Search

A major therapeutic barrier in melanoma is the coexistence of diverse cellular states marked by distinct metabolic traits. Transitioning from a proliferative to an invasive melanoma phenotype is coupled with increased ferroptosis vulnerability. However, the regulatory circuits controlling ferroptosis susceptibility across melanoma cell states are unknown. In this work, we identified Apolipoprotein E ( APOE ) as the top lipid-metabolism gene segregating the melanoma MITF high /AXL low proliferative/ferroptosis-resistant from MITF low /AXL high invasive/ferroptosis-sensitive state. Mechanistically, ApoE secreted by the MITF high /AXL low cells protects the invasive phenotype from ferroptosis-inducing agents by reducing the content of peroxidation-prone polyunsaturated fatty acids and boosting GPX4 levels both in vitro and in vivo. Whole-exome sequencing indicates that APOE high expression in patients with melanoma is associated with resistance to ferroptosis, regardless of APOE germline status. In aggregate, we found a ferroptosis-resistance mechanism between melanoma cell states relying on secreted ApoE and APOE high expression as a potential biomarker for poor ferroptosis response in melanoma.

Dendritic cells (DCs) are critical players at the intersection of innate and adaptive immunity, making them ideal candidates for anticancer vaccine development. DC-based immunotherapies typically involve isolating patient-derived DCs, pulsing them with tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs), and utilizing maturation cocktails to ensure their effective activation. These matured DCs are then reinfused to elicit tumor-specific T-cell responses. While this approach has demonstrated the ability to generate potent immune responses, its clinical efficacy has been limited due to the immunosuppressive tumor microenvironment. Recent efforts have focused on enhancing the immunogenicity of DC-based vaccines, particularly through combination therapies with T cell-targeting immunotherapies. This Trial Watch summarizes recent advances in DC-based cancer treatments, including the development of new preclinical and clinical strategies, and discusses the future potential of DC-based vaccines in the evolving landscape of immuno-oncology., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.)

Many individuals with cancer are resistant to immunotherapies. Here, we identify the gene encoding the pyrimidine salvage pathway enzyme cytidine deaminase (CDA) among the top upregulated metabolic genes in several immunotherapy-resistant tumors. We show that CDA in cancer cells contributes to the uridine diphosphate (UDP) pool. Extracellular UDP hijacks immunosuppressive tumor-associated macrophages (TAMs) through its receptor P2Y 6 . Pharmacologic or genetic inhibition of CDA in cancer cells (or P2Y 6 in TAMs) disrupts TAM-mediated immunosuppression, promoting cytotoxic T cell entry and susceptibility to anti-programmed cell death protein 1 (anti-PD-1) treatment in resistant pancreatic ductal adenocarcinoma (PDAC) and melanoma models. Conversely, CDA overexpression in CDA-depleted PDACs or anti-PD-1-responsive colorectal tumors or systemic UDP administration (re)establishes resistance. In individuals with PDAC, high CDA levels in cancer cells correlate with increased TAMs, lower cytotoxic T cells and possibly anti-PD-1 resistance. In a pan-cancer single-cell atlas, CDA high cancer cells match with T cell cytotoxicity dysfunction and P2RY6 high TAMs. Overall, we suggest CDA and P2Y 6 as potential targets for cancer immunotherapy., (© 2024. The Author(s).)

Despite the success of immunotherapy, overcoming immunoresistance in cancer remains challenging. We identified a unique niche of tumor-associated macrophages (TAMs), coexpressing T cell immunoglobulin and mucin domain-containing 3 (TIM3) and V-domain immunoglobulin suppressor of T cell activation (VISTA), that dominated human and mouse tumors resistant to most of the currently used immunotherapies. TIM3 + VISTA + TAMs were sustained by IL-4-enriching tumors with low (neo)antigenic and T cell-depleted features. TIM3 + VISTA + TAMs showed an anti-inflammatory and protumorigenic phenotype coupled with inability to sense type I interferon (IFN). This was established with cancer cells succumbing to immunogenic cell death (ICD). Dying cancer cells not only triggered autocrine type I IFNs but also exposed HMGB1/VISTA that engaged TIM3/VISTA on TAMs to suppress paracrine IFN-responses. Accordingly, TIM3/VISTA blockade synergized with paclitaxel, an ICD-inducing chemotherapy, to repolarize TIM3 + VISTA + TAMs to proinflammatory TAMs that killed cancer cells via tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) signaling. We propose targeting TIM3 + VISTA + TAMs to overcome immunoresistant tumors.

