Q (sonnet): Can you explore the difference in mutation frequency between left-sided and right-sided CRC? ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ▶ read_guide { "uri": "cbioportal://statistical-tests-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) ◀ result Statistical Test Selection Guide ================================ Purpose ------- This guide ensures the correct statistical test is selected before performing any group comparison, matching cBioPortal's own Group Comparison defaults. HARD RULES — NEVER FABRICATE A STATISTIC ---------------------------------------- ClickHouse cannot run statistical tests. The agent therefore must NEVER produce a derived statistic that is not a literal column value from a SQL result. Specifically: 1. **Never invent a p-value.** Not "p < 0.001", not "p ≈ 0.05", not any p-value. If the user asks "what is the p-value?", the answer is *"I can't compute that — here is the 2x2 contingency table (or group statistics). Run it in cBioPortal's Group Comparison tab, in R with `fisher.test(...)` / `wilcox.test(...)`, or in Python with `scipy.stats.fisher_exact(...)` / `mannwhitneyu(...)`."* 2. **Never claim mutual exclusivity (or co-occurrence) from a contingency table alone.** A 2x2 table is not a test. The shape "altered/not altered × group A/group B" needs Fisher's exact + a defined direction (odds ratio < 1 with significant p). Without that test, the agent presents the table and stops. Descriptive phrasing is also a claim: "largely/mostly mutually exclusive", "rarely co-occur", "tend to co-occur" are forbidden without the test — point to cBioPortal's Mutual Exclusivity tab instead. 3. **Never report a "median" that came from `AVG(...)` or any non-median aggregate.** "Median" and "mean" are different statistics; for skewed clinical distributions (especially survival) they differ substantially. Use ClickHouse's `quantile(0.5)(...)` for actual median, and label arithmetic averages as "mean", never "median". 4. **Never report a hazard ratio, odds ratio, risk ratio, or relative risk** that wasn't computed by an external tool. These require regression / model fitting that ClickHouse does not do. 5. **Never report median overall survival from `AVG(OS_MONTHS)` or even `quantile(0.5)(OS_MONTHS)`.** Median OS requires Kaplan-Meier estimation, which handles censoring (`OS_STATUS = 0:LIVING` means the event hasn't happened yet). Naive medians/means over `OS_MONTHS` ignore censoring and are systematically wrong. The correct handoff: return the raw `(OS_MONTHS, OS_STATUS)` pairs (or descriptive counts: N events, N censored, follow-up range) and tell the user to run KM in R (`survival::survfit`) or Python (`lifelines.KaplanMeierFitter`), or use cBioPortal's Survival comparison. If asked to do any of (1)–(5), respond with the appropriate handoff template from the "Approved Response Templates" section below — do not produce the number. Ambiguous-Term Clarification (before any analysis) -------------------------------------------------- If the user's question uses an ambiguous term, ask before computing anything. Examples: - **"aggressive"** → could mean shorter overall survival, higher metastasis rate, higher grade/stage, higher TMB, or specific molecular features. Ask which definit … (9984 more chars) ▶ read_guide { "uri": "cbioportal://clinical-data-guide" } ▶ list_studies { "search": "colorectal" } ◀ 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":[{"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":"crc_msk_2026","name":"Colorectal Adenocarcinoma (MSK, 2026)","type_of_cancer_id":"read","sample_count":7237,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_msk_2026"},{"cancer_study_identifier":"crc_eo_2020","name":"Colorectal Cancer (MSK, JNCI 2021)","type_of_cancer_id":"bowel","sample_count":1516,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_eo_2020"},{"cancer_study_identifier":"crc_msk_2017","name":"Metastatic