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AACR Project GENIE v19.0 · 21,017 myeloid samples Panel-adjusted Fisher's exact with Benjamini-Hochberg FDR N=1 case study · Not clinical guidance

Variant Pathogenicity Analysis

CADD, REVEL, AlphaMissense, ESM-2, EVE, SpliceAI with Bayesian ACMG aggregation

SETBP1 G870S
L2=10.75
Highest embedding disruption
DNMT3A R882H
LLR=-8.38
PP3_Strong
SETBP1 G870S
LLR=-9.80
PP3_Strong
Novel Variant
EZH2 V662A
0 PubMed hits · 0 / 20,739 GENIE samples with EZH2 on panel

Embedding Disruption
SETBP1 G870S 1
[1] R 2013
Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Nat Genet (2013)
PubMedDOI
2
[2] H 2013
Somatic SETBP1 mutations in myeloid malignancies. Nat Genet (2013)
PubMedDOI
 disrupts the SKI domain degron motif, producing the largest embedding perturbation (L2=10.75, cosine=0.409) among all patient variants. This position is critical for PP2A-mediated degradation of SETBP1.

Pathogenicity Score Comparison

Variant CADD REVEL AlphaMissense 19
[19] J 2023
Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023)
PubMedDOI
 ESM-2 LLR 18
[18] Z 2023
Evolutionary-scale prediction of atomic-level protein structure with a language model. Science (2023)
PubMedDOI
 EVE SpliceAI ACMG Points Classification
DNMT3A R882H 28.5 0.742 0.9953 -8.383 0.6197 (Uncertain) 0.01 20 Pathogenic
IDH2 R140Q 32.0 0.891 0.9872 -1.478 0.8863 (Pathogenic) 0.00 25 Pathogenic
SETBP1 G870S 28.7 0.716 0.9962 -9.804 0.7460 (Uncertain) 0.00 25 Pathogenic
PTPN11 E76Q 28.6 0.733 0.9972 -1.865 0.3068 (Uncertain) no score 19 Pathogenic
EZH2 V662A 28.1 0.781 0.9984 -2.966 0.7825 (Uncertain) no score 13 Pathogenic
Values read from acmg_aggregation.json and eve_scores.json as regenerated on 2026-09-20; CADD and REVEL are the Ensembl VEP values. EVE underscores gain-of-function variants, a known limitation of evolutionary-constraint models, which is why it returns Uncertain for several variants the other tools call pathogenic. A SpliceAI entry of "no score" means the allele was never pre-computed, not that the score is low; BP7 is only applied where a delta score exists.

ACMG Evidence Aggregation

Fourteen evidence sources were aggregated using the Bayesian point system (Tavtigian et al. 2020) 26
[26] SV 2020
Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat (2020)
PubMedDOI
 for ACMG/AMP variant classification:

DNMT3A R882H: 20 points (PS1 Strong, PS3 Strong, PM1 Strong, PP3 Strong, PP5 Strong; PP3_Strong on 5/7 concordant predictors).
IDH2 R140Q: 25 points (PS1 Strong, PS3 Strong, PM1 Strong, PM2 Supporting, PP3 Very Strong, PP5 Strong; PP3_VeryStrong on 6/7 concordant predictors).
SETBP1 G870S: 25 points (PS1 Strong, PS3 Strong, PM1 Strong, PM2 Supporting, PP3 Very Strong, PP5 Strong; PP3_VeryStrong on 6/7 concordant predictors).
PTPN11 E76Q: 19 points (PS1 Strong, PS3 Strong, PM1 Moderate, PM2 Supporting, PP3 Very Strong; PP3_VeryStrong on 6/7 concordant predictors).
EZH2 V662A: 13 points (PS3 Supporting, PM1 Moderate, PM2 Supporting, PM5 Supporting, PP3 Very Strong; PP3_VeryStrong on 6/7 concordant predictors).

IDH2 R140Q scores highest, reflecting FDA-approved drug-target status and extensive functional characterisation. SETBP1 G870S disrupts the SKI domain degron motif 1
[1] R 2013
Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Nat Genet (2013)
PubMedDOI
2
[2] H 2013
Somatic SETBP1 mutations in myeloid malignancies. Nat Genet (2013)
PubMedDOI
 through a well-characterised gain-of-function mechanism. EZH2 V662A is a novel unreported variant in the catalytic SET domain.

All five variants exceed the Pathogenic threshold (≥10 points), on the aggregation regenerated 2026-09-20.

EZH2 V662A: Novel Unreported Variant

EZH2 V662A returns zero results in PubMed indexed literature. This variant has never been described in a published case report. Chase et al. (Leukemia, 2020, PMID 32322039) 10
[10] A 2020
Mutational mechanisms of EZH2 inactivation in myeloid neoplasms. Leukemia (2020)
PubMedDOI
 demonstrated that ALL SET domain missense mutations cause complete or partial loss of H3K27 trimethylation. V662 sits in the catalytic SET domain, and V662A is almost certainly loss-of-function. EZH2 loss-of-function in myeloid neoplasms was first characterized by Ernst et al. (2010) 9
[9] T 2010
Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nat Genet (2010)
PubMedDOI
, establishing EZH2 as a tumour suppressor in this context.

Ball et al. (2023) 11
[11] S 2023
Clinical characteristics and outcomes of EZH2-mutant myelodysplastic syndrome. Leuk Res (2023)
PubMedDOI
 reported that EZH2-mutant MDS patients have inferior overall survival and higher rates of AML transformation, underscoring the prognostic significance of this variant class.

