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• 850k Methyome analysis of snap frozen or FFPE stored human MBM

  We performed a 850k infinium bead chip-based global methylome analysis of MBM.

  Study EGAS00001005975

• The_identification_of_genetic_vulnerabilities_in_head_and_neck_cancers_for_the_development_of_novel_therapies

  CRISPR/Cas9 lethality screens in a set of Asian head and neck cancer cell lines to identify novel targets.

  Study EGAS00001002204

• Single-cell RNA-sequencing of a multi-region pleural mesothelioma case

  Single-cell RNA-seq (Smart-Seq2) from 3 distant biopsies (costal, mediastinal, diaphragmatic) of a pleural mesothelioma case.

  Dataset EGAD00001015519

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015286

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015299

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015298

• TFL unique case study

  Whole exome sequencing of a normal sample, primary tumor sample, and relapse tumor sample of a transformed non-Hodgkins follicular lymphoma patient with extraordinary response to treatment.

  Dataset EGAD00001002707

• Bulk exome sequencing of primary GBM - SF 10360

  We performed bulk exome-seq on a primary GBM and a blood sample from SF10360

  Dataset EGAD00001002274

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015305

• Genome and transcriptome sequence data from a metastatic osteosarcoma tumor patient

  Genome and transcriptome sequence data from a metastatic osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015306

• Genome and transcriptome sequence data from a plexiform neurofibroma tumor patient

  Genome and transcriptome sequence data from a plexiform neurofibroma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015283

• Genome and transcriptome sequence data from a relapsed osteosarcoma tumor patient

  Genome and transcriptome sequence data from a relapsed osteosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015311

• Genome and transcriptome sequence data from a synovial sarcoma tumor patient

  Genome and transcriptome sequence data from a synovial sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015313

• Genome and transcriptome sequence data from a ewing sarcoma tumor patient

  Genome and transcriptome sequence data from a ewing sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015315

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015318

• Genome and transcriptome sequence data from a anaplastic astrocytoma tumor patient

  Genome and transcriptome sequence data from a anaplastic astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015319

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015324

• Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a alveolar rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015329

• Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma tumor patient

  Genome and transcriptome sequence data from a embryonal rhabdomyosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015330

• Genome and transcriptome sequence data from a pIlomyxoid astrocytoma tumor patient

  Genome and transcriptome sequence data from a pIlomyxoid astrocytoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015333

• Genome and transcriptome sequence data from a wilms tumor tumor patient

  Genome and transcriptome sequence data from a wilms tumor patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015334

• Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar tumor patient

  Genome and transcriptome sequence data from a rhabdomyosarcoma, alveolar patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015335

• Genome and transcriptome sequence data from a infantile fibrosarcoma tumor patient

  Genome and transcriptome sequence data from a infantile fibrosarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015263

• Genome and transcriptome sequence data from a CNS sarcoma tumor patient

  Genome and transcriptome sequence data from a CNS sarcoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015268

• Genome and transcriptome sequence data from a ocular melanoma tumor patient

  Genome and transcriptome sequence data from a ocular Melanoma patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015269

• Genome and transcriptome sequence data from a aggressive fibromatosis tumor patient

  Genome and transcriptome sequence data from a aggressive fibromatosis patient, generated as part of the BC Cancer Agency's Pediatric Personalized Onco-Genomics study

