7296 results for "canc*"

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• ER-, HER2-, PR- breast Cancer genome sequencing

  ER-, HER2-, PR- breast Cancer genome sequencing

  Dataset EGAD00001000088

• Matched Ovarian Cancer Sequencing

  Matched Ovarian Cancer Sequencing

  Dataset EGAD00001000084

• 20 Matched Pair Breast Cancer Genomes

  20 Matched Pair Breast Cancer Genomes

  Dataset EGAD00001000082

• Genomics of Colorectal Cancer Metastases - Massively Parallel Sequencing of Matched Primary and Metastatic tumours to Identify a Metastatic Signature of Somatic Mutations (MOSAIC)

  Genomics of Colorectal Cancer Metastases - Massively Parallel Sequencing of Matched Primary and Metastatic tumours to Identify a Metastatic Signature of Somatic Mutations (MOSAIC)

  Dataset EGAD00001000080

• ALK inhibitors in the context of ALK-dependent cancer cell lines

  ALK inhibitors in the context of ALK-dependent cancer cell lines

  Dataset EGAD00001000078

• Cancer Single Cell Sequencing

  Cancer Single Cell Sequencing

  Dataset EGAD00001000067

• Breast Cancer Follow Up Series

  Breast Cancer Follow Up Series

  Dataset EGAD00001000066

• Triple Negative Breast Cancer sequencing

  Triple Negative Breast Cancer sequencing

  Dataset EGAD00001000063

• COLO-829/COLO-829BL

  COLO-829 is a publicly available immortal cancer cell line and COLO-829BL is a lymphoblastoid cell line derived from the same patient

  Dataset EGAD00000000055

• EGAD00000000054

  NCI-H209 is an immortal cell line derived from a bone marrow metastasis of a patient with small cell lung cancer, taken before chemotherapy. The specimen showed histologically typical small cells with classic neuroendocrine features. NCI-BL209 is an EBV-transformed B-cell line derived from the same patient as the small cell lung cancer cell line, NCI-H209

  Dataset EGAD00000000054

• EGAD00000000053

  Sequencing data from Breast Cancer samples

  Dataset EGAD00000000053

• EGAD00000000049

  RNA-SEQ data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

  Dataset EGAD00000000049

• EGAD00000000048

  Sequencing data from oestrogen-receptor-alpha-positive metastatic lobular breast cancer sample

  Dataset EGAD00000000048

• EGAD00000000013

  WTCCC1 project Breast cancer (BC) samples

  Dataset EGAD00000000013

• FFPE tumor RNA exome capture sequencing

  This dataset includes raw, untrimmed FASTQ files generated from bulk RNA exome capture sequencing of FFPE tumor samples from 268 stage III colorectal cancer patients, prior to receiving adjuvant chemotherapy treatment. RNA libraries were prepared with the Illumina TruSeq RNA Exome kit and sequenced (PE51bp) using an Illumina NovaSeq 6000 with an S4 flow cell. The metadata file "RNAseq_metadata.tsv" contains columns with relevant quality metrics "DV200" and "cDNA_ng_ul" per sample, and a column indicating whether the sample was part of one or more published analyses: Franken et al., Eur. J. Cancer, 2026. PMID: 41650786. DOI: https://doi.org/10.1016/j.ejca.2026.116262 Crnovrsanin et al., XXX, 2026. PMID: XXX. DOI: XXX

  Dataset EGAD50000002440

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__clear_cell_renal_cell_carcinoma

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001002608

• TCR β-chain repertoire characterization of regulatory and conventional T cells in peripheral blood from breast cancer patients and healthy individuals.

  Identification of tumor-specific effects on peripheral TCRβ repertoire formation in humans, investigation of the clonal origin of regulatory T cells in breast cancer patients and impact analysis of the tumor-specific conversion of conventional T cells into induced regulatory T cells on the peripheral Treg repertoire in humans.

  Study EGAS00001002699

• Preferential infiltration of distinct Vγ9δ2 T cells into Glioblastoma multiforme

  Glioblastoma multiforme (GBM), the most fatal CNS cancer, is highly infiltrative as a result of phenotypic alteration of cancer cells mainly driven by a mutual interaction among cancer cells, tumor-associated macrophages (TAMs) and other stromal cells. This work aims at characterizing GBM-infiltrating γδ T cells that may regulate the GBM tumor microenvironment and cancer cell gene expression. V(D)J repertoires of tumor-infiltrating and blood circulating γδ T cells from 4 patients were analyzed by NGS-based TCR sequencing. RNA gene expression profiling and immunostaining of cancer tissues were performed as well.

  Study EGAS00001002790

• Sensitive and Frequent Identification of High Avidity Neo-epitope Specific CD8 + T-cells in Immunotherapy-naïve Ovarian Cancer

  Immunotherapy directed against private tumor neo-antigens derived from non-synonymous somatic mutations is a promising strategy of personalized cancer immunotherapy. However, feasibility in low mutational load tumor types remains unknown. Comprehensive and deep analysis of circulating and tumor-infiltrating lymphocytes (TILs) for neo-epitope specific CD8 + T cells allowed prompt identification of oligoclonal and polyfunctional such cells from most immunotherapy-naïve patients with advanced epithelial ovarian cancer studied. Neo-epitope recognition was discordant between circulating T cells and TILs, and was more likely to be found among TILs, which displayed higher functionalavidity and unique TCRs with higher predicted affinity than their blood counterparts. Our results imply that identification of neo-epitope specific CD8 + T cells is achievable even in tumors with relatively low number of somatic mutations, and neo-epitope validation in TILs extends opportunities for mutanome-based personalized immunotherapies to such tumors.

  Study EGAS00001002803

• FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (RNA-seq data)

  PIK3CA, encoding the PI3Kα isoform, is the most frequently mutated oncogene in estrogen receptor-positive breast cancer. Isoform-selective PI3K inhibitors are used clinically but intrinsic and acquired resistance limits their utility. Improved selection of patients that will benefit from these drugs requires novel predictive biomarkers. We show here that persistent FOXM1 expression following drug treatment is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer. Since the transcription factor drives expression of lactate dehydrogenase (LDH) but not hexokinase 2 (HK-II), changes in FOXM1 expression can be detected using magnetic resonance imaging of LDH-catalyzed hyperpolarized 13C label exchange between pyruvate and lactate but not by PET measurements of HK-II-mediated trapping of the glucose analog 2-deoxy-2-[18F]fluorodeoxyglucose.

