5  STX-0712 CCR2-CyTAC for CMML and AML

Comprehensive Analysis with ASH 2024/2025 Data

6 Executive Summary

STX-0712 is a first-in-class CCR2 Cytotoxicity Targeting Chimera (CyTAC) developed by Solu Therapeutics for CMML and monocytic AML. The program has progressed from preclinical validation (ASH 2024) to an ongoing Phase 1 trial (NCT06950034, enrollment began March 2025).

program_data <- tibble(
  Category = c("Target", "Mechanism", "Indications", "Development Stage", 
               "Trial Registration", "Enrollment Start", "Sponsor"),
  Details = c("CCR2 (C-C chemokine receptor type 2)",
              "CyTAC platform - ADCC-mediated killing via NK cells",
              "CMML (Cohort 1) and Monocytic AML (Cohort 2)",
              "Phase 1, First-in-Human",
              "NCT06950034",
              "March 2025 (CMML cohort)",
              "Solu Therapeutics + Mayo Clinic collaboration")
)

program_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**STX-0712 Program Overview**"),
    subtitle = "Key Development Parameters"
  ) %>%
  cols_width(
    Category ~ px(180),
    Details ~ px(450)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Category)
  ) %>%
  tab_style(
    style = cell_fill(color = "#F8F9FA"),
    locations = cells_body(rows = seq(1, 7, 2))
  )

Table 6.1: STX-0712 Program Overview

STX-0712 Program Overview
Key Development Parameters
Category	Details
Target	CCR2 (C-C chemokine receptor type 2)
Mechanism	CyTAC platform - ADCC-mediated killing via NK cells
Indications	CMML (Cohort 1) and Monocytic AML (Cohort 2)
Development Stage	Phase 1, First-in-Human
Trial Registration	NCT06950034
Enrollment Start	March 2025 (CMML cohort)
Sponsor	Solu Therapeutics + Mayo Clinic collaboration

7 Disease Background

7.1 CMML Characteristics

cmml_data <- tibble(
  Characteristic = c("Classification", "Median Age", "Incidence", 
                     "Sex Distribution", "Median OS", "AML Transformation",
                     "Current Standard of Care", "Curative Option", "Key Unmet Need"),
  Description = c("MDS/MPN overlap syndrome",
                  "70-75 years",
                  "~1 per 100,000",
                  "Male predominance (2:1)",
                  "20-40 months",
                  "15-30% of patients",
                  "Hypomethylating agents (azacitidine, decitabine)",
                  "Allogeneic HSCT (limited by age/fitness)",
                  "No approved targeted therapies; HMA responses often transient"),
  Category = c(rep("Epidemiology", 4), rep("Prognosis", 2), 
               rep("Treatment", 2), "Gap")
)

cmml_data %>%
  gt(groupname_col = "Category") %>%
  custom_theme() %>%
  tab_header(
    title = md("**CMML Disease Profile**"),
    subtitle = "Epidemiology, Prognosis, and Treatment Landscape"
  ) %>%
  cols_width(
    Characteristic ~ px(200),
    Description ~ px(400)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_row_groups()
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#FFF3CD"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(rows = Characteristic == "Key Unmet Need")
  )

Table 7.1: CMML Disease Characteristics and Unmet Need

CMML Disease Profile
Epidemiology, Prognosis, and Treatment Landscape
Characteristic	Description
Epidemiology
Classification	MDS/MPN overlap syndrome
Median Age	70-75 years
Incidence	~1 per 100,000
Sex Distribution	Male predominance (2:1)
Prognosis
Median OS	20-40 months
AML Transformation	15-30% of patients
Treatment
Current Standard of Care	Hypomethylating agents (azacitidine, decitabine)
Curative Option	Allogeneic HSCT (limited by age/fitness)
Gap
Key Unmet Need	No approved targeted therapies; HMA responses often transient

7.2 CCR2 as Therapeutic Target

ccr2_data <- tibble(
  Finding = c("Expression Level", 
              "Expression Homogeneity",
              "Progenitor Expression",
              "Survival Pathway",
              "AML Transformation",
              "Venetoclax Resistance"),
  Evidence = c(">98% of CMML monocytes express CCR2",
               "Highly homogeneous across patient samples (6/6)",
               "CCR2+ on CD34+ GMP-like progenitors (disease-specific)",
               "CYTL1-CCR2-ERK autocrine pathway promotes survival",
               "CCR2+ monocytic progenitor expansion drives transformation",
               "CCR2+ progenitors contribute to venetoclax resistance in AML"),
  Source = c("ASH 2024", "ASH 2024", "ASH 2024", 
             "Sevin et al., Blood 2021", "ASH 2025", "ASH 2025"),
  Implication = c("Strong target engagement expected",
                  "Predictable patient selection",
                  "Potential for disease modification",
                  "Combination rationale with MCL1/MEK inhibitors",
                  "Justifies CMML and AML development",
                  "Rationale for venetoclax combinations")
)

