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Testicular cancer mutation landscape

How often each gene is altered in testicular cancer, in each sequenced cohort, over the patients on whom it could have been called. Copy number is its own row. Nothing is pooled.

Retrieved 2026-09-18 · Reference cohort: tgct_tcga_pan_can_atlas_2018 · JSON: /disease/testicular-cancer/mutations.json · Back to the briefing

Answer block

In Testicular Germ Cell Tumors (TCGA, PanCancer Atlas) (149 sequenced patients, exome or genome), the most frequently altered of the 50 genes shown are KIT 13.42%, KRAS 8.72% (amplification), NANOG 6.04% (amplification), JARID2 6.04% (deep deletion), CCND2 5.37% (amplification). Each figure divides by the patients on whom that gene could be called.

Of the briefing's 12 curated targets, 6 are altered in under 2% of this cohort (TP53, POU5F1, SOX2, TERT, CDKN2A, XIST): targets by expression, dependency or drug label, not by mutation. Frequency is not targetability, in either direction.

1 cohorts are shown and none are pooled; overlap between them has not been checked and there is no disease-wide percentage.

Evidence boundary: frequency here is a count in a named cohort. Whether an alteration is a driver, is actionable, or has a drug is the briefing's question and is not inferred from these numbers.

What is altered, by cohort

One row per alteration, not per gene: a gene that is amplified and rarely mutated (ERBB2, MYCN, EGFR) gets a row for each. Every cell divides by its own denominator — the patients in that cohort on whom that gene could be called. Copy-number rows are shown only where at least one cohort reaches 2%.

Alterationtgct_tcga_pan_can_atlas_2018
149 pts · exome or genome
KIT SNV / small indel13.42%20/149
KIT amplification2.01%3/149
KRAS SNV / small indel8.05%12/149
KRAS amplification8.72%13/149
TP53 SNV / small indel0.67%1/149
MDM2 SNV / small indel0%
MDM2 amplification2.68%4/149
NANOG SNV / small indel0.67%1/149
NANOG amplification6.04%9/149
POU5F1 SNV / small indel0%
SOX17 SNV / small indel0%
SOX17 amplification3.36%5/149
SOX2 SNV / small indel0%
CCND2 SNV / small indel0%
CCND2 amplification5.37%8/149
TERT SNV / small indel0%
CDKN2A SNV / small indel0%
XIST SNV / small indel0%
PTMA SNV / small indel5.37%8/149
LZTR1 SNV / small indel4.03%6/149
SRCAP SNV / small indel3.36%5/149
NRAS SNV / small indel3.36%5/149
BIRC6 SNV / small indel2.68%4/149
ZZEF1 SNV / small indel2.01%3/149
VPS13B SNV / small indel2.01%3/149
TRIP12 SNV / small indel2.01%3/149
TET1 SNV / small indel2.01%3/149
SP8 SNV / small indel2.01%3/149
PIK3CA SNV / small indel2.01%3/149
NOTCH3 SNV / small indel2.01%3/149
NISCH SNV / small indel2.01%3/149
LAMA5 SNV / small indel2.01%3/149
KNTC1 SNV / small indel2.01%3/149
KMT2D SNV / small indel2.01%3/149
KDM2A SNV / small indel2.01%3/149
JARID2 SNV / small indel2.01%3/149
JARID2 deep deletion6.04%9/149
EPHB4 SNV / small indel2.01%3/149
EMID1 SNV / small indel2.01%3/149
CPSF1 SNV / small indel2.01%3/149
ATAD5 SNV / small indel2.01%3/149
ANKRD50 SNV / small indel2.01%3/149
ANKRD30A SNV / small indel2.01%3/149
ZFC3H1 SNV / small indel1.34%2/149
ZFC3H1 amplification2.01%3/149
ZBTB10 SNV / small indel1.34%2/149
UTRN SNV / small indel1.34%2/149
UBN1 SNV / small indel1.34%2/149
TTC28 SNV / small indel1.34%2/149
TREX1 SNV / small indel1.34%2/149
TRAM1L1 SNV / small indel1.34%2/149
TLR7 SNV / small indel1.34%2/149
TELO2 SNV / small indel1.34%2/149
TDRD6 SNV / small indel1.34%2/149
TCOF1 SNV / small indel1.34%2/149
TACC2 SNV / small indel1.34%2/149
TAAR2 SNV / small indel1.34%2/149
SYNJ2 SNV / small indel1.34%2/149

observed — shade scales with frequency, full at 30% assayed, none found not on this cohort's panel cohort not readable