An important challenge in the real-world management of patients with advanced clear-cell renal cell carcinoma (aRCC) is determining who might benefit from immune checkpoint blockade (ICB). Here we performed a comprehensive multiomics mapping of aRCC in the context of ICB treatment, involving discovery analyses in a real-world data cohort followed by validation in independent cohorts. We cross-connected bulk-tumor transcriptomes across >1,000 patients with validations at single-cell and spatial resolutions, revealing a patient-specific crosstalk between proinflammatory tumor-associated macrophages and (pre-)exhausted CD8 + T cells that was distinguished by a human leukocyte antigen repertoire with higher preference for tumoral neoantigens. A cross-omics machine learning pipeline helped derive a new tumor transcriptomic footprint of neoantigen-favoring human leukocyte antigen alleles. This machine learning signature correlated with positive outcome following ICB treatment in both real-world data and independent clinical cohorts. In experiments using the RENCA-tumor mouse model, CD40 agonism combined with PD1 blockade potentiated both proinflammatory tumor-associated macrophages and CD8 + T cells, thereby achieving maximal antitumor efficacy relative to other tested regimens. Thus, we present a new multiomics and spatial map of the immune-community architecture that drives ICB response in patients with aRCC., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The quality and quantity of tumor-infiltrating lymphocytes, particularly CD8 + T cells, are important parameters for the control of tumor growth and response to immunotherapy. Here, we show in murine and human cancers that these parameters exhibit circadian oscillations, driven by both the endogenous circadian clock of leukocytes and rhythmic leukocyte infiltration, which depends on the circadian clock of endothelial cells in the tumor microenvironment. To harness these rhythms therapeutically, we demonstrate that efficacy of chimeric antigen receptor T cell therapy and immune checkpoint blockade can be improved by adjusting the time of treatment during the day. Furthermore, time-of-day-dependent T cell signatures in murine tumor models predict overall survival in patients with melanoma and correlate with response to anti-PD-1 therapy. Our data demonstrate the functional significance of circadian dynamics in the tumor microenvironment and suggest the importance of leveraging these features for improving future clinical trial design and patient care., Competing Interests: Declaration of interests C.S. has been a consultant for Bayer and received speaker fees from Abbvie. M.J.P. has been a consultant for AstraZeneca, Debiopharm, Elstar Therapeutics, ImmuneOncia, KSQ Therapeutics, MaxiVax, Merck, Molecular Partners, Third Rock Ventures, and Tidal. F.S. received consulting fees from BMS/Celgene, Incyte, Kite/Gilead, speaker fees from Kite/Gilead, Incyte, travel support from Kite/Gilead, Novartis, AstraZeneca, Neovii, Janssen, and research funding from Kite/Gilead, Novartis, and BMS/Celgene. A.D.G. received consulting/advisory/lecture honoraria or research funding from Boehringer Ingelheim, SOTIO, Miltenyi Biotec, Novigenix, and IsoPlexis. R.B. received speaker fees from Janssen and is a mentee of the ENDEAVOUR-Breast program of Daichii Sankyo. All these relationships are unrelated to this study., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Current immunotherapies provide limited benefits against T cell-depleted tumors, calling for therapeutic innovation. Using multi-omics integration of cancer patient data, we predict a type I interferon (IFN) response HIGH state of dendritic cell (DC) vaccines, with efficacious clinical impact. However, preclinical DC vaccines recapitulating this state by combining immunogenic cancer cell death with induction of type I IFN responses fail to regress mouse tumors lacking T cell infiltrates. Here, in lymph nodes (LNs), instead of activating CD4 + /CD8 + T cells, DCs stimulate immunosuppressive programmed death-ligand 1-positive (PD-L1 + ) LN-associated macrophages (LAMs). Moreover, DC vaccines also stimulate PD-L1 + tumor-associated macrophages (TAMs). This creates two anatomically distinct niches of PD-L1 + macrophages that suppress CD8 + T cells. Accordingly, a combination of PD-L1 blockade with DC vaccines achieves significant tumor regression by depleting PD-L1 + macrophages, suppressing myeloid inflammation, and de-inhibiting effector/stem-like memory T cells. Importantly, clinical DC vaccines also potentiate T cell-suppressive PD-L1 + TAMs in glioblastoma patients. We propose that a multimodal immunotherapy and vaccination regimen is mandatory to overcome T cell-depleted tumors., Competing Interests: Declaration of interests A.D.G. received honoraria/funding from Boehringer Ingelheim, Miltenyi Biotec, Novigenix, SOTIO, and IsoPlexis., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

