. For complex samples, MS2 provides vastly increased ability to resolve the sample contents. Protein contents of biological samples represents both a critically important analysis target and a highly complex sample type. Analysis of such samples using MS2 is known as shotgun proteomics. The vast number of possible peptides in these samples necessitates the use of specialized algorithms when interpreting MS2 results data which aim to find peptide-spectrum matches (PSMs) between spectra and peptides identities. This includes search engines that predict which peptides best match each MS2 precursor ion, as well as library searching which match known peptide spectra to the MS2 spectral data. This project uses a recent advancement in shotgun proteomics, spectrum predictors, in a workflow that predicts peptide fragment spectra based on peptide identities suggested by a search engine, and calculates spectral similarity between the predicted peptide spectra and the experimental spectra which were assigned these peptides. This method aims to combine the strengths of both search engines and library searching, and to use the similarity score between experimental and predicted spectra to increase the number of spectra that can be confidently matched to a peptide identity. This project utilizes the PSM post-processor Percolator to rescore PSMs after introducing predicted spectrum similarity as a feature of the PSMs. The results indicate that the predicted spectrum similarity score is able to identify additional PSMs when used as a Percolator feature, when compared to the default Percolator PSM features. When using a combination of three similarity scores as a Percolator feature set, a number of PSMs are identified with q-values below 0.01 which were not identified by the corresponding Crux followed by Percolator workflow. If the average spectral similarity of predicted- and experimental spectra can be increased, and additional effective similarity scores can be added, this workflow could provide a useful tool for increasing PSM identifications from search engine results.

Background: Pancreatic cancer is the third leading cause of cancer-related mortality. Lack of early detection strategies and therapeutic resistance are main contributors to the poor prognosis. Unfortunately, there are no tissue biomarkers available for the prognosis of pancreatic cancer in routine clinical use.Aim: To identify and validate novel tissue biomarkers for the prognosis of pancreatic cancer.Methods: A mass spectrometry-based proteomic approach was applied to formalin-fixed paraffin-embedded specimens from surgically resected pancreatic cancer in 9 patients with short survival (45 months). The dysregulated biomarkers were further verified by targeted proteomics, parallel reaction monitoring. Finally, we evaluated prognostic candidates (CLCA1, galectin 4, P4HA2, PRTN3 and fibronectin) by tissue microarray and immunohistochemistry in a larger cohort of patients with pancreatic cancer who underwent surgical resection (n=144). Bioinformatic analysis was exploited to assess pathways and networks linked to the prognosis. Kaplan-Meier and Cox proportional hazards modeling were used to explore the association between biomarkers and survival.Results/Conclusion: A total of 24 and 147 proteins were significantly upregulated in patients with short survival and long survival, respectively. Bioinformatic analysis linked proteins representing “activated stroma factors” and “basal tumor factors” to poor prognosis and highlighted TCF1 and CTNNB1 as possible upstream regulators. By targeted proteomics, seven proteins were verified to be upregulated in patients with short survival (MMP9, CLIC3, MMP8, PRTN3, P4HA2, THBS1 and FN1), while 18 proteins were upregulated in patients with long survival, including EPCAM, galectin 4, VIL1, CLCA1 and TPPP3 (I). By immunohistochemical validation, we found that low CLCA1 expression correlated significantly with shorter disease-free survival (II). Furthermore, galectin 4 expression significantly correlated with disease recurrence within 1 year of surgery and with overall survival at 1- and 3-year (III). Besides, a low P4HA2 and high PRTN3 expression pattern correlated with shorter disease-free survival and overall survival (IV). Finally, high stromal FN1 expression was associated with aggressive tumor characteristics in patients with resected pancreatic cancer, although it was not associated with survival (V).

