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      string(258) "NCT02407236 - A Phase 3, Randomized, Double-blind, Placebo-controlled, Parallel-group, Multicenter Protocol to Evaluate the Safety and Efficacy of Ustekinumab Induction and Maintenance Therapy in Subjects With Moderately to Severely Active Ulcerative Colitis"
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  ["project_title"]=>
  string(114) "Mucosal IL-22 score and week-8 remission in ulcerative colitis: a secondary analysis of the randomized UNIFI trial"
  ["project_narrative_summary"]=>
  string(798) "Ulcerative colitis causes inflammation of the bowel, and patients respond differently to treatment. We will reanalyze UNIFI trial data to test whether a published 50-gene IL-22 score measured before treatment helps predict remission at week 8 beyond routine clinical information. Building on our earlier public-data analysis, we will link gene expression and clinical records within the secure platform. We will compare prediction with and without the score and examine whether its association with remission differs between ustekinumab and placebo. We will account for baseline disease severity and other available clinical factors, report uncertainty, and publish results regardless of their direction. This study will assess the score's added value; it is not an independent external validation."
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(3) "Jie"
    ["last_name"]=>
    string(3) "Liu"
    ["degree"]=>
    string(17) "Master’s degree"
    ["primary_affiliation"]=>
    string(24) "Suining Central Hospital"
    ["email"]=>
    string(22) "liujie166475@gmail.com"
    ["state_or_province"]=>
    string(7) "Sichuan"
    ["country"]=>
    string(5) "China"
  }
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    ["value"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1577) "Background: A published mucosal IL-22 program is associated with ulcerative colitis outcomes, but its added value beyond clinical factors and its relation to treatment benefit remain uncertain.

Objective: Assess whether the fixed 50-gene score improves prediction of week-8 remission beyond baseline clinical information.

Study Design: Secondary analysis of randomized UNIFI induction data, extending our prior public-data analysis; not independent external validation.

Participants: Randomized participants with pretreatment mucosal transcriptomic data and linkable clinical records. Availability, exclusions and missingness will be reported.

Primary and Secondary Outcome Measure(s): Primary: protocol-defined week-8 clinical remission. Secondary: week-8 clinical response and endoscopic improvement; paired post-treatment expression changes, if available, will be exploratory.

Statistical Analysis: Clinical and transcriptomic records will be linked only within the approved secure platform. Gene membership will remain fixed. Regression will estimate adjusted score associations and a secondary score-by-treatment interaction. A parsimonious clinical model including treatment arm will be compared with the same model plus the score using internally validated Brier loss, discrimination and calibration. Preprocessing and model fitting will be repeated within resampling. Covariates, missing-data handling and sensitivity analyses will be specified before access. Effect estimates and uncertainty will be reported regardless of direction."
  ["project_brief_bg"]=>
  string(2572) "Pretreatment mucosal gene expression may reflect both inflammatory burden and processes related to treatment response. Pavlidis et al. reported a 50-gene IL-22 response program associated with outcomes in ulcerative colitis (doi:10.1038/s41467-022-33331-8). However, an association with outcome does not establish that a biomarker adds useful information beyond clinical severity or identifies patients who benefit more from one treatment.



Our prior public-data analysis examined this program in UNIFI and additional treatment cohorts. In the UNIFI biosample subset, higher scores were associated with lower remission odds in both randomized arms, while the treatment-by-score interaction was imprecise. Patient-level baseline clinical covariates were not available in the public materials we used. These known findings motivate the present request; this is a planned extension of an existing analysis, not a claim of testing a previously unseen cohort.



UNIFI randomized participants to ustekinumab or placebo during induction (NCT02407236; doi:10.1056/NEJMoa1900750). Access to linked clinical and transcriptomic records would allow us to assess whether the fixed score adds information beyond baseline disease severity and prior treatment history, and to distinguish an overall outcome association from evidence of differential treatment benefit. Janssen has advised us that clinical and transcriptomic data can be linked within the approved secure platform. Exact sample coverage, visits and variables remain to be confirmed.



