WGMAF approach predicts survival for patients in CheckPAC trial
Last updated: 2025-03-27
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New: data/pacto_bins.csv
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New: data/sel_chr.txt
New: data/supplementary_tables.xlsx
New: data/tumor_tmb.txt
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Tree plot showing WGMAF input criteria and response for each patient. The section above the dashed line describes evaluable patients, while the section below the dashed line shows patients who were not evaluable due to unavailable tumor or plasma samples, or an insufficient number of somatic mutations found in tumor. Number of somatic mutations identified in tumors are listed to the left of the tree plot in descending order, colored by mutation type. Baseline MAF is plotted on the left side of tree plot and on-treatment MAF is plotted on the right side. The central spine of the tree plot shows the tumor cellularity using two methods, pathological cellularity evaluated from H&E sample and cellularity assessed from molecular sequencing data, as well as BOR RECIST for each patient.
library(tidyverse)
library(readxl)
library(here)
source(here("code/utility_funcs.R"))#----------------------------------------------------------------------------------
# Prepare the data
#----------------------------------------------------------------------------------
# Tumor mutations
tumor_mut<- read_excel(here("data/i07_input.xlsx"),sheet="tumor_filtered_sbs") %>%
rowwise() %>%
mutate(total = sum(c_across(3:8))) %>%
ungroup()
# Plasma mutations
plasm_mut <- read_excel(here("data/i07_input.xlsx"),sheet="plasma_mafs") %>%
drop_na(patient_id)
plasm_mut$date_blood_draw<-as.Date(plasm_mut$date_blood_draw)
# Pathological cellularity
path_cell <- read_excel(here("data/i07_input.xlsx"),sheet="cellularity_p", range=c("A1:H38")) %>%
rename(patient_id = CGID, cellularity=Path_Cellularity) %>%
select(patient_id, cellularity) %>%
mutate(cellularity = as.numeric(cellularity)/100)Warning: There was 1 warning in `mutate()`.
ℹ In argument: `cellularity = as.numeric(cellularity)/100`.
Caused by warning:
! NAs introduced by coercion# Facets cellularity
fact_cell<- read_excel(here("data/i07_input.xlsx"),sheet="facets_summary") %>%
select(patient_id = id, cellularity = purity, emflags) %>%
mutate(patient_id = substring(patient_id, 1, 9))
fact_cell$cellularity<-as.numeric(fact_cell$cellularity)Warning: NAs introduced by coercion# Best RECIST response
best_resp <- read_excel(here("data/i07_input.xlsx"),sheet="CheckPAC_DATA4") %>%
select(subject_id = StudySubjectID,
bor = BOR_RECIST) %>%
mutate(bor = bor_labels(bor)) %>%
inner_join(read_excel(here("data/i07_input.xlsx"), sheet="ID_comparison") %>%
filter(!unlist(lapply(CGID, function(x) any(str_detect(x, c("CGPLPA225", "CGPLPA783", "CGPLPA795")))))) %>%
select(patient_id = CGID, subject_id = StudySubjectID) %>%
mutate(patient_id = substring(patient_id, 1, 9))) %>%
mutate(bor = ifelse(bor == "Not evaluable", "NE", bor))New names:
Joining with `by = join_by(subject_id)`
• `` -> `...32`# Baseline and on-treatment blood draw dates
blot_date <- read_excel(here("data/i07_input.xlsx"),
sheet = "baseline_p2_dates") %>%
mutate(tx_start_date = as.Date(tx_start_date),
ct_flwup_date = excel_date(ct_flwup_date),
baseline = excel_date(baseline),
endpoint = excel_date(endpoint))Warning: There were 3 warnings in `mutate()`.
The first warning was:
ℹ In argument: `ct_flwup_date = excel_date(ct_flwup_date)`.
