← BioTransferCervical cancer briefingAll diseases

Disease intelligence · mutation landscape

Cervical cancer mutation landscape

How often each gene is altered in cervical 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: cesc_tcga_pan_can_atlas_2018 · JSON: /disease/cervical-cancer/mutations.json · Back to the briefing

Answer block

In Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas) (291 sequenced patients, exome or genome), the most frequently altered of the 48 genes shown are PIK3CA 28.52%, KMT2C 18.56%, KMT2D 14.09%, FBXW7 12.03%, EP300 12.03%. Each figure divides by the patients on whom that gene could be called.

10 of 291 patients are hypermutated (more than 830 non-silent mutations, ten times the cohort median of 83); every gene's frequency without them is beside the headline.

Of the briefing's 12 curated targets, 3 are altered in under 2% of this cohort (CD274, TACSTD2, CDKN2A): targets by expression, dependency or drug label, not by mutation. Frequency is not targetability, in either direction.

2 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%.

Alterationcesc_tcga_pan_can_atlas_2018
291 pts · exome or genome
cervix_msk_2023
177 pts · targeted panel
PIK3CA SNV / small indel28.52%83/29124.86%44/177
PIK3CA amplification15.02%44/2932.26%4/177
TP53 SNV / small indel7.9%23/29111.3%20/177
CD274 SNV / small indel0.69%2/2911.13%2/177
PDCD1 SNV / small indel0.69%2/2911.13%2/177
PDCD1 deep deletion3.41%10/2930%
ERBB2 SNV / small indel4.47%13/29110.73%19/177
ERBB2 amplification5.46%16/2933.95%7/177
KRAS SNV / small indel5.15%15/29111.86%21/177
PTEN SNV / small indel7.22%21/2915.65%10/177
PTEN deep deletion4.78%14/2931.13%2/177
FBXW7 SNV / small indel12.03%35/2918.47%15/177
EGFR SNV / small indel2.41%7/2911.13%2/177
EGFR amplification2.39%7/2930%
TACSTD2 SNV / small indel0%·
TERT SNV / small indel1.37%4/2911.13%2/177
TERT amplification6.48%19/2930.56%1/177
CDKN2A SNV / small indel1.72%5/2912.26%4/177
KMT2C SNV / small indel18.56%54/29110.73%19/177
KMT2D SNV / small indel14.09%41/29113.56%24/177
EP300 SNV / small indel12.03%35/2913.39%6/177
HUWE1 SNV / small indel9.62%28/291·
NAV3 SNV / small indel8.25%24/291·
MDN1 SNV / small indel7.9%23/291·
FAT1 SNV / small indel7.9%23/2916.78%12/177
FAT1 deep deletion3.41%10/2930.56%1/177
EYS SNV / small indel7.9%23/291·
CREBBP SNV / small indel7.56%22/2912.82%5/177
PRKDC SNV / small indel7.22%21/291·
UBR4 SNV / small indel6.87%20/291·
NIPBL SNV / small indel6.87%20/291·
NIPBL amplification5.46%16/2930%
FRAS1 SNV / small indel6.87%20/291·
RB1 SNV / small indel6.53%19/2915.08%9/177
RB1 deep deletion3.07%9/2930%
NOTCH1 SNV / small indel6.53%19/2912.82%5/177
MKI67 SNV / small indel6.53%19/291·
CHD7 SNV / small indel6.53%19/291·
BSN SNV / small indel6.53%19/291·
VPS13B SNV / small indel6.19%18/291·
TAF1 SNV / small indel6.19%18/291·
PKD1 SNV / small indel6.19%18/291·
NSD1 SNV / small indel6.19%18/2912.82%5/177
GOLGB1 SNV / small indel6.19%18/291·
GOLGB1 amplification5.46%16/2930%
ABCA12 SNV / small indel6.19%18/291·
ABCA12 deep deletion2.05%6/2930%
WDFY4 SNV / small indel5.84%17/291·
TG SNV / small indel5.84%17/291·
TG amplification2.39%7/2930%
SRCAP SNV / small indel5.84%17/291·
SPEN SNV / small indel5.84%17/2913.95%7/177
SMG1 SNV / small indel5.84%17/291·
MYH15 SNV / small indel5.84%17/291·
MYH15 amplification4.1%12/2930%
HSPG2 SNV / small indel5.84%17/291·
FLNA SNV / small indel5.84%17/291·
FLNA amplification3.07%9/2930%
FAT2 SNV / small indel5.84%17/291·
DYNC1H1 SNV / small indel5.84%17/291·
DCHS1 SNV / small indel5.84%17/291·
COL6A3 SNV / small indel5.84%17/291·
COL6A3 deep deletion3.07%9/2930%

