Q (sonnet): Do lung adenocarcinoma patients with high EGFR mRNA expression (top quartile) have different survival outcomes than those with low expression?

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

▶ list_guides
{}

◀ 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":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

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

▶ resolve_and_route
{
 "studyKeywords": [
  "TCGA",
  "lung",
  "adenocarcinoma"
 ]
}

◀ 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)

◀ result
{"success":true,"message":"Found 4 matching studies. Pick the best match (prefer TCGA → prefer PanCancer Atlas), use its metadata to call the appropriate navigation tool(s) and generate URLs immediately. Present other studies as alternatives.","data":{"totalCount":4,"studiesWithMetadata":[{"studyId":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","sampleCount":586,"studyViewUrl":"https://www.cbioportal.org/study?id=luad_tcga","metadata":{"clinicalAttributeIds":["AGE","AJCC_CLINICAL_TUMOR_STAGE","AJCC_METASTASIS_CLINICAL_CM","AJCC_METASTASIS_PATHOLOGIC_PM","AJCC_NODES_CLINICAL_CN","AJCC_NODES_CLINICAL_CT","AJCC_NODES_PATHOLOGIC_PN","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","AJCC_TUMOR_PATHOLOGIC_PT","ALK_ANALYSIS_TYPE","ALK_TRANSLOCATION_STATUS","ALK_TRANSLOCATION_VARIANT","CANCER_TYPE","CANCER_TYPE_DETAILED","CARBON_MONOXIDE_DIFFUSION_DLCO","DAYS_TO_COLLECTION","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DAYS_TO_PATIENT_PROGRESSION_FREE","DAYS_TO_SPECIMEN_COLLECTION","DAYS_TO_TUMOR_PROGRESSION","DFS_MONTHS","DFS_STATUS","DISEASE_CODE","ECOG_SCORE","ETHNICITY","EXTRANODAL_INVOLVEMENT","FEV1_FVC_RATIO_POSTBRONCHOLIATOR","FEV1_FVC_RATIO_PREBRONCHOLIATOR","FEV1_PERCENT_REF_POSTBRONCHOLIATOR","FEV1_PERCENT_REF_PREBRONCHOLIATOR","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","HISTOLOGICAL_DIAGNOSIS","HISTORY_IMMUNOLOGICAL_DISEASE","HISTORY_IMMUNOLOGICAL_DISEASE_OTHER","HISTORY_NEOADJUVANT_TRTYN","HISTORY_OTHER_MALIGNANCY","HISTORY_RELEVANT_INFECTIOUS_DX","HIV_STATUS","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","INITIAL_PATHOLOGIC_DX_YEAR","IS_FFPE","KARNOFSKY_PERFORMANCE_SCORE","KRAS_GENE_ANALYSIS_INDICATOR","KRAS_MUTATION","KRAS_MUTATION_IDENTIFIED_TYPE","LATERALITY","LOCATION_LUNG_PARENCHYMA","LONGEST_DIMENSION","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT_OTHER","METHOD_OF_SAMPLE_PROCUREMENT","MUTATION_COUNT","MUTATION_STATUS","MUTATION_TYPE","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","NUMBER_OF_LYMPHNODES_POSITIVE_BY_IHC","OCT_EMBEDDED","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_METHOD_OF_SAMPLE_PROCUREMENT","OTHER_PATIENT_ID","OTHER_SAMPLE_ID","PATHOLOGY_REPORT_FILE_NAME","PATHOLOGY_REPORT_UUID","PERFORMANCE_STATUS_TIMING","PHARMACEUTICAL_TX_ADJUVANT","POS_LYMPH_NODE_LOCATION","PRIMARY_SITE_OTHER","PRIMARY_SITE_PATIENT","PROJECT_CODE","PROSPECTIVE_COLLECTION","PULMONARY_FUNCTION_TEST_INDICATOR","RACE","RADIATION_TREATMENT_ADJUVANT","RESIDUAL_TUMOR","RETROSPECTIVE_COLLECTION","SAMPLE_COUNT","SAMPLE_INITIAL_WEIGHT","SAMPLE_TYPE","SAMPLE_TYPE_ID","SEX","SHORTEST_DIMENSION","SMOKING_PACK_YEARS","SMOKING_YEAR_STARTED","SMOKING_YEAR_STOPPED","SOMATIC_STATUS","SPECIMEN_CURRENT_WEIGHT","SPECIMEN_FREEZING_METHOD","SPECIMEN_SECOND_LONGEST_DIMENSION","STAGE_OTHER","TARGETED_MOLECULAR_THERAPY","TIME_BETWEEN_CLAMPING_AND_FREEZING","TIME_BETWEEN_EXCISION_AND_FREEZING","TISSUE_SOURCE_SITE","TMB_NONSYNONYMOUS","TOBACCO_SMOKING_HISTORY_INDICATOR","TREATMENT_OUTCOME_FIRST_COURS … (6466 more chars)