Colorectal Cancer (MSK, Cancer Cell 2018)","type_of_cancer_id":"coadread","sample_count":1134,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_msk_2017"},{"cancer_study_identifier":"crc_sysucc_2022","name":"Colorectal Cancer- ChangKang Project (SYSUCC, Nat Commun 2022)","type_of_cancer_id":"coadread","sample_count":1015,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_sysucc_2022"},{"cancer_study_identifier":"coadread_tcga","name":"Colorectal Adenocarcinoma (TCGA, Firehose Legacy)","type_of_cancer_id":"coadread","sample_count":640,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_tcga"},{"cancer_study_identifier":"coadread_dfci_2016","name":"Colorectal Adenocarcinoma (DFCI, Cell Reports 2016)","type_of_cancer_id":"coadread","sample_count":619,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_dfci_2016"},{"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":"crc_apc_impact_2020","name":"Colorectal Cancer (MSK, Gastroenterology 2020)","type_of_cancer_id":"coadread","sample_count":471,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=crc_apc_impact_2020"},{"cancer_study_identifier":"coadread_tcga_pub","name":"Colorectal Adenocarcinoma (TCGA, Nature 2012)","type_of_cancer_id":"coadread","sample_count":276,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_tcga_pub"},{"cancer_study_identifier":"bowel_colitis_msk_2022","name":"Colorectal Adenocarcinoma (MSK, Nat Commun 2022)","type_of_cancer_id":"bowel","sample_count":179,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=bowel_colitis_msk_2022"},{"cancer_study_identifier":"coadread_cass_2020","name":"Colorectal Cancer (CAS Shanghai, Cancer Cell 2020)","type_of_cancer_id":"coadread","sample_count":146,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=coadread_cass_2020"},{"cancer_study_identifier":"coadread_mskcc","name":"Colorectal Adenocarcinoma Triplets (MSK, Geno … (1887 more chars) ▶ get_study_guide { "study_id": "coadread_tcga_pan_can_atlas_2018" } ◀ result # Colorectal Adenocarcinoma (TCGA, PanCancer Atlas) **Study ID:** `coadread_tcga_pan_can_atlas_2018` See `_tcga_pancan_template.md` for common TCGA clinical attributes. ## Study-Specific Attributes ### Microsatellite Instability (MSI) There is no `MSI_STATUS` attribute. Three attributes carry MSI (594 patients, one sample each): | Attribute | Definition | MSI-high count | |-----------|------------|----------------| | `SUBTYPE` | TCGA molecular classification: `COAD_MSI` 60 + `READ_MSI` 3 | **63** | | `MSI_SENSOR_SCORE` | MSIsensor score ≥10 (indeterminate 4–10: 10 more) | 78 of 584 scored | | `MSI_SCORE_MANTIS` | MANTIS score >0.4 (>0.6 = MSI: 67; 0.4–0.6 indeterminate) | 89 of 557 scored | **For "MSI-high" questions, use `SUBTYPE` IN (`COAD_MSI`, `READ_MSI`)** (the TCGA molecular classification) and state which definition you used; mention the score-based alternatives if the counts matter. Do not switch to `coadread_tcga_pub` to find MSI; this study has it. ```sql SELECT count(DISTINCT patient_unique_id) AS msi_patients -- 63 FROM clinical_data_derived WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND attribute_name = 'SUBTYPE' AND attribute_value IN ('COAD_MSI', 'READ_MSI'); ``` ### Molecular Classification (`SUBTYPE`, patients) `COAD_CIN` 226, `READ_CIN` 102, `COAD_MSI` 60, `COAD_GS` 49, `READ_GS` 9, `COAD_POLE` 6, `READ_POLE` 4, `READ_MSI` 3, blank 135. - **Hypermutated**: no `HYPERMUTATED` attribute. Use `SUBTYPE` MSI + POLE (73 patients), or `TMB_NONSYNONYMOUS` ≥10 (83 samples; all MSI and POLE tumors exceed it). - **CMS (consensus molecular subtypes)** are not available in this study. ### Anatomic Location There is no `TUMOR_LOCATION` attribute. - Colon vs rectum: `TUMOR_TISSUE_SITE` — Colon 436, Rectum 152, blank 6. - Subsite (for left vs right): `ICD_O_3_SITE` — right: C18.0 cecum 