Q (haiku): Which cancer types have the highest frequency of EGFR mutations across all TCGA Pan-Cancer Atlas studies?

▶ 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 *
FROM gene_mutation_frequency_by_cancer_type(
    preference = 'pan_cancer_tcga',
    gene = 'EGFR'
)
ORDER BY frequency_pct DESC
",
 "max_rows": 50
}

◀ result
{"rows":[{"cancer_type":"Glioblastoma","altered_samples":94,"profiled_samples":397,"frequency_pct":23.7},{"cancer_type":"Non-Small Cell Lung Cancer","altered_samples":84,"profiled_samples":1050,"frequency_pct":8},{"cancer_type":"Melanoma","altered_samples":35,"profiled_samples":440,"frequency_pct":8},{"cancer_type":"Glioma","altered_samples":35,"profiled_samples":514,"frequency_pct":6.8},{"cancer_type":"Endometrial Cancer","altered_samples":36,"profiled_samples":574,"frequency_pct":6.3},{"cancer_type":"Esophagogastric Cancer","altered_samples":26,"profiled_samples":618,"frequency_pct":4.2},{"cancer_type":"Colorectal Cancer","altered_samples":15,"profiled_samples":534,"frequency_pct":2.8},{"cancer_type":"Head and Neck Cancer","altered_samples":13,"profiled_samples":515,"frequency_pct":2.5},{"cancer_type":"Cervical Cancer","altered_samples":7,"profiled_samples":291,"frequency_pct":2.4},{"cancer_type":"Adrenocortical Carcinoma","altered_samples":2,"profiled_samples":91,"frequency_pct":2.2},{"cancer_type":"Bladder Cancer","altered_samples":8,"profiled_samples":410,"frequency_pct":2},{"cancer_type":"Sarcoma","altered_samples":4,"profiled_samples":255,"frequency_pct":1.6},{"cancer_type":"Hepatobiliary Cancer","altered_samples":6,"profiled_samples":366,"frequency_pct":1.6},{"cancer_type":"Breast Cancer","altered_samples":15,"profiled_samples":1066,"frequency_pct":1.4},{"cancer_type":"Non-Seminomatous Germ Cell Tumor","altered_samples":1,"profiled_samples":86,"frequency_pct":1.2},{"cancer_type":"Ovarian Epithelial Tumor","altered_samples":6,"profiled_samples":523,"frequency_pct":1.1},{"cancer_type":"Leukemia","altered_samples":2,"profiled_samples":200,"frequency_pct":1},{"cancer_type":"Pancreatic Cancer","altered_samples":1,"profiled_samples":179,"frequency_pct":0.6},{"cancer_type":"Prostate Cancer","altered_samples":3,"profiled_samples":494,"frequency_pct":0.6},{"cancer_type":"Renal Clear Cell Carcinoma","altered_samples":2,"profiled_samples":402,"frequency_pct":0.5},{"cancer_type":"Renal Non-Clear Cell Carcinoma","altered_samples":1,"profiled_samples":341,"frequency_pct":0.3}]}

▶ resolve_and_route
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018",
  "gbm_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "lgg_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 4 matching studies. Pick the best match (prefer TCGA → prefer PanCancer Atlas), use its metadata to call the appropriate navigation tool(s) and generate URLs immediately. Present other studies as alternatives.","data":{"totalCount":4,"studiesWithMetadata":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566,"studyViewUrl":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","RAGNUM_HYPOXIA_SCORE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT","WINTER_HYPOXIA_SCORE"],"molecularProfileIds":["luad_tcga_pan_can_atlas_2018_gistic","luad_tcga_pan_can_atlas_2018_log2CNA","luad_tcga_pan_can_atlas_2018_mutations","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_tcga_pan_can_atlas_2018_rppa","luad_tcga_pan_can_atlas_2018_rppa_Zscores","luad_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["luad_tcga_pan_can_atlas_2018_armlevel_cna","luad_tcga_pan_can_atlas_2018_genetic_ancestry","luad_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","luad_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}},{"studyId":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multif … (7333 more chars)

▶ navigate_to_results_view
{
 "studyIds": [
  "gbm_tcga_pan_can_atlas_2018",
  "luad_tcga_pan_can_atlas_2018",
  "skcm_tcga_pan_can_atlas_2018",
  "lgg_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "EGFR"
 ],
 "tab": "mutations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/mutations?cancer_study_list=gbm_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=all","url":"https://www.cbioportal.org/results/mutations?cancer_study_list=gbm_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=all","data":{"studyIds":["gbm_tcga_pan_can_atlas_2018","luad_tcga_pan_can_atlas_2018","skcm_tcga_pan_can_atlas_2018","lgg_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592},{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566},{"studyId":"skcm_tcga_pan_can_atlas_2018","name":"Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)","sampleCount":448},{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514}],"genes":["EGFR"],"caseSetId":"all","pageDescription":"Detailed mutation table with lollipop plot showing mutation positions, types, and frequencies for queried genes."}}

═ answer (success)
## EGFR Mutation Frequency Across TCGA Pan-Cancer Atlas Cancer Types

**Sample-level frequency** (mutated / profiled samples × 100):

| Cancer Type | EGFR-Mutated Samples | Profiled Samples | Mutation Frequency |
|---|---:|---:|---:|
| **Glioblastoma** | 94 | 397 | **23.7%** |
| **Non-Small Cell Lung Cancer** | 84 | 1,050 | **8.0%** |
| **Melanoma** | 35 | 440 | **8.0%** |
| **Glioma (Lower Grade)** | 35 | 514 | **6.8%** |
| **Endometrial Cancer** | 36 | 574 | **6.3%** |
| Esophagogastric Cancer | 26 | 618 | 4.2% |
| Colorectal Cancer | 15 | 534 | 2.8% |
| Head and Neck Cancer | 13 | 515 | 2.5% |
| Cervical Cancer | 7 | 291 | 2.4% |
| Adrenocortical Carcinoma | 2 | 91 | 2.2% |

### Key Findings

**EGFR mutations are most frequent in CNS tumors and lung adenocarcinoma:**

1. **Glioblastoma** leads at 23.7%, with nearly 1 in 4 tumors harboring EGFR mutations — the highest frequency across all TCGA PanCancer types.
2. **Non-Small Cell Lung Cancer (NSCLC)** and **Melanoma** are tied at 8%, reflecting known therapeutic targets in these cancers.
3. **Lower-grade gliomas** and **endometrial cancers** also show appreciable frequencies (6.8% and 6.3%, respectively).
4. Most other cancers have EGFR mutation rates <5%, with renal and pancreatic cancers the lowest (<1%).

### Explore the Data

[View EGFR mutations across top cancer types](https://www.cbioportal.org/results/mutations?cancer_study_list=gbm_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=all) — Detailed mutation table with lollipop plot showing specific EGFR variants and their frequencies.