8  Lower-Risk MDS

Evidence-Based Treatment Strategies and Emerging Therapies

8.1 Epidemiology and Disease Overview

Lower-risk MDS (LR-MDS) comprises approximately 58% of all MDS cases, encompassing the very-low and low IPSS-R risk categories. These patients face a median survival of 3–8 years, but the trajectory is heavily influenced by the development and management of anemia.

epi_data <- tribble(
  ~Category, ~Pct, ~Label, ~Color,
  "LR-MDS among\nall MDS", 58, "58%", pal$teal,
  "LR-MDS patients\nwho develop TD anemia", 50, "~50%", pal$amber,
  "5-yr survival\nRBC-TI", 66, "66%", pal$green,
  "5-yr survival\nRBC-TD", 34, "34%", pal$red
)
epi_data$Category <- factor(epi_data$Category, levels = epi_data$Category)

ggplot(epi_data, aes(x = Category, y = Pct, fill = Color)) +
geom_col(width = 0.6, show.legend = FALSE) +
geom_text(aes(label = Label), vjust = -0.4, fontface = "bold", size = 5) +
scale_fill_identity() +
scale_y_continuous(limits = c(0, 80), labels = \(x) paste0(x, "%")) +
labs(
  title = "Key Epidemiologic Facts in Lower-Risk MDS",
  subtitle = "2024 analysis of 1,136 LR-MDS patients across 6 clinical trials: RBC-TI strongly predictive of survival",
  x = NULL, y = NULL,
  caption = "Source: Komrokji et al. Clin Lymph Myeloma Leuk. 2025."
) +
theme_mds() +
theme(axis.text.y = element_blank(), panel.grid.major.y = element_blank())

Figure 8.1: Epidemiology of Anemia in Lower-Risk MDS

ImportantSurvival Impact of Transfusion Independence

A 2024 analysis of 1,136 LR-MDS patients across 6 clinical trials demonstrated that RBC transfusion status is strongly predictive of survival: 5-year OS was 66% for RBC-TI vs. 34% for RBC-TD patients. This underscores that achieving and maintaining transfusion independence is not merely a symptomatic goal but a critical determinant of long-term outcomes.

8.2 Response Criteria

Response in LR-MDS is defined as pRBC transfusion independence or a rise in hemoglobin ≥1.5 g/dL, or both, sustained for at least an 8–16 week period (IWG 2018 criteria).

---

8.3 Treatment Algorithm Overview

algo <- tribble(
  ~Line, ~Setting, ~Therapy, ~Response, ~Evidence, ~y_pos, ~x_pos,
  "1L", "TI, sEPO <200", "ESA (Epoetin-α/Darbepoetin)", "~60% TI", "EU Approval; NCCN", 6, 1,
  "1L", "TD, any sEPO", "Luspatercept", "60% TI≥12wk", "COMMANDS (Phase 3)", 5, 1,
  "1L", "MDS-del(5q)", "Lenalidomide", "67% TI≥8wk", "MDS-003/004", 4, 1,
  "2L", "Post-ESA, RS+", "Luspatercept", "38% TI≥8wk", "MEDALIST (Phase 3)", 3, 2,
  "2L", "Post-ESA, RS-\nor High TB", "Imetelstat", "40% TI≥8wk", "IMerge (Phase 3)", 2, 2,
  "3L", "Post-Luspa", "Imetelstat", "Active post-Luspa", "IMerge subgroup", 1, 3
)

ggplot(algo, aes(x = x_pos, y = y_pos)) +
geom_tile(aes(fill = Line), width = 0.85, height = 0.75, alpha = 0.85, color = "white", linewidth = 1.2) +
geom_text(aes(label = Therapy), fontface = "bold", size = 3.5, vjust = -0.6) +
geom_text(aes(label = paste0(Response, "\n", Evidence)), size = 2.6, vjust = 1, color = "grey20") +
scale_fill_manual(
  values = c("1L" = pal$teal, "2L" = pal$purple, "3L" = pal$amber),
  name = "Line of Therapy"
) +
scale_x_continuous(
  breaks = 1:3, labels = c("First-Line", "Second-Line", "Third-Line"),
  limits = c(0.4, 3.6)
) +
scale_y_continuous(limits = c(0.3, 6.8)) +
labs(
  title = "Treatment Sequencing in Lower-Risk MDS",
  subtitle = "Based on McMahon et al. ASCO Educ Book 2025; Santini/Komrokji recommendations",
  x = NULL, y = NULL,
  caption = "TB = transfusion burden; RS = ring sideroblasts; TD = transfusion dependent; TI = transfusion independent"
) +
theme_mds() +
theme(
  axis.text.y = element_blank(),
  panel.grid = element_blank(),
  axis.text.x = element_text(face = "bold", size = 12)
)

Figure 8.2: Simplified Treatment Algorithm for Anemia in LR-MDS

---

8.4 Erythropoiesis-Stimulating Agents (ESAs)

8.4.1 Patient Selection for ESAs

esa_factors <- tribble(
  ~Factor, ~`Favorable for Response`, ~`Unfavorable for Response`,
  "Serum EPO level", "< 200 U/L (optimal)\n< 500 U/L (acceptable)", "> 500 U/L",
  "Transfusion status", "Transfusion-independent", "Transfusion-dependent",
  "Transfusion burden", "< 2 units / 8 weeks", "≥ 4 units / 8 weeks",
  "Ring sideroblasts", "RS-negative (better ESA response)", "RS-positive (shorter ESA duration)",
  "IPSS-M score", "Very low / Low risk", "Moderate-low or higher",
  "Molecular", "No STAG2 mutation (males)", "STAG2 mutation (males): OR 0.13",
  "Timing", "Early initiation (pre-TD)", "Late / refractory setting"
)

esa_factors %>%
gt(rowname_col = "Factor") %>%
tab_header(
  title = "Predictors of ESA Response in LR-MDS",
  subtitle = "Based on ESA-PSS-M score and multicenter data"
) %>%
cols_label(
  `Favorable for Response` = md("**Favorable**"),
  `Unfavorable for Response` = md("**Unfavorable**")
) %>%
tab_style(
  style = cell_fill(color = pal$green_lt),
  locations = cells_body(columns = `Favorable for Response`)
) %>%
tab_style(
  style = cell_fill(color = pal$red_lt),
  locations = cells_body(columns = `Unfavorable for Response`)
) %>%
tab_source_note(md("Raddi et al. *Blood*. 2025;146(14):1693. McMahon et al. *ASCO Educ Book*. 2025."))

Table 8.1: Factors Influencing ESA Response in LR-MDS

Predictors of ESA Response in LR-MDS
Based on ESA-PSS-M score and multicenter data
	Favorable	Unfavorable
Serum EPO level	< 200 U/L (optimal) < 500 U/L (acceptable)	> 500 U/L
Transfusion status	Transfusion-independent	Transfusion-dependent
Transfusion burden	< 2 units / 8 weeks	≥ 4 units / 8 weeks
Ring sideroblasts	RS-negative (better ESA response)	RS-positive (shorter ESA duration)
IPSS-M score	Very low / Low risk	Moderate-low or higher
Molecular	No STAG2 mutation (males)	STAG2 mutation (males): OR 0.13
Timing	Early initiation (pre-TD)	Late / refractory setting
Raddi et al. Blood. 2025;146(14):1693. McMahon et al. ASCO Educ Book. 2025.

8.4.2 EPO-PRETAR Trial (GFM Group)

The EPO-PRETAR trial randomized LR-MDS patients to early vs. delayed ESA initiation to assess whether starting ESAs before transfusion dependence improves outcomes.

num <- tribble(
  ~Endpoint, ~Metric, ~Early, ~Delayed,
  "HI-E response", "Proportion (%)", 79.5, 54.2,
  "Duration of HI-E", "Median (months)", 30.6, 12.7,
  "Time to RBC transfusion need", "Median (months)", 36.0, 36.0
) %>%
mutate(Difference = Early - Delayed)

num %>%
gt(rowname_col = "Endpoint") %>%
tab_header(
  title = "EPO-PRETAR: Efficacy Summary — Early vs. Delayed ESA",
  subtitle = "GFM Phase 3 Randomized Trial"
) %>%
fmt_number(columns = c(Early, Delayed, Difference), decimals = 1) %>%
cols_label(
  Metric = "Measure",
  Early = md("**Early ESA**"),
  Delayed = md("**Delayed ESA**"),
  Difference = md("**Δ (Early − Delayed)**")
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_body(rows = Endpoint == "HI-E response", columns = c(Early, Difference))
) %>%
tab_footnote("HI-E response and duration favored early ESA; OS/PFS/QoL/AML transformation were not significantly different.") %>%
tab_source_note(md("Park S et al. *Blood*. 2024;144:349-350 (suppl 1)."))

Table 8.2: EPO-PRETAR Trial: Key Efficacy Outcomes

EPO-PRETAR: Efficacy Summary — Early vs. Delayed ESA
GFM Phase 3 Randomized Trial
	Measure	Early ESA	Delayed ESA	Δ (Early − Delayed)
HI-E response	Proportion (%)	79.5	54.2	25.3
Duration of HI-E	Median (months)	30.6	12.7	17.9
Time to RBC transfusion need	Median (months)	36.0	36.0	0.0
HI-E response and duration favored early ESA; OS/PFS/QoL/AML transformation were not significantly different.
Park S et al. Blood. 2024;144:349-350 (suppl 1).

8.4.3 ESA-PSS-M Score

The ESA-PSS-M is a novel molecular predictive score that integrates three variables to predict ESA response: (Raddi et al. 2025)

\[\text{ESA-PSS-M} = -0.05 \times [\text{sEPO (U/L)}] - 4.5 \times [\text{IPSS-M score}] - 5 \times [\text{TD (1=yes; 0=no)}]\]

score_data <- tribble(
  ~Component, ~Weight, ~Direction,
  "sEPO Level (U/L)", -0.05, "Higher EPO → Lower score → Less likely to respond",
  "IPSS-M Score", -4.50, "Higher IPSS-M → Lower score → Less likely to respond",
  "Transfusion\nDependence", -5.00, "TD present → Lower score → Less likely to respond"
)
score_data$Component <- factor(score_data$Component, levels = rev(score_data$Component))

p1 <- ggplot(score_data, aes(x = Component, y = abs(Weight), fill = Component)) +
  geom_col(width = 0.55, show.legend = FALSE) +
  geom_text(aes(label = Weight), hjust = -0.3, fontface = "bold", size = 4) +
  coord_flip() +
  scale_fill_manual(values = c(pal$teal, pal$purple, pal$amber)) +
  scale_y_continuous(limits = c(0, 6.5)) +
  labs(title = "ESA-PSS-M Score Components",
       subtitle = "All coefficients are negative (higher values reduce predicted response)",
       x = NULL, y = "Absolute Weight") +
  theme_mds()

auc_data <- tribble(
  ~Model, ~AUC,
  "sEPO + TD only\n(traditional)", 0.63,
  "sEPO + TD + IPSS-M\n(ESA-PSS-M)", 0.71
)
auc_data$Model <- factor(auc_data$Model, levels = auc_data$Model)

p2 <- ggplot(auc_data, aes(x = Model, y = AUC, fill = Model)) +
  geom_col(width = 0.5, show.legend = FALSE) +
  geom_text(aes(label = AUC), vjust = -0.4, fontface = "bold", size = 5) +
  scale_fill_manual(values = c(pal$slate, pal$teal)) +
  scale_y_continuous(limits = c(0, 0.85)) +
  labs(title = "Predictive Performance",
       subtitle = "AUC improvement: P = 0.0004",
       x = NULL, y = "AUC") +
  theme_mds()

p1 + p2 + plot_layout(widths = c(2, 1))

Figure 8.3: Components of the ESA-PSS-M Score and Performance

NoteSTAG2 and Sex-Specific Resistance

The study also identified that STAG2 mutations (X-linked) are strongly associated with ESA resistance in males (OR 0.13, P = .003), representing the first sex-specific molecular predictor of ESA response in LR-MDS. This finding may guide first-line therapeutic choice between ESAs and luspatercept.

---

8.5 Luspatercept

Luspatercept is a recombinant fusion protein (modified ActRIIB-Fc) that traps TGF-β superfamily ligands (GDF11, GDF8), inhibiting SMAD2/3 signaling to enhance terminal erythroid maturation.

