Immunology · FcRn Inhibitor (VYVGART-like) · gMG
This report analyses simulated Phase II trial data for an FcRn inhibitor (efgartigimod alfa / VYVGART-like) in generalised Myasthenia Gravis (gMG), an IgG-mediated autoimmune neuromuscular disorder. The analysis integrates three analytical streams:
The neonatal Fc receptor (FcRn) salvages IgG antibodies from lysosomal degradation, recycling them into circulation. In gMG, pathogenic anti-AChR and anti-MuSK IgG antibodies impair neuromuscular junction transmission. FcRn inhibition with efgartigimod accelerates IgG catabolism, reducing all IgG subclasses including pathogenic autoantibodies — a mechanism validated across gMG, ITP, CIDP, and pemphigus vulgaris.
Key pharmacology:
data_list <- simulate_trial_data(
n_patients = p("n_patients"),
n_genes = 500,
n_proteins = 50,
seed = p("seed")
)
demo <- data_list$demographics
longitudinal <- data_list$longitudinal
transcriptomics <- data_list$transcriptomics
batch_df <- data_list$batch
proteomics <- data_list$proteomics
survival_df <- data_list$survivaln_act <- sum(demo$treatment == 1)
n_pbo <- sum(demo$treatment == 0)
r_act <- mean(demo$true_responder[demo$treatment == 1])
r_pbo <- mean(demo$true_responder[demo$treatment == 0])
n_genes <- ncol(transcriptomics) - 1
n_prot <- length(data_list$protein_names)
weeks <- unique(longitudinal$week)
tibble(
Parameter = c(
"Total patients enrolled",
sprintf("Active arm (%s)", p("drug_class")),
"Placebo arm",
"Responder rate \u2014 Active",
"Responder rate \u2014 Placebo",
"Transcriptomic features (genes)",
"Proteomic features (Olink proteins)",
"Assessment timepoints (weeks)",
"Total longitudinal records"
),
Value = c(
nrow(demo), n_act, n_pbo,
sprintf("%.1f%%", r_act * 100),
sprintf("%.1f%%", r_pbo * 100),
n_genes, n_prot,
paste(sort(weeks), collapse = ", "),
nrow(longitudinal)
)
) |>
kbl(booktabs = TRUE, align = c("l", "r")) |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE, font_size = 13) |>
row_spec(0, bold = TRUE, color = "white", background = ac)| Parameter | Value |
|---|---|
| Total patients enrolled | 120 |
| Active arm (FcRn Inhibitor) | 63 |
| Placebo arm | 57 |
| Responder rate — Active | 61.9% |
| Responder rate — Placebo | 15.8% |
| Transcriptomic features (genes) | 500 |
| Proteomic features (Olink proteins) | 50 |
| Assessment timepoints (weeks) | 0, 4, 8, 12, 24 |
| Total longitudinal records | 600 |
pca_df <- if (inherits(qc_res$pca_res, "prcomp")) {
as.data.frame(qc_res$pca_res$x[, 1:2]) |>
tibble::rownames_to_column("patient_id") |>
dplyr::left_join(dplyr::select(demo, patient_id, treatment), by = "patient_id")
} else {
df <- as.data.frame(qc_res$pca_res)
if (!"PC1" %in% names(df)) names(df)[1:2] <- c("PC1", "PC2")
if (!"treatment" %in% names(df))
df <- tibble::rownames_to_column(df, "patient_id") |>
dplyr::left_join(dplyr::select(demo, patient_id, treatment), by = "patient_id")
df
}
ggplot(pca_df, aes(PC1, PC2, colour = factor(treatment))) +
geom_point(alpha = 0.75, size = 2.5) +
stat_ellipse(level = 0.90, linetype = "dashed") +
scale_colour_manual(
values = c("0" = "#AAAAAA", "1" = ac),
labels = c("0" = "Placebo", "1" = "Active")
) +
labs(title = "Transcriptomics PCA \u2014 Post Batch Correction",
subtitle = "90% confidence ellipses per arm",
x = "PC1", y = "PC2", colour = "Treatment")
ggplot(de_norm, aes(fc, -log10(pval_raw), colour = sig)) +
geom_point(alpha = 0.55, size = 1.6) +
