Though immune checkpoint inhibition has recently shown encouraging clinical efficacy in mesothelioma, most patients do not respond. Combining immune checkpoint inhibition with radiotherapy presents an attractive option for improving treatment responses owing to the various immunomodulatory effects of radiation on tumors. However, the ideal dosing and scheduling of combined treatment remains elusive, as it is poorly studied in mesothelioma. The present study characterizes the dose- and time-dependent changes to expression of various immune markers and cytokines important to antitumor responses following irradiation of mesothelioma cell lines.Two murine  and two human  mesothelioma cell lines were treated with titrated gamma-radiation doses (1-8 Gy) and the expression of MHC class-I, MHC class-II and PD-L1 was measured over a series of post-irradiation timepoints (1-72 hours) by flow cytometry. Levels of cytokines IL-1α, IL-1β, IL-6, IL-10, IL-12p70, IL-17A, IL-23, IL-27, MCP-1, IFN-β, IFN-γ, TNF-α, and GM-CSF were measured by multiplex immunoassay in murine cell lines following 8 Gy radiation.Following irradiation, a dose-dependent upregulation of MHC-I and PD-L1 was observed on three of the four cell lines studied to varying extents. For all cell lines, the increase in marker expression was most pronounced 72 hours after radiation. At this timepoint, increases in levels of cytokines IFN-β, MCP-1 and IL-6 were observed following irradiation with 8 Gy in AB1 but not AE17, reflecting patterns in marker expression.Overall, this study establishes the dose- and time-dependent changes in immune marker expression of commonly studied mesothelioma cell lines following radiation and will inform future study into optimal dosing and scheduling of combined radiotherapy and immune checkpoint inhibition for mesothelioma. Mesothelioma is an aggressive, incurable cancer in urgent need of more effective therapies. Mesothelioma may develop in any mesothelial surface of the body, but most commonly it arises in the lung pleura following inhalation of asbestos, which acts as a carcinogen . The lonImmune checkpoints such as cytotoxic T-lymphocyte-associated protein-4 (CTLA-4), and the programmed death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) signaling axis are co-inhibitory pathways that physiologically prevent inappropriate activation of immune responses, but are often upregulated by cancers to prevent or dampen T-cell activation and thus suppress antitumor activity . HoweverThe administration of other treatment modalities in conjunction with ICI, such as radiotherapy (RT), may increase both the rate and durability of responses. In mesothelioma, RT is used routinely during surgical resection as well as palliatively to assist in symptoms of pain and dyspnoea . Though Preclinical studies in some cancers have shown improved efficacy of combined RT and ICI over either treatment alone –20, and Murine mesothelioma cell lines AB1 and AE17 were generated as previously described . Human mMurine mesothelioma cell lines were treated with 10 ng/mL recombinant mouse interferon-gamma (IFN-γ)  and incubated for 48 h to induce MHC-I/II and PD-L1 expression. Similarly, human mesothelioma cell lines were subject to a 48 h stimulation with 100 ng/mL recombinant human IFN-γ .6 cells/mL. Cells were irradiated in separate 50 mL tubes by a caesium-137 source at a dose rate of 3.78 Gy/min  at room temperature with doses of 1, 2, 4, 6, and 8 Gy. As the actual radiation dose administered may vary by ± 15% depending on position in the rotating irradiation cannister, tubes were positioned centrally for all irradiations. Immediately after radiation 1 × 106 cells were seeded into separate T175 culture flasks for each timepoint. Cells were then incubated for 1, 6, 24, 48 or 72 h post-irradiation. At the completion of each timepoint, 0.5 × 106 cells were collected per dose and cryopreserved at -80°C until analysis. For cytokine studies, cells were irradiated at 8 Gy and incubated for 72 h, after which cell culture supernatant was collected, centrifuged  to remove debris, and stored at -80°C until analysis. All irradiations were conducted in triplicate.At 80% confluency, cells in culture were harvested and resuspended in R10 at 1 × 10Surface expression of MHC-I, MHC-II and PD-L1 was assessed on irradiated cell lines by flow cytometry. Frozen samples were thawed in a 37°C water bath for one minute, then transferred to fresh 15 mL tubes. Cells were washed in 10 mL R10 by centrifugation . Each sample was resuspended in 200 µl R10, transferred to a 96 well plate, and washed by centrifugation . Samples were washed twice with 200 µL/well PBS . Fixable Viability Dye eFluorTM 506 (eF506) diluted 1/1600 in 20 µL PBS was added to the appropriate wells, and samples were incubated for 20 minutes in the dark at room temperature. Samples were then washed twice in 200 µL flow buffer (PBS/2% NCS/5mM EDTA)/well. Samples were stained with 20 µL of appropriate antibody cocktail . Gating was optimized using FMO controls for each marker  and FACSDiva software (BD Biosciences), and a minimum of 10,000 live events were recorded per sample. Data were analyzed using FlowJo software, version 10.8.0  to generate values for the percentage of cells positive for MHC-I, MHC-II and PD-L1 expression and median fluorescence intensity (MFI). Normalized MFI (nef) was generated by dividing the MFI of each sample with MFI of an unstained control. All radiation doses and timepoints within any individual irradiation experimental repeat were stained and data acquired in the same session.Levels of inflammatory cytokines  were measured in murine mesothelioma cell lines using the LEGENDplex™ 13-plex Mouse Inflammation Panel with Filter Plate . The assay was performed according to manufacturer’s instructions. Standards and samples were assayed in duplicate. Samples were acquired with the FACSCantoII and FACSDiva software (BD Biosciences). Standard curves and cytokine data were generated using the free cloud-based LEGENDplex™ Data Analysis Software Suite (Biolegend).n = 3 independent experimental repeats in all figures. P < 0.05 was considered statistically significant.Data were analyzed and visualized using GraphPad Prism software (version 9.1.2). Immune marker expression was analyzed by two-way analysis of variance (ANOVA), followed by multiple pairwise comparisons with Tukey adjustment. The non-parametric Mann-Whitney U test was used to compare concentrations of a given cytokine in un-irradiated (0 Gy) versus irradiated (8 Gy) samples. Results are presented as mean ± one standard deviation of Prior to irradiation, we first assessed our mesothelioma cell lines for surface expression of three markers commonly found on tumor cells, and of particular relevance to cancer immunotherapy: MHC-I, MHC-II and PD-L1. These were present at varying levels of expression at baseline Figure 1We previously showed that effects of radiation on cell proliferation and survival saturate at doses of 8 Gy in the mesothelioma cell lines studied ; therefoA dose-dependent upregulation of surface MHC-I on both murine mesothelioma cell lines was observed following irradiation , and on the other hand constitutive elevation of PD-L1 to dampen the activity of tumor-specific T cells (as in AB1). Previous preclinical studies in other cancers including hepatocellular carcinoma , pancreain vitro for this cell line. In contrast, both MHC-I and PD-L1 were constitutively expressed by JU77 cells and could be further upregulated by IFN-γ, but expression was unchanged by any dose of radiation administered. It is possible that radiation treatment may have resulted in other immune-related phenotypic or transcriptional changes in this cell line that were beyond the scope of this study.We also sought to compare murine mesothelioma cell lines to human cell lines. Interestingly, though MHC-I was initially poorly expressed by the BYE cell line, radiation strongly induced MHC-I expression in a dose-dependent manner. MHC-I expression was saturated at 6 Gy, showing that such effects can be achieved in humans with clinically used radiation doses. Interestingly, IFN-γ treatment did not induce MHC-I expression on BYE, suggesting an IFNγ-independent mechanism of upregulation Optimizing the timing of delivering ICI with respect to RT is essential to improve antitumor immune responses. However, few studies have measured the dynamic changes to marker expression in response to RT; the present study therefore assessed expression at early (1-6 h) and late (24-72 h) stages post-irradiation. Importantly, we show that where modulation of immune markers is observed, this occurs maximally and, in most cases, solely at the 72 h timepoint, regardless of the level of radiation. This differs from a study in a melanoma cell line, where increased expression of cells irradiated with 10 and 25 Gy was apparent as early as 18 h after radiation; this may have been due to the higher doses administered in this study . Our finin vivo.We hypothesized that observed increases in MHC-I and PD-L1 would associate with changes in the profile of inflammatory cytokines secreted by our cell lines. Although increased levels of MCP-1, IL-6 and IFN-β in AB1 following irradiation with 8 Gy were observed, this did not reach the set level of significance for this study. Interestingly however, the lack of change to these cytokines in AE17 corresponds with the relatively lower increases in MHC-I and PD-L1 expression observed. The increase in MCP-1 observed in AB1 is consistent with a study in a breast carcinoma cell line, where irradiation with 9 and 23 Gy significantly increased levels of this cytokine. Furthermore, IL-6 –35 and Iin vitro, only single doses were administered in this study. Fractionation aims to affect a greater proportion of proliferating tumor cells by delivering multiple low doses at different times, while minimizing toxicity to healthy cells. Future work should establish whether different levels of fractionation result in more pronounced or prolonged antitumor effects compared to single doses. Nevertheless, the studied doses reflect those used in conventional fractionation (2 Gy), as well as hypo-fractionated (> 2 Gy) or hyper-fractionated (< 2 Gy) schedules.One limitation of this study relates to our use of single-dose RT. While fractionated doses of RT are commonly used in the clinic, due to difficulties of studying fractionation in vivo. Another avenue for study is the search for biomarkers to predict likelihood of response to RT and ICI, and dynamic changes to tumor or immune cell expression following radiation or ICI may be one such biomarker  (APP1163065) and the National Health and Medical Research Council (NHMRC) Centres of Research Excellence Scheme (APP1197652 and APP1107043). This material is the responsibility of the authors and does not reflect the views of these funding bodies.The authors acknowledge the facilities, and the scientific and technical assistance of the Centre for Microscopy, Characterization & Analysis, The University of Western Australia, a facility funded by the University, State and Commonwealth Governments. We thank staff managing the NCARD biobank for access to human cell lines in this study. We also thank Dr Tamara Abel, Dr Blake Klyen and Dr Don Heng at the Telethon Kids Institute for training and assistance with the gamma irradiator.