Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics

Abstract

Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding treatment failure. However, the extent of intratumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multisampling scheme to collect spatially distinct tumor fragments from 11 GB patients. We present an integrated genomic analysis that uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Moreover, we reconstructed the phylogeny of the fragments for each patient, identifying copy number alterations in EGFR and CDKN2A/B/p14ARF as early events, and aberrations in PDGFRA and PTEN as later events during cancer progression. We also characterized the clonal organization of each tumor fragment at the single-molecule level, detecting multiple coexisting cell lineages. Our results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.

Fig. 1.

GB sample collection scheme. FGMS (Fluorescence-Guided Multiple Sampling) detects viable tumor tissue in bright pink color [(A): off; (B): on] while avoiding necrotic areas and normal brain tissue during surgery. (C) At the time of resection, multiple fluorescent tumor fragments (T1, T2, T3, …), ∼10 mm apart, were collected from 11 GB patients.

Fig. 2.

Landscape of intratumor heterogeneity at the copy number level. (A) Example of intratumor heterogeneity based on PDGFRA aberrations in patient sp49; whereas fragments T and T2 show no alterations, focal gain and amplification are evident in fragments T3 and T4, respectively (■, PDGFRA location). (B) For each patient, the number of fragments that exhibit CNAs in a putative driver gene is reported. In eight of nine patients, we found putative driver alterations that were not common to the whole tumor (enhanced borders), such as PDGFRA and PTEN. (C) CNAs were classified as common (found in all fragments), shared (found in more than one but not all fragments), and unique (found in only one fragment). All tumors displayed a large yet variable number of shared and unique aberrations. (D) The distribution of common/shared/unique altered probes delineates tumor evolution in the early/middle/late phases, respectively. Alterations focus on chr7 and chr10 as well as CDKN2A/B during an early phase, followed by a middle (shared) phase in which CNAs accumulate further on chr7 and 10, around the PDGFRA locus and on 19p13/12. Finally, during the late phase (corresponding to unique alterations), CNAs spread across the genome, with a peak on 4p16 (GLUT9).

Fig. 3.

Intratumor heterogeneity at the transcriptional level. (A) Clustering based on the gene expression profiles of the full set of 16,811 transcripts results in the grouping of most tumor specimens by patient, with three exceptions (sp42-T4, sp54-T3, and sp56-T3). (B) Despite the overall patient-specific signature, samples taken from the same tumor were classified into different GB subtypes in 6 of 10 patients. (C) Gene ontology analysis of the genes that exhibit intratumor heterogeneity in each of the 10 patients revealed that they were involved in biological processes related to neuron generation/development, cell morphogenesis, and tumor angiogenesis.

Fig. 4.

Multiple mitotic clones coexist within each GB fragment. (A) Fraction of the five most common mitotic clones in each tumor fragment based on molecular clock analysis of eight patients (sp57 was excluded because of PCR failure). (B) Phylogenetic reconstruction within each tumor fragment based on the molecular clock analyses. In these three representative cases, the existing mitotic clones are represented by the leaves of the tree, where a leaf´s thickness is proportional to the abundance of the clone within the fragment. The results indicate clonal heterogeneity within each fragment and the presence of multiple cell lineages that correspond to distinct mitotic clones.

Fig. 5.

Reconstruction of GB progression in time and space. The combination of sampling information (A), reconstructed tumor phylogeny (B), gene expression profiles, and molecular clock data enables temporal and spatial reconstruction of tumor ontogeny (C), as shown here for sp42. The evolution of the malignancy is illustrated by the accumulation of CNAs in different parts of the tumor and the corresponding variation in the gene expression profile, which reveal that T4 is classified as a different subtype (mesenchymal) with respect to the rest of the neoplasm (proneural). Moreover, we report the presence of variable numbers of subclones in each tumor fragment by molecular clock analysis.