Anticancer Response Research Articles

Predicting Anticancer Drug Response With Deep Learning Constrained by Signaling Pathways.

Thanks to the availability of multiomics data of individual cancer patients, precision medicine or personalized medicine is becoming a promising treatment for individual cancer patients. However, the association patterns, that is, the mechanism of response (MoR) between large-scale multiomics features and drug response are complex and heterogeneous and remain unclear. Although there are existing computational models for predicting drug response using the high-dimensional multiomics features, it remains challenging to uncover the complex molecular mechanism of drug responses. To reduce the number of predictors/features and make the model more interpretable, in this study, 46 signaling pathways were used to build a deep learning model constrained by signaling pathways, consDeepSignaling, for anti–drug response prediction. Multiomics data, like gene expression and copy number variation, of individual genes can be integrated naturally in this model. The signaling pathway–constrained deep learning model was evaluated using the multiomics data of ∼1000 cancer cell lines in the Broad Institute Cancer Cell Line Encyclopedia (CCLE) database and the corresponding drug–cancer cell line response data set in the Genomics of Drug Sensitivity in Cancer (GDSC) database. The evaluation results showed that the proposed model outperformed the existing deep neural network models. Also, the model interpretation analysis indicated the distinctive patterns of importance of signaling pathways in anticancer drug response prediction.

Exploring effects of DNA methylation and gene expression on pan-cancer drug response by mathematical models

Since genetic alteration only accounts for 20%-30% in the drug effect-related factors, the role of epigenetic regulation mechanisms in drug response is gradually being valued. However, how epigenetic changes and abnormal gene expression affect the chemotherapy response remains unclear. Therefore, we constructed a variety of mathematical models based on the integrated DNA methylation, gene expression, and anticancer drug response data of cancer cell lines from pan-cancer levels to identify genes whose DNA methylation is associated with drug response and then to assess the impact of epigenetic regulation of gene expression on the sensitivity of anticancer drugs. The innovation of the mathematical models lies in: Linear regression model is followed by logistic regression model, which greatly shortens the calculation time and ensures the reliability of results by considering the covariates. Second, reconstruction of prediction models based on multiple dataset partition methods not only evaluates the model stability but also optimizes the drug-gene pairs. For 368,520 drug-gene pairs with P < 0.05 in linear models, 999 candidate pairs with both AUC ≥ 0.8 and P < 0.05 were obtained by logistic regression models between drug response and DNA methylation. Then 931 drug-gene pairs with 45 drugs and 491 genes were optimized by model stability assessment. Integrating both DNA methylation and gene expression markedly increased predictive power for 732 drug-gene pairs where 598 drug-gene pairs including 44 drugs and 359 genes were prioritized. Several drug target genes were enriched in the modules of the drug-gene-weighted interaction network. Besides, for cancer driver genes such as EGFR, MET, and TET2, synergistic effects of DNA methylation and gene expression can predict certain anticancer drugs' responses. In summary, we identified potential drug sensitivity-related markers from pan-cancer levels and concluded that synergistic regulation of DNA methylation and gene expression affect anticancer drug response.

Mast Cells and Skin and Breast Cancers: A Complicated and Microenvironment-Dependent Role.

Mast cells are important sentinel cells in host defense against infection and major effector cells in allergic disease. The role of these cells in cancer settings has been widely debated. The diverse range of mast cell functions in both immunity and tissue remodeling events, such as angiogenesis, provides multiple opportunities for mast cells to modify the tumor microenvironment. In this review, we consider both skin and breast cancer settings to address the controversy surrounding the importance of mast cells in the host response to tumors. We specifically address the key mediators produced by mast cells which impact tumor development. The role of environmental challenges in modifying mast cell responses and opportunities to modify mast cell responses to enhance anti-tumor immunity are also considered. While the mast cell’s role in many cancer contexts is complicated and poorly understood, the activities of these tissue resident and radioresistant cells can provide important opportunities to enhance anti-cancer responses and limit cancer development.

