Q (sonnet): In colorectal cancer do patients with microsatellite instability in the TCGA dataset have a similar prognosis as those in the MSK Gastroenterology 2020 study?

▶ 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://clinical-data-guide"
}

◀ result
# Clinical Data Query Guide

## Overview
Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis.

## Data Organization

### Patient-Level vs Sample-Level Data
- **Patient-level**: Demographics, overall survival, disease stage (stored once per patient)
- **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient)

### Key Tables
- `clinical_patient`: Patient-level clinical attributes
- `clinical_sample`: Sample-level clinical attributes
- `clinical_data_derived`: Pre-joined view combining both levels
- `clinical_attribute_meta`: Metadata about available clinical attributes

## Recommended Approach: Use clinical_data_derived

The `clinical_data_derived` table is pre-joined and optimized for most queries:

```sql
-- Get clinical data for specific attributes
SELECT
    sample_unique_id,
    patient_unique_id,
    attribute_name,
    attribute_value
FROM clinical_data_derived
WHERE
    cancer_study_identifier = 'your_study_id'
    AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS');
```

## Clinical Attribute Discovery

### Use clinical_attribute_meta for Discovering Available Attributes
**Always start here** to see what clinical attributes are available for a specific study:

```sql
-- Discover available clinical attributes for a study
SELECT
    attr_id,
    description,
    patient_attribute,
    cancer_study_id
FROM clinical_attribute_meta
WHERE
    cancer_study_id = (
        SELECT cancer_study_id
        FROM cancer_study
        WHERE cancer_study_identifier = 'your_study_id'
    )
ORDER BY patient_attribute, attr_id;
```

**Key Fields:**
- **attr_id**: matches attr_id in clinical_sample/clinical_patient tables
- **description**: human-readable description of the attribute
- **patient_attribute**: true = patient attribute, false = sample attribute
- **cancer_study_id**: links to cancer_study table (filter by study)

## Attribute Semantics and Matching

### Case-Insensitive Matching for Attribute Values

Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant.

When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study:

```sql
-- Correct: case-insensitive clinical value filter
SELECT DISTINCT sample_unique_id, patient_unique_id
FROM clinical_data_derived
WHERE cancer_study_identifier = 'your_study_id'
  AND attribute_name = 'MUTATION_STATUS'
  AND upper(attribute_value) = 'GERMLINE';
```

Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study.

### Query the Requested Attribute, Not a Proxy

Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars)

▶ list_studies
{
 "search": "colorectal TCGA"
}

▶ list_studies
{
 "search": "MSK Gastroenterology 2020"
}

◀ result
{"result":[{"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_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":"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"}]}

◀ result
{"result":[{"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"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_id, attr_id, description, patient_attribute
FROM clinical_attribute_meta
WHERE cancer_study_id IN (
    SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier IN ('coadread_tcga_pan_can_atlas_2018','crc_apc_impact_2020')
)
AND (upper(attr_id) LIKE '%MSI%' OR upper(attr_id) LIKE '%MICROSATELLITE%' OR upper(description) LIKE '%MICROSATELLITE%')
ORDER BY cancer_study_id, attr_id"
}

◀ result
{"rows":[{"cancer_study_id":121,"attr_id":"MSI_SCORE","description":"MSI Score","patient_attribute":0},{"cancer_study_id":121,"attr_id":"MSI_TYPE","description":"MSI Type","patient_attribute":0},{"cancer_study_id":756,"attr_id":"MSI_SCORE_MANTIS","description":"MSI Score reported by MANTIS. The suggested thresholds are MSI: >0.6, Indeterminate: 0.4-0.6 and MSS: <0.4.","patient_attribute":0},{"cancer_study_id":756,"attr_id":"MSI_SENSOR_SCORE","description":"MSI Score reported by MSIsensor. The suggested thresholds are MSI: >10, Indeterminate: 4-10 and MSS: <10.","patient_attribute":0}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(DISTINCT sample_unique_id) as n
FROM clinical_data_derived
WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND attribute_name = 'MSI_TYPE'
GROUP BY attribute_value ORDER BY n DESC"
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_name, count(DISTINCT sample_unique_id) as n
FROM clinical_data_derived
WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND (upper(attribute_name) LIKE '%MSI%')
GROUP BY attribute_name ORDER BY n DESC"
}

