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

▶ list_guides
{}

▶ resolve_and_route
{
 "studyKeywords": [
  "lung",
  "squamous",
  "TCGA"
 ]
}

◀ 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"}]}

◀ result
{"success":true,"message":"Found 4 matching studies. Pick the best match (prefer TCGA → prefer PanCancer Atlas), use its metadata to call the appropriate navigation tool(s) and generate URLs immediately. Present other studies as alternatives.","data":{"totalCount":4,"studiesWithMetadata":[{"studyId":"lusc_tcga","name":"Lung Squamous Cell Carcinoma (TCGA, Firehose Legacy)","sampleCount":511,"studyViewUrl":"https://www.cbioportal.org/study?id=lusc_tcga","metadata":{"clinicalAttributeIds":["AGE","AJCC_CLINICAL_TUMOR_STAGE","AJCC_METASTASIS_CLINICAL_CM","AJCC_METASTASIS_PATHOLOGIC_PM","AJCC_NODES_CLINICAL_CN","AJCC_NODES_CLINICAL_CT","AJCC_NODES_PATHOLOGIC_PN","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","AJCC_TUMOR_PATHOLOGIC_PT","ALK_ANALYSIS_TYPE","ALK_TRANSLOCATION_STATUS","ALK_TRANSLOCATION_VARIANT","CANCER_TYPE","CANCER_TYPE_DETAILED","CARBON_MONOXIDE_DIFFUSION_DLCO","DAYS_TO_COLLECTION","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DAYS_TO_PATIENT_PROGRESSION_FREE","DAYS_TO_SPECIMEN_COLLECTION","DAYS_TO_TUMOR_PROGRESSION","DFS_MONTHS","DFS_STATUS","DISEASE_CODE","ECOG_SCORE","ETHNICITY","EXTRANODAL_INVOLVEMENT","FEV1_FVC_RATIO_POSTBRONCHOLIATOR","FEV1_FVC_RATIO_PREBRONCHOLIATOR","FEV1_PERCENT_REF_POSTBRONCHOLIATOR","FEV1_PERCENT_REF_PREBRONCHOLIATOR","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","HISTOLOGICAL_DIAGNOSIS","HISTORY_IMMUNOLOGICAL_DISEASE","HISTORY_IMMUNOLOGICAL_DISEASE_OTHER","HISTORY_NEOADJUVANT_TRTYN","HISTORY_OTHER_MALIGNANCY","HISTORY_RELEVANT_INFECTIOUS_DX","HIV_STATUS","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","INITIAL_PATHOLOGIC_DX_YEAR","IS_FFPE","KARNOFSKY_PERFORMANCE_SCORE","KRAS_GENE_ANALYSIS_INDICATOR","KRAS_MUTATION","KRAS_MUTATION_IDENTIFIED_TYPE","LATERALITY","LOCATION_LUNG_PARENCHYMA","LONGEST_DIMENSION","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT","METHOD_OF_INITIAL_SAMPLE_PROCUREMENT_OTHER","METHOD_OF_SAMPLE_PROCUREMENT","MUTATION_COUNT","MUTATION_STATUS","MUTATION_TYPE","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","NUMBER_OF_LYMPHNODES_POSITIVE_BY_IHC","OCT_EMBEDDED","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_METHOD_OF_SAMPLE_PROCUREMENT","OTHER_PATIENT_ID","OTHER_SAMPLE_ID","PATHOLOGY_REPORT_FILE_NAME","PATHOLOGY_REPORT_UUID","PERFORMANCE_STATUS_TIMING","PHARMACEUTICAL_TX_ADJUVANT","POS_LYMPH_NODE_LOCATION","PRIMARY_SITE_OTHER","PRIMARY_SITE_PATIENT","PROJECT_CODE","PROSPECTIVE_COLLECTION","PULMONARY_FUNCTION_TEST_INDICATOR","RACE","RADIATION_TREATMENT_ADJUVANT","RESIDUAL_TUMOR","RETROSPECTIVE_COLLECTION","SAMPLE_COUNT","SAMPLE_INITIAL_WEIGHT","SAMPLE_TYPE","SAMPLE_TYPE_ID","SEX","SHORTEST_DIMENSION","SITE_OF_TUMOR_TISSUE","SMOKING_PACK_YEARS","SMOKING_YEAR_STARTED","SMOKING_YEAR_STOPPED","SOMATIC_STATUS","SPECIMEN_CURRENT_WEIGHT","SPECIMEN_FREEZING_METHOD","SPECIMEN_SECOND_LONGEST_DIMENSION","STAGE_OTHER","TARGETED_MOLECULAR_THERAPY","TIME_BETWEEN_CLAMPING_AND_FREEZING","TIME_BETWEEN_EXCISION_AND_FREEZING","TISSUE_SOURCE_SITE","TMB_NONSYNONYMOUS","TOBACCO_SMOKING_HISTORY_INDICATOR … (6489 more chars)

