Abstract
BackgroundWhile in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of "sub-types," each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Phylogenetic methods have shown great promise for inferring common patterns of tumor progression, but suffer from limits of the technologies available for assaying differences between and within tumors. One approach to tumor phylogenetics uses differences between single cells within tumors, gaining valuable information about intra-tumor heterogeneity but allowing only a few markers per cell. An alternative approach uses tissue-wide measures of whole tumors to provide a detailed picture of averaged tumor state but at the cost of losing information about intra-tumor heterogeneity.ResultsThe present work applies "unmixing" methods, which separate complex data sets into combinations of simpler components, to attempt to gain advantages of both tissue-wide and single-cell approaches to cancer phylogenetics. We develop an unmixing method to infer recurring cell states from microarray measurements of tumor populations and use the inferred mixtures of states in individual tumors to identify possible evolutionary relationships among tumor cells. Validation on simulated data shows the method can accurately separate small numbers of cell states and infer phylogenetic relationships among them. Application to a lung cancer dataset shows that the method can identify cell states corresponding to common lung tumor types and suggest possible evolutionary relationships among them that show good correspondence with our current understanding of lung tumor development.ConclusionsUnmixing methods provide a way to make use of both intra-tumor heterogeneity and large probe sets for tumor phylogeny inference, establishing a new avenue towards the construction of detailed, accurate portraits of common tumor sub-types and the mechanisms by which they develop. These reconstructions are likely to have future value in discovering and diagnosing novel cancer sub-types and in identifying targets for therapeutic development.
Highlights
While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of “sub-types,” each characterized a roughly equivalent sequence of mutations by which it progresses in different patients
We have developed a novel approach to tumor phylogenetics combining unmixing methods with a cell-by-cell strategy for phylogeny inference
The prototype methods presented here do appear to suffer from insufficiently precise fits of polytopes to the data, especially as the number of components increases, which can in turn result in spurious identification of components in samples that lack them and inaccurate phylogeny inferences
Summary
While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of “sub-types,” each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Our understanding of cancer biology has been radically transformed by new technologies for probing the genome and gene and protein expression profiles of tumors, which have made it possible to identify important sub-types of tumors that may be clinically indistinguishable yet have very different prognoses and responses to treatments [1,2,3,4]. The drug traztuzumab was developed to treat the HER2-overexpressing breast cancer sub-type, yet HER2 overexpression as defined by standard clinical guidelines is not found an all patients who respond to traztuzumab, nor do all patients exhibiting HER2 overexpression respond to traztuzumab [9]. Clinical treatment of cancer could considerably benefit from new ways of identifying sub-types missed by the prevailing expression clustering approaches, better methods of finding diagnostic signatures of those sub-types, and improved techniques for identifying those genes essential to the pathogenicity of particular sub-types
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