Multivariate hazard analyses demonstrate on-treatment ARTEMIS-DELFI scores as independent predictors of overall survival for patients in the CheckPAC trial
Last updated: 2025-03-27
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Multivariate Cox proportional hazard analyses were generated for each molecular method and fit to overall survival adjusting for clinical subgroups. Each of the indicated subgroups that have been shown to be significant on univariate analyses in previous studies (7) have been included in the multivariate analysis. Hazard models are shown using ARTEMIS-DELFI scores at baseline and 8-week follow-up values for patients in the CheckPAC study
library(here)
library(readxl)
library(survival)
source(here("code/hr_plot_funcs.R"))baseline_c <- read_excel("data/CheckPAC_clinical_annotated011622.xlsx") %>%
select(subject_id = StudySubjectID,
age,
weightloss5_yn = weighttloss5_yn,
ecog_ps = PS_cycle1,
bor = `Best overall confirmed response (RECIST)`,
ca199 = LAB_CA199_E1_C4,
crp = LAB_CRP_E1_C4,
bilirubin = LAB_Bilirubin_E1_C4,
albumin = LAB_Albumin_E1_C4) %>%
mutate(mgps = case_when(crp <= 10 ~ 0,
crp > 10 & albumin >= 35 ~ 1,
crp > 10 & albumin < 35 ~ 2))
long_c <- read_excel("data/clinical_073123.xlsx") %>%
select(subject_id = StudySubjectID,
arm = Arm,
nivo_start_date = Nivo_StartDate,
pfs_date = PFS_date,
surv_date = Survival_date,
surv_status = Survival_status) %>%
mutate(surv_status = case_when(surv_status == 2 ~ "Dead",
surv_status == 1 ~ "Alive"),
arm = factor(arm))
clin_c <- inner_join(baseline_c, long_c, by = "subject_id")
# Fix the date formats
for (i in colnames(clin_c)) {
if (str_detect(i, "[Dd]ate")) {
if (!any(class(clin_c[[i]]) == "POSIXt")) {
clin_c[[i]] <- as.Date(as.numeric(clin_c[[i]]), origin = "1899-12-30")
} else {
clin_c[[i]] <- as.Date(clin_c[[i]])
}
}
}
# Compute overall and progression-free survival time
surv_c <- clin_c %>%
mutate(pfs = as.numeric(difftime(pfs_date, nivo_start_date)),
os = as.numeric(difftime(surv_date, nivo_start_date))) %>%
mutate(death = ifelse(surv_status == "Dead", 1, 0))artemis_delfi_c <- read_csv("data/Test_set_pacto.csv") %>%
select(id, score, model) %>%
filter(model == "ARTEMIS_single_DELFI_SSLs_Ensemble")New names:
Rows: 6752 Columns: 6
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(4): id, type, model, set dbl (2): ...1, score
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`# Select baseline and two-week visit timepoints
blood_draw_c <- read_excel("data/checkpac_plasma_manifest.xlsx") %>%
select(cgid = `Sample ID*`, patient_id = Patient, date_blood_draw = Date) %>%
mutate(date_blood_draw = as.Date(date_blood_draw))
# See "/dcs05/scharpf/data/skoul/Projects/checkpac/patient_selection" for more
# information
blood_draw_c[blood_draw_c$cgid == "CGPLPA223P16",
"date_blood_draw"] <- as.Date("2019-12-20")
blood_draw_c[blood_draw_c$cgid == "CGPLPA794P4",
"date_blood_draw"] <- as.Date("2020-02-21")
blood_draw_bl_or_p1_c <- blood_draw_c %>%
mutate(visit = str_extract(cgid, "(?<=CG[A-Z]{1,5}[0-9]{1,5}P).*"),
visit = sapply(visit, function(x) str_split(x, "_")[[1]][1])) %>%
filter(visit == "" | visit == "2")
blood_draw_bl_and_p1_c <- blood_draw_bl_or_p1_c %>%
