Abstract: Infection with SARS-CoV-2 and the resulting COVID-19 can cause both lung and kidney damage. SARS-CoV-2 can directly infect renal cells expressing ACE2 receptors, resulting in kidney damage, and acute kidney injury (AKI) has been reported in COVID-19 hospitalized patients. The pathophysiology of COVID-19-associated AKI is multifactorial. Local and systemic inflammation, immune system dysregulation, blood coagulation disorders, and activation of the renin-angiotensin-aldosterone system (RAAS) are factors that contribute to the development of AKI in COVID 19 disease. COVID-19 patients with kidney involvement have a poor prognosis, and patients with chronic kidney disease (CKD) infected with SARS-CoV-2 have an increased mortality risk. CKD patients with COVID-19 may develop end-stage renal disease (ESRD) requiring dialysis. In particular, patients infected with SARS-CoV-2 and requiring dialysis, as well as patients who have undergone kidney transplantation, have an increased risk of mortality and require special consideration. Nephrologists and infectious disease specialists face several clinical dilemmas in the prophylaxis and treatment of CKD patients with COVID-19. This entry presents recent data showing the effects of COVID-19 on the kidneys and CKD patients and the challenges in the management of CKD patients with COVID-19, and discusses treatment strategies for these patients.

Abstract: Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort ( n  = 278), and then validated in the BeatAML ( n  = 183) and two external cohorts, including a Swedish AML cohort ( n  = 45) and a relapsed/refractory acute leukemia cohort ( n  = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score 〉 0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, –0.49 (95% CI: [–0.53, –0.44] ) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/ .