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Despite decades of clinical use and detailed understandings of their mechanisms of action, clinically useful predictive biomarkers for approved microtubule targeting agents such as eribulin, paclitaxel and vinorelbine have eluded development. Our results now provide the basis for gene expression-based, drug-specific predictive biomarkers for eribulin and vinorelbine, as well as deeper understandings of the molecular pathways associated with eribulin and vinorelbine response.p-values and drug overlaps, yielding 52, 29 and 80 genes uniquely associated with eribulin, paclitaxel and vinorelbine, respectively. Further restriction to minimum 2-fold ratios followed by reintroducing data from the middle two quartiles identified 9 and 13 drug-specific unique fingerprint genes for eribulin and vinorelbine, respectively; surprisingly, no gene met all criteria for paclitaxel. Interactome and Reactome pathway analyses showed that unique fingerprint genes of both drugs were primarily associated with cellular signaling, not microtubule-related pathways, although considerable differences existed in individual pathways identified. Finally, four-gene  and five-gene  multivariate regression models for eribulin and vinorelbine showed high statistical correlation with drug-specific responses across the 100 cell lines and accurately calculated predicted mean IC50s for the most and least sensitive cell line quartiles as surrogates for responders and nonresponders, respectively. Collectively, these results provide a foundation for developing drug-specific predictive biomarkers for eribulin and vinorelbine.Eribulin, a natural product-based microtubule targeting agent with cytotoxic and noncytotoxic mechanisms, is FDA approved for certain patients with advanced breast cancer and liposarcoma. To investigate the feasibility of developing drug-specific predictive biomarkers, we quantified antiproliferative activities of eribulin versus paclitaxel and vinorelbine against 100 human cancer cell lines from the Cancer Cell Line Encyclopedia, and correlated results with publicly available databases to identify genes and pathways associated with eribulin response, either uniquely or shared with paclitaxel or vinorelbine. Mean expression ratios of 11,985 genes between the most and least sensitive cell line quartiles were sorted by  Microtubule targeting agents (MTAs), including eribulin, several taxanes, vinca alkaloids, and at least one epothilone, are important clinical anticancer drugs for several cancer types ,2,3,4. DEribulin, a synthetic analog of the marine sponge natural product halichondrin B , is an M®. Paclitaxel and vinorelbine tartrate  were obtained as dry powders from Selleckchem . Eribulin, paclitaxel and vinorelbine were prepared as 10 mM stock solutions in 100% (v/v) DMSO, aliquoted into small volumes and stored at −20 °C until day of use. Although not a designated test agent for this study, cisplatin was included in all assays as an internal reference control for assay performance; cisplatin was obtained as a laboratory grade liquid formulation from Hospira Australia Pty Ltd. , with storage per manufacturer’s instructions and dilutions for cell culture studies on day of use.Eribulin mesylate  was supplied by Eisai Inc.  as laboratory-grade dry powder active pharmaceutical ingredient (API) obtained from the same manufacturing stream used for Eisai’s branded clinical product, HalavenTM in vitro cell line screening service (see One hundred established human cancer cell lines were selected based on inclusion in both the CCLE and Crown Bioscience’s OmniPanelvice see . A list TM in vitro cell line screening service in their Beijing (China) laboratories. All OmniPanelTM cell lines are routinely tested for mycoplasma and authenticated using short tandem repeat (STR) DNA profiling. Testing was conducted using the CellTiter-Glo® Luminescent Cell Viability Assay  following 72 h compound exposures in 96-well plate assay formats. Compounds were added 24 h after cell seeding into plates, with initial seeding densities for individual cell lines having been previously optimized for OmniPanelTM screening. Based on the authors’ prior experience with the test agents, assays employed 9-step half-log test concentrations of 30 pM–300 nM for eribulin and 100 pM–1 μM for paclitaxel and vinorelbine. Concentrations of test agents inhibiting 50% of viable cell densities in vehicle-treated control wells compared to wells with the highest drug concentrations were defined as IC50s and were determined using GraphPad Prism software . Dose–response curve fitting used a nonlinear regression model with sigmoidal dose response, fixed 100% top y-axis values defined by vehicle and floating bottom y-axis values at either the highest concentration tested or another concentration that resulted in a minimum y-axis reading, ensuring that calculated IC50s represent actual antiproliferative biological responses occurring during the 72 h treatment period without plateau artifacts based on different growth rates of different cell lines.Measurement of antiproliferative activities of test agents against the selected 100 CCLE cell lines was performed by Crown Bioscience under contract from Eisai Inc., using Crown’s OmniPanelIC50s for 5 cell lines for eribulin  and 1 cell line for paclitaxel (HCT-15) exceeded the 300 nM and 1000 nM top concentrations used for these 2 drugs, respectively, so surrogate values of 301 nM eribulin and 1001 nM paclitaxel were assigned for purposes of defining cell lines in the bottom (least sensitive) quartile and calculating means/medians of bottom quartiles. Although using such surrogates does not change assignment of bottom quartiles or median IC50 values, it probably results in minor underestimations of mean bottom quartile IC50s, SDs and SEMs for eribulin and paclitaxel, although such underestimations are likely to be small due to the large overall number of cell lines tested.https://depmap.org/portal/ accessed on 3 August 2021; release version “DepMap Public 18Q2”, 2 May 2018). DepMap data using the symbol SEPT10 were replaced with the currently accepted symbol SEPTIN10. Expression values are log2 gene-level reads per kilobase million (RPKM) derived from RNA Sequencing (RNA-Seq), aligned using TopHat version 1.4 and quantified using the pipeline developed for the Genotype-Tissue Expression (GTEx) project as described by Tsherniak et al. | ≥ 1) and (iii) after reintroducing data from the middle two cell line quartiles, retaining only those genes with statistical significance (p < 0.05) for the cognate drug in full linear regression analysis with all 100 cell lines, but lacking statistical significance for both noncognate drugs. Genes meeting all the above criteria for a given drug were designated as unique fingerprint genes or UFGs for that drug. No gene met all criteria for paclitaxel; thus, no paclitaxel UFGs were identified in this study.Ratios of mean expression levels for each gene between the most and least sensitive cell line quartiles  for each drug were calculated. Genes with top/bottom quartile ratios with ® Cloud  ≥ 1). Following this restriction, data from the middle two cell line quartiles were then reintroduced, and each gene in the three subsets was subjected to full linear regression analysis including expression data and IC50s from all 100 cell lines. Only genes with expression showing statistical significance for a given cognate drug but lacking statistical significance for the other two noncognate drugs were designated as UFGs for each drug. This selection process yielded 9 UFGs for eribulin and 13 UFGs for vinorelbine  not utilizing gene expression information from the 50 cell lines in the middle two quartiles, (ii) accepting a risk that small numbers of cell lines in the top and bottom quartiles might have outlier expression patterns that could disproportionately influence gene selection, and (iii) failing to provide a basis for identifying drug-specific UFGs based on the informational power available from all 100 cell lines. To address these limitations, a final curation of the 52, 29 and 80 ectively F,G, was orelbine . UnexpecC5ORF38 and GPR157, were not themselves captured by network propagation. Interestingly, despite sharing mechanistic similarity as MTAs, only four genes overlapped between the eribulin and vinorelbine 100-gene propagated networks . We speculate that this may relate to the fact that the original UFGs were selected, in part, based on lack of correlations to noncognate drugs.We next asked what mechanisms and pathways were associated with eribulin and vinorelbine UFGs. To this end, network propagation  analysesTo further elucidate the pathways associated with the eribulin and vinorelbine UFG interactomes, the eribulin and vinorelbine 100-gene propagated networks were used as query inputs for Reactome pathway enrichment analyses. The resulting Reactome maps indicate that most pathways for both drugs fall into three main Reactome groupings or ‘islands’: the Signal Transduction, Immune System and Cell Cycle islands C,D. Whilp < 0.05 significance plus q < 0.1 to account for FDR, 26 and 122 Reactome pathways were identified for eribulin and vinorelbine UFG interactomes, respectively  by first calculating each cell line’s predicted IC50 based on individual expression of each of the 9 eribulin or 13 vinorelbine UFGs for that cell line, averaging the resulting 9 or 13 predicted IC50s for each cell line, then comparing the averaged predicted IC50s to actual measured IC50s for each cell line for all three drugs. Although no gene met the criteria for assignment as a paclitaxel UFG, predicted IC50s based on expression of eribulin or vinorelbine UFGs are conceptually drug agnostic, so paclitaxel was included as a drug specificity comparator. Not surprisingly, the resulting MVR models built from all 9 or 13 eribulin or vinorelbine UFGs, respectively, successfully predicted cognate drug IC50s with high statistical significance . Unexpec2 values) yielded a four-gene eribulin MVR model  and a five-gene vinorelbine MVR model ; predicted IC50s from