◀ result
{"rows":[{"attribute_name":"MSI_SCORE","n":471},{"attribute_name":"MSI_TYPE","n":471}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_value, count(DISTINCT sample_unique_id) as n
FROM clinical_data_derived
WHERE cancer_study_identifier = 'crc_apc_impact_2020' AND attribute_name = 'MSI_TYPE'
GROUP BY attribute_value ORDER BY n DESC"
}

◀ result
{"rows":[{"attribute_value":"Stable","n":428},{"attribute_value":"Instable","n":27},{"attribute_value":"Indeterminate","n":8},{"attribute_value":"Do not report","n":7},{"n":1}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT count(*) FROM clinical_data_derived WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND attribute_name IN ('MSI_SCORE','MSI_TYPE')"
}

▶ get_study_guide
{
 "study_id": "coadread_tcga_pan_can_atlas_2018"
}

◀ result
{"rows":[{"count()":0}]}

◀ 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 msi AS (
    SELECT DISTINCT patient_unique_id 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')
),
os AS (
    SELECT patient_unique_id,
        maxIf(toFloat64OrNull(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 = 'coadread_tcga_pan_can_atlas_2018' AND attribute_name IN ('OS_MONTHS','OS_STATUS')
    GROUP BY patient_unique_id
)
SELECT
    count() AS n_patients,
    countIf(startsWith(os_status, '1')) AS n_events,
    countIf(startsWith(os_status, '0')) AS n_censored,
    round(min(os_months),1) AS min_followup,
    round(max(os_months),1) AS max_followup
FROM os
WHERE patient_unique_id IN (SELECT patient_unique_id FROM msi)
  AND os_months IS NOT NULL AND os_status != ''
"
}

◀ result
{"rows":[{"n_patients":63,"n_events":15,"n_censored":48,"min_followup":0,"max_followup":135.6}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH msi AS (
    SELECT DISTINCT patient_unique_id FROM clinical_data_derived
    WHERE cancer_study_identifier = 'crc_apc_impact_2020'
      AND attribute_name = 'MSI_TYPE' AND attribute_value = 'Instable'
),
os AS (
    SELECT patient_unique_id,
        maxIf(toFloat64OrNull(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 = 'crc_apc_impact_2020' AND attribute_name IN ('OS_MONTHS','OS_STATUS')
    GROUP BY patient_unique_id
)
SELECT
    count() AS n_patients,
    countIf(startsWith(os_status, '1')) AS n_events,
    countIf(startsWith(os_status, '0')) AS n_censored,
    round(min(os_months),1) AS min_followup,
    round(max(os_months),1) AS max_followup
FROM os
WHERE patient_unique_id IN (SELECT patient_unique_id FROM msi)
  AND os_months IS NOT NULL AND os_status != ''
"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_attribute_meta"
}

◀ result
{"rows":[{"n_patients":27,"n_events":15,"n_censored":12,"min_followup":2.2,"max_followup":70.9}]}

◀ result
{"columns":[{"name":"attr_id","type":"String"},{"name":"display_name","type":"String"},{"name":"description","type":"String"},{"name":"datatype","type":"String"},{"name":"patient_attribute","type":"Int32"},{"name":"priority","type":"String"},{"name":"cancer_study_id","type":"Int64"}]}

▶ resolve_and_route
{
 "studyIds": [
  "coadread_tcga_pan_can_atlas_2018",
  "crc_apc_impact_2020"
 ]
}

◀ result
{"success":true,"message":"Found 2 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":2,"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_rp … (1172 more chars)

