University of Groningen Evolution of karyotype landscapes in cancer Bakker, Bjorn

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University of Groningen

Evolution of karyotype landscapes in cancer

Bakker, Bjorn

DOI:

10.33612/diss.166886747

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Bakker, B. (2021). Evolution of karyotype landscapes in cancer. University of Groningen. https://doi.org/10.33612/diss.166886747

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224 | Discussion and future perspectives

DISCUSSION AND FUTURE PERSPECTIVES CIN is an accelerator rather than an initiator of tumorigenesis

A question that has been raised ever since Theodor Boveri first proposed a connection between aneuploidy and cancer is whether aneuploidy is causative for cancer1_{. The majority} of tumours show some degree of aneuploidy. To address this question a plethora of mouse models have been engineered that have widely different phenotypes. These differences seem dependent on the kind of mutation introduced to drive the CIN phenotype, and possibly the CIN rate (Chapter 1). Whether CIN and aneuploidy are advantageous or a burden to tumour development then seems a more complex question than it appears at first glance. One key clue learned from the mouse models that show increased tumour incidence is that CIN by itself usually only results in sporadic tumours with a long latency (12 to 24 months) and only in a minority of animals. The formation of tumours through random chromosome mis-segregations alone thus seems a highly stochastic process with a fairly low success rate. This is not surprising, as the immediate effects of aneuploidy are severe. Spontaneously emerging aneuploid cells will typically undergo senescence, apoptosis, or are cleared by the immune system2–5_{. To allow progression towards a tumour, cells will need to adapt to the changes in} metabolic and transcriptional load upon becoming aneuploid6,7_.

When CIN is instead provoked in a predisposed background (e.g. loss of tumour suppressors or carcinogen-induced tumorigenesis) it accelerates the onset and increases the frequency of tumorigenesis. This further supports the idea that once tumour development has started, CIN mediates the rapid acquisition of karyotype aberrations that are beneficial for tumour growth. Our lab developed mouse models having CIN (induced by Mps1 or Mad2 mutation) combined with p53 loss specifically in T-cells. These models revealed that CIN alone was insufficient to trigger efficient tumorigenesis, but a powerful accelerator of malignant transformation in cancer-predisposed backgrounds. These studies emphasized the importance of conditional mouse models to study CIN to the field. In this thesis, I used three separate backgrounds (Mps1 and Mad2 knock-out, and Plk4 overexpression) to drive CIN, mostly in the hematopoietic system and found that CIN accelerates the emergence of one to three T-cell lymphoma clones that acquire a favourable karyotype early in tumour development (Chapters 3, 4, and 6). These clones shared the same karyotypic aberrations (e.g. +4, +5/+9, +14, +15), suggesting that an optimal karyotype landscape exists for T-ALL which confers the greatest cellular fitness. Indeed, such landscapes are tissue-specific as demonstrated by comparing the karyotypes of Mad2-deficient T-ALLs and HCCs (Chapter 4). Studying the karyotype landscapes of human tumours can therefore help to identify the genes driving cancer development8_.

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Discussion and future perspectives | 225

Gradual or punctuated evolution?

According to classical Darwinian evolution, populations change over time by gradual accumulation and propagation of genetic variations. If this were true for how CIN contributes to novel karyotypes we would expect to see more heterogeneity. Our findings from the T-ALL mouse model and simulations are in line with the concept of an early punctuated “burst” during which many karyotypes are generated followed by rapid selection as suggested by for instance the work of the Navin lab9_{(Chapters 3, 4, 5, and} 6). The mutational sequence of events can be determined quite easily by ultra-deep DNA sequencing. However, mapping karyotype evolution for larger chromosome fragments or even whole chromosomes is much more challenging, as they can be easily gained and lost during tumour evolution. Therefore, improved single cell WGS protocols that combine for instance exome sequencing (or other targeted sequencing) with copy number calling are urgently needed.

A delicate balance

Besides its role in early tumour development, CIN is also suspected to drive metastasis and therapy resistance10–12_{. Copy number heterogeneity rather than mutational heterogeneity} has been associated with poor prognosis in a cohort of lung cancer patients13_{. It remains} unclear whether this heterogeneity itself or the CIN underlying the heterogeneity, i.e. the evolution capacity, is responsible for this. It should be noted that not just any level of CIN is beneficial for tumour growth. Too much CIN and the population alters its karyotype too quickly before adaptation to aneuploidy-induced stresses can take place14,15_{. Cells will} inevitably senesce or die. In addition, a rapid turnover in karyotypes prohibits stabilization of the karyotype landscape towards an equilibrium (Chapter 5). This explains why in some mouse models CIN acts tumour suppressive rather than tumour promoting (Chapter 1). On the other hand, too little or no CIN does not allow for any karyotype alteration to efficiently propagate in the population (Chapter 5). An optimal rate of CIN therefore exists that balances rapid acquisition of favourable copy number alterations and the detrimental effects of aneuploidy-induced stresses. This was demonstrated experimentally in a mouse model for colorectal cancers with a range of CIN levels. Only at a specific rate of chromosome mis-segregations do tumours form and evolve efficiently15_{. Moving a tumour} population away from this optimal CIN rate has been suggested as a way of selectively targeting and killing aneuploid cancer cells, for instance by increasing the chromosome missegregation rates using microtubule poisons16,17_{. This strategy is not without risk, as it} may accelerate the evolution of resistant or metastatic subclones. Nevertheless, it seems experimentally effective17,18_.

