The Cancer Biology Program is focused on investigating the mechanisms and signaling pathways involved in malignant transformation. The goals are to advance basic understanding of cancer pathogenesis and to facilitate the translation of basic science discoveries into the clinical arena through collaborations to improve the outcome of patients with cancer. Program research centers on three major scientific themes: Theme 1: Mechanisms and Pathways of Oncogenesis. Investigations are focused on mechanistic analyses of oncogenes and tumor suppressor genes, as well as the control of cell division, genomic stability and DNA damage checkpoints that underlie and drive cancer pathogenesis. Theme 2: Epigenetics. Diverse approaches are being employed to investigate the epigenetic factors and perturbations that are increasingly associated with a wide spectrum of malignancies, with a particular focus on factors that maintain and read the histone code, as well as the chromatin remodeling machinery and IncRNAs that have epigenetic relevance. Theme 3: Epithelial Neoplasia. The molecular and cellular biology of solid tumors, including lung, GI, skin and GU neoplasias, are being investigated using a variety of in vitro and in vivo model systems. Major achievements in this funding period include characterization of oncogenic BAF fusion proteins in synovial sarcomas, derivation of novel techniques for assessing genome-wide chromatin accessibility, discovery and characterization of epigenetic lncRNAs, validation of novel oncogenes in organoid cultures, scaffold targeting of the MAPK pathway, and identification of new downstream targets in the Hedgehog (Hh) signaling pathway. Co-led by Michael Cleary, MD, and Calvin Kuo, MD, PhD, the 40 program members represent three schools (the School of Medicine, the School of Humanities & Sciences and the School of Engineering), 14 Departments, and five Divisions within the Department of Medicine. The research activities of the 40 investigators are supported by 80 peer-reviewed, investigator-initiated grants and 3 T32 training awards. Peer-reviewed funding consists of $28.1M in total costs/year of which $9.5M is from the NCI. Other NIH support amounts to $16.5M and other peer-reviewed support to $2.1M. Since 2009, program investigators have published over 280 manuscripts relevant to cancer biology in peer-reviewed journals of which 15% are intra-programmatic and 48% inter-programmatic, with numerous externally collaborative manuscripts. The SCI will continue to be invaluable in seeding innovative projects and in assisting with the translation of the basic science findings from this program into new approaches to the diagnosis and treatment of patients with cancer.
? OVERALL The Stanford Cancer Institute (SCI) brings together researchers from across Stanford University and from the Cancer Prevention Institute of California with the goals of improving cancer prevention, detection, diagnosis, treatment, and outcomes for individuals and for populations, both within our region and in the world at large. The SCI has a compelling history of cancer discovery that has accelerated with the development of innovative new technologies, a strong focus on understanding the etiologies of cancers in individuals and in populations, and a mission to translate findings to improve the care of cancer patients. With the support of the Cancer Center Support Grant, the SCI will make major contributions across the spectrum of research from basic discovery through clinical application to the population sciences that will enhance our understanding of disease processes, bring new diagnostic and therapeutic modalities to patients, facilitate risk assessment and prevention, and improve the overall outlook for individuals afflicted with or at risk for cancer.
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|Patel, Manali I; Sundaram, Vandana; Desai, Manisha et al. (2018) Effect of a Lay Health Worker Intervention on Goals-of-Care Documentation and on Health Care Use, Costs, and Satisfaction Among Patients With Cancer: A Randomized Clinical Trial. JAMA Oncol 4:1359-1366|
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|Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15|
|Banerjee, Imon; Gensheimer, Michael Francis; Wood, Douglas J et al. (2018) Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives. Sci Rep 8:10037|
|Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14|
|Rogers, Zoë N; McFarland, Christopher D; Winters, Ian P et al. (2018) Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice. Nat Genet 50:483-486|
|Nair, Viswam S; Sundaram, Vandana; Desai, Manisha et al. (2018) Accuracy of Models to Identify Lung Nodule Cancer Risk in the National Lung Screening Trial. Am J Respir Crit Care Med 197:1220-1223|
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