Q (sonnet): How does ERBB2 mRNA expression vary across different cancer types in TCGA Pan-Cancer Atlas studies? ▶ list_guides {} ◀ 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"}]} ▶ read_guide { "uri": "cbioportal://gene-expression-guide" } ▶ read_guide { "uri": "cbioportal://sample-filtering-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 # Sample and Study Filtering Guide ## Overview Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria. ## Study-Level Filtering ### 1. Always Filter by Study Every query should specify a study to ensure consistent results: ```sql -- Always include study filtering SELECT * FROM your_table WHERE cancer_study_identifier = 'your_study_id' -- Additional filters... ``` ### 2. Find Available Studies ```sql -- Discover available studies SELECT cancer_study_identifier, name, description, type_of_cancer_id FROM cancer_study ORDER BY cancer_study_identifier; ``` ### 3. Study Information ```sql -- Get detailed study information SELECT cs.cancer_study_identifier, cs.name as study_name, cs.description, cs.sample_count, COUNT(DISTINCT p.internal_id) as patient_count FROM cancer_study cs LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id WHERE cs.cancer_study_identifier = 'your_study_id' GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count; ``` ### 4. Find Studies by Available Data Types Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter. **Canonical query — studies with ALL requested data types for a cancer type:** ```sql SELECT cancer_study_identifier, name, sample_count, mutation_sample_count, cna_sample_count FROM cancer_study WHERE type_of_cancer_id = 'luad' -- ← OncoTree code from search_oncotree AND mutation_sample_count > 0 -- ← one condition per required data type AND cna_sample_count > 0 ORDER BY sample_count DESC; ``` **Data type → column** (a count of 0 means the study doesn't have that data): | Data type in the user's question | Column (portal "Data type" filter label) | |---|---| | samples in the study | `sample_count` | | mutation / mutations | `mutation_sample_count` ("Mutations") | | copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") | | structural variant / fusion | `structural_variant_sample_count` | | RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" | | specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq | | specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `_mrna` list | | microRNA | `mirna_sample_count` ("miRNA") | | protein / RPPA | `rppa_sample_count` ("RPPA") | | mas … (7083 more chars) ▶ list_studies { "search": "pancancer atlas", "limit": 10 } ◀ result {"result":[{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"brca","sample_count":1084,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"coadread","sample_count":594,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018"},{"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":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","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":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"luad","sample_count":566,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","name":"Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"ucec","sample_count":529,"has_guide":true,"url":"https://www.cbioportal.org/study/summary?id=ucec_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"hnsc_tcga_pan_can_atlas_2018","name":"Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"hnsc","sample_count":523,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=hnsc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"lgg_tcga_pan_can_atlas_2018","name":"Brain Lower Grade Glioma (TCGA, PanCancer