The cellular stress and immunity cycle is a cornerstone of organismal homeostasis. Stress activates intracellular and intercellular communications within a tissue or organ to initiate adaptive responses aiming to resolve the origin of this stress. If such local measures are unable to ameliorate this stress, then intercellular communications expand toward immune activation with the aim of recruiting immune cells to effectively resolve the situation while executing tissue repair to ameliorate any damage and facilitate homeostasis. This cellular stress-immunity cycle is severely dysregulated in diseased contexts like cancer. On one hand, cancer cells dysregulate the normal cellular stress responses to reorient them toward upholding growth at all costs, even at the expense of organismal integrity and homeostasis. On the other hand, the tumors severely dysregulate or inhibit various components of organismal immunity, for example, by facilitating immunosuppressive tumor landscape, lowering antigenicity, and increasing T-cell dysfunction. In this review we aim to comprehensively discuss the basis behind tumoral dysregulation of cellular stress-immunity cycle. We also offer insights into current understanding of the regulators and deregulators of this cycle and how they can be targeted for conceptualizing successful cancer immunotherapy regimen., (© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

CD8 + T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8 + T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8 + T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8 + T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific 'self-like' features. MSI CRC CD8 + T cells showed tumor-specific activation reminiscent of canonical 'T cell hot' tumors, whereas the MSS CRC CD8 + T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of 'pseudo-T cell hot' tumors. Consequently, MSI and MSS CRC CD8 + T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8 + T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8 + T cells to build a bulk tumor transcriptome classification for CRC patients. This "Immune Subtype Classification" (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8 + T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making., (© 2023. The Author(s).)

Obesity is associated with an increased risk of developing breast cancer (BC) and worse prognosis in BC patients, yet its impact on BC biology remains understudied in humans. This study investigates how the biology of untreated primary BC differs according to patients' body mass index (BMI) using data from >2,000 patients. We identify several genomic alterations that are differentially prevalent in overweight or obese patients compared to lean patients. We report evidence supporting an ageing accelerating effect of obesity at the genetic level. We show that BMI-associated differences in bulk transcriptomic profile are subtle, while single cell profiling allows detection of more pronounced changes in different cell compartments. These analyses further reveal an elevated and unresolved inflammation of the BC tumor microenvironment associated with obesity, with distinct characteristics contingent on the estrogen receptor status. Collectively, our analyses imply that obesity is associated with an inflammaging-like phenotype. We conclude that patient adiposity may play a significant role in the heterogeneity of BC and should be considered for BC treatment tailoring., (© 2023. The Author(s).)

