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://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 | `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 |
| `brca_metabric` | ESR1 | PGR | `mrna` | 0.487 | 1980 |

ESR1↔PGR is the textbook breast-cancer estrogen-receptor coregula … (2785 more chars)

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

▶ search_oncotree
{
 "search_term": "NSCLC"
}

◀ 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":"NSCLC","name":"Non-Small Cell Lung Cancer","score":100,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC"},{"code":"NSCLCPD","name":"Poorly Differentiated Non-Small Cell Lung Cancer","score":70,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > NSCLCPD"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    mrna_expression_sample_count
FROM cancer_study
WHERE type_of_cancer_id IN ('nsclc','luad','lusc')
  AND mutation_sample_count > 0
  AND mrna_expression_sample_count > 0
ORDER BY sample_count DESC
"
}

◀ result
{"rows":[{"cancer_study_identifier":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","sample_count":586,"mutation_sample_count":230,"mrna_expression_sample_count":517},{"cancer_study_identifier":"luad_tcga_gdc","name":"Lung Adenocarcinoma (TCGA GDC, 2025)","sample_count":571,"mutation_sample_count":559,"mrna_expression_sample_count":518},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sample_count":566,"mutation_sample_count":566,"mrna_expression_sample_count":510},{"cancer_study_identifier":"lusc_tcga","name":"Lung Squamous Cell Carcinoma (TCGA, Firehose Legacy)","sample_count":511,"mutation_sample_count":178,"mrna_expression_sample_count":504},{"cancer_study_identifier":"lusc_tcga_gdc","name":"Lung Squamous Cell Carcinoma (TCGA GDC, 2025)","sample_count":503,"mutation_sample_count":484,"mrna_expression_sample_count":500},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","sample_count":487,"mutation_sample_count":484,"mrna_expression_sample_count":484},{"cancer_study_identifier":"luad_oncosg_2020","name":"Lung Adenocarcinoma (OncoSG, Nat Genet 2020)","sample_count":305,"mutation_sample_count":302,"mrna_expression_sample_count":169},{"cancer_study_identifier":"luad_cptac_gdc","name":"Lung Adenocarcinoma (CPTAC GDC, 2025)","sample_count":241,"mutation_sample_count":236,"mrna_expression_sample_count":231},{"cancer_study_identifier":"luad_tcga_pub","name":"Lung Adenocarcinoma (TCGA, Nature 2014)","sample_count":230,"mutation_sample_count":230,"mrna_expression_sample_count":230},{"cancer_study_identifier":"lusc_tcga_pub","name":"Lung Squamous Cell Carcinoma (TCGA, Nature 2012)","sample_count":178,"mutation_sample_count":178,"mrna_expression_sample_count":178},{"cancer_study_identifier":"lusc_cptac_gdc","name":"Lung Squamous Cell Carcinoma (CPTAC GDC, 2025)","sample_count":110,"mutation_sample_count":110,"mrna_expression_sample_count":110},{"cancer_study_identifier":"luad_cptac_2020","name":"Lung Adenocarcinoma (CPTAC, Cell 2020)","sample_count":110,"mutation_sample_count":110,"mrna_expression_sample_count":110},{"cancer_study_identifier":"luad_cas_2020","name":"Lung Adenocarcinoma (CAS, Cell 2020)","sample_count":103,"mutation_sample_count":103,"mrna_expression_sample_count":51},{"cancer_study_identifier":"lusc_cptac_2021","name":"Lung Squamous Cell Carcinoma (CPTAC, Cell 2021)","sample_count":80,"mutation_sample_count":108,"mrna_expression_sample_count":108}]}

▶ clickhouse_list_table_columns
{
 "table": "genetic_alteration_derived"
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"hugo_gene_symbol","type":"String"},{"name":"profile_type","type":"LowCardinality(String)"},{"name":"alteration_value","type":"Nullable(String)"}]}