Caused by warning in `as.Date()`:
! NAs introduced by coercion
ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.#----------------------------------------------------------------------------------
# Patient selection for analysis
#----------------------------------------------------------------------------------
# (1) Patients with tumor mutations > 50 AND on-treatment blood draws
anlys_pat <- intersect(pull(filter(tumor_mut, total > 50), patient_id),
pull(drop_na(blot_date, endpoint), patient_id))
lefto_pat <- setdiff(pull(blot_date, patient_id), anlys_pat)
# (2) Order patients based on on-treatment plasma mutations
ot_plasm_mut <- plasm_mut %>%
select(patient_id, date_blood_draw, mf) %>%
inner_join(pivot_longer(select(blot_date, patient_id, baseline, endpoint),
cols = -patient_id,
names_to = "timepoint",
values_to = "date_blood_draw")) %>%
filter(timepoint == "endpoint")Joining with `by = join_by(patient_id, date_blood_draw)`order_pmt <- ot_plasm_mut %>%
arrange(desc(mf)) %>%
pull(patient_id)
# (3) Order left-out patients based on tumor mutations
order_tmt <- tumor_mut %>%
arrange(total) %>%
pull(patient_id)
# (4) Add the remaining patients without on-treatment plasma mutations information
order_std <- c(setdiff(lefto_pat, union(order_tmt, order_pmt)),
intersect(order_tmt[which(!order_tmt %in% order_pmt)], lefto_pat),
intersect(order_pmt[which(!order_pmt %in% order_tmt)], lefto_pat),
order_pmt)
# (4) Split the order into two groups
anlys_ord <- unlist(lapply(order_std, function(x) if(x %in% anlys_pat) x))
lefto_ord <- unlist(lapply(order_std, function(x) if(x %in% lefto_pat) x))
#----------------------------------------------------------------------------------
# Split the plasma mutations into baseline and on-treatment
#----------------------------------------------------------------------------------
plasm_mut_basl <- inner_join(plasm_mut, rename(drop_na(blot_date, baseline), date_blood_draw=baseline))Joining with `by = join_by(patient_id, date_blood_draw)`plasm_mut_ontr <- inner_join(plasm_mut, rename(drop_na(blot_date, endpoint), date_blood_draw=endpoint))Joining with `by = join_by(patient_id, date_blood_draw)`#----------------------------------------------------------------------------------
# Split the data into patients analyzed and left out
#----------------------------------------------------------------------------------
## Tumor mutations
tumor_mut_all1 <- left_join(tibble(patient_id = anlys_ord), tumor_mut)Joining with `by = join_by(patient_id)`tumor_mut_all2 <- left_join(tibble(patient_id = lefto_ord), tumor_mut)Joining with `by = join_by(patient_id)`## Plasma mutations
### Baseline
plasm_mut_bsl1 <- left_join(tibble(patient_id = anlys_ord), plasm_mut_basl)Joining with `by = join_by(patient_id)`plasm_mut_bsl2 <- left_join(tibble(patient_id = lefto_ord), plasm_mut_basl)Joining with `by = join_by(patient_id)`### On-treatment
plasm_mut_ont1 <- left_join(tibble(patient_id = anlys_ord), plasm_mut_ontr)Joining with `by = join_by(patient_id)`plasm_mut_ont2 <- left_join(tibble(patient_id = lefto_ord), plasm_mut_ontr)Joining with `by = join_by(patient_id)`## Cellularity
bins <- seq(0,1,0.2); bins[length(bins)] <- bins[length(bins)]+1e-6
# In pathological cellularity, NA means missing
path_cell_binn <- mutate(path_cell,
cell_binn = cut(cellularity, breaks=bins, include.lowest=TRUE))
