Q (sonnet): What percentage of patients in the MSK-CHORD Study have at least one TP53 mutation?

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

▶ read_guide
{
 "uri": "cbioportal://clinical-data-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 … (35166 more chars)

◀ result
# Clinical Data Query Guide

## Overview
Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis.

## Data Organization

### Patient-Level vs Sample-Level Data
- **Patient-level**: Demographics, overall survival, disease stage (stored once per patient)
- **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient)

### Key Tables
- `clinical_patient`: Patient-level clinical attributes
- `clinical_sample`: Sample-level clinical attributes
- `clinical_data_derived`: Pre-joined view combining both levels
- `clinical_attribute_meta`: Metadata about available clinical attributes

## Recommended Approach: Use clinical_data_derived

The `clinical_data_derived` table is pre-joined and optimized for most queries:

```sql
-- Get clinical data for specific attributes
SELECT
    sample_unique_id,
    patient_unique_id,
    attribute_name,
    attribute_value
FROM clinical_data_derived
WHERE
    cancer_study_identifier = 'your_study_id'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS');
```

## Clinical Attribute Discovery

### Use clinical_attribute_meta for Discovering Available Attributes
**Always start here** to see what clinical attributes are available for a specific study:

```sql
-- Discover available clinical attributes for a study
SELECT
    attr_id,
    description,
    patient_attribute,
    cancer_study_id
FROM clinical_attribute_meta
WHERE
    cancer_study_id = (
        SELECT cancer_study_id
        FROM cancer_study
        WHERE cancer_study_identifier = 'your_study_id'
    )
ORDER BY patient_attribute, attr_id;
```

**Key Fields:**
- **attr_id**: matches attr_id in clinical_sample/clinical_patient tables
- **description**: human-readable description of the attribute
- **patient_attribute**: true = patient attribute, false = sample attribute
- **cancer_study_id**: links to cancer_study table (filter by study)

## Attribute Semantics and Matching

### Case-Insensitive Matching for Attribute Values

Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant.

When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study:

```sql
-- Correct: case-insensitive clinical value filter
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'your_study_id'
  AND attribute_name = 'MUTATION_STATUS'
  AND upper(attribute_value) = 'GERMLINE';
```

Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study.

### Query the Requested Attribute, Not a Proxy

Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH profiled_patients AS (
    SELECT DISTINCT c.patient_unique_id
    FROM (
        SELECT sample_unique_id, cancer_study_identifier
        FROM sample_to_gene_panel_derived stgp
        JOIN gene_panel gp ON stgp.gene_panel_id = gp.stable_id
        JOIN gene_panel_list gpl ON gp.internal_id = gpl.internal_id
        JOIN gene g ON gpl.gene_id = g.entrez_gene_id
        WHERE stgp.alteration_type = 'MUTATION_EXTENDED'
          AND g.hugo_gene_symbol = 'TP53'
          AND stgp.cancer_study_identifier = 'msk_chord_2024'
    ) p
    JOIN clinical_data_derived c
      ON p.sample_unique_id = c.sample_unique_id
    WHERE c.cancer_study_identifier = 'msk_chord_2024'
),
mutated_patients AS (
    SELECT DISTINCT patient_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND hugo_gene_symbol = 'TP53'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      AND off_panel = 0
)
SELECT
    (SELECT count() FROM profiled_patients) AS profiled_patients,
    (SELECT count() FROM mutated_patients WHERE patient_unique_id IN (SELECT patient_unique_id FROM profiled_patients)) AS mutated_patients,
    round(mutated_patients * 100.0 / profiled_patients, 1) AS pct
FROM (SELECT 1)
"
}

◀ result
{"rows":[{"profiled_patients":24950,"mutated_patients":13105,"pct":52.5}]}

▶ resolve_and_route
{
 "studyIds": [
  "msk_chord_2024"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024","metadata":{"clinicalAttributeIds":["ADRENAL_GLANDS","BONE","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_GROUP","CLINICAL_SUMMARY","CNS_BRAIN","CURRENT_AGE_DEID","DIAGNOSIS_DESCRIPTION","ETHNICITY","FRACTION_GENOME_ALTERED","GENDER","GENE_PANEL","GLEASON_FIRST_REPORTED","GLEASON_HIGHEST_REPORTED","GLEASON_SAMPLE_LEVEL","HER2","HISTORY_OF_PDL1","HR","ICD_O_HISTOLOGY_DESCRIPTION","INTRA_ABDOMINAL","LIVER","LUNG","LYMPH_NODES","METASTATIC_SITE","MSI_COMMENT","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","NUM_ICDO_DX","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER","PATHOLOGICAL_GROUP","PDL1_POSITIVE","PLEURA","PRIMARY_SITE","PRIOR_MED_TO_MSK","RACE","REPRODUCTIVE_ORGANS","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SMOKING_PREDICTIONS_3_CLASSES","SOMATIC_STATUS","STAGE_HIGHEST_RECORDED","TMB_NONSYNONYMOUS","TUMOR_PURITY"],"molecularProfileIds":["msk_chord_2024_cna","msk_chord_2024_mutations","msk_chord_2024_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "msk_chord_2024"
 ],
 "genes": [
  "TP53"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=msk_chord_2024&gene_list=TP53&case_set_id=msk_chord_2024_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=msk_chord_2024&gene_list=TP53&case_set_id=msk_chord_2024_all","data":{"studyIds":["msk_chord_2024"],"studies":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040}],"genes":["TP53"],"caseSetId":"msk_chord_2024_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

▶ navigate_to_study_view
{
 "studyIds": [
  "msk_chord_2024"
 ],
 "filterJson": {
  "geneFilters": [
   {
    "molecularProfileIds": [
     "msk_chord_2024_mutations"
    ],
    "geneQueries": [
     [
      {
       "hugoGeneSymbol": "TP53"
      }
     ]
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22TP53%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22msk_chord_2024_mutations%22%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22TP53%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22msk_chord_2024_mutations%22%5D%7D%5D%7D","data":{"studyIds":["msk_chord_2024"],"studies":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040}],"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)
## TP53 Mutation Prevalence in MSK-CHORD

**52.5%** of patients in MSK-CHORD (MSK, Nature 2024) have at least one TP53 mutation.

- **Mutated patients:** 13,105
- **Patients profiled for TP53:** 24,950 (panel-aware denominator, accounting for MSK-IMPACT panel versions across the cohort)
- **Counting unit:** patient-level (prevalence question), on-panel mutations only, excluding UNCALLED status

### Links
- [TP53 OncoPrint in MSK-CHORD](https://www.cbioportal.org/results/oncoprint?cancer_study_list=msk_chord_2024&gene_list=TP53&case_set_id=msk_chord_2024_all) — visual summary of alterations across samples
- [MSK-CHORD Study View filtered to TP53-mutated cases](https://www.cbioportal.org/study/summary?id=msk_chord_2024#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22TP53%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22msk_chord_2024_mutations%22%5D%7D%5D%7D) — cohort overview with TP53 mutation filter applied