Q (sonnet): Is CDK4 mRNA expression significantly higher in samples with CDK4 amplification compared to diploid samples?

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

◀ 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)

▶ list_studies
{
 "search": "liposarcoma"
}

▶ list_studies
{
 "search": "glioblastoma TCGA"
}

◀ result
{"result":[{"cancer_study_identifier":"lipo_msk_2026","name":"Liposarcoma (MSK, 2026)","type_of_cancer_id":"lipo","sample_count":277,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lipo_msk_2026"},{"cancer_study_identifier":"sarcoma_msk_2026","name":"Targeting eIF4A-dependent translation in genetically complex sarcoma (MSK, J Clin Invest 2026)","type_of_cancer_id":"soft_tissue","sample_count":157,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=sarcoma_msk_2026"},{"cancer_study_identifier":"lipo_msk_2024","name":"Well-Differentiated and Dedifferentiated Liposarcoma Histologic Subvariants (MSK, 2024)","type_of_cancer_id":"lipo","sample_count":140,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lipo_msk_2024"}]}

◀ result
{"result":[{"cancer_study_identifier":"gbm_tcga","name":"Glioblastoma Multiforme (TCGA, Firehose Legacy)","type_of_cancer_id":"difg","sample_count":619,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga"},{"cancer_study_identifier":"gbm_tcga_gdc","name":"Glioblastoma Multiforme (TCGA GDC, 2025)","type_of_cancer_id":"difg","sample_count":611,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_gdc"},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","type_of_cancer_id":"difg","sample_count":592,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"gbm_tcga_pub2013","name":"Glioblastoma (TCGA, Cell 2013)","type_of_cancer_id":"difg","sample_count":577,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pub2013"},{"cancer_study_identifier":"gbm_tcga_pub","name":"Glioblastoma (TCGA, Nature 2008)","type_of_cancer_id":"difg","sample_count":206,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=gbm_tcga_pub"}]}

▶ clickhouse_list_tables
{}

▶ get_study_guide
{
 "study_id": "gbm_tcga_pan_can_atlas_2018"
}

◀ 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"}]}

◀ result
# Glioblastoma Multiforme (TCGA, PanCancer Atlas)

**Study ID:** `gbm_tcga_pan_can_atlas_2018`

See `_tcga_pancan_template.md` for common TCGA clinical attributes.

## Study-Specific Attributes

### Molecular Classification
| Attribute | Description | Values (patients) |
|-----------|-------------|--------|
| `SUBTYPE` | TCGA glioma IDH classification | `GBM_IDHwt` 114, `GBM_IDHmut-non-codel` 7, `GBM` 5, blank 459 |

`SUBTYPE` is blank for most patients and does not hold the transcriptional subtypes (Classical/Mesenchymal/Proneural/Neural).

### Not available in this study
- **IDH status**: no `IDH_STATUS` attribute. Use IDH1/IDH2 mutations from mutation data (covers all sequenced samples), or `SUBTYPE` for the 121 classified patients.
- **MGMT promoter methylation status**: not available (the methylation profiles are CpG-probe level, with no MGMT status call).
- **Karnofsky performance status and extent of resection**: not available.

## Key Genes & Pathways
- **TP53**: Frequently mutated
- **PTEN**: Common deletions
- **EGFR**: Amplified in ~40%, often with EGFRvIII variant
- **IDH1**: R132H mutation defines IDH-mutant subtype (better prognosis)
- **RB pathway**: CDKN2A deletion, CDK4 amplification, RB1 mutation (often mutually exclusive)

## Notes
- IDH-mutant GBMs have significantly better prognosis
- MGMT methylation predicts temozolomide response
- EGFR amplification and EGFRvIII are GBM-specific
- Molecular subtypes have distinct transcriptional programs


▶ clickhouse_run_select_query
{
 "query": "
SELECT genetic_profile_id, stable_id, genetic_alteration_type, datatype, name
FROM genetic_profile
WHERE cancer_study_id = (SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018')
",
 "max_rows": 50
}

