Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions

Intratumour heterogeneity is a major cause of treatment failure in cancer. We present in-depth analyses combining transcriptomic and genomic profiling with ultra-deep targeted sequencing of multiregional biopsies in 10 patients with neuroblastoma, a devastating childhood tumour. We observe high spatial and temporal heterogeneity in somatic mutations and somatic copy-number alterations which are reflected on the transcriptomic level. Mutations in some druggable target genes including ALK and FGFR1 are heterogeneous at diagnosis and/or relapse, raising the issue whether current target prioritization and molecular risk stratification procedures in single biopsies are sufficiently reliable for therapy decisions. The genetic heterogeneity in gene mutations and chromosome aberrations observed in deep analyses from patient courses suggest clonal evolution before treatment and under treatment pressure, and support early emergence of metastatic clones and ongoing chromosomal instability during disease evolution. We report continuous clonal evolution on mutational and copy number levels in neuroblastoma, and detail its implications for therapy selection, risk stratification and therapy resistance.

TAMI-76. INTEGRATED MULTI-OMICS REVEAL INTRATUMOUR HETEROGENEITY AND NOVEL THERAPEUTIC TARGETS IN PAEDIATRIC EPENDYMOMA

Ependymoma (EPN) is the second most common malignant paediatric brain tumour with a five-year survival rate of only 25% following relapse. While molecular heterogeneity between EPN tumours is well understood, little is known concerning spatially-distinct intratumour heterogeneity within patients. In this context, we present a multi-omics integration of expression data at transcriptomic and metabolomic levels revealing intratumour heterogeneity and novel therapeutic targets. Surgically resected ependymoma tissue from two epigenetic subgroups, posterior fossa-A (PF-A) and supratentorial RELA, were first homogenised and polar metabolites, lipids and RNA simultaneously extracted from the same cellular population. Using liquid chromatography-mass spectrometry (LC-MS) and RNAseq 115 metabolites and 1580 upregulated genes were identified between the two subgroups, therefore validating previously reported genetic clustering of these two subtypes. Sampling of anatomically distinct regions was performed between eight PF-A EPN patients and multi-omic data was compared across 28 intratumour regions, with at least 3 different regions per patient. Integration of genes and metabolites revealed 124 dysregulated metabolic pathways, encompassing 156 genes and 49 metabolites. A large number of interactions occur in the gluconeogenesis and glycine pathways in 6 out of 8 patients, putatively representing therapeutically relevant ubiquitous metabolic pathways critical for EPN survival. Each anatomical region also presented at least one unique gene-metabolite interaction demonstrating heterogeneity within and across PF-A EPN tumours. A subset of the eight most prevalent genes across patients (GAD1, NT5C, FBP1, FMO3, HK3, TALDO1, NT5E, ALDH3A1) were selected for in vitro metabolic assays using 10 repurposed cytotoxic agents against PF-A EPN cell lines derived from intratumour regions of the same patient. 5/8 genes map within the gluconeogenesis metabolic pathway, further highlighting its significance within PF-A EPN. This is the first instance where multi-omic data integration and intratumour heterogeneity has been investigated for paediatric EPN revealing novel potential targets in the context of gene-metabolite correlations.

Intratumour heterogeneity in microRNAs expression regulates glioblastoma metabolism

While specific microRNA (miRNA) signatures have been identified in glioblastoma (GBM), the intratumour heterogeneity in miRNA expression has not yet been characterised. In this study, we reveal significant alterations in miRNA expression across three GBM tumour regions: the core, rim, and invasive margin. Our miRNA profiling analysis showed that miR-330-5p and miR-215-5p were upregulated in the invasive margin relative to the core and the rim regions, while miR-619-5p, miR-4440 and miR-4793-3p were downregulated. Functional analysis of newly identified miRNAs suggests their involvement in regulating lipid metabolic pathways. Subsequent liquid chromatography–mass spectrometry (LC–MS) and tandem mass spectroscopy (LC–MS/MS) profiling of the intracellular metabolome and the lipidome of GBM cells with dysregulated miRNA expression confirmed the alteration in the metabolite levels associated with lipid metabolism. The identification of regional miRNA expression signatures may underlie the metabolic heterogeneity within the GBM tumour and understanding this relationship may open new avenues for the GBM treatment.

The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.Author summaryThe individual cells within a cancer cell population are not all equal. The heterogeneity among them can strongly affect disease progression and treatment success. Recent diagnostic advances allow measuring how the characteristics of this heterogeneity change over time. To match these advances, we developed deterministic and stochastic trait-based models that capture important characteristics of the intratumour heterogeneity and allow to evaluate different treatment types that either do or do not interact with this heterogeneity. We focus on growth rate as the decisive characteristic of the intratumour heterogeneity. We find that by shifting the trait distribution of the cancer cell population, the growth rate-dependent treatment delays an eventual relapse compared to the growth rate-independent treatment. As a downside, however, we observe a refuge effect where slower-growing subpopulations are less affected by the growth rate-dependent treatment, which may decrease the likelihood of successful therapy. We find that navigating along this trade-off may be achieved by sequentially combining both treatment types, which agrees qualitatively with current clinical practice. Interestingly, even rather large intervals between treatment changes allow for close-to-optimal treatment results, which again hints towards a practical applicability.

Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer

Advanced tumours are often heterogeneous, consisting of subclones with various genetic alterations and functional roles. The precise molecular features that characterize the contributions of multiscale intratumour heterogeneity to malignant progression, metastasis, and poor survival are largely unknown. Here, we address these challenges in breast cancer by defining the landscape of heterogeneous tumour subclones and their biological functions using radiogenomic signatures. Molecular heterogeneity is identified by a fully unsupervised deconvolution of gene expression data. Relative prevalence of two subclones associated with cell cycle and primary immunodeficiency pathways identifies patients with significantly different survival outcomes. Radiogenomic signatures of imaging scale heterogeneity are extracted and used to classify patients into groups with distinct subclone compositions. Prognostic value is confirmed by survival analysis accounting for clinical variables. These findings provide insight into how a radiogenomic analysis can identify the biological activities of specific subclones that predict prognosis in a noninvasive and clinically relevant manner.