featuring the Echo acoustic technology
TITLES and AUTHORS
The High Throughput Biomedicine (HTB) unit at FIMM Technology Centre provides a wide range of biomedical high throughput assays. In collaboration with research groups and the Hospital District of Helsinki and Uusimaa, we have set up drug sensitivity and resistance testing (DSRT) platform with a set of 450 approved and investigational oncology drugs on patient samples. Ex vivo drug testing is currently performed at FIMM with primary cancer cells from patients with leukaemia and multiple myeloma as well as cancer cell lines and the drug sensitivity responses are integrated with molecular profiling such as exome sequencing, transcriptomics and phosphoproteomics. Currently, DSRT is run in 384-well plate format in 5 concentrations for each drug or in 1536-well plate format with 9 concentrations per drug and the assay plates are pre-drugged using acoustic droplet ejection with Labcyte® Echo® 550. The viability and cell death of cells is measured after 72 h and results are analysed using Dotmatics Studies – software and an in-house developed interface, Breeze. Application of the platform to AML patient samples has uncovered taxonomic drug-response subtypes and individualised therapy based on DSRT has resulted in several clinical responses. The DSRT platform enables drug repositioning, provides new combinatorial possibilities and allows for linking drug sensitivities to predictive biomarkers. We are developing the platform with additional readouts and increasing the number of drugs. Our Labcyte Access Workstation, with Echo 550 and Echo® 525 integration, allows development and set-up of miniaturised follow-up assays, such as reverse-phase protein array and qPCR, using non-contact acoustic dispensing.
In vitro studies show that estrogens acutely modulate synaptic function in both sexes. These acute effects may be mediated in vivo by estrogens synthesized within the brain, which could fluctuate more rapidly than circulating estrogens. For this to be the case, brain regions that respond acutely to estrogens should be capable of synthesizing them. To investigate this question, we used quantitative real-time PCR to measure expression of mRNA for the estrogen-synthesizing enzyme, aromatase, in different brain regions of male and female rats. Importantly, because brain aromatase exists in two forms, a long form with aromatase activity and a short form with unknown function, we targeted a sequence found exclusively in long-form aromatase. With this approach, we found highest expression of aromatase mRNA in the amygdala followed closely by the bed nucleus of the stria terminalis (BNST) and preoptic area (POA); we found moderate levels of aromatase mRNA in the dorsal hippocampus and cingulate cortex; and aromatase mRNA was detectable in brainstem and cerebellum, but levels were very low. In the amygdala, gonadal/hormonal status regulated aromatase expression in both sexes; in the BNST and POA, castration of males down-regulated aromatase, whereas there was no effect of estradiol in ovariectomized females. In the dorsal hippocampus and cingulate cortex, there were no differences in aromatase levels between males and females or effects of gonadal/hormonal status. These findings demonstrate that long-form aromatase is expressed in brain regions that respond acutely to estrogens, such as the dorsal hippocampus, and that gonadal/hormonal regulation of aromatase differs among different brain regions.
We developed a systematic algorithmic solution for quantitative drug sensitivity scoring (DSS), based on continuous modeling and integration of multiple dose-response relationships in high-throughput compound testing studies. Mathematical model estimation and continuous interpolation makes the scoring approach robust against sources of technical variability and widely applicable to various experimental settings, both in cancer cell line models and primary patient-derived cells. Here, we demonstrate its improved performance over other response parameters especially in a leukemia patient case study, where differential DSS between patient and control cells enabled identification of both cancer-selective drugs and drug-sensitive patient sub-groups, as well as dynamic monitoring of the response patterns and oncogenic driver signals during cancer progression and relapse in individual patient cells ex vivo. An open-source and easily extendable implementation of the DSS calculation is made freely available to support its tailored application to translating drug sensitivity testing results into clinically actionable treatment options.
The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations.The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation.