  Study EGAS00001004452

• “Castration-persistence” is a distinct state of tolerance to androgen receptor targeting therapies in prostate cancer

  Androgen receptor (AR) signalling is important in prostate cancer progression, and therapies that specifically target this pathway are the mainstay of treatment for advanced disease. Treatment however is non-curative, and resistance develops inevitably with time. Although the mechanisms that drive progressive ‘castration resistant’ disease are reasonably well characterized, how tumours survive acute pathway inhibition is unclear. We performed a neo-adjuvant study of a novel combination of AR targeting therapies, and noted that the objective response to treatment was highly variable. To determine what was driving tumour persistence in poorly responding patients, we comprehensively characterised pre- and post-treatment samples were using both whole genome and RNA-sequencing. We find that ‘castration-persistence’ is a distinct state from ‘castration-resistance’, and is mediated by global transcriptional reprogramming leading to transitional EMT state, which is shared with benign luminal epithelial cells. This appears to be AP-1 and KLF driven, and represents and integration of multiple signalling pathways, particularly IGF2, offering a number of tractable strategies to improve clinical response to AR targeting therapies.

  Study EGAS00001003172

• Lindsey E. Jones et al. Patient-derived cells from recurrent tumors that model the evolution of IDH-mutant glioma. Neuro-Oncology Advances, 2020. We established robust, versatile, and well-characterized Patient Derived Cells (PDC) of an IDH1-mutant astrocytoma and an IDH1-mutant oligodendroglioma that represents defined evolutionary stages of chemotherapy-induced hypermutation. The PDCs retain tumor subtype defining features over time as well as classic hallmarks of cancer, including anchorage independent growth and cell immortality. The integrated phylogenies composed of PDCs,single-cell-derived PDCs, patient-derived xenografts, and corresponding spatiotemporal tumor tissue samples also provide new insight into the clonality, evolutionary pattern, and immense mutational load of hypermutated IDH mutant gliomas.

  IDH-mutant lower-grade gliomas can undergo malignant progression via temozolomide-driven hypermutation. Patient-derived cells (PDC) that model the genetically distinct hypermutated (HM) tumor subgroup are generally lacking, and few if any human brain tumor cell models are from defined evolutionary time points. Here, we characterize multiple PDC derived from independent surgical specimens of IDH1-mutant recurrences, including an ATRX and TP53-mutant astrocytoma and a 1p/19q co-deleted and TERT promoter-mutant oligodendroglioma. We determined the evolutionary time points represented by each PDC using exome sequencing and phylogenetic reconstruction, comparing the PDC and single cell clones of the PDC (scPDC) to multiple spatiotemporal tumor tissue samples, PDC-derived xenografts (PDX) and patient-matched blood. The tumor samples exhibited TMZ-induced mutagenesis and a branching pattern of evolution. We found clear evidence of two fully independent founder HM clones in the tumor tissue that are faithfully represented by independent PDC. The PDC, scPDC and PDX also shared the mutagenesis signature and represent the mid and later evolutionary time points of their corresponding tumors. The PDC maintained the tumor subtype-defining features over many passages, including heterozygosity of the IDH1 R132H mutation, production of 2-hydroxyglutarate (2-HG), and subtype-specific telomere maintenance mechanisms. The PDC from both tumors exhibited anchorage-independent growth in soft agar. The oligodendroglioma PDC formed infiltrative intracranial tumors with characteristic oligodendroglioma histology, initially with a long period to tumor formation. We conclude that the PDC, scPDC and PDX faithfully model the heterogeneous clonal origins of the corresponding tumor tissue. The multilevel analysis also provides new insight into the intratumoral heterogeneity and vast mutational load of HM glioma. The PDC from multiple evolutionary time points presented in the context of full clinical timelines may be useful to model evolution and intratumoral heterogeneity, important sources of therapeutic failure.

  Study EGAS00001003992

• Chemotherapy induces canalization of cell state in childhood B-cell precursor acute lymphoblastic leukemia

  Comparison of intratumor genetic heterogeneity in cancer at diagnosis and relapse suggests that chemotherapy induces bottleneck selection of subclonal genotypes. However, evolutionary events subsequent to chemotherapy could also explain changes in clonal dominance seen at relapse. We therefore investigated the mechanisms of selection in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) during induction chemotherapy where maximal cytoreduction occurs. To distinguish stochastic versus deterministic events, individual leukemias were transplanted into multiple xenografts and chemotherapy administered. Analyses of the immediate post-treatment leukemic residuum at single-cell resolution revealed that chemotherapy has little impact on genetic heterogeneity. Rather, it acts on extensive, previously unappreciated, transcriptional and epigenetic heterogeneity in BCP-ALL, dramatically reducing the spectrum of cell states represented, leaving a genetically polyclonal but phenotypically uniform population, with hallmark signatures relating to developmental stage, cell cycle and metabolism. Hence, canalization of the cell state accounts for a significant component of bottleneck selection during induction chemotherapy.

