Q (sonnet-5.5): I receive a report with the PIK3CA p.*1069Wext*3 mutation. Can you provide details of it

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

▶ read_guide
{
 "uri": "cbioportal://common-pitfalls"
}

◀ 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
# Common Query Pitfalls Guide

## Overview
This guide highlights frequent mistakes when analyzing cBioPortal data and provides solutions to avoid them.

## Critical Pitfalls

### 1. 🚨 CRITICAL MUTATION FREQUENCY ERRORS

#### ❌ WRONG: Using study-wide totals for gene frequencies
```sql
-- INCORRECT - This gives wrong frequencies!
SELECT
    hugo_gene_symbol,
    COUNT(DISTINCT sample_unique_id) as altered_samples,
    (SELECT COUNT(DISTINCT sample_unique_id)
     FROM genomic_event_derived
     WHERE cancer_study_identifier = 'your_study_id') as total_samples
FROM genomic_event_derived
WHERE variant_type = 'mutation' AND cancer_study_identifier = 'your_study_id'
GROUP BY hugo_gene_symbol;
```
**Problem**: Different genes have different profiling coverage - you can't use study-wide totals!

#### ❌ WRONG: Not using gene-specific profiling denominators
```sql
-- INCORRECT - Missing gene-specific denominators
SELECT
    hugo_gene_symbol,
    COUNT(DISTINCT sample_unique_id) as altered_samples
FROM genomic_event_derived
WHERE variant_type = 'mutation'
GROUP BY hugo_gene_symbol;
-- Missing: WHERE ARE THE DENOMINATORS FOR EACH GENE?
```

#### ❌ WRONG: Skipping individual gene profiling queries
**Problem**: Failing to run separate profiling queries for EACH gene in results.
**Each gene has different coverage**: TP53 might be profiled in 25,040 samples, MUC16 in 23,000, etc.

#### ✅ CORRECT: Complete gene-specific workflow
```sql
-- STEP 1: Get altered counts per gene
SELECT
    hugo_gene_symbol,
    entrez_gene_id,
    COUNT(DISTINCT CASE WHEN off_panel = 0 THEN sample_unique_id END) AS numberOfAlteredSamplesOnPanel,
    COUNT(*) AS totalMutationEvents
FROM genomic_event_derived
WHERE variant_type = 'mutation' AND mutation_status != 'UNCALLED'
GROUP BY entrez_gene_id, hugo_gene_symbol
ORDER BY numberOfAlteredSamplesOnPanel DESC;

-- STEP 2: FOR EACH GENE, run this profiling query:
SELECT COUNT(DISTINCT stgp.sample_unique_id) AS numberOfProfiledSamples
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 g.hugo_gene_symbol = 'TP53'  -- Replace with each gene from Step 1
  AND stgp.cancer_study_identifier = 'your_study_id';

-- STEP 3: Calculate frequency = numberOfAlteredSamplesOnPanel / numberOfProfiledSamples * 100
```

#### 🚨 WORKFLOW REQUIREMENTS VIOLATIONS:
- **Missing denominator columns**: Must show gene-specific profiled samples per row
- **Wrong table format**: Should be | Gene | # Mutations | # Samples | Profiled Samples | Sample % |
- **Not replacing gene symbols**: Must replace 'TP53' with actual gene for each query
- **Using study totals**: Never use genomic_event_derived for total sample counts

