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• Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+ T-myeloid cell networks in melanoma

  Adoptive cell therapy (ACT) using ex vivo–expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell–intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor–reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network–based biomarkers that could improve patient selection and guide the design of ACT clinical trials.

  Study EGAS50000001217

• EGAD00010000648

  nccRCC tumor/normal genotypes

  Dataset EGAD00010000648

• HLA-DQ?1 insertion

  Achalasia cases & controls

  Dataset EGAD00010000858

• SEA_610K

  SEA 610K

  Dataset EGAD00010000912

• Whole Genome Sequencing of HCC

  Dataset EGAD00001009743

• Institut Curie Neuroblastoma Whole Genome Sequencing Diagnosis Relapse

  Neuroblastoma, a clinically heterogeneous pediatric cancer, is characterized by distinct genomic profiles but few recurrent mutations. As neuroblastoma is expected to have high degree of genetic heterogeneity, study of neuroblastoma's clonal evolution with deep coverage whole-genome sequencing of diagnosis and relapse samples will lead to a better understanding of the molecular events associated with relapse. Samples were included in this study if sufficient DNA from constitutional, diagnosis and relapse tumors was available for WGS. Whole genome sequencing was performed on trios (constitutional, diagnose and relapse DNA) from eight patients using Illumina Hi-seq2500 leading to paired-ends (PE) 90x90 for 6 of them and 100x100 for two. Expected coverage for sample NB0175 100x100bp was 30X for tumor and constitutional samples. For the seven other patients expected coverage was 80X for tumor samples with PE 100x100, 100X in the other tumor samples and 50X for all constitutional samples (see table 1). Following alignment with BWA (Li et al., Oxford J, 2009 Jul) allowing up to 4% of mismatches, bam files were cleaned up according to the Genome Analysis Toolkit (GATK) recommendations (Van der Auwera et al., Current Protocols in Bioinformatics, 2013, picard-1.45, GenomeAnalysisTK-2.2-16). Variant calling was performed in parallel using 3 variant callers: GenomeAnalysisTK-2.2-16, Samtools-0.1.18 and MuTect-1.1.4 (McKenna et al., Genome Res, 2010; Li et al., Oxford J, 2009 Aug; Cibulskis et al., Nature, 2013). Annovar-v2012-10-23 with cosmic-v64 and dbsnp-v137 were used for the annotation and RefSeq for the structural annotation. For GATK and Samtools, single nucleotide variants (SNVs) with a quality under 30, a depth of coverage under 6 or with less than 2 reads supporting the variant were filter out. MuTect with parameters following GATK and Samtools thresholds have been used to filter our irrelevant variants. .SNVs within and around exons of coding genes overlapping splice sites.. Then,variants reported in more than 1% of the population in the 1000 genomes (1000gAprl_2012) or Exome Sequencing Project (ESP6500) have been discarded in order to filter polymorphisms. Finally, synonymous variants were filtered out. MuTect focuses on somatic by filtering with constitutional sample. Mpileup comparison between constitutional and somatic DNAs allowed us to focus also on tumor specific SNVs with GATK and Samtools. Finally, every SNV called by our pipeline and also supported in any constitutional samples were filtered our in order to prevent putative constitutional DNA coverage deficiency. Then we analyzed CNVs (copy number variants) with HMMcopy-v0.1.1 (Gavin et al., Genome Res, 2012) and control-FREEC-v6.7 (Boeva et al., Bioinformatics 2011) with a respective window of 2000bp and 1000 bp, and auto-correction of normal contamination of tumor samples for Control-FREEC. Finally we explored Structural variants (SVs) including deletions, inversions, tandem duplications and translocations using DELLY-v0.5.5 with standard parameters (Rausch et al., Oxford J, 2012). In tumors, at least 10 supporting reads were required to make a call and 5 supporting reads for the sample NB0175 with a coverage of only 40X (see table 2). To predict SVs in constitutional samples for subsequent somatic filtering, only 2 supporting reads were required in order not to miss one. To identify somatic events, all the SVs in each normal sample were first flanked by 500 bp in both directions and any SVs called in a tumor sample which was in the combined flanked regions of respective normal sample was removed (see graph 1). Deletions with more than 5 genes impacted or larger than 1Mb and inversions or tandem duplications covering more than 4 genes, were removed. We focused on exonic and splicing events for deletions, inversions, and tandem duplications. For translocation, we keep all SVs that occurred in intronic, exonic, 5'UTR, upstream or splicing regions. Bioinformatics detection of variations with Deep sequencing approach Once PE reads merged and adaptors trimmed by SeqPrep with default parameters, merged reads were aligned via the BWA (Li H. and Durbin R. 2009 PMID 19451168) allowing up to 1 differences in the 22-base-long seeds and reporting only unique alignments. Only reads having a mapping quality 20 or more have been further analysed. Variant calling software was not used, since we aimed to predict variations at low frequencies, observed in less than 1% of reads. Such variants require a custom approach. Using DepthOfCoverage functions of the Genome Analysis Toolkit (GATK) v2.13.2 (McKenna A, et al., 2010 Genome Research PMID: 20644199), we focused on high quality coverage of bases A, C, G and T at the targeted variant position. Depth of coverage of each base following a mapping quality higher than 20 and a base quality higher than 10 have been taken into account in order to focus only on high quality data. Aiming to determine the background level of variability at the studied regions, 10 control samples were included in the analysis. The same approach and filtering criteria have been applied as introduced above over the entire amplicons. In order to highlight variants, for each sample the frequencies of each bases at each amplicon position were then compared to those observed in the set of controls. Statistical analyses were performed with the R statistical software (http://www.R-project.org). Fisher’s exact two-sided tests with a Bonferroni correction were performed to compare percentages of bases between the data sets, i.e. for a given base between a case and the controls. Finally, significant variations were filtered-in once (i) a significant increase in the percentage of avariant base and (ii) a significant decrease in the percentage of it's reference base following our p.values criteria was observed (p.val < 0.05).