  Study EGAS00001004407

• Clinical relevance of somatic copy-number alterations in plasma circulating tumor DNA from advanced EGFR-mutated NSCLC patients treated with osimertinib

  Background: Somatic copy-number alterations (SCNAs) are associated with drug resistance in patients with advanced epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) treated with EGFR-tyrosine kinase inhibitors (TKIs). The purpose of our study was to assess the clinical relevance and development of genome-wide SCNAs in plasma circulating tumor DNA (ctDNA) under osimertinib therapy.Methods: We included 43 patients with advanced EGFR T790M-positive lung adenocarcinoma who were treated with osimertinib after progression under previous EGFR-TKI therapy. We performed genomic profiling of ctDNA in plasma samples from each patient obtained pre-osimertinib and after patients developed resistance to osimertinib. SCNAs were detected by shallow whole-genome plasma sequencing and EGFR mutations were assessed by droplet digital PCR.Results: SCNAs in resistance-related genes (rrSCNAs) were detected in 10 out of 39 (25.6%) evaluable patients before start of osimertinib. The presence of rrSCNAs in plasma before initiation of osimertinib therapy was associated with a lower response rate to osimertinib (50% versus 86%, p=0.02) and was an independent predictor for shorter progression-free survival (adjusted HR 3.26, 95% CI 1.37-7.75, p=0.008) and overall survival (adjusted HR 2.41, 95% CI 1.09-5.34, p=0.03).Conclusions: Genomic profiling of plasma ctDNA is clinically relevant and affects the efficacy and clinical outcome of osimertinib. Our approach allows comprehensive assessment of SCNAs in plasma samples of lung adenocarcinoma patients and may help to guide genotype-specific therapeutic strategies in the future.

  Study EGAS00001004539

• TCR β-chain repertoire characterization of regulatory and conventional T cells in breast tumors from breast cancer patients.

  Identification of tumor-specific effects on peripheral TCRβ repertoire formation in humans, investigation of the clonal origin of regulatory T cells in breast cancer patients and impact analysis of the tumor-specific conversion of conventional T cells into induced regulatory T cells on the peripheral Treg repertoire in humans.

  Study EGAS00001004671

• Assessment of cannabidiol and Δ9-tetrahydrocannabiol in mouse models of medulloblastoma

  Phytocannabinoids Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) have been demonstrated to exhibit anti-cancer activity in preclinical models of brain cancer leading to new clinical trials for adults with glioblastoma. We describe here the first report that has investigated a role for THC and CBD in paediatric brain cancer. Cannabinoids had cytotoxic activity against medulloblastoma and ependymoma cells in vitro, functioning in part through the inhibition of cell cycle progression and the induction of autophagy. Despite these effects in vitro, when tested in orthotopic mouse models of medulloblastoma or ependymoma, no impact on animal survival was observed. Furthermore, cannabinoids neither enhanced nor impaired conventional chemotherapy in a medulloblastoma mouse model. These data show that while THC and CBD do have some effects on medulloblastoma and ependymoma cells, are well tolerated and have minimal adverse effects, they do not appear to elicit any survival benefit in preclinical models of paediatric brain cancer.

  Study EGAS00001004963

• Single-cell profiling of neoantigen-specific T cells in lung cancers treated with neoadjuvant PD-1 blockade

  We study the expression profile of MANA-specific T cells, we used the MANA functional expansion of specific T cells (MANAFEST) assay in neoadjuvant anti-PD-1-treated lung cancers and applied their T cell receptor’s (TCR) complementarity-determining region 3 (CDR3) as a “barcode” to track and analyze their transcriptional programs in the tumor microenvironment using single cell TCR sequencing coupled with single cell RNA sequencing. We revealed distinct phenotype for MANA- and virus-specific clones in TIL and adjacent normal lung, regardless of response. MANA-specific T cells show features that drive the differences between responders and non-responders.

  Study EGAS00001005343

• RNA-sequencing of tumors from 45 patients with recurrent or metastatic gastric cancer treated with immune checkpoint inhibitors

  Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this work, we used our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identified four molecular subtypes that were prognostic for survival. We then built a support vector machine with linear kernel and the binary classifier to generate a risk score that is prognostic for 5-year overall survival and validated the risk score using three independent datasets. We also found that the molecular subtypes predicted response to adjuvant 5-fluorouracil and platinum after gastrectomy and immune checkpoint inhibitors in patients with metastatic or recurrent disease. The 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner. This EGA archive includes both St.Mary hospital cohort samples (n=28) and Yonsei immunotherapy patient samples (n=17).

  Study EGAS00001005588

• Identification of biomarkers of response to preoperative talazoparib monotherapy in treatment naïve gBRCA+ breast cancers

  Germline mutations in BRCA1 or BRCA2 exist in ~2-7% of breast cancer patients, which has led to the approval of PARP inhibitors in the advanced setting. We have previously reported a phase II neoadjuvant trial of single agent talazoparib for patients with germline BRCA pathogenic variants with a pathologic complete response (pCR) rate of 53%. As nearly half of the patients treated did not have pCR, better strategies are needed to overcome treatment resistance. To this end, we conducted multi-omic analysis of 13 treatment naïve breast cancer tumors from patients that went on to receive single-agent neoadjuvant talazoparib. We looked for biomarkers that were predictive of response (assessed by residual cancer burden) after 6 months of therapy. We found that all resistant tumors exhibited either the loss of SHLD2, expression of a hypoxia signature, or expression of a stem cell signature. These results indicate that the deep analysis of pre-treatment tumors can identify biomarkers that are predictive of response to talazoparib and potentially other PARP inhibitors, and provides a framework that will allow for better selection of patients for treatment, as well as a roadmap for the development of novel combination therapies to prevent emergence of resistance.

  Study EGAS00001005676

• Mult-omics Palbociclib Resistance Study in HR+/HER2– Metastatic Breast Cancer

  Resistance to CDK4/6 inhibitors and endocrine therapy (ET) are common and poorly understood since they have been real game changer in patients with HR-positive and HER2-negative MBC. A comprehensive genomic and transcriptomic analysis of pre-treatment and post-treatment tumors from patients treated with palbociclib plus ET identified novel markers associated with poor prognosis such as genomic scar features caused by homologous repair deficiency (HRD), estrogen response signatures, and four unique prognostic clusters with distinct molecular features. Tumors with TP53 mutations co-occurring with a unique HRD-high cluster responded poorly. By comparing genomic and transciptomic profiles of paired samples, tumors were found to be further enriched in HRD genomic scars and many had switched to aggressive molecular subtypes. Furthermore, we identified high frequencies of acquired genomic alterations upon disease progression in RB1, ESR1, PTEN, and KMT2C. Our findings provide new insight into potential predictive biomarkers that could be targeted to overcome resistance. (NCT03401359)

  Study EGAS00001005736

• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p < 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at >1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005738

• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation pattern, length and terminal sequence of cell-free DNA (cfDNA) is under the influence of nucleases present in the blood. We hypothesized that differences in the diversity of bases at the end of cfDNA fragments can be leveraged on a genome-wide scale to enhance the sensitivity for detecting the presence of tumor signals in plasma. We surveyed the cfDNA termini in 72 plasma samples from 319 patients with 18 different cancer types using low-coverage whole genome sequencing. The fragment-end sequence and diversity were altered in all cancer types in comparison to 76 healthy controls. We converted the fragment end sequences into a quantitative metric and observed that this correlates with circulating tumor DNA tumor fraction (R = 0.58, p < 0.001, Spearman). Using these metrics, we were able to classify cancer samples from control at a low tumor content (AUROC of 91% at 1% tumor fraction) and shallow sequencing coverage (mean AUROC = 0.99 at >1M fragments). Combining fragment-end sequences and diversity using machine learning, we classified cancer from healthy controls (mean AUROC = 0.99, SD = 0.01). Using unsupervised clustering we showed that early-stage lung cancer can be classified from control or later stages based on fragment-end sequences. We observed that fragment-end sequences can be used for prognostication (hazard ratio: 0.49) and residual disease detection inresectable esophageal adenocarcinoma patients, moving fragmentomics toward a greater clinical implementation.

  Study EGAS00001005747

• early-stage ESCC sequencing study

  We performed a comprehensive multi-omics analysis of 786 trace-tumor-samples from 154 esophageal squamous cell carcinoma phase (ESCC) patients, covering 9 histopathological stages in 3 phases as nontumor phase (NT phase), intraepithelial neoplasia phase (IEN phase), and ESCC phase. Proteogenomics elucidated the stage-specific molecular characterization and defined the cancer-driving waves along with the mutation accumulation in EC progression. The integrated multi-omics uncovered the chromosome 3q gain was the key event in the transmit from the NT to IEN phase, disclosed the top mutation of TP53 enhanced cell cycle and DNA replication in the IEN phase, and revealed the ESCC phase mutations of AKAP9 and MCAF1 elevated glycolysis and Wnt signaling, respectively. Furthermore, the trajectory analysis identified 6 major tracks related to different clinical features during ESCC progression. Growingly enhanced and hyperphosphorylated phosphoglycerate kinase 1 (PGK1, S203) was detected and considered as a drug target in ESCC progression. Collectively, this study provides insight into the understanding of ESCC molecular mechanism and a valuable resource for the development of therapeutic targets.

  Study EGAS00001006126

• Genome-wide cell-free DNA termini in patients with cancer

  The structure, fragmentation patterns and terminal sequences of cell-free DNA (cfDNA) are altered by nucleases and biological mechanisms in the blood of cancer patients. The cfDNA fragment-end composition recovered from low coverage WGS (<1 fold coverage) using a bespoke software (FrEIA) is aberrant in the plasma from cancer patient (n = 418, 655 samples) compared to controls (n = 117). As a standalone test FrEIA allows detection down to ~0.2% tumor fraction in vitro and in silico at 95% specificity, leading to a sensitivity of ~71% for detecting lung cancer (14/22 stage I-II, 27/38 stage III, 92/127 stage IV) and ~68% for detecting esophageal adenocarcinoma (26/44 stage II, 46/62 stage III). Additional cfDNA biological patterns can be combined with FrEIA increasing the diagnostic potential of low coverage WGS at minimal cost (mean AUROC = 0.96). Integrating multiple cfDNA biological signal augments the diagnostic performance of liquid biopsy.

  Study EGAS00001006142

• Accumulation of copy number alterations and clinical progression across advanced prostate cancer

  The burden of copy number alterations positively associated with radiologically-evident distant metastases at diagnosis (P=0.00006) and showed a non-linear relationship with clinical outcome on univariable and multivariable analysis, characterised by a sharp increase in the relative risk of progression (P=0.003) and death (P=0.045) for each unit increase, stabilising into more modest increases with higher burdens. This association between copy number burden and outcome was similar in each of the four metastatic states. Copy number loss occurred significantly more frequently than gain at the lowest copy number burden quartile (q=4.1X10-6). Loss of segments in chromosome 5q21-22 and gains at 8q21-24, respectively including CHD1 and cMYC, occurred more frequently in cases with higher copy number alteration (for either region: Kolmogorov–Smirnov distance, 0.5; adjusted P<0.0001). Copy number alterations showed variability across tumor regions in the same prostate. This variance associated with increased risk of distant metastases (Kruskal-Wallis test P=0.037).

  Study EGAS00001006251

• Intratumoral Heterogeneity and Clonal Evolution Induced by HPV Integration.

  The human papillomavirus (HPV) genome is integrated into host DNA in most HPV-positive cancers, but the consequences for chromosomal integrity are unknown. Continuous long-read sequencing of oropharyngeal cancers and cancer cell lines revealed a unique form of structural variation, termed heterocateny here, characterized by heterogeneous, interrelated, and repetative patterns of concatemerized virus and host DNA segments. Evidence of heterocateny was detected in extrachromosomal and/or intrachromosomal DNA in all cases. Unique breakpoint sequences shared across structurally heterogeneous virus-host concatemers within each cancer facilitated stepwise reconstruction of their evolution from a common molecular ancestor. This analysis revealed that unstable virus and virus-host concatemers in ecDNA or integrated form mediate insertion into and excision from chromosomes, capture, rearrangement, and rolling-circle amplification of host DNA, and chromosomal rearrangements. The data indicate that heterocatena is driven by the dynamic, aberrant replication and recombination of an oncogenic DNA virus, thereby extending known consequences of HPV integration to include promotion of intra-tumoral heterogeneity and clonal evolution.

