Valeria Vitelli

• Liu, Qinghua; Vitelli, Valeria; Mannino, Carlo; Frigessi, Arnoldo & Scheel, Ida (2026). Pseudo-Mallows for Efficient Probabilistic Preference Learning. International Conference on Machine Learning (ICML). doi: https:/arxiv.org/pdf/2205.13911. Full text in Research Archive
• Asgari, Fatemeh; Vitelli, Valeria & Sailer, Uta (2026). Functional Structural Equation Modeling with Latent Variables. Structural Equation Modeling. ISSN 1070-5511. p. 1–9. doi: 10.1080/10705511.2026.2702102. Full text in Research Archive Show summary

  Handling latent variables in Structural Equation Models (SEMs) where both the latent variables and their corresponding indicators are random curves presents significant challenges, especially with sparse data. We develop a novel family of Functional Structural Equation Models (FSEMs) incorporating latent variables modeled as Gaussian Processes (GPs). The model adapts to cases when the random curves’ realizations are observed over a sparse subset of the domain. To extract smooth estimates for the functional parameters, we employ a penalized likelihood approach and evaluate the performance of the proposed model using simulation studies and a real data example, suggesting our model performs well. Supplementary materials are available online.

• Yoon, Chang Ho; Sjöholm, Daniel; Wickstrøm, Kristin; Danielsen, Anders Skyrud; Prebensen, Christian Haugland & Valik, John Karlsson [Show all 12 contributors for this article] (2026). Multicenter Validation of Clinical Sepsis Phenotypes. JAMA Network Open. 9(6). doi: 10.1001/jamanetworkopen.2026.16134. Full text in Research Archive
• Wickstrøm, Kristin; Holten, Aleksander Rygh; Prebensen, Christian Haugland; Luque, Alvaro Kohn; Vitelli, Valeria & Amundsen, Erik Koldberg (2025). Sysmex Cell Population Data for Diagnosing Infection in Patients With Suspected Sepsis in the Emergency Department. International Journal of Laboratory Hematology (IJLH). ISSN 1751-5521. 48(1), p. 71–80. doi: 10.1111/ijlh.70008. Full text in Research Archive Show summary

  Objectives: Early diagnosis of suspected sepsis is crucial to improve patient survival. Cell population (CP) data, a set of leuco-cyte research parameters from hematology instruments, has a potential as markers for infection. The aim of this study was toinvestigate the diagnostic accuracy for infection of CP variables from Sysmex XN instruments in patients with suspected sepsisin the emergency department (ED). Methods: Adult patients with suspected sepsis in the ED were included. CP variables, C-reactive protein (CRP), and post hocassessments of infection were recorded. Logistic regression and machine learning methods were used to develop multivariablemodels, which were evaluated by area under the receiver operating curve (AUC) and calibration plots. Results: The development cohort and the validation cohort consisted of 600 and 656 patients, respectively. Univariate analysesrevealed that complexity in monocytes (MO-X); AUC of 0.78 (0.74, 0.82), reactivity intensity of neutrophils (NEUT-RI); 0.72 (0.67,0.76), and CRP 0.87 (0.84, 0.90) had the highest diagnostic accuracy for infection. A final multivariable model (the optimal model)using Multilayer perceptron (MLP), including MO-X, NEUT-RI, monocyte size (MO-Z), and neutrophil size (NE-FSC), had anAUC of 0.86 (0.85, 0.87) in the development cohort and 0.78 (0.74, 0.82) in the validation cohort with reasonable calibration.Including CRP in this model further improved accuracy and calibration. Conclusions: Sysmex CP variables may help diagnose infections in the ED. However, the lack of well-described calibration pro-cedures and quality assurance for non-IVD approved CP variables is an impediment to clinical implementation

• Kizilaslan, Fatih; Swanson, David Michael & Vitelli, Valeria (2025). A Weibull mixture cure frailty model for high-dimensional covariates. Statistical Methods in Medical Research. ISSN 0962-2802. 34(6), p. 1192–1218. doi: 10.1177/09622802251327687. Full text in Research Archive Show summary

  A novel mixture cure frailty model is introduced for handling censored survival data. Mixture cure models are preferable when the existence of a cured fraction among patients can be assumed. However, such models are heavily underexplored: frailty structures within cure models remain largely undeveloped, and furthermore, most existing methods do not work for high-dimensional datasets, when the number of predictors is significantly larger than the number of observations. In this study, we introduce a novel extension of the Weibull mixture cure model that incorporates a frailty component, employed to model an underlying latent population heterogeneity with respect to the outcome risk. Additionally, high-dimensional covariates are integrated into both the cure rate and survival part of the model, providing a comprehensive approach to employ the model in the context of high-dimensional omics data. We also perform variable selection via an adaptive elastic-net penalization, and propose a novel approach to inference using the expectation–maximization (EM) algorithm. Extensive simulation studies are conducted across various scenarios to demonstrate the performance of the model, and results indicate that our proposed method outperforms competitor models. We apply the novel approach to analyze RNAseq gene expression data from bulk breast cancer patients included in The Cancer Genome Atlas (TCGA) database. A set of prognostic biomarkers is then derived from selected genes, and subsequently validated via both functional enrichment analysis and comparison to the existing biological literature. Finally, a prognostic risk score index based on the identified biomarkers is proposed and validated by exploring the patients’ survival.

• Hyldebrandt, Hanne Kjensli; Stormorken, Astrid T.; Vitelli, Valeria; Geirdal, Amy Østertun & Grindedal, Eli Marie (2025). Experiences with risk-reducing mastectomy in Norwegian BRCA1/2 carriers without prior breast cancer. Familial Cancer. ISSN 1389-9600. 24(3). doi: 10.1007/s10689-025-00484-6. Full text in Research Archive Show summary

  Background: BRCA1 and BRCA2 carriers without prior breast cancer have the option to undergo risk-reducing mastectomy (RRM). The aim of this study was to investigate factors associated with their decision, experiences with the process, and satisfaction with the procedure. Materials and methods: We distributed a cross-sectional survey to 425 women aged 25–70 with a pathogenic BRCA1/2 variant. The survey collected data on sociodemographic factors, family cancer history, reasons for choosing RRM or not, experiences with the decision-making process, support from healthcare, and surgery satisfaction. Multivariate logistic regression analysis identified factors associated with the decision to undergo RRM and satisfaction with having undergone surgery. Results: Of the 272 respondents, 190 (69.9%) had undergone RRM. Having children and being a BRCA1 carrier were both associated with higher probability of choosing RRM, with an OR of 3.5 (p = 0.005 and p < 0.001 respectively). Among those who had undergone RRM, 78.9% (150/190) were satisfied with their decision and would choose the same procedure again. Feeling satisfied with support from the health care system gave an OR of 5.5 for being satisfied with having undergone RRM (p < 0.01). Those who found the decision difficult had lower odds of being satisfied (OR 0.2, p = 0.02). Conclusion: Being a BRCA1 carrier and having children were strongly associated with choosing RRM. Most participants felt relieved after RRM, were satisfied with their decision, and would choose the surgery again. Support from healthcare providers during decision-making was linked to higher satisfaction with having undergone surgery while those who struggled with the decision reported lower satisfaction.

• Brunetti, Marta; Vitelli, Valeria; Naas, Anca Michaela; Eriksson, Ane Gerda Zahl; Haugland, Hans Kristian & Krakstad, Camilla [Show all 7 contributors for this article] (2025). Molecular Landscape of Endometrial Stromal Tumors. JCO Precision Oncology (JCO PO). 9. doi: 10.1200/PO-24-00779. Full text in Research Archive Show summary

  Purpose: The molecular heterogeneity of endometrial stromal tumors (ESTs) is demonstrated by the presence of the same fusion gene in distinct pathologic entities, such as endometrial nodules and low-grade endometrial stromal sarcoma, both exhibiting the JAZ1::SUZ12 chimeric transcript. Given the limited knowledge on these tumors, which is based on a small number of cases studied with a restricted range of techniques, we analyzed 47 ESTs to explore their methylation and transcriptomic landscapes. Materials and Methods: Tumor methylation and transcriptomes profiles were investigated. Results: The methylation profile showed distinct clusters, which correlated with established histopathologic and molecular subtypes. The highest methylation value was reported for nuclear factor of activated T cytoplasmic 1, and the lowest was detected for miR34C. Two different 5'—C—phosphate—G—3' (CpG) sites of LMX1B (LMX1B-cg04996334 and LMX1B), along with miR34C, showed the same methylation pattern in both low-grade and high-grade endometrial stromal sarcoma (HG-ESS). Similarly, CFAP45, HDAC4, ACY3, MOB3A, and XXYLT1 showed identical methylation patterns in HG-ESS and undifferentiated uterine sarcomas, highlighting the similarities between these tumors within the EST spectrum. We identified 13 novel fusion transcripts involving several genes that are active in transcriptional regulation. Conclusion: In ESTs, the genes involved in chromosomal rearrangements function as transcription regulators, either directly through the formation of zinc finger motifs or indirectly through epigenetic regulation. The methylation signature is different for distinct subgroups of the EST spectrum, with more aggressive tumors, HG-ESS, and undifferentiated uterine sarcoma, clustering together. Some genes showed similar methylation levels in different entities, highlighting the presence of a continuum in the tumor profile. Methylation levels of CpG sites at specific gene loci may serve as valuable biomarkers for these tumors.