both models correlated with measured IC50s across all 100 cell lines with high statistical significance for cognate but not noncognate drugs  whose expression is uniquely associated with response to eribulin versus the other two drugs, with four eribulin UFGs being sufficient for a multigene panel that accurately predicts high versus low eribulin response. Separately, 13 UFGs were identified as uniquely associated with vinorelbine response, with 5 of these being sufficient for a predictive vinorelbine multigene panel. Notably, no genes uniquely associated with paclitaxel response were identified using the same stringent criteria used to identify eribulin and vinorelbine UFGs.In addition to its cytotoxic antimitotic activity ,6,7, eri−7 to 10−6 M range for all three drugs, eribulin’s steeper slope resulted primarily from increased responses in its most sensitive cell lines, which collectively dropped into the sub-nM IC50 range. In addition, several nonoverlapping cell lines existed for each drug in their respective top and bottom quartiles, suggesting that aggregating response data across large numbers of cell lines might yield information on drug-specific pathways and mechanisms, a concept first established by the well-known NCI60 cell line screen . Coincidentally, the NCI60 screen was first used to predict a tubulin-based mechanism for eribulin’s natural product parent, halichondrin B [Responses of 100 CCLE human cancer cell lines to eribulin and comparator MTAs paclitaxel and vinorelbine were first quantified by IC50 values and ordered by response. While sensitivities across the 100 cell lines trended in the same direction for the three drugs, the range of eribulin’s response from most to least sensitive was considerably greater than the other two drugs, reflected by a much steeper IC50 slope for eribulin compared to the shallower and parallel slopes for paclitaxel and vinorelbine. Importantly, while IC50s for the least sensitive cell lines were in the same 10ondrin B ,25. We sp-value and gene expression ratios between top and bottom quartiles provided the first indications that the three drugs could be distinguished from each other for biomarker development. Notably, at p < 0.0025 stringency, 52 and 80 genes were unique to eribulin and vinorelbine, respectively, with no genes shared between eribulin and paclitaxel, and only nine genes shared between eribulin and vinorelbine. At the same stringency, vinorelbine and paclitaxel shared only one gene. Thus, paclitaxel, a microtubule stabilizer, showed the least similarity with eribulin and vinorelbine, both microtubule destabilizers, yet even eribulin and vinorelbine could be distinguished from each other based on many more genes uniquely associated with each drug compared to shared genes between them.Using top and bottom quartiles as surrogates for responders and nonresponders, respectively, IC50s were correlated with DepMap gene expression data for 11,985 genes. Z-score heat mapping and filtering on p < 0.0025 genes to include only those with at least 2-fold expression difference between top and bottom quartiles, followed by reintroduction of data from the 50 cell lines in the middle two quartiles, resulted in identification of 9 and 13 UFGs for eribulin and vinorelbine, respectively. Interestingly, no gene met these final UFG criteria for paclitaxel. Notably, seven of the nine eribulin UFGs were upregulated with increased response, while 11 of the 13 vinorelbine UFGs were downregulated with increased response, supporting our hypothesis that increased eribulin response may be driven by upregulation of pathways governing sensitivity, whereas increased vinorelbine response may depend more on downregulation of general resistance pathways.Further restriction of the To elucidate biological pathways associated with eribulin and vinorelbine UFGs, network propagation  was usedFinally, subsets of eribulin and vinorelbine UFGs prioritized by highest individual correlation coefficients were used to build drug-specific MVR models that correlate with responses across all 100 cell lines and accurately predict average response levels of the most and least sensitive quartiles based solely on expression levels of only four or five genes, respectively. While others have reported gene or pathway signatures for MTAs, these have been mainly for paclitaxel ,29,30,31IRX2 is a homeobox gene important in normal embryonic development and has been implicated in cancer, while C5ORF38 is coordinately regulated with IRX2, suggesting both are involved in cellular differentiation and growth regulation [DAAM1 is involved in Wnt signaling and early embryonic gastrulation, both processes associated with cellular differentiation [CD70 codes for a ligand for the immune costimulatory molecule CD27 and thus is involved in the activation and proliferation of T cells, including regulatory T cells [Considering the importance of signaling-related Reactome pathways in eribulin and vinorelbine responses, it is reasonable to ask if the four eribulin and five vinorelbine UFGs in the predictive MVR models directly control responses to the two drugs. Strictly speaking, UFG expression levels were only correlated with response, so firm conclusions of direct involvement cannot be drawn. However, with that caveat, information on known roles of the genes permits some speculation. For eribulin, gulation . Similarntiation . Finally T cells . Thus, wEPHA2 codes for EPH receptor A2, a tyrosine kinase involved in cancer-related signaling, while NGEF codes for a guanine nucleotide exchange factor associated with signaling from EPH receptor A2, RhoA, Rac1 and CDC42 as well as cellular transformation and tumorigenesis [SEPTIN10 is associated with B cell leukemias [TRIP10 is involved in tumorigenesis and cancer progression [VSIG10 codes for an immunoglobulin-related protein associated with both cell adhesion and macrophage involvement in colonic pathologies including colon cancer [For vinorelbine, igenesis ,47. SEPTeukemias , while Tgression . Finallyn cancer . Thus, wIn summary, we have combined response data for eribulin, paclitaxel and vinorelbine from 100 human cancer cell lines together with gene expression data to identify small numbers of genes (UFGs) that are uniquely associated with eribulin and vinorelbine responses. Reactome pathway analyses based on UFG-seeded propagated gene networks revealed that responses to both eribulin and vinorelbine are dominantly associated with cellular signaling processes as opposed to canonical MT-based antimitotic processes, perhaps also explaining our inability to identify paclitaxel UFGs using the same criteria. Despite top-level dependence on cellular signaling for response to both eribulin and vinorelbine, detailed analyses of the specific Reactome pathways identified reveal considerable discrimination between the two drugs. Finally, we show that small subsets of eribulin and vinorelbine UFGs can be successfully combined into MVR models that accurately predict a high versus low response to the two drugs based on expression of only four or five genes. Our results indicate that further investigation of the genes, pathways and MVR panels identified here is warranted, with further validation in both preclinical tumor models and in the clinical setting being of highest priority. Ultimately, our hope is that the current results together with such future validation work will lead to the development of drug-specific, gene expression-based predictive biomarker panels for eribulin and vinorelbine that support improved therapeutic decision-making in clinical settings.


P = 0.04). In contrast, grade III–IV acute  as well as extensive chronic  GVHD correlated with higher nonrelapse mortality leading to lower LFS . In conclusion, these data suggest a dissociation of graft-versus-leukemia effects from GVHD in patients with active AML treated with PTCy-based Haplo-HCT.The association between graft-versus-host disease (GVHD) occurrence and acute myeloid leukemia (AML) relapse in patients treated with HLA-haploidentical allogeneic hematopoietic stem cell transplantation (Haplo-HCT) with post-transplant cyclophosphamide (PTCy)-based GVHD prophylaxis has remained debated. Here, we addressed this issue in patients with active AML at transplantation. 2-year cumulative incidences of relapse and leukemia-free survival (LFS) were 49% and 32.3%, respectively. There were no associations between acute nor chronic GVHD of any grade and lower relapse incidence. However, grade I acute GVHD was associated with better LFS (HR = 0.71, 95% CI 0.51–0.99, The online version contains supplementary material available at 10.1186/s13045-023-01403-x. To the EditorAllogeneic hematopoietic stem cell transplantation  has remained the best option for patients with relapsed/refractory acute myeloid leukemia (AML) . This apPost-transplant cyclophosphamide (PTCy)-based GVHD prophylaxis has revolutionized the field of human leukocyte antigen (HLA)-haploidentical hematopoietic cell transplantation (Haplo-HCT) , 6. ConsP = 0.001) and 16% (P = 0.008), respectively, in PBSC recipients. The 2-year cumulative incidences of chronic and extensive chronic GVHD were 26.8% (95% CI 23.4–30.3%) and 13% (95% CI 10.5–15.8%), respectively. There was no impact of stem cell source on chronic GVHD incidence. However, in vivo T-cell depletion was associated with a lower incidence of chronic GVHD .The analyses were carried out in a total of 670 patients  and a statistical trend for lower LFS   and better LFS  and OS . We do not have a biological explanation for the lower NRM in patients with grade 1 acute GVHD. Future studies needed to evaluate whether this is due to a better immune reconstitution in patients with grade I acute GVHD. Finally, extensive chronic GVHD was associated with higher NRM  and lower LFS  and OS   [n = 805) [Our results differ from what has been observed by the Baltimore group in patients receiving Haplo-HCT with PTCy-based GVHD prophylaxis after nonmyeloablative conditioning as treatment of various hematological malignancies (n = 340) . Indeed,n = 805)  and withn = 805) .The absence of association between GVHD occurrence and the risk of relapse might suggest that in vivo T-cell depletion could be particularly suitable in the Haplo-HCT PTCy setting. However, we observed that ATG was associated with higher relapse incidence in multivariate analysis, without significantly affecting OS and LFS.In conclusion, we demonstrated in a cohort of patients with active AML at transplantation treated with PTCy-based T-cell repleted Haplo-HCT that occurrence of GVHD did not decrease the risk of relapse suggesting a dissociation of GvL effects from GVHD in this transplantation setting.Additional file 1. Supplemental data.


