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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 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.

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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

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Most non–small cell lung cancers (NSCLC) contain nontargetable mutations, including KRAS, TP53, or STK11/LKB1 alterations. By coupling ex vivo drug sensitivity profiling with in vivo drug response studies, we aimed to identify drug vulnerabilities for these NSCLC subtypes. Primary adenosquamous carcinoma (ASC) or adenocarcinoma (AC) cultures were established from KrasG12D/+;Lkb1fl/fl (KL) tumors or AC cultures from KrasG12D/+;p53fl/fl (KP) tumors. Although p53-null cells readily propagated as conventional cultures, Lkb1-null cells required conditional reprograming for establishment. Drug response profiling revealed short-term response to MEK inhibition, yet long-term clonogenic assays demonstrated resistance, associated with sustained or adaptive activation of receptor tyrosine kinases (RTK): activation of ERBBs in KL cultures, or FGFR in AC cultures. Furthermore, pan-ERBB inhibition reduced the clonogenicity of KL cultures, which was exacerbated by combinatorial MEK inhibition, whereas combinatorial MEK and FGFR inhibition suppressed clonogenicity of AC cultures. Importantly, in vivo studies confirmed KL-selective sensitivity to pan-ERBB inhibition, which correlated with high ERBB ligand expression and activation of ERBB receptors, implying that ERBB network activity may serve as a predictive biomarker of drug response. Interestingly, in human NSCLCs, phosphorylation of EGFR or ERBB3 was frequently detected in ASCs and squamous cell carcinomas. We conclude that analysis of in situ ERBB signaling networks in conjunction with ex vivo drug response profiling and biochemical dissection of adaptive RTK activities may serve as a valid diagnostic approach to identify tumors sensitive to ERBB network inhibition.

In adult patients, the treatment outcome of acute lymphoblastic leukemia (ALL) remains suboptimal. Here, we used an ex vivo drug testing platform and comprehensive molecular profiling to discover new drug candidates for B-ALL. We analyzed sensitivity of 18 primary B-ALL adult patient samples to 64 drugs in a physiological concentration range. Whole-transcriptome sequencing and publicly available expression data were used to examine gene expression biomarkers for observed drug responses. Apoptotic modulators targeting BCL2 and MDM2 were highly effective. Philadelphia chromosome-negative (Ph-) samples were sensitive to both BCL2/BCL-W/BCL-XL-targeting agent navitoclax and BCL2-selective venetoclax, whereas Ph-positive (Ph+) samples were more sensitive to navitoclax. Expression of BCL2 was downregulated and BCL-W and BCL-XL upregulated in Ph+ ALL compared with Ph- samples, providing elucidation for the observed difference in drug responses. A majority of the samples were sensitive to MDM2 inhibitor idasanutlin. The regulatory protein MDM2 suppresses the function of tumor suppressor p53, leading to impaired apoptosis. In B-ALL, the expression of MDM2 was increased compared with other hematological malignancies. In B-ALL cell lines, a combination of BCL2 and MDM2 inhibitor was synergistic. In summary, antiapoptotic proteins including BCL2 and MDM2 comprise promising targets for future drug studies in B-ALL.

Here, we describe a scalable and automated, high-content microscopy -based mini-immunofluorescence assay (mini-IFA) for serological testing i.e., detection of antibodies. Unlike conventional IFA, which often relies on the use of cells infected with the target pathogen, our assay employs transfected cells expressing individual viral antigens. The assay builds on a custom neural network-based image analysis pipeline for the automated and multiplexed detection of immunoglobulins (IgG, IgA, and IgM) in patient samples. As a proof-of-concept, we employed high-throughput equipment to set up the assay for measuring antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with spike (S), membrane (M), and nucleo (N) proteins, and the receptor-binding domain (R) as the antigens. We compared the automated mini-IFA results from hundreds of patient samples to the visual observations of human experts and to the results obtained with conventional ELISA. The comparisons demonstrated a high correlation to both, suggesting high sensitivity and specificity of the mini-IFA. By testing pre-pandemic samples and those collected from patients with RT-PCR confirmed SARS-CoV-2 infection, we found mini-IFA to be most suitable for IgG and IgA detection. The results demonstrated N and S proteins as the ideal antigens, and the use of these antigens can serve to distinguish between vaccinated and infected individuals. The assay principle described enables detection of antibodies against practically any pathogen, and none of the assay steps require high biosafety level environment. The simultaneous detection of multiple Ig classes allows for distinguishing between recent and past infection.Public abstractThe manuscript describes a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The automated method builds on machine-learning -guided image analysis with SARS-CoV-2 as the model pathogen. The method enables simultaneous measurement of IgM, IgA, and IgG responses against different virus antigens in a high throughput manner. The assay relies on antigens expressed through transfection and allows for differentiation between vaccine-induced and infection-induced antibody responses. The transfection-based antigen expression enables performing the assay at a low biosafety level laboratory and allows fast adaptation of the assay to emerging pathogens. Our results provide proof-of-concept for the approach, demonstrating fast and accurate measurement of antibody responses in a clinical and research set-up.