  Dataset EGAD00001015279

• WGS and RNA-Seq data from a GBM patient PT-GB9186

  WGS and RNA-Seq data from a GBM patient PT-GB9186

  Dataset EGAD00001004237

• WGS and RNA-Seq data from a GBM patient PT-AH1410

  WGS and RNA-Seq data from a GBM patient PT-AH1410

  Dataset EGAD00001004223

• WGS and RNA-Seq data from a GBM patient PT-AK7565

  WGS and RNA-Seq data from a GBM patient PT-AK7565

  Dataset EGAD00001004224

• WGS and RNA-Seq data from a GBM patient PT-GR2309

  WGS and RNA-Seq data from a GBM patient PT-GR2309

  Dataset EGAD00001004241

• WGS and RNA-Seq data from a GBM patient PT-GB9483

  WGS and RNA-Seq data from a GBM patient PT-GB9483

  Dataset EGAD00001004238

• WGS and RNA-Seq data from a GBM patient PT-HN6692

  WGS and RNA-Seq data from a GBM patient PT-HN6692

  Dataset EGAD00001004242

• WGS and RNA-Seq data from a GBM patient PT-BK0248

  WGS and RNA-Seq data from a GBM patient PT-BK0248

  Dataset EGAD00001004228

• WGS and RNA-Seq data from a GBM patient PT-LS4891

  WGS and RNA-Seq data from a GBM patient PT-LS4891

  Dataset EGAD00001004252

• WGS and RNA-Seq data from a GBM patient PT-AB0029

  WGS and RNA-Seq data from a GBM patient PT-AB0029

  Dataset EGAD00001004221

• WGS and RNA-Seq data from a GBM patient PT-LR9369

  WGS and RNA-Seq data from a GBM patient PT-LR9369

  Dataset EGAD00001004251

• WGS and RNA-Seq data from a GBM patient PT-JP2405

  WGS and RNA-Seq data from a GBM patient PT-JP2405

  Dataset EGAD00001004247

• WGS and RNA-Seq data from a GBM patient PT-JB1730

  WGS and RNA-Seq data from a GBM patient PT-JB1730

  Dataset EGAD00001004245

• WGS and RNA-Seq data from a GBM patient PT-RL7940

  WGS and RNA-Seq data from a GBM patient PT-RL7940

  Dataset EGAD00001004258

• Health and Retirement Study (HRS)

  Introduction to V2: This data release comprises data from the V1 release combined with approximately 3,000 additional samples, collected during the HRS 2010 field period. The 2010 data include samples from a random half of the new cohort enrolled in 2010 along with a significant expansion of the minority sample. Description: The University of Michigan Health and Retirement Study (HRS) is a longitudinal panel study that surveys a representative sample of approximately 20,000 people in America over the age of 50 every two years. Supported by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration, the HRS explores the changes in labor force participation and the health transitions that individuals undergo toward the end of their work lives and in the years that follow. The study collects information about income, work, assets, pension plans, health insurance, disability, physical health and functioning, cognitive functioning, and health care expenditures. Through its unique and in-depth interviews, the HRS provides an invaluable and growing body of multidisciplinary data that researchers can use to address important questions about the challenges and opportunities of aging. Because of its innovation and importance, the HRS has become the model and hub for a growing network of harmonized longitudinal aging studies around the world. Origins of the HRS. As the population ages it is increasingly important to obtain reliable data about aging and topics that are relevant to a range of policy issues in aging. To address this need, the National Institutes on Aging (NIA) established a cooperative agreement with the University of Michigan Institute for Social Research to collect such data. The HRS launched data collection in 1992 and has re-interviewed the original sample of respondents every two years since then. By adding new cohorts and refreshing the sample, the HRS has grown to become the largest, most representative longitudinal panel study of Americans 50 years and older. HRS Study Design. The target population for the original HRS cohort includes all adults in the contiguous United States born during the years 1931-1941 who reside in households, with a 2:1 oversample of African-American and Hispanic populations. The original sample is refreshed with new birth cohorts (51-56 years of age) every six years. The sample has been expanded over the years to include a broader range of birth cohorts as well. The target population for the AHEAD survey consists of United States household residents who were born in 1923 or earlier. Children of the Depression (CODA) recruits households born 1924-1930, War Babies 1942-47, Early Boomers 1948-53, and Mid-Boomers 1954-59. Data collection includes a mixed mode design combining in-person, telephone, mail, and Internet. For consenting respondents, HRS data are linked at the individual level to administrative records from Social Security and Medicare claims. Genetic Research in the HRS. The HRS has genotyped 2.5 million single nucleotide polymorphisms (SNPs) on respondents using Illumina's Human Omni2.5-Quad (Omni2.5) BeadChip. The genotyping was performed by the NIH Center for Inherited Disease Research (CIDR). Saliva was collected on half of the HRS sample each wave starting in 2006. In 2006, saliva was collected using a mouthwash collection method. From 2008 onward, the data collection method switched to the Oragene kit. Saliva completion rates were 83% in 2006, 84% in 2008, and 80% in 2010 among new cohort enrollees. HRS Phenotypic data. Phenotypic data are available on a variety of dimensions. Health measures include physical/psychological self-report, various health conditions, disabilities, cognitive performance, health behaviors (smoking, drinking, exercise), physical performance and anthropomorphic measures, and biomarkers (HbA1c, Total Cholesterol, HDL, CRP, Cystatin-C). Data are also available on health services including utilization, insurance and out-of-pocket spending with linkage to Medicare records. Economic measures include employment status/history, earnings, disability, retirement, type of work, income by source, wealth by asset type, capital gains/debt, consumption, linkage to pensions, Social Security earnings/benefit histories. There is also extensive information on family structure, proximity, transfers to/from of money, time, social and psychological characteristics, as well as a wide range of demographics. Performance on a cognitive test combining immediate and delayed word recall was selected as an example trait for the dbGaP data release. In the immediate word recall task the interviewer reads a list of 10 nouns to the respondent and asks the respondent to recall as many words as possible from the list in any order. After approximately five minutes of asking other survey questions, the respondent is asked to recall the nouns previously presented as part of the immediate recall task. The total recall score is the sum of the correct answers to these two tasks, with a range of 0 to 20. Researchers who wish to link to other HRS measures not in dbGaP will be able to apply for access from HRS. A separate Data Use Agreement (DUA) will be required for linkage to the HRS data. See the HRS website (http://hrsonline.isr.umich.edu/gwas) for details.