### 2. 🚨 OFF-PANEL MUTATION INCLUSION

#### ❌ Wrong: Including off-panel mutations
```sql
-- INCORRECT - Includes mutations outside gene panels
SELEC … (38012 more chars)

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"hugo_gene_symbol","type":"String","comment":"HUGO gene symbol (e.g., TP53, KRAS, BRAF). Use for gene-specific queries."},{"name":"entrez_gene_id","type":"Int32"},{"name":"gene_panel_stable_id","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"genetic_profile_stable_id","type":"LowCardinality(String)"},{"name":"variant_type","type":"LowCardinality(String)","comment":"Type of genomic event: \"mutation\" for SNVs/indels, \"cna\" for copy number alterations, \"structural_variant\" for SVs. Always filter by this."},{"name":"mutation_variant","type":"String","comment":"Protein change notation (e.g., p.V600E, p.R175H). Use for specific variant queries. \"NA\" for non-mutation events."},{"name":"mutation_type","type":"LowCardinality(String)"},{"name":"mutation_status","type":"LowCardinality(String)","comment":"For mutations: Somatic, Germline, UNKNOWN, or UNCALLED. Filter mutation_status != \"UNCALLED\" to exclude uncertain calls. Include all other statuses."},{"name":"driver_filter","type":"LowCardinality(String)"},{"name":"driver_filter_annotation","type":"String"},{"name":"driver_tiers_filter","type":"LowCardinality(String)"},{"name":"driver_tiers_filter_annotation","type":"String"},{"name":"cna_alteration","type":"Nullable(Int8)","comment":"Copy number alteration: only 2 = amplification (AMP) and -2 = deep deletion (HOMDEL) are stored. Shallow deletion (-1), diploid (0) and gain (1) are not in this table; query genetic_alteration_derived WHERE profile_type = 'gistic' (alteration_value is a String, e.g. '-1'). NULL for non-CNA events."},{"name":"cna_cytoband","type":"String"},{"name":"sv_event_info","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"off_panel","type":"Bool","comment":"Boolean: 1 = mutation outside gene panel coverage (off-panel), 0 = within panel (on-panel). Filter off_panel = 0 for reliable frequency calculations."}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier, mutation_variant, mutation_type, mutation_status, driver_filter, count() AS events, uniqExact(sample_unique_id) AS samples, uniqExact(patient_unique_id) AS patients
FROM genomic_event_derived
WHERE hugo_gene_symbol='PIK3CA' AND variant_type='mutation' AND mutation_status!='UNCALLED'
  AND (mutation_variant LIKE '%1069%' OR mutation_type='Nonstop_Mutation')
GROUP BY cancer_study_identifier, mutation_variant, mutation_type, mutation_status, driver_filter
ORDER BY samples DESC"
}

◀ result
{"rows":[{"cancer_study_identifier":"msk_impact_2017","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","mutation_status":"NA","events":3,"samples":3,"patients":3},{"cancer_study_identifier":"msk_impact_50k_2026","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":3,"samples":3,"patients":3},{"cancer_study_identifier":"msk_impact_50k_2026","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","mutation_status":"SOMATIC","events":3,"samples":3,"patients":3},{"cancer_study_identifier":"msk_chord_2024","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"msk_chord_2024","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"cellline_ccle_broad","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"NA","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"msk_impact_50k_2026","mutation_variant":"*1069*","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"pancan_mimsi_msk_2024","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"msk_met_2021","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"msk_met_2021","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"msk_impact_50k_2026","mutation_variant":"*1068*","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"crc_msk_2026","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"ccle_broad_2019","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"NA","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"breast_msk_2026","mutation_variant":"*1069*","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"ccle_broad_2025","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"NA","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"ccle_genentech_2014","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","mutation_status":"NA","events":2,"samples":2,"patients":2},{"cancer_study_identifier":"breast_msk_2026","mutation_variant":"*1064*","mutation_type":"Nonstop_Mutation","mutation_status":"SOMATIC","events":2,"samples":2,"patients":1} … (5754 more chars)

▶ clickhouse_run_select_query
{
 "query": "SELECT * FROM gene_mutation_variants_in_study(study='msk_impact_50k_2026', gene='PIK3CA') WHERE mutation_variant LIKE '*1069%' OR mutation_type='Nonstop_Mutation'"
}

▶ resolve_and_route
{
 "studyIds": [
  "msk_impact_50k_2026"
 ]
}

◀ result