We request individual-level clinical data, pretreatment and available post-treatment mucosal expression data, gene annotations, de-identified participant/sample/visit linkage, and supporting dictionaries and trial documentation. All patient-level linkage and analysis will remain within the authorized environment; external patient-level datasets will not be imported.



The primary contribution is a clinically adjusted assessment of an existing biomarker, using internal validation to quantify its added predictive value. It can clarify whether a complex molecular score warrants further independent evaluation or largely reflects routinely measured disease features. Post-treatment molecular changes will be exploratory and will not be used as baseline predictors. We will report uncertainty, missingness and biosample-selection limitations, and disseminate findings regardless of direction. Access to the same UNIFI participants does not constitute independent external validation."
  ["project_specific_aims"]=>
  string(1572) "1. Primary aim: Determine whether a fixed pretreatment 50-gene IL-22 score adds predictive information for protocol-defined week-8 clinical remission beyond a parsimonious model containing randomized treatment arm and baseline clinical factors. The primary performance comparison will be the internally validated difference in Brier loss between the clinical model and the clinical-plus-score model. Discrimination and calibration will provide complementary assessments.



2. Secondary aim: Estimate the score's association with remission after clinical adjustment. Our directional hypothesis is that higher scores are associated with lower remission probability, but two-sided inference will be used. No genes or score cutoffs will be selected to optimize outcomes.



3. Secondary aim: Estimate the score-by-randomized-treatment interaction and corresponding predicted absolute treatment differences over the observed score range. We will distinguish prognostic association from treatment-effect modification and will not interpret an imprecise interaction as proof of equal treatment benefit.



4. Exploratory aims: Examine week-8 clinical response and endoscopic improvement, robustness to missing-data and preprocessing choices, and paired expression changes if suitable follow-up samples are available. Follow-up analyses will account for treatment and visit timing.



The analysis plan will be finalized before restricted-data access, with prior public-data findings disclosed and subsequent deviations documented."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(14) "indiv_trial_an"
    ["label"]=>
    string(25) "Individual trial analysis"
  }
  ["project_purposes"]=>
  array(2) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
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  ["project_research_methods"]=>
  string(2015) "Source: UNIFI (NCT02407236; CNTO1275UCO3001), using de-identified participant-level clinical and mucosal transcriptomic data supplied through YODA. We request all randomized induction participants' clinical records to describe biosample selection, plus available baseline/follow-up expression matrices, gene annotations, assay/QC metadata, participant-sample-visit linkage, data dictionaries, protocol, statistical analysis plan and CSR.



The primary analysis population will comprise randomized induction participants with ulcerative colitis, a pretreatment mucosal profile passing outcome-blinded technical QC, and an unambiguous platform-provided link to clinical records and treatment assignment. Placebo and both randomized ustekinumab induction doses will be retained; dose allocation will be preserved. We will not restrict eligibility by observed response, score value, prior biologic failure or baseline severity beyond original trial eligibility.



Exclude non-trial controls, post-dose samples from the baseline predictor set, profiles failing documented technical QC, and records with unresolved linkage. Replicate biopsies will be resolved by a documented outcome-blinded rule; each patient will contribute once to baseline analyses. Eligibility will not require a post-treatment biopsy. Covariate missingness alone will not trigger exclusion; missing outcomes and trial-defined treatment failures will be handled under the prespecified outcome rules and sensitivity analyses.



Paired-expression analyses will use the subset with suitable matched follow-up biopsies and will be reported separately. Maintenance-stage and delayed-induction observations will not be pooled with the randomized week-8 induction analysis. Counts and characteristics of included and excluded patients will be reported.



