Q (sonnet): Is there a cohort of NSCLC patient samples that have Kras mutations, wild-type p53, and high expression levels of c-Myc?

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-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 … (36467 more chars)

▶ read_guide
{
 "uri": "cbioportal://gene-expression-guide"
}

◀ result
# Gene Expression Analysis Guide

This guide covers continuous-value genomic data: gene **expression**, **copy number** values, **methylation**, and related profile types. Mutation/CNA/SV *frequency* analysis lives in `cbioportal://mutation-frequency-guide`.

## Where this data lives

Continuous per-sample-per-gene values are stored in `genetic_alteration_derived`:

| Column | Description |
|---|---|
| `sample_unique_id` | `<study>_<sample_stable_id>` |
| `cancer_study_identifier` | study scope |
| `hugo_gene_symbol` | gene |
| `profile_type` | which assay/normalization (see below) |
| `alteration_value` | the actual value — stored as Nullable(String); cast with `toFloat64OrNull` |

`alteration_value` is a string because the same column hosts many different value scales. The `''` and `'NA'` sentinels mean "missing"; always filter them out and use `toFloat64OrNull(alteration_value) IS NOT NULL` for downstream math.

## Discovering profile types for a study

Different studies expose different profile types depending on what assays were run and how the data was normalized. Always check what a specific study supports before picking one:

```sql
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'brca_metabric'
ORDER BY profile_type;
```

Common values across the public portal:

| Family | Profile types |
|---|---|
| mRNA expression | `rna_seq_v2_mrna`, `rna_seq_v2_mrna_median_Zscores`, `rna_seq_v2_mrna_median_all_sample_Zscores` (TCGA PanCancer Atlas), `mrna`, `mrna_median_Zscores`, `mrna_seq_v2_rsem`, `mrna_seq_v2_rsem_Zscores`, `mrna_seq_cpm`, `mrna_seq_fpkm`, `mrna_U133`, `mrna_outliers` |
| Copy number (continuous) | `cna`, `linear_CNA`, `log2CNA`, `cna_consensus`, `cna_rae`, `gistic` |
| Methylation | `methylation_hm27`, `methylation_hm450`, `methylation_epic`, `methylation_promoters_rrbs` |
| miRNA | `mirna`, `mirna_median_Zscores` |
| Protein | `protein_quantification`, `protein_level`, `RPPA` |

**Z-score vs raw choice.** When the user asks "is X correlated with Y", either works for Spearman (rank-based) — Pearson would care. Default to the non-Z-score variant if both exist, and call out which one in the response.

## Canonical recipe — Spearman correlation between two genes

```sql
SELECT *
FROM gene_pair_coexpression(
    study        = 'brca_metabric',
    gene_a       = 'TP53',
    gene_b       = 'MYC',
    profile_type = 'mrna'
);
```

Returns one row: `(gene_a, gene_b, profile_type, spearman_correlation, num_samples)`.

- `spearman_correlation` in [−1, 1]; `NULL` when fewer than 3 valid paired samples.
- Mirrors cbioportal-backend's `ClickhouseCoExpressionMapper.getCoExpressions`, simplified to a pair lookup (the backend computes one ref gene vs ALL other genes for the coexpression page; here the agent asks about a specific pair).

### Verified examples

| Study | gene_a | gene_b | profile_type | spearman | n |
|---|---|---|---|---|---|
| `brca_metabric` | TP53 | MYC | `mrna` | 0.118 | 1980 |
|  … (4931 more chars)

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

▶ search_oncotree
{
 "search_term": "non-small cell lung cancer"
}

◀ 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 … (7866 more chars)

▶ list_studies
{
 "search": "lung adenocarcinoma TCGA"
}

◀ result
{"result":[{"code":"NSCLC","name":"Non-Small Cell Lung Cancer","score":80,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC"},{"code":"ALUCA","name":"Atypical Lung Carcinoid","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET > ALUCA"},{"code":"BLCLC","name":"Basaloid Large Cell Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > BLCLC"},{"code":"CCLC","name":"Clear Cell Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > CCLC"},{"code":"CMPT","name":"Ciliated Muconodular Papillary Tumor of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > CMPT"},{"code":"GCLC","name":"Giant Cell Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > GCLC"},{"code":"IMTL","name":"Inflammatory Myofibroblastic Lung Tumor","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > IMTL"},{"code":"LAIS","name":"Lung Adenocarcinoma In Situ","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LAIS"},{"code":"LAM","name":"Pulmonary Lymphangiomyomatosis","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LAM"},{"code":"LCLC","name":"Large Cell Lung Carcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC"},{"code":"LECLC","name":"Lymphoepithelioma-like Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LCLC > LECLC"},{"code":"LNET","name":"Lung Neuroendocrine Tumor","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET"},{"code":"LUACC","name":"Adenoid Cystic Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > SGTTL > LUACC"},{"code":"LUAD","name":"Lung Adenocarcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAD"},{"code":"LUAS","name":"Lung Adenosquamous Carcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAS"},{"code":"LUCA","name":"Lung Carcinoid","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET > LUCA"},{"code":"LUMEC","name":"Mucoepidermoid Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > SGTTL > LUMEC"},{"code":"LUNE","name":"Large Cell Neuroendocrine Carcinoma","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LNET > LUNE"},{"code":"LUPC","name":"Pleomorphic Carcinoma of the Lung","score":65,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierar … (999 more chars)

