Q (sonnet): what are the most common events in her2- breast cancer?

▶ list_guides
{}

◀ result
{"result":[{"uri":"cbioportal://mutation-frequency-guide","description":"Comprehensive guide for calculating gene mutation frequencies with gene-specific profiling denominators"},{"uri":"cbioportal://clinical-data-guide","description":"Guide for querying clinical data including patient vs sample level considerations"},{"uri":"cbioportal://sample-filtering-guide","description":"Guide for filtering samples and studies in cBioPortal queries"},{"uri":"cbioportal://common-pitfalls","description":"Guide to avoid common mistakes when querying cBioPortal data. If you already know which numbered pitfall applies, fetch just that section via read_guide(\"cbioportal://common-pitfalls#<number>\") (e.g. #16) instead of the full guide"},{"uri":"cbioportal://treatment-guide","description":"Guide for querying treatment/clinical event data including drug agents, timelines, and linking to genomic data"},{"uri":"cbioportal://faq-guide","description":"General cBioPortal FAQ: history, how to cite, data types, reference genome, abbreviations, GISTIC thresholds, API access"},{"uri":"cbioportal://statistical-tests-guide","description":"Statistical test selection guide — decision matrix for choosing Fisher's exact, Wilcoxon, chi-squared, t-test, ANOVA, etc. based on data type and group count"},{"uri":"cbioportal://gene-expression-guide","description":"Gene expression / copy-number / methylation analysis. Covers genetic_alteration_derived, profile_type discovery, and the gene_pair_coexpression view for Spearman correlation between two genes"},{"uri":"cbioportal://external-resources-guide","description":"Guide for finding external linked resources such as imaging, pathology, Minerva, HTAN, or other resource_* table links before declaring data unavailable"},{"uri":"cbioportal://gene-resolution-guide","description":"Guide for resolving ambiguous gene symbols, aliases, gene families, and shorthand such as CD3 before querying expression or alteration data"},{"uri":"cbioportal://study-resolution-guide","description":"Guide for resolving requested studies, avoiding silent substitute cohorts, and redirecting to known external cBioPortal instances when data is not in this deployment"},{"uri":"cbioportal://germline-guide","description":"Guide for querying germline variant data — storage columns, study discovery, query patterns, and somatic vs germline considerations"},{"uri":"cbioportal://study-guide/{study_id}","description":"Dynamic study-specific guide - use get_study_guide(study_id) tool to generate"}]}

▶ 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)

▶ read_guide
{
 "uri": "cbioportal://sample-filtering-guide"
}

▶ search_oncotree
{
 "search_term": "breast"
}

◀ result
# Sample and Study Filtering Guide

## Overview
Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria.

## Study-Level Filtering

### 1. Always Filter by Study
Every query should specify a study to ensure consistent results:

```sql
-- Always include study filtering
SELECT *
FROM your_table
WHERE
    cancer_study_identifier = 'your_study_id'
    -- Additional filters...
```

### 2. Find Available Studies

```sql
-- Discover available studies
SELECT
    cancer_study_identifier,
    name,
    description,
    type_of_cancer_id
FROM cancer_study
ORDER BY cancer_study_identifier;
```

### 3. Study Information

```sql
-- Get detailed study information
SELECT
    cs.cancer_study_identifier,
    cs.name as study_name,
    cs.description,
    cs.sample_count,
    COUNT(DISTINCT p.internal_id) as patient_count
FROM cancer_study cs
LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id
WHERE
    cs.cancer_study_identifier = 'your_study_id'
GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count;
```

### 4. Find Studies by Available Data Types

Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter.

**Canonical query — studies with ALL requested data types for a cancer type:**

```sql
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'luad'          -- ← OncoTree code from search_oncotree
  AND mutation_sample_count > 0           -- ← one condition per required data type
  AND cna_sample_count > 0
ORDER BY sample_count DESC;
```

**Data type → column** (a count of 0 means the study doesn't have that data):

| Data type in the user's question | Column (portal "Data type" filter label) |
|---|---|
| samples in the study | `sample_count` |
| mutation / mutations | `mutation_sample_count` ("Mutations") |
| copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") |
| structural variant / fusion | `structural_variant_sample_count` |
| RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" |
| specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `<study>_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq |
| specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `<study>_mrna` list |
| microRNA | `mirna_sample_count` ("miRNA") |
| protein / RPPA | `rppa_sample_count` ("RPPA") |
| mas … (7083 more chars)

