Q (haiku): What are the top 5 most frequently altered genes in a structural variant in the Osteosarcoma study from TARGET?

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

▶ list_studies
{
 "search": "TARGET osteosarcoma"
}

◀ 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)

◀ result
{"result":[{"cancer_study_identifier":"os_target_gdc","name":"Osteosarcoma (TARGET GDC, 2025)","type_of_cancer_id":"os","sample_count":159,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=os_target_gdc"}]}

▶ get_study_guide
{
 "study_id": "os_target_gdc"
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
# Osteosarcoma (TARGET GDC, 2025)

**Study ID:** `os_target_gdc`

## Overview
Pediatric osteosarcoma study from the TARGET (Therapeutically Applicable Research to Generate Effective Treatments) initiative. Whole exome sequencing data.

## Gene Panel
- **WES** (Whole Exome Sequencing): all coding genes profiled
- **143 of the 160 samples are profiled for mutations.** Use 143 as the mutation-frequency denominator (`sample_to_gene_panel_derived`, `alteration_type = 'MUTATION_EXTENDED'`), not the study's sample count — e.g. TP53 is mutated in 32/143 = 22.4%.

## Patients vs Samples
383 patients have clinical data, but only 153 of them have a sample (159 samples). Patient-level questions (age, sex, survival) use all patients with a value; genomic questions use the 143 mutation-profiled samples.

## Clinical Attributes - Semantic Guide

### Patient Demographics
| Attribute | Description | Notes |
|-----------|-------------|-------|
| `AGE` | Age at diagnosis, **floored at 18** | Every patient younger than 18 is recorded as 18 (241 of 293). **Don't use it for age statistics** — use `DAYS_TO_BIRTH` |
| `DAYS_TO_BIRTH` | Days from birth to diagnosis, negative | Age at diagnosis in years = `-DAYS_TO_BIRTH / 365.25`. 293 patients have a value; 90 are empty |
| `SEX` | Patient sex | Male 172, Female 133, 78 empty |
| `RACE`, `ETHNICITY` | Race, ethnicity | |

### Disease Characteristics
| Attribute | Description | Notes |
|-----------|-------------|-------|
| `CANCER_TYPE_DETAILED` | Cancer type | Osteosarcoma for every sample |
| `PRIMARY_SITE_PATIENT` | Primary site | "Appendicular Skeleton" for every patient with a value — no finer location (femur, tibia) |

No histological subtype, tumor necrosis / chemotherapy response, or event-free survival attributes exist in this study.

### Clinical Outcomes
| Attribute | Description | Notes |
|-----------|-------------|-------|
| `OS_MONTHS` | Overall survival in months | Time from diagnosis |
| `OS_STATUS` | Overall survival status | 0:LIVING 171, 1:DECEASED 105, 107 empty |

## Age at Diagnosis

Compute age from `DAYS_TO_BIRTH`, not `AGE`. A median from `AGE` comes out as 18 because every child is recorded as 18; the real median is about 15 years.

```sql
SELECT
    count() AS patients,
    round(median(-toFloat64OrNull(attribute_value) / 365.25), 1) AS median_age_years,
    round(min(-toFloat64OrNull(attribute_value) / 365.25), 1) AS min_age_years,
    round(max(-toFloat64OrNull(attribute_value) / 365.25), 1) AS max_age_years
FROM clinical_data_derived
WHERE cancer_study_identifier = 'os_target_gdc'
  AND attribute_name = 'DAYS_TO_BIRTH'
  AND toFloat64OrNull(attribute_value) IS NOT NULL;
-- 293 patients, median 15.2, range 3.6-87.1
```

When reporting, say the age comes from `DAYS_TO_BIRTH` and that `AGE` is floored at 18.

## Notes & Caveats
- This is a pediatric cancer cohort; age distribution is younger than adult studies
- WES coverage means no gene panel filtering needed for frequency calculations
- Part  … (66 more chars)

