9 Targeting the Super Elongation Complex in Acute Leukemia

From Menin Inhibitors to MLLT1/3 Degraders: A Comprehensive Review

Author

Clinical Research Analysis

Published

August 19, 2026

9.1 Executive Summary

The Super Elongation Complex (SEC) has emerged as a validated therapeutic target in acute leukemia, with two FDA-approved menin inhibitors and next-generation approaches targeting MLLT1/3 advancing rapidly. This document synthesizes clinical data, resistance mechanisms, and emerging therapeutic strategies including the MLLT1/3 targeted protein degrader (MLLT-TPD) presented at ASH 2025.

Key Clinical Milestones

November 2024: Revumenib (Revuforj®) FDA approval for KMT2A-rearranged acute leukemia
October 2025: Revumenib expanded approval for NPM1-mutated AML
November 2025: Ziftomenib FDA approval for NPM1-mutated AML
January 2026: Amgen acquires Dark Blue Therapeutics (MLLT-TPD program) for up to $840M

9.2 SEC Biology and Therapeutic Rationale

9.2.1 SEC Architecture and Function

The Super Elongation Complex consists of multiple protein components that work together to drive transcriptional elongation:

Show code

sec_components <- tibble(
  Component = c("P-TEFb (CDK9/CycT1)", "MLLT1/ENL", "MLLT3/AF9", 
                "AFF1/AFF4", "ELL1/ELL2", "Menin"),
  Domain_Function = c("Kinase activity", "YEATS chromatin reader", 
                      "YEATS chromatin reader", "Scaffold protein",
                      "Elongation factor", "Adapter protein"),
  Role_in_SEC = c("Phosphorylates RNA Pol II Ser2; releases paused polymerase",
                  "Recruits SEC to acetylated H3K9ac/H3K27ac chromatin",
                  "Recruits SEC to acetylated chromatin; AHD domain binds KMT2A",
                  "Core scaffold; directly fused in KMT2A::AFF1/AFF4",
                  "Stimulates Pol II elongation rate",
                  "Bridges KMT2A to chromatin; NOT a constitutive SEC component"),
  Druggability = c("High (kinase)", "High (hydrophobic pocket)", 
                   "Moderate", "Low", "Low", "High (validated)"),
  Status = c("CDK9i in trials", "TPD in development", 
             "TPD in development", "Not targeted", 
             "Not targeted", "2 FDA approvals")
)

sec_components %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "Super Elongation Complex Components",
    subtitle = "Molecular architecture and therapeutic targeting status"
  ) %>%
  cols_label(
    Component = "Component",
    Domain_Function = "Key Domain/Function",
    Role_in_SEC = "Role in SEC",
    Druggability = "Druggability",
    Status = "Development Status"
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(
      columns = Status,
      rows = Status == "2 FDA approvals"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFF3E0"),
    locations = cells_body(
      columns = Status,
      rows = str_detect(Status, "development|trials")
    )
  ) %>%
  cols_width(
    Component ~ px(140),
    Domain_Function ~ px(140),
    Role_in_SEC ~ px(280),
    Druggability ~ px(100),
    Status ~ px(120)
  )

Table 9.1: Super Elongation Complex Components and Functions

Component	Key Domain/Function	Role in SEC	Druggability	Development Status
Super Elongation Complex Components
Molecular architecture and therapeutic targeting status
P-TEFb (CDK9/CycT1)	Kinase activity	Phosphorylates RNA Pol II Ser2; releases paused polymerase	High (kinase)	CDK9i in trials
MLLT1/ENL	YEATS chromatin reader	Recruits SEC to acetylated H3K9ac/H3K27ac chromatin	High (hydrophobic pocket)	TPD in development
MLLT3/AF9	YEATS chromatin reader	Recruits SEC to acetylated chromatin; AHD domain binds KMT2A	Moderate	TPD in development
AFF1/AFF4	Scaffold protein	Core scaffold; directly fused in KMT2A::AFF1/AFF4	Low	Not targeted
ELL1/ELL2	Elongation factor	Stimulates Pol II elongation rate	Low	Not targeted
Menin	Adapter protein	Bridges KMT2A to chromatin; NOT a constitutive SEC component	High (validated)	2 FDA approvals

9.2.2 Convergent SEC Dependency Across Leukemia Subtypes

Show code

sec_subtypes <- tibble(
  Subtype = c("KMT2A-rearranged AML/ALL", "NPM1-mutated AML", 
              "NUP98-rearranged AML", "UBTF tandem duplications",
              "HOX-high AML (other)", "Core-binding factor AML"),
  Frequency = c("5-10% adult AML; 70-80% infant ALL", "~30% adult AML",
                "4-7% pediatric AML", "~4% pediatric; ~3% adult AML",
                "~10-15% AML", "~15% adult AML"),
  SEC_Mechanism = c("Direct SEC recruitment via fusion partner",
                    "Wild-type KMT2A-mediated; HOX/MEIS1 dependency",
                    "NUP98 fusion interacts with MLL complex",
                    "Co-occupies chromatin with KMT2A/menin at HOX loci",
                    "Variable; requires HOX expression for response",
                    "HOX-low; alternative transcriptional programs"),
  Menin_Sensitivity = c("High", "Moderate-High", "High (preclinical)",
                        "Sensitive (case reports)", "Under investigation",
                        "NOT expected to benefit"),
  Prognosis = c("Poor/Intermediate", "Favorable (without FLT3)",
                "Very poor (<10% EFS)", "Poor", "Variable", "Favorable")
)

sec_subtypes %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "SEC-Dependent Leukemia Subtypes",
    subtitle = "Mechanism of SEC dependency and expected menin inhibitor sensitivity"
  ) %>%
  cols_label(
    Subtype = "Leukemia Subtype",
    Frequency = "Frequency",
    SEC_Mechanism = "Mechanism of SEC Dependency",
    Menin_Sensitivity = "Menin Inhibitor Sensitivity",
    Prognosis = "Baseline Prognosis"
  ) %>%
  tab_style(
    style = list(
      cell_fill(color = "#FFEBEE"),
      cell_text(weight = "bold")
    ),
    locations = cells_body(
      columns = Menin_Sensitivity,
      rows = Menin_Sensitivity == "NOT expected to benefit"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(
      columns = Menin_Sensitivity,
      rows = str_detect(Menin_Sensitivity, "^High")
    )
  ) %>%
  tab_footnote(
    footnote = "Core-binding factor AML includes t(8;21) and inv(16); these are HOX-low subtypes",
    locations = cells_body(columns = Subtype, rows = 6)
  )