8.5.1 Landmark Trials Overview

trials <- tribble(
  ~Trial, ~Phase, ~Setting, ~N, ~Design, ~`Primary Endpoint`, ~`Key Result`,
  "MEDALIST", "Phase 3", "ESA-R/R, MDS-RS", "229", "RCT vs Placebo", "TI ≥8 wk (wk 1-24)", "37.9% vs 13.2% (P<.001)",
  "COMMANDS", "Phase 3", "ESA-naïve, TD LR-MDS", "356", "RCT vs Epoetin-α", "TI ≥12 wk + Hb ≥1.5 g/dL", "59% vs 31% (P<.0001)",
  "MAXILUS", "Phase 3b", "ESA-naïve + ESA-R/R/I", "90", "Single-arm, max dose", "TI ≥8 wk + Hb ≥1.0 g/dL", "76.5% / 65.3%",
  "ELEMENT", "Phase 3", "TI LR-MDS", "—", "Ongoing", "Time to TD", "Pending",
  "COMBOLA", "Phase 2", "Luspa + ESA combo", "—", "GFM study", "Combination efficacy", "Pending"
)

trials %>%
gt(rowname_col = "Trial") %>%
tab_header(
  title = "Luspatercept Clinical Trial Landscape",
  subtitle = "From MEDALIST to ongoing studies"
) %>%
cols_label(
  Phase = "Phase", Setting = "Population", N = "N",
  Design = "Design", `Primary Endpoint` = "Primary EP",
  `Key Result` = "Key Result"
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_body(rows = Trial %in% c("MEDALIST", "COMMANDS"))
) %>%
tab_style(
  style = cell_fill(color = pal$amber_lt),
  locations = cells_body(rows = Trial == "MAXILUS")
) %>%
tab_footnote(
  footnote = "MAXILUS results are preliminary (interim analysis, Jan 2025 cutoff).",
  locations = cells_body(columns = `Key Result`, rows = Trial == "MAXILUS")
)

Table 8.3: Overview of Key Luspatercept Trials in LR-MDS

Luspatercept Clinical Trial Landscape
From MEDALIST to ongoing studies
	Phase	Population	N	Design	Primary EP	Key Result
MEDALIST	Phase 3	ESA-R/R, MDS-RS	229	RCT vs Placebo	TI ≥8 wk (wk 1-24)	37.9% vs 13.2% (P<.001)
COMMANDS	Phase 3	ESA-naïve, TD LR-MDS	356	RCT vs Epoetin-α	TI ≥12 wk + Hb ≥1.5 g/dL	59% vs 31% (P<.0001)
MAXILUS	Phase 3b	ESA-naïve + ESA-R/R/I	90	Single-arm, max dose	TI ≥8 wk + Hb ≥1.0 g/dL	76.5% / 65.3%1
ELEMENT	Phase 3	TI LR-MDS	—	Ongoing	Time to TD	Pending
COMBOLA	Phase 2	Luspa + ESA combo	—	GFM study	Combination efficacy	Pending
1 MAXILUS results are preliminary (interim analysis, Jan 2025 cutoff).

8.5.2 Early vs Delayed Luspatercept

• Amanma I ASH 2025 Poster 4620
• Santini v ash 2025
• Wiliekens C ASH 2025
• This will be answered by ELEMENT clinical trial

8.5.3 COMMANDS Trial

The COMMANDS trial (NCT03682536) is a global, phase 3, open-label, randomized controlled trial comparing luspatercept vs. epoetin alfa as first-line treatment for anemia in ESA-naïve, transfusion-dependent LR-MDS.

8.5.3.1 Primary and Key Secondary Endpoints

commands <- tribble(
  ~Endpoint, ~Luspatercept, ~Epoetin, ~P_value, ~Order,
  "TI ≥12 wk + Hb ≥1.5\n(Primary)", 59, 31, "<0.0001", 1,
  "HI-E ≥8 wk\n(IWG criteria)", 74, 51, "<0.001", 2,
  "TI ≥12 wk\n(any Hb)", 76.4, 55.8, "<0.0001", 3,
  "TI ≥24 wk", 48, 29, "<0.001", 4,
  "TI ≥1 year", 45, 28, "0.0003", 5,
  "TI ≥1.5 years", 30, 14, "<0.0001", 6
)

commands_long <- commands %>%
  pivot_longer(cols = c(Luspatercept, Epoetin), names_to = "Arm", values_to = "Rate") %>%
  mutate(Arm = factor(Arm, levels = c("Luspatercept", "Epoetin")))

ggplot(commands_long, aes(x = reorder(Endpoint, -Order), y = Rate, fill = Arm)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.6) +
  geom_text(
    aes(label = paste0(Rate, "%")),
    position = position_dodge(width = 0.7),
    vjust = -0.4, fontface = "bold", size = 3.5
  ) +
  scale_fill_manual(
    values = c("Luspatercept" = pal$teal, "Epoetin" = pal$slate),
    name = "Treatment Arm"
  ) +
  scale_y_continuous(limits = c(0, 95), labels = \(x) paste0(x, "%")) +
  coord_flip() +
  labs(
    title = "COMMANDS Trial: Luspatercept Superiority Across All Endpoints",
    subtitle = "Long-term follow-up (>2.5 years); N = 356",
    x = NULL, y = "Patients (%)",
    caption = "Platzbecker U, et al. Lancet. 2023;402:373-385.\nGarcia-Manero G, et al. Adv Ther. 2025;42:3576-3589."
  ) +
  theme_mds() +
  theme(panel.grid.major.y = element_blank(), panel.grid.major.x = element_line())

Figure 8.4: COMMANDS: Efficacy Endpoints — Luspatercept vs. Epoetin-α

8.5.3.2 Long-Term Durability by Subgroup

subgroup <- tribble(
  ~Subgroup, ~Luspatercept, ~Epoetin,
  "RS-positive", 32.3, 8.1,
  "RS-negative", 24.5, 13.1,
  "sEPO ≤200 U/L", 35.9, 15.3,
  "sEPO >200 U/L", 16.0, 8.1
)

subgroup_long <- subgroup %>%
  pivot_longer(-Subgroup, names_to = "Arm", values_to = "Rate") %>%
  mutate(Arm = factor(Arm, levels = c("Luspatercept", "Epoetin")))

ggplot(subgroup_long, aes(x = Subgroup, y = Rate, fill = Arm)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.6) +
  geom_text(
    aes(label = paste0(Rate, "%")),
    position = position_dodge(width = 0.7),
    vjust = -0.4, fontface = "bold", size = 4
  ) +
  scale_fill_manual(
    values = c("Luspatercept" = pal$teal, "Epoetin" = pal$slate),
    name = NULL
  ) +
  scale_y_continuous(limits = c(0, 45), labels = \(x) paste0(x, "%")) +
  labs(
    title = "Durable TI ≥1.5-Year Benefit Across Key Subgroups",
    subtitle = "Luspatercept benefit persists in RS-negative patients and across sEPO levels",
    x = NULL, y = "Patients with TI ≥1.5 years (%)",
    caption = "Garcia-Manero G, et al. ASH 2024."
  ) +
  theme_mds()

Figure 8.5: COMMANDS: TI ≥1.5-Year Response Rates by Subgroup

ImportantRS-Negative Patients: An Emerging Story

COMMANDS long-term data show that RS-negative patients achieve 24.5% TI ≥1.5 years with luspatercept (vs. 13.1% with epoetin-α), demonstrating durable benefit in this historically challenging subgroup representing ~80% of LR-MDS cases. However, the absolute TI rate at earlier timepoints is similar between luspatercept and ESA in RS-negative patients (~46–47%), with luspatercept showing advantage in duration of response.

8.5.3.3 Overall Survival Trend

With >2.5 years follow-up, luspatercept showed a trend toward improved overall survival (HR 0.805, 95% CI: 0.565–1.146), representing a ~20% lower risk of death, though not statistically significant. Fewer progressions to HR-MDS were observed in the luspatercept arm.

duration <- tribble(
  ~Metric, ~Luspatercept, ~`Epoetin-α`, ~`P value`,
  "Median cumulative duration of TI (weeks)", "154.7 (95% CI: 118.4-NR)", "91.1 (95% CI: 73.1-123.9)", "0.0016",
  "Approximate duration advantage", "~1 year longer", "—", "—",
  "OS Hazard Ratio", "0.805 (95% CI: 0.565-1.146)", "Reference", "NS"
)

duration %>%
gt(rowname_col = "Metric") %>%
tab_header(title = "COMMANDS: Duration and Survival Outcomes") %>%
cols_label(
  Luspatercept = md("**Luspatercept**"),
  `Epoetin-α` = md("**Epoetin-α**"),
  `P value` = md("***P***")
) %>%
tab_style(
  style = cell_fill(color = pal$teal_lt),
  locations = cells_body(columns = Luspatercept)
)

Table 8.4: COMMANDS: Cumulative Duration of RBC-TI

COMMANDS: Duration and Survival Outcomes
	Luspatercept	Epoetin-α	P
Median cumulative duration of TI (weeks)	154.7 (95% CI: 118.4-NR)	91.1 (95% CI: 73.1-123.9)	0.0016
Approximate duration advantage	~1 year longer	—	—
OS Hazard Ratio	0.805 (95% CI: 0.565-1.146)	Reference	NS

8.5.4 MAXILUS Trial

MAXILUS (Phase 3b, NCT06045689) tests whether starting luspatercept at the maximum-approved dose (1.75 mg/kg SC Q3W) from cycle 1 — without titration — can improve and accelerate erythroid responses in patients with LR-MDS who are ESA-naïve or ESA-R/R/I. The primary analysis (24 weeks; data cutoff October 1, 2025) was reported by Della Porta et al. at EHA 2026 and is a non-randomized, open-label, 2-cohort trial (ESA-naïve, n = 54; ESA-R/R/I, n = 52; 106 treated).

8.5.4.1 Rationale

In both MEDALIST and COMMANDS, the majority of patients required dose escalation to reach 1.75 mg/kg — in COMMANDS, 80.2% of patients received dose escalations to target or maintain a Hb range of 10–12 g/dL. Real-world data show that incomplete or delayed escalation (forgotten escalation, early discontinuation) contributes to suboptimal responses. MAXILUS asks whether initiating at maximum dose removes that barrier.

8.5.4.2 MAXILUS vs COMMANDS: Endpoint Comparison

endpoint_comparison <- tribble(
  ~Feature, ~COMMANDS, ~MAXILUS,
  "Phase", "Phase 3 (registration trial)", "Phase 3b (dose-optimization)",
  "Design", "Randomized 1:1, open-label", "Open-label, non-randomized (2 cohorts)",
  "N (treated)", "356", "106 (54 ESA-naïve + 52 ESA-R/R/I)",
  "Comparator", "Epoetin-α (450–1050 IU/kg weekly)", "None (single-arm per cohort)",
  "Luspatercept Dosing", "Titrated: 1.0 → 1.33 → 1.75 mg/kg Q3W", "Max dose: 1.75 mg/kg Q3W from C1",
  "Primary TI Duration Threshold", "≥12 weeks", "≥8 weeks",
  "Primary Hb Increase Required", "≥1.5 g/dL", "≥1.0 g/dL",
  "Assessment Window", "Weeks 1–24", "Weeks 1–24",
  "Endpoint Stringency", "More stringent", "Less stringent"
)

endpoint_comparison %>%
gt(rowname_col = "Feature") %>%
tab_header(
  title = "MAXILUS vs COMMANDS: Primary Endpoint Definitions",
  subtitle = "Different composite endpoints preclude direct numeric comparison"
) %>%
cols_label(
  COMMANDS = md("**COMMANDS**"),
  MAXILUS = md("**MAXILUS**")
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_column_labels()
) %>%
tab_style(
  style = cell_fill(color = pal$amber_lt),
  locations = cells_body(
    columns = everything(),
    rows = Feature %in% c("Primary TI Duration Threshold", "Primary Hb Increase Required", "Endpoint Stringency")
  )
) %>%
tab_footnote(
  footnote = "Highlighted rows indicate the key differences precluding direct comparison. MAXILUS also reports RBC-TI ≥12 wks + Hb ≥1.5 g/dL as a secondary endpoint, which matches the COMMANDS composite.",
  locations = cells_column_labels(columns = COMMANDS)
) %>%
tab_source_note(
  md("COMMANDS: Platzbecker U, et al. *Lancet*. 2023;402:373-385. MAXILUS: Della Porta MG, et al. EHA 2026 [Abstract S173].")
)

Table 8.5: Primary Endpoint Definitions: MAXILUS vs COMMANDS

MAXILUS vs COMMANDS: Primary Endpoint Definitions
Different composite endpoints preclude direct numeric comparison
	COMMANDS1	MAXILUS
Phase	Phase 3 (registration trial)	Phase 3b (dose-optimization)
Design	Randomized 1:1, open-label	Open-label, non-randomized (2 cohorts)
N (treated)	356	106 (54 ESA-naïve + 52 ESA-R/R/I)
Comparator	Epoetin-α (450–1050 IU/kg weekly)	None (single-arm per cohort)
Luspatercept Dosing	Titrated: 1.0 → 1.33 → 1.75 mg/kg Q3W	Max dose: 1.75 mg/kg Q3W from C1
Primary TI Duration Threshold	≥12 weeks	≥8 weeks
Primary Hb Increase Required	≥1.5 g/dL	≥1.0 g/dL
Assessment Window	Weeks 1–24	Weeks 1–24
Endpoint Stringency	More stringent	Less stringent
1 Highlighted rows indicate the key differences precluding direct comparison. MAXILUS also reports RBC-TI ≥12 wks + Hb ≥1.5 g/dL as a secondary endpoint, which matches the COMMANDS composite.
COMMANDS: Platzbecker U, et al. Lancet. 2023;402:373-385. MAXILUS: Della Porta MG, et al. EHA 2026 [Abstract S173].

WarningInterpretation Caution

Higher primary-endpoint response rates in MAXILUS vs COMMANDS cannot be directly compared because: (1) the MAXILUS primary endpoint uses a less stringent TI threshold (≥8 vs ≥12 weeks), (2) the Hb increase requirement is lower (≥1.0 vs ≥1.5 g/dL), (3) MAXILUS is non-randomized with no concurrent comparator, and (4) sample sizes differ substantially (106 vs 356). Where a like-for-like comparison is desired, use the MAXILUS secondary endpoint (RBC-TI ≥12 wks + Hb ≥1.5 g/dL: 72.2% ESA-naïve, 40.4% ESA-R/R/I).

