Expression Levels of Therapeutic Targets as Indicators of Sensitivity to Targeted Therapeutics

The highly selective and oral phosphoinositide 3-kinase delta (PI3K-δ) inhibitor roginolisib induces apoptosis in mesothelioma cells and increases immune effector cell composition

Targeting aberrantly expressed kinases in malignant pleural mesothelioma (MPM) is a promising therapeutic strategy. We here investigated the effect of the novel and highly selective Phosphoinositide 3-kinase delta (PI3K-δ) inhibitor roginolisib (IOA-244) on MPM cells and on the immune cells in MPM microenvironment. To this aim, we analyzed the expression of PI3K-δ by immunohistochemistry in specimens from primary MPM, cell viability and death in three different MPM cell lines treated with roginolisib alone and in combination with ipatasertib (AKT inhibitor) and sapanisertib (mTOR inhibitor). In a co-culture model of patient-derived MPM cells, autologous peripheral blood mononuclear cells and fibroblasts, the tumor cell viability and changes in immune cell composition were investigated after treatment of roginolisib with nivolumab and cisplatin. PI3K-δ was detected in 66/89 (74%) MPM tumors and was associated with reduced overall survival (12 vs. 25 months, P=0.0452). Roginolisib induced apoptosis in MPM cells and enhanced the anti-tumor efficacy of AKT and mTOR kinase inhibitors by suppressing PI3K-δ/AKT/mTOR and ERK1/2 signaling. Furthermore, the combination of roginolisib with chemotherapy and immunotherapy re-balanced the immune cell composition, increasing effector T-cells and reducing immune suppressive cells. Overall, roginolisib induces apoptosis in MPM cells and increases the antitumor immune cell effector function when combined with nivolumab and cisplatin. These results provide first insights on the potential of roginolisib as a therapeutic agent in patients with MPM and its potential in combination with established immunotherapy regimen.

Discovery of a nitroaromatic nannocystin with potent in vivo anticancer activity against colorectal cancer by targeting AKT1

The development of targeted chemotherapeutic agents against colorectal cancer (CRC), one of the most common cancers with a high mortality rate, is in a constant need. Nannocystins are a family of myxobacterial secondary metabolites featuring a 21-membered depsipeptide ring. The in vitro anti-CRC activity of natural and synthetic nannocystins was well documented, but little is known about their in vivo efficacy and if positive, the underlying mechanism of action. In this study we synthesized a nitroaromatic nannocystin through improved preparation of a key fragment, and characterized its in vitro activity and in vivo efficacy against CRC. We first described the total synthesis of compounds 2-4 featuring Heck macrocyclization to forge their 21-membered macrocycle. In a panel of 7 cancer cell lines from different tissues, compound 4 inhibited the cell viability with IC values of 1-6 nM. In particular, compound 4 (1, 2, 4 nM) inhibited the proliferation of CRC cell lines (HCT8, HCT116 and LoVo) in both concentration and time dependent manners. Furthermore, compound 4 concentration-dependently inhibited the colony formation and migration of CRC cell lines. Moreover, compound 4 induced cell cycle arrest at sub-G1 phase, apoptosis and cellular senescence in CRC cell lines. In three patient-derived CRC organoids, compound 4 inhibited the PDO with IC values of 3.68, 28.93 and 11.81 nM, respectively. In a patient-derived xenograft mouse model, injection of compound 4 (4, 8 mg/kg, i.p.) every other day for 12 times dose-dependently inhibited the tumor growth without significant change in body weight. We conducted RNA-sequencing, molecular docking and cellular thermal shift assay to elucidate the anti-CRC mechanisms of compound 4, and revealed that it exerted its anti-CRC effect at least in part by targeting AKT1.

