University of Groningen Genomic heterogeneity of clear cell renal cell carcinoma Ferronika, Paranita

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University of Groningen

Genomic heterogeneity of clear cell renal cell carcinoma

Ferronika, Paranita

DOI:

10.33612/diss.101437783

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Publication date: 2019

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Ferronika, P. (2019). Genomic heterogeneity of clear cell renal cell carcinoma. Rijksuniversiteit Groningen. https://doi.org/10.33612/diss.101437783

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Summary

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The aim of this thesis was to study somatic genetic tumour heterogeneity between and within cases of clear cell renal cell carcinoma (ccRCC) and their metastases. Because ccRCC is often metastasized at the time of diagnosis, and these cases are difficult to treat, it is often a lethal disease. One of the challenges in improving the clinical management of ccRCC is its inter- and intratumour heterogeneity. At the histomorphological level, this heterogeneity was established many years ago [1, 2]. In contrast, the exploration of somatic genetic heterogeneity, and its connection to histomorphological features, is more recent and has been greatly hastened by the availability of the next generation sequencing (NGS) technology. The most frequently mutated genes have now been clearly established in large series of ccRCCs. However, the prognostic value of these mutated genes, the distribution of their mutations across and within ccRCC samples (including metastases), and their association with histomorphological patterns still need more study.

For this thesis, we used targeted NGS to study mutational profiles in primary ccRCCs and their metastases. We designed a targeted gene panel that included the most frequently mutated genes in ccRCC, as well as some less frequently mutated genes that had been suggested to be associated with choice of treatment and/or clinical outcome. We wished to see the distribution of mutations in these selected genes not only between different tumours, but also within individual tumours. We further broadened our view by using whole exome sequencing to capture mutations in many more genes, arrayCGH to study copy number variation (CNV), and NGS-based gene expression profiling to interrogate changes at the transcriptome level. Wishing to expand our work on ccRCC in the future, we also took the opportunity to experiment with single-cell sequencing, a relatively new technique that can hugely increase the level of detail in mutational profiling.

In chapter 1 we present a general introduction to ccRCC and describe the aims of our studies. Subsequent chapters describe the details of each approach we used to explore the genomic variations among different ccRCC cases, including their associations with cancer-related survival (chapter 2) and their variations within primary ccRCCs (chapter 3) and metastatic ccRCCs (chapter 4). In chapter 5 we report on our single- cell sequencing experiments in a case of small cell lung carcinoma.

We utilized a targeted 42-gene sequencing panel to examine the mutational profile of 252 ccRCC cases from the prospective Netherlands Cohort Study on diet and cancer (NLCS) and analysed its association with ccRCC-specific survival and clinical characteristics. In chapter 2, we present the first data from this project, obtained from 110 cases. From the 42-gene panel, we explored the seven genes with the highest- reported mutation frequencies in ccRCC: VHL, PBRM1, SETD2,

BAP1, MTOR, KDM5C, and TP53 [3, 4]. Mutations in VHL and PBRM1 turned out to be associated

with ccRCC-specific survival, although this was not significant after multiple testing correction. In addition, we observed a statistically significant positive effect on ccRCC-specific survival for combined VHL and PBRM1 mutations. This positive effect might be due to the role of PBRM1 and

VHL mutations as cancer drivers in the early events of ccRCC development and the fact that their

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role as drivers during tumour progression is not as dominant as those of other genes such as

SETD2 and BAP1 [5, 6]. The samples we analysed in this cohort were taken by single sampling from

each ccRCC case, so intratumor genetic heterogeneity was not taken into account [7, 8].

In order to expand on these studies and to connect mutated genes to histomorphological tumour grading, an important tool for guiding the clinical management of ccRCC patients [9, 10], we performed the study described in chapter 3. In this study, we used the same 42-gene panel used in chapter 2 to analyse 31 tumour samples from seven ccRCC patients, each sample corresponding to a tumour subregion with a uniform tumour grade. Not unexpectedly, as VHL is commonly recognized to be an important driver gene in ccRCC, we found mutations in VHL in virtually all samples [5]. In contrast, mutated PBRM1 was present in multiple regions from only four out of seven patients, whereas BAP1 and ROS1 were present in multiple tumour regions of only one patient. A number of genes were privately mutated in specific regions of each patient. We did not observe any firm association between mutated genes and regional tumour grades. We did, however, observe a marked increase in chromosomal instability from low-grade to high-grade samples based on arrayCGH data from the single patient we studied with this technique.