Table 9.2: SEC-Dependent Leukemia Subtypes and Mechanisms

Leukemia Subtype	Frequency	Mechanism of SEC Dependency	Menin Inhibitor Sensitivity	Baseline Prognosis
SEC-Dependent Leukemia Subtypes
Mechanism of SEC dependency and expected menin inhibitor sensitivity
KMT2A-rearranged AML/ALL	5-10% adult AML; 70-80% infant ALL	Direct SEC recruitment via fusion partner	High	Poor/Intermediate
NPM1-mutated AML	~30% adult AML	Wild-type KMT2A-mediated; HOX/MEIS1 dependency	Moderate-High	Favorable (without FLT3)
NUP98-rearranged AML	4-7% pediatric AML	NUP98 fusion interacts with MLL complex	High (preclinical)	Very poor (<10% EFS)
UBTF tandem duplications	~4% pediatric; ~3% adult AML	Co-occupies chromatin with KMT2A/menin at HOX loci	Sensitive (case reports)	Poor
HOX-high AML (other)	~10-15% AML	Variable; requires HOX expression for response	Under investigation	Variable
Core-binding factor AML¹	~15% adult AML	HOX-low; alternative transcriptional programs	NOT expected to benefit	Favorable
¹ Core-binding factor AML includes t(8;21) and inv(16); these are HOX-low subtypes

9.3 FDA-Approved Menin Inhibitors: Clinical Data

9.3.1 Head-to-Head Comparison

Show code

menin_comparison <- tibble(
  Parameter = c(
    # Efficacy - NPM1m
    "CR/CRh (NPM1m)", "ORR (NPM1m)", "MRD negativity (NPM1m)",
    "Duration of CR/CRh (NPM1m)", "Bridge to transplant (NPM1m)",
    # Efficacy - KMT2Ar
    "CR/CRh (KMT2Ar)", "ORR (KMT2Ar)", "MRD negativity (KMT2Ar)",
    "Duration of CR/CRh (KMT2Ar)",
    # Safety
    "Differentiation syndrome (any)", "Differentiation syndrome (≥Gr3)",
    "QTc prolongation (any)", "QTc prolongation (≥Gr3)",
    "Febrile neutropenia", "Infections",
    # Practical
    "Dosing", "CYP3A4 inhibitor required", "Approval pathway",
    "Pediatric indication"
  ),
  Category = c(
    rep("Efficacy (NPM1m)", 5),
    rep("Efficacy (KMT2Ar)", 4),
    rep("Safety", 6),
    rep("Practical Considerations", 4)
  ),
  Revumenib = c(
    "23.4%", "46.9%", "78% of responders",
    "4.7 months", "28%",
    "22.8%", "63%", "70% of responders",
    "6.4 months",
    "27%", "16%",
    "23-29%", "13.8%",
    "32%", "55%",
    "BID (163mg with CYP3A4i)", "Yes (mandatory)",
    "Accelerated → Full", "Yes (≥1 year)"
  ),
  Ziftomenib = c(
    "22-23%", "33%", "65% of responders",
    "3.7-5 months", "~25%",
    "Development halted", "N/A", "N/A",
    "N/A",
    "15-25%", "13-15%",
    "Minimal", "Rare",
    "25%", "45%",
    "QD (600mg)", "No",
    "Full approval", "Under investigation"
  )
)

menin_comparison %>%
  gt(groupname_col = "Category") %>%
  theme_ash() %>%
  tab_header(
    title = "Revumenib vs Ziftomenib: Clinical Comparison",
    subtitle = "Data from AUGMENT-101 and KOMET-001 trials"
  ) %>%
  cols_label(
    Parameter = "Parameter",
    Revumenib = md("**Revumenib**<br>(SNDX-5613)"),
    Ziftomenib = md("**Ziftomenib**<br>(KO-539)")
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_body(
      columns = Revumenib,
      rows = Parameter %in% c("CR/CRh (NPM1m)", "CR/CRh (KMT2Ar)")
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_body(
      columns = Ziftomenib,
      rows = Parameter == "CR/CRh (NPM1m)"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFCDD2"),
    locations = cells_body(
      columns = everything(),
      rows = str_detect(Parameter, "Development halted")
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#C8E6C9"),
    locations = cells_body(
      columns = Ziftomenib,
      rows = Parameter %in% c("QTc prolongation (any)", "QTc prolongation (≥Gr3)")
    )
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_row_groups()
  ) %>%
  tab_source_note(
    source_note = "Sources: FDA approval documents; Issa et al. NEJM 2024; Perner et al. Lancet 2024"
  )

Table 9.3: Comprehensive Comparison of FDA-Approved Menin Inhibitors

Parameter	Revumenib (SNDX-5613)	Ziftomenib (KO-539)
Revumenib vs Ziftomenib: Clinical Comparison
Data from AUGMENT-101 and KOMET-001 trials
Efficacy (NPM1m)
CR/CRh (NPM1m)	23.4%	22-23%
ORR (NPM1m)	46.9%	33%
MRD negativity (NPM1m)	78% of responders	65% of responders
Duration of CR/CRh (NPM1m)	4.7 months	3.7-5 months
Bridge to transplant (NPM1m)	28%	~25%
Efficacy (KMT2Ar)
CR/CRh (KMT2Ar)	22.8%	Development halted
ORR (KMT2Ar)	63%	N/A
MRD negativity (KMT2Ar)	70% of responders	N/A
Duration of CR/CRh (KMT2Ar)	6.4 months	N/A
Safety
Differentiation syndrome (any)	27%	15-25%
Differentiation syndrome (≥Gr3)	16%	13-15%
QTc prolongation (any)	23-29%	Minimal
QTc prolongation (≥Gr3)	13.8%	Rare
Febrile neutropenia	32%	25%
Infections	55%	45%
Practical Considerations
Dosing	BID (163mg with CYP3A4i)	QD (600mg)
CYP3A4 inhibitor required	Yes (mandatory)	No
Approval pathway	Accelerated → Full	Full approval
Pediatric indication	Yes (≥1 year)	Under investigation
Sources: FDA approval documents; Issa et al. NEJM 2024; Perner et al. Lancet 2024