• Asgari, Fatemeh; Alamatsaz, Mohammad H.; Hayati, Saeed & Vitelli, Valeria (2025). Likelihood analysis of latent functional response regression models for sequences of correlated binary data. Scandinavian Journal of Statistics. ISSN 0303-6898. doi: 10.1111/sjos.12773. Full text in Research Archive
• Hyldebrandt, Hanne Kjensli; Stormorken, Astrid T.; Vitelli, Valeria; Mæhle, Lovise Olaug; Schlichting, Ellen & Grindedal, Eli Marie (2025). Risk reducing mastectomy in Norwegian BRCA1/2 carriers. European Journal of Surgical Oncology. ISSN 0748-7983. 51(3), p. 1–6. doi: 10.1016/j.ejso.2024.109571. Full text in Research Archive Show summary

  Background Risk reducing mastectomy (RRM) is an option for women with pathogenic germline variants in BRCA1 or BRCA2 (BRCA1/2). This study investigates and compares RRM-uptake among Norwegian BRCA1/2 carriers from 2008 to 2021, temporal trends, and incidence of breast cancer (BC) after surgery. Methods BRCA1/2 carriers without prior breast or ovarian cancer, tested at Oslo University Hospital between January 1st, 2008 and December 31st, 2021 were included in the study. Data on RRM was obtained from The Norwegian Patient Registry. RRM-uptake, time from genetic testing (GT) to RRM, and RRM-uptake 2- and 5-years post-GT was calculated for all carriers, according to gene and age at GT. RRM-uptake was compared for those tested in 2008/2009 versus 2015/2016. BC diagnoses post-RRM was collected from The Cancer Registry of Norway. Results In total, 1237 BRCA1/2 carriers were included, 679 (54.9 %) BRCA1 and 558 (45.1 %) BRCA2. Six hundred and four (48.8 %) had chosen RRM, 370 (54.5 %) BRCA1 and 234 (42.0 %) BRCA2 (p < 0.001). Mean age at RRM was 40.1 for BRCA1 and 44.6 for BRCA2 (p < 0.001). Mean time from GT to RRM was 2.3 years for BRCA1 and 3 years for BRCA2 (p < 0.001). Women tested in 2015/2016 were 2.6 times more likely to choose RRM compared to those tested in 2008/2009. Two out of the 604 (0.3 %) had developed BC post-RRM. Conclusion Nearly half of BRCA1/2 carriers had chosen RRM, with increasing uptake since 2008. Compared to BRCA2 carriers, more BRCA1 carriers chose RRM, were operated at a younger age and sooner after GT. Post-RRM BC incidence was low.

• Tashbayev, Behzod; Badian, Reza; Chen, Xiangjun; Vitelli, Valeria; Lagali, Neil & Dartt, Darlene [Show all 9 contributors for this article] (2025). Comparison of non-invasive and fluorescein tear film break-up time in a 65-year-old Norwegian population: a cross-sectional study. BMJ Open. 15. doi: 10.1136/bmjopen-2024-090305. Full text in Research Archive Show summary

  Objectives Measurement of tear film stability is central in dry eye disease (DED) diagnosis. In this study, we aimed to compare the performance of two methods of tear film stability measurement: non-invasive tear break-up time (NIBUT) and fluorescein tear film break-up time (FTBUT). Design Cross-sectional study. Setting and participants The study involved 132 subjects of 65-year-old inhabitants of the Oslo region who were not seeking ophthalmic care. Interventions The participants underwent a battery of DED tests, including NIBUT measured on Oculus Keratograph 5M and a traditional method using fluorescein drops (FTBUT). Oculus Keratograph 5M measures two types of NIBUT:; appearance time of the first dry spot (NIBUTFirst) and average NIBUTAvg. Results 74 participants (56%) were female and 58 were male (44%). Subjects presented with varying degrees of DED signs and symptoms. Mean values of NIBUTFirst and FTBUT from all the participants were significantly different (6.2±4.9 s vs 8.6±6.2 s, p<0.0001). There was also a significant difference between NIBUTFirst and NIBUTAvg values (6.2±4.9 s vs 8.3±5.5 s, p<0.0001). In contrast, no difference was observed between FTBUT and NIBUTAvg values (8.6±6.2 s vs 8.3±5.5 s, p=0.655). The receiver operating characteristic curve analysis was performed to compare NIBUT and FTBUT in regards to other clinical tests (Ocular Surface Disease Index, ocular surface staining, blink interval, eye redness, corneal sensitivity, lid debris, Schirmer I test, tear osmolarity, meibum quality, meibum expressibility, lid hyperemia, tear meniscus height,. irregular lid margin, conjunctival hyperaemia, margin telangiectasia, lipid layer and meibomian gland drop-out). While FTBUT demonstrated results with area under the curve>0.6, neither NIBUTFirst nor NIBUTAvg showed significant results. Conclusion NIBUTFirst was shorter than FTBUT. Low correlation between NIBUT and FTBUT indicates that these diagnostic tests are not interchangeable. Other DED tests had correlation, though low, while NIBUT did not demonstrate correlation.

• Hyldebrandt, Hanne Kjensli; Grindedal, Eli Marie; Huppertz-Hauss, Gert; Vitelli, Valeria; Johansen, Nora & Stormorken, Astrid T. (2024). The impact of hysterectomy on subsequent colonoscopy in women with Lynch Syndrome. Scandinavian Journal of Gastroenterology. ISSN 0036-5521. 59(8), p. 1015–1020. doi: 10.1080/00365521.2024.2366969. Full text in Research Archive Show summary

  Background and aims: Women with Lynch Syndrome (LS) have a high risk of colorectal and endometrial cancer. They are recommended regular colonoscopies, and some choose prophylactic hysterectomy. The aim of this study was to determine the impact of hysterectomy on subsequent colonoscopy in these women. Materials and methods: A total of 219 LS women >30 years of age registered in the clinical registry at Section for Hereditary Cancer, Oslo University Hospital, were included. Data included hysterectomy status, other abdominal surgeries, and time of surgery. For colonoscopies, data were collected on cecal intubation rate, challenges, and level of pain. Observations in women with and without hysterectomy, and pre- and post-hysterectomy were compared. Results: Cecal intubation rate was lower in women with hysterectomy than in those without (119/126 = 94.4% vs 88/88 = 100%, p = 0.025). Multivariate regression analysis showed an increased risk of challenging colonoscopies (OR,3.58; CI: 1.52-8.43; p = 0.003), and indicated a higher risk of painful colonoscopy (OR, 3.00; 95%CI: 0.99-17.44, p = 0.052), in women with hysterectomy compared with no hysterectomy. Comparing colonoscopy before and after hysterectomy, we also found higher rates of reported challenging colonoscopies post-hysterectomy (6/69 = 8.7% vs 23/69 = 33.3%, p< 0.001). Conclusions: Women with hysterectomy had a lower cecal intubation rate and a higher number ofreported challenging colonoscopy than women with no hysterectomy. However, completion rate in thehysterectomy group was still as high as 94.4%. Thus, LS women who consider hysterectomy should not be advised against it.

• Vitelli, Valeria (2023). A novel framework for joint sparse clustering and alignment of functional data. Journal of nonparametric statistics (Print). ISSN 1048-5252. doi: 10.1080/10485252.2023.2206499. Full text in Research Archive Show summary

  A novel framework for sparse functional clustering that also embeds an alignment step is here proposed. Sparse functional clustering entails estimating the parts of the curves' domains where their grouping structure shows the most. Misalignment is a well-known issue in functional data analysis, that can heavily affect functional clustering results if not properly handled. Therefore, we develop a sparse functional clustering procedure that accounts for the possible curve misalignment: the coherence of the functional measure used in the clustering step to the class where the warping functions are chosen is ensured, and the well-posedness of the sparse clustering problem is proved. A possible implementing algorithm is also proposed, that jointly performs all these tasks: clustering, alignment, and domain selection. The method is tested on simulated data in various realistic situations, and its application to the Berkeley Growth Study data and to the AneuRisk65 dataset is discussed.

