Clonal Architecture
PyClone-VI Bayesian subclonal clustering, linear evolution model
Preferred Model Strongly favored
Linear
LL = -0.51 vs -14.11 (branching)
Founder Clone CCF 1.00
DNMT3A
R882H, diploid, dominant-negative methylation loss
Late Subclone CCF 0.05
IDH2
R140Q, 2% VAF, only druggable target (enasidenib)
Tumor Purity
0.78
Cross-validated: DNMT3A 0.78, EZH2 (monosomy 7) 0.74
REVOLVER Cohort
7,956 patients
Binary mutation matrix for evolutionary trajectory inference
Cancer Cell Fraction Estimates
Source: Bayesian CCF estimation using
beta-binomial model with Dirichlet Process Mixture clustering
16
[16] S 2020
PyClone-VI: scalable inference of clonal population structures using whole genome data. BMC Bioinformatics (2020)
. Purity estimated from highest diploid
VAF (DNMT3A R882H at 39%).
CCF Detail
| Gene | Variant | VAF | CCF | 95% CI | Pathway |
|---|---|---|---|---|---|
| EZH2 | V662A | 0.59 | 0.9228 | 0.854 to 0.984 | Polycomb complex (epigenetic) |
| DNMT3A | R882H | 0.39 | 1.0000 | 0.878 to 0.999 | DNA methyltransferase (epigenetic regulation) |
| SETBP1 | G870S | 0.34 | 0.8718 | 0.769 to 0.973 | MDS/MPN overlap, PP2A inhibition |
| PTPN11 | E76Q | 0.29 | 0.7436 | 0.646 to 0.849 | RAS-MAPK signaling (gain-of-function) |
| IDH2 | R140Q | 0.02 | 0.0513 | 0.028 to 0.093 | Metabolic (2-HG production) |
CCF computed as VAF * (purity * total_cn + normal_cn * (1-purity)) / (purity * mutant_cn). EZH2 on monosomy 7 (hemizygous).
Linear vs Branching Model
| Model | Log-Likelihood | Interpretation |
|---|---|---|
| Linear (sequential) | -0.51 | Strongly favored. Mutations acquired in strict temporal order. |
| Branching (parallel) | -14.11 | Disfavored. Independent subclones from shared ancestor. |
The linear model (LL = -0.51) is strongly favored over
branching (LL = -14.11), indicating sequential
mutation acquisition. The clonal hierarchy is:
DNMT3A R882H (CCF 1.00) → EZH2 V662A (0.92) → SETBP1 G870S (0.87) → PTPN11 E76Q (0.74) → IDH2 R140Q (0.05)
Each subsequent mutation is nested within the clone above, consistent with a single lineage accumulating driver events over time. The founding clone carries the epigenetic regulator DNMT3A; EZH2 V662A on monosomy 7 expands near-clonally before the MDS/MPN bridge mutation SETBP1 G870S.
DNMT3A R882H (CCF 1.00) → EZH2 V662A (0.92) → SETBP1 G870S (0.87) → PTPN11 E76Q (0.74) → IDH2 R140Q (0.05)
Each subsequent mutation is nested within the clone above, consistent with a single lineage accumulating driver events over time. The founding clone carries the epigenetic regulator DNMT3A; EZH2 V662A on monosomy 7 expands near-clonally before the MDS/MPN bridge mutation SETBP1 G870S.
Type 1 MDS-to-AML Progression
The clonal trajectory matches the Type 1 progression pattern defined by
Makishima et al.
15
PTPN11 E76Q (CCF 0.74) provides the proliferative RAS-MAPK signal for blast expansion. IDH2 R140Q (CCF 0.05) represents a late subclonal event producing 2-hydroxyglutarate, the only FDA-approved druggable target in this profile (enasidenib 21
The therapeutic paradox: the druggable mutation (IDH2) is the smallest subclone. Eliminating it with enasidenib would treat ~5% of the tumor mass. The dominant clones carrying DNMT3A, EZH2, SETBP1, and PTPN11 have no direct targeted therapy.
[15] H 2017
Dynamics of clonal evolution in myelodysplastic syndromes. Nat Genet (2017)
: signaling pathway mutations
(PTPN11, IDH2) are acquired during MDS-to-AML transformation, layered
on top of the founding epigenetic lesions.
PTPN11 E76Q (CCF 0.74) provides the proliferative RAS-MAPK signal for blast expansion. IDH2 R140Q (CCF 0.05) represents a late subclonal event producing 2-hydroxyglutarate, the only FDA-approved druggable target in this profile (enasidenib 21
[21] EM 2017
Enasidenib in mutant IDH2 relapsed or refractory acute myeloid leukemia. Blood (2017)
).
