PharmacoGx: an R package for analysis of large pharmacogenomic datasets

Investigating the impact of SMAD2 and SMAD4 downregulation in colorectal cancer and their correlation with immune markers, prognosis, and drug resistance and sensitivity

BACKGROUND

While many genes linked to colorectal cancer (CRC) contribute to cancer development, a thorough investigation is needed to explore crucial hub genes yet to be fully studied. A pivotal pathway in CRC is transforming growth factor-beta (TGF-β). This study aimed to assess SMAD2 and SMAD4 gene expression from this pathway.

METHODS AND RESULTS

Counted data from the Cancer Genome Atlas (TCGA) were examined, comparing 483 tumor and 41 normal samples. Using clinical data, genes impacting overall survival (OS) were evaluated. GSE39582 was employed to confirmed the levels of genes in CRC compared to the normal samples. Additionally, employing unhealthy samples and the RT-qPCR means our outcomes was validated. Finally, PharmacoGx information were utilized to connect the levels of potential genes to drug tolerance and susceptibility. Our findings showed SMAD2 and SMAD4 levels in TGF-β signaling were more significant than other pathway genes. Our findings indicated that the protein levels of these genes were lower in malignant tissues than in healthy tissues. Results revealed a significant correlation between low levels of SMAD2 and unfavorable OS in CRC individuals. RT-qPCR results demonstrated decreased expressions of both SMAD2 and SMAD4 in cancer tissues compared to elevated levels in adjacent normal samples. Our results showed significant association between selected genes and immune cell infiltration markers such as CD8+, and B-cells. Our results indicated a potential association among the levels of SMAD2 and SMAD4 genes and tolerance and susceptibility to Nilotinib and Panobinostat drugs.

CONCLUSION

Reduced expression of SMAD2 and SMAD4 may be pivotal in CRC progression, impacting downstream genes unrelated to patient OS. These findings suggest a potential role for SMAD2 and SMAD4 as predictive markers for drug response in CRC patients.

Inference of drug off-target effects on cellular signaling using interactome-based deep learning

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning

Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in other, seemingly unrelated, assays. Applications of such models include predicting mechanisms-of-action (MoA) for phenotypic hits, identifying off-target activities, and identifying polypharmacologies. Here, we introduce transcriptomics-to-activity transformer (TAT) models that leverage gene expression profiles observed over compound treatment at multiple concentrations to predict the compound activity in other biochemical or cellular assays. We built TAT models based on gene expression data from a RASL-seq assay to predict the activity of 2692 compounds in 262 dose-response assays. We obtained useful models for 51% of the assays, as determined through a realistic held-out set. Prospectively, we experimentally validated the activity predictions of a TAT model in a malaria inhibition assay. With a 63% hit rate, TAT successfully identified several submicromolar malaria inhibitors. Our results thus demonstrate the potential of transcriptomic responses over compound concentration and the TAT modeling framework as a cost-efficient way to identify the bioactivities of promising compounds across many assays.

TBX15 and SDHB expression changes in colorectal cancer serve as potential prognostic biomarkers

Alterations in the expression of certain genes could be associated with both patient mortality rates and drug resistance. This study aimed to identify genes in colorectal cancer (CRC) that potentially serve as hub genes influencing patient survival rates. RNA-Seq data were downloaded from the cancer genome atlas database, and differential expression analysis was performed between tumors and healthy controls. Through the utilization of univariate and multivariate Cox regression analyses, in combination with the MCODE clustering module, the genes whose expression changes were related to survival rate and the hub genes related to them were identified. The mortality risk model was computed using the hub genes. CRC samples and the RT-qPCR method were utilized to confirm the outcomes. PharmacoGx data were employed to link the expression of potential genes to medication resistance and sensitivity. The results revealed the discovery of seven hub genes, which emerged as independent prognostic markers. These included HOXC6, HOXC13, HOXC8, and TBX15, which were associated with poor prognosis and overexpression, as well as SDHB, COX5A, and UQCRC1, linked to favorable prognosis and downregulation. Applying the risk model developed with the mentioned genes revealed a markedly higher incidence of deceased patients in the high-risk group compared to the low-risk group. RT-qPCR results indicated a decrease in SDHB expression and an elevation in TBX15 levels in cancer samples relative to adjacent healthy tissue. Also, PharmacoGx data indicated that the expression level of SDHB was correlated with drug sensitivity to Crizotinib and Dovitinib. Our findings highlight the potential association between alterations in the expression of genes such as HOXC6, HOXC13, HOXC8, TBX15, SDHB, COX5A, and UQCRC1 and increased mortality rates in CRC patients. As revealed by the PPI network, these genes exhibited the most connections with other genes linked to survival.

Text-mining-based feature selection for anticancer drug response prediction

Motivation

Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes.

Results

In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction.

Availability and implementation

https://github.com/merlab/text_features.

ERBB4-Mediated Signaling Is a Mediator of Resistance to PI3K and BTK Inhibitors in B-cell Lymphoid Neoplasms

BTK and PI3K inhibitors are among the drugs approved for the treatment of patients with lymphoid neoplasms. Although active, their ability to lead to long-lasting complete remission is rather limited, especially in the lymphoma setting. This indicates that tumor cells often develop resistance to the drugs. We started from a marginal zone lymphoma cell line, Karpas-1718, kept under prolonged exposure to the PI3Kδ inhibitor idelalisib until acquisition of resistance, or with no drug. Cells underwent transcriptome, miRNA and methylation profiling, whole-exome sequencing, and pharmacologic screening, which led to the identification of the overexpression of ERBB4 and its ligands HBEGF and NRG2 in the resistant cells. Cellular and genetic experiments demonstrated the involvement of this axis in blocking the antitumor activity of various BTK/PI3K inhibitors, currently used in the clinical setting. Addition of recombinant HBEGF induced resistance to BTK/PI3K inhibitors in parental cells and in additional lymphoma models. Combination with the ERBB inhibitor lapatinib was beneficial in resistant cells and in other lymphoma models already expressing the identified resistance factors. An epigenetic reprogramming sustained the expression of the resistance-related factors, and pretreatment with demethylating agents or EZH2 inhibitors overcame the resistance. Resistance factors were also shown to be expressed in clinical specimens. In conclusion, we showed that the overexpression of ERBB4 and its ligands represents a novel mechanism of resistance for lymphoma cells to bypass the antitumor activity of BTK and PI3K inhibitors and that targeted pharmacologic interventions can restore sensitivity to the small molecules.

A Microfluidic Platform for Evaluating the Internalization of Liposome Drug Carriers in Tumor Spheroids

The development of in vitro models recapitulating nanoparticle transport under physiological flow conditions is of great importance for predicting the efficacy of nanoparticle drug carriers. Liposomes are extensively used for drug delivery owing to their biocompatibility and biodegradability and the ability to carry both hydrophilic and hydrophobic compounds. Here, we used a library of liposomes with various dimensions and a microfluidic platform comprising a large array of uniformly sized breast cancer spheroids to explore size-dependent liposome internalization and retention in the spheroids under close-to-physiological interstitial conditions. Such a platform showed promising applications in the preclinical screening of small molecule drugs; however, the capability to deliver nanoparticles in the spheroid interior under close-to-physiological flow conditions was not explored. For the liposomes with diameters in the range of 45-200 nm, we show experimentally and by simulations that in comparison with liposome delivery solely by diffusion, flow significantly enhances liposome internalization in the microgels and mitigates the size-dependent spheroid penetration by the liposomes. The utility of the microfluidic platform was validated by evaluating the efficacy of clinically approved doxorubicin-loaded liposomes (Doxil), which exhibited superior retention in the spheroids under flow conditions, in comparison with free doxorubicin. This MF platform can serve as an in vitro model for screening the efficacy of drugs encapsulated in liposomes and find applications for screening other types of nanoparticle carriers for vaccine delivery, diagnostics, and skincare.