• Ankill, Jørgen; Aure, Miriam Ragle; Bjørklund, Sunniva; Langberg, Geir Severin Rakh Elvatun; Kristensen, Vessela N. & Vitelli, Valeria [Show all 8 contributors for this article] (2022). Epigenetic alterations at distal enhancers are linked to proliferation in human breast cancer. NAR Cancer. 4(1), p. 1–16. doi: 10.1093/narcan/zcac008. Full text in Research Archive Show summary

  Aberrant DNA methylation is an early event in breast carcinogenesis and plays a critical role in regulating gene expression. Here, we perform genome-wide expression-methylation Quantitative Trait Loci (emQTL) analysis through the integration of DNA methylation and gene expression to identify disease-driving pathways under epigenetic control. By grouping the emQTLs using biclustering we identify associations representing important biological processes associated with breast cancer pathogenesis including regulation of proliferation and tumor-infiltrating fibroblasts. We report genome-wide loss of enhancer methylation at binding sites of proliferation-driving transcription factors including CEBP-β, FOSL1, and FOSL2 with concomitant high expression of proliferation-related genes in aggressive breast tumors as we confirm with scRNA-seq. The identified emQTL-CpGs and genes were found connected through chromatin loops, indicating that proliferation in breast tumors is under epigenetic regulation by DNA methylation. Interestingly, the associations between enhancer methylation and proliferation-related gene expression were also observed within known subtypes of breast cancer, suggesting a common role of epigenetic regulation of proliferation. Taken together, we show that proliferation in breast cancer is linked to loss of methylation at specific enhancers and transcription factor binding and gene activation through chromatin looping.

• Brorson, Ina Skaara; Eriksson, Anna Maria; Høgestøl, Einar August; Leikfoss, Ingvild Sørum; Harbo, Hanne-Cathrin Flinstad & Berge, Tone [Show all 8 contributors for this article] (2022). Global DNA methylation changes in treated and untreated MS patients measured over time. Journal of Neuroimmunology. ISSN 0165-5728. 364. doi: 10.1016/j.jneuroim.2022.577808. Full text in Research Archive Show summary

  Multiple sclerosis (MS) is an autoimmune, neurological disease. We investigated genome-wide DNA methylation profiles of CD4+ and CD8+ T cells from MS patients and healthy controls at baseline and a follow-up visit. Patients were all treatment-naïve at baseline, and either on treatment or remained untreated at the follow-up visit. MS patients show more changes in their T cell DNA methylation profiles as compared to healthy controls over time, with the most pronounced differences observed in the untreated MS patients. These findings underline the potential of DNA methylation as biomarkers in MS.

• Ødegaard, Emilie Eliseussen; Fleischer, Thomas & Vitelli, Valeria (2022). Rank-based Bayesian variable selection for genome-wide transcriptomic analyses. Statistics in Medicine. ISSN 0277-6715. p. 4511–4743. doi: 10.1002/sim.9524. Full text in Research Archive Show summary

  Variable selection is crucial in high-dimensional omics-based analyses, since it is biologically reasonable to assume only a subset of non-noisy features contributes to the data structures. However, the task is particularly hard in an unsupervised setting, and a priori ad hoc variable selection is still a very frequent approach, despite the evident drawbacks and lack of reproducibility. We propose a Bayesian variable selection approach for rank-based unsupervised transcriptomic analysis. Making use of data rankings instead of the actual continuous measurements increases the robustness of conclusions when compared to classical statistical methods, and embedding variable selection into the inferential tasks allows complete reproducibility. Specifically, we develop a novel extension of the Bayesian Mallows model for variable selection that allows for a full probabilistic analysis, leading to coherent quantification of uncertainties. Simulation studies demonstrate the versatility and robustness of the proposed method in a variety of scenarios, as well as its superiority with respect to several competitors when varying the data dimension or data generating process. We use the novel approach to analyze genome-wide RNAseq gene expression data from ovarian cancer patients: several genes that affect cancer development are correctly detected in a completely unsupervised fashion, showing the usefulness of the method in the context of signature discovery for cancer genomics. Moreover, the possibility to also perform uncertainty quantification plays a key role in the subsequent biological investigation.

• Vitelli, Valeria; Fleischer, Thomas; Ankill, Jørgen; Arjas, Elja; Frigessi, Arnoldo & Kristensen, Vessela N. [Show all 7 contributors for this article] (2022). Transcriptomic pan-cancer analysis using rank-based Bayesian inference. Molecular Oncology. ISSN 1574-7891. 00(0), p. 1–16. doi: 10.1002/1878-0261.13354. Full text in Research Archive Show summary

  The analysis of whole genomes of pan-cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank-based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top-ranked genomic features. We applied our method to RNA-seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan-cancer clusters. Importantly, we identified three pan-squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over-represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan-cancer samples.

• Salvanos, Panagiotis; Bjørnsson, Helgi David; Vitelli, Valeria; Bragadottir, Ragnheidur; Moe, Morten Carsten & Utheim, Tor Paaske (2022). Autofluorescence Imaging in the Long-Term Follow-Up of Scleral Buckling Surgery for Retinal Detachment. Journal of Ophthalmology. ISSN 2090-004X. 2022. doi: 10.1155/2022/2119439. Full text in Research Archive Show summary

  Purpose. To analyse fundus autofluorescence (AF) changes in retinal reattachment following primary scleral buckling (SB) surgery for rhegmatogenous retinal detachment (RRD). Methods. Prospective noninterventional chart review study. AF images were reviewed for peripheral and central changes and compared to clinical and OCT findings. Results. A total of 73 eyes from 69 patients were included, four presenting with bilateral RRD. Mean age was 55 ± 12 years, male/female ratio 40/29, fovea-on/-off RRD 43/30, and mean follow-up time 376 ± 270 days, with a mean of 5 ± 3 postoperative visits. Preoperatively, RRD was seen as a hypofluorescent area with a hyperfluorescent leading edge. Immediately postoperatively, three types of cryopexy could be differentiated, gradually transforming to scleral hyperfluorescence. Buckle tightening produced alternating hyper-/hypofluorescent streaks, and demarcation lines showed a persistent rugged hyperfluorescent signal. Choroidal detachment led to transient hypofluorescence, whereas vortex vein compression induced persistent hypofluorescence. Peripheral retinal folds were hyperfluorescent and the drainage site was hypofluorescent. AF was highly sensitive in detecting even small amounts of hyperfluorescent persistent subretinal fluid (SRF) that showed a slow resolution during follow-up. A granular “salt-and-pepper-” like pattern in the central macula was seen in 80% of eyes with fovea-off RRD and alternating streaks in 10%. Findings from OCT imaging correlated well with AF regarding SRF, macular oedema, retinal pigment epithelial detachment, and presence of a subretinal scar, but only moderately in epiretinal membrane formation and choroidal folds. Conclusions. AF is a useful, noninvasive, adjuvant tool in the long-term follow-up after SB surgery.

• Brunetti, Marta; Panagopoulos, Ioannis; Vitelli, Valeria; Andersen, Kristin; Hveem, Tarjei Sveinsgjerd & Davidson, Ben [Show all 10 contributors for this article] (2022). Endometrial Carcinoma: Molecular Cytogenetics and Transcriptomic Profile. Cancers. 14:3536(14), p. 1–16. doi: 10.3390/cancers14143536. Full text in Research Archive Show summary

  Simple Summary Endometrial carcinomas (ECs), the most frequent type of uterine body cancer, are highly heterogeneous with overlapping clinical, pathological, and molecular features. This study aimed to gain insights into these cancers’ chromosomal and genomic aberration patterns as well as their gene and miRNA expression profiles, all of which are both pathogenetically and clinically important disease features. We found that chromosome 1 was the most frequently rearranged chromosome, mostly leading to gain of 1q, that the genes PTEN, PDGFRA, PIK3CA, and KIT were the most frequent pathogenic variants, and that some other genes and miRNAs of known importance in carcinogenesis and the immune response showed consistent deregulation. This study confirms that a high degree of genetic heterogeneity characterizes EC tumors but highlights the nonrandom involvement of some loci. Abstract Endometrial carcinomas (ECs) are histologically classified as endometrioid and nonendometrioid tumors, with each subgroup displaying different molecular profiles and clinical outcomes. Considerable biological and clinical heterogeneity exists within this scheme, however, reflecting its imperfection. We aimed to gather additional data that might help clarify the tumors’ pathogenesis and contribute toward a more meaningful classification scheme. In total, 33 ECs were examined for the presence of chromosomal aberrations, genomic imbalances, pathogenic variants, microsatellite instability, and expression profiles at both gene and miRNA levels. Chromosome 1 was the most frequently rearranged chromosome, showing a gain of all or part of the long arm. Pathogenic variants were found for PTEN (53%), PDGFRA (37%), PIK3CA (34%), and KIT (31%). High microsatellite instability was identified in 15 ECs. Comparing tumors and controls, we identified 23 differentially expressed genes of known importance in carcinogenesis, 15 genes involved in innate and adaptative immune responses, and altered expression of 7 miRNAs. miR-32-5p was the most upregulated. Our series showed a high degree of heterogeneity. Tumors were well-separated from controls, but there was no clear-cut separation between endometrioid and nonendometrioid ECs. Whether this means that the current phenotypic classification is of little relevance or if one still has not detected which genomic parameters to enter into correlation analyses remains unknown. Keywords: endometrial carcinoma; karyotype; chromosome 1; mutation analysis; microsatellite instability; gene expression; cancer pathways; cancer immune profile; miRNA