8.5.4.3 Baseline Demographics & Disease Characteristics

demo <- tribble(
  ~Characteristic, ~`ESA-naïve`, ~`ESA-R/R/I`,
  "Age, median (IQR), years",            "76.0 (71.0–80.0)",       "76.0 (70.5–81.5)",
  "Female, n (%)",                       "17 (31.5)",              "17 (32.7)",
  "Region — Europe, n (%)",              "39 (72.2)",              "47 (90.4)",
  "Region — North America, n (%)",       "15 (27.8)",              "5 (9.6)",
  "Time since MDS dx, median (IQR), mo", "2.2 (0.3–9.2)",          "25.2 (10.0–65.5)",
  "Transfusion burden, median, U/8wk",   "2.0 (1.0–4.0)",          "4.0 (2.0–6.0)",
  "  TB <4 U/8wk, n (%)",                "38 (70.4)",              "25 (48.1)",
  "  TB 4 to <6 U/8wk, n (%)",           "12 (22.2)",              "8 (15.4)",
  "  TB ≥6 U/8wk, n (%)",                "4 (7.4)",                "19 (36.5)",
  "Hb, median (IQR), g/dL",              "8.1 (7.2–8.5)",          "7.5 (7.0–7.8)",
  "  sEPO ≤200 IU/L, n (%)",             "42 (77.8)",              "23 (44.2)",
  "  sEPO >200 IU/L, n (%)",             "11 (20.4)",              "29 (55.8)",
  "  sEPO >500 IU/L, n (%)",             "3 (5.6)",                "17 (32.7)",
  "Serum ferritin, median (IQR), μg/L",  "601.5 (270.0–1130.5)",   "963.2 (469.4–1624.5)",
  "IPSS-R — Very low, n (%)",            "2 (3.7)",                "5 (9.6)",
  "IPSS-R — Low, n (%)",                 "37 (68.5)",              "36 (69.2)",
  "IPSS-R — Intermediate, n (%)",        "14 (25.9)",              "11 (21.2)",
  "RS− status, n (%)",                   "23 (42.6)",              "19 (36.5)",
  "RS+ status, n (%)",                   "31 (57.4)",              "33 (63.5)",
  "SF3B1 mutated, n (%)",                "28 (51.9)",              "30 (57.7)"
)

demo %>%
gt(rowname_col = "Characteristic") %>%
tab_header(
  title = "MAXILUS: Baseline Demographics & Disease Characteristics",
  subtitle = "All-treated population; data cutoff October 1, 2025"
) %>%
cols_label(
  `ESA-naïve` = md("**ESA-naïve**<br>(n = 54)"),
  `ESA-R/R/I` = md("**ESA-R/R/I**<br>(n = 52)")
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_column_labels()
) %>%
tab_footnote(
  footnote = "ESA-R/R/I cohort is a more heavily pre-treated, higher-transfusion-burden, higher-sEPO population (longer time since diagnosis, more TB ≥6 U/8wk, more sEPO >200 IU/L).",
  locations = cells_column_labels(columns = `ESA-R/R/I`)
) %>%
tab_source_note(md("Della Porta MG, et al. EHA 2026 [Abstract S173]."))

Table 8.6: MAXILUS Baseline Demographics and Disease Characteristics (all-treated population)

MAXILUS: Baseline Demographics & Disease Characteristics
All-treated population; data cutoff October 1, 2025
	ESA-naïve (n = 54)	ESA-R/R/I (n = 52)1
Age, median (IQR), years	76.0 (71.0–80.0)	76.0 (70.5–81.5)
Female, n (%)	17 (31.5)	17 (32.7)
Region — Europe, n (%)	39 (72.2)	47 (90.4)
Region — North America, n (%)	15 (27.8)	5 (9.6)
Time since MDS dx, median (IQR), mo	2.2 (0.3–9.2)	25.2 (10.0–65.5)
Transfusion burden, median, U/8wk	2.0 (1.0–4.0)	4.0 (2.0–6.0)
TB <4 U/8wk, n (%)	38 (70.4)	25 (48.1)
TB 4 to <6 U/8wk, n (%)	12 (22.2)	8 (15.4)
TB ≥6 U/8wk, n (%)	4 (7.4)	19 (36.5)
Hb, median (IQR), g/dL	8.1 (7.2–8.5)	7.5 (7.0–7.8)
sEPO ≤200 IU/L, n (%)	42 (77.8)	23 (44.2)
sEPO >200 IU/L, n (%)	11 (20.4)	29 (55.8)
sEPO >500 IU/L, n (%)	3 (5.6)	17 (32.7)
Serum ferritin, median (IQR), μg/L	601.5 (270.0–1130.5)	963.2 (469.4–1624.5)
IPSS-R — Very low, n (%)	2 (3.7)	5 (9.6)
IPSS-R — Low, n (%)	37 (68.5)	36 (69.2)
IPSS-R — Intermediate, n (%)	14 (25.9)	11 (21.2)
RS− status, n (%)	23 (42.6)	19 (36.5)
RS+ status, n (%)	31 (57.4)	33 (63.5)
SF3B1 mutated, n (%)	28 (51.9)	30 (57.7)
1 ESA-R/R/I cohort is a more heavily pre-treated, higher-transfusion-burden, higher-sEPO population (longer time since diagnosis, more TB ≥6 U/8wk, more sEPO >200 IU/L).
Della Porta MG, et al. EHA 2026 [Abstract S173].

8.5.4.4 Preliminary → Primary Efficacy Results

maxilus_eff <- tribble(
  ~Endpoint, ~`ESA-naïve`, ~`ESA-R/R/I`,
  "Primary: RBC-TI ≥8 wks + Hb ≥1.0 g/dL", "81.5% (44/54)", "61.5% (32/52)",
  "RBC-TI ≥8 weeks",                        "83.3% (45/54)", "67.3% (35/52)",
  "RBC-TI ≥12 weeks",                       "75.9% (41/54)", "50.0% (26/52)",
  "RBC-TI ≥12 wks + Hb ≥1.5 g/dL",          "72.2% (39/54)", "40.4% (21/52)",
  "mHI-E (IWG 2018)",                       "87.0% (47/54)", "76.9% (40/52)"
)

maxilus_eff %>%
gt(rowname_col = "Endpoint") %>%
tab_header(
  title = "MAXILUS: Primary Efficacy by Cohort",
  subtitle = "Efficacy-evaluable population (≥24 wks from first dose); data cutoff October 1, 2025"
) %>%
cols_label(
  `ESA-naïve` = md("**Cohort 1: ESA-naïve**<br>(n = 54 evaluable)"),
  `ESA-R/R/I` = md("**Cohort 2: ESA-R/R/I**<br>(n = 52 evaluable)")
) %>%
tab_style(
  style = list(cell_fill(color = pal$green_lt), cell_text(weight = "bold")),
  locations = cells_body(
    columns = everything(),
    rows = Endpoint == "Primary: RBC-TI ≥8 wks + Hb ≥1.0 g/dL"
  )
) %>%
tab_footnote(
  footnote = "Among responders, the majority achieved TI with onset as early as Cycle 1 (ESA-naïve 86.4% [38/44]; ESA-R/R/I 68.8% [22/32]).",
  locations = cells_body(columns = `ESA-naïve`, rows = Endpoint == "Primary: RBC-TI ≥8 wks + Hb ≥1.0 g/dL")
) %>%
tab_source_note(md("Della Porta MG, et al. EHA 2026 [Abstract S173]."))

Table 8.7: MAXILUS Primary Analysis: Efficacy-Evaluable Population, Weeks 1–24

MAXILUS: Primary Efficacy by Cohort
Efficacy-evaluable population (≥24 wks from first dose); data cutoff October 1, 2025
	Cohort 1: ESA-naïve (n = 54 evaluable)	Cohort 2: ESA-R/R/I (n = 52 evaluable)
Primary: RBC-TI ≥8 wks + Hb ≥1.0 g/dL	1 81.5% (44/54)	61.5% (32/52)
RBC-TI ≥8 weeks	83.3% (45/54)	67.3% (35/52)
RBC-TI ≥12 weeks	75.9% (41/54)	50.0% (26/52)
RBC-TI ≥12 wks + Hb ≥1.5 g/dL	72.2% (39/54)	40.4% (21/52)
mHI-E (IWG 2018)	87.0% (47/54)	76.9% (40/52)
1 Among responders, the majority achieved TI with onset as early as Cycle 1 (ESA-naïve 86.4% [38/44]; ESA-R/R/I 68.8% [22/32]).
Della Porta MG, et al. EHA 2026 [Abstract S173].

maxilus_sub <- tribble(
  ~Subgroup, ~`ESA-naïve`, ~`ESA-R/R/I`,
  "Overall",          "75.9% (41/54)", "50.0% (26/52)",
  "RS− status",       "73.9% (17/23)", "36.8% (7/19)",
  "RS+ status",       "77.4% (24/31)", "57.6% (19/33)",
  "sEPO ≤200 IU/L",   "86.0% (37/43)", "56.5% (13/23)",
  "sEPO >200 IU/L",   "36.4% (4/11)",  "44.8% (13/29)"
)

maxilus_sub %>%
gt(rowname_col = "Subgroup") %>%
tab_header(
  title = "MAXILUS: RBC-TI ≥12 Weeks by Subgroup",
  subtitle = "Exploratory subgroup analyses of RBC-TI ≥12 weeks (Weeks 1–24)"
) %>%
cols_label(
  `ESA-naïve` = md("**ESA-naïve**<br>(n = 54)"),
  `ESA-R/R/I` = md("**ESA-R/R/I**<br>(n = 52)")
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_column_labels()
) %>%
tab_style(
  style = cell_fill(color = pal$green_lt),
  locations = cells_body(columns = everything(), rows = Subgroup %in% c("RS− status", "sEPO ≤200 IU/L"))
) %>%
tab_footnote(
  footnote = "Median duration of RBC-TI ≥12 wks: ESA-naïve not reached overall or in any subgroup; ESA-R/R/I 59.6 wks overall, 14.7 wks (RS−), not reached (sEPO ≤200 IU/L).",
  locations = cells_column_labels(columns = `ESA-naïve`)
) %>%
tab_source_note(md("Della Porta MG, et al. EHA 2026 [Abstract S173]."))

Table 8.8: MAXILUS: RBC-TI ≥12 Weeks by RS Status and sEPO Subgroup

MAXILUS: RBC-TI ≥12 Weeks by Subgroup
Exploratory subgroup analyses of RBC-TI ≥12 weeks (Weeks 1–24)
	ESA-naïve (n = 54)1	ESA-R/R/I (n = 52)
Overall	75.9% (41/54)	50.0% (26/52)
RS− status	73.9% (17/23)	36.8% (7/19)
RS+ status	77.4% (24/31)	57.6% (19/33)
sEPO ≤200 IU/L	86.0% (37/43)	56.5% (13/23)
sEPO >200 IU/L	36.4% (4/11)	44.8% (13/29)
1 Median duration of RBC-TI ≥12 wks: ESA-naïve not reached overall or in any subgroup; ESA-R/R/I 59.6 wks overall, 14.7 wks (RS−), not reached (sEPO ≤200 IU/L).
Della Porta MG, et al. EHA 2026 [Abstract S173].

8.5.4.5 Response Comparison Visualization

panel_data <- tribble(
  ~Endpoint, ~Cohort, ~Response, ~n_N, ~Order,
  "Primary\n(TI ≥8wk + Hb ≥1)",  "ESA-naïve",  81.5, "44/54", 1,
  "RBC-TI\n≥8 weeks",            "ESA-naïve",  83.3, "45/54", 2,
  "RBC-TI\n≥12 weeks",           "ESA-naïve",  75.9, "41/54", 3,
  "TI ≥12wk\n+ Hb ≥1.5",         "ESA-naïve",  72.2, "39/54", 4,
  "mHI-E",                        "ESA-naïve",  87.0, "47/54", 5,
  "Primary\n(TI ≥8wk + Hb ≥1)",  "ESA-R/R/I",  61.5, "32/52", 1,
  "RBC-TI\n≥8 weeks",            "ESA-R/R/I",  67.3, "35/52", 2,
  "RBC-TI\n≥12 weeks",           "ESA-R/R/I",  50.0, "26/52", 3,
  "TI ≥12wk\n+ Hb ≥1.5",         "ESA-R/R/I",  40.4, "21/52", 4,
  "mHI-E",                        "ESA-R/R/I",  76.9, "40/52", 5
)
panel_data$Endpoint <- reorder(panel_data$Endpoint, panel_data$Order)
panel_data$Cohort <- factor(panel_data$Cohort, levels = c("ESA-naïve", "ESA-R/R/I"))

ggplot(panel_data, aes(x = Endpoint, y = Response, fill = Cohort)) +
  geom_col(position = position_dodge(width = 0.75), width = 0.7,
           color = "white", linewidth = 0.5) +
  geom_text(aes(label = paste0(Response, "%")),
            position = position_dodge(width = 0.75),
            vjust = -0.4, fontface = "bold", size = 3.6) +
  scale_fill_manual(values = c("ESA-naïve" = pal$teal, "ESA-R/R/I" = pal$amber),
                    name = "Cohort") +
  scale_y_continuous(limits = c(0, 100), labels = \(x) paste0(x, "%")) +
  labs(
    title = "MAXILUS Primary Analysis: Response by Cohort",
    subtitle = "Luspatercept 1.75 mg/kg from C1 · efficacy-evaluable, Weeks 1–24",
    x = NULL, y = "Patients achieving endpoint (%)"
  ) +
  theme_mds()

