Browsing by Subject "CLINICAL-RESPONSE"

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  • Biesiekierski, Jessica R.; Jalanka, Jonna; Staudacher, Heidi M. (2019)
    Dietary intervention is a challenge in clinical practice because of inter-individual variability in clinical response. Gut microbiota is mechanistically relevant for a number of disease states and consequently has been incorporated as a key variable in personalised nutrition models within the research context. This paper aims to review the evidence related to the predictive capacity of baseline microbiota for clinical response to dietary intervention in two specific health conditions, namely, obesity and irritable bowel syndrome (IBS). Clinical trials and larger predictive modelling studies were identified and critically evaluated. The findings reveal inconsistent evidence to support baseline microbiota as an accurate predictor of weight loss or glycaemic response in obesity, or as a predictor of symptom improvement in irritable bowel syndrome, in dietary intervention trials. Despite advancement in quantification methodologies, research in this area remains challenging and larger scale studies are needed until personalised nutrition is realistically achievable and can be translated to clinical practice.
  • Swann, J. R.; Rajilic-Stojanovic, M.; Salonen, A.; Sakwinska, O.; Gill, C.; Meynier, A.; Fanca-Berthon, P.; Schelkle, B.; Segata, N.; Shortt, C.; Tuohy, K.; Hasselwander, O. (2020)
    With the growing appreciation for the influence of the intestinal microbiota on human health, there is increasing motivation to design and refine interventions to promote favorable shifts in the microbiota and their interactions with the host. Technological advances have improved our understanding and ability to measure this indigenous population and the impact of such interventions. However, the rapid growth and evolution of the field, as well as the diversity of methods used, parameters measured and populations studied, make it difficult to interpret the significance of the findings and translate their outcomes to the wider population. This can prevent comparisons across studies and hinder the drawing of appropriate conclusions. This review outlines considerations to facilitate the design, implementation and interpretation of human gut microbiota intervention studies relating to foods based upon our current understanding of the intestinal microbiota, its functionality and interactions with the human host. This includes parameters associated with study design, eligibility criteria, statistical considerations, characterization of products and the measurement of compliance. Methodologies and markers to assess compositional and functional changes in the microbiota, following interventions are discussed in addition to approaches to assess changes in microbiota-host interactions and host responses. Last, EU legislative aspects in relation to foods and health claims are presented. While it is appreciated that the field of gastrointestinal microbiology is rapidly evolving, such guidance will assist in the design and interpretation of human gut microbiota interventional studies relating to foods.
  • Sieberts, Solveig K.; Zhu, Fan; Garcia-Garcia, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornes, Oriol; Guney, Emre; Li, Hongdong; Marin, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O.; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S. K.; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-ud-din, Muhammad; Azencott, Chloe-Agathe; Bellon, Victor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R.; Marttinen, Pekka; Pirinen, Matti; Saarela, Janna; Tang, Jing; Wennerberg, Krister; Rheumatoid Arth Challenge (2016)
    Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.
  • Iivanainen, Sanna; Ahvonen, Jarkko; Knuuttila, Aija; Tiainen, Satu; Koivunen, Jussi Pekka (2019)
    Background Anti-PD-(L)1 agents are standard of care treatments in various cancers but predictive factors for therapy selection are limited. We hypothesised that markers of systemic inflammation would predict adverse outcomes in multiple cancers treated with anti-PD-(L)1 agents. Material and methods Discovery cohort consisted of patients who were treated with anti-programmed cell death protein-1 (PD-1) agents for advanced melanoma (MEL), non-small cell lung cancer (NSCLC) or renal and bladder cancers (GU) at Oulu University Hospital and had pretreatment C reactive protein (CRP), or neutrophil/lymphocyte values available. As a validation cohort, we collected patients treated with anti-PD-1 agents from three other hospitals in Finland. Results In the discovery cohort (n=56, MEL n=23, GU n=17, NSCLC n=16), elevated CRP over the upper limit of normal (ULN) (>10mg/mL) indicated poor progression-free (PFS; p=0.005) and overall survival (OS; p=0.000004) in the whole population and in MEL subgroup. Elevated neutrophil-to-lymphocyte ratio (>2.65) also indicated inferior PFS (p=0.02) and OS (p=0.009). In the validation cohort (n=107,MEL n=44, NSCLC n=42, GU n=17, other n=4), CRP over ULN also was a strong indicator for poor PFS (p=0.0000008), and OS (p=0.000006) in the whole population, and in MEL and NSCLC also. Conclusions Systemic inflammation suggested by elevated CRP is a very strong indicator for adverse prognosis on patients treated with anti-PD-(L)1 agents and has a potential negative predictive value for treatment with anti-PD-(L)1 agents. Prospective trials should investigate whether patients with elevated CRP gain any significant benefit from anti-PD-1 therapy.
