Q (sonnet): What is the correlation between tumor mutational burden and microsatellite instability status in colorectal cancer patients from the MSK-CHORD Study?

▶ 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)

▶ 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)

▶ get_study_guide
{
 "study_id": "msk_chord_2024"
}

▶ search_oncotree
{
 "search_term": "colorectal"
}

◀ result
# MSK-CHORD (MSK, Nature 2024)

**Study ID:** `msk_chord_2024`

## Overview
Targeted sequencing via MSK-IMPACT panels. Clinical annotations include some derived from natural language processing (denoted NLP).

**Exactly five cancer types** (`CANCER_TYPE`, patients): Non-Small Cell Lung Cancer 7,809, Colorectal Cancer 5,543, Breast Cancer 5,368, Prostate Cancer 3,211, Pancreatic Cancer 3,109. There is **no melanoma** or any other cancer type; say so up front if asked, instead of substituting another type.

**No therapy-response variable.** There is no RECIST, objective response, or best-response attribute or event. For treatment-outcome questions (e.g. immunotherapy response), say this first; the only proxies are `OS_MONTHS`/`OS_STATUS`, or NLP radiology progression events (`Diagnosis` events with `SUBTYPE = 'Progression'`, key `PROGRESSION` = Y/N/Indeterminate), in patients with `Treatment` events of the relevant `SUBTYPE` (e.g. `Immuno`: 3,341 patients). Hand off the comparison to cBioPortal group comparison / survival.

**Nearly one sample per patient: 24,950 patients / 25,040 samples.** Only 90 patients have more than one sample, and all 90 have samples from two different cancer types (second primaries); only 26 have both a `Primary` and a `Metastasis` sample. There is no meaningful same-patient (paired) primary-vs-metastasis cohort. For "same patient" / paired questions, say this up front, then offer the **unpaired** comparison of all `Primary` vs `Metastasis` samples (`SAMPLE_TYPE`), labelled as unpaired.
```sql
SELECT countIf(n > 1) AS multi_sample_patients,         -- 90
       countIf(has_p AND has_m) AS primary_and_met      -- 26
FROM (SELECT patient_unique_id, count() AS n,
             has(groupArray(attribute_value), 'Primary') AS has_p,
             has(groupArray(attribute_value), 'Metastasis') AS has_m
      FROM clinical_data_derived
      WHERE cancer_study_identifier = 'msk_chord_2024' AND attribute_name = 'SAMPLE_TYPE'
      GROUP BY patient_unique_id);
```

## Gene Panels
This study uses multiple MSK-IMPACT panel versions:
- **IMPACT341**: Earlier version, 341 genes
- **IMPACT410**: 410 genes
- **IMPACT468**: 468 genes  
- **IMPACT505**: Latest version, 505 genes

**Important:** Different samples may have different gene coverage. Always use gene-specific denominators when calculating mutation frequencies.

## Clinical Attributes - Semantic Guide

### Cancer Classification
| Attribute | Description | Values |
|-----------|-------------|--------|
| `CANCER_TYPE` | Broad cancer category | e.g., "Non-Small Cell Lung Cancer", "Breast Cancer" |
| `CANCER_TYPE_DETAILED` | Specific subtype | e.g., "Lung Adenocarcinoma", "Invasive Ductal Carcinoma" |
| `ONCOTREE_CODE` | OncoTree classification code | Standardized cancer type codes |

### Sample Information
| Attribute | Description | Values |
|-----------|-------------|--------|
| `SAMPLE_TYPE` | Sample origin | Primary, Metastasis, Local Recurrence, Unknown |
| `SAMPLE_CLASS` | Sample … (5439 more chars)

◀ result
{"result":[{"code":"COADREAD","name":"Colorectal Adenocarcinoma","score":60,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD"},{"code":"CAIS","name":"Colon Adenocarcinoma In Situ","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > CAIS"},{"code":"CMC","name":"Medullary Carcinoma of the Colon","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > CMC"},{"code":"COAD","name":"Colon Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > COAD"},{"code":"MACR","name":"Mucinous Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > MACR"},{"code":"READ","name":"Rectal Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > READ"},{"code":"SRCCR","name":"Signet Ring Cell Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > SRCCR"}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH cd AS (
    SELECT patient_unique_id,
        anyIf(attribute_value, attribute_name = 'CANCER_TYPE') AS cancer_type
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name = 'CANCER_TYPE'
    GROUP BY patient_unique_id
),
tmb_msi AS (
    SELECT sample_unique_id, patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'TMB_NONSYNONYMOUS') AS tmb,
        maxIf(attribute_value, attribute_name = 'MSI_TYPE') AS msi_type
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name IN ('TMB_NONSYNONYMOUS', 'MSI_TYPE')
    GROUP BY sample_unique_id, patient_unique_id
)
SELECT
    tm.msi_type AS msi_type,
    count() AS n_samples,
    round(avg(tm.tmb), 2) AS mean_tmb,
    round(quantile(0.5)(tm.tmb), 2) AS median_tmb,
    round(min(tm.tmb), 2) AS min_tmb,
    round(max(tm.tmb), 2) AS max_tmb
FROM tmb_msi tm
JOIN cd ON cd.patient_unique_id = tm.patient_unique_id
WHERE cd.cancer_type = 'Colorectal Cancer'
  AND tm.tmb IS NOT NULL
  AND tm.msi_type != ''
GROUP BY msi_type
ORDER BY mean_tmb DESC
"
}