✗ error
Error POSTing to endpoint: {"jsonrpc":"2.0","id":9,"error":{"code":-32600,"message":"Anthropic Proxy: Invalid content from server","data":null}}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"hugo_gene_symbol","type":"String","comment":"HUGO gene symbol (e.g., TP53, KRAS, BRAF). Use for gene-specific queries."},{"name":"entrez_gene_id","type":"Int32"},{"name":"gene_panel_stable_id","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"genetic_profile_stable_id","type":"LowCardinality(String)"},{"name":"variant_type","type":"LowCardinality(String)","comment":"Type of genomic event: \"mutation\" for SNVs/indels, \"cna\" for copy number alterations, \"structural_variant\" for SVs. Always filter by this."},{"name":"mutation_variant","type":"String","comment":"Protein change notation (e.g., p.V600E, p.R175H). Use for specific variant queries. \"NA\" for non-mutation events."},{"name":"mutation_type","type":"LowCardinality(String)"},{"name":"mutation_status","type":"LowCardinality(String)","comment":"For mutations: Somatic, Germline, UNKNOWN, or UNCALLED. Filter mutation_status != \"UNCALLED\" to exclude uncertain calls. Include all other statuses."},{"name":"driver_filter","type":"LowCardinality(String)"},{"name":"driver_filter_annotation","type":"String"},{"name":"driver_tiers_filter","type":"LowCardinality(String)"},{"name":"driver_tiers_filter_annotation","type":"String"},{"name":"cna_alteration","type":"Nullable(Int8)","comment":"Copy number alteration: only 2 = amplification (AMP) and -2 = deep deletion (HOMDEL) are stored. Shallow deletion (-1), diploid (0) and gain (1) are not in this table; query genetic_alteration_derived WHERE profile_type = 'gistic' (alteration_value is a String, e.g. '-1'). NULL for non-CNA events."},{"name":"cna_cytoband","type":"String"},{"name":"sv_event_info","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"off_panel","type":"Bool","comment":"Boolean: 1 = mutation outside gene panel coverage (off-panel), 0 = within panel (on-panel). Filter off_panel = 0 for reliable frequency calculations."}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
  AND hugo_gene_symbol = 'MYC'
"
}

◀ result
{"rows":[{"profile_type":"gistic"},{"profile_type":"log2CNA"},{"profile_type":"rna_seq_v2_mrna"},{"profile_type":"rna_seq_v2_mrna_median_Zscores"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_Zscores"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores"},{"profile_type":"rppa"},{"profile_type":"rppa_Zscores"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH kras_mut AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'KRAS'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      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 hugo_gene_symbol = 'TP53'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      AND off_panel = 0
),
mutation_profiled AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
),
myc_high AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS z
    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_median_Zscores'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
      AND toFloat64OrNull(alteration_value) > 1
)
SELECT count(DISTINCT k.sample_unique_id) AS cohort_n
FROM kras_mut k
INNER JOIN mutation_profiled mp ON mp.sample_unique_id = k.sample_unique_id
INNER JOIN myc_high m ON m.sample_unique_id = k.sample_unique_id
WHERE k.sample_unique_id NOT IN (SELECT sample_unique_id FROM tp53_mut)
"
}

◀ result
{"rows":[{"cohort_n":18}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH kras_mut AS (
    SELECT DISTINCT sample_unique_id
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'KRAS'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      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 hugo_gene_symbol = 'TP53'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      AND off_panel = 0
),
myc_expr AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS z
    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_median_Zscores'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    count(DISTINCT k.sample_unique_id) AS kras_mut_tp53_wt_with_expr,
    countIf(m.z > 1) AS myc_zscore_gt1,
    countIf(m.z > 2) AS myc_zscore_gt2
FROM kras_mut k
INNER JOIN myc_expr m ON m.sample_unique_id = k.sample_unique_id
WHERE k.sample_unique_id NOT IN (SELECT sample_unique_id FROM tp53_mut)
"
}

◀ result
{"rows":[{"kras_mut_tp53_wt_with_expr":100,"myc_zscore_gt1":18,"myc_zscore_gt2":7}]}