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

Key findings

PIK3CA is mutated in 83 of 291 patients in Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas).
Numerator: 83 · Denominator: 291 · Frequency: 28.52% · Observed in 2 cohorts · Confidence: moderate · Source: cesc_tcga_pan_can_atlas_2018 · Retrieved: 2026-09-18

KMT2C is mutated in 54 of 291 patients in Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas).
Numerator: 54 · Denominator: 291 · Frequency: 18.56% · Observed in 2 cohorts · Confidence: moderate · Source: cesc_tcga_pan_can_atlas_2018 · Retrieved: 2026-09-18

KMT2D is mutated in 41 of 291 patients in Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas).
Numerator: 41 · Denominator: 291 · Frequency: 14.09% · Observed in 2 cohorts · Confidence: moderate · Source: cesc_tcga_pan_can_atlas_2018 · Retrieved: 2026-09-18

Gene table — reference cohort

Headline values are from the reference cohort, cesc_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
PIK3CA curated target SNV / small indel 83 / 291 28.52% 27.05% 2 / 2 24.86–28.52% E545K (n=37), E542K (n=23), E726K (n=6), H1047R (n=2), E453K (n=2)
TP53 curated target SNV / small indel 23 / 291 7.9% 7.12% 2 / 2 7.9–11.3% E285K (n=3), R282W (n=1), R333C (n=1), R196* (n=1), S376C (n=1)
CD274 curated target amplification 5 / 293 1.71% mutation 0.69% 0.36% 2 / 2 0.69–1.13% W167C (n=1), K280N (n=1)
PDCD1 curated target deep deletion 10 / 293 3.41% mutation 0.69% 0.36% 2 / 2 0.69–1.13% G270S (n=1), G119S (n=1), E136Q (n=1)
ERBB2 curated target amplification 16 / 293 5.46% mutation 4.47% 3.56% 2 / 2 4.47–10.73% S310F (n=4), S250Y (n=1), S974F (n=1), P1033Qfs*27 (n=1), E1079K (n=1)
KRAS curated target SNV / small indel 15 / 291 5.15% 5.34% 2 / 2 5.15–11.86% G12D (n=4), G13D (n=4), A146T (n=2), G12C (n=2), G12V (n=2)
PTEN curated target SNV / small indel 21 / 291 7.22% 6.41% 2 / 2 5.65–7.22% R130Q (n=2), R130* (n=2), R173C (n=1), F341V (n=1), R142W (n=1)
FBXW7 curated target SNV / small indel 35 / 291 12.03% 11.03% 2 / 2 8.47–12.03% R505G (n=4), R465C (n=3), R224* (n=2), R658* (n=2), R465H (n=2)
EGFR curated target SNV / small indel 7 / 291 2.41% 1.42% 2 / 2 1.13–2.41% C329Y (n=1), L90I (n=1), S811C (n=1), I789M (n=1), V1097I (n=1)
TACSTD2 curated target amplification 5 / 293 1.71% mutation 0.0% 0.0% 0 / 2 0.0–0.0% none recurrent
TERT curated target amplification 19 / 293 6.48% mutation 1.37% 1.07% 2 / 2 1.13–1.37% R521H (n=1), P627H (n=1), R489K (n=1), A518V (n=1), L350V (n=1)
CDKN2A curated target SNV / small indel 5 / 291 1.72% 1.42% 2 / 2 1.72–2.26% A132V (n=1), D146G (n=1), X153_splice (n=1), E88K (n=1), L31R (n=1)
KMT2C by frequency SNV / small indel 54 / 291 18.56% 16.37% 2 / 2 10.73–18.56% W430* (n=2), Q2220* (n=2), Q2161* (n=2), R4597H (n=1), G2136V (n=1)