◀ result
{"rows":[{"msi_type":"Instable","n_samples":583,"mean_tmb":64.57,"median_tmb":56.77,"min_tmb":0,"max_tmb":490.57},{"msi_type":"Indeterminate","n_samples":190,"mean_tmb":18.4,"median_tmb":6.56,"min_tmb":1.64,"max_tmb":343.53},{"msi_type":"Stable","n_samples":4693,"mean_tmb":7.58,"median_tmb":5.87,"min_tmb":0,"max_tmb":570.96},{"msi_type":"Do not report","n_samples":49,"mean_tmb":7.06,"median_tmb":3.33,"min_tmb":0,"max_tmb":74.31}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH cd AS (
    SELECT patient_unique_id,
        anyIf(attribute_value, attribute_name = 'CANCER_TYPE') AS cancer_type
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name = 'CANCER_TYPE'
    GROUP BY patient_unique_id
),
tmb_msi AS (
    SELECT sample_unique_id, patient_unique_id,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'TMB_NONSYNONYMOUS') AS tmb,
        maxIf(toFloat64OrNull(attribute_value), attribute_name = 'MSI_SCORE') AS msi_score
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name IN ('TMB_NONSYNONYMOUS', 'MSI_SCORE')
    GROUP BY sample_unique_id, patient_unique_id
)
SELECT
    count() AS n_samples,
    round(corr(tmb, msi_score), 4) AS pearson_r,
    round(corr(log10(tmb+1), log10(msi_score+1)), 4) AS pearson_r_log_transformed
FROM tmb_msi tm
JOIN cd ON cd.patient_unique_id = tm.patient_unique_id
WHERE cd.cancer_type = 'Colorectal Cancer'
  AND tm.tmb IS NOT NULL AND tm.msi_score IS NOT NULL
"
}

◀ result
{"rows":[{"n_samples":5515,"pearson_r":0.5829,"pearson_r_log_transformed":null}]}

▶ resolve_and_route
{
 "studyIds": [
  "msk_chord_2024"
 ]
}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024","metadata":{"clinicalAttributeIds":["ADRENAL_GLANDS","BONE","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_GROUP","CLINICAL_SUMMARY","CNS_BRAIN","CURRENT_AGE_DEID","DIAGNOSIS_DESCRIPTION","ETHNICITY","FRACTION_GENOME_ALTERED","GENDER","GENE_PANEL","GLEASON_FIRST_REPORTED","GLEASON_HIGHEST_REPORTED","GLEASON_SAMPLE_LEVEL","HER2","HISTORY_OF_PDL1","HR","ICD_O_HISTOLOGY_DESCRIPTION","INTRA_ABDOMINAL","LIVER","LUNG","LYMPH_NODES","METASTATIC_SITE","MSI_COMMENT","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","NUM_ICDO_DX","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER","PATHOLOGICAL_GROUP","PDL1_POSITIVE","PLEURA","PRIMARY_SITE","PRIOR_MED_TO_MSK","RACE","REPRODUCTIVE_ORGANS","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SMOKING_PREDICTIONS_3_CLASSES","SOMATIC_STATUS","STAGE_HIGHEST_RECORDED","TMB_NONSYNONYMOUS","TUMOR_PURITY"],"molecularProfileIds":["msk_chord_2024_cna","msk_chord_2024_mutations","msk_chord_2024_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}}]}}