# In facets cellularity, if emflags show no confidence, NA => [0,0.2),
# but emflags = NA and NA cellularity => NA cellularity
fact_cell_binn <- mutate(fact_cell,
cellularity = ifelse(is.na(cellularity)&!is.na(emflags), 0.1, cellularity),
cell_binn = cut(cellularity, breaks=bins, include.lowest=TRUE))
path_cell_all1 <- left_join(tibble(patient_id = anlys_ord), path_cell_binn)Joining with `by = join_by(patient_id)`path_cell_all2 <- left_join(tibble(patient_id = lefto_ord), path_cell_binn)Joining with `by = join_by(patient_id)`fact_cell_all1 <- left_join(tibble(patient_id = anlys_ord), fact_cell_binn)Joining with `by = join_by(patient_id)`fact_cell_all2 <- left_join(tibble(patient_id = lefto_ord), fact_cell_binn)Joining with `by = join_by(patient_id)`## Best response
best_resp_all1 <- left_join(tibble(patient_id = anlys_ord), best_resp)Joining with `by = join_by(patient_id)`best_resp_all2 <- left_join(tibble(patient_id = lefto_ord), best_resp)Joining with `by = join_by(patient_id)`#----------------------------------------------------------------------------------
# Plotting presets
#----------------------------------------------------------------------------------
## Panel widths
w1 <- 25; w2 <- 17; w3 <- 2; w4 <- 2; w5 <- 2; w6 <- 17
## Panel heights
h1 <- 20-1 # analyzed samples
h2 <- 20-1 # left out samples
r12 <- h2/h1
## Panel margins
tp <- 5 # top margin
bt <- 1 # bottom margin
## Y-axis extension
yex <- 0.75
## Fix the layout
lay.mat <- layout.matrix(c(h1,h2), c(w1,w2,w3,w4,w5,w6))
layout(lay.mat)
par(oma=c(0,0,0,0)) # Thin outer margins
# Text size
xaxis.cex <- 0.7
xaxis.spine.cex <- 0.6
## Fix the color scheme
## Tumor mutations
tumor_mut_col <- scales::hue_pal()(8-3+1)
## Cellularity
cell_palette <- colorRampPalette(c("white","blue"))(5)
names(cell_palette) <- c("<20", "20-40", "40-60", "60-80", "80-100")
path_cell_col1 <- cell_palette[path_cell_all1$cell_binn]
path_cell_col1[is.na(path_cell_col1)] <- "#000000"
path_cell_col2 <- cell_palette[path_cell_all2$cell_binn]
path_cell_col2[is.na(path_cell_col2)] <- "#000000"
fact_cell_col1 <- cell_palette[fact_cell_all1$cell_binn]
fact_cell_col1[is.na(fact_cell_col1)] <- "#000000"
fact_cell_col2 <- cell_palette[fact_cell_all2$cell_binn]
fact_cell_col2[is.na(fact_cell_col2)] <- "#000000"
## Best response
resp_palette <- c("green", "darkgoldenrod1", "red", "grey")
names(resp_palette) <- c("PR", "SD", "PD", "NE")
best_resp_col1 <- resp_palette[best_resp_all1$bor]
best_resp_col2 <- resp_palette[best_resp_all2$bor]
#----------------------------------------------------------------------------------
# Mutations plot
#----------------------------------------------------------------------------------
tm1plot <- t(as.matrix(tumor_mut_all1[,3:8]))
tm2plot <- t(as.matrix(tumor_mut_all2[,3:8]))
breaks <- pretty(colSums(cbind(tm1plot, tm2plot)))
## ANALYZED panel
par(mar=c(bt,4,tp,1))
# Make the horizontal bars for tumor mutations
ytick1 <- barplot(-tm1plot, horiz=TRUE, axes=FALSE, width=0.8, space=0.25,
#ylim=c(0.6, ncol(tm1plot)-0.4),
ylim=c(0.6-yex, ncol(tm1plot)-0.4+yex),
xlim=sort(-range(breaks)),
col=tumor_mut_col, border=NA,
yaxs="i")
# Annotate total number of mutations
text(-tumor_mut_all1$total-500, ytick1, tumor_mut_all1$total, adj=1, xpd=TRUE, cex=1)
# Adding x-axis at the top
axis(3, at=-breaks, labels=paste0(breaks/1000, c("", rep("k", length(breaks)-1))),