There are over 40 identified human disorders that involve certain proteins folding incorrectly, accumulating in the body causing damage to cells and organs and causing disease. Type 2 Diabetes Mellitus (T2DM) is one of these protein misfolding disorders (PMDs) and involves human islet amyloid polypeptide (hIAPP) misfolding and accumulating in parts of the body, primarily in the pancreas, causing damage to islet cells and affecting glucose regulation. In this review, we have summarised our current understanding of what causes hIAPP to misfold, what conformations are found in different parts of the body with a particular focus on what is known about the structure of hIAPP and how this links to T2DM. Understanding the molecular basis behind these misfolding events is essential for understanding the role of hIAPP to develop better therapeutics since type 2 diabetes currently affects over 4.9 million people in the United Kingdom alone and is predicted to increase as our population ages. Diabetes mellitus is the name given to a group of metabolic diseases characterised by chronic hyperglycaemia as a result of defective glucose regulation. Symptoms of diabetes and the severity can vary depending on the type of diabetes. Diabetes mellitus can be split into type 1 and type 2 diabetes. In type 1 diabetes mellitus  the pancreas is unable to produce any insulin. Type 2 diabetes mellitus (T2DM) usually occurs in adults and is due to the pancreas being unable to produce fully functioning insulin in sufficient quantities. Defective insulin action and/or secretion results in metabolic abnormalities and the associated chronic hyperglycaemia results in long-term damage leading to organ failure. During the early stages of T2DM, patients can be asymptomatic. Symptoms of chronic hyperglycaemia are polyuria, polydipsia, weight loss, polyphagia, and blurred vision in addition to growth impairment and susceptibility to infection . In addiDiabetes is a complex disease and is still not fully understood. Currently, there is no cure. Treatments simply alleviate the symptoms but do not stop the disease from progressing. Diabetes is a financial burden on health systems worldwide. Currently, in the United Kingdom alone, the cost for treatment, intervention, and complications are estimated to cost the National Health Service (NHS) £9.8 billion annually and is predicted to increase to £16.9 billion by 2035 . Hence, Proteinopathies or protein misfolding disorders, PMDs, are diseases resulting from incorrect protein folding, over 40 of which have now been identified . ProteinIn this review, we have reviewed the published structures of hIAPP in its different misfolded conformations  including mutagenic studies to try to understand how different parts of the protein and its different conformations are associated with disease. We have also summarised where these different conformations accumulate in the body and cause damage as it is now known that misfolded hIAPP accumulates in different parts of the body in addition to the pancreas. hIAPP has also been shown to interact with other amyloidogenic proteins accelerating their misfolding. This highlights the importance in understanding hIAPP not just to understand T2DM, but its role in related conditions such as AD and PD. Our review is novel since we have summarised all the mutations, all of the structures of hIAPP and locations of hIAPP deposits that have been published to date thus providing an up-to-date review that summarises what is known about hIAPP misfolding and aggregation and highlight gaps in our knowledge that urgently need addressing.2 and Cys7 is formed. It is then transported to the Golgi complex where it is packed into secretory granules and further cleaved to form a 37-amino acid peptide that is post-translationally modified by amidation at the C-terminal tyrosine. It is this active form that is co-secreted with insulin to regulate glucose levels in the body. IAPP is found in all mammals and healthy individuals and is co-produced with insulin in a molar ratio of 1:100 [HIAPP is expressed in the pancreatic β-cells of the Islets of Langerhans and belongs to the calcitonin family of peptides. IAPP acts in an endocrine as well as auto and paracrine manner involved in the regulation of glucose, bone resorption , renin aof 1:100 .6 and Thr9 yet the physiological role of glycans and their role in disease remains unclear.IAPP can undergo further post-translational modifications such as O-glycosylation  at Thr6 IAPP is normally soluble, monomeric, and natively unfolded in solution in its physiological form. It is described as an intrinsically disordered protein that has no regular three-dimensional structure  and is positively charged at physiological pH . IAPP is25, Ser28, Ser29, and do not form amyloid or T2DM. The S20G mutation which is highly amyloidogenic has been associated with early-onset T2DM and has been found in the Japanese and Chinese populations [Human IAPP is a highly amyloidogenic peptide and there is extensive evidence demonstrating that it can adopt different conformational states. Changes to the local environment, or in particular, changes in hydrophobicity to residues 20–29 , are thought to be crucial for amyloid formation . Sequenculations . IAPP isulations . In T2DMulations . This inThis ability of hIAPP to form amyloid like other PMDs and the observation that over 90% of T2DM patients have hIAPP deposits in the form of extracellular fibrillar aggregates in their pancreas  stronglyAmyloid can be defined as fibrillar deposits of proteins that contain a β-sheet structure and as a result, have enhanced dye-binding capacity to Thioflavin T , Congo RIn addition to being observed in the pancreas, misfolded forms of hIAPP have also been identified in different organs in T2DM patients , includiin vivo. The use of transgenic animal models has provided a longitudinal approach to support the correlation between IAPP aggregation and β-cell loss. Mice and rat IAPP do not aggregate, and these rodents do not normally develop diabetes. Animals where the human IAPP gene is highly expressed, develop diabetes as they grow during their lifetime [The likely event causing hyperglycaemia in T2DM is dysfunction or loss of β-cells leading to reduced secretion of insulin and development of insulin resistance . Here welifetime . The HIPlifetime . At 2 molifetime . Furtherlifetime .In vitro, hIAPP has a strong tendency to aggregate and will do so spontaneously within hours at µM concentrations [In vivo, IAPP is stored in secretory granules at mM concentrations [2+ and insulin at a slightly acidic pH prevents aggregation, especially during assembly of granule contents [in vitro and in vivo. Interaction with membranes can trigger a change in IAPP structure from the natural unfolded state to an α-helical structure that can insert into membranes [in vivo studies have identified oligomeric structures at the site of β-cell loss in pancreatic islets [trations . In vivotrations  but a cocontents ,56. The contents  and IAPPembranes ,58 or foembranes . Studiesembranes , and in c islets .2+ activation of calpain and apoptosis [There are several factors that could disturb proteostasis and induce toxic aggregation of IAPP inside β-cells. Intracellular toxic aggregates of IAPP were identified in biopsies of islets from patients with T2DM with a perinuclear or cytosolic distribution , the latpoptosis , disruptpoptosis  and disrpoptosis ,65.Overexpression of IAPP could activate the unfolded protein response (UPR). A series of three signalling pathways in the ER are triggered by IAPP protein misfolding and result in a local inflammatory response involving the release of pro-inflammatory cytokines IL-6, IL-8, MCP-1, and TNFα . MCP-1 hLarger amounts of oligomers have been found in β-cells of obese T2DM patients compared with non-obese patients, suggesting that other environmental factors such as oxidative stress can affect aggregation. Chronic hyperglycaemia or dyslipidaemia prevalent in T2DM will increase the stress responses . The β-c4 lower than in the pancreas. These findings were corroborated using HIP rat models where hIAPP is expressed only from the pancreas [A number of studies have explored the link between diabetes and neurological pathology mediated via IAPP. One of the first reports of hIAPP deposition in the brain reported that levels of oligomeric hIAPP were highest in brain homogenates taken from T2DM patients with dementia, compared with AD patients and controls . They alpancreas  demonstrpancreas . Loss ofpancreas .A study using brain samples from 2365 patients with and without diabetes, assessed AD neuropathology and concluded that diabetes is not associated with AD pathology . Diffusein vitro for Aβ 42 [There are, however, several studies that have shown a link between T2DM and AD. hIAPP deposits were found in brain blood vessels and associated parenchyma in patients with T2DM and AD some of which also included amyloid-β (Aβ). Interestingly, there were also hIAPP deposits in the AD group who had no apparent diabetes. The data suggested a model in which pancreatic hIAPP creates deposits in brain tissues where the initial aggregation events may be triggered by the high levels of hIAPP that occur in the prediabetic state. The results also demonstrate the potential for co-aggregation with other amyloidogenic proteins potentially influencing amyloid formation. These observations have been confirmed in other studies using animal models. Since rodent IAPP does not aggregate, this property is exploited in transgenic mice and rats expressing the aggregating human form of IAPP. A study using