Q (sonnet): How does the mutation landscape differ between primary and metastatic samples from the same patients in the MSK-CHORD Study?

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-guide"
}

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-guide"
}

◀ result
# Mutation Frequency Analysis Guide

## IMPORTANT: Reporting Mutation Frequencies
- **ALWAYS report frequencies as percentages**, not raw counts: `frequency = (altered_samples / total_profiled_samples) × 100`
- For quick frequency lookups, **prefer the TCGA Pan-Cancer Atlas study first**, then offer to expand to other studies
- When reporting across multiple studies, show **ranges** (e.g., "TP53 is mutated in 30–60% of samples") rather than a single average
- **NEVER** sum mutation events across studies to compute an aggregate frequency — this can exceed 100% due to double-counting
- Warn users that samples may overlap across cohorts (e.g., MSK studies may share patients)
- **Choose and state the counting unit**: use patient-level frequencies for prevalence/rate questions unless the user explicitly asks for samples; use sample-level frequencies when the user asks about samples.
- **For "across cancer types" questions**, jump to the [Cross-Cancer-Type Mutation Frequency](#cross-cancer-type-mutation-frequency) section below — there is one correct recipe and several common wrong ones.

## Counting Unit: Samples vs Patients

Before answering any mutation count or frequency question, decide whether the unit is samples or patients and state that choice in the answer.

| User wording | Counting unit |
|--------------|---------------|
| "prevalence", "rate", "fraction of patients", "patients with", "how common is" | Patient-level: `COUNT(DISTINCT patient_unique_id)` |
| "samples", "specimens", "biopsies", sample-level cohort composition | Sample-level: `COUNT(DISTINCT sample_unique_id)` |
| Ambiguous | Ask, or default to patient-level for prevalence/rate language and say so |

### Cross-study sample-count caveat

When an answer touches more than one study and reports a sample count, prepend a one-line caveat:

> Sample IDs are unique within cBioPortal study prefixes, not guaranteed biological-sample identifiers across studies; overlapping cohorts can count the same patient/sample more than once.

Prefer one of these safer approaches:

- Use a shipped `cancer_study_query_preferences` cohort such as `pan_cancer_tcga` or `all_studies_non_redundant`.
- Restrict to one named study.
- Aggregate by `patient_unique_id` when the biological question is patient prevalence.

## STOP rule: a frequency above 100% means your query is wrong

If your query returns a frequency over 100%, **do not try to debug or explain the data inconsistency to the user**. The cause is always one of these query bugs:

- Summing mutation events instead of `COUNT(DISTINCT sample_unique_id)` for the numerator
- Using a study-wide sample count as the denominator instead of the gene-specific profiled count
- Cross-study aggregation where the same biological sample appears under multiple `sample_unique_id` values (e.g., MSK-IMPACT and MSK-CHORD share patients)
- **Joining the profiled CTE through `gene_panel` / `gene_panel_list` without a WES branch.** `gene_panel_id = 'WES'` is *not* a row in … (36467 more chars)

▶ get_study_guide
{
 "study_id": "msk_chord_2024"
}

◀ result
# Sample and Study Filtering Guide

## Overview
Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria.

## Study-Level Filtering

### 1. Always Filter by Study
Every query should specify a study to ensure consistent results:

```sql
-- Always include study filtering
SELECT *
FROM your_table
WHERE
    cancer_study_identifier = 'your_study_id'
    -- Additional filters...
```

### 2. Find Available Studies

```sql
-- Discover available studies
SELECT
    cancer_study_identifier,
    name,
    description,
    type_of_cancer_id
FROM cancer_study
ORDER BY cancer_study_identifier;
```

### 3. Study Information

```sql
-- Get detailed study information
SELECT
    cs.cancer_study_identifier,
    cs.name as study_name,
    cs.description,
    cs.sample_count,
    COUNT(DISTINCT p.internal_id) as patient_count
FROM cancer_study cs
LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id
WHERE
    cs.cancer_study_identifier = 'your_study_id'
GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count;
```

### 4. Find Studies by Available Data Types

Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter.

