Q (sonnet): show me the distribution of mutations in the tert promoter across cancer types

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-guide"
}

◀ result
# Mutation Frequency Analysis Guide

## IMPORTANT: Reporting Mutation Frequencies
- **ALWAYS report frequencies as percentages**, not raw counts: `frequency = (altered_samples / total_profiled_samples) × 100`
- For quick frequency lookups, **prefer the TCGA Pan-Cancer Atlas study first**, then offer to expand to other studies
- When reporting across multiple studies, show **ranges** (e.g., "TP53 is mutated in 30–60% of samples") rather than a single average
- **NEVER** sum mutation events across studies to compute an aggregate frequency — this can exceed 100% due to double-counting
- Warn users that samples may overlap across cohorts (e.g., MSK studies may share patients)
- **Choose and state the counting unit**: use patient-level frequencies for prevalence/rate questions unless the user explicitly asks for samples; use sample-level frequencies when the user asks about samples.
- **For "across cancer types" questions**, jump to the [Cross-Cancer-Type Mutation Frequency](#cross-cancer-type-mutation-frequency) section below — there is one correct recipe and several common wrong ones.

## Counting Unit: Samples vs Patients

Before answering any mutation count or frequency question, decide whether the unit is samples or patients and state that choice in the answer.

| User wording | Counting unit |
|--------------|---------------|
| "prevalence", "rate", "fraction of patients", "patients with", "how common is" | Patient-level: `COUNT(DISTINCT patient_unique_id)` |
| "samples", "specimens", "biopsies", sample-level cohort composition | Sample-level: `COUNT(DISTINCT sample_unique_id)` |
| Ambiguous | Ask, or default to patient-level for prevalence/rate language and say so |

### Cross-study sample-count caveat

When an answer touches more than one study and reports a sample count, prepend a one-line caveat:

> Sample IDs are unique within cBioPortal study prefixes, not guaranteed biological-sample identifiers across studies; overlapping cohorts can count the same patient/sample more than once.

Prefer one of these safer approaches:

- Use a shipped `cancer_study_query_preferences` cohort such as `pan_cancer_tcga` or `all_studies_non_redundant`.
- Restrict to one named study.
- Aggregate by `patient_unique_id` when the biological question is patient prevalence.

## STOP rule: a frequency above 100% means your query is wrong

If your query returns a frequency over 100%, **do not try to debug or explain the data inconsistency to the user**. The cause is always one of these query bugs:

- Summing mutation events instead of `COUNT(DISTINCT sample_unique_id)` for the numerator
- Using a study-wide sample count as the denominator instead of the gene-specific profiled count
- Cross-study aggregation where the same biological sample appears under multiple `sample_unique_id` values (e.g., MSK-IMPACT and MSK-CHORD share patients)
- **Joining the profiled CTE through `gene_panel` / `gene_panel_list` without a WES branch.** `gene_panel_id = 'WES'` is *not* a row in … (36467 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT preference_name, COUNT(*) AS studies
FROM cancer_study_query_preferences
GROUP BY preference_name
ORDER BY preference_name
"
}

◀ result
{"rows":[{"preference_name":"all_studies_non_redundant","studies":241},{"preference_name":"large_genomic_cohort","studies":1},{"preference_name":"pan_cancer_tcga","studies":32},{"preference_name":"treatment_outcomes","studies":1}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT cancer_study_identifier, genetic_profile_id, genetic_alteration_type, datatype, name
FROM genetic_profile
WHERE lower(genetic_profile_id) LIKE '%promoter%' OR lower(name) LIKE '%promoter%'
ORDER BY cancer_study_identifier
LIMIT 50
"
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 47\n Code: 47. DB::Exception: Unknown expression identifier `cancer_study_identifier` in scope SELECT DISTINCT cancer_study_identifier, genetic_profile_id, genetic_alteration_type, datatype, name FROM genetic_profile WHERE (lower(genetic_profile_id) LIKE '%promoter%') OR (lower(name) LIKE '%promoter%') ORDER BY cancer_study_identifier ASC LIMIT 50. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT variant_type, mutation_type, mutation_variant
FROM genomic_event_derived
WHERE hugo_gene_symbol = 'TERT'
LIMIT 50
"
}

