dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: In the current context of personalized medicine, one of the major challenges in the management of rheumatoid arthritis (RA) is to identify biomarkers that predict drug responsiveness. From the European APPRAISE trial, our main objective was to identify a gene expression profile associated with responsiveness to abatacept (ABA) + methotrexate (MTX) and to understand the involvement of this signature in the pathophysiology of RA. Methods: Whole human genome microarrays (4 × 44 K) were performed from a first subset of 36 patients with RA. Data validation by quantitative reverse-transcription (qRT)-PCR was performed from a second independent subset of 32 patients with RA. Gene Ontology and WikiPathways database allowed us to highlight the specific biological mechanisms involved in predicting response to ABA/MTX.
Patients from the APPRAISE trial A total of 68 patients with RA from the APPRAISE trial were enrolled in this ancillary study  [22, 23]. The AP-PRAISE study assessed the capability of the composite power of Doppler and greyscale ultrasound score to measure the early effect and time course of response to treatment with ABA in biologic-naïve patients with active RA despite MTX therapy. APPRAISE (NCT00767325) initially including 104 patients with RA was a 24-week, Phase IIIb, open-label, multicenter, single-arm study conducted at 21 sites across Europe (Denmark, France, Germany, Hungary, Italy, Norway, Spain and the UK)  [22, 23]. Eligible patients were ≥18 years of age, had American College of Rheumatology (ACR)-defined RA according to the 1987 classification criteria for at least 6 months  [24] , and had been on MTX (≥15 mg/week) for at least 3 months prior to baseline, with a stable MTX dose for at least 28 days before baseline (except in cases of intolerance to MTX).
Image analysis and extraction of raw and normalized signal intensities (lowess) were performed using Feature Extraction Software 10. 5. 1. 1 (Agilent Technologies). The data were in agreement with the guidelines for minimum information about a microarray experiment and were deposited in the database of the National Center for Biotechnology Information Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc=GSE68215). The data are accessible [GEO:GSE68215]. Non-uniform spots and saturated spots or spots with intensities below the background were not taken into account. Only spots that passed these quality controls on 100% of arrays were selected for further analysis. Hierarchical clustering was performed using the Pearson coefficient metric and complete linkage to build the transcripts and sample dendrograms.
Statistical and functional analysis Comparisons of clinical and biological data between R and NR were performed at baseline using Student's t test for continuous variables. Comparisons of R or NR before and after treatment were performed using the paired t test. Identification of clinical parameters able to predict good response to ABA/MTX was performed in two different multivariate analyses: (1) a logistic regression model with variable selection using Bayesian information criterion (BIC) and (  2 ) linear discriminant analysis (LDA), which estimates a coefficient for each variable. Data from transcriptomic analysis were analyzed using GeneSpring GX V. 13. 0 (Agilent Technologies, Les Ulis, France). The normality of log 2 ratio of gene expression was evaluated using the Shapiro-Wilk statistical test.
The unpaired Student's t test (p value < 0. 05), with the Benjamini-Hochberg correction to check the false discovery rate (FDR), was used to determine the statistical significance of differences in gene expression levels between R and NR. Gene Ontology (GO) analysis was used to investigate the biological processes, molecular function or cellular localization enriched in the transcripts list, showing a significant fluctuation in gene expression between R and NR. The p value was computed by standard hypergeometric distribution. The GeneSpring Single Experiment Analysis (SEA) bio-informatics tool was used for computational analysis to identify potential curated canonical pathways with setting parameters (reactome and GenMAPP for pathway source), which are enriched in the differentially expressed transcripts list, using the WikiPathways database (http://www.
Variables selection Fig. 1  Ancillary study design from the APPRAISE trial. Of the 104 patients with rheumatoid arthritis (RA) enrolled in the APPRAISE trial, 68 were included in our ancillary study after discarding patients with missing data or poor-quality RNA samples. Among these 68 patients with RA, two subsets were designated, one for identification and one for validation. The first step of this ancillary study was to identify clinical parameters to predict abatacept (ABA)/methotrexate (MTX) response. The second step was to identify a gene combination able to predict ABA/MTX response. Subset 1, comprising 36 patients with RA, was used to identify clinical parameters or gene combinations to predict drug response. Subset 2, comprising 32 patients with RA, was used to validate these clinical parameters or gene combinations.