geom_hline(yintercept = -log10(0.05), linetype = "dashed", colour = "grey40") +
geom_vline(xintercept = c(-1, 1), linetype = "dashed", colour = "grey40") +
scale_colour_manual(values = c("Up in Active" = "#C0392B",
"Down in Active" = "#2980B9", "NS" = "grey70")) +
labs(title = "Differential Expression: Active vs Placebo (Baseline)",
subtitle = sprintf("%d genes at FDR < 5%% (Welch t-test, BH)", n_de),
x = "log\u2082 Fold Change", y = "-log\u2081\u2080(p-value)", colour = NULL) +
annotate("text", x = Inf, y = Inf, label = sprintf("n DE = %d", n_de),
hjust = 1.1, vjust = 1.3, size = 4, colour = ac, fontface = "bold")
tbl_cols <- intersect(c("gene_id", "fc", "AveExpr", "t_stat", "pval_raw", "fdr"),
names(de_norm))
de_norm |>
dplyr::filter(fdr < 0.05) |>
dplyr::arrange(fdr) |>
dplyr::slice_head(n = 10) |>
dplyr::select(all_of(tbl_cols)) |>
dplyr::rename_with(~ dplyr::case_match(.x,
"gene_id" ~ "Gene", "fc" ~ "log\u2082FC", "AveExpr" ~ "Ave. Expr",
"t_stat" ~ "t", "pval_raw" ~ "p-value", "fdr" ~ "FDR", .default = .x)) |>
mutate(across(where(is.numeric), \(x) round(x, 4))) |>
kbl(booktabs = TRUE) |>
kable_styling(bootstrap_options = c("striped", "hover", "condensed"),
full_width = FALSE) |>
column_spec(length(tbl_cols), bold = TRUE) |>
row_spec(0, bold = TRUE, color = "white", background = ac)| Gene | log₂FC | Ave. Expr | t | p-value | FDR |
|---|---|---|---|---|---|
| GENE0469 | 2.0679 | NA | 5.0466 | 0e+00 | 0.0006 |
| GENE0181 | 1.6008 | NA | 4.6146 | 0e+00 | 0.0015 |
| GENE0386 | 1.6160 | NA | 4.4345 | 0e+00 | 0.0021 |
| GENE0302 | 1.4308 | NA | 3.9602 | 1e-04 | 0.0076 |
| GENE0251 | 1.5233 | NA | 3.9298 | 2e-04 | 0.0076 |
| GENE0369 | 1.5029 | NA | 3.8700 | 2e-04 | 0.0082 |
| GENE0439 | 1.4388 | NA | 3.7685 | 3e-04 | 0.0096 |
| GENE0457 | 1.4095 | NA | 3.6447 | 4e-04 | 0.0133 |
| GENE0039 | 1.3082 | NA | 3.5255 | 7e-04 | 0.0185 |
| GENE0118 | 1.4821 | NA | 3.4783 | 8e-04 | 0.0201 |
prot_long <- local({
df <- as.data.frame(proteomics)
prot_col <- intersect(c("protein","protein_name","analyte","Assay","OlinkID"), names(df))
npx_col <- intersect(c("NPX","npx","value","expression","NPX_value"), names(df))
wk_col <- intersect(c("week","Week","time","timepoint","visit"), names(df))
meta_candidates <- c("patient_id","treatment","week","Week","time","timepoint",
"true_responder")
if (length(prot_col) > 0 && length(npx_col) > 0) {
# ── Already long ─────────────────────────────────────────────────────────
out <- dplyr::rename(df, protein = !!prot_col[1], NPX = !!npx_col[1])
if (length(wk_col) > 0 && wk_col[1] != "week")
out <- dplyr::rename(out, week = !!wk_col[1])
out
} else {
# ── Wide format: pivot all non-metadata columns to long ───────────────────
meta_cols <- intersect(meta_candidates, names(df))
out <- tidyr::pivot_longer(df,
cols = -dplyr::all_of(meta_cols),
names_to = "protein",
values_to = "NPX")
alt <- intersect(c("Week","time","timepoint"), names(out))
if (!"week" %in% names(out) && length(alt) > 0)
out <- dplyr::rename(out, week = !!alt[1])
out
}
})
# ── Resolve which protein to plot ─────────────────────────────────────────────
available_proteins <- unique(prot_long$protein)
plot_protein <- if (p("primary_biomarker") %in% available_proteins) {
p("primary_biomarker")
} else {
# Pick the closest match by name, or fall back to first protein
match_idx <- agrep(p("primary_biomarker"), available_proteins,
ignore.case = TRUE, max.distance = 0.4)
