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• Machine Learning Signal Enrichment for Ultrasensitive Plasma Tumor Burden Monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007451

• Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

  In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by 300X compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer (NSCLC) and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables de novo mutation calling in melanoma and small cell lung cancer (SCLC) without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition (ICI).

  Study EGAS00001007545

• Genetics of Human Inherited Retinal Diseases (GHIRD)

  The goal of this project is to identify novel mutations and genes involved in human retinal disorders, a stated priority of the National Eye Institute. To accomplish this, additional genes whose mutations cause inherited retinal diseases (IRD) will be identified by combining targeted gene panel sequencing, whole exome sequencing, and whole genome sequencing. We have collected about 1050 patient families around the world. In this proposal, we will identify the underlying mutations in these patients using a combination of targeted gene panel sequencing, whole exome sequencing, whole genome sequencing, bioinformatics, statistics, and functional studies. Genes targeted for gene panel sequencing are: ABCA4; ASIC2; JAG1; ABCC6; ARL3; ATP1B2; BBS1; BBS2; BBS4; OPN1SW; CAPN5; C21orf2; CA4; CACNA1F; CDH3; CHM; ERCC8; CLCN2; CLCN3; CLCN7; TPP1; CLN3; CLN5; CNGB1; CNGA1; CNGA3; COL2A1; COL9A1; COL11A1; CRX; VCAN; CTSD; TIMM8A; DMD; CLN8; ERCC6; EFEMP1; OPN1MW; GNAT1; GNAT2; GNGT1; GRM6; GUCA1A; GUCA1B; GUCY2F; GUCY2D; HARS; CFH; HK1; IDH3B; IMPDH1; IMPG1; KCNJ13; KIF11; LRP5; MAK; MITF; TRPM1; MTTP; MVK; MYO7A; NDP; NEK2; NEUROD1; NPHP1; NRL; OAT; OPA1; OTX2; PAX2; PAX6; PDCL; PCYT1A; PDE6A; PDE6C; PDE6G; PDE6H; PDE6B; PEX1; PEX6; PEX7; PEX10; PEX12; PEX13; PEX14; PFDN5; PGK1; PHYH; PIN1; PLA2G5; PPT1; PRKCZ; PEX19; PEX2; PEX5; RB1; RBP3; RBP4; OPN1LW; RDH5; PRPH2; RGR; RHO; GRK1; RLBP1; ROM1; RP9; RP1; RP2; RPGR; RPE65; RS1; SAG; ATXN7; CCL2; SLC6A6; ELOVL4; TEAD1; NR2F1; TIMP3; NR2E1; TTPA; TUB; TULP1; UCHL3; USH2A; CLRN1; VLDLR; BEST1; WFS1; ALMS1; MKKS; FZD4; OFD1; PEX3; CTSF; GBF1; ADAM9; RGS9; PEX11B; PROM1; UNC119; PRPF4; PRPF3; AIFM1; SLC24A1; LRAT; ASIC3; RAB28; PEX16; ITM2B; SLC4A7; IQCB1; CROCC; IFT140; DHX38; MFN2; NR2E3; USH1C; TOPORS; MERTK; FBLN5; CIB2; PRPF8; SDCCAG8; IFT27; TREX1; RRAS2; CEP164; ATF6; TRIM32; RIMS1; SNRNP200; CLUAP1; EMC1; ZNF423; TTLL5; AGTPBP1; RPGRIP1L; CRB1; TSPAN12; ARL2BP; AIPL1; PRPF6; FSCN2; ABHD12; PRPF31; TCTN3; IFT172; CNNM4; NPHP3; INVS; BBS9; FLVCR1; RDH8; IMPG2; WDPCP; RDH11; TMEM216; TMEM138; LZTFL1; CNGB3; AHI1; MKS1; CLN6; BBS7; POMGNT1; PEX26; SPATA7; KIZ; KLHL7; INPP5E; MDM1; CABP4; RPGRIP1; MCOLN1; CC2D2A; KIAA1549; SLC7A14; WDR19; NYX; CDH23; SEMA4A; MPP5; NMNAT1; TMEM237; PCDH15; C5orf42; GNPTAB; TMEM231; TCTN1; SRD5A3; BBS10; ZNF408; TTC21B; CSPP1; LPCAT1; DHDDS; PDZD7; PANK2; CEP290; OPA3; DNAJC5; PITPNM3; MFRP; HMCN1; ADGRV1; ARL6; FAM161A; TMEM126A; GNPTG; GJA10; RAX2; TUBGCP6; TMEM67; ACBD5; C12orf65; CDHR1; BBIP1; REEP6; CACNA2D4; RP1L1; CEP41; C1QTNF5; NXNL1; TTC8; USH1G; BBS5; ZNF513; RDH12; SLC38A8; C8orf37; VPS13B; NXNL2; BBS12; ADGRA3; LCA5; KCNV2; ADAMTS18; ARL13B; ATOH7; MFSD8; NPHP4; CCDC66; CYP4V2; CRB2; RD3; LRIT3; EYS; KIF7; CERKL; RGS9BP; C2orf71; GDF6; DTHD1; GPR179; CISD2; CCR2; PRCD; WHRN; ADGRA3. The current dbGaP study release makes available all sequencing-derived vcf data of study participants.

