Integrated drug profiling and CRISPR screening identify BCR::ABL1-independent vulnerabilities in chronic myeloid leukemia

BCR::ABL1-independent pathways contribute to primary resistance to tyrosine kinase inhibitor (TKI) treatment in chronic myeloid leukemia (CML) and play a role in leukemic stem cell persistence. Here, we perform ex vivo drug screening of CML CD34+ leukemic stem/progenitor cells using 100 single drugs and TKI-drug combinations and identify sensitivities to Wee1, MDM2, and BCL2 inhibitors. These agents effectively inhibit primitive CD34+CD38- CML cells and demonstrate potent synergies when combined with TKIs. Flow-cytometry-based drug screening identifies mepacrine to induce differentiation of CD34+CD38- cells. We employ genome-wide CRISPR-Cas9 screening for six drugs, and mediator complex, apoptosis, and erythroid-lineage-related genes are identified as key resistance hits for TKIs, whereas the Wee1 inhibitor AZD1775 and mepacrine exhibit distinct resistance profiles. KCTD5, a consistent TKI-resistance-conferring gene, is found to mediate TKI-induced BCR::ABL1 ubiquitination. In summary, we delineate potential mechanisms for primary TKI resistance and non-BCR::ABL1-targeting drugs, offering insights for optimizing CML treatment.

Characterisation of new in vitro models and identification of potentially active drugs in angiosarcoma

Angiosarcoma is a rare soft tissue sarcoma originating from endothelial cells. Given that current treatments for advanced disease have shown limited efficacy, alternative therapies need to be identified. In rare diseases, patient-derived cell models are crucial for screening anti-tumour activity. In this study, cell line models were characterised in 2D and 3D cultures. The cell lines' growth, migration and invasion capabilities were explored, confirming them as useful tools for preclinical angiosarcoma studies. By screening a drug library, we identified potentially effective compounds: 8-amino adenosine impacted cell growth and inhibited migration and invasion at considerably low concentrations as a single agent. No synergistic effect was detected when combining with paclitaxel, gemcitabine or doxorubicin. These results suggest that this compound could be a potentially useful drug in the treatment of AGS.

Drug repurposing platform for deciphering the druggable SARS-CoV-2 interactome

The coronavirus disease 2019 (COVID-19) pandemic has heavily challenged the global healthcare system. Despite the vaccination programs, the new virus variants are circulating. Further research is required for understanding of the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and for discovery of therapeutic agents against the virus. Here, we took advantage of drug repurposing to identify if existing drugs could inhibit SARS-CoV-2 infection. We established an open high throughput platform for in vitro screening of drugs against SARS-CoV-2 infection. We screened ∼1000 drugs for their ability to inhibit SARS-CoV-2-induced cell death in the African green monkey kidney cell line (Vero-E6), analyzed how the hit compounds affect the viral N (nucleocapsid) protein expression in human cell lines using high-content microscopic imaging and analysis, determined the hit drug targets in silico, and assessed their ability to cause phospholipidosis, which can interfere with the viral replication. Duvelisib was found by in silico interaction assay as a potential drug targeting virus-host protein interactions. The predicted interaction between PARP1 and S protein, affected by Duvelisib, was further validated by immunoprecipitation. Our results represent a rapidly applicable platform for drug repurposing and evaluation of the new emerging viruses' responses to the drugs. Further in silico studies help us to discover the druggable host pathways involved in the infectious cycle of SARS-CoV-2.

Robust scoring of selective drug responses for patient-tailored therapy selection

Most patients with advanced malignancies are treated with severely toxic, first-line chemotherapies. Personalized treatment strategies have led to improved patient outcomes and could replace one-size-fits-all therapies, yet they need to be tailored by testing of a range of targeted drugs in primary patient cells. Most functional precision medicine studies use simple drug-response metrics, which cannot quantify the selective effects of drugs (i.e., the differential responses of cancer cells and normal cells). We developed a computational method for selective drug-sensitivity scoring (DSS), which enables normalization of the individual patient's responses against normal cell responses. The selective response scoring uses the inhibition of noncancerous cells as a proxy for potential drug toxicity, which can in turn be used to identify effective and safer treatment options. Here, we explain how to apply the selective DSS calculation for guiding precision medicine in patients with leukemia treated across three cancer centers in Europe and the USA; the generic methods are also widely applicable to other malignancies that are amenable to drug testing. The open-source and extendable R-codes provide a robust means to tailor personalized treatment strategies on the basis of increasingly available ex vivo drug-testing data from patients in real-world and clinical trial settings. We also make available drug-response profiles to 527 anticancer compounds tested in 10 healthy bone marrow samples as reference data for selective scoring and de-prioritization of drugs that show broadly toxic effects. The procedure takes <60 min and requires basic skills in R.

Drug repositioning strategy for the identification of novel telomere-damaging agents: A role for NAMPT inhibitors

Drug repositioning strategy represents a valid tool to accelerate the pharmacological development through the identification of new applications for already existing compounds. In this view, we aimed at discovering molecules able to trigger telomere-localized DNA damage and tumor cell death. By applying an automated high-content spinning-disk microscopy, we performed a screening aimed at identifying, on a library of 527 drugs, molecules able to negatively affect the expression of TRF2, a key protein in telomere maintenance. FK866, resulting from the screening as the best candidate hit, was then validated at biochemical and molecular levels and the mechanism underlying its activity in telomere deprotection was elucidated both in vitro and in vivo. The results of this study allow us to discover a novel role of FK866 in promoting, through the production of reactive oxygen species, telomere loss and deprotection, two events leading to an accumulation of DNA damage and tumor cell death. The ability of FK866 to induce telomere damage and apoptosis was also demonstrated in advanced preclinical models evidencing the antitumoral activity of FK866 in triple-negative breast cancer-a particularly aggressive breast cancer subtype still orphan of targeted therapies and characterized by high expression levels of both NAMPT and TRF2. Overall, our findings pave the way to the development of novel anticancer strategies to counteract triple-negative breast cancer, based on the use of telomere deprotecting agents, including NAMPT inhibitors, that would rapidly progress from bench to bedside.

Ex Vivo Drug Testing in Patient-derived Papillary Renal Cancer Cells Reveals EGFR and the BCL2 Family as Therapeutic Targets

BACKGROUND

Immune checkpoint inhibitors and antiangiogenic agents are used for first-line treatment of advanced papillary renal cell carcinoma (pRCC) but pRCC response rates to these therapies are low.

OBJECTIVE

To generate and characterise a functional ex vivo model to identify novel treatment options in advanced pRCC.

DESIGN, SETTING, AND PARTICIPANTS

We established patient-derived cell cultures (PDCs) from seven pRCC samples from patients and characterised them via genomic analysis and drug profiling.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Comprehensive molecular characterisation in terms of copy number analysis and whole-exome sequencing confirmed the concordance of pRCC PDCs with the original tumours. We evaluated their sensitivity to novel drugs by generating drug scores for each PDC.

RESULTS AND LIMITATIONS

PDCs confirmed pRCC-specific copy number variations such as gains in chromosomes 7, 16, and 17. Whole-exome sequencing revealed that PDCs retained mutations in pRCC-specific driver genes. We performed drug screening with 526 novel and oncological compounds. Whereas exposure to conventional drugs showed low efficacy, the results highlighted EGFR and BCL2 family inhibition as the most effective targets in our pRCC PDCs.

CONCLUSIONS

High-throughput drug testing on newly established pRCC PDCs revealed that inhibition of EGFR and BCL2 family members could be a therapeutic strategy in pRCC.

PATIENT SUMMARY

We used a new approach to generate patient-derived cells from a specific type of kidney cancer. We showed that these cells have the same genetic background as the original tumour and can be used as models to study novel treatment options for this type of kidney cancer.

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2

We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics.