{"rows":[{"mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","altered_samples":3,"profiled_samples":54331,"frequency_pct":0,"total_mutation_events":3},{"mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","altered_samples":3,"profiled_samples":54331,"frequency_pct":0,"total_mutation_events":3},{"mutation_variant":"*1068*","mutation_type":"Nonstop_Mutation","altered_samples":2,"profiled_samples":54331,"frequency_pct":0,"total_mutation_events":2},{"mutation_variant":"*1069*","mutation_type":"Nonstop_Mutation","altered_samples":2,"profiled_samples":54331,"frequency_pct":0,"total_mutation_events":2},{"mutation_variant":"*1069Lfs*4","mutation_type":"Frame_Shift_Ins","altered_samples":1,"profiled_samples":54331,"frequency_pct":0,"total_mutation_events":1}]}

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_impact_50k_2026","name":"MSK-IMPACT 50K Clinical Sequencing Cohort (MSK, Cancer Cell 2026)","sampleCount":54331,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_impact_50k_2026","metadata":{"clinicalAttributeIds":["AGE_AT_DX","ANCESTRY_LABEL","CANCER_TYPE","CANCER_TYPE_DETAILED","DISEASE_STATUS","FACETS_PLOIDY","FACETS_PURITY","FACETS_QC","FACETS_SUITE_VERSION","FACETS_WGD","FRACTION_GENOME_ALTERED","GENE_PANEL","HLA-A1","HLA-A2","HLA-B1","HLA-B2","HLA-C1","HLA-C2","HLA_A1_LOH","HLA_A2_LOH","HLA_B1_LOH","HLA_B2_LOH","HLA_C1_LOH","HLA_C2_LOH","HLA_GENOTYPE_AVAILABLE","METASTATIC_SITE","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","N_OTHER_CANCER_TYPES","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PRIMARY_SITE","PURITY_ESTIMATE_FROM_MUTATIONS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SEX","SOMATIC_STATUS","TMB_SCORE","TUMOR_PURITY"],"molecularProfileIds":["msk_impact_50k_2026_gistic","msk_impact_50k_2026_mutations","msk_impact_50k_2026_structural_variants"],"genericAssayProfiles":["msk_impact_50k_2026_armlevel_cna","msk_impact_50k_2026_loh_hla","msk_impact_50k_2026_mutational_signatures_contribution_v2"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","generic_assay_armlevel_cna","generic_assay_loh_hla","generic_assay_mutational_signature"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "msk_impact_50k_2026"
 ],
 "genes": [
  "PIK3CA: MUT"
 ],
 "tab": "mutations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/mutations?cancer_study_list=msk_impact_50k_2026&gene_list=PIK3CA%3A+MUT&case_set_id=msk_impact_50k_2026_all","url":"https://www.cbioportal.org/results/mutations?cancer_study_list=msk_impact_50k_2026&gene_list=PIK3CA%3A+MUT&case_set_id=msk_impact_50k_2026_all","data":{"studyIds":["msk_impact_50k_2026"],"studies":[{"studyId":"msk_impact_50k_2026","name":"MSK-IMPACT 50K Clinical Sequencing Cohort (MSK, Cancer Cell 2026)","sampleCount":54331}],"genes":["PIK3CA"],"caseSetId":"msk_impact_50k_2026_all","pageDescription":"Detailed mutation table with lollipop plot showing mutation positions, types, and frequencies for queried genes."}}

═ answer (success)
**I found no exact match for `PIK3CA p.*1069Wext*3` in cBioPortal, but the closely related `p.*1069Wext*4` is present.**

**Possible discrepancy in your report.** The stop-loss variant recorded in the database at this position is `*1069Wext*4`. The extension length, `*3` versus `*4`, differs from your report. That may be a difference in transcript or annotation, or a typo in the report. Please check the transcript and the cDNA change (c.) in the report. I didn't treat the two as the same variant.

**What the notation means.** The stop codon at position 1069 is changed to tryptophan (W). The protein then extends past its normal end, and a new stop codon follows after the stated number of extra amino acids. It is classified as a Nonstop (stop-loss) mutation.

**Where `*1069Wext*4` appears in cBioPortal.** Each entry below is one sample from one patient unless a count is given.

| Study | Samples |
|---|---|
| MSK-IMPACT 50K (`msk_impact_50k_2026`) | 3 of 54,331 profiled |
| MSK-CHORD 2024 | 2 |
| MSK-MET 2021 | 2 |
| MSI pan-cancer MSK 2024 | 2 |
| CRC MSK 2026 | 2 |
| TCGA glioblastoma (`gbm_tcga`, `gbm_tcga_gdc`, `gbm_tcga_pan_can_atlas_2018`, `gbm_tcga_pub2013`, `lgggbm_tcga_pub`) | 1 each |
| METABRIC breast | 1 |
| CCLE and NCI-60 cell lines, and a colorectal study (`coadread_dfci_2016`) | 1–2 each |

- **Frequency:** In the largest cohort, MSK-IMPACT 50K, the variant is in 3 of 54,331 samples (about 0.006%), so it is very rare.
- **Somatic status:** Where somatic status is recorded, it is called somatic. Several studies leave it blank.
- **Other stop-loss calls nearby:** Other stop-loss variants of PIK3CA at positions 1064–1069 appear in a few samples (for example `*1069*`, `*1068*` and `*1069Rext*4`). Frameshifts at the same codon (`*1069Ffs*5`) are also recorded.

**Limits of this data.**
- I checked whether the study data carries a custom driver annotation for these entries. It doesn't, so I can't say whether this variant is oncogenic, a driver or a passenger.
- Occurrence counts alone don't show functional or clinical significance. For that you need an external curated source such as OncoKB, ClinVar or the primary literature. I did not consult any of these, and this is not clinical advice.

**Explore it yourself.** [PIK3CA Mutations tab, MSK-IMPACT 50K](https://www.cbioportal.org/results/mutations?cancer_study_list=msk_impact_50k_2026&gene_list=PIK3CA%3A+MUT&case_set_id=msk_impact_50k_2026_all) has the lollipop plot and mutation table, where you can find the C-terminal stop-loss variants.

If you send the c. notation and transcript ID from the report, I can look for a closer match.