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

Most non–small cell lung cancers (NSCLC) contain nontargetable mutations, including KRAS, TP53, or STK11/LKB1 alterations. By coupling ex vivo drug sensitivity profiling with in vivo drug response studies, we aimed to identify drug vulnerabilities for these NSCLC subtypes. Primary adenosquamous carcinoma (ASC) or adenocarcinoma (AC) cultures were established from KrasG12D/+;Lkb1fl/fl (KL) tumors or AC cultures from KrasG12D/+;p53fl/fl (KP) tumors. Although p53-null cells readily propagated as conventional cultures, Lkb1-null cells required conditional reprograming for establishment. Drug response profiling revealed short-term response to MEK inhibition, yet long-term clonogenic assays demonstrated resistance, associated with sustained or adaptive activation of receptor tyrosine kinases (RTK): activation of ERBBs in KL cultures, or FGFR in AC cultures. Furthermore, pan-ERBB inhibition reduced the clonogenicity of KL cultures, which was exacerbated by combinatorial MEK inhibition, whereas combinatorial MEK and FGFR inhibition suppressed clonogenicity of AC cultures. Importantly, in vivo studies confirmed KL-selective sensitivity to pan-ERBB inhibition, which correlated with high ERBB ligand expression and activation of ERBB receptors, implying that ERBB network activity may serve as a predictive biomarker of drug response. Interestingly, in human NSCLCs, phosphorylation of EGFR or ERBB3 was frequently detected in ASCs and squamous cell carcinomas. We conclude that analysis of in situ ERBB signaling networks in conjunction with ex vivo drug response profiling and biochemical dissection of adaptive RTK activities may serve as a valid diagnostic approach to identify tumors sensitive to ERBB network inhibition.

The oncogenic protein Bcr-Abl has two major isoforms, p190Bcr-Abl and p210Bcr-Abl. While p210Bcr-Abl is the hallmark of chronic myeloid leukemia (CML), p190Bcr-Abl occurs in the majority of Philadelphia-positive acute lymphoblastic leukemia (Ph + ALL) patients. In CML, p190Bcr-Abl occurs in a minority of patients associating with distinct hematological features and inferior outcomes, yet the pathogenic role of p190Bcr-Abl and potential targeting therapies are largely uncharacterized. We employed next generation sequencing, phospho-proteomic profiling, and drug sensitivity testing to characterize p190Bcr-Abl in CML and hematopoietic progenitor cell line models (Ba/f3 and HPC-LSK). p190Bcr-Abl CML patients demonstrated poor response to imatinib and frequent mutations in epigenetic modifiers genes. In contrast with p210Bcr-Abl, p190Bcr-Abl exhibited specific transcriptional upregulation of interferon, interleukin-1 receptor, and P53 signaling pathways, associated with hyperphosphorylation of relevant signaling molecules including JAK1/STAT1 and PAK1 in addition to Src hyperphosphorylation. Comparable to p190Bcr-Abl CML patients, p190Bcr-Abl cell lines demonstrated similar transcriptional and phospho-signaling signatures. With the drug sensitivity screening we identified targeted drugs with specific activity in p190Bcr-Abl cell lines including IAP-, PAK1-, and Src inhibitors and glucocorticoids. Our results provide novel insights into the mechanisms underlying the distinct features of p190Bcr-Abl CML and promising therapeutic targets for this high-risk patient group.