• Cappelletti, Chiara; Eriksson, Anna Maria; Brorson, Ina Skaara; Leikfoss, Ingvild Sørum; Kråbøl, Oda Glomstad & Høgestøl, Einar August [Show all 12 contributors for this article] (2022). Quantitative proteomics reveals protein dysregulation during T cell activation in multiple sclerosis patients compared to healthy controls. Clinical Proteomics. ISSN 1542-6416. 19(1), p. 1–17. doi: 10.1186/s12014-022-09361-1. Full text in Research Archive Show summary

  Background Multiple sclerosis (MS) is an autoimmune, neurodegenerative disorder with a strong genetic component that acts in a complex interaction with environmental factors for disease development. CD4+ T cells are pivotal players in MS pathogenesis, where peripherally activated T cells migrate to the central nervous system leading to demyelination and axonal degeneration. Through a proteomic approach, we aim at identifying dysregulated pathways in activated T cells from MS patients as compared to healthy controls. Methods CD4+ T cells were purified from peripheral blood from MS patients and healthy controls by magnetic separation. Cells were left unstimulated or stimulated in vitro through the TCR and costimulatory CD28 receptor for 24 h prior to sampling. Electrospray liquid chromatography-tandem mass spectrometry was used to measure protein abundances. Results Upon T cell activation the abundance of 1801 proteins was changed. Among these proteins, we observed an enrichment of proteins expressed by MS-susceptibility genes. When comparing protein abundances in T cell samples from healthy controls and MS patients, 18 and 33 proteins were differentially expressed in unstimulated and stimulated CD4+ T cells, respectively. Moreover, 353 and 304 proteins were identified as proteins exclusively induced upon T cell activation in healthy controls and MS patients, respectively and dysregulation of the Nur77 pathway was observed only in samples from MS patients. Conclusions Our study highlights the importance of CD4+ T cell activation for MS, as proteins that change in abundance upon T cell activation are enriched for proteins encoded by MS susceptibility genes. The results provide evidence for proteomic disturbances in T cell activation in MS, and pinpoint to dysregulation of the Nur77 pathway, a biological pathway known to limit aberrant effector T cell responses.

• Fineide, Fredrik; Chen, Xiangjun; Bjellaas, Thomas; Vitelli, Valeria; Utheim, Tor Paaske & Jensen, Janicke Liaaen [Show all 7 contributors for this article] (2021). Characterization of lipids in saliva, tears and minor salivary glands of sjögren’s syndrome patients using an hplc/ms‐based approach. International Journal of Molecular Sciences. ISSN 1661-6596. 22(16). doi: 10.3390/ijms22168997. Full text in Research Archive Show summary

  The diagnostic work-up of primary Sjögren’s syndrome (pSS) includes quantifying saliva and tear production, evaluation of autoantibodies in serum and histopathological analysis of minor salivary glands. Thus, the potential for further utilizing these fluids and tissues in the quest to find better diagnostic and therapeutic tools should be fully explored. Ten samples of saliva and tears from female patients diagnosed with pSS and ten samples of saliva and tears from healthy females were included for lipidomic analysis of tears and whole saliva using high-performance liquid chromatography coupled to time-of-flight mass spectrometry. In addition, lipidomic analysis was performed on minor salivary gland biopsies from three pSS and three non-SS females. We found significant differences in the lipidomic profiles of saliva and tears in pSS patients compared to healthy controls. Moreover, there were differences in individual lipid species in stimulated saliva that were comparable to those of glandular biopsies, representing an intriguing avenue for further research. We believe a comprehensive elucidation of the changes in lipid composition in saliva, tears and minor salivary glands in pSS patients may be the key to detecting pSS-related dry mouth and dry eyes at an early stage. The identified differences may illuminate the path towards future innovative diagnostic methodologies and treatment modalities for alleviating pSS-related sicca symptoms.

• Jeppestøl, Kristin; Vitelli, Valeria; Kirkevold, Marit & Bragstad, Line Kildal (2021). Factors Associated With Care Trajectory Following Acute Functional Decline in Older Home Nursing Care Patients: A Prospective Observational Study. Home Health Care Management & Practice. ISSN 1084-8223. 34(1), p. 42–51. doi: 10.1177/10848223211034774. Full text in Research Archive Show summary

  Health policies and previous research highlight the importance of early identification and treatment of clinical deterioration in older patients to prevent frailty, higher levels of care, and mortality. This study explores older home nursing care patients’ care trajectories and factors associated with clinical response (type and level of intervention) from the health care services, final level of community care and death within 3 months after an incidence of acute functional decline. This observational study with a prospective, descriptive design includes a sample of 135 older home nursing care patients with acute functional decline. Demographic, health-related, and clinical characteristics were analyzed and prediction models for care trajectories were fitted using Bayesian generalized mixed models. Age ranged from 65 to 100, with a median age of 85. Hospital admission were registered for 13.33% (T1) and 8.77% (T2) of the participants. Nine patients (6.7%) were transferred to a higher level of community care, and 11 patients (8.1%) died. Frequent transitions between levels of care characterized care trajectories for patients experiencing more severe functional decline. Age, living in a private home, and increased Modified Early Warning Scores (MEWS) were associated with level of clinical responses throughout the care trajectory. Living in a private home was associated with the patients’ final level of community care. Female gender, hospital admission, and increased MEWS scores were associated with death. Health care personnel must be vigilant when MEWS scores rise even slightly, as this might be an indication of acute functional decline with possible increased risk of mortality.

• Wickstrøm, Kristin Elisabeth; Vitelli, Valeria; Carr, Ewan; Holten, Aleksander R.; Bendayan, Rebecca & Reiner, Andrew Henry [Show all 15 contributors for this article] (2021). Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort study. PLOS ONE. 16(8), p. 1–13. doi: 10.1371/journal.pone.0255748. Full text in Research Archive Show summary

  Background: Prediction models should be externally validated to assess their performance before implementation. Several prediction models for coronavirus disease-19 (COVID-19) have been published. This observational cohort study aimed to validate published models of severity for hospitalized patients with COVID-19 using clinical and laboratory predictors. Methods: Prediction models fitting relevant inclusion criteria were chosen for validation. The outcome was either mortality or a composite outcome of mortality and ICU admission (severe disease). 1295 patients admitted with symptoms of COVID-19 at Kings Cross Hospital (KCH) in London, United Kingdom, and 307 patients at Oslo University Hospital (OUH) in Oslo, Norway were included. The performance of the models was assessed in terms of discrimination and calibration. Results: We identified two models for prediction of mortality (referred to as Xie and Zhang1) and two models for prediction of severe disease (Allenbach and Zhang2). The performance of the models was variable. For prediction of mortality Xie had good discrimination at OUH with an area under the receiver-operating characteristic (AUROC) 0.87 [95% confidence interval (CI) 0.79–0.95] and acceptable discrimination at KCH, AUROC 0.79 [0.76–0.82]. In prediction of severe disease, Allenbach had acceptable discrimination (OUH AUROC 0.81 [0.74–0.88] and KCH AUROC 0.72 [0.68–0.75]). The Zhang models had moderate to poor discrimination. Initial calibration was poor for all models but improved with recalibration. Conclusions: The performance of the four prediction models was variable. The Xie model had the best discrimination for mortality, while the Allenbach model had acceptable results for prediction of severe disease.

• Yazdani, Mazyar; Fiskådal, Jørgen; Chen, Xiangjun; Utheim, Øygunn Aass; Ræder, Sten & Vitelli, Valeria [Show all 7 contributors for this article] (2021). Tear film break-up time and dry eye disease severity in a large norwegian cohort. Journal of Clinical Medicine. 10(4), p. 1–16. doi: 10.3390/jcm10040884. Full text in Research Archive Show summary

  This study evaluated to what extent tear film break-up time (TFBUT) could discriminate pathological scores for other clinical tests and explore the associations between them. Dry eye patients (n = 2094) were examined for ocular surface disease index (OSDI), tear film osmolarity (Osm), TFBUT, blink interval, ocular protection index (OPI), ocular surface staining (OSS), Schirmer I test, meibomian expressibility, meibomian quality, and meibomian gland dysfunction. The results were grouped into eight levels of break-up time (≤2, ≥3, ≤5, ≥6, ≤10, ≥11, ≤15, and ≥16) with or without sex stratification. Receiver-operating characteristic curve (ROC) analysis and Pearson’s correlation coefficients were used to study TFBUT’s discriminative power and the associations among the tests, respectively. Above and below each TFBUT’s cut-off, all of the parameters indicated significant difference between groups, except OSDI (cut-off 15 s) and Osm (cut-offs 5 s–15 s). At TFBUT cut- off of 2 s, sex difference could be detected for OSDI, Osm, and OSS. OPI presented the strongest discriminative power and association with TFBUT in sharp contrast to Osm, holding the poorest discriminative power with no significant correlation. The remaining parameters were within the poor to very poor categories, both with regard to discrimination and correlation. In conclusion, patients with lower TFBUT presented with more severe DED parameters at all four defined cut-off values

• Sørensen, Øystein; Crispino, Marta; Liu, Qinghua & Vitelli, Valeria (2020). BayesMallows: An R Package for the Bayesian Mallows Model. The R Journal. 12(1), p. 324–342. doi: 10.32614/RJ-2020-026. Full text in Research Archive Show summary

  Abstract BayesMallows is an R package for analyzing preference data in the form of rankings with the Mallows rank model, and its finite mixture extension, in a Bayesian framework. The model is grounded on the idea that the probability density of an observed ranking decreases exponentially with the distance to the location parameter. It is the first Bayesian implementation that allows wide choices of distances, and it works well with a large amount of items to be ranked. BayesMallows handles non-standard data: partial rankings and pairwise comparisons, even in cases including non-transitive preference patterns. The Bayesian paradigm allows coherent quantification of posterior uncertainties of estimates of any quantity of interest. These posteriors are fully available to the user, and the package comes with convienient tools for summarizing and visualizing the posterior distributions.