ccr2_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**CCR2 Target Validation**"),
    subtitle = "Evidence Supporting Therapeutic Targeting"
  ) %>%
  cols_width(
    Finding ~ px(160),
    Evidence ~ px(280),
    Source ~ px(120),
    Implication ~ px(200)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Finding)
  ) %>%
  tab_style(
    style = cell_text(style = "italic", size = px(12)),
    locations = cells_body(columns = Source)
  ) %>%
  data_color(
    columns = Source,
    palette = c("#E3F2FD", "#BBDEFB", "#90CAF9"),
    domain = c("ASH 2024", "ASH 2025", "Sevin et al., Blood 2021")
  )

Table 7.2: Scientific Rationale for CCR2 Targeting

CCR2 Target Validation
Evidence Supporting Therapeutic Targeting
Finding	Evidence	Source	Implication
Expression Level	>98% of CMML monocytes express CCR2	ASH 2024	Strong target engagement expected
Expression Homogeneity	Highly homogeneous across patient samples (6/6)	ASH 2024	Predictable patient selection
Progenitor Expression	CCR2+ on CD34+ GMP-like progenitors (disease-specific)	ASH 2024	Potential for disease modification
Survival Pathway	CYTL1-CCR2-ERK autocrine pathway promotes survival	Sevin et al., Blood 2021	Combination rationale with MCL1/MEK inhibitors
AML Transformation	CCR2+ monocytic progenitor expansion drives transformation	ASH 2025	Justifies CMML and AML development
Venetoclax Resistance	CCR2+ progenitors contribute to venetoclax resistance in AML	ASH 2025	Rationale for venetoclax combinations

8 Preclinical Data (ASH 2024)

8.1 CCR2 Expression Characterization

expression_data <- tibble(
  `Sample Type` = c("Peripheral Blood", "Bone Marrow", "Healthy Controls"),
  N = c("6", "3", "N/A"),
  `Response Rate` = c("6/6 (100%)", "3/3 (100%)", "—"),
  `CCR2+ Population` = c(">98% of monocytes", "Subset of CD34+ cells", "Low/absent on CD34+"),
  `Key Finding` = c("High, homogeneous expression",
                    "GMP-like progenitors CCR2+",
                    "Disease-specific progenitor expression")
)

expression_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**CCR2 Expression Characterization**"),
    subtitle = "Flow Cytometry Analysis of CMML Patient Samples"
  ) %>%
  cols_align(align = "center", columns = c(N, `Response Rate`)) %>%
  tab_style(
    style = list(
      cell_fill(color = "#C8E6C9"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(
      columns = `Response Rate`,
      rows = `Response Rate` != "—"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFECB3"),
    locations = cells_body(rows = `Sample Type` == "Healthy Controls")
  ) %>%
  tab_footnote(
    footnote = "GMP = Granulocyte-Macrophage Progenitor",
    locations = cells_body(columns = `Key Finding`, rows = 2)
  )

Table 8.1: CCR2 Expression in CMML Patient Samples

CCR2 Expression Characterization
Flow Cytometry Analysis of CMML Patient Samples
Sample Type	N	Response Rate	CCR2+ Population	Key Finding
Peripheral Blood	6	6/6 (100%)	>98% of monocytes	High, homogeneous expression
Bone Marrow	3	3/3 (100%)	Subset of CD34+ cells	GMP-like progenitors CCR2+1
Healthy Controls	N/A	—	Low/absent on CD34+	Disease-specific progenitor expression
1 GMP = Granulocyte-Macrophage Progenitor

8.2 Ex Vivo and In Vivo Activity

efficacy_data <- tibble(
  Study = c("Ex Vivo CMML Killing", "Ex Vivo CMML Killing", "Ex Vivo CMML Killing",
            "NHP PK/PD", "NHP PK/PD", "NHP PK/PD", "NHP PK/PD"),
  Parameter = c("Response Rate", "Potency (IC50)", "Killing Efficacy",
                "Monocyte Depletion", "Depletion Depth", "Reversibility", "Tolerability"),
  Result = c("5/5 (100%)", "3 nM", "66-91%",
             "9/9 (100%)", ">95%", "Recovery at <10 ng/mL", "All doses well tolerated"),
  Interpretation = c("Consistent activity across samples",
                     "High potency target engagement",
                     "Effective but variable killing",
                     "Robust in vivo activity",
                     "Near-complete depletion achieved",
                     "Predictable PK-PD relationship",
                     "Mechanism-directed effects only")
)

efficacy_data %>%
  gt(groupname_col = "Study") %>%
  custom_theme() %>%
  tab_header(
    title = md("**Preclinical Efficacy Data**"),
    subtitle = "Ex Vivo Human Samples and Non-Human Primate Studies"
  ) %>%
  cols_width(
    Parameter ~ px(150),
    Result ~ px(150),
    Interpretation ~ px(280)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_row_groups()
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#C8E6C9"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(
      columns = Result,
      rows = Result %in% c("5/5 (100%)", "9/9 (100%)", ">95%")
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_body(
      columns = Result,
      rows = Result == "3 nM"
    )
  )