▶ read_guide
{
 "uri": "cbioportal://gene-expression-guide"
}

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-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)

▶ clickhouse_list_table_columns
{
 "table": "genetic_alteration_derived"
}

◀ result
Statistical Test Selection Guide
================================

Purpose
-------
This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults.

HARD RULES — NEVER FABRICATE A STATISTIC
----------------------------------------
ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically:

1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."*
2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead.
3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median".
4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do.
5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison.

If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number.

Ambiguous-Term Clarification (before any analysis)
--------------------------------------------------
If the user's question uses an ambiguous term, ask before computing anything. Examples:

- **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars)

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"hugo_gene_symbol","type":"String"},{"name":"profile_type","type":"LowCardinality(String)"},{"name":"alteration_value","type":"Nullable(String)"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT DISTINCT hugo_gene_symbol FROM genetic_alteration_derived WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND profile_type = 'rppa' AND hugo_gene_symbol ILIKE '%AKT%'"
}

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

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

◀ 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_run_select_query
{
 "query": "
WITH pten_altered AS (
    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))
),
pakt AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa'
      AND hugo_gene_symbol = 'AKT1_PS473'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    CASE WHEN pa.sample_unique_id IS NOT NULL THEN 'PTEN_altered' ELSE 'PTEN_wildtype' END AS grp,
    count() AS n,
    avg(pk.v) AS mean_pAKT_S473,
    quantile(0.5)(pk.v) AS median_pAKT_S473,
    stddevPop(pk.v) AS sd_pAKT_S473
FROM pakt pk
LEFT JOIN pten_altered pa USING (sample_unique_id)
GROUP BY grp
"
}

◀ result
{"rows":[{"grp":"PTEN_altered","n":317,"mean_pAKT_S473":-0.6432441826182965,"median_pAKT_S473":-0.773582418,"sd_pAKT_S473":0.8398574837387742}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH pten_altered AS (
    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))
),
pakt AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa'
      AND hugo_gene_symbol = 'AKT1_PT308'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    CASE WHEN pa.sample_unique_id IS NOT NULL THEN 'PTEN_altered' ELSE 'PTEN_wildtype' END AS grp,
    count() AS n,
    avg(pk.v) AS mean_pAKT_T308,
    quantile(0.5)(pk.v) AS median_pAKT_T308,
    stddevPop(pk.v) AS sd_pAKT_T308
FROM pakt pk
LEFT JOIN pten_altered pa USING (sample_unique_id)
GROUP BY grp
"
}

◀ result
{"rows":[{"grp":"PTEN_altered","n":317,"mean_pAKT_T308":0.30527145221766566,"median_pAKT_T308":0.150930597,"sd_pAKT_T308":0.7484523626121735}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT
  (SELECT 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') AS pten_mut_events,
  (SELECT uniqExact(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) AS pten_homdel_samples
"
}

◀ result
{"rows":[{"pten_mut_events":53,"pten_homdel_samples":48}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT count() FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
  AND profile_type = 'rppa' AND hugo_gene_symbol = 'AKT1_PS473'
  AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL
"
}

◀ result
{"rows":[{"count()":317}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT sample_unique_id FROM genetic_alteration_derived
WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
  AND profile_type = 'rppa' AND hugo_gene_symbol = 'AKT1_PS473'
LIMIT 5
"
}

◀ result
{"rows":[{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-18-4721-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-18-5592-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1071-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1072-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1075-01"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT sample_unique_id FROM genomic_event_derived
WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'PTEN'
LIMIT 5
"
}

◀ result
{"rows":[{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-18-3406-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-18-3415-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1075-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1081-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-5783-01"}]}

