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

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

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

◀ 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"}]}

◀ 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)

▶ get_study_guide
{
 "study_id": "os_target_gdc"
}

◀ result
# Osteosarcoma (TARGET GDC, 2025)

**Study ID:** `os_target_gdc`

## Overview
Pediatric osteosarcoma study from the TARGET (Therapeutically Applicable Research to Generate Effective Treatments) initiative. Whole exome sequencing data.

## Gene Panel
- **WES** (Whole Exome Sequencing): all coding genes profiled
- **143 of the 160 samples are profiled for mutations.** Use 143 as the mutation-frequency denominator (`sample_to_gene_panel_derived`, `alteration_type = 'MUTATION_EXTENDED'`), not the study's sample count — e.g. TP53 is mutated in 32/143 = 22.4%.

## Patients vs Samples
383 patients have clinical data, but only 153 of them have a sample (159 samples). Patient-level questions (age, sex, survival) use all patients with a value; genomic questions use the 143 mutation-profiled samples.

## Clinical Attributes - Semantic Guide

### Patient Demographics
| Attribute | Description | Notes |
|-----------|-------------|-------|
| `AGE` | Age at diagnosis, **floored at 18** | Every patient younger than 18 is recorded as 18 (241 of 293). **Don't use it for age statistics** — use `DAYS_TO_BIRTH` |
| `DAYS_TO_BIRTH` | Days from birth to diagnosis, negative | Age at diagnosis in years = `-DAYS_TO_BIRTH / 365.25`. 293 patients have a value; 90 are empty |
| `SEX` | Patient sex | Male 172, Female 133, 78 empty |
| `RACE`, `ETHNICITY` | Race, ethnicity | |

### Disease Characteristics
| Attribute | Description | Notes |
|-----------|-------------|-------|
| `CANCER_TYPE_DETAILED` | Cancer type | Osteosarcoma for every sample |
| `PRIMARY_SITE_PATIENT` | Primary site | "Appendicular Skeleton" for every patient with a value — no finer location (femur, tibia) |

No histological subtype, tumor necrosis / chemotherapy response, or event-free survival attributes exist in this study.

### Clinical Outcomes
| Attribute | Description | Notes |
|-----------|-------------|-------|
| `OS_MONTHS` | Overall survival in months | Time from diagnosis |
| `OS_STATUS` | Overall survival status | 0:LIVING 171, 1:DECEASED 105, 107 empty |

## Age at Diagnosis

Compute age from `DAYS_TO_BIRTH`, not `AGE`. A median from `AGE` comes out as 18 because every child is recorded as 18; the real median is about 15 years.

```sql
SELECT
    count() AS patients,
    round(median(-toFloat64OrNull(attribute_value) / 365.25), 1) AS median_age_years,
    round(min(-toFloat64OrNull(attribute_value) / 365.25), 1) AS min_age_years,
    round(max(-toFloat64OrNull(attribute_value) / 365.25), 1) AS max_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;
-- 293 patients, median 15.2, range 3.6-87.1
```

When reporting, say the age comes from `DAYS_TO_BIRTH` and that `AGE` is floored at 18.

## Notes & Caveats
- This is a pediatric cancer cohort; age distribution is younger than adult studies
- WES coverage means no gene panel filtering needed for frequency calculations
- Part  … (66 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    arrayMin(ages) AS min_age, arrayMax(ages) AS max_age,
    countEqual(ages, min_age) AS at_min, countEqual(ages, max_age) 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
    count() AS patients,
    round(median(-toFloat64OrNull(attribute_value) / 365.25), 1) AS median_age_years,
    round(min(-toFloat64OrNull(attribute_value) / 365.25), 1) AS min_age_years,
    round(max(-toFloat64OrNull(attribute_value) / 365.25), 1) AS max_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
"
}

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

◀ result
{"rows":[{"patients":293,"median_age_years":15.2,"min_age_years":3.6,"max_age_years":87.1}]}

◀ 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"]}}]}}

═ answer (success)
## Median Age at Diagnosis — Osteosarcoma (TARGET GDC, 2025)

**Median age: 15.2 years** (n = 293 patients with age data; range 3.6–87.1 years)

**Important note on data quality:** This study's standard `AGE` field is capped at 18 for de-identification — 241 of 293 patients (82%) are recorded simply as "18," which would make a naive median meaningless (it would show as 18). I instead derived exact age at diagnosis from the `DAYS_TO_BIRTH` field, which preserves precise values. This confirms the cohort's expected pediatric age distribution, with one notable outlier at 87.1 years (osteosarcoma can rarely present in older adults, though this is an atypical case for the TARGET pediatric cohort).

Note: 383 patients total have clinical data in this study, but only 293 have a usable `DAYS_TO_BIRTH` value (90 are empty).

[View Osteosarcoma (TARGET GDC, 2025) cohort in Study View](https://www.cbioportal.org/study?id=os_target_gdc)