Q (haiku): write me python code that can query the timeline files for msk-chord ▶ resolve_and_route { "studyKeywords": [ "MSK", "CHORD" ] } ▶ clickhouse_list_tables {} ◀ result {"success":true,"message":"Found 1 study. Use the metadata to call the appropriate navigation tool(s).","data":{"totalCount":1,"studiesWithMetadata":[{"studyId":"msk_chord_2024","name":"MSK-CHORD (MSK, Nature 2024)","sampleCount":25040,"studyViewUrl":"https://www.cbioportal.org/study?id=msk_chord_2024","metadata":{"clinicalAttributeIds":["ADRENAL_GLANDS","BONE","CANCER_TYPE","CANCER_TYPE_DETAILED","CLINICAL_GROUP","CLINICAL_SUMMARY","CNS_BRAIN","CURRENT_AGE_DEID","DIAGNOSIS_DESCRIPTION","ETHNICITY","FRACTION_GENOME_ALTERED","GENDER","GENE_PANEL","GLEASON_FIRST_REPORTED","GLEASON_HIGHEST_REPORTED","GLEASON_SAMPLE_LEVEL","HER2","HISTORY_OF_PDL1","HR","ICD_O_HISTOLOGY_DESCRIPTION","INTRA_ABDOMINAL","LIVER","LUNG","LYMPH_NODES","METASTATIC_SITE","MSI_COMMENT","MSI_SCORE","MSI_TYPE","MUTATION_COUNT","NUM_ICDO_DX","ONCOTREE_CODE","OS_MONTHS","OS_STATUS","OTHER","PATHOLOGICAL_GROUP","PDL1_POSITIVE","PLEURA","PRIMARY_SITE","PRIOR_MED_TO_MSK","RACE","REPRODUCTIVE_ORGANS","SAMPLE_CLASS","SAMPLE_COUNT","SAMPLE_COVERAGE","SAMPLE_TYPE","SMOKING_PREDICTIONS_3_CLASSES","SOMATIC_STATUS","STAGE_HIGHEST_RECORDED","TMB_NONSYNONYMOUS","TUMOR_PURITY"],"molecularProfileIds":["msk_chord_2024_cna","msk_chord_2024_mutations","msk_chord_2024_structural_variants"],"availableComparisonTabs":["overlap","clinical","survival","alterations","mutations"]}}]}} ◀ 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_list_table_columns { "table": "clinical_event_derived" } ▶ clickhouse_list_table_columns { "table": "clinical_event_data_derived" } ◀ result {"columns":[{"name":"clinical_event_id","type":"Int64"},{"name":"patient_id","type":"Nullable(Int64)"},{"name":"patient_stable_id","type":"String"},{"name":"start_date","type":"Nullable(Int64)"},{"name":"stop_date","type":"Nullable(Int64)"},{"name":"event_type","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"}]} ◀ result {"columns":[{"name":"patient_unique_id","type":"String"},{"name":"key","type":"String"},{"name":"value","type":"String"},{"name":"start_date","type":"Int32"},{"name":"stop_date","type":"Int32"},{"name":"event_type","type":"LowCardinality(String)"},{"name":"cancer_study_identifier","type":"LowCardinality(String)"}]} ═ answer (success) Perfect! Here's comprehensive Python code to query the timeline files for MSK-CHORD: ```python import requests import json import pandas as pd from typing import List, Dict, Optional from datetime import datetime class MSKChordTimelineQuerier: """Query timeline/clinical event data for MSK-CHORD study from cBioPortal.""" def __init__(self, database_host: str = "localhost", database_port: int = 8123): """ Initialize the querier. Args: database_host: ClickHouse host (default: localhost) database_port: ClickHouse port (default: 8123) """ self.host = database_host self.port = database_port self.base_url = f"http://{database_host}:{database_port}" self.study_id = "msk_chord_2024" def query_clickhouse(self, sql: str) -> pd.DataFrame: """ Execute a ClickHouse SQL query. Args: sql: SQL SELECT query string Returns: pandas DataFrame with results """ params = { 'query': sql, 'format': 'JSONCompact' } try: response = requests.get(f"{self.base_url}/", params=params) response.raise_for_status() data = response.json() if 'data' in data: # Parse JSONCompact format columns = data.get('meta', []) col_names = [col['name'] for col in columns] df = pd.DataFrame(data['data'], columns=col_names) return df else: return pd.DataFrame() except Exception as e: print(f"Query error: {e}") return pd.DataFrame() def get_all_timeline_events(self, patient_id: Optional[str] = None) -> pd.DataFrame: """ Get all timeline/clinical events for the study or specific patient. Args: patient_id: Optional patient stable ID (e.g., "P-0000001") If None, returns all patients Returns: DataFrame with columns: patient_stable_id, event_type, key, value, start_date, stop_date """ where_clause = f"AND patient_unique_id = '{patient_id}'" if patient_id else "" sql = f""" SELECT patient_unique_id, event_type, key, value, start_date, stop_date FROM clinical_event_data_derived WHERE cancer_study_identifier = '{self.study_id}' {where_clause} ORDER BY patient_unique_id, start_date """ return self.query_clickhouse(sql) def get_treatment_timeline(self, patient_id: Optional[str] = None) -> pd.DataFrame: """ Get treatment timeline events. Args: patient_id: Optional patient stable ID Returns: DataFrame with treatment events """ where_clause = f"AND patient_unique_id = '{patient_id}'" if patient_id else "" sql = f""" SELECT patient_unique_id, key, value, start_date, stop_date, event_type FROM clinical_event_data_derived WHERE cancer_study_identifier = '{self.study_id}' AND event_type = 'TREATMENT' {where_clause} ORDER BY patient_unique_id, start_date """ return self.query_clickhouse(sql) def get_diagnosis_timeline(self, patient_id: Optional[str] = None) -> pd.DataFrame: """ Get diagnosis timeline events. Args: patient_id: Optional patient stable ID Returns: DataFrame with diagnosis events """ where_clause = f"AND patient_unique_id = '{patient_id}'" if patient_id else "" sql = f""" SELECT patient_unique_id, key, value, start_date, stop_date, event_type FROM clinical_event_data_derived WHERE cancer_study_identifier = '{self.study_id}' AND event_type = 'DIAGNOSIS' {where_clause} ORDER BY patient_unique_id, start_date """ return self.query_clickhouse(sql) def get_status_timeline(self, patient_id: Optional[str] = None) -> pd.DataFrame: """ Get status/outcome timeline events. Args: patient_id: Optional patient stable ID Returns: DataFrame with status events """ where_clause = f"AND patient_unique_id = '{patient_id}'" if patient_id else "" sql = f""" SELECT patient_unique_id, key, value, start_date, stop_date, event_type FROM clinical_event_data_derived WHERE cancer_study_identifier = '{self.study_id}' AND event_type = 'STATUS' {where_clause} ORDER BY patient_unique_id, start_date """ return self.query_clickhouse(sql) def get_event_types_summary(self) -> pd.DataFrame: """ Get summary of available event types in the study. Returns: DataFrame with event_type and count """ sql = f""" SELECT event_type, COUNT(DISTINCT patient_unique_id) as num_patients, COUNT(*) as num_events FROM clinical_event_data_derived WHERE cancer_study_identifier = '{self.study_id}' GROUP BY event_type ORDER BY num_events DESC """ return self.query_clickhouse(sql) def get_treatment_history_by_patient(self, patient_id: str) -> Dict: """ Get complete treatment history for a specific patient. Args: patient_id: Patient stable ID Returns: Dictionary with organized timeline data """ treatments = self.get_treatment_timeline(patient_id) diagnoses = self.get_diagnosis_timeline(patient_id) status = self.get_status_timeline(patient_id) return { 'patient_id': patient_id, 'diagnoses': diagnoses.to_dict('records') if not diagnoses.empty else [], 'treatments': treatments.to_dict('records') if not treatments.empty else [], 'status': status.to_dict('records') if not status.empty else [] } def export_timeline_to_csv(self, output_file: str, patient_id: Optional[str] = None): """ Export timeline data to CSV file. Args: output_file: Output CSV file path patient_id: Optional patient ID to filter """ df = self.get_all_timeline_events(patient_id) df.to_csv(output_file, index=False) print(f"Exported {len(df)} events to {output_file}") def export_timeline_to_json(self, output_file: str, patient_id: Optional[str] = None): """ Export timeline data to JSON file. Args: output_file: Output JSON file path patient_id: Optional patient ID to filter """ df = self.get_all_timeline_events(patient_id) df.to_json(output_file, orient='records', indent=2) print(f"Exported {len(df)} events to {output_file}") # Example usage if __name__ == "__main__": # Initialize querier querier = MSKChordTimelineQuerier( database_host="cbioportal-db.example.com", # Update with your host database_port=8123 ) # 1. Get summary of available event types print("Event Types Summary:") print(querier.get_event_types_summary()) print("\n" + "="*50 + "\n") # 2. Get all timeline events (first 100 rows) print("All Timeline Events (first 100):") all_events = querier.get_all_timeline_events() print(all_events.head(100)) print(f"Total events: {len(all_events)}") print("\n" + "="*50 + "\n") # 3. Get treatment timeline for all patients print("Treatment Timeline:") treatments = querier.get_treatment_timeline() print(treatments.head(50)) print(f"Total treatment events: {len(treatments)}") print("\n" + "="*50 + "\n") # 4. Get specific patient timeline (example) if not all_events.empty: sample_patient = all_events.iloc[0]['patient_unique_id'] print(f"Detailed history for patient {sample_patient}:") patient_history = querier.get_treatment_history_by_patient(sample_patient) print(json.dumps(patient_history, indent=2, default=str)) # 5. Export data querier.export_timeline_to_csv("msk_chord_timeline.csv") querier.export_timeline_to_json("msk_chord_timeline.json") ``` ## Key Features 1. **Core Methods:** - `get_all_timeline_events()` — All clinical timeline events - `get_treatment_timeline()` — Treatment-specific events - `get_diagnosis_timeline()` — Diagnosis events - `get_status_timeline()` — Outcome/status events 2. **Filtering:** - Filter by patient ID (optional parameter in all methods) - Automatically scoped to `msk_chord_2024` study 3. **Export Options:** - CSV export via `export_timeline_to_csv()` - JSON export via `export_timeline_to_json()` 4. **Data Columns:** - `patient_unique_id` — Patient identifier - `event_type` — Type of event (TREATMENT, DIAGNOSIS, STATUS, etc.) - `key` — Attribute name (e.g., treatment name, status type) - `value` — Attribute value - `start_date`, `stop_date` — Event timing (Unix timestamps) ## Setup Requirements ```bash pip install requests pandas ``` Update the `database_host` and `database_port` with your cBioPortal ClickHouse connection details.