  • He, Liye; Tang, Jing; Andersson, Emma I.; Timonen, Sanna; Koschmieder, Steffen; Wennerberg, Krister; Mustjoki, Satu; Aittokallio, Tero (2018)
    The molecular pathways that drive cancer progression and treatment resistance are highly redundant and variable between individual patients with the same cancer type. To tackle this complex rewiring of pathway cross-talk, personalized combination treatments targetingmultiple cancer growth and survival pathways are required. Here we implemented a computational-experimental drug combination prediction and testing (DCPT) platform for efficient in silico prioritization and ex vivo testing in patient-derived samples to identify customized synergistic combinations for individual cancer patients. DCPT used drug-target interaction networks to traverse the massive combinatorial search spaces among 218 compounds (a total of 23,653 pairwise combinations) and identified cancer-selective synergies by using differential single-compound sensitivity profiles between patient cells and healthy controls, hence reducing the likelihood of toxic combination effects. A polypharmacology-based machine learning modeling and network visualization made use of baseline genomic and molecular profiles to guide patient-specific combination testing and clinical translation phases. Using T-cell prolymphocytic leukemia (T-PLL) as a first case study, we show how the DCPT platform successfully predicted distinct synergistic combinations for each of the three T-PLL patients, each presenting with different resistance patterns and synergy mechanisms. In total, 10 of 24 (42%) of selective combination predictions were experimentally confirmed to show synergy in patient-derived samples ex vivo. The identified selective synergies among approved drugs, including tacrolimus and temsirolimus combined with BCL-2 inhibitor venetoclax, may offer novel drug repurposing opportunities for treating T-PLL. Significance: An integrated use of functional drug screening combined with genomic and molecular profiling enables patient-customized prediction and testing of drug combination synergies for T-PLL patients. (C) 2018 AACR.
  • Kuuliala, Krista; Kuuliala, Antti; Koivuniemi, Riitta; Kautiainen, Hannu; Repo, Heikki; Leirisalo-Repo, Marjatta (2016)
    Objective To find novel predictors of treatment response to disease-modifying antirheumatic drugs (DMARDs), we studied activation of STAT (signal transducers and activators of transcription) 6 and 1 in circulating leukocytes of patients with rheumatoid arthritis (RA). Methods 19 patients with untreated recent-onset RA, 16 patients with chronic RA irresponsive to synthetic DMARDs and 37 healthy volunteers provided blood samples for whole blood flow cytometric determination of intracellular STAT6 and STAT1 phosphorylation, expressed as relative fluorescence units, in response to IL-4 and IFN-gamma, respectively. Phosphorylation was restudied and treatment response (according to European League Against Rheumatism) determined after 1-year treatment with synthetic DMARDs in recent-onset RA and with biological DMARD in synthetic DMARD-irresponsive RA. Estimation-based exact logistic regression was used to investigate relation of baseline variables to treatment response. 95% confidence intervals of means were estimated by bias-corrected bootstrapping and the significance between baseline and follow-up values was calculated by permutation test. Results At baseline, levels of phosphorylated STAT6 (pSTAT6) induced by IL-4 in monocytes were higher in those who achieved good treatment response to synthetic DMARDs than in those who did not among patients with untreated RA (OR 2.74, 95% CI 1.05 to 9.47), and IFN-gamma-stimulated lymphocyte pSTAT1 levels were higher in those who achieved good treatment response to a biological drug than in those who did not among patients with chronic RA (OR 3.91, 95% CI 1.12 to 20.68). During follow-up, in recent-onset RA patients with good treatment response to synthetic DMARDS, the lymphocyte pSTAT6 levels decreased (p = 0.011), and, consequently, the ratio of pSTAT1/pSTAT6 in lymphocytes increased (p = 0.042). Conclusion Cytokine-stimulated STAT6 and STAT1 phosphorylation in circulating leukocytes was associated with treatment response to DMARDs in this pilot study. The result, if confirmed in larger studies, may aid in developing personalized medicine in RA.