Supramolecular co-assembly of self-adjuvanting nanofibrious peptide hydrogel enhances cancer vaccination by activating MyD88-dependent NF-κB signaling pathway without inflammation

Peptide vaccine targeting tumor-specific antigens is a promising cancer treatment regimen. However, peptide vaccines are commonly low-immunogenic, leading to suboptimal antitumor T-cell responses. Current peptide vaccination approaches are challenged by the variability of peptide physicochemical characters and vaccine formulations, flexibility, and the broad feasibility. Here, the supramolecular co-assembly of antigen epitope-conjugated peptides (ECPs) targeting CD8 or CD4 T-cell receptors was used to engineer a nanofibrious hydrogel vaccine platform. This approach provided precise and tunable loading of peptide antigens in nanofibers, which notably increased the antigen uptake, cross-presentation, and activation of dendritic cells (DCs). Immunization in mice indicated that the co-assembled peptide hydrogel did not induce local inflammation responses and elicited significantly promoted T-cell immunity by activating the MyD88-dependent NF-κB signaling pathway in DCs. Vaccination of mice using co-assembled peptide vaccine stimulated both enhanced CD8 and CD4 T cells against EG.7-OVA tumors without additional immunoadjuvants or delivery systems, and resulted in a more remarkable cancer immunotherapy efficacy, compared with free peptide vaccine or aluminum-adjuvanted peptide formulation. Altogether, peptide co-assembly demonstrated by three independent pairs of ECPs is a facile, customizable, and chemically defined approach for co-delivering peptide antigens in self-adjuvanting hydrogel vaccines that could induce stronger anticancer T-cell responses.

Fecal Metabolic Profiling of Breast Cancer Patients during Neoadjuvant Chemotherapy Reveals Potential Biomarkers.

Breast cancer (BC) is the most common form of cancer among women worldwide. Despite the huge advancements in its treatment, the exact etiology of breast cancer still remains unresolved. There is an increasing interest in the role of the gut microbiome in modulating the anti-cancer therapeutic response. It seems that alteration of the microbiome-derived metabolome potentially promotes carcinogenesis. Taken together, metabolomics has arisen as a fascinating new omics field to screen promising metabolic biomarkers. In this study, fecal metabolite profiling was performed using NMR spectroscopy, to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy (NAC) for breast cancer. Metabolic profiles of feces from patients (n = 8) following chemotherapy treatment cycles were studied. Interestingly, amino acids were found to be upregulated, while lactate and fumaric acid were downregulated in patients under the second and third cycles compared with patients before treatment. Furthermore, short-chain fatty acids (SCFAs) were significantly differentiated between the studied groups. These results strongly suggest that chemotherapy treatment plays a key role in modulating the fecal metabolomic profile of BC patients. In conclusion, we demonstrate the feasibility of identifying specific fecal metabolic profiles reflecting biochemical changes that occur during the chemotherapy treatment. These data give an interesting insight that may complement and improve clinical tools for BC monitoring.

Spike-in normalization for single-cell RNA-seq reveals dynamic global transcriptional activity mediating anticancer drug response.

The transcriptional plasticity of cancer cells promotes intercellular heterogeneity in response to anticancer drugs and facilitates the generation of subpopulation surviving cells. Characterizing single-cell transcriptional heterogeneity after drug treatments can provide mechanistic insights into drug efficacy. Here, we used single-cell RNA-seq to examine transcriptomic profiles of cancer cells treated with paclitaxel, celecoxib and the combination of the two drugs. By normalizing the expression of endogenous genes to spike-in molecules, we found that cellular mRNA abundance shows dynamic regulation after drug treatment. Using a random forest model, we identified gene signatures classifying single cells into three states: transcriptional repression, amplification and control-like. Treatment with paclitaxel or celecoxib alone generally repressed gene transcription across single cells. Interestingly, the drug combination resulted in transcriptional amplification and hyperactivation of mitochondrial oxidative phosphorylation pathway linking to enhanced cell killing efficiency. Finally, we identified a regulatory module enriched with metabolism and inflammation-related genes activated in a subpopulation of paclitaxel-treated cells, the expression of which predicted paclitaxel efficacy across cancer cell lines and in vivo patient samples. Our study highlights the dynamic global transcriptional activity driving single-cell heterogeneity during drug response and emphasizes the importance of adding spike-in molecules to study gene expression regulation using single-cell RNA-seq.