Breeze 2.0: an interactive web-tool for visual analysis and comparison of drug response data

Functional precision medicine (fPM) offers an exciting, simplified approach to finding the right applications for existing molecules and enhancing therapeutic potential. Integrative and robust tools ensuring high accuracy and reliability of the results are critical. In response to this need, we previously developed Breeze, a drug screening data analysis pipeline, designed to facilitate quality control, dose-response curve fitting, and data visualization in a user-friendly manner. Here, we describe the latest version of Breeze (release 2.0), which implements an array of advanced data exploration capabilities, providing users with comprehensive post-analysis and interactive visualization options that are essential for minimizing false positive/negative outcomes and ensuring accurate interpretation of drug sensitivity and resistance data. The Breeze 2.0 web-tool also enables integrative analysis and cross-comparison of user-uploaded data with publicly available drug response datasets. The updated version incorporates new drug quantification metrics, supports analysis of both multi-dose and single-dose drug screening data and introduces a redesigned, intuitive user interface. With these enhancements, Breeze 2.0 is anticipated to substantially broaden its potential applications in diverse domains of fPM.

Comparison of two supporting matrices for patient-derived cancer cells in 3D drug sensitivity and resistance testing assay (3D-DSRT)

Central to the success of functional precision medicine of solid tumors is to perform drug testing of patient-derived cancer cells (PDCs) in tumor-mimicking ex vivo conditions. While high throughput (HT) drug screening methods have been well-established for cells cultured in two-dimensional (2D) format, this approach may have limited value in predicting clinical responses. Here, we describe the results of the optimization of drug sensitivity and resistance testing (DSRT) in three-dimensional (3D) growth supporting matrices in a HT mode (3D-DSRT) using the hepatocyte cell line (HepG2) as an example. Supporting matrices included widely used animal-derived Matrigel and cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. Further, the sensitivity of ovarian cancer PDCs, from two patients included in the functional precision medicine study, was tested for 52 drugs in 5 different concentrations using 3D-DSRT. Shortly, in the optimized protocol, the PDCs are embedded with matrices and seeded to 384-well plates to allow the formation of the spheroids prior to the addition of drugs in nanoliter volumes with acoustic dispenser. The sensitivity of spheroids to drug treatments is measured with cell viability readout (here, 72 h after addition of drugs). The quality control and data analysis are performed with openly available Breeze software. We show the usability of both matrices in established 3D-DSRT, and report 2D vs 3D growth condition dependent differences in sensitivities of ovarian cancer PDCs to MEK-inhibitors and cytotoxic drugs. This study provides a proof-of-concept for robust and fast screening of drug sensitivities of PDCs in 3D-DSRT, which is important not only for drug discovery but also for personalized ex vivo drug testing in functional precision medicine studies. These findings suggest that comparing results of 2D- and 3D-DSRT is essential for understanding drug mechanisms and for selecting the most effective treatment for the patient.

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.

P-TEFb promotes cell survival upon p53 activation by suppressing intrinsic apoptosis pathway

Positive transcription elongation factor b (P-TEFb) is the crucial player in RNA polymerase II (Pol II) pause release that has emerged as a promising target in cancer. Because single-agent therapy may fail to deliver durable clinical response, targeting of P-TEFb shall benefit when deployed as a combination therapy. We screened a comprehensive oncology library and identified clinically relevant antimetabolites and Mouse double minute 2 homolog (MDM2) inhibitors as top compounds eliciting p53-dependent death of colorectal cancer cells in synergy with selective inhibitors of P-TEFb. While the targeting of P-TEFb augments apoptosis by anti-metabolite 5-fluorouracil, it switches the fate of cancer cells by the non-genotoxic MDM2 inhibitor Nutlin-3a from cell-cycle arrest to apoptosis. Mechanistically, the fate switching is enabled by the induction of p53-dependent pro-apoptotic genes and repression of P-TEFb-dependent pro-survival genes of the PI3K-AKT signaling cascade, which stimulates caspase 9 and intrinsic apoptosis pathway in BAX/BAK-dependent manner. Finally, combination treatments trigger apoptosis of cancer cell spheroids. Together, co-targeting of P-TEFb and suppressors of intrinsic apoptosis could become a viable strategy to eliminate cancer cells.

Functional temozolomide sensitivity testing of patient-specific glioblastoma stem cell cultures is predictive of clinical outcome

Serum-free culturing of patient-derived glioblastoma biopsies enrich for glioblastoma stem cells (GSCs) and is recognized as a disease-relevant model system in glioblastoma (GBM). We hypothesized that the temozolomide (TMZ) drug sensitivity of patient-derived GSC cultures correlates to clinical sensitivity patterns and has clinical predictive value in a cohort of GBM patients. To this aim, we established 51 individual GSC cultures from surgical biopsies from both treatment-naïve primary and pretreated recurrent GBM patients. The cultures were evaluated for sensitivity to TMZ over a dosing range achievable in normal clinical practice. Drug efficacy was quantified by the drug sensitivity score. MGMT-methylation status was investigated by pyrosequencing. Correlative, contingency, and survival analyses were performed for associations between experimental and clinical data. We found a heterogeneous response to temozolomide in the GSC culture cohort. There were significant differences in the sensitivity to TMZ between the newly diagnosed and the TMZ-treated recurrent disease (p <0.01). There was a moderate correlation between MGMT-status and sensitivity to TMZ (r=0.459, p=0.0009). The relationship between MGMT status and TMZ efficacy was statistically significant on multivariate analyses (p=0.0051). We found a predictive value of TMZ sensitivity in individual GSC cultures to patient survival (p=0.0089). We conclude that GSC-enriched cultures hold clinical and translational relevance by their ability to reflect the clinical heterogeneity in TMZ-sensitivity, substantiate the association between TMZ-sensitivity and MGMT-promotor methylation status and appear to have a stronger predictive value than MGMT-promotor methylation on clinical responses to TMZ.

Effects of Wee1 inhibitor adavosertib on patient-derived high-grade serous ovarian cancer cells are multiple and independent of homologous recombination status

Objective

A major challenge in the treatment of platinum-resistant high-grade serous ovarian cancer (HGSOC) is lack of effective therapies. Much of ongoing research on drug candidates relies on HGSOC cell lines that are poorly documented. The goal of this study was to screen for effective, state-of-the-art drug candidates using primary HGSOC cells. In addition, our aim was to dissect the inhibitory activities of Wee1 inhibitor adavosertib on primary and conventional HGSOC cell lines.

Methods

A comprehensive drug sensitivity and resistance testing (DSRT) on 306 drug compounds was performed on three patient-derived genetically unique HGSOC cell lines and two commonly used ovarian cancer cell lines. The effect of adavosertib on the cell lines was tested in several assays, including cell-cycle analysis, apoptosis induction, proliferation, wound healing, DNA damage, and effect on nuclear integrity.

Results

Several compounds exerted cytotoxic activity toward all cell lines, when tested in both adherent and spheroid conditions. In further cytotoxicity tests, adavosertib exerted the most consistent cytotoxic activity. Adavosertib affected cell-cycle control in patient-derived and conventional HGSOC cells, inducing G2/M accumulation and reducing cyclin B1 levels. It induced apoptosis and inhibited proliferation and migration in all cell lines. Furthermore, the DNA damage marker γH2AX and the number of abnormal cell nuclei were clearly increased following adavosertib treatment. Based on the homologous recombination (HR) signature and functional HR assays of the cell lines, the effects of adavosertib were independent of the cells' HR status.

Conclusion

Our study indicates that Wee1 inhibitor adavosertib affects several critical functions related to proliferation, cell cycle and division, apoptosis, and invasion. Importantly, the effects are consistent in all tested cell lines, including primary HGSOC cells, and independent of the HR status of the cells. Wee1 inhibition may thus provide treatment opportunities especially for patients, whose cancer has acquired resistance to platinum-based chemotherapy or PARP inhibitors.

Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia

We generated ex vivo drug-response and multiomics profiling data for a prospective series of 252 samples from 186 patients with acute myeloid leukemia (AML). A functional precision medicine tumor board (FPMTB) integrated clinical, molecular, and functional data for application in clinical treatment decisions. Actionable drugs were found for 97% of patients with AML, and the recommendations were clinically implemented in 37 relapsed or refractory patients. We report a 59% objective response rate for the individually tailored therapies, including 13 complete responses, as well as bridging five patients with AML to allogeneic hematopoietic stem cell transplantation. Data integration across all cases enabled the identification of drug response biomarkers, such as the association of IL15 overexpression with resistance to FLT3 inhibitors. Integration of molecular profiling and large-scale drug response data across many patients will enable continuous improvement of the FPMTB recommendations, providing a paradigm for individualized implementation of functional precision cancer medicine. SIGNIFICANCE: Oncogenomics data can guide clinical treatment decisions, but often such data are neither actionable nor predictive. Functional ex vivo drug testing contributes significant additional, clinically actionable therapeutic insights for individual patients with AML. Such data can be generated in four days, enabling rapid translation through FPMTB.See related commentary by Letai, p. 290.This article is highlighted in the In This Issue feature, p. 275.

Functional diagnostics using fresh uncultured lung tumor cells to guide personalized treatments

Functional profiling of a cancer patient's tumor cells holds potential to tailor personalized cancer treatment. Here, we report the utility of fresh uncultured tumor-derived EpCAM+ epithelial cells (FUTCs) for ex vivo drug-response interrogation. Analysis of murine Kras mutant FUTCs demonstrates pharmacological and adaptive signaling profiles comparable to subtype-matched cultured cells. By applying FUTC profiling on non-small-cell lung cancer patient samples, we report robust drug-response data in 19 of 20 cases, with cells exhibiting targeted drug sensitivities corresponding to their oncogenic drivers. In one of these cases, an EGFR mutant lung adenocarcinoma patient refractory to osimertinib, FUTC profiling is used to guide compassionate treatment. FUTC profiling identifies selective sensitivity to disulfiram and the combination of carboplatin plus etoposide, and the patient receives substantial clinical benefit from treatment with these agents. We conclude that FUTC profiling provides a robust, rapid, and actionable assessment of personalized cancer treatment options.

Bayesian multi-source regression and monocyte-associated gene expression predict BCL-2 inhibitor resistance in acute myeloid leukemia

The FDA recently approved eight targeted therapies for acute myeloid leukemia (AML), including the BCL-2 inhibitor venetoclax. Maximizing efficacy of these treatments requires refining patient selection. To this end, we analyzed two recent AML studies profiling the gene expression and ex vivo drug response of primary patient samples. We find that ex vivo samples often exhibit a general sensitivity to (any) drug exposure, independent of drug target. We observe that this "general response across drugs" (GRD) is associated with FLT3-ITD mutations, clinical response to standard induction chemotherapy, and overall survival. Further, incorporating GRD into expression-based regression models trained on one of the studies improved their performance in predicting ex vivo response in the second study, thus signifying its relevance to precision oncology efforts. We find that venetoclax response is independent of GRD but instead show that it is linked to expression of monocyte-associated genes by developing and applying a multi-source Bayesian regression approach. The method shares information across studies to robustly identify biomarkers of drug response and is broadly applicable in integrative analyses.

Breeze: an integrated quality control and data analysis application for high-throughput drug screening

SUMMARY

High-throughput screening (HTS) enables systematic testing of thousands of chemical compounds for potential use as investigational and therapeutic agents. HTS experiments are often conducted in multi-well plates that inherently bear technical and experimental sources of error. Thus, HTS data processing requires the use of robust quality control procedures before analysis and interpretation. Here, we have implemented an open-source analysis application, Breeze, an integrated quality control and data analysis application for HTS data. Furthermore, Breeze enables a reliable way to identify individual drug sensitivity and resistance patterns in cell lines or patient-derived samples for functional precision medicine applications. The Breeze application provides a complete solution for data quality assessment, dose-response curve fitting and quantification of the drug responses along with interactive visualization of the results.

AVAILABILITY AND IMPLEMENTATION

The Breeze application with video tutorial and technical documentation is accessible at https://breeze.fimm.fi; the R source code is publicly available at https://github.com/potdarswapnil/Breeze under GNU General Public License v3.0.

CONTACT

swapnil.potdar@helsinki.fi.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Human Tumor-Derived Matrix Improves the Predictability of Head and Neck Cancer Drug Testing

In vitro cancer drug testing carries a low predictive value. We developed the human leiomyoma–derived matrix “Myogel” to better mimic the human tumor microenvironment (TME). We hypothesized that Myogel could provide an appropriate microenvironment for cancer cells, thereby allowing more in vivo–relevant drug testing. We screened 19 anticancer compounds, targeting the epidermal growth factor receptor (EGFR), MEK, and PI3K/mTOR on 12 head and neck squamous cell carcinoma (HNSCC) cell lines cultured on plastic, mouse sarcoma–derived Matrigel (MSDM), and Myogel. We applied a high-throughput drug screening assay under five different culturing conditions: cells in two-dimensional (2D) plastic wells and on top or embedded in Matrigel or Myogel. We then compared the efficacy of the anticancer compounds to the response rates of 19 HNSCC monotherapy clinical trials. Cancer cells on top of Myogel responded less to EGFR and MEK inhibitors compared to cells cultured on plastic or Matrigel. However, we found a similar response to the PI3K/mTOR inhibitors under all culturing conditions. Cells grown on Myogel more closely resembled the response rates reported in EGFR-inhibitor monotherapy clinical trials. Our findings suggest that a human tumor matrix improves the predictability of in vitro anticancer drug testing compared to current 2D and MSDM methods.

Feasibility study of using high-throughput drug sensitivity testing to target recurrent glioblastoma stem cells for individualized treatment

BACKGROUND

Despite the well described heterogeneity in glioblastoma (GBM), treatment is standardized, and clinical trials investigate treatment effects at population level. Genomics-driven oncology for stratified treatments allow clinical decision making in only a small minority of screened patients. Addressing tumor heterogeneity, we aimed to establish a clinical translational protocol in recurrent GBM (recGBM) utilizing autologous glioblastoma stem cell (GSC) cultures and automated high-throughput drug sensitivity and resistance testing (DSRT) for individualized treatment within the time available for clinical application.

RESULTS

From ten patients undergoing surgery for recGBM, we established individual cell cultures and characterized the GSCs by functional assays. 7/10 GSC cultures could be serially expanded. The individual GSCs displayed intertumoral differences in their proliferative capacity, expression of stem cell markers and variation in their in vitro and in vivo morphology. We defined a time frame of 10 weeks from surgery to complete the entire pre-clinical work-up; establish individualized GSC cultures, evaluate drug sensitivity patterns of 525 anticancer drugs, and identify options for individualized treatment. Within the time frame for clinical translation 5/7 cultures reached sufficient cell yield for complete drug screening. The DSRT revealed significant intertumoral heterogeneity to anticancer drugs (p < 0.0001). Using curated reference databases of drug sensitivity in GBM and healthy bone marrow cells, we identified individualized treatment options in all patients. Individualized treatment options could be selected from FDA-approved drugs from a variety of different drug classes in all cases.

CONCLUSIONS

In recGBM, GSC cultures could successfully be established in the majority of patients. The individual cultures displayed intertumoral heterogeneity in their in vitro and in vivo behavior. Within a time frame for clinical application, we could perform DSRT in 50% of recGBM patients. The DSRT revealed a remarkable intertumoral heterogeneity in sensitivity to anticancer drugs in recGBM that could allow tailored therapeutic options for functional precision medicine.

Prediction of drug combination effects with a minimal set of experiments

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here we implemented DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose-response measurements for accurate prediction of drug combination synergy and antagonism. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines, and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully-measured dose-response matrices. Measuring only the diagonal of the matrix provides an accurate and practical option for combinatorial screening. The open-source web-implementation enables applications of DECREASE to both pre-clinical and translational studies.

