Q (haiku): Help me rank TCGA cancer studies based on median CD3 expression ▶ read_guide { "uri": "cbioportal://clinical-data-guide" } ▶ list_guides {} ◀ 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) ◀ 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://gene-resolution-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 # Gene Resolution Guide Use this guide before querying gene expression, mutation, copy-number, methylation, or structural-variant data when the user's gene term may be ambiguous. ## Routing Triggers Read this guide when the user mentions: - a gene family shorthand: `CD3`, `HLA`, `KRT`, `MUC`, `MT-`, `IGH`, `IGK`, `IGL` - a marker name that may refer to multiple genes or proteins - a gene alias, old symbol, or informal name - a wildcard-like term such as "all CD3 genes" These examples are not exhaustive. Apply this guide to any gene term that may resolve to multiple symbols, aliases, paralogs, family members, or marker genes. ## Core Rule Do not silently aggregate multiple genes when the user names an ambiguous symbol. Either ask for clarification or choose a clearly standard marker and state the choice. For example, "CD3 expression" can refer to `CD3D`, `CD3E`, or `CD3G`; in many immune-marker contexts `CD3E` is the standard marker, but the agent must not average all CD3 genes unless the user asks for a combined signature. ## Gene Discovery Query After validating the gene table exists, search exact symbols first, then prefix/alias-like matches: ```sql SELECT hugo_gene_symbol, entrez_gene_id FROM gene WHERE upper(hugo_gene_symbol) = upper('CD3') OR upper(hugo_gene_symbol) LIKE upper('CD3%') ORDER BY hugo_gene_symbol LIMIT 50; ``` If aliases are available in this deployment, inspect the relevant alias table before assuming no match. If no alias table exists, state that alias resolution is limited to available gene symbols. ## Answer Pattern If multiple plausible genes are found: > "CD3" is ambiguous in cBioPortal gene-symbol terms. I found `CD3D`, `CD3E`, and `CD3G`. Did you mean `CD3E` as a T-cell marker, or should I analyze all three separately? If the user clearly asks for a combined family/signature: - report each gene separately by default - only compute an average/signature if the user explicitly requests it - state exactly how the combined value was calculated ## Do Not - Do not average multiple genes into one expression value without explicit permission. - Do not rewrite an ambiguous symbol to a single gene without telling the user. - Do not treat a prefix match as a validated gene symbol. ▶ clickhouse_list_tables {} ◀ result {"tables":[{"name":"allele_specific_copy_number"},{"name":"alteration_driver_annotation"},{"name":"cancer_study"},{"name":"cancer_study_query_preferences"},{"name":"cancer_study_tags"},{"name":"cbioportal_sequence_state"},{"name":"clinical_attribute_counts"},{"name":"clinical_attribute_meta"},{"name":"clinical_data_derived"},{"name":"clinical_event"},{"name":"clinical_event_data"},{"name":"clinical_event_data_derived"},{"name":"clinical_event_derived"},{"name":"clinical_patient"},{"name":"clinical_sample"},{"name":"cna_event"},{"name":"cna_panel_gene_coverage"},{"name":"cna_wes_coverage"},{"name":"co_altered_genes_in_study"},{"name":"copy_number_seg"},{"name":"copy_number_seg_file"},{"name":"gene"},{"name":"gene_alias"},{"name":"gene_alteration_frequency_by_cancer_type"},{"name":"gene_cna_distribution_in_study"},{"name":"gene_mutation_frequency_by_cancer_type"},{"name":"gene_mutation_frequency_in_studies"},{"name":"gene_mutation_frequency_in_study"},{"name":"gene_mutation_variants_in_study"},{"name":"gene_pair_coexpression"},{"name":"gene_panel"},{"name":"gene_panel_list"},{"name":"gene_panel_to_gene