Q (haiku): How does PTEN alteration (mutations or homozygous deletions) affect pAKT protein levels in lung squamous cell carcinoma?

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

▶ list_guides
{}

◀ 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 … (37891 more chars)

◀ 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://gene-expression-guide"
}

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

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

◀ result
# Gene Expression Analysis Guide

This guide covers continuous-value genomic data: gene **expression**, **copy number** values, **methylation**, and related profile types. Mutation/CNA/SV *frequency* analysis lives in `cbioportal://mutation-frequency-guide`.

## Where this data lives

Continuous per-sample-per-gene values are stored in `genetic_alteration_derived`:

| Column | Description |
|---|---|
| `sample_unique_id` | `<study>_<sample_stable_id>` |
| `cancer_study_identifier` | study scope |
| `hugo_gene_symbol` | gene |
| `profile_type` | which assay/normalization (see below) |
| `alteration_value` | the actual value — stored as Nullable(String); cast with `toFloat64OrNull` |

`alteration_value` is a string because the same column hosts many different value scales. The `''` and `'NA'` sentinels mean "missing"; always filter them out and use `toFloat64OrNull(alteration_value) IS NOT NULL` for downstream math.

## Discovering profile types for a study

Different studies expose different profile types depending on what assays were run and how the data was normalized. Always check what a specific study supports before picking one:

```sql
SELECT DISTINCT profile_type
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'brca_metabric'
ORDER BY profile_type;
```

Common values across the public portal:

| Family | Profile types |
|---|---|
| mRNA expression | `mrna`, `mrna_median_Zscores`, `mrna_seq_v2_rsem`, `mrna_seq_v2_rsem_Zscores`, `mrna_seq_cpm`, `mrna_seq_fpkm`, `mrna_U133`, `mrna_outliers` |
| Copy number (continuous) | `cna`, `linear_CNA`, `log2CNA`, `cna_consensus`, `cna_rae`, `gistic` |
| Methylation | `methylation_hm27`, `methylation_hm450`, `methylation_epic`, `methylation_promoters_rrbs` |
| miRNA | `mirna`, `mirna_median_Zscores` |
| Protein | `protein_quantification`, `protein_level`, `RPPA` |

**Z-score vs raw choice.** When the user asks "is X correlated with Y", either works for Spearman (rank-based) — Pearson would care. Default to the non-Z-score variant if both exist, and call out which one in the response.

## Canonical recipe — Spearman correlation between two genes

```sql
SELECT *
FROM gene_pair_coexpression(
    study        = 'brca_metabric',
    gene_a       = 'TP53',
    gene_b       = 'MYC',
    profile_type = 'mrna'
);
```

Returns one row: `(gene_a, gene_b, profile_type, spearman_correlation, num_samples)`.

- `spearman_correlation` in [−1, 1]; `NULL` when fewer than 3 valid paired samples.
- Mirrors cbioportal-backend's `ClickhouseCoExpressionMapper.getCoExpressions`, simplified to a pair lookup (the backend computes one ref gene vs ALL other genes for the coexpression page; here the agent asks about a specific pair).

### Verified examples

| Study | gene_a | gene_b | profile_type | spearman | n |
|---|---|---|---|---|---|
| `brca_metabric` | TP53 | MYC | `mrna` | 0.118 | 1980 |
| `brca_metabric` | ESR1 | PGR | `mrna` | 0.487 | 1980 |

ESR1↔PGR is the textbook breast-cancer estrogen-receptor coregula … (2785 more chars)

◀ 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
# 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)

▶ search_oncotree
{
 "search_term": "lung squamous cell carcinoma"
}

▶ clickhouse_list_tables
{}

◀ result
{"result":[{"code":"LUSC","name":"Lung Squamous Cell Carcinoma","score":80,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUSC"},{"code":"LUAS","name":"Lung Adenosquamous Carcinoma","score":55,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAS"}]}

◀ 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_run_select_query
{
 "query": "
SELECT
    cancer_study_identifier,
    name,
    sample_count,
    mutation_sample_count,
    cna_sample_count,
    rppa_sample_count,
    mass_spectrometry_sample_count
FROM cancer_study
WHERE type_of_cancer_id = 'lusc'
  AND mutation_sample_count > 0
ORDER BY sample_count DESC
",
 "max_rows": 20
}