Ex Vivo High Salt Activated Tumor-Primed CD4+T Lymphocytes Exert a Potent Anti-Cancer Response.

Simple SummaryCell based immunotherapy is rapidly emerging as a promising cancer treatment. Salt (sodium chloride) treatment to immune cell cultures is known to induce inflammatory activation. In our current study, we analyzed the anti-cancer ability of salt treatment on immune cells outside the host followed by reinfusion into the host. Using a pre-clinical breast cancer model, we demonstrated that external salt treatment on T-cell subset of immune cells produced a viable anti-cancer response, which may have future human clinical application.Cell based immunotherapy is rapidly emerging as a promising cancer treatment. A modest increase in salt (sodium chloride) concentration in immune cell cultures is known to induce inflammatory phenotypic differentiation. In our current study, we analyzed the ability of salt treatment to induce ex vivo expansion of tumor-primed CD4 (cluster of differentiation 4)+T cells to an effector phenotype. CD4+T cells were isolated using immunomagnetic beads from draining lymph nodes and spleens from tumor bearing C57Bl/6 mice, 28 days post-injection of Py230 syngeneic breast cancer cells. CD4+T cells from non-tumor bearing mice were isolated from splenocytes of 12-week-old C57Bl/6 mice. These CD4+T cells were expanded ex vivo with five stimulation cycles, and each cycle comprised of treatment with high salt (Δ0.035 M NaCl) or equimolar mannitol controls along with anti-CD3/CD28 monoclonal antibodies for the first 3 days, followed by the addition of interleukin (IL)-2/IL-7 cytokines and heat killed Py230 for 4 days. Ex vivo high salt treatment induced a two-fold higher Th1 (T helper type 1) expansion and four-fold higher Th17 expansion compared to equimolar mannitol treatment. Importantly, the high salt expanded CD4+T cells retained tumor-specificity, as demonstrated by higher in vitro cytotoxicity against Py230 breast cancer cells and reduced in vivo syngeneic tumor growth. Metabolic studies revealed that high salt treatment enhanced the glycolytic reserve and basal mitochondrial oxidation of CD4+T cells, suggesting a role of high salt in enhanced pro-growth anabolic metabolism needed for inflammatory differentiation. Mechanistic studies demonstrated that the high salt induced switch to the effector phenotype was mediated by tonicity-dependent transcription factor, TonEBP/NFAT5. Using a transgenic murine model, we demonstrated that CD4 specific TonEBP/NFAT5 knock out (CD4cre/creNFAT5flox/flox) abrogated the induction of the effector phenotype and anti-tumor efficiency of CD4+T cells following high salt treatment. Taken together, our data suggest that high salt-mediated ex vivo expansion of tumor-primed CD4+T cells could induce effective tumor specific anti-cancer responses, which may have a novel cell-based cancer immunotherapeutic application.

NLRC5 regulates expression of MHC-I and provides a target for anti-tumor immunity in transmissible cancers.