  Study phs000428

• Cambridge Neoadjuvant Cancer of the Prostate (CANCAP03): A Window Study into the Effects of Olaparib ± Degarelix in Primary Prostate Cancer.

  Study: Cambridge Neoadjuvant Cancer of the Prostate (CANCAP03): A Window Study into the Effects of Olaparib ± Degarelix in Primary Prostate Cancer. EudraCT Number: 2014-004417-86 Clinicaltrials.gov Number: NCT02324998 Purpose: Investigate combined PARP and androgen inhibition in primary prostate cancer. Aim: To understand the biological mechanism(s) underlying clinical efficacy, especially in the absence of mutations in homologous recombination (HR) repair pathways. Dataset: Single-nuclear RNA sequencing data from post-treatment radical prostatectomy tissue cores for six patients, 3 from each of olaparib and olaparib and degarelix cohorts. Samples were processed for nuclear isolation and extraction, prior to single-nuclear RNA sequencing. 10x libraries were constructed using the 10x supplied protocol, briefly: individual nuclei were trapped into droplets with a single bead, and poly A transcripts were barcoded during reverse transcription. Complementary DNA was amplified, and libraries were prepared for sequencing on NovaSeq 6000 using S4 flow cell (100k reads per cell). FASTQ files were processed using CellRanger pipeline (10X Genomics). Outputs from the CellRanger pipeline were analysed using the Seurat package (v4.9.9.9049) in R (v4.2.0). Processed data is provided as a Seurat object.

