Characterizing coexisting clones within a tumor

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One reason tumors can adapt to our treatment is that they are composed of heterogeneous cells. The relative frequencies of subclones within a heterogeneous population changes in response to treatment. The progress in quantifying cellular diversity in the past years has been remarkable, in part thanks to advances in single cell sequencing (sc-Seq). But scDNA-Seq for example is just one perspective on a tumor cell, in turn yielding only one of many perspectives on the tumor’s population composition. Looking at the cell’s RNA instead of the DNA, we will likely see a different population structure. And yet another if we look at imaging data and the cells' microenvironment.

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AwardsAACR-Bristol-Myers Squibb Oncology

Clone specific vulnerabilities

 

Knowing what a clone really is – knowing what constitutes a clone in a tumor – is a powerful metric that informs parameters of tumor evolution that are otherwise difficult to measure directly. Clone characteristics can help inform mathematical models and prediction of therapeutic strategies to steer clonal dynamics into the desired direction. Our lab investigates changes in the clonal composition over time in response to defined selective pressures. Hereby selective pressures are defined as either: cytotoxic therapies (project 1) and/or resource limitations (projects 1, 2).

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But what we ultimately want to know is not what subpopulations differ in their DNA or RNA, but we’re interested in that outline of subpopulations that defines differential drug sensitivities. Data integration is likely to bring us closer to this goal than looking at a single angle. Integrating multiple perspectives – we can better answer the question: what really is a clone?

 

1. Characterizing cytotoxic therapy induced shifts in the cost-to-benefit ratio of high ploidy

Awards: Pathway to Independence Award (K99/R00), NCI;       Dean’s fellowship, Stanford; Best poster award, Cancer Target Discovery. 

 

We previously coined the “tip-over hypothesis of DNA damage therapy sensitivity”, proposing that cytotoxic therapy is effective if it pushes a cell’s somatic copy number alteration (SCNA) load above a tipping point. Variable proximity of co-existing tumor cells to this tipping point imply that dose-response relations need not be monotonic. We test the potential of tumor cell DNA content and dNTP substrate availability to predict a tumor’s vulnerability to increased SCNA rates (e.g. due to cytotoxic therapy).

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Hereby, the aforementioned tipping point is accounted for not by elevated SCNA load alone, but by an inability of the tissue micro-environment (TME) to provide the necessary resources. We integrate single cell sequencing with imaging and mathematical modeling of heterogeneous populations that evolve through chromosome missegregations, to examine observed SCNA landscapes and missegregation tolerances.

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Left: Series of multi-stained IHC images are aligned for one GBM. Vessel characteristics inform transport of O2, PO4, and Glucose through GBM tissue. PDE simulations using vessel characteristics as initial conditions: Cell density- (middle) and energy distributions (right) are shown around vessels.

Experiments are performed in stomach and brain tumors—two cancer types whose TME can “afford” vastly different amounts of DNA. We evaluate Oxygen, Phosphate and Glucose as rate-limiting substrates of dNTP synthesis of co-evolving subpopulations in stomach and brain tissue environments. Our results suggest that limiting dNTP concentrations amplify divergence in S-phase duration between high- and low-ploidy cells.

Opposing selective forces explain why cancer therapies shift the Goldilocks zone of tumor aneuploidy. We distinguish cells along two dimensions: their SCNA load (grayscale, as in A) and their ploidy (drawn as cell size). (A) The Goldilocks zone of tumor aneuploidy lies at intermediate SCNA loads among therapy-naïve patients (-; blue arrow).  For patients exposed to chemo/radiation therapy (+; red arrow), intermediate and high SCNA loads have a similar risk of disease progression. (B) Interaction between two selective pressures – energy scarcity and chromosomal instability (CIN) – can explain differences between therapy-naïve and therapy-exposed patients. Compared to low ploidy cells, high ploidy cells are more likely to survive mutations and accumulate high SCNA loads (y-axis in B). High ploidy cells require more nutrients for growth (x-axis), setting high ploidy cells at a disadvantage when competing in nutrient-scarce micro-environments.

2. Cell adaptations to new growth environments
R03 feasibility award: in collaboration with Laura Heiser (OHSU), Zaid Siddiqui (UPMA), Saeed  Alahmari

2.1 Integrating live-cell imaging with single-cell sequencing
Exposing cancer cells to a new environment influences their growth behavior. For some cells, a moderate growth inhibition is followed by adaptation and return to normal growth rates. Others initially experience a near-complete cytostatic phenotype, only to explode in their growth during later generations, reaching growth rates well beyond baseline. This implies that one can reach opposite conclusions about the relative fitness of two cell lineages, solely depending on timing of measurement. Our goal is the development of a new class of temporal biomarkers that extrapolate from a cell's transcriptome how fit its descendants will be over multiple generations. We record how cells divide, migrate and die, linking the recorded phenotypic differences between cells to differences between their transcriptomes.

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Linking sequenced and imaged cells allows visualizing the spatial segregation of pathways activity inside a cell (here shown for only two compartment: mitochondria and nucleus and 10 pathways: legend). Frequency of each color indicates strength of pathway activity.

We use live-cell imaging to characterize the cell cycle of cancer cell clones as they adapt to new environments. A four-layered approach maps sequenced- and imaged cells in-silico. Hereby biological variability – emerging from multiple growth conditions – acts as an additional barcode during sequencing. This linking matches the sequenced cell's transcriptome to its closest living relative still undergoing live-cell imaging. Integration with live-cell imaging opens the door to leverage the suitability of single cell sequencing for deep learning in a new way – not for solving technical challenges like segmentation and tracking, but for interpretation of genomic information.

2.2 Mathematical modeling of competition dynamics of co-existing tumor clones
 

The transcriptome is part of a cell's "vocabulary" that is used to communicate with its environment. As such, not only the fitness of a cell, but also its transcriptome, is context-dependent -- a function of environmental changes. Here we evaluate how environmental fluctuations – changes in nutrient availability and changes in the cellular population structure – affect the fitness of these subpopulations. If coexisting subpopulations interact with one another, then the selective forces acting on these subpopulations will no longer be constant, but will change with changes in the cellular fraction of each population.

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Evolutionary game theory (EGT) is a mathematical framework designed to model this scenario of dynamic selection. We integrate single-cell sequencing into an EGT framework to restrict the search space of possible interactions between subpopulations and to characterize the nature (i.e., the strength and direction) of these interactions.