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

Co-occurrence Analysis

190 gene-pair Fisher's exact tests, Benjamini-Hochberg FDR, quintuple search

Gene Pairs Tested
190
Significant (BH)
144
p<0.05 after correction
Mutual Exclusions
29
Strongest
FLT3+NPM1
O/E = 9.21
Quintuple Expected
7.7 × 10⁻¹³
1 in 1.3 trillion under independence

Co-occurrence Heatmap

Source: Fisher's exact test 31
[31] C 2013
Mutational landscape and significance across 12 major cancer types. Nature (2013)
, Benjamini-Hochberg FDR correction. GENIE v19.0 17
[17] Consortium 2017
AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov (2017)
, 20,820 myeloid samples. Mutual exclusivity testing follows the pairwise-corrected estimate framework 30
[30] S 2016
A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol (2016)
. Color scale: log2(O/E). Blue = mutual exclusion, red = co-occurrence.

SETBP1 Co-mutation Landscape (OncoPrint)

Figure: SETBP1 co-mutation landscape. 271 SETBP1-mutated myeloid patients from GENIE v19.0 17
[17] Consortium 2017
AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov (2017)
. Columns = patients (sorted by mutation count), rows = 20 myeloid driver genes. Green = missense mutation. Patient's 5 genes highlighted in red. Zero patients carry all five simultaneously.

Top Co-occurrences

Gene PairObservedExpectedO/Ep-valueBH q-valueDirection
FLT3 + NPM142446.19.21< 1e-300< 1e-300Co-occurring
ASXL1 + SRSF2729226.53.221.22e-225< 1e-300Co-occurring
TET2 + SRSF2741244.03.041.32e-212< 1e-300Co-occurring
SRSF2 + RUNX1554151.43.662.75e-191< 1e-300Co-occurring
ASXL1 + RUNX1701235.52.981.72e-190< 1e-300Co-occurring
ASXL1 + EZH239493.44.221.40e-166< 1e-300Co-occurring
DNMT3A + NPM1443118.03.751.24e-162< 1e-300Co-occurring
SRSF2 + IDH237586.14.352.28e-153< 1e-300Co-occurring
ASXL1 + SETBP128957.25.062.06e-151< 1e-300Co-occurring
ASXL1 + STAG234479.54.331.43e-150< 1e-300Co-occurring
SRSF2 + STAG225948.75.312.76e-127< 1e-300Co-occurring
RUNX1 + EZH227662.44.429.23e-112< 1e-300Co-occurring
DNMT3A + FLT3396139.92.831.90e-94< 1e-300Co-occurring
TET2 + ASXL1761383.51.981.95e-94< 1e-300Co-occurring
RUNX1 + BCOR24057.34.198.05e-91< 1e-300Co-occurring

SETBP1 Partnerships

PartnerCo-mutatedExpectedO/Ep-valueBH q-valueDirection
ASXL114530.24.812.12e-72< 1e-300Co-occurring
SRSF27920.23.904.06e-28< 1e-300Co-occurring
CBL326.35.082.11e-14< 1e-300Co-occurring
EZH2368.54.241.18e-13< 1e-300Co-occurring
CSF3R181.89.931.38e-13< 1e-300Co-occurring
U2AF13910.73.641.36e-12< 1e-300Co-occurring
RUNX13919.12.041.58e-57.40e-5Co-occurring
NRAS2912.92.263.31e-51.37e-4Co-occurring
JAK21431.90.442.16e-47.91e-4Exclusive
PTPN11155.82.606.90e-40.0023Co-occurring
NPM1 Depleted212.10.177.87e-40.0024Exclusive
GATA2114.12.670.00280.0076Co-occurring
IDH1 Depleted17.50.130.00750.0191Exclusive
TP531424.60.570.02250.0531Exclusive
SMC341.23.380.03000.0661Co-occurring
BCORL103.80.000.03480.0719Exclusive
STAG2116.31.740.06440.1251Co-occurring
DDX4120.44.590.06910.1266Co-occurring
WT136.80.440.16640.2855Exclusive
RAD2142.21.840.17300.2855Co-occurring
SF3B1813.10.610.19180.3014Exclusive
CALR47.30.550.25480.3822Exclusive
ZRSR264.01.490.30140.4325Co-occurring
CEBPA24.40.460.33030.4542Exclusive
SMC1A01.20.000.63340.8293Exclusive
FLT31011.90.840.65340.8293Exclusive
DNMT3A3028.31.060.68530.8376Co-occurring
BCOR98.01.120.71410.8417Co-occurring
TET23435.50.960.85340.9711Exclusive
KRAS88.30.961.00001.0000Exclusive
IDH21212.40.961.00001.0000Exclusive
MPL33.50.861.00001.0000Exclusive
PHF655.20.971.00001.0000Exclusive
IDH1 depletion (O/E=0.13) and NPM1 depletion (O/E=0.17) stand out as the strongest mutual exclusions with SETBP1 mutations 1
[1] R 2013
Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Nat Genet (2013)
2
[2] H 2013
Somatic SETBP1 mutations in myeloid malignancies. Nat Genet (2013)
. This is biologically consistent: SETBP1-mutant clones occupy a distinct ontogenetic niche (MDS/MPN overlap, CNL, aCML) where NPM1 mutations are rare and IDH1 mutations preferentially co-occur with NPM1 in AML 12
[12] E 2016
Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med (2016)
. The strongest positive associations are CSF3R (O/E=9.93, p=1.4 × 10−13) and ASXL1 (O/E=4.81, p=2.1 × 10−72), reflecting the classical CNL/aCML genotype.
The IDH1+SETBP1 depletion (O/E=0.13) is consistent with SETBP1's role as an epigenetic hub 3
[3] R 2018
SETBP1 induces transcription of a network of development genes by acting as an epigenetic hub. Nat Commun (2018)
, where mutant SETBP1 drives transcriptional deregulation via HCF1/KMT2A interaction and PP2A inhibition. Redundant epigenetic disruption through IDH1-mediated DNA hypermethylation appears to be negatively selected when SETBP1 already provides broad chromatin remodeling. This functional overlap explains the mutual exclusivity: clones carrying both mutations gain no additional fitness advantage, so the combination is purged by clonal competition.