Gene expression profiling associated with response to ABA/MTX With subset 1, the cRNA from 17 R and 19 NR were co-hybridized with a cRNA internal reference from 10 healthy subjects on whole human genome microarrays, by two-color technology. After elimination of spikes and flagged probes, 19,620 probes were detected in all samples. From these 19,620 transcripts, we identified 87 transcripts with statistically significant variation between R and NR according to the t test with correction for multiple testing (FDR, Benjamini-Hochberg correction) (Fig. 3a ). These transcripts are listed in Additional file 2 (69 transcripts are referenced with a Ref Seq accession number while 18 probes were unknown). Since the sex ratio in R and in NR was significantly different, this maldistribution between male and female patients did not introduce bias into our study (data not shown) Finally, we performed hierarchical clustering of the 36 patients from subset 1 based on the levels of the 87 transcripts indicated previously, resulting in almost perfect separation of R and NR into two major clusters.
005). For instance, 6/87 mRNA of genes linked to the electron transport chain (ETC; GO: 0022900) were included in our signature while 101 genes from the whole human genome are known to be relative to this same GO class (corrected p value = 0. 001). The fold enrichment in this GO term is × 16 (Table  2 ). Moreover, these 87 mRNA were submitted to WikiPathways using the SEA tool from GeneSpring software leading us to identify enrichment of four signaling pathways: Electron Transport Chain WP111_41171 (version of 1 March 2011; p value = 1. 6. 10-9), Oxidative Phosphorylation WP623_45305 (version of 7 October 2011; p value = 2. 5. 10-4), Proteasome Degradation WP183_45274 (version of 7 October 2011; p value = 0. 008) and TSH signaling pathway WP2032_44635 (version of 22 September 2011 ; p value = 0.
49; 0. 06 < r 2 < 0. 24) or DAS28(CRP) < r < 0. 43; 0. 10 < r 2 < 0. 18) and gene expression level was observed for the majority of genes except ATP5J. In subset 2, weak positive correlation between CRP or DAS28(CRP) and gene expression data was also found for NDUFA4, COX6A1, COX7A2 and COX7B (data not shown). All these data led us to discard the impact of CRP or disease activity on the dysregulation of the ETC pathway between R and NR at baseline (Additional file 5). Finally, we measured gene expression fluctuation in an independent transcriptomic analysis between baseline and 6 months of treatment by ABA/MTX in R and NR. Only five genes (COX7B, COX11, UQCRC2, NDUFU3, NDUFS1) among the 104 genes involved in the ETC pathway were significantly upregulated in R after 6 months of treatment, while these genes were invariant in NR to ABA/MTX (data not shown).
Next, we validated this signature with an independent subset of 32 patients with RA by means of a minimal combination of four genes with expression levels measured by qRT-PCR, which are more easily usable in routine practice than microarrays. This signature is the optimized combination of genes associated with drug response with good accuracy, because it correctly predicted the future response in 81% of patients with RA (75% sensitivity, 85% specificity, 85% negative predictive value and 75% positive predictive value) prior to treatment (Fig. 4 ). Each gene taken separately was unable to predict response to ABA/MTX (data not shown) but all genes together were associated with response to ABA/MTX with good accuracy. Nevertheless, further validation studies in independent cohorts are essential before considering this signature as a predictive biomarker and of use in clinical practice.
Out of these 4 transcripts, 3 (BLOC1S1, COX6A1 and PTRH2) and 13 probes out of 87 code for proteins located in mitochondria. In addition, some of them are involved in the ETC pathway, suggesting implication of mitochondrial metabolism in response to ABA/MTX. Moreover, GO analysis and SEA are in agreement as these two analyses point to the involvement of ETC in the response to ABA/MTX. Interestingly, we found that seven transcripts with levels that are significantly lower in R than in NR at baseline were similarly regulated in patients in remission and patients not in remission defined by clinical disease activity index (CDAI) in a recent study  [33]. The ETC is a series of five complexes anchored to the inner membrane of mitochondria that transfers electrons via redox reactions, which drives ATP synthesis, generating reactive oxygen species (ROS) and subsequent oxidative stress  [37].