The therapeutic paradox: the druggable mutation (IDH2) is the smallest subclone. Eliminating it with enasidenib would treat ~5% of the tumor mass. The dominant clones carrying DNMT3A, EZH2, SETBP1, and PTPN11 have no direct targeted therapy.
| Phase | Mutation | CCF | Role |
|---|---|---|---|
| Founding | DNMT3A R882H | 1.00 | Epigenetic initiator (dominant-negative methyltransferase) |
| Expansion | EZH2 V662A | 0.92 | Chromatin remodeling loss-of-function (PRC2 axis) |
| MDS bridge | SETBP1 G870S | 0.87 | PP2A inhibition, bridges epigenetic and proliferative axes |
| Transformation | PTPN11 E76Q | 0.74 | RAS-MAPK gain-of-function, blast proliferation signal |
| Late subclone | IDH2 R140Q | 0.05 | Metabolic (2-HG), only druggable target |
Type 1 progression pattern per Makishima et al. Nat Genet 2017.
Triple Epigenetic Catastrophe
The founding clone carries three convergent epigenetic regulators:
1. DNMT3A R882H (CCF 1.00): dominant-negative loss of DNA methyltransferase activity, reducing CpG methylation by ~80%.
2. EZH2 V662A (CCF 0.92): loss-of-function in the PRC2 catalytic subunit, reducing H3K27 trimethylation 10
3. IDH2 R140Q (CCF 0.05): neomorphic production of 2-hydroxyglutarate (2-HG), which inhibits TET2 and other alpha-ketoglutarate-dependent dioxygenases. Though subclonal, even low levels of 2-HG can disrupt the epigenetic landscape globally.
Together, these mutations attack three independent arms of the epigenetic machinery: DNA methylation (DNMT3A), histone methylation (EZH2/PRC2), and metabolic regulation of both (IDH2/TET2). No existing treatment protocol is designed to address this convergent epigenetic collapse. The resulting chromatin state is likely profoundly deregulated, creating a permissive environment for the signaling mutations (SETBP1, PTPN11) that drive transformation.
1. DNMT3A R882H (CCF 1.00): dominant-negative loss of DNA methyltransferase activity, reducing CpG methylation by ~80%.
2. EZH2 V662A (CCF 0.92): loss-of-function in the PRC2 catalytic subunit, reducing H3K27 trimethylation 10
[10] A 2020
Mutational mechanisms of EZH2 inactivation in myeloid neoplasms. Leukemia (2020)
.
3. IDH2 R140Q (CCF 0.05): neomorphic production of 2-hydroxyglutarate (2-HG), which inhibits TET2 and other alpha-ketoglutarate-dependent dioxygenases. Though subclonal, even low levels of 2-HG can disrupt the epigenetic landscape globally.
Together, these mutations attack three independent arms of the epigenetic machinery: DNA methylation (DNMT3A), histone methylation (EZH2/PRC2), and metabolic regulation of both (IDH2/TET2). No existing treatment protocol is designed to address this convergent epigenetic collapse. The resulting chromatin state is likely profoundly deregulated, creating a permissive environment for the signaling mutations (SETBP1, PTPN11) that drive transformation.
REVOLVER Evolutionary Trajectory Cohort
A binary mutation matrix of 7,956 myeloid patients across 34 target
genes has been prepared for REVOLVER
(Repeated Evolution in Cancer) analysis. This matrix enables inference
of repeated evolutionary trajectories across the GENIE v19.0 myeloid
cohort.
The patient's linear trajectory (DNMT3A → EZH2 → SETBP1 → PTPN11 → IDH2) can be compared against population-level evolutionary patterns to determine whether this ordering is recurrent or unique. Preliminary evidence from PyClone-VI 16
The patient's linear trajectory (DNMT3A → EZH2 → SETBP1 → PTPN11 → IDH2) can be compared against population-level evolutionary patterns to determine whether this ordering is recurrent or unique. Preliminary evidence from PyClone-VI 16
[16] S 2020
PyClone-VI: scalable inference of clonal population structures using whole genome data. BMC Bioinformatics (2020)
suggests the linear model is
strongly favored over branching, consistent with a single lineage
accumulating drivers in a stereotyped order.
| Parameter | Value |
|---|---|
| Total patients | 7,956 |
| Genes | 34 myeloid driver genes |
| Matrix format | Binary (mutated/wildtype) |
| Cohort source | GENIE v19.0, myeloid-filtered |
| IDH2-SETBP1 exclusivity | NOT mutually exclusive (DISCOVER permutation test) |
REVOLVER input matrix prepared from GENIE v19.0 myeloid cohort.
References
- Gillis S, Roth A. PyClone-VI: scalable inference of clonal population structures using whole genome data. BMC Bioinformatics (2020). DOI
- Makishima H, Yoshizato T, Yoshida K, et al. Dynamics of clonal evolution in myelodysplastic syndromes. Nat Genet (2017). PubMed
- Chase A, Score J, Lin F, et al. Mutational mechanisms of EZH2 inactivation in myeloid neoplasms. Leukemia (2020). PubMed
- Stein EM, DiNardo CD, Pollyea DA, et al. Enasidenib in mutant IDH2 relapsed or refractory acute myeloid leukemia. Blood (2017). PubMed