▶ get_studyviewfilter_options
{
 "studyId": "msk_chord_2024",
 "attributeIds": [
  "CANCER_TYPE",
  "MSI_TYPE"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"msk_chord_2024","attributes":[{"attributeId":"CANCER_TYPE","displayName":"Cancer Type","description":"The main cancer type as defined by the Oncotree cancer classification system for precision oncology. Source: MPath","datatype":"STRING","values":["Non-Small Cell Lung Cancer","Colorectal Cancer","Prostate Cancer","Pancreatic Cancer","Breast Cancer"]},{"attributeId":"MSI_TYPE","displayName":"MSI Type","description":"Directly related to MSI Score; Possible values include Stable, Instable, Indeterminate, or Do not report. Source: MPath","datatype":"STRING","values":["Instable","NA","Indeterminate","Do not report","Stable"]}]}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "msk_chord_2024"
 ],
 "tab": "clinical",
 "studyViewFilter": {
  "clinicalDataFilters": [
   {
    "attributeId": "CANCER_TYPE",
    "values": [
     {
      "value": "Colorectal Cancer"
     }
    ]
   }
  ]
 },
 "groups": [
  {
   "name": "MSI Instable",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MSI_TYPE",
      "values": [
       {
        "value": "Instable"
       }
      ]
     },
     {
      "attributeId": "CANCER_TYPE",
      "values": [
       {
        "value": "Colorectal Cancer"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "MSI Stable",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MSI_TYPE",
      "values": [
       {
        "value": "Stable"
       }
      ]
     },
     {
      "attributeId": "CANCER_TYPE",
      "values": [
       {
        "value": "Colorectal Cancer"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "Indeterminate",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "MSI_TYPE",
      "values": [
       {
        "value": "Indeterminate"
       }
      ]
     },
     {
      "attributeId": "CANCER_TYPE",
      "values": [
       {
        "value": "Colorectal Cancer"
       }
      ]
     }
    ]
   }
  }
 ]
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5cefee497aad53593c27e","url":"https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5cefee497aad53593c27e","data":{"description":"Group comparison (3 custom groups)","studies":["msk_chord_2024"],"totalGroups":3,"groups":[{"name":"MSI Instable","sampleCount":586},{"name":"MSI Stable","sampleCount":4682},{"name":"Indeterminate","sampleCount":188}],"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%5D%7D","groupUrls":[{"groupName":"MSI Instable","url":"https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22studyIds%22%3A%5B%22msk_chord_2024%22%5D%2C%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22MSI_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Instable%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%5D%7D"},{"groupName":"MSI Stable","url":"https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22studyIds%22%3A%5B%22msk_chord_2024%22%5D%2C%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22MSI_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Stable%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%5D%7D"},{"groupName":"Indeterminate","url":"https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22studyIds%22%3A%5B%22msk_chord_2024%22%5D%2C%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22MSI_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Indeterminate%22%7D%5D%7D%2C%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%5D%7D"}],"pageDescription":"Clinical attribute distributions compared across MSI Instable (586) vs MSI Stable (4682) vs Indeterminate (188)."}}

═ answer (success)
## TMB vs. MSI status in MSK-CHORD colorectal cancer patients

**Cohort:** Colorectal Cancer patients in MSK-CHORD (`msk_chord_2024`), samples with both `TMB_NONSYNONYMOUS` and MSI values recorded.

### Correlation (continuous MSI_SCORE vs. TMB)
Across 5,515 colorectal samples with both values, the Pearson correlation coefficient between `MSI_SCORE` and `TMB_NONSYNONYMOUS` is **r = 0.58** — a moderate-to-strong positive linear association: higher MSI scores track with higher mutational burden, consistent with the expectation that MSI-high tumors are hypermutated.

### TMB by MSI classification (`MSI_TYPE`)

| MSI Type | N samples | Mean TMB (mut/Mb) | Median TMB | Min | Max |
|---|---|---|---|---|---|
| Instable (MSI-H) | 583 | 64.57 | 56.77 | 0 | 490.57 |
| Indeterminate | 190 | 18.40 | 6.56 | 1.64 | 343.53 |
| Stable (MSS) | 4,693 | 7.58 | 5.87 | 0 | 570.96 |
| Do not report | 49 | 7.06 | 3.33 | 0 | 74.31 |

MSI-Instable samples show roughly **8–10× higher mean/median TMB** than MSI-Stable samples, the expected biological pattern (MSI-H colorectal tumors carry a strong hypermutator/mutator phenotype from defective mismatch repair).

**On statistical significance:** This is a 3+ group comparison of a continuous variable (TMB) against a categorical variable (MSI_TYPE). ClickHouse cannot run a Kruskal-Wallis test — I have not computed a p-value. The Pearson *r* above is a legitimate direct correlation coefficient, but for a rigorous group difference (e.g. MSI-H vs. MSS), a **Kruskal-Wallis test** (or Wilcoxon for the 2-group case) should be run on the raw values in R (`kruskal.test`) or via cBioPortal's own comparison tool.

[Compare TMB and clinical attributes: MSI-Instable vs. MSI-Stable vs. Indeterminate colorectal patients](https://www.cbioportal.org/comparison/clinical?comparisonId=6ab5cefee497aad53593c27e) — open the "TMB (nonsynonymous)" chart on this page to see the boxplot/violin distribution with the built-in statistical test.

You can also browse the [full MSK-CHORD colorectal cancer cohort](https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22CANCER_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Colorectal%20Cancer%22%7D%5D%7D%5D%7D) to explore TMB and MSI distributions directly.