ALDH1A1-related stemness in high-grade serous ovarian cancer is a negative prognostic indicator but potentially targetable by EGFR/mTOR-PI3K/aurora kinase inhibitors

Poor chemotherapy response remains a major treatment challenge for high-grade serous ovarian cancer (HGSC). Cancer stem cells are the major contributors to relapse and treatment failure as they can survive conventional therapy. Our objectives were to characterise stemness features in primary patient-derived cell lines, correlate stemness markers with clinical outcome and test the response of our cells to both conventional and exploratory drugs. Tissue and ascites samples, treatment-naive and/or after neoadjuvant chemotherapy, were prospectively collected. Primary cancer cells, cultured under conditions favouring either adherent or spheroid growth, were tested for stemness markers; the same markers were analysed in tissue and correlated with chemotherapy response and survival. Drug sensitivity and resistance testing was performed with 306 oncology compounds. Spheroid growth condition HGSC cells showed increased stemness marker expression (including aldehyde dehydrogenase isoform I; ALDH1A1) as compared with adherent growth condition cells, and increased resistance to platinum and taxane. A set of eight stemness markers separated treatment-naive tumours into two clusters and identified a distinct subgroup of HGSC with enriched stemness features. Expression of ALDH1A1, but not most other stemness markers, was increased after neoadjuvant chemotherapy and its expression in treatment-naive tumours correlated with chemoresistance and reduced survival. In drug sensitivity and resistance testing, five compounds, including two PI3K-mTOR inhibitors, demonstrated significant activity in both cell culture conditions. Thirteen compounds, including EGFR, PI3K-mTOR and aurora kinase inhibitors, were more toxic to spheroid cells than adherent cells. Our results identify stemness markers in HGSC that are associated with a decreased response to conventional chemotherapy and reduced survival if expressed by treatment-naive tumours. EGFR, mTOR-PI3K and aurora kinase inhibitors are candidates for targeting this cell population. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Abstract 2153: Ex vivo drug sensitivity testing of primary cells for precision cancer medicine

Introduction/Purpose:

Cancer therapy is increasingly moving towards individualized care and therapy, but there are still gaps between what is known and described on the molecular level about cancers and what is applied in the clinic. In an attempt to bridge the knowledge gap, we at the Institute for Molecular Medicine Finland (FIMM) have set up an Individualized Systems Medicine program that integrates clinical information, molecular profiling and functional information about individual patients’ cancers (Pemovska et al, Cancer Discov, 2013). Central to this program is the Drug Sensitivity and Resistance Testing (DSRT) where we functionally profile the responses of primary cancer cells to a comprehensive clinical oncology and signal transduction inhibitor drug collection of 528 compounds.

Methods:

Acoustic dispensing platforms are integral to the success of this profiling activity. We have to date produced approximately 3000 drug sets as dose response assay ready plates. The acoustic dispensing allows for making pre-drugged single drug plate sets and/or drug combination plates within hours after sampling of the cells. The plates are also readily sent to researchers anywhere in the world for running comparable assays at other sites. The drugging reproducibility is excellent generating results with correlations of 0.98 or higher in replicate assays. We have developed in-house software solutions to aid these processes: a script for quick creation of transfer list for combination plates and automated analysis pipelines with web-based software interfaces to enable the screening biologists to analyze the screening results effectively.

Results:

The results of these assays are used to explore and understand cancer biology in terms of druggability, functional heterogeneity and mechanism of drug response and resistance. The profiling data can be used to stratify and position the relevance of specific drugs in different diseases and has been used to identify novel clinically relevant activities of existing and investigational drugs (see e.g. Pemovska et al, Nature, 2015). This information is further utilized to establish hypotheses on drug combinations selectively targeting individual cancers and their predictive biomarkers, which can be explored in the clinic by our clinical collaborators to guide the treatment of the individual patient.

Conclusions:

In summary, we describe our platform for a functional drug sensitivity testing within our individualized cancer systems medicine program, which generates consistent biological and clinically relevant data.

Citation Format: Sergey G. Kuznetsov, Alexander Ianevski, Evgeny Kulessky, Karoliina Laamanen, Elina Lehtinen, Maria Nurmi, Swapnil Potdar, Jani Saarela, Katja Suomi, Laura Turunen, Krister Wennerberg, Päivi Tammela. Ex vivo drug sensitivity testing of primary cells for precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2153.

Abstract 458: Precision systems medicine in acute myeloid leukemia: real-time translation of tailored therapeutic opportunities arising from ex-vivo drug sensitivity testing and molecular profiling

Acute myeloid leukemia (AML) is an aggressive disease of clonal hematopoietic progenitor cells. Here, we applied ex-vivo drug sensitivity and resistance testing on AML patient cells with 362 emerging and 153 approved cancer drugs together with genomic and transcriptomic profiling to identify and tailor therapies for patients with advanced disease. Ex-vivo testing with freshly isolated patient cells revealed cancer-specific efficacies of approved drugs in 97% of the 164 patient cases, including 47% of the cases with no actionable driver mutations. We identified 142 statistically significant associations between drug responses and somatic mutations, including increased sensitivity to JAK inhibitors in patients with NPM1 mutations. Transcriptomic profiles predicted drug responses better than genomics and helped to identify additional response markers, especially beyond mutations. For example, overexpression of HOX family genes was associated with sensitivity to JAK inhibitors in patients with NPM1 mutation. In a prospective study, we translated the functional drug response and molecular profile data to the clinic and suggested tailored therapy with targeted drugs for 26 relapsed or refractory AML patients. In an observational intervention study, acting on these recommendations resulted in a temporary complete clinical remission or leukemia-free state in 39% of the cases. In summary, we conclude that ex-vivo testing of drugs on patient AML cells i) revealed clinically actionable drug efficacies in almost all AML patients, including patients with no actionable mutations, ii) predicted cases with actionable driver mutations with no pharmacological dependency on the target, and iii) enabled real-time tailoring of therapy with 39% clinical response rate in chemorefractory advanced AML. Taken together, we believe this real-time systems medicine approach could become a powerful strategy for tailoring therapies for individual patients in the future.

Citation Format: Disha Malani, Ashwni Kumar, Bhagwan Yadav, Mika Kontro, Swapnil Potdar, Oscar Bruck, Säri Kytölä, Jani Saarela, Samuli Eldfors, Riikka Karjalainen, Muntasir M. Majumder, Imre Västrik, Pekka Ellonen, Matti Kankainen, Minna Suvela, Siv Knappila, Alun Parson, Aino Palva, Pirkko Mattila, Evgeny Kulesskiy, Laura Turunen, Karoliina Laamanen, Elina Lehtinen, Maria Nurmi, Katja Suomi, Astrid Muruimägi, Bjorn T. Gjertsen, Satu Mustjoki, Simon Anders, Maija Wolf, Tero Aittokallio, Krister Wennerberg, Caroline Heckman, Kimmo Porkka, Olli Kallioniemi. Precision systems medicine in acute myeloid leukemia: real-time translation of tailored therapeutic opportunities arising from ex-vivo drug sensitivity testing and molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 458.

Intertumoral heterogeneity in patient-specific drug sensitivities in treatment-naïve glioblastoma

Background

A major barrier to effective treatment of glioblastoma (GBM) is the large intertumoral heterogeneity at the genetic and cellular level. In early phase clinical trials, patient heterogeneity in response to therapy is commonly observed; however, how tumor heterogeneity is reflected in individual drug sensitivities in the treatment-naïve glioblastoma stem cells (GSC) is unclear.

Methods

We cultured 12 patient-derived primary GBMs as tumorspheres and validated tumor stem cell properties by functional assays. Using automated high-throughput screening (HTS), we evaluated sensitivity to 461 anticancer drugs in a collection covering most FDA-approved anticancer drugs and investigational compounds with a broad range of molecular targets. Statistical analyses were performed using one-way ANOVA and Spearman correlation.

Results

Although tumor stem cell properties were confirmed in GSC cultures, their in vitro and in vivo morphology and behavior displayed considerable tumor-to-tumor variability. Drug screening revealed significant differences in the sensitivity to anticancer drugs (p < 0.0001). The patient-specific vulnerabilities to anticancer drugs displayed a heterogeneous pattern. They represented a variety of mechanistic drug classes, including apoptotic modulators, conventional chemotherapies, and inhibitors of histone deacetylases, heat shock proteins, proteasomes and different kinases. However, the individual GSC cultures displayed high biological consistency in drug sensitivity patterns within a class of drugs. An independent laboratory confirmed individual drug responses.

