Alejandro Sweet-Cordero, MD
Our goal is to identify novel therapeutic approaches for cancer that target the genetic mutations and altered signaling networks that are specific to cancer cells and their microenvironment. We use functional genomics applied to mouse and human systems (genetically engineered models, patient derived xenografts) to understand the signaling pathways and transcriptional networks that regulate the outcome of specific oncogenic mutations and to identify new approaches for cancer therapy. We have two primary disease interests: lung cancer and pediatric sarcomas. Our research spans the continuum from basic discovery to preclinical target development, done in a dynamic and interactive environment that is highly collaborative.
In our lung cancer work, we use functional genomic approaches to study how KRAS functions as an oncogene and to identify novel therapeutic opportunities. We performed one of the first mouse and human in vivo functional screens to identify WT1 loss as a synthetic vulnerability for KRAS-driven NSCLC (Vicent et al, 2010, JCI). More recently, we described a key role of oncogenic KRAS in regulation of the response to nutrient stress (Gwinn et al 2018, Cancer Cell). Through a multidisciplinary collaboration we have performed a high-throughput proteomic and genomics screen to characterize novel KRAS specific combinatorial vulnerabilities (Kelly, Kostyrko and Han, Cancer Discovery 2020). We are also interested in identifying and characterizing the role of tumor-propagating cells (also called cancer stem cells) in NSCLC. Using a combination of mouse and human systems, we identified a key role for Notch3 as a self-renewal pathway in mouse and human NSCLC (Zheng et al, 2013, Cancer Cell). We also have identified novel methods for targeting tumor-stroma interactions in lung cancer (Kim and Marquez et al, Nature Medicine, 2019). Ongoing projects are seeking to identify other KRAS specific vulnerabilities using 2D and 3D systems in both mouse and human. We are also using single-cell sequencing and other genomics approaches (ATACseq, etc) to study the role of TPCs in lung cancer.
In our sarcoma work, we study mechanisms driving Osteosarcoma and Ewing sarcoma metastatic progression and therapy resistance using in vivo patient-derived xenograft models. These two diseases provide an interesting contrast as clinically they are similar but from a genomic stand point they are quite distinct. We identified EWSAT1 as the first lncRNA involved in the pathogenesis of Ewing sarcoma (Howarth et al, JCI, 2014) and ongoing work is focused on understanding how lncRNAs regulate the oncogenic capacity of the EWS/FLI1 fusion. In osteosarcoma, we are performing translational studies to evaluate the use of targeted therapies for this disease and to understand mechanisms of metastatic progression using PDX and GEM models. Recently, we described how genomic analysis of osteosarcoma can help identify targeted therapies for this disease (Sayles and Breese et al, Cancer Discovery 2019). Our sarcoma work is facilitated by access to a large collection of patient-derived xenograft models and primary tumor samples which have been analyzed by Whole genome sequencing and RNAseq. We are using these models to explore the genomic evolution of sarcomas and define novel therapeutics that are informed by the alterations present in individual tumors.
We make extensive use of computational genomic approaches in our work and we have wide experience in generating and using next-generation sequencing data for gene and network discovery. We lead a multidisciplinary effort to apply next-generation sequencing (WGS/RNAseq etc.) to advance the care of relapsed and other high-risk pediatric cancer patients at UCSF/Benioff Children’s Hospitals (San Francisco and Oakland). To date, our laboratory has sequenced over 200 pediatric tumors by Whole Genome Sequencing and RNAseq. These datasets provide ample research opportunity for trainees interested in the intersection of cancer biology, functional genomics and computational biology. We have an active collaboration with the Genome Institute at the University of California Santa Cruz (D. Haussler and O. Vaske) to develop advance the care of relapsed and metastatic cancer patients using transcriptome analysis (Vaske et al JCI insight, 2019, Pfeil et al Plos Computational Biology, 2020).
