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INTERNATIONAL CONGRESS OF

CancerGenetics

• Integrative Multi-Omics Analysis for Personalized Cancer Therapy Combining genomics, proteomics, and metabolomics data from CPTAC or TARGET to design individualized treatments. - Bioinformatic A...

• Kiana hajavi,^1,* Negar khaki ,²
  1. Azad university
  2. Medical university and Azad university
  
  
  
• Introduction: Introduction to Integrative Multi-Omics Analysis Integrative multi-omics analysis is an innovative approach that combines data from various biological layers, including genomics, proteomics, and metabolomics, to better understand diseases like cancer. This methodology is especially relevant as it enables researchers to explore the interactions among different biological molecules and pathways that influence tumor development and treatment responses. In cancer therapy, where variability in patient responses is common, integrating these diverse data types can facilitate more personalized treatment strategies, helping to ensure that therapies are more effective for individual patients. Two prominent initiatives, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET), stand out in this field. CPTAC focuses on generating and analyzing proteomic data to complement genomic findings, while TARGET aims to improve outcomes for children with cancer by integrating genomic and clinical information. Together, they provide a wealth of data that can inform treatment decisions and enhance our understanding of cancer biology. As researchers apply multi-omics approaches, they can identify key genetic and protein markers associated with drug resistance, enhancing our ability to modify treatment plans. This exploration into the genetic basis of personalized cancer therapy naturally leads to the next area of focus: genomics, which plays a crucial role in identifying specific mutations and alterations that influence therapeutic effectiveness and resistance in cancer patients
• Methods: Genomics in Personalized Cancer Therapy Genomics plays a central role in the development of personalized cancer therapies by providing vital data that can identify specific mutations driving tumor growth. These mutations are often unique to individual patients, making it possible to customize treatment plans that target the biological mechanisms of each cancer. By analyzing tumor DNA, clinicians can pinpoint genetic alterations that may influence how a cancer responds to specific therapies. This approach enables the selection of targeted therapies that are more likely to be effective based on the genomic profile of the tumor. Furthermore, the identification of mutations not only helps in choosing the right drug but also aids in understanding potential resistance mechanisms. For example, if a tumor harbors a mutation that is known to confer resistance to a particular treatment, oncologists can avoid using that therapy and opt for alternatives that have a higher chance of success. This data- driven methodology improves patient outcomes by aligning treatment strategies with the unique characteristics of their cancer. (Bryce et al., 2017) As we consider the role of genomics, it's clear that integrating data from proteomics will further enhance our understanding of cancer biology and treatment responses. By combining genomic insights with protein profiles, researchers can better grasp the dynamics of drug resistance and develop more effective and personalized therapeutic approaches. This integration will lead to more informed decisions in treatment design, ultimately improving care for cancer patients. (Seidel et al., 2013)
• Results: Bioinformatics has become a key tool in understanding drug resistance mechanisms in cancer. By analyzing large datasets that include genomic, proteomic, and metabolomic information, researchers can identify specific signaling pathways and mutations that contribute to a tumor's ability to resist treatment. For example, alterations in pathways like the MAPK and PI3K-Akt can affect how cancer cells respond to therapies, leading to therapeutic failure. These pathways often interact in complex ways, and mutations in associated genes can further complicate responses to drugs like EGFR and VEGFR inhibitors. In addition to identifying key pathways, bioinformatics also helps researchers track mutations that are linked to resistance. These mutations can arise from initial treatments, creating challenges for ongoing therapy. By using advanced computational models, scientists can predict and visualize how these mutations occur over time and how they impact cancer cell behavior. (Feng et al., 2021)
• Conclusion: Looking ahead, future directions in bioinformatics may focus on expanding the range of datasets analyzed to include more diverse cancer types and treatment regimens. Integrating multi-omics data will enhance the understanding of resistance mechanisms and may lead tothe discovery of new drug targets. Ultimately, the insights gained from bioinformatics will help design more effective, personalized treatments by considering both the genetic makeup of tumors and the specific signaling pathways activated in each patient. This approach can improve outcomes and reduce the likelihood of resistance, paving the way for better cancer care.
• Keywords: Cancer therapy Metabolics CPTAC Genomics