◀ result
{"rows":[{"cancer_study_identifier":"lusc_tcga","name":"Lung Squamous Cell Carcinoma (TCGA, Firehose Legacy)","sample_count":511,"mutation_sample_count":178,"cna_sample_count":501,"rppa_sample_count":328,"mass_spectrometry_sample_count":0},{"cancer_study_identifier":"lusc_tcga_gdc","name":"Lung Squamous Cell Carcinoma (TCGA GDC, 2025)","sample_count":503,"mutation_sample_count":484,"cna_sample_count":492,"rppa_sample_count":0,"mass_spectrometry_sample_count":0},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","sample_count":487,"mutation_sample_count":484,"cna_sample_count":487,"rppa_sample_count":317,"mass_spectrometry_sample_count":0},{"cancer_study_identifier":"lusc_tcga_pub","name":"Lung Squamous Cell Carcinoma (TCGA, Nature 2012)","sample_count":178,"mutation_sample_count":178,"cna_sample_count":178,"rppa_sample_count":0,"mass_spectrometry_sample_count":0},{"cancer_study_identifier":"lusc_cptac_gdc","name":"Lung Squamous Cell Carcinoma (CPTAC GDC, 2025)","sample_count":110,"mutation_sample_count":110,"cna_sample_count":110,"rppa_sample_count":0,"mass_spectrometry_sample_count":0},{"cancer_study_identifier":"lusc_cptac_2021","name":"Lung Squamous Cell Carcinoma (CPTAC, Cell 2021)","sample_count":80,"mutation_sample_count":108,"cna_sample_count":0,"rppa_sample_count":0,"mass_spectrometry_sample_count":0}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT DISTINCT
    genetic_profile_id,
    name,
    genetic_alteration_type,
    datatype
FROM genetic_profile
WHERE cancer_study_identifier IN ('lusc_tcga', 'lusc_tcga_pan_can_atlas_2018')
  AND genetic_alteration_type = 'PROTEIN_LEVEL'
ORDER BY genetic_profile_id
",
 "max_rows": 50
}

◀ 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 table expression identifier `cancer_study_identifier` in scope SELECT DISTINCT genetic_profile_id, name, genetic_alteration_type, datatype FROM genetic_profile WHERE (cancer_study_identifier IN ('lusc_tcga', 'lusc_tcga_pan_can_atlas_2018')) AND (genetic_alteration_type = 'PROTEIN_LEVEL') ORDER BY genetic_profile_id 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 DISTINCT
    cs.cancer_study_identifier,
    gp.stable_id,
    gp.name,
    gp.genetic_alteration_type,
    gp.datatype
FROM genetic_profile gp
JOIN cancer_study cs ON gp.cancer_study_id = cs.cancer_study_id
WHERE cs.cancer_study_identifier IN ('lusc_tcga', 'lusc_tcga_pan_can_atlas_2018')
  AND gp.genetic_alteration_type = 'PROTEIN_LEVEL'
ORDER BY cs.cancer_study_identifier, gp.stable_id
",
 "max_rows": 50
}

◀ result
{"rows":[{"cancer_study_identifier":"lusc_tcga","stable_id":"lusc_tcga_rppa","name":"Protein expression (RPPA)","genetic_alteration_type":"PROTEIN_LEVEL","datatype":"LOG2-VALUE"},{"cancer_study_identifier":"lusc_tcga","stable_id":"lusc_tcga_rppa_Zscores","name":"Protein expression z-scores (RPPA)","genetic_alteration_type":"PROTEIN_LEVEL","datatype":"Z-SCORE"},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","stable_id":"lusc_tcga_pan_can_atlas_2018_rppa","name":"Protein expression (RPPA)","genetic_alteration_type":"PROTEIN_LEVEL","datatype":"LOG2-VALUE"},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","stable_id":"lusc_tcga_pan_can_atlas_2018_rppa_Zscores","name":"Protein expression z-scores (RPPA)","genetic_alteration_type":"PROTEIN_LEVEL","datatype":"Z-SCORE"}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Step 1: Get PTEN alterations (mutations and homozygous deletions) and pAKT levels
WITH pten_alterations AS (
    SELECT 
        sample_unique_id,
        'MUTATION' as alteration_type,
        COUNT(*) as event_count
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
    GROUP BY sample_unique_id
    
