Drug Response Pattern Research Articles

Micro-organospheres: An automated patient-derived model platform for rapid drug screening of breast cancer.

e12628 Background: Patient-derived breast cancer (BC) organoids are valuable preclinical models to study patient drug responses, demonstrating good correlations with patients’ clinical outcomes. However, establishment and expansion of such organoids from patient tumors for drug screening is currently a time-consuming and labor-intensive process. A more rapid and high-throughput method will enable broader utility in diagnostics and drug development. Methods: An automated, rapid and scalable microfluidic platform was used to process and develop BC micro-organospheres. Drug sensitivities studies on BC micro-organospheres were performed on day 3 and day 6 using 10-FDA approved drugs, including palbociclib, adriamycin, 5-FU, gemcitabine, methotrexate, everolimus, paclitaxel, docetaxel, ixabepilone, and vinblastine. The responses of micro-organospheres and organoids to the drugs were assessed by CellTiter 3D Glo assay on day 6 after the drug treatment. The growth and establishment of the micro-organospheres by imaging. The drug sensitivity and resistance of the micro-organospheres were analyzed by calculating the percentage cell viability and normalized growth rate inhibition (GRI) and compared to organoids. Results: We successfully established micro-organospheres from eight patient-derived BC organoids with a 100% success rate. The micro-organospheres preserved similar cell morphologies to the bulk organoids. 7/8 micro-organosphere models had similar drug response patterns to organoids between day 3 and day 6 as evident by the GRI heatmap. Specifically, we treated matching micro-organospheres and conventional organoids from two patients with 10 frontline BC chemotherapy drugs, and both showed similar response patterns with GRI heatmap. For the other 6 patient-derived models, the responses of micro-organospheres to docetaxel and everolimus also matched the historical drug responses of in bulk organoid culture with similar GRI heatmap. Conclusions: We have now shown the feasibility of establishing micro-organospheres as a rapid, scalable, and cost-effective platform to study patient-derived BC drug response. This technology has the potential to be used for both diagnostics to guide patient treatment and as a screening platform for new BC drug discovery.

Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer

BackgroundGastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics.MethodsWe integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation.ResultsWe comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value.ConclusionsThese findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.

Abstract PO-064: Predicting tumor sensitivity to chemotherapeutic drugs using molecular, cellular and computational methods

Abstract Oral squamous cell carcinoma (OSCC) patients respond poorly to chemotherapy. Predicting anticancer drug response before the treatment of OSCC is a significant challenge. Recently, gene expression profiling and cell-based assays reveals drug sensitivity that could be implemented for drug selection for patients. We analyzed the expression of 11 drug response-related genes in 31 OSCC biopsies collected prior to any treatment, using custom designed PCR array. Further, we investigated drug response pattern of selected anticancer drugs using BH3 apoptotic profiling in the primary cells isolated from OSCC tissues. Then, we correlated the results of drug-response gene expression pattern with apoptotic priming to predict tumor response to chemotherapy. The best performing drug (BPD) and response differences (RD) between the drugs were identified using statistical methods to select the best choice of drug in a personalized manner. Based on the Pierson correlation, we classified OSCC tumors as sensitive, moderately responsive or resistant to chemotherapy. We observed that 2 patients were resistant, 17 patients were moderately responsive and 12 patients were sensitive to chemotherapeutic drugs. We found that up-regulation of genes linked to drug resistance facilitates survival of tumor samples, which was revealed by the percentage of apoptotic priming. Moreover, we found that paclitaxel induced 40-45% apoptotic priming compared to other drugs. Average response difference (RD) analysis showed that 80% of tumors responded well to paclitaxel as compared to other drugs studied that were less effective. The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. We developed drug efficacy prediction models using multiple tumor features by employing the statistical methods like multi linear regression (MLR), modified MLR-weighted least square (MLR-WLS) and enhanced MLR-WLS. All the three developed drug efficacy prediction models were then validated using the data of actual OSCC samples (train-test ratio 31: 31) and actual Vs hypothetical samples (train-test ratio 31: 30). The selected best statistical model i.e. enhanced MLR-WLS has then been cross-validated (CV) using 341 theoretical tumor data. Finally, the performances of the models were assessed by the level of learning confidence, significance, accuracy and error terms. We observed that the enhanced MLR-WLS model was the best fit to predict anticancer drug efficacy which may have translational applications. Therefore, drug-response gene expression pattern with the status of apoptotic priming in the tumor cells may be translated to the clinic and which will make impact in precision medicine. Citation Format: N. Rajendra Prasad, Kanimozhi Govindaswamy, G.R. Brindha. Predicting tumor sensitivity to chemotherapeutic drugs using molecular, cellular and computational methods [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-064.