◀ 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
    hugo_gene_symbol,
    COUNT(DISTINCT sample_unique_id) AS altered_samples,
    total_sv_events
FROM genomic_event_derived
WHERE cancer_study_identifier = 'os_target_gdc'
  AND variant_type = 'structural_variant'
GROUP BY hugo_gene_symbol
ORDER BY altered_samples DESC
LIMIT 5
",
 "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: Unknown expression identifier `total_sv_events` in scope SELECT hugo_gene_symbol, COUNTDistinct(sample_unique_id) AS altered_samples, total_sv_events FROM genomic_event_derived WHERE (cancer_study_identifier = 'os_target_gdc') AND (variant_type = 'structural_variant') GROUP BY hugo_gene_symbol ORDER BY altered_samples DESC LIMIT 5. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
SELECT *
FROM top_sv_genes_in_study(study = 'os_target_gdc', top_n = 5)
",
 "max_rows": 100
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT variant_type, COUNT(*) as event_count
FROM genomic_event_derived
WHERE cancer_study_identifier = 'os_target_gdc'
GROUP BY variant_type
ORDER BY event_count DESC
",
 "max_rows": 100
}

◀ result
{"rows":[{"variant_type":"cna","event_count":99668},{"variant_type":"mutation","event_count":2627}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    genetic_profile_id,
    genetic_alteration_type,
    datatype,
    name,
    description
FROM genetic_profile
WHERE cancer_study_identifier = 'os_target_gdc'
ORDER BY genetic_alteration_type
",
 "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: Unknown expression or function identifier `cancer_study_identifier` in scope SELECT genetic_profile_id, genetic_alteration_type, datatype, name, description FROM genetic_profile WHERE cancer_study_identifier = 'os_target_gdc' ORDER BY genetic_alteration_type ASC. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_list_table_columns
{
 "table": "genetic_profile"
}

◀ result
{"columns":[{"name":"genetic_profile_id","type":"Int64"},{"name":"stable_id","type":"String"},{"name":"cancer_study_id","type":"Int64"},{"name":"genetic_alteration_type","type":"String"},{"name":"generic_assay_type","type":"Nullable(String)"},{"name":"datatype","type":"String"},{"name":"name","type":"String"},{"name":"description","type":"Nullable(String)"},{"name":"show_profile_in_analysis_tab","type":"Int32"},{"name":"pivot_threshold","type":"Nullable(Float64)"},{"name":"sort_order","type":"Nullable(String)"},{"name":"patient_level","type":"Nullable(Int32)"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT 
    gp.stable_id,
    gp.genetic_alteration_type,
    gp.datatype,
    gp.name
FROM genetic_profile gp
JOIN cancer_study cs ON gp.cancer_study_id = cs.cancer_study_id
WHERE cs.cancer_study_identifier = 'os_target_gdc'
ORDER BY gp.genetic_alteration_type
",
 "max_rows": 100
}

◀ result
{"rows":[{"stable_id":"os_target_gdc_cna","genetic_alteration_type":"COPY_NUMBER_ALTERATION","datatype":"DISCRETE","name":"Putative copy-number alterations from GISTIC"},{"stable_id":"os_target_gdc_mrna_seq_tpm","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"CONTINUOUS","name":"mRNA Expression (TPM)"},{"stable_id":"os_target_gdc_mrna_seq_tpm_Zscores","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"Z-SCORE","name":"mRNA Expression TPM z-scores"},{"stable_id":"os_target_gdc_mrna_seq_fpkm","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"CONTINUOUS","name":"mRNA Expression (FPKM)"},{"stable_id":"os_target_gdc_mrna_seq_fpkm_Zscores","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"Z-SCORE","name":"mRNA Expression FPKM z-scores"},{"stable_id":"os_target_gdc_rna_seq_mrna","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"CONTINUOUS","name":"mRNA Expression"},{"stable_id":"os_target_gdc_mrna_seq_read_counts_Zscores","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"Z-SCORE","name":"mRNA Expression z-scores"},{"stable_id":"os_target_gdc_mutations","genetic_alteration_type":"MUTATION_EXTENDED","datatype":"MAF","name":"Mutations"}]}

═ answer (success)
## Result

The **Osteosarcoma (TARGET GDC, 2025) study does not have structural variant data**. 

Based on the available molecular profiles, this study (`os_target_gdc`) contains:
- **Mutations** (from whole exome sequencing)
- **Copy number alterations** (GISTIC discrete CNA)
- **mRNA expression** (multiple data types)

There is **no structural variant profile** (`STRUCTURAL_VARIANT` alteration type) in this study. The data availability is limited to point mutations and copy-number events.

If you're interested in the most frequently altered genes in a different alteration type, I can help you find:
- **Top genes with copy number alterations (amplifications/deep deletions)**
- **Top mutated genes**

Would either of those alternatives be helpful?