_derived"},{"name":"generic_assay_data_derived"},{"name":"generic_assay_meta_derived"},{"name":"generic_assay_profile_entity_derived"},{"name":"generic_entity_properties"},{"name":"geneset"},{"name":"geneset_gene"},{"name":"geneset_hierarchy_leaf"},{"name":"geneset_hierarchy_node"},{"name":"genetic_alteration"},{"name":"genetic_alteration_derived"},{"name":"genetic_entity"},{"name":"genetic_profile"},{"name":"genetic_profile_link"},{"name":"genetic_profile_samples"},{"name":"genomic_event_derived"},{"name":"gistic"},{"name":"gistic_to_gene"},{"name":"info"},{"name":"mut_sig"},{"name":"mutation"},{"name":"mutation_count_by_keyword"},{"name":"mutation_derived"},{"name":"mutation_event"},{"name":"mutation_panel_gene_coverage"},{"name":"mutation_wes_coverage"},{"name":"patient"},{"name":"reference_genome"},{"name":"reference_genome_gene"},{"name":"resource_definition"},{"name":"resource_patient"},{"name":"resource_sample"},{"name":"resource_study"},{"name":"sample"},{"name":"sample_cna_event"},{"name":"sample_derived"},{"name":"sample_list"},{"name":"sample_list_list"},{"name":"sample_profile"},{"name":"sample_to_gene_panel_derived"},{"name":"structural_variant"},{"name":"sv_panel_gene_coverage"},{"name":"sv_wes_coverage"},{"name":"top_cna_genes_in_study"},{"name":"top_mutated_genes_in_cohort"},{"name":"top_mutated_genes_in_study"},{"name":"top_sv_genes_in_study"},{"name":"treatment_counts_in_study"},{"name":"treatment_regimens_in_study"},{"name":"type_of_cancer"}]} ▶ clickhouse_run_select_query { "query": " SELECT hugo_gene_symbol, entrez_gene_id FROM gene WHERE upper(hugo_gene_symbol) = upper('CD3') OR upper(hugo_gene_symbol) LIKE upper('CD3%') ORDER BY hugo_gene_symbol LIMIT 50 " } ◀ result {"rows":[{"hugo_gene_symbol":"CD300A","entrez_gene_id":11314},{"hugo_gene_symbol":"CD300C","entrez_gene_id":10871},{"hugo_gene_symbol":"CD300E","entrez_gene_id":342510},{"hugo_gene_symbol":"CD300H","entrez_gene_id":100130520},{"hugo_gene_symbol":"CD300LB","entrez_gene_id":124599},{"hugo_gene_symbol":"CD300LD","entrez_gene_id":100131439},{"hugo_gene_symbol":"CD300LD-AS1","entrez_gene_id":146723},{"hugo_gene_symbol":"CD300LF","entrez_gene_id":146722},{"hugo_gene_symbol":"CD300LG","entrez_gene_id":146894},{"hugo_gene_symbol":"CD302","entrez_gene_id":9936},{"hugo_gene_symbol":"CD320","entrez_gene_id":51293},{"hugo_gene_symbol":"CD33","entrez_gene_id":945},{"hugo_gene_symbol":"CD34","entrez_gene_id":947},{"hugo_gene_symbol":"CD36","entrez_gene_id":948},{"hugo_gene_symbol":"CD37","entrez_gene_id":951},{"hugo_gene_symbol":"CD38","entrez_gene_id":952},{"hugo_gene_symbol":"CD38-AS1","entrez_gene_id":143433680},{"hugo_gene_symbol":"CD3D","entrez_gene_id":915},{"hugo_gene_symbol":"CD3E","entrez_gene_id":916},{"hugo_gene_symbol":"CD3G","entrez_gene_id":917}]} ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT cancer_study_identifier FROM cancer_study WHERE cancer_study_identifier LIKE 'tcga%' AND cancer_study_identifier LIKE '%_pan_can%' ORDER BY cancer_study_identifier LIMIT 1 " } ◀ result {"rows":[]} ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT cancer_study_identifier, name FROM cancer_study WHERE cancer_study_identifier LIKE 'tcga%' ORDER BY cancer_study_identifier LIMIT 50 " } ◀ result {"rows":[]} ▶ clickhouse_run_select_query { "query": " SELECT cancer_study_identifier, name, type_of_cancer_id FROM cancer_study LIMIT 50 " } ◀ result {"rows":[{"cancer_study_identifier":"acc_tcga","name":"Adrenocortical Carcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"acc"},{"cancer_study_identifier":"acyc_mskcc_2013","name":"Adenoid Cystic Carcinoma (MSK, Nat Genet 2013)","type_of_cancer_id":"acyc"},{"cancer_study_identifier":"acyc_fmi_2014","name":"Adenoid Cystic Carcinoma (FMI, Am J Surg Pathl. 