This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Divergence of treatment responses to chemotherapy exists across patients (often with underlying mechanisms unknown). More important, certain patients exhibit worsened outcome upon treatment. Genomic approaches (such as microarray and whole-genome sequencing) hold promise for transforming the cancer treatment landscape, particularly, in tailoring drug regimen to specific patients. Nevertheless, formulating effective personalized treatment via profiling the mutational landscape remains difficult, due to current deficiencies in predicting drug sensitivity from genotype. Xenografts, both indirect (via cell line) and direct (from primary tumours), are good physiologic models of cancers. Their utility in informing cancer treatment, however, is constrained by high cost of generating and maintaining genetically modified animals, and the paucity of tissue samples from biopsies. Advent of high throughput biomolecular profiling tools finally enables reading out the expansive molecular fingerprints that encode observed phenotypes in xenografts. Using pheochromocytoma (an adrenal medulla cancer) as example, this short essay attempts to provide a broad overview of the scientific and clinical possibilities offered by xenograft models for understanding resistance mechanisms to particular chemotherapeutic regimens, and upon identification of the putative mutations, confirms their functional roles as either oncogenes or tumour suppressors. Additionally, workflow involved in generating a predictive platform, based on non-invasive blood biomarkers, for informing drug treatment options is discussed. Known as an integrated genomic classifier, combination of physiological response of direct xenografts to drug treatment and bioinformatics-enabled correlation of blood biomarkers to observed phenotype at cellular and animal levels, provides the biological basis for predicting patients’ prognosis without invasive biopsy of solid tumours. Elucidation of drug resistance mechanisms entails: (i) recapitulating in vivo tumour behavior using cell lines derived from primary tumour; (ii) identification of aetiological mutations and longitudinal profiling of phenotypic response; and (iii) validation of mutations and phenotype via both knockout mice and direct allogenic xenografts. Biological models seek to recapitulate human physiology at specific levels of abstraction for answering particular questions, but incongruence in phenotype is inevitable. Nevertheless, xenografts (especially those derived from patients, PDTX), are powerful tools for addressing basic science, clinical and treatment-related questions using close functional replicas of patient physiology in an animal model. Residual incompatibility between model and patient response would require the expertise and clinical experience of oncologists for fine-tuning model suggested drug regimen to particular patients.
This is a new version acknowledging Miss Seah Kwi Shan's help in review of preprint, as well as providing some improvements in sentence flow.