Conclusions

This study demonstrates that patient-derived and treatment-naïve GSC cultures maintain patient-specific traits and display intertumoral heterogeneity in drug sensitivity to anticancer drugs. The heterogeneity in patient-specific drug responses highlights the difficulty in applying targeted treatment strategies at the population level to GBM patients. However, HTS can be applied to uncover patient-specific drug sensitivities for functional precision medicine.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5861-4) contains supplementary material, which is available to authorized users.

Abstract A57: Drug sensitivity and resistance testing (DSRT) of clinically important compounds on primary ovarian cancer cell lines

The heterogeneity of chemoresponses in high-grade serous ovarian cancer (HGS-OvCa) presents a major clinical challenge. The inherited or acquired nonresponsiveness to current therapies is likely to be one reason for relapse and treatment failure. Better models for predicting effective treatment options and drug combinations are needed. Although most tumors initially respond to standard platinum-taxane combination therapy, treatment resistance eventually evolves in 80-90% of the patients.

Drug sensitivity and resistance testing (DSRT) was performed by using high-throughput screening (HTS) of 306 small-drug molecules on six primary cell lines from patients with disseminated HGS-OvCa. Commonly available ovarian cancer cell line OVCAR8 was used as a control. The drug effect was investigated with help of a quantitative scoring approach, drug sensitivity score (DSS). Sixteen drugs of clinical interest were investigated in more detail.

Drug sensitivity varied greatly within primary cell lines. Out of 306 compounds, 29 were effective on the most resistant cell line, whereas 102 compounds showed effect on the most sensitive line. We found that 9 out of the 16 clinically important compounds gave no response on the tested cell lines. Seven compounds provided response on the screened cell lines: four HSP90 inhibitors, an HSP70 inhibitor, a Wee1 inhibitor, and a proteasome inhibitor. Of these seven compounds, the most effective drug compounds (the Wee1 inhibitor and two HSP90 inhibitors) were chosen for validation on primary HGS-OvCa cell lines to verify the HTS result. The effectiveness of currently used clinical treatment-line drugs, cisplatin and paclitaxel, was compared to the putative novel drug compounds on the primary cell lines and the control cell line.

One primary cell line was completely resistant to the validated drug compounds as well as to cisplatin and paclitaxel combination treatment. Five cell lines were sensitive to cisplatin-paclitaxel treatment, but the sensitivity to the three most effective compounds varied greatly. The commonly available cell line OVCAR8 was the most sensitive of all tested cell lines.

In conclusion, we found three potential drug compounds of clinical interest effective for disseminated HGS-OvCa. The analyses demonstrate that patient-derived primary HGS-OvCa cell lines have unique heterogeneous characteristics that are likely to have an effect on the choice of best drug candidates for each individual cell line, and furthermore for each patient. Consequently, more combinatorial drug treatment strategies should be validated to find new specific personalized drug treatment strategies.

Citation Format: Pia Roering, Piia Mikkonen, Swapnil Potdar, Krister Wennerberg, Johanna Hynninen, Seija Grénman, Annika Auranen, Olli Carpén, Katja Kaipio. Drug sensitivity and resistance testing (DSRT) of clinically important compounds on primary ovarian cancer cell lines. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr A57.

Abstract 3883: Gene expression predicts ex vivo drug sensitivity in acute myeloid leukemia

Introduction: Ex vivo drug sensitivity studies of samples derived from acute myeloid leukemia (AML) patients have been shown to be predictive of in vivo response. These findings are based on a limited number of well-characterized agents for which in vivo patient response data and ex vivo drug sensitivity data—on that same patient—are available. To show the feasibility of scaling such ex vivo studies to large drug screens, we characterized the reproducibility of expression-based models of drug response across two independent data sets—one generated at the Oregon Health and Science University (OHSU) and the second at the Institute for Molecular Medicine Finland (FIMM).

Methods: We harmonized two large-scale AML ex vivo studies screened for drug response and profiled transcriptomically—OHSU (303 AML patient samples and 160 drugs) and FIMM (48 AML samples and 480 drugs). The two panels have 94 drugs in common. Log-logistic curves were fit to the dose-response data and area under the dose-response curves (AUCs) were calculated. Predictive modeling using Ridge regression or an integrative Bayesian approach was performed for each drug AUC independently using 202 highly-variable and/or cancer-associated genes as features.

Results: For each of the 94 drugs in common between the two data sets, we trained a Ridge regression model on the OHSU data set, used the model to predict response in the FIMM data set, and calculated the Pearson correlation between the predicted and observed FIMM responses. 41 of the 94 drug models had a positive and statistically significant correlation [false discovery rate (FDR) &lt; 20%; mean ρ = 0.43; 95% CI = 0.29 – 0.77]. Drugs corresponding to the top decile of these significant models (mean ρ = 0.54; 95% CI = 0.48 – 0.77) clustered into four primary classes: MEK inhibitors (PD184352, Selumetinib, and Trametinib), EGFR/VEGFR inhibitors (Cabozantinib, Erlotinib, Foretinib, and Sorafenib), and singletons Venetoclax and Sirolimus. To confirm these results, we applied a second modeling approach—an integrative Bayesian machine learning method—that allows systematic combination of both data sets. Training and evaluation of this approach using 10-fold cross validation yielded 82 positive and statistically significant correlations (FDR &lt; 20%; mean ρ = 0.35; 95% CI = 0.13 – 0.58). Five of nine drugs (Cabozantinib, Selumetinib, Sirolimus, Sorafenib, and Trametinib) corresponding to the top decile of these significant models (mean ρ = 0.54; 95% CI = 0.49 – 0.60) overlapped with drugs from the top decile of Ridge results (one-sided Fisher p = 2.5 x 10-4)

Conclusions: Our results using independent data sets and two statistical approaches suggest that certain drugs (including MEK and EGFR/VEGFR inhibitors) are amenable to expression-based predictive modeling in AML. Future work will focus on inferring individual biomarkers of response.

Citation Format: Brian S. White, Suleiman A. Khan, Muhammad Ammad-ud-din, Swapnil Potdar, Mike J. Mason, Cristina E. Tognon, Brian J. Druker, Caroline A. Heckman, Olli P. Kallioniemi, Stephen E. Kurtz, Kimmo Porkka, Jeffrey W. Tyner, Tero Aittokallio, Krister Wennerberg, Justin Guinney. Gene expression predicts ex vivo drug sensitivity in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3883.

Abstract 3899: Discovery and clinical implementation of individualized therapies in acute myeloid leukemia based on ex vivo drug sensitivity testing and multi-omics profiling

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by multiple molecular subtypes and lack of effective targeted therapies. Here, we performed extensive molecular profiling and ex vivo drug testing with 515 approved and emerging cancer drugs on 164 AML patient samples. The aim was to i) assign individualized treatment options to advanced AML patients in real time, ii) explore drug response patterns across the molecular subtypes of AML and iii) identify opportunities to repurpose existing and emerging cancer drugs.

Bone marrow samples (n=164) from 129 consecutive AML patients and 17 healthy donors were studied from the Helsinki University Hospital and the Haukeland University Hospital, Bergen. Mononuclear cells were resuspended either in mononuclear cell medium (MCM) or stroma conditioned medium (CM) and tested for drug sensitivity and resistance as previously described (PMID: 24056683) and studied by exome and transcriptome sequencing. The study protocol allowed us to return data to the clinician for consideration of novel treatment options. For the meta-analysis of associations between drug responses and molecular and clinical parameters, Wilcoxon signed ranked test and logistic regression were applied.

Clustering of all patient samples based on ex vivo drug response patterns in both media types identified 7 distinct functional groups of AML. For example, a subgroup of samples was highly resistant to chemotherapeutics and all targeted drugs except BCL-2 inhibitors. The differences in drug responses in the two media types highlighted the importance of assay conditions for ex vivo drug testing. Strong clustering of several drugs in the same drug classes was often observed as well as clustering across different classes, for example between BET (JQ1, I-BET151, birabresib) and MEK (trametinib, cobimetinib) inhibitors. About 24 percent of the FLT3 negative AML patients manifested strong ex vivo sensitivity to glucocorticoids, highlighting a potential drug repositioning opportunity in this subset of AML patients. Overall, we identified 320 significant associations between drugs and mutated driver genes including association between NPM1 mutation and sensitivity to JAK inhibitors.

