Q (haiku):
TCGA PanCancer Atlas の乳がんで TP53 変異の頻度を教えてください。
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。
Continue this conversation: reply to the user's new message.
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。
▶ read_guide
{
"uri": "cbioportal://mutation-frequency-guide"
}
▶ clickhouse_list_table_columns
{
"table": "cancer_study"
}
◀ 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)
▶ clickhouse_list_table_columns
{
"table": "genetic_alteration_derived"
}
◀ result
{"columns":[{"name":"cancer_study_id","type":"Int64","comment":"Internal numeric ID. Prefer cancer_study_identifier for queries as it is more readable and stable."},{"name":"cancer_study_identifier","type":"Nullable(String)","comment":"Stable string identifier for the study (e.g., \"msk_chord_2024\", \"brca_tcga\"). Use this for filtering, not cancer_study_id."},{"name":"type_of_cancer_id","type":"String"},{"name":"name","type":"String","comment":"Full descriptive name of the study (e.g., \"MSK-CHORD (MSK, Nature 2024)\")."},{"name":"description","type":"String"},{"name":"public","type":"Int32"},{"name":"pmid","type":"Nullable(String)"},{"name":"citation","type":"Nullable(String)"},{"name":"groups","type":"Nullable(String)"},{"name":"status","type":"Nullable(Int64)"},{"name":"import_date","type":"Nullable(DateTime64(6))"},{"name":"reference_genome_id","type":"Nullable(Int64)"},{"name":"sample_count","type":"UInt32","comment":"Samples in the study (members of _all), as shown in the portal study list. Precomputed daily at LLM-prep time."},{"name":"mutation_sample_count","type":"UInt32","comment":"Samples profiled for mutations (_sequenced) — portal \"Data type\" filter: \"Mutations\". 0 = no mutation data."},{"name":"cna_sample_count","type":"UInt32","comment":"Samples profiled for copy-number alterations (_cna) — \"CNA\". 0 = no CNA data."},{"name":"structural_variant_sample_count","type":"UInt32","comment":"Distinct samples with at least one structural variant (fusions etc.). 0 = none."},{"name":"rna_seq_sample_count","type":"UInt32","comment":"Samples with RNA-Seq expression (_rna_seq_v2_mrna) — \"RNA-Seq\"."},{"name":"mrna_microarray_sample_count","type":"UInt32","comment":"Samples with microarray mRNA expression (_mrna) — \"RNA (microarray)\"."},{"name":"mirna_sample_count","type":"UInt32","comment":"Samples with microRNA expression (_microrna) — \"miRNA\"."},{"name":"rppa_sample_count","type":"UInt32","comment":"Samples with RPPA protein levels (_rppa) — \"RPPA\"."},{"name":"mass_spectrometry_sample_count","type":"UInt32","comment":"Samples with mass-spectrometry protein quantification (_protein_quantification) — \"Protein Mass-Spectrometry\"."},{"name":"treatment_patient_count","type":"UInt32","comment":"PATIENTS (not samples) with treatment clinical events — \"Treatment\". 0 = no treatment data."},{"name":"resource_sample_counts","type":"Map(String, UInt32)","comment":"Samples with each linked resource, keyed by display name: imaging and pathology such as 'Slide Microscopy', 'Computed Tomography', 'Magnetic Resonance', 'H&E Slide', 'MxIF Image'. Query with mapKeys(resource_sample_counts) or resource_sample_counts['Slide Microscopy'] > 0."},{"name":"mrna_expression_sample_count","type":"UInt32","comment":"Samples in any mRNA expression profile (RNA-Seq, microarray, TPM, z-scores; from sample_profile). Use this for \"does the study have expression data\" — rna_seq_sample_count and mrna_m … (136 more chars)
◀ 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": "
WITH tp53_mutations AS (
SELECT COUNT(DISTINCT sample_unique_id) AS tp53_mutated
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'
),
tp53_homdel AS (
SELECT COUNT(DISTINCT sample_unique_id) AS tp53_homdel
FROM genomic_event_derived
WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
AND hugo_gene_symbol = 'TP53'
AND variant_type = 'cna'
AND cna_alteration = -2 -- HOMDEL
),
tp53_any_alteration AS (
SELECT COUNT(DISTINCT sample_unique_id) AS tp53_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)
)
),
profiled_for_mutations AS (
SELECT COUNT(DISTINCT sample_unique_id) AS mutation_profiled
FROM sample_to_gene_panel_derived
WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
AND alteration_type = 'MUTATION_EXTENDED'
),
profiled_for_cna AS (
SELECT COUNT(DISTINCT sample_unique_id) AS cna_profiled
FROM sample_to_gene_panel_derived
WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018'
AND alteration_type = 'COPY_NUMBER_ALTERATION'
)
SELECT
(SELECT tp53_mutated FROM tp53_mutations) AS tp53_mutations,
(SELECT tp53_homdel FROM tp53_homdel) AS tp53_homdel,
(SELECT tp53_either FROM tp53_any_alteration) AS tp53_mutations_or_homdel,
(SELECT mutation_profiled FROM profiled_for_mutations) AS mutation_profiled_samples,
(SELECT cna_profiled FROM profiled_for_cna) AS cna_profiled_samples,
round((SELECT tp53_mutated FROM tp53_mutations) * 100.0 / (SELECT mutation_profiled FROM profiled_for_mutations), 1) AS mutation_frequency_pct,
round((SELECT tp53_homdel FROM tp53_homdel) * 100.0 / (SELECT cna_profiled FROM profiled_for_cna), 1) AS homdel_frequency_pct,
round((SELECT tp53_either FROM tp53_any_alteration) * 100.0 / GREATEST((SELECT mutation_profiled FROM profiled_for_mutations), (SELECT cna_profiled FROM profiled_for_cna)), 1) AS combined_frequency_pct
"
}
◀ result
{"rows":[{"tp53_mutations":347,"tp53_homdel":9,"tp53_mutations_or_homdel":354,"mutation_profiled_samples":1066,"cna_profiled_samples":1070,"mutation_frequency_pct":32.6,"homdel_frequency_pct":0.8,"combined_frequency_pct":33.1}]}
═ answer (success)
TCGA PanCancer Atlas乳がん（Breast Invasive Carcinoma）の **TP53のホモ接合性欠失（HOMDEL）を含めた頻度** は以下の通りです：
| TP53変異タイプ | 変異サンプル数 | プロファイル数 | 頻度 |
|---|---|---|---|
| **点突然変異** | 347 | 1,066 | **32.6%** |
| **ホモ接合性欠失（HOMDEL）** | 9 | 1,070 | **0.8%** |
| **突然変異またはHOMDEL** | 354 | 1,070 | **33.1%** |
**結論：** TP53の機能喪失イベント（点突然変異またはホモ接合性欠失）は、乳がんサンプルの約 **33%** に見られます。ホモ接合性欠失は比較的少数（9サンプル）ですが、点突然変異のわずか約2.6%に相当します。