• Pladsen, Arne Valebjørg; Nilsen, Gro; Rueda, Oscar M.; Aure, Miriam Ragle; Borgan, Ørnulf & Liestøl, Knut [Show all 18 contributors for this article] (2020). DNA copy number motifs are strong and independent predictors of survival in breast cancer. Communications Biology. 3. doi: 10.1038/s42003-020-0884-6. Full text in Research Archive Show summary

  Somatic copy number alterations are a frequent sign of genome instability in cancer. A precise characterization of the genome architecture would reveal underlying instability mechanisms and provide an instrument for outcome prediction and treatment guidance. Here we show that the local spatial behavior of copy number profiles conveys important information about this architecture. Six filters were defined to characterize regional traits in copy number profiles, and the resulting Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to tumors in four breast cancer cohorts (n = 2919). The derived motifs represent a layer of information that complements established molecular classifications of breast cancer. A score reflecting presence or absence of motifs provided a highly significant independent prognostic predictor. Results were consistent between cohorts. The nonsite-specific occurrence of the detected patterns suggests that CARMA captures underlying replication and repair defects and could have a future potential in treatment stratification.

• Tashbayev, Behzod; Utheim, Tor Paaske; Utheim, Øygunn Aass; Ræder, Sten; Jensen, Janicke Cecilie Liaaen & Yazdani, Mazyar [Show all 10 contributors for this article] (2020). Utility of Tear Osmolarity Measurement in Diagnosis of Dry Eye Disease. Scientific Reports. 10, p. 1–7. doi: 10.1038/s41598-020-62583-x. Full text in Research Archive Show summary

  The prevalence of dry eye disease is high worldwide and poses a great burden on patients’ daily lives. Accurate diagnosis of the disease is important, and it requires application of various methods. Hyperosmolarity is believed to be the disease marker and thus measuring it provides useful information. In this study we investigated utility of tear osmolarity measured with TearLab osmometer, along with other diagnostic tests (Ocular Surface Disease Index questionnaire, Tear flm break-up time, Ocular Protection Index, Ocular Surface Staining, Schirmer I test, Meibomian gland functionality in 757 patients (1514 eyes) with dry eye disease and 29 healthy controls (58 eyes). Statistical diferences between the patient group and the control group were observed for all the tests apart from tear osmolarity, regardless of cut-of value (>308mOsm/L, >316mOsm/L, and inter-eye diference >8mOsm/L). Moreover, in the receiver operating characteristics curve analyses tear osmolarity measurement could not discriminate dry eye disease pathological scores. Therefore, our study suggests that tear osmolarity measured with TearLab osmometer cannot be used as a key indicator of DED.

• Brorson, Ina Skaara; Eriksson, Anna Maria; Leikfoss, Ingvild Sørum; Vitelli, Valeria; Celius, Elisabeth Gulowsen & Luders, Torben [Show all 10 contributors for this article] (2020). CD8 + T cell gene expression analysis identifies differentially expressed genes between multiple sclerosis patients and healthy controls. Multiple Sclerosis Journal, Experimental, Translational and Clinical. doi: 10.1177/2055217320978511. Full text in Research Archive Show summary

  Background: Genetic and clinical observations have indicated T cells are involved in MS pathology. There is little insight in how T cells are involved and whether or not these can be used as markers for MS. Objectives: Analysis of the gene expression profiles of circulating CD8þ T cells of MS patients compared to healthy controls. Methods: RNA from purified CD8þ T cells was sequenced and analyzed for differential gene expression. Pathway analyses of genes at several p-value cutoffs were performed to identify putative pathways involved. Results: We identified 36 genes with significant differential gene expression in MS patients. Four genes reached at least 2-fold differences in expression. The majority of differentially expressed genes was higher expressed in MS patients. Genes associated to MS in GWAS showed enrichment amongst the differentially expressed genes. We did not identify enrichment of specific pathways amongst the differentially expressed genes in MS patients. Conclusions: CD8þ T cells of MS patients show differential gene expression, with predominantly higher activity of genes in MS patients. We do not identify specific biological pathways in our study. More detailed analysis of CD8þ T cells and subtypes of these may increase understanding of how T cells are involved in MS.

• Chernyakov, Gleb A.; Vitelli, Valeria; Alexandrin, Mikhail Y.; Grachev, Alexei M.; Mikhalenko, Vladimir & Kozachek, Anna V. [Show all 8 contributors for this article] (2020). Dynamics of seasonal patterns in geochemical, isotopic, and meteorological records of the elbrus region derived from functional data clustering. Geography, Environment, Sustainability. ISSN 2071-9388. 13(3), p. 110–116. doi: 10.24057/2071-9388-2019-180. Full text in Research Archive Show summary

  A nonparametric clustering method, the Bagging Voronoi K-Medoid Alignment algorithm, which simultaneously clusters and aligns spatially/temporally dependent  curves,  is applied to study various data series from the Elbrus  region (Central Caucasus). We used the algorithm to cluster annual curves obtained by smoothing of the following synchronous data series: titanium concentrations in varved (annually laminated) bottom sediments of proglacial  Lake Donguz-Orun;  an oxygen-18 isotope record in an ice core from Mt. Elbrus; temperature and precipitation observations with a monthly resolution from Teberda and Terskol meteorological stations. The data of different types were clustered independently. Due to restrictions concerned with the availability of meteorological data, we have fulfilled the clustering procedure separately for two periods: 1926–2010 and 1951–2010. The study is aimed to determine whether the instrumental period could be reasonably divided (clustered)  into several sub-periods using different climate and proxy time series; to examine the interpretability of the resulting borders of the clusters (resulting time periods); to study typical patterns of intra-annual variations of the data series. The results of clustering suggest that the precipitation and to a lesser degree titanium decadal-scale data may be reasonably grouped, while the temperature and oxygen-18 series are too short to form meaningful clusters; the intercluster boundaries show a notable degree of coherence between temperature and oxygen-18 data, and less between titanium and oxygen-18 as well as for precipitation series; the annual curves for titanium and partially precipitation data reveal much more pronounced intercluster  variability than the annual patterns of temperature and oxygen-18 data.

• Engebretsen, Stine; Bogstrand, Stig Tore; Jacobsen, Dag; Vitelli, Valeria & Rimstad, Rune (2020). NEWS2 versus a single-parameter system to identify critically ill medical patients in the emergency department. Resuscitation Plus. 3. doi: 10.1016/j.resplu.2020.100020. Full text in Research Archive
• Crispino, Marta; Arjas, Elja; Vitelli, Valeria; Barrett, Natasha & Rattalma, Arnoldo Frigessi Di (2019). A Bayesian Mallows approach to nontransitive pair comparison data: How human are sounds? Annals of Applied Statistics. ISSN 1932-6157. 13(1), p. 492–519. doi: 10.1214/18-AOAS1203. Full text in Research Archive Show summary

  We are interested in learning how listeners perceive sounds as having human origins. An experiment was performed with a series of electronically synthesized sounds, and listeners were asked to compare them in pairs. We propose a Bayesian probabilistic method to learn individual preferences from nontransitive pairwise comparison data, as happens when one (or more) individual preferences in the data contradicts what is implied by the others. We build a Bayesian Mallows model in order to handle nontransitive data, with a latent layer of uncertainty which captures the generation of preference misreporting. We then develop a mixture extension of the Mallows model, able to learn individual preferences in a heterogeneous population. The results of our analysis of the musicology experiment are of interest to electroacoustic composers and sound designers, and to the audio industry in general, whose aim is to understand how computer generated sounds can be produced in order to sound more human.