▶ clickhouse_run_select_query
{
 "query": "
SELECT
  quantile(0.25)(toFloat64OrNull(alteration_value)) AS q1,
  quantile(0.75)(toFloat64OrNull(alteration_value)) AS q3,
  count() AS n
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
  AND profile_type = 'rna_seq_v2_mrna'
  AND hugo_gene_symbol = 'EGFR'
  AND alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(alteration_value) IS NOT NULL
"
}

◀ result
{"rows":[{"q1":539.147,"q3":1766.0475000000001,"n":510}]}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
{"columns":[{"name":"internal_id","type":"Int32"},{"name":"sample_unique_id","type":"String","comment":"Globally unique sample ID: cancer_study_identifier + \"_\" + sample.stable_id. Empty for patient-level attributes. Use this for sample filtering and joins."},{"name":"patient_unique_id","type":"String","comment":"Globally unique patient ID: cancer_study_identifier + \"_\" + patient.stable_id. Present for both sample and patient-level attributes."},{"name":"attribute_name","type":"LowCardinality(String)","comment":"Clinical attribute name (e.g., SAMPLE_TYPE, CANCER_TYPE, AGE, OS_MONTHS). Use with attribute_value for filtering. AGE may be floored or capped for de-identification (e.g. all children recorded as 18, or everyone 89+ recorded as 89 or 90): before age statistics check for a pile-up at the min/max, and if present compute age from DAYS_TO_BIRTH (-days / 365.25)."},{"name":"attribute_value","type":"String","comment":"Value of the clinical attribute (String). For SAMPLE_TYPE: Primary, Metastasis, Local Recurrence, Unknown. Missing values are empty strings, so use toFloat64OrNull(attribute_value) for numeric comparisons — CAST fails on them."},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"type","type":"LowCardinality(String)","comment":"Data level: \"sample\" for sample-level attributes (e.g., SAMPLE_TYPE), \"patient\" for patient-level attributes (e.g., AGE, OS_MONTHS)."}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH egfr AS (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS expr
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    AND profile_type = 'rna_seq_v2_mrna'
    AND hugo_gene_symbol = 'EGFR'
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
),
cutoffs AS (
  SELECT quantile(0.25)(expr) AS q1, quantile(0.75)(expr) AS q3 FROM egfr
),
grouped AS (
  SELECT
    e.sample_unique_id AS sample_unique_id,
    e.expr AS expr,
    CASE WHEN e.expr >= c.q3 THEN 'Top quartile (high EGFR)'
         WHEN e.expr <= c.q1 THEN 'Bottom quartile (low EGFR)'
         ELSE 'Middle 50%' END AS grp
  FROM egfr e CROSS JOIN cutoffs c
),
os AS (
  SELECT patient_unique_id,
         maxIf(attribute_value, attribute_name = 'OS_MONTHS') AS os_months,
         maxIf(attribute_value, attribute_name = 'OS_STATUS') AS os_status
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
    AND type = 'patient'
  GROUP BY patient_unique_id
)
SELECT
  g.grp AS group_name,
  count() AS n_samples,
  countIf(toFloat64OrNull(o.os_months) IS NOT NULL AND o.os_status != '') AS n_with_survival,
  countIf(o.os_status = '1:DECEASED') AS n_events,
  countIf(o.os_status = '0:LIVING') AS n_censored,
  min(toFloat64OrNull(o.os_months)) AS min_followup,
  max(toFloat64OrNull(o.os_months)) AS max_followup
FROM grouped g
LEFT JOIN os o ON o.patient_unique_id = substring(g.sample_unique_id, 1, length(g.sample_unique_id) - 3)
WHERE g.grp IN ('Top quartile (high EGFR)', 'Bottom quartile (low EGFR)')
GROUP BY g.grp
ORDER BY g.grp
"
}

