Q (sonnet-5.5): Can you please generate a survival curve for colorectal cancer patients based on the expression levels of IMPDH2?

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

◀ 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)

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

▶ list_studies
{
 "search": "colorectal",
 "limit": 40
}

◀ 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)

◀ 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":"crc_msk_2026","name":"Colorectal Adenocarcinoma (MSK, 2026)","type_of_cancer_id":"read","sample_count":7237,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_msk_2026"},{"cancer_study_identifier":"crc_eo_2020","name":"Colorectal Cancer (MSK, JNCI 2021)","type_of_cancer_id":"bowel","sample_count":1516,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_eo_2020"},{"cancer_study_identifier":"crc_msk_2017","name":"Metastatic Colorectal Cancer (MSK, Cancer Cell 2018)","type_of_cancer_id":"coadread","sample_count":1134,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_msk_2017"},{"cancer_study_identifier":"crc_sysucc_2022","name":"Colorectal Cancer- ChangKang Project (SYSUCC, Nat Commun 2022)","type_of_cancer_id":"coadread","sample_count":1015,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_sysucc_2022"},{"cancer_study_identifier":"coadread_tcga","name":"Colorectal Adenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"coadread","sample_count":640,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_tcga"},{"cancer_study_identifier":"coadread_dfci_2016","name":"Colorectal Adenocarcinoma (DFCI, Cell Reports 2016)","type_of_cancer_id":"coadread","sample_count":619,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_dfci_2016"},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"coadread","sample_count":594,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"crc_apc_impact_2020","name":"Colorectal Cancer (MSK, Gastroenterology 2020)","type_of_cancer_id":"coadread","sample_count":471,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_apc_impact_2020"},{"cancer_study_identifier":"coadread_tcga_pub","name":"Colorectal Adenocarcinoma (TCGA, Nature 2012)","type_of_cancer_id":"coadread","sample_count":276,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_tcga_pub"},{"cancer_study_identifier":"bowel_colitis_msk_2022","name":"Colorectal Adenocarcinoma (MSK, Nat Commun 2022)","type_of_cancer_id":"bowel","sample_count":179,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=bowel_colitis_msk_2022"},{"cancer_study_identifier":"coadread_cass_2020","name":"Colorectal Cancer (CAS Shanghai, Cancer Cell 2020)","type_of_cancer_id":"coadread","sample_count":146,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_cass_2020"},{"cancer_study_identifier":"coadread_mskcc","name":"Colorectal Adenocarcinoma Triplets (MSK, Geno … (2148 more chars)

▶ get_study_guide
{
 "study_id": "coadread_tcga_pan_can_atlas_2018"
}

◀ result
# Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)

**Study ID:** `coadread_tcga_pan_can_atlas_2018`

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

## Study-Specific Attributes

### Microsatellite Instability (MSI)
There is no `MSI_STATUS` attribute. Three attributes carry MSI (594 patients, one sample each):

| Attribute | Definition | MSI-high count |
|-----------|------------|----------------|
| `SUBTYPE` | TCGA molecular classification: `COAD_MSI` 60 + `READ_MSI` 3 | **63** |
| `MSI_SENSOR_SCORE` | MSIsensor score ≥10 (indeterminate 4–10: 10 more) | 78 of 584 scored |
| `MSI_SCORE_MANTIS` | MANTIS score >0.4 (>0.6 = MSI: 67; 0.4–0.6 indeterminate) | 89 of 557 scored |

**For "MSI-high" questions, use `SUBTYPE` IN (`COAD_MSI`, `READ_MSI`)** (the TCGA molecular classification) and state which definition you used; mention the score-based alternatives if the counts matter. Do not switch to `coadread_tcga_pub` to find MSI; this study has it.
```sql
SELECT count(DISTINCT patient_unique_id) AS msi_patients   -- 63
FROM clinical_data_derived
WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018'
  AND attribute_name = 'SUBTYPE' AND attribute_value IN ('COAD_MSI', 'READ_MSI');
```

### Molecular Classification (`SUBTYPE`, patients)
`COAD_CIN` 226, `READ_CIN` 102, `COAD_MSI` 60, `COAD_GS` 49, `READ_GS` 9, `COAD_POLE` 6, `READ_POLE` 4, `READ_MSI` 3, blank 135.
- **Hypermutated**: no `HYPERMUTATED` attribute. Use `SUBTYPE` MSI + POLE (73 patients), or `TMB_NONSYNONYMOUS` ≥10 (83 samples; all MSI and POLE tumors exceed it).
- **CMS (consensus molecular subtypes)** are not available in this study.