  Study EGAS00001006652

• RNA-seq of DF149 cells – a patient-derived xenograft model of ascites-derived, homologous recombination repair-proficient, high-grade serous ovarian carcinoma – cultured in vitro and isolated after 8 hours treatment with DMSO control (3 x biological replicates) and 2.5 µM CBL0137 (3 x biological replicates)

  Background: High-grade serous ovarian carcinomas (HGSCs) are a heterogeneous subtype of epithelial ovarian cancers and include serous cancers arising in the fallopian tube and peritoneum. These cancers are now subdivided into homologous recombination repair (HR)-deficient and proficient subgroups as this classification impacts on management and prognosis. PARP inhibitors (PARPi) have shown significant clinical efficacy, particularly as maintenance therapy following response to platinum based chemotherapy in BRCA-mutant or homologous recombination (HR)-deficient HGSCs in both the 1st and 2nd line settings. However, PARPi have limited clinical benefit in HR-proficient HGSCs which make up almost 50% of HGSC and improving outcomes in these patients is now a high priority due to the poor prognosis with current standard of care. There are a number of potential lines of investigation including efforts in sensitizing HR-proficient tumors to PARPi. Herein, we aimed to develop a novel combination therapy by targeting SSRP1 using a small molecule inhibitor CBL0137 with PARPi in HR-proficient HGSOCs. Experimental design: We tested anti-cancer activity of CBL0137 monotherapy using a panel of HGSOC cell lines and patient-derived tumor cells in vitro. RNA sequencing was used to map global transcriptomic changes in CBL0137-treated patient-derived HR-proficient HGSOC cells. We tested efficacy of CBL0137 in combination with PARPi using HGSOC cell lines and patient-derived tumor cells in vitro and in vivo. Results: We show that SSRP1 inhibition using a small molecule, CBL0137, that traps SSRP1 onto chromatin, exerts a significant anti-growth activity in vitro against HGSC cell lines and patient-derived tumor cells, and also reduces tumor burden in vivo. CBL0137 induced DNA repair deficiency via inhibition of the HR repair pathway and sensitized SSRP1-high HR-proficient HGSC cell lines and patient-derived tumor cells/xenografts to the PARPi, Olaparib in vitro and in vivo. CBL0137 also enhanced the efficacy of DNA damaging platinum-based chemotherapy in HGSC patient-derived xenografts. Conclusion: Our findings strongly suggest that combination of CBL0137 and PARP inhibition represents a novel therapeutic strategy for HR-proficient HGSCs that express high levels of SSRP1 and should be investigated in the clinic.

  Study EGAS00001006662

• Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification (H021)

  Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

  Study EGAS00001006706

• Non-muscle Invasive Bladder Cancer Molecular Subtypes Predict Differential Response to Intravesical Bacillus Calmette-Guérin

  The recommended treatment for patients with high-risk non-muscle invasive bladder cancer (HR-NMIBC) is tumor resection followed by adjuvant Bacillus Calmette-Guérin (BCG) bladder instillations. However, only 50% of patients benefit from this therapy. In case of progression to advanced disease, patients must undergo a radical cystectomy with significant morbidity and have a poor clinical outcome. Identifying tumors least likely to respond to BCG can translate into alternative treatments, such as early radical cystectomy or novel targeted or immunotherapies. Here we present molecular profiling of 132 BCG-naive, HR-NMIBC patients, and 44 post-BCG recurrences (34 matched), which uncovered three distinct BCG Response Subtypes (BRS1-3). Patients with BRS3 tumors have reduced recurrence and progression-free survival compared to BRS1-2. BRS3 tumors expressed high EMT-basal markers and had an immunosuppresive profile, which was confirmed with spatial proteomics. Tumors which recurred post-BCG were enriched for BRS3. BRS stratification was validated in a second cohort of 151 BCG-naive HR-NMIBC patients and the molecular subtypes outperformed guideline recommended risk stratification based on clinicopathological variables. For clinical application, we validated that a commercially approved assay was able to accurately predict BRS3 tumors (AUROC 0.86). Our findings provide a potential clinical tool for improved identification of HR-NMIBC patients at the highest risk of progression, which can be used to select patients for early radical cystectomy or novel subtype-directed therapies.

  Study EGAS00001006879

• 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

• ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA

  Background Liquid biopsies, and the dynamic tracking of somatic mutations within circulating tumour DNA (ctDNA) can provide insight into the dynamics of cancer evolution and the intra-tumour heterogeneity that fuels treatment resistance. However, identifying and tracking dynamic changes in somatic copy-number alterations (SCNAs), which have been associated with poor outcome and metastasis, using ctDNA is challenging. Pancreatic adenocarcinoma is a disease which has been considered to harbour early punctuated events in its evolution, leading to an early fitness peak, with minimal further subclonal evolution. Methods To interrogate the role of SCNAs in pancreatic adenocarcinoma cancer evolution, we applied whole-exome sequencing of 55 longitudinal cell-free DNA (cfDNA) samples taken from 24 patients (including 8 from whom a patient-derived xenograft (PDX) was derived) with metastatic disease prospectively recruited into a clinical trial. We developed a method, Aneuploidy in Circulating Tumour DNA (ACT-Discover), that leverages haplotype phasing of paired tumour biopsies or PDXs to identify SCNAs in cfDNA with greater sensitivity. Results SCNAs were observed within 28 of 47 evaluable cfDNA samples. Of these events, 30% could only be identified by harnessing the haplotype-aware approach leveraged in ACT-Discover. The exceptional purity of PDX tumours enabled near-complete phasing of genomic regions in allelic imbalance, highlighting an important auxiliary function of PDXs. Finally, although the classical model of pancreatic cancer evolution emphasises the importance of early, homogenous somatic events as a key requirement for cancer development, ACT-Discover identified substantial heterogeneity of SCNAs, including parallel focal and arm-level events, affecting different parental alleles within individual tumours. Indeed, ongoing acquisition of SCNAs was identified within tumours throughout the disease course, including within an untreated metastatic tumour. Conclusions This work demonstrates the power of haplotype phasing to study genomic variation in cfDNA samples and reveals undiscovered intra-tumour heterogeneity with important scientific and clinical implications. Implementation of ACT-Discover could lead to important insights from existing cohorts, or underpin future prospective studies seeking to characterise the landscape of tumour evolution through liquid biopsy.