Tracing back primed resistance in cancer via sister cells

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance. Applying ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells reveals pre-resistant phenotypes defined by proteostatic and mRNA surveillance features, reflecting traits enriched in the upcoming subclonal selection. Furthermore, we show that DNA repair deficiency renders cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leverage the obtained pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

Homologous Recombination Deficiency Unrelated to Platinum and PARP Inhibitor Response in Cell Line Libraries

While large publicly available cancer cell line databases are invaluable for preclinical drug discovery and biomarker development, the association between homologous recombination deficiency (HRD) and drug sensitivity in these resources remains unclear. In this study, we comprehensively analyzed molecular profiles and drug screening data from the Cancer Cell Line Encyclopedia. Unexpectedly, gene alterations in BRCA1/2 or homologous recombination-related genes, HRD scores, or mutational signature 3 were not positively correlated with sensitivity to platinum agents or PARP inhibitors. Rather, higher HRD scores and mutational signature 3 were significantly associated with resistance to these agents in multiple assays. These findings were consistent when analyzing exclusively breast and ovarian cancer cell lines and when using data from the COSMIC Cell Line Project. Collectively, the existing data from established cancer cell lines do not reflect the expected association between HRD status and drug response to platinum agents and PARP inhibitors in clinical tumors. This discrepancy may extend to other tumor characteristics, highlighting the importance of recognizing potential limitations in cell line data for researchers.

Identifying squalene epoxidase as a metabolic vulnerability in high-risk osteosarcoma using an artificial intelligence-derived prognostic index

BACKGROUND

Osteosarcoma (OSA) presents a clinical challenge and has a low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high-risk OSA patients and guide treatment.

METHODS

We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA-seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways.

RESULTS

The artificial intelligence-derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high-AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K-Akt signalling, and diminished immune infiltration. ScRNA-seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE-specific inhibitor FR194738 demonstrated anti-OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents.

CONCLUSIONS

AIDPI is a robust biomarker for identifying the high-risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.

Integrated drug response prediction models pinpoint repurposed drugs with effectiveness against rhabdomyosarcoma

Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying cancer-targeting compounds with few side effects, among the many potential compounds, is expensive and time-consuming. A computational approach to reduce the number of potential candidate drugs can facilitate the discovery of attractive lead compounds. To address this and obtain reliable predictions of novel cell-line-specific drugs, we apply prediction models that have the potential to improve drug discovery approaches for RMS treatment. The results of two prediction models were ensemble and validated via in vitro experiments. The computational models were trained using data extracted from the Genomics of Drug Sensitivity in Cancer database and tested on two RMS cell lines to select potential RMS drug candidates. Among 235 candidate drugs, 22 were selected following the result of the computational approach, and three candidate drugs were identified (NSC207895, vorinostat, and belinostat) that showed selective effectiveness in RMS cell lines in vitro via the induction of apoptosis. Our in vitro experiments have demonstrated that our proposed methods can effectively identify and repurpose drugs for treating RMS.

A glycosylation-related gene signature predicts prognosis, immune microenvironment infiltration, and drug sensitivity in glioma

Glioma represents the most common primary cancer of the central nervous system in adults. Glycosylation is a prevalent post-translational modification that occurs in eukaryotic cells, leading to a wide array of modifications on proteins. We obtained the clinical information, bulk RNA-seq data, and single-cell RNA sequencing (scRNA-seq) from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Gene Expression Omnibus (GEO), and Repository of Molecular Brain Neoplasia Data (Rembrandt) databases. RNA sequencing data for normal brain tissues were accessed from the Genotype-Tissue Expression (GTEx) database. Then, the glycosylation genes that were differentially expressed were identified and further subjected to variable selection using a least absolute shrinkage and selection operator (LASSO)-regularized Cox model. We further conducted enrichment analysis, qPCR, nomogram, and single-cell transcriptome to detect the glycosylation signature. Drug sensitivity analysis was also conducted. A five-gene glycosylation signature (CHPF2, PYGL, GALNT13, EXT2, and COLGALT2) classified patients into low- or high-risk groups. Survival analysis, qPCR, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group. Furthermore, GSEA and immune infiltration analysis indicated that the glycosylation signature has the potential to predict the immune response in glioma. In addition, four drugs (crizotinib, lapatinib, nilotinib, and topotecan) showed different responses between the two risk groups. Glioma cells had been classified into seven lines based on single-cell expression profiles. The five-gene glycosylation signature can accurately predict the prognosis of glioma and may offer additional guidance for immunotherapy.

Identification of Prognostic Markers and Potential Therapeutic Targets using Gene Expression Profiling and Simulation Studies in Pancreatic Cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage.

OBJECTIVE

The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer.

METHODS

Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC.

RESULTS

Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer.

CONCLUSION

This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.

PI3Kδ Inhibition Potentiates Glucocorticoids in B-lymphoblastic Leukemia by Decreasing Receptor Phosphorylation and Enhancing Gene Regulation

Glucocorticoids are the cornerstone of B-lymphoblastic leukemia (B-ALL) therapy. Because response to glucocorticoids alone predicts overall outcomes for B-ALL, enhancing glucocorticoid potency should improve treatment. We previously showed that inhibition of the lymphoid-restricted PI3Kδ with idelalisib enhances glucocorticoid activity in B-ALL cells. Here, we show that idelalisib enhances glucocorticoid potency in 90% of primary B-ALL specimens and is most pronounced at sub-saturating doses of glucocorticoids near the EC50. Potentiation is associated with enhanced regulation of all glucocorticoid-regulated genes, including genes that drive B-ALL cell death. Idelalisib reduces phosphorylation of the glucocorticoid receptor (GR) at PI3Kδ/MAPK1 (ERK2) targets S203 and S226. Ablation of these phospho-acceptor sites enhances sensitivity to glucocorticoids with ablation of S226 in particular reducing synergy. We also show that phosphorylation of S226 reduces the affinity of GR for DNA in vitro. We propose that PI3Kδ inhibition improves glucocorticoid efficacy in B-ALL in part by decreasing GR phosphorylation, increasing DNA binding affinity, and enhancing downstream gene regulation. This mechanism and the response of patient specimens suggest that idelalisib will benefit most patients with B-ALL, but particularly patients with less responsive, including high-risk, disease. This combination is also promising for the development of less toxic glucocorticoid-sparing therapies.

CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose-response curves

Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a measured response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models. Here, we present CurveCurator, an open-source software that provides reliable dose-response characteristics by computing p-values and false discovery rates based on a recalibrated F-statistic and a target-decoy procedure that considers dataset-specific effect size distributions. The application of CurveCurator to three large-scale datasets enables a systematic drug mode of action analysis and demonstrates its scalable utility across several application areas, facilitated by a performant, interactive dashboard for fast data exploration.

PI3Kδ activation, IL6 over-expression, and CD37 loss cause resistance to the targeting of CD37-positive lymphomas with the antibody-drug conjugate naratuximab emtansine

Purpose

The transmembrane protein CD37 is expressed almost exclusively in lymphoid tissues, with the highest abundance in mature B cells. CD37-directed antibody- and, more recently, cellular-based approaches have shown preclinical and promising early clinical activity. Naratuximab emtansine (Debio 1562, IMGN529) is an antibodydrug conjugate (ADC) that incorporates an anti-CD37 monoclonal antibody conjugated to the maytansinoid DM1 as payload. Naratuximab emtansine has shown activity as a single agent and in combination with the anti-CD20 monoclonal antibody rituximab in B cell lymphoma patients.

Experimental Design

We assessed the activity of naratuximab emtansine using in vitro models of lymphomas, correlated its activity with CD37 expression levels, characterized two resistance mechanisms to the ADC, and identified combination partners providing synergy.

Results

The anti-tumor activity of naratuximab emtansine was tested in 54 lymphoma cell lines alongside its free payload. The median IC 50 of naratuximab emtansine was 780 pM, and the activity, primarily cytotoxic, was more potent in B than in T cell lymphoma cell lines. In the subgroup of cell lines derived from B cell lymphoma, there was some correlation between sensitivity to DM1 and sensitivity to naratuximab emtansine (r=0.28, P = 0.06). After prolonged exposure to the ADC, one diffuse large B cell lymphoma (DLBCL) cell line developed resistance to the ADC due to the biallelic loss of the CD37 gene. After CD37 loss, we also observed upregulation of IL6 (IL-6) and other transcripts from MYD88/IL6-signaling. Recombinant IL6 led to resistance to naratuximab emtansine, while the anti-IL6 antibody tocilizumab improved the cytotoxic activity of the ADC in CD37-positive cells. In a second model, resistance was sustained by an activating mutation in the PIK3CD gene, associated with increased sensitivity to PI3K δ inhibition and a switch from functional dependence on the anti-apoptotic protein MCL1 to reliance on BCL2. The addition of idelalisib or venetoclax to naratuximab emtansine overcame resistance to the ADC in the resistant derivative while also improving the cytotoxic activity of the ADC in the parental cells.

Conclusions

Targeting B cell lymphoma with the CD37 targeting ADC naratuximab emtansine showed vigorous anti-tumor activity as a single agent, which was also observed in models bearing genetic lesions associated with inferior outcomes, such as MYC translocations and TP53 inactivation or resistance to R-CHOP. Resistance DLBCL models identified active combinations of naratuximab emtansine with drugs targeting IL6, PI3K δ , and BCL2. Despite notable progress in recent decades, we still face challenges in achieving a cure for a substantial number of lymphoma patients (1,2). A pertinent example is diffuse large B cell lymphoma (DLBCL), the most prevalent type of lymphoma (3). More than half of DLBCL patients can achieve remission, but around 40% of them experience refractory disease or relapse following an initial positive response (3). Regrettably, the prognosis for many of these cases remains unsatisfactory despite introducing the most recent antibody-based or cellular therapies (3,4), underscoring the importance of innovating new therapeutic strategies and gaining insights into the mechanisms of therapy resistance. CD37 is a transmembrane glycoprotein belonging to the tetraspanin family, primarily expressed on the surface of immune cells, principally in mature B cells but also, at lower levels, in T cells, macrophages/monocytes, granulocytes and dendritic cells (5) (6-8). CD37 plays a crucial role in various immune functions, including B cell activation, proliferation, and signaling, although its precise role still needs to be fully elucidated. CD37 interacts with multiple molecules, including SYK, LYN, CD19, CD22, PI3K δ , PI3K γ , and different integrins, among others (6-8). In mice, the lack of CD37 is paired with reduced T cell-dependent antibody-secreting cells and memory B cells, apparently due to the loss of CD37-mediated clustering of α 4 β 1 integrins (VLA-4) on germinal center B cells and decreased downstream activation of PI3K/AKT signaling and cell survival (5). Reflecting the expression pattern observed in normal lymphocytes, CD37 exhibits elevated expression in all mature B-cell lymphoid neoplasms, including most lymphoma subtypes, and absence in early progenitor cells or terminally differentiated plasma cells (6,8-14). In DLBCL, CD37 expression has been reported between 40% and 90% of cases across multiple studies performed using different antibodies (10,14-16). CD37-directed antibody- and, more recently, cellular-based approaches have shown preclinical (7,10-14,17-23) and early promising clinical activity (24-32). Among the CD37-targeting agents, naratuximab emtansine (Debio 1562, IMGN529) is an antibody-drug conjugate (ADC) that incorporates the anti-CD37 humanized IgG1 monoclonal antibody K7153A conjugated to the maytansinoid DM1, as payload, via the thioether linker, N-succinimidyl-4-(N-maleimidomethyl)cyclohexane-1-carboxylate (SMCC) (10). Based on the initial in vitro and in vivo evidence of anti-tumor activity in lymphoma and chronic lymphocytic leukemia (CLL) (7,10), naratuximab emtansine entered the clinical evaluation as a single agent. The phase 1 study exploring naratuximab emtansine enrolled 39 patients with relapsed/refractory B cell lymphoma (27). The overall response rate (ORR) was 13% across all patients and 22% in DLBCL patients, including the only observed complete remission (CR) (27). In preliminary results of a phase 2 trial exploring the combination of naratuximab emtansine with the anti-CD20 monoclonal antibody rituximab (18), based on positive preclinical data (18), the ORR was 45% in 76 patients with DLBCL with 24 CRs (32%), 57% in 14 patients with follicular lymphoma (five CR), 50% in four MCL patients (2 CR) (31). Here, we studied the pattern of activity of naratuximab emtansine across a large panel of cell lines derived from DLBCL and other lymphoma subtypes and characterized two resistance mechanisms to the ADC.

Integrative analysis of multi-omics data reveals a pseudouridine-related lncRNA signature for prediction of glioma prognosis and chemoradiotherapy sensitivity

BACKGROUND

Glioblastoma is the most common type of glioma with a high incidence and poor prognosis, and effective medical treatment remains challenging. Pseudouridine (Ψ) is the first post-transcriptional modification discovered and one of the most abundant modifications to RNA. However, the prognostic value of Ψ-related lncRNAs (ΨrLs) for glioma patients has never been systematically evaluated. This study aims to construct a risk model based on ΨrLs signature and to validate the predictive efficiency of the model.

METHOD

Transcriptomic data, genomic data, and relevant clinical data of glioma patients were extracted from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). ΨrLs with significant correlation with Ψ-related genes were identified, and univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were used to further select biomarkers and construct a ΨrLs signature risk model. Then, the expression of lncRNAs of ΨrLs signature in multiple glioma cell lines was detected by qPCR. Further, ROC analysis, stratification analysis, correlation analysis, survival analysis, nomogram, enrichment analysis, immune infiltration analysis, chemoradiotherapy sensitivity analysis, somatic mutation, and recurrent copy number variation (CNV) analysis were used to validate the predictive efficiency of ΨrLs signature in TCGA and CGGA datasets.