All linkage and participant-level analysis will occur within the approved secure platform. No external patient-level data will be imported or pooled."
  ["project_main_outcome_measure"]=>
  string(1946) "Primary clinical outcome: clinical remission at induction week 8, using the published UNIFI full-Mayo definition: total Mayo score 1. The trial-derived endpoint and its documented visit window, treatment-failure and nonresponder-imputation rules will be used and checked against component scores where available. An absent value will not automatically be recoded as nonremission unless required by those documented rules. Remaining unknown outcomes will be reported and addressed in prespecified missing-outcome sensitivity analyses.



Secondary clinical outcomes at induction week 8:

1. Clinical response: a decrease from baseline in total Mayo score of >=3 points and >=30%, together with a rectal-bleeding subscore decrease of >=1 point or an absolute rectal-bleeding subscore of 0 or 1.

2. Endoscopic improvement: Mayo endoscopic subscore of 0 or 1, using the trial-designated assessment. This will not be treated as histological healing or combined histo-endoscopic healing.



The primary added-prediction metric is the internally validated difference in Brier loss (clinical-plus-score minus clinical-only model) for week-8 remission; negative values favor adding the score. AUC, calibration and adjusted associations are complementary assessments. Treatment-by-score interaction is secondary, not an alternative primary endpoint.



Exploratory molecular outcome: within-patient change from pretreatment in the fixed IL-22 score at documented post-treatment biopsy visits, analyzed separately by visit with consistent scoring and treatment accounted for. Its association with contemporaneous outcomes will not be presented as baseline prediction.



Endpoint definitions and coding will be reconciled with the supplied protocol and dictionaries before outcome modeling. Any necessary change or unavailable outcome will be documented rather than silently substituted."
  ["project_main_predictor_indep"]=>
  string(2008) "The main predictor is a continuous pretreatment mucosal expression summary of the published 50-gene IL-22 response program (Pavlidis et al., doi:10.1038/s41467-022-33331-8). Gene membership and direction will be frozen before restricted-data access; no outcome-based gene selection, learned gene weights or optimized score threshold will be used.



For the primary added-prediction analysis, the score will be the arithmetic mean of gene-level z scores. For each gene, the mean and sample standard deviation will be estimated from training participants only and applied unchanged to held-out participants. The resulting panel score will also be standardized using training-set parameters; associations will be reported per standard-deviation increase. Higher values indicate higher program expression. This is a prespecified operational summary of the published program, not a claim to reproduce the original numerical score exactly.



Gene identifiers will be mapped using supplied annotations. Expression units, transformation, replicate handling and technical QC will be documented. The full 50-gene panel is preferred. If genes are unavailable or invariant, an outcome-blinded technical audit will define and report a reduced measured panel; analyses with fewer than 40 measurable genes will be descriptive only. Missing gene measurements will not be coded as zero. Rules will be fixed before score-outcome modeling.



Original published scores, if supplied, and a GSVA reimplementation will be secondary measurement-sensitivity analyses. Scores computed from the full cohort will be labeled conditional analyses and will not replace the primary training-only prediction evaluation.



Randomized induction allocation will be coded as placebo, ustekinumab 130 mg, or approximately 6 mg/kg. A secondary model will include the continuous score, treatment indicators and both score-by-treatment terms. No response-derived patient subgroups will be created."
  ["project_other_variables_interest"]=>
  string(2055) "The core clinical comparison model will contain randomized induction arm (three categories), baseline total Mayo score (0-12, continuous), prior biologic treatment failure (yes/no, using the trial definition), and baseline systemic corticosteroid use (yes/no). Both comparator models will use identical patients and clinical variables; the extended model adds only the IL-22 score. Missing availability of a core variable will be reported, with no substitution based on observed associations.



Additional variables requested for sample description and prespecified sensitivity analyses are age (years), sex, disease duration (years), disease extent, geographic region/randomization strata, individual Mayo subscores, prior anti-TNF and vedolizumab failure, baseline steroid dose, immunomodulator use, C-reactive protein, albumin and fecal calprotectin. Units, sampling windows and trial coding will be checked. Total Mayo and its component subscores will not be entered together in the core model.



