Liquid biopsy analysis refers to methods designed to detect tumour-specific material (e.g., circulating tumour cells or tumour DNA) in body fluids, such as urine or blood samples. A widely-used liquid biopsy approach consists of genotyping the circulating tumour DNA (ctDNA) through sequencing of plasma/serum DNA. Although useful in the context of metastatic disease (where the concentration of ctDNA is high), current liquid biopsy technologies show limited sensitivity of detection for the early detection of cancer, and low specificity, as cancer-related mutations accumulate in healthy tissues as part of the ageing process, thus making it difficult to distinguish these from tumour mutations, and because sequencing errors and true mutations show overlapping profiles. Therefore, developing liquid biopsy protocols with increased sensitivity and specificity represents an urgent clinical need. Here we harness extrachromosomal circular DNA (eccDNA) elements, which are circular DNA structures physically separated from the chromosomes of up to several Mbp long pervasive in human cancers, for liquid biopsy analysis. In this pilot study we will focus on the analysis of glioblastomas, because there is strong evidence for the presence of eccDNA in these tumour types, and because developing liquid biopsy approaches for brain tumours to reduce the invasiveness of brain tumour biopsies remains an unmet clinical need.
10x Sequencing of 6 patient derived organoid cell models. Each model was derived from a piece of patient tumour taken following surgical rescetion of the tumour. All model derivations took place with the CGaP facility in Sanger. This 10x data will be combined with other sequencing data in order to generate accurate reference cancer genomes.
Brainstem gliomas are the most devastating and lethal tumors. Survival rates are among the lowest in all cancers and options for intervention are likewise low. Due to anatomical delicacy of these areas, resection of tumors is particularly difficult and attempted resections have high perioperative mortality rates. Genomic and epigenetic studies often provide a gateway to functional studies of specific classifications of tumors that can lead to major breakthroughs in diagnosis and treatment options.
To elucidate the timing and mechanism of the clonal expansion of somatic mutations in cancer-associated genes in the normal endometrium, we conducted whole-exome and whole-genome sequencing for 56 endometrial glands and matched blood samples from 4 women. By collecting endometrial glands from different parts of the endometrium, we showed that multiple glands with the same somatic mutations occupied substantial areas of the endometrium.
Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human. In this study we generated Whole Exome Sequencing (WES) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA.
To elucidate the timing and mechanism of the clonal expansion of somatic mutations in cancer-associated genes in the normal endometrium, we conducted target sequencing of 112 genes for 1,298 endometrial glands and matched blood samples from 36 women. By collecting endometrial glands from different parts of the endometrium, we showed that multiple glands with the same somatic mutations occupied substantial areas of the endometrium. The 112 genes are as follows: ABCC1, ACRC, ANK3, ARHGAP35, ARID1A, ARID5B, ATCAY, ATM, ATR, BARD1, BCOR, BRCA1, BRCA2, BRD4, BRIP1, CAMTA1, CDC23, CDYL, CFAP54, CHD4, CHEK1, CHEK2, CTCF, CTNNB1, CUX1, DGKA, DISP2, DYNC2H1, EMSY, FAAP24, FAM135B, FAM175A, FAM65C, FANCA, FANCB, FANCC, FANCD2, FANCE, FANCF, FANCG, FANCI, FANCL, FANCM, FAT1, FAT3, FBN2, FBXW7, FGFR2, FRG1, GPR50, HEATR1, HIST1H4B, HNRNPCL1, HOOK3, KIAA1109, KIF26A, KMT2B, KMT2C, KRAS, LAMA2, LRP1B, MLH1, MON2, MRE11A, MSH2, MSH6, MTOR, NBN, PALB2, PHEX, PIK3CA, PIK3R1, PLXNB2, PLXND1, PMS2, POLE, POLR3B, PPP2R1A, PTEN, PTPN13, RAD50, RAD51, RAD51B, RAD51C, RAD51D, RAD52, RAD54B, RAD54L, RICTOR, SACS, SIGLEC9, SLC19A1, SLX4, SPEG, STT3A, TAF1, TAF2, TAS2R31, TFAP2C, TNC, TONSL, TP53, TTC6, UBA7, VNN1, WT1, XIRP2, ZBED6, ZC3H13, ZFHX3, ZFHX4, ZMYM4.
Genomic determination for Homologous Recombination Deficiency (HRD) by shallow Whole Genome Sequencing (sWGS) with shallowHRD (PMID : 32315385) on 55 triple-negative breast cancer Patient Derived-Xenograft (PDX) treated with platinum.
While childhood cancer survivors are known to be at increased risk of subsequent neoplasms (SNs), the contribution of germline mutations in cancer predisposition genes to the risk of SN is largely unknown. Whole-genome sequencing (30X) was performed on 3,006 5+ year survivors of childhood cancer (median age, 35.8 [range: 7.1-69.8] years) from the St. Jude Lifetime Cohort. Survivors underwent a comprehensive clinical assessment. Germline mutations in 156 cancer predisposition genes (SJCPG156), including a subset of 60 (SJCPG60) autosomal dominant genes with moderate to high penetrance, were classified for their pathogenicity. Piecewise exponential regression, stratified by radiation exposure, was used to evaluate the relative rate (RR) and 95% confidence interval (95% CI) of SN occurrence by mutation status. Pathogenic or likely pathogenic mutations in SJCPG156 and SJCPG60 were identified in 11.7% and 5.8% of survivors, respectively. The most frequently mutated SJCPG60 genes included RB1 (n=43), NF1 (n=22), BRCA2 (n=14), BRCA1 (n=12) and TP53 (n=10). Mutations in SJCPG60 were associated with the rate of subsequent sarcoma (RR, 10.3; 95% CI, 4.2 to 25.6) and breast cancer (RR, 13.3; 95% CI, 5.8 to 30.5) among irradiated survivors, and the rate of developing any SN (RR, 4.8; 95% CI, 2.4 to 9.4) and breast cancer (RR, 7.8; 95% CI, 2.5 to 24.0) among non-irradiated survivors. The rate of developing ≥2 histologically distinct SNs was increased 17.8-fold (95% CI, 3.5 to 89.8) among SJCPG60 mutation carriers treated without radiotherapy. Similar but attenuated associations were observed for mutations in SJCPG156. Survivors who develop a SN without prior radiotherapy or who are diagnosed with a subsequent sarcoma or breast cancer within a site of prior radiotherapy should be referred to genetic counseling.
The project aims to look at mutational signatures in a rare inherited skin tumour syndrome called CYLD cutaneous syndrome. These patients develop multiple skin tumours that are seen at sun exposed and sun protected sites. We plan to carry out WGS on carefully curated tumours from such patients. We then plan to analyse this data for mutational signatures, comparing this between sun exposed and sun protected sites.
Purpose: Cell-free DNA (cfDNA) offers a non-invasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including MRD, recurrence, evolution, and second primary cancers. Experimental Design: Three simulation datasets were generated from 26 cancer patients to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n=76) from cancer patients (n=35) with six different cancer types were used for performance validation during various treatments.Results: We present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fraction ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fraction ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods.Conclusion: Our results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.
