| 1. |
- Brueffer, Christian, et al.
(författare)
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The Mutational Landscape of the SCAN-B Real-World Primary Breast Cancer Transcriptome
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Breast cancer is a disease of genomic alterations, of which the complete panorama of somatic mutations and how these relate to molecular subtypes and therapy response is incompletely understood. Within the Sweden Cancerome Analysis Network–Breast project (SCAN-B; ClinicalTrials.govNCT02306096), an ongoing study elucidating the tumor transcriptomic profiles for thousands of breast cancers prospectively, we developed an optimized pipeline for detection of single nucleotide variants and small insertions and deletions from RNA sequencing (RNA-seq) data, and profiled a large real-world population-based cohort of 3,217 breast tumors. We use it to describe the mutational landscape of primary breast cancer viewed through the transcriptome of a large population-based cohort of patients, and relate it to patient overall survival. We demonstrate that RNA-seq can be used to call mutations in important breast cancer genes such asPIK3CA,TP53, andERBB2, as well as the status of key molecular pathways and tumor mutational burden, and identify potentially druggable genes in 86.8% percent of tumors. To make this rich and growing mutational portraiture of breast cancer available for the wider research community, we developed an open source web-based application, the SCAN-B MutationExplorer, accessible athttp://oncogenomics.bmc.lu.se/MutationExplorer. These results add another dimension to the use of RNA-seq as a potential clinical tool, where both gene expression-based and gene mutation-based biomarkers can be interrogated simultaneously and in real-time within one week of tumor sampling.
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| 2. |
- Milosevic, Jelena, et al.
(författare)
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PPM1D is a neuroblastoma oncogene and therapeutic target in childhood neural tumors
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Majority of cancers harbor alterations of the tumor suppressor TP53. However, childhood cancers, including unfavorable neuroblastoma, often lack TP53 mutations despite frequent loss of p53 function, suggesting alternative p53 inactivating mechanisms.Here we show that p53-regulating PPM1D at chromosome 17q22.3 is linked to aggressive tumors and poor prognosis in neuroblastoma. We identified that WIP1-phosphatase encoded by PPM1D, is activated by frequent segmental 17q-gain further accumulated during clonal evolution, gene-amplifications, gene-fusions or gain-of-function somatic and germline mutations. Pharmacological and genetic manipulation established WIP1 as a druggable target in neuroblastoma. Genome-scale CRISPR-Cas9 screening demonstrated PPM1D genetic dependency in TP53 wild-type neuroblastoma cell lines, and shRNA PPM1D knockdown significantly delayed in vivo tumor formation. Establishing a transgenic mouse model overexpressing PPM1D showed that these mice develop cancers phenotypically and genetically similar to tumors arising in mice with dysfunctional p53 when subjected to low-dose irradiation. Tumors include T-cell lymphomas harboring Notch1-mutations, Pten-deletions and p53-accumulation, adenocarcinomas and PHOX2B-expressing neuroblastomas establishing PPM1D as a bona fide oncogene in wtTP53 cancer and childhood neuroblastoma. Pharmacological inhibition of WIP1 suppressed the growth of neural tumors in nude mice proposing WIP1 as a therapeutic target in neural childhood tumors.
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| 3. |
- Andersson, Alma, et al.
(författare)
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Spatial Deconvolution of HER2-positive Breast Tumors Reveals Novel Intercellular Relationships
- 2020
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra-and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. We integrate and spatially map tumor-associated types from single cell data to find: segregated epithelial cells, interactions between B and T-cells and myeloid cells, co-localization of macrophage and T-cell subsets. A model is constructed to infer presence of tertiary lymphoid structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define novel interactions between tumor-infiltrating cells in breast cancer and provide tools generalizing across tissues and diseases.
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