  Dataset EGAD00001001356

• Cooperative Study of Sickle Cell Disease (CSSCD)

  The Cooperative Study of Sickle Cell Disease was initiated in 1977 to determine the natural history of sickle cell disease (SCD) from birth to death in order to identify those factors contributing to the morbidity and mortality of the disease. Specific objectives included: 1) to study the effect of sickle cell disease on growth and development from birth through adolescence 2) to study the conditions or events that may be related to the onset of painful crises 3) to obtain data on the nature, duration, and outcome of major complications of SCD 4) determine the nature, prevalence, and age- related incidence of organ damage due to SCD, and 5) study the role of SCD and its interaction with selected health events.Phases 2 and 3 of the study involved followup of the infant cohort. A total of 709 infants (age less than 6 months) were enrolled during Phase 1 of the Cooperative Study of Sickle Cell Disease (CSSCD), and Phases 2 and 3 of the CSSCD was designed to follow these children for an additional 10 years. The study objectives included: 1) define prospectively the natural history of sickle cell disease; 2) determine the relationships between cognitive and academic functioning and brain status as determined by MRI; 3) determine the cognitive or behavioral markers of silent infarct; 4) determine the relationship of family functioning on the Family Environment Scale (FES) to brain status, cognitive functioning, and social and demographic factors; 5) continue studies that will enhance the state of knowledge on the influence of sickle cell disease on the psychosocial adjustment of children and adolescents. Phase 2A of the study sought to examine the progression of organ damage in the heart, lung, kidney, and liver in adult cohort patients (born before 1/1/56) enrolled in phase 1 of the study between 3/79 and 5/81. A total of 620 patients from 11 centers were eligible for phase 2A.Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