Key findings

KIT is mutated in 20 of 149 patients in Testicular Germ Cell Tumors (TCGA, PanCancer Atlas).
Numerator: 20 · Denominator: 149 · Frequency: 13.42% · Observed in 1 cohorts · Confidence: moderate · Source: tgct_tcga_pan_can_atlas_2018 · Retrieved: 2026-09-18

KRAS is amplified in 13 of 149 patients in Testicular Germ Cell Tumors (TCGA, PanCancer Atlas).
Numerator: 13 · Denominator: 149 · Frequency: 8.72% · Observed in 1 cohorts · Confidence: moderate · Source: tgct_tcga_pan_can_atlas_2018 · Retrieved: 2026-09-18

NANOG is amplified in 9 of 149 patients in Testicular Germ Cell Tumors (TCGA, PanCancer Atlas).
Numerator: 9 · Denominator: 149 · Frequency: 6.04% · Observed in 1 cohorts · Confidence: moderate · Source: tgct_tcga_pan_can_atlas_2018 · Retrieved: 2026-09-18

Gene table — reference cohort

Headline values are from the reference cohort, tgct_tcga_pan_can_atlas_2018; the matrix above keeps every cohort separate. "Curated" marks a gene the disease briefing lists as a target; the rest are here because they are among the most frequently mutated genes in the reference cohort. Recurrent changes are the reference cohort's commonest protein changes.

