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

▶ read_guide
{
 "uri": "cbioportal://common-pitfalls#16"
}

◀ result
### 16. 🚨 SILENT QUERY SUBSTITUTION ("did you mean...")

When the user's wording differs from canonical terminology (e.g. "V600V" looks like "V600E" with a typo, or "point mutation" sounds like "missense"), it is forbidden to silently rewrite the question and answer the rewritten version. Doing so produces an answer that looks confident but is for a different question — the user cannot tell what was changed.

#### ❌ Wrong: silently substitute

> User: *"Find patients in colorectal cancer with the V600V alteration in BRAF"*
> Agent: *(internally treats this as V600E)* "I found 412 samples with BRAF V600E in colorectal studies..."

> User: *"What is the most prevalent TP53 mutation in uterine cancer that is not a point mutation?"*
> Agent: *(internally treats "point mutation" = "missense", silently excludes only missense)* "The most prevalent non-missense TP53 mutation is..."

#### ✅ Correct: answer the literal question, flag any normalization

For an unusual-looking variant the user may have typed deliberately:
- Query for what was asked, literally.
- If 0 rows come back, **explain *why* zero is the expected answer** before suggesting a likely-intended alternative. For synonymous variants (e.g. BRAF V600V, TP53 R175R), the explanation is: *cBioPortal's mutation tables filter out synonymous (silent) variants in most studies, so 0 hits means "filtered upstream", not "no such variant exists in any patient"*. Then ask: *"Did you mean V600E (the canonical activating variant)? Or would you like me to look for V600V in the studies that do retain synonymous calls?"*
- If the wording is ambiguous (e.g. "point mutation"), ask the user which definition they meant before querying — do not pick one silently.

#### Mutation-type terminology mapping (use this to disambiguate)

| User says | Canonical definition | `mutation_type` filter |
|---|---|---|
| "point mutation" | Any SNV (single-nucleotide variant) — includes missense, nonsense, synonymous, splice-site SNVs | `mutation_type IN ('Missense_Mutation','Nonsense_Mutation','Silent','Splice_Site')` — **but ask the user to confirm scope first** |
| "missense" | Single amino-acid substitution that changes the protein | `mutation_type = 'Missense_Mutation'` |
| "nonsense" / "stop-gain" | Premature stop codon | `mutation_type = 'Nonsense_Mutation'` |
| "synonymous" / "silent" | Nucleotide change with no amino-acid change | `mutation_type = 'Silent'` (**often filtered out of public datasets** — see below) |
| "splice site" | Mutation in canonical splice acceptor/donor | `mutation_type = 'Splice_Site'` |
| "frameshift" | Indel changing reading frame | `mutation_type IN ('Frame_Shift_Ins','Frame_Shift_Del')` |
| "indel" / "in-frame" | In-frame insertion or deletion | `mutation_type IN ('In_Frame_Ins','In_Frame_Del')` |
| "truncating" | Anything that disrupts the protein early | `mutation_type IN ('Nonsense_Mutation','Frame_Shift_Ins','Frame_Shift_Del','Splice_Site','Nonstop_Mutation')` |

**Synonymous-variant filter. … (785 more chars)

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-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 … (35166 more chars)

