Q (sonnet-5.5): How does PTEN alteration (mutations or homozygous deletions) affect pAKT protein levels in lung squamous cell carcinoma?

▶ read_guide
{
 "uri": "cbioportal://gene-expression-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 | `rna_seq_v2_mrna`, `rna_seq_v2_mrna_median_Zscores`, `rna_seq_v2_mrna_median_all_sample_Zscores` (TCGA PanCancer Atlas), `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 |
|  … (4931 more chars)

▶ read_guide
{
 "uri": "cbioportal://statistical-tests-guide"
}

▶ list_studies
{
 "search": "lung squamous"
}

◀ 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
{"result":[{"cancer_study_identifier":"lusc_tcga","name":"Lung Squamous Cell Carcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"lusc","sample_count":511,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lusc_tcga"},{"cancer_study_identifier":"lusc_tcga_gdc","name":"Lung Squamous Cell Carcinoma (TCGA GDC, 2025)","type_of_cancer_id":"lusc","sample_count":503,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lusc_tcga_gdc"},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"lusc","sample_count":487,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lusc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lusc_tcga_pub","name":"Lung Squamous Cell Carcinoma (TCGA, Nature 2012)","type_of_cancer_id":"lusc","sample_count":178,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lusc_tcga_pub"},{"cancer_study_identifier":"lusc_cptac_gdc","name":"Lung Squamous Cell Carcinoma (CPTAC GDC, 2025)","type_of_cancer_id":"lusc","sample_count":110,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lusc_cptac_gdc"},{"cancer_study_identifier":"lusc_cptac_2021","name":"Lung Squamous Cell Carcinoma (CPTAC, Cell 2021)","type_of_cancer_id":"lusc","sample_count":80,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lusc_cptac_2021"}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT cancer_study_identifier, profile_type, count(DISTINCT sample_unique_id) n FROM genetic_alteration_derived WHERE cancer_study_identifier IN ('lusc_tcga_pan_can_atlas_2018','lusc_tcga','lusc_cptac_2021','lusc_tcga_pub') AND hugo_gene_symbol='AKT1' AND profile_type ILIKE ANY ('%rppa%','%protein%') GROUP BY 1,2"
}

▶ clickhouse_list_table_columns
{
 "table": "genetic_alteration_derived"
}

◀ result
{"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 46\n Code: 46. DB::Exception: Function with name `ANY` does not exist. In scope SELECT cancer_study_identifier, profile_type, countDistinct(sample_unique_id) AS n FROM genetic_alteration_derived WHERE (cancer_study_identifier IN ('lusc_tcga_pan_can_atlas_2018', 'lusc_tcga', 'lusc_cptac_2021', 'lusc_tcga_pub')) AND (hugo_gene_symbol = 'AKT1') AND (profile_type ILIKE ANY('%rppa%', '%protein%')) GROUP BY 1, 2. Maybe you meant: ['any','and']. (UNKNOWN_FUNCTION) (version 26.4.1.2509 (official build))\n"}

◀ 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 cancer_study_identifier, profile_type, hugo_gene_symbol, count(DISTINCT sample_unique_id) n FROM genetic_alteration_derived WHERE cancer_study_identifier IN ('lusc_tcga_pan_can_atlas_2018','lusc_tcga','lusc_cptac_2021','lusc_tcga_pub') AND hugo_gene_symbol IN ('AKT1','AKT2','AKT3') AND (profile_type ILIKE '%rppa%' OR profile_type ILIKE '%protein%') GROUP BY 1,2,3"
}

◀ result
{"rows":[{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","profile_type":"rppa_Zscores","hugo_gene_symbol":"AKT1","n":317},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","profile_type":"rppa","hugo_gene_symbol":"AKT2","n":317},{"cancer_study_identifier":"lusc_tcga","profile_type":"rppa","hugo_gene_symbol":"AKT3","n":328},{"cancer_study_identifier":"lusc_tcga","profile_type":"rppa_Zscores","hugo_gene_symbol":"AKT1","n":328},{"cancer_study_identifier":"lusc_cptac_2021","profile_type":"protein_quantification","hugo_gene_symbol":"AKT1","n":108},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","profile_type":"rppa","hugo_gene_symbol":"AKT3","n":317},{"cancer_study_identifier":"lusc_tcga","profile_type":"rppa","hugo_gene_symbol":"AKT2","n":328},{"cancer_study_identifier":"lusc_cptac_2021","profile_type":"protein_quantification","hugo_gene_symbol":"AKT3","n":108},{"cancer_study_identifier":"lusc_tcga","profile_type":"rppa_Zscores","hugo_gene_symbol":"AKT3","n":328},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","profile_type":"rppa_Zscores","hugo_gene_symbol":"AKT2","n":317},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","profile_type":"rppa","hugo_gene_symbol":"AKT1","n":317},{"cancer_study_identifier":"lusc_tcga","profile_type":"rppa","hugo_gene_symbol":"AKT1","n":328},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","profile_type":"rppa_Zscores","hugo_gene_symbol":"AKT3","n":317},{"cancer_study_identifier":"lusc_tcga","profile_type":"rppa_Zscores","hugo_gene_symbol":"AKT2","n":328},{"cancer_study_identifier":"lusc_cptac_2021","profile_type":"protein_quantification","hugo_gene_symbol":"AKT2","n":108}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT hugo_gene_symbol, count() rows, count(DISTINCT sample_unique_id) samples FROM genetic_alteration_derived WHERE cancer_study_identifier='lusc_tcga_pan_can_atlas_2018' AND profile_type='rppa' AND hugo_gene_symbol IN ('AKT1','AKT2','AKT3','PTEN') GROUP BY 1"
}

