Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our understanding of disease pathogenesis and changed the way we diagnose and treat disease leading to more precise, predictable and powerful health care that is customized for the individual patient. Genetic, genomics, and epigenetic alterations appear to be contributing to different diseases. Deep clinical phenotyping, combined with advanced molecular phenotypic profiling, enables the construction of causal network models in which a genomic region is proposed to influence the levels of transcripts, proteins, and metabolites. Phenotypic analysis bears great importance to elucidat the pathophysiology of networks at the molecular and cellular level. Digital biomarkers (BMs) can have several applications beyond clinical trials in diagnostics—to identify patients affected by a disease or to guide treatment. Digital BMs present a big opportunity to measure clinical endpoints in a remote, objective and unbiased manner. However, the use of “omics” technologies and large sample sizes have generated massive amounts of data sets, and their analyses have become a major bottleneck requiring sophisticated computational and statistical methods. With the wealth of information for different diseases and its link to intrinsic biology, the challenge is now to turn the multi-parametric taxonomic classification of a disease into better clinical decision-making by more precisely defining a disease. As a result, the big data revolution has provided an opportunity to apply artificial intelligence (AI) and machine learning algorithms to this vast data set. The advancements in digital health opportunities have also arisen numerous questions and concerns on the future of healthcare practices in particular with what regards the reliability of AI diagnostic tools, the impact on clinical practice and vulnerability of algorithms. AI, machine learning algorithms, computational biology, and digital BMs will offer an opportunity to translate new data into actionable information thus, allowing earlier diagnosis and precise treatment options. A better understanding and cohesiveness of the different components of the knowledge network is a must to fully exploit the potential of it.

/pdf/are-innovation-and-new-technologies-in-precision-medicine-3c5vfqqdx2.pdf

Are innovation and new technologies in precision medicine paving a new era in patients centric care

RNA interference (RNAi) leads to sequence-specific knockdown of gene function. The approach can be used in large-scale screens to interrogate function in various model organisms and an increasing number of other species. Genome-scale RNAi screens are routinely performed in cultured or primary cells or in vivo in organisms such as C. elegans. High-throughput RNAi screening is benefitting from the development of sophisticated new instrumentation and software tools for collecting and analyzing data, including high-content image data. The results of large-scale RNAi screens have already proved useful, leading to new understandings of gene function relevant to topics such as infection, cancer, obesity, and aging. Nevertheless, important caveats apply and should be taken into consideration when developing or interpreting RNAi screens. Some level of false discovery is inherent to high-throughput approaches and specific to RNAi screens, false discovery due to off-target effects (OTEs) of RNAi reagents remains a problem. The need to improve our ability to use RNAi to elucidate gene function at large scale and in additional systems continues to be addressed through improved RNAi library design, development of innovative computational and analysis tools and other approaches.

/pdf/rnai-screening-new-approaches-understandings-and-organisms-ohn48rl15z.pdf

RNAi screening: new approaches, understandings, and organisms.

Neratinib, an irreversible inhibitor of epidermal growth factor receptor and human epidermal receptor 2, is in phase III clinical trials for patients with human epidermal receptor 2-positive, locally advanced or metastatic breast cancer. The objective of this study was to explore the ability of neratinib to reverse tumor multidrug resistance attributable to overexpression of ATP-binding cassette (ABC) transporters. Our results showed that neratinib remarkably enhanced the sensitivity of ABCB1-overexpressing cells to ABCB1 substrates. It is noteworthy that neratinib augmented the effect of chemotherapeutic agents in inhibiting the growth of ABCB1-overexpressing primary leukemia blasts and KBv200 cell xenografts in nude mice. Furthermore, neratinib increased doxorubicin accumulation in ABCB1-overexpressing cell lines and Rhodamine 123 accumulation in ABCB1-overexpressing cell lines and primary leukemia blasts. Neratinib stimulated the ATPase activity of ABCB1 at low concentrations but inhibited it at high concentrations. Likewise, neratinib inhibited the photolabeling of ABCB1 with [125I]iodoarylazidoprazosin in a concentration-dependent manner (IC50 = 0.24 μM). Neither the expression of ABCB1 at the mRNA and protein levels nor the phosphorylation of Akt was affected by neratinib at reversal concentrations. Docking simulation results were consistent with the binding conformation of neratinib within the large cavity of the transmembrane region of ABCB1, which provides computational support for the cross-reactivity of tyrosine kinase inhibitors with human ABCB1. In conclusion, neratinib can reverse ABCB1-mediated multidrug resistance in vitro, ex vivo, and in vivo by inhibiting its transport function.

https://molpharm.aspetjournals.org/content/molpharm/early/2012/04/04/mol.111.076299.full.pdf

Neratinib reverses ATP-binding cassette B1-mediated chemotherapeutic drug resistance in vitro, in vivo, and ex vivo.

