It has been hypothesized that chemotherapy resistance reflects selection for a mutant clone of tumor cells that is intrinsically resistant to chemotherapy due to its unique genetics. However, recent reports demonstrate only a weak correlation between acute myeloid leukemia genotype and chemotherapy resistance. As an alternative, we propose that intratumoral heterogeneity (ITH, i.e. clonal diversity) may be a predictor of chemotherapy resistance. While one might hypothesize that increased levels of ITH relates to less tractable disease, little data is available that definitively links ITH with outcome, let alone a relationship between the presence of genetic diversity and gene expression or other phenotypic changes. While the role of clonal evolution during leukemia development and therapy has been a focus for a number of avenues of research, the ability to deduce the clonal composition of individual samples has been limited by the use of data from bulk tumor samples. Many of the genetic assessments of clonality performed on cancer specimens will require the final description of clonality to be informed by single cell data rather than solely relying on computational deconvolution of the clonal structure. Unfortunately, despite sequencing cost reductions, the challenge of generating statistically meaningful data from single cells makes most techniques not cost effective or flat out uninformative for this purpose. Beyond the costs, the enormous technical and computational challenges that exist for generating and analyzing the data limit single cell analysis to research laboratories often not involved with clinical research. Refining the technical ability to derive accurate data at the bulk and single cell levels, appropriately process and interpret these data, and apply this approach to larger cohorts of patients are crucial next steps for making relevant biological conclusions from ITH analyses.
We aim to address this challenge by combining bulk- level ITH deconvolution with single cell targeted genetic analysis using our novel microfluidic chip, and extending this technique to include downstream transcriptomic assessments. The ability to identify genetic diversity, track it through therapy, and connect this diversity with corresponding gene expression changes would all provide a substantial improvement in our clinical understanding of the role of ITH in cancer therapy. With the possibility to more directly query the genetic variability and possible transcriptomic implications of this in both model systems as well as in primary human specimens, we can more clearly understand what role intratumoral heterogeneity plays in human malignancy. Alternative therapies designed to level the evolutionary playing field for all clones and reduce their frequency to a manageable level, could essentially transform an acute disease to a chronic one. This possibility would be a valuable new clinical option for especially toxic, or poorly tolerated therapies designed to abolish all clones that often result in more aggressive relapsed disease.
Refining the technical ability to derive accurate data at the bulk and single cell levels, appropriately process and interpret these data, and apply this approach to larger cohorts of cancer patients are crucial next steps for making therapeutically relevant conclusions from single cell and bioinformatic analyses of clonal diversity in cancer. We aim to apply our novel multi-scale approach to understand how tumor cell genetic diversity impacts outcome in acute myeloid leukemia as a demonstration of how this approach might be extended to other cancer types or used in the context of a clinical trial.