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Correspondence to: Dr Sara G.M. Piccirillo, Division of Hematology and Oncology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA. Tel: +1-2146489691;
Dr Andrea Sottoriva, Evolutionary Genomics and Modelling Lab, Centre for Evolution and Cancer, The Institute of Cancer Research, 15 Cotswold Road, Sutton, London, Surrey SM2 5NG, UK. Tel: +44-2087224072;
Glioblastoma is the most common and aggressive adult brain malignancy against which conventional surgery and chemoradiation provide limited benefit. Even when a good treatment response is obtained, recurrence inevitably occurs either locally (∼80%) or distally (∼20%), driven by cancer clones that are often genomically distinct from those in the primary tumour. Glioblastoma cells display a characteristic infiltrative phenotype, invading the surrounding tissue and often spreading across the whole brain. Cancer cells responsible for relapse can reside in two compartments of residual disease that are left behind after treatment: the infiltrated normal brain parenchyma and the sub-ventricular zone. However, these two sources of residual disease in glioblastoma are understudied because of the difficulty in sampling these regions during surgery.
Patient and methods
Here, we present the results of whole-exome sequencing of 69 multi-region samples collected using fluorescence-guided resection from 11 patients, including the infiltrating tumour margin and the sub-ventricular zone for each patient, as well as matched blood. We used a phylogenomic approach to dissect the spatio-temporal evolution of each tumour and unveil the relation between residual disease and the main tumour mass. We also analysed two patients with paired primary-recurrence samples with matched residual disease.
Our results suggest that infiltrative subclones can arise early during tumour growth in a subset of patients. After treatment, the infiltrative subclones may seed the growth of a recurrent tumour, thus representing the ‘missing link’ between the primary tumour and recurrent disease.
These results are consistent with recognised clinical phenotypic behaviour and suggest that more specific therapeutic targeting of cells in the infiltrated brain parenchyma may improve patient’s outcome.
In this study, we investigate the evolution of residual disease in glioblastoma that is left behind after surgery, in particular cancer cells in the infiltrative tumour margin and in the sub-ventricular zone. We found that residual disease samples contained early precursor cancer clones that were involved in the development of the disease and may contribute to recurrence.
Glioblastoma (GB) is a lethal brain cancer against which effective therapeutic options are lacking [
]. The disease aetiology and clinical course have distinct features compared with other cancers. Unlike other solid tumours, GB rarely metastasises outside the brain, but it invariably recurs, limiting the median survival to approximately 14 months [
]. In approximately 80% of aggressively treated patients, disease progression/recurrence occurs within 2 cm of the resection margin. In the remaining patients, even when complete surgical removal of the primary lesion was possible, the tumour recurs distally [
The accumulating clinical and genomic evidence suggests that infiltration may be a very early event in GB development, so that no matter how early a cancer is detected, it has already spread to distal regions of the brain, including the normal brain parenchyma and the SVZ (Figure 1A). After treatment (Figure 1B), infiltrative cells in the brain parenchyma and in the SVZ can drive relapse. Thus, even in the case of optimal surgery and when the tumour mass has been macroscopically resected, residual disease will trigger new growth, giving rise to recurrence, either locally or distally (Figure 1C). We argue that the recent seminal studies carried out on primary-recurrent-matched GB samples [
] point at the infiltrative population as the ‘missing link’, connecting the primary and the recurrent malignant clone in the evolution of the disease.
Patient cohort and samples
Sixty-nine tissue samples were collected from neurosurgical fluorescence-guided resections carried out on 10 IDH1 wildtype GB patients and one IDH1 mutant anaplastic astrocytoma patient (see supplementary Table S1, available at Annals of Oncology online for clinical information). Between five and nine multiple samples from the tumour mass (T, at least 1 cm apart), SVZ and infiltrative margin (M) areas were collected from each patient (Figure 2). Tumour mass samples were numbered as the surgery progressed and hence T4 samples tend to be deeper into the resection cavity than T1 samples. SVZ samples are taken after T4. In the case of the two primary/recurrence cases, three samples (T, SVZ and M) were taken during the primary and secondary surgical resections for a total of six specimens per patient. Thirty 10-μm cryosections were taken from each frozen tissue for DNA extraction using the DNeasy Blood & Tissue kit (Qiagen, Hilden, Germany). Patient informed consent was obtained and tissue collection/storage protocols were compliant with the UK Human Tissue Act 2004 and approved by the Local Regional Ethics Committee (LREC ref 04/Q0108/60). No difference in 5-ALA labelling capacity was observed between patients.
