Q (sonnet): What is the median age at diagnosis for osteosarcoma patients in the TARGET study?

▶ list_guides
{}

▶ list_studies
{
 "search": "TARGET osteosarcoma"
}

◀ result
{"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

◀ result
{"result":[{"cancer_study_identifier":"os_target_gdc","name":"Osteosarcoma (TARGET GDC, 2025)","type_of_cancer_id":"os","sample_count":159,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=os_target_gdc"}]}

▶ read_guide
{
 "uri": "cbioportal://clinical-data-guide"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ 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)

◀ result
{"columns":[{"name":"internal_id","type":"Int32"},{"name":"sample_unique_id","type":"String","comment":"Globally unique sample ID: cancer_study_identifier + \"_\" + sample.stable_id. Empty for patient-level attributes. Use this for sample filtering and joins."},{"name":"patient_unique_id","type":"String","comment":"Globally unique patient ID: cancer_study_identifier + \"_\" + patient.stable_id. Present for both sample and patient-level attributes."},{"name":"attribute_name","type":"LowCardinality(String)","comment":"Clinical attribute name (e.g., SAMPLE_TYPE, CANCER_TYPE, AGE, OS_MONTHS). Use with attribute_value for filtering. AGE may be floored or capped for de-identification (e.g. all children recorded as 18, or everyone 89+ recorded as 89 or 90): before age statistics check for a pile-up at the min/max, and if present compute age from DAYS_TO_BIRTH (-days / 365.25)."},{"name":"attribute_value","type":"String","comment":"Value of the clinical attribute (String). For SAMPLE_TYPE: Primary, Metastasis, Local Recurrence, Unknown. Missing values are empty strings, so use toFloat64OrNull(attribute_value) for numeric comparisons — CAST fails on them."},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"type","type":"LowCardinality(String)","comment":"Data level: \"sample\" for sample-level attributes (e.g., SAMPLE_TYPE), \"patient\" for patient-level attributes (e.g., AGE, OS_MONTHS)."}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT
    arrayMin(ages) AS min_age, arrayMax(ages) AS max_age,
    countEqual(ages, arrayMin(ages)) AS at_min, countEqual(ages, arrayMax(ages)) AS at_max, length(ages) AS patients
FROM (
    SELECT groupArray(toFloat64OrNull(attribute_value)) AS ages
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'os_target_gdc' AND attribute_name = 'AGE'
      AND toFloat64OrNull(attribute_value) IS NOT NULL
)"
}

◀ result
{"rows":[{"min_age":18,"max_age":87,"at_min":241,"at_max":1,"patients":293}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT attribute_name
FROM clinical_data_derived
WHERE cancer_study_identifier = 'os_target_gdc'
  AND (attribute_name LIKE '%AGE%' OR attribute_name LIKE '%BIRTH%')"
}

◀ result
{"rows":[{"attribute_name":"AGE"},{"attribute_name":"DAYS_TO_BIRTH"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT
    count() AS n_patients,
    round(median(age_years), 2) AS median_age,
    round(avg(age_years), 2) AS mean_age,
    round(min(age_years), 2) AS min_age,
    round(max(age_years), 2) AS max_age
FROM (
    SELECT patient_unique_id,
        -toFloat64OrNull(attribute_value) / 365.25 AS age_years
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'os_target_gdc'
      AND attribute_name = 'DAYS_TO_BIRTH'
      AND toFloat64OrNull(attribute_value) IS NOT NULL
)"
}

◀ result
{"rows":[{"n_patients":293,"median_age":15.16,"mean_age":16.22,"min_age":3.56,"max_age":87.13}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT
    round(quantile(0.25)(age_years),2) AS q1,
    round(quantile(0.5)(age_years),2) AS median,
    round(quantile(0.75)(age_years),2) AS q3,
    countIf(age_years > 40) AS n_over_40
FROM (
    SELECT -toFloat64OrNull(attribute_value) / 365.25 AS age_years
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'os_target_gdc'
      AND attribute_name = 'DAYS_TO_BIRTH'
      AND toFloat64OrNull(attribute_value) IS NOT NULL
)"
}

◀ result
{"rows":[{"q1":12.22,"median":15.16,"q3":17.66,"n_over_40":6}]}

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

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"os_target_gdc","name":"Osteosarcoma (TARGET GDC, 2025)","sampleCount":159,"studyViewUrl":"https://www.cbioportal.org/study?id=os_target_gdc","metadata":{"clinicalAttributeIds":["AGE","CANCER_TYPE","CANCER_TYPE_DETAILED","DAYS_TO_BIRTH","DAYS_TO_DEATH","DISEASE_TYPE","ETHNICITY","MUTATION_COUNT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PRIMARY_DIAGNOSIS","PRIMARY_SITE_PATIENT","PROJECT_ID","RACE","SAMPLE_COUNT","SEX","SPECIMEN_TYPE","TMB_NONSYNONYMOUS","VITAL_STATUS"],"molecularProfileIds":["os_target_gdc_cna","os_target_gdc_mrna_seq_fpkm","os_target_gdc_mrna_seq_fpkm_Zscores","os_target_gdc_mrna_seq_read_counts_Zscores","os_target_gdc_mrna_seq_tpm","os_target_gdc_mrna_seq_tpm_Zscores","os_target_gdc_mutations","os_target_gdc_rna_seq_mrna"],"heatmapProfileIds":["os_target_gdc_mrna_seq_fpkm_Zscores","os_target_gdc_mrna_seq_read_counts_Zscores","os_target_gdc_mrna_seq_tpm_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna"]}}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "os_target_gdc"
 ],
 "tab": "clinicalData"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/clinicalData?id=os_target_gdc","url":"https://www.cbioportal.org/study/clinicalData?id=os_target_gdc","data":{"studyIds":["os_target_gdc"],"studies":[{"studyId":"os_target_gdc","name":"Osteosarcoma (TARGET GDC, 2025)","sampleCount":159}],"tab":"clinicalData","hasFilters":false,"hasPlotsConfig":false,"pageDescription":"Table of clinical attributes per sample."}}

═ answer (success)
[View Clinical Data — Osteosarcoma (TARGET GDC, 2025)](https://www.cbioportal.org/study/clinicalData?id=os_target_gdc)

Table of clinical attributes per sample. Note: the portal's built-in `AGE` chart on the Study View summary tab will show a spike at 18 due to de-identification flooring — refer to the median above (computed from `DAYS_TO_BIRTH`) rather than the displayed age histogram for accurate age statistics.