◀ result
{"rows":[{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"D1038N"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"E555Q"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"A93V"},{"variant_type":"mutation","mutation_type":"5'Flank","mutation_variant":"Promoter"},{"variant_type":"cna","mutation_type":"NA","mutation_variant":"NA"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"P376L"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"T618M"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"I364V"},{"variant_type":"mutation","mutation_type":"Frame_Shift_Del","mutation_variant":"Q450Pfs*54"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"R108H"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"L601M"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"R466W"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"L616M"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"A242V"},{"variant_type":"mutation","mutation_type":"Nonsense_Mutation","mutation_variant":"C403*"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"V407E"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"A615T"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"L85V"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"R48C"},{"variant_type":"mutation","mutation_type":"Splice_Site","mutation_variant":"X823_splice"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"P391H"},{"variant_type":"structural_variant","mutation_type":"NA","mutation_variant":"NA"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"P313S"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"R268C"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"E247K"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"V826I"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"R447C"},{"variant_type":"mutation","mutation_type":"Frame_Shift_Del","mutation_variant":"G110Afs*18"},{"variant_type":"mutation","mutation_type":"Nonsense_Mutation","mutation_variant":"R8*"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"R743Q"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"A180T"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"V148A"},{"variant_type":"mutation","mutation_type":"Missense_Mutation","mutation_variant":"P721R"},{"variant_type":"mutatio … (1533 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH cohort AS (
    SELECT cancer_study_identifier
    FROM cancer_study_query_preferences
    WHERE preference_name = 'pan_cancer_tcga'
),
sample_cancer_type AS (
    SELECT cd.sample_unique_id, cd.attribute_value AS cancer_type
    FROM clinical_data_derived cd
    JOIN cohort c USING (cancer_study_identifier)
    WHERE cd.attribute_name = 'CANCER_TYPE'
),
altered AS (
    SELECT sct.cancer_type,
           COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    WHERE ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
      AND ged.hugo_gene_symbol = 'TERT'
      AND ged.mutation_type = '5\\'Flank'
      AND ged.off_panel = 0
    GROUP BY sct.cancer_type
),
profiled_samples_for_gene AS (
    SELECT sample_unique_id, cancer_study_identifier
    FROM mutation_panel_gene_coverage
    WHERE hugo_gene_symbol = 'TERT'
    UNION ALL
    SELECT sample_unique_id, cancer_study_identifier
    FROM mutation_wes_coverage
),
profiled AS (
    SELECT sct.cancer_type,
           COUNT(DISTINCT p.sample_unique_id) AS profiled_samples
    FROM profiled_samples_for_gene p
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    GROUP BY sct.cancer_type
)
SELECT a.cancer_type,
       a.altered_samples,
       p.profiled_samples,
       ROUND(a.altered_samples * 100.0 / NULLIF(p.profiled_samples, 0), 1) AS frequency_pct
FROM altered a
JOIN profiled p USING (cancer_type)
WHERE p.profiled_samples >= 50
ORDER BY frequency_pct DESC
"
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT cancer_study_identifier, mutation_type, mutation_variant, count() AS n
FROM genomic_event_derived
WHERE hugo_gene_symbol = 'TERT' AND variant_type = 'mutation' AND mutation_variant = 'Promoter'
GROUP BY cancer_study_identifier, mutation_type, mutation_variant
ORDER BY n DESC
LIMIT 50
"
}