Urinary bladder cancer is a heterogeneous disease appearing in different forms, e.g. non-muscle invasive and muscle invasive. For all variants, the expression of proteins is interesting to analyze for diagnostic, predictive, prognostic and drug targeting purposes, since it reflects the altered gene expression causing the cancer. Since urothelial cells of the bladder are in direct contact with urine it is likely that this body fluid contains cancer-related proteins. In Paper I, unbiased analysis of proteins in urine from urinary bladder cancer patients and controls, using label-free quantification by mass spectrometry, was applied and four interesting proteins APOE, FGB, LRG and SERPINA1 were selected and further analyzed with western and dot blot. In Paper II, two more proteins, POLR1E and TOP2A, were validated as relevant proteins in bladder cancer urine. In Paper III and IV, the proteins GAL1 and STMN1 were investigated for their prognostic and therapeutic target potential in bladder cancer. In Paper II, III and IV, the expression of seven of the proteins were analyzed on tissue microarrays representing tumour tissue from 360 patients with different tumour stages. For the proteins identified by the urine screening approach, their protein expressions were confirmed in bladder cancer tissue. The expression level in tissue of five of the proteins, APOE, FGB, POLR1E (Paper II), GAL1 (Paper III) and STMN1 (Paper IV), increased with tumour stage, showing diagnostic relevance and three of the proteins, SERPINA1 (Paper II), STMN1 (Paper IV) and GAL1 (Paper III) had prognostic potential in urinary bladder cancer. In addition, GAL1 and STMN1 were demonstrated to be highly expressed in metastatic disease and inhibition of STMN1 reduced cell growth (Paper III and IV), indicating that these proteins are promising drug targets in urinary bladder cancer. In conclusion, the approach of this thesis has generated several candidate protein biomarkers in urine and tissue, validated with independent methods, which have the potential to improve the care for bladder cancer patients.

A proteomic approach was applied to examine the protein composition of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) in humans. LDL and HDL were isolated by density gradient ultracentrifugation, and proteins were separated with twodimensional gel electrophoresis (2-DE) and identified with peptide mass fingerprinting, using matrix-assisted laser desorption/ionization-time of flight mass spectrometry, and with amino acid sequencing using electrospray ionization tandem mass spectrometry. To improve the identification of low abundant proteins in silver stained 2-DE gels, 2,5-dihydroxybenzoic acid was used instead of α-cyano-4-hydroxycinnamic acid as matrix in the peptide mass fingerprinting procedure; this was demonstrated to give more matching peptide peaks, higher sequence coverage, and higher signal to noise ratio. Altogether 18 different proteins were demonstrated in LDL and/or HDL: three of these (calgranulin A, lysozyme C and transthyretin) have not been identified in LDL before. Apo C-II, apo C-III, apo E, apo A-I, apo A-IV, apo J, apo M, serum amyloid A-IV and α1-antitrypsin were found in both LDL and HDL, while apo B-100 (clone), calgranulin A, lysozyme C and transthyretin were found only in LDL, and apo A-II, apo C-I, and serum amyloid A only in HDL. Salivary α-amylase wass identified only in HDL2, and apo L and glycosylated apo A-II only in HDL3. Many of the proteins occurred in a number of isoforms: in all, 47 different isoform identities were demonstrated. A 2-DE mobility shift assay and deglycosylation experiments were used to demonstrate, for the first time, that apo M in LDL and HDL occurs in five isoforms; three that are both N-glycosylated and sialylated, one that is N-glycosylated but not sialylated and one that is neither N-glycosylated nor sialylated. LDL from obese subjects was found to contain more apo J, apo C-II, apo M, α1-antitrypsin and serum amyloid A-IV than LDL from controls,, and also more of an acidic isoform (pI/Mr; 5.2 / 23 100) of apo A-I. In addition, the new LDLassociated protein transthyretin, was found to be significantly more abundant in LDL from obese subjects. On the other hand, the amounts of apo A-IV and the major isoform of apo A-I (pI/Mr; 5.3 / 23 100) were significantly less. Altogether, these findings (i) illustrate the power of 2-DE and mass spectrometry for detailed mapping of the proteins and their isoforms in human lipoproteins; (ii) demonstrate the presence of a number of new proteins in LDL (calgranulin A, lysozyme C and transthyretin); (iii) give precise biochemical clues to the polymorphism of apo M in LDL and HDL, and; (iv) indicate that obesity is associated with significant changes in the protein profile of LDL. It is concluded that new information on lipoproteins can easily be obtained through a proteomic approach, thus facilitating the development of a new proteomic field: lipoproteomics. Much further investigation in this field is warranted, particularly because newly discovered LDL and HDL proteins may play hitherto unknown role(s) in inflammatory reactions of the arterial wall and evolve as useful biomarkers in cardiovascular disease.