An expanded, shrinkage-based sensitivity model will add age, sex, disease duration, log-transformed CRP and albumin when sufficiently available; it will not use stepwise or univariable significance screening. Highly incomplete optional variables (>40% missing) will remain descriptive. Further additions, including fecal calprotectin and region, will be explicitly exploratory and judged against sample size and event counts.



Continuous predictors will remain continuous. Transformation constants for skewed biomarkers will be based on assay units/detection limits and fixed before outcome modeling. Post-baseline treatment changes, response status and follow-up biomarkers will not be adjustment variables in baseline prediction models.



Technical metadata include biopsy site, visit, assay batch, RNA quality, processing method and availability of paired samples. These support outcome-blinded QC, batch-sensitivity checks and comparison of profiled versus non-profiled randomized participants."
  ["project_stat_analysis_plan"]=>
  string(4817) "1. Scope and audit. Analyses will be performed in the approved secure platform. Before restricted-data access, we will freeze the gene list, hypotheses and analysis plan, disclose prior public-data results, and log deviations. After access, an outcome-blinded technical review will resolve assay mapping and variable coding before fitting outcome models. Report patient flow, exclusions, missingness, randomized-arm counts, events and gene coverage. Compare participants with versus without usable baseline profiles using descriptive summaries and standardized differences. Results concern the available biosample subset, not automatically all randomized patients.



2. Primary prediction comparison. Model A contains the prespecified core clinical variables and randomized arm; Model B adds the mean-z IL-22 score. Fit ridge logistic regression with an unpenalized intercept and treatment indicators, retaining all prespecified predictors. Select the penalty using inner cross-validation minimizing Brier loss. Evaluate with 10 repeats of five-fold patient-level cross-validation using identical folds for A and B, stratified by remission and treatment where feasible. All samples from one patient stay in one fold. Covariate imputation, expression transformations requiring cohort estimates, gene/score scaling and penalty tuning must use training data only, including within inner folds. Validation outcomes will never enter predictor construction or imputation.



Primary performance is the paired difference in mean held-out Brier loss, B minus A; negative values favor adding the score. Average performance over repeats. Report AUC, log loss, calibration intercept/slope and calibration plots as complementary measures, including within-arm results where estimable. Use 200 patient-level bootstrap resamples rerunning the paired validation procedure for approximate 95% intervals; duplicate copies of a resampled patient remain grouped. Do not treat folds or repeated predictions as independent observations. Record seeds, convergence and failed resamples. If event counts cannot support five folds, use three folds and report the change; if even this is infeasible, restrict conclusions to descriptive estimates.



3. Association and treatment interaction. Firth logistic regression will estimate the adjusted score odds ratio per SD and profile-likelihood 95% interval using the same core covariates. A secondary model adds two score-by-treatment terms, with a joint two-degree-of-freedom penalized-likelihood test and dose-specific estimates. Display standardized predicted remission probabilities and absolute treatment differences over the observed score range, avoiding extrapolation. This is a secondary treatment-effect-modification assessment in a selected biosample subset; neither a prognostic association nor a nonsignificant interaction establishes score-guided treatment benefit.



4. Missing data. Follow documented trial endpoint rules for treatment failure and nonresponder imputation. Participants whose remission status remains unknown will be excluded from the primary scored-outcome evaluation, counted explicitly, and assessed using all-unknown-as-failure/all-unknown-as-success sensitivity analyses; missing-at-random outcome imputation will be exploratory. For clinical predictors, primary prediction uses training-set median/mode imputation with missingness indicators. This evaluates a deployable missing-data strategy and is not assumed to remove missingness bias. Association analyses will use complete core-covariate records, with 20 chained-equation imputations under a missing-at-random assumption as a sensitivity analysis. Inference from imputed fits will use appropriate pooled estimates, with sparse-data limitations reported. Any imputed prediction sensitivity must fit imputation on training data without using held-out outcomes.