lwd=1, font=1)
mtext("Number of tumor somatic mutations", 3, cex=xaxis.cex, line=2.5)
# Adding y-axis on the right
axis(4, at=ytick1, labels=anlys_ord, line=-0.5, las=2, lwd=0, font=1)
#----------------------------------------------------------------------------------
# Adding floating legend for tumor mutations
#----------------------------------------------------------------------------------
add.legend(title="Mutation type", x=-45e3, y=15, xadj=1e3, labels=rownames(tm1plot))
#----------------------------------------------------------------------------------
# Adding floating legend for cellularity
#----------------------------------------------------------------------------------
add.legend(title="Cellularity", x=-45e3, y=6, xadj=1e3,
labels=c(rev(names(cell_palette)),"NA"), col_palette=c(rev(cell_palette),"#000000"), border="grey")
#----------------------------------------------------------------------------------
## LEFT OUT panel
par(mar=c(r12*bt,4,r12*tp,1))
# Make the horizontal bars for tumor mutations
ytick2 <- barplot(-tm2plot, horiz=TRUE, axes=FALSE, width=0.8, space=0.25,
#ylim=c(0.6, ncol(tm2plot)-0.4),
ylim=c(0.6-r12*yex, ncol(tm2plot)-0.4+r12*yex),
xlim=sort(-range(breaks)),
col=tumor_mut_col, border=NA,
yaxs="i")
# Annotate total number of mutations
text(-tumor_mut_all2$total-500, ytick1, tumor_mut_all2$total, adj=1, xpd=TRUE, cex=1)
# Adding x-axis at the top
#axis(3, at=-breaks, labels=paste0(breaks/1000, c("", rep("k", length(breaks)-1))),
# lwd=1, font=1)
#mtext("Number of tumor somatic mutations", 3, cex=xaxis.cex, line=2.5)
# Adding y-axis on the right
axis(4, at=ytick2, labels=lefto_ord, line=-0.5, las=2, lwd=0, font=1)
#----------------------------------------------------------------------------------
# Adding floating legend for BOR
#----------------------------------------------------------------------------------
add.legend(title="BOR RECIST", x=-45e3, y=15, xadj=1e3,
labels=names(resp_palette), col_palette=resp_palette, border="grey")
#----------------------------------------------------------------------------------
# Baseline MAF
#----------------------------------------------------------------------------------
breaks <- pretty(c(plasm_mut_basl$mf, plasm_mut_ontr$mf))
## ANALYZED panel
par(mar=c(bt,4,tp,0))
# Make the lollipop plot
# Empty plot - draw the points on top of sticks
plot(x = -plasm_mut_bsl1$mf, y = c(1:nrow(plasm_mut_bsl1)), bty="n", axes=FALSE,
xlab = "", ylab = "", pch = NA, xlim = sort(-range(breaks)), cex=2,
ylim = c(1-yex, nrow(plasm_mut_bsl1)+yex),
yaxs="i")
# Draw the sticks
sapply(1:nrow(plasm_mut_bsl1), function(i) {
lines(c(-plasm_mut_bsl1$mf[c(1:nrow(plasm_mut_bsl1)) == i], 0), c(i,i),
col="grey", lwd=2)})[[1]]
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points(x=-plasm_mut_bsl1$mf, y=1:nrow(plasm_mut_bsl1), pch=16)
# Draw y-axis using abline
abline(v=0, lwd=1)
# Draw x-axis at the top
axis(3, at=-breaks, labels=breaks*100, lwd=1, font=1)
mtext("Baseline MAF (%)", 3, cex=xaxis.cex, line=2.5)
## LEFT OUT panel
par(mar=c(r12*bt,4,r12*tp,0))
# Make the lollipop plot
# Empty plot - draw the points on top of sticks
plot(x = plasm_mut_bsl2$mf, y = c(1:nrow(plasm_mut_bsl2)), bty="n", axes=FALSE,
xlab = "", ylab = "", pch = NA, xlim = sort(-range(breaks)), cex=2,
ylim = c(1-r12*yex, nrow(plasm_mut_bsl2)+r12*yex),
yaxs="i")
