<p>Supplementary experimental procedures: this file contains an expanded materials and methods section on 5-ALA administration and sample collection, cell lines propagation, immunofluorescence and in vivo assays, genomic and molecular clock analysis, fluorescence in situ hybridization, drug concentration used in the proliferation assay and MGMT promoter methylation analysis. Supplementary Figure S1: this figure illustrates the identification of the sub-ependymal zone (SEZ) in two GB patients included in this study. Supplementary Figure S2: this figure shows the sampling and morphology of the SEZ tissue. Supplementary Figure S3: this figure summarizes the results of real time analysis for markers of glial, precursor, stem cells and proliferation in paired T and SEZ from three GB patients. Supplementary Figure S4: this figure shows an example of common copy number aberration breakpoints between the SEZ and the corresponding T in each of the GB patients included in this study. Supplementary Figure S5: this figure summarizes the fluorescence in situ hybridization (FISH) results for EGFR, MET and PTEN in three GB patients. Supplementary Figure S6: this figure shows the copy number aberration breakpoints between T and SEZ and the corresponding cell lines in patient sp14. Supplementary Figure S7: this figure shows the results of growth curve analysis of tumor-initiating cells (TICs) derived from matched T and SEZ of five GBs. Supplementary Figure S8: this figure summarizes the results of clonogenic index between three paired T and SEZ cells and multipotency of SEZ cells. Supplementary Figure S9: this figure shows the immunofluorescence results for stem cell markers on T and SEZ cells in two GBs. Supplementary Figure S10: this figure summarizes the results of cumulative Kaplan-Meier survival analysis from five additional GBs. Supplementary Figure S11: this figure shows an analysis of the in vivo properties of TICs isolated from the SEZ of three GBs. Supplementary Figure S12: this figure shows the results of the cell proliferation assay of 7 paired TICs using 50μM of Temozolomide (TMZ). Supplementary Figure S13: this figure summarizes the cell proliferation data of additional 20 TICs treated with TMZ for dose escalation analysis. Supplementary Figure S14: this figure shows the results of cell proliferation assay of additional 4 paired TICs treated with TMZ, Cisplatin and Cediranib. Supplementary Figure S15: this figure shows a gel image of MGMT promoter status analysis for T and SEZ of the analyzed 7 paired TICs. Supplementary Figure S16: this figure summarizes the immunofluorescence results for vascular endothelial growth factor receptor 2 (Vegfr2) on T and SEZ cells from three GBs. Supplementary Figure S17: this figure summarizes the cell proliferation data of three control lines. Supplementary Figure S18: this figure illustrates a model of residual disease in human GB and summarizes the cardinal features of T and SEZ. Supplementary Figure S19: this figure shows the results of growth curve analysis of two SEZ in absence of fluorescence. Supplementary Table S1: this table summarizes the results of the histological features of T and SEZ tissues. Supplementary Table S2: this table provides a summary of the samples used for the genomic analysis and of the clinical information available for the eleven GB patients included in this study. Supplementary Table S3: this table shows the gene ontology (GO) terms whose genes are differentially expressed between SEZ and T. Supplementary Table S4: this table provides a summary of the results of MGMT methylation analysis by pyrosequencing in sixteen GBs. Supplementary Table S5: this table summarizes the overall survival of the eight GB patients whose TICs were used for the cell proliferation assay and includes the MGMT methylation by pyrosequencing. Supplementary Table S6: this table shows the gene expression data of PDGF and VEGF receptors in T and SEZ.</p>
Abstract Circulating tumour DNA (ctDNA) allows tracking of the evolution of human cancers at high resolution, overcoming many limitations of tissue biopsies. However, exploiting ctDNA to determine how a patient’s cancer is evolving in order to aid clinical decisions remains difficult. This is because ctDNA is a mix of fragmented alleles, and the contribution of different cancer deposits to ctDNA is largely unknown. Profiling ctDNA almost invariably requires prior knowledge of what genomic alterations to track. Here, we leverage on a rapid autopsy programme to demonstrate that unbiased genomic characterisation of several metastatic sites and concomitant ctDNA profiling at whole-genome resolution reveals the extent to which ctDNA is representative of widespread disease. We also present a methylation profiling method that allows tracking evolutionary changes in ctDNA at single-molecule resolution without prior knowledge. These results have critical implications for the use of liquid biopsies to monitor cancer evolution in humans and guide treatment.
Abstract Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade-offs. This weakness can be exploited in the clinic using an approach called ‘evolutionary herding’ that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 10 8 –10 9 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.
Abstract The dominant mutational signature in colorectal cancer genomes is C > T deamination (COSMIC Signature 1) and, in a small subgroup, mismatch repair signature (COSMIC signatures 6 and 44). Mutations in common colorectal cancer driver genes are often not consistent with those signatures. Here we perform whole-genome sequencing of normal colon crypts from cancer patients, matched to a previous multi-omic tumour dataset. We analyse normal crypts that were distant vs adjacent to the cancer. In contrast to healthy individuals, normal crypts of colon cancer patients have a high incidence of pks + (polyketide synthases) E.coli ( Escherichia coli ) mutational and indel signatures, and this is confirmed by metagenomics. These signatures are compatible with many clonal driver mutations detected in the corresponding cancer samples, including in chromatin modifier genes, supporting their role in early tumourigenesis. These results provide evidence that pks + E.coli is a potential driver of carcinogenesis in the human gut.
Abstract Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity 1 . The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather ‘plastic’. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis -acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.
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