  Study phs002362

• Methylation biomarker study of magnesium deficiency and colorectal cancer

  Five US studies using the Mg tolerance test, the "gold standard" test of Mg status, indicated that >50% participants had Mg deficiency. In our ongoing US trial, we have found a similar result. In growing recognition of the importance of Mg in human health, very recently, Mg was selected by the US Federal Dietary Reference Intake (DRI) Committee to update the DRI. In US and other populations at high risk of Mg deficiency, high Mg intake has been linked to reduced risk of colorectal neoplasia, insulin resistance, metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD). Conversely, in populations not at high risk of Mg deficiency, high Mg intake has been related to increased risk of total mortality (e.g. total cancer, colorectal cancer, and CVD), particularly when calcium intake is low. These observations suggest that the associations between high Mg intake and disease risks may completely differ by the underlying Mg status. Due to major limitations, the Mg tolerance test is not used in conventional clinical practice and rarely used in research. Instead, serum Mg is used for clinical diagnosis. However, serum Mg performs very poorly at identifying those with Mg deficiency. There is a great need to develop implementable, sensitive, and specific biomarkers which can be easily used for identifying people with Mg deficiency. Without such a marker, it is impossible to develop effective strategies that minimize adverse effects by targeting those who, as a result of Mg deficiency, are at risk of common diseases. It is known that DNA methylation changes are inducible by environmental exposures, including nutrients, and reversible when the exposure disappears. There are two major types of cytosine modifications in DNA, 5-hydroxymethylcytosine (5-hmC) and 5-methylcytosine (5-mC). 5-mC is often associated with suppressed gene expression. 5-hmC, generated by the oxidation of 5-mC, is specifically enriched in expressed genes and plays a critical role in activating and/or maintaining gene expression. However, current epigenomic profiling platforms cannot distinguish 5-hmC from 5-mC. Just recently, our collaborator, Dr. Chuan He (HHMI Investigator), established a novel and state of the art technique, TAB-Seq and TAB-Array protocol, which can separate 5-hmC from 5-mC in the genome. Our pilot studies indicate that Mg intake or treatment significantly affects methylation capacity, 5-mC and 5-hmC biomarkers. Our findings suggest blood leukocyte 5-hmC/5-mC biomarkers may serve as more sensitive biomarkers to identify Mg deficiency than serum Mg. Thus, a comprehensive epigenome-wide association study (EWAS) may help to identify the most sensitive 5-hmC/ 5- mC biomarkers for Mg deficiency. We propose to identify 5-hmC/5-mC biomarkers for Mg deficiency by a 4- phase EWAS study in the "Personalized Prevention of Colorectal Cancer Trial [PPCCT, R01CA149633; PI, Dai and Yu]" with a total of 240 participants. Mg tolerance test will be used as the gold standard. Finally, using newly identified biomarkers, we will evaluate if 12-week Mg treatment reduces TRPM7 expression, essential in Mg homeostasis and colorectal carcinogenesis, in rectal tissues only among those with Mg deficiency.

  Study phs002037

• Whole genome sequencing of circulating cell-free DNA on Illumina and Ultima platforms

  The dataset includes Illumina (n=1) and Ultima (n=92) sequencing of circulating cell-free DNA. The dataset was generated through standard whole genome sequencing (Illumina n=1 & Ultima n = 15) and using a duplex unique molecular identifier whole genome sequencing workflow (Ultima, n=77). This dataset includes cancer samples and cancer-free control samples.

  Dataset EGAD50000001234

• Synthetic - GDI synthetic dataset (Population 11 Finland, Subgroup 2)

  Synthetic - This dataset contains the pheno-clinical and genomic information of 42046 individuals from COVID Population 11 Finland, Subgroup 2. 2010 are affected by Phenotype 1, 2010 are affected by Phenotype 2, 188 are affected by Phenotype 1 and 2, and 37838 are control. The dataset also contains the information about the smoking habits of each individual.

  Dataset EGAD50000000955