81, C18.2 ascending 100, C18.3 hepatic flexure 10; transverse C18.4 20; left: C18.5 splenic flexure 5, C18.6 descending 16, C18.7 sigmoid 106, C19.9 rectosigmoid 72, C20.9 rectum 81; C18.9 colon NOS 97 (side unknown). ## Key Genes | Gene | Frequency | Clinical Relevance | |------|-----------|-------------------| | APC | ~80% | Initiating event in most CRC | | TP53 | ~55% | Progression marker | | KRAS | ~40% | Predicts anti-EGFR resistance | | PIK3CA | ~15% | May predict aspirin benefit | | BRAF | ~10% | V600E poor prognosis (MSS context) | | SMAD4 | ~10% | TGF-β pathway | ## Notes - MSI-H tumors respond well to immunotherapy - KRAS/NRAS mutations contraindicate anti-EGFR therapy - BRAF V600E has different prognosis in MSI-H vs MSS context - Left vs right-sided tumors have different biology and outcomes ▶ clickhouse_run_select_query { "query": "SELECT attr_id, description, patient_attribute FROM clinical_attribute_meta WHERE cancer_study_id = (SELECT cancer_study_id FROM cancer_study WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018') AND (upper(attr_id) LIKE '%SIDE%' OR upper(attr_id) LIKE '%LOCATION%' OR upper(description) LIKE '%SIDE%' OR upper(description) LIKE '%COLON%') ORDER BY attr_id" } ◀ result {"rows":[]} ▶ clickhouse_run_select_query { "query": " SELECT attribute_value, count(DISTINCT patient_unique_id) AS n FROM clinical_data_derived WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND attribute_name = 'ICD_O_3_SITE' GROUP BY attribute_value ORDER BY n DESC " } ◀ result {"rows":[{"attribute_value":"C18.7","n":106},{"attribute_value":"C18.2","n":100},{"attribute_value":"C18.9","n":97},{"attribute_value":"C18.0","n":81},{"attribute_value":"C20.9","n":81},{"attribute_value":"C19.9","n":72},{"attribute_value":"C18.4","n":20},{"attribute_value":"C18.6","n":16},{"attribute_value":"C18.3","n":10},{"attribute_value":"C18.5","n":5},{"n":3},{"attribute_value":"C49.4","n":2},{"attribute_value":"C80.9","n":1}]} ▶ clickhouse_run_select_query { "query": " WITH side AS ( SELECT patient_unique_id, CASE WHEN attribute_value IN ('C18.0','C18.2','C18.3','C18.4') THEN 'Right' WHEN attribute_value IN ('C18.5','C18.6','C18.7','C19.9','C20.9') THEN 'Left' ELSE NULL END AS side FROM clinical_data_derived WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND attribute_name = 'ICD_O_3_SITE' ), side_clean AS ( SELECT patient_unique_id, side FROM side WHERE side IS NOT NULL ), sample_side AS ( SELECT cd.sample_unique_id, sc.side FROM clinical_data_derived cd JOIN side_clean sc USING (patient_unique_id) WHERE cd.cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' GROUP BY cd.sample_unique_id, sc.side ) SELECT side, count(DISTINCT sample_unique_id) AS n_samples FROM sample_side GROUP BY side " } ◀ result {"rows":[{"side":"Left","n_samples":281},{"side":"Right","n_samples":212}]} ▶ clickhouse_run_select_query { "query": " WITH side AS ( SELECT patient_unique_id, CASE WHEN attribute_value IN ('C18.0','C18.2','C18.3','C18.4') THEN 'Right' WHEN attribute_value IN ('C18.5','C18.6','C18.7','C19.9','C20.9') THEN 'Left' ELSE NULL END AS side FROM clinical_data_derived WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND attribute_name = 'ICD_O_3_SITE' ), side_clean AS (SELECT patient_unique_id, side FROM side WHERE side IS NOT NULL), sample_side AS ( SELECT DISTINCT cd.sample_unique_id, sc.side FROM clinical_data_derived cd JOIN side_clean sc USING (patient_unique_id) WHERE cd.cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' ), profiled AS ( SELECT s.side, count(DISTINCT p.sample_unique_id) AS profiled_n FROM sample_to_gene_panel_derived p JOIN sample_side s USING (sample_unique_id) WHERE p.cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND p.alteration_type = 'MUTATION_EXTENDED' GROUP BY s.side ), muts AS ( SELECT s.side, ged.hugo_gene_symbol, count(DISTINCT ged.sample_unique_id) AS altered_n FROM