The Importance of Analytical Chemistry in Therapeutic Drug Monitoring for Personalized Medicine

Personalized therapy (PM) has the potential to adapt treatment with the best response and highest safety to provide better patient care. Key data is drug concentration of biological materials such as plasma and serum.Individual drug therapy means, choice of a drug and its dose regime should fit every individual specifically. Thus efficacy of a drug treatment would improve significantly. When developing an analytical method for (Therapeutic drug monitoring) TDM, it is important to choose a clinically relevant calibration range. This quantitation range should be built around the proposed target concentration, covering majority of samples as seen in the clinic (Ciocan-Cartita et al. 2019).Inter-individual variability in Pharmacokinetic variables may affect the blood concentration of drug so TDM approaches could solve the dosing problem.To achieve individual drug therapy with a reasonably predictive outcome, one must further account for different patterns of drug response among geographically and ethnically distinct populations. Keywords: LC-MS/MS, Therapeutic Drug Monitoring, Lenalidomide, Anastrozole DOI: 10.7176/CMR/12-7-05 Publication date: September 30 th 2020

Systematic mapping of cancer cell target dependencies using high-throughput drug screening in triple-negative breast cancer

While high-throughput drug screening offers possibilities to profile phenotypic responses of hundreds of compounds, elucidation of the cell context-specific mechanisms of drug action requires additional analyses. To that end, we developed a computational target deconvolution pipeline that identifies the key target dependencies based on collective drug response patterns in each cell line separately. The pipeline combines quantitative drug-cell line responses with drug-target interaction networks among both intended on- and potent off-targets to identify pharmaceutically actionable and selective therapeutic targets. To demonstrate its performance, the target deconvolution pipeline was applied to 310 small molecules tested on 20 genetically and phenotypically heterogeneous triple-negative breast cancer (TNBC) cell lines to identify cell line-specific target mechanisms in terms of cytotoxic and cytostatic drug target vulnerabilities. The functional essentiality of each protein target was quantified with a target addiction score (TAS), as a measure of dependency of the cell line on the therapeutic target. The target dependency profiling was shown to capture inhibitory information that is complementary to that obtained from the structure or sensitivity of the drugs. Comparison of the TAS profiles and gene essentiality scores from CRISPR-Cas9 knockout screens revealed that certain proteins with low gene essentiality showed high target addictions, suggesting that they might be functioning as protein groups, and therefore be resistant to single gene knock-out. The comparative analysis discovered protein groups of potential multi-target synthetic lethal interactions, for instance, among histone deacetylases (HDACs). Our integrated approach also recovered a number of well-established TNBC cell line-specific drivers and known TNBC therapeutic targets, such as HDACs and cyclin-dependent kinases (CDKs). The present work provides novel insights into druggable vulnerabilities for TNBC, and opportunities to identify multi-target synthetic lethal interactions for further studies.

Modulating cancer stemness provides luminal a breast cancer cells with HER2 positive-like features.