RESULTS

A four-lncRNA ΨrLs signature (DNAJC27-AS1, GDNF-AS1, ZBTB20-AS4, and DNMBP-AS1) risk model was constructed. By ROC analysis, stratified analysis, correlation analysis, survival analysis, and nomogram, the signature showed satisfactory predictive efficiency. Functional enrichment analysis revealed the differences in immune-related biological processes between high- and low-risk groups. Immune infiltration analysis showed that the high-risk group had lower tumor purity and higher stromal, immune and ESTIMATE scores. Mitoxantrone was identified as effective drug for low-risk group of glioma patients. Key genes in glioma development, including IDH1, EGFR, PTEN, etc., were differentially mutated between risk groups. The main recurrent CNVs in low-risk groups were 19q13.42 deletion and 7q34 amplification; 10q23.31 deletion and 12q14.1 in the high-risk group.

CONCLUSIONS

Our study identified a four-lncRNA ΨrLs signature that effectively predicts the prognosis of glioma patients and may serve as a diagnostic tool. Risk scores of glioma patients generated by the signature is associated with immune-related biological processes and chemoradiotherapy sensitivity. These findings may inform the development of more targeted and effective therapies for glioma patients.

Jean DJ, Tieu T, Ladd B, Hilbert B, Wang W, Alltucker JT, Manimala S, Kryukov GV, Brooijmans N, Dowdell G, Jonsson P, Huff M, Guzman-Perez A, Jackson EL, Goncalves MD, Stuart DD. STX-478, a Mutant-Selective, Allosteric PI3Kα Inhibitor Spares Metabolic Dysfunction and Improves Therapeutic Response in PI3Kα-Mutant Xenografts

Phosphoinositide 3-kinase α (PIK3CA) is one of the most mutated genes across cancers, especially breast, gynecologic, and head and neck squamous cell carcinoma tumors. Mutations occur throughout the gene, but hotspot mutations in the helical and kinase domains predominate. The therapeutic benefit of isoform-selective PI3Kα inhibition was established with alpelisib, which displays equipotent activity against the wild-type and mutant enzyme. Inhibition of wild-type PI3Kα is associated with severe hyperglycemia and rash, which limits alpelisib use and suggests that selectively targeting mutant PI3Kα could reduce toxicity and improve efficacy. Here we describe STX-478, an allosteric PI3Kα inhibitor that selectively targets prevalent PI3Kα helical- and kinase-domain mutant tumors. STX-478 demonstrated robust efficacy in human tumor xenografts without causing the metabolic dysfunction observed with alpelisib. Combining STX-478 with fulvestrant and/or cyclin-dependent kinase 4/6 inhibitors was well tolerated and provided robust and durable tumor regression in ER+HER2- xenograft tumor models.

SIGNIFICANCE

These preclinical data demonstrate that the mutant-selective, allosteric PI3Kα inhibitor STX-478 provides robust efficacy while avoiding the metabolic dysfunction associated with the nonselective inhibitor alpelisib. Our results support the ongoing clinical evaluation of STX-478 in PI3Kα-mutated cancers, which is expected to expand the therapeutic window and mitigate counterregulatory insulin release. See related commentary by Kearney and Vasan, p. 2313. This article is featured in Selected Articles from This Issue, p. 2293.

Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning

Targeting lncRNAs by small molecules (SM-lncR) to alter their expression levels has emerged as an important therapeutic modality for disease treatment. To date, no computational tools have been dedicated to predicting small molecule-mediated upregulation or downregulation of lncRNA expression. Here, we introduce DeepdlncUD, which integrates predictions of nine deep learning algorithms together, to infer the regulation types of small molecules on modulating lncRNA expression. Through systematic optimization on a training set of 771 upregulation and 739 downregulation SM-lncR pairs, each encoding 1369 sequence, representational, and physiochemical features, this method outperforms a recently released program, DeepsmirUD, by achieving 0.674 in AUC (area under the receiver operating characteristic curve), 0.722 in AUCPR (area under the precision-recall curve), 0.681 in F1-score, and 0.516 in Jaccard Index on a test set of 222 SM-lncR pairs. By extracting 125 upregulation and 46 downregulation SM-lncR pairs that involve disease-associated lncRNAs, DeepdlncUD is shown to gain an accuracy of 0.700 in the pathological context. Using connectivity scores, around half of the small molecules are correctly estimated as drugs to treat lncRNA-regulated diseases. This tool can be run at a fast speed to assist the discovery of potential small molecule drugs of lncRNA targets on a large scale. DeepdlncUD is publicly available at https://github.com/2003100127/deepdlncud.

The prediction of drug sensitivity by multi-omics fusion reveals the heterogeneity of drug response in pan-cancer

Cancer drug response prediction based on genomic information plays a crucial role in modern pharmacogenomics, enabling individualized therapy. Given the expensive and complexity of biological experiments, computational methods serve as effective tools in predicting cancer drug sensitivity. In this study, we proposed a novel method called Multi-Omics Integrated Collective Variational Autoencoders (MOICVAE), which leverages integrated omics knowledge, including genomic and transcriptomic data, to fill in missing cancer-drug associations and enhance drug sensitivity prediction. Our method employs an encoder-decoder network to learn latent feature representations from cell lines. These learned feature vectors are then fed into a collective variational autoencoder network to train an association matrix. We evaluated MOICVAE on the GDSC and CCLE benchmark datasets using 10-fold cross-validation and achieved impressive AUCs of 0.856 and 0.808, respectively, outperforming state-of-the-art methods. Furthermore, on the TCGA dataset, consisting of 25 drugs across 7 cancer types, MOICVAE exhibited an average AUC of 0.91 in predicting drug sensitivity. Additionally, significant differences were observed in survival, tumor inflammatory assessment, and tumor microenvironment between the predicted drug-sensitive and drug-resistant groups. These results are consistent with predictions made on the METABRIC dataset. Moreover, we discovered that fusing omics data based on mRNA and CNV (copy number variations) yielded superior results in drug sensitivity prediction. MOICVAE not only achieved higher accuracy in drug sensitivity prediction but also provided additional value for combining immunotherapy with chemotherapy, offering patients with more precise treatment options. The code and dataset for MOICVAE are freely available at https://github.com/wanggnoc/MOICVAE.

Discovering the mechanism of action of drugs with a sparse explainable network

BACKGROUND

Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models.

METHODS

We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs.

FINDINGS

SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035.

INTERPRETATION

SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response.

FUNDING

The Accelerator Award Programme funded by Cancer Research UK [C355/A26819], Fundación Científica de la AECC and Fondazione AIRC, Project PIBA_2020_1_0055 funded by the Basque Government and the Synlethal Project (RETOS Investigacion, Spanish Government).