Table 8.2: STX-0712 Preclinical Efficacy Summary

Preclinical Efficacy Data
Ex Vivo Human Samples and Non-Human Primate Studies
Parameter	Result	Interpretation
Ex Vivo CMML Killing
Response Rate	5/5 (100%)	Consistent activity across samples
Potency (IC50)	3 nM	High potency target engagement
Killing Efficacy	66-91%	Effective but variable killing
NHP PK/PD
Monocyte Depletion	9/9 (100%)	Robust in vivo activity
Depletion Depth	>95%	Near-complete depletion achieved
Reversibility	Recovery at <10 ng/mL	Predictable PK-PD relationship
Tolerability	All doses well tolerated	Mechanism-directed effects only

preclinical_summary <- tibble(
  Study = c("CCR2 Expression\n(PB)", "CCR2 Expression\n(BM)", 
            "Ex Vivo Killing", "NHP Depletion"),
  Response = c(100, 100, 100, 100),
  N_Total = c(6, 3, 5, 9),
  N_Responders = c(6, 3, 5, 9),
  Metric = c(">98% CCR2+", "CD34+ subset", "66-91% killing", ">95% depletion")
)

ggplot(preclinical_summary, aes(x = Study, y = Response)) +

  geom_col(fill = "#2E75B6", width = 0.6) +
  geom_text(aes(label = paste0(N_Responders, "/", N_Total)), 
            vjust = -0.5, fontface = "bold", size = 5) +
  geom_text(aes(label = Metric, y = 50), color = "white", 
            fontface = "bold", size = 4) +
  scale_y_continuous(limits = c(0, 115), breaks = seq(0, 100, 25),
                     labels = function(x) paste0(x, "%")) +
  labs(title = "STX-0712 Preclinical Response Rates",
       subtitle = "100% response across all preclinical studies",
       x = NULL, y = "Response Rate") +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", color = "#1F4E79", size = 16),
    plot.subtitle = element_text(color = "#666666"),
    axis.text.x = element_text(face = "bold"),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank()
  )

Figure 8.1: Preclinical Response Summary

9 Phase 1 Clinical Trial Design (ASH 2025)

9.1 Trial Overview

trial_data <- tibble(
  Parameter = c("Trial Type", "Sponsor", "Location", "Sites", 
                "Administration", "Schedule", "Treatment Duration"),
  Details = c("Phase 1, First-in-Human, Open-label, Multicenter",
              "Solu Therapeutics",
              "United States",
              "MD Anderson, Stanford, Dana-Farber, Moffitt, OHSU, Tennessee Oncology, Mayo Clinic",
              "Intravenous (IV) infusion",
              "Every 3 weeks (Q3W); additional schedules may be explored",
              "Until progression, death, intolerance, or withdrawal")
)

trial_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**Phase 1 Trial Design**"),
    subtitle = "NCT06950034 | Enrollment began March 2025"
  ) %>%
  cols_width(
    Parameter ~ px(160),
    Details ~ px(480)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Parameter)
  ) %>%
  tab_style(
    style = cell_fill(color = "#F8F9FA"),
    locations = cells_body(rows = seq(1, 7, 2))
  )

Table 9.1: Phase 1 Trial Design Summary (NCT06950034)

Phase 1 Trial Design
NCT06950034 | Enrollment began March 2025
Parameter	Details
Trial Type	Phase 1, First-in-Human, Open-label, Multicenter
Sponsor	Solu Therapeutics
Location	United States
Sites	MD Anderson, Stanford, Dana-Farber, Moffitt, OHSU, Tennessee Oncology, Mayo Clinic
Administration	Intravenous (IV) infusion
Schedule	Every 3 weeks (Q3W); additional schedules may be explored
Treatment Duration	Until progression, death, intolerance, or withdrawal

9.2 Patient Cohorts

cohort_data <- tibble(
  Parameter = c("Population", "Planned N", "Response Criteria", 
                "Enrollment Status", "Dose Escalation Design"),
  `Cohort 1: CMML` = c("Refractory/Resistant CMML",
                       "~47 patients",
                       "MDS/MPN IWG 2015",
                       "✓ Enrolling (since March 2025)",
                       "BOIN design, minimum 3 pts/dose"),
  `Cohort 2: AML` = c("R/R Monocytic or Monocytic-Predominant AML",
                      "~58 patients",
                      "ELN 2022",
                      "Not yet enrolling",
                      "BOIN design, minimum 3 pts/dose")
)

cohort_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**Patient Cohorts**"),
    subtitle = "Planned enrollment ~105 patients total"
  ) %>%
  cols_width(
    Parameter ~ px(180),
    `Cohort 1: CMML` ~ px(220),
    `Cohort 2: AML` ~ px(250)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Parameter)
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#C8E6C9"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(
      columns = `Cohort 1: CMML`,
      rows = Parameter == "Enrollment Status"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFECB3"),
    locations = cells_body(
      columns = `Cohort 2: AML`,
      rows = Parameter == "Enrollment Status"
    )
  ) %>%
  tab_spanner(
    label = md("**Study Cohorts**"),
    columns = c(`Cohort 1: CMML`, `Cohort 2: AML`)
  )