transgenic hIAPP mice focussed on the ability of α-synuclein to enhance amyloid formation involving co-aggregation with hIAPP in islets  using boor Aβ 42 , and α-sor Aβ 42 .Further work has focussed on the potential role of other factors that could modulate IAPP aggregation, in particular obesity or the pre-diabetic state. A positive correlation between brain amyloid deposits and levels of fasting plasma glucose, triglyceride, and HDL has been observed in cynomolgus monkeys  and in mDiabetes and hypertension, individually or combined are thought to account for 80% of all cases of end-stage renal disease. Nephropathy caused by diabetes is due to damage to blood vessels which leads to chronic kidney disease. A single study on humans has described deposits of IAPP in kidneys of patients with diabetic nephropathy (DN) . hIAPP d2) binding [hIAPP monomers have been detected in the plasma, white blood cells, and red blood cells in blood from a T2DM patient with nephropathy . In a co binding . hIAPP d binding . ElevateLarger conformers of misfolded hIAPP have also been identified in the blood of T2DM patients. Oligomers in the form of homodimers  and heterooligomers varying in size (1–400 nm) have been identified from sera of children with T2DM that were also present in obese children and those with T1DM . FibrilsDiabetes is known to be a risk factor for cardiovascular disease, attributed to the damage caused by high-glucose levels on vasculature supplying the heart. Here we consider whether IAPP could contribute to the risk of heart disease for diabetics by directly affecting the function of cardiomyocytes and surrounding cells. There are a very limited number of studies that have investigated IAPP aggregation in human heart tissue. Despa et al.  found IAIn a follow-up study  oligomerThese findings in patients suggest a correlation between IAPP aggregation, lipid oxidation, inflammatory stress, and myocyte damage, which was investigated using rat models and cultures of cardiomyocytes . Treatme2+ movements in HIP cardiomyocytes not found in UCD-T2M myocytes. Prediabetic HIP rats show signs of cardiac abnormality, with accumulation hIAPP, whereas UCD-T2M prediabetic rats do not.HIP rats were compared with the UCD-T2M rat model, which is not transgenic for hIAPP and in which diabetes develops due to β-cell dysfunction . In the 2+ movements in myocytes by hIAPP suggests an initial attachment to the sarcolemma, and then the formation of oligomers that could create cation-selective channels in the sarcolemma and also develop into fibrils. The membrane-attached fibrils could amplify structural alteration of membrane and hence Ca2+ dysregulation, leading to remodelling of myocytes via a Ca2+-dependent activation of HDAC NFAT transcription factors [A model of the disruption of Ca factors .Liu et al.  further 2+ signalling. The model was advantageous since other aspects of diabetes pathology such as hyperglycaemia or cardiac remodelling found in diabetes would be absent.Similar results have been found in mice perfused with aggregated hIAPP, which induced formation of hIAPP HNE– and MDA–adducts in the heart and activation of IL-1β. In a mouse knock-out model which does not express endogenous IAPP , adminisThese studies suggest that hIAPP can bind to cardiomyocytes, aggregate, and induce stress but it also seems possible that accumulation in vasculature of the heart could also contribute to compromising heart function.IAPP has been found in the eye of individuals with AD ,87. It iin vitro studies so the exact mechanism of misfolding in vivo remains unclear. IAPP amyloid formation is thought to occur via a nucleation-dependent polymerisation mechanism in vitro [Most of our information regarding the misfolding pathways come from in vitro . Evidencin vitro .The three main stages involved in amyloid formation are thought to include: (1) primary nucleation (lag phase) where small soluble oligomers are formed from monomeric peptides followed by (2) elongation (log phase) involving the formation of protofibrils, and fibril growth followed by (3) the plateau (or saturation) phase where mature amyloid fibrils dominate. hIAPP amyloid fibrils are further stabilised by their interactions with proteins such as serum amyloid P, apolipoprotein E, and perlecan . PerlecaGlycosaminoglycans such as heparan sulphate, are negatively charged polymers found in the extracellular matrix or on the membrane surface of cells. They have been found associated with hIAPP deposits in T2DM and are thought to be essential for amyloid formation . Lipid mLike many PMDs, there is still debate over what conformation of the amyloidogenic protein is associated with disease. Since fibrillar amyloid deposits of hIAPP have been observed in islet cells in the majority of T2DM patients , it is t18 and Tyr37 [In vitro experiments suggest that oligomers contribute to toxicity by disrupting cell membranes by forming pores that allow too many Ca2+ ions to enter that can lead to apoptosis [in vivo including studying their structure in more detail (if indeed they exist in vivo) to understand how they function in disease. Two-dimensional infrared spectroscopy experiments have revealed that an oligomeric intermediate form of hIAPP consists of parallel β-sheets [Oligomers ranging from dimers to hexamers have been identified during the early stages of aggregation  and in pnd Tyr37 . Residuend Tyr37 . Moleculnd Tyr37 . In vitrpoptosis . More evβ-sheets . Interesin vitro experiments suggest that fibrillisation of 1–37 is a synchronous process where protofibril elongation and mature fibril formation occur simultaneously [in vivo experiments are needed to corroborate these findings.Recent aneously  rather taneously . These sWhilst there is ample evidence linking hIAPP with T2DM, there is some evidence in the literature suggesting other proteins may misfold or somehow contribute to the misfolding of other proteins involved in T2DM. ProIAPP also appears to misfold since amyloid deposits composed of fibrillar aggregates containing proIAPP have been observed in secretory granules in some β-cells in human IAPP transgenic mice .Insulin degradation enzyme (IDE), which also degrades hIAPP , appearsFamilial mutations in ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1) are associated with PD where the variant I93M is found to aggregate . UCH-L1 In vivo studies show that intravenous injection of Aβ fibrils into hIAPP transgenic mice cause increased amyloid formation in islet cells [Drosophila melanogaster models result in co-deposition of amyloid peptides [Aβ and α-synuclein, proteins that misfold in AD and PD respectively have been associated with IAPP misfolding and T2DM. et cells  and co-epeptides . A signipeptides .Understanding the structures of hIAPP assemblies is crucial for understanding exactly how IAPP is linked to T2DM. There are now many hIAPP structures that have been solved  many to Structures have revealed that monomers tend to have a predominantly α-helical content . The natbone fideex-vivo structures remain unknown.Fibrils tend to be characteristic of amyloid fibrils as seen in other PMDs usually being composed of two protofilaments and having a predominantly β-sheet content, (containing β-strands that run perpendicular to the fibre axis) that can be identified using X-ray fibre diffraction and cryo-electron microscopy (EM) . Like ot37 is consistent with the importance of amidation at the C-terminal thought to be important in fibril formation. The structures also explain one reason why the S20G hereditary mutation may facilitate hIAPP fibrillisation since glycine residues allow a more kinked conformation that in turn may allow the peptide chain to adopt a fibril-forming fold thus more easily forming a stable fibril structure. Since three of the polymorphs do not resemble any structures produced in vitro it suggests in vitro structures may differ from those in vivo as previously summarised for other amyloidogenic proteins [ex-vivo fibrils being more stable and protease-resistant which has important consequences for therapeutic design [A promising paper showed that hIAPP fibrils extracted from a T2DM patient could be used to seed synthetic hIAPP peptides to produce enough fibrils to study by cryo-EM . Four diproteins  due to ec design .Oligomers have been less well studied. Some research suggests they have a more α-helical content , whilst in vitro from peptides. Not all the structures are full-length, and the preparation methods used differ between studies. Hence, these factors may contribute to the different polymorphs produced as previously suggested [in vivo.Most of the structures solved to date are produced uggested  arguing We have summarised the current understanding behind the misfolding and aggregation of hIAPP and its link to T2DM in a simplified cartoon diagram shown in 5MGQ and 6Y1A), [Many mutational analysis experiments have been performed on hIAPP to understand the mechanism of hIAPP amyloid formation. To date, a large amount of data assessing the importance of certain residues in the sequence of hIAPP involved in amyloid formation have been collated. However, a systematic analysis of all possible mutations for positions of IAPP has not yet been published. Here we have summarised all mutational analysis studies that have been done to date using full-length (Supplementary File S1 and Table S1) or an 8–37 fragment of hIAPP (Supplementary File S1 and Table S2). Studies using shorter hIAPP fragments have been undertaken but are not included in this review as we deem them less physiologically relevant. Most of the studies summarised used hIAPP that is amidated at