• Liu, Qinghua; Crispino, Marta; Scheel, Ida; Vitelli, Valeria & Rattalma, Arnoldo Frigessi Di (2019). Model-based learning from preference data. Annual Review of Statistics and Its Application. ISSN 2326-8298. 6, p. 329–354. doi: 10.1146/annurev-statistics-031017-100213. Full text in Research Archive
• Vitelli, Valeria; Sørensen, Øystein; Crispino, Marta; Rattalma, Arnoldo Frigessi Di & Arjas, Elja (2018). Probabilistic preference learning with the Mallows rank model. Journal of machine learning research. ISSN 1532-4435. 18, p. 1–49. Full text in Research Archive Show summary

  Ranking and comparing items is crucial for collecting information about preferences in many areas, from marketing to politics. The Mallows rank model is among the most successful approaches to analyse rank data, but its computational complexity has limited its use to a particular form based on Kendall distance. We develop new computationally tractable methods for Bayesian inference in Mallows models that work with any right-invariant distance. Our method performs inference on the consensus ranking of the items, also when based on partial rankings, such as top-k items or pairwise comparisons. We prove that items that none of the assessors has ranked do not influence the maximum a posteriori consensus ranking, and can therefore be ignored. When assessors are many or heterogeneous, we propose a mixture model for clustering them in homogeneous subgroups, with cluster-specific consensus rankings. We develop approximate stochastic algorithms that allow a fully probabilistic analysis, leading to coherent quantifications of uncertainties. We make probabilistic predictions on the class membership of assessors based on their ranking of just some items, and predict missing individual preferences, as needed in recommendation systems. We test our approach using several experimental and benchmark datasets.

• Ak, Ronay; Li, Yan-Fu; Vitelli, Valeria & Zio, Enrico (2018). Adequacy assessment of a wind-integrated system using neural network-based interval predictions of wind power generation and load. International Journal of Electrical Power & Energy Systems. ISSN 0142-0615. 95, p. 213–226. doi: 10.1016/j.ijepes.2017.08.012. Full text in Research Archive
• Asfaw, Derbachew; Vitelli, Valeria; Sørensen, Øystein; Arjas, Elja & Frigessi, Arnoldo (2017). Time-varying rankings with the Bayesian Mallows model. Stat. 6(1), p. 14–30. doi: 10.1002/sta4.132. Full text in Research Archive Show summary

  We present new statistical methodology for analysing rank data, where the rankings are allowed to vary in time. Such data arise, for example, when the assessments are based on a performance measure of the items, which varies in time, or if the criteria, according to which the items are ranked, change in time. Items can also be absent when the assessments are made, because of delayed entry or early departure, or purely randomly. In such situations, also the dimension of the rank vectors varies in time. Rank data in a time‐dependent setting thus lead to challenging statistical problems. These problems are further complicated, from the perspective of computation, by the large dimension of the sample space consisting of all permutations of the items. Here, we focus on introducing and developing a Bayesian version of the Mallows rank model, suitable for situations in which the ranks vary in time and the assessments can be incomplete. The consequent missing data problems are handled by applying Bayesian data augmentation within Markov chain Monte Carlo. Our method is also adapted to the task of future rank prediction. The method is illustrated by analysing some aspects of a data set describing the academic performance, measured by a series of tests, of a class of high school students over a period of 4 years. © 2017 Wiley

• Floriello, Davide & Vitelli, Valeria (2017). Sparse clustering of functional data. Journal of Multivariate Analysis. ISSN 0047-259X. 154, p. 1–18. doi: 10.1016/j.jmva.2016.10.008. Full text in Research Archive
• Aure, Miriam Ragle; Vitelli, Valeria; Jernström, Sandra Johanna; Kumar, Surendra; Krohn, Marit & Due, Eldri Undlien [Show all 35 contributors for this article] (2017). Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome. Breast Cancer Research. ISSN 1465-5411. 19(44). doi: 10.1186/s13058-017-0812-y. Full text in Research Archive Show summary

  Background Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.

• Brix, Maiken Kirkegaard; Westman, Eric; Simmons, Andrew; Ringstad, Geir; Eide, Per Kristian & Wagner-Larsen, Kari Strøno [Show all 9 contributors for this article] (2017). The Evans' Index revisited: New cut-off levels for use in radiological assessment of ventricular enlargement in the elderly. European Journal of Radiology. ISSN 0720-048X. 95, p. 28–32. doi: 10.1016/j.ejrad.2017.07.013. Full text in Research Archive
• Wyller, Vegard Bruun; Vitelli, Valeria; Sulheim, Dag; Fagermoen, Frode Even; Winger, Anette & Godang, Kristin [Show all 7 contributors for this article] (2016). Altered neuroendocrine control and association to clinical symptoms in adolescent chronic fatigue syndrome: a cross-sectional study. Journal of Translational Medicine. 14(121). doi: 10.1186/s12967-016-0873-1. Full text in Research Archive Show summary

  Background Chronic fatigue syndrome (CFS) is a common and disabling disorder, and a major threat against adolescent health. The pathophysiology is unknown, but alteration of neuroendocrine control systems might be a central element, resulting in attenuation of the hypothalamus–pituitary–adrenalin (HPA) axis and enhancement of the sympathetic/adrenal medulla (SAM) system. This study explored differences in neuroendocrine control mechanisms between adolescent CFS patients and healthy controls, and whether characteristics of the control mechanisms are associated with important clinical variables within the CFS group. Methods CFS patients 12–18 years of age were recruited nation-wide to a single referral center as part of the NorCAPITAL project. A broad case definition of CFS was applied. A comparable group of healthy controls were recruited from local schools. A total of nine hormones were assayed and subjected to network analyses using the ARACNE algorithm. Symptoms were charted by a questionnaire, and daily physical activity was recorded by an accelerometer. Results A total of 120 CFS patients and 68 healthy controls were included. CFS patients had significantly higher levels of plasma norepinephrine, plasma epinephrine and plasma FT4, and significantly lower levels of urine cortisol/creatinine ratio. Subgrouping according to other case definitions as well as adjusting for confounding factors did not alter the results. Multivariate linear regression models as well as network analyses revealed different interrelations between hormones of the HPA axis, the SAM system, and the thyroid system in CFS patients and healthy controls. Also, single hormone degree centrality was associated with clinical markers within the CFS group. Conclusion This study reveals different interrelation between hormones of the HPA axis, the SAM system, and the thyroid system in CFS patients and healthy controls, and an association between hormone control characteristics and important clinical variables in the CFS group. These results add to the growing insight of CFS disease mechanisms.

• Pihlstrøm, Lasse; Morset, Kristina Rebekka; Grimstad, Espen; Vitelli, Valeria & Toft, Mathias (2016). A cumulative genetic risk score predicts progression in Parkinson's disease. Movement Disorders. ISSN 0885-3185. 31(4), p. 487–490. doi: 10.1002/mds.26505. Full text in Research Archive
• Lesurf, Robert; Aure, Miriam Ragle; Mørk, Hanne Håberg; Vitelli, Valeria; Lundgren, Steinar & Børresen-Dale, Anne-Lise [Show all 10 contributors for this article] (2016). Molecular Features of Subtype-Specific Progression from Ductal Carcinoma In Situ to Invasive Breast Cancer. Cell reports. 16(4), p. 1166–1179. doi: 10.1016/j.celrep.2016.06.051. Full text in Research Archive Show summary

  Breast cancer consists of at least five main molecular “intrinsic” subtypes that are reflected in both pre-invasive and invasive disease. Although previous studies have suggested that many of the molecular features of invasive breast cancer are established early, it is unclear what mechanisms drive progression and whether the mechanisms of progression are dependent or independent of subtype. We have generated mRNA, miRNA, and DNA copy-number profiles from a total of 59 in situ lesions and 85 invasive tumors in order to comprehensively identify those genes, signaling pathways, processes, and cell types that are involved in breast cancer progression. Our work provides evidence that there are molecular features associated with disease progression that are unique to the intrinsic subtypes. We additionally establish subtype-specific signatures that are able to identify a small proportion of pre-invasive tumors with expression profiles that resemble invasive carcinoma, indicating a higher likelihood of future disease progression.