Identification of Long Non-Coding RNA Profiles and Potential Therapeutic Agents for Fibrolamellar Carcinoma Based on RNA-Sequencing Data

BACKGROUND

Fibrolamellar carcinoma (FLC) is a rare type of liver cancer that primarily affects adolescents and young adults without prior liver disease or viral infections. Patients with FLC generally have non-specific symptoms, are often diagnosed at a later stage, and experience a higher frequency of metastases compared to patients with other liver cancers. A fusion transcript of DNAJB1 and PRKACA, which can lead to increased activity of PKA and cellular proliferation, has been identified in all FLC patients, but the exact mechanism through which FLC develops remains unclear. In this study, we investigated common lncRNA profiles in various FLC samples using bioinformatics analyses.

METHODS

We analyzed differentially expressed (DE) lncRNAs from three RNA sequencing datasets. Using lncRNAs and DE mRNAs, we predicted potential lncRNA target genes and performed Gene Ontology (GO) and KEGG analyses with the DE lncRNA target genes. Moreover, we screened for small-molecule compounds that could act as therapeutic targets for FLC.

RESULTS

We identified 308 DE lncRNAs from the RNA sequencing datasets. In addition, we performed a trans-target prediction analysis and identified 454 co-expressed pairs in FLC. The GO analysis showed that the lncRNA-related up-regulated mRNAs were enriched in the regulation of protein kinase C signaling and cAMP catabolic processes, while lncRNA-related down-regulated mRNAs were enriched in steroid, retinol, cholesterol, and xenobiotic metabolic processes. The analysis of small-molecule compounds for FLC treatment identified vitexin, chlorthalidone, triamterene, and amiloride, among other compounds.

CONCLUSIONS

We identified potential therapeutic targets for FLC, including lncRNA target genes as well as small-molecule compounds that could potentially be used as treatments. Our findings could contribute to furthering our understanding of FLC and providing potential avenues for diagnosis and treatment.

Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response

The spatial organization of the tumor microenvironment has a profound impact on biology and therapy response. Here, we perform an integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize malignant cells in tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, neighboring cellular compositions, and ligand-receptor interactions. We demonstrate that the gene expression profile associated with the LE is conserved across different cancers while the TC is tissue specific, highlighting common mechanisms underlying tumor progression and invasion. Additionally, we find our LE gene signature is associated with worse clinical outcomes while TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an in silico modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and interactive spatial atlases ( http://www.pboselab.ca/spatial_OSCC/ ; http://www.pboselab.ca/dynamo_OSCC/ ) that can be foundational for developing novel targeted therapies.

iBT-Net: an incremental broad transformer network for cancer drug response prediction

In modern precision medicine, it is an important research topic to predict cancer drug response. Due to incomplete chemical structures and complex gene features, however, it is an ongoing work to design efficient data-driven methods for predicting drug response. Moreover, since the clinical data cannot be easily obtained all at once, the data-driven methods may require relearning when new data are available, resulting in increased time consumption and cost. To address these issues, an incremental broad Transformer network (iBT-Net) is proposed for cancer drug response prediction. Different from the gene expression features learning from cancer cell lines, structural features are further extracted from drugs by Transformer. Broad learning system is then designed to integrate the learned gene features and structural features of drugs to predict the response. With the capability of incremental learning, the proposed method can further use new data to improve its prediction performance without retraining totally. Experiments and comparison studies demonstrate the effectiveness and superiority of iBT-Net under different experimental configurations and continuous data learning.

Therapeutic effects of guanidine hydrochloride on breast cancer through targeting KCNG1 gene

BACKGROUND

Triple-negative breast cancer (TNBC) is one of the subtypes of breast cancer (BC) that is associated with poor survival rates and failure to respond to hormonal and targeted therapies.

OBJECTIVE

The aim of this study was to identify a specific gene at the expression level for TNBC and targeting of this type of breast cancer based on it. Using TCGA database, genes that are particularly high expression in TNBC subtypes compared to other BC subtypes (in terms of receptor status) and normal samples were identified and their sensitivity and specificity were evaluated. Using PharmacoGX and Drug Bank data, drug sensitivity and drug-appropriate genes were identified, respectively. The effects of the identified drug on triple-negative cell lines (MDA-MB-468) were evaluated in comparison with the cell line of other subtypes (MCF7) by apoptosis and MTS tests.

RESULTS

Data analyzes showed that the expression level of KCNG1 gene in the TNBC subgroup was significantly higher compared to other BC subtypes from the KCN gene family and ROC results showed that this gene had highest sensitivity and specificity in TNBC subtype. The results of drug resistance and sensitivity showed that an increase in the expression level of KCNG1 was associated with sensitivity to Cisplatin and Oxaliplatin. Moreover, Drug Bank results showed that Guanidine hydrochloride (GuHCl) was a suitable inhibitor for KCNG1. In vitro results showed that the expression level of KCNG1 was higher in MDA-MB-468 compared to MCF7. In addition, the rate of apoptosis in response to GuHCl treatment in MDA-MB-468 cell line as TNBC cell model was higher than MCF7 in the same concentration.

CONCLUSION

This study revealed that GuHCl could be a suitable treatment for TNBC subtype by targeting of KCNG1.

Leveraging multi-source to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction

Pharmacogenomics datasets have been generated for various purposes, such as investigating different biomarkers. However, when studying the same cell line with the same drugs, differences in drug responses exist between studies. These variations arise from factors such as inter-tumoral heterogeneity, experimental standardization, and the complexity of cell subtypes. Consequently, drug response prediction suffers from limited generalizability. To address these challenges, we propose a computational model based on Federated Learning (FL) for drug response prediction. By leveraging three pharmacogenomics datasets (CCLE, GDSC2, and gCSI), we evaluate the performance of our model across diverse cell line-based databases. Our results demonstrate superior predictive performance compared to baseline methods and traditional FL approaches through various experimental tests. This study underscores the potential of employing FL to leverage multiple data sources, enabling the development of generalized models that account for inconsistencies among pharmacogenomics datasets. By addressing the limitations of low generalizability, our approach contributes to advancing drug response prediction in precision oncology.

MKX-AS1 Gene Expression Associated with Variation in Drug Response to Oxaliplatin and Clinical Outcomes in Colorectal Cancer Patients

Oxaliplatin (OXAL) is a commonly used chemotherapy for treating colorectal cancer (CRC). A recent genome wide association study (GWAS) showed that a genetic variant (rs11006706) in the lncRNA gene MKX-AS1 and partnered sense gene MKX could impact the response of genetically varied cell lines to OXAL treatment. This study found that the expression levels of MKX-AS1 and MKX in lymphocytes (LCLs) and CRC cell lines differed between the rs11006706 genotypes, indicating that this gene pair could play a role in OXAL response. Further analysis of patient survival data from the Cancer Genome Atlas (TCGA) and other sources showed that patients with high MKX-AS1 expression status had significantly worse overall survival (HR = 3.2; 95%CI = (1.17-9); p = 0.024) compared to cases with low MKX-AS1 expression status. Alternatively, high MKX expression status had significantly better overall survival (HR = 0.22; 95%CI = (0.07-0.7); p = 0.01) compared to cases with low MKX expression status. These results suggest an association between MKX-AS1 and MKX expression status that could be useful as a prognostic marker of response to OXAL and potential patient outcomes in CRC.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

RYK Gene Expression Associated with Drug Response Variation of Temozolomide and Clinical Outcomes in Glioma Patients

Temozolomide (TMZ) chemotherapy is an important tool in the treatment of glioma brain tumors. However, variable patient response and chemo-resistance remain exceptionally challenging. Our previous genome-wide association study (GWAS) identified a suggestively significant association of SNP rs4470517 in the RYK (receptor-like kinase) gene with TMZ drug response. Functional validation of RYK using lymphocytes and glioma cell lines resulted in gene expression analysis indicating differences in expression status between genotypes of the cell lines and TMZ dose response. We conducted univariate and multivariate Cox regression analyses using publicly available TCGA and GEO datasets to investigate the impact of RYK gene expression status on glioma patient overall (OS) and progression-free survival (PFS). Our results indicated that in IDH mutant gliomas, RYK expression and tumor grade were significant predictors of survival. In IDH wildtype glioblastomas (GBM), MGMT status was the only significant predictor. Despite this result, we revealed a potential benefit of RYK expression in IDH wildtype GBM patients. We found that a combination of RYK expression and MGMT status could serve as an additional biomarker for improved survival. Overall, our findings suggest that RYK expression may serve as an important prognostic or predictor of TMZ response and survival for glioma patients.

Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology

More information about a person's genetic makeup, drug response, multi-omics response, and genomic response is now available leading to a gradual shift towards personalized treatment. Additionally, the promotion of non-animal testing has fueled the computational toxicogenomics as a pivotal part of the next-gen risk assessment paradigm. Artificial Intelligence (AI) has the potential to provid new ways analyzing the patient data and making predictions about treatment outcomes or toxicity. As personalized medicine and toxicogenomics involve huge data processing, AI can expedite this process by providing powerful data processing, analysis, and interpretation algorithms. AI can process and integrate a multitude of data including genome data, patient records, clinical data and identify patterns to derive predictive models anticipating clinical outcomes and assessing the risk of any personalized medicine approaches. In this article, we have studied the current trends and future perspectives in personalized medicine & toxicology, the role of toxicogenomics in connecting the two fields, and the impact of AI on personalized medicine & toxicology. In this work, we also study the key challenges and limitations in personalized medicine, toxicogenomics, and AI in order to fully realize their potential.

Clinical and genome-wide association analysis of chemoradiation-induced hearing loss in nasopharyngeal carcinoma

Chemoradiation-induced hearing loss (CRIHL) is one of the most devasting side effects for nasopharyngeal carcinoma (NPC) patients, which seriously affects survivors' long-term quality of life. However, few studies have comprehensively characterized the risk factors for CRIHL. In this study, we found that age at diagnosis, tumor stage, and concurrent cisplatin dose were positively associated with chemoradiation-induced hearing loss. We performed a genome-wide association study (GWAS) in 777 NPC patients and identified rs1050851 (within the exon 2 of NFKBIA), a variant with a high deleteriousness score, to be significantly associated with hearing loss risk (HR = 5.46, 95% CI 2.93-10.18, P = 9.51 × 10^-08). The risk genotype of rs1050851 was associated with higher NFKBIA expression, which was correlated with lower cellular tolerance to cisplatin. According to permutation-based enrichment analysis, the variants mapping to 149 hereditary deafness genes were significantly enriched among GWAS top signals, which indicated the genetic similarity between hereditary deafness and CRIHL. Pathway analysis suggested that synaptic signaling was involved in the development of CRIHL. Additionally, the risk score integrating genetic and clinical factors can predict the risk of hearing loss with a relatively good performance in the test set. Collectively, this study shed new light on the etiology of chemoradiation-induced hearing loss, which facilitates high-risk individuals' identification for personalized prevention and treatment.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

Protocol for 3D drug sensitivity and resistance testing of patient-derived cancer cells in 384-well plates

Establishment of drug testing of patient-derived cancer cells (PDCs) in physiologically relevant 3-dimensional (3D) culture is central for drug discovery and cancer research, as well as for functional precision medicine. Here, we describe the detailed protocol allowing the 3D drug testing of PDCs - or any type of cells of interest - in Matrigel in 384-well plate format using automation. We also provide an alternative protocol, which does not require supporting matrices. The cancer tissue is obtained directly from clinics (after surgery or biopsy) and processed into single cell suspension. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the PDCs directly after cancer cell isolation from tissue or on cells expanded for a few passages. In the 3D-DSRT assay, the PDCs are plated in 384-well plates in Matrigel, grown as spheroids, and treated with compounds of interest for 72 h. The cell viability is directly measured using a luminescence-based assay. Alternatively, prior to the cell viability measurement, drug-treated cells can be directly subjected to automated high-content bright field imaging or stained for fluorescence (live) cell microscopy for further image analysis. This is followed by the quality control and data analysis. The 3D-DSRT can be performed within a 1-3-week timeframe of the clinical sampling of cancer tissue, depending on the amount of the obtained tissue, growth rate of cancer cells, and the number of drugs being tested. The 3D-DSRT method can be flexibly modified, e.g., to be carried out with or without supporting matrices with U-bottom 384-well plates when appropriate for the PDCs or other cell models used.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

A comparative study of COVID-19 transcriptional signatures between clinical samples and preclinical cell models in the search for disease master regulators and drug repositioning candidates

Coronavirus disease 2019 (COVID-19) is an acute viral disease with millions of cases worldwide. Although the number of daily new cases and deaths has been dropping, there is still a need for therapeutic alternatives to deal with severe cases. A promising strategy to prospect new therapeutic candidates is to investigate the regulatory mechanisms involved in COVID-19 progression using integrated transcriptomics approaches. In this work, we aimed to identify COVID-19 Master Regulators (MRs) using a series of publicly available gene expression datasets of lung tissue from patients which developed the severe form of the disease. We were able to identify a set of six potential COVID-19 MRs related to its severe form, namely TAL1, TEAD4, EPAS1, ATOH8, ERG, and ARNTL2. In addition, using the Connectivity Map drug repositioning approach, we identified 52 different drugs which could be used to revert the disease signature, thus being candidates for the design of novel clinical treatments. Furthermore, we compared the identified signature and drugs with the ones obtained from the analysis of nasopharyngeal swab samples from infected patients and preclinical cell models. This comparison showed significant similarities between them, although also revealing some limitations on the overlap between clinical and preclinical data in COVID-19, highlighting the need for careful selection of the best model for each disease stage.

Low level of antioxidant capacity biomarkers but not target overexpression predicts vulnerability to ROS-inducing drugs

Despite a strong rationale for why cancer cells are susceptible to redox-targeting drugs, such drugs often face tumor resistance or dose-limiting toxicity in preclinical and clinical studies. An important reason is the lack of specific biomarkers to better select susceptible cancer entities and stratify patients. Using a large panel of lung cancer cell lines, we identified a set of "antioxidant-capacity" biomarkers (ACB), which were tightly repressed, partly by STAT3 and STAT5A/B in sensitive cells, rendering them susceptible to multiple redox-targeting and ferroptosis-inducing drugs. Contrary to expectation, constitutively low ACB expression was not associated with an increased steady state level of reactive oxygen species (ROS) but a high level of nitric oxide, which is required to sustain high replication rates. Using ACBs, we identified cancer entities with a high percentage of patients with favorable ACB expression pattern, making it likely that more responders to ROS-inducing drugs could be stratified for clinical trials.