◀ result
{"result":[{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"luad","sample_count":566,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"luad","sample_count":586,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga"},{"cancer_study_identifier":"luad_tcga_gdc","name":"Lung Adenocarcinoma (TCGA GDC, 2025)","type_of_cancer_id":"luad","sample_count":571,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_gdc"},{"cancer_study_identifier":"luad_tcga_pub","name":"Lung Adenocarcinoma (TCGA, Nature 2014)","type_of_cancer_id":"luad","sample_count":230,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pub"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH profiled_kras AS (
    SELECT sample_unique_id 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 = 'KRAS'
      AND stgp.cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    UNION DISTINCT
    SELECT sample_unique_id FROM sample_to_gene_panel_derived
    WHERE alteration_type = 'MUTATION_EXTENDED' AND gene_panel_id = 'WES'
      AND cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
),
kras_mut AS (
    SELECT DISTINCT sample_unique_id FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
      AND variant_type = 'mutation' AND mutation_status != 'UNCALLED'
      AND hugo_gene_symbol = 'KRAS' AND off_panel = 0
),
tp53_mut AS (
    SELECT DISTINCT sample_unique_id FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
      AND variant_type = 'mutation' AND mutation_status != 'UNCALLED'
      AND hugo_gene_symbol = 'TP53' AND off_panel = 0
),
myc_expr AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'MYC' AND profile_type = 'rna_seq_v2_mrna'
      AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL
),
myc_q AS (
    SELECT quantile(0.75)(v) AS q75 FROM myc_expr
)
SELECT
  (SELECT count() FROM profiled_kras) AS profiled_for_kras,
  (SELECT count() FROM kras_mut) AS kras_mutant_total,
  (SELECT count() FROM kras_mut WHERE sample_unique_id NOT IN (SELECT sample_unique_id FROM tp53_mut)) AS kras_mut_tp53_wt,
  (SELECT round(q75,2) FROM myc_q) AS myc_q75_rsem,
  (SELECT count() FROM kras_mut k
     WHERE k.sample_unique_id NOT IN (SELECT sample_unique_id FROM tp53_mut)
       AND k.sample_unique_id IN (SELECT sample_unique_id FROM myc_expr WHERE v >= (SELECT q75 FROM myc_q))
  ) AS final_cohort_kras_tp53wt_myc_high
"
}

◀ result
{"rows":[{"profiled_for_kras":566,"kras_mutant_total":168,"kras_mut_tp53_wt":106,"myc_q75_rsem":1937.64,"final_cohort_kras_tp53wt_myc_high":22}]}

▶ resolve_and_route
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566,"studyViewUrl":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","RAGNUM_HYPOXIA_SCORE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT","WINTER_HYPOXIA_SCORE"],"molecularProfileIds":["luad_tcga_pan_can_atlas_2018_gistic","luad_tcga_pan_can_atlas_2018_log2CNA","luad_tcga_pan_can_atlas_2018_mutations","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_tcga_pan_can_atlas_2018_rppa","luad_tcga_pan_can_atlas_2018_rppa_Zscores","luad_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["luad_tcga_pan_can_atlas_2018_armlevel_cna","luad_tcga_pan_can_atlas_2018_genetic_ancestry","luad_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","luad_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","luad_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "KRAS: MUT",
  "TP53",
  "MYC"
 ],
 "tab": "oncoprint"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%0AMYC&case_set_id=luad_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%0AMYC&case_set_id=luad_tcga_pan_can_atlas_2018_all","data":{"studyIds":["luad_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566}],"genes":["KRAS","TP53","MYC"],"caseSetId":"luad_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
[View KRAS mutations, TP53 status, and MYC alterations — OncoPrint](https://www.cbioportal.org/results/oncoprint?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%0AMYC&case_set_id=luad_tcga_pan_can_atlas_2018_all)

*Visual summary of alterations in queried genes across samples.*

This OncoPrint shows all 566 LUAD samples with KRAS mutation status, TP53 alteration status, and MYC alteration/expression tracks side by side, so you can visually confirm the 22-sample triple-positive group and inspect individual cases. For a continuous view of MYC mRNA levels stratified by KRAS/TP53 status, you can also add MYC as a heatmap track or use the Plots tab from that same page.

A caveat worth flagging: cBioPortal doesn't have a single built-in filter that combines "gene mutated," "gene wild-type," and "expression above a percentile" in one click in the study view UI — the 22-sample number above came from a direct database query using a top-quartile MYC cutoff, which is a reasonable but somewhat arbitrary threshold for "high expression." If you have a specific z-score or fold-change cutoff for c-Myc in mind, let me know and I can recompute the cohort size.