Immunogenic cell death (ICD) refers to an immunologically distinct process of regulated cell death that activates, rather than suppresses, innate and adaptive immune responses. Such responses culminate into T cell-driven immunity against antigens derived from dying cancer cells. The potency of ICD is dependent on the immunogenicity of dying cells as defined by the antigenicity of these cells and their ability to expose immunostimulatory molecules like damage-associated molecular patterns (DAMPs) and cytokines like type I interferons (IFNs). Moreover, it is crucial that the host's immune system can adequately detect the antigenicity and adjuvanticity of these dying cells. Over the years, several well-known chemotherapies have been validated as potent ICD inducers, including (but not limited to) anthracyclines, paclitaxels, and oxaliplatin. Such ICD-inducing chemotherapeutic drugs can serve as important combinatorial partners for anti-cancer immunotherapies against highly immuno-resistant tumors. In this Trial Watch, we describe current trends in the preclinical and clinical integration of ICD-inducing chemotherapy in the existing immuno-oncological paradigms., Competing Interests: No potential conflict of interest was reported by the authors., (© 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.)

Immunotherapy based on immunecheckpoint blockade (ICB) using antibodies induces rejection of tumours and brings clinical benefit in patients with various cancer types 1 . However, tumours often resist immune rejection. Ongoing efforts trying to increase tumour response rates are based on combinations of ICB with compounds that aim to reduce immunosuppression in the tumour microenvironment but usually have little effect when used as monotherapies 2,3 . Here we show that agonists of α2-adrenergic receptors (α2-AR) have very strong anti-tumour activity when used as monotherapies in multiple immunocompetent tumour models, including ICB-resistant models, but not in immunodeficient models. We also observed marked effects in human tumour xenografts implanted in mice reconstituted with human lymphocytes. The anti-tumour effects of α2-AR agonists were reverted by α2-AR antagonists, and were absent in Adra2a-knockout (encoding α2a-AR) mice, demonstrating on-target action exerted on host cells, not tumour cells. Tumours from treated mice contained increased infiltrating T lymphocytes and reduced myeloid suppressor cells, which were more apoptotic. Single-cell RNA-sequencing analysis revealed upregulation of innate and adaptive immune response pathways in macrophages and T cells. To exert their anti-tumour effects, α2-AR agonists required CD4 + T lymphocytes, CD8 + T lymphocytes and macrophages. Reconstitution studies in Adra2a-knockout mice indicated that the agonists acted directly on macrophages, increasing their ability to stimulate T lymphocytes. Our results indicate that α2-AR agonists, some of which are available clinically, could substantially improve the clinical efficacy of cancer immunotherapy., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan-dioxygenase (TDO) are enzymes catabolizing the essential amino acid tryptophan into kynurenine. Expression of these enzymes is frequently observed in advanced-stage cancers and is associated with poor disease prognosis and immune suppression. Mechanistically, the respective roles of tryptophan shortage and kynurenine production in suppressing immunity remain unclear. Kynurenine was proposed as an endogenous ligand for the aryl hydrocarbon receptor (AHR), which can regulate inflammation and immunity. However, controversy remains regarding the role of AHR in IDO1/TDO-mediated immune suppression, as well as the involvement