◀ result
{"result":[{"code":"BREAST","name":"Breast","score":100,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST"},{"code":"BA","name":"Breast Angiosarcoma","score":60,"mainType":"Breast Sarcoma","tissue":"Breast","hierarchy":"BREAST > PBS > BA"},{"code":"BFN","name":"Breast Fibroepithelial Neoplasms","score":60,"mainType":"Breast Sarcoma","tissue":"Breast","hierarchy":"BREAST > BFN"},{"code":"BIALCL","name":"Breast Implant-Associated Anaplastic Large-Cell Lymphoma","score":60,"mainType":"Mature T and NK Neoplasms","tissue":"Lymphoid","hierarchy":"LYMPH > LNM > NHL > MTNN > ALCL > BIALCL"},{"code":"BNNOS","name":"Breast Neoplasm, NOS","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BNNOS"},{"code":"BRCANOS","name":"Breast Invasive Cancer, NOS","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > BRCANOS"},{"code":"BRCNOS","name":"Breast Invasive Carcinoma, NOS","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > BRCNOS"},{"code":"BRSRCC","name":"Breast Carcinoma with Signet Ring","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > BRSRCC"},{"code":"CSNOS","name":"Breast Invasive Carcinosarcoma, NOS","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > CSNOS"},{"code":"DCIS","name":"Breast Ductal Carcinoma In Situ","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > DCIS"},{"code":"IDC","name":"Breast Invasive Ductal Carcinoma","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > IDC"},{"code":"ILC","name":"Breast Invasive Lobular Carcinoma","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > ILC"},{"code":"IMMC","name":"Breast Invasive Mixed Mucinous Carcinoma","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > IMMC"},{"code":"LCIS","name":"Breast Lobular Carcinoma In Situ","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > LCIS"},{"code":"MDLC","name":"Breast Mixed Ductal and Lobular Carcinoma","score":60,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > MDLC"},{"code":"PBS","name":"Breast Sarcoma","score":60,"mainType":"Breast Sarcoma","tissue":"Breast","hierarchy":"BREAST > PBS"},{"code":"ACBC","name":"Adenoid Cystic Breast Cancer","score":50,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRCA > ACBC"},{"code":"BLPT","name":"Borderline Phyllodes Tumor of the Breast","score":50,"mainType":"Breast Sarcoma","tissue":"Breast","hierarchy":"BREAST > BFN > PT > BLPT"},{"code":"BPT","name":"Benign Phyllodes Tumor of the Breast","score":50,"mainType":"Breast Sarcoma","tissue":"Breast","hierarchy":"BREAST > BFN > PT > BPT"},{"code":"BRAME","name":"Adenomyoepithelioma of the Breast","score":50,"mainType":"Breast Cancer","tissue":"Breast","hierarchy":"BREAST > BRAME"},{"code":"BRCA","name":"Invasiv … (682 more chars)

▶ clickhouse_list_table_columns
{
 "table": "clinical_data_derived"
}

◀ 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 DISTINCT attribute_name FROM clinical_data_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND lower(attribute_name) LIKE '%her2%'"
}

◀ result
{"rows":[]}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier, COUNT(DISTINCT attribute_name) FROM clinical_data_derived WHERE lower(attribute_name) LIKE '%her2%' GROUP BY cancer_study_identifier ORDER BY cancer_study_identifier"
}