▶ 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_study_view
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ],
 "filterJson": {
  "geneFilters": [
   {
    "molecularProfileIds": [
     "luad_tcga_pan_can_atlas_2018_mutations"
    ],
    "geneQueries": [
     [
      {
       "hugoGeneSymbol": "KRAS"
      }
     ]
    ]
   }
  ],
  "mutationDataFilters": [
   {
    "hugoGeneSymbol": "TP53",
    "profileType": "mutations",
    "categorization": "MUTATED",
    "values": [
     [
      {
       "value": "NOT_MUTATED"
      }
     ]
    ]
   }
  ],
  "genomicDataFilters": [
   {
    "hugoGeneSymbol": "MYC",
    "profileType": "rna_seq_v2_mrna_median_Zscores",
    "values": [
     {
      "start": 1
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22KRAS%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22luad_tcga_pan_can_atlas_2018_mutations%22%5D%7D%5D%2C%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22MYC%22%2C%22profileType%22%3A%22rna_seq_v2_mrna_median_Zscores%22%2C%22values%22%3A%5B%7B%22start%22%3A1%7D%5D%7D%5D%2C%22mutationDataFilters%22%3A%5B%7B%22categorization%22%3A%22MUTATED%22%2C%22hugoGeneSymbol%22%3A%22TP53%22%2C%22profileType%22%3A%22mutations%22%2C%22values%22%3A%5B%5B%7B%22value%22%3A%22NOT_MUTATED%22%7D%5D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22KRAS%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22luad_tcga_pan_can_atlas_2018_mutations%22%5D%7D%5D%2C%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22MYC%22%2C%22profileType%22%3A%22rna_seq_v2_mrna_median_Zscores%22%2C%22values%22%3A%5B%7B%22start%22%3A1%7D%5D%7D%5D%2C%22mutationDataFilters%22%3A%5B%7B%22categorization%22%3A%22MUTATED%22%2C%22hugoGeneSymbol%22%3A%22TP53%22%2C%22profileType%22%3A%22mutations%22%2C%22values%22%3A%5B%5B%7B%22value%22%3A%22NOT_MUTATED%22%7D%5D%5D%7D%5D%7D","data":{"studyIds":["luad_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":566}],"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."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "KRAS: MUT",
  "TP53: MUT",
  "MYC"
 ],
 "tab": "oncoprint",
 "oncoprintHeatmapTracks": [
  {
   "molecularProfileId": "luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores",
   "entities": [
    "MYC"
   ]
  }
 ]
}

◀ 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%3A+MUT%0AMYC&case_set_id=luad_tcga_pan_can_atlas_2018_all&heatmap_track_groups=luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores%2CMYC","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%3A+MUT%0AMYC&case_set_id=luad_tcga_pan_can_atlas_2018_all&heatmap_track_groups=luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores%2CMYC","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","oncoprintHeatmapTracks":[{"molecularProfileId":"luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","entities":["MYC"]}],"pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
Here are direct links to explore this cohort in cBioPortal:

- **[Study View — pre-filtered cohort](https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22KRAS%22%7D%5D%5D%2C%22molecularProfileIds%22%3A%5B%22luad_tcga_pan_can_atlas_2018_mutations%22%5D%7D%5D%2C%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22MYC%22%2C%22profileType%22%3A%22rna_seq_v2_mrna_median_Zscores%22%2C%22values%22%3A%5B%7B%22start%22%3A1%7D%5D%7D%5D%2C%22mutationDataFilters%22%3A%5B%7B%22categorization%22%3A%22MUTATED%22%2C%22hugoGeneSymbol%22%3A%22TP53%22%2C%22profileType%22%3A%22mutations%22%2C%22values%22%3A%5B%5B%7B%22value%22%3A%22NOT_MUTATED%22%7D%5D%5D%7D%5D%7D)** — automatically filters to KRAS-mutant, TP53-wild-type, MYC Z-score > 1 samples (the 18 identified above). You can click into any chart to refine further or adjust the MYC threshold.
- **[OncoPrint / gene view (KRAS, TP53, MYC with MYC expression heatmap)](https://www.cbioportal.org/results/oncoprint?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=KRAS%3A+MUT%0ATP53%3A+MUT%0AMYC&case_set_id=luad_tcga_pan_can_atlas_2018_all&heatmap_track_groups=luad_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores%2CMYC)** — shows the full 566-sample cohort with KRAS/TP53 mutation calls and MYC expression as a heatmap track, so you can visually inspect the co-occurrence pattern.

**Summary:** Yes — such a cohort exists. In the TCGA Lung Adenocarcinoma PanCancer Atlas study, of 100 samples that are KRAS-mutant and TP53 wild-type with MYC expression data, **18 samples (18%)** also show elevated MYC mRNA (Z-score > 1); 7 show a more stringent Z-score > 2. Note this study is adenocarcinoma-specific — KRAS mutations are rare in lung squamous cell carcinoma, so I did not pool the two histologies together.