• Crispino, Marta; Arjas, Elja; Vitelli, Valeria & Frigessi, Arnoldo (2016). Recommendation from intransitive pairwise comparisons. CEUR Workshop Proceedings. 1688. Full text in Research Archive
• Abramowicz, Konrad; Arnqvist, Per; Secchi, Piercesare; Luna, Sara Sjöstedt de; Vantini, Simone & Vitelli, Valeria (2016). Clustering misaligned dependent curves applied to varved lake sediment for climate reconstruction. Stochastic environmental research and risk assessment (Print). ISSN 1436-3240. 31(1), p. 71–85. doi: 10.1007/s00477-016-1287-6. Full text in Research Archive
• Ak, Ronay; Vitelli, Valeria & Zio, Enrico (2015). An Interval-Valued Neural Network Approach for Uncertainty Quantification in Short-Term Wind Speed Prediction. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 26(11), p. 2787–2800. doi: 10.1109/TNNLS.2015.2396933. Full text in Research Archive
• Liu, Jie; Vitelli, Valeria; Zio, Enrico & Seraoui, Redouane (2015). A Novel Dynamic-Weighted Probabilistic Support Vector Regression-Based Ensemble for Prognostics of Time Series Data. IEEE Transactions on Reliability. ISSN 0018-9529. 64(4), p. 1203–1213. doi: 10.1109/TR.2015.2427156. Full text in Research Archive
• Secchi, Piercesare; Vantini, Simone & Vitelli, Valeria (2015). Analysis of spatio-temporal mobile phone data: a case study in the metropolitan area of Milan. Statistical Methods & Applications. ISSN 1618-2510. 24(2), p. 279–300. doi: 10.1007/s10260-014-0294-3. Full text in Research Archive
• Secchi, Piercesare; Vantini, Simone & Vitelli, Valeria (2015). Rejoinder to the discussion of “Analysis of Spatio-Temporal Mobile Phone Data: a Case Study in the Metropolitan Area of Milan”. Statistical Methods & Applications. ISSN 1618-2510. 24(2), p. 335–338. doi: 10.1007/s10260-015-0325-8. Full text in Research Archive
• Liu, Jie; Vitelli, Valeria; Zio, Enrico & Seraoui, Redouane (2015). A dynamic weighted RBF-based ensemble for prediction of time series data from nuclear components. International Journal of Prognostics and Health Management. 6(3), p. 1–9. doi: 10.1109/tr.2015.2427156. Full text in Research Archive
• Liu, Jie; Seraoui, Redouane; Vitelli, Valeria & Zio, Enrico (2013). Nuclear Power Plant Components Condition Monitoring by Probabilistic Support Vector Machine. Annals of Nuclear Energy. ISSN 0306-4549. 56, p. 23–33. doi: 10.1016/j.anucene.2013.01.005. Full text in Research Archive
• Ak, Ronay; Li, Yanfu; Vitelli, Valeria & Zio, Enrico (2013). NSGA-II-Trained Neural Network Approach to the Estimation of Prediction Intervals of Scale Deposition Rate in Oil & Gas Equipment. Expert Systems With Applications. ISSN 0957-4174. 40(4), p. 1205–1212. doi: 10.1016/j.eswa.2012.08.018. Full text in Research Archive
• Ieva, F.; Paganoni, Anna Maria; Pigoli, Davide & Vitelli, Valeria (2013). Multivariate functional clustering for the morphological analysis of electrocardiograph curves. Journal of the Royal Statistical Society, Series C (Applied Statistics). ISSN 0035-9254. 62(3), p. 401–418. doi: 10.1111/j.1467-9876.2012.01062.x. Full text in Research Archive
• Baraldo, Stefano; Ieva, F.; Paganoni, Anna Maria & Vitelli, Valeria (2013). Outcome Prediction for Heart Failure Telemonitoring Via Generalized Linear Models with Functional Covariates. Scandinavian Journal of Statistics. ISSN 0303-6898. 40(3), p. 403–416. doi: 10.1111/j.1467-9469.2012.00818.x. Full text in Research Archive
• Secchi, Piercesare; Vantini, Simone & Vitelli, Valeria (2013). Bagging Voronoi classifiers for clustering spatial functional data. International Journal of Applied Earth Observation and Geoinformation. ISSN 1569-8432. 22, p. 53–64. doi: 10.1016/j.jag.2012.03.006. Full text in Research Archive
• Vitelli, Valeria; Sangalli, Laura Maria; Secchi, Piercesare & Vantini, Simone (2010). K-means alignment for curve clustering. Computational Statistics & Data Analysis. ISSN 0167-9473. 54(5), p. 1219–1233. Full text in Research Archive
• Vitelli, Valeria; Sangalli, Laura Maria; Secchi, Piercesare & Vantini, Simone (2010). Functional Clustering and Alignment Methods with Applications. Communications in Applied and Industrial Mathematics. 1(1), p. 205–224. doi: 10.1685/2010CAIM486. Full text in Research Archive Show summary

  We consider the issue of classification of functional data and, in particular, we deal with the problem of curve clustering when curves are misaligned. In the proposed setting, we aim at jointly aligning and clustering the curves, via the solution of an optimization problem. We describe an iterative procedure for the solution of the optimization problem, and we detail two alternative specifications of the procedure, a k-mean version and a k-medoid version. We illustrate via applications to real data the robustness of the alignment and clustering procedure under the different specifications.

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• Vitelli, Valeria; Passamonti, Federica; Vantini, Simone & Secchi, Piercesare (2022). A Nonparametric Algorithm for Spatially Dependent Functional Data: Bagging Voronoi for Clustering, Dimensional Reduction, and Regression. John Wiley & Sons. ISBN 9781119387848. 432 p. Full text in Research Archive Show summary

  Bagging Voronoi is a flexible methodology for the nonparametric analysis of functional data indexed by the sites of a spatial lattice, when spatial dependence is deemed to be a key issue of the analysis. This system of methods can target different goals: classification, dimensional reduction, and regression. By following a bagging perspective, the Bagging Voronoi strategy generates an ensemble of weak learners, each trained on a data sample where spatial dependence is captured by a random system of Voronoi neighborhoods. Results generated by the weak learners are finally aggregated into a conclusive strong result. An application to the analysis of the Erlang measures recording, along space and time, the load of the mobile phone network covering the metropolitan region of Milano (Italy), motivates the methodology and drives its illustration.

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• Greve, Jan; Grün, Bettina; Malsiner-Walli, Gertraud; Frühwirth-Schnatter, Sylvia & Vitelli, Valeria (2026). Induced Priors in Bayesian Mixture Models [R package fipp version 1.0.1]. Full text in Research Archive
• Vitelli, Valeria (2026). AI instruments for better health. doi: https:/www.uio.no/english/research/strategic-research-areas/life-science/norway-life-science-conference/11-february-2026.html. Full text in Research Archive
• Vitelli, Valeria (2026). The Bayesian Mallows Model for Tied Rankings. doi: https:/ssc.ca/sites/default/files/meetings/ssc2026-program-without-abstracts.pdf. Full text in Research Archive
• Vitelli, Valeria (2026). Latent Function-on-Scalar Regression Models for Observed Sequences of Binary Data. doi: https:/ifcs2026.unimib.it/scientific-conference-program/. Full text in Research Archive
• Vitelli, Valeria (2026). Bayesian genome-wide clustering and variable selection of transcriptomic data via rank-based mixtures. doi: https:/events.conf.app/event/fe1bf927-baa2-4933-9e99-2efcafa0fcdd/agenda/6f738381-9780-46d6-bf62-f16dd1ba843f. Full text in Research Archive
• Wally, Youssef; Ell, Basil; Ricaud, Benjamin; Mylius-Kroken, Johan; Giese, Martin & Kampffmeyer, Michael [Show all 10 contributors for this article] (2026). Biologically Informed Across Space and Time Modelling of the Tumour Microenvironment. Full text in Research Archive Show summary

  Understanding spatial cell organisation in the tumour microenvironment is critical for cancer research, as it provides key insights into disease mechanisms. In this ongoing work, we explore two approaches that incorporate biological knowledge while analysing the tumour microenvironment. Firstly, we embed single-cell instances into a Lorentzian latent space using a fully hyperbolic variational autoencoder, which we have previously shown to retain significantly more biologically relevant information than Euclidean alternatives. Secondly, we adopt taxonomy-aware spatial association rule mining for quantifying cell interaction behaviour beyond pairwise interactions.

• Wally, Youssef; Mylius-Kroken, Johan; Kampffmeyer, Michael; Ehsani, Rezvan; Ell, Basil & Ricaud, Benjamin [Show all 10 contributors for this article] (2026). Biologically Informed Modelling of the Tumour Microenvironment: Hyperbolic Representation Learning and Hierarchical Association Rules. Full text in Research Archive Show summary

  Understanding spatial cell organisation in the tumour microenvironment is critical for cancer research, as it provides key insights into disease mechanisms. In this ongoing work, we explore two approaches that incorporate biological knowledge while analysing the tumour microenvironment. Firstly, we embed single-cell instances into a Lorentzian latent space using a fully hyperbolic variational autoencoder, which we have previously shown to retain significantly more biologically relevant information than Euclidean alternatives. Secondly, we adopt taxonomy-aware spatial association rule mining for quantifying cell interaction behaviour beyond pairwise interactions.