Acknowledgements The authors would like to thank  Manuela Le Bars  and  Corine Gaillez  from  Bristol-Myers Squibb  for designing the study and providing study logistics. They also wish to thank the principal investigators of the APPRAISE study. APPRAISE principal investigators are  Silvano Adami ,  Vivi Bakkenheim ,  Hilde Berner Hammer ,  Stefano Bombardieri ,  Maria-Antonietta D'Agostino ,  Paul Emery ,  Liana Euller-Ziegler ,  Gianfranco Ferraccioli ,  Maurizio Galeazzi ,  Philippe Gaudin ,  Walter Grassi ,  Annamaria Iagnocco ,  Herbert Kellner ,  Thierry Lequerré ,  Ingrid Möller ,  Esperanza Naredo ,  Mikkel Østergaard ,  Fredeswinda Romero ,  Istvan Szombati ,  Lene Terslev ,  Jacqueline Uson ,  Esther Vicente ,  Olivier Vittecoq  and  Richard Wakefield. The authors are grateful to  Nikki Sabourin-Gibbs ,  Rouen University Hospital , for her help in editing the manuscript.
Methods: The population studied corresponded to all adults with metastatic melanoma hospitalised in France between 1st January 2011 and 31st December 2013 who required chemotherapy, immunotherapy or radiotherapy due to tumour progression and unresectable Stage III or Stage IV melanoma. Metastatic melanoma was identified by ICD-10 codes documented in the hospital patient discharge records. For each patient, hospital stays were stratified into a pre-or post-progression health state using proxy variables for the RECIST criteria. All healthcare expenditure documented in the French national hospital claims system database and incurred between the index hospitalisation (or change of progression state) and the end of follow-up were analysed. For the principal analysis, valuation of healthcare resource consumption was performed using official national hospitalisation tariffs.
Any expensive therapy administered during the stay was documented from a linked database of expensive drugs (FICHCOMP). Results: Seventy-eight thousand seven hundred fifty hospital stays by 10,337 patients with metastatic melanoma were identified over the three-year study period. Annual per capita costs of hospitalisation were € 5046 in the pre-progression stage and € 19,006 in the post-progression stage. Hospitalisations attributed to adverse drug reactions to chemotherapy or immunotherapy were observed in 27% of patients. Annual per capita costs of these hospitalisations related to adverse drug reactions were € 3762 in the pre-progression stage and € 5523 in the post-progression stage. Conclusions: Hospitalisation costs related to metastatic melanoma rise substantially as the disease progresses.
Methods This cohort study (MELISSA) was a retrospective analysis of data on hospitalisations and associated costs extracted from the French national hospital claims system database (PMSI, Programme de Médicalisation des Systèmes d'Information) relating to all patients with a first hospitalisation with metastatic melanoma between 1st January 2011 and 31st December 2013. Costs were analysed from the government payer perspective. A brief description of the French health system is provided in Additional file 1.
Data source Data were extracted PMSI database. This database contains exhaustive medico-administrative information registered in the patient medical records for all hospitalisations in France in acute care (medicine, surgery or obstetrics), short-term rehabilitation facilities and home care, covering both the public and private sectors. For the present study, stays in psychiatric institutions were excluded from the analysis. In 2013, a total of around 26. 5 million individual hospital stays were documented in the PMSI acute care database. Individual patients can be tracked across multiple hospitalisations through a unique anonymous patient identifier, which is conserved until the patient dies. The reasons for hospitalisation are identified for each DRG using the diagnostic codes of the International Classification of Diseases 10th Revision (ICD-10)  [11].