Deregulated miRNA expression has been suggested in several stages of breast cancer pathogenesis. We have studied the miR-30 family, in particular miR-30d, in relation to breast cancer patient survival and treatment outcomes. With tumor specimens from 1238 breast cancer patients, we analyzed the association of miR-30d expression with tumor characteristics with the 5-year occurrence of breast cancer-specific death or distant metastasis (BDDM), and with 10-year breast cancer survival (BCS). We conducted a two-stage drug-screen to investigate the impact of miR-30 family members (miR-30a-30e) on sensitivity to doxorubicin and lapatinib in six breast cancer cell lines HCC1937, HCC1954, MDA-MB-361, MCF7, MDA-MB-436 and CAL-120, using drug sensitivity scores (DSS) to compare the miR-30 family mimics to their specific inhibitors. The study was complemented with Ingenuity Pathway Analysis (IPA) with the METABRIC data. We found that while high miR-30d expression is typical for aggressive tumors, it predicts better metastasis-free (pBDDM = 0.035, HR = 0.63, 95% CI = 0.4–0.9) and breast cancer-specific survival (pBCS = 0.018, HR = 0.61, 95% CI = 0.4–0.9), especially in HER2-positive (pBDDM = 0.0009), ER-negative (pBDDM = 0.003), p53-positive (pBDDM = 0.011), and highly proliferating (pBDDM = 0.0004) subgroups, and after adjuvant chemotherapy (pBDDM = 0.035). MiR-30d predicted survival independently of standard prognostic markers (pBDDM = 0.0004). In the drug-screening test, the miR-30 family sensitized the HER2-positive HCC1954 cell line to lapatinib (p &lt, 10−2) and HCC1937, MDA-MB-361, MDA-MB-436 and CAL120 to doxorubicin (p &lt, 10−4) with an opposite impact on MCF7. According to the pathway analysis, the miR-30 family has a suppressive effect on cell motility and metastasis in breast cancer. Our results suggest prognostic and predictive potential for the miR-30 family, which warrants further investigation.

SUMMARYFunctional 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 (FUTC) for ex vivo drug response interrogation. Analysis of murine Kras mutant FUTCs demonstrated pharmacological and adaptive signaling profiles comparable to subtype-matched cultured cells. Applying FUTC profiling on non-small cell lung cancer patient samples, we generated robust drug response data in 18 of 19 cases, where the cells exhibited targeted drug sensitivities corresponding to their oncogenic drivers. In one of these cases, an EGFR mutant lung adenocarcinoma patient refractory to osimertinib, FUTC profiling was used to guide compassionate treatment. FUTC profiling identified selective sensitivity to disulfiram and the combination of carboplatin plus etoposide and the patient received substantial clinical benefit from the treatment with these agents. We conclude that FUTC profiling provides a robust, rapid, and actionable assessment of personalized cancer treatment options.

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.

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.

In vitro cancer drug testing carries a low predictive value. We developed the human leiomyoma&ndash, derived matrix &ldquo, Myogel&rdquo, 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&ndash, 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&ndash, 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.

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

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

Additional file 3. Preclinical characterization of recGSC cultures. MRI, in vitro spheroid and differentiation morphology and the subsequent xenograft upon transplantation of immunodeficient mice. T = Tumor.

Validation of selected compounds from the drug screening. We identified drugs with a high DSS and increased patient-specificity (sDSS) and verified the pattern of drug responses in an independent laboratory. (A-C) T1454, (D-F) T1456, and (G-I) T1459. The dose-response curves in the validation experiments are calculated from the mean ± standard error of the mean in five independent experiments and fitted on the basis of a four-parameter sigmoidal logistic fit function. (PDF 342 kb)

Global gene expression analyses. (A) Correspondence analysis of global gene expression data displayed a tumor distribution contrasting the overall drug sensitivity analyses with no clear separation of the two most sensitive tumors from the others. Each dot in the scatter plot represents individual genes (rows), while individual tumors are highlighted (columns). (B) Unsupervised hierarchical clustering with distance matrix (average linkage, Pearson correlation). (PDF 657 kb)

