Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Bayesian Hidden Markov Modeling of Array CGH Data

Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data. We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Since the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation-based inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme and breast cancer are analyzed, and comparisons are made with some widely-used algorithms to illustrate the reliability and success of the technique

Learning mutational graphs of individual tumour evolution from single-cell and multi-region sequencing data

Background. A large number of algorithms is being developed to reconstruct evolutionary models of individual tumours from genome sequencing data. Most methods can analyze multiple samples collected either through bulk multi-region sequencing experiments or the sequencing of individual cancer cells. However, rarely the same method can support both data types. Results. We introduce TRaIT, a computational framework to infer mutational graphs that model the accumulation of multiple types of somatic alterations driving tumour evolution. Compared to other tools, TRaIT supports multi-region and single-cell sequencing data within the same statistical framework, and delivers expressive models that capture many complex evolutionary phenomena. TRaIT improves accuracy, robustness to data-specific errors and computational complexity compared to competing methods. Conclusions. We show that the application of TRaIT to single-cell and multi-region cancer datasets can produce accurate and reliable models of single-tumour evolution, quantify the extent of intra-tumour heterogeneity and generate new testable experimental hypotheses

INVESTIGATING INVASION IN DUCTAL CARCINOMA IN SITU WITH TOPOGRAPHICAL SINGLE CELL GENOME SEQUENCING

Synchronous Ductal Carcinoma in situ (DCIS-IDC) is an early stage breast cancer invasion in which it is possible to delineate genomic evolution during invasion because of the presence of both in situ and invasive regions within the same sample. While laser capture microdissection studies of DCIS-IDC examined the relationship between the paired in situ (DCIS) and invasive (IDC) regions, these studies were either confounded by bulk tissue or limited to a small set of genes or markers. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS), which combines laser-catapulting with single cell DNA sequencing to measure genomic copy number profiles from single tumor cells while preserving their spatial context. We applied TSCS to sequence 1,293 single cells from 10 synchronous DCIS patients. We also applied deep-exome sequencing to the in situ, invasive and normal tissues for the DCIS-IDC patients. Previous bulk tissue studies had produced several conflicting models of tumor evolution. Our data support a multiclonal invasion model, in which genome evolution occurs within the ducts and gives rise to multiple subclones that escape the ducts into the adjacent tissues to establish the invasive carcinomas. In summary, we have developed a novel method for single cell DNA sequencing, which preserves spatial context, and applied this method to understand clonal evolution during the transition between carcinoma in situ to invasive ductal carcinoma

Simultaneous evolutionary expansion and constraint of genomic heterogeneity in multifocal lung cancer.

Recent genomic analyses have revealed substantial tumor heterogeneity across various cancers. However, it remains unclear whether and how genomic heterogeneity is constrained during tumor evolution. Here, we sequence a unique cohort of multiple synchronous lung cancers (MSLCs) to determine the relative diversity and uniformity of genetic drivers upon identical germline and environmental background. We find that each multicentric primary tumor harbors distinct oncogenic alterations, including novel mutations that are experimentally demonstrated to be functional and therapeutically targetable. However, functional studies show a strikingly constrained tumorigenic pathway underlying heterogeneous genetic variants. These results suggest that although the mutation-specific routes that cells take during oncogenesis are stochastic, genetic trajectories may be constrained by selection for functional convergence on key signaling pathways. Our findings highlight the robust evolutionary pressures that simultaneously shape the expansion and constraint of genomic diversity, a principle that holds important implications for understanding tumor evolution and optimizing therapeutic strategies.Across cancer types tumor heterogeneity has been observed, but how this relates to tumor evolution is unclear. Here, the authors sequence multiple synchronous lung cancers, highlighting the evolutionary pressures that simultaneously shape the expansion and constraint of genomic heterogeneity