    UNION ALL
    
    SELECT
        sample_unique_id,
        'HOMODELETION' as alteration_type,
        1 as event_count
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'cna'
      AND cna_alteration = -2
),
pten_status AS (
    SELECT
        sample_unique_id,
        groupArray(alteration_type) as alteration_types,
        sum(event_count) as total_events,
        if(length(alteration_types) > 0, 'ALTERED', 'WILDTYPE') as status
    FROM pten_alterations
    GROUP BY sample_unique_id
),
pakt_levels AS (
    SELECT
        sample_unique_id,
        toFloat64OrNull(alteration_value) as pakt_value
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol IN ('AKT1', 'AKT_pS473')  -- pAKT (phosphorylated AKT at Ser473)
      AND profile_type LIKE '%rppa%'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
),
combined_data AS (
    SELECT
        ps.sample_unique_id,
        ps.status as pten_status,
        arrayStringConcat(ps.alteration_types, ', ') as pten_alterations,
        ps.total_events,
        pk.pakt_value,
        if(pk.pakt_value IS NOT NULL, 'HAS_PAKT', 'NO_PAKT') as pakt_available
    FROM pten_status ps
    LEFT JOIN pakt_levels pk ON ps.sample_unique_id = pk.sample_unique_id
)
SELECT
    pten_status,
    pakt_available,
    count(*) as sample_count,
    count(pakt_value) as pakt_measured,
    round(avg(pakt_value), 3) as avg_pakt_level,
    round(stddevPop(pakt_value), 3) as std_pakt_level,
    round(min(pakt_value), 3) as min_pakt_level,
    round(max(pakt_value), 3) as max_pakt_level
FROM combined_data
GROUP BY pten_status, pakt_available
ORDER BY pten_status, pakt_available
",
 "max_rows": 100
}

◀ result
{"rows":[{"pten_status":"ALTERED","pakt_available":"HAS_PAKT","sample_count":134,"pakt_measured":134,"avg_pakt_level":0.255,"std_pakt_level":0.933,"min_pakt_level":-4.755,"max_pakt_level":1.429},{"pten_status":"ALTERED","pakt_available":"NO_PAKT","sample_count":30,"pakt_measured":0}]}

▶ clickhouse_run_select_query
{
 "query": "
-- First, let's see what AKT/phospho-AKT proteins are available in RPPA
SELECT DISTINCT
    hugo_gene_symbol,
    profile_type,
    count(*) as data_points
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
  AND (hugo_gene_symbol LIKE '%AKT%' OR hugo_gene_symbol LIKE '%pAKT%' OR hugo_gene_symbol LIKE '%phospho%')
  AND profile_type LIKE '%rppa%'
GROUP BY hugo_gene_symbol, profile_type
ORDER BY data_points DESC
",
 "max_rows": 50
}

◀ result
{"rows":[{"hugo_gene_symbol":"AKT1_PT308","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT3_PT308","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT3_PT308","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT1S1_PT246","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT2_PT308","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT1_PT308","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT2_PS473","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT2","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT3_PS473","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT1","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT2_PT308","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT1_PS473","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT3_PS473","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT2","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT1S1_PT246","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT1_PS473","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT1","profile_type":"rppa_Zscores","data_points":317},{"hugo_gene_symbol":"AKT2_PS473","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT3","profile_type":"rppa","data_points":317},{"hugo_gene_symbol":"AKT3","profile_type":"rppa_Zscores","data_points":317}]}

▶ clickhouse_run_select_query
{
 "query": "
-- PTEN alterations correlated with pAKT protein levels (RPPA)
-- Using AKT1_PS473, AKT2_PS473, AKT3_PS473 (phosphorylated AKT)
WITH pten_alterations AS (
    -- Get all PTEN mutations
    SELECT 
        sample_unique_id,
        'MUTATION' as alteration_type,
        mutation_variant
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
    
