Q (sonnet): show me EGFR expression across cancer types ▶ list_guides {} ▶ resolve_and_route { "studyKeywords": [ "pan_can_atlas", "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#\") (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 32 matching studies (top 5 with full metadata, rest with basic info). 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":32,"studiesWithMetadata":[{"studyId":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","sampleCount":1084,"studyViewUrl":"https://www.cbioportal.org/study?id=brca_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":["brca_tcga_pan_can_atlas_2018_gistic","brca_tcga_pan_can_atlas_2018_log2CNA","brca_tcga_pan_can_atlas_2018_mutations","brca_tcga_pan_can_atlas_2018_protein_quantification","brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_pan_can_atlas_2018_rppa","brca_tcga_pan_can_atlas_2018_rppa_Zscores","brca_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["brca_tcga_pan_can_atlas_2018_armlevel_cna","brca_tcga_pan_can_atlas_2018_genetic_ancestry","brca_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","brca_tcga_pan_can_atlas_2018_methylation_hm450","brca_tcga_pan_can_atlas_2018_phosphoprotein_quantification"],"heatmapProfileIds":["brca_tcga_pan_can_atlas_2018_protein_quantification_zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","brca_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","brca_tcga_ … (22287 more chars) ▶ read_guide { "uri": "cbioportal://gene-expression-guide" } ▶ list_studies { "search": "pan-cancer" } ◀ 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 {"result":[{"cancer_study_identifier":"msk_impact_50k_2026","name":"MSK-IMPACT 50K Clinical Sequencing Cohort (MSK, Cancer Cell 2026)","type_of_cancer_id":"mixed","sample_count":54331,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=msk_impact_50k_2026"},{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","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":"pan_origimed_2020","name":"China Pan-cancer (OrigiMed, Nature 2022)","type_of_cancer_id":"mixed","sample_count":10194,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pan_origimed_2020"},{"cancer_study_identifier":"pancan_pcawg_2020","name":"Pan-cancer analysis of whole genomes (ICGC/TCGA, Nature 2020)","type_of_cancer_id":"mixed","sample_count":2922,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancan_pcawg_2020"},{"cancer_study_identifier":"pancan_hcmi_2025","name":"Pan-cancer Analysis of Organoid Samples (HCMI, 2025)","type_of_cancer_id":"mixed","sample_count":1183,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancan_hcmi_2025"},{"cancer_study_identifier":"pediatric_dkfz_2017","name":"Pediatric Pan-Cancer (DKFZ, Nature 2017)","type_of_cancer_id":"mixed","sample_count":961,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pediatric_dkfz_2017"},{"cancer_study_identifier":"pog570_bcgsc_2020","name":"Pan-cancer Analysis of Advanced and Metastatic Tumors (BCGSC, Nature Cancer 2020)","type_of_cancer_id":"mixed","sample_count":570,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pog570_bcgsc_2020"},{"cancer_study_identifier":"pan_pdo_wcm_2026","name":"Pan-Cancer Patient-Derived Organoids (WCM, 2026)","type_of_cancer_id":"mixed","sample_count":227,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pan_pdo_wcm_2026"},{"cancer_study_identifier":"mixed_pipseq_2017","name":"Pediatric Pan-cancer (Columbia U, Genome Med 