Figure 8.6: MAXILUS Primary Analysis: RBC-TI and mHI-E responses by cohort

bar_data <- tribble(
  ~Study_Arm, ~Response, ~Endpoint_Label, ~Fill_Group, ~Order,
  "COMMANDS\nLuspatercept", 59.0, "TI ≥12 wks + Hb ≥1.5", "COMMANDS", 1,
  "COMMANDS\nEpoetin-α", 31.0, "TI ≥12 wks + Hb ≥1.5", "COMMANDS_ctrl", 2,
  "MAXILUS\nESA-naïve", 81.5, "TI ≥8 wks + Hb ≥1.0", "MAXILUS", 3,
  "MAXILUS\nESA-R/R/I", 61.5, "TI ≥8 wks + Hb ≥1.0", "MAXILUS", 4
)
bar_data$Study_Arm <- factor(bar_data$Study_Arm, levels = bar_data$Study_Arm)

ggplot(bar_data, aes(x = Study_Arm, y = Response, fill = Fill_Group)) +
  geom_col(width = 0.6, color = "white", linewidth = 0.6) +
  geom_text(aes(label = paste0(Response, "%")), vjust = -0.5, fontface = "bold", size = 4.5) +
  scale_fill_manual(
    values = c(
      "COMMANDS" = pal$teal, "COMMANDS_ctrl" = pal$slate,
      "MAXILUS" = pal$amber
    ),
    labels = c("COMMANDS (Luspatercept)", "COMMANDS (Epoetin-α)", "MAXILUS (Max-dose Luspatercept)"),
    name = "Study Arm"
  ) +
  annotate("text", x = 1.5, y = 72,
           label = "Endpoint:\nTI ≥12 wks + Hb ≥1.5 g/dL",
           size = 3, color = pal$teal, fontface = "italic") +
  annotate("text", x = 3.5, y = 90,
           label = "Endpoint:\nTI ≥8 wks + Hb ≥1.0 g/dL",
           size = 3, color = "#B45309", fontface = "italic") +
  geom_vline(xintercept = 2.5, linetype = "dashed", color = "grey60", linewidth = 0.4) +
  scale_y_continuous(limits = c(0, 100), labels = \(x) paste0(x, "%")) +
  labs(
    title = "Primary Endpoint Responses: COMMANDS vs MAXILUS",
    subtitle = "⚠ Different endpoint definitions — direct comparison not appropriate",
    x = NULL, y = "Patients Achieving Primary Endpoint (%)"
  ) +
  theme_mds()

Figure 8.7: Primary Endpoint Response: COMMANDS vs MAXILUS (⚠ different endpoint definitions)

8.5.4.6 Hemoglobin Trajectory

hb <- tribble(
  ~Parameter, ~`Naïve — eval`, ~`Naïve — resp`, ~`R/R/I — eval`, ~`R/R/I — resp`,
  "Baseline Hb, median (IQR), g/dL",       "8.1 (7.2–8.5)",  "8.2 (7.7–8.6)",  "7.5 (7.0–7.8)",  "7.3 (6.9–7.7)",
  "Maximum Hb, median (IQR), g/dL",        "11.3 (9.9–12.4)","11.5 (10.7–12.7)","9.8 (8.9–10.7)","10.2 (9.3–11.0)",
  "Change to maximum, median (IQR), g/dL", "3.2 (2.5–4.1)",  "3.3 (3.0–4.2)",  "2.4 (1.6–3.5)",  "3.0 (2.2–3.8)"
)

hb %>%
gt(rowname_col = "Parameter") %>%
tab_header(
  title = "MAXILUS: Hemoglobin Trajectory",
  subtitle = "Median peak Hb 11.3 g/dL (ESA-naïve) and 9.8 g/dL (ESA-R/R/I) within 24 weeks"
) %>%
tab_spanner(label = md("**ESA-naïve**"), columns = c(`Naïve — eval`, `Naïve — resp`)) %>%
tab_spanner(label = md("**ESA-R/R/I**"), columns = c(`R/R/I — eval`, `R/R/I — resp`)) %>%
cols_label(
  `Naïve — eval` = md("Evaluable<br>(n=54)"),
  `Naïve — resp` = md("Responders<br>(n=44)"),
  `R/R/I — eval` = md("Evaluable<br>(n=52)"),
  `R/R/I — resp` = md("Responders<br>(n=32)")
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_column_spanners()
) %>%
tab_source_note(md("Responders = primary-endpoint responders. Della Porta MG, et al. EHA 2026 [Abstract S173]."))

Table 8.9: MAXILUS: Hemoglobin Change from Baseline to Maximum (Weeks 1–24)

MAXILUS: Hemoglobin Trajectory
Median peak Hb 11.3 g/dL (ESA-naïve) and 9.8 g/dL (ESA-R/R/I) within 24 weeks
	ESA-naïve		ESA-R/R/I
	Evaluable (n=54)	Responders (n=44)	Evaluable (n=52)	Responders (n=32)
Baseline Hb, median (IQR), g/dL	8.1 (7.2–8.5)	8.2 (7.7–8.6)	7.5 (7.0–7.8)	7.3 (6.9–7.7)
Maximum Hb, median (IQR), g/dL	11.3 (9.9–12.4)	11.5 (10.7–12.7)	9.8 (8.9–10.7)	10.2 (9.3–11.0)
Change to maximum, median (IQR), g/dL	3.2 (2.5–4.1)	3.3 (3.0–4.2)	2.4 (1.6–3.5)	3.0 (2.2–3.8)
Responders = primary-endpoint responders. Della Porta MG, et al. EHA 2026 [Abstract S173].

8.5.4.7 Treatment Exposure & Disposition

expo <- tribble(
  ~Metric, ~`ESA-naïve`, ~`ESA-R/R/I`,
  "Median (range) treatment exposure, weeks", "47.3 (10.7–71.1)", "43.4 (2.1–97.1)",
  "On active treatment at cutoff, n (%)",     "38 (70.4)",        "20 (38.5)",
  "Completed treatment, n (%)",               "1 (1.9)",          "1 (1.9)",
  "Discontinued treatment, n (%)",            "15 (27.8)",        "31 (59.6)",
  "  — Lack of efficacy",                     "6 (11.1)",         "12 (23.1)",
  "  — Withdrawal by patient",                "3 (5.6)",          "5 (9.6)",
  "  — Progressive disease",                  "2 (3.7)",          "4 (7.7)",
  "  — Adverse event",                        "2 (3.7)",          "1 (1.9)",
  "  — Death",                                "1 (1.9)",          "3 (5.8)",
  "≥1 dose delay, n (%)",                     "28 (51.9)",        "22 (42.3)",
  "  — Predose Hb ≥12 g/dL",                  "18 (33.3)",        "2 (3.8)",
  "≥1 dose reduction, n (%)",                 "13 (24.1)",        "5 (9.6)",
  "  — Hb increase >2 g/dL vs prior dose",    "10 (18.5)",        "2 (3.8)"
)

expo %>%
gt(rowname_col = "Metric") %>%
tab_header(
  title = "MAXILUS: Treatment Exposure & Disposition",
  subtitle = "Data cutoff October 1, 2025"
) %>%
cols_label(
  `ESA-naïve` = md("**ESA-naïve** (n=54)"),
  `ESA-R/R/I` = md("**ESA-R/R/I** (n=52)")
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_column_labels()
) %>%
tab_footnote(
  footnote = "In the ESA-naïve cohort, dose delays were driven predominantly by predose Hb ≥12 g/dL (33.3%) and reductions by Hb rise >2 g/dL (18.5%) — i.e., managing efficacy, not toxicity.",
  locations = cells_column_labels(columns = `ESA-naïve`)
) %>%
tab_source_note(md("Della Porta MG, et al. EHA 2026 [Abstract S173]."))

Table 8.10: MAXILUS: Treatment Exposure and Disposition (all-treated population)

MAXILUS: Treatment Exposure & Disposition
Data cutoff October 1, 2025
	ESA-naïve (n=54)1	ESA-R/R/I (n=52)
Median (range) treatment exposure, weeks	47.3 (10.7–71.1)	43.4 (2.1–97.1)
On active treatment at cutoff, n (%)	38 (70.4)	20 (38.5)
Completed treatment, n (%)	1 (1.9)	1 (1.9)
Discontinued treatment, n (%)	15 (27.8)	31 (59.6)
— Lack of efficacy	6 (11.1)	12 (23.1)
— Withdrawal by patient	3 (5.6)	5 (9.6)
— Progressive disease	2 (3.7)	4 (7.7)
— Adverse event	2 (3.7)	1 (1.9)
— Death	1 (1.9)	3 (5.8)
≥1 dose delay, n (%)	28 (51.9)	22 (42.3)
— Predose Hb ≥12 g/dL	18 (33.3)	2 (3.8)
≥1 dose reduction, n (%)	13 (24.1)	5 (9.6)
— Hb increase >2 g/dL vs prior dose	10 (18.5)	2 (3.8)
1 In the ESA-naïve cohort, dose delays were driven predominantly by predose Hb ≥12 g/dL (33.3%) and reductions by Hb rise >2 g/dL (18.5%) — i.e., managing efficacy, not toxicity.
Della Porta MG, et al. EHA 2026 [Abstract S173].

8.5.4.8 Safety

safety <- tribble(
  ~Event, ~`ESA-naïve (n=54)`, ~`ESA-R/R/I (n=52)`,
  "Any-grade TEAE", "94.4%", "100%",
  "  — Treatment-related", "29.6%", "46.2%",
  "Grade 3/4 TEAE", "68.5%", "53.8%",
  "  — Treatment-related", "11.1%", "7.7%",
  "Grade 5 TEAE", "0", "7.7% (n=4)",
  "  — Treatment-related", "0", "0",
  "Serious TEAE", "40.7%", "38.5%",
  "  — Treatment-related", "1.9%", "0",
  "TEAE → drug interruption", "20.4%", "21.2%",
  "TEAE → dose reduction", "3.7%", "1.9%",
  "TEAE → drug withdrawn", "9.3%", "3.8%",
  "Progression to HR-MDS", "3.7%", "3.8%",
  "Progression to AML", "0", "0"
)

safety %>%
gt(rowname_col = "Event") %>%
tab_header(title = "MAXILUS: Safety Summary", subtitle = "Data cutoff October 1, 2025") %>%
cols_label(
  `ESA-naïve (n=54)` = md("**ESA-naïve (n=54)**"),
  `ESA-R/R/I (n=52)` = md("**ESA-R/R/I (n=52)**")
) %>%
tab_style(
  style = list(cell_fill(color = pal$green_lt), cell_text(weight = "bold")),
  locations = cells_body(
    columns = everything(),
    rows = Event == "Progression to AML"
  )
) %>%
tab_footnote(
  footnote = "Grade 5 TEAEs (ESA-R/R/I, n=4): death, general physical health deterioration, nasopharyngeal cancer, and pancytopenia (n=1 each); none treatment-related.",
  locations = cells_body(columns = `ESA-R/R/I (n=52)`, rows = Event == "Grade 5 TEAE")
) %>%
tab_source_note(md("Della Porta MG, et al. EHA 2026 [Abstract S173]."))

Table 8.11: MAXILUS Safety Overview by Cohort (all-treated population)

MAXILUS: Safety Summary
Data cutoff October 1, 2025
	ESA-naïve (n=54)	ESA-R/R/I (n=52)
Any-grade TEAE	94.4%	100%
— Treatment-related	29.6%	46.2%
Grade 3/4 TEAE	68.5%	53.8%
— Treatment-related	11.1%	7.7%
Grade 5 TEAE	0	7.7% (n=4)1
— Treatment-related	0	0
Serious TEAE	40.7%	38.5%
— Treatment-related	1.9%	0
TEAE → drug interruption	20.4%	21.2%
TEAE → dose reduction	3.7%	1.9%
TEAE → drug withdrawn	9.3%	3.8%
Progression to HR-MDS	3.7%	3.8%
Progression to AML	0	0
1 Grade 5 TEAEs (ESA-R/R/I, n=4): death, general physical health deterioration, nasopharyngeal cancer, and pancytopenia (n=1 each); none treatment-related.
Della Porta MG, et al. EHA 2026 [Abstract S173].

eoi <- tribble(
  ~EOI, ~`ESA-naïve (n=54)`, ~`ESA-R/R/I (n=52)`,
  "≥1 treatment-emergent EOI", "48.1%", "65.4%",
  "Asthenia (incl. fatigue)",  "16.7%", "36.5%",
  "Fractures",                 "13.0%", "11.5%",
  "Hypertension",              "11.1%", "13.5%",
  "Kidney toxicity",           "11.1%", "11.5%",
  "Malignancies",              "5.6%",  "5.8%",
  "Premalignant disorders",    "5.6%",  "1.9%"
)

eoi %>%
gt(rowname_col = "EOI") %>%
tab_header(title = "MAXILUS: Events of Interest", subtitle = "Data cutoff October 1, 2025") %>%
cols_label(
  `ESA-naïve (n=54)` = md("**ESA-naïve (n=54)**"),
  `ESA-R/R/I (n=52)` = md("**ESA-R/R/I (n=52)**")
) %>%
tab_style(
  style = list(cell_fill(color = pal$amber_lt), cell_text(weight = "bold")),
  locations = cells_body(columns = everything(), rows = EOI == "≥1 treatment-emergent EOI")
) %>%
tab_footnote(
  footnote = "No patients experienced EOIs of extramedullary hematopoiesis; no thromboembolic EOIs were listed among reported events of interest.",
  locations = cells_column_labels(columns = `ESA-naïve (n=54)`)
) %>%
tab_source_note(md("Della Porta MG, et al. EHA 2026 [Abstract S173]."))

Table 8.12: MAXILUS: Treatment-Emergent Events of Interest (EOI)

MAXILUS: Events of Interest
Data cutoff October 1, 2025
	ESA-naïve (n=54)1	ESA-R/R/I (n=52)
≥1 treatment-emergent EOI	48.1%	65.4%
Asthenia (incl. fatigue)	16.7%	36.5%
Fractures	13.0%	11.5%
Hypertension	11.1%	13.5%
Kidney toxicity	11.1%	11.5%
Malignancies	5.6%	5.8%
Premalignant disorders	5.6%	1.9%
1 No patients experienced EOIs of extramedullary hematopoiesis; no thromboembolic EOIs were listed among reported events of interest.
Della Porta MG, et al. EHA 2026 [Abstract S173].