9.3.2 Frontline Combination Data

Show code

frontline_data <- tibble(
  Trial = c("BEAT AML", "BEAT AML", "Phase 1b/2"),
  Regimen = c(
    "Revumenib + Ven + Aza (NPM1m)",
    "Revumenib + Ven + Aza (KMT2Ar)",
    "Ziftomenib + 7+3 (KMT2Ar/NPM1m)"
  ),
  N = c(28, 15, 35),
  CR = c("67%", "47%", "46%"),
  CRc = c("81%", "73%", "69%"),
  MRD_Negativity = c("100%", "82%", "75%"),
  Median_OS = c("15.5 mo", "Not reached", "Not reported"),
  Bridge_to_HCT = c("42%", "53%", "38%"),
  Key_Toxicity = c(
    "DS 35%, QTc 28%",
    "DS 40%, QTc 33%",
    "DS 30%, Mucositis 45%"
  )
)

frontline_data %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "Frontline Menin Inhibitor Combinations",
    subtitle = "Preliminary data from ongoing trials in newly diagnosed AML"
  ) %>%
  cols_label(
    Trial = "Trial",
    Regimen = "Regimen",
    N = "N",
    CR = "CR",
    CRc = "CRc",
    MRD_Negativity = "MRD Neg",
    Median_OS = "mOS",
    Bridge_to_HCT = "→HCT",
    Key_Toxicity = "Key Toxicities"
  ) %>%
  tab_spanner(
    label = "Efficacy",
    columns = c(CR, CRc, MRD_Negativity, Median_OS, Bridge_to_HCT)
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(columns = MRD_Negativity, rows = MRD_Negativity == "100%")
  ) %>%
  tab_footnote(
    footnote = "Ven = venetoclax; Aza = azacitidine; DS = differentiation syndrome; HCT = transplant",
    locations = cells_column_labels(columns = Regimen)
  )

Table 9.4: Frontline Combination Trial Results

Trial	Regimen¹	N	Efficacy					Key Toxicities
Frontline Menin Inhibitor Combinations
Preliminary data from ongoing trials in newly diagnosed AML
Trial	Regimen¹	N	CR	CRc	MRD Neg	mOS	→HCT	Key Toxicities
BEAT AML	Revumenib + Ven + Aza (NPM1m)	28	67%	81%	100%	15.5 mo	42%	DS 35%, QTc 28%
BEAT AML	Revumenib + Ven + Aza (KMT2Ar)	15	47%	73%	82%	Not reached	53%	DS 40%, QTc 33%
Phase 1b/2	Ziftomenib + 7+3 (KMT2Ar/NPM1m)	35	46%	69%	75%	Not reported	38%	DS 30%, Mucositis 45%
¹ Ven = venetoclax; Aza = azacitidine; DS = differentiation syndrome; HCT = transplant

9.4 Resistance Mechanisms and Monitoring

9.4.1 MEN1 Resistance Mutations

Show code

resistance_data <- tibble(
  Mutation = c("M327I", "M327V", "T349M", "G331R", "G331D", 
               "S160C", "S160T", "T345A", "Y276C", "Other"),
  Location = c("Drug binding pocket", "Drug binding pocket", 
               "KMT2A interface", "Drug binding pocket", "Drug binding pocket",
               "Menin surface", "Menin surface", "KMT2A interface",
               "Drug binding pocket", "Various"),
  Frequency = c("18%", "8%", "12%", "7%", "5%", 
                "6%", "4%", "3%", "2%", "~35%"),
  Fold_Resistance_Revumenib = c("51x", "35x", "28x", "42x", "38x",
                                "15x", "12x", "22x", "18x", "Variable"),
  Cross_Resistance = c("Pan-class", "Pan-class", "Pan-class", 
                       "Pan-class", "Pan-class",
                       "Partial", "Partial", "Pan-class",
                       "Pan-class", "Variable"),
  Bleximenib_Activity = c("Retained (~10x)", "Retained", "Retained (~10x)",
                          "Reduced", "Reduced", 
                          "Retained", "Retained", "Reduced",
                          "Reduced", "Variable")
)

resistance_data %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "MEN1 Resistance Mutations in Menin Inhibitor-Treated Patients",
    subtitle = "Frequency and cross-resistance patterns"
  ) %>%
  cols_label(
    Mutation = "Mutation",
    Location = "Structural Location",
    Frequency = "Frequency",
    Fold_Resistance_Revumenib = "Fold ↑ IC50",
    Cross_Resistance = "Cross-Resistance",
    Bleximenib_Activity = "Bleximenib Activity"
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFCDD2"),
    locations = cells_body(
      columns = Cross_Resistance,
      rows = Cross_Resistance == "Pan-class"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#C8E6C9"),
    locations = cells_body(
      columns = Bleximenib_Activity,
      rows = str_detect(Bleximenib_Activity, "Retained")
    )
  ) %>%
  tab_footnote(
    footnote = "Mutations detected in ~39% of patients after ≥2 cycles; median time to detection ~56 days",
    locations = cells_column_labels(columns = Frequency)
  ) %>%
  tab_source_note(
    source_note = "Source: Perner et al. Nature 2023; ASH 2024 abstracts"
  )

Table 9.5: MEN1 Resistance Mutations Identified in Clinical Trials

Mutation	Structural Location	Frequency¹	Fold ↑ IC50	Cross-Resistance	Bleximenib Activity
MEN1 Resistance Mutations in Menin Inhibitor-Treated Patients
Frequency and cross-resistance patterns
M327I	Drug binding pocket	18%	51x	Pan-class	Retained (~10x)
M327V	Drug binding pocket	8%	35x	Pan-class	Retained
T349M	KMT2A interface	12%	28x	Pan-class	Retained (~10x)
G331R	Drug binding pocket	7%	42x	Pan-class	Reduced
G331D	Drug binding pocket	5%	38x	Pan-class	Reduced
S160C	Menin surface	6%	15x	Partial	Retained
S160T	Menin surface	4%	12x	Partial	Retained
T345A	KMT2A interface	3%	22x	Pan-class	Reduced
Y276C	Drug binding pocket	2%	18x	Pan-class	Reduced
Other	Various	~35%	Variable	Variable	Variable
¹ Mutations detected in ~39% of patients after ≥2 cycles; median time to detection ~56 days
Source: Perner et al. Nature 2023; ASH 2024 abstracts