The impact of violating the independence assumption in meta-analysis on biomarker discovery

With rapid advancements in high-throughput sequencing technologies, massive amounts of "-omics" data are now available in almost every biomedical field. Due to variance in biological models and analytic methods, findings from clinical and biological studies are often not generalizable when tested in independent cohorts. Meta-analysis, a set of statistical tools to integrate independent studies addressing similar research questions, has been proposed to improve the accuracy and robustness of new biological insights. However, it is common practice among biomarker discovery studies using preclinical pharmacogenomic data to borrow molecular profiles of cancer cell lines from one study to another, creating dependence across studies. The impact of violating the independence assumption in meta-analyses is largely unknown. In this study, we review and compare different meta-analyses to estimate variations across studies along with biomarker discoveries using preclinical pharmacogenomics data. We further evaluate the performance of conventional meta-analysis where the dependence of the effects was ignored via simulation studies. Results show that, as the number of non-independent effects increased, relative mean squared error and lower coverage probability increased. Additionally, we also assess potential bias in the estimation of effects for established meta-analysis approaches when data are duplicated and the assumption of independence is violated. Using pharmacogenomics biomarker discovery, we find that treating dependent studies as independent can substantially increase the bias of meta-analyses. Importantly, we show that violating the independence assumption decreases the generalizability of the biomarker discovery process and increases false positive results, a key challenge in precision oncology.

CD73 Inhibits cGAS-STING and Cooperates with CD39 to Promote Pancreatic Cancer

The ectonucleotidases CD39 and CD73 catalyze extracellular ATP to immunosuppressive adenosine, and as such, represent potential cancer targets. We investigated biological impacts of CD39 and CD73 in pancreatic ductal adenocarcinoma (PDAC) by studying clinical samples and experimental mouse tumors. Stromal CD39 and tumoral CD73 expression significantly associated with worse survival in human PDAC samples and abolished the favorable prognostic impact associated with the presence of tumor-infiltrating CD8+ T cells. In mouse transplanted KPC tumors, both CD39 and CD73 on myeloid cells, as well as CD73 on tumor cells, promoted polarization of infiltrating myeloid cells towards an M2-like phenotype, which enhanced tumor growth. CD39 on tumor-specific CD8+ T cells and pancreatic stellate cells also suppressed IFNγ production by T cells. Although therapeutic inhibition of CD39 or CD73 alone significantly delayed tumor growth in vivo, targeting of both ectonucleotidases exhibited markedly superior antitumor activity. CD73 expression on human and mouse PDAC tumor cells also protected against DNA damage induced by gemcitabine and irradiation. Accordingly, large-scale pharmacogenomic analyses of human PDAC cell lines revealed significant associations between CD73 expression and gemcitabine chemoresistance. Strikingly, increased DNA damage in CD73-deficient tumor cells associated with activation of the cGAS-STING pathway. Moreover, cGAS expression in mouse KPC tumor cells was required for antitumor activity of the CD73 inhibitor AB680 in vivo. Our study, thus, illuminates molecular mechanisms whereby CD73 and CD39 seemingly cooperate to promote PDAC progression.

ERBB4-mediated signaling is a mediator of resistance to BTK and PI3K inhibitors in B cell lymphoid neoplasms

BTK and PI3K inhibitors are among the drugs approved for the treatment of patients with lymphoid neoplasms. Although active, their ability to lead as single agents to long-lasting complete remission is rather limited especially in the lymphoma setting. This indicates that tumor cells often develop resistance to the drugs. Here, we show that the overexpression of ERBB4 and its ligands represents a modality for B cell neoplastic cells to bypass the anti-tumor activity of BTK and PI3K inhibitors and that targeted pharmacological interventions can restore sensitivity to the small molecules. We started from a marginal zone lymphoma (MZL) cell line, Karpas-1718, kept under prolonged exposure to the PI3Kδ inhibitor idelalisib until acquisition of resistance, or with no drug. Cells underwent transcriptome, miRNA and methylation profiling, whole exome sequencing, and pharmacological screening which led to the identification of the overexpression of ERBB4 and its ligands HBEGF and NRG2 in the resistant cells. Cellular and genetic experiments demonstrated the involvement of this axis in blocking the anti-tumor activity of various BTK and PI3K inhibitors, currently used in the clinical setting. Addition of recombinant HBEGF induced resistance to BTK and PI3K inhibitors in parental cells but also in additional lymphoma models. Combination with the ERBB inhibitor lapatinib was beneficial in resistant cells and in other lymphoma models already expressing the identified resistance factors. Multi-omics analysis underlined that an epigenetic reprogramming affected the expression of the resistance-related factors, and pretreatment with demethylating agents or EZH2 inhibitors overcame the resistance. Resistance factors were shown to be expressed in clinical samples, further extending the findings of the study. In conclusions, we identified a novel ERBB4-driven mechanism of resistance to BTK and PI3K inhibitors and treatments that appear to overcome it.

Key points

A mechanism of secondary resistance to the PI3Kδ and BTK inhibitors in B cell neoplasms driven by secreted factors.Resistance can be reverted by targeting ERBB signaling.

Overview of Bioinformatics Software and Databases for Metabolic Engineering

The explosion of the "omics" era has introduced a growing number of sets and tools that facilitate molecular interrogation of the metabolome. These include various bioinformatics and pharmacogenomics resources that can be utilized independently or collectively to facilitate metabolic engineering across disease, clinical oncology, and understanding of molecular changes across larger systems. This review provides starting points for accessing publicly available data and computational tools that support assessment of metabolic profiles and metabolic regulation, providing both a depth-and-breadth approach toward understanding the metabolome. We focus in particular on pathway databases and tools, which provide in-depth analysis of metabolic pathways, which is at the heart of metabolic engineering.

Utilization of Cancer Cell Line Screening to Elucidate the Anticancer Activity and Biological Pathways Related to the Ruthenium-Based Therapeutic BOLD-100

BOLD-100 (sodium trans-[tetrachlorobis(1H indazole)ruthenate(III)]) is a ruthenium-based anticancer compound currently in clinical development. The identification of cancer types that show increased sensitivity towards BOLD-100 can lead to improved developmental strategies. Sensitivity profiling can also identify mechanisms of action that are pertinent for the bioactivity of complex therapeutics. Sensitivity to BOLD-100 was measured in a 319-cancer-cell line panel spanning 24 tissues. BOLD-100's sensitivity profile showed variation across the tissue lineages, including increased response in esophageal, bladder, and hematologic cancers. Multiple cancers, including esophageal, bile duct and colon cancer, had higher relative response to BOLD-100 than to cisplatin. Response to BOLD-100 showed only moderate correlation to anticancer compounds in the Genomics of Drug Sensitivity in Cancer (GDSC) database, as well as no clear theme in bioactivity of correlated hits, suggesting that BOLD-100 may have a differentiated therapeutic profile. The genomic modalities of cancer cell lines were modeled against the BOLD-100 sensitivity profile, which revealed that genes related to ribosomal processes were associated with sensitivity to BOLD-100. Machine learning modeling of the sensitivity profile to BOLD-100 and gene expression data provided moderative predictive value. These findings provide further mechanistic understanding around BOLD-100 and support its development for additional cancer types.

