Advancing Precision Oncology Through Genetics, Immunology, and Innovation
Research Overview
The Siolas Lab is dedicated to uncovering how genetic changes and the immune system influence cancer development, progression, and response to therapy. We focus on some of the most challenging cancers—including colorectal and pancreatic cancer—and combine laboratory research, clinical studies, and computational tools to find new ways to improve patient outcomes. By identifying how tumors evade the immune system and predicting who is at risk for relapse, we work toward building personalized, more effective cancer treatments for the future.
Colorectal Cancer
Our lab studies how genetic changes and the surrounding environment of colorectal tumors influence cancer growth, recurrence, and response to treatment. We identified genomic signatures that can predict which patients are more likely to experience a return of their cancer after surgery (Metzger et al., Annals of Surgical Oncology, 2025). We also explore new immunotherapy strategies for patients whose tumors typically do not respond to immune-based treatments. By understanding how tumors interact with the immune system, we aim to develop more personalized treatment plans that improve outcomes and reduce the risk of relapse.
Pancreatic Cancer
Our research focuses on how specific genetic mutations in pancreatic cancer—especially changes in the KRAS and p53 genes—alter the immune environment around tumors. We have shown that different KRAS mutations create unique immune landscapes, leading to differences in how pancreatic tumors grow and respond to therapy (McIntyre et al., Cancer Cell, 2024). We also discovered that mutations in p53 can drive immune suppression by attracting certain immune cells that protect the tumor. By uncovering these mechanisms, we are working toward designing smarter therapies that enable the immune system to better detect and destroy pancreatic cancer cells.
Precision Oncology and Computational Approaches
We apply cutting-edge genomic analyses and artificial intelligence tools, like natural language processing (NLP), to predict cancer behavior and personalize patient care. Our work has identified genetic markers that forecast the likelihood of cancer returning after surgery in colon cancer (Metzger et al., Annals of Surgical Oncology, 2025) and used AI to find high-risk bladder cancer patients in large clinical datasets (Narayan et al., JCO Clinical Cancer Informatics, 2023). By combining genomics, big data, and clinical research, we aim to make cancer diagnosis and treatment smarter, more predictive, and tailored to each individual’s needs.