PurposeDownregulation of MHC class I (MHC-I) is a common immune evasion strategy of many cancers. Similarly, two allogeneic clonal transmissible cancers have killed thousands of wild Tasmanian devils (Sarcophilus harrisii) and also modulate MHC-I expression to evade anti-cancer and allograft responses. IFNG treatment restores MHC-I expression on devil facial tumor (DFT) cells but is insufficient to control tumor growth. Transcriptional co-activator NLRC5 is a master regulator of MHC-I in humans and mice but its role in transmissible cancers remains unknown. In this study, we explored the regulation and role of MHC-I in these unique genetically mis-matched tumors.MethodsWe used transcriptome and flow cytometric analyses to determine how MHC-I shapes allogeneic and anti-tumor responses. Cell lines that overexpress NLRC5 to drive antigen presentation, and B2M-knockout cell lines incapable of presenting antigen on MHC-I were used to probe the role of MHC-I in rare cases of tumor regressions.ResultsTranscriptomic results suggest that NLRC5 plays a major role in MHC-I regulation in devils. NLRC5 was shown to drive the expression of many components of the antigen presentation pathway but did not upregulate PDL1. Serum from devils with tumor regressions showed strong binding to IFNG-treated and NLRC5 cell lines; antibody binding to IFNG-treated and NRLC5 transgenic tumor cells was diminished or absent following B2M knockout.ConclusionMHC-I could be identified as a target for anti-tumor and allogeneic immunity. Consequently, NLRC5 could be a promising target for immunotherapy and vaccines to protect devils from transmissible cancers and inform development of transplant and cancer therapies for humans.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00432-021-03601-x.

Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling.

Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high‐dimensional settings, whereas high‐dimensional molecular profiling technologies (“omics”) are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high‐dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics‐based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two‐step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient‐derived xenograft (PDX) models treated with a variety of mono‐ and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates‐derived TGI parameter values were regressed using the LASSO on high‐dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.

Biogenic nanosized gold particles: Physico-chemical characterization and its anticancer response against breast cancer

With the advancement of nanotechnology, the nano-sized particles make an imprint on our daily lives.The present investigation revealed that biomolecules present in seed exudates of Vigna radiata are responsible for the synthesis of AuNPs, confirmed by the routine characterization techniques. Anticancer efficacy showed by AuNPs might be due to the release of phytochemicals in the exudate which is being adsorbed on the surface of AuNPs referencing their anticancer efficacy against the tested breast cancer cell lines. Inhibition of clonogenicity and cell cycle arrest at G2/M phase then apoptosis of AuNPs was also observed, but found nontoxic to the human PBMC cells which further confirms its biocompatible property Among the various physicochemical study, present AuNPs shows unique information, they show photoluminescent property which may be used for bioimaging purposes. However, further molecular analysis needs to be explored to understand the underlying mechanism for therapeutic and biomedical application.

Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression.

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.

Potential chemopreventive, anticancer and anti-inflammatory properties of a refined artocarpin-rich wood extract of Artocarpus heterophyllus Lam.

Colorectal cancer (CRC) represents the third leading cause of death among cancer patients below the age of 50, necessitating improved treatment and prevention initiatives. A crude methanol extract from the wood pulp of Artocarpus heterophyllus was found to be the most bioactive among multiple others, and an enriched extract containing 84% (w/v) artocarpin (determined by HPLC–MS–DAD) was prepared. The enriched extract irreversibly inhibited the activity of human cytochrome P450 CYP2C9, an enzyme previously shown to be overexpressed in CRC models. In vitro evaluations on heterologously expressed microsomes, revealed irreversible inhibitory kinetics with an IC50 value of 0.46 µg/mL. Time- and concentration-dependent cytotoxicity was observed on human cancerous HCT116 cells with an IC50 value of 4.23 mg/L in 72 h. We then employed the azoxymethane (AOM)/dextran sodium sulfate (DSS) colitis-induced model in C57BL/6 mice, which revealed that the enriched extract suppressed tumor multiplicity, reduced the protein expression of proliferating cell nuclear antigen, and attenuated the gene expression of proinflammatory cytokines (Il-6 and Ifn-γ) and protumorigenic markers (Pcna, Axin2, Vegf, and Myc). The extract significantly (p = 0.03) attenuated (threefold) the gene expression of murine Cyp2c37, an enzyme homologous to the human CYP2C9 enzyme. These promising chemopreventive, cytotoxic, anticancer and anti-inflammatory responses, combined with an absence of toxicity, validate further evaluation of A. heterophyllus extract as a therapeutic agent.

Immunomodulatory and Anticancer Activities of Hyacinthus orientalis L.: An In Vitro and In Vivo Study.