Table 9.2: Patient Population by Cohort

Patient Cohorts
Planned enrollment ~105 patients total
Parameter	Study Cohorts
	Cohort 1: CMML	Cohort 2: AML
Population	Refractory/Resistant CMML	R/R Monocytic or Monocytic-Predominant AML
Planned N	~47 patients	~58 patients
Response Criteria	MDS/MPN IWG 2015	ELN 2022
Enrollment Status	✓ Enrolling (since March 2025)	Not yet enrolling
Dose Escalation Design	BOIN design, minimum 3 pts/dose	BOIN design, minimum 3 pts/dose

9.3 Study Endpoints

endpoints_data <- tibble(
  Category = c(rep("Primary", 3), rep("Secondary", 3), rep("Exploratory", 4)),
  Endpoint = c(
    "Safety and tolerability of STX-0712",
    "Determine minimal safe/biologically effective dose, RP2D, and/or MTD",
    "Characterize dose-limiting toxicities (DLTs)",
    "Preliminary antitumor activity",
    "Pharmacokinetic (PK) parameters",
    "Pharmacodynamic effects on CCR2+ cells (blood and bone marrow)",
    "Changes in CCR2+ tumor cells post-dosing",
    "Candidate biomarker evaluation",
    "T cell, B cell, NK cell characterization over treatment",
    "Patient-reported outcomes (MPN-SAF-TSS for CMML; EQ-5D-5L for AML)"
  ),
  `Assessment Type` = c(
    "Safety", "Dose-finding", "Safety",
    "Efficacy", "PK", "PD",
    "PD/Biomarker", "Biomarker", "Immunology", "QoL"
  )
)

endpoints_data %>%
  gt(groupname_col = "Category") %>%
  custom_theme() %>%
  tab_header(
    title = md("**Study Endpoints**"),
    subtitle = "Primary, Secondary, and Exploratory Objectives"
  ) %>%
  cols_width(
    Endpoint ~ px(450),
    `Assessment Type` ~ px(120)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_row_groups()
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_row_groups(groups = "Primary")
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_row_groups(groups = "Secondary")
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFF8E1"),
    locations = cells_row_groups(groups = "Exploratory")
  ) %>%
  data_color(
    columns = `Assessment Type`,
    palette = "Set3",
    domain = unique(endpoints_data$`Assessment Type`)
  )

Table 9.3: Study Endpoints by Category

Study Endpoints
Primary, Secondary, and Exploratory Objectives
Endpoint	Assessment Type
Primary
Safety and tolerability of STX-0712	Safety
Determine minimal safe/biologically effective dose, RP2D, and/or MTD	Dose-finding
Characterize dose-limiting toxicities (DLTs)	Safety
Secondary
Preliminary antitumor activity	Efficacy
Pharmacokinetic (PK) parameters	PK
Pharmacodynamic effects on CCR2+ cells (blood and bone marrow)	PD
Exploratory
Changes in CCR2+ tumor cells post-dosing	PD/Biomarker
Candidate biomarker evaluation	Biomarker
T cell, B cell, NK cell characterization over treatment	Immunology
Patient-reported outcomes (MPN-SAF-TSS for CMML; EQ-5D-5L for AML)	QoL

10 Competitive Landscape

10.1 Head-to-Head Comparison

competitive_data <- tibble(
  Agent = c("STX-0712", "IO-202 + Azacitidine", "Tagraxofusp"),
  Developer = c("Solu Therapeutics", "Immune-Onc Therapeutics", "Menarini Group"),
  Target = c("CCR2", "LILRB4 (ILT3)", "CD123 (IL-3Rα)"),
  Mechanism = c("CyTAC (NK-mediated ADCC)", "mAb (ADCC/ADCP)", "Toxin conjugate (DT)"),
  Stage = c("Phase 1", "Phase 1/2", "Phase 1/2 (completed)"),
  Population = c("R/R CMML", "HMA-naïve CMML", "TN and R/R CMML"),
  `CR Rate` = c("TBD", "27.8%", "0%"),
  `ORR` = c("TBD", "66.7%", "0%"),
  `Clinical Benefit` = c("TBD", "100%", "27-40% (stable disease)")
)