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Learning the rules of the game

Cancer cells continuously undergo Darwinian evolution19–21_{. This is subject to time,} population size, rate of change, and natural selection22_{. These variables constrain a} population of organisms or cells to evolve following a limited set of rules. It should in principle be possible to learn these evolutionary rules and thus predict the most likely course of tumour evolution. A recent study in clear-cell renal carcinoma revealed that loss of 9p is a critical aberration required for metastasis23,24_{. Furthermore, metastases had fewer subclonal} mutations than the primary tumour. This is suggestive of an evolutionary bottleneck: only specific mutations and CNAs enable or permit metastasis. When it comes to treating cancer, knowing the routes of cancer evolution can be an invaluable asset to determine the most suitable therapy before or during treatment. By anticipating the emergence of a recurring copy number alterations known to cause drug resistance or drive metastasis, treating physicians can instead choose to apply pre-emptive combination therapy. Methods to accurately map the karyotype landscapes of tumours and determine their rates of CIN could therefore help to improve patient outcome (Chapters 2 and 5). In addition, we would need to understand how the karyotype determines the ultimate phenotype. In Chapter 7, we make a first step towards this aim. Here, we find that karyotype-stratified groups were maintained in the tumour transcriptomes. Furthermore, the tumour transcriptomes revealed distinct gene expression signatures including an altered metabolism for aneuploid BCCs and a DNA damage signature for CIN BCCs. Our findings also indicated that even transcriptome data by itself can already to some extent predict an ongoing CIN phenotype in the tumour. In Chapter 8, we show that similar concepts are true for other tumour types as well, as we find that the risk of recurrence is proportional to the degree of genomic heterogeneity (both mutational and karyotypic). In addition, we found that mutations arising early in neuroblastoma and rhabdomyosarcoma affect the risk of relapse, whereas in Wilms tumours such events occur later in tumour development, again highlighting how CIN impacts tumour evolution and thus patient prognosis. Therefore, understanding how chromosome copy number, epigenetics and post-transcriptional regulation shape the phenotype of cancer cells will help to further determine the oncogenicity of karyotype alterations.

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Discussion and future perspectives | 227

Concluding remarks: an answer to Boveri’s speculation

At the start of the 20th_{century, Theodor Boveri published the results of his experiments} with sea urchins. Twelve years later in 1914 he wrote: “I tacked onto the results of my experiments on the development of doubly fertilized sea urchin eggs the speculation that malignant tumours might be the consequence of a certain abnormal chromosome constitution” 1,25_{. After more than a century of research using technology well beyond the} imagination of Boveri, we can look back on his speculation. The presence of aneuploidy in cancer has been thoroughly documented and indeed the majority of tumours display some degree of whole chromosome gain and loss. It seems unlikely that aneuploidy is a

bona fide initiator. Instead, CIN accelerates cancer evolution through the generation of

aneuploid cells with highly variable karyotypes and corresponding phenotypes. Early in tumour development a lucky few clones with the right combination of chromosomal gains and losses will emerge and grow. Natural selection will further shape the karyotype landscape of the tumour as it grows, selecting for those chromosome aberrations that further increase cellular fitness. All the while cells continue to mis-segregate chromosomes, thereby generating new karyotypes with new phenotypic traits, and converge towards a karyotypic equilibrium by the forces of selection. When a tumour is challenged, as for instance happens during chemotherapy, this equilibrium can shift (summarized in Figure 1). If we are to treat cancer effectively, we should learn the rules of the evolutionary game of CIN.

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Normal

cell Cancercell

Low fitness CIN cell

Non-CIN cell High fitness CIN cell

Emergence of aneuploid clones

Early selection for fit karyotypes

Expansion of dominant karyotypes Time Karyotypes Euploid karyotype Optimal tumour karyotype

Low fitness CIN cell Non-CIN cell

High fitness CIN cell Normal cell Cancer cell Karyotypic equilibrium Cell death Cell cycle arrest Expansion Severe mis-segegation Minor mis-segregation STOP

a

b

High fitness Low fitness Chemotherapy STOP Equilibrium shift

Fig. 1 Evolution towards karyotypic equilibrium in CIN-driver cancers. a Aneuploid clones emerge early

in tumour development through random chromosome mis-segregations. Cells will acquire varying levels of fitness depending on their karyotypes. Unfit clones (yellow, purple, and red cells) are rapidly outcompeted by cells that have obtained favourable chromosome aberrations (green cell). b In the fitness landscape of cancer there are karyotypes with an associated optimum in relative fitness (green band). Throughout tumour development random chromosome mis-segregations will enhance or decrease cellular fitness, ultimately resulting in cell death, cell cycle arrest or accelerated expansion. Once a population of cells emerges that have obtained a karyotype close to the optimal, the population is expands in a state of karyotypic equilibrium as random mis-segregations can more likely decrease fitness. Other karyotype alterations may become more advantageous when stresses such as chemotherapy or metastasis are introduced. Ongoing CIN will generate new karyotypes and selection will subsequently funnel the population towards a different karyotypic equilibrium.

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