### Anatomic Location
There is no `TUMOR_LOCATION` attribute.
- Colon vs rectum: `TUMOR_TISSUE_SITE` — Colon 436, Rectum 152, blank 6.
- Subsite (for left vs right): `ICD_O_3_SITE` — right: C18.0 cecum 81, C18.2 ascending 100, C18.3 hepatic flexure 10; transverse C18.4 20; left: C18.5 splenic flexure 5, C18.6 descending 16, C18.7 sigmoid 106, C19.9 rectosigmoid 72, C20.9 rectum 81; C18.9 colon NOS 97 (side unknown).

## Key Genes
| Gene | Frequency | Clinical Relevance |
|------|-----------|-------------------|
| APC | ~80% | Initiating event in most CRC |
| TP53 | ~55% | Progression marker |
| KRAS | ~40% | Predicts anti-EGFR resistance |
| PIK3CA | ~15% | May predict aspirin benefit |
| BRAF | ~10% | V600E poor prognosis (MSS context) |
| SMAD4 | ~10% | TGF-β pathway |

## Notes
- MSI-H tumors respond well to immunotherapy
- KRAS/NRAS mutations contraindicate anti-EGFR therapy
- BRAF V600E has different prognosis in MSI-H vs MSS context
- Left vs right-sided tumors have different biology and outcomes


▶ clickhouse_run_select_query
{
 "query": "WITH e AS (SELECT sample_unique_id, replaceOne(sample_unique_id, 'coadread_tcga_pan_can_atlas_2018_', '') sid, toFloat64OrNull(alteration_value) v FROM genetic_alteration_derived WHERE cancer_study_identifier='coadread_tcga_pan_can_atlas_2018' AND profile_type='rna_seq_v2_mrna' AND hugo_gene_symbol='IMPDH2' AND toFloat64OrNull(alteration_value) IS NOT NULL),
m AS (SELECT median(v) med FROM e)
SELECT count() n, (SELECT med FROM m) med, min(v) mn, max(v) mx FROM e"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
{"rows":[{"n":592,"med":4992.9871375,"mn":1708.776571,"mx":20587.46131}]}

◀ 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 e AS (SELECT sample_unique_id, toFloat64OrNull(alteration_value) v FROM genetic_alteration_derived WHERE cancer_study_identifier='coadread_tcga_pan_can_atlas_2018' AND profile_type='rna_seq_v2_mrna' AND hugo_gene_symbol='IMPDH2' AND toFloat64OrNull(alteration_value) IS NOT NULL),
p AS (SELECT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier='coadread_tcga_pan_can_atlas_2018' AND type='sample' AND attribute_name='SAMPLE_TYPE' GROUP BY 1,2),
os AS (SELECT patient_unique_id, anyIf(toFloat64OrNull(attribute_value), attribute_name='OS_MONTHS') t, anyIf(attribute_value, attribute_name='OS_STATUS') s FROM clinical_data_derived WHERE cancer_study_identifier='coadread_tcga_pan_can_atlas_2018' AND attribute_name IN ('OS_MONTHS','OS_STATUS') GROUP BY patient_unique_id),
med AS (SELECT median(v) m FROM e)
SELECT if(e.v>=(SELECT m FROM med),'High','Low') grp, count() n, countIf(os.s LIKE '1:%') events, countIf(os.s LIKE '0:%') censored, round(min(os.t),1) mint, round(max(os.t),1) maxt
FROM e JOIN p USING sample_unique_id JOIN os USING patient_unique_id WHERE os.t IS NOT NULL GROUP BY grp"
}