**Canonical query — studies with ALL requested data types for a cancer type:**

```sql
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'          -- ← OncoTree code from search_oncotree
  AND mutation_sample_count > 0           -- ← one condition per required data type
  AND cna_sample_count > 0
ORDER BY sample_count DESC;
```

**Data type → column** (a count of 0 means the study doesn't have that data):

| Data type in the user's question | Column (portal "Data type" filter label) |
|---|---|
| samples in the study | `sample_count` |
| mutation / mutations | `mutation_sample_count` ("Mutations") |
| copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") |
| structural variant / fusion | `structural_variant_sample_count` |
| RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" |
| specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `<study>_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq |
| specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `<study>_mrna` list |
| microRNA | `mirna_sample_count` ("miRNA") |
| protein / RPPA | `rppa_sample_count` ("RPPA") |
| mas … (7866 more chars)

◀ 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)

▶ clickhouse_run_select_query
{
 "query": "
WITH primary_samples AS (
    SELECT DISTINCT sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name = 'SAMPLE_TYPE'
      AND attribute_value = 'Primary'
),
met_samples AS (
    SELECT DISTINCT sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name = 'SAMPLE_TYPE'
      AND attribute_value = 'Metastasis'
),
mut_primary AS (
    SELECT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) AS altered
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      AND off_panel = 0
      AND sample_unique_id IN (SELECT sample_unique_id FROM primary_samples)
    GROUP BY hugo_gene_symbol
),
mut_met AS (
    SELECT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) AS altered
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      AND off_panel = 0
      AND sample_unique_id IN (SELECT sample_unique_id FROM met_samples)
    GROUP BY hugo_gene_symbol
),
profiled_primary AS (
    SELECT stgp.hugo_gene_symbol, COUNT(DISTINCT stgp.sample_unique_id) AS profiled
    FROM (
        SELECT g.hugo_gene_symbol, stgp.sample_unique_id
        FROM sample_to_gene_panel_derived stgp
        JOIN gene_panel gp ON stgp.gene_panel_id = gp.stable_id
        JOIN gene_panel_list gpl ON gp.internal_id = gpl.internal_id
        JOIN gene g ON gpl.gene_id = g.entrez_gene_id
        WHERE stgp.alteration_type = 'MUTATION_EXTENDED'
          AND stgp.cancer_study_identifier = 'msk_chord_2024'
          AND stgp.sample_unique_id IN (SELECT sample_unique_id FROM primary_samples)
    ) stgp
    GROUP BY stgp.hugo_gene_symbol
),
profiled_met AS (
    SELECT stgp.hugo_gene_symbol, COUNT(DISTINCT stgp.sample_unique_id) AS profiled
    FROM (
        SELECT g.hugo_gene_symbol, stgp.sample_unique_id
        FROM sample_to_gene_panel_derived stgp
        JOIN gene_panel gp ON stgp.gene_panel_id = gp.stable_id
        JOIN gene_panel_list gpl ON gp.internal_id = gpl.internal_id
        JOIN gene g ON gpl.gene_id = g.entrez_gene_id
        WHERE stgp.alteration_type = 'MUTATION_EXTENDED'
          AND stgp.cancer_study_identifier = 'msk_chord_2024'
          AND stgp.sample_unique_id IN (SELECT sample_unique_id FROM met_samples)
    ) stgp
    GROUP BY stgp.hugo_gene_symbol
)
SELECT
    p.hugo_gene_symbol,
    p.altered AS primary_altered,
    pp.profiled AS primary_profiled,
    ROUND(p.altered * 100.0 / pp.profiled, 1) AS primary_pct,
    m.altered AS met_altered,
    mp.profiled AS met_profiled,
    ROUND(m.altered * 100.0 / mp.profiled, 1) AS met_pct,
    ROUND((m.altered * 100.0 / mp.profiled) - (p.altered * 100.0 / pp.profiled), 1) AS pct_point_diff
FROM mut_primary p
JOIN profiled_primary pp ON p.hugo_gene_symbol = pp.hugo_gene_symbol
JOIN mut_met m ON p.hugo_gene_symbol = m.hugo_gene_symbol
JOIN profiled_met mp ON m.hugo_gene_symbol = mp.hugo_gene_symbol
WHERE pp.profiled >= 500 AND mp.profiled >= 500
ORDER BY (p.altered + m.altered) DESC
LIMIT 25
",
 "max_rows": 30
}