Purpose
Neratinib is an oral, small-molecule inhibitor that irreversibly binds to pan-HER (ErbB) receptor tyrosine kinases. Studies suggest that dual anti-HER therapies utilized in breast cancer patients are more efficacious than single agents in both the metastatic and neoadjuvant settings. In this phase I study, neratinib was combined with trastuzumab and paclitaxel in metastatic HER2-positive patients.

Safety and efficacy of neratinib in combination with weekly paclitaxel and trastuzumab in women with metastatic HER2‑positive breast cancer: an NSABP Foundation Research Program phase I study.

Neratinib (HKI-272) is a small molecule tyrosine kinase inhibitor of the ErbB receptor family currently in Phase III clinical trials. Despite its efficacy, the mechanism of potential cellular resistance to neratinib and genes involved with it remains unknown. We have used a pool-based lentiviral genome-wide functional RNAi screen combined with a lethal dose of neratinib to discover chemoresistant interactions with neratinib. Our screen has identified a collection of genes whose inhibition by RNAi led to neratinib resistance including genes involved in oncogenesis (e.g. RAB33A, RAB6A and BCL2L14), transcription factors (e.g. FOXP4, TFEC, ZNF), cellular ion transport (e.g. CLIC3, TRAPPC2P1, P2RX2), protein ubiquitination (e.g. UBL5), cell cycle (e.g. CCNF), and genes known to interact with breast cancer-associated genes (e.g. CCNF, FOXP4, TFEC, several ZNF factors, GNA13, IGFBP1, PMEPA1, SOX5, RAB33A, RAB6A, FXR1, DDO, TFEC, OLFM2). The identification of novel mediators of cellular resistance to neratinib could lead to the identification of new or neoadjuvant drug targets. Their use as patient or treatment selection biomarkers could make the application of anti-ErbB therapeutics more clinically effective.

A genome-wide RNAi screen identifies novel targets of neratinib resistance leading to identification of potential drug resistant genetic markers

ErbB2 is frequently activated in tumors, and influences a wide array of cellular functions, including proliferation, apoptosis, cell motility and adhesion. HKI-272 (neratinib) is a small molecule pan-kinase inhibitor of the ErbB family of receptor tyrosine kinases, and shows strong antiproliferative activity in ErbB2-overexpressing breast cancer cells. We undertook a genome-wide pooled lentiviral RNAi screen to identify synthetic lethal or enhancer (synthetic modulator screen) genes that interact with neratinib in a human breast cancer cell line (SKBR-3). These genes upon knockdown would modulate cell viability in the presence of subeffective concentrations of neratinib. We discovered a diverse set of genes whose depletion selectively impaired or enhanced the viability of SKBR-3 cells in the presence of neratinib. We observed diverse pathways including EGFR, hypoxia, cAMP, and protein ubiquitination that, when co-treated with RNAi and neratinib, resulted in arrest of cell proliferation. Examining the changes of these genes and their protein products also led to a rationale for clinically relevant drug combination treatments. Treatment of cells with either paclitaxel or cytarabine in combination with neratinib resulted in a strong antiproliferative effect. The identification of novel mediators of cellular response to neratinib and the development of potential drug combination treatments have expanded our understanding of neratinib's mode-of-action for the development of more effective therapeutic regimens. Notably, our findings support a paclitaxel and neratinib phase III clinical trial in breast cancer patients.

/pdf/a-genome-wide-rnai-screen-identifies-novel-targets-of-2xyd7eynq9.pdf

A genome-wide RNAi screen identifies novel targets of neratinib sensitivity leading to neratinib and paclitaxel combination drug treatments

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A genome-wide RNAi screen identifies novel targets of neratinib sensitivity leading to neratinib and paclitaxel combination drug treatments