# Draw the sticks
sapply(1:nrow(plasm_mut_bsl2), function(i) {
lines(c(-plasm_mut_bsl2$mf[c(1:nrow(plasm_mut_bsl2)) == i], 0), c(i,i),
col="grey", lwd=2)})[[1]]
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points(x=-plasm_mut_bsl2$mf, y=1:nrow(plasm_mut_bsl2), pch=16)
# Draw y-axis using abline
abline(v=0, lwd=1)
# Draw x-axis at the top
#axis(3, at=-breaks, labels=breaks*100, lwd=1, font=1)
#mtext("Baseline MAF (%)", 3, cex=xaxis.cex, line=2.5)
#----------------------------------------------------------------------------------
# Cellularity plot
#----------------------------------------------------------------------------------
# Pathology
## ANALYZED panel
par(mar=c(bt,0,tp,0))
plot(x=rep(0, nrow(path_cell_all1)), y=1:nrow(path_cell_all1),
pch=22, col="grey", bg=path_cell_col1,
bty="n", axes=FALSE, xlab="", ylab="", cex=2, font=1,
ylim = c(1-yex, nrow(path_cell_all1)+yex),
yaxs="i")
mtext("Pathalogical\ncellularity", 3, las=2, cex=xaxis.spine.cex)
## LEFT OUT panel
par(mar=c(r12*bt,0,r12*tp,0))
plot(x=rep(0, nrow(path_cell_all2)), y=1:nrow(path_cell_all2),
pch=22, col="grey", bg=path_cell_col2,
bty="n", axes=FALSE, xlab="", ylab="", cex=2, font=1,
ylim = c(1-r12*yex, nrow(path_cell_all2)+r12*yex),
yaxs="i")
#mtext("Pathalogical\ncellularity", 3, las=2, cex=xaxis.spine.cex)
# Facets
## ANALYZED panel
par(mar=c(bt,0,tp,0))
plot(x=rep(0, nrow(fact_cell_all1)), y=1:nrow(fact_cell_all1),
pch=22, col="grey", bg=fact_cell_col1,
bty="n", axes=FALSE, xlab="", ylab="", cex=2, font=1,
ylim = c(1-yex, nrow(fact_cell_all1)+yex),
yaxs="i")
mtext("WGS\ncellularity", 3, las=2, cex=xaxis.spine.cex)
## LEFT OUT panel
par(mar=c(r12*bt,0,r12*tp,0))
plot(x=rep(0, nrow(fact_cell_all2)), y=1:nrow(fact_cell_all2),
pch=22, col="grey", bg=fact_cell_col2,
bty="n", axes=FALSE, xlab="", ylab="", cex=2, font=1,
ylim = c(1-r12*yex, nrow(fact_cell_all2)+r12*yex),
yaxs="i")
#mtext("WGS\ncellularity", 3, las=2, cex=xaxis.spine.cex)
#----------------------------------------------------------------------------------
# Best RECIST response
#----------------------------------------------------------------------------------
## ANALYZED panel
par(mar=c(bt,0,tp,0))
plot(x=rep(0, nrow(best_resp_all1)), y=1:nrow(best_resp_all1),
pch=22, col="grey", bg=best_resp_col1,
bty="n", axes=FALSE, xlab="", ylab="", cex=2, font=1,
ylim = c(1-yex, nrow(best_resp_all1)+yex),
yaxs="i")
mtext("BOR\nRECIST", 3, las=2, cex=xaxis.spine.cex)
## LEFT OUT panel
par(mar=c(r12*bt,0,r12*tp,0))
plot(x=rep(0, nrow(best_resp_all2)), y=1:nrow(best_resp_all2),
pch=22, col="grey", bg=best_resp_col2,
bty="n", axes=FALSE, xlab="", ylab="", cex=2, font=1,
ylim = c(1-r12*yex, nrow(best_resp_all2)+r12*yex),
yaxs="i")
#mtext("BOR\nRECIST", 3, las=2, cex=xaxis.spine.cex)
#----------------------------------------------------------------------------------
# On-treatment MAF
#----------------------------------------------------------------------------------
breaks <- pretty(c(plasm_mut_basl$mf, plasm_mut_ontr$mf))
## ANALYZED panel
par(mar=c(bt,0,tp,4))
# Make the lollipop plot
# Empty plot - draw the points on top of sticks
plot(x = plasm_mut_ont1$mf, y = c(1:nrow(plasm_mut_ont1)), bty="n", axes=FALSE,
xlab = "", ylab = "", pch = NA, xlim = range(breaks), cex=2,
ylim = c(1-yex, nrow(plasm_mut_ont1)+yex),
yaxs="i")
# Draw the sticks
sapply(1:nrow(plasm_mut_ont1), function(i) {