9.4.2 Resistance Mechanisms Overview

Show code

resistance_mechanisms <- tibble(
  Mechanism = c(
    "MEN1 point mutations",
    "PRC1.1 component loss",
    "KAT6A upregulation",
    "UTX loss",
    "Chromatin state remodeling",
    "Clonal evolution"
  ),
  Frequency = c("~40%", "~15%", "~10%", "~5%", "~20%", "~10%"),
  Detection_Method = c(
    "NGS, cfDNA monitoring",
    "RNA-seq, protein expression",
    "ChIP-seq, expression",
    "WES/WGS",
    "ATAC-seq, ChIP-seq",
    "Serial NGS"
  ),
  Time_to_Emergence = c(
    "~56 days (median)",
    "Variable",
    "Rapid (days-weeks)",
    "At relapse",
    "Weeks to months",
    "At relapse"
  ),
  Potential_Strategies = c(
    "Next-gen menin inhibitors; MLLT1/3 degraders",
    "Venetoclax combination (hypersensitive)",
    "KAT6A inhibitors (PF-9363)",
    "Further investigation needed",
    "Epigenetic combinations",
    "Combination therapy upfront"
  )
)

resistance_mechanisms %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "Mechanisms of Resistance to Menin Inhibitors",
    subtitle = "Genetic and non-genetic resistance pathways"
  ) %>%
  cols_label(
    Mechanism = "Mechanism",
    Frequency = "Est. Frequency",
    Detection_Method = "Detection Method",
    Time_to_Emergence = "Timing",
    Potential_Strategies = "Potential Strategies"
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFF3E0"),
    locations = cells_body(rows = 1)
  ) %>%
  tab_footnote(
    footnote = "MEN1 mutations are the dominant mechanism and confer pan-class resistance",
    locations = cells_body(columns = Mechanism, rows = 1)
  )

Table 9.6: Comprehensive Resistance Mechanisms to Menin Inhibitors

Mechanism	Est. Frequency	Detection Method	Timing	Potential Strategies
Mechanisms of Resistance to Menin Inhibitors
Genetic and non-genetic resistance pathways
MEN1 point mutations¹	~40%	NGS, cfDNA monitoring	~56 days (median)	Next-gen menin inhibitors; MLLT1/3 degraders
PRC1.1 component loss	~15%	RNA-seq, protein expression	Variable	Venetoclax combination (hypersensitive)
KAT6A upregulation	~10%	ChIP-seq, expression	Rapid (days-weeks)	KAT6A inhibitors (PF-9363)
UTX loss	~5%	WES/WGS	At relapse	Further investigation needed
Chromatin state remodeling	~20%	ATAC-seq, ChIP-seq	Weeks to months	Epigenetic combinations
Clonal evolution	~10%	Serial NGS	At relapse	Combination therapy upfront
¹ MEN1 mutations are the dominant mechanism and confer pan-class resistance

9.5 MLLT1/3 Targeted Protein Degraders: ASH 2025 Data

9.5.1 Dark Blue Therapeutics MLLT-TPD (ASH 2025 Oral Presentation)

Show code

mllt_tpd_data <- tibble(
  Parameter = c(
    "Target", "Mechanism", "DC50 (MLLT1)",
    "Degradation kinetics", "Activity in cell lines",
    "Activity vs menin inhibitor",
    "Activity in MEN1-mutant cells",
    "In vivo activity",
    "Oral bioavailability",
    "Development status"
  ),
  MLLT_TPD = c(
    "MLLT1 (ENL) + MLLT3 (AF9)",
    "Targeted protein degradation via proteasome",
    "<1 nM",
    "Rapid and sustained (4 hours)",
    "~80% of AML/ALL cell lines sensitive",
    "Broader activity; affects genes menin inhibitor does not (e.g., MYC)",
    "Full activity retained against M327I, T349M, G331R mutations",
    "Marked anticancer activity in multiple AML xenografts at low oral doses",
    "Orally bioavailable",
    "IND-enabling; acquired by Amgen (Jan 2026)"
  ),
  Significance = c(
    "Targets constitutive SEC components vs menin adapter",
    "Complete protein elimination vs inhibition",
    "Highly potent; comparable to best PROTACs",
    "Faster SEC disruption vs menin inhibitors",
    "Includes cell lines insensitive to menin inhibitors",
    "MYC transcription blocked by MLLT-TPD but not menin inhibitor",
    "Bypasses dominant resistance mechanism",
    "Well-tolerated with strong efficacy signal",
    "Practical for chronic administration",
    "Validation: $840M acquisition by Amgen"
  )
)

mllt_tpd_data %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "MLLT1/3 Targeted Protein Degrader (Dark Blue Therapeutics)",
    subtitle = "ASH 2025 Oral Presentation: Best-in-Class SEC Targeting"
  ) %>%
  cols_label(
    Parameter = "Parameter",
    MLLT_TPD = "MLLT-TPD Data",
    Significance = "Clinical Significance"
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(
      rows = Parameter == "Activity in MEN1-mutant cells"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_body(
      rows = Parameter == "Activity vs menin inhibitor"
    )
  ) %>%
  tab_source_note(
    source_note = "Source: Maloney et al. Blood 2025;146(Suppl 1):320-321. DOI: 10.1182/blood-2025-320"
  )

Table 9.7: MLLT1/3 TPD Preclinical Data from ASH 2025

Parameter	MLLT-TPD Data	Clinical Significance
MLLT1/3 Targeted Protein Degrader (Dark Blue Therapeutics)
ASH 2025 Oral Presentation: Best-in-Class SEC Targeting
Target	MLLT1 (ENL) + MLLT3 (AF9)	Targets constitutive SEC components vs menin adapter
Mechanism	Targeted protein degradation via proteasome	Complete protein elimination vs inhibition
DC50 (MLLT1)	<1 nM	Highly potent; comparable to best PROTACs
Degradation kinetics	Rapid and sustained (4 hours)	Faster SEC disruption vs menin inhibitors
Activity in cell lines	~80% of AML/ALL cell lines sensitive	Includes cell lines insensitive to menin inhibitors
Activity vs menin inhibitor	Broader activity; affects genes menin inhibitor does not (e.g., MYC)	MYC transcription blocked by MLLT-TPD but not menin inhibitor
Activity in MEN1-mutant cells	Full activity retained against M327I, T349M, G331R mutations	Bypasses dominant resistance mechanism
In vivo activity	Marked anticancer activity in multiple AML xenografts at low oral doses	Well-tolerated with strong efficacy signal
Oral bioavailability	Orally bioavailable	Practical for chronic administration
Development status	IND-enabling; acquired by Amgen (Jan 2026)	Validation: $840M acquisition by Amgen
Source: Maloney et al. Blood 2025;146(Suppl 1):320-321. DOI: 10.1182/blood-2025-320