8.5.4.9 Key Takeaways

• No titration needed: Initiating at 1.75 mg/kg from C1 was feasible and well-tolerated; the majority of responders reached transfusion independence with onset as early as Cycle 1 (ESA-naïve 86.4%; ESA-R/R/I 68.8%)
• ESA-naïve primary endpoint 81.5% (44/54); ESA-R/R/I 61.5% (32/52)
• Activity in RS− disease: ESA-naïve RS− patients achieved 73.9% RBC-TI ≥12 weeks (RS+ 77.4%); ESA-R/R/I RS− 36.8%
• sEPO ≤200 IU/L subgroup had the highest responses (ESA-naïve 86.0%; ESA-R/R/I 56.5%)
• Durability: median duration of RBC-TI ≥12 wks not reached in ESA-naïve (overall and subgroups); 59.6 weeks overall in ESA-R/R/I
• No progression to AML in either cohort; low discontinuation; grade 3/4 treatment-related TEAEs 11.1% / 7.7%
• Dose delays/reductions in the ESA-naïve cohort were driven mainly by Hb rising too high (predose Hb ≥12 g/dL, Hb rise >2 g/dL), not toxicity
• Primary analysis complete (cutoff Oct 1, 2025); extension phase (up to 2 years) ongoing

8.5.5 ELEMENT Trial

8.5.6 Real-world treatment patterns and outcomes of first-line luspatercept in patients with myelodysplastic syndromes in the United States

• Sekeres et.al presented the data at (Sekeres et al. 2025)ASH 2025

8.5.7 ESAs + Luspatercept

• (Komrokji et al. 2023)Data from the Moffitt group

8.6 Imetelstat

Imetelstat (RYTELO) is a first-in-class oligonucleotide telomerase (hTERT) competitive inhibitor. FDA-approved June 2024 for adults with low- to intermediate-1 risk MDS with TD anemia requiring ≥4 RBC units/8 weeks who are ESA-R/R or ESA-ineligible.

8.6.1 IMerge Phase 3 Trial

imerge <- tribble(
  ~Endpoint, ~Imetelstat, ~Placebo, ~P_value, ~Order,
  "TI ≥8 weeks\n(Primary)", 39.8, 15.0, "<0.001", 1,
  "TI ≥24 weeks\n(Key Secondary)", 28.0, 3.3, "<0.001", 2,
  "TI ≥1 year\n(Post hoc)", 13.6, 1.7, "—", 3,
  "HI-E per IWG\n(Key Secondary)", 63.6, 51.7, "NS", 4
)

imerge_long <- imerge %>%
  pivot_longer(c(Imetelstat, Placebo), names_to = "Arm", values_to = "Rate") %>%
  mutate(Arm = factor(Arm, levels = c("Imetelstat", "Placebo")))

ggplot(imerge_long, aes(x = reorder(Endpoint, -Order), y = Rate, fill = Arm)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.6) +
  geom_text(
    aes(label = paste0(Rate, "%")),
    position = position_dodge(width = 0.7),
    vjust = -0.4, fontface = "bold", size = 3.8
  ) +
  scale_fill_manual(
    values = c("Imetelstat" = pal$purple, "Placebo" = pal$slate),
    name = NULL
  ) +
  scale_y_continuous(limits = c(0, 80), labels = \(x) paste0(x, "%")) +
  coord_flip() +
  labs(
    title = "IMerge Phase 3: Imetelstat vs. Placebo in ESA-R/R LR-MDS",
    subtitle = "N = 178 (2:1 randomization); Median follow-up 19.5 months",
    x = NULL, y = "Patients (%)",
    caption = "Platzbecker U, et al. Lancet. 2024;403:249-260. Kim et al. JCO 2025."
  ) +
  theme_mds() +
  theme(panel.grid.major.y = element_blank(), panel.grid.major.x = element_line())

Figure 8.8: IMerge Phase 3: Key Efficacy Results

8.6.2 Disease-Modifying Evidence

Unlike ESAs and luspatercept, imetelstat demonstrates disease-modifying activity:

dm_evidence <- tribble(
  ~Evidence, ~Detail,
  "VAF Reduction", ">50% decrease in variant allele frequency of SF3B1, TET2, DNMT3A in long-term responders",
  "Cytogenetic Response", "~35% of treated patients achieve cytogenetic complete response",
  "Hemoglobin Normalization", "Responders achieving ≥1-year TI reach median Hb of 13.1 g/dL",
  "Durable TI", "18% of all treated patients achieved TI ≥1 year; median DoR 51.6 weeks for ≥8-wk responders",
  "Activity Post-Prior Therapies", "Effective after ESAs, luspatercept, lenalidomide, and HMAs"
)

dm_evidence %>%
gt(rowname_col = "Evidence") %>%
tab_header(
  title = "Imetelstat: Disease-Modifying Evidence from IMerge",
  subtitle = "Unique among LR-MDS therapies"
) %>%
cols_label(Detail = md("**Finding**")) %>%
tab_style(
  style = cell_fill(color = pal$purple_lt),
  locations = cells_body()
) %>%
tab_source_note(md("Santini V. *Clin Adv Hematol Oncol*. 2025;23(7):432-434. Kim et al. *JCO*. 2025."))

Table 8.13: Evidence for Disease-Modifying Effects of Imetelstat

Imetelstat: Disease-Modifying Evidence from IMerge
Unique among LR-MDS therapies
	Finding
VAF Reduction	>50% decrease in variant allele frequency of SF3B1, TET2, DNMT3A in long-term responders
Cytogenetic Response	~35% of treated patients achieve cytogenetic complete response
Hemoglobin Normalization	Responders achieving ≥1-year TI reach median Hb of 13.1 g/dL
Durable TI	18% of all treated patients achieved TI ≥1 year; median DoR 51.6 weeks for ≥8-wk responders
Activity Post-Prior Therapies	Effective after ESAs, luspatercept, lenalidomide, and HMAs
Santini V. Clin Adv Hematol Oncol. 2025;23(7):432-434. Kim et al. JCO. 2025.

8.6.3 Safety Profile

safety_imerge <- tribble(
  ~AE, ~Imetelstat_any, ~Imetelstat_g34, ~Placebo_any, ~Placebo_g34,
  "Thrombocytopenia", 97, 65, 34, 8,
  "Neutropenia", 92, 72, 47, 7,
  "Fatigue", 29, 0, 20, 3.4,
  "Infections (any)", 47, 11, 34, 14,
  "Arthralgia/Myalgia", 25, 2.5, 19, 5,
  "Hemorrhage", 22, 2.5, 12, 1.7
)

safety_long <- safety_imerge %>%
  select(AE, Imetelstat_g34, Placebo_g34) %>%
  pivot_longer(-AE, names_to = "Arm", values_to = "Rate") %>%
  mutate(
    Arm = case_when(
      Arm == "Imetelstat_g34" ~ "Imetelstat (Grade 3/4)",
      Arm == "Placebo_g34"    ~ "Placebo (Grade 3/4)"
    ),
    AE = factor(AE, levels = rev(safety_imerge$AE))
  )

ggplot(safety_long, aes(x = AE, y = Rate, fill = Arm)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.6) +
  geom_text(
    aes(label = paste0(Rate, "%")),
    position = position_dodge(width = 0.7),
    hjust = -0.2, size = 3.2
  ) +
  coord_flip() +
  scale_fill_manual(
    values = c("Imetelstat (Grade 3/4)" = pal$red, "Placebo (Grade 3/4)" = pal$slate),
    name = NULL
  ) +
  scale_y_continuous(limits = c(0, 90), labels = \(x) paste0(x, "%")) +
  labs(
    title = "IMerge: Grade 3/4 Adverse Events",
    subtitle = "Cytopenias are the predominant safety signal; reversible and manageable",
    x = NULL, y = "Patients (%)",
    caption = "Kim et al. JCO 2025. ODAC voted 12:2 that benefits outweigh risks."
  ) +
  theme_mds() +
  theme(panel.grid.major.y = element_blank(), panel.grid.major.x = element_line())

Figure 8.9: IMerge: Key Safety Signals — Imetelstat vs. Placebo

TipPractical Imetelstat Management (per Dr. Santini)

• Treatment duration: Administer for at least 6 months before judging response
• Monitoring: Blood counts, Hb, and transfusion number; bone marrow only if loss of response/progression suspected
• Cytopenia management: Dose interruptions and reductions; cytopenias generally short-lived (median 1.9 weeks for grade 3/4 thrombocytopenia)
• Best candidates: TD patients, especially RS-negative and high transfusion burden; avoid if severe pre-existing cytopenias (ANC <1.5 × 10⁹/L, Plt <75 × 10⁹/L)

8.6.4 Mutations Predicting Response

Preclinical studies suggest that mutations in TET2, RAD21, and NRAS may be associated with preferential response to imetelstat. In long-term responders, parallel decreases in VAF of SF3B1, TET2, and DNMT3A provide evidence of clonal suppression.

8.6.5 Pharmacokinetics and Renal Clearance

Imetelstat is metabolized by tissue nucleases (not hepatic CYP enzymes) and excreted renally as fragments. Dose adjustments are not warranted for mild/moderate hepatic or renal impairment.

---

8.7 Elritercept (KER-050)

• An Emerging TGF-β Pathway Agent

NoteNew Section — Based on Advisory Board Insights

This section incorporates data from the Takeda Global Advisory Board on elritercept development in LR-MDS.

Elritercept is a ligand trap targeting downstream TGF-β superfamily signaling, specifically blocking Activin A, Activin E, GDF8, and GDF11. Its key mechanistic distinction from luspatercept is that it does not bind Activin B in the same manner and may affect both early and late hematopoietic differentiation (vs. luspatercept’s primarily late-stage activity).

8.7.1 Mechanism of Action Comparison

moa <- tribble(
  ~Feature, ~Luspatercept, ~Elritercept,
  "Target pathway", "TGF-β / ActRIIB ligand trap", "TGF-β superfamily ligand trap",
  "Key ligands trapped", "Activin A, Activin B, GDF11, GDF8", "Activin A, Activin E, GDF8, GDF11",
  "Activin B binding", "Yes", "No (key differentiator)",
  "Stage of differentiation", "Primarily late-stage erythroid maturation", "Early + late hematopoietic differentiation",
  "Multilineage potential", "Primarily erythroid", "Erythroid + neutrophil + platelet (non-myelotoxic)",
  "Muscle mass hypothesis", "No effect (ACE536 mouse data)", "Positive effect (preclinical + NHP data via Activin A blockade)",
  "Route / Schedule", "SC Q3W", "SC Q4W",
  "Disease modification", "No evidence of VAF reduction", "Unknown (no evidence yet)"
)

moa %>%
gt(rowname_col = "Feature") %>%
tab_header(
  title = "Mechanistic Comparison: Luspatercept vs. Elritercept",
  subtitle = "Different ligand binding profiles may yield distinct clinical effects"
) %>%
cols_label(
  Luspatercept = md("**Luspatercept**<br>(Reblozyl)"),
  Elritercept = md("**Elritercept**<br>(KER-050)")
) %>%
tab_style(
  style = cell_fill(color = pal$amber_lt),
  locations = cells_body(
    rows = Feature %in% c("Activin B binding", "Muscle mass hypothesis", "Stage of differentiation")
  )
) %>%
tab_source_note("Based on Takeda advisory board data and preclinical/NHP studies.")

Table 8.14: Mechanism of Action: Luspatercept vs. Elritercept

Mechanistic Comparison: Luspatercept vs. Elritercept
Different ligand binding profiles may yield distinct clinical effects
	Luspatercept (Reblozyl)	Elritercept (KER-050)
Target pathway	TGF-β / ActRIIB ligand trap	TGF-β superfamily ligand trap
Key ligands trapped	Activin A, Activin B, GDF11, GDF8	Activin A, Activin E, GDF8, GDF11
Activin B binding	Yes	No (key differentiator)
Stage of differentiation	Primarily late-stage erythroid maturation	Early + late hematopoietic differentiation
Multilineage potential	Primarily erythroid	Erythroid + neutrophil + platelet (non-myelotoxic)
Muscle mass hypothesis	No effect (ACE536 mouse data)	Positive effect (preclinical + NHP data via Activin A blockade)
Route / Schedule	SC Q3W	SC Q4W
Disease modification	No evidence of VAF reduction	Unknown (no evidence yet)
Based on Takeda advisory board data and preclinical/NHP studies.

8.7.2 CARE-050 MD201 Phase 2 Data

elri_eff <- tribble(
  ~Population, ~`TI Rate`, ~Notes,
  "All patients (ITT)", "47.5%", "N = 96 at RP2D",
  "High transfusion burden", "37.2%", "Clinically meaningful in this difficult-to-treat group",
  "EPO <500, RS-positive", "48.9%", "Comparable to MEDALIST luspatercept rates",
  "EPO <500, RS-negative", "43.8%", "Potentially higher than luspatercept in RS-neg",
  "EPO >500 (n=17)", "~6%", "Near-absence of response → excluded from Phase 3"
)

elri_eff %>%
gt(rowname_col = "Population") %>%
tab_header(
  title = "Elritercept Phase 2: Transfusion Independence Rates",
  subtitle = "CARE-050 MD201 (Recommended Phase 2 Dose)"
) %>%
cols_label(
  `TI Rate` = md("**TI Rate**"),
  Notes = md("**Clinical Context**")
) %>%
tab_style(
  style = cell_fill(color = pal$green_lt),
  locations = cells_body(rows = Population == "EPO <500, RS-negative")
) %>%
tab_style(
  style = cell_fill(color = pal$red_lt),
  locations = cells_body(rows = Population == "EPO >500 (n=17)")
) %>%
tab_source_note("Felnar L. Takeda Advisory Board Presentation 2025.")

Table 8.15: Elritercept Phase 2 (CARE-050 MD201): Efficacy by Subgroup

Elritercept Phase 2: Transfusion Independence Rates
CARE-050 MD201 (Recommended Phase 2 Dose)
	TI Rate	Clinical Context
All patients (ITT)	47.5%	N = 96 at RP2D
High transfusion burden	37.2%	Clinically meaningful in this difficult-to-treat group
EPO <500, RS-positive	48.9%	Comparable to MEDALIST luspatercept rates
EPO <500, RS-negative	43.8%	Potentially higher than luspatercept in RS-neg
EPO >500 (n=17)	~6%	Near-absence of response → excluded from Phase 3
Felnar L. Takeda Advisory Board Presentation 2025.

