Fragmentation patterns for patients in CheckPAC trial with partial response or stable disease are more closely correlated to healthy plasma
Last updated: 2025-03-28
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Knit directory: hruban_wflow/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 29a8876 | Shashikant Koul | 2025-03-28 | Fix patient selection in S8 and S22 |
| html | 29a8876 | Shashikant Koul | 2025-03-28 | Fix patient selection in S8 and S22 |
| Rmd | 9bbcb47 | Shashikant Koul | 2025-03-27 | Initial commit |
| html | 9bbcb47 | Shashikant Koul | 2025-03-27 | Initial commit |
cfDNA fragmentation profiles from CheckPAC patients are shown as short to long ratios of fragment size in 473 5-Mb bins across the genome. Profiles are shown for all patients with plasma samples at baseline, and at follow-up for each of the clinical RECIST 1.1 response categories. Each profile is colored by correlation to the median profile of healthy reference samples
library(cowplot)
library(fs)
library(grid)
library(here)
library(readxl)
library(tidyverse)
devtools::load_all(here("code/rlucas"))df <- list.files(path = here("data/checkpac_5mb_bins"), full.names = TRUE) %>%
lapply(read_csv) %>%
bind_rows %>%
filter(chr != "chrX") %>%
as_tibble() %>%
mutate(ratio.cor = short.cor / long.cor) %>%
group_by(id) %>%
mutate(ratio.centered = scale(ratio.cor, scale = FALSE)[, 1])
clean_data <- read_excel(here("data/supplementary_tables.xlsx"),
sheet = "Table S1", range = "A2:X45")
delfi_pred <- read_excel(here("data/supplementary_tables.xlsx"),
sheet = "Table S2", range = "A2:N219")clean_data <- clean_data %>%
filter(`BOR RECIST 1.1` != "Not Evaluable")
delfi_pred <- delfi_pred %>%
filter(Timepoint %in% c("Baseline", "Endpoint"))
DELFI_complete <- delfi_pred %>%
inner_join(clean_data, by = "Patient") %>%
mutate(class = case_when((Timepoint == "Baseline") ~ "Baseline",
TRUE ~ `BOR RECIST 1.1`)) %>%
distinct(Patient, class, .keep_all = TRUE)
DELFI_complete$id <- DELFI_complete$Sample
fp2 <- inner_join(DELFI_complete, df, by = "id")
fp2 <- arrange(fp2, id, bin) %>%
mutate(bin=factor(bin),
arm=factor(arm, levels=unique(arm))) %>%
mutate(dx=factor(class, levels=c("Baseline","Progressive Disease","Stable Disease","Partial Response")))
panel.labels <- fp2 %>%
group_by(dx) %>%
summarize(n=length(unique(id)),
.groups="drop") %>%
mutate(labels=paste0(c("Baseline (n=",
"Progressive Disease (n=",
"Stable Disease (n=",
"Partial Response (n="
),
n, ")"),
arm="1p") %>%
mutate(x=rep(5,4), y=rep(0.2, 4))
arm <- fp2 %>% group_by(arm) %>%
summarize(n=n(), .groups="drop") %>%
mutate(arm = as.character(arm))
arm.labels <- setNames(arm$arm, arm$arm)#baseline
b<-read.csv(here("data/fp2_lucas_healthy.csv"))
medians<-b%>%group_by(bin)%>% summarise(Median=median(ratio.centered))
medians$bin <- as.factor(medians$bin)
cors<-left_join(fp2, medians, by="bin")
cors<-cors %>% group_by(id) %>% summarize(c=cor(ratio.centered, Median, method="spearman"))
fp2<-left_join(fp2,cors, by = "id")
df5<-fp2
df5<-df5 %>% mutate(d= case_when((c<=.25) ~ "0.00-0.25",
(c<=.5) ~ "0.25-0.50",
(c<= .75) ~ "0.50-0.75",
(c<=1) ~"0.75-1.00"))
df5$class <- ordered(df5$class, levels = c("Baseline","Progressive Disease" , "Stable Disease","Partial Response"))fig <- df5 %>%
group_by(dx) %>%
ggplot(aes(x = bin, y = ratio.centered, group = reorder(id, -c), col = d)) +