◀ result
{"rows":[{"cancer_study_identifier":"msk_impact_50k_2026","mutation_type":"5'Flank","mutation_variant":"Promoter","n":6870},{"cancer_study_identifier":"msk_met_2021","mutation_type":"5'Flank","mutation_variant":"Promoter","n":2583},{"cancer_study_identifier":"msk_impact_2017","mutation_type":"5'Flank","mutation_variant":"Promoter","n":1358},{"cancer_study_identifier":"paired_bladder_2022","mutation_type":"5'Flank","mutation_variant":"Promoter","n":1209},{"cancer_study_identifier":"pan_origimed_2020","mutation_type":"5'Flank","mutation_variant":"Promoter","n":738},{"cancer_study_identifier":"mel_mskimpact_2020","mutation_type":"5'Flank","mutation_variant":"Promoter","n":625},{"cancer_study_identifier":"glioma_mskcc_2019","mutation_type":"5'Flank","mutation_variant":"Promoter","n":596},{"cancer_study_identifier":"tmb_mskcc_2018","mutation_type":"5'Flank","mutation_variant":"Promoter","n":510},{"cancer_study_identifier":"msk_chord_2024","mutation_type":"5'Flank","mutation_variant":"Promoter","n":389},{"cancer_study_identifier":"bladder_msk_2023","mutation_type":"5'Flank","mutation_variant":"Promoter","n":378},{"cancer_study_identifier":"ccle_broad_2025","mutation_type":"5'Flank","mutation_variant":"Promoter","n":257},{"cancer_study_identifier":"hcc_msk_2024","mutation_type":"5'Flank","mutation_variant":"Promoter","n":224},{"cancer_study_identifier":"mycf_msk_2026","mutation_type":"5'Flank","mutation_variant":"Promoter","n":207},{"cancer_study_identifier":"blca_msk_2025","mutation_type":"5'Flank","mutation_variant":"Promoter","n":193},{"cancer_study_identifier":"hcc_clca_2024","mutation_type":"5'Flank","mutation_variant":"Promoter","n":170},{"cancer_study_identifier":"pancan_mimsi_msk_2024","mutation_type":"5'Flank","mutation_variant":"Promoter","n":169},{"cancer_study_identifier":"pancan_hcmi_2025","mutation_type":"5'Flank","mutation_variant":"Promoter","n":165},{"cancer_study_identifier":"crc_msk_2026","mutation_type":"5'Flank","mutation_variant":"Promoter","n":161},{"cancer_study_identifier":"msk_ctdna_vte_2024","mutation_type":"5'Flank","mutation_variant":"Promoter","n":159},{"cancer_study_identifier":"blca_msk_2024","mutation_type":"5'Flank","mutation_variant":"Promoter","n":148},{"cancer_study_identifier":"hcc_inserm_fr_2015","mutation_type":"5'Flank","mutation_variant":"Promoter","n":138},{"cancer_study_identifier":"sarcoma_mskcc_2022","mutation_type":"5'Flank","mutation_variant":"Promoter","n":120},{"cancer_study_identifier":"bladder_columbia_msk_2018","mutation_type":"5'Flank","mutation_variant":"Promoter","n":112},{"cancer_study_identifier":"blca_msk_tcga_2020","mutation_type":"5'Flank","mutation_variant":"Promoter","n":99},{"cancer_study_identifier":"msk_access_2021","mutation_type":"5'Flank","mutation_variant":"Promoter","n":95},{"cancer_study_identifier":"msk_ch_2023","mutation_type":"5'Flank","mutation_variant":"Promoter","n":85},{"cancer_study_identifier":"acc_2019","mutation_type":"5'Flank","mutation_variant":"Promoter","n":82},{"canc … (2552 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH cohort AS (
    SELECT cancer_study_identifier
    FROM cancer_study_query_preferences
    WHERE preference_name = 'large_genomic_cohort'
),
sample_cancer_type AS (
    SELECT cd.sample_unique_id, cd.attribute_value AS cancer_type
    FROM clinical_data_derived cd
    JOIN cohort c USING (cancer_study_identifier)
    WHERE cd.attribute_name = 'CANCER_TYPE'
),
altered AS (
    SELECT sct.cancer_type,
           COUNT(DISTINCT ged.sample_unique_id) AS altered_samples
    FROM genomic_event_derived ged
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    WHERE ged.variant_type = 'mutation'
      AND ged.mutation_status != 'UNCALLED'
      AND ged.hugo_gene_symbol = 'TERT'
      AND ged.mutation_variant = 'Promoter'
      AND ged.off_panel = 0
    GROUP BY sct.cancer_type
),
profiled_samples_for_gene AS (
    SELECT sample_unique_id, cancer_study_identifier
    FROM mutation_panel_gene_coverage
    WHERE hugo_gene_symbol = 'TERT'
    UNION ALL
    SELECT sample_unique_id, cancer_study_identifier
    FROM mutation_wes_coverage
),
profiled AS (
    SELECT sct.cancer_type,
           COUNT(DISTINCT p.sample_unique_id) AS profiled_samples
    FROM profiled_samples_for_gene p
    JOIN cohort c USING (cancer_study_identifier)
    JOIN sample_cancer_type sct USING (sample_unique_id)
    GROUP BY sct.cancer_type
)
SELECT a.cancer_type,
       a.altered_samples,
       p.profiled_samples,
       ROUND(a.altered_samples * 100.0 / NULLIF(p.profiled_samples, 0), 1) AS frequency_pct
FROM altered a
JOIN profiled p USING (cancer_type)
WHERE p.profiled_samples >= 50
ORDER BY frequency_pct DESC
LIMIT 40
"
}