8.7.3 Phase 3 Development Program

elri_trials <- tribble(
  ~Study, ~Setting, ~Design, ~`Primary EP`, ~Status,
  "RENEW", "2L (post-ESA or sEPO 200–500)", "RCT 2:1 vs placebo", "TI ≥8 wk (wk 1-24)", ">40 enrolled; >100 screened",
  "ARISE MDS", "1L (mirrors COMMANDS)", "RCT 1:1 vs ESA", "TI ≥12 wk + Hb ≥1.5 g/dL", "First US sites activated",
  "Cohort G", "Post-luspatercept (1L luspa failure)", "Single-arm (within CARE-050)", "Efficacy/safety", "First US site active; enrollment imminent",
  "Japan Bridging", "Phase 2", "Bridging study", "Safety/efficacy", "Newly initiated"
)

elri_trials %>%
gt(rowname_col = "Study") %>%
tab_header(
  title = "Elritercept Clinical Development Program",
  subtitle = "Spanning first-line through post-luspatercept settings"
) %>%
cols_label(
  Setting = "Population", Design = "Design",
  `Primary EP` = "Primary Endpoint", Status = "Current Status"
) %>%
tab_style(
  style = list(cell_fill(color = pal$amber_lt), cell_text(weight = "bold")),
  locations = cells_body(rows = Study == "Cohort G")
)

Table 8.16: Elritercept Phase 3 Development in LR-MDS

Elritercept Clinical Development Program
Spanning first-line through post-luspatercept settings
	Population	Design	Primary Endpoint	Current Status
RENEW	2L (post-ESA or sEPO 200–500)	RCT 2:1 vs placebo	TI ≥8 wk (wk 1-24)	>40 enrolled; >100 screened
ARISE MDS	1L (mirrors COMMANDS)	RCT 1:1 vs ESA	TI ≥12 wk + Hb ≥1.5 g/dL	First US sites activated
Cohort G	Post-luspatercept (1L luspa failure)	Single-arm (within CARE-050)	Efficacy/safety	First US site active; enrollment imminent
Japan Bridging	Phase 2	Bridging study	Safety/efficacy	Newly initiated

8.7.4 Potential Differentiators from Luspatercept

diff_data <- tribble(
  ~Area, ~Strength, ~Category,
  "RS-Negative Activity", 4, "Efficacy",
  "Muscle Mass / Physical\nFunction (Activin A)", 3, "Novel MOA",
  "Fatigue Profile\n(no drug-induced fatigue)", 3, "Tolerability",
  "Multilineage Effects\n(Plt + ANC improvement)", 2, "Efficacy",
  "Post-Luspatercept\nSetting", 4, "Unmet Need",
  "Time to Response\n(Hb changes within 2 wk)", 2, "Speed"
)
diff_data$Area <- factor(diff_data$Area, levels = rev(diff_data$Area))

ggplot(diff_data, aes(x = Area, y = Strength, fill = Category)) +
  geom_col(width = 0.55) +
  coord_flip() +
  scale_fill_manual(values = c(
    "Efficacy" = pal$teal, "Novel MOA" = pal$purple,
    "Tolerability" = pal$green, "Unmet Need" = pal$amber,
    "Speed" = pal$blue
  )) +
  scale_y_continuous(limits = c(0, 5), breaks = 1:4,
                     labels = c("Preliminary", "Emerging", "Supportive", "Strong")) +
  labs(
    title = "Elritercept: Potential Differentiators from Luspatercept",
    subtitle = "Advisory board consensus on strength of available evidence",
    x = NULL, y = "Evidence Strength",
    caption = "Based on expert advisor assessments. Takeda Global Advisory Board 2025."
  ) +
  theme_mds()

Figure 8.10: Potential Differentiation Points for Elritercept vs. Luspatercept

---

8.8 Comprehensive Treatment Comparison

comparison <- tribble(
  ~Feature, ~ESAs, ~Luspatercept, ~Imetelstat, ~Elritercept,
  "Mechanism", "EPO receptor agonism", "ActRIIB ligand trap\n(GDF11/GDF8 → SMAD2/3)", "Telomerase (hTERT)\ninhibitor", "TGF-β ligand trap\n(ActA/GDF8/GDF11)",
  "Route / Schedule", "SC weekly/Q2W", "SC Q3W", "IV Q4W", "SC Q4W",
  "1L TI Rate (TD pts)", "~35% (TI≥12wk)", "59% (TI≥12wk + Hb≥1.5)", "Not studied in 1L", "Phase 3 ongoing (ARISE)",
  "2L TI Rate", "Limited; short-lived", "38% (TI≥8wk; MEDALIST)", "40% (TI≥8wk; IMerge)", "47.5% (Phase 2)",
  "Activity in RS-negative", "Good (primary response)", "Comparable rate; longer DoR", "40% of pts were RS-neg;\nequally active", "43.8% TI (RS-neg, Phase 2)",
  "High TB efficacy", "Poor (>4U/8wk)", "Limited >6U/8wk", "45% TI in RS+ with >6U", "37.2% TI",
  "Disease modification", "No", "No", "Yes (VAF reduction,\ncytogenetic response)", "No evidence",
  "Key toxicities", "Hypertension, TE events", "Fatigue, peripheral edema,\nhypertension", "Cytopenias (Plt 65%,\nANC 72% Grade 3/4)", "Diarrhea, fatigue\n(no drug-induced fatigue signal)",
  "Post-luspatercept", "Modest/short-lived (~3-4mo)", "N/A", "Active (IMerge subgroup)", "Cohort G ongoing",
  "FDA Status", "Not FDA-approved for MDS (US)", "Approved (1L + 2L RS+)", "Approved (2L/3L)", "Phase 2/3"
)

comparison %>%
gt(rowname_col = "Feature") %>%
tab_header(
  title = "Head-to-Head Comparison: LR-MDS Anemia Therapies",
  subtitle = "Approved agents and emerging data"
) %>%
cols_label(
  ESAs = md("**ESAs**"),
  Luspatercept = md("**Luspatercept**"),
  Imetelstat = md("**Imetelstat**"),
  Elritercept = md("**Elritercept**")
) %>%
tab_style(
  style = cell_fill(color = pal$teal_lt),
  locations = cells_body(columns = Luspatercept)
) %>%
tab_style(
  style = cell_fill(color = pal$purple_lt),
  locations = cells_body(columns = Imetelstat)
) %>%
tab_style(
  style = cell_fill(color = pal$amber_lt),
  locations = cells_body(columns = Elritercept)
) %>%
tab_style(
  style = cell_text(size = "small"),
  locations = cells_body()
)

Table 8.17: Comprehensive Comparison of Approved and Emerging Therapies for LR-MDS Anemia

Head-to-Head Comparison: LR-MDS Anemia Therapies
Approved agents and emerging data
	ESAs	Luspatercept	Imetelstat	Elritercept
Mechanism	EPO receptor agonism	ActRIIB ligand trap (GDF11/GDF8 → SMAD2/3)	Telomerase (hTERT) inhibitor	TGF-β ligand trap (ActA/GDF8/GDF11)
Route / Schedule	SC weekly/Q2W	SC Q3W	IV Q4W	SC Q4W
1L TI Rate (TD pts)	~35% (TI≥12wk)	59% (TI≥12wk + Hb≥1.5)	Not studied in 1L	Phase 3 ongoing (ARISE)
2L TI Rate	Limited; short-lived	38% (TI≥8wk; MEDALIST)	40% (TI≥8wk; IMerge)	47.5% (Phase 2)
Activity in RS-negative	Good (primary response)	Comparable rate; longer DoR	40% of pts were RS-neg; equally active	43.8% TI (RS-neg, Phase 2)
High TB efficacy	Poor (>4U/8wk)	Limited >6U/8wk	45% TI in RS+ with >6U	37.2% TI
Disease modification	No	No	Yes (VAF reduction, cytogenetic response)	No evidence
Key toxicities	Hypertension, TE events	Fatigue, peripheral edema, hypertension	Cytopenias (Plt 65%, ANC 72% Grade 3/4)	Diarrhea, fatigue (no drug-induced fatigue signal)
Post-luspatercept	Modest/short-lived (~3-4mo)	N/A	Active (IMerge subgroup)	Cohort G ongoing
FDA Status	Not FDA-approved for MDS (US)	Approved (1L + 2L RS+)	Approved (2L/3L)	Phase 2/3

---

8.9 Lenalidomide

Lenalidomide is the standard of care for transfusion-dependent (TD) MDS-del(5q), where it induced RBC-transfusion independence (RBC-TI) ≥8 weeks in ~67% of patients in the pivotal MDS-003 study (list2006?) and secured FDA/EMA approval on the basis of the phase 3 MDS-004 trial (fenaux2011?). The SintraREV trial extends this evidence to the non-transfusion-dependent setting, asking whether earlier intervention at a lower dose can delay the onset of transfusion dependence rather than treat it once established.

8.9.1 Sintra-REV Trial

SintraREV is the first randomised, double-blind, placebo-controlled phase 3 trial of lenalidomide in non-TD low-risk del(5q) MDS (diezcampelo2024?). Conducted at 22 university hospitals in Spain, France, and Germany (enrolment 2010–2018), it randomised 61 patients 2:1 to lenalidomide 5 mg/day continuously (d1–28 of a 28-day cycle) versus placebo for 2 years, followed by 2 years of surveillance (extended to March 2022). Eligibility required IPSS low- or intermediate-1-risk del(5q) MDS with symptomatic anaemia (baseline Hb ≤12 g/dL) but no prior RBC transfusions for MDS; patients were ESA-naïve (ESAs were not permitted on study) with ECOG ≤2, and were excluded for grade 4 neutropenia, platelets <25 ×10⁹/L, or prior MDS therapy. The primary endpoint was time to transfusion dependence from the start of treatment (TD defined as ≥2 RBC units every 56 days for ≥112 days for symptomatic anaemia), adjudicated by blinded independent central review; secondary endpoints included erythroid (HI-E) and cytogenetic response (IWG 2006), OS, EFS, AML transformation, time to TD from diagnosis, and safety, with a 65-gene NGS panel applied post hoc (Table 8.18).

library(gt)
library(tibble)

sintrarev_baseline <- tribble(
  ~Characteristic,                          ~Value,
  "Design",                                 "Phase 3, randomised (2:1), double-blind, placebo-controlled",
  "Arms",                                   "Lenalidomide 5 mg/day continuous vs placebo × 2 yr",
  "Patients (ITT / safety)",                "61 (40 LEN / 21 placebo) / 59",
  "Median age, yr (IQR)",                   "72.2 (65.4–81.9)",
  "Female",                                 "82%",
  "Median time from diagnosis, mo (IQR)",   "3.6 (1.0–12.1)",
  "Median haemoglobin, g/dL (IQR)",         "9.8 (9.1–10.5)",
  "IPSS risk (low / int-1)",                "70% / 30%",
  "Isolated del(5q) / del(5q) + 1",         "89% / 11%",
  "SF3B1 / TP53 mutated (NGS, n = 47)",     "26% / 21% (all monoallelic)",
  "Median follow-up, mo (IQR)",             "60.6 (32.1–73.9)"
) |>
  gt() |>
  tab_options(table.font.size = px(13)) |>
  cols_label(Characteristic = "", Value = "")

sintrarev_baseline

Table 8.18: SintraREV design and baseline characteristics (ITT population, n = 61).

Design	Phase 3, randomised (2:1), double-blind, placebo-controlled
Arms	Lenalidomide 5 mg/day continuous vs placebo × 2 yr
Patients (ITT / safety)	61 (40 LEN / 21 placebo) / 59
Median age, yr (IQR)	72.2 (65.4–81.9)
Female	82%
Median time from diagnosis, mo (IQR)	3.6 (1.0–12.1)
Median haemoglobin, g/dL (IQR)	9.8 (9.1–10.5)
IPSS risk (low / int-1)	70% / 30%
Isolated del(5q) / del(5q) + 1	89% / 11%
SF3B1 / TP53 mutated (NGS, n = 47)	26% / 21% (all monoallelic)
Median follow-up, mo (IQR)	60.6 (32.1–73.9)

Early low-dose lenalidomide met the primary endpoint: only 25% (10/40) of the lenalidomide arm developed transfusion dependence versus 65% (13/21) of the placebo arm, with median time to TD not reached versus 11.6 months — a 69.8% reduction in risk (HR 0.302, 95% CI 0.132–0.692; p=0.0046), reproduced in the evaluable population (HR 0.240, 95% CI 0.097–0.590) and when measured from diagnosis (NR vs 25.9 months; HR 0.306). Response quality was high and confined entirely to the lenalidomide arm — erythroid response in 70% (78% among evaluable patients, median Hb rise +2.7 g/dL) and cytogenetic response in 80% (94% among patients with ≥1 assessment; complete in 70%) — rates that exceed those historically reported in the transfusion-dependent MDS-003/004 trials despite the lower 5 mg dose. Event-free survival favoured lenalidomide (6.9 vs 1.7 yr; HR 0.466), but the trial was underpowered for the time-dependent endpoints: neither progression-free survival (HR 0.878) nor overall survival (HR 0.75) differed, and AML transformation was numerically lower with lenalidomide (15% vs 24%, NS). The contrast between the strong TD/EFS benefit and the null survival endpoints is summarised in Table 8.19 and Figure 8.11.

sintrarev_eff <- tribble(
  ~Endpoint,                              ~Lenalidomide,     ~Placebo,   ~Effect,
  "Developed transfusion dependence",     "10/40 (25%)",     "13/21 (65%)", "—",
  "Median time to TD (from treatment)",   "Not reached",     "11.6 mo",   "HR 0.302 (0.132–0.692); p=0.0046",
  "Median time to TD (from diagnosis)",   "Not reached",     "25.9 mo",   "HR 0.306 (0.132–0.708); p=0.0056",
  "Erythroid response (HI-E)ᵃ",           "28/40 (70%)",     "0",         "p<0.001",
  "Cytogenetic responseᵇ",                "32/40 (80%)",     "0",         "—",
  "Event-free survival (median)",         "6.9 yr",          "1.7 yr",    "HR 0.466 (0.22–0.95); p=0.032",
  "Progression-free survival (median)",   "6.8 yr",          "5.3 yr",    "HR 0.878 (0.389–1.984); p=0.75",
  "Overall survival (median)",            "8.4 yr",          "7.4 yr",    "HR 0.75 (0.311–1.82); p=0.53",
  "AML transformation",                   "6/40 (15%)",      "5/21 (24%)","NS"
) |>
  gt() |>
  tab_options(table.font.size = px(13)) |>
  tab_footnote("78% (28/36) among evaluable patients; median Hb rise +2.7 g/dL.",
               locations = cells_body(columns = Endpoint, rows = 4)) |>
  tab_footnote("Complete in 70% (28/40); 94% (32/34) among patients with ≥1 cytogenetic assessment.",
               locations = cells_body(columns = Endpoint, rows = 5)) |>
  cols_align(align = "left", columns = Endpoint)

sintrarev_eff

Table 8.19: SintraREV efficacy outcomes (ITT). HR < 1 favours lenalidomide.