Breast cancers can be classified into luminal A, luminal B, HER2 positive and triple-negative subtypes, each with a distinct therapeutic response. Tumor stemness drives cancer malignancy that challenges cancer control. Understanding the revolutionary relationships driven by tumor stemness among breast cancer subtypes is fundamental to identifying feasible therapeutic modalities for each breast cancer subtype.Utilizing the endogenous tRNA-processing system, we established a multiplexing CRISPR/dCas9 system in breast cancer cells, and applied it to a four-gene panel controlling cell potency, i.e., OCT4, KLF, MYC, SOX2. The stable cell strain, OKMS#1 was obtained through concomitantly over-expressing these genes in luminal A breast cancer cells. OKMS#1 cells showed increased invasion, proliferation and cancer stemness, shared similar drug response pattern with HER2 positive cells, and exhibited altered MAPK and enhanced NFkB signaling. This study contributes in providing an efficient multiplexing CRISPR/dCas9 system that enriches our genetic modulation tool box, and suggests that HER2 positive cells are potential progenitors of luminal A cells and that these two breast cancer subtypes may share similar treatment strategies once rewired between the two states. Our results also implicate that triple negative breast cancer cells, though sharing similar cancer stemness with HER2 positive cells, represent a distinct type of disease and require unique treatment solutions.

Deep learning of pharmacogenomics resources: moving towards precision oncology.

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

Multi-Parametric Single Cell Profiling Defines Distinct Drug Responses in Healthy Hematological Cell Lineages That Are Retained in Corresponding Malignant Cell Types

▪BackgroundDuring hematopoiesis, multipotent stem cells and pluripotent precursors undergo a complex differentiation program to generate a diverse set of blood cell types with wide-ranging phenotypes and functions that may shape their initial response to therapies. Therefore, innate drug sensitivity in healthy cells provides insight into cell specific responses, aids identification of lineage specific anticancer therapies and reveals off-target effects. To characterize the diversity in drug response and intracellular signal transduction patterns in the major hematopoietic cell types, we simultaneously assessed their sensitivity to 71 small molecules utilizing a multi-parametric flow cytometry assay and mapped onto their proteomic and basal signaling profiles.MethodsHuman BM aspirates and PB samples were collected after written informed consent in compliance with the Declaration of Helsinki. Mononuclear cells from 331 BM aspirates or PB samples were isolated by density gradient centrifugation (Ficoll-Paque Premium; GE Healthcare). Cytometric analyses of drug responses were performed assay using a high throughput flow cytometer (iQue®Screener PLUS) in both 384 well (in 3 healthy samples, 71 drugs) and 96 well plate formats (in 26 samples, 6 drugs) to study drug effects in 5 concentrations (1-10,000 nM). Effect of bortezomib, clofarabine, dexamethasone, omipalisib, venetoclax and navitoclax were assessed on 10 cell populations, namely hematopoietic stem cells (HSCs) (CD34+CD38-), common progenitor cells (CPCs) (CD34+CD38+), monocytes (CD14+), B cells (CD45+CD19+), cytotoxic T cells (CD45+CD3+CD8+), T helper cells (CD45+CD3+CD4+), NK-T cells (CD45+CD3+CD56+), NK cells (CD45+CD56+CD3-), plasma cells (CD138+CD38) and granulocytes (CD45+, SSC++). Mass cytometry (CyTOF) was applied to investigate basal signaling activity of 9 proteins involved in MAPK, JAK-STAT, NF-κB and PI3K-mTOR signaling. Protein abundances in CD3+, CD14+ and CD19+ cells were investigated in six samples using mass spectrometry. To evaluate whether the distinct drug sensitivities detected in the cells of origin could be exploited in the malignant cell counterpart cell, ex vivo drug responses detected in healthy cells were compared to a cohort of 281 primary samples derived from multiple hematological malignancies.ResultsUnsupervised hierarchical clustering of drug response to 71 small molecules identified distinct drug responses in healthy cell subsets based on their cellular lineage. Compared to other cell types, B and NK cells were more sensitive to dexamethasone, venetoclax and midostaurin. Monocytes were more sensitive to trametinib which did not correlate to resting ERK1/2 phosphorylation. Venetoclax exhibited dose dependent cell selectivity to lymphocytes that inversely correlated to STAT3 phosphorylation. Elevated expression of catalase (CAT) and calprotectin (S100A8/S100A9) in monocytes corresponded to their intrinsic resistance to dexamethasone and venetoclax. Comparison of drug responses for six aforementioned drugs in healthy and neoplastic cells across 281 patient samples showed that healthy cell responses are predictive of the corresponding malignant cell response.ConclusionApplying a high content, multi-parametric single-cell assay, we could assess the diversity in drug effects on 10 different cell populations in individual donor samples. Our results demonstrate that cell subtypes are drastically different from each other with respect to protein abundance, signaling profiles and drug-response patterns against a diverse collection of anticancer drugs. Importantly, cell subset specific sensitivity and resistance mechanisms were clearly reflected in their malignant state. Taken together, understanding drug sensitivity in the healthy cell-of-origin provides opportunities to obtain a new level of therapy precision and avoid off-target toxicity. DisclosuresMustjoki:Ariad: Research Funding; Pfizer: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Celgene: Honoraria; Bristol-Myers Squibb: Honoraria, Research Funding. Wennerberg:Novartis: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Gjertsen:Seattle Genetics: Consultancy; BerGenBio: Consultancy; Kinn Therapeutics: Membership on an entity's Board of Directors or advisory committees; Alden Cancer Therapy 2: Equity Ownership; Boehringer Ingelheim: Research Funding; Alden Cancer Therapy 2: Membership on an entity's Board of Directors or advisory committees; Alden Cancer Therapy 2: Patents & Royalties: Alden Cancer Therapy II patent application in relation to CryoIT trial.; Kinn Therapeutics: Equity Ownership; Novartis: Consultancy. Heckman:Novartis: Research Funding; Orion Pharma: Research Funding; Celgene: Research Funding.

