Nucleotide metabolism in cancer cells fuels a UDP-driven macrophage cross-talk, promoting immunosuppression and immunotherapy resistance.

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 P2Y6. Pharmacologic or genetic inhibition of CDA in cancer cells (or P2Y6 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, CDAhigh cancer cells match with T cell cytotoxicity dysfunction and P2RY6high TAMs. Overall, we suggest CDA and P2Y6 as potential targets for cancer immunotherapy.

A spatial architecture-embedding HLA signature to predict clinical response to immunotherapy in renal cell carcinoma.

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.

Circadian tumor infiltration and function of CD8+ T cells dictate immunotherapy efficacy.

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.

Lymph node and tumor-associated PD-L1+ macrophages antagonize dendritic cell vaccines by suppressing CD8+ T cells.

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) responseHIGH 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.

The cell stress and immunity cycle in cancer: Toward next generation of cancer immunotherapy.

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.

Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer.

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.

Obesity-associated changes in molecular biology of primary breast cancer.

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.

Tryptophan depletion sensitizes the AHR pathway by increasing AHR expression and GCN2/LAT1-mediated kynurenine uptake, and potentiates induction of regulatory T lymphocytes.

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.

Trial watch: chemotherapy-induced immunogenic cell death in oncology.

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.

Tumour immune rejection triggered by activation of α2-adrenergic receptors.

Immunotherapy based on immunecheckpoint blockade (ICB) using antibodies induces rejection of tumours and brings clinical benefit in patients with various cancer types1. 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 monotherapies2,3. 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.

Multiomics and spatial mapping characterizes human CD8+ T cell states in cancer.

Clinically relevant immunological biomarkers that discriminate between diverse hypofunctional states of tumor-associated CD8+ T cells remain disputed. Using multiomics analysis of CD8+ T cell features across multiple patient cohorts and tumor types, we identified tumor niche-dependent exhausted and other types of hypofunctional CD8+ T cell states. CD8+ T cells in "supportive" niches, like melanoma or lung cancer, exhibited features of tumor reactivity-driven exhaustion (CD8+ TEX). These included a proficient effector memory phenotype, an expanded T cell receptor (TCR) repertoire linked to effector exhaustion signaling, and a cancer-relevant T cell-activating immunopeptidome composed of largely shared cancer antigens or neoantigens. In contrast, "nonsupportive" niches, like glioblastoma, were enriched for features of hypofunctionality distinct from canonical exhaustion. This included immature or insufficiently activated T cell states, high wound healing signatures, nonexpanded TCR repertoires linked to anti-inflammatory signaling, high T cell-recognizable self-epitopes, and an antiproliferative state linked to stress or prodeath responses. In situ spatial mapping of glioblastoma highlighted the prevalence of dysfunctional CD4+:CD8+ T cell interactions, whereas ex vivo single-cell secretome mapping of glioblastoma CD8+ T cells confirmed negligible effector functionality and a promyeloid, wound healing-like chemokine profile. Within immuno-oncology clinical trials, anti-programmed cell death protein 1 (PD-1) immunotherapy facilitated glioblastoma's tolerogenic disparities, whereas dendritic cell (DC) vaccines partly corrected them. Accordingly, recipients of a DC vaccine for glioblastoma had high effector memory CD8+ T cells and evidence of antigen-specific immunity. Collectively, we provide an atlas for assessing different CD8+ T cell hypofunctional states in immunogenic versus nonimmunogenic cancers.

The Interface of Tumour-Associated Macrophages with Dying Cancer Cells in Immuno-Oncology.

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.

Trial watch: Dendritic cell (DC)-based immunotherapy for cancer.

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.

New Insights into the Mechanisms of Proteasome-Mediated Peptide Splicing Learned from Comparing Splicing Efficiency by Different Proteasome Subtypes.

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 ß1-ß2-ß5i (single intermediate proteasome) and ß1i-ß2-ß5i (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-5172-176/217-220, tyrosinase368-373/336-340, and gp10040-42/47-52 are better produced by the SP than the other proteasome subtypes. The peptides SP110296-301/286-289, gp100195-202/191or192, and gp10047-52/40-42 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 (gp10040-42/47-52) and QLYPEW_RTK (gp10047-52/40-42) 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.

T Cell-Mediated Targeted Delivery of Anti-PD-L1 Nanobody Overcomes Poor Antibody Penetration and Improves PD-L1 Blocking at the Tumor Site.

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.

Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles.

(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.

Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.

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.

Osmotic stress inhibits leaf growth of Arabidopsis thaliana by enhancing ARF-mediated auxin responses.

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::ß-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.

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data.

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.