◀ result
{"rows":[{"cancer_type":"Bladder Cancer","altered_samples":1781,"profiled_samples":2526,"frequency_pct":70.5},{"cancer_type":"Glioma","altered_samples":1528,"profiled_samples":2322,"frequency_pct":65.8},{"cancer_type":"Melanoma","altered_samples":1037,"profiled_samples":1831,"frequency_pct":56.6},{"cancer_type":"Thyroid Cancer","altered_samples":475,"profiled_samples":867,"frequency_pct":54.8},{"cancer_type":"Sex Cord Stromal Tumor","altered_samples":35,"profiled_samples":91,"frequency_pct":38.5},{"cancer_type":"Vaginal Cancer","altered_samples":20,"profiled_samples":57,"frequency_pct":35.1},{"cancer_type":"Head and Neck Cancer","altered_samples":199,"profiled_samples":657,"frequency_pct":30.3},{"cancer_type":"Skin Cancer, Non-Melanoma","altered_samples":136,"profiled_samples":455,"frequency_pct":29.9},{"cancer_type":"Embryonal Tumor","altered_samples":14,"profiled_samples":80,"frequency_pct":17.5},{"cancer_type":"Hepatobiliary Cancer","altered_samples":217,"profiled_samples":1431,"frequency_pct":15.2},{"cancer_type":"Cancer of Unknown Primary","altered_samples":180,"profiled_samples":1581,"frequency_pct":11.4},{"cancer_type":"Renal Cell Carcinoma","altered_samples":111,"profiled_samples":1209,"frequency_pct":9.2},{"cancer_type":"Salivary Gland Cancer","altered_samples":36,"profiled_samples":411,"frequency_pct":8.8},{"cancer_type":"Miscellaneous Brain Tumor","altered_samples":5,"profiled_samples":58,"frequency_pct":8.6},{"cancer_type":"Cervical Cancer","altered_samples":28,"profiled_samples":351,"frequency_pct":8},{"cancer_type":"Soft Tissue Sarcoma","altered_samples":177,"profiled_samples":2307,"frequency_pct":7.7},{"cancer_type":"Adrenocortical Carcinoma","altered_samples":6,"profiled_samples":88,"frequency_pct":6.8},{"altered_samples":3,"profiled_samples":50,"frequency_pct":6},{"cancer_type":"Mesothelioma","altered_samples":23,"profiled_samples":408,"frequency_pct":5.6},{"cancer_type":"Bone Cancer","altered_samples":29,"profiled_samples":590,"frequency_pct":4.9},{"cancer_type":"Anal Cancer","altered_samples":7,"profiled_samples":143,"frequency_pct":4.9},{"cancer_type":"CNS Cancer","altered_samples":8,"profiled_samples":170,"frequency_pct":4.7},{"cancer_type":"Ampullary Cancer","altered_samples":5,"profiled_samples":134,"frequency_pct":3.7},{"cancer_type":"Small Bowel Cancer","altered_samples":6,"profiled_samples":161,"frequency_pct":3.7},{"cancer_type":"Wilms Tumor","altered_samples":2,"profiled_samples":68,"frequency_pct":2.9},{"cancer_type":"Nerve Sheath Tumor","altered_samples":4,"profiled_samples":142,"frequency_pct":2.8},{"cancer_type":"Uterine