of kynurenine. In this study, we aimed to clarify the link between IDO1/TDO expression, AHR pathway activation and immune suppression., Methods: AHR expression and activation was analyzed by RT-qPCR and western blot analysis in cells engineered to express IDO1/TDO, or cultured in medium mimicking tryptophan catabolism by IDO1/TDO. In vitro differentiation of naïve CD4 + T cells into regulatory T cells (Tregs) was compared in T cells isolated from mice bearing different Ahr alleles or a knockout of Ahr , and cultured in medium with or without tryptophan and kynurenine., Results: We confirmed that IDO1/TDO expression activated AHR in HEK-293-E cells, as measured by the induction of AHR target genes. Unexpectedly, AHR was also overexpressed on IDO1/TDO expression. AHR overexpression did not depend on kynurenine but was triggered by tryptophan deprivation. Multiple human tumor cell lines overexpressed AHR on tryptophan deprivation. AHR overexpression was not dependent on general control non-derepressible 2 (GCN2), and strongly sensitized the AHR pathway. As a result, kynurenine and other tryptophan catabolites, which are weak AHR agonists in normal conditions, strongly induced AHR target genes in tryptophan-depleted conditions. Tryptophan depletion also increased kynurenine uptake by increasing SLC7A5 (LAT1) expression in a GCN2-dependent manner. Tryptophan deprivation potentiated Treg differentiation from naïve CD4 + T cells isolated from mice bearing an AHR allele of weak affinity similar to the human AHR., Conclusions: Tryptophan deprivation sensitizes the AHR pathway by inducing AHR overexpression and increasing cellular kynurenine uptake. As a result, tryptophan catabolites such as kynurenine more potently activate AHR, and Treg differentiation is promoted. Our results propose a molecular explanation for the combined roles of tryptophan deprivation and kynurenine production in mediating IDO1/TDO-induced immune suppression., Competing Interests: Competing interests: CAO is founder and managing director of cAHRmeleon Bioscience. CAO has patents on AHR inhibitors in cancer (WO2013034685); a method to multiplex tryptophan and its metabolites (WO2017072368); a transcriptional signature to determine AHR activity (WO2020201825); Interleukin-4-induced gene 1 (IL4I1) as a biomarker (WO2020208190) Interleukin-4-induced gene 1 (il4i1) and its metabolites as biomarkers for cancer (WO2021116357). BJVdE is cofounder of iTeos Therapeutics., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Tumour-associated macrophages (TAMs) are essential players in the tumour microenvironment (TME) and modulate various pro-tumorigenic functions such as immunosuppression, angiogenesis, cancer cell proliferation, invasion and metastasis, along with resistance to anti-cancer therapies. TAMs also mediate important anti-tumour functions and can clear dying cancer cells via efferocytosis. Thus, not surprisingly, TAMs exhibit heterogeneous activities and functional plasticity depending on the type and context of cancer cell death that they are faced with. This ultimately governs both the pro-tumorigenic and anti-tumorigenic activity of TAMs, making the interface between TAMs and dying cancer cells very important for modulating cancer growth and the efficacy of chemo-radiotherapy or immunotherapy. In this review, we discuss the interface of TAMs with cancer cell death from the perspectives of cell death pathways, TME-driven variations, TAM heterogeneity and cell-death-inducing anti-cancer therapies. We believe that a better understanding of how dying cancer cells influence TAMs can lead to improved combinatorial anti-cancer therapies, especially in combination with TAM-targeting immunotherapies.