GeneWhy listedLargest alterationAltered / testedFrequencyWithout hypermutatedCohorts observedRange across cohortsRecurrent changes
KIT curated target SNV / small indel 20 / 149 13.42% 1 / 1 13.42–13.42% D816H (n=4), D816Y (n=3), D816V (n=3), N822K (n=3), A829P (n=2)
KRAS curated target amplification 13 / 149 8.72% mutation 8.05% 1 / 1 8.05–8.05% G12V (n=3), Q61L (n=2), G12R (n=2), E63K (n=1), G12A (n=1)
TP53 curated target SNV / small indel 1 / 149 0.67% 1 / 1 0.67–0.67% G334W (n=1)
MDM2 curated target amplification 4 / 149 2.68% mutation 0.0% 0 / 1 0.0–0.0% none recurrent
NANOG curated target amplification 9 / 149 6.04% mutation 0.67% 1 / 1 0.67–0.67% N208K (n=1)
POU5F1 curated target SNV / small indel 0 / 149 0.0% 0 / 1 0.0–0.0% none recurrent
SOX17 curated target amplification 5 / 149 3.36% mutation 0.0% 0 / 1 0.0–0.0% none recurrent
SOX2 curated target amplification 2 / 149 1.34% mutation 0.0% 0 / 1 0.0–0.0% none recurrent
CCND2 curated target amplification 8 / 149 5.37% mutation 0.0% 0 / 1 0.0–0.0% none recurrent
TERT curated target deep deletion 2 / 149 1.34% mutation 0.0% 0 / 1 0.0–0.0% none recurrent
CDKN2A curated target amplification 1 / 149 0.67% mutation 0.0% 0 / 1 0.0–0.0% none recurrent
XIST curated target SNV / small indel 0 / 149 0.0% 0 / 1 0.0–0.0% none recurrent
PTMA by frequency SNV / small indel 8 / 149 5.37% 1 / 1 5.37–5.37% *112Qext*9 (n=7), E62G (n=1)
LZTR1 by frequency SNV / small indel 6 / 149 4.03% 1 / 1 4.03–4.03% X217_splice (n=6)
SRCAP by frequency SNV / small indel 5 / 149 3.36% 1 / 1 3.36–3.36% P2791T (n=1), K2383* (n=1), A1542Gfs*306 (n=1), P1356A (n=1), P2544S (n=1)
NRAS by frequency SNV / small indel 5 / 149 3.36% 1 / 1 3.36–3.36% Q61R (n=2), G12D (n=1), Q61K (n=1), G12S (n=1)
BIRC6 by frequency SNV / small indel 4 / 149 2.68% 1 / 1 2.68–2.68% S2820* (n=1), A1762V (n=1), S1757C (n=1), S1399I (n=1)
ZZEF1 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% A320V (n=1), L2166M (n=1), A613V (n=1)
VPS13B by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% R1200G (n=1), L2349F (n=1), L1465V (n=1)
TRIP12 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% X809_splice (n=1), X1623_splice (n=1), A1076T (n=1)
TET1 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% T1472S (n=1), S483* (n=1), T188I (n=1)
SP8 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% G138S (n=3)
PIK3CA by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% E545K (n=2), N345K (n=1)
NOTCH3 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% T445R (n=1), R1893Q (n=1), Q1552R (n=1)
NISCH by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% N80K (n=1), N937S (n=1), R82T (n=1)
LAMA5 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% P1920L (n=1), A2518V (n=1), G391V (n=1)
KNTC1 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% X674_splice (n=1), I2133M (n=1), L438V (n=1)
KMT2D by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% Q3905del (n=1), F883L (n=1), T2131P (n=1)
KDM2A by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% X87_splice (n=1), Q68* (n=1), C1129Y (n=1)
JARID2 by frequency deep deletion 9 / 149 6.04% mutation 2.01% 1 / 1 2.01–2.01% G54E (n=1), D654N (n=1), V608I (n=1)
EPHB4 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% G82Afs*78 (n=1), C255G (n=1), V228A (n=1)
EMID1 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% K155N (n=1), G193V (n=1), P417T (n=1)
CPSF1 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% S56N (n=1), E662G (n=1), A1174T (n=1)
ATAD5 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% K658T (n=1), S584I (n=1), D1100E (n=1)
ANKRD50 by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% S57F (n=1), V637M (n=1), K1278N (n=1)
ANKRD30A by frequency SNV / small indel 3 / 149 2.01% 1 / 1 2.01–2.01% C516Y (n=1), M1305T (n=1), C525W (n=1), C763W (n=1)
ZFC3H1 by frequency amplification 3 / 149 2.01% mutation 1.34% 1 / 1 1.34–1.34% K756* (n=1), N1270Kfs*2 (n=1)
ZBTB10 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% F250L (n=1), V636M (n=1)
UTRN by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% W3173* (n=1), R2859C (n=1)
UBN1 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% S671F (n=1), T1075R (n=1)
TTC28 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% A472T (n=1), V1097L (n=1)
TREX1 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% G78S (n=1), L142P (n=1)
TRAM1L1 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% L72F (n=1), E41Q (n=1)
TLR7 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% T33Lfs*15 (n=1), R186L (n=1)
TELO2 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% E529D (n=1), A88S (n=1)
TDRD6 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% G1282R (n=1), C1936Y (n=1)
TCOF1 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% A490V (n=1), G1407W (n=1)
TACC2 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% T1955N (n=1), E929V (n=1)
TAAR2 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% K20Rfs*45 (n=1), L124M (n=1)
SYNJ2 by frequency SNV / small indel 2 / 149 1.34% 1 / 1 1.34–1.34% T86I (n=1), D1435N (n=1)

Cohorts

Listed in the disease profile, not searched: a name search returns the same patients under several accessions. Patients are unique patient ids in the study's sequenced sample list. Hypermutated: more than ten times the cohort's median non-silent mutations per sample, and at least 100.

CohortAccessionPatientsSamples sequenced / in studyAssayPanels (samples)BuildProfiles readHypermutated patientsMedian mutations / sample
Testicular Germ Cell Tumors (TCGA, PanCancer Atlas) reference
Testicular Germ Cell Tumors (TCGA, PanCancer Atlas)
tgct_tcga_pan_can_atlas_2018149 observed149 / 149exome or genomeWES (149)hg19SNV, small indel, amplification, deep deletion, structural variant (profile present, not read)012

Copy-number events

Discrete calls from each study's copy-number profile: 2 is high-level amplification, −2 deep deletion. Gains and shallow losses are not counted. Denominators are the cohort's copy-number sample list, which differs from its sequenced list. Rows at 2% or more.

