Q (sonnet-5.5): What cancer types in MSK-IMPACT have mutations in TP53?

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

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

◀ 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)

◀ result
{"result":[{"cancer_study_identifier":"msk_impact_50k_2026","name":"MSK-IMPACT 50K Clinical Sequencing Cohort (MSK, Cancer Cell 2026)","type_of_cancer_id":"mixed","sample_count":54331,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_impact_50k_2026"},{"cancer_study_identifier":"msk_ch_2023","name":"Cancer Therapy and Clonal Hematopoiesis (MSK, Blood Adv 2023)","type_of_cancer_id":"mixed","sample_count":42714,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_ch_2023"},{"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"},{"cancer_study_identifier":"msk_ch_2020","name":"Cancer Therapy and Clonal Hematopoiesis (MSK, Nat Genet 2020)","type_of_cancer_id":"mixed","sample_count":24146,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_ch_2020"},{"cancer_study_identifier":"msk_impact_2017","name":"MSK-IMPACT Clinical Sequencing Cohort (MSK, Nat Med 2017)","type_of_cancer_id":"mixed","sample_count":10945,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_impact_2017"},{"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":"breast_msk_2026","name":"CCNE1 Amplifications in Breast Cancer (MSK, 2026)","type_of_cancer_id":"breast","sample_count":6318,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=breast_msk_2026"},{"cancer_study_identifier":"pancan_mimsi_msk_2024","name":"Mixed Tumors - MiMSI Cohort (MSK, Nat Commun 2024)","type_of_cancer_id":"mixed","sample_count":5033,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancan_mimsi_msk_2024"},{"cancer_study_identifier":"breast_msk_2025","name":"Breast Cancer (MSK, Nat Genet 2025)","type_of_cancer_id":"brca","sample_count":3879,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=breast_msk_2025"},{"cancer_study_identifier":"mycf_msk_2026","name":"MYC Family Amplifications in MSK-IMPACT Solid Tumors (MSK, 2026)","type_of_cancer_id":"mixed","sample_count":3442,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=mycf_msk_2026"},{"cancer_study_identifier":"heme_msk_impact_2022","name":"MSK-IMPACT Heme Tumors (MSK, 2022)","type_of_cancer_id":"mixed","sample_count":2383,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=heme_msk_impact_2022"},{"cancer_study_identifier":"pdac_msk_2024","name":"Pancreatic Adenocarcinoma (MSK, Nat Med 2024)","type_of_cancer_id":"pancreas","sample_count":2336,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pdac_msk_2024"},{"cancer_study_identifier":"prostate_msk_2024","name":"Prostate Cancer (MSK, Clin Cancer Res 2024)","type_of_cancer_id" … (1829 more chars)

═ answer (success)
"MSK-IMPACT" matches more than one study in cBioPortal. Which one do you want?

- **MSK-IMPACT 50K Clinical Sequencing Cohort** (`msk_impact_50k_2026`): 54,331 samples. This is the largest and most current, and it covers many cancer types.
- **MSK-IMPACT Clinical Sequencing Cohort, Nat Med 2017** (`msk_impact_2017`): 10,945 samples across mixed cancer types.
- **MSK-IMPACT Heme Tumors** (`heme_msk_impact_2022`): 2,383 samples of hematologic malignancies.
- **MYC Family Amplifications in MSK-IMPACT Solid Tumors** (`mycf_msk_2026`): 3,442 samples. This is a specialized sub-cohort.
- **MSK-IMPACT and MSK-ACCESS Mixed Cohort** (`msk_access_2021`): 1,446 samples.

The MSK-IMPACT studies overlap in patients, so I'd rather not combine them. If you want a single answer, the 50K cohort is the usual choice. I can rank cancer types by TP53 mutation frequency there, with altered and profiled sample counts, and give you a link to the results.

Should I use `msk_impact_50k_2026`, or a different one (or all of them, reported separately)?