▶ clickhouse_run_select_query
{
 "query": "
SELECT uniqExact(sample_unique_id) AS n 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))
"
}

◀ result
{"rows":[{"n":97}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH pten_altered AS (
    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))
),
pakt AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa'
      AND hugo_gene_symbol = 'AKT1_PS473'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT pk.sample_unique_id, pk.v, pa.sample_unique_id AS matched
FROM pakt pk
LEFT JOIN pten_altered pa ON pk.sample_unique_id = pa.sample_unique_id
LIMIT 10
"
}

◀ result
{"rows":[{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-18-4721-01","v":-0.583782556},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-18-5592-01","v":-1.421245785},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1071-01","v":0.004583385},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1072-01","v":0.620156021},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1075-01","v":-0.067119426,"matched":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1075-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1076-01","v":-1.412203675},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1079-01","v":-0.512596156},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1080-01","v":-1.400767662},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1081-01","v":-0.442841839,"matched":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1081-01"},{"sample_unique_id":"lusc_tcga_pan_can_atlas_2018_TCGA-21-1082-01","v":-0.795068277}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH pten_altered AS (
    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))
),
pakt AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa'
      AND hugo_gene_symbol = 'AKT1_PS473'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    CASE WHEN pa.sample_unique_id != '' THEN 'PTEN_altered' ELSE 'PTEN_wildtype' END AS grp,
    count() AS n,
    avg(pk.v) AS mean_v,
    quantile(0.5)(pk.v) AS median_v,
    stddevPop(pk.v) AS sd_v
FROM pakt pk
LEFT JOIN pten_altered pa ON pk.sample_unique_id = pa.sample_unique_id
GROUP BY grp
"
}

◀ result
{"rows":[{"grp":"PTEN_wildtype","n":250,"mean_v":-0.785136678316,"median_v":-0.8795417725,"sd_v":0.7561033596090063},{"grp":"PTEN_altered","n":67,"mean_v":-0.11379457180597018,"median_v":-0.157027605,"sd_v":0.9212388935765028}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH pten_altered AS (
    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))
),
pakt AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa'
      AND hugo_gene_symbol = 'AKT1_PT308'
      AND alteration_value NOT IN ('', 'NA')
      AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    CASE WHEN pa.sample_unique_id != '' THEN 'PTEN_altered' ELSE 'PTEN_wildtype' END AS grp,
    count() AS n,
    avg(pk.v) AS mean_v,
    quantile(0.5)(pk.v) AS median_v,
    stddevPop(pk.v) AS sd_v
FROM pakt pk
LEFT JOIN pten_altered pa ON pk.sample_unique_id = pa.sample_unique_id
GROUP BY grp
"
}

◀ result
{"rows":[{"grp":"PTEN_wildtype","n":250,"mean_v":0.16535944556400003,"median_v":0.0072118955,"sd_v":0.6636213407716294},{"grp":"PTEN_altered","n":67,"mean_v":0.8273311785373133,"median_v":0.837577675,"sd_v":0.813364014408818}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH pten_mut AS (
    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'
),
pten_homdel AS (
    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 = 'cna' AND cna_alteration = -2
),
pakt AS (
    SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS v
    FROM genetic_alteration_derived
    WHERE cancer_study_identifier = 'lusc_tcga_pan_can_atlas_2018'
      AND profile_type = 'rppa' AND hugo_gene_symbol = 'AKT1_PS473'
      AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL
)
SELECT
    multiIf(m.sample_unique_id != '' AND h.sample_unique_id != '', 'PTEN_mut+homdel',
            m.sample_unique_id != '', 'PTEN_mut_only',
            h.sample_unique_id != '', 'PTEN_homdel_only',
            'PTEN_wildtype') AS grp,
    count() AS n,
    avg(pk.v) AS mean_pAKT_S473,
    quantile(0.5)(pk.v) AS median_pAKT_S473
FROM pakt pk
LEFT JOIN pten_mut m ON pk.sample_unique_id = m.sample_unique_id
LEFT JOIN pten_homdel h ON pk.sample_unique_id = h.sample_unique_id
GROUP BY grp
ORDER BY grp
"
}

