scholarly journals Compendium of Synovial Signatures Identifies Pathologic Characteristics for Predicting Treatment Response in Rheumatoid Arthritis

2018 ◽  
Author(s):  
Ki-Jo Kim ◽  
Minseung Kim ◽  
Ilias Tagkopoulos

ABSTRACTTreatment of patients with rheumatoid arthritis (RA) is challenging due to clinical heterogeneity and variability. Integration of RA synovial genome-scale transcriptomic profiling of different patient cohorts can provide insights on the causal basis of drug responses. A normalized compendium was built that consists of 256 RA synovial samples that cover an intersection of 11,769 genes from 11 datasets. Differentially expression genes (DEGs) that were identified in three independent methods were fed into functional network analysis, with subsequent grouping of the samples based on a non-negative matrix factorization method. Finally, we built a predictive model for treatment response by using RA-relevant pathway activation scores and four machine learning classification techniques. We identified 876 up-regulated DEGs including 24 known genetic risk factors and 8 drug targets. DEG-based subgrouping revealed 3 distinct RA patient clusters with distinct activity signatures for RA-relevant pathways. In the case of infliximab, we constructed a classifier of drug response that was highly accurate with an AUC/AUPR of 0.92/0.86. The most informative pathways in achieving this performance were the NFκB-, FcεRI- TCR-, and TNF signaling pathways. Similarly, the expression of the HMMR, PRPF4B, EVI2A, RAB27A, MALT1, SNX6, and IFIH1 genes contributed in predicting the patient outcome. Construction and analysis of normalized synovial transcriptomic compendia can provide useful insights for understanding RA-related pathway involvement and drug responses for individual patients. The efficacy of a predictive model for personalized drug response has been demonstrated and can be generalized to several drugs, co-morbidities, and other relevant features.

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0167975 ◽  
Author(s):  
Krista Kuuliala ◽  
Antti Kuuliala ◽  
Riitta Koivuniemi ◽  
Hannu Kautiainen ◽  
Heikki Repo ◽  
...  

2017 ◽  
Author(s):  
Mi Yang ◽  
Jaak Simm ◽  
Chi Chung Lam ◽  
Pooya Zakeri ◽  
Gerard J.P. van Westen ◽  
...  

ABSTRACTDespite the abundance of large-scale molecular and drug-response data, the insights gained about the mechanisms underlying treatment efficacy in cancer has been in general limited. Machine learning algorithms applied to those datasets most often are used to provide predictions without interpretation, or reveal single drug-gene association and fail to derive robust insights. We propose to use Macau, a bayesian multitask multi-relational algorithm to generalize from individual drugs and genes and explore the interactions between the drug targets and signaling pathways’ activation. A typical insight would be: “Activation of pathway Y will confer sensitivity to any drug targeting protein X”. We applied our methodology to the Genomics of Drug Sensitivity in Cancer (GDSC) screening, using gene expression of 990 cancer cell lines, activity scores of 11 signaling pathways derived from the tool PROGENy as cell line input and 228 nominal targets for 265 drugs as drug input. These interactions can guide a tissue-specific combination treatment strategy, for example suggesting to modulate a certain pathway to maximize the drug response for a given tissue. We confirmed in literature drug combination strategies derived from our result for brain, skin and stomach tissues. Such an analysis of interactions across tissues might help target discovery, drug repurposing and patient stratification strategies.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 54.1-54
Author(s):  
S. Benamar ◽  
C. Lukas ◽  
C. Daien ◽  
C. Gaujoux-Viala ◽  
L. Gossec ◽  
...  

