Q (sonnet-5.5): <conversation_so_far>
<user>
TCGA PanCancer Atlas の乳がんで TP53 変異の頻度を教えてください。
</user>

<assistant>
Breast Invasive Carcinoma (TCGA, PanCancer Atlas) では、TP53 変異はプロファイルされた 1,066 サンプル中 347 サンプル（32.6%）に見られます。
</assistant>
</conversation_so_far>

Continue this conversation: reply to the user's new message.

<user>
変異だけでなく、ホモ接合性欠失（deep deletion）も含めて計算してください。
</user>

▶ 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": "WITH cs AS (
    SELECT concat('brca_tcga_pan_can_atlas_2018_', s.stable_id) AS sid
    FROM sample_list_list sll
    JOIN sample_list sl ON sl.list_id = sll.list_id
    JOIN sample s ON s.internal_id = sll.sample_id
    WHERE sl.stable_id = 'brca_tcga_pan_can_atlas_2018_cnaseq'
)
SELECT (SELECT count() FROM cs) AS n,
  uniqExactIf(sample_unique_id, variant_type='mutation') AS mut,
  uniqExactIf(sample_unique_id, variant_type='cna' AND cna_alteration=-2) AS homdel,
  uniqExact(sample_unique_id) AS either
FROM genomic_event_derived
WHERE cancer_study_identifier='brca_tcga_pan_can_atlas_2018' AND hugo_gene_symbol='TP53'
  AND ((variant_type='mutation' AND mutation_status!='UNCALLED') OR (variant_type='cna' AND cna_alteration=-2))
  AND sample_unique_id IN (SELECT sid FROM cs)"
}

▶ resolve_and_route
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"rows":[{"n":996,"mut":344,"homdel":9,"either":351}]}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_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":["brca_tcga_pan_can_atlas_2018_gistic","brca_tcga_pan_can_atlas_2018_log2CNA","brca_tcga_pan_can_atlas_2018_mutations","brca_tcga_pan_can_atlas_2018_protein_quantification","brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa","brca_tcga_pan_can_atlas_2018_rppa_Zscores","brca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["brca_tcga_pan_can_atlas_2018_armlevel_cna","brca_tcga_pan_can_atlas_2018_genetic_ancestry","brca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","brca_tcga_pan_can_atlas_2018_methylation_hm450","brca_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_anc … (83 more chars)

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "TP53: MUT HOMDEL"
 ],
 "caseSetId": "brca_tcga_pan_can_atlas_2018_cnaseq",
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT+HOMDEL&case_set_id=brca_tcga_pan_can_atlas_2018_cnaseq","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT+HOMDEL&case_set_id=brca_tcga_pan_can_atlas_2018_cnaseq","data":{"studyIds":["brca_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084}],"genes":["TP53"],"caseSetId":"brca_tcga_pan_can_atlas_2018_cnaseq","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
Breast Invasive Carcinoma (TCGA, PanCancer Atlas) では、TP53 の変異またはホモ接合性欠失（deep deletion）を持つサンプルは、変異と CNA の両方がプロファイルされた 996 サンプル中 351 サンプル（35.2%）でした。

| 区分 | サンプル数 | 割合 (/996) |
|---|---|---|
| TP53 変異 | 344 | 34.5% |
| TP53 ホモ接合性欠失 | 9 | 0.9% |
| 変異または欠失（合算） | 351 | 35.2% |

- 単位はサンプルです。変異と欠失の両方を持つサンプルは重複して数えていません。合算が 344 + 9 = 353 ではなく 351 なのはそのためです。
- 分母は、変異と CNA の両方がプロファイルされたサンプル（cBioPortal の「Samples with mutation and CNA data」）の 996 です。
- 前回の 32.6%（347/1,066）は変異のみで、分母が異なります（1,066 は変異プロファイル済みサンプル数）。そのため、今回の変異のみの値 34.5%（344/996）とは一致しません。
- 欠失は GISTIC の deep deletion（−2）のみを数えています。浅い欠失（−1）は含めていません。

[TP53 の OncoPrint を開く（MUT + HOMDEL）](https://www.cbioportal.org/results/oncoprint?cancer_study_list=brca_tcga_pan_can_atlas_2018&gene_list=TP53%3A+MUT+HOMDEL&case_set_id=brca_tcga_pan_can_atlas_2018_cnaseq)

OncoPrint は、複数の遺伝子をまとめて可視化するタブです。