Abstract: Background: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in the western world and shows a very heterogeneous clinical course. While the genetic landscape of CLL has been well characterized during recent years it can only partially explain the underlying biology of this heterogeneity. Proteogenomics could offer a valuable tool to fill this gap and improve the understanding of CLL biology. Methods: Here, we performed a large proteogenomic analysis (n=263) of three clinically annotated CLL cohorts: For the discovery cohort (Germany_1: n=68) we performed in-depth HiRIEF LC-MS based proteomics (more than 9000 proteins quantified) alongside genome-, transcriptome and ex-vivo drug response-profiling with 43 clinically established drugs. The proteome of two additional validation cohorts (Germany_2: n=44, Sweden_1: n=89), were characterized by data-independent acquisition (DIA) mass spectrometry. Results: To connect the CLL genotype with the molecular phenotype, we investigated associations between recurrent genetic alterations of CLL, mRNA expression and protein abundance. We found that trisomy 12, IGHV status and SF3B1 mutations had the greatest impact on protein abundances. CLL specific recurrent chromosomal deletions and gains (trisomy 12, del17p, del13q, del11q, gain8q24) consistently impacted on gene expression and protein abundance through gene dosage effects. We explored functional consequences of these gene dosage effects and found that the additional copy of chromosome 12 increased the abundance of central B-cell receptor (BCR) protein complexes through cis- and trans-effects, which could explain the increased response of trisomy 12 patient samples to BCR inhibition. Somatic mutations of TP53, ATM and XPO1 were associated with less, but specific and biologically-relevant protein abundance changes. p53 for instance, was the most upregulated protein in TP53 mutated samples, owing to the known stabilisation of mutant p53. This effect was not detectable on transcript level. ATM and XPO1 protein abundances were significantly lower in ATM and XPO1 mutated cases, indicating loss-of-function phenotypes of these mutations. To understand global similarities and differences between CLL patients on the proteomic level, we performed unsupervised clustering and identified clinically meaningful subgroups. Unsupervised clustering of the proteomics data identified six subgroups with contrasting clinical behaviour. TP53 mutations, IGHV status, trisomy 12 and their interactions explained five subgroups. These results show that quantitative mass spectrometry-based proteomics distinguished clinically relevant subgroups of CLL. Most importantly, we identified a previously unappreciated subgroup of CLL, comprising 20% of all cases, which could be uncovered by proteomic profiling and showed no association with frequent genetic or transcriptional alterations. This new CLL subgroup was characterized by accelerated disease progression, SF3B1 mutation-independent splicing alterations, metabolomic reprogramming and increased vulnerability to inhibitors of metabolic enzymes and the proteasome. Surprisingly, major BCR signaling proteins were downregulated in this subgroup, suggesting less dependence on BCR activity. In accordance with this observation, an unsupervised analysis revealed that low levels of many BCR signaling proteins (e.g. PLCG2 and PIK3CD) were associated with short time to next treatment. The existence of this subgroup could be confirmed in the validation cohorts. Finally, we performed an unsupervised multi-omics factor analysis (MOFA) across all omics data sets in parallel. This unsupervised analysis confirmed the existence of the above identified CLL subgroups and an important role of SF3B1 mutation-independent splicing alterations in CLL. Conclusion: Our integrative multi-omics analysis provides the first comprehensive overview of the interplay between genetic variants, the transcriptome, and the proteome, along with functional consequences for drug response and clinical outcome in CLL. Importantly, we identified a new subgroup with accelerated disease progression, a distinct proteomic signature and a clinically exploitable drug sensitivity profile. Figure Disclosures Mueller-Tidow: BiolineRx: Research Funding; Daiichi Sankyo: Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMBF: Research Funding; Wilhelm-Sander-Stiftung: Research Funding; Jose-Carreras-Siftung: Research Funding; Bayer AG: Research Funding; Deutsche Krebshilfe: Research Funding; Deutsche Forschungsgemeinschaft: Research Funding; Janssen-Cilag Gmbh: Membership on an entity's Board of Directors or advisory committees. Dreger:Neovii: Research Funding; Roche: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Janssen: Consultancy; Gilead: Consultancy, Speakers Bureau; AstraZeneca: Consultancy; AbbVie: Consultancy, Speakers Bureau. Stilgenbauer:Pharmacyclics: Consultancy, Honoraria, Other, Research Funding; Novartis: Consultancy, Honoraria, Other, Research Funding; Mundipharma: Consultancy, Honoraria, Other, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Other: travel support, Research Funding; GlaxoSmithKline: Consultancy, Honoraria, Other: travel support, Research Funding; Gilead: Consultancy, Honoraria, Other: travel support, Research Funding; Genzyme: Consultancy, Honoraria, Other: travel support, Research Funding; Genentech: Consultancy, Honoraria, Other: travel support, Research Funding; F. Hoffmann-LaRoche: Consultancy, Honoraria, Other: travel support, Research Funding; Celgene: Consultancy, Honoraria, Other: travel support, Research Funding; Boehringer-Ingelheim: Consultancy, Honoraria, Other: travel support, Research Funding; Amgen: Consultancy, Honoraria, Other: travel support, Research Funding; AbbVie: Consultancy, Honoraria, Other: travel support, Research Funding. Tausch:Roche: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Janssen-Cilag: Consultancy, Honoraria, Research Funding. Dietrich:Roche: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; KITE: Membership on an entity's Board of Directors or advisory committees.

Abstract: Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic-based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterize the proteome and transcriptome alongside genetic and ex-vivo drug response profiling in a clinically annotated CLL discovery cohort (n = 68). Unsupervised clustering of the proteome data reveals six subgroups. Five of these proteomic groups are associated with genetic features, while one group is only detectable at the proteome level. This new group is characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). Classifiers developed to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n = 165, n = 169) confirm that ASB-CLL comprises about 20% of CLL patients. The inferior overall survival in ASB-CLL is also independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling.

Abstract: In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.

Abstract: Hyperdiploidy, i.e. gain of whole chromosomes, is one of the most common genetic features of childhood acute lymphoblastic leukemia (ALL), but its pathogenetic impact is poorly understood. Here, we report a proteogenomic analysis on matched datasets from genomic profiling, RNA-sequencing, and mass spectrometry-based analysis of 〉 8,000 genes and proteins as well as Hi-C of primary patient samples from hyperdiploid and ETV6 / RUNX1 -positive pediatric ALL. We show that CTCF and cohesin, which are master regulators of chromatin architecture, display low expression in hyperdiploid ALL. In line with this, a general genome-wide dysregulation of gene expression in relation to topologically associating domain (TAD) borders were seen in the hyperdiploid group. Furthermore, Hi-C of a limited number of hyperdiploid childhood ALL cases revealed that 2/4 cases displayed a clear loss of TAD boundary strength and 3/4 showed reduced insulation at TAD borders, with putative leukemogenic effects.

Abstract: Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/forall .