    UNION ALL
    
    -- Get PTEN homozygous deletions
    SELECT
        sample_unique_id,
        'HOMODELETION' as alteration_type,
        'HOMDEL (-2)' as mutation_variant
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'cna'
      AND cna_alteration = -2
),
pten_status AS (
    SELECT
        sample_unique_id,
        'PTEN_ALTERED' as group_label
    FROM pten_alterations
    GROUP BY sample_unique_id
),
-- Get all samples for wildtype classification
all_samples AS (
    SELECT DISTINCT sample_unique_id
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa'
),
pakt_levels AS (
    SELECT
        sample_unique_id,
        hugo_gene_symbol,
        toFloat64OrNull(alteration_value) as pakt_value
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol IN ('AKT1_PS473', 'AKT2_PS473', 'AKT3_PS473')
      AND profile_type = 'rppa'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
),
pakt_aggregated AS (
    SELECT
        sample_unique_id,
        round(avg(pakt_value), 4) as avg_pakt_level,
        count(*) as pakt_isoforms_measured
    FROM pakt_levels
    GROUP BY sample_unique_id
),
combined AS (
    SELECT
        COALESCE(ps.group_label, 'PTEN_WILDTYPE') as pten_status,
        pa.sample_unique_id,
        pa.avg_pakt_level,
        pa.pakt_isoforms_measured
    FROM all_samples s
    LEFT JOIN pten_status ps ON s.sample_unique_id = ps.sample_unique_id
    LEFT JOIN pakt_aggregated pa ON s.sample_unique_id = pa.sample_unique_id
    WHERE pa.avg_pakt_level IS NOT NULL
)
SELECT
    pten_status,
    count(*) as n_samples,
    round(avg(avg_pakt_level), 4) as mean_pakt,
    round(stddevPop(avg_pakt_level), 4) as stddev_pakt,
    round(min(avg_pakt_level), 4) as min_pakt,
    round(max(avg_pakt_level), 4) as max_pakt,
    round(quantile(avg_pakt_level)(0.25), 4) as q1_pakt,
    round(median(avg_pakt_level), 4) as median_pakt,
    round(quantile(avg_pakt_level)(0.75), 4) as q3_pakt
FROM combined
GROUP BY pten_status
ORDER BY pten_status DESC
",
 "max_rows": 100
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 36\n Code: 36. DB::Exception: Parameter for function 'quantile' expected to have constant value. Actual: avg_pakt_level. In scope WITH pten_alterations AS (SELECT sample_unique_id, 'MUTATION' AS alteration_type, mutation_variant FROM genomic_event_derived WHERE (cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'PTEN') AND (variant_type = 'mutation') AND (mutation_status != 'UNCALLED') UNION ALL SELECT sample_unique_id, 'HOMODELETION' AS alteration_type, 'HOMDEL (-2)' AS mutation_variant FROM genomic_event_derived WHERE (cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol = 'PTEN') AND (variant_type = 'cna') AND (cna_alteration = -2)), pten_status AS (SELECT sample_unique_id, 'PTEN_ALTERED' AS group_label FROM pten_alterations GROUP BY sample_unique_id), all_samples AS (SELECT DISTINCT sample_unique_id FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') AND (profile_type = 'rppa')), pakt_levels AS (SELECT sample_unique_id, hugo_gene_symbol, toFloat64OrNull(alteration_value) AS pakt_value FROM genetic_alteration_derived WHERE (cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018') AND (hugo_gene_symbol IN ('AKT1_PS473', 'AKT2_PS473', 'AKT3_PS473')) AND (profile_type = 'rppa') AND (alteration_value NOT IN ('', 'NA')) AND (toFloat64OrNull(alteration_value) IS NOT NULL)), pakt_aggregated AS (SELECT sample_unique_id, round(avg(pakt_value), 4) AS avg_pakt_level, count(*) AS pakt_isoforms_measured FROM pakt_levels GROUP BY sample_unique_id), combined AS (SELECT COALESCE(ps.group_label, 'PTEN_WILDTYPE') AS pten_status, pa.sample_unique_id, pa.avg_pakt_level, pa.pakt_isoforms_measured FROM all_samples AS s LEFT JOIN pten_status AS ps ON s.sample_unique_id = ps.sample_unique_id LEFT JOIN pakt_aggregated AS pa ON s.sample_unique_id = pa.sample_unique_id WHERE pa.avg_pakt_level IS NOT NULL) SELECT pten_status, count(*) AS n_samples, round(avg(avg_pakt_level), 4) AS mean_pakt, round(stddevPop(avg_pakt_level), 4) AS stddev_pakt, round(min(avg_pakt_level), 4) AS min_pakt, round(max(avg_pakt_level), 4) AS max_pakt, round(quantile(avg_pakt_level)(0.25), 4) AS q1_pakt, round(median(avg_pakt_level), 4) AS median_pakt, round(quantile(avg_pakt_level)(0.75), 4) AS q3_pakt FROM combined GROUP BY pten_status ORDER BY pten_status DESC. (BAD_ARGUMENTS) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
-- PTEN alterations correlated with pAKT protein levels (RPPA)
-- Using AKT1_PS473, AKT2_PS473, AKT3_PS473 (phosphorylated AKT)
WITH pten_alterations AS (
    -- Get all PTEN mutations
    SELECT 
        sample_unique_id,
        'MUTATION' as alteration_type
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
    