9.5.2 MLLT-TPD vs Menin Inhibitor: Mechanistic Comparison

Show code

mllt_comparison <- tibble(
  Feature = c(
    "Target in SEC",
    "SEC dependency",
    "MYC transcription",
    "HOXA9/MEIS1 suppression",
    "LSC signature",
    "Differentiation induction",
    "MEN1 mutation resistance",
    "Non-KMT2Ar/NPM1m AML",
    "RUNX1-RUNX1T1 activity"
  ),
  MLLT_TPD = c(
    "MLLT1/3 (core scaffolding proteins)",
    "All SECs depend on MLLT1/3",
    "Blocked",
    "Rapid downregulation",
    "Rapid downregulation",
    "Mature myeloid signatures upregulated",
    "Full activity retained",
    "Active in multiple subtypes",
    "Active; downregulates RUNX1-fusion transcript"
  ),
  Menin_Inhibitor = c(
    "Menin (adapter protein)",
    "Only KMT2A-containing SECs",
    "NOT blocked",
    "Slower downregulation",
    "Slower downregulation",
    "Differentiation induction",
    "Resistance develops in ~40%",
    "Limited activity in most",
    "No activity"
  ),
  Implication = c(
    "MLLT-TPD targets universal SEC requirement",
    "Broader applicability expected",
    "Key oncogene not addressed by menin inhibitors",
    "More rapid transcriptional collapse",
    "More rapid LSC targeting",
    "Both induce differentiation",
    "MLLT-TPD may rescue resistant patients",
    "Expanded patient population",
    "New indication for MLLT-TPD"
  )
)

mllt_comparison %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "MLLT-TPD vs Menin Inhibitors: Key Differentiators",
    subtitle = "Mechanistic advantages supporting best-in-class potential"
  ) %>%
  cols_label(
    Feature = "Feature",
    MLLT_TPD = "MLLT-TPD",
    Menin_Inhibitor = "Menin Inhibitor",
    Implication = "Clinical Implication"
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(
      columns = MLLT_TPD,
      rows = Feature %in% c("MYC transcription", "MEN1 mutation resistance", 
                           "Non-KMT2Ar/NPM1m AML", "RUNX1-RUNX1T1 activity")
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFCDD2"),
    locations = cells_body(
      columns = Menin_Inhibitor,
      rows = Feature %in% c("MYC transcription", "MEN1 mutation resistance",
                           "Non-KMT2Ar/NPM1m AML", "RUNX1-RUNX1T1 activity")
    )
  )

Table 9.8: Mechanistic Comparison: MLLT-TPD vs Menin Inhibitors

Feature	MLLT-TPD	Menin Inhibitor	Clinical Implication
MLLT-TPD vs Menin Inhibitors: Key Differentiators
Mechanistic advantages supporting best-in-class potential
Target in SEC	MLLT1/3 (core scaffolding proteins)	Menin (adapter protein)	MLLT-TPD targets universal SEC requirement
SEC dependency	All SECs depend on MLLT1/3	Only KMT2A-containing SECs	Broader applicability expected
MYC transcription	Blocked	NOT blocked	Key oncogene not addressed by menin inhibitors
HOXA9/MEIS1 suppression	Rapid downregulation	Slower downregulation	More rapid transcriptional collapse
LSC signature	Rapid downregulation	Slower downregulation	More rapid LSC targeting
Differentiation induction	Mature myeloid signatures upregulated	Differentiation induction	Both induce differentiation
MEN1 mutation resistance	Full activity retained	Resistance develops in ~40%	MLLT-TPD may rescue resistant patients
Non-KMT2Ar/NPM1m AML	Active in multiple subtypes	Limited activity in most	Expanded patient population
RUNX1-RUNX1T1 activity	Active; downregulates RUNX1-fusion transcript	No activity	New indication for MLLT-TPD

9.6 Alternative SEC-Targeting Approaches

9.6.1 CDK9 Inhibitors

Show code

cdk9_data <- tibble(
  Agent = c("SLS009 (tambiciclib)", "AZD4573", "VIP152", "Alvocidib/Flavopiridol"),
  Developer = c("SELLAS Life Sciences", "AstraZeneca", "Vincerx", "Tolero/Sumitomo"),
  CDK9_IC50 = c("<1 nM", "<3 nM", "<3 nM", "~10 nM (pan-CDK)"),
  Selectivity = c(">100x vs CDK2", ">10x vs other CDKs", ">10x vs other CDKs", "Non-selective"),
  Stage = c("Phase 2a", "Phase 1/2", "Phase 1", "Deprioritized"),
  Key_Data = c(
    "33% ORR with Ven+Aza in R/R AML post-venetoclax; 56% in ASXL1-mutated",
    "Responses in hematologic malignancies; cardiac toxicity concerns",
    "Preclinical efficacy in CLL models",
    "Historical toxicity; hybridization with ara-C ongoing"
  ),
  Administration = c("IV", "IV", "IV", "IV")
)

cdk9_data %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "CDK9 Inhibitors Targeting P-TEFb",
    subtitle = "Clinical development status in acute leukemia"
  ) %>%
  cols_label(
    Agent = "Agent",
    Developer = "Developer",
    CDK9_IC50 = "CDK9 IC50",
    Selectivity = "Selectivity",
    Stage = "Stage",
    Key_Data = "Key Clinical Data",
    Administration = "Route"
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(rows = 1)
  ) %>%
  tab_footnote(
    footnote = "SLS009 showed particular activity in ASXL1-mutated disease (56% response)",
    locations = cells_body(columns = Agent, rows = 1)
  )