KMT2D by frequency SNV / small indel 41 / 291 14.09% 12.81% 2 / 2 13.56–14.09% R4904* (n=1), R5533W (n=1), R5229H (n=1), L2398V (n=1), Q3580* (n=1)
EP300 by frequency SNV / small indel 35 / 291 12.03% 11.03% 2 / 2 3.39–12.03% D1399N (n=4), S281* (n=2), W1436R (n=2), G201E (n=1), R2308C (n=1)
HUWE1 by frequency SNV / small indel 28 / 291 9.62% 8.19% 1 / 2 9.62–9.62% S661I (n=1), C955R (n=1), S3277L (n=1), S2527Y (n=1), X216_splice (n=1)
NAV3 by frequency SNV / small indel 24 / 291 8.25% 6.76% 1 / 2 8.25–8.25% K1051Sfs*23 (n=1), G1031Dfs*43 (n=1), D1692N (n=1), P449S (n=1), E943K (n=1)
MDN1 by frequency SNV / small indel 23 / 291 7.9% 5.69% 1 / 2 7.9–7.9% R1257C (n=1), Q4328* (n=1), L916I (n=1), A5388D (n=1), R873Q (n=1)
FAT1 by frequency SNV / small indel 23 / 291 7.9% 7.47% 2 / 2 6.78–7.9% T180I (n=1), G407E (n=1), S541* (n=1), H2508Qfs*27 (n=1), S4557Y (n=1)
EYS by frequency SNV / small indel 23 / 291 7.9% 6.41% 1 / 2 7.9–7.9% L1641* (n=1), F767V (n=1), E743K (n=1), E1482Q (n=1), S1648T (n=1)
CREBBP by frequency SNV / small indel 22 / 291 7.56% 5.69% 2 / 2 2.82–7.56% R1985C (n=1), P2094L (n=1), P225L (n=1), P1488R (n=1), S1680del (n=1)
PRKDC by frequency SNV / small indel 21 / 291 7.22% 5.69% 1 / 2 7.22–7.22% Q3568E (n=2), A1947T (n=1), Q1048H (n=1), M3449T (n=1), A3729T (n=1)
UBR4 by frequency SNV / small indel 20 / 291 6.87% 6.05% 1 / 2 6.87–6.87% Y3302H (n=1), S1737C (n=1), P4371L (n=1), E645Q (n=1), H4698Y (n=1)
NIPBL by frequency SNV / small indel 20 / 291 6.87% 5.69% 1 / 2 6.87–6.87% V1424Cfs*6 (n=2), R1372Q (n=1), P2630L (n=1), E1635V (n=1), K1341* (n=1)
FRAS1 by frequency SNV / small indel 20 / 291 6.87% 6.05% 1 / 2 6.87–6.87% T1322I (n=1), A11T (n=1), E3555Q (n=1), D2002N (n=1), R3070T (n=1)
RB1 by frequency SNV / small indel 19 / 291 6.53% 6.05% 2 / 2 5.08–6.53% R418Sfs*9 (n=1), G836C (n=1), W563S (n=1), X500_splice (n=1), G449V (n=1)
NOTCH1 by frequency SNV / small indel 19 / 291 6.53% 5.69% 2 / 2 2.82–6.53% S2467L (n=1), E569K (n=1), G1250S (n=1), N982S (n=1), H2018Lfs*9 (n=1)
MKI67 by frequency SNV / small indel 19 / 291 6.53% 4.98% 1 / 2 6.53–6.53% V729A (n=1), A1826D (n=1), S94P (n=1), P1112S (n=1), A3210V (n=1)
CHD7 by frequency SNV / small indel 19 / 291 6.53% 5.34% 1 / 2 6.53–6.53% D1755G (n=1), P1705S (n=1), R459H (n=1), R2027* (n=1), R2303T (n=1)
BSN by frequency SNV / small indel 19 / 291 6.53% 4.27% 1 / 2 6.53–6.53% A3430T (n=1), A2565T (n=1), V2701I (n=1), D2642N (n=1), E2590K (n=1)
VPS13B by frequency SNV / small indel 18 / 291 6.19% 4.27% 1 / 2 6.19–6.19% R1462C (n=1), D1593G (n=1), F1430L (n=1), D361N (n=1), Q331* (n=1)
TAF1 by frequency SNV / small indel 18 / 291 6.19% 4.63% 1 / 2 6.19–6.19% R996C (n=2), K1412N (n=1), R997H (n=1), N483K (n=1), I1270M (n=1)
PKD1 by frequency SNV / small indel 18 / 291 6.19% 4.63% 1 / 2 6.19–6.19% V1687M (n=1), R3115W (n=1), S2715N (n=1), T3134A (n=1), L544I (n=1)