The Influence of the Bone Marrow Niche on Drug Response Phenotypes of Blood Cancers

Background: Signals provided by the microenvironment can modify and circumvent pathway activities that are therapeutically targeted by drugs. However, a systems-level understanding of how the microenvironment and the genetic and molecular alterations of the tumor interact with each other and contribute to drug resistance is lacking.Methods: To address this unmet need, we established an automated microscopy-based phenotyping platform that uses co-culture conditions mimicking the bone marrow environment. We cultured primary tumor cells from more than 100 leukemia patients (CLL, AML, MCL, T-PLL, HCL) with and without bone marrow stroma cell support in DMEM and 10% human serum and treated each condition with 57 drugs in 3 concentrations. After 72h of incubation, 22 000 images per patient were acquired and processed by our custom made image analysis pipeline. Our set-up allows us to increase sensitivity far beyond simple viability testing, as it reads out additional cell type specific features such as cell morphology, autophagy and cell-cell interactions.Results: Quality assessment revealed that in contrast to mono-culture conditions, assay plate edge effects can be avoided under stable stroma cell co-culture conditions. Correlation of replicated patient samples were comparable between mono- and co-cultures (R2>0.75).In the absence of their native microenvironment, primary leukemia cells undergo spontaneous apoptosis ex-vivo. Viability at culture start was always >90% and dropped to a median of 51% (viability range: 17%-90%) after 72h in mono-cultures. Among CLL samples spontaneous apoptosis was not dependent on either IGHV mutation status or any major cytogenetic risk group. Bone marrow stroma cell co-culture conditions protected tumor cells from spontaneous apoptosis (p=8.2e-6, paired t-test). Patient samples with a high degree of spontaneous apoptosis benefited most from co-culture conditions (p=7.2e-10, Pearson correlation).To model interactions of stroma cell conditions and drug-induced apoptosis we established the following linear model: Viability ~ drug-effect + culture-model + drug-effect:culture-model. While activity of some drugs was significantly altered under co-culture conditions, we could also identify drugs with similar activity in mono- and co-cultures. For instance, the activity of common chemotherapeutics (fludarabine: p=0.002 at 0.6µM, cytarabine: p=0.001 at 1.5µM, ANOVA) or bromodomain inhibitors (I-BET-762: p=5.9e-5 at 4.5µM, JQ1: p=1.5e-8 at 1.5µM, ANOVA) was significantly reduced under co-culture conditions. In contrast, PI3K inhibitors idelalisib and duvelisib had a similar activity in mono-culture and stroma co-culture conditions and might represent a starting point to overcome stroma cell mediated drug resistance. In CLL, we identified IGHV mutation status and trisomy 12 as important determinants of response to kinase inhibitors. We confirmed these findings in stroma cell co-cultures, e.g. a better activity of B-cell receptor inhibitors in trisomy 12 and IGHV unmutated CLL. A systematic comparison of ex-vivo drug response pattern in mono- and co-cultures across 171 drug conditions will be presented.Conclusion: Our results suggest that high throughput co-culture drug testing can be robustly performed and provide an unprecedented understanding of how the stroma cell microenvironment and the genetic make-up of tumor cells contribute to drug resistance and sensitivity.Figure: Over 2 million microscopy images were acquired and analysed to assess drug resistance and sensitivity in a co-culture model of primary leukemia and bone marrow stroma cells. blue= Hoechst33342, green=Calcein AM, red=lysosomal dye NIR [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Predicting Tumor Sensitivity to Chemotherapeutic Drugs in Oral Squamous Cell Carcinoma Patients