if (length(match_idx) > 0) available_proteins[match_idx[1]] else available_proteins[1]
}
if (plot_protein != p("primary_biomarker"))
message(sprintf("Primary biomarker '%s' not found in proteomics data. Plotting '%s' instead.",
p("primary_biomarker"), plot_protein))
md <- as.data.frame(prot_res$mean_delta)
pc_col <- intersect(c("protein","protein_name","analyte"), names(md))[1]
get_d <- function(trt) {
rows <- !is.na(pc_col) & md[[pc_col]] == plot_protein & md$treatment == trt
if (any(rows, na.rm = TRUE)) md$mean_delta[rows][1] else NA_real_
}
act_pb <- get_d(1); pbo_pb <- get_d(0)
sub_txt <- if (!is.na(act_pb) && !is.na(pbo_pb))
sprintf("Active \u0394 (Wk0\u219212): %+.3f | Placebo \u0394: %+.3f", act_pb, pbo_pb) else
sprintf("%s NPX change from baseline", plot_protein)
prot_plot <- prot_long |>
dplyr::filter(protein == plot_protein)
# Only join true_responder from demo if the column isn't already present
if (!"true_responder" %in% names(prot_plot))
prot_plot <- dplyr::left_join(
prot_plot, dplyr::select(demo, patient_id, true_responder), by = "patient_id"
)
prot_plot |>
dplyr::mutate(Arm = ifelse(treatment == 1, "Active", "Placebo"),
Responder = ifelse(true_responder == 1, "Responder", "Non-Responder")) |>
dplyr::group_by(Arm, Responder, week) |>
dplyr::summarise(mn = mean(NPX), se = sd(NPX)/sqrt(dplyr::n()), .groups = "drop") |>
ggplot(aes(week, mn, colour = Arm, linetype = Responder, fill = Arm)) +
geom_ribbon(aes(ymin = mn - se, ymax = mn + se), alpha = 0.15, colour = NA) +
geom_line(linewidth = 1) + geom_point(size = 2.5) +
scale_colour_manual(values = c("Active" = ac, "Placebo" = "#AAAAAA")) +
scale_fill_manual(values = c("Active" = ac, "Placebo" = "#AAAAAA")) +
scale_x_continuous(breaks = sort(unique(prot_long$week))) +
labs(title = sprintf("%s NPX Over Time", p("primary_biomarker")),
subtitle = sub_txt, x = "Week", y = "NPX (log\u2082)",
colour = "Treatment", fill = "Treatment", linetype = "Responder Status")
tryCatch({
grid::grid.newpage()
plot_multiomics(qc_res$expr_filtered, de_df, demo, proteomics,
omics_int$cross_corr, qc_res$pca_res)
}, error = function(e) {
png_path <- file.path("outputs", "01_multiomics_analysis.png")
if (file.exists(png_path)) knitr::include_graphics(png_path)
else message("plot_multiomics() error: ", conditionMessage(e))
})
\[Y_{ij} = \beta_0 + \beta_1\,\text{week}_{ij} + \beta_2\,\text{trt}_i + \beta_3\,(\text{week}\times\text{trt})_{ij} + b_i + \varepsilon_{ij}\]
lme_tbl <- if (requireNamespace("broom.mixed", quietly = TRUE)) {
broom.mixed::tidy(lme_fit, effects = "fixed", conf.int = TRUE) |>
dplyr::select(dplyr::any_of(c("term","estimate","std.error","statistic","conf.low","conf.high")))
} else {
sm <- summary(lme_fit); cm <- as.data.frame(sm$coefficients)
tibble::tibble(term = rownames(cm), estimate = cm[[1]], std.error = cm[[2]],
statistic = cm[[grep("t.value|t value|z.value|z value", names(cm))[1]]])
}
int_rows <- which(grepl("week.*trt|trt.*week|week:treat|treat.*:.*week",
lme_tbl$term, ignore.case = TRUE))
lme_kbl <- lme_tbl |>
dplyr::mutate(dplyr::across(where(is.numeric), \(x) round(x, 3))) |>
dplyr::rename(Term = term, Estimate = estimate, SE = std.error,
`t-stat` = dplyr::any_of("statistic"),
`CI Low` = dplyr::any_of("conf.low"),
`CI High` = dplyr::any_of("conf.high")) |>
kbl(booktabs = TRUE) |>
kable_styling(bootstrap_options = c("striped","hover","condensed"), full_width = FALSE) |>
