Q (haiku): What is the most common cancer type in the MSK-CHORD Study based on sample count?

▶ list_studies
{
 "search": "MSK-CHORD"
}

◀ result
{"result":[{"cancer_study_identifier":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","type_of_cancer_id":"mixed","sample_count":25040,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=msk_chord_2024"}]}

▶ get_study_guide
{
 "study_id": "msk_chord_2024"
}

◀ 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": "
SELECT 
  type_of_cancer,
  COUNT(DISTINCT sample_id) as sample_count
FROM genetic_alteration_derived gad
INNER JOIN sample_derived sd ON gad.sample_id = sd.sample_id
INNER JOIN cancer_study cs ON sd.cancer_study_id = cs.cancer_study_id
WHERE cs.cancer_study_identifier = 'msk_chord_2024'
GROUP BY type_of_cancer
ORDER BY sample_count DESC
LIMIT 20
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 47\n Code: 47. DB::Exception: Identifier 'gad.sample_id' cannot be resolved from table with name gad. In scope SELECT type_of_cancer, COUNTDistinct(sample_id) AS sample_count FROM genetic_alteration_derived AS gad INNER JOIN sample_derived AS sd ON gad.sample_id = sd.sample_id INNER JOIN cancer_study AS cs ON sd.cancer_study_id = cs.cancer_study_id WHERE cs.cancer_study_identifier = 'msk_chord_2024' GROUP BY type_of_cancer ORDER BY sample_count DESC LIMIT 20. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_list_table_columns
{
 "table": "sample_derived"
}

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"sample_unique_id_base64","type":"String"},{"name":"sample_stable_id","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"patient_unique_id_base64","type":"String"},{"name":"patient_stable_id","type":"String"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"internal_id","type":"Int32"},{"name":"patient_internal_id","type":"Int32"},{"name":"sample_type","type":"String"},{"name":"sequenced","type":"Int32"},{"name":"copy_number_segment_present","type":"Int32"}]}

◀ 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": "
SELECT 
  attribute_value as cancer_type,
  COUNT(DISTINCT sample_unique_id) as sample_count
FROM clinical_data_derived
WHERE cancer_study_identifier = 'msk_chord_2024'
  AND attribute_name = 'CANCER_TYPE'
GROUP BY attribute_value
ORDER BY sample_count DESC
",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_type":"Non-Small Cell Lung Cancer","sample_count":7809},{"cancer_type":"Colorectal Cancer","sample_count":5543},{"cancer_type":"Breast Cancer","sample_count":5368},{"cancer_type":"Prostate Cancer","sample_count":3211},{"cancer_type":"Pancreatic Cancer","sample_count":3109}]}

═ answer (success)
## Answer

In the **MSK-CHORD Study** (25,040 samples), the most common cancer type by sample count is:

**Non-Small Cell Lung Cancer** with **7,809 samples (31.2%)** of the cohort.

### Cancer Type Breakdown:
| Cancer Type | Sample Count | % of Total |
|---|---|---|
| Non-Small Cell Lung Cancer | 7,809 | 31.2% |
| Colorectal Cancer | 5,543 | 22.1% |
| Breast Cancer | 5,368 | 21.4% |
| Prostate Cancer | 3,211 | 12.8% |
| Pancreatic Cancer | 3,109 | 12.4% |

The MSK-CHORD study includes exactly these five cancer types — no other cancer types are represented in the cohort.