GeneEventObserved patientsTested patientsFrequencyCohortProfile
KRASamplification131498.72%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic
NANOGamplification91496.04%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic
JARID2deep deletion91496.04%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic
CCND2amplification81495.37%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic
SOX17amplification51493.36%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic
MDM2amplification41492.68%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic
KITamplification31492.01%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic
ZFC3H1amplification31492.01%tgct_tcga_pan_can_atlas_2018tgct_tcga_pan_can_atlas_2018_gistic

Cohort-aware frequencies

Each row is calculated from unique patients in that study's sequenced sample list. The range is descriptive; no pooled estimate is shown because cross-study overlap and assay comparability have not been checked.

GeneRangePer cohort (altered / tested)
KIT13.42–13.42%tgct_tcga_pan_can_atlas_2018: 20/149 (13.42%)
KRAS8.05–8.05%tgct_tcga_pan_can_atlas_2018: 12/149 (8.05%)
TP530.67–0.67%tgct_tcga_pan_can_atlas_2018: 1/149 (0.67%)
MDM20.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
NANOG0.67–0.67%tgct_tcga_pan_can_atlas_2018: 1/149 (0.67%)
POU5F10.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
SOX170.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
SOX20.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
CCND20.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
TERT0.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
CDKN2A0.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
XIST0.0–0.0%tgct_tcga_pan_can_atlas_2018: 0/149 (0.0%)
PTMA5.37–5.37%tgct_tcga_pan_can_atlas_2018: 8/149 (5.37%)
LZTR14.03–4.03%tgct_tcga_pan_can_atlas_2018: 6/149 (4.03%)
SRCAP3.36–3.36%tgct_tcga_pan_can_atlas_2018: 5/149 (3.36%)
NRAS3.36–3.36%tgct_tcga_pan_can_atlas_2018: 5/149 (3.36%)
BIRC62.68–2.68%tgct_tcga_pan_can_atlas_2018: 4/149 (2.68%)
ZZEF12.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
VPS13B2.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
TRIP122.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
TET12.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
SP82.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
PIK3CA2.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
NOTCH32.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
NISCH2.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
LAMA52.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
KNTC12.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
KMT2D2.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
KDM2A2.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
JARID22.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
EPHB42.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
EMID12.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
CPSF12.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
ATAD52.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
ANKRD502.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
ANKRD30A2.01–2.01%tgct_tcga_pan_can_atlas_2018: 3/149 (2.01%)
ZFC3H11.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
ZBTB101.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
UTRN1.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
UBN11.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TTC281.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TREX11.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TRAM1L11.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TLR71.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TELO21.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TDRD61.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TCOF11.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TACC21.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
TAAR21.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)
SYNJ21.34–1.34%tgct_tcga_pan_can_atlas_2018: 2/149 (1.34%)

What this page does not do

Structural variants
Read the structural-variant profiles the studies carry; fusions are the defining event in several of these diseases.

Context
Stage, subtype, age and treatment line are not attached to any count; the cohorts differ on all four.

Interpretation
Activating versus inactivating, actionable versus not, and evidence level are not inferred here; the briefing's target table carries the drug and trial facts.

Limitations

How a machine should read this page

  1. Denominators: every frequency divides by the patients in one named cohort on whom the gene could be called; there is no disease-wide figure.
  2. Missing values: not_assayed (the panel did not carry the gene), not_observed (assayed, none found) and not_evaluable (the cohort could not be read) are three different facts and are never converted to zero.
  3. Counting: patients, not samples; several samples from one patient count once. Non-silent calls only.
  4. Copy number: a separate assay with a separate roster; discrete calls at ±2 only.
  5. Hypermutation: flagged per cohort; the headline keeps all patients and the frequency without them is reported beside it.
  6. Provenance: every value carries the study id, the retrieval date and the processing version; the source is the cBioPortal public API.

Machine endpoints: full landscape · genes · cohorts · the disease's own facts: /disease/testicular-cancer.json.

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