▶ clickhouse_list_tables
{}

◀ result
{"rows":[{"hugo_gene_symbol":"AKT2","rows":317,"samples":317},{"hugo_gene_symbol":"AKT1","rows":317,"samples":317},{"hugo_gene_symbol":"PTEN","rows":317,"samples":317},{"hugo_gene_symbol":"AKT3","rows":317,"samples":317}]}

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

▶ clickhouse_list_table_columns
{
 "table": "genetic_entity"
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"columns":[{"name":"id","type":"Int64"},{"name":"entity_type","type":"String"},{"name":"stable_id","type":"Nullable(String)"}]}

◀ result
{"columns":[{"name":"sample_unique_id","type":"String"},{"name":"hugo_gene_symbol","type":"String","comment":"HUGO gene symbol (e.g., TP53, KRAS, BRAF). Use for gene-specific queries."},{"name":"entrez_gene_id","type":"Int32"},{"name":"gene_panel_stable_id","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"},{"name":"genetic_profile_stable_id","type":"LowCardinality(String)"},{"name":"variant_type","type":"LowCardinality(String)","comment":"Type of genomic event: \"mutation\" for SNVs/indels, \"cna\" for copy number alterations, \"structural_variant\" for SVs. Always filter by this."},{"name":"mutation_variant","type":"String","comment":"Protein change notation (e.g., p.V600E, p.R175H). Use for specific variant queries. \"NA\" for non-mutation events."},{"name":"mutation_type","type":"LowCardinality(String)"},{"name":"mutation_status","type":"LowCardinality(String)","comment":"For mutations: Somatic, Germline, UNKNOWN, or UNCALLED. Filter mutation_status != \"UNCALLED\" to exclude uncertain calls. Include all other statuses."},{"name":"driver_filter","type":"LowCardinality(String)"},{"name":"driver_filter_annotation","type":"String"},{"name":"driver_tiers_filter","type":"LowCardinality(String)"},{"name":"driver_tiers_filter_annotation","type":"String"},{"name":"cna_alteration","type":"Nullable(Int8)","comment":"Copy number alteration: only 2 = amplification (AMP) and -2 = deep deletion (HOMDEL) are stored. Shallow deletion (-1), diploid (0) and gain (1) are not in this table; query genetic_alteration_derived WHERE profile_type = 'gistic' (alteration_value is a String, e.g. '-1'). NULL for non-CNA events."},{"name":"cna_cytoband","type":"String"},{"name":"sv_event_info","type":"String"},{"name":"patient_unique_id","type":"String"},{"name":"off_panel","type":"Bool","comment":"Boolean: 1 = mutation outside gene panel coverage (off-panel), 0 = within panel (on-panel). Filter off_panel = 0 for reliable frequency calculations."}]}

▶ clickhouse_run_select_query
{
 "query": "WITH alt 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 off_panel=0
    AND ((variant_type='mutation' AND mutation_status!='UNCALLED' AND upper(mutation_status)!='GERMLINE') OR (variant_type='cna' AND cna_alteration=-2))
),
mutonly 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' AND upper(mutation_status)!='GERMLINE'
),
delonly 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
)
SELECT g.hugo_gene_symbol,
  multiIf(g.sample_unique_id IN (SELECT * FROM mutonly) AND g.sample_unique_id IN (SELECT * FROM delonly),'both',
          g.sample_unique_id IN (SELECT * FROM mutonly),'mut',
          g.sample_unique_id IN (SELECT * FROM delonly),'homdel','unaltered') AS grp,
  count() n, round(avg(toFloat64OrNull(g.alteration_value)),3) mean_rppa, round(median(toFloat64OrNull(g.alteration_value)),3) med_rppa,
  round(min(toFloat64OrNull(g.alteration_value)),2) mn, round(max(toFloat64OrNull(g.alteration_value)),2) mx
FROM genetic_alteration_derived g
WHERE g.cancer_study_identifier='lusc_tcga_pan_can_atlas_2018' AND g.profile_type='rppa' AND g.hugo_gene_symbol IN ('AKT1','AKT2','AKT3')
  AND toFloat64OrNull(g.alteration_value) IS NOT NULL
GROUP BY 1,2 ORDER BY 1,2"
}