◀ result
{"rows":[{"grp":"PTEN_homdel_only","n":34,"mean_pAKT_S473":-0.1384449290588235,"median_pAKT_S473":-0.138940344},{"grp":"PTEN_mut_only","n":33,"mean_pAKT_S473":-0.08839723403030303,"median_pAKT_S473":-0.204561106},{"grp":"PTEN_wildtype","n":250,"mean_pAKT_S473":-0.785136678316,"median_pAKT_S473":-0.8795417725}]}

▶ get_studyviewfilter_options
{
 "studyId": "lusc_tcga_pan_can_atlas_2018",
 "geneSpecificQueries": [
  {
   "hugoGeneSymbol": "PTEN",
   "profileType": "mutations"
  },
  {
   "hugoGeneSymbol": "PTEN",
   "profileType": "gistic"
  }
 ]
}

◀ result
{"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"lusc_tcga_pan_can_atlas_2018","geneSpecificCounts":[{"hugoGeneSymbol":"PTEN","profileType":"mutations","counts":[{"value":"Nonsense_Mutation","label":"Nonsense Mutation","count":12},{"value":"Frame_Shift_Ins","label":"Frame Shift Ins","count":4},{"value":"Splice_Site","label":"Splice Site","count":4},{"value":"Frame_Shift_Del","label":"Frame Shift Del","count":11},{"value":"Missense_Mutation","label":"Missense Mutation","count":22}]},{"hugoGeneSymbol":"PTEN","profileType":"gistic","counts":[{"value":"NA","label":"NA","count":0},{"value":"-2","label":"Homozygously deleted","count":48},{"value":"1","label":"Gained","count":36},{"value":"-1","label":"Heterozygously deleted","count":218},{"value":"0","label":"Diploid","count":185}]}]}}

▶ navigate_to_group_comparison
{
 "studyIds": [
  "lusc_tcga_pan_can_atlas_2018"
 ],
 "tab": "protein",
 "groups": [
  {
   "name": "PTEN altered (mut or HOMDEL)",
   "studyViewFilter": {
    "geneFilters": [
     {
      "molecularProfileIds": [
       "lusc_tcga_pan_can_atlas_2018_mutations",
       "lusc_tcga_pan_can_atlas_2018_gistic"
      ],
      "geneQueries": [
       [
        {
         "hugoGeneSymbol": "PTEN",
         "alterations": [
          "HOMDEL"
         ]
        },
        {
         "hugoGeneSymbol": "PTEN"
        }
       ]
      ]
     }
    ]
   }
  },
  {
   "name": "PTEN wildtype / other",
   "isUnselected": true
  }
 ]
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/protein?comparisonId=6ab5ba3ae497aad53593c253","url":"https://www.cbioportal.org/comparison/protein?comparisonId=6ab5ba3ae497aad53593c253","data":{"description":"Group comparison (2 custom groups)","studies":["lusc_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"PTEN altered (mut or HOMDEL)","sampleCount":97},{"name":"PTEN wildtype / other","sampleCount":390}],"studyViewUrl":"https://www.cbioportal.org/study?id=lusc_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"PTEN altered (mut or HOMDEL)","url":"https://www.cbioportal.org/study?id=lusc_tcga_pan_can_atlas_2018#filterJson=%7B%22geneFilters%22%3A%5B%7B%22molecularProfileIds%22%3A%5B%22lusc_tcga_pan_can_atlas_2018_gistic%22%2C%22lusc_tcga_pan_can_atlas_2018_mutations%22%5D%2C%22geneQueries%22%3A%5B%5B%7B%22hugoGeneSymbol%22%3A%22PTEN%22%2C%22alterations%22%3A%5B%22HOMDEL%22%5D%7D%2C%7B%22hugoGeneSymbol%22%3A%22PTEN%22%7D%5D%5D%7D%5D%7D"}],"pageDescription":"Protein expression for all genes compared across PTEN altered (mut or HOMDEL) (97) vs PTEN wildtype / other (390)."}}