    UNION ALL
    
    -- Get PTEN homozygous deletions
    SELECT
        sample_unique_id,
        'HOMODELETION' as alteration_type
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'cna'
      AND cna_alteration = -2
),
pten_status AS (
    SELECT
        sample_unique_id,
        'PTEN_ALTERED' as group_label
    FROM pten_alterations
    GROUP BY sample_unique_id
),
-- Get all samples for wildtype classification
all_samples AS (
    SELECT DISTINCT sample_unique_id
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa'
),
pakt_levels AS (
    SELECT
        sample_unique_id,
        hugo_gene_symbol,
        toFloat64OrNull(alteration_value) as pakt_value
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol IN ('AKT1_PS473', 'AKT2_PS473', 'AKT3_PS473')
      AND profile_type = 'rppa'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
),
pakt_aggregated AS (
    SELECT
        sample_unique_id,
        round(avg(pakt_value), 4) as avg_pakt_level,
        count(*) as pakt_isoforms_measured
    FROM pakt_levels
    GROUP BY sample_unique_id
),
combined AS (
    SELECT
        COALESCE(ps.group_label, 'PTEN_WILDTYPE') as pten_status,
        pa.sample_unique_id,
        pa.avg_pakt_level,
        pa.pakt_isoforms_measured
    FROM all_samples s
    LEFT JOIN pten_status ps ON s.sample_unique_id = ps.sample_unique_id
    LEFT JOIN pakt_aggregated pa ON s.sample_unique_id = pa.sample_unique_id
    WHERE pa.avg_pakt_level IS NOT NULL
)
SELECT
    pten_status,
    count(*) as n_samples,
    round(avg(avg_pakt_level), 4) as mean_pakt,
    round(stddevPop(avg_pakt_level), 4) as stddev_pakt,
    round(min(avg_pakt_level), 4) as min_pakt,
    round(max(avg_pakt_level), 4) as max_pakt,
    round(median(avg_pakt_level), 4) as median_pakt
FROM combined
GROUP BY pten_status
ORDER BY pten_status DESC
",
 "max_rows": 100
}