▶ clickhouse_run_select_query
{
 "query": "SELECT stable_id FROM genetic_entity WHERE stable_id ILIKE '%AKT%' LIMIT 20"
}

◀ result
{"rows":[{"hugo_gene_symbol":"AKT1","grp":"homdel","n":34,"mean_rppa":0.744,"med_rppa":0.699,"mn":-0.15,"mx":1.4},{"hugo_gene_symbol":"AKT1","grp":"mut","n":33,"mean_rppa":0.82,"med_rppa":0.939,"mn":-1.12,"mx":1.43},{"hugo_gene_symbol":"AKT1","grp":"unaltered","n":250,"mean_rppa":0.928,"med_rppa":0.991,"mn":-1.88,"mx":1.82},{"hugo_gene_symbol":"AKT2","grp":"homdel","n":34,"mean_rppa":0.744,"med_rppa":0.699,"mn":-0.15,"mx":1.4},{"hugo_gene_symbol":"AKT2","grp":"mut","n":33,"mean_rppa":0.82,"med_rppa":0.939,"mn":-1.12,"mx":1.43},{"hugo_gene_symbol":"AKT2","grp":"unaltered","n":250,"mean_rppa":0.928,"med_rppa":0.991,"mn":-1.88,"mx":1.82},{"hugo_gene_symbol":"AKT3","grp":"homdel","n":34,"mean_rppa":0.744,"med_rppa":0.699,"mn":-0.15,"mx":1.4},{"hugo_gene_symbol":"AKT3","grp":"mut","n":33,"mean_rppa":0.82,"med_rppa":0.939,"mn":-1.12,"mx":1.43},{"hugo_gene_symbol":"AKT3","grp":"unaltered","n":250,"mean_rppa":0.928,"med_rppa":0.991,"mn":-1.88,"mx":1.82}]}

◀ result
{"rows":[{"stable_id":"AKT1_acetylprotein"},{"stable_id":"AKT1S1_acetylprotein"},{"stable_id":"AKT2_acetylprotein"},{"stable_id":"AKT3_acetylprotein"},{"stable_id":"AKTIP_acetylprotein"},{"stable_id":"circ_chr1_243545510_243573048_AKT3"},{"stable_id":"circ_chr1_243545510_243695716_AKT3"},{"stable_id":"circ_chr1_243613671_243637742_AKT3"},{"stable_id":"circ_chr1_243613671_243695716_AKT3"},{"stable_id":"circ_chr1_243645893_243843282_AKT3"},{"stable_id":"AKT3_S472:NP_005456.1"},{"stable_id":"AKT3_S474:NP_005456.1"},{"stable_id":"AKT3_S476:NP_005456.1"},{"stable_id":"chr16_53498391_53500329_AKTIP"},{"stable_id":"AKT1_S122"},{"stable_id":"AKT1_S124"},{"stable_id":"AKT1_S126"},{"stable_id":"AKT1_S129"},{"stable_id":"AKT1_T450"},{"stable_id":"AKT1S1_S108"}]}

