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Integration of pharmacology, molecular pathology, and population data science to support precision gastrointestinal
Shuji Ogino1,2,3, Iny Jhun1, Douglas A Mata1
1Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Abstract:
Precision medicine has a goal of customizing disease prevention and treatment strategies. Under the precision medicine paradigm, each patient has unique pathologic processes resulting from cellular genomic, epigenomic, proteomic, and metabolomic alterations, which are influenced by pharmacological, environmental, microbial, dietary, and lifestyle factors. Hence, to realize the promise of precision medicine, multi-level research methods that can comprehensively analyze many of these variables are needed. In order to address this gap, the integrative field of molecular pathology and population data science (i.e., molecular pathological epidemiology) has been developed to enable such multi-level analyses, especially in gastrointestinal cancer research. Further integration of pharmacology can improve our understanding of drug effects, and inform decision-making of drug use at both the individual and population levels. Such integrative research demonstrated potential benefits of aspirin in colorectal carcinoma with PIK3CA mutations, providing the basis for new clinical trials. Evidence also suggests that HPGD (15-PDGH) expression levels in normal colon and the germline rs6983267 polymorphism that relates to tumor CTNNB1 (β-catenin)/WNT signaling status may predict the efficacy of aspirin for cancer chemoprevention. As immune checkpoint blockade targeting the CD274 (PD-L1)/PDCD1 (PD-1) pathway for microsatellite instability-high (or mismatch repair-deficient) metastatic gastrointestinal or other tumors has become standard of care, potential modifying effects of dietary, lifestyle, microbial, and environmental factors on immunotherapy need to be studied to further optimize treatment strategies. With its broad applicability, our integrative approach can provide insights into the interactive role of medications, exposures, and molecular pathology, and guide the development of precision medicine.
Insights
Precision medicine tailors treatments by analyzing molecular and lifestyle factors. Molecular pathological epidemiology integrates data to optimize cancer prevention and therapies, like aspirin for specific colorectal cancers.
Area of Science:
- Integrative molecular pathology and population data science (molecular pathological epidemiology).
- Translational research in precision medicine for gastrointestinal cancers.
Background:
- Precision medicine aims to customize disease prevention and treatment.
- Individual variations in genomic, epigenomic, proteomic, and metabolomic profiles influence disease.
- Pharmacological, environmental, microbial, dietary, and lifestyle factors impact patient outcomes.
Purpose of the Study:
- To address the need for multi-level research methods in precision medicine.
- To integrate molecular pathology and population data science for comprehensive analyses.
- To enhance understanding of drug effects and inform clinical decision-making.
Main Methods:
- Development and application of molecular pathological epidemiology.
- Analysis of multi-level data including molecular alterations and external factors.
- Integration of pharmacological data with molecular and population data.
Main Results:
- Demonstrated potential benefits of aspirin in colorectal carcinoma with PIK3CA mutations.
- Identified HPGD expression and rs6983267 polymorphism as predictors of aspirin efficacy.
- Highlighted the need to study modifying effects of lifestyle and environmental factors on immunotherapy.
Conclusions:
- The integrative approach provides insights into the interplay of medications, exposures, and molecular pathology.
- This approach guides the development of precision medicine strategies for gastrointestinal cancers.
- Further research can optimize immunotherapy and other treatments by considering diverse patient factors.
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