2014)","type_of_cancer_id":"acyc"},{"cancer_study_identifier":"acyc_jhu_2016","name":"Adenoid Cystic Carcinoma (JHU, Cancer Prev Res 2016)","type_of_cancer_id":"acyc"},{"cancer_study_identifier":"acyc_mda_2015","name":"Adenoid Cystic Carcinoma (MDA, Clin Cancer Res 2015)","type_of_cancer_id":"acyc"},{"cancer_study_identifier":"acyc_mgh_2016","name":"Adenoid Cystic Carcinoma (MGH, Nat Gen 2016)","type_of_cancer_id":"acyc"},{"cancer_study_identifier":"acyc_sanger_2013","name":"Adenoid Cystic Carcinoma (Sanger/MDA, JCI 2013)","type_of_cancer_id":"acyc"},{"cancer_study_identifier":"all_stjude_2015","name":"Acute Lymphoblastic Leukemia (St Jude, Nat Genet 2015)","type_of_cancer_id":"bll"},{"cancer_study_identifier":"ampca_bcm_2016","name":"Ampullary Carcinoma (Baylor College of Medicine, Cell Reports 2016)","type_of_cancer_id":"ampca"},{"cancer_study_identifier":"all_stjude_2013","name":"Hypodiploid Acute Lymphoid Leukemia (St Jude, Nat Genet 2013)","type_of_cancer_id":"myeloid"},{"cancer_study_identifier":"all_stjude_2016","name":"Acute Lymphoblastic Leukemia (St Jude, Nat Genet 2016)","type_of_cancer_id":"bll"},{"cancer_study_identifier":"all_phase2_target_2018_pub","name":"Pediatric Acute Lymphoid Leukemia - Phase II (TARGET, 2018)","type_of_cancer_id":"bll"},{"cancer_study_identifier":"aml_target_2018_pub","name":"Pediatric Acute Myeloid Leukemia (TARGET, 2018)","type_of_cancer_id":"aml"},{"cancer_study_identifier":"aml_ohsu_2018","name":"Acute Myeloid Leukemia (OHSU, Nature 2018)","type_of_cancer_id":"aml"},{"cancer_study_identifier":"acc_2019","name":"Adenoid Cystic Carcinoma Project (J Clin Invest 2019)","type_of_cancer_id":"acyc"},{"cancer_study_identifier":"angs_painter_2020","name":"The Angiosarcoma Project (Provisional, July 2020)","type_of_cancer_id":"angs"},{"cancer_study_identifier":"bcc_unige_2016","name":"Basal Cell Carcinoma (UNIGE, Nat Genet 2016)","type_of_cancer_id":"bcc"},{"cancer_study_identifier":"blca_dfarber_mskcc_2014","name":"Bladder Urothelial Carcinoma (DFCI/MSK, Cancer Discov 2014)","type_of_cancer_id":"blca"},{"cancer_study_identifier":"blca_bgi","name":"Bladder Urothelial Carcinoma (BGI, Nat Genet 2013)","type_of_cancer_id":"blca"},{"cancer_study_identifier":"angs_project_painter_2018","name":"The Angiosarcoma Project - Count Me In (Nature Medicine, 2020)","type_of_cancer_id":"angs"},{"cancer_study_identifier":"bfn_duke_nus_2015","name":"Breast Fibroepithelial Tumors (Duke-NUS, Nat Genet 2015)","type_of_cancer_id":"bfn"},{"cancer_study_identifier":"blca_cornell_2016","name":"Urothelial Carcinoma (Cornell/Trento, Nat Gen 2016)","type_of_cancer_id":"blca"},{"cancer_stu … (3861 more chars) ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT cancer_study_identifier, name FROM cancer_study WHERE cancer_study_identifier LIKE 'tcga%' AND cancer_study_identifier NOT LIKE '%firehose%' AND cancer_study_identifier NOT LIKE '%provisional%' ORDER BY cancer_study_identifier LIMIT 100 " } ◀ result {"rows":[]} ▶ clickhouse_run_select_query { "query": " SELECT DISTINCT cs.cancer_study_identifier, cs.name FROM cancer_study cs WHERE cs.cancer_study_identifier LIKE '%tcga%' ORDER BY cs.cancer_study_identifier LIMIT 100 " } ◀ result {"rows":[{"cancer_study_identifier":"acc_tcga","name":"Adrenocortical Carcinoma (TCGA, Firehose Legacy)"},{"cancer_study_identifier":"acc_tcga_gdc","name":"Adrenocortical Carcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","name":"Adrenocortical Carcinoma (TCGA, PanCancer Atlas)"},{"cancer_study_identifier":"aml_tcga_gdc","name":"Acute Myeloid Leukemia (TCGA GDC, 2025)"},{"cancer_study_identifier":"blca_msk_tcga_2020","name":"Bladder Cancer (MSK/TCGA, Eur Urol 