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

◀ result
{"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"lusc_tcga_pan_can_atlas_2018","name":"Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","sampleCount":487,"studyViewUrl":"https://www.cbioportal.org/study?id=lusc_tcga_pan_can_atlas_2018","metadata":{"clinicalAttributeIds":["AGE","AJCC_PATHOLOGIC_TUMOR_STAGE","AJCC_STAGING_EDITION","ANEUPLOIDY_SCORE","BUFFA_HYPOXIA_SCORE","CANCER_TYPE","CANCER_TYPE_ACRONYM","CANCER_TYPE_DETAILED","DAYS_LAST_FOLLOWUP","DAYS_TO_BIRTH","DAYS_TO_INITIAL_PATHOLOGIC_DIAGNOSIS","DFS_MONTHS","DFS_STATUS","DSS_MONTHS","DSS_STATUS","ETHNICITY","FORM_COMPLETION_DATE","FRACTION_GENOME_ALTERED","GENETIC_ANCESTRY_LABEL","GRADE","HISTORY_NEOADJUVANT_TRTYN","ICD_10","ICD_O_3_HISTOLOGY","ICD_O_3_SITE","INFORMED_CONSENT_VERIFIED","IN_PANCANPATHWAYS_FREEZE","MSI_SCORE_MANTIS","MSI_SENSOR_SCORE","MUTATION_COUNT","NEW_TUMOR_EVENT_AFTER_INITIAL_TREATMENT","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER_PATIENT_ID","PATH_M_STAGE","PATH_N_STAGE","PATH_T_STAGE","PERSON_NEOPLASM_CANCER_STATUS","PFS_MONTHS","PFS_STATUS","PRIMARY_LYMPH_NODE_PRESENTATION_ASSESSMENT","PRIOR_DX","RACE","RADIATION_THERAPY","RAGNUM_HYPOXIA_SCORE","SAMPLE_COUNT","SAMPLE_TYPE","SEX","SOMATIC_STATUS","SUBTYPE","TBL_SCORE","TISSUE_PROSPECTIVE_COLLECTION_INDICATOR","TISSUE_RETROSPECTIVE_COLLECTION_INDICATOR","TISSUE_SOURCE_SITE","TISSUE_SOURCE_SITE_CODE","TMB_NONSYNONYMOUS","TUMOR_TISSUE_SITE","TUMOR_TYPE","WEIGHT","WINTER_HYPOXIA_SCORE"],"molecularProfileIds":["lusc_tcga_pan_can_atlas_2018_gistic","lusc_tcga_pan_can_atlas_2018_log2CNA","lusc_tcga_pan_can_atlas_2018_mutations","lusc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","lusc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lusc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lusc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","lusc_tcga_pan_can_atlas_2018_rppa","lusc_tcga_pan_can_atlas_2018_rppa_Zscores","lusc_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["lusc_tcga_pan_can_atlas_2018_armlevel_cna","lusc_tcga_pan_can_atlas_2018_genetic_ancestry","lusc_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","lusc_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["lusc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","lusc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","lusc_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","lusc_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_cna","generic_assay_genetic_ancestry","generic_assay_methylation"]}}]}}

▶ navigate_to_results_view
{
 "studyIds": [
  "lusc_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "PTEN: MUT HOMDEL"
 ],
 "tab": "comparison/protein"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/comparison/protein?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+MUT+HOMDEL&case_set_id=lusc_tcga_pan_can_atlas_2018_all","url":"https://www.cbioportal.org/results/comparison/protein?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+MUT+HOMDEL&case_set_id=lusc_tcga_pan_can_atlas_2018_all","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","pageDescription":"Protein expression for all genes compared across groups defined by query genes."}}

═ answer (success)
In the TCGA PanCancer Atlas LUSC cohort, samples with PTEN alterations had slightly lower RPPA AKT values than unaltered samples. I couldn't confirm that these values are phospho-AKT (pS473 or pT308), so I can't answer the pAKT question directly.

I used the PanCancer Atlas cohort because it has RPPA protein data. Other lung squamous studies are available, including the TCGA Firehose Legacy, TCGA GDC and CPTAC studies. Tell me if you want a different one.

**Results for 317 patient samples with RPPA data:**

| PTEN status | Samples | Mean RPPA AKT (median) |
|---|---|---|
| Unaltered | 250 | 0.93 (0.99) |
| Mutated | 33 | 0.82 (0.94) |
| Homozygous deletion | 34 | 0.74 (0.70) |

- **Direction:** Both PTEN-altered groups sit lower than the unaltered group, and the deletion group is lowest. No sample had both a mutation and a deletion.
- **Not what you'd expect for pAKT:** PTEN loss would normally be expected to raise pAKT. This is the opposite direction.
- **Overlap:** The ranges overlap heavily. For example, the deletion group runs from −0.15 to 1.40 and the unaltered group from −1.88 to 1.82.
- **No p-value:** I can't compute a significance test, so I'm not claiming the differences are significant. The portal's Protein tab below runs the appropriate test.

**Why I can't confirm this is pAKT:** The database gives one RPPA value per sample for AKT, and it is identical for AKT1, AKT2 and AKT3. That means it's a single merged AKT readout. I can't tell which antibody it comes from. The specific antibodies (for example AKT_pS473 and AKT_pT308) should be selectable in the portal.

**Counting notes:** Mutations exclude germline calls, and homozygous deletion is GISTIC −2. Only PTEN-profiled samples with RPPA data are counted.

[Open the PTEN-altered vs unaltered protein comparison](https://www.cbioportal.org/results/comparison/protein?cancer_study_list=lusc_tcga_pan_can_atlas_2018&gene_list=PTEN%3A+MUT+HOMDEL&case_set_id=lusc_tcga_pan_can_atlas_2018_all) (LUSC, TCGA PanCancer Atlas). It compares protein expression across the altered and unaltered groups, so you can look for the phospho-AKT antibodies there. The [study summary page](https://www.cbioportal.org/study?id=lusc_tcga_pan_can_atlas_2018) has the cohort overview.