5. Sensitivities and interpretation. Examine the expanded clinical model, alternate score summaries, dose-specific analyses and documented batch effects. If supplied normalization cannot be reconstructed within training sets, label the affected evaluation conditional on that preprocessing rather than fully independent pipeline validation. Secondary clinical endpoints use the same prespecified framework. Paired expression changes will be examined by visit using patient-level paired summaries or mixed models with treatment, visit and their interaction; these are exploratory and will not use post-treatment expression to predict earlier outcomes. Report two-sided estimates and uncertainty, identify all secondary/exploratory analyses, and avoid selecting conclusions by significance. This is internal validation and extension of UNIFI, not independent external validation."
  ["project_software_used"]=>
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      ["label"]=>
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  ["project_timeline"]=>
  string(1405) "The project will begin when the data use agreement is executed and access to the required clinical and transcriptomic datasets is activated (Month 0). The analysis plan and gene list will be finalized before restricted-data access.



Months 1-2: Review documentation, verify participant-sample linkage and gene coverage, conduct technical quality control, and document missingness and the analysis population.



Months 3-5: Complete primary clinical-model comparisons, internal validation, and adjusted association analyses.



Months 6-7: Complete secondary treatment-interaction analyses, sensitivity analyses, and exploratory paired-expression analyses if suitable follow-up samples are available.



Months 8-9: Complete reproducibility checks, interpret findings, and prepare the manuscript and supplementary materials.



Month 10: Submit the manuscript to a peer-reviewed journal and provide YODA with a results summary and submission details.



Months 11-12: Address editorial feedback where feasible, finalize permitted research outputs, and provide YODA with an updated project report. Publication details will be reported when available.



The target is to complete analyses and first manuscript submission within the 12-month access period. Any necessary extension will be requested before access expires."
  ["project_dissemination_plan"]=>
  string(1526) "We plan to prepare one primary research manuscript reporting the incremental predictive value of the fixed mucosal IL-22 program beyond baseline clinical variables in UNIFI. Secondary treatment-interaction and exploratory longitudinal findings will be clearly distinguished from the primary analysis. Results will be reported regardless of statistical significance or direction.



The intended audience includes inflammatory bowel disease clinicians, translational researchers, trial investigators and researchers evaluating clinical biomarkers. Potential journals include PLOS ONE and specialist gastroenterology journals, with the final choice determined by the completed study's scope.



The manuscript will disclose and cite our earlier public-data analysis, currently submitted to PLOS ONE, and explain the additional clinical information and analyses enabled by YODA access. Overlapping UNIFI participants will be identified; the study will not be described as independent external validation. Any publication will clearly distinguish new contributions and avoid duplicate reporting.



We will provide YODA with project updates, a results summary and publication details. A plain-language summary will describe the findings and limitations. Analysis code and non-disclosive aggregate outputs will be shared where permitted by the data use agreement and platform review procedures. Restricted participant-level clinical or transcriptomic data will remain within the secure platform."
  ["project_bibliography"]=>
  string(1567) "

Pavlidis P, Tsakmaki A, Pantazi E, et al. Interleukin-22 regulates neutrophil recruitment in ulcerative colitis and is associated with resistance to ustekinumab therapy. Nature Communications. 2022;13:5820. https://doi.org/10.1038/s41467-022-33331-8
Sands BE, Sandborn WJ, Panaccione R, et al. Ustekinumab as Induction and Maintenance Therapy for Ulcerative Colitis. New England Journal of Medicine. 2019;381:1201-1214. https://doi.org/10.1056/NEJMoa1900750
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. https://doi.org/10.1186/1471-2105-14-7
Friedman J, Hastie T, Tibshirani R. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software. 2010;33(1):1-22. https://doi.org/10.18637/jss.v033.i01
Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Statistics in Medicine. 2002;21(16):2409-2419. https://doi.org/10.1002/sim.1047
van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software. 2011;45(3):1-67. https://doi.org/10.18637/jss.v045.i03

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