  Study EGAS00001007077

• Implementation of pediatric precision oncology into clinical practice: The individualized Therapies for Children with cancer program “iTHER”

  iTHER is a prospective national precision oncology program aiming to define tumor molecular profiles in children and adolescents with primary very high-risk, relapsed, or refractory pediatric tumors in order to identify relevant aberrations to inform treatment. From April 2017 to April 2021, 302 samples of 253 patients were included. Comprehensive molecular profiling utilizing lcWGS, WES, RNAseq, Affymetrix, and/or 850k methylation profiling was performed for 226 samples. Germline pathogenic variants were identified in 16% of patients. At least one somatic alteration was detected in 204 (90.3%), and 185 (81.9%) were considered druggable, with priority levels very high (6.1%), high (21.3%), moderate (26.0%), intermediate (36.1%), and borderline (10.5%). Diagnosis was revised or refined in 8 patients (3.5%). Spatial and temporal heterogeneity was observed in paired samples of 15 patients, indicating the value of sequential analysis. Of 137 patients with follow-up beyond 12 months, 21 molecularly matched treatments were applied in 19 patients (13.9%) with modest response. Most relevant barriers to not applying targeted therapies included poor performance status, as well as limited access to drugs within clinical trial. iTHER demonstrates feasibility of comprehensive molecular profiling across all ages, tumor types and stages in pediatric cancers, informing of diagnostic, prognostic, and targetable alterations as well as reportable germline variants within a clinically relevant timeframe. Therefore, WES and RNAseq is implemented into standard clinical care at the Princess Máxima Center for all children. Improved access to targeted treatments within biology-driven combination trials is required to eventually improve survival.

  Study EGAS00001007099

• Single-cell decoding of drug induced transcriptomic reprogramming in triple negative breast cancers

  Background The encoding of cell intrinsic drug resistance states in breast cancer reflects the contributions of genomic and non-genomic variations and requires accurate estimation of clonal fitness from co-measurement of transcriptomic and genomic data. Somatic copy number (CN) variation is the dominant mutational mechanism leading to transcriptional variation and notably contributes to platinum chemotherapy resistance cell states. Here, we deploy time series measurements of triple negative breast cancer (TNBC) single-cell transcriptomes, along with co-measured single-cell CN fitness, identifying genomic and transcriptomic mechanisms in drug associated transcriptional cell states. Results We present scRNA-seq data (53,641 filtered cells) from serial passaging TNBC patient-derived xenograft (PDX) experiments spanning 2.5 years, matched with genomic single-cell CN data from the same samples. Our findings reveal distinct clonal responses within TNBC tumors exposed to platinum. Clones with high drug fitness undergo clonal sweeps and show subtle transcriptional reversion, while those with weak fitness exhibit dynamic transcription upon drug withdrawal. Pathway analysis highlights convergence on epithelial-mesenchymal transition and cytokine signaling, associated with resistance. Furthermore, pseudotime analysis demonstrates hysteresis in transcriptional reversion, indicating generation of new intermediate transcriptional states upon platinum exposure. Conclusions Within a polyclonal tumor, clones with strong genotype-associated fitness under platinum remained fixed, minimizing transcriptional reversion upon drug withdrawal. Conversely, clones with weaker fitness display non-genomic transcriptional plasticity. This suggests CN-associated and CN-independent transcriptional states could both contribute to platinum resistance. The dominance of genomic or non-genomic mechanisms within polyclonal tumors has implications for drug sensitivity, restoration and re-treatment strategies.

  Study EGAS00001007242

• Clinical outcomes and immune correlates of response to nivolumab plus chemoradiotherapy in women with locally-advanced cervical cancer – NiCOL study

  Concurrent chemoradiotherapy (CRT) with blockade of the PD-1 pathway may enhance immune-mediated tumor control through increased phagocytosis, cell death, and antigen presentation. The NiCOL phase 1 trial was designed to determine safety, tolerance, and immune correlates of response to the PD-1 blocking antibody, nivolumab, with and following concurrent CRT in 16 women with locally advanced cervical cancer (LACC). Three patients experienced grade 3 dose-limiting toxicities. The overall response rate was 93.8% [95%CI: 69.8%-99.8%] with a 2-year PFS of 75% [95% CI: 56.5%-99.5%]. Compared to patients with progressive disease (PD), progression-free (PF) subjects showed a brisker stromal immune infiltrate, higher proximity of tumor-infiltrating CD3+ T cells to PD-L1+ tumor cells and of Foxp3+ T cells to proliferating CD11c+ myeloid cells. PF had higher baseline levels of PD-1 on EMRA CD4+ T cells and ICOS-L on tumor-associated macrophages (TAMs) vs PD, which instead displayed enhanced PD-L1 expression on TAMs and higher, circulating frequencies of proliferating Tregs at baseline. Concomitant nivolumab plus definitive CRT is safe and associated with encouraging PFS rates. Further validation in the subset of LACC displaying pre-existing, adaptive immune activation is warranted.

  Study EGAS00001007297

• Pediatric Patient-Derived-Xenograft development in MAPPYACTS – international pediatric cancer precision medicine trial in relapsed and refractory tumors

  Pediatric patients with recurrent and refractory cancers are in most need for new treatments. This study developed patient-derived-xenograft (PDX) models within the European MAPPYACTS cancer precision medicine trial (NCT02613962). To date, 131 PDX models were established following heterotopical and/or orthotopical implantation in immunocompromised mice: 76 sarcomas, 25 other solid tumors, 12 central nervous system tumors, 15 acute leukemias, and 3 lymphomas. PDX establishment rate was 43%. Histology, whole exome and RNA sequencing revealed a high concordance with the primary patient’s tumor profile, human leukocyte-antigen characteristics and specific metabolic pathway signatures. A detailed patient molecular characterization, including specific mutations prioritized in the clinical molecular tumor boards are provided. Ninety models were shared with the IMI2 ITCC Paediatric Preclinical Proof-of-concept Platform (IMI2 ITCC-P4) for further exploitation. This new PDX biobank of unique recurrent childhood cancers provides an essential support for basic and translational research and new treatments development in advanced pediatric malignancies.

