Browsing by Subject "precision medicine"

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  • Zusinaite, Eva; Ianevski, Aleksandr; Niukkanen, Diana; Poranen, Minna M.; Bjoras, Magnar; Afset, Jan Egil; Tenson, Tanel; Velagapudi, Vidya; Merits, Andres; Kainov, Denis E. (2018)
    There are dozens of approved, investigational and experimental antiviral agents. Many of these agents cause serious side effects, which can only be revealed after drug administration. Identification of the side effects prior to drug administration is challenging. Here we describe an ex vivo approach for studying immuno- and neuro-modulatory properties of antiviral agents, which may be associated with potential side effects of these therapeutics. The current approach combines drug toxicity/efficacy tests and transcriptomics, which is followed by mRNA, cytokine and metabolite profiling. We demonstrated the utility of this approach with several examples of antiviral agents. We also showed that the approach can utilize different immune stimuli and cell types. It can also include other omics techniques, such as genomics and epigenomics, to allow identification of individual markers associated with adverse reactions to antivirals with immuno- and neuro-modulatory properties.
  • Bousquet, Jean; Pfaar, Oliver; Agache, Ioana; Bedbrook, Anna; Akdis, Cezmi A.; Canonica, G. Walter; Chivato, Tomas; Al-Ahmad, Mona; Abdul Latiff, Amir H.; Ansotegui, Ignacio J.; Bachert, Claus; Baharuddin, Abdullah; Bergmann, Karl-Christian; Bindslev-Jensen, Carsten; Bjermer, Leif; Bonini, Matteo; Bosnic-Anticevich, Sinthia; Bosse, Isabelle; Brough, Helen A.; Brussino, Luisa; Calderon, Moises A.; Caraballo, Luis; Cardona, Victoria; Carreiro-Martins, Pedro; Casale, Tomas; Cecchi, Lorenzo; Cepeda Sarabia, Alfonso M.; Chkhartishvili, Ekaterine; Chu, Derek K.; Cirule, Ieva; Cruz, Alvaro A.; Czarlewski, Wienczyslawa; del Giacco, Stefano; Demoly, Pascal; Devillier, Philippe; Dokic, Dejan; Durham, Stephen L.; Ebisawa, Motohiro; El-Gamalt, Yehia; Emuzyte, Regina; Gamkrelidze, Amiran; Fauquert, Jean Luc; Fiocchi, Alessandro; Fokkens, Wytske J.; Fonseca, Joao A.; Fontaine, Jean-Francois; Gawlik, Radoslaw; Gelincik, Asli; Gemicioglu, Bilun; Gereda, Jose E.; Gerth van Wijk, Roy; Gomez, R. Maximiliano; Gotua, Maia; Grisle, Ineta; Guzman, Maria-Antonieta; Haahtela, Tari; Halken, Susanne; Heffler, Enrico; Hoffmann-Sommergruber, Karin; Hossny, Elham; Hrubisko, Martin; Irani, Carla; Ivancevich, Juan Carlos; Ispayeva, Zhanat; Julge, Kaja; Kaidashev, Igor; Kalayci, Omer; Khaitov, Musa; Klimek, Ludger; Knol, Edward; Kowalski, Marek L.; Kraxner, Helga; Kull, Inger; Kuna, Piotr; Kvedariene, Violeta; Kritikos, Vicky; Lauerma, Antti; Lau, Susanne; Laune, Daniel; Levin, Michael; Larenas-Linnemann, Desiree E.; Lodrup Carlsen, Karin C.; Lombardi, Carlo; Lourenco, Olga M.; Mahboub, Bassam; Malling, Hans-Jorgen; Manning, Patrick; Marshall, Gailen D.; Melen, Erik; Meltzer, Eli O.; Miculinic, Neven; Milenkovic, Branislava; Moin, Mostafa; Montefort, Stephen; Morais-Almeida, Mario; Mortz, Charlotte G.; Mosges, Ralph; Mullol, Joaquim; Namazova Baranova, Leyla; Neffen, Hugo; Nekam, Kristof; Niedoszytko, Marek; Odemyr, Mikaela; O'Hehir, Robyn E.; Ollert, Markus; O'Mahony, Liam; Ohta, Ken; Okamoto, Yoshitaka; Okubo, Kimi; Pajno, Giovanni B.; Palomares, Oscar; Palkonen, Susanna; Panzner, Petr; Papadopoulos, Nikolaos; Park, Hae-Sim; Passalacqua, Giovanni; Patella, Vincenzo; Pawankar, Ruby; Pham-Thi, Nhan; Plavec, Davor; Popov, Todor A.; Recto, Marysia; Regateiro, Frederico S.; Riggioni, Carmen; Roberts, Graham; Rodriguez-Gonzales, Monica; Rosario, Nelson; Rottem, Menachem; Rouadi, Philip W.; Ryan, Dermot; Samolinski, Boleslaw; Sanchez-Borgest, Mario; Serpa, Faradiba S.; Sastre, Joaquin; Scadding, Glenis K.; Shamji, Mohamed H.; Schmid-Grendelmeier, Peter; Schunemann, Holger J.; Sheikh, Aziz; Scichilone, Nicola; Sisul, Juan Carlos; Sofiev, Mikhail; Sole, Dirceu; Sooronbaev, Talant; Soto-Martinez, Manuel; Soto-Quiros, Manuel; Sova, Milan; Schwarze, Jurgen; Skypala, Isabel; Suppli-Ulrik, Charlotte; Taborda-Barata, Luis; Todo-Bom, Ana; Torres, Maria J.; Valentin-Rostan, Marylin; Tomazic, Peter-Valentin; Valero, Antonio; Toppila-Salmi, Sanna; Tsiligianni, Ioanna; Untersmayr, Eva; Urrutia-Pereira, Marilyn; Valiulis, Arunas; Valovirta, Erkka; Vandenplas, Olivier; Ventura, Maria Teresa; Vichyanond, Pakit; Wagenmann, Martin; Wallace, Dana; Walusiak-Skorupa, Jolanta; Wang, De Yun; Waserman, Susan; Wong, Gary W. K.; Yorgancioglu, Arzu; Yusuf, Osman M.; Zernotti, Mario; Zhang, Luo; Zidarn, Mihaela; Zuberbier, Torsten; Jutel, Marek (2021)