Calculation of sDSS, distribution and correspondence analysis of sDSS from the DSRT. (A) Dose-response curves to bortezomib in GSC cultures ranging from the least sensitive tumor (upper curve, T1461) with a DSS of 7.6 to the most sensitive tumor (T1547, lower curve) with a DSS of 29.1. Average DSS across all cultures is highlighted in blue. (B) By using the average DSS in all GBM as a reference, the cultures were classified according to the relative increased or decreased sensitivity to bortezomib presented as selective DSS (sDSS) in the waterfall plot. (C) Distribution of sDSS of the entire drug collection significantly differed among the cultures (p

Heat map of DSS in all drugs. Heat map and unsupervised hierarchical clustering of absolute effects (DSS) of the entire drug collection. Gray: failed/missing drug response. (PDF 148 kb)

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. (c) 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

BACKGROUND: Heterogeneity and low incidence comprise the biggest challenge in sarcoma diagnosis and treatment. Chemotherapy, although efficient for some sarcoma subtypes, generally results in poor clinical responses and is mostly recommended for advanced disease. Specific genomic aberrations have been identified in some sarcoma subtypes but few of them can be targeted with approved drugs. METHODS: We cultured and characterised patient-derived sarcoma cells and evaluated their sensitivity to 525 anti-cancer agents including both approved and non-approved drugs. In total, 14 sarcomas and 5 healthy mesenchymal primary cell cultures were studied. The sarcoma biopsies and derived cells were characterised by gene panel sequencing, cancer driver gene expression and by detecting specific fusion oncoproteins in situ in sarcomas with translocations. RESULTS: Soft tissue sarcoma cultures were established from patient biopsies with a success rate of 58%. The genomic profile and drug sensitivity testing on these samples helped to identify targeted inhibitors active on sarcomas. The cSrc inhibitor Dasatinib was identified as an active drug in sarcomas carrying chromosomal translocations. The drug sensitivity of the patient sarcoma cells ex vivo correlated with the response to the former treatment of the patient. CONCLUSIONS: Our results show that patient-derived sarcoma cells cultured in vitro are relevant and practical models for genotypic and phenotypic screens aiming to identify efficient drugs to treat sarcoma patients with poor treatment options.

Heat map of sDSS in all drugs. Heat map and unsupervised hierarchical clustering of relative effects (sDSS) of the entire drug collection. Gray: failed/missing drug response. (PDF 148 kb)

Gene expression analyses of GSC cultures related to selected genes of drug resistance, metabolism, GSC- and GBM genes. Unsupervised hierarchical clustering of expressed genes related to (A) drug resistance, (B) drug metabolism, (C) GSCs, and (D) GBM. In all analyses of selected gene panels, the clusters do not separate the most sensitive tumors from the others. Scale bar in all heat maps: log2-values. The cultures highlighted in red text were the two most sensitive GSC cultures from the drug screening. (PDF 289 kb)

Most non-small cell lung cancers (NSCLC) contain nontargetable mutations, including

