Q (sonnet): How does ERBB2 mRNA expression vary across different cancer types in TCGA Pan-Cancer Atlas studies? ▶ read_guide { "uri": "cbioportal://gene-expression-guide" } ▶ read_guide { "uri": "cbioportal://clinical-data-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) ◀ result # Clinical Data Query Guide ## Overview Clinical data in cBioPortal is stored at both patient and sample levels. Understanding the distinction is crucial for accurate analysis. ## Data Organization ### Patient-Level vs Sample-Level Data - **Patient-level**: Demographics, overall survival, disease stage (stored once per patient) - **Sample-level**: Sample type, sequencing platform, purity (can have multiple per patient) ### Key Tables - `clinical_patient`: Patient-level clinical attributes - `clinical_sample`: Sample-level clinical attributes - `clinical_data_derived`: Pre-joined view combining both levels - `clinical_attribute_meta`: Metadata about available clinical attributes ## Recommended Approach: Use clinical_data_derived The `clinical_data_derived` table is pre-joined and optimized for most queries: ```sql -- Get clinical data for specific attributes SELECT sample_unique_id, patient_unique_id, attribute_name, attribute_value FROM clinical_data_derived WHERE cancer_study_identifier = 'your_study_id' AND attribute_name IN ('CANCER_TYPE', 'SAMPLE_TYPE', 'TMB_NONSYNONYMOUS'); ``` ## Clinical Attribute Discovery ### Use clinical_attribute_meta for Discovering Available Attributes **Always start here** to see what clinical attributes are available for a specific study: ```sql -- Discover available clinical attributes for a study SELECT attr_id, description, patient_attribute, cancer_study_id FROM clinical_attribute_meta WHERE cancer_study_id = ( SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'your_study_id' ) ORDER BY patient_attribute, attr_id; ``` **Key Fields:** - **attr_id**: matches attr_id in clinical_sample/clinical_patient tables - **description**: human-readable description of the attribute - **patient_attribute**: true = patient attribute, false = sample attribute - **cancer_study_id**: links to cancer_study table (filter by study) ## Attribute Semantics and Matching ### Case-Insensitive Matching for Attribute Values Clinical values are free text across studies and may differ only by case. For example, a controlled-looking value such as germline mutation status may appear as `GERMLINE`, `Germline`, or another case variant. When filtering `clinical_data_derived.attribute_value`, use case-insensitive matching unless you have already profiled the exact values in the target study: ```sql -- Correct: case-insensitive clinical value filter SELECT DISTINCT sample_unique_id, patient_unique_id FROM clinical_data_derived WHERE cancer_study_identifier = 'your_study_id' AND attribute_name = 'MUTATION_STATUS' AND upper(attribute_value) = 'GERMLINE'; ``` Do not write `attribute_value = 'GERMLINE'` without first checking all distinct values for that attribute in the study. ### Query the Requested Attribute, Not a Proxy Do not infer one clinical attribute from a related subtype or marker. Query the actual requested attribute when it … (17073 more chars) ▶ clickhouse_run_select_query { "query": " SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ORDER BY cancer_study_identifier " } ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT profile_type FROM genetic_alteration_derived WHERE cancer_study_identifier = 'brca_tcga_pan_can_atlas_2018' AND profile_type ILIKE '%mrna%' ORDER BY profile_type " } ◀ result {"rows":[{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"gbm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"laml_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"meso_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"skcm_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"tgct_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"thym_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucs_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"uvm_tcga_pan_can_atlas_2018"}]} ◀ result {"rows":[{"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":"rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores"}]} ▶ clickhouse_run_select_query { "query": " SELECT cancer_study_identifier AS study, count(v) AS n_samples, round(avg(v), 3) AS mean_zscore, round(quantile(0.5)(v), 3) AS median_zscore, round(min(v), 2) AS min_zscore, round(max(v), 2) AS max_zscore FROM ( SELECT cancer_study_identifier, toFloat64OrNull(alteration_value) AS v FROM genetic_alteration_derived WHERE hugo_gene_symbol = 'ERBB2' AND profile_type = 'rna_seq_v2_mrna_median_Zscores' AND cancer_study_identifier IN ( SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ) AND alteration_value NOT IN ('', 'NA') ) WHERE v IS NOT NULL GROUP BY study ORDER BY mean_zscore DESC " } ◀ result {"rows":[{"study":"stad_tcga_pan_can_atlas_2018","n_samples":412,"mean_zscore":6.293,"median_zscore":0.185,"min_zscore":-2.11,"max_zscore":230.51},{"study":"esca_tcga_pan_can_atlas_2018","n_samples":181,"mean_zscore":5.45,"median_zscore":0.238,"min_zscore":-1.58,"max_zscore":208.56},{"study":"brca_tcga_pan_can_atlas_2018","n_samples":1082,"mean_zscore":3.255,"median_zscore":-0.131,"min_zscore":-2.22,"max_zscore":113.26},{"study":"cesc_tcga_pan_can_atlas_2018","n_samples":294,"mean_zscore":3.127,"median_zscore":-0.023,"min_zscore":-1.81,"max_zscore":197.36},{"study":"ucec_tcga_pan_can_atlas_2018","n_samples":527,"mean_zscore":1.784,"median_zscore":-0.057,"min_zscore":-2.14,"max_zscore":157.88},{"study":"coadread_tcga_pan_can_atlas_2018","n_samples":592,"mean_zscore":1.764,"median_zscore":-0.03,"min_zscore":-2.55,"max_zscore":258.54},{"study":"ucs_tcga_pan_can_atlas_2018","n_samples":57,"mean_zscore":1.581,"median_zscore":0.262,"min_zscore":-1.68,"max_zscore":37.55},{"study":"blca_tcga_pan_can_atlas_2018","n_samples":407,"mean_zscore":1.147,"median_zscore":0.028,"min_zscore":-1.27,"max_zscore":54.94},{"study":"lusc_tcga_pan_can_atlas_2018","n_samples":484,"mean_zscore":0.841,"median_zscore":-0.122,"min_zscore":-1.93,"max_zscore":207.79},{"study":"luad_tcga_pan_can_atlas_2018","n_samples":510,"mean_zscore":0.74,"median_zscore":0.126,"min_zscore":-1.98,"max_zscore":94.14},{"study":"paad_tcga_pan_can_atlas_2018","n_samples":177,"mean_zscore":0.7,"median_zscore":0.01,"min_zscore":-2.19,"max_zscore":106.72},{"study":"hnsc_tcga_pan_can_atlas_2018","n_samples":515,"mean_zscore":0.686,"median_zscore":-0.138,"min_zscore":-1.84,"max_zscore":150.87},{"study":"kirp_tcga_pan_can_atlas_2018","n_samples":283,"mean_zscore":0.469,"median_zscore":0.311,"min_zscore":-1.57,"max_zscore":6.3},{"study":"ov_tcga_pan_can_atlas_2018","n_samples":300,"mean_zscore":0.449,"median_zscore":-0.762,"min_zscore":-2.35,"max_zscore":154.27},{"study":"tgct_tcga_pan_can_atlas_2018","n_samples":149,"mean_zscore":0.218,"median_zscore":-0.134,"min_zscore":-0.99,"max_zscore":5.62},{"study":"chol_tcga_pan_can_atlas_2018","n_samples":36,"mean_zscore":0.112,"median_zscore":0.221,"min_zscore":-1.87,"max_zscore":1.99},{"study":"thym_tcga_pan_can_atlas_2018","n_samples":119,"