Most cancers are not driven by a single gene mutation, but rather an accumulation of mutations that develop over the evolution of tumor growth. While mouse models are useful for identifying cancer drivers and therapeutic strategies, human cancers are highly complex and the models “we have historically used do not approach this complexity and have underrepresented the diversity of human cancer,” states Dr. John Lee, an Associate Professor, previously in the Human Biology Division at Fred Hutch, now at UCLA. To better model the heterogeneity of human cancers, Dr. Lee’s research group wanted to “develop a system that could rapidly generate more complex but genetically defined models of cancer that would better recapitulate the diverse genotypes and phenotypes seen in human cancer,” he adds. In work recently published in Nature Genetics, researchers from the Lee lab describe a combinatorial genetic strategy to generate genetically diverse and clinically relevant models for bladder and prostate cancer.
To develop genetically diverse models for human cancers, the researchers generated a lentiviral library consisting of a pool of plasmids that each encode a cancer-inducing gene with a unique barcode, which was then introduced into normal bladder and prostate cells. To generate the genetic complexity seen in cancer cells, the authors added this lentiviral library in a highly efficient, concentrated way that would “deliver multiple lentiviruses at random and in combination” to each cell, explains Dr. Lee. With this approach, many of the cells would have received several different genes at random, so that each cell would then express a unique combination of cancer-driving genes. “These cells were then transplanted into mice to evaluate for the formation of tumors. We expected that only a subset of cells receiving certain combinations of oncogenic events would form tumors and we were able to analyze tumors to decode the lentiviral barcodes (and cancer-inducing genes) in each cell by single-cell sequencing, thereby providing insight into the types of genetic signals that may cooperate during tumor initiation.”
Using this approach, the researchers were able to generate “a broad range of bladder and prostate cancer histologies such as those seen in humans, many of which had yet to be modeled in traditional genetically engineered mouse models of cancer,” describes Dr. Lee. He adds that the group was able to “functionally attribute specific genetic alterations to particular cancer histologies.” For example, the researchers showed that a mutated form of the gene Fgfr3 drove a specific bladder cancer phenotype, luminal urothelial cancer, while loss of another gene, Kmt2c, was associated with a pleomorphic giant cell carcinoma phenotype. These findings demonstrate the strength of this model in linking complex genotypes with cancer phenotypes.
This work pushes beyond the standard genetic models for cancer research and demonstrates “the ability to rapidly generate genotypically and phenotypically diverse cancer models more reflective of human cancer,” states Dr. Lee. “We believe that these tumor models will be especially important in interrogating genetic interactions that promote tumor initiation and progression but also understanding mechanisms of response/resistance to therapeutics,” he continues. Using this model generated in this study, the Lee lab now aims to understand “the stepwise acquisition of genetic alterations and how this impacts cancer evolution” as well as “how polyclonal tumor populations marked by different genetic alterations may demonstrate different responses to immune checkpoint therapies and other targeted therapeutic agents.”
This work was supported by the National Institutes of Health, the Bladder Cancer Advocacy Network and the Department of Defense.
Fred Hutch/UW/Seattle Children’s Cancer Consortium members Drs. John Lee, Michael Haffner, Andrew Hsieh, Hung-Ming Lam, Jonathan Wright and Qian Wu contributed to this study.
Li S, Wong A, Sun H, Bhatia V, Javier G, Jana S, Wu Q, Montgomery RB, Wright JL, Lam HM, Hsieh AC, Faltas BM, Haffner MC, Lee JK. A combinatorial genetic strategy for exploring complex genotype-phenotype associations in cancer. Nature Genetics. 2024.