Single-cell RNA-seq reveals the metabolic status of immune cells response to immunotherapy in triple-negative breast cancer

Immune checkpoint blockade (ICB) therapy offers promise in the treatment of triple-negative breast cancer (TNBC); however, its limited efficacy in certain TNBC patients poses a challenge. In this study, we elucidated the metabolic mechanism at 'sub-subtype' resolution underlying the non-response to ICB therapy in TNBC. Here, an analytic pipeline was developed to reveal the metabolic heterogeneity, which is correlated with the ICB outcomes, within each immune cell subtype. First, we identified metabolic 'sub-subtypes' within certain cell subtypes, predominantly T cell subsets, which are enriched in ICB non-responders and named as non-responder-enriched (NR-E) clusters. Notably, most of NR-E T metabolic cells exhibit globally higher metabolic activities compared to other cells within the same individual subtype. Further, we investigated the extra-cellular signals that trigger the metabolic status of NR-E T cells. In detail, the prediction of cell-to-cell communication indicated that NR-E T cells are regulated by plasmatic dendritic cells (pDCs) through TNFSF9, as well as by macrophages expressing SIGLEC9. In addition, we also validate the communication between TNFSF9+ pDCs and NR-E T cells utilizing deconvolution of spatial transcriptomics analysis. In summary, our research identified specific metabolic 'sub-subtypes' associated with ICB non-response and uncovered the mechanisms of their regulation in TNBC. And the proposed analytical pipeline can be used to examine metabolic heterogeneity within cell types that correlate with diverse phenotypes.

Robust analysis of a novel PANoptosis-related prognostic gene signature model for hepatocellular carcinoma immune infiltration and therapeutic response

PANoptosis, an interplay between pyroptosis, apoptosis, and necroptosis, is deeply involved in cancer development and immunity. However, the influence of PANoptosis in hepatocellular carcinoma (HCC) remains to be further investigated. The differentially expressed PANoptosis-related genes (PANRGs) was screened in The Cancer Genome Atlas (TCGA) database. Accordingly, mutation, bioinformatics, and consensus clustering analyses were performed. Then, a prognostic risk model was developed by least absolute shrinkage and selection operator (LASSO) Cox regression. Furthermore, the prognostic value, immunity correlation and therapeutic response prediction ability of risk model were explored. A total of 18 PANRGs were differently expressed in the TCGA-HCC cohort and were mainly involved in cancer- and cell death-related signal pathways. Using unsupervised clustering method, we identified two PANRGs-mediated clustering patterns. The remarkable differences between the two clusters on overall survival (OS) and clinical features were demonstrated respectively. Based on the five-gene prognostic risk model, the calculated PANRG-scores were used to categorize the subgroups as high- and low-risk. Notably, the high-risk subgroup had a dismal prognosis and exhibited much lower immune infiltration levels of mast cells, nature killer cells and pDCs, but higher levels of aDCs, iDCs and Treg cells than those in the low-risk subgroup. Furthermore, we constructed a reliable nomogram combining clinical traits and PANRG-score to predict the OS of HCC patients. The significantly negative correlation between PANoptosis and tumor mutation burden (TMB), ferroptosis were revealed. In drug sensitivity analysis, the high-risk subgroup had a considerably lower TIDE score, suggesting a preferable response to immunotherapy, and may be more sensitive to Tipifarnib, Imatinib, Doxorubicin, and Gemcitabine. The upregulated mRNA expressions of FADD were validated in 16 paired HCC tissues of Guangxi cohort. Based on PANoptosis-related genes, an integrated risk signature was constructed to provide a roadmap for patient stratification and predict HCC patient's prognosis. The patients with the higher PANRG-score may carry a dismal survival and relatively low immune infiltration, but a potential better immunotherapy response. Therefore, future HCC therapy perspectives should emphasize the setting of PANoptosis to achieve a personalized, practicable and effective therapeutic regimen.

Synergistic disruption of BTK and BCL-2 causes apoptosis while inducing ferroptosis in double-hit lymphoma

