Q (sonnet): Can you explore the difference in mutation frequency between left-sided and right-sided CRC?

▶ 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#<number>\") (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://statistical-tests-guide"
}

▶ read_guide
{
 "uri": "cbioportal://mutation-frequency-guide"
}

◀ 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"
}

◀ 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
# 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)

▶ search_oncotree
{
 "search_term": "colorectal"
}

▶ list_studies
{
 "search": "colorectal TCGA"
}

◀ result
{"result":[{"code":"COADREAD","name":"Colorectal Adenocarcinoma","score":60,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD"},{"code":"CAIS","name":"Colon Adenocarcinoma In Situ","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > CAIS"},{"code":"CMC","name":"Medullary Carcinoma of the Colon","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > CMC"},{"code":"COAD","name":"Colon Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > COAD"},{"code":"MACR","name":"Mucinous Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > MACR"},{"code":"READ","name":"Rectal Adenocarcinoma","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > READ"},{"code":"SRCCR","name":"Signet Ring Cell Adenocarcinoma of the Colon and Rectum","score":45,"mainType":"Colorectal Cancer","tissue":"Bowel","hierarchy":"BOWEL > COADREAD > SRCCR"}]}

◀ result
{"result":[{"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_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":"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"}]}

▶ 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(attr_id) LIKE '%ANATOMIC%' OR upper(attr_id) LIKE '%SITE%')
ORDER BY attr_id"
}

◀ result
{"rows":[{"attr_id":"ICD_O_3_SITE","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.","patient_attribute":1},{"attr_id":"TISSUE_SOURCE_SITE","description":"A Tissue Source Site collects samples (tissue, cell, or blood) and cliincal metadata which are then sent to tha Biospecimen Core Resource.","patient_attribute":0},{"attr_id":"TISSUE_SOURCE_SITE_CODE","description":"Tissue Source Site Code","patient_attribute":0},{"attr_id":"TUMOR_TISSUE_SITE","description":"Text term that describes the anatomic site of the tumor or disease.","patient_attribute":0}]}

▶ clickhouse_run_select_query
{
 "query": "SELECT attribute_name, 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 IN ('ICD_O_3_SITE','TUMOR_TISSUE_SITE')
GROUP BY attribute_name, attribute_value
ORDER BY attribute_name, n DESC"
}

◀ result
{"rows":[{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.7","n":106},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.2","n":100},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.9","n":97},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.0","n":81},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C20.9","n":81},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C19.9","n":72},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.4","n":20},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.6","n":16},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.3","n":10},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C18.5","n":5},{"attribute_name":"ICD_O_3_SITE","n":3},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C49.4","n":2},{"attribute_name":"ICD_O_3_SITE","attribute_value":"C80.9","n":1},{"attribute_name":"TUMOR_TISSUE_SITE","attribute_value":"Colon","n":436},{"attribute_name":"TUMOR_TISSUE_SITE","attribute_value":"Rectum","n":152},{"attribute_name":"TUMOR_TISSUE_SITE","n":6}]}

▶ 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'
)
SELECT side, COUNT(*) as n_patients
FROM side
WHERE side IS NOT NULL
GROUP BY side
"
}

◀ result
{"rows":[{"side":"Left","n_patients":280},{"side":"Right","n_patients":211}]}

▶ 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'
),
sample_side AS (
    SELECT DISTINCT cd.sample_unique_id, s.side
    FROM clinical_data_derived cd
    JOIN side s USING (patient_unique_id)
    WHERE cd.cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018'
      AND s.side IS NOT NULL
),
profiled AS (
    -- WES study: all samples profiled for mutation are profiled for every gene
    SELECT sample_unique_id, side
    FROM sample_side ss
    WHERE ss.sample_unique_id IN (
        SELECT sample_unique_id FROM sample_to_gene_panel_derived
        WHERE cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018'
          AND alteration_type = 'MUTATION_EXTENDED'
    )
),
altered AS (
    SELECT ged.hugo_gene_symbol, ss.side, COUNT(DISTINCT ged.sample_unique_id) as altered_samples
    FROM genomic_event_derived ged
    JOIN sample_side ss 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','TCF7L2','ARID1A','SOX9')
    GROUP BY ged.hugo_gene_symbol, ss.side
),
prof_counts AS (
    SELECT side, COUNT(*) as profiled_samples FROM profiled GROUP BY side
)
SELECT a.hugo_gene_symbol,
       a.side,
       a.altered_samples,
       p.profiled_samples,
       ROUND(a.altered_samples * 100.0 / p.profiled_samples, 1) as freq_pct
FROM altered a
JOIN prof_counts p USING (side)
ORDER BY a.hugo_gene_symbol, a.side
"
}