the C-terminal with point mutations altering a single amino acid at a time, to assess whether certain residues are involved in the ability of hIAPP to form amyloid (Supplementary Tables S1 and S2). For clarity, we have highlighted these residues in solved 3D structures of monomeric and fibrillar hIAPP (PDB codes: d 6Y1A),  as shownd 6Y1A), . These fd 6Y1A), .d-amino acid)) amyloid formation of hIAPP. Understanding the molecular basis behind the link between different residues and amyloidosis clearly requires further exploration [Mutating asparagine at position 14 can abolish  or accelerate (N14D) amyloid formation. Hydrophobic residues  abolish amyloid formation whereas mutating to a negatively charged aspartic acid accelerates amyloid formation ,145. Simloration .18. The protonation of histidine in an acidic environment results in the prevention of cation–π interactions and the formation of hydrogen bonds with other polar or charged residues. Whereas in a neutral environment, deprotonation of His18 favours these interactions [It should be noted that it is difficult to compare data between these different studies. This is due to different lengths of the protein being used (fragment vs full-length protein), the C-terminal amidation status differs and buffer type, buffer co-solutes, pH, ionic strength, and temperature used in the assays differ. In the truncated studies (Supplementary Table S2), even though the truncation is the first seven amino acids, outside the reported amyloid core, it may still affect the fibrillation rate and structures produced. Additionally, thioflavin-T binding assays used to assess hIAPP amyloid formation ,148 usedractions . In addiractions ,151. HenIn summary, from the studies summarised in Supplementary Tables S1 and S2, various mutations in the SRE conserved region  of IAPP 60IZ show similarities with hIAPP 7M61 (chain A), Aβ 2M4J with hIAPP 7M61 (chain B) and 50QV and 6SHS matching with hIAPP 7M62 highlighting residues 19–29 of hIAPP and 24–34 Aβ as having structurally similar regions that may be responsible for cross-seeding [In vivo, numerous studies have demonstrated that hIAPP can leave the pancreas and cross the blood–brain barrier and co-localise with Aβ in the brain contributing to the onset and progression of AD [There are numerous reports that hIAPP can interact with other amyloidogenic proteins promoting amyloidosis, thus linking T2DM to other disorders such as AD . This is-seeding . In vivoon of AD . The simon of AD .in vitro and in vivo in mice and human islet cells [in vitro could explain why having T2DM increases a person’s risk of getting PD but not the other way round since α-synuclein amyloid seeds actually inhibit IAPP amyloid production [T2DM has been linked to PD. Recently, α-synuclein and IAPP have been shown to co-aggregate et cells . The facoduction .Another amyloidogenic protein, the prion protein, PrP (fragment 106–126) and IAPP are reported to co-aggregate which is interesting since they lack sequence homology . Perhapsin vivo is needed for therapeutic design. Fibril formation is thought to involve a slow nucleation phase that may take many months or years as observed in other disorders such as AD [in vitro where hIAPP fibrils have been shown to occur in minutes [In addition to understanding the structure of hIAPP to high resolution, understanding the mechanism of amyloid formation ch as AD  where in minutes .Treatments for diabetes alleviate the symptoms but do not resolve the cause. Current treatments for diabetes include insulin, hIAPP analogues, biguanides, sulphonylureas, α-glucosidase inhibitors, dipeptidyl peptidase- 4 inhibitors, glucagon-like peptide receptor agonists, thiazolidinediones, SGLT2 inhibitors, and meglitinides .Destruction of β-cells is linked to hIAPP and insulin deficiency observed in T1DM and T2DM ,161, henAnother analogue called Davalintide, not yet approved, binds to the amylin receptor . HoweverKBP-088 is another DACRA and has been tested in clinical trials to treat weight loss and diabetes. Due to its high potency to bind both calcitonin and amylin receptors, it is a potent weight loss inducer. Using rats fed on a high-fat diet, those fed 5 μg/kg of KBP-088 per day for 11 weeks showed a 16% reduction in body weight . HoweverCagrilintide, which is a long-acting acylated IAPP analogue, is currently undergoing clinical trials for weight loss and glycaemic control in T2DM patients ,173. It Using hIAPP analogues in combination with other peptide hormones may accommodate the needs of individuals by mimicking the natural, energy homeostatic signalling pathways to create a specific desired physiological effect. For example, leptin, insulin, and peptide YY 3-36, a gut metabolic hormone, could be used in combination with an hIAPP analogue . Clearly25, Ser28, Ser29 in their IAPP sequence) form amyloidogenic forms of hIAPP and naturally get T2DM shows a direct link between the protein itself and T2DM.There are numerous studies that prove IAPP can form amyloid and a clear link between the accumulation of IAPP in T2DM patients and animal models strongly suggesting misfolded IAPP is linked to T2DM. The fact that only certain species (that have Ala2+ activation of calpain and apoptosis [There is extensive evidence linking dysfunction or loss of β-cells with reduced secretion of insulin and development of insulin resistance which is linked to IAPP amyloid formation . Howeverpoptosis , disruptpoptosis , disruptpoptosis ,65, and poptosis . The subpoptosis . Environpoptosis .ex vivo fibrils still remains unknown. In addition, the ex vivo/in vivo structure of smaller conformations of IAPP that precede fibrils is unknown. Understanding the structure of these conformers is key to developing therapeutics since fibrillar conformers are linked to late stages of T2DM so being able to prevent or slow fibril formation may prevent onset of the disease. A recent study showed that the molecular chaperone Hsp70 binds to oligomers but not monomers [As summarised in this review, evidence suggests that oligomers and fibrils are linked to T2DM  however monomers  hence exin vitro experiments. Using ex-vivo aggregates to seed fibrillisation reactions produced different conformations [It appears that hIAPP can form more than one conformer of fibrils and oligomers . This cormations  suggestirmations ,177 is uIAPP analogues such as Pramlintide, Davalintide, KBP-088, and Cagrilintide have all shown promise as a treatment for T2DM however issues with efficacy, solubility, and side effects need optimisation before they can be used as an effective T2DM treatment. Using natural compounds instead  or in coMore research is needed to identify what conformation hIAPP adopts when it deposits in different parts of the body  and in aT2DM is clearly a complex disease. Interestingly, not all T2DM patients have hIAPP deposits  and hIAPClick here for additional data file.


The curcumin analogues (3a–c) were tested for antiproliferative activity against a total of five dozen cancer cell lines in a single (10 µM) and five dose (0.001 to 100 µM) assays. 3,5-Bis(4-hydroxy-3-methoxystyryl)-1H-pyrazole-1-yl-(phenoxy)ethanone (3b) and 3,5-bis(4-hydroxy-3-methoxystyryl)-1H-pyrazole-1-yl-ethanone (3c) demonstrated the most promising antiproliferative activity against the cancer cell lines with growth inhibitions of 92.41% and 87.28%, respectively, in a high single dose of 10 µM and exhibited good antiproliferative activity (%GIs > 68%) against 54 out of 56 cancer cell lines and 54 out of 60 cell lines, respectively. The compound 3b and 3c demonstrated the most potent antiproliferative activity in a 5-dose assay with GI50 values ranging between 0.281 and 5.59 µM and 0.39 and 0.196 and 3.07 µM, respectively. The compound 3b demonstrated moderate selectivity against a leukemia panel with a selectivity ratio of 4.59. The HOMO-LUMO energy-gap (∆E) of the compounds in the order of 3a > 3b > 3c, was found to be in harmony with the anticancer activity in the order of 3c ≥ 3b > 3a. Following that, all of the curcumin analogues were molecular docked against EGFR, one of the most appealing targets for antiproliferative activity. In a molecular docking simulation, the ligand 3b exhibited three different types of interactions: H-bond, π-π-stacking and π-cationic. The ligand 3b displayed three H-bonds with the residues Met793 (with methoxy group), Lys875 (with phenolic group) and Asp855 (with methoxy group). The π-π-stacking interaction was observed between the phenyl (of phenoxy) and the residue Phe997, while π-cationic interaction was displayed between the phenyl (of curcumin) and the residue Arg841. Similarly, the ligand 3c displayed five H-bonds with the residue Met793 (with methoxy and phenolic groups), Lys845 (methoxy group), Cys797 (phenoxy oxygen), and Asp855 (phenolic group), as well as a halogen bond with residue Cys797 (chloro group). Furthermore, all the compound 3a–c demonstrated significant binding affinity  against the active site of EGFR. The curcumin analogues described in the current work might offer beneficial therapeutic intervention for the treatment and prevention of cancer. Future anticancer drug discovery programs can be expedited by further modifying these analogues to create new compounds with powerful anticancer potentials.With 19.3 million new cases and almost 10 million deaths in 2020, cancer has become a leading cause of death today. Curcumin and its analogues were found to have promising anticancer activity. Inspired by curcumin’s promising anticancer activity, we prepared three semi-synthetic analogues by chemically modifying the diketone function of curcumin to its