Altogether, targeted treatment opportunities were clinically tested in 25 occasions in chemorefractory AML patients. The tailored clinical therapy led to transient complete remission or leukemia free state in 36% (9/25) of these cases.

In conclusion, we discovered and clinically implemented individualized therapeutic options for AML patients, which resulted in a 36% clinical responses in a non-randomized proof-of-concept study. The associations identified between ex-vivo drug response and driver mutations provided novel drug repositioning opportunities in specific molecular subtypes.

Citation Format: Disha Malani, Ashwini Kumar, Bhagwan Yadav, Mika Kontro, Swapnil Potdar, Oscar Brück, Sari Kytölä, Jani Saarela, Samuli Eldfors, Poojitha Ojamies, Karjalainen Riikka, Muntasir Mamun Majumder, Imre Västrik, Pekka Ellonen, Matti Kankainen, Minna Suvela, Siv Knappila, Alun Parson, Aino Palva, Pirkko Mattila, Evgeny Kulesskiy1, Laura Turunen, Karoliina Laamanen, Elina Lehtinen, Piia Mikkonen, Maria Nurmi, Sanna Timonen, Astrid Murumägi, Bjorn Tore Gjersten, Satu Mustjoki, Tero Aittokallio, Krister Wennerberg, Simon Anders, Maija Wolf, Caroline Heckman, Kimmo Porkka, Olli Kallioniemi. Discovery and clinical implementation of individualized therapies in acute myeloid leukemia based on ex vivo drug sensitivity testing and multi-omics profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3899.

Abstract 5029: Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models

We have set up a precision medicine strategy for solid tumors to i) understand biological heterogeneity and driver signaling pathways in cancer, ii) identify new drug opportunities, iii) develop biomarkers for drug responses, and iv) eventually tailor effective treatments for individual patients. Fresh cancer tissue is obtained directly from clinics and processed to provide patient -derived cells (PDCs). PDCs and original tumor tissues are characterized using genetic profiling and image-based phenotyping, phenomics. Systematic drug sensitivity and resistance testing (DSRT) is carried out on the representative PDC models. The PDCs are plated in 384-well plates and treated with oncological compounds, each in five concentrations in 384-well plates. The cell viability and toxicity are measured as drug responses using plate readers. Alternatively, drug-treated cells are immunostained and subjected to automated high-content imaging and image analysis.

Central to the success of this approach is to grow and test the patient derived cells in ex vivo conditions mimicking the tumor microenvironment. To this aim, we have developed 3D drug profiling for PDC spheroids from ovarian and renal cancers. For the 3D drug profiling, cells were cultivated either in Matrigel or in cellulose-based hydrogel, GrowDex, which has earlier been shown to support 3D growth of cell lines and stem cells. The pipetting robot Biomek FXp (Beckman Coulter) was utilized for transfer of matrices and cells to 384-well plates, and acoustic dispenser Echo 550 (Labcyte) for delivery of tailored drug library of 52 drugs to the spheroids. The drug sensitivities of cancer cell spheroids were scored using cell viability measurement as well as high-content confocal imaging.

Our results with primary cells and cell lines suggest that both Matrigel and cellulose-based hydrogel are applicable in the 384-well plate drug profiling assay, and PDC spheroids are formed in both conditions. When the drug responses of ovarian cancer PDCs grown in different 2D and 3D conditions were systematically compared, we observed significant differences in sensitivity to several drugs. Here, we describe the individual drug effects in all conditions, such as some chemotherapeutics being less effective in 3D. As a conclusion, the comparison of results from 2D and 3D drug profiling increases our understanding of mechanisms of drugs, and may aid to select the most representative drugs for the patient.

Citation Format: Piia Mikkonen, Laura Turunen, Lauri Paasonen, Swapnil Potdar, Lassi Paavolainen, Astrid Murumägi, Olli Kallioniemi, Vilja M. Pietiäinen. Precision cancer medicine based on 3D drug profiling of patient-derived cancer cell spheroid models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5029.

Differentiation status of primary chronic myeloid leukemia cells affects sensitivity to BCR-ABL1 inhibitors

Tyrosine kinase inhibitors (TKI) are the mainstay treatment of BCR-ABL1-positive leukemia and virtually all patients with chronic myeloid leukemia in chronic phase (CP CML) respond to TKI therapy. However, there is limited information on the cellular mechanisms of response and particularly on the effect of cell differentiation state to TKI sensitivity in vivo and ex vivo/in vitro. We used multiple, independent high-throughput drug sensitivity and resistance testing platforms that collectively evaluated 295 oncology compounds to characterize ex vivo drug response profiles of primary cells freshly collected from newly-diagnosed patients with BCR-ABL1-positive leukemia (n = 40) and healthy controls (n = 12). In contrast to the highly TKI-sensitive cells from blast phase CML and Philadelphia chromosome-positive acute lymphoblastic leukemia, primary CP CML cells were insensitive to TKI therapy ex vivo. Despite maintaining potent BCR-ABL1 inhibitory activity, ex vivo viability of cells was unaffected by TKIs. These findings were validated in two independent patient cohorts and analysis platforms. All CP CML patients under study responded to TKI therapy in vivo. When CP CML cells were sorted based on CD34 expression, the CD34-positive progenitor cells showed good sensitivity to TKIs, whereas the more mature CD34-negative cells were markedly less sensitive. Thus in CP CML, TKIs predominantly target the progenitor cell population while the differentiated leukemic cells (mostly cells from granulocytic series) are insensitive to BCR-ABL1 inhibition. These findings have implications for drug discovery in CP CML and indicate a fundamental biological difference between CP CML and advanced forms of BCR-ABL1-positive leukemia.

Abstract 3838: Drug sensitivity profile of 5TGM1 murine multiple myeloma cell line emphasizes the translational potential of the syngeneic in vivo model

Multiple myeloma (MM) is the second most common hematologic malignancy that originates from B-cells (plasma cells) and causes 2% of cancer-related deaths. Symptoms of MM include bone pain caused by multiple osteolytic lesions, pathologic fractures, and hypercalcemia. Typically, MM has a low growth fraction and it is highly dependent on the microenvironment. These properties have made it hard to target by conventional chemotherapy, but could now be exploited by novel stroma-targeting drugs and immunotherapy. These new approaches underline the need for well characterized models with functional immune system and appropriate tumor microenvironment. To gain additional information supporting the use of the syngeneic 5TGM1 murine multiple myeloma model in drug development, we tested drug sensitivity of 5TGM1 cells by screening an extensive panel of drugs.

The compound library consisting of 460 compounds included conventional chemotherapy, kinase inhibitors, metabolic modifiers, rapalogs, differentiating/epigenetic modifiers, kinesin inhibitors, apoptotic modulators, NSAIDs, hormone therapy, immunomodulators and HSP inhibitors. The compounds were tested in five concentrations covering a 10.000-fold drug-relevant concentration range in 384-well format. Cells were seeded to plates with a compound library, followed by cell viability measurements (CellTiter-Glo) after 72 hours. Maximal and minimal responses to drugs were analyzed, and the EC50 values were calculated. Drug Sensitivity Score (DSS) was calculated for each drug as a measure of reduced viability.

According to DSS analysis, 5TGM1 cells showed sensitivity to conventional chemotherapy, such as antimitotic drugs, and kinase inhibitors, such as MEK1/2 inhibitors. In addition, the cells showed particular sensitivity to several HSP90 inhibitors currently in phase I/II clinical development for MM. Lenalidomide and pomalidomide, efficient in treating multiple myeloma in humans, both gave low DSS value indicating that 5TGM1 cells are not sensitive to these drugs, which is expected because they do not bind to murine form of the target cereblon. In contrast, 5TGM1 cells were highly sensitive to the proteasome inhibitor bortezomib (DSS 32.2), which is currently in clinical use.

