Predicting resistance to chemotherapy using chromosomal instability signatures Joe Sneath Thompson1,2,*, Laura Madrid2,*, Barbara Hernando1,*, Carolin M. Sauer3, Maria Vias3, Maria Escobar-Rey1,2, Wing-Kit Leung2,3, Diego Garcia-Lopez2, Jamie Huckstep3, Magdalena Sekowska3, Karen Hosking4,5, Mercedes Jimenez-Linan5,6, Marika A. V. Reinius3,5,6, Abhipsa Roy2, Omar Abdulle2, Justina Pangonyte3, Harry Dobson2, Amy Cullen2,3, Dilrini De Silva2, David Gómez-Sánchez1,7, Marina Torres1, Ángel Fernández-Sanromán1, Deborah Sanders3, Filipe Correia Martins3,5,6, Ionut-Gabriel Funingana3,4,5, Giovanni Codacci-Pisanelli3,4,8, Miguel Quintela-Fandino1, Florian Markowetz2,3,4, Jason Yip2, James D. Brenton2,3,4,5,6, Anna M. Piskorz#,2,3, Geoff Macintyre#,1,2 1 Spanish National Cancer Research Centre (CNIO), Madrid, Spain 2 Tailor Bio Ltd, Cambridge, UK 3 Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK 4 Department of Oncology, University of Cambridge, Cambridge, UK 5 Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK 6 Cancer Research UK Major Centre - Cambridge, University of Cambridge, Cambridge, UK 7 H12O-CNIO Lung Cancer Clinical Research Unit, Health Research Institute Hospital 12 de Octubre (imas12), Madrid, Spain 8 University of Rome "la Sapienza", Rome, Italy
The dataset comprises targeted deep methylation sequencing data used for both tissue-of-origin determination and donor-derived cfDNA quantification. It includes plasma cfDNA data from stable kidney transplant recipients (n = 31), stable liver transplant recipients (n = 20) and healthy controls (n = 23). In addition, plasma cfDNA samples collected early after transplantation were analyzed from kidney transplant recipients (n = 44) and liver transplant recipients (n = 40). The dataset also features genomic DNA data generated from whole blood (n = 10) and buffy coat (n = 3) from healthy controls, as well as from T cells (n = 1), B cells (n = 2), hepatocytes (n = 1) and kidney epithelium (n = 1). Lastly, genomic DNA data was generated from buffy coat of transplant recipients (n = 17). Here, “n” indicates the sample number.
To overcome the challenges of low DNA yields, degraded DNA by formalin fixation and diluted signal of genomic aberrations by non-carcinoma components in the heterogeneous FFPE samples, we isolated pure carcinoma and stromal cells using the DEPArray™ NxT system, a microchip-based digital sorter that allows isolation of pure, homogeneous subpopulations of cells from FFPE samples. We isolated pure carcinoma and stromal cell populations from 12 FFPE tissues, including tissues from 9 primary and metastatic breast cancer and 3 primary ovarian high-grade serous carcinomas. This was followed by downstream shallow whole genome sequencing (WGS) for copy number landscape profiling for 10 samples. Seven out of 10 samples (even some with low tumour content or of old age) produced good quality genomic data, detecting sCNA in all carcinoma population samples but not in the stromal populations.
This study investigates the clonal evolution and metastatic dissemination of prostate cancer using multiregional single-nuclei RNA sequencing (snRNA-seq) and low-pass whole-genome sequencing (WGS) data from 43 spatially distinct tumor areas in five patients with locally advanced prostate cancer, including both primary and regional lymph node samples. We employed the Chromium Next GEM Single Cell 3’ Kit (v3/3.1, 10x Genomics) for single-nuclei transcriptome profiling and constructed bulk WGS libraries using the NEBNext Ultra II FS DNA Library Prep Kit (New England Biolabs) from the same pool of nuclei extracted from each area. snRNA-seq data were aligned to the human genome (GRCh38-1.2.0_premrna) using 10x Genomics Cell Ranger v5.0.0, while WGS reads were aligned to the human reference genome (GRCh38) with BWA MEM, achieving an average sequencing depth of 0.3X for low-pass WGS and 16X after resequencing.
Highly purified mesenchymal cells (CD45-/7AAD-/CD235a-/CD31-/CD271+/CD105+) were prospectively FACS-isolated from bone marrow specimens of 45 low-risk myelodysplastic syndrome (LRMDS) cases. Gene expression profiles (GEPs) of the 45 LRMDS have been compared to GEPs derived from likewise highly purified mesenchymal cells obtained from bone marrow specimens of healthy donors for the identification of inflammatory signatures. Additionally, an overlap in inflammatory signatures has been determined by comparing the GEPs of these 45 LRMDS cases to the GEPs of 4 Shwachman-Diamond syndrome and 3 Diamond-Blackfan anemia cases, both representing different subclasses of congenital pre-leukemia syndromes with a tendency of leukemic progression and perturbed niche compartment. Finally, the GEPs and gene expression signatures have been utilized for prognostication and the prediction of leukemic progression.