geom_line(size = 0.75) +
scale_color_manual(values = c("#B71643CA", "#EB6429FF", "grey70")) +
labs(x = "", y = "Fragmentation profile\n", color = "") +
facet_grid(dx ~ arm,
space = "free_x", scales = "free_x",
labeller = labeller(arm = arm.labels),
switch = "x") +
theme_classic(base_size = 25) +
theme(axis.text.x = element_blank(),
panel.spacing.y = unit(2, "lines"),
panel.spacing.x = unit(.2, "lines"),
axis.ticks.x = element_blank(),
strip.background = element_blank(),
strip.text.y = element_blank(),
axis.text.y = element_text(size = 26),
strip.text.x = element_text(angle = 90),
axis.title.y = element_text(size = 30),
legend.position = c(0.8, 0.95),
legend.direction = "horizontal") +
scale_y_continuous(breaks = c(-0.2, -0.1, 0, 0.1, 0.2)) +
coord_cartesian(ylim = c(-0.19, 0.19))Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.ggdraw(fig) +
draw_grob(textGrob(label = panel.labels$labels[1],
x = unit(0.15, "npc"),
y = unit(0.97, "npc"),
hjust = 0,
gp = gpar(cex = 2))) +
draw_grob(textGrob(label = panel.labels$labels[2],
x = unit(0.15, "npc"),
y = unit(0.75, "npc"),
hjust = 0,
gp = gpar(cex = 2))) +
draw_grob(textGrob(label = panel.labels$labels[3],
x = unit(0.15, "npc"),
y = unit(0.53, "npc"),
hjust = 0,
gp = gpar(cex = 2))) +
draw_grob(textGrob(label = panel.labels$labels[4],
x = unit(0.15, "npc"),
y = unit(0.30, "npc"),
hjust = 0,
gp = gpar(cex = 2)))
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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] rlucas_0.0.3 lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1
[5] dplyr_1.1.4 purrr_1.0.4 readr_2.1.5 tidyr_1.3.1
[9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 readxl_1.4.5
[13] here_1.0.1 fs_1.6.5 cowplot_1.1.3 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] remotes_2.5.0 rlang_1.1.5
[3] magrittr_2.0.3 git2r_0.35.0
[5] matrixStats_1.5.0 compiler_4.4.1
[7] getPass_0.2-4 callr_3.7.6
[9] vctrs_0.6.5 profvis_0.4.0
[11] pkgconfig_2.0.3 crayon_1.5.3
[13] fastmap_1.2.0 XVector_0.44.0
[15] ellipsis_0.3.2 promises_1.3.2
[17] rmarkdown_2.29 sessioninfo_1.2.3
[19] tzdb_0.4.0 UCSC.utils_1.0.0
[21] ps_1.9.0 bit_4.6.0
[23] xfun_0.51 zlibbioc_1.50.0
[25] cachem_1.1.0 GenomeInfoDb_1.40.1
[27] jsonlite_1.9.1 later_1.4.1
[29] DelayedArray_0.30.1 parallel_4.4.1
[31] R6_2.6.1 bslib_0.9.0
[33] stringi_1.8.4 pkgload_1.4.0
[35] GenomicRanges_1.56.2 jquerylib_0.1.4
[37] cellranger_1.1.0 Rcpp_1.0.14
[39] SummarizedExperiment_1.34.0 knitr_1.49
[41] usethis_3.1.0 IRanges_2.38.1
[43] httpuv_1.6.15 Matrix_1.7-3
[45] timechange_0.3.0 tidyselect_1.2.1
[47] rstudioapi_0.17.1 abind_1.4-8
[49] yaml_2.3.10 miniUI_0.1.1.1
[51] processx_3.8.6 pkgbuild_1.4.6
[53] lattice_0.22-6 shiny_1.10.0
[55] Biobase_2.64.0 withr_3.0.2
[57] evaluate_1.0.3 desc_1.4.3
[59] urlchecker_1.0.1 pillar_1.10.1
[61] MatrixGenerics_1.16.0 whisker_0.4.1
[63] stats4_4.4.1 generics_0.1.3
[65] vroom_1.6.5 rprojroot_2.0.4
[67] S4Vectors_0.42.1 hms_1.1.3
[69] munsell_0.5.1 scales_1.3.0
[71] xtable_1.8-4 glue_1.8.0
[73] tools_4.4.1 devtools_2.4.5
[75] colorspace_2.1-1 GenomeInfoDbData_1.2.12
[77] cli_3.6.4 S4Arrays_1.4.1
[79] rematch_2.0.0 gtable_0.3.6
[81] sass_0.4.9 digest_0.6.37
[83] BiocGenerics_0.50.0 SparseArray_1.4.8
[85] farver_2.1.2 htmlwidgets_1.6.4
[87] memoise_2.0.1 htmltools_0.5.8.1
[89] lifecycle_1.0.4 httr_1.4.7
[91] mime_0.12 bit64_4.6.0-1