competitive_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**CMML Targeted Therapy Landscape**"),
    subtitle = "Comparative Analysis of Leading Agents"
  ) %>%
  cols_width(
    Agent ~ px(130),
    Developer ~ px(130),
    Target ~ px(100),
    Mechanism ~ px(130),
    Stage ~ px(90),
    Population ~ px(100),
    `CR Rate` ~ px(70),
    `ORR` ~ px(70),
    `Clinical Benefit` ~ px(100)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Agent)
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#C8E6C9"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(
      columns = c(`CR Rate`, `ORR`, `Clinical Benefit`),
      rows = Agent == "IO-202 + Azacitidine"
    )
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#FFCDD2"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(
      columns = c(`CR Rate`, `ORR`),
      rows = Agent == "Tagraxofusp"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_body(rows = Agent == "STX-0712")
  ) %>%
  tab_footnote(
    footnote = "HMA = Hypomethylating Agent; TN = Treatment-Naïve; R/R = Relapsed/Refractory",
    locations = cells_column_labels(columns = Population)
  ) %>%
  tab_footnote(
    footnote = "IO-202 data from Blood Neoplasia 2025; Tagraxofusp data from Blood Neoplasia 2025",
    locations = cells_column_labels(columns = `CR Rate`)
  )

Table 10.1: Competitive Landscape: CMML Targeted Therapies

CMML Targeted Therapy Landscape
Comparative Analysis of Leading Agents
Agent	Developer	Target	Mechanism	Stage	Population1	CR Rate2	ORR	Clinical Benefit
STX-0712	Solu Therapeutics	CCR2	CyTAC (NK-mediated ADCC)	Phase 1	R/R CMML	TBD	TBD	TBD
IO-202 + Azacitidine	Immune-Onc Therapeutics	LILRB4 (ILT3)	mAb (ADCC/ADCP)	Phase 1/2	HMA-naïve CMML	27.8%	66.7%	100%
Tagraxofusp	Menarini Group	CD123 (IL-3Rα)	Toxin conjugate (DT)	Phase 1/2 (completed)	TN and R/R CMML	0%	0%	27-40% (stable disease)
1 HMA = Hypomethylating Agent; TN = Treatment-Naïve; R/R = Relapsed/Refractory
2 IO-202 data from Blood Neoplasia 2025; Tagraxofusp data from Blood Neoplasia 2025

10.2 Efficacy Visualization

efficacy_visual <- tibble(
  Agent = rep(c("IO-202 + AZA\n(LILRB4)", "Tagraxofusp\n(CD123)", "STX-0712\n(CCR2)"), 3),
  Metric = rep(c("CR Rate", "ORR", "Clinical Benefit"), each = 3),
  Value = c(27.8, 0, NA, 66.7, 0, NA, 100, 33, NA),
  Label = c("27.8%", "0%", "TBD", "66.7%", "0%", "TBD", "100%", "33%", "TBD")
)

efficacy_visual$Agent <- factor(efficacy_visual$Agent, 
                                 levels = c("IO-202 + AZA\n(LILRB4)", 
                                           "Tagraxofusp\n(CD123)", 
                                           "STX-0712\n(CCR2)"))
efficacy_visual$Metric <- factor(efficacy_visual$Metric,
                                  levels = c("CR Rate", "ORR", "Clinical Benefit"))

ggplot(efficacy_visual, aes(x = Agent, y = Value, fill = Metric)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.7, na.rm = TRUE) +
  geom_text(aes(label = Label, y = ifelse(is.na(Value), 5, Value + 3)), 
            position = position_dodge(width = 0.8), 
            fontface = "bold", size = 4, vjust = 0) +
  scale_fill_manual(values = c("CR Rate" = "#1F4E79", 
                               "ORR" = "#2E75B6", 
                               "Clinical Benefit" = "#7FB3D5")) +
  scale_y_continuous(limits = c(0, 110), breaks = seq(0, 100, 25),
                     labels = function(x) paste0(x, "%")) +
  labs(title = "Clinical Efficacy of CMML Targeted Therapies",
       subtitle = "IO-202 + azacitidine sets the efficacy benchmark for future agents",
       x = NULL, y = "Response Rate (%)", fill = "Endpoint") +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", color = "#1F4E79", size = 16),
    plot.subtitle = element_text(color = "#666666"),
    legend.position = "bottom",
    panel.grid.major.x = element_blank(),
    axis.text.x = element_text(face = "bold", size = 11)
  )