◀ result
{"rows":[{"p.hugo_gene_symbol":"TP53","primary_altered":8104,"primary_profiled":15928,"primary_pct":50.9,"met_altered":4890,"met_profiled":8878,"met_pct":55.1,"pct_point_diff":4.2},{"p.hugo_gene_symbol":"KRAS","primary_altered":4751,"primary_profiled":15928,"primary_pct":29.8,"met_altered":2326,"met_profiled":8878,"met_pct":26.2,"pct_point_diff":-3.6},{"p.hugo_gene_symbol":"APC","primary_altered":3345,"primary_profiled":15928,"primary_pct":21,"met_altered":1400,"met_profiled":8878,"met_pct":15.8,"pct_point_diff":-5.2},{"p.hugo_gene_symbol":"PIK3CA","primary_altered":2351,"primary_profiled":15928,"primary_pct":14.8,"met_altered":1327,"met_profiled":8878,"met_pct":14.9,"pct_point_diff":0.2},{"p.hugo_gene_symbol":"EGFR","primary_altered":1338,"primary_profiled":15928,"primary_pct":8.4,"met_altered":779,"met_profiled":8878,"met_pct":8.8,"pct_point_diff":0.4},{"p.hugo_gene_symbol":"ARID1A","primary_altered":1199,"primary_profiled":15928,"primary_pct":7.5,"met_altered":634,"met_profiled":8878,"met_pct":7.1,"pct_point_diff":-0.4},{"p.hugo_gene_symbol":"SMAD4","primary_altered":1142,"primary_profiled":15928,"primary_pct":7.2,"met_altered":643,"met_profiled":8878,"met_pct":7.2,"pct_point_diff":0.1},{"p.hugo_gene_symbol":"KMT2D","primary_altered":1203,"primary_profiled":15928,"primary_pct":7.6,"met_altered":569,"met_profiled":8878,"met_pct":6.4,"pct_point_diff":-1.1},{"p.hugo_gene_symbol":"KMT2C","primary_altered":1059,"primary_profiled":15928,"primary_pct":6.6,"met_altered":552,"met_profiled":8878,"met_pct":6.2,"pct_point_diff":-0.4},{"p.hugo_gene_symbol":"ATM","primary_altered":890,"primary_profiled":15928,"primary_pct":5.6,"met_altered":483,"met_profiled":8878,"met_pct":5.4,"pct_point_diff":-0.1},{"p.hugo_gene_symbol":"CDKN2A","primary_altered":846,"primary_profiled":15928,"primary_pct":5.3,"met_altered":483,"met_profiled":8878,"met_pct":5.4,"pct_point_diff":0.1},{"p.hugo_gene_symbol":"FAT1","primary_altered":818,"primary_profiled":15928,"primary_pct":5.1,"met_altered":421,"met_profiled":8878,"met_pct":4.7,"pct_point_diff":-0.4},{"p.hugo_gene_symbol":"ZFHX3","primary_altered":828,"primary_profiled":15394,"primary_pct":5.4,"met_altered":382,"met_profiled":8393,"met_pct":4.6,"pct_point_diff":-0.8},{"p.hugo_gene_symbol":"NF1","primary_altered":778,"primary_profiled":15928,"primary_pct":4.9,"met_altered":424,"met_profiled":8878,"met_pct":4.8,"pct_point_diff":-0.1},{"p.hugo_gene_symbol":"PTEN","primary_altered":782,"primary_profiled":15928,"primary_pct":4.9,"met_altered":407,"met_profiled":8878,"met_pct":4.6,"pct_point_diff":-0.3},{"p.hugo_gene_symbol":"STK11","primary_altered":755,"primary_profiled":15928,"primary_pct":4.7,"met_altered":428,"met_profiled":8878,"met_pct":4.8,"pct_point_diff":0.1},{"p.hugo_gene_symbol":"PTPRT","primary_altered":780,"primary_profiled":15928,"primary_pct":4.9,"met_altered":401,"met_profiled":8878,"met_pct":4.5,"pct_point_diff":-0.4},{"p.hugo_gene_symbol":"KEAP1","primary_altered":702,"primary_profiled":15928,"primary_pct":4.4," … (1255 more chars)

