Cancer Systems Laboratory (CSL) views cancer as a complex system whose components can be reverse-engineered for the purposes of understanding the underlying mechanisms of cancer progression and identifying approaches for more effective cancer control strategies. Currently, our laboratory infers complex features of cancer progression through a variety of approaches that include: (1) reconstructing molecular networks of cancer, (2) integrating a diversity of molecular, pathological, imaging and clinical cancer data, and (3) mathematically modeling the progression of primary disease to metastatic stages in patients. Ultimately, our goal is to develop a comprehensive, multiscale view of cancer progression that merges these various approaches.

(1) Reconstructing molecular networks: We apply a wide range of computational and statistical techniques to infer molecular networks underlying cancer using genomic, transcriptomic and proteomic data. These networks often represent interactions between genes or sets of genes, mediated by a diversity of molecular regulators. We use these networks to generate new hypotheses about cancer initiation and progression. Recently, we have been funded by the NCI Integrative Cancer Biology Program as a national Center for Cancer Systems Biology to promote this research with a grant entitled "Modeling the Role of Differentiation in Cancer Progression," which focus on hematologic malignancies with a multi-disciplinary team across the Stanford campus. We are establishing a new wet-lab to experimentally validate our computationally-derived findings. With this new experimental laboratory, we are now expanding our molecular-network-based research to the analysis of solid tumors, specifically focusing on the microenvironment of breast and lung cancer.

(2) Integrating a diversity of molecular, imaging and clinical data: We have embarked on numerous projects that involve the integration of multi-platform cancer datasets through probabilistic modeling. In a recent collaborative effort through ISIS, with investigators from the Stanford Departments of Radiology and Surgery, we are creating an association map between CT and PET image features and gene expression microarrays of human non-small cell lung carcinoma. This map provides a molecular characterization of imaging features of lung cancer. It also enable us to identify prognostic significance image biomarkers by leveraging on a vast amount of clinically annotated, publically available lung cancer gene expression microarray.

(3) Mathematically modeling clinical cancer progression and cancer control health policies: We develop multi-scale models of the natural history of cancer that describe the stochastic behavior of tumor growth and metastatic spread. We have used these models to address important health policy questions related to early detection, such as: how does screening mammography and MRI impact breast cancer mortality; and how would CT screening for lung cancer impact lung cancer mortality rates? This effort has been funded for over 10 years and has been renewed for an additional 5 years of funding through the NCI Cancer Intervention and Surveillance Network (CISNET).

In summary, CSL brings together computational and biomathematical modelers, engineers, biological experimentalists and clinical researchers to ensure the biological and clinical relevance and translation.

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