▶ clickhouse_list_tables
{}

◀ result
{"tables":[{"name":"allele_specific_copy_number"},{"name":"alteration_driver_annotation"},{"name":"cancer_study"},{"name":"cancer_study_query_preferences"},{"name":"cancer_study_tags"},{"name":"cbioportal_sequence_state"},{"name":"clinical_attribute_counts"},{"name":"clinical_attribute_meta"},{"name":"clinical_data_derived"},{"name":"clinical_event"},{"name":"clinical_event_data"},{"name":"clinical_event_data_derived"},{"name":"clinical_event_derived"},{"name":"clinical_patient"},{"name":"clinical_sample"},{"name":"cna_event"},{"name":"cna_panel_gene_coverage"},{"name":"cna_wes_coverage"},{"name":"co_altered_genes_in_study"},{"name":"copy_number_seg"},{"name":"copy_number_seg_file"},{"name":"gene"},{"name":"gene_alias"},{"name":"gene_alteration_frequency_by_cancer_type"},{"name":"gene_cna_distribution_in_study"},{"name":"gene_mutation_frequency_by_cancer_type"},{"name":"gene_mutation_frequency_in_studies"},{"name":"gene_mutation_frequency_in_study"},{"name":"gene_mutation_variants_in_study"},{"name":"gene_pair_coexpression"},{"name":"gene_panel"},{"name":"gene_panel_list"},{"name":"gene_panel_to_gene_derived"},{"name":"generic_assay_data_derived"},{"name":"generic_assay_meta_derived"},{"name":"generic_assay_profile_entity_derived"},{"name":"generic_entity_properties"},{"name":"geneset"},{"name":"geneset_gene"},{"name":"geneset_hierarchy_leaf"},{"name":"geneset_hierarchy_node"},{"name":"genetic_alteration"},{"name":"genetic_alteration_derived"},{"name":"genetic_entity"},{"name":"genetic_profile"},{"name":"genetic_profile_link"},{"name":"genetic_profile_samples"},{"name":"genomic_event_derived"},{"name":"gistic"},{"name":"gistic_to_gene"},{"name":"info"},{"name":"mut_sig"},{"name":"mutation"},{"name":"mutation_count_by_keyword"},{"name":"mutation_derived"},{"name":"mutation_event"},{"name":"mutation_panel_gene_coverage"},{"name":"mutation_wes_coverage"},{"name":"patient"},{"name":"reference_genome"},{"name":"reference_genome_gene"},{"name":"resource_definition"},{"name":"resource_patient"},{"name":"resource_sample"},{"name":"resource_study"},{"name":"sample"},{"name":"sample_cna_event"},{"name":"sample_derived"},{"name":"sample_list"},{"name":"sample_list_list"},{"name":"sample_profile"},{"name":"sample_to_gene_panel_derived"},{"name":"structural_variant"},{"name":"sv_panel_gene_coverage"},{"name":"sv_wes_coverage"},{"name":"top_cna_genes_in_study"},{"name":"top_mutated_genes_in_cohort"},{"name":"top_mutated_genes_in_study"},{"name":"top_sv_genes_in_study"},{"name":"treatment_counts_in_study"},{"name":"treatment_regimens_in_study"},{"name":"type_of_cancer"}]}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

▶ clickhouse_list_table_columns
{
 "table": "mutation_event"
}

◀ 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."}]}

◀ result
{"columns":[{"name":"mutation_event_id","type":"Int64"},{"name":"entrez_gene_id","type":"Int64"},{"name":"chr","type":"Nullable(String)"},{"name":"start_position","type":"Nullable(Int64)"},{"name":"end_position","type":"Nullable(Int64)"},{"name":"reference_allele","type":"Nullable(String)"},{"name":"tumor_seq_allele","type":"Nullable(String)"},{"name":"protein_change","type":"Nullable(String)"},{"name":"mutation_type","type":"Nullable(String)"},{"name":"ncbi_build","type":"Nullable(String)"},{"name":"strand","type":"Nullable(String)"},{"name":"variant_type","type":"Nullable(String)"},{"name":"db_snp_rs","type":"Nullable(String)"},{"name":"db_snp_val_status","type":"Nullable(String)"},{"name":"refseq_mrna_id","type":"Nullable(String)"},{"name":"codon_change","type":"Nullable(String)"},{"name":"uniprot_accession","type":"Nullable(String)"},{"name":"protein_pos_start","type":"Nullable(Int64)"},{"name":"protein_pos_end","type":"Nullable(Int64)"},{"name":"canonical_transcript","type":"Nullable(Int32)"},{"name":"keyword","type":"Nullable(String)"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT
    hugo_gene_symbol,
    mutation_variant,
    mutation_type,
    COUNT(DISTINCT sample_unique_id) as samples_count,
    COUNT(DISTINCT cancer_study_identifier) as studies_count
FROM genomic_event_derived
WHERE hugo_gene_symbol = 'PIK3CA'
  AND (
      mutation_variant LIKE '%1069%'
      OR mutation_variant LIKE '%Wext%'
      OR mutation_variant LIKE '%*1069%'
  )
  AND variant_type = 'mutation'
  AND mutation_status != 'UNCALLED'
GROUP BY hugo_gene_symbol, mutation_variant, mutation_type
ORDER BY samples_count DESC
LIMIT 20
",
 "max_rows": 100
}

◀ result
{"rows":[{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","samples_count":27,"studies_count":17},{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","samples_count":21,"studies_count":15},{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069*","mutation_type":"Nonstop_Mutation","samples_count":8,"studies_count":6},{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069fs*","mutation_type":"Frame_Shift_Del","samples_count":2,"studies_count":2},{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Lfs*4","mutation_type":"Frame_Shift_Ins","samples_count":1,"studies_count":1},{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Lfs*5","mutation_type":"Frame_Shift_Ins","samples_count":1,"studies_count":1},{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Rext*4","mutation_type":"Nonstop_Mutation","samples_count":1,"studies_count":1}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT
    hugo_gene_symbol,
    mutation_variant,
    mutation_type,
    COUNT(DISTINCT sample_unique_id) as samples_count,
    COUNT(DISTINCT cancer_study_identifier) as studies_count
FROM genomic_event_derived
WHERE hugo_gene_symbol = 'PIK3CA'
  AND mutation_variant LIKE '%1069Wext%'
  AND variant_type = 'mutation'
  AND mutation_status != 'UNCALLED'
GROUP BY hugo_gene_symbol, mutation_variant, mutation_type
ORDER BY samples_count DESC
LIMIT 20
",
 "max_rows": 100
}

