19  CH and Sickle Cell Disease

# Create a data frame comparing the three key studies
scd_ch_studies <- data.frame(
  Study = c("Liggett et al. (2022)", "Pincez et al. (2021)", "Spencer Chapman et al. (2023)"),
  Cohort_Size = c("74,190 (3,090 SCD, 71,100 controls)", "8,307 (1,459 SCD, 6,848 controls)", "6 SCD patients (pre/post gene therapy)"),
  Method = c("Whole-genome sequencing (WGS)", "Whole-exome sequencing (WES)", "Whole-genome sequencing (WGS)"),
  CH_Detection_Threshold = c("VAF >10% (partially assessed 5-10%)", "VAF 2.5-26% (median 7%)", "Various, tracking HSCs pre/post therapy"),
  Primary_Finding = c("No significant difference in CH prevalence", "Higher CH prevalence in SCD despite younger cohort", "Increased frequency of driver mutations post-therapy"),
  Implication = c("CH may not be major driver of SCD cancer risk", "CH could contribute to increased leukemia risk", "Gene therapy may amplify existing CH clones")
)

# Load necessary libraries
library(knitr)
library(kableExtra)

# Create a formatted table
scd_ch_table <- kable(scd_ch_studies, format = "html", 
                      caption = "Comparison of Key Studies on Clonal Hematopoiesis in Sickle Cell Disease") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"), 
                full_width = TRUE) %>%
  column_spec(1, bold = TRUE) %>%
  column_spec(5, width = "20em")

# Display the table
scd_ch_table

Comparison of Key Studies on Clonal Hematopoiesis in Sickle Cell Disease

Study	Cohort_Size	Method	CH_Detection_Threshold	Primary_Finding	Implication
Liggett et al. (2022)	74,190 (3,090 SCD, 71,100 controls)	Whole-genome sequencing (WGS)	VAF >10% (partially assessed 5-10%)	No significant difference in CH prevalence	CH may not be major driver of SCD cancer risk
Pincez et al. (2021)	8,307 (1,459 SCD, 6,848 controls)	Whole-exome sequencing (WES)	VAF 2.5-26% (median 7%)	Higher CH prevalence in SCD despite younger cohort	CH could contribute to increased leukemia risk
Spencer Chapman et al. (2023)	6 SCD patients (pre/post gene therapy)	Whole-genome sequencing (WGS)	Various, tracking HSCs pre/post therapy	Increased frequency of driver mutations post-therapy	Gene therapy may amplify existing CH clones

# Additional visualization of cohort characteristics
library(ggplot2)

# Creating a summary of cohort ages for visualization
cohort_ages <- data.frame(
  Study = c("Liggett", "Liggett", "Pincez", "Pincez"),
  Group = c("SCD", "Controls", "SCD", "Controls"),
  Mean_Age = c(NA, NA, 23.6, 50.1),  # Liggett didn't specify exact mean ages
  Age_Range = c("Up to 70 years", "Up to 70 years", "Younger cohort", "Older cohort")
)

# Create ggplot visualization
age_plot <- ggplot(data = cohort_ages, aes(x = Study, y = Mean_Age, fill = Group)) +
  geom_bar(stat = "identity", position = position_dodge()) +
  labs(title = "Mean Age Comparison Between Studies",
       y = "Mean Age (years)",
       caption = "Note: Exact means for Liggett et al. not specified; broad age range reported") +
  theme_minimal()

# Display the plot
# Note: This would be rendered in an actual R environment

19.1 SCD Gene therapies

# Load necessary libraries
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.1     ✔ readr     2.2.0
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ lubridate 1.9.5     ✔ tibble    3.3.1
✔ purrr     1.2.2     ✔ tidyr     1.3.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::group_rows() masks kableExtra::group_rows()
✖ dplyr::lag()        masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(kableExtra)
library(gt)

# Create data frame of FDA-approved gene therapies for SCD
scd_gene_therapies <- data.frame(
  Therapy_Name = c("Casgevy (exagamglogene autotemcel)", "Lyfgenia (lovotibeglogene autotemcel)"),
  Companies = c("Vertex Pharmaceuticals & CRISPR Therapeutics", "Bluebird Bio"),
  Technology = c("CRISPR/Cas9 gene editing", "Lentiviral vector gene addition"),
  Mechanism = c("Edits genome to reactivate fetal hemoglobin (HbF) production", 
                "Adds modified gene to produce HbAT87Q (anti-sickling hemoglobin)"),
  FDA_Approval_Date = c("December 8, 2023", "December 8, 2023"),
  Eligible_Population = c("Patients ≥12 years with recurrent VOCs", 
                          "Patients ≥12 years with history of VOEs"),
  Clinical_Efficacy = c("93.5% (29/31) achieved freedom from severe VOCs", 
                        "88% (28/32) achieved complete resolution of VOEs"),
  Safety_Concerns = c("Myeloablative conditioning required; potential fertility impacts", 
                      "Hematologic malignancy risk (black box warning); myeloablative conditioning required"),
  Estimated_Cost = c("$2.2 million per patient", "$2+ million per patient")
)