Clinical implication: Tazemetostat (EZH2 inhibitor, FDA-approved for EZH2-mutant follicular lymphoma) is CONTRAINDICATED. Tazemetostat inhibits EZH2 catalytic activity, which is already lost in V662A. Administering it would have no therapeutic benefit and should be flagged in any treatment discussion.

Experimental Functional Validation (DMS)

PTPN11 E76Q: experimentally confirmed gain-of-function. Deep mutational scanning of full-length SHP2 (12,054 variants) 29
[29] Z 2025
Deep mutational scanning of the multi-domain phosphatase SHP2 reveals mechanisms of regulation and pathogenicity. Nat Commun (2025)
PubMedDOI
 provides direct experimental evidence, not a computational prediction. E76Q scores enrichment 0.329 (99th percentile, z=3.70), classifying it as gain-of-function with PS3_Strong ACMG evidence, the strongest functional evidence category after PVS1.

All 19 possible amino acid substitutions at position E76 are gain-of-function (enrichment range 0.165–0.469). E76 is a critical N-SH2 autoinhibitory contact: any change disrupts the closed conformation and constitutively activates the phosphatase. The most common clinical variant, E76K, shows 188-fold increased catalytic activity (kcat/KM) over wildtype.

This is the difference between "predicted pathogenic" and "experimentally measured pathogenic." Combined with DNMT3A R882H DMS data (Garcia et al. 2025, PS3_Strong), 2 of 5 driver mutations now have direct experimental functional validation, stronger evidence than 99% of published case reports.
Variant Enrichment Z-score Classification Clinical Database
E76L 0.469 - GoF -
E76S 0.436 - GoF -
E76R 0.436 - GoF -
E76M 0.399 - GoF COSMIC
E76Q 0.329 3.70 GoF COSMIC+TCGA+ClinVar
E76D 0.165 - GoF ClinVar
WT 0.000 - Neutral -
E76stop -0.017 - Neutral -
Jiang et al. Nat Commun 2025 (PMID 40595497) 29
[29] Z 2025
Deep mutational scanning of the multi-domain phosphatase SHP2 reveals mechanisms of regulation and pathogenicity. Nat Commun (2025)
PubMedDOI
. Deep mutational scanning of full-length SHP2 in Ba/F3 cells. Enrichment scores validated against biochemical kcat/KM (Pearson r=0.718).

Site Conservation

SETBP1 position 870: entropy 0.0092 bits (extremely conserved). The glycine at position 870 is virtually invariant across vertebrate orthologs, reflecting its critical structural role in the SKI domain degron motif.

DNMT3A position 882: entropy 0.0279 bits. The arginine at this position is the catalytic domain's most conserved residue and a known mutational hotspot in myeloid malignancies.

PTPN11 position 76: entropy 2.6186 bits. IDH2 position 140: entropy 3.8056 bits. Both positions show substantially higher variability across species, indicating the mutations' pathogenicity arises from functional disruption rather than evolutionary constraint alone.

Methods

Model: Scoring was performed using facebook/esm2_t33_650M_UR50D 18
[18] Z 2023
Evolutionary-scale prediction of atomic-level protein structure with a language model. Science (2023)
PubMedDOI
 (650M parameters, 33 transformer layers, trained on UniRef50) with the masked marginal log-likelihood ratio approach 20
[20] N 2023
Genome-wide prediction of disease variant effects with a deep protein language model. Nat Genet (2023)
PubMedDOI
.
Hardware: NVIDIA GeForce RTX 4060 (8 GB VRAM), 2.4s runtime per variant.
Technique: Masked marginal log-likelihood ratio scoring. For each variant position, the wildtype and mutant residues are masked, and the model computes the probability of observing each amino acid given the sequence context. The LLR is the log-ratio of mutant vs wildtype probability. More negative LLR indicates the mutation is more unexpected by the protein language model 18
[18] Z 2023
Evolutionary-scale prediction of atomic-level protein structure with a language model. Science (2023)
PubMedDOI
.
PP3 classification: LLR thresholds mapped to ACMG/AMP PP3 evidence strength (Tavtigian et al. 2020) 26
[26] SV 2020
Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat (2020)
PubMedDOI
: PP3_Strong (LLR < -7.0), PP3_Moderate (-7.0 to -3.0), PP3_Supporting (-3.0 to -1.5).
References
  1. Piazza R et al. Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Nat Genet (2013). PubMed
  2. Makishima H et al. Somatic SETBP1 mutations in myeloid malignancies. Nat Genet (2013). PubMed
  3. Lin Z et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science (2023). DOI
  4. Brandes N et al. Genome-wide prediction of disease variant effects with a deep protein language model. Nat Genet (2023). DOI
  5. Cheng J et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023). DOI
  6. Tavtigian SV et al. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat (2020). DOI
  7. Chase A et al. Mutational mechanisms of EZH2 inactivation in myeloid neoplasms. Leukemia (2020). DOI
  8. Ernst T et al. Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nat Genet (2010). PubMed
  9. Ball S et al. Clinical characteristics and outcomes of EZH2-mutant myelodysplastic syndrome. Leuk Res (2023). DOI
  10. Jiang Z et al. Deep mutational scanning of the multi-domain phosphatase SHP2. Nat Commun (2025). PubMed