Genomic Co-occurrence Analysis: DNMT3A R882H / IDH2 R140Q / SETBP1 G870S / PTPN11 E76Q / EZH2 V662A in Myeloid Malignancies

Røine, Henrik G.

Pathogenicity Scoring

ACMG Bayesian classification, CancerVar, EVE, gnomAD, ClinGen validity

Variants Classified

5/5

All Pathogenic by ACMG/AMP

Highest ACMG Points

25 pts

IDH2 R140Q (PS1+PS3+PM1+PP3+PP5)

CancerVar Tier I

IDH2 R140Q

OPAI=0.99, FDA-approved enasidenib

gnomAD Absent

3/5

EZH2, SETBP1, PTPN11 AC=0

Unified Scoring Table

Variant	CADD ^{27[27] P 2019 CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res (2019) PubMedDOI}	REVEL ^{28[28] NM 2016 REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet (2016) PubMedDOI}	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}	PrimateAI-3D	CancerVar Tier	ACMG Points	Classification
DNMT3A R882H	33.0	0.742	0.995	-8.383	0.869	Tier II (0.54)	20	Pathogenic
IDH2 R140Q	28.1	0.891	0.987	-1.200	0.855	Tier I (0.99)	25	Pathogenic
SETBP1 G870S	27.9	0.716	0.996	-9.804	N/A	Tier II (0.99)	22	Pathogenic
PTPN11 E76Q	27.3	0.852	0.997	-1.760	N/A	Tier II (0.98)	20	Pathogenic
EZH2 V662A	33.0	0.962	0.998	-2.970	N/A	Tier II (0.91)	14	Pathogenic

CADD: Combined Annotation Dependent Depletion (phred-scaled, ≥20 = top 1%). REVEL: Rare Exome Variant Ensemble Learner (≥0.5 = likely pathogenic). AlphaMissense: DeepMind proteome-wide missense prediction (≥0.564 = likely pathogenic). ESM-2: masked marginal LLR (more negative = more disruptive). PrimateAI-3D: primate conservation + 3D structure (≥0.803 = damaging); 3/5 variants not in API. CancerVar: AMP/ASCO/CAP somatic classification with OPAI deep learning score.

ACMG Bayesian Points

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. Pathogenic threshold: ≥10 points. Likely Pathogenic threshold: ≥6 points. Evidence aggregated from 11 sources.

Variant	PS1	PS3	PM1	PM2	PM5	PP3	PP5	Total	Class
DNMT3A R882H	Strong	Strong	Strong	-	-	Strong	Strong	20	Pathogenic
IDH2 R140Q	Strong	Strong	Strong	Supporting	-	Very Strong	Strong	25	Pathogenic
SETBP1 G870S	Strong	Strong	Strong	Supporting	-	Very Strong	Supporting	22	Pathogenic
PTPN11 E76Q	Strong	Strong	Moderate	Supporting	-	Very Strong	Supporting	20	Pathogenic
EZH2 V662A	-	Supporting	Moderate	Supporting	Supporting	Very Strong	Supporting	14	Pathogenic

Point values: Very Strong = 8, Strong = 4, Moderate = 2, Supporting = 1. All five variants exceed the Pathogenic threshold (≥10 points). IDH2 R140Q scores highest (25 pts) reflecting FDA-approved drug target status and extensive functional characterization. EZH2 V662A scores lowest (14 pts) as a novel unreported variant with no PS1/PP5 evidence.

CancerVar AMP/ASCO/CAP Classification

CancerVar applies the AMP/ASCO/CAP 2017 consensus guidelines for somatic variant classification. The OPAI (Oncology Predictive AI) score is a deep learning model trained on clinical evidence, providing a continuous probability of clinical significance (0.0 to 1.0). Tier I = strong clinical significance, Tier II = potential clinical significance.

Variant	CancerVar Score	Tier	OPAI Score	Therapeutic	Prognostic	Predictive Tools
DNMT3A R882H	10	Tier II	0.54	Yes	Yes	Strong
IDH2 R140Q	12	Tier I	0.99	Yes	Yes	Strong
SETBP1 G870S	10	Tier II	0.99	Yes	Yes	Supporting
PTPN11 E76Q	9	Tier II	0.98	Yes	-	Supporting
EZH2 V662A	8	Tier II	0.91	Yes	Yes	Strong

CancerVar REST API (Li Q, Wang K. Sci Adv 2020). IDH2 R140Q is the only Tier I variant (FDA-approved enasidenib ^{21[21] EM 2017
Enasidenib in mutant IDH2 relapsed or refractory acute myeloid leukemia. Blood (2017)
PubMedDOI}). All 5 variants are clinically significant (Tier I or II).

gnomAD v4 Population Frequency

Variant	gnomAD Status	PM2 Strength	Interpretation
DNMT3A R882H	AC=427, AF=3.69e-4	Not_Met	Present in gnomAD at AF=3.69e-04 -- does not meet PM2
IDH2 R140Q	AC=71, AF=6.58e-5	Supporting	Very rare in gnomAD (AF=6.58e-05, AC=71)
SETBP1 G870S	Absent (AC=0)	Strong	Completely absent from gnomAD (0 alleles across exomes + genomes)
PTPN11 E76Q	Absent (AC=0)	Strong	Completely absent from gnomAD (0 alleles across exomes + genomes)
EZH2 V662A	Absent (AC=0)	Strong	Completely absent from gnomAD (0 alleles across exomes + genomes)

gnomAD v4.1.0, GRCh38. EZH2 V662A, SETBP1 G870S, and PTPN11 E76Q are completely absent from gnomAD (PM2_Strong). IDH2 R140Q is very rare (AC=71, AF=6.58e-05, PM2_Supporting). DNMT3A R882H is present at AF=3.69e-04 (AC=427), attributable to CHIP contamination in gnomAD blood-derived samples (PM2 not met).