Oral Squamous Cell Carcinoma (OSCC) patients respond poorly to chemotherapy. We analyzed the expression of 11 drug response-related genes in 31 OSCC biopsies, collected prior to any treatment, using custom-designed PCR array. Further, we investigated the drug response pattern of selected anticancer drugs by BH3 (Bcl2 Homology-3) profiling in the primary cells isolated from OSCC tissues. Then, we correlated the results of drug-response gene expression pattern with apoptotic priming to predict tumor response to chemotherapy. The best performing drug (BPD) and response differences (RD) between the drugs were identified using statistical methods to select the best choice of drug in a personalized manner. Based on the correlation, we classified OSCC tumors as sensitive (13 tumors), moderately responsive (16 tumors) or resistant (2 tumors) to chemotherapy. We found that up-regulation of genes linked with drug resistance facilitates survival of tumor samples, which was revealed by the percentage of apoptotic priming. Moreover, we found that paclitaxel-induced 40–45% apoptotic priming compared to other drugs. Average response difference (RD) analysis showed that 80% of tumors responded well to paclitaxel as compared to other drugs studied. Therefore, gene expression analysis with BH3 profiling reveals drug sensitivity that could be translated for drug selection before treatment.

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species. We identified drug response‐related ceRNA (DRCEs) by combining the sequence and expression data of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), and the survival data of cancer patients treated with drugs. We constructed a patient–drug two‐layer integrated network and used a linear weighting method to predict individual drug responses. DRCEs were found to be significantly enriched in known cancer and drug‐associated data resources, involved in biological processes known to mediate drug responses, and correlated to drug activity in cancer cell lines. The dysregulation of DRCE expression influenced drug response‐associated functions and pathways, suggesting DRCEs as potential therapeutic targets affecting drug responses. A further case study in breast invasive carcinoma (BRCA) found that DRCE expression was consistent with the drug response pattern and the aberrant expression of the two NEAT1‐related DRCEs may lead to poor response to tamoxifen therapy for patients with TP53 mutations. In summary, this study provides a framework for ceRNA‐based evaluation of clinical drug responses across multiple cancer types. Understanding the underlying molecular mechanisms of drug responses will allow improved response to chemotherapy and outcomes of cancer treatment.