pyrazole counterpart. The curcumin analogues ( Curcuma longa Linn.), belongs to the family Zingiberaceae is a β-diketone that showed an antiproliferative effect on various panels of cancer cell lines [2-), aryl side chain, diketone group, and carbon-carbon double bonds (-CH=CH-) to create a number of semi-synthetic curcumin analogues with improved bioactivity [The development of new antiproliferative agents with greater efficacy and a smaller amount of side effects remains a modern scientific research challenge. Over the last few decades, there has been a surge in interest in using natural products for therapeutic purposes. Although this concept appears to be new, our forefathers have used natural compounds/extracts to treat illness since time immemorial. The development of synthetic pharmaceuticals over the last century not only revolutionized modern medicine, but also accrued the undesirable properties and side effects associated with these drugs. The un-favorable properties of synthetic drugs have prompted a search for natural alternatives, with the hope that naturally occurring compounds will be better tolerated and safer than their synthesized counterparts ,2. Curcull lines . Medicinactivity . In thisactivity . Curcumiactivity ,7,8,9,10activity ,15,16,17activity ,19.Cancer is a disease in which a few of the body’s cells grow out of control and spread to other parts of the body. With 19.3 million new cases and almost 10 million deaths in 2020, cancer has become a leading cause of death today. Today, breast cancer is the most commonly diagnosed cancer, followed by lung cancer. Breast, lung, colorectal, liver, and stomach cancers account for 11.7, 11.4, 10, 7.3, and 5.6 percent of all new cancer cases, respectively . TobaccoThe EGFR is a key anticancer target and the most studied receptor in the tyrosine kinase super-family ,33. Curc3a–c) synthesized in the present investigation are summarized in 3a) was prepared by heating an equimolar mixture of curcumin (1)  and N-(2-methoxyphenyl)hydrazine carboxamide (2a)  in glacial acetic acid at 80 °C. Similarly, 1--4-hydroxy-3-methoxystyryl)-1H-pyrazol-1-yl)-2-(substituted phenoxy)ethan-1-ones (3b–c) were prepared by heating an equimolar mixture of curcumin (1)  and substituted phenoxy-acetohydrazide (2b–c) in glacial acetic acid at 80 °C. In this method (Method A), the reaction mixture was heated with continuous stirring for 10 h to complete the reaction while thin layer chromatography (TLC) in n-hexane: ethylacetate (6:4) was used to monitor the reaction throughout. The curcumin analogues were also prepared by another method (Method B). The method B glycerol-water system (1:2) was taken as solvent and the reaction was charged at 60–80 °C with continuous stirring for 6 h. The yields and time taken in the reaction are compared in 3a–c). The synthetic protocol is summarized in 2a, and 2b–c were prepared using the methods described elsewhere [3a–c) were confirmed by spectroscopic techniques. The IR spectra of curcumin analogues 3b–c showed acyl carbonyl (-CH2C=O) peak at 1650 and 1600 cm−1, while pyrazole C=N of compounds 3a–c was observed at 1544 to 1548 cm−1. The phenolic function (ArOH) of compounds 3a–c, while the phenoxy (-O-) functions of compounds 3b–c were observed ranging between 3427–3431 and 1276–1280 cm−1, respectively. The peak of the C-Cl function of 3c was observed at 680 cm−1. The 1H NMR showed a singlet for the six protons the methoxy function (OCH3) of curcumin in compounds 3a–c at δ 3.82–3.83 ppm, while the methoxy function (OCH3) present in the phenyl ring of compound 3a at δ 3.69 ppm. The methylene bridge (-CH2-) function of compounds 3b and 3c were observed at δ 4.60 and 4.80 ppm, respectively. The pyrazole CH of compounds 3a–c was observed as a singlet at 6.61 ppm and a broad singlet for the two protons of phenolic function (ArOH) at δ 9.14–9.91 ppm. The secondary amine peak of compounds 3a was observed as a singlet at δ 10.56 ppm. Two doublets were observed at δ 6.73–6.77 ppm corresponding to the -CH=CH- with coupling constant (J) of 12.4–14.4 Hz confirming the trans couplings. The aromatic protons of compounds 3a–c were observed as a singlet, and multiplet depending upon the nature of protons at δ 7.80–7.56 ppm. The 13C NMR of the prototype compound 3b displayed different carbon peaks at δ 160.71, 151.32, 147.46, 144.89, 137.31, 133.41, 129.83, 128.81, 123.6, 121.18, 120.19, 116.80, 114.31, 112.101, 107.72, 72.09, and 56.41 ppm. The ES-MS spectra of compound 3b showed a peak at m/z, 498.1 and 499.1 corresponding to M+ and (M + 1)+ corresponding to the molecular formula C29H26N2O6.The curcumin analogues  studies are critical for comprehending intermolecular dynamics and designing molecules with desired pharmaceutical characteristics. This tool may be used to identify a molecule’s fundamental properties such as its frontier molecular orbital energy levels, chemical reactivity, and stability, among others ,37. To f3a–c was determined to be 3.55, 3.40 and 3.35 eV, respectively. This is a typical value for tiny organic cores and corresponds to the prior study [As shown in the or study .3a displayed less antiproliferative activity and was found to displayed moderate activity against NCI-H522 and HT29 with growth percent (GP) of 73.19 and 80.40 percent, respectively. The compound 3b displayed maximum anticancer activity with growth percent (GP) 7.59 percent  followed by compound 3c  and 3a . The curcumin analogues 3b and 3c were found to be highly sensitive against all the leukemia cancer lines. The antiproliferative data of curcumin was retrieved from the NCI data base with NSC 32982 [3c showed a lethal effect on leukemia cell lines HL-60 (TB) (%GI = 118.70%) and RPMI-8226 (%GI = 106.80%), while the compound 3b displayed a lethal effect on RPMI-8226 (%GI = 103.54%). Similarly, the compounds 3b and 3c displayed a lethal effect on non-small lung cancer cell lines NCI-H522 with %GIs of 139.28 and 128.71, respectively. The compound 3b and 3c displayed more antiproliferative activity than curcumin against non-small lung cancer cell lines panel. The compounds 3b and 3c were found to be active against all colon cancer cell lines except COLO205 and HCC-2998 and a lethal effect was observed on the colon cancer cell line HT29 with %GIs of 133.23% by the compound 3b. The compounds 3b and 3c were also found to be active on all the CNS cancer cell lines, while lethal effect was observed against SF-539 (%GI = 110.32%) by compound 3b. The compounds 3b and 3c displayed more promising antiproliferative activity than curcumin against most of the colon and CNS cancer cell lines. The compound 3c exhibited a lethal effect on melanoma cell lines MDA-MB-435, SK-MEL-2, and SK-MEL-5 with %GIs of 112.15, 104.36 and 112.95 percent, respectively, while the compound 3b exhibited a lethal effect on melanoma cell line SK-MEL-2, and SK-MEL-5 with %GIs of 118.73, and 128.80, respectively. The compounds 3b and 3c were found to be active against all the melanoma cancer cell lines and also displayed superior antiproliferative activity to curcumin. The compound 3b exhibited a lethal effect on ovarian cancer cell lines OVCAR-3 and SK-OV-3 with %GIs of 104.46 and 128.58%, respectively. The compounds 3b and 3c also displayed superior antiproliferative activity to curcumin against ovarian cancer cell lines. The curcumin analogues 3b and 3c displayed promising antiproliferative activity against melanoma and ovarian cancer panels and their activity was found to be superior to curcumin. The compound 3b and 3c displayed a lethal effect on renal cancer cell lines with %GIs of 144.52 and 109.98 percent, while the compound 3b showed a lethal effect on breast cancer cell lines BT-549 and MDA-MB-468 with %GIs of 103.68 and 101.76 percent. Both the compounds were found to be active against all the cell lines of renal, prostate and breast cancer cell line panels with %GIs > 68 percent and displayed superior antiproliferative activity to curcumin. The mean GPs and %GIs showed that the curcumin analogues 3b and 3c showed superior antiproliferative activity to curcumin as shown in 3a–c) in term %GIs is given in the 3a–c) in term GP is given in the 3c demonstrated maximum sensitivity against NCI-H522 with GP of −28.71% (%GI = 128.71%) and least sensitivity against COLO205 with GP of 82.37% (%GI = 17.63%). The curcumin analogues 3b and 3c displayed promising antiproliferative activity in the one dose assay (10 µM). The compound 3b exhibited good inhibitions against 54 cancer cell lines out of 56 cancer cell lines, similarly the compound 3c exhibited good inhibitions against 54 cancer cell lines out of 60 cancer cell lines at 10 µM. The antiproliferative data of curcumin analogues (3a–c) in a single dose assay at 10 µM are given in 3b and 3c) demonstrated promising antiproliferative activity in a single dose assay, they qualified for 5-dose assay testing [The antiproliferative activity of the curcumin analogues was carried out against five dozen cancer cell lines derived from nine diverse panels including, breast, colon, CNS, leukemia, melanoma, non-small cell lung, ovarian, renal, and prostate cancer cell lines at one dose (10 µM) and five dose assay , as per the National Cancer Institute US ,40,41,42SC 32982 . The comSC 32982 ,44. The 3b demonstrated promising antiproliferative activity against 60 NCI cell lines with GI50 values ranging between 0.281 and 5.59 µM, TGI values ranging between 0.49 and >100 µM, and LC50 values 4.48 and >100 µM. Similarly, the compound 3c exhibited promising antiproliferative activity with GI50 values ranging between 0.224 and 3.82 µM, TGI values ranging between 0.837 