Whole-exome and targeted sequencing
Between 100 and 300 ng of DNA from each of the 69 tumour specimens and 11 blood samples were used for whole-exome sequencing using the Agilent SureSelectXT Human All Exon V5 Kit (Agilent Technologies, Santa Clara, CA, US). Median coverage was ×157 (min. ×108, max. ×187). A custom targeted sequencing panel for 891 single nucleotide variants (SNVs) (covered by a total of 5090 amplicons) identified from the exome sequencing data was designed using Agilent’s Haloplex technology (TES1). In addition, we designed a separate Agilent SureSelect XT2 capture panel to specifically validate 1054 SNVs found in the M and SVZ samples across all patients (TES2). Both amplicon (TES1) and targeted capture (TES2) libraries were sequenced on an Illumina HiSeq2500 obtaining a median coverage of ×4050 and ×1128, respectively, in reported variants (Figure 3A and supplementary Figure S1, available at Annals of Oncology online). Copy number alterations per sample are reported in Figure 3B. See supplementary Material and methods, available at Annals of Oncology online for details about bioinformatics analysis.
ITH in the tumour mass and residual disease
We carried out fluorescence-guided multi-region sampling of different regions from primary tumours (T1, T2, T3, …; Figure 2A) and also collected samples from the infiltrative margin M (n = 11; Figure 2B) and the SVZ (n = 15; Figure 2C). The margin is defined by non-fluorescent tissue beyond the fluorescent tumour mass. We reported previously that this area appears histologically as normal brain and is composed by only 5%–10% of tumour cells [
]. Clinical and follow-up information, as well as imaging was available (supplementary Table S1, available at Annals of Oncology online). Samples from the tumour mass and SVZ from 7/11 patients were common to our previous studies, for which we had carried out microarray copy number profiling and gene expression alone [
]. The margin samples are presented here for the first time.
In the present study, multi-region whole-exome sequencing (see supplementary Material and methods, available at Annals of Oncology online) identified extensive ITH at the level of SNVs. Heterogeneity at the level of SNV putative drivers (from ref. [
]) was evident in 5/11 patients, especially in EGFR, PIK3R1 and TP53 (Figure 3A and supplementary Figure S1, available at Annals of Oncology online). Copy number profiles inferred from the whole-exome sequencing confirmed the heterogeneity levels reported in previous studies (Figure 3B and supplementary Figure S2, available at Annals of Oncology online). Copy number events were highly recurrent, especially EGFR amplification (SP42, SP54, SP55, A23 and A34), chromosome 10 loss containing PTEN (all patients), and CDKN2A homozygous deletion (SP42, SP52, SP55, SP56, SP57 and A44), corroborating the findings from large scale studies [
Because the infiltrative margin (M) samples consist of scattered cancer cells in the surrounding normal brain, the purity of those samples was expectedly low (5%–10%). Purity is a confounding factor for calling mutations, leading to possible false-negatives that may impact the phylogenetic analysis [
]. To tackle this problem, we designed a second targeted sequencing panel (TES2) specifically to validate whether mutations that were present in all the other tumour samples were really absent from the infiltrative areas (indicating the margin or SVZ as an ancestral subclone). This panel confirmed that several mutations that appeared truncal to the tumour mass (putative truncal) were not present in the margin sample (Figure 3A and supplementary Figure S1, available at Annals of Oncology online, TES2 panel). In addition, to further support our results, we developed a statistical method to test whether genomic variants in targeted sequencing that are not found in the margin are likely to be truly absent, rather than being false negatives (i.e. in the second case, if there is no power to determine with reasonable certainty that the mutation is not present).