◀ result
{"rows":[{"hugo_gene_symbol":"APC","side":"Left","altered_samples":199,"profiled_samples":254,"freq_pct":78.3},{"hugo_gene_symbol":"APC","side":"Right","altered_samples":124,"profiled_samples":188,"freq_pct":66},{"hugo_gene_symbol":"ARID1A","side":"Left","altered_samples":17,"profiled_samples":254,"freq_pct":6.7},{"hugo_gene_symbol":"ARID1A","side":"Right","altered_samples":29,"profiled_samples":188,"freq_pct":15.4},{"hugo_gene_symbol":"BRAF","side":"Left","altered_samples":10,"profiled_samples":254,"freq_pct":3.9},{"hugo_gene_symbol":"BRAF","side":"Right","altered_samples":42,"profiled_samples":188,"freq_pct":22.3},{"hugo_gene_symbol":"FBXW7","side":"Left","altered_samples":37,"profiled_samples":254,"freq_pct":14.6},{"hugo_gene_symbol":"FBXW7","side":"Right","altered_samples":34,"profiled_samples":188,"freq_pct":18.1},{"hugo_gene_symbol":"KRAS","side":"Left","altered_samples":83,"profiled_samples":254,"freq_pct":32.7},{"hugo_gene_symbol":"KRAS","side":"Right","altered_samples":92,"profiled_samples":188,"freq_pct":48.9},{"hugo_gene_symbol":"PIK3CA","side":"Left","altered_samples":45,"profiled_samples":254,"freq_pct":17.7},{"hugo_gene_symbol":"PIK3CA","side":"Right","altered_samples":74,"profiled_samples":188,"freq_pct":39.4},{"hugo_gene_symbol":"SMAD4","side":"Left","altered_samples":29,"profiled_samples":254,"freq_pct":11.4},{"hugo_gene_symbol":"SMAD4","side":"Right","altered_samples":28,"profiled_samples":188,"freq_pct":14.9},{"hugo_gene_symbol":"SOX9","side":"Left","altered_samples":18,"profiled_samples":254,"freq_pct":7.1},{"hugo_gene_symbol":"SOX9","side":"Right","altered_samples":33,"profiled_samples":188,"freq_pct":17.6},{"hugo_gene_symbol":"TCF7L2","side":"Left","altered_samples":27,"profiled_samples":254,"freq_pct":10.6},{"hugo_gene_symbol":"TCF7L2","side":"Right","altered_samples":23,"profiled_samples":188,"freq_pct":12.2},{"hugo_gene_symbol":"TP53","side":"Left","altered_samples":174,"profiled_samples":254,"freq_pct":68.5},{"hugo_gene_symbol":"TP53","side":"Right","altered_samples":93,"profiled_samples":188,"freq_pct":49.5}]}

▶ 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'
),
tmb AS (
    SELECT cd.patient_unique_id, toFloat64OrNull(cd.attribute_value) as tmb_val
    FROM clinical_data_derived cd
    WHERE cd.cancer_study_identifier = 'coadread_tcga_pan_can_atlas_2018'
      AND cd.attribute_name = 'TMB_NONSYNONYMOUS'
)
SELECT s.side, count(*) as n, round(avg(t.tmb_val),2) as mean_tmb, round(quantile(0.5)(t.tmb_val),2) as median_tmb,
       countIf(t.tmb_val > 10) as n_hypermutated_gt10
FROM side s JOIN tmb t USING (patient_unique_id)
WHERE s.side IS NOT NULL AND t.tmb_val IS NOT NULL
GROUP BY s.side
"
}

◀ result
{"rows":[{"side":"Left","n":254,"mean_tmb":8.63,"median_tmb":3.07,"n_hypermutated_gt10":14},{"side":"Right","n":188,"mean_tmb":21.98,"median_tmb":4.85,"n_hypermutated_gt10":58}]}

▶ resolve_and_route
{
 "studyIds": [
  "coadread_tcga_pan_can_atlas_2018"
 ]
}

▶ get_studyviewfilter_options
{}

◀ result
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✗ error
<tool_use_error>Error: No such tool available: mcp__claude_ai_cBioPortal_MCP__get_studyviewfilter_options</tool_use_error>

▶ 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": "alterations"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/comparison/alterations?comparisonId=6ab5c7fbe497aad53593c266","url":"https://www.cbioportal.org/comparison/alterations?comparisonId=6ab5c7fbe497aad53593c266","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"}],"pageDescription":"Comparison of genomic alteration rates across Right-sided (cecum-transverse colon) (211) vs Left-sided (splenic flexure-rectum) (280)."}}

