Q (sonnet): Which cBioPortal studies include lung adenocarcinoma samples with mutation and copy-number data?

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-guide"
}

▶ search_oncotree
{
 "search_term": "lung adenocarcinoma"
}

◀ result
# Sample and Study Filtering Guide

## Overview
Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria.

## Study-Level Filtering

### 1. Always Filter by Study
Every query should specify a study to ensure consistent results:

```sql
-- Always include study filtering
SELECT *
FROM your_table
WHERE
    cancer_study_identifier = 'your_study_id'
    -- Additional filters...
```

### 2. Find Available Studies

```sql
-- Discover available studies
SELECT
    cancer_study_identifier,
    name,
    description,
    type_of_cancer_id
FROM cancer_study
ORDER BY cancer_study_identifier;
```

### 3. Study Information

```sql
-- Get detailed study information
SELECT
    cs.cancer_study_identifier,
    cs.name as study_name,
    cs.description,
    cs.sample_count,
    COUNT(DISTINCT p.internal_id) as patient_count
FROM cancer_study cs
LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id
WHERE
    cs.cancer_study_identifier = 'your_study_id'
GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count;
```

### 4. Find Studies by Available Data Types

Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter.

**Canonical query — studies with ALL requested data types for a cancer type:**

```sql
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'          -- ← OncoTree code from search_oncotree
  AND mutation_sample_count > 0           -- ← one condition per required data type
  AND cna_sample_count > 0
ORDER BY sample_count DESC;
```

**Data type → column** (a count of 0 means the study doesn't have that data):

| Data type in the user's question | Column (portal "Data type" filter label) |
|---|---|
| samples in the study | `sample_count` |
| mutation / mutations | `mutation_sample_count` ("Mutations") |
| copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") |
| structural variant / fusion | `structural_variant_sample_count` |
| RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" |
| specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `<study>_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq |
| specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `<study>_mrna` list |
| microRNA | `mirna_sample_count` ("miRNA") |
| protein / RPPA | `rppa_sample_count` ("RPPA") |
| mas … (7083 more chars)

◀ result
{"result":[{"code":"LUAD","name":"Lung Adenocarcinoma","score":80,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAD"},{"code":"LAIS","name":"Lung Adenocarcinoma In Situ","score":60,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LAIS"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'
  AND mutation_sample_count > 0
  AND cna_sample_count > 0
ORDER BY sample_count DESC",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_study_identifier":"luad_mskcc_2023_met_organotropism","name":"Lung Adenocarcinoma Met Organotropism (MSK, Cancer Cell 2023)","sample_count":2653,"mutation_sample_count":2653,"cna_sample_count":2422},{"cancer_study_identifier":"lung_msk_2017","name":"Non-Small Cell Cancer (MSK, Cancer Discov 2017)","sample_count":915,"mutation_sample_count":915,"cna_sample_count":915},{"cancer_study_identifier":"luad_mskcc_2020","name":"Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)","sample_count":604,"mutation_sample_count":604,"cna_sample_count":604},{"cancer_study_identifier":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","sample_count":586,"mutation_sample_count":230,"cna_sample_count":516},{"cancer_study_identifier":"luad_tcga_gdc","name":"Lung Adenocarcinoma (TCGA GDC, 2025)","sample_count":571,"mutation_sample_count":559,"cna_sample_count":508},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sample_count":566,"mutation_sample_count":566,"cna_sample_count":511},{"cancer_study_identifier":"luad_msk_npjpo_2021","name":"Lung Adenocarcinoma (MSK, NPJ Precision Oncology 2021)","sample_count":426,"mutation_sample_count":426,"cna_sample_count":426},{"cancer_study_identifier":"luad_oncosg_2020","name":"Lung Adenocarcinoma (OncoSG, Nat Genet 2020)","sample_count":305,"mutation_sample_count":302,"cna_sample_count":302},{"cancer_study_identifier":"luad_cptac_gdc","name":"Lung Adenocarcinoma (CPTAC GDC, 2025)","sample_count":241,"mutation_sample_count":236,"cna_sample_count":232},{"cancer_study_identifier":"luad_tcga_pub","name":"Lung Adenocarcinoma (TCGA, Nature 2014)","sample_count":230,"mutation_sample_count":230,"cna_sample_count":230},{"cancer_study_identifier":"luad_mskimpact_2021","name":"Lung Adenocarcinoma (MSK, 2021)","sample_count":186,"mutation_sample_count":186,"cna_sample_count":186},{"cancer_study_identifier":"luad_broad","name":"Lung Adenocarcinoma (Broad, Cell 2012)","sample_count":183,"mutation_sample_count":183,"cna_sample_count":183},{"cancer_study_identifier":"luad_cptac_2020","name":"Lung Adenocarcinoma (CPTAC, Cell 2020)","sample_count":110,"mutation_sample_count":110,"cna_sample_count":110}]}