Double-hit lymphoma (DHL) is an aggressive subset of Diffuse Large B-cell Lymphoma (DLBCL) with poor outcomes and without satisfying treatment options. BTK inhibitor monotherapy is ineffective to suppress aggressive lymphoma. Hence, combination with other potential agents is warranted. Here, we demonstrated the second generation of BTK inhibitor, zanubrutinib, and a BCL-2 inhibitor, navitoclax, worked in synergistic manner to suppress DHL. Comprehensive in silico approach by interrogating single-cell to bulk-level profiling was employed along with in vitro and in vivo validation in DHL cell lines. Ablation of BTK enhanced sensitivity to navitoclax and suppressed proliferation of DHL cells. Combination of second generation of BTK inhibitor with navitoclax synergistically suppressed DLBCL cells with higher synergy score in DHL subset. The drug combination triggered apoptosis and ferroptosis, with the latter being characterized by reactive oxygen species (ROS) accumulation, extensive lipid peroxidation, and depletion of reduced glutathione. Moreover, ablation of BTK sensitized DHL cells to ferroptosis. Mechanistically, disruption of BTK and BCL-2 triggered ferroptosis by downregulating NRF2 and HMOX1, while deactivating GPX4. Combination of zanubrutinib and navitoclax effectively suppressed tumor growth in vivo. Our data suggest that zanubrutinib and navitoclax synergistically suppressed DHL by inducing apoptosis and ferroptosis.

SPHINX-Based Combination Therapy as a Potential Novel Treatment Strategy for Acute Myeloid Leukaemia

Introduction: Dysregulated alternative splicing is a prominent feature of cancer. The inhibition and knockdown of the SR splice factor kinase SRPK1 reduces tumour growth in vivo. As a result several SPRK1 inhibitors are in development including SPHINX, a 3-(trifluoromethyl)anilide scaffold. The objective of this study was to treat two leukaemic cell lines with SPHINX in combination with the established cancer drugs azacitidine and imatinib. Materials and Methods: We selected two representative cell lines; Kasumi-1, acute myeloid leukaemia, and K562, BCR-ABL positive chronic myeloid leukaemia. Cells were treated with SPHINX concentrations up to 10μM, and in combination with azacitidine (up to 1.5 μg/ml, Kasumi-1 cells) and imatinib (up to 20 μg/ml, K562 cells). Cell viability was determined by counting the proportion of live cells and those undergoing apoptosis through the detection of activated caspase 3/7. SRPK1 was knocked down with siRNA to confirm SPHINX results. Results: The effects of SPHINX were first confirmed by observing reduced levels of phosphorylated SR proteins. SPHINX significantly reduced cell viability and increased apoptosis in Kasumi-1 cells, but less prominently in K562 cells. Knockdown of SRPK1 by RNA interference similarly reduced cell viability. Combining SPHINX with azacitidine augmented the effect of azacitidine in Kasumi-1 cells. In conclusion, SPHINX reduces cell viability and increases apoptosis in the acute myeloid leukaemia cell line Kasumi-1, but less convincingly in the chronic myeloid leukaemia cell line K562. Conclusion: We suggest that specific types of leukaemia may present an opportunity for the development of SRPK1-targeted therapies to be used in combination with established chemotherapeutic drugs.

Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts

Computational models have been successful in predicting drug sensitivity in cancer cell line data, creating an opportunity to guide precision medicine. However, translating these models to tumors remains challenging. We propose a new transfer learning workflow that transfers drug sensitivity predicting models from large-scale cancer cell lines to both tumors and patient derived xenografts based on molecular pathways derived from genomic features. We further compute feature importance to identify pathways most important to drug response prediction. We obtained good performance on tumors (AUROC = 0.77) and patient derived xenografts from triple negative breast cancers (RMSE = 0.11). Using feature importance, we highlight the association between ER-Golgi trafficking pathway in everolimus sensitivity within breast cancer patients and the role of class II histone deacetylases and interlukine-12 in response to drugs for triple-negative breast cancer. Pathway information support transfer of drug response prediction models from cell lines to tumors and can provide biological interpretation underlying the predictions, serving as a steppingstone towards usage in clinical setting.

Regulatory pattern of abnormal promoter CpG island methylation in the glioblastoma multiforme classification

Glioblastoma (GBM) is characterized by extensive genetic and phenotypic heterogeneity. However, it remains unexplored primarily how CpG island methylation abnormalities in promoter mediate glioblastoma typing. First, we presented a multi-omics scale map between glioblastoma sample clusters constructed based on promoter CpG island (PCGI) methylation-driven genes, using datasets including methylation profiles, expression profiles, and single-cell sequencing data from multiple highly annotated public clinical cohorts. Second, we identified differences in the tumor microenvironment between the two glioblastoma sample clusters and resolved key signaling pathways between cell clusters at the single-cell level based on comprehensive comparative analyses to investigate the reasons for survival differences between two of these clusters. Finally, we developed a diagnostic map and a prediction model for glioblastoma, and compared theoretical differences of drug sensitivity between two glioblastoma sample clusters. In summary, this study established a classification system for dissecting promoter CpG island methylation heterogeneity in glioblastoma and provides a new perspective for the diagnosis and treatment of glioblastoma.