Sarcoma","altered_samples":9,"profiled_samples":344,"frequency_pct":2.6},{"cancer_type":"Ovarian Cancer","altered_samples":54,"profiled_samples":2140,"frequency_pct":2.5},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":189,"profiled_samples":7867,"frequency_pct":2.4},{"cancer_type":"Endometrial Cancer","altered_samples":59,"profiled_samples":2445,"frequency_pct":2.4},{"cancer_type":"S … (1024 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "msk_impact_50k_2026"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_impact_50k_2026","name":"MSK-IMPACT 50K Clinical Sequencing Cohort (MSK, Cancer Cell 2026)","sampleCount":54331,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_impact_50k_2026","metadata":{"clinicalAttributeIds":["AGE_AT_DX","ANCESTRY_LABEL","CANCER_TYPE","CANCER_TYPE_DETAILED","DISEASE_STATUS","FACETS_PLOIDY","FACETS_PURITY","FACETS_QC","FACETS_SUITE_VERSION","FACETS_WGD","FRACTION_GENOME_ALTERED","GENE_PANEL","HLA-A1","HLA-A2","HLA-B1","HLA-B2","HLA-C1","HLA-C2","HLA_A1_LOH","HLA_A2_LOH","HLA_B1_LOH","HLA_B2_LOH","HLA_C1_LOH","HLA_C2_LOH","HLA_GENOTYPE_AVAILABLE","METASTATIC_SITE","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","N_OTHER_CANCER_TYPES","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PRIMARY_SITE","PURITY_ESTIMATE_FROM_MUTATIONS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SEX","SOMATIC_STATUS","TMB_SCORE","TUMOR_PURITY"],"molecularProfileIds":["msk_impact_50k_2026_gistic","msk_impact_50k_2026_mutations","msk_impact_50k_2026_structural_variants"],"genericAssayProfiles":["msk_impact_50k_2026_armlevel_cna","msk_impact_50k_2026_loh_hla","msk_impact_50k_2026_mutational_signatures_contribution_v2"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","generic_assay_armlevel_cna","generic_assay_loh_hla","generic_assay_mutational_signature"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "msk_impact_50k_2026"
 ],
 "genes": [
  "TERT: MUT"
 ],
 "tab": "cancerTypesSummary"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/cancerTypesSummary?cancer_study_list=msk_impact_50k_2026&gene_list=TERT%3A+MUT&case_set_id=msk_impact_50k_2026_all","url":"https://www.cbioportal.org/results/cancerTypesSummary?cancer_study_list=msk_impact_50k_2026&gene_list=TERT%3A+MUT&case_set_id=msk_impact_50k_2026_all","data":{"studyIds":["msk_impact_50k_2026"],"studies":[{"studyId":"msk_impact_50k_2026","name":"MSK-IMPACT 50K Clinical Sequencing Cohort (MSK, Cancer Cell 2026)","sampleCount":54331}],"genes":["TERT"],"caseSetId":"msk_impact_50k_2026_all","pageDescription":"Alteration frequencies for queried genes by cancer type."}}

