Research Goal

We are particularly interested in elucidating tumor evolutionary dynamics, novel therapeutic targets, and the genotype to phenotype map in cancer. A unifying theme of our research is to exploit ‘omic’ data derived from clinically annotated samples in robust computational frameworks coupled with iterative experimental validation in order to advance our understanding of cancer systems biology. In particular, we employ advanced genomic techniques, computational and mathematical modeling, and elegant model systems in order to:

1) Model the evolutionary dynamics of tumor progression and therapeutic resistance

2) Elucidate disease etiology and novel molecular targets through integrative analyses of high-throughput omic data

3) Develop techniques for the systems-level interpretation of genotype-phenotype associations in cancer

Selected Publications

A Big Bang Model of human colorectal tumor growth. Nature Genetics, 2015. [link]

** Recommended by Faculty of 1000 Prime

- Nature Genetics News and Views Commentary: Big Bang and Context Driven Collapse [link]

- Nature Reviews Cancer Research Highlight: Explosive Beginnings [link]

Contributions to drug resistance in glioblastoma derived from malignant cells in the sub-ependymal zone. Cancer Res 75(1), 2015. [pubmed]

Genome-driven integrated classification of breast cancer validated in over 7,500 samples. Genome Biology 15(8), 2014. [pubmed]

Comprehensive Molecular Characterization of Gastric Adenocarcinoma. Nature, 2014, 513, 202-209. [pubmed]

The breast cancer oncogene EMSY represses transcription of anti-metastatic microRNA miR-31. Molecular Cell, 53(5):806-18, 2014. [pubmed]

The shaping and functional consequence of the miRNA landscape in breast cancer. Nature, 497(7449):378-82, 2013. [pubmed]

Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. PNAS, 110(10):4009-4014, 2013 [Epub 2013 Feb 14]. [pubmed]

Single-molecule genomic data delineate patient-specific tumor profiles and cancer stem cell organization. Cancer Research, 73(1): 41-49, 2013 [Epub 2012 Oct 22]. [pubmed]

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 486(7403):346-52, 2012. [pubmed]

View all publications

A Big Bang Model of human colorectal tumor growth

Figure 1. The Big Bang model of tumor growth.

(a) After initiation, a tumor grows predominantly as a single expansion populated by numerous heterogeneous subclones. ITH results from private alterations (colored arrowheads) that continuously accumulate owing to replication errors. In addition to public alterations present in the first transformed cell, private alterations acquired early persist and become pervasive in the final tumor although remaining non-dominant (colored segments). Late-arising alterations are only present in small regions of the tumor.

(b) In the Big Bang model, the pervasiveness of private alterations depends on when the alteration occurs during growth, rather than on selection for that alteration. The schematic illustrates how early private alterations, despite remaining non-dominant, are pervasive within the tumor (for example, red and yellow) and can be found in distant regions, thus appearing variegated (for example, red). This is owing to aberrant subclone mixing in the primordial tumor, followed by scattering during expansion. Late alterations are restricted to small regions (for example, black, pink, gray) and are essentially undetectable by conventional bulk genomic profiling. Distance from the dashed vertical axis corresponds to increasingly late onset for alterations. Dashed boxes represent sampled regions.

(c) We sampled an average of 23 individual tumor glands (<10,000 cells) from distant regions (~0.5 cm3 in size) and bulk (left and right) samples. Samples were profiled using several genomic techniques, including copy number analysis, whole-exome and targeted sequencing, neutral methylation tag sequencing and FISH, providing a panoramic view of genomic alterations throughout the tumor on multiple spatial scales.

Figure 2. The spatial distribution of ITH shows subclone mixing and the absence of clonal expansions.

(a) Circos plot representation of CNAs in individual glands and bulk samples for carcinoma M (shown throughout this figure). LOH, loss of heterozygosity; L, left; R, right.

(b) Gland-level copy number analysis was employed to reconstruct the tumor phylogeny. Mixing of glands from opposite regions is apparent, where glands from left and right bulk samples are colored purple and orange, respectively.

(c) Targeted sequencing of patient-specific mutations in individual glands showed variegation in subsets of glands from opposite sides, thus confirming subclone mixing at the mutational level. A public APC mutation is shown as a clonal control (with LOH noted on chromosome 5). WES, whole-exome sequencing; TS, targeted sequencing; syn, synonomous; stp, stop gain.