  Study EGAS00001007327

• Genome-wide cell-free DNA biological patterns in patients with cancer

  The structure of cell-free DNA (cfDNA) is altered in the blood of cancer patients. From whole genome sequencing we retrieved the cfDNA fragment-end composition using a new software (FrEIA), as well as the cfDNA size and tumor fraction in three independent cohorts (n=925 cancer from >10 types and 321 control samples). At 95% specificity, we detect 72% of cancer samples using at least one cfDNA measure, including 64% of early-stage cancer (n=220). cfDNA detection correlates with a shorter overall (p=0.018) and recurrence-free (p=0.006) survival in patients with resectable esophageal adenocarcinoma. Integrating cfDNA measures with machine learning in an independent test set (n=396 cancer, 90 controls) achieve a detection accuracy of 82% and AUROC of 96%. In conclusion, harnessing the biological features of cfDNA can improve at no-extra cost the diagnostic performance of liquid biopsies.

  Study EGAS00001007400

• SMARCB1 loss activates patient-specific distal oncogenic enhancers in malignant rhabdoid tumors

  Malignant rhabdoid tumor (MRT) is a highly malignant and often lethal childhood cancer. MRTs are genetically defined by bi-allelic inactivating mutations in SMARCB1, a member of the BRG1/BRM-associated factors (BAF) chromatin remodeling complex. Mutations in BAF complex members are common in human cancer, yet their contribution to tumorigenesis remains in many cases poorly understood. Here, we studied derailed regulatory landscapes as a consequence of SMARCB1 loss in the context of MRT. Our multi-omics approach on patient-derived MRT organoids revealed a dramatic reshaping of the regulatory landscape upon SMARCB1 reconstitution. Chromosome conformation capture experiments subsequently revealed patient-specific looping of distal enhancer regions with the promoter of the MYC oncogene. This intertumoral heterogeneity in MYC enhancer utilization is also present in patient MRT tissues as shown by combined single-cell RNA-seq and ATAC-seq. We show that loss of SMARCB1 drives patient-specific epigenetic reprogramming underlying MRT tumorigenesis.

  Study EGAS00001007590

• Metabolic profiling of patient-derived organoids reveals nucleotide synthesis as a metabolic vulnerability in malignant rhabdoid tumors

  Malignant rhabdoid tumor (MRT) is one of the most aggressive childhood cancers for which no effective treatment options are available. Reprogramming of cellular metabolism is an important hallmark of cancer, with various metabolism-based drugs being approved cancer treatment. In this study, we use patient derived tumor organoids (tumoroids) to map the metabolic landscape of several pediatric cancers. Combining gene expression analyses and metabolite profiling using mass spectrometry, we find nucleotide biosynthesis to be a particular vulnerability of MRT. Treatment of MRT tumoroids with de novo nucleotide synthesis inhibitors lowers nucleotide levels in MRT tumoroids and induces apoptosis. Lastly, we demonstrate invivo efficacy of methotrexate in an MRT PDX mouse model. Our study revealsnucleotide biosynthesis as an MRT-specific metabolic vulnerability, which can ultimately lead to better treatment options for children suffering from this lethal pediatric malignancy.

  Study EGAS00001007877

• Genome-wide association study identifies common variants associated with breast cancer in South African Black women

  Genome-wide association studies (GWAS) have characterized the contribution of common variants to the development of breast cancer (BC) in populations of European descent, however no GWAS have been reported in resident African populations. This GWAS included 2,485 resident African BC cases and 1,101 population matched controls. Two risk loci were identified, one on chromosome 15, located between UNC13C and RAB27A (rs7181788, p = 1.01x10-08) and a second in an intron of USP22 on chromosome 17 (rs899342, p = 4.62x10-08). RAB27A is a member of the RAS oncogene family associated with invasive growth and metastasis, and UNC13C is involved in tumor progression. The rs899342 SNP is an eQTL for expression of USP22 in a wide range of tissues. Several genome-wide significant signals were detected in a hormone receptor subtype analysis. Comparison of a meta-analysis of other African ancestry BC GWAS datasets predominantly of West African ancestry with our South African data showed limited evidence of shared risk loci, suggesting potential heterogeneity in genetic risk factors for breast cancer in different African populations. A European ancestry derived polygenic risk model for BC was applied to our data, explaining only 0.79% of variance, with an area under the curve of 0.56. This study identified genetic risk factors for BC in a resident African population, but larger studies in this and other African populations are needed to further define the contribution of common variants to BC risk.

  Study EGAS00001008032

• Cancer of Low survival and UnMet Need – Pathologist Initiated

  Despite significant advances in diagnostic cancer histopathology, a subset of tumors remain unable to be classified using WHO criteria. The resulting diagnostic uncertainty can result in inappropriate clinical management and negative patient outcomes. We investigated whether combining histopathology with whole genome and transcriptome sequencing (WGTS) could improve the classification of tumors that posed diagnostic dilemmas despite extensive histopathology and standard molecular work-up at a quaternary oncology center. We successfully sequenced 45 tumors from an initial set of 54 unclassified tumors (83% success rate). A confident diagnosis was made for 38/45 tumors (84%). Additionally, potential treatment targets were identified in 21/45 tumors (47%). Theoretical comparison with alternative assays demonstrated that WGTS was uniquely capable of detecting critical diagnostic findings in 15/38 tumors (39%). This work supports augmenting histopathology and standard molecular pathology with WGTS in the classification of difficult-to-diagnose tumors.

  Study EGAS00001008112

• Sequencing data from MethylScan assay on plasma

  Machine learning models in biomedical research are often hindered by demographic imbalances in clinical datasets, leading to biased predictions that disadvantage minority populations. Existing bias correction methods face limitations in handling the heterogeneity of biomedical data and the complexity of demographic influences. Here, we present DeBias, a computational framework for mitigating demographic biases in high-dimensional biomedical datasets. DeBias eliminates bias-associated subspaces within the entire feature space using control samples and enables global bias correction across all features, effectively preserving disease-specific signals while minimizing demographic distortions. To demonstrate its effectiveness, we applied DeBias to cfDNA methylation data for cancer detection. It achieved a >6-fold reduction in the number of features containing biases and outperformed existing methods in improving the cancer detection performance for minority populations. Our method represented a promising step toward developing machine learning models that are more accurate, reliable, and equitable for minority populations.