Our test scans mutations that were identified as ‘putatively truncal’ from the tumour mass samples (T1, T2, …). These mutations have high cancer cell fraction (CCF) in all T samples (Figure 4A, representative example of patient SP52). We reasoned that if the cancer subclones in the margin were just infiltrative cells deriving from the tumour mass—rather than an early ancestor—then ‘putative truncal’ mutations should also be found in the margin (‘truly truncal’). However, a failure to identify these mutations in the margin, despite the very high depth of sequencing of targeted panels, may occur as a false-negative owing to low purity. Similar to previous approaches [
], our statistical method accounts for the confounding factor of purity, and tests the null hypothesis that ‘putative truncal’ mutations are present in the margin, but miss to be detected in the margin. Rejecting the null indicates that these mutations are unlikely to be present in M.
We first fit a beta-binomial distribution to the set of putative truncal mutations, separated by copy number state (Figure 4B, see supplementary Material and methods, available at Annals of Oncology online for details). This provides the expected variant allele frequency of a mutation in a high purity sample, such as T1–4. This model allows calculation of the expected frequency of a mutant given any purity value, for example the 5% purity of the margin sample. We then examine the whole-exome and targeted sequencing data (TES1 and TES2) from margin samples and consider those putative truncal SNVs that were not detected by any assay (Figure 4C). Given the coverage achieved at the locus of the missing mutation, and the beta-binomial model trained on putative truncal SNVs, we can calculate the likelihood of the data assuming the null is true (i.e. the mutation is truly truncal but 0 mutant reads are found at the locus, for a given purity). Application of our method to our dataset revealed that a considerable proportion of testable mutations (see supplementary Material and methods, available at Annals of Oncology online for details) were unlikely to be present in the margin (P<0.05 using Bonferroni correction), even when we assumed the margin purity to be as low as 1% (Figure 4D). These results indicate that these SNVs are likely to be absent in M, and hence cannot be truncal. The method is potentially applicable to any genomic dataset to test true negatives (see supplementary Material and methods and Figure S3, available at Annals of Oncology online). The results of the test for all patients are reported in Figure 3A and supplementary Figure S1, available at Annals of Oncology online, left hand side of each heatmap. This allowed us to carry out a more reliable phylogenetic analysis for each patient that included residual disease in the SVZ and the infiltrative margin M.
Evolutionary trajectories suggest early ancestor clones within residual disease
Residual disease samples diverged early from the rest of the tumour mass in the majority of patients for which M samples were available, with particular evidence for SP49, SP52, SP57, A44, SP28 for which we could apply our test (Figure 5, testable mutations that did not pass our test were excluded from the phylogeny). Importantly, residual disease was also found at recurrence, demonstrating the presence of a reservoir of cancer cells in the infiltrative margin at relapse (patients A23 and SP28). In case A23, the primary tumour mass appeared to have originated from earlier cancer cell lineages located in the margin M and SVZ collected at primary resection. However, at the time of relapse, whereas both T and SVZ appear to have acquired additional new mutations (long branch), the ‘M recurrence’ lineage has remained similar to the primary tumour. A comparable pattern is observed in SP28 where ‘M recurrence’ also shows as an early residual clone present at relapse. Hence, early ancestral clones are present both at primary and recurrence and are not generally resected. We do acknowledge that the bulk tumour mass at relapse can also be driven by incomplete resection of the primary tumour due to the neoplastic tissue extending to vital parts of the brain that cannot be removed. We note that A23 and SP28 were local recurrences and more residual disease samples from distal recurrent tumours will need to be collected in the future.
As a whole, this analysis indicates that subclones present in M may arise early during tumour growth. Moreover, in 6/11 patients, the SVZ appeared also as an early ancestor, as we previously reported [
]. See supplementary Figure S4, available at Annals of Oncology online for bootstrapping values. We do acknowledge however that in those branches some mutations may be missing due to limits of detectability. To validate further these results, we also carried out single-allele methylation molecular clock analysis [
] on the same samples for a subset of patients, in particularly those where we had primary-recurrence pairs (supplementary Figure S5, available at Annals of Oncology online). Methylation molecular clock haplotyping is a single-molecule approach that allows reading the status of single CpGs in CpG island from the same DNA molecule. We have shown that some of these CpG island loci can be used for phylogenetic reconstruction, both colorectal cancer and GB [
]. Importantly, because this assay is ‘single-molecule’, alleles that come from non-proliferative normal cells (e.g. normal contamination from neurons in the margin samples) can be discarded because of their low methylation status. Therefore, this analysis does not suffer from the problems of tumour purity of the exome analysis (see supplementary Material and methods, available at Annals of Oncology online). Eliminating the methylation haplotypes coming from non-cancer cells allows reconstructing the tumour phylogeny orthogonally with respect to the exome trees. The results strikingly confirmed the structure of the phylogenetic trees inferred from exome sequencing, thus corroborating the results in Figure 5.