We then set out to expand our study of intratumoural genomic heterogeneity to the metastases and to try to capture the heterogeneity between samples at the RNA- rather than DNA-level. In chapter 4, we describe a study in which we extensively analysed one ccRCC case using samples from multiple regions of the primary tumour, a sample from a tumour thrombus reaching into the inferior vena cava, and samples from four different lung metastases. In addition to sharing 18 single nucleotide variants (SNVs), all samples presented with a more-or-less equal number of private mutations. We also observed heterogeneity among different regions of the primary tumours, the venous tumour thrombus, and the metastases, at the level of CNVs and gene expression. We detected an increased complexity in the copy number profiles for the higher tumour grades and the metastases, as compared to the low-grade samples, a situation very similar to what we observed in chapter 3. Overall, our findings suggest a scenario in which metastasis development could start from punctuated evolution in a subregion of a primary tumour, with parallel clonal evolution occurring over time in all primary regions and metastases, which then introduces more tumour subclones unique to each particular region.

Taking multiple samples from a tumour, rather than a single sample, for genomic studies allows us to study intratumor heterogeneity. However, we know that individual tumour cells can have different mutational and expressional profiles, even within a single sample. In the study presented in chapter 4, we were already seeing copy number heterogeneity and SNVs with a low minor allele frequency as an indication of this. The relatively new technique of single-cell sequencing allows for the study of this inter- tumour cell heterogeneity. Wishing to introduce this technique into our ccRCC research in the future, we took the opportunity to use it in a small-cell lung cancer case for which the necessary fresh-frozen tissue was available (as opposed to our ccRCC series). This study is reported in chapter 5. Our single-cell CNV analyses revealed a high degree Chapter 6

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of intratumour heterogeneity between single cells from the primary tumour, lymph node and adrenal metastases, but a much lower degree of intratumour heterogeneity in the liver metastasis. The low degree of intratumour heterogeneity in the liver suggests a monoclonal seeding pattern [11] to that organ. In contrast, the higher degree of intratumour heterogeneity observed in the lymph node and adrenal metastases suggests a much more polyclonal seeding to those sites. Sources of polyclonal seeding to a metastatic site can either be a single primary tumour subclone, together with one or more other metastatic sites, or multiple primary tumour subclones with or without contribution from one or more other metastatic sites [12, 13].

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References

Ficarra V, Righetti R, Martignoni G et al. Prognostic value of renal cell carcinoma nuclear grading: multivariate analysis of 333 cases. Urol Int 2001; 67: 130-134.

Fuhrman SA, Lasky LC, Limas C. Prognostic significance of morphologic parameters in renal cell carcinoma. Am J Surg Pathol 1982; 6: 655-663.

Forbes SA, Beare D, Boutselakis H et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res 2017; 45: D777-D783.

Network CGAR. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013; 499: 43-49.

Turajlic S, Xu H, Litchfield K et al. Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal. Cell 2018; 173: 595-610 e511.

Sato Y, Yoshizato T, Shiraishi Y et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet 2013; 45: 860-867.

Gerlinger M, Rowan AJ, Horswell S et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012; 366: 883-892.

Gerlinger M, Horswell S, Larkin J et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet 2014; 46: 225-233.

Dagher J, Delahunt B, Rioux-Leclercq N et al. Clear cell renal cell carcinoma: validation of World Health Organization/International Society of Urological Pathology grading. Histopathology 2017; 71: 918-925. Delahunt B, Srigley JR, Egevad L et al. International Society of Urological Pathology grading and other prognostic factors for renal neoplasia. Eur Urol 2014; 66: 795-798.

Ni X, Zhuo M, Su Z et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients. Proc Natl Acad Sci U S A 2013; 110: 21083-21088.

Gundem G, Van Loo P, Kremeyer B et al. The evolutionary history of lethal metastatic prostate cancer. Nature 2015; 520: 353-357.

Hong MK, Macintyre G, Wedge DC et al. Tracking the origins and drivers of subclonal metastatic expansion in prostate cancer. Nat Commun 2015; 6: 6605.

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. Chapter 6

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