2016)","type_of_cancer_id":"mixed","sample_count":103,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=mixed_pipseq_2017"},{"cancer_study_identifier":"mixed_impact_subset_2022","name":"Pan-Cancer MSK-IMPACT MET Validation Cohort (MSK 2022)","type_of_cancer_id":"mixed","sample_count":69,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=mixed_impact_subset_2022"},{"cancer_study_identifier":"pancan_pdmr_2025","name":"NCI Patient-Derived Models Repository (PDMR, 2025)","type_of_cancer_id":"mixed","sample_count":6272,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=pancan_pdmr_2025"},{"cancer_study_identifier":"ccle_broad_2025","name":"Cancer Cell Line Encyclopedia (Broad, Nat Rev Cancer 2025)","type_of_cancer_id":"mixed","sample_count":1981,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=ccle_broad_2025"},{"cancer_study_identifier":"nscl … (2026 more chars) ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ◀ 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 cohort AS ( SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ), sample_cancer_type AS ( SELECT cd.sample_unique_id, cd.cancer_study_identifier, cd.attribute_value AS cancer_type FROM clinical_data_derived cd JOIN cohort c USING (cancer_study_identifier) WHERE cd.attribute_name = 'CANCER_TYPE' ), expr AS ( SELECT gad.sample_unique_id, gad.cancer_study_identifier, toFloat64OrNull(gad.alteration_value) AS zscore FROM genetic_alteration_derived gad JOIN cohort c USING (cancer_study_identifier) WHERE gad.hugo_gene_symbol = 'EGFR' AND gad.profile_type = 'rna_seq_v2_mrna_median_Zscores' AND gad.alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(gad.alteration_value) IS NOT NULL ) SELECT sct.cancer_type, COUNT(*) AS n_samples, ROUND(AVG(e.zscore), 3) AS mean_zscore, ROUND(median(e.zscore), 3) AS median_zscore, ROUND(quantile(0.25)(e.zscore), 3) AS q1, ROUND(quantile(0.75)(e.zscore), 3) AS q3 FROM expr e JOIN sample_cancer_type sct USING (sample_unique_id) GROUP BY sct.cancer_type HAVING n_samples >= 30 ORDER BY mean_zscore DESC ", "max_rows": 40 } ◀ result {"rows":[{"cancer_type":"Glioblastoma","n_samples":160,"mean_zscore":6.907,"median_zscore":1.637,"q1":-0.291,"q3":8.767},{"cancer_type":"Esophagogastric Cancer","n_samples":593,"mean_zscore":1.688,"median_zscore":-0.018,"q1":-0.615,"q3":0.758},{"cancer_type":"Bladder Cancer","n_samples":407,"mean_zscore":1.338,"median_zscore":-0.166,"q1":-0.685,"q3":0.984},{"cancer_type":"Head and Neck Cancer","n_samples":515,"mean_zscore":1.285,"median_zscore":0.212,"q1":-0.55,"q3":1.155},{"cancer_type":"Glioma","n_samples":514,"mean_zscore":1.132,"median_zscore":-0.108,"q1":-0.659,"q3":0.602},{"cancer_type":"Non-Small Cell Lung Cancer","n_samples":994,"mean_zscore":0.801,"median_zscore":-0.01,"q1":-0.47,"q3":0.76},{"cancer_type":"Breast Cancer","n_samples":1082,"mean_zscore":0.589,"median_zscore":-0.284,"q1":-0.478,"q3":0.2},{"cancer_type":"Colorectal Cancer","n_samples":592,"mean_zscore":0.577,"median_zscore":0.159,"q1":-0.451,"q3":1.179},{"cancer_type":"Cervical Cancer","n_samples":294,"mean_zscore":0.493,"median_zscore":-0.277,"q1":-0.7,"q3":0.562},{"cancer_type":"Miscellaneous Neuroepithelial Tumor","n_samples":31,"mean_zscore":0.351,"median_zscore":-0.33,"q1":-0.472,"q3":0},{"cancer_type":"Mature B-Cell Neoplasms","n_samples":48,"mean_zscore":0.184,"median_zscore":-0.311,"q1":-0.565,"q3":0.522},{"cancer_type":"Sarcoma","n_samples":253,"mean_zscore":0.176,"median_zscore":-0.212,"q1":-0.454,"q3":0.282},{"cancer_type":"Pleural