◀ result
{"rows":[{"pten_status":"PTEN_ALTERED","n_samples":67,"mean_pakt":-0.1138,"stddev_pakt":0.9212,"min_pakt":-2.1709,"max_pakt":2.3692,"median_pakt":-0.157},{"n_samples":250,"mean_pakt":-0.7851,"stddev_pakt":0.7561,"min_pakt":-2.1009,"max_pakt":2.017,"median_pakt":-0.8795}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Detailed breakdown of PTEN alterations and pAKT levels
WITH pten_mutations AS (
    SELECT 
        sample_unique_id,
        'MUTATION' as alteration_type,
        mutation_variant,
        mutation_type
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
),
pten_deletions AS (
    SELECT
        sample_unique_id,
        'HOMODELETION' as alteration_type,
        'HOMDEL (-2)' as mutation_variant,
        'CNA' as mutation_type
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'cna'
      AND cna_alteration = -2
),
pakt_levels AS (
    SELECT
        sample_unique_id,
        toFloat64OrNull(alteration_value) as pakt_value
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol IN ('AKT1_PS473', 'AKT2_PS473', 'AKT3_PS473')
      AND profile_type = 'rppa'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
),
pakt_aggregated AS (
    SELECT
        sample_unique_id,
        round(avg(pakt_value), 4) as avg_pakt_level
    FROM pakt_levels
    GROUP BY sample_unique_id
),
all_pten_alterations AS (
    SELECT * FROM pten_mutations
    UNION ALL
    SELECT * FROM pten_deletions
),
combined AS (
    SELECT
        pa.alteration_type,
        pa.mutation_type,
        pk.avg_pakt_level
    FROM all_pten_alterations pa
    LEFT JOIN pakt_aggregated pk ON pa.sample_unique_id = pk.sample_unique_id
)
SELECT
    alteration_type,
    mutation_type,
    count(*) as n_samples,
    countIf(avg_pakt_level IS NOT NULL) as n_with_pakt_data,
    round(avg(avg_pakt_level), 4) as mean_pakt,
    round(stddevPop(avg_pakt_level), 4) as stddev_pakt,
    round(median(avg_pakt_level), 4) as median_pakt,
    round(min(avg_pakt_level), 4) as min_pakt,
    round(max(avg_pakt_level), 4) as max_pakt
FROM combined
GROUP BY alteration_type, mutation_type
ORDER BY alteration_type, mutation_type
",
 "max_rows": 100
}

◀ result
{"rows":[{"alteration_type":"HOMODELETION","mutation_type":"CNA","n_samples":48,"n_with_pakt_data":34,"mean_pakt":-0.1384,"stddev_pakt":0.9204,"median_pakt":-0.139,"min_pakt":-2.1709,"max_pakt":1.5714},{"alteration_type":"MUTATION","mutation_type":"Frame_Shift_Del","n_samples":11,"n_with_pakt_data":8,"mean_pakt":0.0891,"stddev_pakt":0.7647,"median_pakt":-0.145,"min_pakt":-0.8446,"max_pakt":1.6611},{"alteration_type":"MUTATION","mutation_type":"Frame_Shift_Ins","n_samples":4,"n_with_pakt_data":3,"mean_pakt":-0.0217,"stddev_pakt":0.9349,"median_pakt":-0.0669,"min_pakt":-1.1434,"max_pakt":1.1453},{"alteration_type":"MUTATION","mutation_type":"Missense_Mutation","n_samples":22,"n_with_pakt_data":14,"mean_pakt":-0.1052,"stddev_pakt":0.8878,"median_pakt":-0.2266,"min_pakt":-1.0683,"max_pakt":2.3692},{"alteration_type":"MUTATION","mutation_type":"Nonsense_Mutation","n_samples":12,"n_with_pakt_data":6,"mean_pakt":0.0872,"stddev_pakt":0.7669,"median_pakt":0.0358,"min_pakt":-0.9916,"max_pakt":1.4679},{"alteration_type":"MUTATION","mutation_type":"Splice_Site","n_samples":4,"n_with_pakt_data":3,"mean_pakt":-0.9401,"stddev_pakt":1.0858,"median_pakt":-1.7074,"min_pakt":-1.7084,"max_pakt":0.5954}]}

▶ clickhouse_run_select_query
{
 "query": "
-- Summary statistics on PTEN alteration frequency and pAKT levels
WITH pten_events AS (
    -- PTEN mutations
    SELECT 
        sample_unique_id,
        'PTEN_MUTATED' as status
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
    
    UNION ALL
    
    -- PTEN homozygous deletions
    SELECT
        sample_unique_id,
        'PTEN_DELETED' as status
    FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'cna'
      AND cna_alteration = -2
),
pten_unique AS (
    SELECT DISTINCT sample_unique_id FROM pten_events
),
-- Profiled samples for PTEN
pten_profiled AS (
    SELECT COUNT(DISTINCT sample_unique_id) as profiled_count
    FROM mutation_panel_gene_coverage
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND alteration_type = 'MUTATION_EXTENDED'
    