  Study EGAS50000000880

• NCI COVID-19 in Cancer Patients Study (NCCAPS): A Longitudinal Natural History Study

  NCCAPS is a longitudinal prospective cohort study designed to assess patient, disease and treatment factors associated with short- and long-term outcomes in patients with cancer who develop COVID-19. We also aimed to describe COVID-19 symptoms and treatments, and to describe disruptions in cancer treatment due to COVID-19. The study enrolled patients with cancer on active treatment or with a history of stem cell transplant or CAR-T cell therapy with new, active COVID-19, defined as being within 14 days of their first positive SARS-CoV-2 test. The study found that receipt of chemotherapy and baseline history of stroke, atrial fibrillation or pulmonary embolism were associated with a higher risk of hospitalization for COVID-19. Vaccination for SARS-CoV-2 was associated with a lower risk of hospitalization. Death due to COVID-19 was highest in patients with lymphoma, intermediate in those with acute leukemia and lung cancer, and lowest in those with other solid tumors and other hematologic cancers. Disruptions of cancer treatment were frequent in the first 30 days after SARS-CoV-2 infection.

  Study phs004178

• A Therapeutic Vaccine for Fibrolamellar Hepatocellular Carcinoma

  Fibrolamellar hepatocellular carcinoma (FLC) is a rare form of liver cancer affecting 33 children and young adults that is driven by a chimeric protein, DNAJ-PKAc. We conducted a phase 1 clinical trial of a therapeutic vaccine targeting DNAJ-PKAc 38 (FLC-Vac), in combination with nivolumab and ipilimumab, in children and adults with advanced FLC. Among 16 patients enrolled, 12 completed the vaccine priming phase and were evaluable for both immunological and clinical endpoints. Grade 3 treatment-related adverse events were reported by six patients (37.5%). DNAJ43 PKAc-specific T cell responses were detected in 9/12 patients after treatment. In the subset of patients who completed the initial priming phase the disease control rate (DCR) was 75% (9/12), with three partial responses (25%). All patients with clinical responses also had DNAJ-PKAc specific T cell responses, from which we identified multiple class II-restricted T cell receptors (TCRs) with specificity for DNAJ-PKAc. Correlates of response included both functional neoantigen reactivity and changes in TCR repertoire features over time. Immune escape in two patients corresponded with immune exhaustion rather than neoantigen escape or HLA loss.

  Study phs003970

• Cross-tissue transcriptomic analysis of human secondary lymphoid organ residing ILC3 reveals a default quiescent state in the absence of inflammation

  A cross tissue transcriptional comparison of human ILC3 isolated from non-inflamed lymph nodes (LN) and spleens, inflamed tonsils and the peripheral blood (PB). Using uniform gating and high-purity cell-sorting, ILC3 were sorted as Lineage(CD3,CD19,CD34,CD14,CD94)-CD45intCD127+CRTH2-CD117+. From LN, spleen, tonsils and PB, NKp44- ILC3 were isolated and, in addition, NKp44+ ILC3 were sorted from spleen and tonsils. RNA quality was measured on a RNA 6000 Pico Kit (Agilent Technologies) using a 2100 Bioanalyzer (Agilent Technologies). 100 pg high-quality RNA was isolated and cDNA was generated using the SMART-Seq (v3) Ultra Low Input RNA Kit (Clontech) with 15 cycles of amplification. Amplified and Covaris-fragmented cDNA was quality-checked on a High Sensitivity DNA chip (Agilent Technologies) and further processed according the TruSeq Nano DNA Library Preparation Kit (Illumina). Data was processed as follows: SMARTer adapters were trimmed with cutadapt and the resulting sequences were aligned to the human RefSeq transcriptome using 14 TopHat2 (Kim et al., 2013). Normalization and quantification was performed using Cufflinks (Trapnell et al., 2012). FPKM counts were determined per gene with HTSeq-count.