Mesothelioma","n_samples":87,"mean_zscore":0.139,"median_zscore":-0.152,"q1":-0.632,"q3":0.666},{"cancer_type":"Renal Clear Cell Carcinoma","n_samples":510,"mean_zscore":0.138,"median_zscore":-0.162,"q1":-0.618,"q3":0.461},{"cancer_type":"Endometrial Cancer","n_samples":584,"mean_zscore":0.136,"median_zscore":-0.224,"q1":-0.659,"q3":0.407},{"cancer_type":"Renal Non-Clear Cell Carcinoma","n_samples":348,"mean_zscore":0.118,"median_zscore":-0.21,"q1":-0.608,"q3":0.585},{"cancer_type":"Melanoma","n_samples":443,"mean_zscore":0.113,"median_zscore":-0.313,"q1":-0.39,"q3":0.003},{"cancer_type":"Non-Seminomatous Germ Cell Tumor","n_samples":86,"mean_zscore":0.113,"median_zscore":-0.531,"q1":-0.744,"q3":0.223},{"cancer_type":"Pancreatic Cancer","n_samples":177,"mean_zscore":0.101,"median_zscore":-0.075,"q1":-0.512,"q3":0.515},{"cancer_type":"Hepatobiliary Cancer","n_samples":366,"mean_zscore":0.095,"median_zscore":-0.269,"q1":-0.673,"q3":0.454},{"cancer_type":"Ovarian Epithelial Tumor","n_samples":300,"mean_zscore":0.081,"median_zscore":-0.218,"q1":-0.605,"q3":0.493},{"cancer_type":"Adrenocortical Carcinoma","n_samples":78,"mean_zscore":0.079,"median_zscore":-0.248,"q1":-0.45,"q3":0.307},{"cancer_type":"Thymic Epithelial Tumor","n_samples":119,"mean_zscore":0.026,"median_zscore":-0.344,"q1":-0.569,"q3":0.204},{"cancer_type":"Prostate Cancer","n_samples":493,"mean_zscore":-0.001,"median_zscore":-0.256,"q1":-0.725,"q3":0.488},{"cancer_type":"Ocular Melanoma","n_samples":80,"mean_zscore":-0.003,"median_zscore":-0.375,"q1":-0.598,"q3":0.234},{"canc … (568 more chars) ▶ resolve_and_route { "studyIds": [ "brca_tcga_pan_can_atlas_2018", "coadread_tcga_pan_can_atlas_2018", "gbm_tcga_pan_can_atlas_2018", "ov_tcga_pan_can_atlas_2018", "luad_tcga_pan_can_atlas_2018", "ucec_tcga_pan_can_atlas_2018", "hnsc_tcga_pan_can_atlas_2018", "lgg_tcga_pan_can_atlas_2018", "kirc_tcga_pan_can_atlas_2018", "thca_tcga_pan_can_atlas_2018", "prad_tcga_pan_can_atlas_2018", "lusc_tcga_pan_can_atlas_2018", "skcm_tcga_pan_can_atlas_2018", "stad_tcga_pan_can_atlas_2018", "blca_tcga_pan_can_atlas_2018", "lihc_tcga_pan_can_atlas_2018", "cesc_tcga_pan_can_atlas_2018", "kirp_tcga_pan_can_atlas_2018", "sarc_tcga_pan_can_atlas_2018", "laml_tcga_pan_can_atlas_2018", "paad_tcga_pan_can_atlas_2018", "esca_tcga_pan_can_atlas_2018", "pcpg_tcga_pan_can_atlas_2018", "tgct_tcga_pan_can_atlas_2018", "thym_tcga_pan_can_atlas_2018", "acc_tcga_pan_can_atlas_2018", "meso_tcga_pan_can_atlas_2018", "uvm_tcga_pan_can_atlas_2018", "kich_tcga_pan_can_atlas_2018", "ucs_tcga_pan_can_atlas_2018", "dlbc_tcga_pan_can_atlas_2018", "chol_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-491rduy0/6173d884-4d2c-4e2d-b346-06bc8081501d/tool-results/mcp-navigator-resolve_and_route-1790298443969.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-491rduy0/6173d884-4d2c-4e2d-b346-06bc8081501d/tool-results/mcp-navigator-resolve_and_route-1790298443969.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-491rduy0/6173d884-4d2c-4e2d-b346-06bc8081501d/tool-results/mcp-navigator-resolve_and_route-1790298443969.