  Study EGAS00001008127

• Single-cell and spatial transcriptomic profiling of hormone-naïve localised prostate cancer

  Localised prostate cancers (PCa) are heterogeneous and multifocal, with diverse outcomes. Current prognostic methods are epithelium-centric, overlooking the complex cellular landscape within the tumour microenvironment (TME), which remains incompletely characterised. We performed a comprehensive analysis of cancerous and adjacent-benign cores from 24 patients with hormone therapy-naïve localised PCa using single-cell RNA-sequencing. By integrating copy number variation and transcriptional signatures, we classified epithelial cells across a malignant spectrum, revealing widespread molecular perturbation. We found an expansion of Club cell phenotypes, suggestive of Luminal dedifferentiation. We also performed a detailed annotation of stromal phenotypes, focusing on fibroblasts, and identified a novel peri-neural fibroblast population. Spatial transcriptomics elucidated the precise anatomical distribution of CAFs within the PCa TME. This study provides a valuable foundation for advancing our understanding of PCa pathobiology and developing a comprehensive cellular model of the disease.

  Study EGAS00001008332

• Genome and transcriptome sequence data from a metastatic cholangiocarcinoma patient

  Genome and transcriptome sequence data from a metastatic cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002976

• Genome and transcriptome sequence data from a breast cancer patient

  Genome and transcriptome sequence data from a breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002972

• Genome and transcriptome sequence data from a rectal adenocarcinoma patient

  Genome and transcriptome sequence data from a rectal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002973

• Genome and transcriptome sequence data from a metastatic gastric adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic gastric adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002974

• Genome and transcriptome sequence data from a metastatic neuroendocrine carcinoma of unknown primary patient

  Genome and transcriptome sequence data from a metastatic neuroendocrine carcinoma of unknown primary patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002975

• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002977

• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002978

• Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient

  Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002979

• Genome and transcriptome sequence data from a metastatic fibrolamellar hepatocelluar carcinoma patient

  Genome and transcriptome sequence data from a metastatic fibrolamellar hepatocelluar carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002980

• Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient

  Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002981

• Genome and transcriptome sequence data from a metastatic rectosigmoid adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic rectosigmoid adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002982

• Genome and transcriptome sequence data from a metastatic carcinoma of the lung patient

  Genome and transcriptome sequence data from a metastatic carcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002983

• Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient

  Genome and transcriptome sequence data from a metastatic pancreatic carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002984

• Genome and transcriptome sequence data from a adenocarcinoma of the lung patient

  Genome and transcriptome sequence data from a adenocarcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002985

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002986

• Genome and transcriptome sequence data from a metastatic endocervical adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic endocervical adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002987

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002988

• Genome and transcriptome sequence data from a medullary thyroid cancer patient

  Genome and transcriptome sequence data from a medullary thyroid cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002989

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002990

• Genome and transcriptome sequence data from a cholangiocarcinoma patient

  Genome and transcriptome sequence data from a cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002991

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002992

• Genome and transcriptome sequence data from a metastatic carcinoma of primary unknown patient

  Genome and transcriptome sequence data from a metastatic carcinoma of primary unknown patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002993

• Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of anus patient

  Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of anus patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002994

• Genome and transcriptome sequence data from a carcinosarcoma of the uterus patient

  Genome and transcriptome sequence data from a carcinosarcoma of the uterus patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002995

• Genome and transcriptome sequence data from a leiomyosarcoma patient

  Genome and transcriptome sequence data from a leiomyosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002996

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002997

• Genome and transcriptome sequence data from a metastatic large cell neuroendocrine tumour of the lung patient

  Genome and transcriptome sequence data from a metastatic large cell neuroendocrine tumour of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003017

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002998

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002999

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003000

• Genome and transcriptome sequence data from a serous carcinoma of fallopian tube patient

  Genome and transcriptome sequence data from a serous carcinoma of fallopian tube patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003001

• Genome and transcriptome sequence data from a metastatic adult granulosa cell tumour patient

  Genome and transcriptome sequence data from a metastatic adult granulosa cell tumour patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003002

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003003

• Genome and transcriptome sequence data from a glioblastoma multiforme patient

  Genome and transcriptome sequence data from a glioblastoma multiforme patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003004

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003005

• Genome and transcriptome sequence data from a metastatic medullary thyroid cancer patient

  Genome and transcriptome sequence data from a metastatic medullary thyroid cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003006

• Genome and transcriptome sequence data from a metastatic colorectal cancer patient

  Genome and transcriptome sequence data from a metastatic colorectal cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003007

• Genome and transcriptome sequence data from a metastatic adenocarcinoma presumably of ovarian origin patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma presumably of ovarian origin patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003008

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003009

• Genome and transcriptome sequence data from a metastatic uterine leiomyosarcoma patient

  Genome and transcriptome sequence data from a metastatic uterine leiomyosarcoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003010

• Genome and transcriptome sequence data from a squamous cell carcinoma of the lung patient

  Genome and transcriptome sequence data from a squamous cell carcinoma of the lung patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003011

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the rectum patient

  Genome and transcriptome sequence data from a metastatic adenocarcinoma of the rectum patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003012

• Genome and transcriptome sequence data from a metastatic gastric cancer patient

  Genome and transcriptome sequence data from a metastatic gastric cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003013

• Genome and transcriptome sequence data from a metastatic colon cancer patient

  Genome and transcriptome sequence data from a metastatic colon cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003014

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003015

• Genome and transcriptome sequence data from a metastatic ductal carcinoma of the breast patient

  Genome and transcriptome sequence data from a metastatic ductal carcinoma of the breast patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003016

• Genome and transcriptome sequence data from a metastatic breast cancer patient

  Genome and transcriptome sequence data from a metastatic breast cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003035

• Genome and transcriptome sequence data from an ovarian adenocarcinoma patient

  Genome and transcriptome sequence data from an ovarian adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003036