Endpoint	Lenalidomide	Placebo	Effect
Developed transfusion dependence	10/40 (25%)	13/21 (65%)	—
Median time to TD (from treatment)	Not reached	11.6 mo	HR 0.302 (0.132–0.692); p=0.0046
Median time to TD (from diagnosis)	Not reached	25.9 mo	HR 0.306 (0.132–0.708); p=0.0056
Erythroid response (HI-E)ᵃ1	28/40 (70%)	0	p<0.001
Cytogenetic responseᵇ2	32/40 (80%)	0	—
Event-free survival (median)	6.9 yr	1.7 yr	HR 0.466 (0.22–0.95); p=0.032
Progression-free survival (median)	6.8 yr	5.3 yr	HR 0.878 (0.389–1.984); p=0.75
Overall survival (median)	8.4 yr	7.4 yr	HR 0.75 (0.311–1.82); p=0.53
AML transformation	6/40 (15%)	5/21 (24%)	NS
1 78% (28/36) among evaluable patients; median Hb rise +2.7 g/dL.
2 Complete in 70% (28/40); 94% (32/34) among patients with ≥1 cytogenetic assessment.

library(ggplot2)
library(ggtext)
library(dplyr)

forest <- tibble::tribble(
  ~endpoint,                          ~hr,    ~lo,    ~hi,     ~p,
  "Time to TD (from treatment)",      0.302,  0.132,  0.692,   0.0046,
  "Time to TD (evaluable)",           0.240,  0.097,  0.590,   0.0019,
  "Time to TD (from diagnosis)",      0.306,  0.132,  0.708,   0.0056,
  "Event-free survival",              0.466,  0.220,  0.950,   0.032,
  "Progression-free survival",        0.878,  0.389,  1.984,   0.75,
  "Overall survival",                 0.750,  0.311,  1.820,   0.53
) |>
  mutate(
    sig      = hi < 1,
    endpoint = factor(endpoint, levels = rev(endpoint)),
    label    = sprintf("%.2f (%.2f–%.2f)", hr, lo, hi)
  )

# Palette — swap for your project tokens if defined
col_sig <- "#2C7FB8"   # significant (favours lenalidomide)
col_ns  <- "#9AA0A6"   # non-significant

ggplot(forest, aes(x = hr, y = endpoint, colour = sig)) +
  geom_vline(xintercept = 1, linetype = "dashed", colour = "grey55") +
  geom_errorbarh(aes(xmin = lo, xmax = hi), height = 0.18, linewidth = 0.7) +
  geom_point(size = 3) +
  geom_text(aes(label = label), hjust = 0, nudge_y = 0.28, size = 3.1,
            colour = "grey25") +
  scale_x_log10(breaks = c(0.1, 0.25, 0.5, 1, 2),
                limits = c(0.08, 2.5)) +
  scale_colour_manual(values = c(`TRUE` = col_sig, `FALSE` = col_ns),
                      guide = "none") +
  annotate("richtext", x = 0.12, y = 0.55,
           label = "&larr; favours lenalidomide", size = 3, colour = "grey45",
           fill = NA, label.color = NA) +
  annotate("richtext", x = 1.9, y = 0.55,
           label = "favours placebo &rarr;", size = 3, colour = "grey45",
           fill = NA, label.color = NA) +
  labs(
    title    = "SintraREV: early low-dose lenalidomide in non-TD del(5q) LR-MDS",
    subtitle = "Hazard ratios with 95% CI &mdash; <span style='color:#2C7FB8'>**significant**</span> vs <span style='color:#9AA0A6'>**non-significant**</span>",
    x = "Hazard ratio (log scale)", y = NULL,
    caption  = "Díez-Campelo et al. Lancet Haematol 2024;11(9):e659–e670 (N=61)"
  ) +
  theme_minimal(base_size = 12) +          # replace with your custom theme_*()
  theme(
    plot.title       = element_text(face = "bold"),
    plot.subtitle    = element_markdown(),
    plot.caption     = element_text(hjust = 0, size = 8, colour = "grey40"),
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    plot.title.position = "plot"
  )

Figure 8.11: Hazard ratios for time-to-event endpoints in SintraREV. Points left of 1 favour lenalidomide; the strong effect on transfusion dependence and event-free survival is not matched by progression-free or overall survival.

Molecular subgroups reinforced the disease-modifying signal: all SF3B1-mutated patients on lenalidomide achieved both erythroid and cytogenetic responses (though with shorter median cytogenetic response duration, 12.0 vs 24.6 months), and TP53-mutated patients — despite lower response rates (erythroid 40%, cytogenetic 60%) — showed reductions in variant allele frequency (median −10%) rather than clonal expansion, with no clear excess of AML transformation attributable to early lenalidomide. Toxicity was consistent with the drug’s known profile and manageable at 5 mg: treatment-related AEs occurred in 87% versus 33%, dominated by myelosuppression, without treatment-related deaths (Table 8.20). Three patients (8%) discontinued for drug-related toxicity, and there was no increased signal for second malignancies over the follow-up period.

sintrarev_safety <- tribble(
  ~`Adverse event`,        ~`Lenalidomide, G3 / G4`,  ~`Placebo, G3 / G4`,
  "Neutropenia",           "45% / 3%",                "5% / 0",
  "Thrombocytopenia",      "5% / 0",                  "0 / 0",
  "Anaemia",               "3% / 0",                  "0 / 0",
  "Febrile neutropenia",   "3% / 0",                  "0 / 0",
  "Rash / erythema",       "3% / 0",                  "0 / 0",
  "Pulmonary embolism",    "3% / 0",                  "0 / 0"
) |>
  gt() |>
  tab_options(table.font.size = px(13)) |>
  tab_source_note(md(
    "Any treatment-related AE: **87% (LEN) vs 33% (placebo)**. Common all-grade LEN events: neutropenia 63%, rash 24%, constipation 18%, thrombocytopenia 18%. Serious AEs: 18 (LEN) vs 1 (placebo); no treatment-related deaths."
  )) |>
  cols_align(align = "left", columns = `Adverse event`)

sintrarev_safety

Table 8.20: SintraREV grade 3–4 treatment-related adverse events (safety population: LEN n = 38, placebo n = 21).

Adverse event	Lenalidomide, G3 / G4	Placebo, G3 / G4
Neutropenia	45% / 3%	5% / 0
Thrombocytopenia	5% / 0	0 / 0
Anaemia	3% / 0	0 / 0
Febrile neutropenia	3% / 0	0 / 0
Rash / erythema	3% / 0	0 / 0
Pulmonary embolism	3% / 0	0 / 0
Any treatment-related AE: 87% (LEN) vs 33% (placebo). Common all-grade LEN events: neutropenia 63%, rash 24%, constipation 18%, thrombocytopenia 18%. Serious AEs: 18 (LEN) vs 1 (placebo); no treatment-related deaths.

NotePositioning

SintraREV is the first prospective evidence that treating del(5q) anaemia before transfusion dependence — with a lower 5 mg dose — delays TD and delivers high-quality erythroid and cytogenetic responses without an AML or clonal-evolution penalty, including in TP53- and SF3B1-mutated disease. Key caveats for interpretation: the trial was small (n=61) and underpowered for OS/PFS (both null), quality of life was not formally assessed, ESAs were prohibited, and the non-TD indication remains off-label. It complements the ESA early-intervention data (EPO-PRETAR/Park) in supporting earlier action to delay transfusion dependence, while the durability and survival questions await larger studies.

---

8.10 TPO Agonists

Severe thrombocytopenia carries poor prognosis in MDS.

tpo <- tribble(
  ~Feature, ~Romiplostim, ~Eltrombopag,
  "Study design", "Phase 2 RCT vs. placebo", "Phase 2 RCT (EQOL-MDS)",
  "Population", "LR-MDS with Plt <20×10⁹/L or bleeding hx", "LR-MDS with thrombocytopenia",
  "Platelet response", "HI-Plt 36.5% vs 3.6%", "42.3% vs 11.1%",
  "Bleeding reduction", "Significant", "Significant (WHO grade ≥2)",
  "AML progression", "Trial stopped: transient blast increases", "No increased risk observed",
  "Long-term F/U", "5-year: similar OS, no increased AML risk\n(Kantarjian 2018)", "Requires regular LFT monitoring",
  "Status", "Not routinely used for MDS", "Limited to select patients"
)

tpo %>%
gt(rowname_col = "Feature") %>%
tab_header(
  title = "TPO Agonists for Thrombocytopenia in LR-MDS",
  subtitle = "Limited but emerging role"
) %>%
cols_label(
  Romiplostim = md("**Romiplostim**"),
  Eltrombopag = md("**Eltrombopag**")
) %>%
tab_style(
  style = cell_fill(color = pal$amber_lt),
  locations = cells_body(rows = Feature == "AML progression")
) %>%
tab_source_note(md("Giagounidis et al. 2014; Kantarjian et al. 2018; Oliva et al. 2017, 2023."))

Table 8.21: TPO Agonists in LR-MDS: Romiplostim and Eltrombopag

TPO Agonists for Thrombocytopenia in LR-MDS
Limited but emerging role
	Romiplostim	Eltrombopag
Study design	Phase 2 RCT vs. placebo	Phase 2 RCT (EQOL-MDS)
Population	LR-MDS with Plt <20×10⁹/L or bleeding hx	LR-MDS with thrombocytopenia
Platelet response	HI-Plt 36.5% vs 3.6%	42.3% vs 11.1%
Bleeding reduction	Significant	Significant (WHO grade ≥2)
AML progression	Trial stopped: transient blast increases	No increased risk observed
Long-term F/U	5-year: similar OS, no increased AML risk (Kantarjian 2018)	Requires regular LFT monitoring
Status	Not routinely used for MDS	Limited to select patients
Giagounidis et al. 2014; Kantarjian et al. 2018; Oliva et al. 2017, 2023.

---

8.11 HMAs in LR-MDS

• Deciatbeine 3 days vs Azacitidine 3 days (Sasaki et al. 2022)

• Azacitidine x5 days has better efficacy than azacitidine x3 days or decitabine x3 days.

8.12 Emerging Clinical Trials and Pipeline

pipeline <- tribble(
  ~Agent, ~Target, ~Phase, ~Mechanism, ~`Key Features`,
  "Elritercept (KER-050)", "TGF-β (Activin A/E/GDF8/GDF11)", "Phase 2/3", "Ligand trap (broader than luspatercept)", "Multilineage; muscle mass hypothesis; post-luspa cohort",
  "R289 (Rigel)", "IRAK 1/4 inhibitor", "Phase 1b", "Anti-inflammatory (targets innate immune pathway)", "Oral; mutation-agnostic; early Hb improvement data",
  "AG-946 / Tebapivat", "Pyruvate kinase activator", "Phase 2", "Enhances RBC glycolysis and survival", "Oral; targets RBC intrinsic metabolic defect",
  "Roxadustat (FibroGen)", "HIF-PH inhibitor", "Phase 3", "Stabilizes HIF → ↑ endogenous EPO + iron utilization", "Oral; TI observed in clinical use; well-designed Ph3 needed",
  "AND017", "Prolyl hydroxylase inhibitor", "Phase 1/2", "Small molecule PHD inhibitor", "Similar pathway to roxadustat",
  "Ofirnoflast (HT-6184)", "NEK7 inhibitor", "Phase 1", "NLRP3 inflammasome pathway modulation", "Anti-inflammatory approach to MDS",
  "Momelotinib", "JAK1/JAK2 + ACVR1", "Exploratory", "JAK inhibition + activin signaling", "Under investigation in Germany (per Prof. Goetz)"
)

pipeline %>%
gt(rowname_col = "Agent") %>%
tab_header(
  title = "Emerging Therapeutic Pipeline for Lower-Risk MDS",
  subtitle = "From anti-inflammatory to disease-modifying approaches"
) %>%
cols_label(
  Target = "Target", Phase = "Phase",
  Mechanism = "Mechanism", `Key Features` = "Differentiating Features"
) %>%
tab_style(
  style = list(cell_fill(color = pal$teal_lt), cell_text(weight = "bold")),
  locations = cells_body(rows = Agent == "Elritercept (KER-050)")
) %>%
tab_style(
  style = cell_fill(color = pal$blue_lt),
  locations = cells_body(rows = Agent %in% c("Roxadustat (FibroGen)", "R289 (Rigel)"))
) %>%
tab_source_note("McMahon et al. ASCO Educ Book 2025; Advisory Board expert perspectives.")