  • Saeed, Khalid; Ojamies, Poojitha; Pellinen, Teijo; Eldfors, Samuli; Turkki, Riku; Lundin, Johan; Järvinen, Petrus; Nisén, Harry; Taari, Kimmo; af Hällström, Taija Maria; Rannikko, Antti; Mirtti, Tuomas; Kallioniemi, Olli-Pekka; Östling, Päivi (2019)
    Renal cell cancer (RCC) has become a prototype example of the extensive intratumor heterogeneity and clonal evolution of human cancers. However, there is little direct evidence on how the genetic heterogeneity impacts on drug response profiles of the cancer cells. Our goal was to determine how genomic clonal evolution impacts drug responses. Finding from our study could help to define the challenge that clonal evolution poses on cancer therapy. We established multiple patient-derived cells (PDCs) from different tumor regions of four RCC patients, verified their clonal relationship to each other and to the uncultured tumor tissue by genome sequencing. Furthermore, comprehensive drug-sensitivity testing with 460 oncological drugs was performed on all PDC clones. The PDCs retained many cancer-specific copy number alterations and mutations in driver genes such as VHL, PBRM1, PIK3C2A, KMD5C and TSC2 genes. The drug testing highlighted vulnerability in the PDCs toward approved RCC drugs, such as the mTOR-inhibitor temsirolimus, but also novel sensitivities were uncovered. The individual PDC clones from different tumor regions in a patient showed distinct drug-response profiles, suggesting that genomic heterogeneity contributes to the variability in drug responses. Studies of multiple PDCs from a patient with cancer are informative for elucidating cancer heterogeneity and for the determination on how the genomic evolution is manifested in cancer drug responsiveness. This approach could facilitate tailoring of drugs and drug combinations to individual patients.
  • Zagidullin, B; Wang, Z; Guan, Y; Pitkänen, E; Tang, J (2021)
    Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.
  • Benedek, P.; Jiao, H.; Duvefelt, K.; Skoog, T.; Linde, M.; Kiviluoma, P.; Kere, J.; Eriksson, M.; Angelin, B. (2021)
    Aim To investigate whether genotyping could be used as a cost-effective screening step, preceding next-generation sequencing (NGS), in molecular diagnosis of familial hypercholesterolaemia (FH) in Swedish patients. Methods and results Three hundred patients of Swedish origin with clinical suspicion of heterozygous FH were analysed using a specific array genotyping panel embedding 112 FH-causing mutations in the LDLR, APOB and PCSK9 genes. The mutations had been selected from previous reports on FH patients in Scandinavia and Finland. Mutation-negative cases were further analysed by NGS. In 181 patients with probable or definite FH using the Dutch lipid clinics network (DLCN) criteria (score >= 6), a causative mutation was identified in 116 (64%). Of these, 94 (81%) were detected by genotyping. Ten mutations accounted for more than 50% of the positive cases, with APOB c.10580G>A being the most common. Mutations in LDLR predominated, with (c.2311+1_2312-1)(2514)del (FH Helsinki) and c.259T>G having the highest frequency. Two novel LDLR mutations were identified. In patients with DLCN score < 6, mutation detection rate was significantly higher at younger age. Conclusion A limited number of mutations explain a major fraction of FH cases in Sweden. Combination of selective genotyping and NGS facilitates the clinical challenge of cost-effective genetic screening in suspected FH. The frequency of APOB c.10580G>A was higher than previously reported in Sweden. The lack of demonstrable mutations in the LDLR, APOB and PCSK9 genes in similar to 1/3 of patients with probable FH strongly suggests that additional genetic mechanisms are to be found in phenotypic FH.