Background:Polypharmacy is steadily increasing in patients with rheumatoid arthritis (RA). They may interfere with treatment response and the occurrence of serious adverse events. Medications taken by a patient may reflect active comorbidities, whereas comorbidity indices usually used include past or current diseases.Objectives:To evaluate whether polypharmarcy is associated with treatment response and adverse events in an early RA cohort and to establish whether polypharmacy could represent a substitute of comorbidities.Methods:We used data from the French cohort ESPOIR, including 813 patients with early onset arthritis. Patients included the current study had to start their first disease modifying anti-rheumatic drug (DMARD) within 24 months of inclusion in the cohort. Disease activity data were collected at one, five and ten years from the initiation of the first DMARD. For each patient, treatments were collected at baseline and at five years. Medications count included all specialties other than background RA therapy, analgesics/NSAIDs and topicals. Polypharmacy was defined as a categorical variable based on the median and tertiles of distribution in the cohort. Treatment response was assessed by achieving DAS28 ESR remission (REM) at 1 year, 5 years and 10 years from the initiation of the first DMARD. The occurrence of severe adverse events (SAE) was measured by the occurrence of severe infection, hospitalization, or death during the 10-year follow-up. The association between patient’s characteristics and achievement of REM and occurrence of SAE were tested in univariate analysis. A logistic regression model was used to evaluate associations between polypharmacy and REM at 1 year, 5 years and 10 years (we used baseline polypharmacy for the 1-year analysis and five years polypharmacy for the 5- and 10-years analyses). Multivariate adjustment was made for age, sex, BMI, duration of disease, initial DAS28 ESR, initial HAQ, smoking status, rheumatic disease comorbidity index (RDCI).Results:The proportion of patients who achieved REM one year after the initiation of the first DMARD was 32.1% in the polypharmacy according to the median group (patients taken ≥2 medication) versus 67.9% in the non-polypharmacy group (p=0.07). At 5 years after the first DMARD, the proportion of patients with REM was 45.0% in the polypharmacy group versus 56.3% in the non-polypharmacy group (p=0.03). At 10 years the proportion of patients with REM was 32.5% in the polypharmacy group versus 67.5% (p=0.06). Patients who take greater or equal to 2 medications had a 40% lower probability of achieving REM (OR = 0.60 [0.38-0.94] p = 0.03) at 5 years from the first DMARD (if RDCI index was not included in the model). At 10 years, patients receiving multiple medications had a 43% lower probability of achieving REM (OR = 0.57 [0.34-0.94] p = 0.02). SAE incidence was 61 per 1000 patient-years. For patients who developed SAE all causes 71.4% where in the polypharmacy group versus 57.8% were in the non-polypharmacy group (p = 0.03; univariate analysis). These results are no longer significant after adjustment for comorbidities indices.Conclusion:In this early RA cohort, polypharmacy is associated with a poorer treatment response and increased risk of adverse events. Polypharmacy may represent a good substitute of comorbidities for epidemiological studies.Acknowledgements:We are grateful to Nathalie Rincheval (Montpellier) who did expert monitoring and data management and all theinvestigators who recruited and followed the patients (F. Berenbaum, Paris-Saint Antoine; MC. Boissier, Paris-Bobigny; A. Cantagrel, Toulouse; B. Combe, Montpellier; M. Dougados, Paris-Cochin; P. Fardellone and P. Boumier, Amiens; B. Fautrel, Paris-La Pitié; RM. Flipo, Lille; Ph. Goupille, Tours; F. Liote, Paris- Lariboisière; O. Vittecoq, Rouen; X. Mariette, Paris-Bicêtre; P. Dieude, Paris Bichat; A. Saraux, Brest; T. Schaeverbeke, Bordeaux; and J. Sibilia, Strasbourg).The work reported on in the manuscript did not benefit from any financial support. The ESPOIR cohort is sponsored by the French Society for Rheumatology. An unrestricted grant from Merck Sharp and Dohme (MSD) was allocated for the first 5 years. Two additional grants from INSERM were obtained to support part of the biological database. Pfizer, Abbvie, Lilly and more recently Fresenius and Biogen also supported the ESPOIR cohort.Disclosure of Interests:Soraya Benamar: None declared, Cédric Lukas Speakers bureau: Abbvie, Amgen, Janssen, Lilly, MSD, Novartis, Pfizer, Roche-Chugai, UCB, Consultant of: Abbvie, Amgen, Janssen, Lilly, MSD, Novartis, Pfizer, Roche-Chugai, UCB, Grant/research support from: Pfizer, Novartis and Roche-Chugai, Claire Daien Speakers bureau: AbbVie, Abivax, BMS, MSD, Roche, Chugai, Novartis, Pfizer, Sandoz, Lilly, Consultant of: AbbVie, Abivax, BMS, MSD, Roche, Chugai, Novartis, Pfizer, Sandoz, Lilly, Cécile Gaujoux-Viala Speakers bureau: Abbvie, BMS, Celgene, Janssen, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi, Roche-Chugai, UCB, Consultant of: Abbvie, BMS, Celgene, Janssen, Medac, MSD, Nordic Pharma, Novartis, Pfizer, Sanofi, Roche-Chugai, UCB, Grant/research support from: Pfizer, Laure Gossec Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sandoz, Sanofi-Aventis et UCB, Consultant of: AbbVie, Amgen, Biogen, Celgene, Janssen, Lilly, Novartis, Pfizer, Sandoz, Sanofi-Aventis et UCB, Anne-Christine Rat Speakers bureau: Pfizer, Lilly, Consultant of: Pfizer, Lilly, Bernard Combe Speakers bureau: AbbVie; Bristol-Myers Squibb; Gilead; Janssen; Lilly; Merck; Novartis; Pfizer; Roche-Chugai; and Sanofi;, Consultant of: AbbVie; Bristol-Myers Squibb; Gilead; Janssen; Lilly; Merck; Novartis; Pfizer; Roche-Chugai; and Sanofi;, Grant/research support from: Novartis, Pfizer, and Roche-Chugai., Jacques Morel Speakers bureau: Abbvie, BMS, Lilly, Médac, MSD, Nordic Pharma, Pfizer, UCB, Consultant of: Abbvie, BMS, Lilly, Médac, MSD, Nordic Pharma, Pfizer, UCB, Grant/research support from: BMS, Pfizer