Table 9.9: CDK9 Inhibitors in Clinical Development for AML

Agent	Developer	CDK9 IC50	Selectivity	Stage	Key Clinical Data	Route
CDK9 Inhibitors Targeting P-TEFb
Clinical development status in acute leukemia
SLS009 (tambiciclib)¹	SELLAS Life Sciences	<1 nM	>100x vs CDK2	Phase 2a	33% ORR with Ven+Aza in R/R AML post-venetoclax; 56% in ASXL1-mutated	IV
AZD4573	AstraZeneca	<3 nM	>10x vs other CDKs	Phase 1/2	Responses in hematologic malignancies; cardiac toxicity concerns	IV
VIP152	Vincerx	<3 nM	>10x vs other CDKs	Phase 1	Preclinical efficacy in CLL models	IV
Alvocidib/Flavopiridol	Tolero/Sumitomo	~10 nM (pan-CDK)	Non-selective	Deprioritized	Historical toxicity; hybridization with ara-C ongoing	IV
¹ SLS009 showed particular activity in ASXL1-mutated disease (56% response)

9.6.2 Comprehensive SEC-Targeting Pipeline

Show code

pipeline_data <- tibble(
  Target = c(
    "Menin", "Menin", "Menin", "Menin",
    "MLLT1/3", "MLLT1/3", 
    "CDK9", "CDK9",
    "ENL YEATS", "BRD4/BET"
  ),
  Agent = c(
    "Revumenib", "Ziftomenib", "Bleximenib (JNJ-75276617)", "BMF-219",
    "DBT 3757 (MLLT-TPD)", "MS41",
    "SLS009", "AZD4573",
    "TDI-11055", "Molibresib"
  ),
  Mechanism = c(
    "Menin-KMT2A inhibitor", "Menin-KMT2A inhibitor", 
    "Menin-KMT2A inhibitor (2nd gen)", "Covalent menin inhibitor",
    "MLLT1/3 degrader (TPD)", "ENL PROTAC degrader",
    "CDK9 inhibitor", "CDK9 inhibitor",
    "ENL YEATS inhibitor", "BET inhibitor"
  ),
  Stage = c(
    "FDA approved", "FDA approved", "Phase 2", "Phase 1",
    "IND-enabling", "Preclinical",
    "Phase 2a", "Phase 1/2",
    "Preclinical", "Phase 1/2"
  ),
  Key_Differentiation = c(
    "First-in-class; KMT2Ar + NPM1m", "Once-daily; NPM1m only", 
    "Activity vs some MEN1 mutations", "Covalent binding; may overcome mutations",
    "Bypasses MEN1 resistance; broader activity", "Academic PROTAC",
    "Active post-venetoclax; ASXL1-mut", "Selective CDK9",
    "Small molecule; oral", "SEC-adjacent mechanism"
  ),
  Developer = c(
    "Syndax", "Kura/Zeno", "J&J", "Biomea Fusion",
    "Amgen (Dark Blue)", "Academic",
    "SELLAS", "AstraZeneca",
    "Academic", "GSK"
  )
)

pipeline_data %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "SEC-Targeting Therapeutic Pipeline",
    subtitle = "Agents in development targeting the Super Elongation Complex"
  ) %>%
  cols_label(
    Target = "Target",
    Agent = "Agent",
    Mechanism = "Mechanism",
    Stage = "Stage",
    Key_Differentiation = "Key Differentiation",
    Developer = "Developer"
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(columns = Stage, rows = Stage == "FDA approved")
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFF3E0"),
    locations = cells_body(columns = Stage, rows = str_detect(Stage, "Phase"))
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_body(columns = Stage, rows = Stage == "IND-enabling")
  )

Table 9.10: SEC-Targeting Therapeutic Pipeline

Target	Agent	Mechanism	Stage	Key Differentiation	Developer
SEC-Targeting Therapeutic Pipeline
Agents in development targeting the Super Elongation Complex
Menin	Revumenib	Menin-KMT2A inhibitor	FDA approved	First-in-class; KMT2Ar + NPM1m	Syndax
Menin	Ziftomenib	Menin-KMT2A inhibitor	FDA approved	Once-daily; NPM1m only	Kura/Zeno
Menin	Bleximenib (JNJ-75276617)	Menin-KMT2A inhibitor (2nd gen)	Phase 2	Activity vs some MEN1 mutations	J&J
Menin	BMF-219	Covalent menin inhibitor	Phase 1	Covalent binding; may overcome mutations	Biomea Fusion
MLLT1/3	DBT 3757 (MLLT-TPD)	MLLT1/3 degrader (TPD)	IND-enabling	Bypasses MEN1 resistance; broader activity	Amgen (Dark Blue)
MLLT1/3	MS41	ENL PROTAC degrader	Preclinical	Academic PROTAC	Academic
CDK9	SLS009	CDK9 inhibitor	Phase 2a	Active post-venetoclax; ASXL1-mut	SELLAS
CDK9	AZD4573	CDK9 inhibitor	Phase 1/2	Selective CDK9	AstraZeneca
ENL YEATS	TDI-11055	ENL YEATS inhibitor	Preclinical	Small molecule; oral	Academic
BRD4/BET	Molibresib	BET inhibitor	Phase 1/2	SEC-adjacent mechanism	GSK

9.7 Expanded Patient Populations

Show code

expanded_pop <- tibble(
  Population = c(
    "KMT2A-rearranged AML/ALL",
    "NPM1-mutated AML",
    "NUP98-rearranged AML",
    "UBTF tandem duplications",
    "HOX-high AML (biomarker)",
    "Menin inhibitor-resistant",
    "Venetoclax-refractory AML"
  ),
  Frequency = c(
    "5-10% adult AML; 70-80% infant ALL",
    "~30% adult AML",
    "4-7% pediatric AML",
    "~4% pediatric; ~3% adult AML",
    "~50% of AML (under investigation)",
    "~40% of menin inhibitor-treated",
    "30-40% of treated patients"
  ),
  Current_Status = c(
    "FDA approved (revumenib)",
    "FDA approved (revumenib, ziftomenib)",
    "Clinical trials (NCT trials)",
    "Case reports; preclinical data",
    "Phase 2 trial (NCT06229912)",
    "Unmet need",
    "Unmet need"
  ),
  Menin_Inhibitor_Expected = c(
    "Effective", "Effective", "Effective (preclinical)",
    "Effective (limited data)", "Under investigation",
    "Resistant", "Variable"
  ),
  MLLT_TPD_Rationale = c(
    "Equivalent or superior activity expected",
    "Equivalent or superior activity expected",
    "Active if SEC-dependent",
    "Active if SEC-dependent",
    "Broader activity across HOX-high AML",
    "Bypasses MEN1 mutations",
    "Orthogonal mechanism"
  )
)