Interestingly, histopathology reports (pre WHO 2016 revision) are congruent with these phylogenetic data in multiple cases, for example SP49 GB with low-grade areas, SP52 and SP28 GB with oligodendroglial component. Together these data indicate a less aggressively proliferative phenotype at early stages of the evolution of the malignancy. After the primary tumour has been treated with radio- and chemotherapy, the quiescent residual subclones may trigger new growth and further clonal evolution, producing the divergence that has been observed by us and others between primary and recurrent samples in GB. This interpretation of the data is consistent with an early onset of tumour cell infiltration. Residual ancestral disease present in the SVZ and in the infiltrative margin is the source of the inevitable relapse that occurs in GB patients. This model is also consistent with the high incidence of multifocal lesions and the accumulating evidence of evolutionary divergence that is emerging from genomic data [
A key to understanding cancer is not just exposing ITH, the natural process that underlines clonal evolution, but also to understand such heterogeneity in a way that is clinically relevant and therapeutically tractable. An important aspect of genomic ITH is that it embeds the evolutionary history of the tumour, a fundamental biological element that cannot be directly measured in humans. Nevertheless, inferring and understanding that history may be critical in developing a rationale for combinatorial therapeutics [
Specifically, in this study, we leveraged the spatio-temporal decomposition of the clonal architecture of the tumour to understand the link between subclones in the main tumour mass and in residual disease left behind in the surrounding brain parenchyma and SVZ following surgery. This residual disease is a key factor contributing to GB treatment failure because of resistance to radiation and alkylating chemotherapy coupled with an inherent ability to seed re-growth. Therefore, the main message of this study is that residual disease and not just the main tumour mass [
] must be investigated in depth from the point of view of tumour evolution if we are to understand how treatment-resistant disease develops. We acknowledge that to study the mechanisms that link residual disease to tumour relapse, additional analysis is needed, especially of primary-recurrence pairs from distal relapses where also SVZ and M would be collected. However, this remains a technical and ethical challenge.
This observation presents a challenge to the GB research community to develop the tools and strategies needed to collect and robustly analyse difficult samples from residual disease areas in prospective cohorts. Due to the limited number of patients in this study, additional analyses on larger cohorts are necessary to validate these findings. We acknowledge that analysing sparse cancer cells in the margin remains challenging, even with the most advanced sequencing and bioinformatics approaches currently available. Further work is also needed to improve purification of margin samples, which is not currently possible due to lack of reliable markers to sort GB cells. Therefore, new efforts of collecting infiltrative cells that lay distant from the main tumour mass will be needed to study residual disease with more accuracy. To do this, post-mortem efforts such as the PEACE study (Postohumous Evaluation of Advanced Cancer Environment) are likely to play a key role in revealing the biology of infiltrative disease in GB.
We acknowledge support from the NIHR tissue bank at the University of Cambridge.
Data access: Sequence data have been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001003043. Further information about EGA can be found on https://ega-archive.org.
This work was supported by a Schottlander Research Charitable Trust Innovation Award to the Sottoriva Laboratory. AS is also supported by the Wellcome Trust (202778/B/16/Z), Cancer Research UK (A22909) and the Chris Rokos Fellowship in Evolution and Cancer. BW is supported by the Geoffrey W. Lewis Post-Doctoral Training fellowship. This work was also supported by a Wellcome Trust award to the Centre for Evolution and Cancer (105104/Z/14/Z). CW and SP are supported by The Brain Tumour Charity (10/136) and Cancer Research UK (C9221/A11869 and C9221/A16700).
The authors have declared no conflicts of interest.