Study population The study population comprised all adults (≥18 years at the time of first hospitalisation) with metastatic melanoma hospitalised in France between 1st January 2011 and 31st December 2013 who required chemotherapy, immunotherapy or radiotherapy due to tumour progression and unresectable Stage III or Stage IV melanoma. This population corresponds to patients eligible for immunotherapy with ipilimumab. In order to align all included patients at the same stage of disease (first hospitalisation for treatment of metastatic disease), the analysis was restricted to patients who had not previously been treated for metastatic melanoma. To this end, a retrospective search of the PMSI database was performed to identify and exclude any patients with a metastatic melanoma-related hospital stay in the 2 years prior to the index hospitalisation using the same decision rules (see below) as those used for inclusion (see below).
Overall, 78,750 hospital stays by 10,337 patients with metastatic melanoma (and no other primary cancer) identified in the SDS were listed in the PMSI database for the 3 years of the study  (2011) (2012) (2013). Of these, 8294 stays (10. 5%) were excluded since they were not directly related to melanoma, and 7188 stays (9. 1%) related to melanoma were excluded since they had previously received chemotherapy for melanoma and were thus not treatment naïve. The remaining 63,268 stays made by 8862 treatment-naïve patients with metastatic melanoma were the subject matter of the study (Fig. 2 ).
Data sources Using the patient unique identifier number, all hospital stays from the first documented during the study period until the patient dies or until the 31st December 2013 (whichever came first) were extracted for costing. Stays considered unrelated to melanoma were excluded using the same rules as those applied for patient eligibility. Each stay related to melanoma was assigned to a pre-or post-progression state as specified above. Stays related to administration of chemotherapy or immunotherapy were identified from the DRG codes assigned to 'Sessions of chemotherapy for tumour (ambulatory)' and 'Chemotherapy for tumour (full hospitalisation)' (codes 28Z07Z and 17 M06*). The FICHCOMP database was used to identify any stay at which fotemustine or ipilimumab had been administered. In addition, stays presumed to be related to treatment of adverse drug reactions to melanoma treatment were extracted.
Data collection All documented healthcare resource consumption in the PMSI database was identified for the period between the index hospitalisation (or change of progression state) and the end of follow-up. The end of follow-up was defined as the end of the observation period (31st December 2013), death or loss to health insurance of the patient, or change of health state due to disease progression. Any acquisition costs of fotemustine or ipilimumab during this period were identified from the FICHCOMP database. Healthcare resources documented in the PMSI database include costs of radiotherapy, acquisition costs for standard chemotherapy (acquisition costs for immunotherapy and innovative chemotherapy are documented separately in the FICHCOMP database) and administration costs for all chemotherapy and immunotherapy.
They also include all diagnostic tests performed during hospital stays or day hospitalisations (for example, PET, MRI, CT), all medical and paramedical care provided in hospital including palliative care, intensive care and psychosocial support. Healthcare resources not documented in the PMSI database include any consultations in community medicine (for example, primary care, community-based specialists, psychological support, nursing), as well as outpatient hospital consultations, medications prescribed outside the hospital (and notably BRAF inhibitors), laboratory and other diagnostic tests (for example, echography, CT scans). In patients followed in large university hospitals, most diagnostic tests and consultations Table  1  Listing of ICD-10 codes used to assign diagnosis of metastatic melanoma with other physicians are generally provided during a comprehensive day hospitalisation, whereas for patients followed in small local hospitals, these services are generally provided in the community.
Determination of mortality Mortality was documented through the PMSI database, where all in-hospital deaths are identified although cause of death is not reported. Mortality documented in the PMSI database thus does not fully reflect death from metastatic melanoma for two reasons, firstly because deaths occurring outside hospital are not accounted for and secondly because in-hospital deaths due to causes other than melanoma such as accidents may artificially inflate mortality rates. These sources of imprecision are expected to be relatively minor, since the vast majority of patients with cancer in France die in hospital  [12]  and most patients with metastatic melanoma die from it  [13]. In order to take into account these sources of imprecision, a correction was made using data from the national deaths registry in which all deaths in France are entered, together with the cause of death.
This correction was based on a comparison between the number of deaths due to melanoma in 2011 documented in the national deaths registry (1732 deaths) and the corresponding number of all-cause deaths in patients with metastatic melanoma documented in the PMSI database over the same period (1368 deaths). This indicates that 79% of deaths occur in hospital and 21% elsewhere. For each year of follow-up, the number of non-hospital deaths was thus estimated as 0. 21/0. 79 × the number of in-hospital deaths.