expanded_pop %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "Expanded Patient Populations for SEC-Targeting",
    subtitle = "Beyond KMT2Ar and NPM1m AML"
  ) %>%
  cols_label(
    Population = "Population",
    Frequency = "Estimated Frequency",
    Current_Status = "Current Status",
    Menin_Inhibitor_Expected = "Menin Inhibitor Activity",
    MLLT_TPD_Rationale = "MLLT-TPD Rationale"
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(
      columns = Current_Status,
      rows = str_detect(Current_Status, "FDA approved")
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFCDD2"),
    locations = cells_body(
      columns = Menin_Inhibitor_Expected,
      rows = Menin_Inhibitor_Expected == "Resistant"
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#E3F2FD"),
    locations = cells_body(
      columns = MLLT_TPD_Rationale,
      rows = str_detect(MLLT_TPD_Rationale, "Bypasses|Broader")
    )
  )

Table 9.11: Expanded Patient Populations for SEC-Targeting Strategies

Population	Estimated Frequency	Current Status	Menin Inhibitor Activity	MLLT-TPD Rationale
Expanded Patient Populations for SEC-Targeting
Beyond KMT2Ar and NPM1m AML
KMT2A-rearranged AML/ALL	5-10% adult AML; 70-80% infant ALL	FDA approved (revumenib)	Effective	Equivalent or superior activity expected
NPM1-mutated AML	~30% adult AML	FDA approved (revumenib, ziftomenib)	Effective	Equivalent or superior activity expected
NUP98-rearranged AML	4-7% pediatric AML	Clinical trials (NCT trials)	Effective (preclinical)	Active if SEC-dependent
UBTF tandem duplications	~4% pediatric; ~3% adult AML	Case reports; preclinical data	Effective (limited data)	Active if SEC-dependent
HOX-high AML (biomarker)	~50% of AML (under investigation)	Phase 2 trial (NCT06229912)	Under investigation	Broader activity across HOX-high AML
Menin inhibitor-resistant	~40% of menin inhibitor-treated	Unmet need	Resistant	Bypasses MEN1 mutations
Venetoclax-refractory AML	30-40% of treated patients	Unmet need	Variable	Orthogonal mechanism

9.8 Summary Visualization

Show code

# Create summary data for visualization
summary_data <- tibble(
  Agent_Class = factor(c(
    "Menin Inhibitors\n(Revumenib)", 
    "Menin Inhibitors\n(Ziftomenib)",
    "Next-Gen Menin\n(Bleximenib)",
    "MLLT1/3 TPD\n(Dark Blue)",
    "CDK9 Inhibitors\n(SLS009)"
  ), levels = c(
    "Menin Inhibitors\n(Revumenib)", 
    "Menin Inhibitors\n(Ziftomenib)",
    "Next-Gen Menin\n(Bleximenib)",
    "MLLT1/3 TPD\n(Dark Blue)",
    "CDK9 Inhibitors\n(SLS009)"
  )),
  Development_Stage = c(5, 5, 3, 2, 3),
  Efficacy_KMT2Ar = c(4, 1, 4, 5, 3),
  Efficacy_NPM1m = c(4, 4, 4, 4, 3),
  Resistance_Profile = c(2, 2, 3, 5, 4),
  Broader_Activity = c(2, 2, 2, 5, 3)
)

# Reshape for radar-style bar chart
summary_long <- summary_data %>%
  pivot_longer(
    cols = -Agent_Class,
    names_to = "Metric",
    values_to = "Score"
  ) %>%
  mutate(
    Metric = factor(Metric, 
                    levels = c("Development_Stage", "Efficacy_KMT2Ar", 
                              "Efficacy_NPM1m", "Resistance_Profile", "Broader_Activity"),
                    labels = c("Development Stage", "KMT2Ar Efficacy", 
                              "NPM1m Efficacy", "Resistance Profile", "Broader Activity"))
  )

ggplot(summary_long, aes(x = Metric, y = Score, fill = Agent_Class)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.7) +
  scale_fill_manual(values = c(
    "Menin Inhibitors\n(Revumenib)" = "#1E88E5",
    "Menin Inhibitors\n(Ziftomenib)" = "#43A047",
    "Next-Gen Menin\n(Bleximenib)" = "#FB8C00",
    "MLLT1/3 TPD\n(Dark Blue)" = "#8B0000",
    "CDK9 Inhibitors\n(SLS009)" = "#7B1FA2"
  )) +
  scale_y_continuous(breaks = 1:5, labels = c("1-Low", "2", "3-Mod", "4", "5-High"),
                     limits = c(0, 5.5)) +
  labs(
    title = "Comparative Assessment of SEC-Targeting Approaches",
    subtitle = "Scoring: 1 = Low/Early, 5 = High/Approved",
    x = NULL,
    y = "Score",
    fill = "Agent Class"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title = element_text(face = "bold", size = 16, color = "#1E3A5F"),
    plot.subtitle = element_text(size = 11, color = "#666666"),
    axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
    legend.position = "bottom",
    legend.title = element_text(face = "bold"),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank()
  ) +
  geom_hline(yintercept = c(1, 3, 5), linetype = "dashed", alpha = 0.3)