The High Throughput Biomedicine (HTB) unit at the Institute for Molecular Medicine Finland FIMM was established in 2010 to serve as a national and international academic screening unit providing access to state of the art instrumentation for chemical and RNAi-based high throughput screening. The initial focus of the unit was multiwell plate based chemical screening and high content microarray-based siRNA screening. However, over the first four years of operation, the unit has moved to a more flexible service platform where both chemical and siRNA screening is performed at different scales primarily in multiwell plate-based assays with a wide range of readout possibilities with a focus on ultraminiaturization to allow for affordable screening for the academic users. In addition to high throughput screening, the equipment of the unit is also used to support miniaturized, multiplexed and high throughput applications for other types of research such as genomics, sequencing and biobanking operations. Importantly, with the translational research goals at FIMM, an increasing part of the operations at the HTB unit is being focused on high throughput systems biological platforms for functional profiling of patient cells in personalized and precision medicine projects.
Single-cell genomics is a powerful tool for exploring the genetic makeup of environmental microorganisms, the vast majority of which are difficult, if not impossible, to cultivate with current approaches. Here we present a comprehensive protocol for obtaining genomes from uncultivated environmental microbes via high-throughput single-cell isolation by FACS. The protocol encompasses the preservation and pretreatment of differing environmental samples, followed by the physical separation, lysis, whole-genome amplification and 16S rRNA–based identification of individual bacterial and archaeal cells. The described procedure can be performed with standard molecular biology equipment and a FACS machine. It takes <12 h of bench time over a 4-d time period, and it generates up to 1 μg of genomic DNA from an individual microbial cell, which is suitable for downstream applications such as PCR amplification and shotgun sequencing. The completeness of the recovered genomes varies, with an average of ∼50%.
Inositol-requiring enzyme 1 alpha (IRE1α) is a transmembrane sensor protein with both kinase and ribonuclease activity, which plays a crucial role in the unfolded protein response (UPR). Protein misfolding in the endoplasmic reticulum (ER) lumen triggers dimerization and subsequent trans-autophosphorylation of IRE1α. This leads to the activation of its endoribonuclease (RNase) domain and splicing of the mRNA of the transcriptional activator XBP1, ultimately generating an active XBP1 (XBP1s) implicated in multiple myeloma survival. Previously, we have identified human IRE1α as a target for the development of kinase inhibitors that could modulate the UPR in human cells, which has particular relevance for multiple myeloma and other secretory malignancies. Here we describe the development and validation of a 384-well high-throughput screening assay using DELFIA technology that is specific for IRE1α autophosphorylation. Using this format, a focused library of 2312 potential kinase inhibitors was screened, and several novel IRE1α kinase inhibitor scaffolds were identified that could potentially be developed toward new therapies to treat multiple myeloma.
With the aim of fuelling open-source, translational, early-stage drug discovery activities, the results of the recently completed antimycobacterial phenotypic screening campaign against Mycobacterium bovis BCG with hit confirmation in M. tuberculosis H37Rv were made publicly accessible. A set of 177 potent noncytotoxic H37Rv hits was identified and will be made available to maximize the potential impact of the compounds toward a chemical genetics/proteomics exercise, while at the same time providing a plethora of potential starting points for new synthetic lead-generation activities. Two additional drug-discovery- relevant datasets are included: a) a drug-like property analysis reflecting the latest lead-like guidelines and b) an early lead-generation package of the most promising hits within the clusters identified.
We present an individualized systems medicine (ISM) approach to optimize cancer drug therapies one-patient-at-a-time. ISM is based on i) molecular profiling and ex vivo drug sensitivity and resistance testing (DSRT) of patients' cancer cells to 187 oncology drugs, ii) clinical implementation of therapies predicted to be effective and iii) studying consecutive samples from the treated patients to understand the basis of resistance. Here, application of ISM to 28 samples from patients with acute myeloid leukemia (AML) uncovered five major taxonomic drug response subtypes based on DSRT profiles, some with distinct genomic features (e.g. MLL gene fusions in subgroup IV and FLT3-ITD mutations in subgroup V). Therapy based on DSRT resulted in several clinical responses. After progression under DSRT-guided therapies, AML cells displayed significant clonal evolution, novel genomic changes potentially explaining resistance, while ex vivo DSRT data showed resistance to the clinically applied drugs and new vulnerabilities to previously ineffective drugs.
A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of nonadditive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.