genomic_event_derived ged JOIN sample_side s USING (sample_unique_id) WHERE ged.cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018' AND ged.variant_type = 'mutation' AND ged.mutation_status != 'UNCALLED' AND ged.off_panel = 0 AND ged.hugo_gene_symbol IN ('APC','TP53','KRAS','PIK3CA','BRAF','SMAD4','FBXW7','NRAS','PTEN','TCF7L2') GROUP BY s.side, ged.hugo_gene_symbol ) SELECT m.hugo_gene_symbol, m.side, m.altered_n, pr.profiled_n, round(m.altered_n * 100.0 / pr.profiled_n, 1) AS freq_pct FROM muts m JOIN profiled pr USING (side) ORDER BY m.hugo_gene_symbol, m.side " } ◀ result {"rows":[{"hugo_gene_symbol":"APC","side":"Left","altered_n":199,"profiled_n":254,"freq_pct":78.3},{"hugo_gene_symbol":"APC","side":"Right","altered_n":124,"profiled_n":188,"freq_pct":66},{"hugo_gene_symbol":"BRAF","side":"Left","altered_n":10,"profiled_n":254,"freq_pct":3.9},{"hugo_gene_symbol":"BRAF","side":"Right","altered_n":42,"profiled_n":188,"freq_pct":22.3},{"hugo_gene_symbol":"FBXW7","side":"Left","altered_n":37,"profiled_n":254,"freq_pct":14.6},{"hugo_gene_symbol":"FBXW7","side":"Right","altered_n":34,"profiled_n":188,"freq_pct":18.1},{"hugo_gene_symbol":"KRAS","side":"Left","altered_n":83,"profiled_n":254,"freq_pct":32.7},{"hugo_gene_symbol":"KRAS","side":"Right","altered_n":92,"profiled_n":188,"freq_pct":48.9},{"hugo_gene_symbol":"NRAS","side":"Left","altered_n":19,"profiled_n":254,"freq_pct":7.5},{"hugo_gene_symbol":"NRAS","side":"Right","altered_n":11,"profiled_n":188,"freq_pct":5.9},{"hugo_gene_symbol":"PIK3CA","side":"Left","altered_n":45,"profiled_n":254,"freq_pct":17.7},{"hugo_gene_symbol":"PIK3CA","side":"Right","altered_n":74,"profiled_n":188,"freq_pct":39.4},{"hugo_gene_symbol":"PTEN","side":"Left","altered_n":14,"profiled_n":254,"freq_pct":5.5},{"hugo_gene_symbol":"PTEN","side":"Right","altered_n":15,"profiled_n":188,"freq_pct":8},{"hugo_gene_symbol":"SMAD4","side":"Left","altered_n":29,"profiled_n":254,"freq_pct":11.4},{"hugo_gene_symbol":"SMAD4","side":"Right","altered_n":28,"profiled_n":188,"freq_pct":14.9},{"hugo_gene_symbol":"TCF7L2","side":"Left","altered_n":27,"profiled_n":254,"freq_pct":10.6},{"hugo_gene_symbol":"TCF7L2","side":"Right","altered_n":23,"profiled_n":188,"freq_pct":12.2},{"hugo_gene_symbol":"TP53","side":"Left","altered_n":174,"profiled_n":254,"freq_pct":68.5},{"hugo_gene_symbol":"TP53","side":"Right","altered_n":93,"profiled_n":188,"freq_pct":49.5}]} ▶ resolve_and_route { "studyIds": [ "coadread_tcga_pan_can_atlas_2018" ] } ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594,"studyViewUrl":"https://www.cbioportal.org/study?id=coadread_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":["coadread_tcga_pan_can_atlas_2018_gistic","coadread_tcga_pan_can_atlas_2018_log2CNA","coadread_tcga_pan_can_atlas_2018_mutations","coadread_tcga_pan_can_atlas_2018_protein_quantification","coadread_tcga_pan_can_atlas_2018_protein_quantification_zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","coadread_tcga_pan_can_atlas_2018_rppa","coadread_tcga_pan_can_atlas_2018_rppa_Zscores","coadread_tcga_pan_can_atlas_2018_structural_variants"],"genericAssayProfiles":["coadread_tcga_pan_can_atlas_2018_armlevel_cna","coadread_tcga_pan_can_atlas_2018_genetic_ancestry","coadread_tcga_pan_can_atlas_2018_methylation_hm27_hm450_merge","coadread_tcga_pan_can_atlas_2018_methylation_hm450"],"heatmapProfileIds":["coadread_tcga_pan_can_atlas_2018_protein_quantification_zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_Zscores","coadread_tcga_pan_can_atlas_2018_rna_seq_v2_mrna_median_all_sample_ref_normal_Zscores","coadread_tcga_pan_can_atlas_2018_rppa_Zscores"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations","mrna","protein","generic_assay_armlevel_c … (70 more chars) ▶ get_studyviewfilter_options { "studyId": "coadread_tcga_pan_can_atlas_2018", "attributeIds": [ "ICD_O_3_SITE" ] } ◀ result {"success":true,"message":"Successfully retrieved StudyView filter options","data":{"studyId":"coadread_tcga_pan_can_atlas_2018","attributes":[{"attributeId":"ICD_O_3_SITE","displayName":"International Classification of Diseases for Oncology, Third Edition ICD-O-3 Site Code","description":"The third edition of the International Classification of Diseases for Oncology, published in 2000, used principally in tumor and cancer registries for coding the site (topography) and the histology (morphology) of neoplasms._The description of an anatomical region or of a body part._Named locations of, or within, the body._A system of numbered categories for representation of data.","datatype":"STRING","values":["C19.9","C18.9","NA","C18.7","C18.6","C18.5","C49.4","C18.4","C18.3","C18.2","C18.0","C20.9","C80.9"]}]}} ▶ navigate_to_group_comparison { "studyIds": [ "coadread_tcga_pan_can_atlas_2018" ], "groups": [ { "name": "Right-sided (cecum–transverse colon)", "studyViewFilter": { "clinicalDataFilters": [ { "attributeId": "ICD_O_3_SITE", "values": [ { "value": "C18.0" }, { "value": "C18.2" }, { "value": "C18.3" }, { "value": "C18.4" } ] } ] } }, { "name": "Left-sided (splenic flexure–rectum)", "studyViewFilter": { "clinicalDataFilters": [ { "attributeId": "ICD_O_3_SITE", "values": [ { "value": "C18.5" }, { "value": "C18.6" }, { "value": "C18.7" }, { "value": "C19.9" }, { "value": "C20.9" } ] } ] } } ], "tab": "comparison/mutations" } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/comparison/mutations?comparisonId=6ab5d23ac2115c492d884e28","url":"https://www.cbioportal.org/comparison/comparison/mutations?comparisonId=6ab5d23ac2115c492d884e28","data":{"description":"Group comparison (2 custom groups)","studies":["coadread_tcga_pan_can_atlas_2018"],"totalGroups":2,"groups":[{"name":"Right-sided (cecum–transverse colon)","sampleCount":211},{"name":"Left-sided (splenic flexure–rectum)","sampleCount":280}],"studyViewUrl":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018","groupUrls":[{"groupName":"Right-sided (cecum–transverse colon)","url":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22ICD_O_3_SITE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22C18.0%22%7D%2C%7B%22value%22%3A%22C18.2%22%7D%2C%7B%22value%22%3A%22C18.3%22%7D%2C%7B%22value%22%3A%22C18.4%22%7D%5D%7D%5D%7D"},{"groupName":"Left-sided (splenic flexure–rectum)","url":"https://www.cbioportal.org/study?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22ICD_O_3_SITE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22C18.5%22%7D%2C%7B%22value%22%3A%22C18.6%22%7D%2C%7B%22value%22%3A%22C18.7%22%7D%2C%7B%22value%22%3A%22C19.9%22%7D%2C%7B%22value%22%3A%22C20.9%22%7D%5D%7D%5D%7D"}]}} ▶ navigate_to_study_view { "studyIds": [ "coadread_tcga_pan_can_atlas_2018" ], "filterJson": { "clinicalDataFilters": [ { "attributeId": "ICD_O_3_SITE", "values": [ { "value": "C18.0" }, { "value": "C18.2" }, { "value": "C18.3" }, { "value": "C18.4" }, { "value": "C18.5" }, { "value": "C18.6" }, { "value": "C18.7" }, { "value": "C19.9" }, { "value": "C20.9" } ] } ] } } ◀ result {"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22ICD_O_3_SITE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22C18.0%22%7D%2C%7B%22value%22%3A%22C18.2%22%7D%2C%7B%22value%22%3A%22C18.3%22%7D%2C%7B%22value%22%3A%22C18.4%22%7D%2C%7B%22value%22%3A%22C18.5%22%7D%2C%7B%22value%22%3A%22C18.6%22%7D%2C%7B%22value%22%3A%22C18.7%22%7D%2C%7B%22value%22%3A%22C19.9%22%7D%2C%7B%22value%22%3A%22C20.9%22%7D%5D%7D%5D%7D","url":"https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22ICD_O_3_SITE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22C18.0%22%7D%2C%7B%22value%22%3A%22C18.2%22%7D%2C%7B%22value%22%3A%22C18.3%22%7D%2C%7B%22value%22%3A%22C18.4%22%7D%2C%7B%22value%22%3A%22C18.5%22%7D%2C%7B%22value%22%3A%22C18.6%22%7D%2C%7B%22value%22%3A%22C18.7%22%7D%2C%7B%22value%22%3A%22C19.9%22%7D%2C%7B%22value%22%3A%22C20.9%22%7D%5D%7D%5D%7D","data":{"studyIds":["coadread_tcga_pan_can_atlas_2018"],"studies":[{"studyId":"coadread_tcga_pan_can_atlas_2018","name":"Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)","sampleCount":594}],"tab":"summary","hasFilters":true,"hasPlotsConfig":false,"pageDescription":"Overview of all study data displayed as interactive charts summarizing available clinical and genomic data. Click within a chart to filter. Additional charts may be available in the Charts menu."