═ answer (success)
**13 studies** in cBioPortal contain lung adenocarcinoma (LUAD) samples with both mutation and copy-number (CNA) data. Sample counts shown are totals per study; mutation/CNA counts are the profiled subset (denominator for any frequency calculation):

| Study | Total samples | Mutation-profiled | CNA-profiled |
|---|---|---|---|
| [Lung Adenocarcinoma Met Organotropism (MSK, Cancer Cell 2023)](https://www.cbioportal.org/study/summary?id=luad_mskcc_2023_met_organotropism) | 2,653 | 2,653 | 2,422 |
| [Non-Small Cell Cancer (MSK, Cancer Discov 2017)](https://www.cbioportal.org/study/summary?id=lung_msk_2017) | 915 | 915 | 915 |
| [Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)](https://www.cbioportal.org/study/summary?id=luad_mskcc_2020) | 604 | 604 | 604 |
| [Lung Adenocarcinoma (TCGA, Firehose Legacy)](https://www.cbioportal.org/study/summary?id=luad_tcga) | 586 | 230 | 516 |
| [Lung Adenocarcinoma (TCGA GDC, 2025)](https://www.cbioportal.org/study/summary?id=luad_tcga_gdc) | 571 | 559 | 508 |
| [Lung Adenocarcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018) | 566 | 566 | 511 |
| [Lung Adenocarcinoma (MSK, NPJ Precision Oncology 2021)](https://www.cbioportal.org/study/summary?id=luad_msk_npjpo_2021) | 426 | 426 | 426 |
| [Lung Adenocarcinoma (OncoSG, Nat Genet 2020)](https://www.cbioportal.org/study/summary?id=luad_oncosg_2020) | 305 | 302 | 302 |
| [Lung Adenocarcinoma (CPTAC GDC, 2025)](https://www.cbioportal.org/study/summary?id=luad_cptac_gdc) | 241 | 236 | 232 |
| [Lung Adenocarcinoma (TCGA, Nature 2014)](https://www.cbioportal.org/study/summary?id=luad_tcga_pub) | 230 | 230 | 230 |
| [Lung Adenocarcinoma (MSK, 2021)](https://www.cbioportal.org/study/summary?id=luad_mskimpact_2021) | 186 | 186 | 186 |
| [Lung Adenocarcinoma (Broad, Cell 2012)](https://www.cbioportal.org/study/summary?id=luad_broad) | 183 | 183 | 183 |
| [Lung Adenocarcinoma (CPTAC, Cell 2020)](https://www.cbioportal.org/study/summary?id=luad_cptac_2020) | 110 | 110 | 110 |

Note: totals across studies aren't additive — cohorts overlap (e.g., MSK-IMPACT studies share patients across releases).

For the most commonly used reference cohort, here's the TCGA PanCancer Atlas overview with mutation and CNA data pre-filtered:

[View TCGA LUAD PanCancer Atlas — Study Overview](https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018)

If you'd like, I can open any specific study (e.g., the largest — MSK Met Organotropism 2023) or cross-study OncoPrint analysis instead — just let me know which cohort(s) to focus on.