An atlas of bloodstream-accessible bone marrow proteins for site-directed therapy of acute myeloid leukemia

Adrenomedullin-CALCRL axis controls relapse-initiating drug tolerant acute myeloid leukemia cells

Drug tolerant/resistant leukemic stem cell (LSC) subpopulations may explain frequent relapses in acute myeloid leukemia (AML), suggesting that these relapse-initiating cells (RICs) persistent after chemotherapy represent bona fide targets to prevent drug resistance and relapse. We uncover that calcitonin receptor-like receptor (CALCRL) is expressed in RICs, and that the overexpression of CALCRL and/or of its ligand adrenomedullin (ADM), and not CGRP, correlates to adverse outcome in AML. CALCRL knockdown impairs leukemic growth, decreases LSC frequency, and sensitizes to cytarabine in patient-derived xenograft models. Mechanistically, the ADM-CALCRL axis drives cell cycle, DNA repair, and mitochondrial OxPHOS function of AML blasts dependent on E2F1 and BCL2. Finally, CALCRL depletion reduces LSC frequency of RICs post-chemotherapy in vivo. In summary, our data highlight a critical role of ADM-CALCRL in post-chemotherapy persistence of these cells, and disclose a promising therapeutic target to prevent relapse in AML.

Leukemic stem cells which are resistant to chemotherapy are proposed as relapse-initiating cells (RICs). Here, the authors show that targeting the adrenomedullin-calcitonin receptor-like receptor decreases RICs frequency improving chemotherapy response in AML preclinical models.

Low-density lipoprotein receptor (LDLR) is an independent adverse prognostic factor in acute myeloid leukaemia

The low-density lipoprotein receptor (LDLR) is a membrane receptor that mediates the endocytosis of low-density lipoprotein (LDL). Uptake of LDL has been proposed to contribute to chemotherapy resistance of acute myeloid leukaemia (AML) cell lines in vitro. In the present study, we analysed LDLR expression and survival using bone marrow biopsies from 187 intensively treated patients with AML. Here, increasing LDLR expression was associated with decreasing overall (58·4%, 44·2%, and 24·4%; P = 0·0018), as well as event-free survival (41·7%, 18·1%, and 14·3%; P = 0·0077), and an increasing cumulative incidence of relapse (33·9%, 55·1%, and 71·4%; P = 0·0011). Associations of LDLR expression with survival were confirmed in 557 intensively treated patients from two international validation cohorts. In the analytic and validation cohorts, LDLR expression remained associated with outcome in multivariable regression analyses including the European LeukemiaNet genetic risk classification. Thus, LDLR predicts outcome of patients with AML beyond existing risk factors. Furthermore, we found low expression levels of LDLR in most healthy tissues, suggesting it as a promising target for antibody-based pharmacodelivery approaches in AML.

A mass spectrometry guided approach for the identification of novel vaccine candidates in gram-negative pathogens

Vaccination is the most effective method to prevent infectious diseases. However, approaches to identify novel vaccine candidates are commonly laborious and protracted. While surface proteins are suitable vaccine candidates and can elicit antibacterial antibody responses, systematic approaches to define surfomes from gram-negatives have rarely been successful. Here we developed a combined discovery-driven mass spectrometry and computational strategy to identify bacterial vaccine candidates and validate their immunogenicity using a highly prevalent gram-negative pathogen, Helicobacter pylori, as a model organism. We efficiently isolated surface antigens by enzymatic cleavage, with a design of experiment based strategy to experimentally dissect cell surface-exposed from cytosolic proteins. From a total of 1,153 quantified bacterial proteins, we thereby identified 72 surface exposed antigens and further prioritized candidates by computational homology inference within and across species. We next tested candidate-specific immune responses. All candidates were recognized in sera from infected patients, and readily induced antibody responses after vaccination of mice. The candidate jhp_0775 induced specific B and T cell responses and significantly reduced colonization levels in mouse therapeutic vaccination studies. In infected humans, we further show that jhp_0775 is immunogenic and activates IFNγ secretion from peripheral CD4+ and CD8+ T cells. Our strategy provides a generic preclinical screening, selection and validation process for novel vaccine candidates against gram-negative bacteria, which could be employed to other gram-negative pathogens.

Pan-Cancer analysis of the expression and regulation of matrisome genes across 32 tumor types

The microenvironment plays a central role in cancer, and neoplastic cells actively shape it to their needs by complex arrays of extracellular matrix (ECM) proteins, enzymes, cytokines and growth factors collectively referred to as the matrisome. Studies on the cancer matrisome have been performed for single or few neoplasms, but a more systematic analysis is still missing. Here we present a Pan-Cancer study of matrisome gene expression in 10,487 patients across 32 tumor types, supplemented with transcription factors (TFs) and driver genes/pathways regulating each tumor's matrisome. We report on 919 TF-target pairs, either used specifically or shared across tumor types, and their prognostic significance, 40 master regulators, 31 overarching regulatory pathways and the potential for druggability with FDA-approved cancer drugs. These results provide a comprehensive transcriptional architecture of the cancer matrisome and suggest the need for development of specific matrisome-targeting approaches for future therapies.

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In-depth characterization of matrisome gene expression and regulation in 10,487 patients across 32 human tumor types.

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Identification of transcription factor (TF) and “master regulators” governing each cancer’s matrisome.

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Analysis unveils therapeutic possibilities and suggests new treatments by repurposing of FDA-approved cancer drugs.

Stromal collagen type VI associates with features of malignancy and predicts poor prognosis in salivary gland cancer

PURPOSE

Collagen Type VI (COLVI) is an extracellular matrix protein that is upregulated in various solid tumours during tumour progression and has been shown to stimulate proliferation, suppress apoptosis and promote invasion and metastasis. It has also been described as a mediator of chemotherapy resistance and as a therapeutic target in preclinical cancer models. Here, we aimed to analyse the prognostic role of COLVI in salivary gland cancer (SGC).

METHODS

Stromal COLVI protein expression was assessed in primary SGC specimens of 91 patients using immunohistochemistry (IHC). The IHC expression patterns obtained were subsequently correlated with various survival and clinicopathological features, including Ki-67 and p53 expression.

RESULTS

We found that COLVI was expressed in all SGC specimens. High expression was found to be associated with features of malignancy such as high histologic grades, advanced and invasive T stages and metastatic lymph node involvement (p < 0.05 for all variables). COLVI expression was also found to correlate with both Ki-67 and p53 expression (p < 0.01). We found that high COLVI expression predicted a significantly inferior 5-year overall survival (38.3%, 55.1% and 93.8%; p = 0.002) and remained a significant predictor of prognosis in a multivariate Cox regression analysis (hazard ratio, 2.62; 95% confidence interval, 1.22-5.61; p = 0.013). In all low-risk subgroups COLVI expression identified patients with an adverse outcome. Patients receiving adjuvant radiotherapy had a poor survival when expressing high levels of COLVI.

CONCLUSIONS

Our data indicate that stromal COLVI expression associates with key features of malignancy, represents a novel independent prognostic factor and may affect response to radiotherapy in SGC. Although our results warrant validation in an independent cohort, assessing stromal COLVI expression may be suitable for future diagnostic and therapeutic decision making in patients with SGC.