Q (sonnet): give me a contingency table with the number of lung cancer patients with EGFR and/or KRAS alterations

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

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

◀ result
Statistical Test Selection Guide
================================

Purpose
-------
This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults.

HARD RULES — NEVER FABRICATE A STATISTIC
----------------------------------------
ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically:

1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."*
2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead.
3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median".
4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do.
5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison.

If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number.

Ambiguous-Term Clarification (before any analysis)
--------------------------------------------------
If the user's question uses an ambiguous term, ask before computing anything. Examples:

- **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars)

◀ result
{"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","type_of_cancer_id":"mixed","sample_count":25775,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_met_2021"},{"cancer_study_identifier":"luad_mskcc_2023_met_organotropism","name":"Lung Adenocarcinoma Met Organotropism (MSK, Cancer Cell 2023)","type_of_cancer_id":"luad","sample_count":2653,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2023_met_organotropism"},{"cancer_study_identifier":"lung_msk_2017","name":"Non-Small Cell Cancer (MSK, Cancer Discov 2017)","type_of_cancer_id":"luad","sample_count":915,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_2017"},{"cancer_study_identifier":"luad_mskcc_2020","name":"Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)","type_of_cancer_id":"luad","sample_count":604,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2020"},{"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_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_msk_npjpo_2021","name":"Lung Adenocarcinoma (MSK, NPJ Precision Oncology 2021)","type_of_cancer_id":"luad","sample_count":426,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_msk_npjpo_2021"},{"cancer_study_identifier":"luad_oncosg_2020","name":"Lung Adenocarcinoma (OncoSG, Nat Genet 2020)","type_of_cancer_id":"luad","sample_count":305,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_oncosg_2020"},{"cancer_study_identifier":"lung_msk_mind_2020","name":"Lung Adenocarcinoma (MSK Mind,Nature Cancer 2022)","type_of_cancer_id":"lung","sample_count":247,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_mind_2020"},{"cancer_study_identifier":"luad_cptac_gdc","name":"Lung Adenocarcinoma (CPTAC GDC, 2025)","type_of_cancer_id":"luad","sample_count":241,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_cptac_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"},{"cancer_study_identifier":"luad_mskimpact_2021","name":"Lung Adenocarcinoma (MSK, 2021)","type_of_cancer_id":"luad","sample_count":186,"has_guide":false, … (1174 more chars)

▶ get_study_guide
{
 "study_id": "luad_tcga_pan_can_atlas_2018"
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
# Lung Adenocarcinoma (TCGA, PanCancer Atlas)

**Study ID:** `luad_tcga_pan_can_atlas_2018`

See `_tcga_pancan_template.md` for common TCGA clinical attributes.

## Study-Specific Attributes

### Not available in this study
- **Smoking history / pack-years**: no smoking attribute. Say smoking status is not available here rather than inferring it.
- **EGFR / KRAS / ALK status**: no clinical status attributes. Derive EGFR and KRAS status from mutation data, and ALK fusions from structural-variant data (5 samples with an ALK SV in `genomic_event_derived`).
- `SUBTYPE` is `LUAD` (502) or blank (64) and carries no molecular subtype. `GRADE` is blank for all samples.

## Key Genes - Driver Mutations
| Gene | Frequency | Notes |
|------|-----------|-------|
| KRAS | ~30% | G12C targetable with sotorasib/adagrasib |
| EGFR | ~15% | L858R, exon 19 del targetable with TKIs |
| ALK | ~5% | Fusions targetable with crizotinib, etc. |
| BRAF | ~5% | V600E targetable |
| ROS1 | ~2% | Fusions targetable |
| RET | ~2% | Fusions targetable |
| MET | ~3% | Exon 14 skipping, amplification |

## Notes
- Driver mutations are largely mutually exclusive
- STK11 and KEAP1 mutations associated with poor immunotherapy response


◀ 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
  countDistinctIf(patient_unique_id, hugo_gene_symbol = 'EGFR' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND off_panel = 0) AS egfr_altered_any,
  countDistinctIf(patient_unique_id, hugo_gene_symbol = 'KRAS' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND off_panel = 0) AS kras_altered_any
FROM genomic_event_derived
WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
"
}

◀ result
{"rows":[{"egfr_altered_any":70,"kras_altered_any":168}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH patient_status AS (
  SELECT
    patient_unique_id,
    maxIf(1, hugo_gene_symbol = 'EGFR' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND off_panel = 0) AS egfr_mut,
    maxIf(1, hugo_gene_symbol = 'KRAS' AND variant_type = 'mutation' AND mutation_status != 'UNCALLED' AND off_panel = 0) AS kras_mut
  FROM genomic_event_derived
  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    AND variant_type = 'mutation'
  GROUP BY patient_unique_id
),
all_profiled AS (
  SELECT DISTINCT patient_unique_id
  FROM genomic_event_derived
  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
),
full_status AS (
  SELECT
    a.patient_unique_id AS patient_unique_id,
    coalesce(p.egfr_mut, 0) AS egfr_mut,
    coalesce(p.kras_mut, 0) AS kras_mut
  FROM all_profiled a
  LEFT JOIN patient_status p ON a.patient_unique_id = p.patient_unique_id
)
SELECT
  egfr_mut,
  kras_mut,
  count(*) AS n_patients
FROM full_status
GROUP BY egfr_mut, kras_mut
ORDER BY egfr_mut DESC, kras_mut DESC
"
}

◀ result
{"rows":[{"egfr_mut":1,"kras_mut":1,"n_patients":1},{"egfr_mut":1,"kras_mut":0,"n_patients":69},{"egfr_mut":0,"kras_mut":1,"n_patients":167},{"egfr_mut":0,"kras_mut":0,"n_patients":329}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT count(DISTINCT patient_unique_id) AS total_profiled_patients
FROM genomic_event_derived
WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
"
}

◀ result
{"rows":[{"total_profiled_patients":566}]}

▶ 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": [
  "EGFR: MUT",
  "KRAS: MUT"
 ],
 "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=EGFR%3A+MUT%0AKRAS%3A+MUT&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=EGFR%3A+MUT%0AKRAS%3A+MUT&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":["EGFR","KRAS"],"caseSetId":"luad_tcga_pan_can_atlas_2018_all","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
[Explore EGFR/KRAS mutations in the OncoPrint](https://www.cbioportal.org/results/oncoprint?cancer_study_list=luad_tcga_pan_can_atlas_2018&gene_list=EGFR%3A+MUT%0AKRAS%3A+MUT&case_set_id=luad_tcga_pan_can_atlas_2018_all) — this view visualizes each patient's mutation pattern and lets you run the built-in Mutual Exclusivity test directly on the two genes.

For the Fisher's exact test itself, use cBioPortal's **Mutual Exclusivity** tab from that page (available once both genes are queried), or the R/Python snippets above.