◀ result
{"rows":[{"hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","samples_count":27,"studies_count":17}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check for p.*1069Wext*3 in raw mutation_event table
SELECT DISTINCT
    protein_change,
    mutation_type,
    COUNT(*) as occurrences
FROM mutation_event
WHERE entrez_gene_id IN (SELECT entrez_gene_id FROM gene WHERE hugo_gene_symbol = 'PIK3CA')
  AND (protein_change LIKE '%1069W%' OR protein_change LIKE '%*1069%')
GROUP BY protein_change, mutation_type
ORDER BY occurrences DESC
LIMIT 30
",
 "max_rows": 100
}

◀ result
{"rows":[{"protein_change":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","occurrences":5},{"protein_change":"*1069*","mutation_type":"Nonstop_Mutation","occurrences":3},{"protein_change":"*1069Wext*4","mutation_type":"Nonstop_Mutation","occurrences":2},{"protein_change":"*1069Rext*4","mutation_type":"Nonstop_Mutation","occurrences":1},{"protein_change":"*1069Lfs*4","mutation_type":"Frame_Shift_Ins","occurrences":1},{"protein_change":"*1069Lfs*5","mutation_type":"Frame_Shift_Ins","occurrences":1},{"protein_change":"*1069fs*","mutation_type":"Frame_Shift_Del","occurrences":1}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get sample details for p.*1069Wext*4 and all 1069 variants
SELECT 
    ged.cancer_study_identifier,
    ged.hugo_gene_symbol,
    ged.mutation_variant,
    ged.mutation_type,
    ged.driver_filter_annotation,
    COUNT(DISTINCT ged.sample_unique_id) as sample_count
FROM genomic_event_derived ged
WHERE ged.hugo_gene_symbol = 'PIK3CA'
  AND ged.mutation_variant LIKE '%1069%'
  AND ged.variant_type = 'mutation'
  AND ged.mutation_status != 'UNCALLED'
GROUP BY 
    ged.cancer_study_identifier,
    ged.hugo_gene_symbol,
    ged.mutation_variant,
    ged.mutation_type,
    ged.driver_filter_annotation
ORDER BY sample_count DESC
LIMIT 50
",
 "max_rows": 100
}

◀ result
{"rows":[{"cancer_study_identifier":"msk_impact_50k_2026","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":3},{"cancer_study_identifier":"msk_impact_50k_2026","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","sample_count":3},{"cancer_study_identifier":"msk_impact_2017","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","sample_count":3},{"cancer_study_identifier":"msk_met_2021","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","sample_count":2},{"cancer_study_identifier":"pancan_mimsi_msk_2024","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"msk_met_2021","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"msk_chord_2024","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"ccle_broad_2025","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"crc_msk_2026","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"ccle_broad_2019","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"msk_impact_50k_2026","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069*","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"breast_msk_2026","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069*","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"msk_chord_2024","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","sample_count":2},{"cancer_study_identifier":"ccle_genentech_2014","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"cellline_ccle_broad","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","sample_count":2},{"cancer_study_identifier":"rectal_msk_2019","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","sample_count":1},{"cancer_study_identifier":"crc_msk_2017","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","sample_count":1},{"cancer_study_identifier":"msk_met_2021","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069fs*","mutation_type":"Frame_Shift_Del","sample_count":1},{"cancer_study_identifier":"brca_metabric","hugo_gene_symbol":"PIK3CA","mutation_variant":"*1069Lfs*5","mutation_type":"Frame_Shift_Ins","sample_c … (3785 more chars)

▶ clickhouse_run_select_query
{
 "query": "
-- Get comprehensive information about PIK3CA gene
SELECT 
    hugo_gene_symbol,
    entrez_gene_id,
    COUNT(DISTINCT CASE WHEN variant_type = 'mutation' AND mutation_status != 'UNCALLED' THEN sample_unique_id END) as total_mutated_samples,
    COUNT(DISTINCT CASE WHEN variant_type = 'cna' AND cna_alteration = 2 THEN sample_unique_id END) as amplified_samples,
    COUNT(DISTINCT cancer_study_identifier) as studies_with_mutations
FROM genomic_event_derived
WHERE hugo_gene_symbol = 'PIK3CA'
GROUP BY hugo_gene_symbol, entrez_gene_id
",
 "max_rows": 10
}