Dendritic cell (DC)-based vaccination for cancer treatment has seen considerable development over recent decades. However, this field is currently in a state of flux toward niche-applications, owing to recent paradigm-shifts in immuno-oncology mobilized by T cell-targeting immunotherapies. DC vaccines are typically generated using autologous (patient-derived) DCs exposed to tumor-associated or -specific antigens (TAAs or TSAs), in the presence of immunostimulatory molecules to induce DC maturation, followed by reinfusion into patients. Accordingly, DC vaccines can induce TAA/TSA-specific CD8 + /CD4 + T cell responses. Yet, DC vaccination still shows suboptimal anti-tumor efficacy in the clinic. Extensive efforts are ongoing to improve the immunogenicity and efficacy of DC vaccines, often by employing combinatorial chemo-immunotherapy regimens. In this Trial Watch, we summarize the recent preclinical and clinical developments in this field and discuss the ongoing trends and future perspectives of DC-based immunotherapy for oncological indications., Competing Interests: ADG has received speaker/lecture remuneration or consultancy fees from Boehringer Ingelheim, Miltenyi Biotec or Isoplexis. AC is a contracted researcher for Oncoinvent AS and Novocure and a consultant for Sotio a.s., (© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.)

By tying peptide fragments originally distant in parental proteins, the proteasome can generate spliced peptides that are recognized by CTL. This occurs by transpeptidation involving a peptide-acyl-enzyme intermediate and another peptide fragment present in the catalytic chamber. Four main subtypes of proteasomes exist: the standard proteasome (SP), the immunoproteasome, and intermediate proteasomes β1-β2-β5i (single intermediate proteasome) and β1i-β2-β5i (double intermediate proteasome). In this study, we use a tandem mass tag-quantification approach to study the production of six spliced human antigenic peptides by the four proteasome subtypes. Peptides fibroblast growth factor-5 172-176/217-220, tyrosinase 368-373/336-340 , and gp100 40-42/47-52 are better produced by the SP than the other proteasome subtypes. The peptides SP110 296-301/286-289 , gp100 195-202/191or192 , and gp100 47-52/40-42 are better produced by the immunoproteasome and double intermediate proteasome. The current model of proteasome-catalyzed peptide splicing suggests that the production of a spliced peptide depends on the abundance of the peptide splicing partners. Surprisingly, we found that despite the fact that reciprocal peptides RTK_QLYPEW (gp100 40-42/47-52 ) and QLYPEW_RTK (gp100 47-52/40-42 ) are composed of identical splicing partners, their production varies differently according to the proteasome subtype. These differences were maintained after in vitro digestions involving identical amounts of the splicing fragments. Our results indicate that the amount of splicing partner is not the only factor driving peptide splicing and suggest that peptide splicing efficiency also relies on other factors, such as the affinity of the C-terminal splice reactant for the primed binding site of the catalytic subunit., (Copyright © 2022 by The American Association of Immunologists, Inc.)

Monoclonal antibodies (mAbs) blocking immune checkpoints such as programmed death ligand 1 (PD-L1) have yielded strong clinical benefits in many cancer types. Still, the current limitations are the lack of clinical response in a majority of patients and the development of immune-related adverse events in some. As an alternative to PD-L1-specific antibody injection, we have developed an approach based on the engineering of tumor-targeting T cells to deliver intratumorally an anti-PD-L1 nanobody. In the MC38-OVA model, our strategy enhanced tumor control as compared with injection of PD-L1-specific antibody combined with adoptive transfer of tumor-targeting T cells. As a possible explanation for this, we demonstrated that PD-L1-specific antibody massively occupied PD-L1 in the periphery but failed to penetrate to PD-L1-expressing cells at the tumor site. In sharp contrast, locally delivered anti-PD-L1 nanobody improved PD-L1 blocking at the tumor site while avoiding systemic exposure. Our approach appears promising to overcome the limitations of immunotherapy based on PD-L1-specific antibodies., (©2022 American Association for Cancer Research.)

(1) Background: Inter-tumour heterogeneity is one of cancer's most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only available for a handful of cases (each case being a given drug acting on tumors of a specific cancer type). A way to generate predictive models for the remaining cases is with suitable machine learning algorithms that are yet to be applied to existing in vitro pharmacogenomics datasets. Here, we apply XGBoost integrated with a stringent feature selection approach, which is an algorithm that is advantageous for these high-dimensional problems. Thus, we identified and validated 118 predictive models for 62 drugs across five cancer types by exploiting four molecular profiles (sequence mutations, copy-number alterations, gene expression, and DNA methylation). Predictive models were found in each cancer type and with every molecular profile. On average, no omics profile or cancer type obtained models with higher predictive accuracy than the rest. However, within a given cancer type, some molecular profiles were overrepresented among predictive models. For instance, CNA profiles were predictive in breast invasive carcinoma (BRCA) cell lines, but not in small cell lung cancer (SCLC) cell lines where gene expression (GEX) and DNA methylation profiles were the most predictive. Lastly, we identified the best XGBoost model per cancer type and analyzed their selected features. For each model, some of the genes in the selected list had already been found to be individually linked to the response to that drug, providing additional evidence of the usefulness of these models and the merits of the feature selection scheme.