Quintuple Co-occurrence Analysis

The quadruple analysis assumed four driver mutations. With EZH2 V662A reclassified from VUS to Pathogenic (5/5 computational models concordant, EVE 0.9997), this becomes a quintuple co-occurrence search. The expected frequency drops from 1.13e-4 to 7.7 × 10−13, over a billion times rarer.
EZH2 V662A has 0 carriers in the entire GENIE v19.0 17
[17] Consortium 2017
AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov (2017)
myeloid cohort (0/20,739). The variant itself does not exist in the database, making any multi-gene search involving this specific variant a guaranteed zero. Under a statistical independence assumption 30
[30] S 2016
A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol (2016)
, the probability of all five specific variants (DNMT3A R882H + IDH2 R140Q + SETBP1 G870S 1
[1] R 2013
Recurrent SETBP1 mutations in atypical chronic myeloid leukemia. Nat Genet (2013)
2
[2] H 2013
Somatic SETBP1 mutations in myeloid malignancies. Nat Genet (2013)
+ PTPN11 E76Q + EZH2 V662A) co-occurring in a single myeloid patient is approximately 7.7 × 10−13, roughly 1 in 1.3 trillion. Even if all 8 billion humans had myeloid cancer, this profile would still be unique, 162 times over.
CombinationObservedExpectedNotes
DNMT3A p.R882H + IDH2 p.R140Q + SETBP1 p.G870S0~0.21No triple match in GENIE
DNMT3A p.R882H + IDH2 p.R140Q + PTPN11 p.E76Q0~0.02No triple match in GENIE
DNMT3A p.R882H + SETBP1 p.G870S + PTPN11 p.E76Q0~0.00No triple match in GENIE
IDH2 p.R140Q + SETBP1 p.G870S + PTPN11 p.E76Q0~0.00No triple match in GENIE
All four variants (original)0~0.000Expected: 1.13e-4
EZH2 V662A (fifth driver)000/20,739 carriers; variant absent from GENIE
Gene-level quintuple (any variant)00.0034Any DNMT3A+IDH2+SETBP1+PTPN11+EZH2 variant
All five specific variants07.7 × 10−131 in 1.3 trillion under independence
Source: Expected values calculated under independence assumption 30
[30] S 2016
A novel independence test for somatic alterations in cancer shows that biology drives mutual exclusivity but chance explains most co-occurrence. Genome Biol (2016)
: P(quint) = P(DNMT3A R882H) x P(IDH2 R140Q) x P(SETBP1 G870S) x P(PTPN11 E76Q) x P(EZH2 V662A). Individual variant frequencies from GENIE v19.0 17
[17] Consortium 2017
AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov (2017)
myeloid panel-eligible cohort (N=21,017 myeloid, N=18,625 panel-eligible for original four). EZH2 V662A frequency = 0/20,739. Gene-level quintuple uses gene-level (any coding variant) frequencies.
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. AACR Project GENIE Consortium. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov (2017). DOI
  4. Kandoth C et al. Mutational landscape and significance across 12 major cancer types. Nature (2013). PubMed
  5. Canisius S et al. A novel independence test for somatic alterations in cancer. Genome Biol (2016). DOI
  6. Papaemmanuil E et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med (2016). PubMed
  7. Piazza R et al. SETBP1 induces transcription of a network of development genes by acting as an epigenetic hub. Nat Commun (2018). PubMed