In conclusion, the murine 5TGM1 cells show sensitivity to various MM drugs used in the clinic and under development. Evaluating the effects of the microenvironment on the growth and drug sensitivity of 5TGM1 cells in vitro and in vivo will be essential. Furthermore, the cell-based compound screening combined with DSS analysis provides a possibility to profile cellular responses to an extensive collection of anti-cancer compounds enabling identification of vulnerabilities in cancer cells and functional investigation of cellular pathways behind drug sensitivity or resistance.

Citation Format: Jenni Mäki-Jouppila, Jenni Bernoulli, Mari I. Suominen, Tiina Kähkönen, Jussi M. Halleen, Sanna Timonen, Elina Huovari, Katja Suomi, Swapnil Potdar, Maria Nurmi, Päivi Östling, Jani Saarela, Katja M. Fagerlund. Drug sensitivity profile of 5TGM1 murine multiple myeloma cell line emphasizes the translational potential of the syngeneic in vivo model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3838. doi:10.1158/1538-7445.AM2017-3838

Abstract 3854: Precision medicine approach: analysis of renal cancer patient-derived cells with phenomics, genomics and drug sensitivity profiling

We have built up the precision medicine approach for solid tumors to understand biological heterogeneity and driver signalling pathways in cancer, to develop pharmacogenomic biomarkers and to ultimately tailor effective treatments for patients. In addition to genetic profiling of original tumor tissues, we have developed patient -derived tumor cell (PDC) models, and characterized these models with genomics, image-based phenotyping (phenomics) and high throughput drug profiling.

Here, we describe our approach for a clear cell renal cell carcinoma (ccRCC) patient. PDC cultures were initiated using conditional reprogramming method from fresh primary tumor, protrusion to vena cava, and the benign tissue samples. The exome-sequencing of tissues and PDCs showed that several copy number variations and somatic driver mutations were shared between the cancer tissue and corresponding cell models, including TSC2 and VHL mutations. Using image-based phenotyping, the PDCs from the benign tissue were observed to be strongly positive to cytokeratin 7 (CK7), while original tumor tissue and PDCs from tumor tissue were predominantly CK7 negative. The PDCs expressed vimentin, a mesenchymal marker, but the epithelial marker E-cadherin was downregulated, suggesting for epithelial-mesenchymal transition. The PDCs from primary tumor were exposed to drug sensitivity profiling with a library of 525 approved and investigational oncology compounds in five different concentrations and imaged for proliferation with Ki-67. PI3K/mTOR pathway inhibitors were found to be among the drugs inhibiting the cell proliferation, in agreement with detected somatic mutations affecting these pathways. In addition, our image-based drug sensitivity testing revealed the intra-sample heterogeneity in drug responses and resistance, and we are now further investigating the mechanisms of most potent drugs and their combinations in PDCs at single cell level.

As a conclusion, our results implicate the importance of comprehensive characterization of PDCs and their drug responses in translational research. We also foresee that these approaches may potentially improve the translation of results back to clinic and support the design of combination therapies in cancer.

Citation Format: Vilja M. Pietiäinen, Piia Mikkonen, Khalid Saeed, Lassi Paavolainen, Teijo Pellinen, Swapnil Potdar, John Mpindi, Harry Nisén, Antti Rannikko, Tuomas Mirtti, Peter Horvath, Päivi Östling, Olli Kallioniemi. Precision medicine approach: analysis of renal cancer patient-derived cells with phenomics, genomics and drug sensitivity profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3854. doi:10.1158/1538-7445.AM2017-3854

Abstract 4207: Selective drug sensitivity score (DSS) for indolent and aggressive prostate cancer cell lines

Prostate cancer (PC) is the most common malignancy in men and the second leading cause of cancer-related deaths. The majority of the PCs are classified as adenocarcinomas characterized by the expression of androgen receptor (AR) and prostate-specific antigen (PSA). Two of the most commonly used cell lines are LNCaP and PC-3 cells, derived from lymph node and bone metastases, respectively. Also VCaP cells, derived from vertebral metastases, are widely used in prostate cancer research. It has been well established that LNCaP and VCaP cells represent the conventional indolent form of PC expressing AR and PSA and are androgen-dependent. PC-3 cells, on the other hand, do not express AR and PSA, are androgen-independent, and represent the highly aggressive form.

The drug sensitivity of the cell lines was assessed by applying a large panel of drugs covering cancer chemotherapeutics and clinically available and emerging drugs including conventional chemotherapy, kinase inhibitors, metabolic modifiers, rapalogs, differentiating/epigenetic modifiers, kinesin inhibitors, apoptotic modulators, NSAIDs, hormone therapy, immunomodulators and HSP inhibitors. A panel of 460 compounds was tested in five concentrations covering a 10.000-fold drug-relevant concentration range in 384-well format. Cells were seeded to pre-drugged plates, followed by cell viability measurements (CellTiter-Glo) after 72 hours. Maximal and minimal responses to drugs were analyzed, the EC50 values were calculated and Drug Sensitivity Score (DSS) was calculated for each drug as a measure of reduced viability. A selective Drug Sensitivity Score (sDSS) was calculated to identify the selective drug response pattern of each three cancer cell lines.

As expected, the results indicate that LNCaP and VCaP cells in general were more sensitive to drugs of different categories than PC-3 cells. According to DSS analysis, all three cell lines showed sensitivity to conventional chemotherapy and kinase inhibitors. However, PC-3 cells were more sensitive to kinase inhibitors than conventional chemotherapy. Determining sDSS revealed specific sensitivities of each cell line. LNCaP cells were sensitive to kinase inhibitors, such as mTOR and AKT inhibitors. Also VCaP cells showed selective sensitivity to kinase inhibitors, especially Aurora kinase and IGF1R inhibitors. In addition to kinase inhibitors, VCaP cells were selectively sensitive to HDAC inhibitors. Furthermore, PC-3 cells were sensitive to e.g. CDK inhibitors.

We conclude that the cell-based compound screening combined with DSS and sDSS analysis provides a possibility to profile cellular responses to an extensive collection of anti-cancer compounds enabling repurposing of existing drugs to new indications, identification of vulnerabilities in different types of cancer cells and functional investigation of cellular pathways behind drug sensitivity or resistance.

Citation Format: Jenni Mäki-Jouppila, Jenni Bernoulli, Johanna Tuomela, Mari I. Suominen, Jussi M. Halleen, Sanna Timonen, Elina Huovari, Katja Suomi, Swapnil Potdar, Päivi Östling, Jani Saarela, Katja M. Fagerlund. Selective drug sensitivity score (DSS) for indolent and aggressive prostate cancer cell lines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4207. doi:10.1158/1538-7445.AM2017-4207

Abstract 5732: PI3K/Akt activity regulates androgen receptor expression and predicts poor clinical outcome in non-metastatic hormone-naïve prostate cancer

Activation of PI3K/Akt pathway is associated with adverse outcome and aggressive disease in many cancers. In prostate cancer (PCa), the activity of this pathway has been shown to promote disease progression and metastasis. However, it is still controversial how PI3K/Akt regulates androgen receptor (AR), a central signaling molecule in prostate pathophysiology, and whether it has an active role in hormone naïve non-metastatic PCa. Here, we show using immunohistochemistry (IHC) and advanced quantitative multiplexed IHC that the expression of phosphorylated-Akt(S473) and AR are highly correlated in clinical PCa, even at the cellular level. Furthermore, we found that high expression of p-Akt(S473) predicts poor clinical outcome in two independent hormone-naïve non-metastatic PCa cohorts. To study whether PI3K/Akt regulates AR expression, we performed an in vitro drug screen with 32 PI3K/Akt/mTOR inhibitors in PC346C, an AR expressing cell line derived from a hormone-naïve primary tumor of prostate. We observed a strong correlation between p-Akt(S473) and AR also in vitro in individual cells independent of the inhibitor used. Although both PI3K and Akt specific inhibition reduced cell viability, the response in nuclear expression of AR was highly dependent on the target of inhibition: Akt specific inhibition reduced AR nuclear expression and resulted in large, spindle-shaped cells, whereas PI3K specific inhibition increased AR nuclear expression and resulted in smaller, round-shaped cells. These data suggest that PI3K and Akt have different roles in sustaining AR activity in PCa as perturbations of the two components leads to differential responses in terms of AR nuclear expression and cell morphology. In conclusion, activated Akt associates with AR expression and predicts poor clinical outcome in hormone-naïve non-metastatic PCa. Furthermore, the differing roles of PI3K and Akt in AR regulation warrants for further studies as it may have implications in the design of PCa therapy targeting PI3K/Akt, especially when the inhibitors are administered in combination with anti-androgens.