Figure 10.1: Clinical Efficacy Comparison of CMML Targeted Therapies

10.3 Detailed Competitor Profiles

io202_data <- tibble(
  Endpoint = c("Complete Response (CR)", "Overall Response Rate (ORR)", 
               "Clinical Benefit Rate", "Bridge to Transplant",
               "Stable Disease", "Median Follow-up"),
  Result = c("27.8%", "66.7%", "100%", "38.9%", "33.3%", "Not reported"),
  `N Evaluable` = c("18", "18", "18", "18", "18", "—"),
  Significance = c("Benchmark for future CMML therapies",
                   "Meaningful activity in combination",
                   "Universal symptomatic improvement",
                   "Effective bridge strategy",
                   "Disease stabilization achieved",
                   "—")
)

io202_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**IO-202 + Azacitidine in HMA-naïve CMML**"),
    subtitle = "Phase 1b Expansion Results (Blood Neoplasia 2025)"
  ) %>%
  cols_width(
    Endpoint ~ px(180),
    Result ~ px(100),
    `N Evaluable` ~ px(100),
    Significance ~ px(280)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Endpoint)
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#C8E6C9"),
      cell_text(weight = "bold", size = px(16))
    ),
    locations = cells_body(
      columns = Result,
      rows = Endpoint %in% c("Complete Response (CR)", "Overall Response Rate (ORR)")
    )
  ) %>%
  tab_source_note(
    source_note = "Source: Aribi A, et al. Blood Neoplasia 2025;2(4):100126"
  )

Table 10.2: IO-202 + Azacitidine Clinical Results (HMA-naïve CMML)

IO-202 + Azacitidine in HMA-naïve CMML
Phase 1b Expansion Results (Blood Neoplasia 2025)
Endpoint	Result	N Evaluable	Significance
Complete Response (CR)	27.8%	18	Benchmark for future CMML therapies
Overall Response Rate (ORR)	66.7%	18	Meaningful activity in combination
Clinical Benefit Rate	100%	18	Universal symptomatic improvement
Bridge to Transplant	38.9%	18	Effective bridge strategy
Stable Disease	33.3%	18	Disease stabilization achieved
Median Follow-up	Not reported	—	—
Source: Aribi A, et al. Blood Neoplasia 2025;2(4):100126

tagraxofusp_data <- tibble(
  Endpoint = c("Complete Response (CR)", "Partial Response (PR)",
               "Stable Disease (TN)", "Stable Disease (R/R)",
               "Median OS (TN)", "Median OS (R/R)",
               "Capillary Leak Syndrome", "Tumor Lysis Syndrome"),
  Result = c("0%", "0%", "40%", "59%", "11.2 months", "15.6 months", "23%", "13%"),
  Category = c(rep("Efficacy", 6), rep("Safety", 2))
)

tagraxofusp_data %>%
  gt(groupname_col = "Category") %>%
  custom_theme() %>%
  tab_header(
    title = md("**Tagraxofusp in CMML**"),
    subtitle = "Phase 1/2 Final Results (Blood Neoplasia 2025)"
  ) %>%
  cols_width(
    Endpoint ~ px(250),
    Result ~ px(150)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_row_groups()
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#FFCDD2"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(
      columns = Result,
      rows = Endpoint %in% c("Complete Response (CR)", "Partial Response (PR)")
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFECB3"),
    locations = cells_body(
      columns = Result,
      rows = Endpoint %in% c("Capillary Leak Syndrome", "Tumor Lysis Syndrome")
    )
  ) %>%
  tab_source_note(
    source_note = "Source: Patnaik MM, et al. Blood Neoplasia 2025;2(4):100115"
  ) %>%
  tab_footnote(
    footnote = "TN = Treatment-Naïve; R/R = Relapsed/Refractory",
    locations = cells_body(columns = Endpoint, rows = 3)
  )

Table 10.3: Tagraxofusp Clinical Results in CMML

Tagraxofusp in CMML
Phase 1/2 Final Results (Blood Neoplasia 2025)
Endpoint	Result
Efficacy
Complete Response (CR)	0%
Partial Response (PR)	0%
Stable Disease (TN)1	40%
Stable Disease (R/R)	59%
Median OS (TN)	11.2 months
Median OS (R/R)	15.6 months
Safety
Capillary Leak Syndrome	23%
Tumor Lysis Syndrome	13%
1 TN = Treatment-Naïve; R/R = Relapsed/Refractory
Source: Patnaik MM, et al. Blood Neoplasia 2025;2(4):100115

11 Critical Analysis

11.1 SWOT Summary

swot_data <- tibble(
  Category = c(rep("Strengths", 4), rep("Weaknesses", 4), 
               rep("Opportunities", 4), rep("Threats", 4)),
  Item = c(
    # Strengths
    "Validated target: >98% CCR2+ on CMML monocytes",
    "Novel CyTAC mechanism differentiates from failed approaches",
    "CCR2+ on CD34+ progenitors suggests disease modification potential",
    "100% preclinical response rates (ex vivo and NHP)",
    # Weaknesses
    "Small preclinical sample sizes (n=3-6)",
    "No in vivo efficacy models presented",
    "Normal monocyte depletion may cause immunosuppression",
    "Starting as monotherapy (IO-202 best results in combination)",
    # Opportunities
    "Combination with azacitidine could enhance efficacy",
    "Venetoclax resistance rationale expands AML opportunity",
    "MCL1/MEK inhibitor combinations based on CYTL1-CCR2-ERK biology",
    "Disease modification if progenitor targeting translates clinically",
    # Threats
    "IO-202 + AZA sets high efficacy bar (CR 27.8%, ORR 66.7%)",
    "Tagraxofusp failure highlights CMML development challenges",
    "Infection risk from monocyte depletion",
    "Competitive landscape evolving rapidly"
  )
)