Atlas)","type_of_cancer_id":"difg","sample_count":514,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lgg_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018","name":"Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"ccrcc","sample_count":512,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=kirc_tcga_pan_can_atlas_2018"},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018","name":"Thyroid Carcinoma (TCGA, PanCancer Atlas)","type_of_cancer_id":"thpa","sample_count":500,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=thca_tcga_pan_can_atlas_2018"}]} ▶ clickhouse_run_select_query { "query": "SELECT preference_name, count(*) as n FROM cancer_study_query_preferences GROUP BY preference_name ORDER BY n DESC LIMIT 20" } ◀ result {"rows":[{"preference_name":"all_studies_non_redundant","n":241},{"preference_name":"pan_cancer_tcga","n":32},{"preference_name":"treatment_outcomes","n":1},{"preference_name":"large_genomic_cohort","n":1}]} ▶ 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' 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":"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":"rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores"},{"profile_type":"rppa"},{"profile_type":"rppa_Zscores"}]} ▶ clickhouse_run_select_query { "query": " WITH studies AS ( SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ), expr AS ( SELECT g.sample_unique_id, g.cancer_study_identifier, toFloat64OrNull(g.alteration_value) AS zscore FROM genetic_alteration_derived g INNER JOIN studies s ON g.cancer_study_identifier = s.cancer_study_identifier WHERE g.hugo_gene_symbol = 'ERBB2' AND g.profile_type = 'rna_seq_v2_mrna_median_all_sample_Zscores' AND g.alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(g.alteration_value) IS NOT NULL ), ct AS ( SELECT sample_unique_id, attribute_value AS cancer_type FROM clinical_data_derived WHERE attribute_name = 'CANCER_TYPE' AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM studies) ) SELECT ct.cancer_type, e.cancer_study_identifier, count() AS n_samples, round(avg(e.zscore), 3) AS mean_zscore, round(median(e.zscore), 3) AS median_zscore, round(stddevSamp(e.zscore), 3) AS sd_zscore, round(max(e.zscore), 2) AS max_zscore FROM expr e INNER JOIN ct ON e.sample_unique_id = ct.sample_unique_id GROUP BY ct.cancer_type, e.cancer_study_identifier ORDER BY mean_zscore DESC ", "max_rows": 40 } ◀ result {"rows":[{"cancer_type":"Miscellaneous Neuroepithelial Tumor","cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018","n_samples":31,"mean_zscore":0.773,"median_zscore":0.729,"sd_zscore":0.969,"max_zscore":2.58},{"cancer_type":"Non-Seminomatous Germ Cell Tumor","cancer_study_identifier":"tgct_tcga_pan_can_atlas_2018","n_samples":86,"mean_zscore":0.628,"median_zscore":0.583,"sd_zscore":0.757,"max_zscore":2.48},{"cancer_type":"Esophagogastric Cancer","cancer_study_identifier":"esca_tcga_pan_can_atlas_2018","n_samples":181,"mean_zscore":0,"median_zscore":-0.13,"sd_zscore":1.003,"max_zscore":4.08},{"cancer_type":"Non-Small Cell Lung Cancer","cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","n_samples":510,"mean_zscore":0,"median_zscore":0.037,"sd_zscore":1.001,"max_zscore":6.04},{"cancer_type":"Melanoma","cancer_study_identifier":"skcm_tcga_pan_can_atlas_2018","n_samples":443,"mean_zscore":0,"median_zscore":0.161,"sd_zscore":1.001,"max_zscore":2.52},{"cancer_type":"Renal Non-Clear Cell Carcinoma","cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018","n_samples":283,"mean_zscore":0,"median_zscore":0.058,"sd_zscore":1.002,"max_zscore":2.98},{"cancer_type":"Pleural Mesothelioma","cancer_study_identifier":"meso_tcga_pan_can_atlas_2018","n_samples":87,"mean_zscore":0,"median_zscore":-0.084,"sd_zscore":1.006,"max_zscore":2.17},{"cancer_type":"Bladder Cancer","cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","n_samples":407,"mean_zscore":0,"median_zscore":0.034,"sd_zscore":1.001,"max_zscore":3.86},{"cancer_type":"Ovarian