and >100 µM, and LC50 values 11.4 and >100 µM. The antiproliferative activity of compounds 3b and 3c against 60 NCI cancer cell lines in the 5-dose screening in terms of GI50, TGI, and LC50 are given in 3b displayed the most promising antiproliferative activity against CCRF-CEM (GI50 = 0.281 µM), while the compound 3c displayed the most promising activity against SR (GI50 = 0.224 µM) among the leukemia cell lines. The compound 3b displayed the most promising antiproliferative activity against HOP-62 (GI50 = 1.08 µM), while the compound 3c displayed the most promising activity against NCI-H460 (GI50 = 0.517 µM) among the non-small lung cancer cell line. The compound 3b displayed the most promising antiproliferative activity against HCT-116 (GI50 = 0.386 µM), while the compound 3c displayed the most promising activity against HCT-15 (GI50 = 0.342 µM) among the colon cancer cell line. The compounds 3b and 3c displayed the most promising antiproliferative activity against the CNS cancer cell line SF-539 with GI50 values of 0.354 and 0.38 µM, respectively. The compounds 3b and 3c displayed the most promising antiproliferative activity against the melanoma cancer cell line MDA-MB-435 with GI50 values of 0.21 and 0.243 µM, respectively. The compounds 3b and 3c displayed the most promising antiproliferative activity against the ovarian cancer cell line OVCAR-3 with GI50 values of 0.552 and 0.511 µM, respectively. The compound 3b and 3c displayed the most promising antiproliferative activity against the renal cancer cell line UO-31 with GI50 values of 0.302 and 0.362 µM, respectively. The compounds 3b and 3c displayed the most promising antiproliferative activity against the prostate cancer cell line DU-145 with GI50 values of 1.37 and 0.833 µM, respectively. The compounds 3b and 3c displayed the most promising antiproliferative activity against the breast cancer cell line MCF7 with GI50 values of 0.343 and 0.336 µM, respectively. The compound 3b exhibited less selectivity against all the eight panels (except leukemia) of cancer cell lines with a selectivity ratio (SR) ranging between 0.59 and 1.63, similarly the compound 3c exhibited less selectivity against all the panels of cancer cell lines with SR ranging between 0.80 and 2.94  (a) and compared with that of the curcumin  implies a high chemical reactivity as well as biological activity [3c might possess a relatively high anticancer activity. The anticancer activity of 3b,c was found to be promising in a single dose assay with %GIs of 92.41 and 87.28, respectively, at 10 µM, while the mean GI50 was calculated as 1.241 and 1.149 µM, respectively, in the five dose assay. Both the compounds were found to be active against 54 cancer cell lines. Comparing the anticancer activity of the three compounds, the order followed as 3c ≥ 3b > 3a  and the lowest unoccupied molecular orbital (LUMO) ,48,49. Tactivity . This su 3b > 3a .3a displayed two H-bonds one between the phenolic function and the residue Leu718 and another between the carbonyl function and residue Thr854. The ligand 3c displayed four H-bonds and a halogen bond, the two H-bonds between one of the phenolic and methoxy functions and the most important residue Met793, while one H-bond was present between one of the phenolic functions and the residue Asp855 and one H-bond between the methoxy function and the residue Lys875. The 3D interaction of the ligands  is shown in 3b was found to be the most active compound in the series exhibiting three different types of interactions, including H-bond, π-π-stacking and π-cationic interactions. The ligand 3b displayed three H-bonds, one H-bond between the methoxy function and the most important residue Met793, one H-bond between another methoxy function and the residue Lys875 and one H-bond between the phenolic function and the residue Asp855. The π-π-stacking interaction was observed between phenyl (of phenoxy) and the residue Phe997, while π-cationic interaction was displayed between phenyl (of curcumin) and the residue Arg841. The 3D interactions of ligand 3b against the active site of EGFR are shown in The anti-EGFR activity of curcumin and curcumin analogues was well documented in the literature ,18. The 3a, which showed a mean percent GP of 99.22 percent [50 of 7.1 µM against MCF7 cell line [50 values of 5.6 ± 2.0, 5.9 ± 0.6 and 6.6 ± 1.9 µM, respectively [50 values ranging between 12.5 and 50 µM [50 values of 54.8 ± 2.5 and 52.1 ± 4.8 µM, respectively [50 values of 5.31, 8.33, 7.69, 8.62, and 8.19 µM and also inhibited Akt and STAT3 phosphorylation and increased ERK phosphorylation [50 value of 16 µM [3b and 3c) in the current investigation showed superior antiproliferative activity to curcumin in the one dose as well as the five dose assay. The curcumin analogues  displayed maximum sensitivity against the leukemia cell line panel with GI50 values ranging between 0.224 and 0.569 µM  displayed mean GPs of 7.59 and 12.72, respectively [50 ranging between 3.13 and 93.40 µM, while compounds 3b (GI50 = 0.281 µM) and 3c (GI50 = 1.34 µM) in the current investigation showed superior activity against the same cell lines [3b,c) were reported for the first time in the current investigation and they demonstrated promising antiproliferative activity against 5 dozen cancer cell lines derived from nine diverse panels. Some of the curcumin analogues reported earlier showed IC50 values of 16.71 (curcumin pyrazole) and 5.85 µM (curcumin semicarbazide) in an SRB assay against the HCT 116 cell line, while our compounds 3b and 3c displayed superior anticancer activity with GI50 values of 0.386 and 0.416 µM, respectively [3b was found to be moderately selective against the leukemia panel with an SR of 4.59. In our previous work the curcumin analogues were found to be non-selective towards the cancer cell line panels [3a–c) in the current investigation showed encouraging binding affinity against the EGFR, this information may also help to highlight the biological significance of curcumin analogues.The anticancer activity of six 3,5-bis(4-hydroxy-3-methylstyryl)-N-(substitutedphenyl)-1H-pyrazole-1-carboxamides has previously been reported, and their antiproliferative properties in terms of GPs ranged between 7.23 and −19.19 percent, which were found to be higher than that of compound  percent . The curell line . The curectively . The curnd 50 µM . The cucectively . The currylation . The curof 16 µM . The cur0.569 µM , while tectively ,55,59,60ll lines . The come panels ,64. Sinc1)  and N-(2-methoxyphenyl)-hydrazine carboxamide (2a)  in glacial acetic acid was stirred continuously at 80 °C in a sand bath for 10 h. The reaction mixture was then concentrated under a vacuum to remove excess solvent before being poured into crushed ice, filtered, dried, and recrystallized with ethanol to yield the compound 3a. The intermediate 2a was prepared as per the reported method in two steps starting from o-anisidine [An equimolar mixture of curcumin (nisidine . Method 1)  and substituted phenoxy acetohydrazide (2b–c) (1 mmol) in glacial acetic acid was stirred continuously at 80 °C in the sand bath for 10 h. The reaction mixture was then concentrated under a vacuum to remove excess solvent before being poured into crushed ice, filtered, dried, and recrystallized with ethanol to yield compound 3b–c. The intermediates 2b–c were prepared as per the reported method in two steps starting from substituted phenol as per the reported method [An equimolar mixture of curcumin (d method . Method http://www.chemaxon.com/ accessed on 24 July 2022), then transformed to a 3D structure and saved in xyz format. The DFT calculations were carried out in the gas phase using the Orca 5.03 package [https://www.chemcraftprog.com accessed on 24 July 2022) and Avogadro programs were used for analysis of the DFT results [The ligand’s 2D structure was created in Marvin Sketch  was evaluated against nine diverse panels of 60 cancer cell lines at 10 µM according to the National Cancer Institute (NCI US) protocol [3b–c) were treated against the cell lines in the given concentrations of 0.001 to 100 µM and there different parameters viz. GI50, TGI and LC50 were calculated for each cell line [50, TGI and LC50 are the molar concentration producing 50% growth inhibition, total growth inhibition (TGI) and a 50% cellular death, respectively.The antiproliferative activity of the curcumin analogues  [3a–c were saved as mol files, and docking was performed according to the reported protocol [The ligands 3a-c were molecular docked against EGFR. The protein data bank provided the EGFR (PDB: 3W2R) X-ray crystal structure with a resolution of 2.05 Å; R-value 0.220 (observed) (ly 2022) . The ligprotocol ,51.3a–c). The antiproliferative activity of compounds 3b and 3c showed promising anticancer activity in a one dose as well as a five dose assay. The compound 3b showed the most promising antiproliferative activity among the series of curcumin analogues and showed moderate selectivity against leukemia with an SR value of 4.59. The curcumin analogues  demonstrated superior antiproliferative activity to curcumin. HOMO-LUMO gaps (∆E) from DFT analysis and anticancer activities were discovered to be in agreement. All the curcumin analogues (3a–c) demonstrated significant binding affinity against the EGFR, a potential anticancer target. Because the compounds demonstrated promising anticancer activity and significant binding affinity against the EGFR, the current report has the potential to enhance the anticancer research development program in the future.We have prepared and reported the antiproliferative activity of three curcumin analogues (