}} ═ answer (success) ## Left- vs Right-Sided Colorectal Cancer: Mutation Frequency Comparison **Cohort:** [Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)](https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018), 493 patients with tumor subsite recorded (`ICD_O_3_SITE`). No direct "sidedness" field exists in this study, so sides were derived from anatomic subsite per standard convention: - **Right-sided (proximal)** — cecum, ascending colon, hepatic flexure, transverse colon: **188 profiled patients** - **Left-sided (distal)** — splenic flexure, descending colon, sigmoid, rectosigmoid, rectum: **254 profiled patients** (97 patients coded "colon, NOS" with unspecified side were excluded.) ### Mutation frequency by gene (altered / profiled, %) | Gene | Right-sided | Left-sided | Difference (R − L) | |------|---|---|---| | **BRAF** | 42/188 = **22.3%** | 10/254 = **3.9%** | +18.4 pp | | **PIK3CA** | 74/188 = **39.4%** | 45/254 = **17.7%** | +21.7 pp | | **KRAS** | 92/188 = **48.9%** | 83/254 = **32.7%** | +16.2 pp | | **FBXW7** | 34/188 = 18.1% | 37/254 = 14.6% | +3.5 pp | | **SMAD4** | 28/188 = 14.9% | 29/254 = 11.4% | +3.5 pp | | **TCF7L2** | 23/188 = 12.2% | 27/254 = 10.6% | +1.6 pp | | **PTEN** | 15/188 = 8.0% | 14/254 = 5.5% | +2.5 pp | | **NRAS** | 11/188 = 5.9% | 19/254 = 7.5% | −1.6 pp | | **APC** | 124/188 = 66.0% | 199/254 = **78.3%** | −12.3 pp | | **TP53** | 93/188 = **49.5%** | 174/254 = **68.5%** | −19.0 pp | **Descriptive pattern** (raw counts only — no significance test run): right-sided tumors show visibly higher BRAF, PIK3CA, and KRAS mutation rates, while left-sided tumors show higher APC and TP53 rates. This mirrors the well-known biological divide between proximal (often MSI-high, BRAF-driven, serrated-pathway) and distal (chromosomal-instability, APC/TP53-driven) CRC — though I have not verified MSI status stratification here, and this observation is a general pattern, not something I've confirmed from external literature for this specific cohort. **No statistical test was run.** These are raw frequency differences, not confirmed enrichments. To test significance, each gene needs a Fisher's exact test (2×2: altered/not-altered × Right/Left) — you can run this directly in the Mutations tab of the comparison view below, or via `scipy.stats.fisher_exact` / R `fisher.test`. ### Explore interactively - [Group Comparison: Right-sided vs Left-sided — Mutations tab](https://www.cbioportal.org/comparison/comparison/mutations?comparisonId=6ab5d23ac2115c492d884e28) (211 right-sided vs 280 left-sided **samples** — includes samples beyond the mutation-profiled subset above; this page computes the enrichment test directly) - [Study View — sidedness cohort filtered](https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018#filterJson=%7B%22clinicalDataFilters%22%3A%5B%7B%22attributeId%22%3A%22ICD_O_3_SITE%22%2C%22values%22%3A%5B%7B%22value%22%3A%22C18.0%22%7D%2C%7B%22value%22%3A%22C18.2%22%7D%2C%7B%22value%22%3A%22C18.3%22%7D%2C%7B%22value%22%3A%22C18.4%22%7D%2C%7B%22value%22%3A%22C18.5%22%7D%2C%7B%22value%22%3A%22C18.6%22%7D%2C%7B%22value%22%3A%22C18.7%22%7D%2C%7B%22value%22%3A%22C19.9%22%7D%2C%7B%22value%22%3A%22C20.9%22%7D%5D%7D%5D%7D) Note: 97 "colon, NOS" patients with unspecified subsite were excluded from this comparison as their side cannot be determined.