NSD1 by frequency SNV / small indel 18 / 291 6.19% 4.98% 2 / 2 2.82–6.19% S1679* (n=2), R1471* (n=1), R2199C (n=1), F1711S (n=1), Y905C (n=1)
GOLGB1 by frequency SNV / small indel 18 / 291 6.19% 3.91% 1 / 2 6.19–6.19% A3102V (n=1), L1618V (n=1), H2802D (n=1), E2853Q (n=1), D2436H (n=1)
ABCA12 by frequency SNV / small indel 18 / 291 6.19% 4.98% 1 / 2 6.19–6.19% N752D (n=1), R1912H (n=1), P700S (n=1), Y1150* (n=1), S2118L (n=1)
WDFY4 by frequency SNV / small indel 17 / 291 5.84% 4.27% 1 / 2 5.84–5.84% E332K (n=1), D2664N (n=1), S1957Y (n=1), Y1032H (n=1), Y1392C (n=1)
TG by frequency SNV / small indel 17 / 291 5.84% 4.98% 1 / 2 5.84–5.84% Q515R (n=1), Q829H (n=1), E2240Q (n=1), R2489H (n=1), G1195R (n=1)
SRCAP by frequency SNV / small indel 17 / 291 5.84% 4.63% 1 / 2 5.84–5.84% R940Q (n=1), R1025* (n=1), R2721Q (n=1), R2398H (n=1), S351C (n=1)
SPEN by frequency SNV / small indel 17 / 291 5.84% 3.91% 2 / 2 3.95–5.84% R1137C (n=1), T3048M (n=1), E2183K (n=1), S1109* (n=1), Q3304E (n=1)
SMG1 by frequency SNV / small indel 17 / 291 5.84% 5.34% 1 / 2 5.84–5.84% X1412_splice (n=1), R903H (n=1), S1907F (n=1), S3184Y (n=1), H2754N (n=1)
MYH15 by frequency SNV / small indel 17 / 291 5.84% 3.56% 1 / 2 5.84–5.84% A660V (n=1), E876D (n=1), S254P (n=1), K1105* (n=1), E1796K (n=1)
HSPG2 by frequency SNV / small indel 17 / 291 5.84% 4.27% 1 / 2 5.84–5.84% R4174H (n=1), R1716W (n=1), R624H (n=1), G2775D (n=1), G2775S (n=1)
FLNA by frequency SNV / small indel 17 / 291 5.84% 4.98% 1 / 2 5.84–5.84% A1205V (n=1), A2274V (n=1), A1141T (n=1), A1082V (n=1), R1272C (n=1)
FAT2 by frequency SNV / small indel 17 / 291 5.84% 3.91% 1 / 2 5.84–5.84% S1385* (n=2), W31* (n=1), D1163Y (n=1), K1985Q (n=1), R2303Q (n=1)
DYNC1H1 by frequency SNV / small indel 17 / 291 5.84% 4.27% 1 / 2 5.84–5.84% R3654W (n=1), F3996L (n=1), R4178C (n=1), F3996V (n=1), R4638W (n=1)
DCHS1 by frequency SNV / small indel 17 / 291 5.84% 3.56% 1 / 2 5.84–5.84% A2517T (n=1), R688Q (n=1), Q2697H (n=1), D467N (n=1), E1762Q (n=1)
COL6A3 by frequency SNV / small indel 17 / 291 5.84% 3.91% 1 / 2 5.84–5.84% R2313I (n=1), V512A (n=1), G1957V (n=1), R341H (n=1), K2937E (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
Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas) reference
Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
cesc_tcga_pan_can_atlas_2018291 observed291 / 297exome or genomeWES (291)hg19SNV, small indel, amplification, deep deletion, structural variant (profile present, not read)1083
Cervical Cancer (MSK, Clin Cancer Res 2023)
Cervical Cancer (MSK, Clin Cancer Res 2023)
cervix_msk_2023177 observed177 / 177targeted panelIMPACT468 (130), IMPACT410 (33), IMPACT341 (14)hg19SNV, small indel, amplification, deep deletion, structural variant (profile present, not read)04