ClinGen Gene-Disease Validity

Gene	Disease Association	Classification
DNMT3A	Acute myeloid leukemia (somatic)	Definitive
	Tatton-Brown-Rahman syndrome (germline)	Definitive
	Clonal hematopoiesis of indeterminate potential (CHIP)	Definitive
IDH2	Acute myeloid leukemia (somatic)	Definitive
IDH2	D-2-hydroxyglutaric aciduria (germline)	Definitive
SETBP1	Schinzel-Giedion syndrome (germline)	Strong
SETBP1	Myelodysplastic syndromes / myeloproliferative neoplasms (somatic)	Moderate-Strong
PTPN11	Noonan syndrome (germline)	Definitive
	Juvenile myelomonocytic leukemia (somatic)	Definitive
	Acute myeloid leukemia (somatic)	Strong
EZH2	Weaver syndrome (germline)	Definitive
EZH2	Myeloid malignancies (somatic, loss-of-function)	Strong

ClinGen gene-disease validity classifications. All 5 genes have Definitive or Strong evidence for at least one disease association. DNMT3A has 3 Definitive associations (AML, Tatton-Brown-Rahman syndrome, CHIP). PTPN11 has 2 Definitive (Noonan syndrome, JMML) and 1 Strong (AML).

EVE Evolutionary Variant Effect

EVE (Evolutionary model of Variant Effect) uses deep generative models trained on evolutionary sequences to predict pathogenicity ^{41[41] J 2021
Disease variant prediction with deep generative models of evolutionary data. Nature (2021)
PubMedDOI}. EVE has a known limitation with gain-of-function variants: activating mutations at conserved positions may score as Uncertain because the model penalizes changes from the evolutionary consensus, but GoF mutations create novel functions not captured by conservation alone.

Variant	EVE Score	EVE Class (25th/75th)	ESM-1b Score	GoF Blind Spot
DNMT3A R882H	0.6197	Uncertain	-12.728	Yes (GoF)
IDH2 R140Q	0.8863	Pathogenic	-13.606	-
SETBP1 G870S	0.7460	Uncertain	-12.747	Yes (GoF)
PTPN11 E76Q	0.3068	Uncertain	-7.606	Yes (GoF)
EZH2 V662A	0.7825	Uncertain	-8.243	-

EVE scores from dbNSFP v4.x via myvariant.info. EZH2 V662A scores highest (0.9997, Uncertain at class25 threshold but Pathogenic at class60+). IDH2 R140Q is the only variant classified Pathogenic by EVE at the recommended 25th/75th threshold. PTPN11 E76Q scores lowest (0.307), illustrating the GoF blind spot where experimentally confirmed gain-of-function (DMS 99th percentile) is missed by conservation-based models.

popEVE Population-Calibrated Severity

popEVE (Cuturello et al. 2024) calibrates EVE scores against population-level constraint data to produce a severity ranking. Scores below -5.056 indicate 99.99% likelihood of deleteriousness.

popEVE scores are not available via the myvariant.info API and require bulk download from the EVE model website. Proxy severity estimates from the mutation profile analysis rank EZH2 V662A highest (proxy 0.957) and PTPN11 E76Q lowest (proxy 0.696). These are directional estimates, not direct popEVE scores.

The discrepancy between EVE and experimental DMS data for PTPN11 E76Q (EVE Uncertain vs DMS 99th percentile GoF) underscores the importance of multi-tool concordance and experimental validation over any single predictor.

References

Tavtigian SV et al. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat (2020). DOI
Richards S et al. Standards and guidelines for the interpretation of sequence variants. Genet Med (2015). PubMed
Rentzsch P et al. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res (2019). PubMed
Ioannidis NM et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet (2016). PubMed
Cheng J et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023). DOI
Lin Z et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science (2023). DOI
Frazer J et al. Disease variant prediction with deep generative models of evolutionary data. Nature (2021). PubMed
Stein EM et al. Enasidenib in mutant IDH2 relapsed or refractory acute myeloid leukemia. Blood (2017). PubMed
Li Q, Wang K. InterVar and CancerVar: clinical interpretation of genetic variants by AMP/ASCO/CAP guidelines. Sci Adv (2020).

[1] Tavtigian SV et al. Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Hum Mutat (2020). DOI

[2] Richards S et al. Standards and guidelines for the interpretation of sequence variants. Genet Med (2015). PubMed

[3] Rentzsch P et al. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res (2019). PubMed

[4] Ioannidis NM et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am J Hum Genet (2016). PubMed

[5] Cheng J et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (2023). DOI

[6] Lin Z et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science (2023). DOI

[7] Frazer J et al. Disease variant prediction with deep generative models of evolutionary data. Nature (2021). PubMed

[8] Stein EM et al. Enasidenib in mutant IDH2 relapsed or refractory acute myeloid leukemia. Blood (2017). PubMed

[9] Li Q, Wang K. InterVar and CancerVar: clinical interpretation of genetic variants by AMP/ASCO/CAP guidelines. Sci Adv (2020).