Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling

The development of cancer therapies may be improved by the discovery of tumor-specific molecular dependencies. The requisite tools include genetic and chemical perturbations, each with its strengths and limitations. Drug perturbations can be readily applied to primary cancer samples at a large scale, but mechanistic understanding of hits and further pharmaceutical development is often complicated by the fact that a small compound has a range of affinities to multiple proteins. To computationally infer the molecular dependencies of individual cancers from their ex-vivo drug sensitivity profiles, we developed a mathematical model that deconvolutes these data using measurements of protein-drug affinity profiles. Our method, DepInfeR, correctly identified known dependencies, including EGFR dependence in Her2+ breast cancer cell line, FLT3 dependence in AML tumors with FLT3-ITD mutations, and the differential dependencies on the B-cell receptor pathway in two major subtypes of chronic lymphocytic leukemia (CLL). Furthermore, our method uncovered new subgroup-specific dependencies, including a previously unreported dependence of high-risk CLL on Checkpoint kinase 1 (CHEK1). The method also produced a more accurate map of the molecular dependencies in a heterogeneous set of 117 CLL samples. The ability to deconvolute polypharmacological phenotypes into underlying causal molecular dependencies should increase the utility of high-throughput drug response assays for functional precision oncology.

3D nanomechanical mapping of subcellular and sub-nuclear structures of living cells by multi-harmonic AFM with long-tip microcantilevers

Recent developments such as multi-harmonic Atomic Force Microscopy (AFM) techniques have enabled fast, quantitative mapping of nanomechanical properties of living cells. Due to their high spatiotemporal resolution, these methods provide new insights into changes of mechanical properties of subcellular structures due to disease or drug response. Here, we propose three new improvements to significantly improve the resolution, identification, and mechanical property quantification of sub-cellular and sub-nuclear structures using multi-harmonic AFM on living cells. First, microcantilever tips are streamlined using long-carbon tips to minimize long-range hydrodynamic interactions with the cell surface, to enhance the spatial resolution of nanomechanical maps and minimize hydrodynamic artifacts. Second, simultaneous Spinning Disk Confocal Microscopy (SDC) with live-cell fluorescent markers enables the unambiguous correlation between observed heterogeneities in nanomechanical maps with subcellular structures. Third, computational approaches are then used to estimate the mechanical properties of sub-nuclear structures. Results are demonstrated on living NIH 3T3 fibroblasts and breast cancer MDA-MB-231 cells, where properties of nucleoli, a deep intracellular structure, were assessed. The integrated approach opens the door to study the mechanobiology of sub-cellular structures during disease or drug response.

Combinatorial nanodroplet platform for screening antibiotic combinations

We have developed a combinatorial nanodroplet platform for screening antibiotic combinations and successfully screened drug response of pairwise antibiotic combinations from selected antibiotics using the platform.

The Role of Drug Resistance Causing lncRNAs in Breast Cancer: In-silico Analysis

Background: Breast cancer is the most common cancer in women globally. LncRNAs are non-coding RNAs that play an essential role in biological pathways. Many lncRNAs have been discovered to influence cancer medication resistance. As a result, identifying how lncRNAs may cause drug resistance is vital.Method: Breast cancer TCGA RNA-seq data was applied in this study. We used the PharmacoGX package to explore lncRNAs with drug resistance or sensitivity effect through GDSC and CCLE data. Differential gene expression analysis (DGE) was used to find dysregulated lncRNAs (P<0.01). Survival analysis was performed to identify lncRNAs associated with patient survival, and a model based on them was developed. Multivariate cox regression analysis and ROC curve analysis were applied to assess the model. The TCGA-BRCA and two independent datasets (GSE21653 and GSE20685) were used to study the relevance of lncRNAs in biological pathways. lncRNA-miRNA-mRNA interaction network was investigated. The connections of lncRNAs with MRPs were analysed through the correlation test. Finally, lncRNA and MRP mRNAs attachment sites were analysed through the LncRRisearch tool.Result: According to our data, thirty-eight lncRNAs were associated with cell drug response in breast cancer cells. IL12A-AS1, AC137723.1, LINC00667, SVIL-AS1, CYTOR, and MIR4435-2HG linked to patient survival (P<0.05). AC137723.1 and LINC00667 were identified as good prognostic genes, while the others were discovered to have poor prognostic effects. Moreover, the risk score model separated patients perfectly, in which about 45% of high-risk patients were dead; by contrast, around 95% of low-risk patients could survive. ROC curve results proved that CYTOR, MIR4435-2HG, and LINC00667 are potential biomarkers in breast cancer with AUC >0.8. Pathway analysis revealed that CYTOR and MIR4435-2HG are highly correlated with the Epithelial-Mesenchymal transition pathway, while AC137723.1 and LINC00667 were negatively correlated with the pathway. AC128688.2, CYTOR, TDRKH-AS1 and LINC00667 can participate in lncRNA-miRNA-mRNA networks. Also, MIR22HG might influence drug resistance by attaching to MRP mRNAs.Conclusion: Our findings revealed 38 lncRNAs involved in cancer cell treatment resistance and sensitivity. They can participate in patients’ prognosis, diagnosis and cellular pathways. Also, they may influence cell drug response through connections with CSPs, lncRNA-miRNA-mRNA networks and MRPs.

Determining the Factors Predicting the Response to Anti-HER2 Therapy in HER2-Positive Breast Cancer Patients

Background: We aimed to identify overexpressed genes or related pathways associated with good responses to anti-HER2 therapy and to suggest a model for predicting drug response in neoadjuvant therapy with trastuzumab in HER2-positive breast cancer patients. Methods: We recruited 64 women with breast cancer and categorized them into three groups according to anti-HER2 therapy response. RNA from twenty core needle biopsy paraffin-embedded tissues and four cultured cell was extracted, reverse transcribed, and subjected to microarray. The obtained data were analyzed using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and database for annotation, visualization, and integrated discovery. Results: In total, 6,656 genes differentially expressed between trastuzumab-susceptible and trastuzumab-resistant cell lines (3,224 upregulated and 3,432 downregulated). Expression changes in 34 genes in several pathways were found to be related to the response to trastuzumab-containing treatment, interfering with adhesion to other cells or tissues (focal adhesion) and regulating extracellular matrix interactions and phagosome action. Thus, decreased tumor invasiveness and enhanced drug effects might be the mechanisms explaining the better drug response in complete response group. Conclusions: This multigene assay-based study provides insights into breast cancer signal transduction and the possible prediction of treatment response to targeted therapies such as trastuzumab.