Outcomes As specified in the study protocol, the costs per patientyear of all hospitalisations related to metastatic melanoma were determined, both overall and for hospitalisations at which chemotherapy or immunotherapy was administered. Such hospitalisations were identified through documentation in the FICHCOMP database for fotemustine or ipilimumab and through documentation of the ICD-10 code for administration of antineoplastic chemotherapy (Z511) in the SDS for other treatments. These hospitalisations included both programmed visits, for example for chemotherapy or radiotherapy sessions, and unprogrammed visits, for example for management of an adverse drug reaction. The costs per patient-year of all hospitalisations related to clinical events presumably related to melanoma immunotherapy (ipilimumab) and BRAF targeted treatments were also determined.
Since the cause of clinical events is not explicitly defined in the SDS, this could not be directly attributed to melanoma treatment. For this reason, a list of frequently associated adverse drug reactions was established a priori by the Steering Committee of the study. These events included infections, myalgia/pain, skin reactions, fatigue, diarrhoea, nausea/vomiting, colitis, dyspnoea, anaemia, thrombocytopenia, neutropenia, decreased blood corticotrophin, glomerulonephritis, skin carcinoma and neuropathies (sensory/motor). All additional stays of each patient for which these ADRs were documented in the SDS as a PD/RD (irrespective of whether metastatic melanoma was documented as an SAD) were extracted from the PMSI database and the related cost determined. The costs per patient-year of all hospitalisations involving palliative care were also determined.
Valuation Healthcare resource consumption costs were determined for each progression state and for each patient using three different costing conventions. The first of these used official French tariffs which are established by the national health insurance system for accounting and resource allocation purposes. In our study, the total per capita cost accrued was calculated from the DRG-specific unit tariffs attributed to each stay, together with the cost of any expensive chemotherapy or immunotherapy administered during the stay that was documented in the FICHCOMP database. The DRG-specific unit cost was adjusted for the actual length of the hospital stay by adding or subtracting daily hospitalisation rates. The second valuation method used the unit costs determined in the Études Nationales de Coûts à Méthodologie Commune (ENCC).
This is a bottom-up costing study in a representative sample of all French hospitals designed to evaluate the real DRG-based aggregate costs of hospitalisation. These cost determinations are important for the national health insurance system to align national tariffs with real costs. The mean ENCC costs include the cost of expensive chemotherapy. The third valuation method used the DRG-specific unit ENCC costs adjusted for the use of expensive chemotherapy and immunotherapy. For each stay, the cost of expensive drugs determined in the ENCC was subtracted from the total cost of the stay and replaced by the actual cost of reimbursed drugs as specified in the FICHCOMP database administered during the individual hospital stay, on a patient-by-patient basis. All costs are presented as inflationadjusted 2017 Euros.
Data analysis The possible trajectories of individually patients that can be followed in the PMSI database are illustrated in Fig. 1. The individual follow-up duration in days of each patient per progression health stage was determined. In the pre-progression stage, the duration of follow-up time was defined for each patient as the time between the date of index hospitalisation and the date of progression, the date of death or the date of censuring (31st December 2013). In the post-progression stage, the duration of follow-up time was defined for each patient as the time between the date of progression and the date of death or the date of censuring. For patients who progressed, the date of the hospitalisation at which the criteria for progression were met was taken as the date of progression.
For the latter group, the cumulative follow-up duration was then truncated to take into account the shorter actual follow-up duration of patients who died outside hospital. In order to do this, the number of patients estimated to have died outside hospital for each of the three calendar years (2011, 2012 and 2013) was calculated as described above (determination of mortality). As a conservative hypothesis, these deaths were assumed to have occurred on the 1st January of the given Fig. 1  Patient trajectories in the PMSI database. Patients are identified in the PMSI database when they undergo a hospitalisation fulfilling the eligibility criteria for metastatic melanoma. This is the index hospitalisation. At this time, the patient is attributed to the pre-progression stage or the post-progression stage.