2020)"},{"cancer_study_identifier":"blca_tcga","name":"Bladder Urothelial Carcinoma (TCGA, Firehose Legacy)"},{"cancer_study_identifier":"blca_tcga_gdc","name":"Bladder Urothelial Carcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","name":"Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)"},{"cancer_study_identifier":"blca_tcga_pub","name":"Bladder Urothelial Carcinoma (TCGA, Nature 2014)"},{"cancer_study_identifier":"blca_tcga_pub_2017","name":"Bladder Cancer (TCGA, Cell 2017)"},{"cancer_study_identifier":"brca_tcga","name":"Breast Invasive Carcinoma (TCGA, Firehose Legacy)"},{"cancer_study_identifier":"brca_tcga_gdc","name":"Invasive Breast Carcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)"},{"cancer_study_identifier":"brca_tcga_pub","name":"Breast Invasive Carcinoma (TCGA, Nature 2012)"},{"cancer_study_identifier":"brca_tcga_pub2015","name":"Breast Invasive Carcinoma (TCGA, Cell 2015)"},{"cancer_study_identifier":"ccrcc_tcga_gdc","name":"Renal Clear Cell Carcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"cesc_tcga","name":"Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA, Firehose Legacy)"},{"cancer_study_identifier":"cesc_tcga_gdc","name":"Cervical Squamous Cell Carcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018","name":"Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)"},{"cancer_study_identifier":"chol_tcga","name":"Cholangiocarcinoma (TCGA, Firehose Legacy)"},{"cancer_study_identifier":"chol_tcga_gdc","name":"Cholangiocarcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","name":"Cholangiocarcinoma (TCGA, PanCancer Atlas)"},{"cancer_study_identifier":"chrcc_tcga_gdc","name":"Chromophobe Renal Cell Carcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"coad_tcga_gdc","name":"Colon Adenocarcinoma (TCGA GDC, 2025)"},{"cancer_study_identifier":"coadread_tcga","name":"Colorectal Adenocarcinoma (TCGA, Firehose Legacy)"},{"cancer_study_identifier":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)"},{"cancer_study_identifier":"coadread_tcga_pub","name":"Colorectal Adenocarcinoma (TCGA, Nature 2012)"},{"cancer_study_identifier":"difg_tcga_gdc","name":"Diffuse Glioma (TCGA GDC, 2025)"},{"cancer_study_identifier":"dlbc_tcga","name":"Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (TCGA, … (7391 more chars) ▶ clickhouse_run_select_query { "query": " SELECT gad.cancer_study_identifier, count(gad.sample_unique_id) as samples_with_cd3e, round(median(toFloat64OrNull(gad.alteration_value)), 2) as median_cd3e_expression, round(avg(toFloat64OrNull(gad.alteration_value)), 2) as mean_cd3e_expression, round(min(toFloat64OrNull(gad.alteration_value)), 2) as min_cd3e, round(max(toFloat64OrNull(gad.alteration_value)), 2) as max_cd3e FROM genetic_alteration_derived gad WHERE gad.cancer_study_identifier LIKE '%_tcga_pan_can_atlas_2018%' AND gad.hugo_gene_symbol = 'CD3E' AND gad.alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(gad.alteration_value) IS NOT NULL GROUP BY gad.cancer_study_identifier ORDER BY median_cd3e_expression DESC ", "max_rows": 50 } ◀ result {"rows":[{"cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018","samples_with_cd3e":240,"median_cd3e_expression":0.11,"mean_cd3e_expression":452.53,"min_cd3e":-2.54,"max_cd3e":12312},{"cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","samples_with_cd3e":390,"median_cd3e_expression":0.04,"mean_cd3e_expression":10.14,"min_cd3e":-3.36,"max_cd3e":360.06},{"cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018","samples_with_cd3e":3058,"median_cd3e_expression":0.01,"mean_cd3e_expression":110.6,"min_cd3e":-4.48,"max_cd3e":4298.2},{"cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","samples_with_cd3e":3062,"median_cd3e_expression":0.01,"mean_cd3e_expression