Additional file 9: Figure S9. of DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer

Role of MAPK and PI3K pathways in hydroxyurea-induced APOBEC3 activation. a MCF10A cells were treated with the indicated drugs for 48 h followed by cell viability determination by CellTiter-Glo. b MCF7 cells were treated with the indicated drugs for 48 h followed by analysis of cell cycle distribution by FACS analysis. c MCF7 cells were transfected with PTEN siRNA. After 72 h the extent of knockdown was determined by quantitative PCR. d PTEN levels were depleted from MCF7 cells by RNAi. After 72 h cells were harvested and western blots were probed with the indicated antibodies. e MCF7 cells were transfected with PTEN siRNA or ubiquitin (UBB) control, followed by cell viability determination by CellTiter-Glo. f MCF7 cells were treated as in c followed by cell cycle distribution analysis by FACS. (TIF 35459 kb

Additional file 10: Figure S10. of DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer

Role for DNA damage signalling in APOBEC3 activation. a BT474 cells were treated with the indicated doses of Chk1 inhibitor CCT244747 followed by APOBEC3 cytidine deamination assay. b ATR and CHEK1 were depleted from MCF10A cells by RNAi. After 24-h transfection, cells were treated with hydroxyurea for a further 48 h prior to lysis and cytidine deamination assay for APOBEC3 actvity. c Validation of the extent of silencing of ATR and CHEK1 in MCF10A cells. Cells were treated with the indicated siRNAs for 72 h followed by mRNA isolation, cDNA synthesis and quantitative PCR for ATR and CHEK1 mRNA expression levels. d MCF10A cells were treated with hydroxyurea in the presence or absence of Chk1 inhibitor UCN-01 for 48 h prior to mRNA isolation, cDNA synthesis and quantitative PCR for APOBEC3B levels. e Eight breast cancer cell lines were treated with ten doses of CCT244747 for two population doublings followed by sulforhodamine B staining and GI50 determination. f MCF10A cells were treated with the indicated drugs for 48 h followed by cell viability determination using CellTiter-Glo. g BT474 cells were treated with 9 ÎźM CCT244747 followed by analysis of cell cycle distribution by FACS. h MCF10A cells were treated with the indicated drugs for 48 h followed by analysis of cell cycle distribution by FACS. (TIF 34524 kb

Additional file 7: Figure S7. of DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer

Role of receptor tyrosine kinase signalling in APOBEC3 activation. a BT474 and MDA-MB-361 cells were treated with RNAi targeting ERBB2. After 72 h, cells were harvested, lysed and western blots were probed with the indicated antibodies to determine the extent of ERBB2 silencing. b BT474 cells were treated with 10 nM afatinib or 30 nM lapatinib for 24 h followed by lysis. Western blots were probed with the indicated antibodies. c HCC1419 cells were treated with 2 mM hydroxyurea in the presence or absence of 30 nM lapatinib. Following mRNA isolation and cDNA synthesis, APOBEC3B mRNA expression levels were determined by quantitative PCR. d HCC1419 cells were treated as in c and, following lysis, oligonucleotide-based cytidine deamination assays were performed for APOBEC3 activity using probe 1. e HCC1419 cells were treated as in c and, following lysis, APOBEC3 cytidine deamination assays were performed using probe 2. f HCC1419 cells were treated as in c. Cells were lysed and western blots were probed with the indicated antibodies. (TIF 34667 kb

Workflow diagram giving overview of methodology.

<p>Workflow diagram giving overview of methodology.</p

Mutational signatures in TN1 and ER2, and ctDNA assays in ER2.

<p>(A) Reconstruction of mutational context plot with deconstructSigs for TN1 using COSMIC signatures 17, 24, and 29. 5′ and 3′ nucleotides are indicated by colour code on <i>x</i>-axis. (B) Contribution of each signature per sample. (C) Subclonal phylogeny for TN1 showing private and public subclones. Private subclones in dark blue. Mutations arising from signature 17 represented in pink along branches. (D) Reconstruction of mutational context for ER2 using APOBEC signatures 2 and 13 detected with deconstructSigs, with origin of key mutations overlaid. Colour codings as for (A). (E) Subclonal phylogeny for ER2, with private subclones in dark blue. (F) Bar charts show ctDNA results at time of liver biopsy and later at death. <i>y</i>-axis unit is copies per millilitre.</p

Genomic-transcriptomic evolution in lung cancer and metastasis.

Intratumour heterogeneity (ITH) fuels lung cancer evolution, which leads to immune evasion and resistance to therapy1. Here, using paired whole-exome and RNA sequencing data, we investigate intratumour transcriptomic diversity in 354 non-small cell lung cancer tumours from 347 out of the first 421 patients prospectively recruited into the TRACERx study2,3. Analyses of 947 tumour regions, representing both primary and metastatic disease, alongside 96 tumour-adjacent normal tissue samples implicate the transcriptome as a major source of phenotypic variation. Gene expression levels and ITH relate to patterns of positive and negative selection during tumour evolution. We observe frequent copy number-independent allele-specific expression that is linked to epigenomic dysfunction. Allele-specific expression can also result in genomic-transcriptomic parallel evolution, which converges on cancer gene disruption. We extract signatures of RNA single-base substitutions and link their aetiology to the activity of the RNA-editing enzymes ADAR and APOBEC3A, thereby revealing otherwise undetected ongoing APOBEC activity in tumours. Characterizing the transcriptomes of primary-metastatic tumour pairs, we combine multiple machine-learning approaches that leverage genomic and transcriptomic variables to link metastasis-seeding potential to the evolutionary context of mutations and increased proliferation within primary tumour regions. These results highlight the interplay between the genome and transcriptome in influencing ITH, lung cancer evolution and metastasis