◀ result
{"rows":[{"group_name":"Bottom quartile (low EGFR)","n_samples":128,"n_with_survival":127,"n_events":49,"n_censored":79,"min_followup":0.427392577,"max_followup":232.1727981},{"group_name":"Top quartile (high EGFR)","n_samples":128,"n_with_survival":126,"n_events":51,"n_censored":77,"min_followup":0,"max_followup":163.0995825}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
  AND type = 'sample'
LIMIT 5
"
}

◀ result
{"rows":[{"sample_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4244-01","patient_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4244"},{"sample_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4249-01","patient_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4249"},{"sample_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4250-01","patient_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4250"},{"sample_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4382-01","patient_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4382"},{"sample_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4384-01","patient_unique_id":"luad_tcga_pan_can_atlas_2018_TCGA-05-4384"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH egfr AS (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS expr
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    AND profile_type = 'rna_seq_v2_mrna'
    AND hugo_gene_symbol = 'EGFR'
    AND alteration_value NOT IN ('', 'NA')
    AND toFloat64OrNull(alteration_value) IS NOT NULL
),
cutoffs AS (
  SELECT quantile(0.25)(expr) AS q1, quantile(0.75)(expr) AS q3 FROM egfr
),
grouped AS (
  SELECT
    e.sample_unique_id AS sample_unique_id,
    e.expr AS expr,
    CASE WHEN e.expr >= c.q3 THEN 'Top quartile (high EGFR)'
         WHEN e.expr <= c.q1 THEN 'Bottom quartile (low EGFR)'
         ELSE 'Middle 50%' END AS grp
  FROM egfr e CROSS JOIN cutoffs c
),
sample_to_patient AS (
  SELECT DISTINCT sample_unique_id, patient_unique_id
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    AND type = 'sample'
),
os AS (
  SELECT patient_unique_id,
         maxIf(attribute_value, attribute_name = 'OS_MONTHS') AS os_months,
         maxIf(attribute_value, attribute_name = 'OS_STATUS') AS os_status
  FROM clinical_data_derived
  WHERE cancer_study_identifier = 'luad_tcga_pan_can_atlas_2018'
    AND attribute_name IN ('OS_MONTHS','OS_STATUS')
    AND type = 'patient'
  GROUP BY patient_unique_id
)
SELECT
  g.grp AS group_name,
  count() AS n_samples,
  countIf(toFloat64OrNull(o.os_months) IS NOT NULL AND o.os_status != '') AS n_with_survival,
  countIf(o.os_status = '1:DECEASED') AS n_events,
  countIf(o.os_status = '0:LIVING') AS n_censored,
  min(toFloat64OrNull(o.os_months)) AS min_followup_months,
  max(toFloat64OrNull(o.os_months)) AS max_followup_months
FROM grouped g
JOIN sample_to_patient stp ON stp.sample_unique_id = g.sample_unique_id
LEFT JOIN os o ON o.patient_unique_id = stp.patient_unique_id
WHERE g.grp IN ('Top quartile (high EGFR)', 'Bottom quartile (low EGFR)')
GROUP BY g.grp
ORDER BY g.grp
"
}

◀ result
{"rows":[{"group_name":"Bottom quartile (low EGFR)","n_samples":128,"n_with_survival":127,"n_events":49,"n_censored":79,"min_followup_months":0.427392577,"max_followup_months":232.1727981},{"group_name":"Top quartile (high EGFR)","n_samples":128,"n_with_survival":126,"n_events":51,"n_censored":77,"min_followup_months":0,"max_followup_months":163.0995825}]}