▶ resolve_and_route
{
 "studyIds": [
  "coadread_tcga_pan_can_atlas_2018"
 ]
}

◀ result
{"rows":[{"grp":"Low","n":295,"events":66,"censored":229,"mint":0,"maxt":140.4},{"grp":"High","n":293,"events":53,"censored":240,"mint":0,"maxt":148}]}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594,"studyViewUrl":"https://www.cbioportal.org/study?id=coadread_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":["coadread_tcga_pan_can_atlas_2018_gistic","coadread_tcga_pan_can_atlas_2018_log2CNA","coadread_tcga_pan_can_atlas_2018_mutations","coadread_tcga_pan_can_atlas_2018_protein_quantification","coadread_tcga_pan_can_atlas_2018_protein_quantification_zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","coadread_tcga_pan_can_atlas_2018_rppa","coadread_tcga_pan_can_atlas_2018_rppa_Zscores","coadread_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["coadread_tcga_pan_can_atlas_2018_armlevel_cna","coadread_tcga_pan_can_atlas_2018_genetic_ancestry","coadread_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","coadread_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["coadread_tcga_pan_can_atlas_2018_protein_quantification_zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","coadread_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_c … (70 more chars)

▶ navigate_to_group_comparison
{
 "studyIds": [
  "coadread_tcga_pan_can_atlas_2018"
 ],
 "tab": "survival",
 "groups": [
  {
   "name": "IMPDH2 high (≥ median)",
   "studyViewFilter": {
    "genomicDataFilters": [
     {
      "hugoGeneSymbol": "IMPDH2",
      "profileType": "rna_seq_v2_mrna",
      "values": [
       {
        "start": 4992.99,
        "end": 20600
       }
      ]
     }
    ]
   }
  },
  {
   "name": "IMPDH2 low (< median)",
   "studyViewFilter": {
    "genomicDataFilters": [
     {
      "hugoGeneSymbol": "IMPDH2",
      "profileType": "rna_seq_v2_mrna",
      "values": [
       {
        "start": 1700,
        "end": 4992.99
       }
      ]
     }
    ]
   }
  }
 ]
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6abb4223e497aad53593ca50","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6abb4223e497aad53593ca50","data":{"description":"Group comparison (2 custom groups)","studies":["coadread_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"IMPDH2 high (≥ median)","sampleCount":296},{"name":"IMPDH2 low (< median)","sampleCount":296}],"studyViewUrl":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"IMPDH2 high (≥ median)","url":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22IMPDH2%22%2C%22profileType%22%3A%22rna_seq_v2_mrna%22%2C%22values%22%3A%5B%7B%22start%22%3A4992.99%2C%22end%22%3A20600%7D%5D%7D%5D%7D"},{"groupName":"IMPDH2 low (< median)","url":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22genomicDataFilters%22%3A%5B%7B%22hugoGeneSymbol%22%3A%22IMPDH2%22%2C%22profileType%22%3A%22rna_seq_v2_mrna%22%2C%22values%22%3A%5B%7B%22start%22%3A1700%2C%22end%22%3A4992.99%7D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing IMPDH2 high (≥ median) (296) vs IMPDH2 low (< median) (296)."}}

═ answer (success)
I can't draw the curve here, so I set up the comparison in cBioPortal, which will plot it with the log-rank p-value.

[View IMPDH2 high vs low survival comparison](https://www.cbioportal.org/comparison/survival?comparisonId=6abb4223e497aad53593ca50)

**Cohort:** Colorectal Adenocarcinoma (TCGA, PanCancer Atlas), `coadread_tcga_pan_can_atlas_2018`. I chose it because it has both RNA-seq expression and overall survival data. Many other colorectal studies are available, so tell me if you'd rather use a different one.

**Grouping:** I split the patients at the median IMPDH2 mRNA level (RSEM, about 4,993). This is one common cutoff. The portal lets you change the ranges if you want quartiles or another threshold.

**Overall survival summary** (patients with expression and survival data):

| Group | Patients | Deaths | Censored | Follow-up (months) |
|---|---|---|---|---|
| IMPDH2 high (≥ median) | 293 | 53 | 240 | 0–148 |
| IMPDH2 low (< median) | 295 | 66 | 229 | 0–140 |

- **Group sizes:** The portal shows 296 samples per group because it counts samples rather than patients with survival data.
- **Median survival:** In both groups, fewer than half of patients died. The Kaplan-Meier median survival is therefore probably not reached in either group.
- **Statistics:** I haven't calculated a p-value or hazard ratio. The log-rank p-value will appear on the linked page. A hazard ratio would need a Cox model in R or Python.
- **Follow-up:** Most patients were censored, so the right-hand end of the curve rests on few patients.