  Study phs001517

• Induction Failure in Childhood and Young Adult T-cell Acute Lymphoblastic Leukemia

  We carried out WGS and RNAseq on a cohort of 48 children and young adults with induction failure in T-cell Acute Lymphoblastic Leukemia (T-ALL) to identify genomic drivers of treatment resistance. The study includes WGS for 33 tumour/normal pairs and 15 tumour-only samples. In addition, there is RNAseq data for 37 cases.

  Dataset EGAD00001009058

• Targeted sequencing of cell-free DNA and white blood cells from 24 men with metastatic prostate cancer

  This dataset consists of aligned DNA sequencing data in BAM file format from cell-free DNA and white blood cells from 24 men with metastatic prostate cancer. One cell-free DNA sample and one white blood cell sample is available for each patient, resulting in 48 total BAM files in this dataset. The sequencing was performed using a hybrid capture-based targeted panel of 73 prostate cancer driver genes.

  Dataset EGAD00001004486

• The genomic landscape of follicular and diffuse large B-cell lymphoma

  As part of the ICGC, the ICGC MMML-Seq project performed whole genome sequencing and RNA sequencing of follicular and diffuse large B-cell lymphoma. Follicular and diffuse large B-cell lymphomas are the most common B-cell lymphomas in adulthood. Analyses were performed in concordance with the guidelines of the ICGC. The results define the genomic landscape of structural variants, somatic single nucleotide variants and mutational signatures in these lymphomas

  Study EGAS00001002199

• Aligned reads from specific genomic locations derived from Illumina HiSeqX and HiSeq2000 whole genome sequence data

  This dataset includes bam files from 3,001 samples. These bam files include all read pairs where at least one of the reads aligns within 1kb of the C9orf72 repeat expansion. Additionally, these bam files also contain reads that are aligned to any of 29 pre-determined off target locations where the aligners are known to mis-align reads associated with this repeat expansion. These samples were sequenced using a combination of 2x100bp reads on an Illumina HiSeq2000 and 2x150bp reads on an Illumina HiSeqX sequencer and aligned using the Isaac aligner.

  Dataset EGAD00001003513

• Whole_exome_sequencing_of_rare_autoimmune_related_phenotypes

  We aim to whole-exome sequence DNA samples from 75 individuals with severe forms of Inflammatory Bowel Disease and related autoimmune diseases to identify the rare, highly penetrant, variants that we believe underlie these phenotypes. Case samples will be obtained from both new and existing (UK IBD Genetics Consortium) collaborators to ensure only the most extreme cases are sequenced.

  Study EGAS00001000228

• Collection of Genotypic and Ethnographic Information from Individuals of South African Ethnic Groups

  The hunter-gatherers and pastoralists of South Africa retain the highest genetic diversity of any population. Genetic determinants of light skin pigmentation, reduced stature etc and other basic biomedical phenotypes are unique to samples with Southern African hunter-gatherer and pastoralist ancestry or retain ancestral haplotypes not found in other populations.

  Study EGAS00001004472

• ExHiBITT shows that microbiome from colon biopsies, caecal fluid from colonoscopies and faecal samples shape different microbiome-host interactions

  The objective of the colonoscopy study is to carry out the 16s sequencing of colon biopsies and faecal samples provided in ExHiBITT study to compare potential fluctuations in the microbiota of different sites.

  Study EGAS00001007313