• Zuccato, Lorenzo; George, Anne-Marie; Vitelli, Valeria; Frigessi, Arnoldo; Giese, Martin & Scheel, Ida (2026). Bridging Statistical and Logical Perspectives on Inconsistency. Full text in Research Archive
• Wally, Youssef; Ell, Basil; Ricaud, Benjamin; Mylius-Kroken, Johan; Giese, Martin & Kampffmeyer, Michael [Show all 10 contributors for this article] (2026). Extracting Knowledge from Spatial Biology: Evaluating Cell Type Hierarchies in Breast Cancer Imaging Data. Full text in Research Archive Show summary

  Hyperbolic representation learning has shown compelling advantages over conventional Eu- clidean representation learning in modelling hierarchical relationships in data. In this work, we evaluate its potential to capture biological relations between cell types in highly multiplexed imaging data, where capturing subtle, hierarchical relationships between cell types is crucial to understand tissue composi- tion and functionality. Using a recent and thoroughly validated 42-marker Imaging Mass Cytometry (IMC) dataset of breast cancer tissue, we embed cells into both Euclidean and Lorentzian latent spaces via a fully hyperbolic variational autoencoder. We then introduce an information-theoretic framework based on k-nearest neighbour estimators to rigorously quantify the clustering performance in each geom- etry using mutual information and conditional mutual information. Our results reveal that hyperbolic embeddings retain significantly more biologically relevant information than their Euclidean counter- parts. We further provide open-source tools to extend Kraskov-Stögbauer-Grassberger based mutual information estimation to Lorentzian geodesic spaces, and to enable UMAP visualizations with hyper- bolic distance metrics. This work contributes a principled evaluation method for geometry-aware learning and supports the growing evidence of hyperbolic geometry’s benefits in spatial biology. Code is available at: https://github.com/youssefwally/FlatlandandBeyond

• Wally, Youssef; Ell, Basil; Ricaud, Benjamin; Giese, Martin; Vitelli, Valeria & Milosevic, Vladan [Show all 7 contributors for this article] (2026). Representing cell type hierarchies in breast cancer. Full text in Research Archive Show summary

  Understanding the spatial organisation of cells within tissues is fundamental for cell niches discovery. Advances in spatial omics technologies now yield high-resolution maps of cell populations across tissue sections, yet computational methods for systematically discovering recurrent, interpretable multi-cell assemblies remain limited, particularly when cell type annotations are organised in hierarchical taxonomies. Cell type ontologies in modern atlases naturally form hierarchies, ignoring this structure leads to either redundant or missed niche patterns. We present our on-going work for cell niche discovery based on spatial association rule mining that is explicitly aware of these hierarchical relationships. By propagating colocation evidence through the taxonomy, we are able to identify multi-cell-type assemblies that co-occur significantly across tissue regions at both fine-grained and coarser levels of cell-type resolution, reducing redundancy while improving interpretability

• Wally, Youssef; Ell, Basil; Ricaud, Benjamin; Giese, Martin; Vitelli, Valeria & Milosevic, Vladan [Show all 7 contributors for this article] (2026). Cell Niche discovery via taxonomy-aware spatial association rule mining. Full text in Research Archive Show summary

  Understanding the spatial organisation of cells within tissues is fundamental for cell niches discovery. Advances in spatial omics technologies now yield high-resolution maps of cell populations across tissue sections, yet computational methods for systematically discovering recurrent, interpretable multi-cell assemblies remain limited, particularly when cell type annotations are organised in hierarchical taxonomies. Cell type ontologies in modern atlases naturally form hierarchies, ignoring this structure leads to either redundant or missed niche patterns. We present our on-going work for cell niche discovery based on spatial association rule mining that is explicitly aware of these hierarchical relationships. By propagating colocation evidence through the taxonomy, we are able to identify multi-cell-type assemblies that co-occur significantly across tissue regions at both fine-grained and coarser levels of cell-type resolution, reducing redundancy while improving interpretability

• Wally, Youssef; Ell, Basil; Ricaud, Benjamin; Giese, Martin; Milosevic, Vladan & Vitelli, Valeria [Show all 7 contributors for this article] (2026). Cell Niche discovery via taxonomy-aware spatial association rule mining. Full text in Research Archive Show summary

  Understanding the spatial organisation of cells within tissues is fundamental for cell niches discovery. Advances in spatial omics technologies now yield high-resolution maps of cell populations across tissue sections, yet computational methods for systematically discovering recurrent, interpretable multi-cell assemblies remain limited, particularly when cell type annotations are organised in hierarchical taxonomies. Cell type ontologies in modern atlases naturally form hierarchies, ignoring this structure leads to either redundant or missed niche patterns. We present our on-going work for cell niche discovery based on spatial association rule mining that is explicitly aware of these hierarchical relationships. By propagating colocation evidence through the taxonomy, we are able to identify multi-cell-type assemblies that co-occur significantly across tissue regions at both fine-grained and coarser levels of cell-type resolution, reducing redundancy while improving interpretability

• Denault, William R. P.; Hubin, Aliaksandr; Vitelli, Valeria & Richardson, Sylvia (2024). Contributed Discussion to Sparse Bayesian Factor Analysis When the Number of Factors Is Unknown. doi: 10.1214/24-BA1423. Full text in Research Archive Show summary

  There has been increased research interest in the subfield of sparse Bayesian factor analysis with shrinkage priors, which achieve additional sparsity beyond the natural parsimonity of factor models. In this spirit, we estimate the number of common factors in the widely applied sparse latent factor model with spike-and-slab priors on the factor loadings matrix. Our framework leads to a natural, efficient and simultaneous coupling of model estimation and selection on one hand and model identification and rank estimation (number of factors) on the other hand. More precisely, by embedding the unordered generalised lower triangular loadings representation into overfitting sparse factor modelling, we obtain posterior summaries regarding factor loadings, common factors as well as the factor dimension via postprocessing draws from our efficient and customized Markov chain Monte Carlo scheme.

• Vitelli, Valeria (2022). Variational Bayes Pseudo-Mallows approach for Efficient Probabilistic Preference Learning. Full text in Research Archive
• Vitelli, Valeria (2021). Lower-dimensional Bayes Mallows model with application to cancer genomics. Full text in Research Archive
• Vitelli, Valeria (2021). Rank-based Bayesian inference for transcriptomic analyses in cancer. Full text in Research Archive
• Vitelli, Valeria (2020). Probabilistic Preference Learning via the Mallows rank model: some recent advances. Full text in Research Archive
• Bos, Steffan Daniel; Brorson, Ina Skaara; Eriksson, Anna; Leikfoss, Ingvild Sørum; Vitelli, Valeria & Celius, Elisabeth Gulowsen [Show all 10 contributors for this article] (2019). Differential gene expression analysis of CD8+ T cells from multiple sclerosis patients and healthy controls. Full text in Research Archive
• Wyller, Vegard Bruun Bratholm; Vitelli, Valeria; Sulheim, Dag; Fagermoen, Frode Even; Winger, Anette & Godang, Kristin [Show all 7 contributors for this article] (2017). Altered neuroendocrine control and association to clinical symptoms in adolescent chronic fatigue syndrome: A cross-sectional study. Journal of Translational Medicine. 15(157). doi: 10.1186/s12967-017-1261-1. Full text in Research Archive
• Frigessi, Arnoldo; Vitelli, Valeria; Crispino, Marta & Arjas, Elja (2016). Recommendations from non-transitive pairwise comparisons. Full text in Research Archive
• Wickstrøm, Kristin; Amundsen, Erik Koldberg; Vitelli, Valeria & Prebensen, Christian Haugland (2026). Supervised and unsupervised modeling on routine clinical and laboratory variables for diagnosis, prognosis and subgrouping of infectious diseases. Universitetet i Oslo. ISSN 9788234807787. Full text in Research Archive Show summary

  In this thesis we used supervised and unsupervised models on patients with infectious diseases, with a focus on laboratory variables from patients admitted to the emergency department (ED). These models are used for diagnosis, prognosis, and subgrouping of patients. In the first part of the thesis, we evaluated four published supervised models for prediction of outcomes in COVID‑19 patients. Using cohorts from Oslo and London, we found that all models performed worse and with greater variability than in their original studies. Recalibration improved performance and the results highlight the importance of validating prediction models across different populations. Next, we developed a supervised model to predict infection using Sysmex cell population (CP) data from patients with suspected sepsis. These research parameters are produced by hematology instruments when analyzing white blood cells and reflect the cells morphology and degree of activation. Among the parameters, monocyte complexity (MO‑X) and neutrophil fluorescence activity (NEUT‑RI) showed the strongest individual predictive value. Using LASSO and backward selection, we created a four‑variable model that performed slightly below C‑reactive protein (CRP) alone. The multilayer perceptron model demonstrated the best calibration, particularly when CRP was added. Further standardization and external validation are required before clinical use. Finally, we explored unsupervised clustering to identify subgroups of sepsis patients. Unlike the SENECA study, which reported four phenotypes, our analyses did not reproduce these subgroups. A large multicenter replication study with cohorts from Oslo, Karolinska, and Oxford also failed to reproduce the SENECA subgroups, suggesting that these phenotypes may not generalize across populations. In the Oslo cohort, seven clusters were more stable and clinically distinct. Further validation of the seven clusters should be conducted.

• Vitelli, Valeria; Sørensen, Øystein; Frigessi, Arnoldo & Arjas, Elja (2018). Probabilistic preference learning with the Mallows rank model. Journal of Machine Learning Research. Full text in Research Archive

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