◀ result
{"rows":[{"cancer_study_identifier":"acbc_mskcc_2015","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"biliary_tract_adc_targets_msk_2026","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_aurora_2023","COUNTDistinct(attribute_name)":5},{"cancer_study_identifier":"brca_bccrc","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_broad","COUNTDistinct(attribute_name)":2},{"cancer_study_identifier":"brca_dfci_2020","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_igr_2015","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_jup_msk_2020","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_mbcproject_2022","COUNTDistinct(attribute_name)":16},{"cancer_study_identifier":"brca_mbcproject_wagle_2017","COUNTDistinct(attribute_name)":12},{"cancer_study_identifier":"brca_metabric","COUNTDistinct(attribute_name)":2},{"cancer_study_identifier":"brca_pareja_msk_2020","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_sanger","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_tcga","COUNTDistinct(attribute_name)":22},{"cancer_study_identifier":"brca_tcga_pub","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"brca_tcga_pub2015","COUNTDistinct(attribute_name)":16},{"cancer_study_identifier":"breast_msk_2018","COUNTDistinct(attribute_name)":11},{"cancer_study_identifier":"breast_msk_cfdna_2026","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"egc_msk_2017","COUNTDistinct(attribute_name)":9},{"cancer_study_identifier":"egc_trap_ccr_msk_2023","COUNTDistinct(attribute_name)":4},{"cancer_study_identifier":"egc_trap_msk_2020","COUNTDistinct(attribute_name)":6},{"cancer_study_identifier":"ilc_msk_2023","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"mbc_msk_2021","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"mixed_msk_tcga_2021","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"msk_chord_2024","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"stad_utokyo","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"stes_tcga_pub","COUNTDistinct(attribute_name)":1},{"cancer_study_identifier":"ucs_msk_2024","COUNTDistinct(attribute_name)":3}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_name, attribute_value, COUNT(*) FROM clinical_data_derived WHERE cancer_study_identifier = 'brca_metabric' AND lower(attribute_name) LIKE '%her2%' GROUP BY attribute_name, attribute_value ORDER BY attribute_name, attribute_value"
}

◀ result
{"rows":[{"attribute_name":"HER2_SNP6","COUNT()":529},{"attribute_name":"HER2_SNP6","attribute_value":"GAIN","COUNT()":438},{"attribute_name":"HER2_SNP6","attribute_value":"LOSS","COUNT()":101},{"attribute_name":"HER2_SNP6","attribute_value":"NEUTRAL","COUNT()":1436},{"attribute_name":"HER2_SNP6","attribute_value":"UNDEF","COUNT()":5},{"attribute_name":"HER2_STATUS","COUNT()":529},{"attribute_name":"HER2_STATUS","attribute_value":"Negative","COUNT()":1733},{"attribute_name":"HER2_STATUS","attribute_value":"Positive","COUNT()":247}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH her2neg AS (
    SELECT DISTINCT sample_unique_id
    FROM clinical_data_derived
    WHERE cancer_study_identifier = 'brca_metabric'
      AND attribute_name = 'HER2_STATUS'
      AND attribute_value = 'Negative'
),
altered AS (
    SELECT hugo_gene_symbol, COUNT(DISTINCT sample_unique_id) AS altered_samples, COUNT(*) AS mutation_events
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'brca_metabric'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
      AND off_panel = 0
      AND sample_unique_id IN (SELECT sample_unique_id FROM her2neg)
    GROUP BY hugo_gene_symbol
)
SELECT * FROM altered ORDER BY altered_samples DESC LIMIT 20
"
}

◀ result
{"rows":[{"hugo_gene_symbol":"PIK3CA","altered_samples":717,"mutation_events":839},{"hugo_gene_symbol":"TP53","altered_samples":498,"mutation_events":516},{"hugo_gene_symbol":"MUC16","altered_samples":271,"mutation_events":339},{"hugo_gene_symbol":"AHNAK2","altered_samples":262,"mutation_events":356},{"hugo_gene_symbol":"GATA3","altered_samples":219,"mutation_events":227},{"hugo_gene_symbol":"KMT2C","altered_samples":210,"mutation_events":240},{"hugo_gene_symbol":"SYNE1","altered_samples":200,"mutation_events":233},{"hugo_gene_symbol":"MAP3K1","altered_samples":191,"mutation_events":273},{"hugo_gene_symbol":"CDH1","altered_samples":160,"mutation_events":166},{"hugo_gene_symbol":"AHNAK","altered_samples":153,"mutation_events":173},{"hugo_gene_symbol":"DNAH11","altered_samples":145,"mutation_events":154},{"hugo_gene_symbol":"DNAH2","altered_samples":134,"mutation_events":147},{"hugo_gene_symbol":"KMT2D","altered_samples":124,"mutation_events":133},{"hugo_gene_symbol":"USH2A","altered_samples":119,"mutation_events":122},{"hugo_gene_symbol":"DNAH5","altered_samples":117,"mutation_events":130},{"hugo_gene_symbol":"RYR2","altered_samples":116,"mutation_events":124},{"hugo_gene_symbol":"HERC2","altered_samples":115,"mutation_events":126},{"hugo_gene_symbol":"AKAP9","altered_samples":102,"mutation_events":109},{"hugo_gene_symbol":"TG","altered_samples":102,"mutation_events":106},{"hugo_gene_symbol":"PDE4DIP","altered_samples":95,"mutation_events":103}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT COUNT(DISTINCT sample_unique_id) AS profiled_her2neg
FROM sample_to_gene_panel_derived
WHERE cancer_study_identifier = 'brca_metabric'
  AND alteration_type = 'MUTATION_EXTENDED'
  AND sample_unique_id IN (
      SELECT DISTINCT sample_unique_id
      FROM clinical_data_derived
      WHERE cancer_study_identifier = 'brca_metabric'
        AND attribute_name = 'HER2_STATUS'
        AND attribute_value = 'Negative'
  )
"
}

