Survival time calculation for subjects that are still "Alive"

scripts/run_bone.py

@@ -1,14 +1,17 @@
 import os
 from google.protobuf.json_format import MessageToJson
 
-from cdapython import Q
 from oncoexporter.cda import CdaTableImporter, configure_cda_table_importer
 
-table_importer: CdaTableImporter = configure_cda_table_importer()
+######   Input parameters  ########
+table_importer: CdaTableImporter = configure_cda_table_importer(use_cache=True)
 
-Tsite = Q('primary_diagnosis_site = "%bone%" OR primary_diagnosis_site = "%osseous%"', )
+Query = {'match_any': ['primary_diagnosis_site = *bone*',
+                       'primary_diagnosis_site = *osseous*'],
+         'data_source': 'GDC'}
 cohort_name = 'Bone'
-p = table_importer.get_ga4gh_phenopackets(Tsite, cohort_name=cohort_name)
+
+p = table_importer.get_ga4gh_phenopackets(Query, cohort_name=cohort_name)
 
 result_dir = os.path.abspath(os.path.join('phenopackets', cohort_name))
 os.makedirs(result_dir, exist_ok=True)
@@ -19,3 +22,4 @@
     with open(file_path, 'w') as fh:
         json = MessageToJson(pp)
         fh.write(json)
+

scripts/run_cervix.py

@@ -1,15 +1,17 @@
 import os
 from google.protobuf.json_format import MessageToJson
 
-from cdapython import Q
 from oncoexporter.cda import CdaTableImporter, configure_cda_table_importer
 
-table_importer: CdaTableImporter = configure_cda_table_importer()
+######   Input parameters  ########
+table_importer: CdaTableImporter = configure_cda_table_importer(use_cache=True)
 
-Tsite = Q('primary_diagnosis_site = "%uter%" OR primary_diagnosis_site = "%cerv%"', )
+Query = {'match_any': ['primary_diagnosis_site = *uter*',
+                       'primary_diagnosis_site = *cerv*'],
+         'data_source': 'GDC'}
 cohort_name = 'Cervix'
-p = table_importer.get_ga4gh_phenopackets(Tsite, cohort_name=cohort_name)
 
+p = table_importer.get_ga4gh_phenopackets(Query, cohort_name=cohort_name)
 
 result_dir = os.path.abspath(os.path.join('phenopackets', cohort_name))
 os.makedirs(result_dir, exist_ok=True)
@@ -20,3 +22,4 @@
     with open(file_path, 'w') as fh:
         json = MessageToJson(pp)
         fh.write(json)
+

src/oncoexporter/cda/_gdc.py

@@ -43,6 +43,8 @@ def __init__(
         ))
         self._case_fields = ','.join((
             "demographic.vital_status",
+            "diagnoses.days_to_last_follow_up",
+            "diagnoses.age_at_diagnosis",
         ))
 
     def _fetch_data_from_gdc(self, url: str, subject_id: str, fields: typing.List[str]=None) -> typing.Any:
@@ -53,7 +55,7 @@ def _fetch_data_from_gdc(self, url: str, subject_id: str, fields: typing.List[st
             return data
         else:
             raise ValueError(f'Failed to fetch data from {url} due to {response.status_code}: {response.reason}')
-    
+
     def _prepare_query_params(self, subject_id: str, fields: typing.List[str]=None) -> typing.Dict:
         filters = {
             "op": "in",
@@ -80,7 +82,23 @@ def fetch_variants(self, subject_id: str) -> typing.Sequence[pp.VariantInterpret
             mutation_details.append(vi)
 
         return mutation_details
-
+
+    def _calculate_survival_time_when_alive(self, subject_id: str) -> typing.Optional[int]:
+        diagnosis_data = self._fetch_data_from_gdc(self._cases, subject_id, self._case_fields)
+        hits = diagnosis_data.get("data", {}).get("hits", [])
+        if len(hits) > 1:
+            self._logger.warning(f"Multiple diagnoses found for subject {subject_id}. Using the first one.")
+        if hits:
+            diagnoses = hits[0].get("diagnoses", [])
+            if diagnoses:
+                last_follow_up = diagnoses[0].get("days_to_last_follow_up")
+                # Via these docs:
+                # https://docs.gdc.cancer.gov/Data_Dictionary/viewer/#?view=table-definition-view&id=diagnosis&anchor=days_to_last_follow_up
+                # last_follow_up is days since diagnosis (not days since birth)^^
+                if last_follow_up is None:
+                    self._logger.info(f"Cannot calculate survival time for subject {subject_id} due to missing data")
+                return last_follow_up
+
     def fetch_vital_status(self, subject_id: str) -> pp.VitalStatus:
         survival_data = self._fetch_data_from_gdc(self._survival_url, subject_id)
         vital_status_data = self._fetch_data_from_gdc(self._cases, subject_id, self._case_fields)
@@ -100,14 +118,21 @@ def fetch_vital_status(self, subject_id: str) -> pp.VitalStatus:
             vital_status = demographic.get("vital_status")
 
         vital_status_obj = pp.VitalStatus()
-        vital_status_obj.survival_time_in_days = int(survival_time) if survival_time is not None else 0
         if vital_status == "Dead":
             vital_status_obj.status = pp.VitalStatus.Status.DECEASED
+            if survival_time is not None:
+                vital_status_obj.survival_time_in_days = int(survival_time)
         elif vital_status == "Alive":
             vital_status_obj.status = pp.VitalStatus.Status.ALIVE
+            survival_time = self._calculate_survival_time_when_alive(subject_id)
+            if survival_time is not None:
+                if survival_time < 0:
+                    self._logger.warning(f"Survival time for subject {subject_id} is negative: {survival_time}")
+                else:
+                    vital_status_obj.survival_time_in_days = int(survival_time)
         else:
             vital_status_obj.status = pp.VitalStatus.Status.UNKNOWN_STATUS
-        
+
         return vital_status_obj
 
     def _map_mutation_to_variant_interpretation(self, mutation) -> pp.VariantInterpretation:
@@ -161,4 +186,4 @@ def _map_consequence_to_expression(csq) -> typing.Optional[pp.Expression]:
         ann = tx['annotation']['hgvsc']
         expression.value = f'{tx_id}:{ann}'
 
-        return expression
+        return expression