  • Majumder, Muntasir Mamun; Silvennoinen, Raija; Anttila, Pekka; Tamborero, David; Eldfors, Samuli; Yadav, Bhagwan; Karjalainen, Riikka; Kuusanmaki, Heikki; Lievonen, Juha; Parsons, Alun; Suvela, Minna; Jantunen, Esa; Porkka, Kimmo; Heckman, Caroline A. (2017)
    Novel agents have increased survival of multiple myeloma (MM) patients, however high-risk and relapsed/refractory patients remain challenging to treat and their outcome is poor. To identify novel therapies and aid treatment selection for MM, we assessed the ex vivo sensitivity of 50 MM patient samples to 308 approved and investigational drugs. With the results we i) classified patients based on their ex vivo drug response profile; ii) identified and matched potential drug candidates to recurrent cytogenetic alterations; and iii) correlated ex vivo drug sensitivity to patient outcome. Based on their drug sensitivity profiles, MM patients were stratified into four distinct subgroups with varied survival outcomes. Patients with progressive disease and poor survival clustered in a drug response group exhibiting high sensitivity to signal transduction inhibitors. Del(17p) positive samples were resistant to most drugs tested with the exception of histone deacetylase and BCL2 inhibitors. Samples positive for t(4; 14) were highly sensitive to immunomodulatory drugs, proteasome inhibitors and several targeted drugs. Three patients treated based on the ex vivo results showed good response to the selected treatments. Our results demonstrate that ex vivo drug testing may potentially be applied to optimize treatment selection and achieve therapeutic benefit for relapsed/refractory MM.
  • Neiman, Maja; Hellstrom, Cecilia; Just, David; Mattsson, Cecilia; Fagerberg, Linn; Schuppe-Koistinen, Ina; Gummesson, Anders; Bergström, Göran; Kallioniemi, Olli; Achour, Adnane; Sallinen, Riitta; Uhlen, Mathias; Nilsson, Peter (2019)
    In the era towards precision medicine, we here present the individual specific autoantibody signatures of 193 healthy individuals. The self-reactive IgG signatures are stable over time in a way that each individual profile is recognized in longitudinal sampling. The IgG autoantibody reactivity towards an antigen array comprising 335 protein fragments, representing 204 human proteins with potential relevance to autoimmune disorders, was measured in longitudinal plasma samples from 193 healthy individuals. This analysis resulted in unique autoantibody barcodes for each individual that were maintained over one year's time. The reactivity profiles, or signatures, are person specific in regards to the number of reactivities and antigen specificity. Two independent data sets were consistent in that each healthy individual displayed reactivity towards 0-16 antigens, with a median of six. Subsequently, four selected individuals were profiled on in-house produced high-density protein arrays containing 23,000 protein fragments representing 14,000 unique protein coding genes. Based on a unique, broad and deep longitudinal profiling of autoantibody reactivities, our results demonstrate a unique autoreactive profile in each analyzed healthy individual. The need and interest for broad-ranged and high-resolution molecular profiling of healthy individuals is rising. We have here generated and assessed an initial perspective on the global distribution of the self-reactive IgG repertoire in healthy individuals, by investigating 193 well-characterized healthy individuals.