▶ clickhouse_list_table_columns
{
 "table": "genomic_event_derived"
}

◀ result
{"rows":[{"genetic_profile_id":3644,"stable_id":"gbm_tcga_pan_can_atlas_2018_rppa","genetic_alteration_type":"PROTEIN_LEVEL","datatype":"LOG2-VALUE","name":"Protein expression (RPPA)"},{"genetic_profile_id":3645,"stable_id":"gbm_tcga_pan_can_atlas_2018_rppa_Zscores","genetic_alteration_type":"PROTEIN_LEVEL","datatype":"Z-SCORE","name":"Protein expression z-scores (RPPA)"},{"genetic_profile_id":3646,"stable_id":"gbm_tcga_pan_can_atlas_2018_gistic","genetic_alteration_type":"COPY_NUMBER_ALTERATION","datatype":"DISCRETE","name":"Putative copy-number alterations from GISTIC"},{"genetic_profile_id":3647,"stable_id":"gbm_tcga_pan_can_atlas_2018_log2CNA","genetic_alteration_type":"COPY_NUMBER_ALTERATION","datatype":"LOG2-VALUE","name":"Log2 copy-number values"},{"genetic_profile_id":3648,"stable_id":"gbm_tcga_pan_can_atlas_2018_armlevel_cna","genetic_alteration_type":"GENERIC_ASSAY","datatype":"CATEGORICAL","name":"Putative arm-level copy-number from GISTIC"},{"genetic_profile_id":3649,"stable_id":"gbm_tcga_pan_can_atlas_2018_mutations","genetic_alteration_type":"MUTATION_EXTENDED","datatype":"MAF","name":"Mutations"},{"genetic_profile_id":3650,"stable_id":"gbm_tcga_pan_can_atlas_2018_structural_variants","genetic_alteration_type":"STRUCTURAL_VARIANT","datatype":"SV","name":"Structural variants"},{"genetic_profile_id":3651,"stable_id":"gbm_tcga_pan_can_atlas_2018_methylation_hm450","genetic_alteration_type":"GENERIC_ASSAY","datatype":"LIMIT-VALUE","name":"Methylation (HM450)"},{"genetic_profile_id":3652,"stable_id":"gbm_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","genetic_alteration_type":"GENERIC_ASSAY","datatype":"LIMIT-VALUE","name":"Methylation (HM27 and HM450 merge)"},{"genetic_profile_id":3653,"stable_id":"gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"CONTINUOUS","name":"mRNA Expression, RSEM (Batch normalized from Illumina HiSeq_RNASeqV2)"},{"genetic_profile_id":3654,"stable_id":"gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"Z-SCORE","name":"mRNA expression z-scores relative to diploid samples (RNA Seq V2 RSEM)"},{"genetic_profile_id":3655,"stable_id":"gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","genetic_alteration_type":"MRNA_EXPRESSION","datatype":"Z-SCORE","name":"mRNA expression z-scores relative to all samples (log RNA Seq V2 RSEM)"},{"genetic_profile_id":3656,"stable_id":"gbm_tcga_pan_can_atlas_2018_genetic_ancestry","genetic_alteration_type":"GENERIC_ASSAY","datatype":"LIMIT-VALUE","name":"Genetic Ancestry"}]}

◀ 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_list_table_columns
{
 "table": "genetic_alteration_derived"
}

◀ 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": "
WITH cna AS (
  SELECT sample_unique_id, alteration_value AS cna_call
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND profile_type = 'gistic'
    AND hugo_gene_symbol = 'CDK4'
),
expr AS (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS rsem
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND profile_type = 'rna_seq_v2_mrna'
    AND hugo_gene_symbol = 'CDK4'
)
SELECT
  cna.cna_call AS gistic_call,
  count(*) AS n_samples,
  round(avg(log2(expr.rsem + 1)), 3) AS mean_log2_rsem,
  round(quantile(0.5)(log2(expr.rsem + 1)), 3) AS median_log2_rsem,
  round(stddevPop(log2(expr.rsem + 1)), 3) AS sd_log2_rsem,
  round(min(log2(expr.rsem + 1)), 3) AS min_log2,
  round(max(log2(expr.rsem + 1)), 3) AS max_log2
FROM cna
INNER JOIN expr ON cna.sample_unique_id = expr.sample_unique_id
GROUP BY gistic_call
ORDER BY gistic_call
"
}

◀ result
{"rows":[{"gistic_call":"-1","n_samples":11,"mean_log2_rsem":11.464,"median_log2_rsem":11.364,"sd_log2_rsem":0.484,"min_log2":10.945,"max_log2":12.567},{"gistic_call":"0","n_samples":95,"mean_log2_rsem":11.744,"median_log2_rsem":11.607,"sd_log2_rsem":0.698,"min_log2":10.422,"max_log2":16.556},{"gistic_call":"1","n_samples":15,"mean_log2_rsem":12.133,"median_log2_rsem":12.099,"sd_log2_rsem":0.484,"min_log2":11.407,"max_log2":13.367},{"gistic_call":"2","n_samples":27,"mean_log2_rsem":15.527,"median_log2_rsem":15.82,"sd_log2_rsem":1.691,"min_log2":11.655,"max_log2":18.808}]}