":83.63,"min_cd3e":-6.88,"max_cd3e":3085.04},{"cancer_study_identifier":"esca_tcga_pan_can_atlas_2018","samples_with_cd3e":1088,"median_cd3e_expression":0,"mean_cd3e_expression":44.24,"min_cd3e":-2.51,"max_cd3e":5156.31},{"cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","samples_with_cd3e":2444,"median_cd3e_expression":0,"mean_cd3e_expression":47.39,"min_cd3e":-2.56,"max_cd3e":3558.63},{"cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018","samples_with_cd3e":2198,"median_cd3e_expression":0,"mean_cd3e_expression":41.51,"min_cd3e":-5.85,"max_cd3e":4552.19},{"cancer_study_identifier":"prad_tcga_pan_can_atlas_2018","samples_with_cd3e":2950,"median_cd3e_expression":0,"mean_cd3e_expression":32,"min_cd3e":-4.85,"max_cd3e":2691.59},{"cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018","samples_with_cd3e":1265,"median_cd3e_expression":0,"mean_cd3e_expression":73.64,"min_cd3e":-2.88,"max_cd3e":6037.85},{"cancer_study_identifier":"ucec_tcga_pan_can_atlas_2018","samples_with_cd3e":3154,"median_cd3e_expression":0,"mean_cd3e_expression":45.42,"min_cd3e":-5.7,"max_cd3e":4547.64},{"cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","samples_with_cd3e":2044,"median_cd3e_expression":0,"mean_cd3e_expression":23.36,"min_cd3e":-3.15,"max_cd3e":1332.67},{"cancer_study_identifier":"uvm_tcga_pan_can_atlas_2018","samples_with_cd3e":400,"median_cd3e_expression":0,"mean_cd3e_expression":27.23,"min_cd3e":-2.06,"max_cd3e":1432.21},{"cancer_study_identifier":"pcpg_tcga_pan_can_atlas_2018","samples_with_cd3e":856,"median_cd3e_expression":0,"mean_cd3e_expression":11.11,"min_cd3e":-2.77,"max_cd3e":298.79},{"cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","samples_with_cd3e":216,"median_cd3e_expression":0,"mean_cd3e_expression":82.07,"min_cd3e":-4.4,"max_cd3e":9258.43},{"cancer_study_identifier":"lusc_tcga_pan_can_atlas_2018","samples_with_cd3e":2910,"median_cd3e_expression":0,"mean_cd3e_expression":64.99,"min_cd3e":-9.11,"max_cd3e":5240.29},{"cancer_study_identifier":"acc_tcga_pan_can_atlas_2018","samples_with_cd3e":412,"median_cd3e_expression":0,"mean_cd3e_expression":13.29,"min_cd3e":-1.94,"max_cd3e":677.35},{"cancer_study_identifier":"laml_tcga_pan_can_atlas_2018","samples_with_cd3e":901,"median_cd3e_expression":0,"mean_cd3e_expression":81.32,"min_cd3e":-2.55,"max_cd3e":2328.86},{"cancer … (2628 more chars) ▶ clickhouse_run_select_query { "query": " SELECT cs.cancer_study_identifier, cs.name, round(median(toFloat64OrNull(gad.alteration_value)), 2) as median_cd3e_expression, round(avg(toFloat64OrNull(gad.alteration_value)), 2) as mean_cd3e_expression, count(gad.sample_unique_id) as samples_with_data FROM genetic_alteration_derived gad JOIN cancer_study cs ON gad.cancer_study_identifier = cs.cancer_study_identifier WHERE gad.cancer_study_identifier LIKE '%_tcga_pan_can_atlas_2018%' AND gad.hugo_gene_symbol = 'CD3E' AND gad.alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(gad.alteration_value) IS NOT NULL GROUP BY gad.cancer_study_identifier, cs.name ORDER BY median_cd3e_expression DESC ", "max_rows": 50 } ◀ result {"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 215\n Code: 215. DB::Exception: Column 'cs.cancer_study_identifier' is not under aggregate function and not in GROUP BY keys. In query SELECT cs.cancer_study_identifier, cs.name, round(median(toFloat64OrNull(gad.alteration_value)), 2) AS median_cd3e_expression, round(avg(toFloat64OrNull(gad.alteration_value)), 2) AS mean_cd3e_expression, count(gad.sample_unique_id) AS samples_with_data FROM genetic_alteration_derived AS gad INNER JOIN cancer_study AS cs ON gad.cancer_study_identifier = cs.cancer_study_identifier WHERE (gad.cancer_study_identifier LIKE '%_tcga_pan_can_atlas_2018%') AND (gad.hugo_gene_symbol = 'CD3E') AND (gad.alteration_value NOT IN ('', 'NA')) AND (toFloat64OrNull(gad.alteration_value) IS NOT NULL) GROUP BY gad.cancer_study_identifier, cs.name ORDER BY median_cd3e_expression DESC. (NOT_AN_AGGREGATE) (version 26.4.1.2359 (official build))\n"} ▶ clickhouse_run_select_query { "query": " WITH cd3_stats AS ( SELECT cancer_study_identifier, round(median(toFloat64OrNull(alteration_value)), 2) as median_cd3e_expression, round(avg(toFloat64OrNull(alteration_value)), 2) as mean_cd3e_expression, count(sample_unique_id) as samples_with_data FROM genetic_alteration_derived WHERE cancer_study_identifier LIKE '%_tcga_pan_can_atlas_2018%' AND hugo_gene_symbol = 'CD3E' AND alteration_value NOT IN ('', 'NA') AND toFloat64OrNull(alteration_value) IS NOT NULL GROUP BY cancer_study_identifier ) SELECT cs.cancer_study_identifier, cs.name, cd3_stats.median_cd3e_expression, cd3_stats.mean_cd3e_expression, cd3_stats.samples_with_data FROM cd3_stats JOIN cancer_study cs ON cd3_stats.cancer_study_identifier = cs.cancer_study_identifier ORDER BY cd3_stats.median_cd3e_expression DESC ", "max_rows": 50 } ◀ result {"rows":[{"cs.cancer_study_identifier":"dlbc_tcga_pan_can_atlas_2018","name":"Diffuse Large B-Cell Lymphoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0.11,"mean_cd3e_expression":452.53,"samples_with_data":240},{"cs.cancer_study_identifier":"kich_tcga_pan_can_atlas_2018","name":"Kidney Chromophobe (TCGA, PanCancer Atlas)","median_cd3e_expression":0.04,"mean_cd3e_expression":10.14,"samples_with_data":390},{"cs.cancer_study_identifier":"kirc_tcga_pan_can_atlas_2018","name":"Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0.01,"mean_cd3e_expression":110.6,"samples_with_data":3058},{"cs.cancer_study_identifier":"luad_tcga_pan_can_atlas_2018","name":"Lung Adenocarcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0.01,"mean_cd3e_expression":83.63,"samples_with_data":3062},{"cs.cancer_study_identifier":"esca_tcga_pan_can_atlas_2018","name":"Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":44.24,"samples_with_data":1088},{"cs.cancer_study_identifier":"uvm_tcga_pan_can_atlas_2018","name":"Uveal Melanoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":27.23,"samples_with_data":400},{"cs.cancer_study_identifier":"cesc_tcga_pan_can_atlas_2018","name":"Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":90.74,"samples_with_data":1468},{"cs.cancer_study_identifier":"ucs_tcga_pan_can_atlas_2018","name":"Uterine Carcinosarcoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":30.19,"samples_with_data":283},{"cs.cancer_study_identifier":"lihc_tcga_pan_can_atlas_2018","name":"Liver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":41.51,"samples_with_data":2198},{"cs.cancer_study_identifier":"prad_tcga_pan_can_atlas_2018","name":"Prostate Adenocarcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":32,"samples_with_data":2950},{"cs.cancer_study_identifier":"sarc_tcga_pan_can_atlas_2018","name":"Sarcoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":73.64,"samples_with_data":1265},{"cs.cancer_study_identifier":"brca_tcga_pan_can_atlas_2018","name":"Breast Invasive Carcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":60.6,"samples_with_data":6610},{"cs.cancer_study_identifier":"ov_tcga_pan_can_atlas_2018","name":"Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":23.36,"samples_with_data":2044},{"cs.cancer_study_identifier":"blca_tcga_pan_can_atlas_2018","name":"Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":47.39,"samples_with_data":2444},{"cs.cancer_study_identifier":"chol_tcga_pan_can_atlas_2018","name":"Cholangiocarcinoma (TCGA, PanCancer Atlas)","median_cd3e_expression":0,"mean_cd3e_expression":82.07,"samp … (3456 more chars) ▶ resolve_and_route { "studyIds": [ "dlbc_tcga_pan_can_atlas_2018", "kich_tcga_pan_can_atlas_2018", "kirc_tcga_pan_can_atlas_2018", "luad_tcga_pan_can_atlas_2018", "esca_tcga_pan_can_atlas_2018", "uvm_tcga_pan_can_atlas_2018", "cesc_tcga_pan_can_atlas_2018", "ucs_tcga_pan_can_atlas_2018", "lihc_tcga_pan_can_atlas_2018", "prad_tcga_pan_can_atlas_2018", "sarc_tcga_pan_can_atlas_2018", "brca_tcga_pan_can_atlas_2018", "ov_tcga_pan_can_atlas_2018", "blca_tcga_pan_can_atlas_2018", "chol_tcga_pan_can_atlas_2018", "coadread_tcga_pan_can_atlas_2018", "acc_tcga_pan_can_atlas_2018", "laml_tcga_pan_can_atlas_2018", "lgg_tcga_pan_can_atlas_2018", "thca_tcga_pan_can_atlas_2018", "lusc_tcga_pan_can_atlas_2018", "pcpg_tcga_pan_can_atlas_2018", "kirp_tcga_pan_can_atlas_2018", "paad_tcga_pan_can_atlas_2018", "meso_tcga_pan_can_atlas_2018", "stad_tcga_pan_can_atlas_2018", "gbm_tcga_pan_can_atlas_2018", "ucec_tcga_pan_can_atlas_2018", "hnsc_tcga_pan_can_atlas_2018", "thym_tcga_pan_can_atlas_2018", "skcm_tcga_pan_can_atlas_2018", "tgct_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-v4bwnhqw/2eb1bd0d-30ea-4b3a-9afa-3b666b97b87f/tool-results/mcp-navigator-resolve_and_route-1790297519033.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-v4bwnhqw/2eb1bd0d-30ea-4b3a-9afa-3b666b97b87f/tool-results/mcp-navigator-resolve_and_route-1790297519033.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-v4bwnhqw/2eb1bd0d-30ea-4b3a-9afa-3b666b97b87f/tool-results/mcp-navigator-resolve_and_route-1790297519033.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. ═ answer (success) The resolver response is very large. Let me work with a subset of the top studies and navigate to those: ## **TCGA Cancer Studies Ranked by Median CD3E Expression** Based on a query of CD3E (standard T-cell marker) expression across TCGA PanCancer Atlas studies, here's the ranking: | Rank | Cancer Type | Study | Median CD3E | Mean CD3E | Samples | |------|------------|-------|-----------|---------|---------| | 1 | **Diffuse Large B-Cell Lymphoma (DLBC)** | dlbc_tcga_pan_can_atlas_2018 | **0.11** | 452.53 | 240 | | 2 | **Kidney Chromophobe (KICH)** | kich_tcga_pan_can_atlas_2018 | **0.04** | 10.14 | 390 | | 3 | **Kidney Renal Clear Cell (KIRC)** | kirc_tcga_pan_can_atlas_2018 | **0.01** | 110.6 | 3,058 | | 4 | **Lung Adenocarcinoma (LUAD)** | luad_tcga_pan_can_atlas_2018 | **0.01** | 83.63 | 3,062 | | 5 | **Esophageal Adenocarcinoma (ESCA)** | esca_tcga_pan_can_atlas_2018 | **0.00** | 44.24 | 1,088 | | 6–31 | **All other cancers** | (Various) | **0.00 to −0.21** | 4.52–90.74 | 216–6,610 | ### Key Findings - **DLBC (lymphoma)** has the highest median CD3E expression by far (0.11), which makes biological sense — B-cell lymphomas contain abundant T-cell infiltrates. - **KIRC and LUAD** have very low medians (0.01) despite large sample sizes and high mean values, indicating highly right-skewed distributions (some tumors with very high immune infiltration, many with low). - Many solid tumors cluster at median = **0.00**, suggesting CD3E is absent or minimal in most samples but present in immunologically "hot" subsets. - **Note:** Mean values are much higher than medians in immune-infiltrated tumors (DLBC mean 452.53 vs median 0.11), indicating outlier-driven distributions. ### Navigate to Studies I can generate direct links to visualize CD3E expression in cBioPortal. Would you like me to create navigation links to: 1. **Top 3 studies** (DLBC, KICH, KIRC) to explore CD3E distribution interactively? 2. **A specific study** for detailed gene expression analysis? 3. **All 32 studies** as a comparison view? Let me know which you'd prefer, and I'll provide the direct URLs.