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": [ "brca_tcga_pan_can_atlas_2018", "coadread_tcga_pan_can_atlas_2018", "gbm_tcga_pan_can_atlas_2018", "ov_tcga_pan_can_atlas_2018", "luad_tcga_pan_can_atlas_2018", "ucec_tcga_pan_can_atlas_2018", "hnsc_tcga_pan_can_atlas_2018", "lgg_tcga_pan_can_atlas_2018", "kirc_tcga_pan_can_atlas_2018", "thca_tcga_pan_can_atlas_2018", "prad_tcga_pan_can_atlas_2018", "lusc_tcga_pan_can_atlas_2018", "skcm_tcga_pan_can_atlas_2018", "stad_tcga_pan_can_atlas_2018", "blca_tcga_pan_can_atlas_2018", "lihc_tcga_pan_can_atlas_2018", "cesc_tcga_pan_can_atlas_2018", "kirp_tcga_pan_can_atlas_2018", "sarc_tcga_pan_can_atlas_2018", "laml_tcga_pan_can_atlas_2018", "paad_tcga_pan_can_atlas_2018", "esca_tcga_pan_can_atlas_2018", "pcpg_tcga_pan_can_atlas_2018", "tgct_tcga_pan_can_atlas_2018", "thym_tcga_pan_can_atlas_2018", "acc_tcga_pan_can_atlas_2018", "meso_tcga_pan_can_atlas_2018", "uvm_tcga_pan_can_atlas_2018", "kich_tcga_pan_can_atlas_2018", "ucs_tcga_pan_can_atlas_2018", "dlbc_tcga_pan_can_atlas_2018", "chol_tcga_pan_can_atlas_2018" ], "genes": [ "EGFR" ], "tab": "plots", "plotsHorzSelection": { "dataType": "clinical", "selectedDataSourceOption": "CANCER_TYPE" }, "plotsVertSelection": { "dataType": "mrna", "selectedGeneOption": "EGFR" } } ◀ result {"success":true,"message":"Navigating to 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… (4566 more chars) ═ answer (success) [View EGFR mRNA Expression by Cancer Type (Plots Tab)](https://www.cbioportal.org/results/plots?cancer_study_list=brca_tcga_pan_can_atlas_2018%2Ccoadread_tcga_pan_can_atlas_2018%2Cgbm_tcga_pan_can_atlas_2018%2Cov_tcga_pan_can_atlas_2018%2Cluad_tcga_pan_can_atlas_2018%2Cucec_tcga_pan_can_atlas_2018%2Chnsc_tcga_pan_can_atlas_2018%2Clgg_tcga_pan_can_atlas_2018%2Ckirc_tcga_pan_can_atlas_2018%2Cthca_tcga_pan_can_atlas_2018%2Cprad_tcga_pan_can_atlas_2018%2Clusc_tcga_pan_can_atlas_2018%2Cskcm_tcga_pan_can_atlas_2018%2Cstad_tcga_pan_can_atlas_2018%2Cblca_tcga_pan_can_atlas_2018%2Clihc_tcga_pan_can_atlas_2018%2Ccesc_tcga_pan_can_atlas_2018%2Ckirp_tcga_pan_can_atlas_2018%2Csarc_tcga_pan_can_atlas_2018%2Claml_tcga_pan_can_atlas_2018%2Cpaad_tcga_pan_can_atlas_2018%2Cesca_tcga_pan_can_atlas_2018%2Cpcpg_tcga_pan_can_atlas_2018%2Ctgct_tcga_pan_can_atlas_2018%2Cthym_tcga_pan_can_atlas_2018%2Cacc_tcga_pan_can_atlas_2018%2Cmeso_tcga_pan_can_atlas_2018%2Cuvm_tcga_pan_can_atlas_2018%2Ckich_tcga_pan_can_atlas_2018%2Cucs_tcga_pan_can_atlas_2018%2Cdlbc_tcga_pan_can_atlas_2018%2Cchol_tcga_pan_can_atlas_2018&gene_list=EGFR&case_set_id=all&plots_horz_selection=%7B%22dataType%22%3A%22clinical%22%2C%22selectedDataSourceOption%22%3A%22CANCER_TYPE%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%221956%22%2C%22dataType%22%3A%22mrna%22%7D) Scatter/box plots: EGFR mRNA (y-axis) by cancer type (x-axis), across all 32 TCGA PanCancer Atlas studies (~9,900 samples). **Summary of query results** (mRNA Z-scores, `rna_seq_v2_mrna_median_Zscores`, ≥30 samples/type): | Cancer Type | N | Mean Z | Median Z | |---|---|---|---| | **Glioblastoma** | 160 | **6.91** | 1.64 | | Esophagogastric Cancer | 593 | 1.69 | -0.02 | | Bladder Cancer | 407 | 1.34 | -0.17 | | Head and Neck Cancer | 515 | 1.29 | 0.21 | | Glioma | 514 | 1.13 | -0.11 | | Non-Small Cell Lung Cancer | 994 | 0.80 | -0.01 | | Breast Cancer | 1082 | 0.59 | -0.28 | | ... (26 more types, trending toward Z≈0 and below) | | | | | Seminoma | 63 | -0.69 | -0.82 | Glioblastoma shows the most extreme EGFR overexpression, with a markedly right-skewed distribution (median 1.64 vs. mean 6.91) — consistent with a subset of highly overexpressing samples pulling the mean up, plausibly reflecting *EGFR* amplification status (not assessed here — this is expression only, not driver annotation).