Epithelial Tumor","cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","n_samples":300,"mean_zscore":0,"median_zscore":-0.107,"sd_zscore":1.002,"max_zscore":7.43},{"cancer_type":"Leukemia","cancer_study_identifier":"laml_tcga_pan_can_atlas_2018","n_samples":173,"mean_zscore":0,"median_zscore":-0.024,"sd_zscore":1.003,"max_zscore":2.41},{"cancer_type":"Cholangiocarcinoma","cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","n_samples":36,"mean_zscore":0,"median_zscore":0.31,"sd_zscore":1.014,"max_zscore":1.01},{"cancer_type":"Renal Non-Clear Cell Carcinoma","cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","n_samples":65,"mean_zscore":0,"median_zscore":0.122,"sd_zscore":1.008,"max_zscore":1.74},{"cancer_type":"Thyroid Cancer","cancer_study_identifier":"thca_tcga_pan_can_atlas_2018","n_samples":498,"mean_zscore":0,"median_zscore":0.076,"sd_zscore":1.001,"max_zscore":2.29},{"cancer_type":"Pancreatic Cancer","cancer_study_identifier":"paad_tcga_pan_can_atlas_2018","n_samples":177,"mean_zscore":0,"median_zscore":0.142,"sd_zscore":1.003,"max_zscore":5.61},{"cancer_type":"Colorectal Cancer","cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","n_samples":592,"mean_zscore":0,"median_zscore":-0.075,"sd_zscore":1.001,"max_zscore":7.48},{"cancer_type":"Ocular Melanoma","cancer_study_identifier":"uvm_tcga_pan_can_atlas_2018","n_samples":80,"mean_zscore":0,"median_zscore":-0.231,"sd_zscore":1.006,"max_zscore":2.78 … (3312 more chars) ▶ clickhouse_run_select_query { "query": " WITH studies AS ( SELECT cancer_study_identifier FROM cancer_study_query_preferences WHERE preference_name = 'pan_cancer_tcga' ), expr AS ( SELECT g.sample_unique_id, g.cancer_study_identifier, toFloat64OrNull(g.alteration_value) AS rsem FROM genetic_alteration_derived g INNER JOIN studies s ON g.cancer_study_identifier = s.cancer_study_identifier WHERE g.hugo_gene_symbol = 'ERBB2' AND g.profile_type = 'rna_seq_v2_mrna' AND g.alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(g.alteration_value) IS NOT NULL AND toFloat64OrNull(g.alteration_value) > 0 ), ct AS ( SELECT sample_unique_id, attribute_value AS cancer_type FROM clinical_data_derived WHERE attribute_name = 'CANCER_TYPE' AND cancer_study_identifier IN (SELECT cancer_study_identifier FROM studies) ) SELECT e.cancer_study_identifier, any(ct.cancer_type) AS cancer_type, count() AS n_samples, round(avg(log2(e.rsem + 1)), 3) AS mean_log2_rsem, round(median(e.rsem), 1) AS median_rsem, round(quantile(0.25)(e.rsem), 1) AS q1_rsem, round(quantile(0.75)(e.rsem), 1) AS q3_rsem, round(max(e.rsem), 1) AS max_rsem FROM expr e INNER JOIN ct ON e.sample_unique_id = ct.sample_unique_id GROUP BY e.cancer_study_identifier ORDER BY median_rsem DESC ", "max_rows": 40 } ◀ result {"rows":[{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","cancer_type":"Bladder Cancer","n_samples":407,"mean_log2_rsem":12.828,"median_rsem":7514.4,"q1_rsem":3949.1,"q3_rsem":12122.1,"max_rsem":307567},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","cancer_type":"Breast Cancer","n_samples":1082,"mean_log2_rsem":12.973,"median_rsem":7025.3,"q1_rsem":4476.1,"q3_rsem":10792.2,"max_rsem":380668},{"cancer_study_identifier":"thca_tcga_pan_can_atlas_2018","cancer_type":"Thyroid Cancer","n_samples":498,"mean_log2_rsem":12.669,"median_rsem":6700.8,"q1_rsem":5442.9,"q3_rsem":8372.4,"max_rsem":15358.2},{"cancer_study_identifier":"kirp_tcga_pan_can_atlas_2018","cancer_type":"Renal Non-Clear Cell Carcinoma","n_samples":283,"mean_log2_rsem":12.655,"median_rsem":6622.1,"q1_rsem":4916.2,"q3_rsem":8779.7,"max_rsem":25050.9},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","cancer_type":"Non-Small Cell Lung