Hyacinthus orientalis L. (family Hyacinthaceae) is traditionally used to treat different diseases including cancer. In this study, the anticancer and immunomodulatory effects of this plant were evaluated. Hydroalcoholic extract was prepared, and different solvent fractions were obtained using solvent–solvent extraction. In the anticancer part, MTT assay and caspase-3 ELISA kits were used to measure the antiproliferative and apoptosis induction ability for each extract, respectively. In the immunomodulatory part, lymphocyte proliferation assay and cytokines detection kit were used to measure the effect of extracts of acquired immunity. Phagocytosis and pinocytosis induction were used to evaluate the effect of extracts on the innate immunity. GC–MS, LC–MS, and Foline–Ciocalteu assays were used to identify the chemical composition of the plant. Balb/C mice were inoculated with breast cancer and treated with hydroalcoholic extract of H. orientalis L. Results showed that hydroalcoholic extract and n-hexane fraction were highly effective in apoptosis induction. Both extract and fraction were also effective in stimulating lymphocytes proliferation and phagocytosis. Significant reduction in tumor size was achieved after treating tumor-bearing mice with hydroalcoholic extract. Additionally, high cure percentages (50%) were obtained in treated mice. Results of this study showed that H. orientalis L. has promising anticancer and immunomodulatory activities. However, further studies are needed to explore more details of apoptosis induction ability and other mechanisms of action and to measure different signaling pathways responsible for the anticancer and immunomodulatory response.

Nanotechnology synergized immunoengineering for cancer

Novel strategies modulating the immune system yielded enhanced anticancer responses and improved cancer survival. Nevertheless, the success rate of immunotherapy in cancer treatment has been below expectation(s) due to unpredictable efficacy and off-target effects from systemic dosing of immunotherapeutic(s). As a result, there is an unmet clinical need for improving conventional immunotherapy. Nanotechnology offers several new strategies, multimodality, and multiplex biological targeting advantage to overcome many of these challenges. These efforts enable programming the pharmacodynamics, pharmacokinetics, and delivery of immunomodulatory agents/co-delivery of compounds to prime at the tumor sites for improved therapeutic benefits. This review provides an overview of the design and clinical principles of biomaterials driven nanotechnology and their potential use in personalized nanomedicines, vaccines, localized tumor modulation, and delivery strategies for cancer immunotherapy. In this review, we also summarize the latest highlights and recent advances in combinatorial therapies availed in the treatment of cold and complicated tumors. It also presents key steps and parameters implemented for clinical success. Finally, we analyse, discuss, and provide clinical perspectives on the integrated opportunities of nanotechnology and immunology to achieve synergistic and durable responses in cancer treatment.

Applications of Melanin and Melanin-Like Nanoparticles in Cancer Therapy: A Review of Recent Advances.

Thanks to the growing knowledge about cancers and their interactions with the immune system, a huge number of therapeutic cancer vaccines have been developed in the past two decades. Despite encouraging results in pre-clinical models, cancer vaccines have not yet achieved significant clinical efficacy. Several factors may contribute to such poor results, including the difficulty of triggering a strong immune response and the immunosuppressive tumor microenvironment. Many strategies are currently being explored. Different types of adjuvants have been incorporated into vaccine formulations to improve their efficacy, as cancer antigens are usually poorly immunogenic. Nanoparticle systems are promising tools as they act as carriers for antigens and can be surface-modified so that they specifically target antigen-presenting cells in lymph nodes. Bioinspired nanomaterials are ideal candidates thanks to their biocompatibility. Recently, melanin-based nanoparticles were reported to efficiently localize into draining lymphoid tissues and trigger immune responses against loaded antigens. In addition, by virtue of their photochemical properties, melanin-based nanoparticles can also play an immunomodulatory role to promote anti-cancer responses in the context of photothermal therapy. In this review, we discuss the above-mentioned properties of melanin, and summarize the promising results of the melanin-based cancer vaccines recently reported in preclinical models.

