Selection of metastasis competent subclones in the tumour interior

Abstract

The genetic evolutionary features of solid tumour growth are becoming increasingly well described, but the spatial and physical nature of subclonal growth remains unclear. Here, we utilize 102 macroscopic whole-tumour images from clear cell renal cell carcinoma patients, with matched genetic and phenotypic data from 756 biopsies. Utilizing a digital image processing pipeline, a renal pathologist marked the boundaries between tumour and normal tissue and extracted positions of boundary line and biopsy regions to X and Y coordinates. We then integrated coordinates with genomic data to map exact spatial subclone locations, revealing how genetically distinct subclones grow and evolve spatially. We observed a phenotype of advanced and more aggressive subclonal growth in the tumour centre, characterized by an elevated burden of somatic copy number alterations and higher necrosis, proliferation rate and Fuhrman grade. Moreover, we found that metastasizing subclones preferentially originate from the tumour centre. Collectively, these observations suggest a model of accelerated evolution in the tumour interior, with harsh hypoxic environmental conditions leading to a greater opportunity for driver somatic copy number alterations to arise and expand due to selective advantage. Tumour subclone growth is predominantly spatially contiguous in nature. We found only two cases of subclone dispersal, one of which was associated with metastasis. The largest subclones spatially were dominated by driver somatic copy number alterations, suggesting that a large selective advantage can be conferred to subclones upon acquisition of these alterations. In conclusion, spatial dynamics is strongly associated with genomic alterations and plays an important role in tumour evolution.

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Figure 1. Study overview.

A) Macroscopic images taken after surgery were reviewed by a pathologist to determine the physical boundary between tumour and normal tissues, and spatial information was subsequently gathered to generate digital tumour maps. Scale bar shows 1 cm in length on the photograph. B) Spatial data were combined with multi-regional sequencing data to study spatial dynamics and tumour evolution. C) Distribution of tumour size measured in millimetres. D) Distribution of distances between each biopsy region and its nearest tumour boundary on the tumour maps, measured in millimetres.

Figure 2. Comparison between regions in the tumour centre and regions in the tumour margin.

A) Definition of tumour centre and tumour margin. Biopsies less than 1 centimetre to its boundary were defined as marginal regions, while biopsies more than 1 centimetre to its boundary were defined as central regions. B) Comparison of weighted genome integrity index (wGII) which is a measurement of burden of somatic copy number alterations between tumour centre (n=323) and tumour margin (n=425). C) Comparison of Ki67 between tumour centre (n=204) and tumour margin (n=250). D) Comparison of frequency of necrotic regions between tumour centre (n=379) and tumour margin (n=464). E) Comparison of frequencies of regions of different Fuhrman grades in clear cell renal cell carcinoma between tumour centre and tumour margin (n=495). F) Comparison of frequencies of metastasising clones and non-metastasising clones between tumour centre (n=131) and tumour margin (n=128) in a subset of metastatic tumours. G) A cartoon summarizing the differences observed between tumour centre and tumour margin, which might be explained by the differences of blood supply of different parts of the tumour. In all boxplots in this figure the centre line is the median, the bounds of the box represent the inter-quartile range, the lower whisker = max(min(x), Q_1 – 1.5 × IQR) and upper whisker = min(max(x), Q_3 + 1.5 × IQR).

Figure 3. Phylogenetic trees and tumour maps showing the possible metastasising routes of 4 example cases on a 2-dimention level.

A) In case K153, metastasising subclones and non-metastasising subclones originate from different branches of the phylogenetic tree. Regions containing metastasising subclones are more enriched in the tumour centre. B) In case K180, the 1st-level metastasising subclone and non-metastasising subclone originate from different branches of the phylogenetic tree, while the 1st-level metastasising subclone also gives rise to the 2nd-level non-metastasising subclone. Regions containing metastasising subclones are more enriched in the tumour centre. C) In case K446, the non-metastasising subclones are all originated from their parent metastasising subclones. Regions containing metastasising subclones are more enriched in the tumour centre and occupy more space. D) In case K280_2, the 1st-level metastasising subclone gives rise to a 2nd-level metastasising subclone and a 2nd-level non-metastasising subclone. Regions containing metastasising subclones are more enriched in the tumour centre and occupy more space.

Figure 4. Computational modelling supports the preferential localisation of SCNA subclones in more necrotic regions of a tumour.

A) Schematic illustrating the emergence of necrosis in a growing tumour. Tumour areas in yellow experience a high death rate due to necrosis. B) Schematic illustrating the definition of central versus margin areas in a simulated tumour. C) Spatial pattern of subclones in a representative simulated tumour. Tumour areas in yellow are necrotic. Founder clone is in grey while other subclones are in randomly assigned colours. D) Spatial pattern of loss_8p and loss_14q in the representative simulated tumour shown in (C). E) Spatial maps of the birth time of subclones relative to the age of the simulated tumour. Darker blue reflects later birth. F) Number of SCNAs in central (“At.Centre”, n=1280) versus marginal (“At.Margin”, n=568) biopsies. G) Number of SCNAs in less versus more necrotic regional biopsies. “Less necrotic” biopsies (n=931) refer to the regional biopsies with a necrotic fraction less than or equal to median while “More necrotic” biopsies (n=917) refer to those with necrotic fraction greater than median. The 20% most necrotic biopsies are excluded from analysis. H) Average subclone birth time relative to the age of the tumour in central (“At.Centre”) versus marginal (“At.Margin”) biopsies, analysed in simulations without necrosis (“no necrosis”) and those with necrosis (“necrosis”). For the “At.Centre” group, N = 1527 biopsies for “necrosis” simulations and N = 1922 biopsies for “no necrosis” simulations. For the “At.Margin” group, N = 568 biopsies for “necrosis” simulations and N = 572 biopsies for “no necrosis” simulations.

Figure 5. Integrated analysis of genomic and spatial distances.

A) A cartoon showing how spatial and genomic distances were measured. Spatial distances between each two biopsy regions were calculated using the spatial data previously retrieved, and genomic distances between each two biopsy regions were calculated based on the genomic alterations they harboured using Euclidean metrics. B) Correlation between spatial distance measured in millimetres and genomic distance across all samples is shown. C) Maximum spatial distance occupied by each subclonal driver event measured in millimetres is shown, ordered from the highest to the lowest. D) Maximum spatial distance occupied in millimetres versus subclonal frequency, showing the relationship between space occupied and timing of the event. Higher subclonal frequency indicates later occurrence of an event. In all boxplots in this figure the centre line is the median, the bounds of the box represent the inter-quartile range, the lower whisker = max(min(x), Q_1 – 1.5 × IQR) and upper whisker = min(max(x), Q_3 + 1.5 × IQR).

Figure 6. Phylogenetic trees and tumour maps showing the inferred clonal expansion pattern and tumour metastasising routes.

A) Most cases had a contiguous clonal expansion, while one case of clonal dispersal was seen in 2 out of 79 samples. B) A tumour map and a phylogenetic tree of case K096 showing a pattern of contiguous clonal expansion. C) Tumour maps and phylogenetic trees of cases G_K234 and D) K163 showing clonal dispersal (dotted line). In case K234, the dispersing clone formed the metastasising branch and gave rise to its descendant clone which was also found in a venous thrombus. In case K163, the dispersing clone wasn’t associated with metastasis.