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• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 596 samples; filetype=bam

  Dataset EGAD00001004731

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90554C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1735 samples; filetype=bam

  Dataset EGAD00001004732

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90560A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 585 samples; filetype=bam

  Dataset EGAD00001004733

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90560B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 766 samples; filetype=bam

  Dataset EGAD00001004734

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90600C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 2055 samples; filetype=bam

  Dataset EGAD00001004735

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90648B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 620 samples; filetype=bam

  Dataset EGAD00001004736

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90682

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 624 samples; filetype=bam

  Dataset EGAD00001004737

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90685

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 481 samples; filetype=bam

  Dataset EGAD00001004738

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 378 samples; filetype=bam

  Dataset EGAD00001004739

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 735 samples; filetype=bam

  Dataset EGAD00001004740

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90689C

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 718 samples; filetype=bam

  Dataset EGAD00001004741

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90694A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 493 samples; filetype=bam

  Dataset EGAD00001004742

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90694B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1222 samples; filetype=bam

  Dataset EGAD00001004743

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A90706

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 522 samples; filetype=bam

  Dataset EGAD00001004744

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95621B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 488 samples; filetype=bam

  Dataset EGAD00001004745

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95622A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 509 samples; filetype=bam

  Dataset EGAD00001004746

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95624A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 604 samples; filetype=bam

  Dataset EGAD00001004747

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95632B

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 626 samples; filetype=bam

  Dataset EGAD00001004748

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95632D

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 635 samples; filetype=bam

  Dataset EGAD00001004749

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95660A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 1522 samples; filetype=bam

  Dataset EGAD00001004750

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires for library A95664A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 465 samples; filetype=bam

  Dataset EGAD00001004751

• Multi-omic and functional analysis for classification and treatment of sarcomas with FUS-TFCP2 or EWSR1-TFCP2 fusions(H021/INF)

  Many rare cancers are not well understood, and pathogenesis-directed therapies are often lacking, resulting in poor patient outcomes. Leveraging two (inter)national precision oncology trials that enroll large numbers of rare cancers, we investigated the clinical, histopathologic, molecular, and functional characteristics of rhabdomyosarcoma (RMS) with fusions of FUS or EWSR1 to the TFCP2 transcription factor, a recently discovered ultra-rare entity whose classification, pathogenesis, and optimal treatment are unclear. Unusually for fusion-driven sarcomas, most cases had highly rearranged genomes, including chromothripsis, and signs of defective homologous recombination DNA repair. All tumors showed extreme expression of a truncated TERT variant and the ALK receptor tyrosine kinase, which was additionally affected by intragenic deletions and aberrant splicing, resulting in the expression of shortened variants in 58% of cases. Three ALK variants were oncogenic in immortalized cells, and patient-derived tumor cells expressing two variants responded to certain ALK inhibitors. Other recurrent alterations included CDKN2A/MTAP co-deletions and mutations in PAPPA2, encoding an IGFBP5-specific proteinase, in 67% and 25% of cases, respectively. DNA methylation analysis, along with 19 other soft-tissue sarcoma classes, revealed a close relationship with undifferentiated sarcoma but not other RMS subtypes, suggesting that TFCP2-rearranged RMS is a separate entity, possibly arising from a distinct cell of origin. Expression of TFCP2 fusions in immortalized human cells blocked late myogenic differentiation and significantly induced genes that were also highly expressed in patient tumors, including ALK, TERT, and two known regulators of skeletal muscle cells, IGFBP5 and PTH1R. Genome-wide chromatin profiling demonstrated direct binding of FUS-TFCP2 to the ALK and TERT loci outside their regular promoters, which correlated with the expression of alternative transcript variants. Finally, FUS-TFCP2 inhibited the repair of DNA double-strand breaks in immortalized cells, rendering them sensitive to treatment with cisplatin. This study provides insight into the pathogenesis and therapeutic vulnerabilities of a new RMS subtype and illustrates the value of linking comprehensive molecular diagnostics and functional annotation within multicenter consortia to improve the understanding and clinical management of rare cancers.