filter(patient_id %in% pull(filter(count(group_by(blood_draw_bl_or_p1_c,
patient_id)),
n > 1),
patient_id))
# Join the scores with timepoint
artemis_delfi_blp1_c <- inner_join(artemis_delfi_c, blood_draw_bl_and_p1_c,
by = join_by(id == cgid)) %>%
mutate(visit = ifelse(visit == "", "baseline", "week8"))exclude_c <- c("CGPLPA225", "CGPLPA783", "CGPLPA795")
id_map_c <- read_excel("data/ID_comparison.xlsx") %>%
filter(!unlist(lapply(CGID, function(x) any(str_detect(x, exclude_c))))) %>%
select(patient_id = CGID, subject_id = StudySubjectID) %>%
mutate(patient_id = substring(patient_id, 1, 9))
surv_artemis_delfi_c <- surv_c %>%
inner_join(id_map_c) %>%
inner_join(artemis_delfi_blp1_c) %>%
select(subject_id, patient_id, score, model,
arm, ca199, bilirubin, age, mgps, ecog_ps, weightloss5_yn, visit,
pfs, os, death) %>%
pivot_wider(names_from = visit, values_from = score) %>%
mutate(bilirubin_bin = bilirubin > 25,
age_bin = age <= 65,
mgps_bin = factor(mgps, levels = c(0, 1, 2)),
ecog_ps_bin = ecog_ps == 1,
ca199_bin = ca199 > 37,
weightloss5_yn_bin = weightloss5_yn == 1)Joining with `by = join_by(subject_id)`
Joining with `by = join_by(patient_id)`var_labels <- c("Age, years",
"> 65",
"\u2264 65",
"ECOG PS",
"0",
"1",
"Weight loss, %",
"< 5",
"\u2265 5",
"MGPS",
"0",
"1",
"2",
"Treatment",
"RT+Nivo",
"RT+Nivo/Ipi",
"ARTEMIS DELFI score",
"Baseline",
"ARTEMIS DELFI score",
"On-treatment (week 8)")
os_mf_c <- coxph(Surv(os, death) ~ age_bin + ecog_ps_bin +
weightloss5_yn_bin +
mgps_bin + arm + baseline + week8,
surv_artemis_delfi_c)
os_cox_tbl <- get_hr_tbl(surv_artemis_delfi_c, os_mf_c)
print(plot_hr(os_cox_tbl, var_labels = var_labels))Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).Warning: Removed 21 rows containing missing values or values outside the scale range
(`geom_point()`).
pfs_mf_c <- coxph(Surv(pfs, death) ~ age_bin + ecog_ps_bin +
weightloss5_yn_bin +
mgps_bin + arm + baseline + week8,
surv_artemis_delfi_c)
pfs_cox_tbl <- get_hr_tbl(surv_artemis_delfi_c, pfs_mf_c)
print(plot_hr(pfs_cox_tbl, var_labels = var_labels))
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] patchwork_1.3.0 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 survival_3.8-3
[13] readxl_1.4.5 here_1.0.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.6 xfun_0.51 bslib_0.9.0 processx_3.8.6
[5] lattice_0.22-6 callr_3.7.6 tzdb_0.4.0 vctrs_0.6.5
[9] tools_4.4.1 ps_1.9.0 generics_0.1.3 parallel_4.4.1
[13] pkgconfig_2.0.3 Matrix_1.7-3 lifecycle_1.0.4 compiler_4.4.1
[17] farver_2.1.2 git2r_0.35.0 munsell_0.5.1 getPass_0.2-4
[21] httpuv_1.6.15 htmltools_0.5.8.1 sass_0.4.9 yaml_2.3.10
[25] crayon_1.5.3 later_1.4.1 pillar_1.10.1 jquerylib_0.1.4
[29] whisker_0.4.1 cachem_1.1.0 tidyselect_1.2.1 digest_0.6.37
[33] stringi_1.8.4 labeling_0.4.3 splines_4.4.1 rprojroot_2.0.4
[37] fastmap_1.2.0 grid_4.4.1 colorspace_2.1-1 cli_3.6.4
[41] magrittr_2.0.3 withr_3.0.2 scales_1.3.0 promises_1.3.2
[45] bit64_4.6.0-1 timechange_0.3.0 rmarkdown_2.29 httr_1.4.7
[49] bit_4.6.0 cellranger_1.1.0 hms_1.1.3 evaluate_1.0.3
[53] knitr_1.49 rlang_1.1.5 Rcpp_1.0.14 glue_1.8.0
[57] rstudioapi_0.17.1 vroom_1.6.5 jsonlite_1.9.1 R6_2.6.1
[61] fs_1.6.5