Abstract 5302: Phenotypic heterogeneity of patient-derived tumor cells visualized by unsupervised analysis in cell-based personalized drug testing

Abstract Patient-derived tumor cells are highly variable in their morphology and molecular phenotypes and after exposure to a library of drugs their heterogeneity further increases as the cells respond to the perturbations. The complete phenotypic diversity of the tumor cells and drug response patterns may remain undetectable if only few simple readouts, such as individual biomarkers in pre-defined tumor cells, are considered. Therefore, novel techniques that capture and visualize the entire spectrum of heterogeneous phenotypic changes in ex-vivo patient-derived tumor cells are needed. Here, we applied unsupervised machine learning for comprehensive visualization of cell phenotypes with the aim to improve the analysis of image-based drug screening results. We performed high content screening of patient-derived renal clear cell carcinoma cells, exposed to 36 drugs in nine concentrations applied in 384-well format. The nuclei were stained with Hoechst and cell proliferation using Ki67, and images were acquired with a high-content imaging microscope (PE Operetta, 20x). To quantitatively describe the cell phenotypes, we used CellProfiler for cell segmentation and feature extraction and then applied a large-scale data embedding method (LargeVis) to cluster the cells based on their phenotypes. Using the clustering, we created an image-based phenotype-map to describe the phenotypic landscape. Overlaying the drug treatments on different concentrations on top of the map allowed for visualization of unique phenotypic fingerprints for each drug at the single cell resolution. Visual inspection of the phenotype-map displayed visually similar cells clustering together. Likewise, a comparison with an expert trained supervised classification indicated consistency with the phenotype-map. As expected, Ki67-positive and -negative cells as well as dying cells formed clusters. Interestingly, the phenotype-map identified sub-clusters within the Ki67-positive and -negative cells. Furthermore, the map also allowed us to adjust for classification errors occurring in the supervised classification. In conclusion, we find that phenotypic signatures provide an intuitive way of linking and comparing phenotypes of distinct populations of drug-treated cells at a single-cell level. This will reveal additional systematic information of the drug responses, complementing traditional readout, such as cell viability, by highlighting the phenotypic changes over concentrations. Citation Format: Riku Turkki, Lassi Paavolainen, Piia Mikkonen, Päivi Östling, Peter Horvath, Vilja Pietiäinen, Olli Kallioniemi. Phenotypic heterogeneity of patient-derived tumor cells visualized by unsupervised analysis in cell-based personalized drug testing [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 5302.

Abstract 4099: Novel patient-derived 3D (PD3D) cell models and matched patient-derived xenografts (PDX) from peritoneal metastasis of colorectal cancer for drug testing and biomarker analysis

Abstract For patients with colorectal cancer (CRC) peritoneal metastases (PM) represent a terminal tumor stage with limited therapeutic options. To increase therapeutic efficacy and overall survival, availability of appropriate patient-derived 3D cell culture (PD3D) and patient-derived xenografts (PDX) models of PM could help improving the predictability of drug response for a specific tumor, but also foster the identification of novel biomarkers and therapeutic targets for CRC-PM patients. In this context, we generated the first scaffold-based PD3D and matching PDX models of CRC-PM as platform to test for chemotherapy response and to identify novel biomarkers. For model establishment, surgical specimens were processed either for PD3D cell culture or transplanted subcutaneously (s.c.) onto immunocompromized NOD scid gamma (NSG) mice. For 3D cell culture models, tissue was dissected, digested and filtered before embedding into a scaffold matrix. For PDX models engrafted tumors were transferred to NMRI nu/nu mice for further passaging. They were characterized by histopathology, immunohistochemistry and gene expression analyses using real-time RT-PCR. Chemosensitivity of both sibling models was evaluated on a panel of conventional chemotherapeutic and of targeted drugs. The same panel of drugs was used in 15 matched PD3D/PDX models and revealed individual response patterns both in PD3D and PDX. Most interestingly, different drug response pattern was observed in models derived from tumor tissue of the omentum vs. tissue from the peritoneum of the same patient. Our results demonstrate, that matched models maintain basic characteristics such as the morphology of the patient tumor in early passages, reflect heterogeneous response rates, and can be used as preclinical platform for translational studies of potential clinical use. Citation Format: Christian RA Regenbrecht, Guido Gambara, Eva Pachmayr, Alessandra Silvestri, Mathias Dahlmann, Bernadette Brzezicha, Britta Buettner, Beate Rau, Ulrich Keilholz, Urike Stein, Wolfgang Walther. Novel patient-derived 3D (PD3D) cell models and matched patient-derived xenografts (PDX) from peritoneal metastasis of colorectal cancer for drug testing and biomarker analysis [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 4099.