▶ clickhouse_run_select_query
{
 "query": "
WITH s AS (
    SELECT sample_unique_id, attribute_value AS sample_type
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name = 'SAMPLE_TYPE'
      AND attribute_value IN ('Primary','Metastasis')
),
tmb AS (
    SELECT sample_unique_id, toFloat64OrNull(attribute_value) AS tmb
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'msk_chord_2024'
      AND attribute_name = 'TMB_NONSYNONYMOUS'
)
SELECT s.sample_type,
       count() AS n_samples,
       round(avg(tmb.tmb),2) AS mean_tmb,
       round(quantile(0.5)(tmb.tmb),2) AS median_tmb
FROM s
JOIN tmb USING (sample_unique_id)
WHERE tmb.tmb IS NOT NULL
GROUP BY s.sample_type
"
}

◀ result
{"rows":[{"sample_type":"Primary","n_samples":15928,"mean_tmb":7.77,"median_tmb":4.1},{"sample_type":"Metastasis","n_samples":8878,"mean_tmb":6.35,"median_tmb":4.32}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
    cd_type.attribute_value AS sample_type,
    cd_cancer.attribute_value AS cancer_type,
    COUNT(DISTINCT cd_type.sample_unique_id) AS n
FROM clinical_data_derived cd_type
JOIN clinical_data_derived cd_cancer
  ON cd_type.sample_unique_id = cd_cancer.sample_unique_id
WHERE cd_type.cancer_study_identifier = 'msk_chord_2024'
  AND cd_type.attribute_name = 'SAMPLE_TYPE'
  AND cd_type.attribute_value IN ('Primary','Metastasis')
  AND cd_cancer.cancer_study_identifier = 'msk_chord_2024'
  AND cd_cancer.attribute_name = 'CANCER_TYPE'
GROUP BY sample_type, cancer_type
ORDER BY cancer_type, sample_type
"
}

◀ result
{"rows":[{"sample_type":"Metastasis","cancer_type":"Breast Cancer","n":2464},{"sample_type":"Primary","cancer_type":"Breast Cancer","n":2859},{"sample_type":"Metastasis","cancer_type":"Colorectal Cancer","n":1457},{"sample_type":"Primary","cancer_type":"Colorectal Cancer","n":4054},{"sample_type":"Metastasis","cancer_type":"Non-Small Cell Lung Cancer","n":2763},{"sample_type":"Primary","cancer_type":"Non-Small Cell Lung Cancer","n":4908},{"sample_type":"Metastasis","cancer_type":"Pancreatic Cancer","n":1124},{"sample_type":"Primary","cancer_type":"Pancreatic Cancer","n":1977},{"sample_type":"Metastasis","cancer_type":"Prostate Cancer","n":1070},{"sample_type":"Primary","cancer_type":"Prostate Cancer","n":2130}]}

▶ 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": [
  "SAMPLE_TYPE"
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"msk_chord_2024","attributes":[{"attributeId":"SAMPLE_TYPE","displayName":"Sample Type","description":"The type assigned to a sample (i.e., normal, primary, met, recurrence). Source: MPath","datatype":"STRING","values":["Local Recurrence","Unknown","Primary","Metastasis"]}]}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "msk_chord_2024"
 ],
 "groups": [
  {
   "name": "Primary",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SAMPLE_TYPE",
      "values": [
       {
        "value": "Primary"
       }
      ]
     }
    ]
   }
  },
  {
   "name": "Metastasis",
   "studyViewFilter": {
    "clinicalDataFilters": [
     {
      "attributeId": "SAMPLE_TYPE",
      "values": [
       {
        "value": "Metastasis"
       }
      ]
     }
    ]
   }
  }
 ],
 "tab": "alterations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/alterations?comparisonId=6ab49e90c2115c492d884a5c","url":"https://www.cbioportal.org/comparison/alterations?comparisonId=6ab49e90c2115c492d884a5c","data":{"description":"Group comparison (2 custom groups)","studies":["msk_chord_2024"],"totalGroups":2,"groups":[{"name":"Primary","sampleCount":15928},{"name":"Metastasis","sampleCount":8878}],"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024","groupUrls":[{"groupName":"Primary","url":"https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SAMPLE_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Primary%22%7D%5D%7D%5D%7D"},{"groupName":"Metastasis","url":"https://www.cbioportal.org/study?id=msk_chord_2024#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22SAMPLE_TYPE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Metastasis%22%7D%5D%7D%5D%7D"}],"pageDescription":"Comparison of genomic alteration rates across Primary (15928) vs Metastasis (8878)."}}