lines(c(0,plasm_mut_ont1$mf[c(1:nrow(plasm_mut_ont1)) == i]), c(i,i),
col="grey", lwd=2)})[[1]]
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points(x=plasm_mut_ont1$mf, y=1:nrow(plasm_mut_ont1), pch=16)
# Draw y-axis using abline
abline(v=0, lwd=1)
# Draw x-axis at the top
axis(3, at=breaks, labels=breaks*100, lwd=1, font=1)
mtext("On-treatment MAF (%)", 3, cex=xaxis.cex, line=2.5)
## LEFT OUT panel
par(mar=c(r12*bt,0,r12*tp,4))
# Make the lollipop plot
# Empty plot - draw the points on top of sticks
plot(x = plasm_mut_ont2$mf, y = c(1:nrow(plasm_mut_ont2)), bty="n", axes=FALSE,
xlab = "", ylab = "", pch = NA, xlim = range(breaks), cex=2,
ylim = c(1-r12*yex, nrow(plasm_mut_ont2)+r12*yex),
yaxs="i")
# Draw the sticks
sapply(1:nrow(plasm_mut_ont2), function(i) {
lines(c(0,plasm_mut_ont2$mf[c(1:nrow(plasm_mut_ont2)) == i]), c(i,i),
col="grey", lwd=2)})[[1]]
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points(x=plasm_mut_ont2$mf, y=1:nrow(plasm_mut_ont2), pch=16)
# Draw y-axis using abline
abline(v=0, lwd=1)
# Draw x-axis at the top
#axis(3, at=breaks, labels=breaks*100, lwd=1, font=1)
#mtext("On-treatment MAF (%)", 3, cex=xaxis.cex, line=2.5)
#----------------------------------------------------------------------------------
# Annotations
#----------------------------------------------------------------------------------
par(fig=c(0,1,0,1), mar=c(0,1,0,1), new=TRUE)
# Samples that are not evaluable
#plot(c(-10,10), 12.10*c(1,1), lty=3, bty="n", ylim=c(0,25), type="l", axes=FALSE, xpd=TRUE)
#text(1,11.60, "Not evaluable \n(Below analytic threshold)", adj=0.5)
# Samples that were dropped from the analysis, because of missing data
plot(c(-10,10), 12.10*c(1,1), lty=0, bty="n", ylim=c(0,25), type="l", axes=FALSE, xpd=TRUE)
text(0, 12, "Not evaluable \n(Missing tumor sample or blood draw or\nbelow analytic threshold)", adj=0.5)
draw.bracket(0, 11, -10, 10)
# To explain why the MAF is high for low mutation count
smpl_plasm_mut <- filter(plasm_mut_ont2, patient_id == "CGPLPA779")
n_ref <- smpl_plasm_mut$n_ref
n_alt <- smpl_plasm_mut$n_alt
text(8.85,6.60,paste(n_alt,n_alt+n_ref,sep="/"))
#----------------------------------------------------------------------------------
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 15.3.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] here_1.0.1 readxl_1.4.5 lubridate_1.9.4 forcats_1.0.0
[5] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4 readr_2.1.5
[9] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
[13] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] sass_0.4.9 generics_0.1.3 stringi_1.8.4 rematch_2.0.0
[5] hms_1.1.3 digest_0.6.37 magrittr_2.0.3 timechange_0.3.0
[9] evaluate_1.0.3 grid_4.4.1 fastmap_1.2.0 cellranger_1.1.0
[13] rprojroot_2.0.4 jsonlite_1.9.1 processx_3.8.6 whisker_0.4.1
[17] ps_1.9.0 promises_1.3.2 httr_1.4.7 scales_1.3.0
[21] jquerylib_0.1.4 cli_3.6.4 rlang_1.1.5 munsell_0.5.1
[25] withr_3.0.2 cachem_1.1.0 yaml_2.3.10 tools_4.4.1
[29] tzdb_0.4.0 colorspace_2.1-1 httpuv_1.6.15 vctrs_0.6.5
[33] R6_2.6.1 lifecycle_1.0.4 git2r_0.35.0 fs_1.6.5
[37] pkgconfig_2.0.3 callr_3.7.6 pillar_1.10.1 bslib_0.9.0
[41] later_1.4.1 gtable_0.3.6 glue_1.8.0 Rcpp_1.0.14
[45] xfun_0.51 tidyselect_1.2.1 rstudioapi_0.17.1 knitr_1.49
[49] farver_2.1.2 htmltools_0.5.8.1 rmarkdown_2.29 compiler_4.4.1
[53] getPass_0.2-4