Collaborative effect of Csnk1a1 haploinsufficiency and mutant p53 in Myc induction can promote leukemic transformation.

ABSTRACT: It is still not fully understood how genetic haploinsufficiency in del(5q) myelodysplastic syndrome (MDS) contributes to malignant transformation of hematopoietic stem cells. We asked how compound haploinsufficiency for Csnk1a1 and Egr1 in the common deleted region on chromosome 5 affects hematopoietic stem cells. Additionally, Trp53 was disrupted as the most frequently comutated gene in del(5q) MDS using CRISPR/Cas9 editing in hematopoietic progenitors of wild-type (WT), Csnk1a1-/+, Egr1-/+, Csnk1a1/Egr1-/+ mice. A transplantable acute leukemia only developed in the Csnk1a1-/+Trp53-edited recipient. Isolated blasts were indefinitely cultured ex vivo and gave rise to leukemia after transplantation, providing a tool to study disease mechanisms or perform drug screenings. In a small-scale drug screening, the collaborative effect of Csnk1a1 haploinsufficiency and Trp53 sensitized blasts to the CSNK1 inhibitor A51 relative to WT or Csnk1a1 haploinsufficient cells. In vivo, A51 treatment significantly reduced blast counts in Csnk1a1 haploinsufficient/Trp53 acute leukemias and restored hematopoiesis in the bone marrow. Transcriptomics on blasts and their normal counterparts showed that the derived leukemia was driven by MAPK and Myc upregulation downstream of Csnk1a1 haploinsufficiency cooperating with a downregulated p53 axis. A collaborative effect of Csnk1a1 haploinsufficiency and p53 loss on MAPK and Myc upregulation was confirmed on the protein level. Downregulation of Myc protein expression correlated with efficient elimination of blasts in A51 treatment. The "Myc signature" closely resembled the transcriptional profile of patients with del(5q) MDS with TP53 mutation.

Reconstructing Phylogenetic Relationship in Bladder Cancer: A Methodological Overview.

Bladder cancer (BC) expresses itself as a highly heterogeneous disease both at the histological and molecular level, often occurring as synchronous or metachronous multifocal disease with high risk of recurrence and potential to metastasize. Multiple sequencing studies focusing on both non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) gave insights into the extent of both inter- and intrapatient heterogeneity, but many questions on clonal evolution in BC remain unanswered. In this review article, we provide an overview over the technical and theoretical concepts linked to reconstructing evolutionary trajectories in BC and propose a set of tools and established software for phylogenetic analysis.

Artificial Intelligence-based Detection of FGFR3 Mutational Status Directly from Routine Histology in Bladder Cancer: A Possible Preselection for Molecular Testing?

BACKGROUND: Fibroblast growth factor receptor (FGFR) inhibitor treatment has become the first clinically approved targeted therapy in bladder cancer. However, it requires previous molecular testing of each patient, which is costly and not ubiquitously available. OBJECTIVE: To determine whether an artificial intelligence system is able to predict mutations of the FGFR3 gene directly from routine histology slides of bladder cancer. DESIGN, SETTING, AND PARTICIPANTS: We trained a deep learning network to detect FGFR3 mutations on digitized slides of muscle-invasive bladder cancers stained with hematoxylin and eosin from the Cancer Genome Atlas (TCGA) cohort (n = 327) and validated the algorithm on the "Aachen" cohort (n = 182; n = 121 pT2-4, n = 34 stroma-invasive pT1, and n = 27 noninvasive pTa tumors). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary endpoint was the area under the receiver operating curve (AUROC) for mutation detection. Performance of the deep learning system was compared with visual scoring by an uropathologist. RESULTS AND LIMITATIONS: In the TCGA cohort, FGFR3 mutations were detected with an AUROC of 0.701 (p < 0.0001). In the Aachen cohort, FGFR3 mutants were found with an AUROC of 0.725 (p < 0.0001). When trained on TCGA, the network generalized to the Aachen cohort, and detected FGFR3 mutants with an AUROC of 0.625 (p = 0.0112). A subgroup analysis and histological evaluation found highest accuracy in papillary growth, luminal gene expression subtypes, females, and American Joint Committee on Cancer (AJCC) stage II tumors. In a head-to-head comparison, the deep learning system outperformed the uropathologist in detecting FGFR3 mutants. CONCLUSIONS: Our computer-based artificial intelligence system was able to detect genetic alterations of the FGFR3 gene of bladder cancer patients directly from histological slides. In the future, this system could be used to preselect patients for further molecular testing. However, analyses of larger, multicenter, muscle-invasive bladder cancer cohorts are now needed in order to validate and extend our findings. PATIENT SUMMARY: In this report, a computer-based artificial intelligence (AI) system was applied to histological slides to predict genetic alterations of the FGFR3 gene in bladder cancer. We found that the AI system was able to find the alteration with high accuracy. In the future, this system could be used to preselect patients for further molecular testing.