row_spec(0, bold = TRUE, color = "white", background = ac)
if (length(int_rows) > 0)
lme_kbl <- kableExtra::row_spec(lme_kbl, int_rows, bold = TRUE, background = "#EAF3FB")
lme_kbl| Term | Estimate | SE | t-stat | CI Low | CI High |
|---|---|---|---|---|---|
| (Intercept) | 6.445 | 0.156 | 41.344 | 6.140 | 6.751 |
| week | -0.010 | 0.012 | -0.812 | -0.034 | 0.014 |
| treatment | -0.136 | 0.215 | -0.632 | -0.558 | 0.286 |
| week:treatment | -0.089 | 0.017 | -5.281 | -0.122 | -0.056 |
\[E(t) = E_0 - \frac{E_{\max}\cdot t^\gamma}{EC_{50}^\gamma + t^\gamma}\]
tryCatch({
grid::grid.newpage()
plot_longitudinal_survival(longitudinal, survival_df, lme_fit, emax_res, km_res, cox_res)
}, error = function(e) {
png_path <- file.path("outputs", "02_longitudinal_survival.png")
if (file.exists(png_path)) knitr::include_graphics(png_path)
else message("plot_longitudinal_survival() error: ", conditionMessage(e))
})#>
#> ✓ Saved: outputs/02_longitudinal_survival.png| Responders | Non.Responders | |
|---|---|---|
| emax | 84.680 | 40.926 |
| ec50 | 4.287 | 5.311 |
tryCatch({
if (!requireNamespace("survminer", quietly = TRUE)) stop("survminer not installed")
print(survminer::ggsurvplot(
km_res$km_fit, data = survival_df,
palette = c("#AAAAAA", ac), conf.int = TRUE, pval = TRUE, risk.table = TRUE,
ggtheme = theme_trial(), legend.labs = c("Placebo","Active"),
title = "Time to Sustained Clinical Response",
xlab = "Time (weeks)", ylab = "Response-free probability"
))
}, error = function(e) {
plot(km_res$km_fit, col = c("#AAAAAA", ac), lwd = 2,
xlab = "Time (weeks)", ylab = "Response-free probability",
main = "Time to Sustained Clinical Response")
legend("topright", legend = c("Placebo","Active"), col = c("#AAAAAA", ac), lwd = 2)
})
cox_tbl <- broom::tidy(cox_res$cox_fit, exponentiate = TRUE, conf.int = TRUE) |>
dplyr::select(dplyr::any_of(c("term","estimate","conf.low","conf.high","p.value")))
trt_rows <- which(grepl("treatment|trt", cox_tbl$term, ignore.case = TRUE))
cox_kbl <- cox_tbl |>
dplyr::mutate(
dplyr::across(dplyr::any_of(c("estimate","conf.low","conf.high")), \(x) round(x, 3)),
p.value = ifelse(p.value < 0.001, "<0.001", as.character(round(p.value, 3)))
) |>
dplyr::rename(Covariate = term, HR = estimate,
`95% CI Low` = dplyr::any_of("conf.low"),
`95% CI High` = dplyr::any_of("conf.high"),
`p-value` = p.value) |>
kbl(booktabs = TRUE) |>
kable_styling(bootstrap_options = c("striped","hover","condensed"), full_width = FALSE) |>
row_spec(0, bold = TRUE, color = "white", background = ac)
if (length(trt_rows) > 0)
cox_kbl <- kableExtra::row_spec(cox_kbl, trt_rows, bold = TRUE, background = "#FEF3E8")
cox_kbl| Covariate | HR | 95% CI Low | 95% CI High | p-value |
|---|---|---|---|---|
| treatment | 6.666 | 2.907 | 15.284 | <0.001 |
| baseline_igg_z | 0.833 | 0.586 | 1.185 | 0.31 |
| latent_biology_z | 2.511 | 1.887 | 3.341 | <0.001 |
#>
#> ✓ Saved: outputs/03_ml_pipeline.pngcluster_tbl <- tryCatch({
df <- as.data.frame(cluster_res$cluster_summary)
if (nrow(df) == 0) stop("empty")
df
}, error = function(e) {
cv <- if (!is.null(cluster_res$clusters)) cluster_res$clusters else
if (!is.null(cluster_res$cluster)) cluster_res$cluster else
if (!is.null(cluster_res$assignments)) cluster_res$assignments else
stop("No cluster vector in cluster_res: ", paste(names(cluster_res), collapse = ", "))
ci <- as.integer(unlist(cv))
n <- nrow(demo)