The Abscopal Effect of Radiation Therapy

Radiation therapy (RT) in some cases results in a systemic anticancer response known as the abscopal effect. Multiple hypotheses support the role of immune activation initiated by RT-induced DNA damage. Optimal radiation dose is necessary to promote the cGAS-STING pathway in response to radiation and initiate an IFN-1 signaling cascade that promotes the maturation and migration of dendritic cells to facilitate antigen presentation and stimulation of cytotoxic T cells. T cells then exert a targeted response throughout the body at areas not subjected to RT. These effects are further augmented through the use of immunotherapeutic drugs resulting in increased T-cell activity. Tumor-infiltrating lymphocyte presence and TREX1, KPNA2 and p53 signal expression are being explored as prognostic biomarkers.

Abstract P08: Adenosine-based cancer immunotherapy for COVID-19

Abstract Cancer immunotherapy has shaped the way in which we design cancer treatments, introducing the paradigm of taking advantage of our immune system to fight cancer. We propose that the same concept could be applied to infectious diseases and, especially, to those that hamper the immune system like COVID-19. It is well known that an adaptive immune response is able to eradicate viral infections and CD8+ T-cells are key in such anti-viral response. Furthermore, several studies reported that the number of CD8+ T-cells is reduced in COVID-19 patients since the beginning of SARS-CoV-2 infection and further decreased in severe cases. CD8+ T-cells often show signs of exhaustion, loss of T-cell functions and suppression in COVID-19 patients, suggesting that hampering CD8+ T-cells could be a way by which SARS-CoV-2 infection progresses. Importantly, the number of CD8+ T cells appears to re-increase in patients that are recovering from COVID-19, suggesting that CD8+ T-cells could be a key factor that determines whether our body can recover from the disease. We propose that therapies that boost CD8+ T cells could be effective in clearing SARS-CoV-2 infection in COVID-19. To test this, we will take advantage of the adenosine-mediated immunomodulation. Adenosine, an ATP metabolite that is produced during inflammation, hypoxia and in the tumor microenvironment, was found to suppress the immune response through activation of adenosine receptors present on immune cells. Small molecules antagonists that block one of these receptors, adenosine A2A receptor (A2AR) antagonists, are currently being studied to boost anti-cancer T-cell mediated immune responses. Our data show that treatment with an A2AR antagonist restores and stabilizes Notch1, a key pathway for T-cell functions, along with production of INF-gamma and Granzyme B and proliferation in CD8+ T-cells. As a proof of concept, our data indicates that treatment with an A2AR antagonist increases CD8+ T-cells anti-cancer response in tumor-derived organoids, suggesting that the treatment boosts CD8+ T-cells-mediated immune response. Ongoing work aims to test whether the A2AR antagonist treatment restores several parameters of T-cell function that are specifically modified in COVID-19. This analysis will help to predict the action of the A2AR antagonist on T-cells in vivo and we ultimately aim to test this treatment in a mouse model for COVID-19. Overall, our work could introduce a new paradigm and new therapies for COVID-19 and other infectious diseases. Citation Format: Giulia Monticone, Alistair Ramsay, Lucio Miele. Adenosine-based cancer immunotherapy for COVID-19 [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2021 Feb 3-5. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(6_Suppl):Abstract nr P08.

The Multifaceted Roles of EGFL7 in Cancer and Drug Resistance.