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
PIK3CAamplification4429315.02%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
TERTamplification192936.48%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
ERBB2amplification162935.46%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
NIPBLamplification162935.46%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
GOLGB1amplification162935.46%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
PTENdeep deletion142934.78%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
MYH15amplification122934.1%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
ERBB2amplification71773.95%cervix_msk_2023cervix_msk_2023_gistic
PDCD1deep deletion102933.41%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
FAT1deep deletion102933.41%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
RB1deep deletion92933.07%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
FLNAamplification92933.07%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
COL6A3deep deletion92933.07%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
EGFRamplification72932.39%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
TGamplification72932.39%cesc_tcga_pan_can_atlas_2018cesc_tcga_pan_can_atlas_2018_gistic
PIK3CAamplification41772.26%cervix_msk_2023cervix_msk_2023_gistic
ABCA12deep deletion62932.05%cesc_tcga_pan_can_atlas_2018cesc_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)
PIK3CA24.86–28.52%cesc_tcga_pan_can_atlas_2018: 83/291 (28.52%) · cervix_msk_2023: 44/177 (24.86%)
TP537.9–11.3%cesc_tcga_pan_can_atlas_2018: 23/291 (7.9%) · cervix_msk_2023: 20/177 (11.3%)
CD2740.69–1.13%cesc_tcga_pan_can_atlas_2018: 2/291 (0.69%) · cervix_msk_2023: 2/177 (1.13%)
PDCD10.69–1.13%cesc_tcga_pan_can_atlas_2018: 2/291 (0.69%) · cervix_msk_2023: 2/177 (1.13%)
ERBB24.47–10.73%cesc_tcga_pan_can_atlas_2018: 13/291 (4.47%) · cervix_msk_2023: 19/177 (10.73%)
KRAS5.15–11.86%cesc_tcga_pan_can_atlas_2018: 15/291 (5.15%) · cervix_msk_2023: 21/177 (11.86%)
PTEN5.65–7.22%cesc_tcga_pan_can_atlas_2018: 21/291 (7.22%) · cervix_msk_2023: 10/177 (5.65%)
FBXW78.47–12.03%cesc_tcga_pan_can_atlas_2018: 35/291 (12.03%) · cervix_msk_2023: 15/177 (8.47%)
EGFR1.13–2.41%cesc_tcga_pan_can_atlas_2018: 7/291 (2.41%) · cervix_msk_2023: 2/177 (1.13%)
TACSTD20.0–0.0%cesc_tcga_pan_can_atlas_2018: 0/291 (0.0%) · cervix_msk_2023: not assayed
TERT1.13–1.37%cesc_tcga_pan_can_atlas_2018: 4/291 (1.37%) · cervix_msk_2023: 2/177 (1.13%)
CDKN2A1.72–2.26%cesc_tcga_pan_can_atlas_2018: 5/291 (1.72%) · cervix_msk_2023: 4/177 (2.26%)
KMT2C10.73–18.56%cesc_tcga_pan_can_atlas_2018: 54/291 (18.56%) · cervix_msk_2023: 19/177 (10.73%)
KMT2D13.56–14.09%cesc_tcga_pan_can_atlas_2018: 41/291 (14.09%) · cervix_msk_2023: 24/177 (13.56%)
EP3003.39–12.03%cesc_tcga_pan_can_atlas_2018: 35/291 (12.03%) · cervix_msk_2023: 6/177 (3.39%)
