KRAS-driven cancers are one of the most common, yet most treatment-refractory of all human cancers. Mutations in KRAS render the enzyme constitutively active, leading to hyper-activation of a complex network of downstream effector pathways that mediate oncogenic transformation. Despite many years of effort, KRAS itself is still undruggable, and inhibition of single downstream effectors fails to inhibit the growth of KRAS- mutant tumors. We hypothesize that the vast KRAS signaling network, which regulates at least 40 effector nodes, requires potent inhibition of multiple effectors to staunch oncogenic KRAS signaling. We further hypothesize that not all KRAS-mutant cancers rely on the same set of critical effectors. We have thus developed novel tools and assays to 1) systematically explore combination effector inhibition and 2) stratify KRAS-mutant cell lines based on effector dependency and novel biomarkers. These goals will help us understand how to effectively treat KRAS-mutant cancers. We developed a Sensor siRNA library targeting all 40 KRAS effector nodes, which are represented by 84 genes. These validated siRNAs ablate their targets specifically and potently and are uniquely amenable for use in high-order combinations. We have shown that functional redundancy in effector gene families (i.e., ARAF, BRAF and CRAF of the RAF effector node) requires inhibition of all related isoforms to achieve complete effector inhibition. This unique library thus allows us to deplete complete effector nodes as well as node combinations by potently targeting up to 8 genes at once with minimal off-target effects. We propose to utilize the K99 phase of this grant to screen a panel of KRAS-mutant cell lines with ~1000 Sensor siRNA combinations to identify the most critical effector nodes involved in oncogenic KRAS signaling. We will also validate these hits by delivering siRNA combination payloads to tumors in vivo using nanoparticle-based RNAi therapy. A thorough interrogation of the molecular mechanisms behind successful siRNA payloads will be pursued in the R00 phase, in addition to further development of the very promising field of RNAi therapy. We will also utilize the K99 phase of this grant to address the problem of heterogeneity in KRAS-mutant cancer. KRAS-mutant cell lines are variably addicted to KRAS itself as well as to downstream effectors. Using our Sensor siRNA library, we developed a novel single cell-based assay to compile comprehensive effector- dependency profiles of 135 cell lines (74 KRAS-mutant and 61 KRAS-wildtype). We propose to stratify KRAS- mutant cancers into subclasses based on KRAS/effector-dependencies and to identify unique genomic/expression biomarkers that characterize each subclass. In the R00 phase I will use this data to unravel the causal relationships between biomarkers and effectors, design personalized siRNA/drug treatments specific to each KRAS-mutant subclass, and test if our biomarkers can be used in the clinic to identify patients with the highest likelihood of response to targeted therapies. In addition to the scientific goals of this proposal, I have also proposed a comprehensive training program in the K99 phase that will prepare me for research as an independent investigator. This includes guidance from a renown mentor and advisory committee, acquisition of new skills in preclinical trial design and bioinformatics analysis, training on state-of- the-art equipment at UCSF and development of professional skills to guide my transition to independence.
KRAS mutations occur in 35% of lung, 45% of colorectal and 95% of pancreatic cancers, yet these cancers remain unresponsive to chemotherapy and current targeted therapies. The goal of this project is to identify novel combinatorial targets for the treatment of KRAS-driven cancers and to understand how the complex effector network downstream of KRAS drives tumorigenesis. We will also stratify KRAS-mutant cancers into subclasses based on effector-dependency to gain insight into the heterogeneity of the disease and how patients in the clinic may be stratified and benefit from personalized treatment.