Simple SummaryCancer growth and metastasis require interactions with the extracellular matrix (ECM), which is home to many biomolecules that support the formation of new vessels and cancer growth. One of these biomolecules is epidermal growth factor-like protein-7 (EGFL7). EGFL7 alters cellular adhesion to the ECM and migratory behavior of tumor and immune cells contributing to tumor metastasis. EGFL7 is engaged in the formation of new vessels and changes in ECM stiffness. One of its binding partners on the endothelial and cancer cell surface is beta 3 integrin. Beta 3 integrin pathways are under intense investigation in search of new therapies to kill cancer cells. All these properties enable EGFL7 to contribute to drug resistance. In this review, we give insight into recent studies on EGFL7 and its engagement with beta3 integrin, a marker predicting cancer stem cells and drug resistance.Invasion of cancer cells into surrounding tissue and the vasculature is an important step for tumor progression and the establishment of distant metastasis. The extracellular matrix (ECM) is home to many biomolecules that support new vessel formation and cancer growth. Endothelial cells release growth factors such as epidermal growth factor-like protein-7 (EGFL7), which contributes to the formation of the tumor vasculature. The signaling axis formed by EGFL7 and one of its receptors, beta 3 integrin, has emerged as a key mediator in the regulation of tumor metastasis and drug resistance. Here we summarize recent studies on the role of the ECM-linked angiocrine factor EGFL7 in primary tumor growth, neoangiogenesis, tumor metastasis by enhancing epithelial-mesenchymal transition, alterations in ECM rigidity, and drug resistance. We discuss its role in cellular adhesion and migration, vascular leakiness, and the anti-cancer response and provide background on its transcriptional regulation. Finally, we discuss its potential as a drug target as an anti-cancer strategy.

Neoadjuvant chemoradiation alters biomarkers of anticancer immunotherapy responses in locally advanced rectal cancer

BackgroundNeoadjuvant chemoradiation therapy (CRT) is a widely used preoperative treatment strategy for locally advanced rectal cancer (LARC). However, a few studies have evaluated the molecular changes caused by neoadjuvant CRT...

Abstract PO-064: Predicting tumor sensitivity to chemotherapeutic drugs using molecular, cellular and computational methods

Abstract Oral squamous cell carcinoma (OSCC) patients respond poorly to chemotherapy. Predicting anticancer drug response before the treatment of OSCC is a significant challenge. Recently, gene expression profiling and cell-based assays reveals drug sensitivity that could be implemented for drug selection for patients. We analyzed the expression of 11 drug response-related genes in 31 OSCC biopsies collected prior to any treatment, using custom designed PCR array. Further, we investigated drug response pattern of selected anticancer drugs using BH3 apoptotic profiling in the primary cells isolated from OSCC tissues. Then, we correlated the results of drug-response gene expression pattern with apoptotic priming to predict tumor response to chemotherapy. The best performing drug (BPD) and response differences (RD) between the drugs were identified using statistical methods to select the best choice of drug in a personalized manner. Based on the Pierson correlation, we classified OSCC tumors as sensitive, moderately responsive or resistant to chemotherapy. We observed that 2 patients were resistant, 17 patients were moderately responsive and 12 patients were sensitive to chemotherapeutic drugs. We found that up-regulation of genes linked to drug resistance facilitates survival of tumor samples, which was revealed by the percentage of apoptotic priming. Moreover, we found that paclitaxel induced 40-45% apoptotic priming compared to other drugs. Average response difference (RD) analysis showed that 80% of tumors responded well to paclitaxel as compared to other drugs studied that were less effective. The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. We developed drug efficacy prediction models using multiple tumor features by employing the statistical methods like multi linear regression (MLR), modified MLR-weighted least square (MLR-WLS) and enhanced MLR-WLS. All the three developed drug efficacy prediction models were then validated using the data of actual OSCC samples (train-test ratio 31: 31) and actual Vs hypothetical samples (train-test ratio 31: 30). The selected best statistical model i.e. enhanced MLR-WLS has then been cross-validated (CV) using 341 theoretical tumor data. Finally, the performances of the models were assessed by the level of learning confidence, significance, accuracy and error terms. We observed that the enhanced MLR-WLS model was the best fit to predict anticancer drug efficacy which may have translational applications. Therefore, drug-response gene expression pattern with the status of apoptotic priming in the tumor cells may be translated to the clinic and which will make impact in precision medicine. Citation Format: N. Rajendra Prasad, Kanimozhi Govindaswamy, G.R. Brindha. Predicting tumor sensitivity to chemotherapeutic drugs using molecular, cellular and computational methods [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-064.