We investigated the interaction between osmotic stress and auxin signaling in leaf growth regulation. Therefore, we grew Arabidopsis thaliana seedlings on agar media supplemented with mannitol to impose osmotic stress and 1-naphthaleneacetic acid (NAA), a synthetic auxin. We performed kinematic analysis and flow-cytometry to quantify the effects on cell division and expansion in the first leaf pair, determined the effects on auxin homeostasis and response (DR5::β-glucuronidase), performed a next-generation sequencing transcriptome analysis and investigated the response of auxin-related mutants. Mannitol inhibited cell division and expansion. NAA increased the effect of mannitol on cell division, but ameliorated its effect on expansion. In proliferating cells, NAA and mannitol increased free IAA concentrations at the cost of conjugated IAA and stimulated DR5 promotor activity. Transcriptome analysis shows a large overlap between NAA and osmotic stress-induced changes, including upregulation of auxin synthesis, conjugation, transport and TRANSPORT INHIBITOR RESPONSE1 (TIR1) and AUXIN RESPONSE FACTOR (ARF) response genes, but downregulation of Aux/IAA response inhibitors. Consistently, arf7/19 double mutant lack the growth response to auxin and show a significantly reduced sensitivity to osmotic stress. Our results show that osmotic stress inhibits cell division during leaf growth of A. thaliana at least partly by inducing the auxin transcriptional response., (© 2020 The Authors. New Phytologist © 2020 New Phytologist Trust.)

Drug combinations are of great interest for cancer treatment. Unfortunately, the discovery of synergistic combinations by purely experimental means is only feasible on small sets of drugs. In silico modeling methods can substantially widen this search by providing tools able to predict which of all possible combinations in a large compound library are synergistic. Here we investigate to which extent drug combination synergy can be predicted by exploiting the largest available dataset to date (NCI-ALMANAC, with over 290,000 synergy determinations). Each cell line is modeled using primarily two machine learning techniques, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), on the datasets provided by NCI-ALMANAC. This large-scale predictive modeling study comprises more than 5,000 pair-wise drug combinations, 60 cell lines, 4 types of models, and 5 types of chemical features. The application of a powerful, yet uncommonly used, RF-specific technique for reliability prediction is also investigated. The evaluation of these models shows that it is possible to predict the synergy of unseen drug combinations with high accuracy (Pearson correlations between 0.43 and 0.86 depending on the considered cell line, with XGBoost providing slightly better predictions than RF). We have also found that restricting to the most reliable synergy predictions results in at least 2-fold error decrease with respect to employing the best learning algorithm without any reliability estimation. Alkylating agents, tyrosine kinase inhibitors and topoisomerase inhibitors are the drugs whose synergy with other partner drugs are better predicted by the models. Despite its leading size, NCI-ALMANAC comprises an extremely small part of all conceivable combinations. Given their accuracy and reliability estimation, the developed models should drastically reduce the number of required in vitro tests by predicting in silico which of the considered combinations are likely to be synergistic.

Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models., Competing Interests: CONFLICTS OF INTEREST The authors declare no conflicts of interest.

Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC 50 10 µM. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions.

Withaferin A (WA), a natural phytochemical derived from the plant Withania somnifera, is a well-studied bioactive compound exerting a broad spectrum of health promoting effects. To gain better insight in the potential therapeutic capacity of WA, we evaluated the transcriptional effects of WA on primary human umbilical vein endothelial cells (HUVECs) and an endothelial cell line (EA.hy926). RNA microarray analysis of WA treated HUVEC cells demonstrated increased expression of the antioxidant gene heme oxygenase (HO-1). Transcriptional regulation of this gene is strongly dependent on the transcription factor NF-E2-related factor 2 (Nrf2), which senses chemical changes in the cell and coordinates transcriptional responses to maintain chemical homeostasis via expression of antioxidant genes and cytoprotective Phase II detoxifying enzymes. Under normal conditions, Nrf2 is kept in the cytoplasm by Kelch-like ECH-associated protein 1 (Keap1), an adaptor protein controlling the half-life of Nrf2 via constant proteasomal degradation. In this study we demonstrate that WA time- and concentration-dependently induces HO-1 expression in endothelial cells via upregulation and increased nuclear translocation of Nrf2. According to the crucial negative regulatory role of Keap1 in Nrf2 expression levels, a direct interaction of WA with Keap1 could be demonstrated. In vitro and in silico evaluations suggest that specific cysteine residues in Keap1 might be involved in the interaction with WA., (Copyright © 2016 Elsevier Inc. All rights reserved.)