2021 ◽  
Vol 22 (5) ◽  
pp. 2426
Author(s):  
Askhat Myngbay ◽  
Limara Manarbek ◽  
Steve Ludbrook ◽  
Jeannette Kunz

Rheumatoid arthritis (RA) is a chronic autoimmune disease causing inflammation of joints, cartilage destruction and bone erosion. Biomarkers and new drug targets are actively sought and progressed to improve available options for patient treatment. The Collagen Triple Helix Repeat Containing 1 protein (CTHRC1) may have an important role as a biomarker for rheumatoid arthritis, as CTHRC1 protein concentration is significantly elevated in the peripheral blood of rheumatoid arthritis patients compared to osteoarthritis (OA) patients and healthy individuals. CTHRC1 is a secreted glycoprotein that promotes cell migration and has been implicated in arterial tissue-repair processes. Furthermore, high CTHRC1 expression is observed in many types of cancer and is associated with cancer metastasis to the bone and poor patient prognosis. However, the function of CTHRC1 in RA is still largely undefined. The aim of this review is to summarize recent findings on the role of CTHRC1 as a potential biomarker and pathogenic driver of RA progression. We will discuss emerging evidence linking CTHRC1 to the pathogenic behavior of fibroblast-like synoviocytes and to cartilage and bone erosion through modulation of the balance between bone resorption and repair.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 933.2-934
Author(s):  
A. Julià ◽  
M. Lopez Lasanta ◽  
F. Blanco ◽  
A. Gómez ◽  
I. Haro ◽  
...  