▶ navigate_to_results_view
{
 "studyIds": [
  "lusc_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "PTEN: MUT HOMDEL",
  "AKT1"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "GENOTYPE",
  "selectedGeneOption": "PTEN"
 },
 "plotsVertSelection": {
  "dataType": "protein",
  "selectedGeneOption": "AKT1",
  "selectedDataSourceOption": "rppa"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+MUT+HOMDEL%0AAKT1&case_set_id=lusc_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%225728%22%2C%22dataType%22%3A%22GENOTYPE%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%22207%22%2C%22dataType%22%3A%22protein%22%2C%22selectedDataSourceOption%22%3A%22rppa%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+MUT+HOMDEL%0AAKT1&case_set_id=lusc_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%225728%22%2C%22dataType%22%3A%22GENOTYPE%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%22207%22%2C%22dataType%22%3A%22protein%22%2C%22selectedDataSourceOption%22%3A%22rppa%22%7D","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","AKT1"],"caseSetId":"lusc_tcga_pan_can_atlas_2018_all","pageDescription":"Scatter/box plots: AKT1 protein (y-axis) by PTEN genotype (x-axis)."}}

═ answer (success)
## Results (LUSC, TCGA PanCancer Atlas, n=487)

**Cohort:** 97 PTEN-altered samples (49 mutation-only, 48 homozygous deletion-only, 33 with a coding mutation; some overlap) vs. 250 PTEN-wildtype samples with RPPA data available (317 total RPPA-profiled samples).

**pAKT (Ser473) — AKT1_PS473 RPPA antibody:**

| Group | N | Mean | Median | SD |
|---|---|---|---|---|
| PTEN altered (mut or HOMDEL) | 67 | −0.114 | −0.157 | 0.921 |
| PTEN wildtype | 250 | −0.785 | −0.880 | 0.756 |

**pAKT (Thr308) — AKT1_PT308 RPPA antibody:**

| Group | N | Mean | Median | SD |
|---|---|---|---|---|
| PTEN altered (mut or HOMDEL) | 67 | 0.827 | 0.838 | 0.813 |
| PTEN wildtype | 250 | 0.165 | 0.007 | 0.664 |

**By alteration subtype (pAKT S473):**

| Subtype | N | Mean | Median |
|---|---|---|---|
| PTEN mutation only | 33 | −0.088 | −0.205 |
| PTEN HOMDEL only | 34 | −0.138 | −0.139 |
| PTEN wildtype | 250 | −0.785 | −0.880 |

**Interpretation of the raw numbers:** RPPA values are median-centered protein Z-scores, so higher values indicate higher relative phosphorylation. PTEN-altered samples show markedly higher pAKT levels at both phosphosites (S473 and T308) relative to PTEN-wildtype samples — consistent with PTEN's canonical role as a negative regulator of PI3K/AKT signaling. Mutation-only and HOMDEL-only subgroups show similarly elevated pAKT relative to wildtype, without a striking difference between the two alteration types.

**Statistical caveat:** This is a 2-group comparison of continuous RPPA protein values (a per-antibody Z-score). Per the decision matrix, the appropriate test is a **Student's t-test** (or Wilcoxon rank-sum if normality is questionable given the skew). I have not computed a p-value — do this in cBioPortal's Group Comparison → Protein tab (below) or via `scipy.stats.ttest_ind` / `mannwhitneyu`. Given the effect size and sample sizes, a significant difference is plausible, but I cannot state significance without running the test.

**Caveat on gene identity:** This uses the AKT1 phospho-antibodies from the LUSC RPPA panel (AKT1_PS473, AKT1_PT308) — the RPPA platform's phospho-AKT antibodies are pan-AKT reactive in practice but are annotated under AKT1 in this dataset.

---

### Navigate

[Group Comparison: PTEN-altered vs PTEN-wildtype, Protein tab](https://www.cbioportal.org/comparison/protein?comparisonId=6ab5ba3ae497aad53593c253) — 97 PTEN-altered vs 390 wildtype/other samples; shows AKT1 (and all other) protein/RPPA comparisons with box plots and (unlike ClickHouse) a computed p-value.

[Plots Tab: AKT1 protein by PTEN genotype](https://www.cbioportal.org/results/plots?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+MUT+HOMDEL%0AAKT1&case_set_id=lusc_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%225728%22%2C%22dataType%22%3A%22GENOTYPE%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%22207%22%2C%22dataType%22%3A%22protein%22%2C%22selectedDataSourceOption%22%3A%22rppa%22%7D) — scatter/box plot of AKT1 protein (RPPA, y-axis) stratified by PTEN mutation/HOMDEL status (x-axis); switch the data-source dropdown on the y-axis to view AKT1_PS473 or AKT1_PT308 specifically.