═ answer (success)
## TERT Promoter Mutation Frequency Across Cancer Types

**A note on scope:** TERT promoter mutations (e.g., C228T/-124C>T, C250T/-146C>T) are non-coding alterations upstream of the TERT coding sequence, tracked separately from coding TERT mutations in this database (flagged as `5'Flank`/`Promoter` records). Coding TERT mutations are excluded from the numbers below.

**Cohort caveat:** TCGA PanCancer Atlas (whole-exome sequencing) does not reliably call TERT promoter mutations — the region is poorly captured/annotated by WES pipelines, so that cohort returned essentially no promoter-mutation records. I instead used the **MSK-IMPACT 50K Clinical Sequencing Cohort** (`msk_impact_50k_2026`, Cancer Cell 2026), a single non-overlapping targeted-panel cohort with dedicated TERT promoter coverage — 54,331 samples across many cancer types. Frequencies below are **sample-level** (altered samples / samples profiled for TERT × 100), restricted to cancer types with ≥50 profiled samples.

| Cancer Type | Altered | Profiled | Frequency |
|---|---|---|---|
| Bladder Cancer | 1,781 | 2,526 | **70.5%** |
| Glioma | 1,528 | 2,322 | **65.8%** |
| Melanoma | 1,037 | 1,831 | **56.6%** |
| Thyroid Cancer | 475 | 867 | **54.8%** |
| Sex Cord Stromal Tumor | 35 | 91 | 38.5% |
| Vaginal Cancer | 20 | 57 | 35.1% |
| Head and Neck Cancer | 199 | 657 | 30.3% |
| Skin Cancer, Non-Melanoma | 136 | 455 | 29.9% |
| Embryonal Tumor | 14 | 80 | 17.5% |
| Hepatobiliary Cancer | 217 | 1,431 | 15.2% |
| Cancer of Unknown Primary | 180 | 1,581 | 11.4% |
| Renal Cell Carcinoma | 111 | 1,209 | 9.2% |
| Salivary Gland Cancer | 36 | 411 | 8.8% |
| Cervical Cancer | 28 | 351 | 8.0% |
| Soft Tissue Sarcoma | 177 | 2,307 | 7.7% |
| Adrenocortical Carcinoma | 6 | 88 | 6.8% |
| Mesothelioma | 23 | 408 | 5.6% |
| Bone Cancer | 29 | 590 | 4.9% |
| Anal Cancer | 7 | 143 | 4.9% |
| CNS Cancer | 8 | 170 | 4.7% |
| Ovarian Cancer | 54 | 2,140 | 2.5% |
| Non-Small Cell Lung Cancer | 189 | 7,867 | 2.4% |
| Endometrial Cancer | 59 | 2,445 | 2.4% |
| Colorectal Cancer | 99 | 5,277 | 1.9% |
| Esophagogastric Cancer | 18 | 1,834 | 1.0% |
| Breast Cancer | 46 | 6,807 | 0.7% |
| Pancreatic Cancer | 15 | 2,851 | 0.5% |

This matches the well-established pattern: TERT promoter mutations are highest in **bladder cancer, glioma (especially IDH-wildtype glioblastoma), melanoma, and thyroid cancer**, and rare in most other solid tumors.

[View TERT alteration frequencies by cancer type (MSK-IMPACT 50K)](https://www.cbioportal.org/results/cancerTypesSummary?cancer_study_list=msk_impact_50k_2026&gene_list=TERT%3A+MUT&case_set_id=msk_impact_50k_2026_all)

Note: this ResultsView link shows all TERT mutations (promoter + any coding) by default — use its Mutations tab to visually confirm the '5-Flank/Promoter' variants specifically.