Q (haiku): What are the most frequently altered genes in KRAS wild-type lung adenocarcinoma patients? ▶ read_guide { "uri": "cbioportal://mutation-frequency-guide" } ▶ read_guide { "uri": "cbioportal://sample-filtering-guide" } ▶ search_oncotree { "search_term": "lung adenocarcinoma" } ◀ 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 # Sample and Study Filtering Guide ## Overview Proper filtering is essential for meaningful cBioPortal analysis. This guide covers filtering by studies, sample types, and other criteria. ## Study-Level Filtering ### 1. Always Filter by Study Every query should specify a study to ensure consistent results: ```sql -- Always include study filtering SELECT * FROM your_table WHERE cancer_study_identifier = 'your_study_id' -- Additional filters... ``` ### 2. Find Available Studies ```sql -- Discover available studies SELECT cancer_study_identifier, name, description, type_of_cancer_id FROM cancer_study ORDER BY cancer_study_identifier; ``` ### 3. Study Information ```sql -- Get detailed study information SELECT cs.cancer_study_identifier, cs.name as study_name, cs.description, cs.sample_count, COUNT(DISTINCT p.internal_id) as patient_count FROM cancer_study cs LEFT JOIN patient p ON cs.cancer_study_id = p.cancer_study_id WHERE cs.cancer_study_identifier = 'your_study_id' GROUP BY cs.cancer_study_identifier, cs.name, cs.description, cs.sample_count; ``` ### 4. Find Studies by Available Data Types Use this when the user asks *"which studies have mutation and copy-number data for X"*, *"studies with expression for Y"*, *"is there any imaging data"*, or any *"studies with Z data"* question. `cancer_study` has one precomputed sample count per data type — filter on them. No join and no schema exploration needed. These are the same counts as the portal's study list and its "Data type" filter. **Canonical query — studies with ALL requested data types for a cancer type:** ```sql SELECT cancer_study_identifier, name, sample_count, mutation_sample_count, cna_sample_count FROM cancer_study WHERE type_of_cancer_id = 'luad' -- ← OncoTree code from search_oncotree AND mutation_sample_count > 0 -- ← one condition per required data type AND cna_sample_count > 0 ORDER BY sample_count DESC; ``` **Data type → column** (a count of 0 means the study doesn't have that data): | Data type in the user's question | Column (portal "Data type" filter label) | |---|---| | samples in the study | `sample_count` | | mutation / mutations | `mutation_sample_count` ("Mutations") | | copy-number / CNA / amplification / deletion | `cna_sample_count` ("CNA") | | structural variant / fusion | `structural_variant_sample_count` | | RNA / mRNA / gene expression (any platform) | `mrna_expression_sample_count` — use this for "which studies have expression data" | | specifically the portal's "RNA-Seq" filter | `rna_seq_sample_count` ("RNA-Seq") — counts only the `_rna_seq_v2_mrna` list; 0 for many GDC and iAtlas studies that do have RNA-Seq | | specifically the portal's "RNA (microarray)" filter | `mrna_microarray_sample_count` ("RNA (microarray)") — counts only the `_mrna` list | | microRNA | `mirna_sample_count` ("miRNA") | | protein / RPPA | `rppa_sample_count` ("RPPA") | | mas … (7083 more chars) ◀ result {"result":[{"code":"LUAD","name":"Lung Adenocarcinoma","score":80,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > NSCLC > LUAD"},{"code":"LAIS","name":"Lung Adenocarcinoma In Situ","score":60,"mainType":"Non-Small Cell Lung Cancer","tissue":"Lung","hierarchy":"LUNG > LAIS"}]} ▶ clickhouse_run_select_query { "query": " SELECT preference_name, COUNT(*) AS studies FROM cancer_study_query_preferences GROUP BY preference_name ORDER BY preference_name; " } ◀ result {"error_message":"Query execution failed: HTTPDriver for https://dl96orhu96.us-east-1.aws.clickhouse.cloud:8443 received ClickHouse error code 241\n Code: 241. DB::Exception: (total) memory limit exceeded: would use 31.73 GiB (attempt to allocate chunk of 0.00 B), current RSS: 31.72 GiB, maximum: 28.80 GiB. (MEMORY_LIMIT_EXCEEDED)\n"} ▶ list_studies { "search": "lung adenocarcinoma", "verbose": true } ◀ result {"result":[{"cancer_study_identifier":"msk_met_2021","name":"MSK MetTropism (MSK, Cell 2021)","description":"MSK-MET (Memorial Sloan Kettering - Metastatic Events and Tropisms) is a pan-cancer cohort of tumor genomic and clinical outcome data from 25,000 patients. The dataset identifies associations between tumor genomic alterations and patterns of metastatic dissemination across 50 tumor types; showing that chromosomal instability is strongly correlated with metastatic burden in some tumor types, like prostate and lung adenocarcinomas and HR+/HER2+ breast ductal carcinoma, but not in others, such as colorectal MSS, pancreatic adenocarcinoma and high-grade serous ovarian cancer. The study also identifies somatic alterations associated with increased metastatic burden and routes of metastatic spread. Our data offers a resource for the investigation of the biologic basis for metastatic spread and highlights the role of chromosomal instability in cancer progression. This data is available under the Creative Commons BY-NC-ND 4.0 license.","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":"luad_mskcc_2023_met_organotropism","name":"Lung Adenocarcinoma Met Organotropism (MSK, Cancer Cell 2023)","description":"Targeted and whole-exome sequencing of 2653 lung adenocarcinoma tumor/normal sample pairs.","type_of_cancer_id":"luad","sample_count":2653,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2023_met_organotropism"},{"cancer_study_identifier":"lung_msk_2017","name":"Non-Small Cell Cancer (MSK, Cancer Discov 2017)","description":"Targeted sequencing of 860 patients with metastatic lung adenocarcinoma","type_of_cancer_id":"luad","sample_count":915,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=lung_msk_2017"},{"cancer_study_identifier":"luad_mskcc_2020","name":"Lung Adenocarcinoma (MSK, J Thorac Oncol 2020)","description":"Targeted sequencing of 604 lung adenocarcinoma tumor/normal pairs via MSK-IMPACT.","type_of_cancer_id":"luad","sample_count":604,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_mskcc_2020"},{"cancer_study_identifier":"luad_tcga","name":"Lung Adenocarcinoma (TCGA, Firehose Legacy)","description":"TCGA Lung Adenocarcinoma. Source data from GDAC Firehose. Previously known as TCGA Provisional.","type_of_cancer_id":"luad","sample_count":586,"has_guide":false,"url":"https://www.cbioportal.org/study/summary?id=luad_tcga"},{"cancer_study_identifier":"luad_tcga_gdc","name":"Lung Adenocarcinoma (TCGA GDC, 2025)","description":"TCGA Lung Adenocarcinoma. Source data from NCI GDC and generated in Aug 2025 using