if (length(ci) %% n == 0 && length(ci) > n) ci <- ci[seq_len(n)]
demo |>
dplyr::mutate(Cluster = ci) |>
dplyr::group_by(Cluster) |>
dplyr::summarise(N = dplyr::n(),
`Active (%)` = round(mean(treatment == 1) * 100, 1),
`Responders (%)` = round(mean(true_responder == 1) * 100, 1),
.groups = "drop")
})
cluster_tbl |>
dplyr::mutate(dplyr::across(where(is.numeric), \(x) round(x, 2))) |>
kbl(booktabs = TRUE) |>
kable_styling(bootstrap_options = c("striped","hover","condensed"), full_width = FALSE) |>
row_spec(0, bold = TRUE, color = "white", background = ac)| Cluster | N | Active (%) | Responders (%) |
|---|---|---|---|
| NA | 120 | 52.5 | 40 |
tibble::tibble(Feature = ml_res$selected_feats) |>
dplyr::mutate(Rank = dplyr::row_number()) |>
dplyr::relocate(Rank) |>
kbl(booktabs = TRUE) |>
kable_styling(bootstrap_options = c("striped","hover","condensed"),
full_width = FALSE, font_size = 12) |>
row_spec(0, bold = TRUE, color = "white", background = ac)| Rank | Feature |
|---|---|
| 1 | GENE0025 |
| 2 | GENE0038 |
| 3 | GENE0039 |
| 4 | GENE0041 |
| 5 | GENE0064 |
| 6 | GENE0117 |
| 7 | GENE0118 |
| 8 | GENE0127 |
| 9 | GENE0128 |
| 10 | GENE0130 |
| 11 | GENE0161 |
| 12 | GENE0163 |
| 13 | GENE0168 |
| 14 | GENE0169 |
| 15 | GENE0170 |
| 16 | GENE0172 |
| 17 | GENE0177 |
| 18 | GENE0181 |
| 19 | GENE0189 |
| 20 | GENE0191 |
| 21 | GENE0201 |
| 22 | GENE0213 |
| 23 | GENE0216 |
| 24 | GENE0233 |
| 25 | GENE0235 |
| 26 | GENE0251 |
| 27 | GENE0262 |
| 28 | GENE0291 |
| 29 | GENE0292 |
| 30 | GENE0294 |
| 31 | GENE0299 |
| 32 | GENE0302 |
| 33 | GENE0308 |
| 34 | GENE0313 |
| 35 | GENE0320 |
| 36 | GENE0324 |
| 37 | GENE0332 |
| 38 | GENE0343 |
| 39 | GENE0350 |
| 40 | GENE0360 |
| 41 | GENE0365 |
| 42 | GENE0367 |
| 43 | GENE0368 |
| 44 | GENE0369 |
| 45 | GENE0377 |
| 46 | GENE0381 |
| 47 | GENE0384 |
| 48 | GENE0385 |
| 49 | GENE0386 |
| 50 | GENE0387 |
| 51 | GENE0397 |
| 52 | GENE0407 |
| 53 | GENE0420 |
| 54 | GENE0425 |
| 55 | GENE0439 |
| 56 | GENE0442 |
| 57 | GENE0457 |
| 58 | GENE0468 |
| 59 | GENE0469 |
| 60 | GENE0477 |
| 61 | GENE0496 |
| 62 | IL6 |
| 63 | TNF |
| 64 | IL17A |
| 65 | CXCL13 |
| 66 | IL4 |
| 67 | PROT007 |
| 68 | PROT013 |
| 69 | PROT020 |
imp_df <- as.data.frame(ml_res$importance_df)
feat_col <- intersect(c("feature","Feature","variable","Variable","gene"), names(imp_df))[1]
imp_col <- intersect(c("importance","MeanDecreaseGini","MeanDecreaseAccuracy",
"IncNodePurity","Overall"), names(imp_df))[1]
if (is.na(feat_col) || is.na(imp_col))
stop("Cannot identify columns in ml_res$importance_df: ",
paste(names(imp_df), collapse = ", "))
imp_df <- dplyr::rename(imp_df, feature = !!feat_col, importance = !!imp_col)
imp_df |>
dplyr::slice_head(n = min(20L, nrow(imp_df))) |>
dplyr::mutate(feature = forcats::fct_reorder(feature, importance)) |>
ggplot(aes(importance, feature, fill = importance)) +
geom_col(show.legend = FALSE) +
scale_fill_gradient(low = "#CCCCCC", high = ac) +
labs(title = "Random Forest \u2014 Variable Importance",
subtitle = sprintf("Top biomarker: %s | OOB AUROC = %.3f",
imp_df$feature[which.max(imp_df$importance)], ml_res$auc_oob),
x = "Mean Decrease in Gini Impurity", y = NULL)
tryCatch({
if (!requireNamespace("pROC", quietly = TRUE)) stop("pROC not installed")
scores <- if (!is.null(ml_res$oob_probs)) ml_res$oob_probs else
if (!is.null(ml_res$oob_pred)) ml_res$oob_pred else
stop("No OOB scores in ml_res")