▶ navigate_to_group_comparison
{
 "studyIds": [
  "coadread_tcga_pan_can_atlas_2018"
 ],
 "groups": [
  {
   "name": "MSI-H (COAD_MSI/READ_MSI)",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SUBTYPE",
      "values": [
       {
        "value": "COAD_MSI"
       },
       {
        "value": "READ_MSI"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "Non-MSI-H",
   "isUnselected": true
  }
 ],
 "tab": "survival"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6ab7f740c2115c492d88514a","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6ab7f740c2115c492d88514a","data":{"description":"Group comparison (2 custom groups)","studies":["coadread_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"MSI-H (COAD_MSI/READ_MSI)","sampleCount":63},{"name":"Non-MSI-H","sampleCount":531}],"studyViewUrl":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"MSI-H (COAD_MSI/READ_MSI)","url":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SUBTYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22COAD_MSI%22%7D%2C%7B%22value%22%3A%22READ_MSI%22%7D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing MSI-H (COAD_MSI/READ_MSI) (63) vs Non-MSI-H (531)."}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "crc_apc_impact_2020"
 ],
 "groups": [
  {
   "name": "MSI-Instable",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MSI_TYPE",
      "values": [
       {
        "value": "Instable"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "MSS/Other",
   "isUnselected": true
  }
 ],
 "tab": "survival"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/survival?comparisonId=6ab5d10fc2115c492d884e23","url":"https://www.cbioportal.org/comparison/survival?comparisonId=6ab5d10fc2115c492d884e23","data":{"description":"Group comparison (2 custom groups)","studies":["crc_apc_impact_2020"],"totalGroups":2,"groups":[{"name":"MSI-Instable","sampleCount":27},{"name":"MSS/Other","sampleCount":444}],"studyViewUrl":"https://www.cbioportal.org/study?id=crc_apc_impact_2020","groupUrls":[{"groupName":"MSI-Instable","url":"https://www.cbioportal.org/study?id=crc_apc_impact_2020#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22MSI_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Instable%22%7D%5D%7D%5D%7D"}],"pageDescription":"Kaplan-Meier survival analysis comparing MSI-Instable (27) vs MSS/Other (444)."}}

═ answer (success)
## Summary

I can't compute a survival comparison directly — Kaplan-Meier estimation with censoring handling is needed, which isn't something the underlying database can calculate. But here are the descriptive numbers for MSI-high patients with overall survival (OS) data in each cohort, plus direct links to run the Kaplan-Meier comparison in each cBioPortal study.

**Important framing difference between the two cohorts:**
- **TCGA PanCancer Atlas** ([Colorectal Adenocarcinoma](https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018), 594 patients) — a mixed-stage, largely resectable/primary cohort. MSI-high defined via the TCGA molecular `SUBTYPE` classification (`COAD_MSI`/`READ_MSI`).
- **MSK Gastroenterology 2020** ([Colorectal Cancer](https://www.cbioportal.org/study/summary?id=crc_apc_impact_2020), 471 patients) — this is specifically a cohort enriched for **metastatic** colorectal cancer (it has fields like age-at-metastasis, first-line metastatic treatment, metastatic site). MSI status here comes from the study's own `MSI_TYPE` attribute ("Instable" vs "Stable").

This means the two cohorts are **not stage-matched** — the MSK cohort skews toward more advanced/metastatic disease, which alone would be expected to affect survival independent of MSI status. Any prognosis comparison must account for that confound.

**Descriptive OS summary (patients with OS data available):**

| Cohort | N (MSI-H, with OS data) | Events (deceased) | Censored (living) | Follow-up range (months) |
|---|---|---|---|---|
| TCGA PanCancer Atlas (COAD_MSI/READ_MSI) | 63 | 15 (24%) | 48 (76%) | 0 – 135.6 |
| MSK Gastroenterology 2020 (MSI-Instable) | 27 | 15 (56%) | 12 (44%) | 2.2 – 70.9 |

Since fewer than half of the TCGA MSI-H group experienced an event, the Kaplan-Meier median OS for that group is likely **not reached**. The MSK MSI-Instable group has a considerably higher event rate (56% vs 24%), consistent with its more advanced-disease composition — but this is a raw count observation, not a validated statistical difference, and it's confounded by the different disease-stage mix of the two cohorts.

To get an actual median OS estimate and a log-rank p-value for each cohort's MSI-H vs. MSS split, use these direct links:

- [TCGA PanCancer Atlas: MSI-H vs Non-MSI-H survival comparison](https://www.cbioportal.org/comparison/survival?comparisonId=6ab7f740c2115c492d88514a) (MSI-H n=63, Non-MSI-H n=531)
- [MSK Gastroenterology 2020: MSI-Instable vs MSS/Other survival comparison](https://www.cbioportal.org/comparison/survival?comparisonId=6ab5d10fc2115c492d884e23) (MSI-Instable n=27, MSS/Other n=444)

These links open the Kaplan-Meier curves with log-rank test results directly. Because the two studies differ so much in stage composition (largely resectable TCGA vs. metastatic-enriched MSK), I'd caution against directly equating "prognosis" across them without adjusting for stage — the MSK cohort's shorter follow-up and higher event rate most likely reflect its more advanced-disease population rather than a biological difference in how MSI behaves.