Figure 9.1: SEC-Targeting Therapeutic Landscape

9.9 Clinical Implications and Recommendations

Show code

recommendations <- tibble(
  Clinical_Scenario = c(
    "Newly diagnosed KMT2Ar AML",
    "Newly diagnosed NPM1m AML",
    "R/R KMT2Ar acute leukemia",
    "R/R NPM1m AML",
    "MEN1 mutation at relapse",
    "Venetoclax-refractory AML",
    "NUP98-rearranged AML",
    "UBTF-TD AML"
  ),
  Recommended_Approach = c(
    "Consider clinical trial with menin inhibitor + standard induction",
    "Consider clinical trial with menin inhibitor + Ven/Aza",
    "Revumenib (FDA approved)",
    "Revumenib or Ziftomenib (both FDA approved)",
    "Clinical trial with next-gen menin inhibitor or MLLT-TPD",
    "Consider SLS009 + Ven/Aza trial; MLLT-TPD when available",
    "Revumenib (clinical trial); MLLT-TPD when available",
    "Revumenib (clinical trial); data limited"
  ),
  Monitoring = c(
    "MRD by flow/NGS; consider cfDNA for MEN1 mutations",
    "MRD by flow/NGS; consider cfDNA for MEN1 mutations",
    "MRD; cfDNA monitoring for MEN1 mutations",
    "MRD; cfDNA monitoring for MEN1 mutations",
    "Confirm mutation; evaluate bleximenib or MLLT-TPD trials",
    "Standard monitoring; consider CDK9i trial",
    "MRD; research on SEC biomarkers",
    "MRD; limited published guidance"
  ),
  Key_Considerations = c(
    "EVOLVE trials ongoing; bridge to HCT",
    "High MRD negativity rates with triplet",
    "DS prophylaxis; QTc monitoring (revumenib)",
    "Choose based on tolerability profile",
    "MEN1 mutations confer pan-class resistance to current menin inhibitors",
    "Orthogonal mechanisms may overcome resistance",
    "Very poor prognosis; early HCT if responding",
    "Emerging entity; case-by-case evaluation"
  )
)

recommendations %>%
  gt() %>%
  theme_ash() %>%
  tab_header(
    title = "Clinical Decision Framework for SEC-Targeting",
    subtitle = "Recommendations based on disease characteristics and treatment history"
  ) %>%
  cols_label(
    Clinical_Scenario = "Clinical Scenario",
    Recommended_Approach = "Recommended Approach",
    Monitoring = "Monitoring",
    Key_Considerations = "Key Considerations"
  ) %>%
  tab_style(
    style = cell_fill(color = "#E8F5E9"),
    locations = cells_body(
      columns = Recommended_Approach,
      rows = str_detect(Recommended_Approach, "FDA approved")
    )
  ) %>%
  tab_style(
    style = cell_fill(color = "#FFF3E0"),
    locations = cells_body(
      columns = Recommended_Approach,
      rows = str_detect(Recommended_Approach, "Clinical trial|trial")
    )
  )

Table 9.12: Clinical Recommendations for SEC-Targeting Strategies

Clinical Scenario	Recommended Approach	Monitoring	Key Considerations
Clinical Decision Framework for SEC-Targeting
Recommendations based on disease characteristics and treatment history
Newly diagnosed KMT2Ar AML	Consider clinical trial with menin inhibitor + standard induction	MRD by flow/NGS; consider cfDNA for MEN1 mutations	EVOLVE trials ongoing; bridge to HCT
Newly diagnosed NPM1m AML	Consider clinical trial with menin inhibitor + Ven/Aza	MRD by flow/NGS; consider cfDNA for MEN1 mutations	High MRD negativity rates with triplet
R/R KMT2Ar acute leukemia	Revumenib (FDA approved)	MRD; cfDNA monitoring for MEN1 mutations	DS prophylaxis; QTc monitoring (revumenib)
R/R NPM1m AML	Revumenib or Ziftomenib (both FDA approved)	MRD; cfDNA monitoring for MEN1 mutations	Choose based on tolerability profile
MEN1 mutation at relapse	Clinical trial with next-gen menin inhibitor or MLLT-TPD	Confirm mutation; evaluate bleximenib or MLLT-TPD trials	MEN1 mutations confer pan-class resistance to current menin inhibitors
Venetoclax-refractory AML	Consider SLS009 + Ven/Aza trial; MLLT-TPD when available	Standard monitoring; consider CDK9i trial	Orthogonal mechanisms may overcome resistance
NUP98-rearranged AML	Revumenib (clinical trial); MLLT-TPD when available	MRD; research on SEC biomarkers	Very poor prognosis; early HCT if responding
UBTF-TD AML	Revumenib (clinical trial); data limited	MRD; limited published guidance	Emerging entity; case-by-case evaluation

9.10 Conclusions

Key Takeaways

Menin inhibitors are now standard of care for R/R KMT2Ar and NPM1m acute leukemia, with revumenib (KMT2Ar + NPM1m) and ziftomenib (NPM1m) both FDA-approved.
MEN1 resistance mutations emerge in ~40% of patients within 2 cycles, representing the dominant resistance mechanism with pan-class cross-resistance.
MLLT1/3 degraders offer a mechanistically differentiated approach that bypasses MEN1 mutations and shows broader activity across leukemia subtypes, including menin inhibitor-resistant disease.
Frontline combinations (menin inhibitor + venetoclax + azacitidine) achieve remarkably high MRD negativity rates (up to 100%), supporting investigation in phase 3 trials.
cfDNA monitoring can detect emerging MEN1 mutations months before clinical relapse, potentially guiding treatment switches.
Patient populations extend beyond current approvals to include NUP98-rearranged, UBTF-TD, and potentially HOX-high AML regardless of genotype.

9.11 References

Maloney A, et al. MLLT1/3 targeted protein degradation as the best-in-class approach for targeting the super elongation complex (SEC) in acute leukemia, including menin inhibitor refractory disease. Blood 2025;146(Suppl 1):320-321. DOI: 10.1182/blood-2025-320
Issa GC, et al. Menin Inhibition With Revumenib for KMT2A-Rearranged Relapsed or Refractory Acute Leukemia (AUGMENT-101). J Clin Oncol 2024. DOI: 10.1200/JCO.24.00826
Perner F, et al. MEN1 mutations mediate clinical resistance to menin inhibition. Nature 2023;615:913-919. DOI: 10.1038/s41586-023-05755-9
Stein EM, et al. The menin inhibitor revumenib in KMT2A-rearranged or NPM1-mutant leukaemia. Nature 2023;615:920-924. DOI: 10.1038/s41586-023-05812-3
Wang E, et al. A potent and selective ENL degrader suppresses oncogenic gene expression and leukemia progression. Science Advances 2024;10:eado1432. DOI: 10.1126/sciadv.ado1432
Krivtsov AV, et al. A menin-MLL inhibitor induces specific chromatin changes and eradicates disease in models of MLL-rearranged leukemia. Cancer Cell 2019;36:660-673.

Document generated: 2026-08-19

This document is for educational and research purposes. Clinical decisions should be made in consultation with treating physicians and based on individual patient characteristics. ```