Table 8.22: Emerging Therapies in the LR-MDS Pipeline

Emerging Therapeutic Pipeline for Lower-Risk MDS
From anti-inflammatory to disease-modifying approaches
	Target	Phase	Mechanism	Differentiating Features
Elritercept (KER-050)	TGF-β (Activin A/E/GDF8/GDF11)	Phase 2/3	Ligand trap (broader than luspatercept)	Multilineage; muscle mass hypothesis; post-luspa cohort
R289 (Rigel)	IRAK 1/4 inhibitor	Phase 1b	Anti-inflammatory (targets innate immune pathway)	Oral; mutation-agnostic; early Hb improvement data
AG-946 / Tebapivat	Pyruvate kinase activator	Phase 2	Enhances RBC glycolysis and survival	Oral; targets RBC intrinsic metabolic defect
Roxadustat (FibroGen)	HIF-PH inhibitor	Phase 3	Stabilizes HIF → ↑ endogenous EPO + iron utilization	Oral; TI observed in clinical use; well-designed Ph3 needed
AND017	Prolyl hydroxylase inhibitor	Phase 1/2	Small molecule PHD inhibitor	Similar pathway to roxadustat
Ofirnoflast (HT-6184)	NEK7 inhibitor	Phase 1	NLRP3 inflammasome pathway modulation	Anti-inflammatory approach to MDS
Momelotinib	JAK1/JAK2 + ACVR1	Exploratory	JAK inhibition + activin signaling	Under investigation in Germany (per Prof. Goetz)
McMahon et al. ASCO Educ Book 2025; Advisory Board expert perspectives.

pipe_plot <- tribble(
  ~Agent, ~Phase_num, ~Category,
  "ESAs (Epoetin-α)", 5, "EPO Pathway",
  "Luspatercept", 5, "TGF-β/ActRIIB",
  "Imetelstat", 5, "Telomerase",
  "Lenalidomide", 5, "Immunomodulatory",
  "Elritercept", 3.3, "TGF-β/ActRIIB",
  "Roxadustat", 3, "HIF-PH",
  "R289", 1.5, "Anti-Inflammatory",
  "AG-946/Tebapivat", 2, "RBC Metabolism",
  "Ofirnoflast", 1, "NLRP3/NEK7",
  "AND017", 1.5, "HIF-PH",
  "Momelotinib", 0.5, "JAK/ACVR1"
)

ggplot(pipe_plot, aes(x = Phase_num, y = reorder(Agent, Phase_num), fill = Category)) +
  geom_point(shape = 21, size = 7, stroke = 1.2) +
  scale_x_continuous(
    breaks = c(0.5, 1, 1.5, 2, 3, 3.3, 5),
    labels = c("Preclinical", "Phase 1", "Phase 1b", "Phase 2", "Phase 3", "Phase 2/3", "Approved"),
    limits = c(0, 5.5)
  ) +
  scale_fill_brewer(palette = "Set2", name = "Mechanism Class") +
  labs(
    title = "LR-MDS Treatment Landscape: Development Stage",
    subtitle = "Pipeline agents span multiple mechanistic approaches",
    x = "Development Stage", y = NULL
  ) +
  theme_mds() +
  theme(panel.grid.major.y = element_line(linetype = "dotted", color = "grey80"))

Figure 8.12: LR-MDS Therapeutic Pipeline: Development Stage by Mechanism

---

8.13 Unmet Needs and Special Populations

needs <- tribble(
  ~Population, ~`Current Challenge`, ~`Potential Solutions`,
  "RS-negative patients (~80% of LR-MDS)", "Luspatercept TI rate ≈ ESA in RS-neg; benefit is in DoR", "Elritercept RS-neg enrichment (GFM study); imetelstat equally active",
  "High transfusion burden (≥6U/8wk)", "Luspatercept loses efficacy; ESAs ineffective", "Imetelstat: 45% TI in RS+ with high TB; elritercept: 37.2%",
  "Post-luspatercept failure", "Limited options; ESA post-luspa gives ~3-4mo response", "Imetelstat (active post-luspa); elritercept Cohort G; R289",
  "Del(5q) post-lenalidomide", "Excluded from luspatercept and imetelstat trials", "Elritercept under exploration; no standard 2L option",
  "Non-transfusion-dependent", "Most trials require TD; clinicians want to treat earlier", "ELEMENT trial; NTD studies under development",
  "MDS/MPN overlap (without thrombocytosis)", "No treatment options; excluded from trials", "Elritercept CMML cohort (limited); advocacy for inclusion"
)

needs %>%
gt(rowname_col = "Population") %>%
tab_header(
  title = "Unmet Needs in Lower-Risk MDS",
  subtitle = "Informing future clinical development"
) %>%
cols_label(
  `Current Challenge` = md("**Current Challenge**"),
  `Potential Solutions` = md("**Emerging Solutions**")
) %>%
tab_style(
  style = cell_fill(color = pal$red_lt),
  locations = cells_body(columns = `Current Challenge`)
) %>%
tab_style(
  style = cell_fill(color = pal$green_lt),
  locations = cells_body(columns = `Potential Solutions`)
)

Table 8.23: Key Unmet Needs in LR-MDS — Expert Consensus

Unmet Needs in Lower-Risk MDS
Informing future clinical development
	Current Challenge	Emerging Solutions
RS-negative patients (~80% of LR-MDS)	Luspatercept TI rate ≈ ESA in RS-neg; benefit is in DoR	Elritercept RS-neg enrichment (GFM study); imetelstat equally active
High transfusion burden (≥6U/8wk)	Luspatercept loses efficacy; ESAs ineffective	Imetelstat: 45% TI in RS+ with high TB; elritercept: 37.2%
Post-luspatercept failure	Limited options; ESA post-luspa gives ~3-4mo response	Imetelstat (active post-luspa); elritercept Cohort G; R289
Del(5q) post-lenalidomide	Excluded from luspatercept and imetelstat trials	Elritercept under exploration; no standard 2L option
Non-transfusion-dependent	Most trials require TD; clinicians want to treat earlier	ELEMENT trial; NTD studies under development
MDS/MPN overlap (without thrombocytosis)	No treatment options; excluded from trials	Elritercept CMML cohort (limited); advocacy for inclusion

---

8.14 Key Discussion Questions for Practice

questions <- tribble(
  ~Question, ~`Evidence-Based Guidance`,
  "When to start treatment?", "Initiate when Hb <10-10.5 g/dL or symptomatic, ideally before TD. Early ESA improves HI-E rate (79.5% vs 54.2%) and duration (30.6 vs 12.7 months).",
  "ESA vs. Luspatercept 1L?", "TD patients: luspatercept preferred (superior TI rate, duration, OS trend). TI patients with sEPO <200: ESA preferred (pending ELEMENT data). Use ESA-PSS-M to optimize selection.",
  "When to switch therapy?", "After confirmed ESA failure/relapse. Do not delay: ESA post-failure gives diminishing returns. Assess sEPO at relapse to inform next-line selection.",
  "Luspatercept dosing: titrate or max?", "MAXILUS suggests starting at 1.75 mg/kg is safe and effective. Pending final results, max-dose initiation may simplify practice. No thromboembolic events observed.",
  "Imetelstat vs. Luspatercept 2L?", "RS-neg or high TB → imetelstat preferred. RS+ → luspatercept (MEDALIST). Either after ESA failure. Imetelstat uniquely active as 3L post-luspatercept.",
  "How long to treat imetelstat?", "Minimum 6 months before judging response. Continue until loss of response or progression. Monitor for cytopenias and dose-modify accordingly.",
  "Role of molecular testing?", "IPSS-M for risk stratification and ESA-PSS-M for response prediction. STAG2 in males → ESA resistance → consider luspatercept 1L. SF3B1 informs RS status and prognosis."
)

questions %>%
gt(rowname_col = "Question") %>%
tab_header(
  title = "Clinical Decision Support: Frequently Encountered Questions",
  subtitle = "Synthesized from current evidence and expert opinion"
) %>%
cols_label(`Evidence-Based Guidance` = md("**Guidance**")) %>%
tab_style(
  style = cell_text(weight = "bold", color = pal$teal),
  locations = cells_stub()
)

Table 8.24: Clinical Decision Points in LR-MDS Management

Clinical Decision Support: Frequently Encountered Questions
Synthesized from current evidence and expert opinion
	Guidance
When to start treatment?	Initiate when Hb <10-10.5 g/dL or symptomatic, ideally before TD. Early ESA improves HI-E rate (79.5% vs 54.2%) and duration (30.6 vs 12.7 months).
ESA vs. Luspatercept 1L?	TD patients: luspatercept preferred (superior TI rate, duration, OS trend). TI patients with sEPO <200: ESA preferred (pending ELEMENT data). Use ESA-PSS-M to optimize selection.
When to switch therapy?	After confirmed ESA failure/relapse. Do not delay: ESA post-failure gives diminishing returns. Assess sEPO at relapse to inform next-line selection.
Luspatercept dosing: titrate or max?	MAXILUS suggests starting at 1.75 mg/kg is safe and effective. Pending final results, max-dose initiation may simplify practice. No thromboembolic events observed.
Imetelstat vs. Luspatercept 2L?	RS-neg or high TB → imetelstat preferred. RS+ → luspatercept (MEDALIST). Either after ESA failure. Imetelstat uniquely active as 3L post-luspatercept.
How long to treat imetelstat?	Minimum 6 months before judging response. Continue until loss of response or progression. Monitor for cytopenias and dose-modify accordingly.
Role of molecular testing?	IPSS-M for risk stratification and ESA-PSS-M for response prediction. STAG2 in males → ESA resistance → consider luspatercept 1L. SF3B1 informs RS status and prognosis.

---

8.15 Summary and Future Directions

The treatment landscape for LR-MDS has evolved dramatically. Key paradigm shifts include:

1. Early intervention before transfusion dependence develops is associated with better hematologic response rates and longer response duration, though OS impact remains unproven.

2. Luspatercept as preferred 1L for transfusion-dependent LR-MDS, with a ~20% OS benefit trend and significantly longer duration of TI vs. epoetin alfa.

3. Imetelstat as a disease-modifying therapy with unique activity in RS-negative patients, high transfusion burden, and post-luspatercept settings — representing the first agent to demonstrate clonal suppression (VAF reduction) in LR-MDS.

4. Sequential therapy (ESAs → luspatercept → imetelstat) is the primary treatment paradigm, with each therapy maintaining activity after prior failures due to distinct mechanisms.

5. Elritercept and novel agents are addressing remaining unmet needs in RS-negative patients, the post-luspatercept setting, and populations excluded from pivotal trials (del(5q), MDS/MPN overlap).

paradigm <- tribble(
  ~Era, ~Year, ~Key_Advance, ~y,
  "ESA Era", 1, "ESAs established as 1L\n(EU approval; NCCN guidelines)", 1,
  "Luspatercept\nApproval", 2, "MEDALIST → 2L RS+ approval (2020)\nCOMMANDS → 1L approval (2023)", 2,
  "Imetelstat\nApproval", 3, "IMerge → FDA approval (2024)\nFirst disease-modifying agent", 3,
  "Optimization", 4, "MAXILUS: Max-dose from C1\nESA-PSS-M: Molecular selection\nLong-term COMMANDS data", 4,
  "Emerging\nLandscape", 5, "Elritercept (Phase 2/3)\nR289, Roxadustat, AG-946\nPost-luspa/del(5q) solutions", 5
)

ggplot(paradigm, aes(x = Year, y = 0)) +
  geom_segment(aes(x = 0.5, xend = 5.5, y = 0, yend = 0),
               linewidth = 2, color = pal$teal, arrow = arrow(length = unit(0.3, "cm"))) +
  geom_point(size = 8, color = pal$teal) +
  geom_text(aes(label = Era), vjust = -2.5, fontface = "bold", size = 3.8) +
  geom_text(aes(label = Key_Advance), vjust = 2.5, size = 2.8, lineheight = 0.9, color = "grey30") +
  scale_x_continuous(limits = c(0, 6)) +
  scale_y_continuous(limits = c(-1.5, 1.5)) +
  labs(
    title = "Timeline of Paradigm Shifts in LR-MDS Treatment",
    subtitle = "From supportive care to disease modification and precision medicine"
  ) +
  theme_void(base_size = 13) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(color = "grey40", hjust = 0.5)
  )

Figure 8.13: Evolving Treatment Paradigm for LR-MDS Anemia

---

References

Komrokji, Rami S., Luis E. Aguirre, Najla H. Al Ali, et al. 2023. “Activity of Luspatercept and ESAs Combination for Treatment of Anemia in Lower-Risk Myelodysplastic Syndromes.” Blood Advances 7 (14): 3677–79. https://doi.org/10.1182/bloodadvances.2023009781.

Raddi, Marco Gabriele, Sophie Park, Michaela Fontenay, et al. 2025. “IPSS-m Risk and Specific Sex-Associated Somatic Mutations Predict Response to ESA Therapy in LR-MDS: Building a New Score.” Blood Journal, ahead of print, June 25. https://doi.org/10.1182/blood.2024027540.

Sasaki, Koji, Elias Jabbour, Guillermo Montalban-Bravo, et al. 2022. “Low-Dose Decitabine Versus Low-Dose Azacitidine in Lower-Risk MDS.” NEJM Evidence 1 (10). https://doi.org/10.1056/EVIDoa2200034.

Sekeres, Mikkael, Ahmed Hnoosh, Aylin Yucel, et al. 2025. “Real-World Treatment Patterns and Outcomes of First-Line Luspatercept in Patients with Myelodysplastic Syndromes in the United States.” Blood 146 (Supplement 1): 5638–38. https://doi.org/10.1182/blood-2025-5638.