labels <- if (!is.null(ml_res$y_true)) ml_res$y_true else
if (!is.null(ml_res$labels)) ml_res$labels else
stop("No true labels in ml_res")
roc_obj <- pROC::roc(labels, scores, quiet = TRUE)
pROC::ggroc(roc_obj, colour = ac, linewidth = 1.1) +
geom_abline(slope = 1, intercept = 1, linetype = "dashed", colour = "grey60") +
annotate("text", x = 0.25, y = 0.1,
label = sprintf("AUROC = %.3f", pROC::auc(roc_obj)),
size = 5, colour = ac, fontface = "bold") +
labs(title = "Random Forest \u2014 OOB ROC Curve",
x = "Specificity", y = "Sensitivity")
}, error = function(e) message("ROC skipped: ", conditionMessage(e)))tibble::tibble(
Domain = c("Multi-Omics","Multi-Omics",
"Longitudinal","Longitudinal","Longitudinal","Longitudinal",
"ML Pipeline","ML Pipeline","ML Pipeline"),
Finding = c(
sprintf("%d genes differentially expressed at baseline (Welch t-test, FDR < 5%%)", n_de),
sprintf("%s NPX \u0394 (Wk0\u219212): Active %s vs Placebo %s",
p("primary_biomarker"), fmt_d(act_pb), fmt_d(pbo_pb)),
"lme4 LME: significant week \u00d7 treatment interaction",
"Emax PD: Responders ~80% vs Non-Responders ~40% primary-biomarker reduction",
sprintf("Log-rank p = %.5f (active responds significantly earlier)", km_res$lr_p),
sprintf("Cox PH treatment HR = %.2f\u00d7 (adjusted)", cox_res$trt_hr),
sprintf("Elastic-net selected %d features at optimal \u03bb", length(ml_res$selected_feats)),
sprintf("Random forest OOB AUROC = %.3f", ml_res$auc_oob),
sprintf("Top predictive biomarker: %s", top_b)
)
) |>
kbl(booktabs = TRUE) |>
kable_styling(bootstrap_options = c("striped","hover","condensed"), full_width = TRUE) |>
column_spec(1, bold = TRUE, width = "3cm") |>
row_spec(0, bold = TRUE, color = "white", background = ac) |>
kableExtra::collapse_rows(columns = 1, valign = "top")| Domain | Finding |
|---|---|
| Multi-Omics | 18 genes differentially expressed at baseline (Welch t-test, FDR < 5%) |
| IgG_total NPX Δ (Wk0→12): Active -1.674 vs Placebo -0.020 | |
| Longitudinal | lme4 LME: significant week × treatment interaction |
| Emax PD: Responders ~80% vs Non-Responders ~40% primary-biomarker reduction | |
| Log-rank p = 0.00000 (active responds significantly earlier) | |
| Cox PH treatment HR = 6.67× (adjusted) | |
| ML Pipeline | Elastic-net selected 69 features at optimal λ |
| Random forest OOB AUROC = 0.960 | |
| Top predictive biomarker: GENE0181 |
#> ─ Session info ─────────────────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.2 (2024-10-31)
#> os macOS 26.3
#> system aarch64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Europe/Brussels
#> date 2026-05-13
#> pandoc 3.6.3 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
#> quarto 1.7.32 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/quarto
#>
#> ─ Packages ─────────────────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> abind 1.4-8 2024-09-12 [1] CRAN (R 4.4.1)
#> askpass 1.2.1 2024-10-04 [1] CRAN (R 4.4.1)
#> backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.1)
#> Biobase * 2.66.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> BiocGenerics * 0.52.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> boot 1.3-32 2025-08-29 [1] CRAN (R 4.4.1)
#> broom * 1.0.12 2026-01-27 [1] CRAN (R 4.4.3)
#> broom.mixed 0.2.9.7 2026-02-17 [1] CRAN (R 4.4.3)
#> car 3.1-3 2024-09-27 [1] CRAN (R 4.4.1)
#> carData 3.0-6 2026-01-30 [1] CRAN (R 4.4.3)
#> caret 7.0-1 2024-12-10 [1] CRAN (R 4.4.1)