◀ result
{"rows":[{"hugo_gene_symbol":"PIK3CA","entrez_gene_id":5290,"total_mutated_samples":41531,"amplified_samples":5501,"studies_with_mutations":463}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Get detailed information about all position 1069 variants with driver status
SELECT 
    mutation_variant,
    mutation_type,
    COUNT(DISTINCT sample_unique_id) as samples,
    COUNT(DISTINCT cancer_study_identifier) as studies,
    driver_filter_annotation,
    driver_tiers_filter_annotation
FROM genomic_event_derived
WHERE hugo_gene_symbol = 'PIK3CA'
  AND mutation_variant LIKE '%1069%'
  AND variant_type = 'mutation'
  AND mutation_status != 'UNCALLED'
GROUP BY mutation_variant, mutation_type, driver_filter_annotation, driver_tiers_filter_annotation
ORDER BY samples DESC
",
 "max_rows": 20
}

◀ result
{"rows":[{"mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation","samples":27,"studies":17},{"mutation_variant":"*1069Ffs*5","mutation_type":"Frame_Shift_Ins","samples":21,"studies":15},{"mutation_variant":"*1069*","mutation_type":"Nonstop_Mutation","samples":8,"studies":6},{"mutation_variant":"*1069fs*","mutation_type":"Frame_Shift_Del","samples":2,"studies":2},{"mutation_variant":"*1069Lfs*5","mutation_type":"Frame_Shift_Ins","samples":1,"studies":1},{"mutation_variant":"*1069Rext*4","mutation_type":"Nonstop_Mutation","samples":1,"studies":1},{"mutation_variant":"*1069Lfs*4","mutation_type":"Frame_Shift_Ins","samples":1,"studies":1}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Check for the exact p.*1069Wext*3 variant notation (in case it's a formatting variant)
SELECT DISTINCT
    mutation_variant,
    mutation_type,
    driver_filter_annotation
FROM genomic_event_derived
WHERE hugo_gene_symbol = 'PIK3CA'
  AND mutation_variant IN ('*1069Wext*3', 'p.*1069Wext*3', '*1069Wext*4', 'p.*1069Wext*4')
  AND variant_type = 'mutation'
  AND mutation_status != 'UNCALLED'
ORDER BY mutation_variant
",
 "max_rows": 20
}

◀ result
{"rows":[{"mutation_variant":"*1069Wext*4","mutation_type":"Nonstop_Mutation"}]}

═ answer (success)
## Summary: PIK3CA p.*1069Wext*3 Mutation Analysis

Based on my search of the cBioPortal database, **the exact variant `p.*1069Wext*3` was not found in the current dataset**. However, I found a closely related variant and can provide detailed information about this mutation class:

---

### **Related Variant Found: p.*1069Wext*4**

| Feature | Details |
|---------|---------|
| **Gene** | PIK3CA (Phosphatidylinositol-3-kinase, catalytic subunit alpha) |
| **Mutation Type** | **Nonstop Mutation** (nonsense mutation with readthrough) |
| **Samples** | 27 samples across 17 different cancer studies |
| **Notation** | `*1069Wext*4` (your report may list `*1069Wext*3` or a similar variant) |
| **Position** | Position 1069 in the PIK3CA protein sequence |

---

### **What This Mutation Represents**

**Nonstop mutations** at position 1069 are **truncation-escape variants**:
- The asterisk (*) represents a **stop codon** (premature termination)
- **W** (tryptophan) is the amino acid that replaces the stop codon (readthrough)
- **ext\*4/\*3** means the protein is **extended by 3-4 additional amino acids** before hitting another stop codon
- This type of mutation can produce a **longer, abnormal protein** with altered function

---

### **PIK3CA Gene Context**

PIK3CA is one of the most frequently altered genes in cancer:
- **Total mutated samples in database**: 41,531 samples
- **Amplified samples**: 5,501 samples
- **Studies containing PIK3CA mutations**: 463 different cancer studies
- **Gene function**: Encodes the catalytic subunit of phosphatidylinositol 3-kinase (PI3K), a critical regulator of cell growth and survival signaling

---

### **Related Variants at Position 1069**

Other variants at this position include:
- **p.*1069Ffs*5** (Frame Shift Insertion) — 21 samples
- **p.*1069*** (simple stop codon) — 8 samples
- **p.*1069Rext*4** (Arginine readthrough) — 1 sample

---

### **Clinical/Research Notes**

1. **No exact `*1069Wext*3` match** — The `*4` readthrough extension is more commonly reported. The *3 variant may represent a sequencing artifact, a variant calling difference, or data from a source not currently in cBioPortal.

2. **Nonstop mutations are rare** compared to frameshift or missense variants; they may not be well-characterized functionally.

3. **PIK3CA mutations are generally oncogenic** — many variants in this gene are associated with **activating mutations**, particularly in the kinase domain and helical domain.

4. **Recommendation** — If you have the genomic coordinates (chromosome, position, reference/tumor alleles) from your report, I can provide more precise details or check external databases like ClinVar or OncoKB.

Would you like me to explore this further (e.g., find studies where this variant appears, check cancer type distributions, or get clinical context)?