  • Lahteenmaki, Jaakko; Vuorinen, AL; Pajula, J; Harno, K; Lehto, M; Niemi, M; Van Gils, M (2021)
    Aim: This case study aimed to investigate the process of integrating resources of multiple biobanks and health-care registers, especially addressing data permit application, time schedules, co-operation of stakeholders, data exchange and data quality. Methods: We investigated the process in the context of a retrospective study: Pharmacogenomics of antithrombotic drugs (PreMed study). The study involved linking the genotype data of three Finnish biobanks (Auria Biobank, Helsinki Biobank and THL Biobank) with register data on medicine dispensations, health-care encounters and laboratory results. Results: We managed to collect a cohort of 7005 genotyped individuals, thereby achieving the statistical power requirements of the study. The data collection process took 16 months, exceeding our original estimate by seven months. The main delays were caused by the congested data permit approval service to access national register data on health-care encounters. Comparison of hospital data lakes and national registers revealed differences, especially concerning medication data. Genetic variant frequencies were in line with earlier data reported for the European population. The yearly number of international normalised ratio (INR) tests showed stable behaviour over time. Conclusions: A large cohort, consisting of versatile individual-level phenotype and genotype data, can be constructed by integrating data from several biobanks and health data registers in Finland. Co-operation with biobanks is straightforward. However, long time periods need to be reserved when biobank resources are linked with national register data. There is a need for efforts to define general, harmonised co-operation practices and data exchange methods for enabling efficient collection of data from multiple sources.
  • Lin, Jizhen; Hafren, Lena; Kerschner, Joseph; Li, Jian-Dong; Brown, Steve; Zheng, Qing Y.; Preciado, Diego; Nakamura, Yoshihisa; Huang, Qiuhong; Zhang, Yan (2017)
    Objective. The objective is to perform a comprehensive review of the literature up to 2015 on the genetics and precision medicine relevant to otitis media. Data Sources. PubMed database of the National Library of Medicine. Review Methods. Two subpanels were formed comprising experts in the genetics and precision medicine of otitis media. Each of the panels reviewed the literature in their respective fields and wrote draft reviews. The reviews were shared with all panel members, and a merged draft was created. The entire panel met at the 18th International Symposium on Recent Advances in Otitis Media in June 2015 and discussed the review and refined the content. A final draft was made, circulated, and approved by the panel members. Conclusion. Many genes relevant to otitis media have been identified in the last 4 years in advancing our knowledge regarding the predisposition of the middle ear mucosa to commensals and pathogens. Advances include mutant animal models and clinical studies. Many signaling pathways are involved in the predisposition of otitis media. Implications for Practice. New knowledge on the genetic background relevant to otitis media forms a basis of novel potential interventions, including potential new ways to treat otitis media.
  • ARIA EPOS Working Groups; Haahtela, Tari (2017)
    Precision medicine (PM) is increasingly recognized as the way forward for optimizing patient care. Introduced in the field of oncology, it is now considered of major interest in other medical domains like allergy and chronic airway diseases, which face an urgent need to improve the level of disease control, enhance patient satisfaction and increase effectiveness of preventive interventions. The combination of personalized care, prediction of treatment success, prevention of disease and patient participation in the elaboration of the treatment plan is expected to substantially improve the therapeutic approach for individuals suffering from chronic disabling conditions. Given the emerging data on the impact of patient stratification on treatment outcomes, European and American regulatory bodies support the principles of PM and its potential advantage over current treatment strategies. The aim of the current document was to propose a consensus on the position and gradual implementation of the principles of PM within existing adult treatment algorithms for allergic rhinitis (AR) and chronic rhinosinusitis (CRS). At the time of diagnosis, prediction of success of the initiated treatment and patient participation in the decision of the treatment plan can be implemented. The second-level approach ideally involves strategies to prevent progression of disease, in addition to prediction of success of therapy, and patient participation in the long-term therapeutic strategy. Endotype-driven treatment is part of a personalized approach and should be positioned at the tertiary level of care, given the efforts needed for its implementation and the high cost of molecular diagnosis and biological treatment.
  • Helle, Emmi; Pihkala, Jaana; Turunen, Riitta; Ruotsalainen, Hanna; Tuupanen, Sari; Koskenvuo, Juha; Ojala, Tiina (2020)
    Myocardial dysfunction is a known risk factor for morbidity and mortality in hypoplastic left heart syndrome (HLHS). Variants in some transcription factor and contractility genes, which are known to cause cardiomyopathy, have previously been associated with impaired right ventricular function in some HLHS patients. The care of HLHS patients is resource demanding. Identifying genetic variants associated with myocardial dysfunction would be helpful in tailoring the follow-up and therapeutic strategies. We tested whether a commercial cardiomyopathy gene panel could serve as a diagnostic tool in a Finnish cohort of HLHS patients with impaired right ventricular function to identify potentially pathogenic variants associated with poor prognosis. None of the patients had pathogenic or likely pathogenic variants in the studied cardiomyopathy-associated genes. Thus, our approach of performing a cardiomyopathy gene panel to identify pathogenic variants as directly causal or as modifiers for worse outcomes in hypoplastic left heart syndrome is not useful in clinical practice at the moment.
  • Jafari, Mohieddin; Wang, Yinyin; Amiryousefi, Ali; Tang, Jing (2020)
    The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.