▶ get_studyviewfilter_options
{
 "studyId": "luad_tcga_pan_can_atlas_2018",
 "geneSpecificQueries": [
  {
   "hugoGeneSymbol": "EGFR",
   "profileType": "rna_seq_v2_mrna"
  }
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"luad_tcga_pan_can_atlas_2018","geneSpecificCounts":[{"hugoGeneSymbol":"EGFR","profileType":"rna_seq_v2_mrna","bins":[{"end":538.388,"count":128},{"start":538.388,"end":994.5969999999995,"count":127},{"start":994.5969999999995,"end":1772.42,"count":128},{"start":1772.42,"count":127}]}]}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "luad_tcga_pan_can_atlas_2018"
 ],
 "tab": "survival",
 "groups": [
  {
   "name": "High EGFR mRNA (top quartile)",
   "studyViewFilter": {
    "genomicDataFilters": [
     {
      "hugoGeneSymbol": "EGFR",
      "profileType": "rna_seq_v2_mrna",
      "values": [
       {
        "start": 1772.42
       }
      ]
     }
    ]
   }
  },
  {
   "name": "Low EGFR mRNA (bottom quartile)",
   "studyViewFilter": {
    "genomicDataFilters": [
     {
      "hugoGeneSymbol": "EGFR",
      "profileType": "rna_seq_v2_mrna",
      "values": [
       {
        "end": 538.388
       }
      ]
     }
    ]
   }
  }
 ]
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6ab5ba3be497aad53593c254","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6ab5ba3be497aad53593c254","data":{"description":"Group comparison (2 custom groups)","studies":["luad_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"High EGFR mRNA (top quartile)","sampleCount":127},{"name":"Low EGFR mRNA (bottom quartile)","sampleCount":128}],"studyViewUrl":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"High EGFR mRNA (top quartile)","url":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22EGFR%22%2C%22profileType%22%3A%22rna_seq_v2_mrna%22%2C%22values%22%3A%5B%7B%22start%22%3A1772.42%7D%5D%7D%5D%7D"},{"groupName":"Low EGFR mRNA (bottom quartile)","url":"https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22EGFR%22%2C%22profileType%22%3A%22rna_seq_v2_mrna%22%2C%22values%22%3A%5B%7B%22end%22%3A538.388%7D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing High EGFR mRNA (top quartile) (127) vs Low EGFR mRNA (bottom quartile) (128)."}}

═ answer (success)
## Results

**Study:** Lung Adenocarcinoma (TCGA, PanCancer Atlas) — `luad_tcga_pan_can_atlas_2018`, EGFR mRNA expression (`rna_seq_v2_mrna`, n=510 profiled samples).

ClickHouse cannot compute a Kaplan-Meier estimate or log-rank p-value directly, so I cannot report a survival difference or median OS here — that requires KM estimation to properly handle censoring. Here is the cohort summary needed for that analysis:

| Group | EGFR mRNA range | N samples | N with OS data | Events (deceased) | Censored (living) | Follow-up range (months) |
|---|---|---|---|---|---|---|
| **Top quartile (high EGFR)** | ≥ 1772.42 | 128 | 126 | 51 | 77 | 0 – 163.1 |
| **Bottom quartile (low EGFR)** | ≤ 538.39 | 128 | 127 | 49 | 79 | 0.4 – 232.2 |

Event rates look similar (51/126 ≈ 40.5% vs. 49/127 ≈ 38.6%), but this raw comparison ignores censoring and time-to-event structure — it is **not** a substitute for KM/log-rank. To get a proper answer, run:
- **R:** `survival::survfit(Surv(OS_MONTHS, OS_STATUS=="1:DECEASED") ~ group, data=...)` + `survdiff(...)` for the log-rank p-value
- **Python:** `lifelines.KaplanMeierFitter` + `lifelines.statistics.logrank_test`
- **cBioPortal's Group Comparison → Survival tab** (linked below) — this runs the log-rank test automatically and plots the KM curves.

### [View KM survival comparison: High vs Low EGFR mRNA (top vs bottom quartile)](https://www.cbioportal.org/comparison/survival?comparisonId=6ab5ba3be497aad53593c254)

Kaplan-Meier survival analysis comparing High EGFR mRNA (top quartile) (127 samples) vs Low EGFR mRNA (bottom quartile) (128 samples). This page will render the KM curves and log-rank p-value directly.

Companion cohort view: [Study View – luad_tcga_pan_can_atlas_2018](https://www.cbioportal.org/study?id=luad_tcga_pan_can_atlas_2018)