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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 ratios and P values are indicated on the right for individuals in the CheckPAC study, after excluding those who do not secrete CA19-9.

library(here)
library(readxl)
library(survival)
source(here("code/hr_plot_funcs.R"))
baseline_c <- read_excel(here("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(here("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))
ca199 <- read.csv(here("data/checkpac_ca199.csv")) %>%
  mutate(visit = ifelse(lab_suffix == "E1_C4",
                        "baseline",
                        as.character(cyclenumber)),
         ca199 = ifelse(ca199 == -999, NA, ca199),
         subject_id = str_pad(subject_id, 3, "left", "0")) %>%
  select(-c(lab_suffix, cyclenumber, visit)) %>%
  drop_na()
secretor <- ca199 %>%
  group_by(subject_id) %>%
  summarize(secretor = all(ca199 > 37))
is_secretor <- secretor$secretor
names(is_secretor) <- secretor$subject_id

blood_draw_c <- read.csv(here("data/checkpac_ca199_baseline_p4_dates.csv"))
blood_draw_bl_and_p1_c <- blood_draw_c %>%
  filter(!is.na(baseline) & !is.na(endpoint)) %>%
  select(patient_id, baseline, endpoint) %>%
  pivot_longer(cols = c("baseline", "endpoint"),
               names_to = "visit",
               values_to = "date_blood_draw")
exclude_c <- c("CGPLPA225", "CGPLPA783", "CGPLPA795")
id_map_c <- read_excel(here("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 %>%
  select(-ca199) %>%
  inner_join(id_map_c) %>%
  inner_join(blood_draw_bl_and_p1_c) %>%
  inner_join(ca199,
             by = join_by("subject_id", "date_blood_draw" == "lab_date")) %>%
  select(subject_id, patient_id,
         arm, ca199, bilirubin, age, mgps, ecog_ps, weightloss5_yn, visit,
         pfs, os, death) %>%
  pivot_wider(names_from = visit, values_from = ca199) %>%
  filter(is_secretor[subject_id]) %>%
  mutate(bilirubin_bin = bilirubin > 25,
         age_bin = age <= 65,
         mgps_bin = factor(mgps, levels = c(0, 1, 2)),
         ecog_ps_bin = ecog_ps == 1,
         weightloss5_yn_bin = weightloss5_yn == 1)
Joining with `by = join_by(subject_id)`
Joining with `by = join_by(patient_id)`
covar_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")

surv_data <- surv_artemis_delfi_c %>%
  mutate(baseline = scale(baseline)[, 1],
         endpoint = scale(endpoint)[, 1])

mf <- coxph(Surv(os, death) ~ age_bin + ecog_ps_bin + weightloss5_yn_bin +
              mgps_bin + arm + baseline + endpoint, surv_data)

var_labels <- c(covar_labels,
                "CA19-9",
                "Baseline",
                "CA19-9",
                "On-treatment (week 8)")

cox_tb <- get_hr_tbl(surv_data, mf)

print(plot_hr(cox_tb, 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()`).


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] sass_0.4.9        generics_0.1.3    stringi_1.8.4     lattice_0.22-6   
 [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    Matrix_1.7-3      processx_3.8.6   
[17] whisker_0.4.1     ps_1.9.0          promises_1.3.2    httr_1.4.7       
[21] scales_1.3.0      jquerylib_0.1.4   cli_3.6.4         rlang_1.1.5      
[25] munsell_0.5.1     splines_4.4.1     withr_3.0.2       cachem_1.1.0     
[29] yaml_2.3.10       tools_4.4.1       tzdb_0.4.0        colorspace_2.1-1 
[33] httpuv_1.6.15     vctrs_0.6.5       R6_2.6.1          lifecycle_1.0.4  
[37] git2r_0.35.0      fs_1.6.5          pkgconfig_2.0.3   callr_3.7.6      
[41] pillar_1.10.1     bslib_0.9.0       later_1.4.1       gtable_0.3.6     
[45] glue_1.8.0        Rcpp_1.0.14       xfun_0.51         tidyselect_1.2.1 
[49] rstudioapi_0.17.1 knitr_1.49        farver_2.1.2      htmltools_0.5.8.1
[53] labeling_0.4.3    rmarkdown_2.29    compiler_4.4.1    getPass_0.2-4