  Study EGAS00001006939

• University of Pennsylvania CAR T Cell Responding and Non-responding Patients

  The adoptive transfer of autologous T cells genetically modified to express a CD19-specific, 4-1BB/CD3zeta-signaling chimeric antigen receptor (CAR; CTL019) has shown remarkable activity in patients with B acute lymphoblastic leukemia. Similar therapy can induce long-term remissions for relapsed/refractory chronic lymphocytic leukemia (CLL) patients, but in only a small subset of subjects. The determinants of response and resistance to CTL019 therapy of CLL are not fully understood. We employed next generation sequencing of RNA (RNA-seq) to identify predictive indicators of response to CTL019 treatment. We performed RNA-seq on leukapheresis and manufactured infusion product T cells from patients with heavily pre-treated and high-risk disease. To characterize potency, we also performed RNA-seq on the cellular infusion product after CAR-specific stimulation. Our findings indicate that durable remission in CLL is associated with gene expression signatures of early memory T cell differentiation (e.g., STAT3), while T cells from poorly- or non-responding patients exhibited elevated expression of key regulators of late memory as well as effector T cell differentiation, apoptosis, aerobic glycolysis, hypoxia and exhaustion. These gene expression signatures, along with additional immunological biomarkers, may be used to identify which patients are most likely to respond to cellular therapies and suggest manufacturing modifications that might potentiate the generation of maximally efficacious infusion products. In a follow-up study, we report multiplex CRISPR-Cas9 gene editing on T cells in humans to test the hypothesis that disrupting genes encoding the endogenous T cell receptor (TCR), TCRα (TRAC) and TCRβ (TRBC), as well as PD-1 (PDCD1) would enhance the efficacy of autologous T cells engineered to express a transgenic NY-ESO TCR. We used single cell RNA-seq to comprehensively characterize the transcriptomic phenotype of this T cell product and its evolution over time in a patient with sarcoma. Notably, this patient's transferred T cells that persisted in vivo demonstrated increases in expression of genes associated with a central memory state (IL7R, TCF7). These findings may guide future trials involving administration of genetically redirected, genome -edited T cells.In this follow-up study, we investigated whether CRISPR-Cas9-mediated disruption of PDCD1 and/or CTLA-4 could restore CART dysfunction in healthy donors and/or CLL patients. We show that depletion of CTLA-4 rescues the function of T cells from leukemia patients that previously failed CART cell treatment. We performed single-cell RNA sequencing and characterized the transcriptomic landscape of CTLA-4 KO CART19 cells that showed higher proliferation and anti-tumor efficacy by permitting unopposed CD28 signaling and maintenance of CAR expression on the T cell surface under stress-test conditions of chronic antigen exposure. Our findings reveal that selective disruption of CTLA-4 invigorates dysfunctional patient T cells, providing a strategy for increasing patient responses to CART cell therapy.

  Study phs001707

• Defining T cell States Associated with Response to Checkpoint Immunotherapy in Melanoma

