Patient recruitment and sample collection
Patients were from the TRACERx Renal trial (NCT03226886), a multi-centre prospective longitudinal cohort study. The inclusion and exclusion criteria, along with full clinical, histological and follow-up data were described previously5,15. In brief, primary tumours were dissected along the longest axes and spatially separated regions sampled from the “tumour slice” using a 6mm punch biopsy needle. Primary tumour regions were labelled as R1, R2, R3… Rn with locations recorded.
Macroscopic photo acquisition, quality control and pathological review
Macroscopic photos (n = 102) were taken both before and after multi-regional sampling and were taken from directly above the tumour. Photos were then assessed by a renal pathologist (JIL) for quality control to ensure high-quality tumour images including the tumour boundary were visible. Cases in which the whole tumour couldn’t be imaged to high quality (n = 9), without a clearly defined boundary between tumour and normal tissues (n = 5), or without exact positions of the biopsy regions (n = 9) were excluded. In total, 79 tumour sections of 66 unique primary tumours were included in this study.
Boundary drawing and digital tumour map processing
Boundaries between tumour and normal tissues were marked independently by a renal pathologist (JIL), after which the boundaries, along with the biopsy regions were extracted into X- and Y-coordinates using WebPlotDigitizer24. 2-dimensional size in millimetres of the tumours was set in accordance with the pathology reports after surgery. For those cases with only maximum length available, the length along the axis perpendicular to that of the maximum length was re-scaled based on the length on the photos. Digital tumour maps were then generated using R (version 3.6.2)25.
Microscopic pathology review, immunohistochemical staining and digital image analysis of Ki67
As previously described, histological sections, along with the immunohistochemical staining and digital image analysis of Ki67 of each region in each case were evaluated by the same pathologist (JIL)5,15. Briefly, tumour type, tumour architecture, tumour grading and the presence of necrosis and microvascular invasion were determined. Immunohistochemical staining of Ki67 was performed on 468 biopsy regions using a fully automated system and ready-to-use optimized reagents according to the manufacturer’s recommendations (Ventana Discovery Ultra, Ventana, Arizona, USA). Mouse intestine tissue was used as positive control, and regions containing tumour tissue were identified and marked by a pathologist and subsequently scanned into digital images, which were then subjected to automated image analysis for Ki67 quantification. Results were depicted as total percentage of Ki67-positive nuclei.
Targeted sequencing and genomic data processing
DNA extraction and multi-regional panel sequencing were as previously described5,15. Driver gene panels (Panel_v3, Panel_v5 and Panel_v6) were used in this study. Panel_v3, Panel_v5 and panel_v6 included 110, 119 and 130 putative driver genes, respectively. Driver genes were selected from genes that are frequently mutated in TCGA and other studies2-4. Mutation and somatic copy number alteration (SCNA) calling, clonality estimation of genomic alterations and phylogenetic tree reconstruction were done as previously described5,15. Subclonal frequency was defined as the proportion of an event that appeared to be subclonal across all samples. A matrix with presence and absence of nonsynonymous and synonymous point mutations, dinucleotide substitutions (DNVs), INDELs and arm level SCNAs was created for each tumour, and all the events were clustered as previously described5,15.
Definition of tumour centre and tumour margin
Tumour centre was defined as the area within the tumour where the distance between a biopsy region and the tumour boundary was more than or equal to 1cm, while tumour margin was defined as the area within the tumour where the distance between a biopsy region and the tumour boundary was less than 1cm (Fig. 2a).
Spatial and genomic distance calculation
Spatial distance between biopsy regions was measured using the Euclidean metric (Fig. 3a). Each biopsy region was represented as a point on the tumour map, with the X- and Y-coordinates referring to the actual position on the macroscopic photos. Distance between each biopsy region to the tumour boundary was calculated as the Euclidean distance between the biopsy region and its nearest boundary.
Genomic distance was calculated by taking the Euclidean distance of the detected genomic events (non-synonymous mutations and SCNAs). Mutations and SCNAs of all biopsy regions on a slice were represented as a matrix with 1 defined as an existing event and 0 as a non-existing event. Pairwise Euclidean distances were then calculated between all biopsy regions.
Calculation of space occupied by driver events
Driver event status of all biopsy regions was encoded in a matrix with 1 defined as an existing event and 0 as a non-existing event. The space occupied by a driver event was defined as the maximum spatial distance between any two regions containing the same driver event on a tumour slice, measured in millimetres.
Inference of metastasising routes on tumour maps
A matrix for all biopsy regions of all samples showing the relationship of clusters and biopsy regions was generated. Clusters containing nonsynonymous and synonymous point mutations, DNVs, INDELs and arm level SCNAs that appear in all the biopsy regions in a tumour were considered truncal clones, while clusters containing nonsynonymous and synonymous point mutations, DNVs, INDELs and arm level SCNAs that appear only in part of the biopsy regions in a tumour were considered subclones. The parent and child relationship of clusters were based on whether the genetic alterations each cluster contained were detected in other clusters. A secondary subclone was a cluster that contained not only point mutations, DNVs, INDELs and arm level SCNAs detected in the truncal clone, but also point mutations, DNVs, INDELs or arm level SCNAs that were not found in the truncal clone. A third subclone was a cluster that contained not only those genomic alterations detected in a secondary subclone, but also genomic alterations that were not found in the secondary subclone. The parent-and-child relationship went on and on until it reached a terminal clone which harboured genomic alterations that were not shared by any other clusters. Metastasising routes were inferred on tumour maps based on this parent-and-child relationship. Levels of the biopsy regions were set depending on the highest-level subclone it contained. Regions containing a parent clone as the highest-level subclone would point to regions containing a child clone as the highest-level subclone, while regions containing same levels of subclones were lined without arrows.