═ answer (success)
## Important caveat first: this is not a paired (same-patient) comparison

MSK-CHORD contains 24,950 patients but only 25,040 samples — the cohort is almost entirely one sample per patient. Only **26 patients** in the entire study have *both* a Primary and a Metastasis sample. There is no meaningful same-patient paired primary-vs-metastatic cohort to analyze here.

What I can report instead is an **unpaired** comparison: all Primary tumor samples (n=15,928, mostly from different patients) vs. all Metastasis samples (n=8,878, mostly from different patients), each profiled by MSK-IMPACT.

## Top mutated genes, Primary vs. Metastasis (unpaired, % of profiled samples)

| Gene | Primary altered/profiled (%) | Metastasis altered/profiled (%) | Difference (pp) |
|---|---|---|---|
| TP53 | 8,104/15,928 (50.9%) | 4,890/8,878 (55.1%) | +4.2 |
| KRAS | 4,751/15,928 (29.8%) | 2,326/8,878 (26.2%) | −3.6 |
| APC | 3,345/15,928 (21.0%) | 1,400/8,878 (15.8%) | −5.2 |
| PIK3CA | 2,351/15,928 (14.8%) | 1,327/8,878 (14.9%) | +0.2 |
| EGFR | 1,338/15,928 (8.4%) | 779/8,878 (8.8%) | +0.4 |
| FBXW7 | 876/15,928 (5.5%) | 238/8,878 (2.7%) | −2.8 |
| BRAF | 826/15,928 (5.2%) | 307/8,878 (3.5%) | −1.7 |
| KEAP1 | 702/15,928 (4.4%) | 475/8,878 (5.4%) | +0.9 |
| GATA3 | 634/15,928 (4.0%) | 458/8,878 (5.2%) | +1.2 |
| CDH1 | 554/15,928 (3.5%) | 406/8,878 (4.6%) | +1.1 |

Notable patterns (descriptive only — no significance testing done):
- **TP53** is somewhat more frequent in metastatic samples, consistent with the general expectation that metastatic lesions are enriched for later, higher-burden clones — but I have not run a statistical test to confirm this is significant.
- **APC and FBXW7** are noticeably less frequent in metastases — largely a composition effect: Colorectal Cancer (where APC is common) is disproportionately Primary (4,054 primary vs. 1,457 metastasis samples), while other cancer types are more evenly split.
- **KRAS and BRAF** are also modestly lower in the metastatic group, again potentially confounded by cancer-type mix.

**Cancer-type composition differs by sample type** (this drives much of the above): Colorectal Cancer is 74% Primary/26% Metastasis samples, whereas Prostate Cancer is 67%/33% and Breast Cancer is 54%/46%. Any Primary-vs-Metastasis mutation difference in this pooled, 5-cancer-type cohort partly reflects this mix rather than a pure primary→metastasis biological effect. A cleaner comparison would restrict to one cancer type at a time.

**Tumor mutational burden:** mean TMB is slightly higher in Primary samples (7.77 vs. 6.35 mutations/Mb) but median TMB is nearly identical (4.10 vs. 4.32 mutations/Mb) — the means are pulled by a right tail of hypermutated samples, so I'm not characterizing this as a real group difference without a proper test.

None of these differences have been tested for statistical significance — for that, use cBioPortal's Mutual Exclusivity / Comparison tools directly.

## Explore interactively

[Primary vs. Metastasis mutation/CNA comparison in MSK-CHORD](https://www.cbioportal.org/comparison/alterations?comparisonId=6ab49e90c2115c492d884a5c) — Comparison of genomic alteration rates across Primary (15,928 samples) vs Metastasis (8,878 samples), with built-in enrichment statistics (Fisher's exact / log-odds ratio) for each gene.

[MSK-CHORD Study Overview](https://www.cbioportal.org/study?id=msk_chord_2024)