swot_data %>%
  gt(groupname_col = "Category") %>%
  custom_theme() %>%
  tab_header(
    title = md("**STX-0712 Strategic Analysis**"),
    subtitle = "Strengths, Weaknesses, Opportunities, and Threats"
  ) %>%
  cols_width(Item ~ px(550)) %>%
  tab_style(
    style = list(
      cell_fill(color = "#C8E6C9"),
      cell_text(weight = "bold")
    ),
    locations = cells_row_groups(groups = "Strengths")
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#FFCDD2"),
      cell_text(weight = "bold")
    ),
    locations = cells_row_groups(groups = "Weaknesses")
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#BBDEFB"),
      cell_text(weight = "bold")
    ),
    locations = cells_row_groups(groups = "Opportunities")
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#FFE0B2"),
      cell_text(weight = "bold")
    ),
    locations = cells_row_groups(groups = "Threats")
  )

Table 11.1: STX-0712 SWOT Analysis

STX-0712 Strategic Analysis
Strengths, Weaknesses, Opportunities, and Threats
Item
Strengths
Validated target: >98% CCR2+ on CMML monocytes
Novel CyTAC mechanism differentiates from failed approaches
CCR2+ on CD34+ progenitors suggests disease modification potential
100% preclinical response rates (ex vivo and NHP)
Weaknesses
Small preclinical sample sizes (n=3-6)
No in vivo efficacy models presented
Normal monocyte depletion may cause immunosuppression
Starting as monotherapy (IO-202 best results in combination)
Opportunities
Combination with azacitidine could enhance efficacy
Venetoclax resistance rationale expands AML opportunity
MCL1/MEK inhibitor combinations based on CYTL1-CCR2-ERK biology
Disease modification if progenitor targeting translates clinically
Threats
IO-202 + AZA sets high efficacy bar (CR 27.8%, ORR 66.7%)
Tagraxofusp failure highlights CMML development challenges
Infection risk from monocyte depletion
Competitive landscape evolving rapidly

11.2 Key Development Questions

questions_data <- tibble(
  Question = c(
    "What is the therapeutic index?",
    "Will progenitor targeting translate to disease modification?",
    "What is the optimal dosing schedule?",
    "How will AML cohort efficacy compare to CMML?",
    "What is the combination strategy?"
  ),
  Rationale = c(
    "CCR2 is also on normal monocytes; balance efficacy vs. infection risk",
    "CD34+ GMP-like cell targeting is novel; clinical benefit unclear",
    "Q3W may need adjustment based on monocyte recovery kinetics",
    "CCR2+ AML may have different biology than CMML",
    "IO-202 best results in combination; monotherapy may limit signals"
  ),
  `Data to Watch` = c(
    "DLTs, infection rates, normal monocyte dynamics",
    "Duration of response, bone marrow CCR2+ depletion",
    "PK/PD modeling, monocyte recovery data",
    "Cohort 2 enrollment and early response signals",
    "Biomarker data supporting rational combinations"
  ),
  Timeline = c(
    "Dose escalation (2025-2026)",
    "Expansion cohort (2026+)",
    "Dose escalation (2025-2026)",
    "Post Cohort 2 enrollment",
    "Based on PD/biomarker data"
  )
)

questions_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**Key Questions for STX-0712 Development**"),
    subtitle = "Critical watchpoints for clinical success"
  ) %>%
  cols_width(
    Question ~ px(200),
    Rationale ~ px(220),
    `Data to Watch` ~ px(200),
    Timeline ~ px(140)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Question)
  ) %>%
  tab_style(
    style = cell_fill(color = "#F8F9FA"),
    locations = cells_body(rows = seq(1, 5, 2))
  )

Table 11.2: Critical Questions for Clinical Development

Key Questions for STX-0712 Development
Critical watchpoints for clinical success
Question	Rationale	Data to Watch	Timeline
What is the therapeutic index?	CCR2 is also on normal monocytes; balance efficacy vs. infection risk	DLTs, infection rates, normal monocyte dynamics	Dose escalation (2025-2026)
Will progenitor targeting translate to disease modification?	CD34+ GMP-like cell targeting is novel; clinical benefit unclear	Duration of response, bone marrow CCR2+ depletion	Expansion cohort (2026+)
What is the optimal dosing schedule?	Q3W may need adjustment based on monocyte recovery kinetics	PK/PD modeling, monocyte recovery data	Dose escalation (2025-2026)
How will AML cohort efficacy compare to CMML?	CCR2+ AML may have different biology than CMML	Cohort 2 enrollment and early response signals	Post Cohort 2 enrollment
What is the combination strategy?	IO-202 best results in combination; monotherapy may limit signals	Biomarker data supporting rational combinations	Based on PD/biomarker data

