Cinnamides Target Leishmania amazonensis Arginase Selectively

Caffeic acid and related natural compounds were previously described as Leishmania amazonensis arginase (L-ARG) inhibitors, and against the whole parasite in vitro. In this study, we tested cinnamides that were previously synthesized to target human arginase. The compound caffeic acid phenethyl amide (CAPA), a weak inhibitor of human arginase (IC50 = 60.3 ± 7.8 μM) was found to have 9-fold more potency against L-ARG (IC50 = 6.9 ± 0.7 μM). The other compounds that did not inhibit human arginase were characterized as L-ARG, showing an IC50 between 1.3-17.8 μM, and where the most active was compound 15 (IC50 = 1.3 ± 0.1 μM). All compounds were also tested against L. amazonensis promastigotes, and only the compound CAPA showed an inhibitory activity (IC50 = 80 μM). In addition, in an attempt to gain an insight into the mechanism of competitive L-ARG inhibitors, and their selectivity over mammalian enzymes, we performed an extensive computational investigation, to provide the basis for the selective inhibition of L-ARG for this series of compounds. In conclusion, our results indicated that the compounds based on cinnamoyl or 3,4-hydroxy cinnamoyl moiety could be a promising starting point for the design of potential antileishmanial drugs based on selective L-ARG inhibitors

Pulmonary venous circulating tumor cell dissemination before tumor resection and disease relapse

Approximately 50% of patients with early-stage non-small-cell lung cancer (NSCLC) who undergo surgery with curative intent will relapse within 5 years1,2. Detection of circulating tumor cells (CTCs) at the time of surgery may represent a tool to identify patients at higher risk of recurrence for whom more frequent monitoring is advised. Here we asked whether CellSearch-detected pulmonary venous CTCs (PV-CTCs) at surgical resection of early-stage NSCLC represent subclones responsible for subsequent disease relapse. PV-CTCs were detected in 48% of 100 patients enrolled into the TRACERx study3, were associated with lung-cancer-specific relapse and remained an independent predictor of relapse in multivariate analysis adjusted for tumor stage. In a case study, genomic profiling of single PV-CTCs collected at surgery revealed higher mutation overlap with metastasis detected 10 months later (91%) than with the primary tumor (79%), suggesting that early-disseminating PV-CTCs were responsible for disease relapse. Together, PV-CTC enumeration and genomic profiling highlight the potential of PV-CTCs as early predictors of NSCLC recurrence after surgery. However, the limited sensitivity of PV-CTCs in predicting relapse suggests that further studies using a larger, independent cohort are warranted to confirm and better define the potential clinical utility of PV-CTCs in early-stage NSCLC

Anti-estrogen Resistance in Human Breast Tumors Is Driven by JAG1-NOTCH4-Dependent Cancer Stem Cell Activity

Breast cancers (BCs) typically express estrogen receptors (ERs) but frequently exhibit de novo or acquired resistance to hormonal therapies. Here, we show that short-term treatment with the anti-estrogens tamoxifen or fulvestrant decrease cell proliferation but increase BC stem cell (BCSC) activity through JAG1-NOTCH4 receptor activation both in patient-derived samples and xenograft (PDX) tumors. In support of this mechanism, we demonstrate that high ALDH1 predicts resistance in women treated with tamoxifen and that a NOTCH4/HES/HEY gene signature predicts for a poor response/prognosis in 2 ER+ patient cohorts. Targeting of NOTCH4 reverses the increase in Notch and BCSC activity induced by anti-estrogens. Importantly, in PDX tumors with acquired tamoxifen resistance, NOTCH4 inhibition reduced BCSC activity. Thus, we establish that BCSC and NOTCH4 activities predict both de novo and acquired tamoxifen resistance and that combining endocrine therapy with targeting JAG1-NOTCH4 overcomes resistance in human breast cancers

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. Video Abstract [Figure presented] Development of the bioinformatics tool LOHHLA allows precise measurement of allele-specific HLA copy number, improves the accuracy in neoantigen prediction, and uncovers insights into how immune escape contributes to tumor evolution in non-small-cell lung cancer

Fc-Optimized Anti-CD25 Depletes Tumor-Infiltrating Regulatory T Cells and Synergizes with PD-1 Blockade to Eradicate Established Tumors

CD25 is expressed at high levels on regulatory T (Treg) cells and was initially proposed as a target for cancer immunotherapy. However, anti-CD25 antibodies have displayed limited activity against established tumors. We demonstrated that CD25 expression is largely restricted to tumor-infiltrating Treg cells in mice and humans. While existing anti-CD25 antibodies were observed to deplete Treg cells in the periphery, upregulation of the inhibitory Fc gamma receptor (FcγR) IIb at the tumor site prevented intra-tumoral Treg cell depletion, which may underlie the lack of anti-tumor activity previously observed in pre-clinical models. Use of an anti-CD25 antibody with enhanced binding to activating FcγRs led to effective depletion of tumor-infiltrating Treg cells, increased effector to Treg cell ratios, and improved control of established tumors. Combination with anti-programmed cell death protein-1 antibodies promoted complete tumor rejection, demonstrating the relevance of CD25 as a therapeutic target and promising substrate for future combination approaches in immune-oncology

Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies

Computer-driven development of an in silico tool for finding selective histone deacetylase 1 inhibitors

Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R2 = 0.958) and a satisfactory predictive power (Q2 = 0.822; Q2F3 = 0.894). The model was validated (r2ext_ts = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner–Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity

A repurposing approach for uncovering the anti-tubercular activity of FDA-approved drugs with Potential Multi-Targeting Profiles

Tuberculosis (TB) is one of the top 10 causes of death worldwide. This scenario is further complicated by the insurgence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. The identification of appropriate drugs with multi-target affinity profiles is considered to be a widely accepted strategy to overcome the rapid development of resistance. The aim of this study was to discover Food and Drug Administration (FDA)-approved drugs possessing antimycobacterial activity, potentially coupled to an effective multi-target profile. An integrated screening platform was implemented based on computational procedures (high-throughput docking techniques on the target enzymes peptide deformylase and Zmp1) and in vitro phenotypic screening assays using two models to evaluate the activity of the selected drugs against Mycobacterium tuberculosis (Mtb), namely, growth of Mtb H37Rv and of two clinical isolates in axenic media, and infection of peripheral blood mononuclear cells with Mtb. Starting from over 3000 FDA-approved drugs, we selected 29 marketed drugs for submission to biological evaluation. Out of 29 drugs selected, 20 showed antimycobacterial activity. Further characterization suggested that five drugs possessed promising profiles for further studies. Following a repurposing strategy, by combining computational and biological efforts, we identified marketed drugs with relevant antimycobacterial profiles