Cancer","n_samples":510,"mean_log2_rsem":12.542,"median_rsem":6104.5,"q1_rsem":4308.5,"q3_rsem":8446,"max_rsem":262936},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018","cancer_type":"Prostate Cancer","n_samples":493,"mean_log2_rsem":12.421,"median_rsem":5518.6,"q1_rsem":4542.8,"q3_rsem":6912.6,"max_rsem":110785},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","cancer_type":"Cholangiocarcinoma","n_samples":36,"mean_log2_rsem":12.035,"median_rsem":5469.6,"q1_rsem":3339.2,"q3_rsem":6754.1,"max_rsem":9980.4},{"cancer_study_identifier":"paad_tcga_pan_can_atlas_2018","cancer_type":"Pancreatic Cancer","n_samples":177,"mean_log2_rsem":12.142,"median_rsem":4992.9,"q1_rsem":3425.4,"q3_rsem":6359.8,"max_rsem":233627},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","cancer_type":"Endometrial Cancer","n_samples":527,"mean_log2_rsem":12.351,"median_rsem":4853.2,"q1_rsem":3573.5,"q3_rsem":6650.1,"max_rsem":358261},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","cancer_type":"Colorectal Cancer","n_samples":592,"mean_log2_rsem":12.265,"median_rsem":4695.9,"q1_rsem":3633.2,"q3_rsem":6079.9,"max_rsem":511917},{"cancer_study_identifier":"ucs_tcga_pan_can_atlas_2018","cancer_type":"Endometrial Cancer","n_samples":57,"mean_log2_rsem":12.277,"median_rsem":4555.4,"q1_rsem":3131,"q3_rsem":7076,"max_rsem":72554.7},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","cancer_type":"Renal Non-Clear Cell Carcinoma","n_samples":65,"mean_log2_rsem":12.045,"median_rsem":4552.7,"q1_rsem":3217.8,"q3_rsem":6434.7,"max_rsem":12264.7},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018","cancer_type":"Cervical Cancer","n_samples":294,"mean_log2_rsem":12.28,"median_rsem":4534.1,"q1_rsem":3271,"q3_rsem":6071.4,"max_rsem":431024},{"cancer_study_identifier":"stad_tcga_pan_can_atlas_2018","cancer_type":"Esophagogastric Cancer","n_samples":412,"mean_log2_rsem":12.292,"median_rsem":4249.7,"q1_rsem":3006.7,"q3_rsem":6358.1,"max_rsem":415387.1},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","cancer_type":"Ovarian … (3583 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-3sjqt3v5/a2b86c57-10f9-4e66-a78e-aed9fd838b95/tool-results/mcp-navigator-resolve_and_route-1790294653534.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-3sjqt3v5/a2b86c57-10f9-4e66-a78e-aed9fd838b95/tool-results/mcp-navigator-resolve_and_route-1790294653534.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-3sjqt3v5/a2b86c57-10f9-4e66-a78e-aed9fd838b95/tool-results/mcp-navigator-resolve_and_route-1790294653534.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" }, "plotsVertSelection": { "dataType": "mrna", "selectedGeneOption": "ERBB2", "logScale": "true" } } ◀ 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%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna%22%2C%22logScale%22%3A%22true%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%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna%22%2C%22logScale%22%3A%22true%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_atlas_2018","dlbc_tcga_pan_can_atlas_2018","esca_tcga_pan_can_atlas_2018","gbm_tcga_pan_can_atlas_2018" … (4653 more chars) ═ answer (success) [View ERBB2 mRNA Expression by Cancer Type (Pan-Cancer Atlas)](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%22%7D&plots_vert_selection=%7B%22selectedGeneOption%22%3A%222064%22%2C%22dataType%22%3A%22mrna%22%2C%22logScale%22%3A%22true%22%7D) Scatter/box plots: ERBB2 mRNA (log scale, y-axis) by cancer type (x-axis) across all 32 TCGA PanCancer Atlas studies (~13,300 samples total). This matches the query results above — bladder, breast, thyroid, papillary renal (KIRP), and lung adenocarcinoma show the highest median ERBB2 expression, while AML, adrenocortical carcinoma, and DLBCL show the lowest.