mean_zscore":0.099,"median_zscore":-0.338,"min_zscore":-1.03,"max_zscore":7.22},{"study":"skcm_tcga_pan_can_atlas_2018","n_samples":443,"mean_zscore":0.086,"median_zscore":-0.108,"min_zscore":-1.5,"max_zscore":7.59},{"study":"uvm_tcga_pan_can_atlas_2018","n_samples":80,"mean_zscore":0.078,"median_zscore":-0.302,"min_zscore":-1.18,"max_zscore":4.23},{"study":"lihc_tcga_pan_can_atlas_2018","n_samples":366,"mean_zscore":0.074,"median_zscore":-0.192,"min_zscore":-1.2,"max_zscore":7.43},{"study":"prad_tcga_pan_can_atlas_2018","n_samples":493,"mean_zscore":0.048,"median_zscore":-0.209,"min_zscore":-2.77,"max_zscore":54.91},{"study":"gbm_tcga_pan_can_atlas_2018","n_samples":160,"mean_zscore":0.016,"median_zscore":-0.153,"min_zscore … (1399 more chars) ▶ resolve_and_route { "studyIds": [ "acc_tcga_pan_can_atlas_2018", "blca_tcga_pan_can_atlas_2018", "brca_tcga_pan_can_atlas_2018", "cesc_tcga_pan_can_atlas_2018", "chol_tcga_pan_can_atlas_2018", "coadread_tcga_pan_can_atlas_2018", "dlbc_tcga_pan_can_atlas_2018", "esca_tcga_pan_can_atlas_2018", "gbm_tcga_pan_can_atlas_2018", "hnsc_tcga_pan_can_atlas_2018", "kich_tcga_pan_can_atlas_2018", "kirc_tcga_pan_can_atlas_2018", "kirp_tcga_pan_can_atlas_2018", "laml_tcga_pan_can_atlas_2018", "lgg_tcga_pan_can_atlas_2018", "lihc_tcga_pan_can_atlas_2018", "luad_tcga_pan_can_atlas_2018", "lusc_tcga_pan_can_atlas_2018", "meso_tcga_pan_can_atlas_2018", "ov_tcga_pan_can_atlas_2018", "paad_tcga_pan_can_atlas_2018", "pcpg_tcga_pan_can_atlas_2018", "prad_tcga_pan_can_atlas_2018", "sarc_tcga_pan_can_atlas_2018", "skcm_tcga_pan_can_atlas_2018", "stad_tcga_pan_can_atlas_2018", "tgct_tcga_pan_can_atlas_2018", "thca_tcga_pan_can_atlas_2018", "thym_tcga_pan_can_atlas_2018", "ucec_tcga_pan_can_atlas_2018", "ucs_tcga_pan_can_atlas_2018", "uvm_tcga_pan_can_atlas_2018" ] } ◀ result Error: result (81,891 characters across 1 line) exceeds maximum allowed tokens. Output has been saved to /home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-ly8cg8ra/ddc7e87e-e3b0-482a-aed0-2378eb2d8853/tool-results/mcp-navigator-resolve_and_route-1790300220897.txt. Format: Plain text - For targeted searches (find a string): use grep on the file directly. - For analysis or summarization that requires reading the full content: the file's lines are too long for Read's offset/limit. Slice by character range via Bash instead — e.g. python3 -c 'print(open("/home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-ly8cg8ra/ddc7e87e-e3b0-482a-aed0-2378eb2d8853/tool-results/mcp-navigator-resolve_and_route-1790300220897.txt").read()[A:B])' in ~80,000-char spans until you have read 100% of it. - If the Agent tool is available, do this inside a subagent so the full output stays out of your main context. Give it the instruction above verbatim, and be explicit about what it must return — e.g. "Slice /home/ino/.claude-cbioportal-team/projects/-tmp-mcp-qa-claude-ly8cg8ra/ddc7e87e-e3b0-482a-aed0-2378eb2d8853/tool-results/mcp-navigator-resolve_and_route-1790300220897.txt in ~80,000-char spans via python (read()[A:B]) until you have read all 81,891 characters, then summarize and quote any key findings verbatim." A vague "summarize this" may lose detail. ▶ navigate_to_results_view { "studyIds": [ "acc_tcga_pan_can_atlas_2018", "blca_tcga_pan_can_atlas_2018", "brca_tcga_pan_can_atlas_2018", "cesc_tcga_pan_can_atlas_2018", "chol_tcga_pan_can_atlas_2018", "coadread_tcga_pan_can_atlas_2018", "dlbc_tcga_pan_can_atlas_2018", "esca_tcga_pan_can_atlas_2018", "gbm_tcga_pan_can_atlas_2018", "hnsc_tcga_pan_can_atlas_2018", "kich_tcga_pan_can_atlas_2018", "kirc_tcga_pan_can_atlas_2018", "kirp_tcga_pan_can_atlas_2018", "laml_tcga_pan_can_atlas_2018", "lgg_tcga_pan_can_atlas_2018", "lihc_tcga_pan_can_atlas_2018", "luad_tcga_pan_can_atlas_2018", "lusc_tcga_pan_can_atlas_2018", "meso_tcga_pan_can_atlas_2018", "ov_tcga_pan_can_atlas_2018", "paad_tcga_pan_can_atlas_2018", "pcpg_tcga_pan_can_atlas_2018", "prad_tcga_pan_can_atlas_2018", "sarc_tcga_pan_can_atlas_2018", "skcm_tcga_pan_can_atlas_2018", "stad_tcga_pan_can_atlas_2018", "tgct_tcga_pan_can_atlas_2018", "thca_tcga_pan_can_atlas_2018", "thym_tcga_pan_can_atlas_2018", "ucec_tcga_pan_can_atlas_2018", "ucs_tcga_pan_can_atlas_2018", "uvm_tcga_pan_can_atlas_2018" ], "genes": [ "ERBB2" ], "tab": "plots", "plotsHorzSelection": { "dataType": "clinical_attribute", "selectedDataSourceOption": "CANCER_TYPE_DETAILED" }, "plotsVertSelection": { "dataType": "mrna", "selectedGeneOption": "ERBB2", "selectedDataSourceOption": "rna_seq_v2_mrna_median_Zscores" } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/results/plots?cancer_study_list=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=all&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22CANCER_TYPE_DETAILED%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna_median_Zscores%22%7D","url":"https://www.cbioportal.org/results/plots?cancer_study_list=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=all&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22CANCER_TYPE_DETAILED%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna_median_Zscores%22%7D","data":{"studyIds":["acc_tcga_pan_can_atlas_2018","blca_tcga_pan_can_atlas_2018","brca_tcga_pan_can_atlas_2018","cesc_tcga_pan_can_atlas_2018","chol_tcga_pan_can_atlas_2018","coadread_tcga_pan_can_a … (4764 more chars) ═ answer (success) [View ERBB2 mRNA expression by cancer type across TCGA PanCancer Atlas studies](https://www.cbioportal.org/results/plots?cancer_study_list=acc_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Cbrca_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018&gene_list=ERBB2&case_set_id=all&plots_horz_selection=%7B%22dataType%22%3A%22clinical_attribute%22%2C%22selectedDataSourceOption%22%3A%22CANCER_TYPE_DETAILED%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna%22%2C%22selectedDataSourceOption%22%3A%22rna_seq_v2_mrna_median_Zscores%22%7D) This opens a box/scatter plot of ERBB2 mRNA (RNA-Seq V2, Z-score) by detailed cancer type across all 32 TCGA PanCancer Atlas cohorts, letting you inspect the outlier-driven amplified subgroups directly. **Summary:** Gastric (STAD), esophageal (ESCA), breast (BRCA), cervical (CESC), and endometrial (UCEC) carcinomas show the widest right-tailed distributions and highest mean Z-scores — consistent with a HER2-amplified minority subgroup within each of these cancer types — while sarcoma, kidney chromophobe, adrenocortical, and pheochromocytoma/paraganglioma show the lowest, most homogeneous ERBB2 expression. Because a handful of extreme outliers (Z-scores up to ~230–260 in some samples) skew the means, the median is the more representative summary of the "typical" tumor in each cohort, and it clusters near zero for most cancer types except esophageal, uterine carcinosarcoma, and papillary renal cell carcinoma, which have slightly elevated medians. **Caveat:** This aggregates 32 separate TCGA studies queried in parallel — since they are non-overlapping primary tumor cohorts (no shared patients across cancer types), there is no double-counting concern here, unlike combining overlapping cohorts.