  Study EGAS00001002636

• Rucaparib in patients presenting a metastatic breast cancer with Homologous Recombination Deficiency, without germline BRCA1/2 mutation, pronostic value of high LOH genomic score and predictive value of HRDetect (microarray)

  Breast cancer may present genomic alterations leading to homologous recombination deficiency. PARP inhibitors have proved their efficacy in patients with HER2-negative metastatic breast cancer (mBC) harboring germline (g) BRCA1/2 mutations. We conducted the phase 2 RUBY trial to assess the efficacy of rucaparib in HER2-negative mBC with high genomic loss of heterozygosity (LOH) score or somatic, without gBRCA1/2 mutation. 220 of 711 patients with mBC screened for LOH presented high LOH score which was associated with a higher likelihood of death (HR = 1.39, 95% CI: 1.11-1.75, p = 0.005). The primary objective was not reached with a clinical benefit rate (objective response or SD>16 weeks) of 13.5%. Two LOH-high patients, without somatic BRCA1/2 mutation, presented a complete and durable response (14 and 32 months). HRDetect tended to be associated with response to rucaparib, whithout reaching statistical significance (median HRDetect responders versus non responders: 0.465 versus, 0.040, p = 0.2135). Our data suggests that a small subset of patients with high LOH score could derive benefit from PARP inhibitors.

  Study EGAS00001004755

• Familial psychosis associated with a missense mutation at MACF1 gene combined with the rare duplications DUP3p26.3 and DUP16p23.3, affecting the CNTN6 and CDH13 genes

  Psychosis is a highly heritable and heterogeneous psychiatric condition. Its genetic architecture is thought to be the result of the joint effect of common and rare variants. Families with high prevalence are an interesting approach to shed light on the rare variant's contribution without the need of collecting large cohorts. To unravel the genomic architecture of a family enriched for psychosis, with four affected individuals, we applied a system genomic approach based on karyotyping, genotyping by whole-exome sequencing to search for rare single nucleotide polymorphisms (SNPs) and SNP array to search for copy-number variants (CNVs). We identified a rare non-synonymous variant, g.39914279 C>G, in the MACF1 gene, segregating with psychosis. Rare variants in the MACF1 gene have been previously detected in SCZ patients. Besides, two rare CNVs, DUP3p26.3 and DUP16p23.3, were also identified in the family affecting relevant genes (CNTN6 and CDH13, respectively). We hypothesize that the co-segregation of these duplications with the rare variant g.39914279 C>G of MACF1 gene precipitated with schizophrenia and schizoaffective disorder.

  Study EGAS00001004791

• A Single-cell Atlas of Progressive TKI Resistance in Chronic Myeloid Leukemia

  High-dimensional scRNA seq and CyTOF were used to identify features predictive of treatment outcome at diagnosis. An optimal TKI response was characterised by erythroid lineage skewing of the CD34+ HSPC compartment. While TKI-resistant CP was reminiscent of a BC-like state, where HSPCs were enriched for inflammation, stemness and quiescence. We designed a machine-learning approach which assessed the prognostic potential of all 32 sub-populations of our scRNA seq dataset. LSCs and NK populations harboured the highest prognostic power at diagnosis. Within the LSCs, erythroid lineage priming and MYC activation were features of an optimal and poor TKI responses, respectively. With the NK cell compartment, apart from a higher abundance of NK cells in TKI optimal responders, a memory-like HLA-DR+ adaptive NK and KLRC1+ NK populations were hallmarks of an optimal and poor TKI responses, respectively. Mechanistically, both cell intrinsic and extrinsic mechanisms contributed towards the higher sensitivity of EPs to TKI therapy. In summary, our high-dimensional single-cell atlas of TKI resistance highlights pivotal transcriptional features associated with TKI-resistance disease, some of which have the potential to be exploited as biomarkers.