Citation Format: Sami Blom, Petra Mäki-Teeri, Andrew Erickson, Lassi Paavolainen, Tuomas Mirtti, Antti Rannikko, Swapnil Potdar, Päivi Östling, Wytske van Weerden, Olli Kallioniemi, Teijo Pellinen. PI3K/Akt activity regulates androgen receptor expression and predicts poor clinical outcome in non-metastatic hormone-naïve prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5732. doi:10.1158/1538-7445.AM2017-5732

Relationship of locus of control with plaque and gingival status before and after oral health education in a group of college students - an experimental study

OBJECTIVES

In health psychology, several models are being constructed to understand human behaviour. Multidimensional health locus of control (MHLC) is one among them. We sought to know the relationship of MHLC with dental plaque and gingival status before and after oral health education programme among 286 college students, aged 18-21 years in Davangere city.

METHODS

Multidimensional health locus of control questionnaire consisting of questions measuring internal health locus of control (IHLC), powerful others health locus of control (PHLC) and chance health locus of control (CHLC) was administered to students. Dental plaque and gingival health status were recorded using Plaque Index (PLI) and Gingival Index (GI), 1967. Oral health education was provided using power point presentation after the baseline oral examination. After 10 weeks of intervention, the students were given the same proforma followed by the assessment of plaque and gingival status.

RESULTS

A negative correlation was observed between PHLC and IHLC with PLI and GI and positive correlation of CHLC with PLI and GI at a level of P < 0.01. The difference between 'pre-test' and 'post-test' mean PLI scores, GI scores, PHLC was found to be statistically significant at a level of P < 0.05.

CONCLUSION

Oral health education was found to be effective and this could change the behaviour of individuals.

Use of vaginal pH in diagnosis of infections and its association with reproductive manifestations

Increase in vaginal secretion pH is an indicator of bacterial vaginosis (BV), but is yet to be in use as a diagnostic tool by clinicians. Similarly, no reports are available on the effect of cervical chlamydia infection and different reproductive manifestations on vaginal secretion pH. This study evaluated the use of vaginal pH for screening of BV, the effect of Chlamydia trachomatis (C. trachomatis) infection, and different reproductive manifestations on vaginal pH of women attending the gynecology outpatient department of a general hospital. Vaginal pH was recorded while diagnosing infections in 358 women, among which 45 were with repeated spontaneous abortion, 79 with infertility, 185 had sign and symptoms of lower genital tract infection, and 49 had no history or symptom of any complications or infections. Normal vaginal pH, BV, and C. trachomatis infection were observed in 72.6, 21.5, and 10.1% of women, respectively. BV and C. trachomatis were observed in 78.6 and 4.1% of women, respectively, with high vaginal pH; 12.3% of women with normal vaginal pH had C. trachomatis infection. C. trachomatis infection or different reproductive manifestations do not lead to change in vaginal pH but high vaginal pH correlated with BV and should be used as a simple tool for its diagnosis.

Kidney injury molecule-1 expression in transplant biopsies is a sensitive measure of cell injury

Kidney injury molecule-1 (KIM-1) is a specific histological biomarker for diagnosing early tubular injury on renal biopsies. In this study, KIM-1 expression was quantitated in renal transplant biopsies by immunohistochemistry and correlated with renal function. None of the 25 protocol biopsies showed detectable tubular injury on histologic examination, yet 28% had focal positive KIM-1 expression. Proximal tubule KIM-1 expression was present in all biopsies from patients with histological changes showing acute tubular damage and deterioration of kidney function. In this group, higher KIM-1 staining predicted a better outcome with improved blood urea nitrogen (BUN), serum creatinine, and estimated glomerular filtration rate (eGFR) over an ensuing 18 months. KIM-1 was expressed focally in affected tubules in 92% of kidney biopsies from patients with acute cellular rejection. By contrast, there was little positive staining for Ki-67, a cell proliferation marker, in any of the groups. KIM-1 expression significantly correlated with serum creatinine and BUN, and inversely with the eGFR on the biopsy day. Our study shows that KIM-1 staining sensitively and specifically identified proximal tubular injury and correlated with the degree of renal dysfunction. KIM-1 expression is more sensitive than histology for detecting early tubular injury, and its level of expression in transplant biopsies may indicate the potential for recovery of kidney function.

Dissociation of depletional induction and posttransplant lymphoproliferative disease in kidney recipients treated with alemtuzumab

Transplant patients are at the risk for posttransplant lymphoproliferative disease (PTLD), a virally-driven malignancy. Induction with the depleting antibody preparations Thymoglobulin and OKT3 is associated with PTLD suggesting that the T-cell depletion increases PTLD risk. We therefore studied 59 560 kidney recipients from the Organ Procurement and Transplantation Network/United Network for Organ Sharing (OPTN/UNOS) database for a relationship between induction agent use and PTLD. Two agents with comparable T-cell depletional effects, alemtuzumab and Thymoglobulin, were compared to nondepletional induction agents or no induction. The overall incidence of PTLD was 0.46% and differed significantly by induction strategy (p < 0.01): without induction (0.43%), basiliximab (0.38%), daclizumab (0.33%), Thymoglobulin (0.67%) and alemtuzumab (0.37%). Thymoglobulin was associated with significantly increased PTLD risk (p = 0.0025), but alemtuzumab (p = 0.74), basiliximab (p = 0.33) and daclizumab, which trended toward a protective effect (p = 0.06), were not. Alemtuzumab and Thymoglobulin treated patients did not differ in any established parameter affecting PTLD risk although alemtuzumab is known to have a more pronounced B-cell depleting effect. Interestingly, maintenance therapy with an mTOR inhibitor was strongly associated with PTLD (0.71%, p < 0.0001). Thus, depletional induction is not an independent risk factor for PTLD. Rather, maintenance drug selection or perhaps the balance between B- and T-cell depletion may be more relevant determinants of PTLD risk.

Percutaneous Ultrasound-Guided Pancreas Allograft Biopsy: A Single-Center Experience

Percutaneous ultrasound-guided pancreas allograft biopsy is the preferred technique for evaluating pancreas allograft rejection. Experience from large centers has shown it to be safe and effective. We report our experience with 120 percutaneous allograft biopsies performed at a single center. Biopsy tissue was obtained in 54 patients. Thirty-three patients received simultaneous pancreas and kidney transplants, 14 received isolated pancreas transplants, and 7 received a pancreas transplant after kidney transplantation. Biopsies were performed by pancreas transplantation surgeons with the assistance of radiologists under ultrasound guidance using an Acuson XP 128/10 ultrasound machine. One hundred twenty allograft biopsies were performed in 54 patients. Twenty-seven (50%) patients underwent multiple biopsies. In 102 (85%) biopsies the specimens were adequate for examination. Eighteen (15%) biopsy samples had no pancreatic tissue and showed surrounding fat and small bowel. 1 (1.8%) patient bleeding developed that required transfusion of 3 units of packed red blood cells, but no surgical intervention was necessary. One (1.8%) patient had a pancreatic fistula, which healed with nonoperative management. Biochemical evidence of pancreatitis was noted in 5 (9.2%) patients, but none of these patients had clinical signs of pancreatitis. Percutaneous ultrasound-guided pancreas allograft biopsy is a safe procedure with a low complication rate and a high tissue yield for histopathologic examination.