◀ result
{"rows":[{"profiled_her2neg":1668}]}

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

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"brca_metabric","name":"Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016)","sampleCount":2509,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_metabric","metadata":{"clinicalAttributeIds":["AGE_AT_DIAGNOSIS","BREAST_SURGERY","CANCER_TYPE","CANCER_TYPE_DETAILED","CELLULARITY","CHEMOTHERAPY","CLAUDIN_SUBTYPE","COHORT","ER_IHC","ER_STATUS","GRADE","HER2_SNP6","HER2_STATUS","HISTOLOGICAL_SUBTYPE","HORMONE_THERAPY","INFERRED_MENOPAUSAL_STATE","INTCLUST","LATERALITY","LYMPH_NODES_EXAMINED_POSITIVE","MUTATION_COUNT","NPI","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","PR_STATUS","RADIO_THERAPY","RFS_MONTHS","RFS_STATUS","SAMPLE_COUNT","SAMPLE_TYPE","SEX","THREEGENE","TMB_NONSYNONYMOUS","TUMOR_SIZE","TUMOR_STAGE","VITAL_STATUS"],"molecularProfileIds":["brca_metabric_cna","brca_metabric_methylation_promoters_rrbs","brca_metabric_mrna","brca_metabric_mrna_median_all_sample_Zscores","brca_metabric_mutations"],"heatmapProfileIds":["brca_metabric_methylation_promoters_rrbs","brca_metabric_mrna_median_all_sample_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","dna_methylation"]}}]}}

▶ navigate_to_study_view
{
 "studyIds": [
  "brca_metabric"
 ],
 "filterJson": {
  "clinicalDataFilters": [
   {
    "attributeId": "HER2_STATUS",
    "values": [
     {
      "value": "Negative"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D","data":{"studyIds":["brca_metabric"],"studies":[{"studyId":"brca_metabric","name":"Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016)","sampleCount":2509}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "brca_metabric"
 ],
 "genes": [
  "PIK3CA",
  "TP53",
  "MUC16",
  "AHNAK2",
  "GATA3",
  "KMT2C",
  "SYNE1",
  "MAP3K1",
  "CDH1",
  "AHNAK"
 ],
 "tab": "oncoprint",
 "studyViewFilter": {
  "clinicalDataFilters": [
   {
    "attributeId": "HER2_STATUS",
    "values": [
     {
      "value": "Negative"
     }
    ]
   }
  ]
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?session_id=6ab5c65fe497aad53593c264","url":"https://www.cbioportal.org/results/oncoprint?session_id=6ab5c65fe497aad53593c264","data":{"studyIds":["brca_metabric"],"studies":[{"studyId":"brca_metabric","name":"Breast Cancer (METABRIC, Nature 2012 & Nat Commun 2016)","sampleCount":2509}],"genes":["PIK3CA","TP53","MUC16","AHNAK2","GATA3","KMT2C","SYNE1","MAP3K1","CDH1","AHNAK"],"filteredSampleCount":1733,"caseSetId":"-1","sessionId":"6ab5c65fe497aad53593c264","studyViewUrl":"https://www.cbioportal.org/study?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D","pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
## Links

[Study View — HER2-Negative METABRIC Cohort](https://www.cbioportal.org/study/summary?id=brca_metabric#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22HER2_STATUS%22%2C%22values%22%3A%5B%7B%22value%22%3A%22Negative%22%7D%5D%7D%5D%7D) — 1,733 HER2-negative samples out of 2,509 total. Overview of clinical and genomic data summarized as interactive charts.

[OncoPrint — Top 10 Mutated Genes in HER2-Negative Samples](https://www.cbioportal.org/results/oncoprint?session_id=6ab5c65fe497aad53593c264) — visual summary of alterations in PIK3CA, TP53, MUC16, AHNAK2, GATA3, KMT2C, SYNE1, MAP3K1, CDH1, and AHNAK across the 1,733 filtered samples.