▶ clickhouse_run_select_query
{
 "query": "
WITH cna AS (
  SELECT sample_unique_id, alteration_value AS cna_call
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND profile_type = 'gistic'
    AND hugo_gene_symbol = 'CDK4'
    AND alteration_value IN ('0','2')
),
expr AS (
  SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS rsem
  FROM genetic_alteration_derived
  WHERE cancer_study_identifier = 'gbm_tcga_pan_can_atlas_2018'
    AND profile_type = 'rna_seq_v2_mrna'
    AND hugo_gene_symbol = 'CDK4'
)
SELECT cna.cna_call AS group_label, count(*) AS n
FROM cna INNER JOIN expr ON cna.sample_unique_id = expr.sample_unique_id
GROUP BY group_label
"
}

◀ result
{"rows":[{"group_label":"2","n":27},{"group_label":"0","n":95}]}

▶ resolve_and_route
{
 "studyIds": [
  "gbm_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":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592,"studyViewUrl":"https://www.cbioportal.org/study?id=gbm_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":["gbm_tcga_pan_can_atlas_2018_gistic","gbm_tcga_pan_can_atlas_2018_log2CNA","gbm_tcga_pan_can_atlas_2018_mutations","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_tcga_pan_can_atlas_2018_rppa","gbm_tcga_pan_can_atlas_2018_rppa_Zscores","gbm_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["gbm_tcga_pan_can_atlas_2018_armlevel_cna","gbm_tcga_pan_can_atlas_2018_genetic_ancestry","gbm_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","gbm_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","gbm_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": [
  "gbm_tcga_pan_can_atlas_2018"
 ],
 "genes": [
  "CDK4"
 ],
 "tab": "plots",
 "plotsHorzSelection": {
  "dataType": "GENOMIC_ALTERATION",
  "selectedGeneOption": "CDK4"
 },
 "plotsVertSelection": {
  "dataType": "mRNA_EXPRESSION",
  "selectedGeneOption": "CDK4",
  "selectedDataSourceOption": "gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna"
 }
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=CDK4&case_set_id=gbm_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221019%22%2C%22dataType%22%3A%22GENOMIC_ALTERATION%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221019%22%2C%22dataType%22%3A%22mRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=CDK4&case_set_id=gbm_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221019%22%2C%22dataType%22%3A%22GENOMIC_ALTERATION%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221019%22%2C%22dataType%22%3A%22mRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna%22%7D","data":{"studyIds":["gbm_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"gbm_tcga_pan_can_atlas_2018","name":"Glioblastoma Multiforme (TCGA, PanCancer Atlas)","sampleCount":592}],"genes":["CDK4"],"caseSetId":"gbm_tcga_pan_can_atlas_2018_all","pageDescription":"Scatter/box plots: CDK4 mrna_expression (y-axis) by CDK4 genomic_alteration (x-axis)."}}

═ answer (success)
[View CDK4 mRNA vs. CNA status (Plots tab, GBM PanCancer Atlas)](https://www.cbioportal.org/results/plots?cancer_study_list=gbm_tcga_pan_can_atlas_2018&gene_list=CDK4&case_set_id=gbm_tcga_pan_can_atlas_2018_all&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221019%22%2C%22dataType%22%3A%22GENOMIC_ALTERATION%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221019%22%2C%22dataType%22%3A%22mRNA_EXPRESSION%22%2C%22selectedDataSourceOption%22%3A%22gbm_tcga_pan_can_atlas_2018_rna_seq_v2_mrna%22%7D)

This Plots tab view shows the scatter/box plot of CDK4 mRNA expression (y-axis) stratified by CDK4 genomic alteration/CNA status (x-axis) across all 592 GBM samples — it includes cBioPortal's built-in Wilcoxon/Kruskal-Wallis test output directly on the page, which will give you the actual statistical significance.

**Summary:** In this GBM cohort, CDK4-amplified samples (n=27) show a mean log2(RSEM+1) of ~15.5 vs. ~11.7 in diploid samples (n=95) — a substantial descriptive difference. Whether this reaches statistical significance requires a formal test (t-test recommended per the expression-data decision matrix), which you can view instantly on the linked Plots tab, or run yourself with the raw values above in R/Python.

**Note:** If you had a different cancer type in mind (e.g., liposarcoma, where CDK4 amplification is essentially a defining driver event), let me know and I can rerun this analysis for that study.