HUWE19.62–9.62%cesc_tcga_pan_can_atlas_2018: 28/291 (9.62%) · cervix_msk_2023: not assayed
NAV38.25–8.25%cesc_tcga_pan_can_atlas_2018: 24/291 (8.25%) · cervix_msk_2023: not assayed
MDN17.9–7.9%cesc_tcga_pan_can_atlas_2018: 23/291 (7.9%) · cervix_msk_2023: not assayed
FAT16.78–7.9%cesc_tcga_pan_can_atlas_2018: 23/291 (7.9%) · cervix_msk_2023: 12/177 (6.78%)
EYS7.9–7.9%cesc_tcga_pan_can_atlas_2018: 23/291 (7.9%) · cervix_msk_2023: not assayed
CREBBP2.82–7.56%cesc_tcga_pan_can_atlas_2018: 22/291 (7.56%) · cervix_msk_2023: 5/177 (2.82%)
PRKDC7.22–7.22%cesc_tcga_pan_can_atlas_2018: 21/291 (7.22%) · cervix_msk_2023: not assayed
UBR46.87–6.87%cesc_tcga_pan_can_atlas_2018: 20/291 (6.87%) · cervix_msk_2023: not assayed
NIPBL6.87–6.87%cesc_tcga_pan_can_atlas_2018: 20/291 (6.87%) · cervix_msk_2023: not assayed
FRAS16.87–6.87%cesc_tcga_pan_can_atlas_2018: 20/291 (6.87%) · cervix_msk_2023: not assayed
RB15.08–6.53%cesc_tcga_pan_can_atlas_2018: 19/291 (6.53%) · cervix_msk_2023: 9/177 (5.08%)
NOTCH12.82–6.53%cesc_tcga_pan_can_atlas_2018: 19/291 (6.53%) · cervix_msk_2023: 5/177 (2.82%)
MKI676.53–6.53%cesc_tcga_pan_can_atlas_2018: 19/291 (6.53%) · cervix_msk_2023: not assayed
CHD76.53–6.53%cesc_tcga_pan_can_atlas_2018: 19/291 (6.53%) · cervix_msk_2023: not assayed
BSN6.53–6.53%cesc_tcga_pan_can_atlas_2018: 19/291 (6.53%) · cervix_msk_2023: not assayed
VPS13B6.19–6.19%cesc_tcga_pan_can_atlas_2018: 18/291 (6.19%) · cervix_msk_2023: not assayed
TAF16.19–6.19%cesc_tcga_pan_can_atlas_2018: 18/291 (6.19%) · cervix_msk_2023: not assayed
PKD16.19–6.19%cesc_tcga_pan_can_atlas_2018: 18/291 (6.19%) · cervix_msk_2023: not assayed
NSD12.82–6.19%cesc_tcga_pan_can_atlas_2018: 18/291 (6.19%) · cervix_msk_2023: 5/177 (2.82%)
GOLGB16.19–6.19%cesc_tcga_pan_can_atlas_2018: 18/291 (6.19%) · cervix_msk_2023: not assayed
ABCA126.19–6.19%cesc_tcga_pan_can_atlas_2018: 18/291 (6.19%) · cervix_msk_2023: not assayed
WDFY45.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
TG5.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
SRCAP5.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
SPEN3.95–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: 7/177 (3.95%)
SMG15.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
MYH155.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
HSPG25.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
FLNA5.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
FAT25.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
DYNC1H15.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
DCHS15.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed
COL6A35.84–5.84%cesc_tcga_pan_can_atlas_2018: 17/291 (5.84%) · cervix_msk_2023: not assayed

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/cervical-cancer.json.

Built by the BioTransfer briefings pipeline from the cBioPortal public API. The neuroblastoma page was assembled by hand and set the rules this page follows; how these are built.