Q (sonnet): In TCGA lower grade glioma, show me samples with EGFR gains.

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

▶ list_studies
{
 "search": "lower grade glioma TCGA"
}

◀ 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 … (35166 more chars)

◀ result
{"result":[{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","type_of_cancer_id":"difg","sample_count":514,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lgg_tcga","name":"Brain Lower Grade Glioma (TCGA, Firehose Legacy)","type_of_cancer_id":"difg","sample_count":530,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_tcga"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT alteration_value, count() AS samples
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND hugo_gene_symbol = 'EGFR'
  AND profile_type = 'gistic'
  AND alteration_value NOT IN ('', 'NA')
GROUP BY alteration_value
ORDER BY alteration_value"
}

▶ clickhouse_list_table_columns
{
 "table": "genetic_alteration_derived"
}

◀ result
{"rows":[{"alteration_value":"-1","samples":4},{"alteration_value":"0","samples":381},{"alteration_value":"1","samples":87},{"alteration_value":"2","samples":39}]}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"hugo_gene_symbol","type":"String"},{"name":"profile_type","type":"LowCardinality(String)"},{"name":"alteration_value","type":"Nullable(String)"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT sample_unique_id
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lgg_tcga_pan_can_atlas_2018'
  AND hugo_gene_symbol = 'EGFR'
  AND profile_type = 'gistic'
  AND alteration_value = '1'
ORDER BY sample_unique_id",
 "max_rows": 100
}

◀ result
{"rows":[{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-4942-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-5395-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-5396-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-5397-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-6186-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-6665-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-CS-6670-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DB-5270-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DB-5277-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DB-A4X9-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DB-A4XE-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DB-A64O-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DH-A669-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DH-A66G-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DH-A7UT-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DH-A7UV-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6392-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6395-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6400-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6402-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6404-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6407-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-6410-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-7006-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-7011-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-7012-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-7014-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-7015-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-8161-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-8163-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-8165-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-8166-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A5TR-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A5TT-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A5TU-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A5TW-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A5TY-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A76K-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-DU-A7TA-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-E1-5304-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-E1-5322-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-E1-A7YL-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-E1-A7YM-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas_2018_TCGA-E1-A7YN-01"},{"sample_unique_id":"lgg_tcga_pan_can_atlas … (2839 more chars)

▶ resolve_and_route
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514,"studyViewUrl":"https://www.cbioportal.org/study?id=lgg_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":["lgg_tcga_pan_can_atlas_2018_gistic","lgg_tcga_pan_can_atlas_2018_log2CNA","lgg_tcga_pan_can_atlas_2018_mutations","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lgg_tcga_pan_can_atlas_2018_rppa","lgg_tcga_pan_can_atlas_2018_rppa_Zscores","lgg_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["lgg_tcga_pan_can_atlas_2018_armlevel_cna","lgg_tcga_pan_can_atlas_2018_genetic_ancestry","lgg_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","lgg_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lgg_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lgg_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"]}}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "lgg_tcga_pan_can_atlas_2018"
 ],
 "filterJson": {
  "genomicDataFilters": [
   {
    "hugoGeneSymbol": "EGFR",
    "profileType": "gistic",
    "values": [
     {
      "value": "1"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22EGFR%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%221%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22EGFR%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%221%22%7D%5D%7D%5D%7D","data":{"studyIds":["lgg_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","sampleCount":514}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}}

═ answer (success)
## EGFR Copy-Number Gains — TCGA Lower Grade Glioma (PanCancer Atlas)

**Study:** [Brain Lower Grade Glioma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018) — 514 samples total.

Note: "gain" here refers to the low-level copy-number **gain** GISTIC category (value = +1) — a shallow copy-number increase, distinct from focal **amplification** (GISTIC value = +2).

**Full EGFR discrete CNA distribution** (511 samples with a called GISTIC value):
| CNA category | Samples |
|---|---|
| Homozygous deletion (−2) | 0 |
| Heterozygous loss (−1) | 4 |
| Diploid (0) | 381 |
| **Gain (+1)** | **87** |
| Amplification (+2) | 39 |

**87 of 511 profiled samples (17.0%)** show an EGFR gain. Sample IDs include TCGA-CS-4942-01, TCGA-CS-5395-01, TCGA-CS-5396-01, TCGA-DB-5270-01, TCGA-DU-6392-01, TCGA-E1-5304-01, TCGA-FG-5962-01, TCGA-HT-7473-01, TCGA-P5-A5EZ-01, TCGA-QH-A6CS-01, TCGA-S9-A6TY-01, TCGA-TM-A7CA-01, TCGA-TQ-A7RH-01, TCGA-WY-A85E-01, and 73 more (full list of 87 available on request).

[View EGFR-gain samples in the Study View](https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22EGFR%22%2C%22profileType%22%3A%22gistic%22%2C%22values%22%3A%5B%7B%22value%22%3A%221%22%7D%5D%7D%5D%7D) — this pre-filters the cohort to the 87 samples with an EGFR gain, letting you cross-tabulate with clinical attributes (e.g., IDH status, grade, 1p/19q codeletion) or browse individual cases.

Separately, if you're interested in EGFR **amplification** (the more clinically discussed alteration in glioma) rather than shallow gain, that's the 39-sample (7.6%) group shown above — let me know if you'd like that view instead or in addition.