The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ''omics'' data, this paper addresses some of the specific challenges and solutions of the proteomics field., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences., (© The Author 2013. Published by Oxford University Press.)

Unlabelled: Postherpetic neuralgia (PHN) is the most common complication of herpes zoster and is typified by a lingering pain that can last months or years after the characteristic herpes zoster rash disappears. It is well known that there are risk factors for the development of PHN, such as its association with certain HLA alleles. In this study, previous HLA genotyping results were collected and subjected to a meta-analysis with increased statistical power. This work shows that the alleles HLA-A*33 and HLA-B*44 are significantly enriched in PHN patients, while HLA-A*02 and HLA-B*40 are significantly depleted. Prediction of the varicella-zoster virus (VZV) peptide affinity for these four HLA variants by using one in-house-developed and two existing state-of-the-art major histocompatibility complex (MHC) class I ligand prediction methods reveals that there is a great difference in their absolute and relative peptide binding repertoires. It was observed that HLA-A*02 displays a high affinity for an ∼7-fold-higher number of VZV peptides than HLA-B*44. Furthermore, after correction for HLA allele-specific limitations, the relative affinity of HLA-A*33 and HLA-B*44 for VZV peptides was found to be significantly lower than those of HLA-A*02 and HLA-B*40. In addition, HLA peptide affinity calculations indicate strong trends for VZV to avoid high-affinity peptides in some of its proteins, independent of the studied HLA allele., Importance: Varicella-zoster virus can cause two distinct diseases: chickenpox (varicella) and shingles (herpes zoster). Varicella is a common disease in young children, while herpes zoster is more frequent in older individuals. A common complication of herpes zoster is postherpetic neuralgia, a persistent and debilitating pain that can remain months up to years after the resolution of the rash. In this study, we show that the relative affinity of HLA variants associated with higher postherpetic neuralgia risk for varicella-zoster virus peptides is lower than that of variants with a lower risk. These results provide new insight into the development of postherpetic neuralgia and strongly support the hypothesis that one of its possible underlying causes is a suboptimal anti-VZV immune response due to weak HLA binding peptide affinity., (Copyright © 2015, American Society for Microbiology. All Rights Reserved.)

Glucocorticoids (GCs) selectively trigger cell death in the multiple myeloma cell line MM1S which express NR3C1/Glucocorticoid Receptor (GR) protein, but fail to kill MM1R cells which lack GR protein. Given recent demonstrations of altered microRNA profiles in a diverse range of haematological malignancies and drug resistance, we characterized GC inducible mRNA and microRNA transcription profiles in GC sensitive MM1S as compared to GC resistant MM1R cells. Transcriptome analysis revealed that GCs regulate expression of multiple genes involved in cell cycle control, cell organization, cell death and immunological disease in MM1S cells, which remain unaffected in MM1R cells. With respect to microRNAs, mir-150-5p was identified as the most time persistent GC regulated microRNA, out of 5 QPCR validated microRNAs (mir-26b, mir-125a-5p, mir-146-5p, mir-150-5p, and mir-184), which are GC inducible in MM1S but not in MM1R cells. Functional studies further revealed that ectopic transfection of a synthetic mir-150-5p mimics GR dependent gene expression changes involved in cell death and cell proliferation pathways. Remarkably, despite the gene expression changes observed, overexpression of mir-150-5p in absence of GCs did not trigger significant cytotoxicity in MM1S or MM1R cells. This suggests the requirement of additional steps in GC induced cell death, which can not be mimicked by mir-150-5p overexpression alone. Interestingly, a combination of mir-150-5p transfection with low doses GC in MM1S cells was found to sensitize therapy response, whereas opposite effects could be observed with a mir-150-5p specific antagomir. Although mir-150-5p overexpression did not substantially change GR expression levels, it was found that mir-150-5p evokes GR specific effects through indirect mRNA regulation of GR interacting transcription factors and hormone receptors, GR chaperones, as well as various effectors of unfolded protein stress and chemokine signalling. Altogether GC-inducible mir-150-5p adds another level of regulation to GC specific therapeutic responses in multiple myeloma.