  Study EGAS00001005509

• Diagnostic Genomic Analysis is Prognostic in AYA ALL Patients Treated on a MRD-Stratified Paediatric Protocol

  Background: Over the last decade, next generation sequencing has enabled classification of multiple new recurrent genomic drivers of acute lymphoblastic leukemia (ALL). We provide a comprehensive genomic survey of adolescent and young adult (AYA) patients treated on the Australasian Leukaemia and Lymphoma Group (ALLG) ALL06 study. ALL06 clinical trial described the efficacy and tolerability of AYA ALL patients aged 15-39 years treated on a single minimal residual disease (MRD)-stratified platform. In this study we have taken the ALL06 cohort and retrospectively performed comprehensive genomic analyses assessing response of AYA patients with various genomic subtypes treated with a paediatric approach. Methods: Genomic risk classification of B- and T-ALL patients (n=57) enrolled to the study was based on mRNA-Sequencing, MLPA, immunophenotyping and cytogenetics. Results: Using this approach, 36/40 (90%) B- and 13/17 (76.5%) T-ALL patients were classified according to genomic risk. We have linked outcome data with each patient’s genomic risk and found a strong correlation between adverse genomic risk and MRD at the end of consolidation, translating to inferior overall and relapse free survival. Patients with adverse risk genomics who achieved negative MRD status had improved responses compared to those with persistent MRD. Patients with standard risk genomics had excellent responses regardless of their MRD status. Conclusion: Our data argue strongly for incorporation of a genomic risk classification into future ALL treatment paradigms at the time of diagnosis.

  Study EGAS50000000752

• Homozygous inactivation of CHEK2 is linked to a familial case of multiple primary lung cancer with accompanying cancers in other organs

  In clinical practice, there are a number of cancer patients with clear family histories, but not belonging to existing familial malignancies. Recent advances in genomic technologies have enhanced the possibility of identifying causative genes in such cases. Two siblings, an elder sister and a younger brother, were found to have multiple primary lung cancers at the age of 60. The former subsequently developed breast cancer, and had a history of myoma uteri. The latter had initially developed prostate cancer at the age of 59, and had a history of colon cancer. SNP genotyping revealed that approximately 10% of the genomes were homozygous in both patients. Exome sequencing revealed non-synonymous mutations in four genes in the runs of homozygosity: CHEK2, FCGRT, INPP5J, and SFI1. Evolutionary conservation of primary protein structures suggested the functional importance of the CHEK2 mutation, R474C. This mutation altered the tertiary structure of CHK2 by disrupting the salt bridge between R474 and E394. No such structural changes were observed with the other three mutated genes. Subsequent cell-based transfection analysis revealed that CHK2 R474C was unstable and scarcely activated. We concluded that CHEK2 is a causative gene in this case of familial cancer. Although homozygous inactivation of CHEK2 in mice led to cancers in multiple organs, accumulation of additional human cases is needed to establish its pathogenic role in humans.

  Study JGAS000057

• Identification of a Fumarate-Hydratase Deficient-like RCC Subtype with Convergent Pathway Alterations

  The development of molecularly targeted therapies builds upon the identification of tumor clades with shared molecular abnormalities. Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is a familial syndrome resulting from germline mutation of the two-hit tumor suppressor gene, Fumarate Hydratase (FH). HLRCC renal tumors present often in young adults, exhibit unique histological features and are particularly aggressive. HLRCC is uncommon and poorly understood, and whether there are sporadic renal tumors with similar characteristics, is unclear. Notably, FH can also be inactivated somatically in RCC, and should be included among FH-deficient (FHD) kidney tumors. We identified a tumor clade without FH mutations but with similar morphological and molecular characteristics as well as aggressive clinical behavior, which we name FHDL (FHD-like). We show that FHD/FHDL tumors represent a unique subtype of CIMP (CpG Island Methylator Phenotype) with the highest levels of overall genome methylation and CDKN2A silencing. FHD/FHDL tumors are characterized by convergent activation of the Hippo pathway as determined by frequent and mutually exclusive mutations in NF2 and PTPN14. AKR1B10, an NRF2 target, was upregulated in FHD/FHDL tumors as well as GATA3, which suggests a shared origin in the renal medulla and is consistent with topological studies. There is loss of 5-hydroxymethylcytosine in both FHD and FHDL tumors. These data define a group of particularly aggressive renal cancers with shared molecular features and potential targets for therapeutic intervention.

  Study EGAS00001002646