One mouse, one patient paradigm: New avatars of personalized cancer therapy

Abstract

Over the last few decades, study of cancer in mouse models has gained popularity. Sophisticated genetic manipulation technologies and commercialization of these murine systems have made it possible to generate mice to study human disease. Given the large socio-economic burden of cancer, both on academic research and the health care industry, there is a need for in vivo animal cancer models that can provide a rationale that is translatable to the clinic. Such a bench-to-bedside transition will facilitate a long term robust strategy that is economically feasible and clinically effective to manage cancer. The major hurdles in considering mouse models as a translational platform are the lack of tumor heterogeneity and genetic diversity, which are a hallmark of human cancers. The present review, while critical of these pitfalls, discusses two newly emerging concepts of personalized mouse models called “Mouse Avatars” and Co-clinical Trials. Development of “Mouse Avatars” entails implantation of patient tumor samples in mice for subsequent use in drug efficacy studies. These avatars allow for each patient to have their own tumor growing in an in vivo system, thereby allowing the identification of a personalized therapeutic regimen, eliminating the cost and toxicity associated with non-targeted chemotherapeutic measures. In Co-clinical Trials, genetically engineered mouse models (GEMMs) are used to guide therapy in an ongoing human patient trial. Murine and patient trials are conducted concurrently, and information obtained from the murine system is applied towards future clinical management of the patient’s tumor. The concurrent trials allow for a real-time integration of the murine and human tumor data. In combination with several molecular profiling techniques, the “Mouse Avatar” and Co-clinical Trial concepts have the potential to revolutionize the drug development and health care process. The present review outlines the current status, challenges and the future potential of these two new in vivo approaches in the field of personalized oncology.

c-Myc Alterations Confer Therapeutic Response and Acquired Resistance to c-Met Inhibitors in MET-Addicted Cancers

“…Given the strictly limited accessibility to clinical test of c-Met inhibitors, we alternatively resorted to patient-derived xenograft (PDX) models, which faithfully recapitulate tumor heterogeneity and histologic characteristics of primary tumors, to mimic the clinical settings (42)(43)(44).…”

Patient-Derived-Tumor-Xenograft: modeling cancer for basic and translational cancer research

“…A plethora of evidence, including high fidelity in mutational status, transcriptome, histology, polymorphism and copy number variation, also supports the notion that PDTX models remarkably resemble the pathophysiology of human tumors more closely than traditional Cancer-derived xenograft (CDX) models [16]. This convincing data suggests PDTX are informative models to study clonal evolution along serial passages, at the rates reported in primary tumors [17][18][19][20][21][22][23][24]. Notably, PDTX grow within a rich host environment and the relationship between human tumors and mouse host elements have been proven to be critical for the successful engraftment, growth and response to therapy [25].…”

An Integrated Analysis of Heterogeneous Drug Responses in Acute Myeloid Leukemia That Enables the Discovery of Predictive Biomarkers

“…Thus, drugs that impair leukemic engraftment may affect outcomes, a possibility that must ultimately be tested in clinical trials. Patient-derived tissues that have been serially propagated as xenografts (distinct from primary patient samples) have been used previously by the Pediatric Preclinical Testing Program (29) and others (30) to test a panel of therapeutic agents against a limited number of individual tumors. Although effective in screening potential new drugs, this approach does not capture interpatient tumor heterogeneity nor allow for identification of drug response biomarkers.…”