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

Abstract 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 2684: Drug sensitivity profiling of BT-474 breast cancer cell line for identification of novel therapies targeting HER2-positive breast cancer

Abstract Developing targeted therapy for breast cancer expressing hormone receptors such as estrogen receptor (ER) and progesterone receptor (PR) as well as human epidermal growth factor receptor 2 (HER2) is essential. However, breast cancer has high susceptibility to spread despite the targeted therapies and metastases often turn resistant to the used treatment. Therefore, new therapies are needed especially at the stage when cancer becomes resistant to hormonal therapy. The aim of the study was to investigate drug sensitivity of BT-474 breast cancer cell line and to recognize possible vulnerabilities especially in HER2+ breast cancer. BT-474 cell line (ER+, PR+, HER2+) is originally derived from human solid, invasive ductal carcinoma of the breast. Drug sensitivity of the cell line was assessed by screening of a large compound library consisting of many clinically available and emerging anti-cancer drugs including e.g. conventional chemotherapy, metabolic modifiers, kinesin inhibitors, apoptotic modulators, immunomodulators and kinase inhibitors such as HER2, pan-HER2 and EGFR-HER2 inhibitors. A panel of 525 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 DSS (sDSS) was calculated to identify the selective drug response pattern. DSS analysis of BT-474 cell line showed sensitivity to conventional chemotherapy including paclitaxel and docetaxel and to kinase inhibitors such as EGFR and HER2 inhibitors neratinib, afatinib, mubritinib, poziotinib, dacomitinib and lapatinib with nanomolar EC50 values. Moreover, BT-474 cells showed sensitivity to PI3K and mTOR inhibitors, such as copanlisib and omipalisib, and to HSP90 and HDAC inhibitors, such as tanespimycin and abexinostat. Screening of large compound libraries combined with DSS and sDSS analysis enables drug sensitivity profiling of BT-474 breast cancer cells for discovery of novel therapies against HER+ breast cancer. Furthermore, drug sensitivity profiling enables repurposing of existing drugs to new indications and identification of vulnerabilities in different types of cancer cells. Citation Format: Jenni H. Mäki-Jouppila, Tiina E. Kähkönen, Mari I. Suominen, Jussi M. Halleen, Jenni Bernoulli, Jani Saarela, Katja Fagerlund. Drug sensitivity profiling of BT-474 breast cancer cell line for identification of novel therapies targeting HER2-positive breast cancer [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 2684.

In Vivo-Like Cell-Cycle Phase Distribution of Cancer Cells in Gelfoam® Histoculture Observed in Real Time by FUCCI Imaging.

FUCCI color codes cells as they express different color fluorescent proteins as they go through phases of the cell cycle. The cell cycle phase distribution of cancer cells in Gelfoam® histoculture was similar to in vivo tumors, whereby only the surface cells proliferate and interior cells are quiescent in G0/G1. In contrast, in 2D cancer-cell culture, most of the cells are always cycling. The cancer cells responded similarly to toxic chemotherapy in Gelfoam® culture as in vivo; as such, therapy is cell cycle dependent. In 2D culture, cancer cells were much more chemosensitive. These results indicate why the drug response pattern of tumors in Gelfoam® histoculture reflects what is observed in vivo.