Breast cancer stage prediction: a computational approach guided by transcriptome analysis

Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics.

N6-methyladenosine (m6A) regulator expression pattern correlates with the immune landscape in lung adenocarcinoma

Lung adenocarcinoma is the leading cause of tumor-related death. The tumor microenvironment (TME) may determine anti-tumor treatment responses. We focused on 23 m6A regulators, and analyzed m6A regulator expression patterns in 995 lung adenocarcinoma samples collected from 7 publicly available datasets. Two m6A clusters were identified, wherein gene clusters and m6A score were generated using unsupervised clustering and principal component analysis based on differentially expressed genes with prognostic significance. Further, three independent datasets from TCGA-LUAD and GEO were employed to validate the impact of m6A signatures and score. We found that m6A cluster 1 with high m6A score was associated with an inflamed TME, higher neoantigen and tumor mutation burden and improved response to immunotherapy. However, anti-tumor immunity cells were exhausted in high m6A score patients; thus, the prognosis of these patients was poor. Elucidation of m6A regulator expression pattern may facilitate the development of effective treatment strategies for lung adenocarcinoma.

Systematic identification of biomarker-driven drug combinations to overcome resistance

The ability to understand and predict variable responses to therapeutic agents may improve outcomes in patients with cancer. We hypothesized that the basal gene-transcription state of cancer cell lines, coupled with cell viability profiles of small molecules, might be leveraged to nominate specific mechanisms of intrinsic resistance and to predict drug combinations that overcome resistance. We analyzed 564,424 sensitivity profiles to identify candidate gene-compound pairs, and validated nine such relationships. We determined the mechanism of a novel relationship, in which expression of the serine hydrolase enzymes monoacylglycerol lipase (MGLL) or carboxylesterase 1 (CES1) confers resistance to the histone lysine demethylase inhibitor GSK-J4 by direct enzymatic modification. Insensitive cell lines could be sensitized to GSK-J4 by inhibition or gene knockout. These analytical and mechanistic studies highlight the potential of integrating gene-expression features with small-molecule response to identify patient populations that are likely to benefit from treatment, to nominate rational candidates for combinations and to provide insights into mechanisms of action.

Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

A recombinant antibody fragment directed to the thymic stromal lymphopoietin receptor (CRLF2) efficiently targets pediatric Philadelphia chromosome-like acute lymphoblastic leukemia

Antibody fragments are promising building blocks for developing targeted therapeutics, thus improving treatment efficacy while minimising off-target toxicity. Despite recent advances in targeted therapeutics, patients with Philadelphia-like acute lymphoblastic leukemia (Ph-like ALL), a high-risk malignancy, lack specific and effective targeted treatments. Cytokine receptor-like factor 2 (CRLF2) is overexpressed in 50% of Ph-like ALL cases, conferring the survival of leukemia blasts through activation of the JAK/STAT signalling pathway. Targeting such a vital cell-surface protein could result in potent anti-leukaemic efficacy and reduce the likelihood of relapse associated with antigen loss. Herein, we developed a novel single-chain variable fragment (scFv) against CRLF2 based on a monoclonal antibody raised against the recombinant extracellular domain of human TSLPRα chain. The scFv fragment demonstrated excellent binding affinity with CRLF2 protein in the nanomolar range. Cellular association studies in vitro using an inducible CRLF2 knockdown cell line and ex vivo using patient-derived xenografts revealed the selective association of the scFv with CRLF2. The fragment exhibited significant receptor antagonistic effects on STAT5 signalling, suggesting possible therapeutic implications in vivo. This study is the first to describe the potential use of a novel scFv for targeting Ph-like ALL.