    UNION ALL
    
    SELECT COUNT(DISTINCT sample_unique_id) as profiled_count
    FROM mutation_wes_coverage
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
),
mutation_and_cna_profiled AS (
    -- Get samples profiled for both mutation and CNA
    SELECT COUNT(DISTINCT s1.sample_unique_id) as mutation_cna_profiled
    FROM mutation_panel_gene_coverage s1
    WHERE s1.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND s1.hugo_gene_symbol = 'PTEN'
      AND s1.alteration_type = 'MUTATION_EXTENDED'
      AND EXISTS (
        SELECT 1 FROM cna_panel_gene_coverage s2
        WHERE s2.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
          AND s2.hugo_gene_symbol = 'PTEN'
          AND s2.alteration_type = 'COPY_NUMBER_ALTERATION'
          AND s2.sample_unique_id = s1.sample_unique_id
      )
)
SELECT
    count(DISTINCT sample_unique_id) as pten_altered_samples,
    (SELECT mutation_cna_profiled FROM mutation_and_cna_profiled) as samples_profiled_for_pten_alterations,
    round(100.0 * count(DISTINCT sample_unique_id) / 
          (SELECT mutation_cna_profiled FROM mutation_and_cna_profiled), 2) as pten_alteration_frequency_pct
FROM pten_unique
",
 "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: Identifier 's1.alteration_type' cannot be resolved from table with name s1. In scope  mutation_and_cna_profiled. (UNKNOWN_IDENTIFIER) (version 26.4.1.2359 (official build))\n"}

▶ clickhouse_run_select_query
{
 "query": "
-- Summary: PTEN mutation/deletion frequency and association with pAKT levels
-- Lung Squamous Cell Carcinoma (TCGA PanCancer Atlas)

-- Count PTEN-altered samples
SELECT 
    'PTEN alterations' as metric,
    count(DISTINCT sample_unique_id) as value
FROM (
    SELECT sample_unique_id FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'mutation'
      AND mutation_status != 'UNCALLED'
    
    UNION ALL
    
    SELECT sample_unique_id FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND variant_type = 'cna'
      AND cna_alteration = -2
)

UNION ALL

-- Total samples with RPPA data
SELECT 
    'Samples with pAKT (RPPA) data' as metric,
    COUNT(DISTINCT sample_unique_id) as value
FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
  AND hugo_gene_symbol IN ('AKT1_PS473', 'AKT2_PS473', 'AKT3_PS473')
  AND profile_type = 'rppa'
  AND alteration_value NOT IN ('', 'NA')
  AND toFloat64OrNull(alteration_value) IS NOT NULL

UNION ALL

-- PTEN-altered samples with pAKT data
SELECT 
    'PTEN-altered with pAKT data' as metric,
    COUNT(DISTINCT p.sample_unique_id) as value
FROM (
    SELECT DISTINCT sample_unique_id FROM genomic_event_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND hugo_gene_symbol = 'PTEN'
      AND (
          (variant_type = 'mutation' AND mutation_status != 'UNCALLED')
          OR (variant_type = 'cna' AND cna_alteration = -2)
      )
) p
INNER JOIN genetic_alteration_derived g ON p.sample_unique_id = g.sample_unique_id
WHERE g.cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
  AND g.hugo_gene_symbol IN ('AKT1_PS473', 'AKT2_PS473', 'AKT3_PS473')
  AND g.profile_type = 'rppa'
  AND g.alteration_value NOT IN ('', 'NA')
",
 "max_rows": 100
}

◀ result
{"rows":[{"metric":"Samples with pAKT (RPPA) data","value":317},{"metric":"PTEN alterations","value":97},{"metric":"PTEN-altered with pAKT data","value":67}]}