Characterization of an RNA binding protein interactome reveals a context-specific post-transcriptional landscape of MYC-amplified medulloblastoma

Pediatric medulloblastoma (MB) is the most common solid malignant brain neoplasm, with Group 3 (G3) MB representing the most aggressive subgroup. MYC amplification is an independent poor prognostic factor in G3 MB, however, therapeutic targeting of the MYC pathway remains limited and alternative therapies for G3 MB are urgently needed. Here we show that the RNA-binding protein, Musashi-1 (MSI1) is an essential mediator of G3 MB in both MYC-overexpressing mouse models and patient-derived xenografts. MSI1 inhibition abrogates tumor initiation and significantly prolongs survival in both models. We identify binding targets of MSI1 in normal neural and G3 MB stem cells and then cross referenced these data with unbiased large-scale screens at the transcriptomic, translatomic and proteomic levels to systematically dissect its functional role. Comparative integrative multi-omic analyses of these large datasets reveal cancer-selective MSI1-bound targets sharing multiple MYC associated pathways, providing a valuable resource for context-specific therapeutic targeting of G3 MB.

The E3 ubiquitin ligase WWP2 regulates pro-fibrogenic monocyte infiltration and activity in heart fibrosis

Non-ischemic cardiomyopathy (NICM) can cause left ventricular dysfunction through interstitial fibrosis, which corresponds to the failure of cardiac tissue remodeling. Recent evidence implicates monocytes/macrophages in the etiopathology of cardiac fibrosis, but giving their heterogeneity and the antagonizing roles of macrophage subtypes in fibrosis, targeting these cells has been challenging. Here we focus on WWP2, an E3 ubiquitin ligase that acts as a positive genetic regulator of human and murine cardiac fibrosis, and show that myeloid specific deletion of WWP2 reduces cardiac fibrosis in hypertension-induced NICM. By using single cell RNA sequencing analysis of immune cells in the same model, we establish the functional heterogeneity of macrophages and define an early pro-fibrogenic phase of NICM that is driven by Ccl5-expressing Ly6c^high monocytes. Among cardiac macrophage subtypes, WWP2 dysfunction primarily affects Ly6c^high monocytes via modulating Ccl5, and consequentially macrophage infiltration and activation, which contributes to reduced myofibroblast trans-differentiation. WWP2 interacts with transcription factor IRF7, promoting its non-degradative mono-ubiquitination, nuclear translocation and transcriptional activity, leading to upregulation of Ccl5 at transcriptional level. We identify a pro-fibrogenic macrophage subtype in non-ischemic cardiomyopathy, and demonstrate that WWP2 is a key regulator of IRF7-mediated Ccl5/Ly6c^high monocyte axis in heart fibrosis.

Multi-Omics Alleviates the Limitations of Panel Sequencing for Cancer Drug Response Prediction

Comprehensive genomic profiling using cancer gene panels has been shown to improve treatment options for a variety of cancer types. However, genomic aberrations detected via such gene panels do not necessarily serve as strong predictors of drug sensitivity. In this study, using pharmacogenomics datasets of cell lines, patient-derived xenografts, and ex vivo treated fresh tumor specimens, we demonstrate that utilizing the transcriptome on top of gene panel features substantially improves drug response prediction performance in cancer.

Computational pharmacogenomic screen identifies drugs that potentiate the anti-breast cancer activity of statins

Statins, a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the mevalonate metabolic pathway, have demonstrated anticancer activity. Evidence shows that dipyridamole potentiates statin-induced cancer cell death by blocking a restorative feedback loop triggered by statin treatment. Leveraging this knowledge, we develop an integrative pharmacogenomics pipeline to identify compounds similar to dipyridamole at the level of drug structure, cell sensitivity and molecular perturbation. To overcome the complex polypharmacology of dipyridamole, we focus our pharmacogenomics pipeline on mevalonate pathway genes, which we name mevalonate drug-network fusion (MVA-DNF). We validate top-ranked compounds, nelfinavir and honokiol, and identify that low expression of the canonical epithelial cell marker, E-cadherin, is associated with statin-compound synergy. Analysis of remaining prioritized hits led to the validation of additional compounds, clotrimazole and vemurafenib. Thus, our computational pharmacogenomic approach identifies actionable compounds with pathway-specific activities.

Precision oncology using ex vivo technology: a step towards individualised cancer care?

Despite advances in cancer genomics and the increased use of genomic medicine, metastatic cancer is still mostly an incurable and fatal disease. With diminishing returns from traditional drug discovery strategies, and high clinical failure rates, more emphasis is being placed on alternative drug discovery platforms, such as ex vivo approaches. Ex vivo approaches aim to embed biological relevance and inter-patient variability at an earlier stage of drug discovery, and to offer more precise treatment stratification for patients. However, these techniques also have a high potential to offer personalised therapies to patients, complementing and enhancing genomic medicine. Although an array of approaches are available to researchers, only a minority of techniques have made it through to direct patient treatment within robust clinical trials. Within this review, we discuss the current challenges to ex vivo approaches within clinical practice and summarise the contemporary literature which has directed patient treatment. Finally, we map out how ex vivo approaches could transition from a small-scale, predominantly research based technology to a robust and validated predictive tool. In future, these pre-clinical approaches may be integrated into clinical cancer pathways to assist in the personalisation of therapy choices and to hopefully improve patient experiences and outcomes.

Gene expression based inference of cancer drug sensitivity

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.

Predicting treatment response in cancer remains a highly complex task. Here, the authors develop Precily, a deep neural network framework to predict treatment response in cancer by considering gene expression, pathway activity estimates and drug features, and test this method in multiple datasets and preclinical models.

Elevated CDK5R1 expression associated with poor prognosis, proliferation, and drug resistance in colorectal and breast malignancies: CDK5R1 as an oncogene in cancers

BACKGROUND

Studies have shown that the CDK5R1 gene could have a part in some types of cancer. This study sought to examine the relationship between CDK5R1 expression and prognosis and medication resistance in 13 commonly occurring cancers.

METHOD

The cancer genome atlas data and clinical data were utilized to assess the role of CDK5R1 in malignancies. The expression data of 13 cancers were also integrated and used for the co-expression network. The relationship between CDK5R1 expression and drug resistance and sensitivity was evaluated using pharmacogenomics data. The colorectal cancer (CRC) and breast cancer (BC) were used to confirm the results through the RT-qPCR method.

RESULTS

With the exception of gastric cancer, all common malignancies showed an increase in CDK5R1 expression. Also, outcomes of sensitivity and specificity showed that CDK5R1 level could be a really good potential biomarker. Additionally, CDK5R1 expression was higher in CRC and BC samples compared to adjacent normal, according to RT-qPCR data. In six types of tumors and combined data, a poor prognosis was associated with increased CDK5R1 expression. The CDK5R1-associated genes were connected to the primary oncogenic pathways in cancer cells, according to the co-expression network. Also, CDK5R1 level was significantly linked to the resistance and sensitivity of several chemotherapy drugs and caused the highest resistance to cyclophosphamide.

CONCLUSION

CDK5R1 expression is upregulated in 12 prevalent cancers and can play an oncogenic role. Also, this gene's expression could be used as a biomarker to predict patient survival and medication resistance.