Evaluation of Therapeutic Targets in Histological Subtypes of Bladder Cancer.

Histologically, bladder cancer is a heterogeneous group comprising urothelial carcinoma (UC), squamous cell carcinoma, adenocarcinomas (ACs), urachal carcinomas (UrCs), and small cell neuroendocrine carcinomas (SCCs). However, all bladder cancers have been treated so far uniformly, and targeted therapy options are still limited. Thus, we aimed to determine the protein expression/molecular status of commonly used cancer targets (programmed cell death 1 ligand 1 (PD-L1), mismatch repair (MMR), androgen and estrogen receptors (AR/ER), Nectin-4, tumor-associated calcium signal transducer 2 (Tacstd2, Trop-2), epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), and fibroblast growth factor receptor 3 (FGFR3)) to give first insights into whether patients with SCC, AC/UrCs, and squamous-differentiated carcinomas (Sq-BLCA) of the bladder could be eligible for targeted therapies. In addition, for MMR-deficient tumors, microsatellite instability was analyzed. We completed our own data with molecular data from The Cancer Genome Atlas (TCGA). We present ratios for each drug and cumulative ratios for multiple therapeutic options for each nonurothelial subtype. For example, 58.9% of SCC patients, 33.5% of AC/UrCs patients, and 79.3% of Sq-BLCA patients would be eligible for at least one of the analyzed targets. In conclusion, our findings hold promise for targeted therapeutic approaches in selected patients in the future, as various drugs could be applied according to the biomarker status.

SWI/SNF Alterations in Squamous Bladder Cancers.

Dysfunction of the SWI/SNF complex has been observed in various cancers including urothelial carcinomas. However, the clinical impact of the SWI/SNF complex in squamous-differentiated bladder cancers (sq-BLCA) remains unclear. Therefore, we aimed to analyze potential expression loss and genetic alterations of (putative) key components of the SWI/SNF complex considering the co-occurrence of genetic driver mutations and PD-L1 expression as indicators for therapeutic implications. Assessment of ARID1A, SMARCA2, SMARCA4, SMARCB1/INI1, SMARCC1, SMARCC2 and PBRM1 mutations in a TCGA data set of sq-BLCA (n = 45) revealed that ARID1A was the most frequently altered SWI/SNF gene (15%) while being associated with protein downregulation. Genetic alterations and loss of ARID1A were confirmed by Targeted Next Generation Sequencing (NGS) (3/6) and immunohistochemistry (6/116). Correlation with further mutational data and PD-L1 expression revealed co-occurrence of ARID1A loss and TP53 mutations, while positive correlations with other driver mutations such as PIK3CA were not observed. Finally, a rare number of sq-BLCA samples were characterized by both ARID1A protein loss and strong PD-L1 expression suggesting a putative benefit upon immune checkpoint inhibitor therapy. Hence, for the first time, our data revealed expression loss of SWI/SNF subunits in sq-BLCA, highlighting ARID1A as a putative target of a small subgroup of patients eligible for novel therapeutic strategies.

Haplotype threading: accurate polyploid phasing from long reads.

Resolving genomes at haplotype level is crucial for understanding the evolutionary history of polyploid species and for designing advanced breeding strategies. Polyploid phasing still presents considerable challenges, especially in regions of collapsing haplotypes.We present WHATSHAP POLYPHASE, a novel two-stage approach that addresses these challenges by (i) clustering reads and (ii) threading the haplotypes through the clusters. Our method outperforms the state-of-the-art in terms of phasing quality. Using a real tetraploid potato dataset, we demonstrate how to assemble local genomic regions of interest at the haplotype level. Our algorithm is implemented as part of the widely used open source tool WhatsHap.