12 Conclusions

conclusions_data <- tibble(
  Aspect = c("Scientific Rationale", "Development Progress", 
             "Competitive Position", "Key Risks", "Timeline"),
  Assessment = c(
    "Strong: CCR2 validated as therapeutic target with compelling biology (CYTL1-CCR2-ERK pathway)",
    "Rapid: Preclinical (ASH 2024) → Phase 1 FIH enrolling (March 2025) in ~1 year",
    "Evolving: Must match IO-202 + AZA efficacy (CR 27.8%, ORR 66.7%); differentiated mechanism",
    "Moderate: Normal monocyte depletion, monotherapy start, high competitive bar",
    "Initial safety/PK data: Late 2025; Preliminary efficacy: Mid-2026"
  ),
  `Overall Rating` = c("✓✓✓", "✓✓✓", "✓✓", "✓✓", "—")
)

conclusions_data %>%
  gt() %>%
  custom_theme() %>%
  tab_header(
    title = md("**STX-0712 Development Assessment**"),
    subtitle = "Summary of key findings and outlook"
  ) %>%
  cols_width(
    Aspect ~ px(140),
    Assessment ~ px(450),
    `Overall Rating` ~ px(100)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold", color = "#1F4E79"),
    locations = cells_body(columns = Aspect)
  ) %>%
  cols_align(align = "center", columns = `Overall Rating`) %>%
  tab_style(
    style = list(
      cell_fill(color = "#C8E6C9"),
      cell_text(size = px(18))
    ),
    locations = cells_body(
      columns = `Overall Rating`,
      rows = `Overall Rating` == "✓✓✓"
    )
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#FFF9C4"),
      cell_text(size = px(18))
    ),
    locations = cells_body(
      columns = `Overall Rating`,
      rows = `Overall Rating` == "✓✓"
    )
  ) %>%
  tab_footnote(
    footnote = "Rating: ✓✓✓ = Strong; ✓✓ = Moderate; ✓ = Weak",
    locations = cells_column_labels(columns = `Overall Rating`)
  )

Table 12.1: Summary and Outlook

STX-0712 Development Assessment
Summary of key findings and outlook
Aspect	Assessment	Overall Rating1
Scientific Rationale	Strong: CCR2 validated as therapeutic target with compelling biology (CYTL1-CCR2-ERK pathway)	✓✓✓
Development Progress	Rapid: Preclinical (ASH 2024) → Phase 1 FIH enrolling (March 2025) in ~1 year	✓✓✓
Competitive Position	Evolving: Must match IO-202 + AZA efficacy (CR 27.8%, ORR 66.7%); differentiated mechanism	✓✓
Key Risks	Moderate: Normal monocyte depletion, monotherapy start, high competitive bar	✓✓
Timeline	Initial safety/PK data: Late 2025; Preliminary efficacy: Mid-2026	—
1 Rating: ✓✓✓ = Strong; ✓✓ = Moderate; ✓ = Weak

13 References

1. Turunen B, et al. Preclinical Evaluation of STX-0712, a CCR2 Cytotoxicity Targeting Chimera for the Treatment of CMML. Blood 2024;144(Suppl 1):2771-2772. DOI: 10.1182/blood-2024-209024

2. Montalban-Bravo G, et al. A First-in-Human Study of STX-0712, a CCR2+ CyTAC in CMML and AML. Blood 2025;146(Suppl 1):3874-3875. DOI: 10.1182/blood-2025-3874

3. Sevin M, et al. Cytokine-like protein 1-induced survival of monocytes suggests a combined strategy targeting MCL1 and MAPK in CMML. Blood 2021;137(24):3390-3402. DOI: 10.1182/blood.2020008729

4. Patnaik MM, et al. Tagraxofusp, a CD123-targeted therapy, for CMML: final results of a phase 1/2 study. Blood Neoplasia 2025;2(4):100115. DOI: 10.1016/j.bneo.2025.100115

5. Aribi A, et al. A phase 1 study of IO-202, an anti-LILRB4 antibody, in CMML and AML. Blood Neoplasia 2025;2(4):100126. DOI: 10.1016/j.bneo.2025.100126

6. Steinauer N, Patnaik MM. Splicing Factor Mutations in CMML: Biological Consequences and Therapeutic Implications. Curr Hematol Malig Rep 2025;20(1):16. DOI: 10.1007/s11899-025-00763-0

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Report generated: 2026-08-19