#> class 7.3-23 2025-01-01 [1] CRAN (R 4.4.1)
#> cli 3.6.5 2025-04-23 [1] CRAN (R 4.4.1)
#> cluster * 2.1.8.1 2025-03-12 [1] CRAN (R 4.4.1)
#> codetools 0.2-20 2024-03-31 [1] CRAN (R 4.4.2)
#> crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.1)
#> data.table 1.18.2.1 2026-01-27 [1] CRAN (R 4.4.3)
#> DelayedArray 0.32.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> digest 0.6.39 2025-11-19 [1] CRAN (R 4.4.3)
#> dplyr * 1.2.0 2026-02-03 [1] CRAN (R 4.4.3)
#> evaluate 1.0.5 2025-08-27 [1] CRAN (R 4.4.1)
#> farver 2.1.2 2024-05-13 [1] CRAN (R 4.4.1)
#> fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.1)
#> forcats 1.0.1 2025-09-25 [1] CRAN (R 4.4.1)
#> foreach 1.5.2 2022-02-02 [1] CRAN (R 4.4.0)
#> Formula 1.2-5 2023-02-24 [1] CRAN (R 4.4.1)
#> furrr 0.3.1 2022-08-15 [1] CRAN (R 4.4.0)
#> future 1.69.0 2026-01-16 [1] CRAN (R 4.4.3)
#> future.apply 1.20.1 2025-12-09 [1] CRAN (R 4.4.3)
#> generics 0.1.4 2025-05-09 [1] CRAN (R 4.4.1)
#> GenomeInfoDb * 1.42.3 2025-01-27 [1] Bioconductor 3.20 (R 4.4.2)
#> GenomeInfoDbData 1.2.13 2026-05-07 [1] Bioconductor
#> GenomicRanges * 1.58.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> ggplot2 * 4.0.1 2025-11-14 [1] CRAN (R 4.4.3)
#> ggpubr 0.6.2 2025-10-17 [1] CRAN (R 4.4.1)
#> ggrepel * 0.9.6 2024-09-07 [1] CRAN (R 4.4.1)
#> ggsignif 0.6.4 2022-10-13 [1] CRAN (R 4.4.0)
#> glmnet * 4.1-10 2025-07-17 [1] CRAN (R 4.4.1)
#> globals 0.18.0 2025-05-08 [1] CRAN (R 4.4.1)
#> glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
#> gower 1.0.2 2024-12-17 [1] CRAN (R 4.4.1)
#> gridExtra * 2.3 2017-09-09 [1] CRAN (R 4.4.1)
#> gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.1)
#> hardhat 1.4.2 2025-08-20 [1] CRAN (R 4.4.1)
#> htmltools 0.5.9 2025-12-04 [1] CRAN (R 4.4.3)
#> htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
#> httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
#> ipred 0.9-15 2024-07-18 [1] CRAN (R 4.4.0)
#> IRanges * 2.40.1 2024-12-05 [1] Bioconductor 3.20 (R 4.4.2)
#> iterators 1.0.14 2022-02-05 [1] CRAN (R 4.4.1)
#> jsonlite 2.0.0 2025-03-27 [1] CRAN (R 4.4.1)
#> kableExtra * 1.4.0 2024-01-24 [1] CRAN (R 4.4.0)
#> km.ci 0.5-6 2022-04-06 [1] CRAN (R 4.4.0)
#> KMsurv 0.1-6 2025-05-20 [1] CRAN (R 4.4.1)
#> knitr * 1.51 2025-12-20 [1] CRAN (R 4.4.3)
#> labeling 0.4.3 2023-08-29 [1] CRAN (R 4.4.1)
#> lattice 0.22-7 2025-04-02 [1] CRAN (R 4.4.1)
#> lava 1.8.2 2025-10-30 [1] CRAN (R 4.4.1)
#> lifecycle 1.0.5 2026-01-08 [1] CRAN (R 4.4.3)
#> limma * 3.62.2 2025-01-09 [1] Bioconductor 3.20 (R 4.4.2)
#> listenv 0.10.0 2025-11-02 [1] CRAN (R 4.4.1)
#> lme4 * 1.1-38 2025-12-02 [1] CRAN (R 4.4.3)
#> lmerTest * 3.2-1 2026-03-05 [1] CRAN (R 4.4.3)
#> lubridate 1.9.4 2024-12-08 [1] CRAN (R 4.4.1)
#> magrittr 2.0.4 2025-09-12 [1] CRAN (R 4.4.1)
#> MASS 7.3-65 2025-02-28 [1] CRAN (R 4.4.1)
#> Matrix * 1.7-4 2025-08-28 [1] CRAN (R 4.4.1)
#> MatrixGenerics * 1.18.1 2025-01-09 [1] Bioconductor 3.20 (R 4.4.2)
#> matrixStats * 1.5.0 2025-01-07 [1] CRAN (R 4.4.1)
#> minqa 1.2.8 2024-08-17 [1] CRAN (R 4.4.1)
#> ModelMetrics 1.2.2.2 2020-03-17 [1] CRAN (R 4.4.1)
#> nlme 3.1-168 2025-03-31 [1] CRAN (R 4.4.1)
#> nloptr 2.2.1 2025-03-17 [1] CRAN (R 4.4.1)
#> nnet 7.3-20 2025-01-01 [1] CRAN (R 4.4.1)
#> numDeriv 2016.8-1.1 2019-06-06 [1] CRAN (R 4.4.1)
#> openssl 2.3.4 2025-09-30 [1] CRAN (R 4.4.1)
#> otel 0.2.0 2025-08-29 [1] CRAN (R 4.4.1)
#> parallelly 1.46.1 2026-01-08 [1] CRAN (R 4.4.3)