Abstract A151: Drug sensitivity profiling of BT-474 breast cancer cell line for identification of novel therapies against HER2-positive breast cancer

Abstract Introduction: Targeted therapy for breast cancer expressing hormone receptors such as estrogen receptor (ER) and progesterone receptor (PR), as well as human epidermal growth factor receptor (HER2), is essential. In spite of current targeted therapies, breast cancer has high susceptibility to spread and metastases often turn resistant to the used treatment. Therefore, new therapies are needed, especially when cancer becomes resistant to hormonal therapy. The aim of the study was to investigate drug sensitivity and resistance of BT-474 breast cancer cell line and to recognize possible vulnerabilities, especially in hormone therapy resistant HER2+ breast cancer. Materials and Methods: BT-474 cell line (ER+, PR+, HER2+) is originally derived from human solid, invasive ductal carcinoma of the breast. The drug sensitivity of the cell line was assessed by applying a large panel of drugs covering cancer chemotherapeutics and clinically available and emerging drugs including, e.g., conventional chemotherapy and kinase inhibitors such as HER2, pan-HER2, and EGFR-HER2 inhibitors. Altogether a panel of 525 compounds was tested in five concentrations covering a 10,000-fold drug-relevant concentration range in 384-well format. Cells were seeded to predrugged 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. Results: DSS analysis of BT-474 cell line showed sensitivity to conventional chemotherapy including paclitaxel and docetaxel and to kinase inhibitors such as EGFR inhibitors neratinib and afatinib. In the absence of supplemented estradiol, the cell line was not sensitive to hormonal therapies, including antiestrogens and selective estrogen receptor modulators. However, BT-474 cells were sensitive to HER2, pan-HER2, and EGFR-HER2 inhibitors including mubritinib, poziotinib, dacomitinib, and lapatinib with EC50 range from 2 to 98 nM. The BT-474 cells also showed sensitivity against PI3K and mTOR inhibitors, such as copanlisib and omipalisib. Conclusions: Screening of large compound libraries combined with DSS and sDSS analysis enables drug sensitivity profiling of HER2+ BT-474 breast cancer cells for identification of novel anticancer compounds against breast cancer, especially for the hormonal-resistant stage of the disease. Moreover, the assay enables 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, Tiina Kähkönen, Mari I. Suominen, Katja M. Fagerlund, Jussi M. Halleen, Jani Saarela, Jenni Bernoulli. Drug sensitivity profiling of BT-474 breast cancer cell line for identification of novel therapies against HER2-positive breast cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A151.

Nonlinear mixed effects dose response modeling in high throughput drug screens: application to melanoma cell line analysis.

Cancer cell lines are often used in high throughput drug screens (HTS) to explore the relationship between cell line characteristics and responsiveness to different therapies. Many current analysis methods infer relationships by focusing on one aspect of cell line drug-specific dose-response curves (DRCs), the concentration causing 50% inhibition of a phenotypic endpoint (IC50). Such methods may overlook DRC features and do not simultaneously leverage information about drug response patterns across cell lines, potentially increasing false positive and negative rates in drug response associations. We consider the application of two methods, each rooted in nonlinear mixed effects (NLME) models, that test the relationship relationships between estimated cell line DRCs and factors that might mitigate response. Both methods leverage estimation and testing techniques that consider the simultaneous analysis of different cell lines to draw inferences about any one cell line. One of the methods is designed to provide an omnibus test of the differences between cell line DRCs that is not focused on any one aspect of the DRC (such as the IC50 value). We simulated different settings and compared the different methods on the simulated data. We also compared the proposed methods against traditional IC50-based methods using 40 melanoma cell lines whose transcriptomes, proteomes, and, importantly, BRAF and related mutation profiles were available. Ultimately, we find that the NLME-based methods are more robust, powerful and, for the omnibus test, more flexible, than traditional methods. Their application to the melanoma cell lines reveals insights into factors that may be clinically useful.

A precision medicine approach to haematological malignancies

A precision medicine approach to haematological malignancies

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

Abstract 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