Background:Blocking of the Tumor Necrosis Factor (TNF) activity is a successful therapeutic approach for 2 out of 3 Rheumatoid Arthritis patients. Identifying the patients that will not respond to this therapeutic approach is a major translational goal in RA. Association of seropositivity to rheumatoid factor (RF) or anti-cyclic-citrullinated antibodies (anti-CCP) with anti-TNF response has proven inconclusive, suggesting that other yet unexplored biomarkers could be more informative for this goal.Objectives:We tested the association of two recently introduced biomarkers in RA: anti-carbamylated protein antibodies (anti-CarP) and anti-peptidylarginine deiminase type 4 (anti-PAD4).Methods:A prospective cohort of n=80 RA patients starting anti-TNF therapy was recruited and levels for all four autoantibodies -RF, anti-CCP, anti-CarP and anti-PAD4- were measured at baseline. The change in DAS28 score between baseline and week 12 of therapy was used as the clinical endpoint.Results:Single marker-analysis showed no significant association with drug response. However, when testing for interactions between autoantibodies, we found highly significant associations with drug response. Anti-CCP and RF showed a positive interaction with the response to anti-TNF therapy (P=0.00068), and anti-PAD4 and antiCarP titers showed a negative interaction with the clinical response at week 12 (P=0.0062). Using an independent retrospective sample (n=199 patients), we validated the interaction between anti-CCP and RF with the clinical response to anti-TNF agents. (P=0.044).Conclusion:The results of this study show that interactions between antibodies are important in the response to anti-TNF therapy and suggest potential pathogenic relationships.Acknowledgments :We would like to thank the clinical researchers and patients participating in the IMID Consortium for their collaborationDisclosure of Interests:Antonio Julià: None declared, Maria Lopez Lasanta: None declared, Francisco Blanco: None declared, Antonio Gómez: None declared, Isabel Haro: None declared, Antonio Juan Mas: None declared, Alba Erra: None declared, Mª Luz García Vivar: None declared, Jordi Monfort: None declared, Simon Sánchez Fernandez: None declared, Isidoro González-Álvaro Grant/research support from: Roche Laboratories, Consultant of: Lilly, Sanofi, Paid instructor for: Lilly, Speakers bureau: Abbvie, MSD, Roche, Lilly, Mercedes Alperi-López: None declared, Raúl Castellanos: None declared, Antonio Fernandez-Nebro: None declared, Cesar Diaz Torne: None declared, Núria Palau: None declared, Raquel M Lastra: None declared, Jordi Lladós: None declared, Raimon Sanmarti: None declared, Sara Marsal: None declared


Reumatismo ◽  
2020 ◽  
Vol 72 (1) ◽  
pp. 16-20 ◽  
Author(s):  
M. Bellan ◽  
D. Soddu ◽  
E. Zecca ◽  
A. Croce ◽  
R. Bonometti ◽  
...  

Red cell distribution width (RDW) is an unconventional biomarker of inflammation. We aimed to explore its role as a predictor of treatment response in rheumatoid arthritis (RA). Eighty-two RA patients (55 females), median age [interquartile range] 63 years [52-69], were selected by scanning the medical records of a rheumatology clinic, to analyze the associations between baseline RDW, disease activity scores and inflammatory markers, as well as the relationship between RDW changes following methotrexate (MTX) and treatment response. The lower the median baseline RDW, the greater were the chances of a positive EULAR response at three months, 13.5% [13.0-14.4] being among those with good response, vs 14.0% [13.2-14.7] and 14.2% [13.5- 16.0] (p=0.009) among those with moderate and poor response, respectively. MTX treatment was followed by a significant RDW increase (p<0.0001). The increase of RDW was greater among patients with good EULAR response, becoming progressively smaller in cases with moderate and poor response (1.0% [0.4-1.4] vs. 0.7 [0.1-2.0] vs. 0.3 [-0.1-0.8]; p=0.03). RDW is a strong predictor of early response to MTX in RA. RDW significantly increases after MTX initiation in parallel to treatment response, suggesting a role as a marker of MTX effectiveness.


2013 ◽  
Vol 15 (6) ◽  
Author(s):  
John M Davis ◽  
Keith L Knutson ◽  
Michael A Strausbauch ◽  
Abigail B Green ◽  
Cynthia S Crowson ◽  
...  

Microbiology ◽  
2014 ◽  
Vol 160 (6) ◽  
pp. 1252-1266 ◽  
Author(s):  
Hassan B. Hartman ◽  
David A. Fell ◽  
Sergio Rossell ◽  
Peter Ruhdal Jensen ◽  
Martin J. Woodward ◽  
...  

Salmonella enterica sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of S. Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of Salmonella when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the catabolic core). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.


Sign in / Sign up

Export Citation Format

Share Document