#> patchwork * 1.3.2 2025-08-25 [1] CRAN (R 4.4.1)
#> pheatmap * 1.0.13 2025-06-05 [1] CRAN (R 4.4.1)
#> pillar 1.11.1 2025-09-17 [1] CRAN (R 4.4.1)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.1)
#> plyr 1.8.9 2023-10-02 [1] CRAN (R 4.4.1)
#> png 0.1-8 2022-11-29 [1] CRAN (R 4.4.1)
#> pROC * 1.19.0.1 2025-07-31 [1] CRAN (R 4.4.1)
#> prodlim 2025.04.28 2025-04-28 [1] CRAN (R 4.4.1)
#> purrr * 1.2.1 2026-01-09 [1] CRAN (R 4.4.3)
#> R6 2.6.1 2025-02-15 [1] CRAN (R 4.4.1)
#> ragg 1.5.0 2025-09-02 [1] CRAN (R 4.4.1)
#> randomForest * 4.7-1.2 2024-09-22 [1] CRAN (R 4.4.1)
#> rbibutils 2.4.1 2026-01-21 [1] CRAN (R 4.4.3)
#> RColorBrewer * 1.1-3 2022-04-03 [1] CRAN (R 4.4.1)
#> Rcpp 1.1.1 2026-01-10 [1] CRAN (R 4.4.3)
#> Rdpack 2.6.5 2026-01-23 [1] CRAN (R 4.4.3)
#> recipes 1.3.1 2025-05-21 [1] CRAN (R 4.4.1)
#> reformulas 0.4.3.1 2026-01-08 [1] CRAN (R 4.4.3)
#> reshape2 1.4.5 2025-11-12 [1] CRAN (R 4.4.1)
#> reticulate 1.44.1 2025-11-14 [1] CRAN (R 4.4.3)
#> rlang 1.2.0 2026-04-06 [1] CRAN (R 4.4.2)
#> rmarkdown 2.30 2025-09-28 [1] CRAN (R 4.4.1)
#> rpart 4.1.24 2025-01-07 [1] CRAN (R 4.4.1)
#> RSpectra 0.16-2 2024-07-18 [1] CRAN (R 4.4.0)
#> rstatix 0.7.3 2025-10-18 [1] CRAN (R 4.4.1)
#> rstudioapi 0.18.0 2026-01-16 [1] CRAN (R 4.4.3)
#> Rtsne 0.17 2023-12-07 [1] CRAN (R 4.4.1)
#> S4Arrays 1.6.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> S4Vectors * 0.44.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> S7 0.2.1 2025-11-14 [1] CRAN (R 4.4.3)
#> scales 1.4.0 2025-04-24 [1] CRAN (R 4.4.1)
#> sessioninfo 1.2.3 2025-02-05 [1] CRAN (R 4.4.1)
#> shape 1.4.6.1 2024-02-23 [1] CRAN (R 4.4.1)
#> SparseArray 1.6.2 2025-02-20 [1] Bioconductor 3.20 (R 4.4.2)
#> statmod 1.5.1 2025-10-09 [1] CRAN (R 4.4.1)
#> stringi 1.8.7 2025-03-27 [1] CRAN (R 4.4.1)
#> stringr 1.6.0 2025-11-04 [1] CRAN (R 4.4.1)
#> SummarizedExperiment * 1.36.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> survival * 3.8-6 2026-01-16 [1] CRAN (R 4.4.3)
#> survminer 0.5.1 2025-09-02 [1] CRAN (R 4.4.1)
#> survMisc 0.5.6 2022-04-07 [1] CRAN (R 4.4.0)
#> svglite 2.2.2 2025-10-21 [1] CRAN (R 4.4.1)
#> systemfonts 1.3.1 2025-10-01 [1] CRAN (R 4.4.1)
#> textshaping 1.0.4 2025-10-10 [1] CRAN (R 4.4.1)
#> tibble * 3.3.1 2026-01-11 [1] CRAN (R 4.4.3)
#> tidyr * 1.3.2 2025-12-19 [1] CRAN (R 4.4.3)
#> tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
#> timechange 0.4.0 2026-01-29 [1] CRAN (R 4.4.3)
#> timeDate 4052.112 2026-01-28 [1] CRAN (R 4.4.3)
#> UCSC.utils 1.2.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> umap 0.2.10.0 2023-02-01 [1] CRAN (R 4.4.0)
#> utf8 1.2.6 2025-06-08 [1] CRAN (R 4.4.1)
#> vctrs 0.7.1 2026-01-23 [1] CRAN (R 4.4.3)
#> viridisLite 0.4.2 2023-05-02 [1] CRAN (R 4.4.1)
#> withr 3.0.2 2024-10-28 [1] CRAN (R 4.4.1)
#> xfun 0.56 2026-01-18 [1] CRAN (R 4.4.3)
#> xml2 1.5.2 2026-01-17 [1] CRAN (R 4.4.3)
#> xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.1)
#> XVector 0.46.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> yaml 2.3.12 2025-12-10 [1] CRAN (R 4.4.3)
#> zlibbioc 1.52.0 2024-11-08 [1] Bioconductor 3.20 (R 4.4.1)
#> zoo 1.8-15 2025-12-15 [1] CRAN (R 4.4.3)
#>
#> [1] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
#> * ── Packages attached to the search path.
#>
#> ────────────────────────────────────────────────────────────────────────────────────────Report generated with Quarto · R 4.4.2 · 13 May 2026, 09:35 CEST