Definition of clonal expansion patterns
Types of clonal expansion patterns were defined based on the metastasising routes on the tumour maps. Contiguous growth was defined as the growth pattern where nearest subclones from the same branch of the phylogenetic tree were lined one by one, without any subclones from the other branch in between. Clonal dispersal was defined as the growth pattern where nearest subclones from the same branch of the phylogenetic tree crossed the border formed by the subclones from the other branch.
Circularity score calculation
Circularity score was calculated to assess the regularity of tumour boundaries:
![](https://myfiles.space/user_files/44073_bbc9ffc5e562ebe7/44073_custom_files/img1598627230.png)
where area and perimeter of the tumour slice were captured based on the tumour maps using ImageJ version 1.52q. A score of 1 indicates perfect circularity, and lower scores indicate increasing irregularity in shape.
Computational modelling
Simulation of tumour growth and evolution, as determined by the 26 ccRCC drivers, and in the context of necrosis, was achieved by the development of a coarse-grained cellular automata model (12 driver genes and 14 SCNAs). A model tumour comprises units called tumour voxels, each of which reflects a volume of 1 mm3. In the basic model, growth takes place in the form of probabilistic voxel duplication. A tumour voxel can only duplicate if surrounded by at least one empty site. Upon growth, a tumour voxel randomly acquires additional driver events.
The growth probability of a tumour voxel in the model further depends on the drivers harboured within. Two levels of relative driver advantage in growth are set and reflect the ranking of driver advantages by their association with regional Ki67 expression in the TRACERx Renal study. Four SCNAs (1q gain, 7q gain, 4q loss, and 9p loss) endow a tumour voxel with the strongest growth advantage; a tumour voxel harbouring any of these SCNAs grows with a probability of 1.0 every simulation step. Another five SCNAs (8q gain, 12p gain, 20p gain, 1p loss, and 14q loss) endow a tumour voxel with a lesser growth advantage; a tumour voxel harbouring any of these SCNAs, but not none of the above four strongest SCNAs, grows with a probability of 0.5 every simulation step. If a tumour voxel doesn’t harbour any of these nine drivers, it grows with a probability of 0.25 every simulation step.
The acquisition probability of driver gene mutations is 1×10-4 per simulation step. Mutations in two driver genes, BAP1 and PBRM1, are defined to promote the acquisition of SCNAs. In tumour voxels harbouring these two mutations, the acquisition probability of SCNAs is also 1×10-4 per simulation step. Otherwise, the acquisition of SCNAs is defined to take place with a probability of 1×10-7 per simulation step. The loss of 3p and VHL in the model are defined to be always truncal and already harboured in the first tumour voxel.
Extending the basic model, we implemented central necrosis to explore the impact of enhanced cell death at the tumour core on tumour growth and, of particular interest, the evolution of SCNA-harbouring subclones. Specifically, we defined that all tumour voxels located farther than 1.5 cm from the tumour surface become necrotic with a probability of 0.5 every simulation step. The chosen distance threshold of 1.5 cm reflects the clinical observations of distances between any regional biopsies having necrosis and the lack of necrosis at the tumour contour (mean: 12.83 mm; range: 3.71 – 27.11 mm). If a tumour voxel is chosen to undergo necrosis, the site occupied by that tumour voxel is labelled as necrotic; consequently, adjacent tumour voxels are allowed to grow and fill in the necrotic site.
Each simulation is performed in three dimensions (3D) until reaching a size of at least 1 million tumour voxels after the last simulation step. Upon completion, a two-dimensional (2D) slice is collected from the middle of the 3D simulated tumour in 3D. Within the tumour slice in 2D, regional biopsies (each with a size reflecting a diameter of 1 cm) are collected uniformly, with a spacing of 1 cm. The necrotic status and driver composition within each regional biopsy, from these simulations, are recorded for further analysis.
The necrotic fraction of a regional biopsy is calculated as the number of tumour voxels that are necrotic divided by the total number of tumour voxels in that biopsy. For the comparison of the number of SCNAs within each region, all regional biopsies are ranked according to the necrotic fraction and grouped into “less necrotic” (necrotic fraction between 0 and 0.4) and “more necrotic” (necrotic fraction between 0.4 and 0.8) categories. Due to the small amount of alive tumour voxels, biopsies with a necrotic fraction greater than 0.8 were dropped from the analysis. The number of SCNAs in a regional biopsy is defined as the number of unique SCNAs found in any tumour voxels in that biopsy.
Statistical analyses
Non-parametric Wilcoxon signed-rank test was used to compare medians of groups of continuous variables. Fisher’s exact test was used to compare proportions of counts of categorical variables. Non-parametric Spearman’s rank correlation coefficient was used to assess the relationship between pairs of variables, as opposed to testing for a difference in medians between two continuous variables. Kaplan-Meier survival curves and log-rank tests were used to analyse patients’ survival. Progression-free survival was defined as the time to disease recurrence or relapse, or death without disease recurrence, whichever came first. Overall survival was defined as the time to cancer specific death. Significance level was set 0.05. All statistical analyses were conducted using R version 3.6.2.