(d) FISH performed using HER2 probes (red) and corresponding chromosome 17 centromeric probes (green) showed high variability in copy number states between cells within a gland, as summarized by the Shannon index. For each group, box plots show the median, limited by the 25th (Q1) and 75th (Q3) percentiles, where whiskers represent the extremes of the maximum or Q3 + 1.5(Q3 − Q1) and the minimum or Q1 − 1.5(Q3 − Q1). The maximum possible ITH value (“maximum heterogeneity”) corresponds to an index of 1.79 (99% of the FISH counts; range of 0–5).

(e) Summary of the characteristic spatial patterns and types of alterations in each tumor. Whereas adenomas were characterized by low chromosomal instability and the segregation of alterations, carcinomas harbored side-variegated and variegated alterations (7/11 at the copy number level and 6/6 at the mutational level). Yellow shading highlights variegated alterations.

Figure 3.

Single-gland sequencing confirms the predictions of the Big Bang model and exposes variegation in carcinomas but not adenomas. Heat maps indicate the presence of representative public and private mutations across multiple individual glands per tumor. In all the adenomas, private mutations are confined to a single tumor side (regional and side-specific events), whereas, in invasive carcinomas, the same private mutation is found in distant regions of the neoplasm, despite remaining non-dominant. These patterns of genetic variegation are indicative of subclone mixing in the early neoplasm followed by scattering, as predicted by the Big Bang tumor model. For representative carcinoma M, the mutational data are summarized according to the schematic in Figure 1b, where variegated mutations (red) occurred early and scattered to distant tumor regions. Regional mutations (yellow) occurred later and were confined to smaller regions of the neoplasm. Heat maps indicate the presence of representative public and private mutations (nsn, nonsynonymous; syn, synonymous; stp, stop gain; NA, not available) across multiple individual glands per tumor.

Figure 4. Inference on the genomic data verifies that most detectable ITH occurs early during tumor growth.

(a) The inferred mutational timeline is indicated for different classes of CNAs for all tumors, where time is represented in relation to tumor volume. In particular, the posterior probability distribution of tumor size (number of cells) is illustrated for each class of alteration. The results show that both public alterations and the majority of private alterations (including side-specific, side-variegated and variegated events) occur very early after the transition to an advanced neoplasm, when the tumor is composed of fewer than 1 million cells, whereas unique mutations occur late. Error bars, s.d.

(b) A schematic of the mutational timeline (from a) illustrates that the majority of detectable non-unique alterations occur early, when the tumor is orders of magnitude smaller than can be clinically detected. As reliable estimates of cell cycle duration are not available and somatic alterations depend on cell division rates rather than time, tumor size is used as a surrogate for time.

(c) By applying the inferred mutational timeline to the whole-genome CNA profiles for each patient, it is apparent that early CNAs dominate the genomic landscape. Here early events correspond to alterations that took place when the tumor had <1x10^6 cells and late alterations correspond to those that occurred after the tumor reached 1x10^9 cells. For simplicity, only private alterations are represented, as all public alterations occur early.

Figure 5. Schematic of spatiotemporal Big Bang growth dynamics.

For each tumor profiled at the mutational level, the phylogeny was reconstructed from the single-gland and bulk tumor data to define subclones. The relative timing during which each subclone arose was specified on the basis of the inferred mutational timeline for the different classes of private alteration (variegated, side specific, regional and unique). By combining information on the mutational timeline and tumor subclonal architecture, we can approximately reconstruct patient-specific spatiotemporal evolutionary dynamics, as shown in this schematic. The topographical distribution of different subclones is illustrated by distinct colors, and distance from the tumor origin (arrowhead) corresponds to the increasingly late onset of alterations. Variegated and side-variegated subclones occurred very early within the primordial tumor (<1 million cells) and are shown within the inset square representing a magnified view of the primordial neoplasm. Regional and unique subclones arose later and are represented outside the inset square. Dashed boxes represent the regions of the tumor that were experimentally sampled. This schematic shows how, in the Big Bang tumor model, the prevalence of a private mutation depends on when it arose during tumor expansion, rather than on selection for that mutation. The schematic also illustrates that, although all tumors exhibit Big Bang dynamics, subclone mixing is restricted to carcinomas, whereas adenomas are characterized by subclone segregation.

The Curtis laboratory is focused on the development and application of innovative experimental, computational, and analytical approaches to improve the diagnosis, treatment, and early detection of cancer.