# Create a formatted table using gt
scd_therapies_table <- gt(scd_gene_therapies) %>%
  tab_header(
    title = "FDA-Approved Gene Therapies for Sickle Cell Disease",
    subtitle = "Comparison of Key Characteristics (As of May 2025)"
  ) %>%
  cols_label(
    Therapy_Name = "Therapy Name",
    Companies = "Manufacturer",
    Technology = "Technology Platform",
    Mechanism = "Mechanism of Action",
    FDA_Approval_Date = "FDA Approval Date",
    Eligible_Population = "Eligible Population",
    Clinical_Efficacy = "Clinical Efficacy",
    Safety_Concerns = "Safety Concerns",
    Estimated_Cost = "Estimated Cost"
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#e6f3ff"),
      cell_text(weight = "bold")
    ),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_body(columns = Therapy_Name)
  )

# Display the table
scd_therapies_table

FDA-Approved Gene Therapies for Sickle Cell Disease
Comparison of Key Characteristics (As of May 2025)
Therapy Name	Manufacturer	Technology Platform	Mechanism of Action	FDA Approval Date	Eligible Population	Clinical Efficacy	Safety Concerns	Estimated Cost
Casgevy (exagamglogene autotemcel)	Vertex Pharmaceuticals & CRISPR Therapeutics	CRISPR/Cas9 gene editing	Edits genome to reactivate fetal hemoglobin (HbF) production	December 8, 2023	Patients ≥12 years with recurrent VOCs	93.5% (29/31) achieved freedom from severe VOCs	Myeloablative conditioning required; potential fertility impacts	$2.2 million per patient
Lyfgenia (lovotibeglogene autotemcel)	Bluebird Bio	Lentiviral vector gene addition	Adds modified gene to produce HbAT87Q (anti-sickling hemoglobin)	December 8, 2023	Patients ≥12 years with history of VOEs	88% (28/32) achieved complete resolution of VOEs	Hematologic malignancy risk (black box warning); myeloablative conditioning required	$2+ million per patient

# Create a data frame for challenges in implementing these therapies
implementation_challenges <- data.frame(
  Challenge_Category = c("Clinical", "Clinical", "Access", "Access", "Access", "Long-term", "Long-term", "Research"),
  Challenge_Description = c(
    "Myeloablative conditioning toxicity and fertility preservation", 
    "Risk of clonal hematopoiesis (CH) mutations post-therapy", 
    "High cost ($2+ million per patient)",
    "Limited treatment centers (~40-50 in US only)",
    "Healthcare disparities affecting predominantly minority populations",
    "Uncertain durability beyond clinical trial periods",
    "Lifelong monitoring for potential malignancies required",
    "Discrepancies in CH research findings as highlighted in original text"
  )
)

# Create a challenges table
challenges_table <- gt(implementation_challenges) %>%
  tab_header(
    title = "Implementation Challenges for SCD Gene Therapies",
    subtitle = "Key Barriers to Widespread Adoption"
  ) %>%
  cols_label(
    Challenge_Category = "Category",
    Challenge_Description = "Challenge Description"
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#fff0e6"),
      cell_text(weight = "bold")
    ),
    locations = cells_column_labels()
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_body(columns = Challenge_Category)
  )

# Display the challenges table
challenges_table

Implementation Challenges for SCD Gene Therapies
Key Barriers to Widespread Adoption
Category	Challenge Description
Clinical	Myeloablative conditioning toxicity and fertility preservation
Clinical	Risk of clonal hematopoiesis (CH) mutations post-therapy
Access	High cost ($2+ million per patient)
Access	Limited treatment centers (~40-50 in US only)
Access	Healthcare disparities affecting predominantly minority populations
Long-term	Uncertain durability beyond clinical trial periods
Long-term	Lifelong monitoring for potential malignancies required
Research	Discrepancies in CH research findings as highlighted in original text