▶ resolve_and_route
{
 "studyIds": [
  "lusc_tcga_pan_can_atlas_2018"
 ]
}

◀ result
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▶ navigate_to_group_comparison
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◀ result
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▶ navigate_to_results_view
{
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◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/oncoprint?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN&case_set_id=lusc_tcga_pan_can_atlas_2018_all&heatmap_track_groups=lusc_tcga_pan_can_atlas_2018_rppa_Zscores%2CAKT1_PS473%2CAKT2_PS473%2CAKT3_PS473","url":"https://www.cbioportal.org/results/oncoprint?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN&case_set_id=lusc_tcga_pan_can_atlas_2018_all&heatmap_track_groups=lusc_tcga_pan_can_atlas_2018_rppa_Zscores%2CAKT1_PS473%2CAKT2_PS473%2CAKT3_PS473","data":{"studyIds":["lusc_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","sampleCount":487}],"genes":["PTEN"],"caseSetId":"lusc_tcga_pan_can_atlas_2018_all","oncoprintHeatmapTracks":[{"molecularProfileId":"lusc_tcga_pan_can_atlas_2018_rppa_Zscores","entities":["AKT1_PS473","AKT2_PS473","AKT3_PS473"]}],"pageDescription":"Visual summary of alterations in queried genes across samples."}}

═ answer (success)
Perfect! Now let me create a summary of my findings:

## Summary: PTEN Alterations and pAKT Protein Levels in Lung Squamous Cell Carcinoma

Based on analysis of the **TCGA PanCancer Atlas Lung Squamous Cell Carcinoma (LUSC)** cohort (487 samples), here are the key findings:

### **pAKT Expression in PTEN-Altered vs Wildtype Samples**

| Group | N Samples | Mean pAKT (RPPA) | Median pAKT | Std Dev | Min | Max |
|-------|-----------|-----------------|------------|---------|-----|-----|
| **PTEN-Altered** | 67 | **-0.114** | **-0.157** | 0.921 | -2.171 | 2.369 |
| **PTEN Wildtype** | 250 | **-0.785** | **-0.880** | 0.756 | -2.101 | 2.017 |

### **Key Finding:**
**PTEN-altered samples have HIGHER pAKT levels** (mean: -0.114) compared to PTEN wildtype samples (mean: -0.785), a difference of **0.67 log2-units**. This result **supports the expected biological relationship**: loss of PTEN phosphatase function impairs dephosphorylation of AKT, leading to increased phosphorylated AKT (pAKT) accumulation.

### **PTEN Alteration Breakdown (N=97 samples):**

| Alteration Type | N | Type(s) | Mean pAKT | Median pAKT |
|-----------------|---|---------|-----------|------------|
| **Homozygous Deletion** | 48 | Copy number -2 | -0.138 | -0.139 |
| **Missense Mutation** | 22 | Point mutations | -0.105 | -0.227 |
| **Frameshift Deletion** | 11 | Truncating | +0.089 | -0.145 |
| **Nonsense Mutation** | 12 | Truncating | +0.087 | +0.036 |
| **Splice Site** | 4 | Truncating | -0.940 | -1.707 |
| **Frameshift Insertion** | 4 | Truncating | -0.022 | -0.067 |

### **Sample Coverage:**
- **PTEN-altered samples in cohort:** 97 (20% of 487 LUSC samples)
- **With pAKT RPPA data:** 67 (69% of altered samples)
- **Total samples with pAKT data:** 317 (65% of cohort)

### **Biological Interpretation:**
The **positive association** between PTEN loss (mutations or deletions) and higher pAKT levels is consistent with known PI3K/AKT/mTOR pathway biology:
- **PTEN** is a phosphatase that negatively regulates the PI3K pathway
- **Loss of PTEN** removes this inhibition, allowing **increased AKT phosphorylation** and activation
- **Phosphorylated AKT (pAKT)** is the active form that drives pro-survival signaling

### **Interactive Visualization:**

[View PTEN alterations with pAKT heatmap in OncoPrint](https://www.cbioportal.org/results/oncoprint?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN&case_set_id=lusc_tcga_pan_can_atlas_2018_all&heatmap_track_groups=lusc_tcga_pan_can_atlas_2018_rppa_Zscores%2CAKT1_PS473%2CAKT2_PS473%2CAKT3_PS473)

This visualization displays:
- **OncoPrint:** PTEN mutations and deletions across 487 LUSC samples
- **Heatmap tracks:** pAKT levels (phospho-AKT at Serine 473) for AKT1, AKT2, and AKT3 isoforms (RPPA z-scores)
- **Color intensity:** Red = elevated pAKT; Blue = reduced pAKT

**Note:** These are correlative findings from genomic/proteomic data; causation would require functional validation.