  Checkpoint blockade therapy using antibodies targeting CTLA4, PD1 or PDL1 have changed the way cancer patients with advanced disease are being treated, as evident by their FDA approval in a wide variety of malignancies, and their ability to induce high response rates when compared to conventional therapies (e.g. chemotherapy). However, even in melanoma, despite the high response rate, most patients are refractory to therapy or acquire resistance in 10-12 months. To identify key immunological elements coupled with response or resistance to checkpoint immunotherapy, we performed an unbiased analysis on 16,291 CD45+ immune cells from 32 patients (48 samples) treated with checkpoint therapy (anti-PD1=37; anti-PD1/CTLA4=11), using single cell transcriptomics. Initial unsupervised clustering of all CD45+ cells identified 11 clusters. When examining the association of these clusters with clinical outcome, we found that two clusters (B-cells, p=0.005; memory T-cells, p=0.03) were significantly enriched in responder lesions while 4 clusters (monocytes/macrophages, p=0.003; dendritic cells, p=0.015; exhausted CD8+ T-cells, p=0.005; and exhausted/cell-cycle lymphocytes, 1.3x10-5) are enriched in non-responder lesions. Next, by leveraging our unbiased single cell approach, we identified not only clusters but also specific markers associated with either responder lesions (PLAC8, LTB, LY9, SELL, TCF7, IGKC and CCR7) or non-responder ones (CCL3, CD38, HAVCR2, ENTPD1 and WARS), many of which were not previously reported to be associated with clinical outcome to checkpoint therapy. Due to the importance of CD8+ T-cells in controlling tumors, the significant association of T-cell states with clinical outcome, and their high abundance in melanoma tumors, we next focused our analysis on CD8+ T-cells and identified 2 main clusters CD8_G (with increased expression of genes linked to memory) and CD8_B (enriched for genes linked to cell dysfunction), that were significantly enriched in responder (CD8_G, p=1.4x10-6) and non-responder (CD8_B, p=0.005) lesions, respectively. Since cells with both states coexist in each of the responder and non-responder lesions, we decided to calculate the ratio between the number of cells in these 2 clusters and observed a significant separation between responders (CD8_G/CD8_B>1) and non-responders (CD8_G/CD8_B<1) when looking at all samples, baseline or post samples separately. Similar results were detected when looking at the expression of a single transcription factor, TCF7, in CD8 T-cells in an independent cohort (n=43) using a simple immunofluorescence assay. Additionally, we validated the identity and function of some of the newly identified cell states as well as their epigenetic landscape, and found that cells expressing the inhibitory molecules CD39 and TIM3, on top of being enriched in non-responder lesions, are likely to play a crucial role in T-cell exhaustion in melanoma. Collectively, our study provides extensive unbiased data in human tumors for discovery of predictors and therapeutic targets to checkpoint immunotherapy.

  Study phs001680

• Blood Somatic Mutations in TCGA Donors Suffering from Solid Tumors

  Blood somatic mutations identified across 8,530 solid tumor patients in TCGA cohort. We reasoned that low-coverage whole-genome sequencing of blood samples routinely carried out in cancer genomics projects may be repurposed to detect clonal hematopoiesis (CH). Thus, inspired by a previous approach to identify early mutations in the development of the hematopoietic system, we implemented a pipeline to systematically carry out this "reverse" somatic mutation calling on the paired blood/tumor samples. Blood somatic mutations were called using a 'reverse' approach, in which the tumor sample taken from each patient was used as control of their germline genome. Blood somatic mutations are thus detected as variants in the blood (with respect to the human reference genome) that are absent in the tumor sample. The variant calling was carried out in our in house cluster on downloaded blood/tumor BAM files. The matched blood and tumoral BAM files -- masked and deduplicated using GATK -- of 8,530 whole-exome patients were obtained as described above. The variant calling was carried out using Strelka2 (employing default parameters) with the blood sample as the tumoral input and the tumor sample as control (reverse calling). All variants with two or more supporting reads matching the caller PASS filter and with VAF<0.5 were kept. Mutations in lowly mappable regions as defined by the DUST algorithm (k=30) and UMAP68 (36-kmers) were excluded. Contiguous variants were merged into double-base substitutions. Variants with greater frequency across the cohorts than the DNMT3A R882H or JAK2 V617F hotspot in a cohort-specific Panel of Normals and in gnomAD v2.1 were removed. This was equivalent to discarding variants present in these datasets with an allele frequency greater than 0.008 in PoN TCGA and 0.0003 in gnomAD v2.1. Additionally, common SNPs as defined by the dbSNP 151 Common UCSC track and dbSNP were excluded. Mutations within segmental duplications, simple repeats and masked regions as defined in UCSC tracks were also removed. Finally, samples with the mutation count above the 97.5 percentile of the mutation burden across the cohort were deemed unreliable and excluded for further analyses. We call the set of variants obtained after the application of these filters the full set. A more conservative subset of somatic mutations was generated from the full set of blood somatic mutations. To this end, we applied MosaicForecast, a software designed to phase mutations to polymorphisms with the aim of identifying somatic mutations in a small number of cells and also predict mosaicism for the unphased ones with a random forest classifier. As a result, we obtained a subset of mosaic-phased mutations, and a subset of mutations likely to be somatic (mosaic set).In order to obtain access to this study, please first obtain access to TCGA study (phs000178).

  Study phs002867