▶ navigate_to_study_view
{
 "studyIds": [
  "coadread_tcga_pan_can_atlas_2018"
 ],
 "filterAttributeId": "ICD_O_3_SITE",
 "filterValues": "C18.0,C18.2,C18.3,C18.4,C18.5,C18.6,C18.7,C19.9,C20.9"
}

◀ result
{"success":true,"message":"Navigating to https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018&filterAttributeId=ICD_O_3_SITE&filterValues=C18.0%2CC18.2%2CC18.3%2CC18.4%2CC18.5%2CC18.6%2CC18.7%2CC19.9%2CC20.9","url":"https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018&filterAttributeId=ICD_O_3_SITE&filterValues=C18.0%2CC18.2%2CC18.3%2CC18.4%2CC18.5%2CC18.6%2CC18.7%2CC19.9%2CC20.9","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:** TCGA Colorectal Adenocarcinoma (PanCancer Atlas), sidedness classified via `ICD_O_3_SITE`:
- **Right-sided (proximal):** cecum, ascending colon, hepatic flexure, transverse colon (C18.0/C18.2/C18.3/C18.4) — n=211
- **Left-sided (distal):** splenic flexure, descending colon, sigmoid, rectosigmoid, rectum (C18.5/C18.6/C18.7/C19.9/C20.9) — n=280
- Excluded as non-classifiable: "Colon, NOS" (C18.9), overlapping/other sites

### Gene-level mutation frequency (sample %, gene-specific profiled denominator, WES study)

| Gene | Left (n=254 profiled) | Right (n=188 profiled) |
|---|---|---|
| **APC** | 199/254 = 78.3% | 124/188 = 66.0% |
| **TP53** | 174/254 = 68.5% | 93/188 = 49.5% |
| **KRAS** | 83/254 = 32.7% | 92/188 = 48.9% |
| **BRAF** | 10/254 = 3.9% | 42/188 = 22.3% |
| **PIK3CA** | 45/254 = 17.7% | 74/188 = 39.4% |
| **SOX9** | 18/254 = 7.1% | 33/188 = 17.6% |
| **ARID1A** | 17/254 = 6.7% | 29/188 = 15.4% |
| **SMAD4** | 29/254 = 11.4% | 28/188 = 14.9% |
| **FBXW7** | 37/254 = 14.6% | 34/188 = 18.1% |
| **TCF7L2** | 27/254 = 10.6% | 23/188 = 12.2% |

### Tumor mutational burden (descriptive)
| Side | N | Mean TMB (mut/Mb) | Median TMB | % with TMB > 10 |
|---|---|---|---|---|
| Left | 254 | 8.63 | 3.07 | 5.5% (14/254) |
| Right | 188 | 21.98 | 4.85 | 30.9% (58/188) |

**Interpretation notes:**
- Right-sided tumors show a much larger hypermutated fraction (consistent with known MSI-high enrichment in proximal colon), a strong confounder for any gene-frequency comparison — genes like BRAF, PIK3CA, ARID1A, SOX9 are classically enriched in MSI-high tumors, so part of their higher right-sided frequency likely reflects this hypermutator phenotype rather than a side-specific driver effect per se.
- APC and TP53 are more frequent on the left; KRAS, BRAF, and PIK3CA are more frequent on the right — this mirrors the established molecular epidemiology of proximal vs. distal CRC.
- These are descriptive frequencies only. Whether any gene-level difference is statistically significant requires **Fisher's exact test** (2×2: altered vs. not-altered × left vs. right) — I have not computed a p-value. Use cBioPortal's Comparison → Mutations/Alterations tab (linked below, which runs this test automatically) or `scipy.stats.fisher_exact` / R `fisher.test`.

### Navigate

[Group Comparison: Right- vs. Left-sided CRC — Alterations tab](https://www.cbioportal.org/comparison/alterations?comparisonId=6ab5c7fbe497aad53593c266) — Right-sided (211) vs. Left-sided (280); includes built-in Fisher's exact enrichment testing per gene.

[Study View: Colorectal Adenocarcinoma (filtered to classifiable sidedness)](https://www.cbioportal.org/study/summary?id=coadread_tcga_pan_can_atlas_2018&filterAttributeId=ICD_O_3_SITE&filterValues=C18.0%2CC18.2%2CC18.3%2CC18.4%2CC18.5%2CC18.6%2CC18.7%2CC19.9%2CC20.9)