Q (haiku): What is the correlation between EGFR mRNA expression and EGFR protein levels in ovarian cancer samples? ▶ 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` | `_` | | `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) ▶ list_studies { "search": "ovarian", "limit": 20, "verbose": true } ◀ result {"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","description":"MSK-MET (Memorial Sloan Kettering - Metastatic Events and Tropisms) is a pan-cancer cohort of tumor genomic and clinical outcome data from 25,000 patients. The dataset identifies associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types; showing that chromosomal instability is strongly correlated with metastatic burden in some tumor types, like prostate and lung adenocarcinomas and HR+/HER2+ breast ductal carcinoma, but not in others, such as colorectal MSS, pancreatic adenocarcinoma and high-grade serous ovarian cancer. The study also identifies somatic alterations associated with increased metastatic burden and routes of metastatic spread. Our data offers a resource for the investigation of the biologic basis for metastatic spread and highlights the role of chromosomal instability in cancer progression. This data is available under the Creative Commons BY-NC-ND 4.0 license.","type_of_cancer_id":"mixed","sample_count":25775,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_met_2021"},{"cancer_study_identifier":"ov_tcga","name":"Ovarian Serous Cystadenocarcinoma (TCGA, Firehose Legacy)","description":"TCGA Ovarian Serous Cystadenocarcinoma. Source data from GDAC Firehose. Previously known as TCGA Provisional.","type_of_cancer_id":"hgsoc","sample_count":617,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ov_tcga"},{"cancer_study_identifier":"hgsoc_tcga_gdc","name":"High-Grade Serous Ovarian Cancer (TCGA GDC, 2025)","description":"TCGA High-Grade Serous Ovarian Cancer. Source data from NCI GDC and generated in Aug 2025 using Cancer Data Aggregator.","type_of_cancer_id":"hgsoc","sample_count":604,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=hgsoc_tcga_gdc"},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","description":"Ovarian Serous Cystadenocarcinoma TCGA PanCancer data. The original data is here. The publications are here.","type_of_cancer_id":"hgsoc","sample_count":585,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=ov_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ovary_geomx_gray_foundation_2024","name":"Ovarian Cancer (Gray Foundation, Cancer Discov 2024)","description":"Microregion transcriptomic data (GeoMx) and multiplex imaging (CyCIF) for High-Grade Serous ovarian Cancer Cases (HGSOC). This resou … (4038 more chars) ▶ get_study_guide { "study_id": "ov_tcga_pan_can_atlas_2018" } ◀ result # Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas) **Study ID:** `ov_tcga_pan_can_atlas_2018` See `_tcga_pancan_template.md` for common TCGA clinical attributes. ## Study-Specific Attributes ### Histology | Attribute | Description | Values (samples) | |-----------|-------------|--------| | `GRADE` | Histologic grade | G3 400, G2 65, GX 7, G1 5, GB 2, G4 1, blank 105 | | `CANCER_TYPE_DETAILED` | Histology | Serous Ovarian Cancer (all 585) | ### Not available in this study - **Platinum sensitivity and residual disease**: no treatment-response or surgical-outcome attributes. Survival (`OS_*`, `PFS_*`, `DFS_*`, `DSS_*`) is the only outcome data. - **BRCA / HRD status**: no clinical status attributes. Derive BRCA1/BRCA2 status from mutation (somatic calls only) and CNA data; there is no HRD score. - `AJCC_PATHOLOGIC_TUMOR_STAGE` is blank for all samples. `SUBTYPE` is `OV` (177) or blank (408) and carries no molecular subtype. ## Key Genes | Gene | Frequency | Notes | |------|-----------|-------| | TP53 | ~96% | Near-universal in high-grade serous | | BRCA1 | ~10% germline | PARP inhibitor sensitivity | | BRCA2 | ~6% germline | PARP inhibitor sensitivity | | NF1 | ~4% | RAS pathway | | RB1 | ~2% | Cell cycle | | CDK12 | ~3% | DNA repair | ## Notes - High-grade serous ovarian cancer (HGSOC) is genomically distinct from other ovarian subtypes - TP53 is mutated in virtually all HGSOC - BRCA1/2 mutations (germline or somatic) predict PARP inhibitor response - HRD score predicts platinum and PARP sensitivity even without BRCA mutation - Copy number alterations more prominent than mutations (except TP53) ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT profile_type FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pan_can_atlas_2018' AND hugo_gene_symbol = 'EGFR' ORDER BY profile_type " } ◀ result {"rows":[{"profile_type":"gistic"},{"profile_type":"log2CNA"},{"profile_type":"protein_quantification"},{"profile_type":"protein_quantification_zscores"},{"profile_type":"rna_seq_v2_mrna"},{"profile_type":"rna_seq_v2_mrna_median_Zscores"},{"profile_type":"rna_seq_v2_mrna_median_all_sample_Zscores"},{"profile_type":"rppa"},{"profile_type":"rppa_Zscores"}]} ▶ clickhouse_run_select_query { "query": " WITH mrna_data AS ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS mrna_value FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pan_can_atlas_2018' AND profile_type = 'rna_seq_v2_mrna' AND hugo_gene_symbol = 'EGFR' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ), protein_data AS ( SELECT sample_unique_id, toFloat64OrNull(alteration_value) AS protein_value FROM genetic_alteration_derived WHERE cancer_study_identifier = 'ov_tcga_pan_can_atlas_2018' AND profile_type = 'protein_quantification' AND hugo_gene_symbol = 'EGFR' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL ) SELECT rankCorr(m.mrna_value, p.protein_value) AS spearman_correlation, count() AS num_samples, min(m.mrna_value) AS mrna_min, max(m.mrna_value) AS mrna_max, min(p.protein_value) AS protein_min, max(p.protein_value) AS protein_max FROM mrna_data m INNER JOIN protein_data p USING (sample_unique_id) ", "max_rows": 10 } ◀ result {"rows":[{"spearman_correlation":0.6751129211403184,"num_samples":74,"mrna_min":25.3552508440214,"mrna_max":1414.83335531395,"protein_min":-0.652,"protein_max":0.7525}]} ▶ resolve_and_route { "studyIds": [ "ov_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":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":585,"studyViewUrl":"https://www.cbioportal.org/study?id=ov_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":["ov_tcga_pan_can_atlas_2018_gistic","ov_tcga_pan_can_atlas_2018_log2CNA","ov_tcga_pan_can_atlas_2018_mutations","ov_tcga_pan_can_atlas_2018_protein_quantification","ov_tcga_pan_can_atlas_2018_protein_quantification_zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ov_tcga_pan_can_atlas_2018_rppa","ov_tcga_pan_can_atlas_2018_rppa_Zscores","ov_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["ov_tcga_pan_can_atlas_2018_armlevel_cna","ov_tcga_pan_can_atlas_2018_genetic_ancestry","ov_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","ov_tcga_pan_can_atlas_2018_methylation_hm450","ov_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["ov_tcga_pan_can_atlas_2018_protein_quantification_zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","ov_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","ov_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","generic_assay_phosphosite_quantification"]}}]}} ▶ navigate_to_results_view { "studyIds": [ "ov_tcga_pan_can_atlas_2018" ], "genes": [ "EGFR" ], "tab": "plots", "plotsHorzSelection": { "selectedGeneOption": "EGFR", "dataType": "rna_seq_v2_mrna" }, "plotsVertSelection": { "selectedGeneOption": "EGFR", "dataType": "protein_quantification" }, "profileFilter": "rna_seq_v2_mrna,protein_quantification" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=ov_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=ov_tcga_pan_can_atlas_2018_all&profileFilter=rna_seq_v2_mrna%2Cprotein_quantification&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22rna_seq_v2_mrna%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22protein_quantification%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=ov_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=ov_tcga_pan_can_atlas_2018_all&profileFilter=rna_seq_v2_mrna%2Cprotein_quantification&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22rna_seq_v2_mrna%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22protein_quantification%22%7D","data":{"studyIds":["ov_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":585}],"genes":["EGFR"],"caseSetId":"ov_tcga_pan_can_atlas_2018_all","pageDescription":"Scatter/box plots: EGFR protein_quantification (y-axis) by EGFR rna_seq_v2_mrna (x-axis)."}} ═ answer (success) ## Results **EGFR mRNA–Protein Correlation in Ovarian Cancer (TCGA PanCancer Atlas)** **Spearman Correlation: 0.675** (n = 74 samples) This represents a **moderate-to-strong positive correlation** between EGFR mRNA expression and EGFR protein levels in the 74 ovarian cancer samples with paired measurements available. The correlation suggests that mRNA abundance reasonably predicts protein levels for this gene in high-grade serous ovarian cancer, though there is still considerable sample-to-sample variation (r² ≈ 0.46, meaning ~46% of variance in protein is explained by mRNA). **Data characteristics:** - **mRNA range:** 25.4 – 1,414.8 (RNA-seq v2 counts) - **Protein range:** −0.65 – 0.75 (quantification units) - **Study:** Ovarian Serous Cystadenocarcinoma (TCGA PanCancer Atlas), 585 total samples; 74 had both measurements --- **[View interactive EGFR mRNA vs. Protein plot](https://www.cbioportal.org/results/plots?cancer_study_list=ov_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=ov_tcga_pan_can_atlas_2018_all&profileFilter=rna_seq_v2_mrna%2Cprotein_quantification&plots_horz_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22rna_seq_v2_mrna%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22protein_quantification%22%7D)** — This link opens the Plots tab with the scatter plot pre-configured to show EGFR mRNA (x-axis) vs. protein (y-axis), where you can explore individual samples and download the data.