Introduction: The oncoprotein Bcr-Abl has two major isoforms, depending on the breakpoint in BCR gene, p190 and p210. While p210 is the hallmark of chronic myeloid leukemia (CML), p190 occurs in the majority of Philadelphia-positive acute lymphoblastic leukemia (Ph+ ALL) patients. p190 occurs as a sole transcript in 1-2% CML patients, associated with distinct features like monocytosis and frequent additional cytogenetic abnormalities (ACA) at diagnosis. It also confers a risk of treatment failure and progression in chronic phase (CP) CML patients. However, the underlying mechanisms are largely unknown. Here we explore the characteristics of p190 and p210 in CML and ALL patients using next generation sequencing, phospho-flowcytometry and high throughput drug testing. Patients and methods: Peripheral blood mononuclear cells (PMNC) were collected at diagnosis from four CP-CML patients harboring p190 isoform from Helsinki University Hospital. Genetic alterations were identified by whole exome sequencing. RNA sequencing was employed to analyze transcriptional profiles of p190 CML (n=3) in contrast to p210 CML patients (n=4). A thorough transcriptional, phosphorylation and drug sensitivity profiling were applied to five p190- and three p210-expressing Ph+ALL patients. Expression alterations were further characterized in two cell line models mimicking BCR-ABL positive leukemia (Ba/F3 and HPCLSK). Phosphorylation profiles were analyzed by flowcytometry and phospho-array (Tyrosine Phosphorylation ProArray, Full Moon Biosystems). For drug sensitivity and resistance testing (DSRT), a custom plate set comprising 75 approved and investigational oncology drugs was used for patient samples and more extensive 528-drugs plates were used for the cell lines. Results: CML patients with p190 had a median age of 72.5 years at the diagnosis (range: 50-80) and all received imatinib as a frontline treatment. Only one patient achieved a fluctuating major molecular response (MMR) by 12 months while the rest of the patients showed primary resistance to treatment and were shifted to a 2nd line TKI, nilotinib (n=2) or proceeded to HSCT (n=1). By exome sequencing we identified 26 variants in p190-CML samples (median per patient=7, range: 2-10), including variants in ASXL1, DNMT3A and KDM4D genes. RNA-sequencing analysis identified 19 and 97 dysregulated genes (Q Conclusions: In CML, p190 isoform of BCR-ABL1 is associated with distinct features and should be considered as a high-risk group. Combining clinical, genomic, phosphorylation and drug sensitivity data, we demonstrated that p190 activates specific cancer pathways, notably Src signaling and interferon pathways. Data also suggests that CML patients with p190 could benefit from broad spectrum TKI with Src inhibiting activity or combination of TKI with MDM2- or IAP-inhibitors. Disclosures Heckman: Orion Pharma: Research Funding; Celgene: Research Funding; Novartis: Research Funding; Oncopeptides: Research Funding. Porkka:Celgene: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding. Mustjoki:Novartis: Research Funding; Pfizer: Research Funding; BMS: Honoraria, Research Funding.

Objectives Oral squamous cell carcinoma (OSCC) is the sixth most common cancer worldwide. Traditionally, cancer cell lines cultured in 2D are used to predict the efficacy of new anti-cancer compounds. However, this method has low predicting value for efficacy since more than 80% of the cancer drugs, which have promising effect in pre-clinical studies, fail in Phase II clinical trials. Our group has developed human matrix based product, Myogel, which is extracted from leiomyoma tissue. Our hypothesis is that Myogel represents better the in vivo condition compared with the 2D plastic wells, or even wells coated with mouse derived Matrigel®. We selected 12 OSCC cell lines and 19 anti-cancer compounds, targeting mTOR and epidermal growth factor receptor (EGFR) signalling pathways. The High Throughput Drug Screening method with five different conditions were used: cells in 2D plastic wells; on top and within Matrigel® or Myogel. Additionally, the morphology of OSCC cells and EGRF location were studied using immunofluorescence staining and confocal microscope. Findings Cancer cells on top and within Myogel were less responsive to EGFR inhibitors compared to cells cultured in 2D plastic or Matrigel®. However, in case of mTOR inhibitors, similar efficacy of the drugs in all conditions was seen. The morphology of the carcinoma cells differed depending on the matrix. Within Matrigel, the cells formed isolated round-shaped organoids, whereas the cells within Myogel were stellate-shaped. Immunofluorescent staining revealed that in 2D and Matrigel, EGFR was located primarily on the cell membranes, while in Myogel, the staining was mainly in the cytoplasm. Conclusions Carcinoma cells showed different behaviours and responses to anti-cancer compounds depending on the testing conditions. Comparison between clinical data and our in vitroresults are still needed to reveal the most reliable condition for cancer drug testing.

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.

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.

// Paavo O. Pietarinen 1 , Christopher A. Eide 2, 3 , Pilar Ayuda-Duran 4 , Swapnil Potdar 5 , Heikki Kuusanmaki 5 , Emma I. Andersson 1 , John P. Mpindi 5 , Tea Pemovska 5, 6 , Mika Kontro 1 , Caroline A. Heckman 5 , Olli Kallioniemi 5 , Krister Wennerberg 5 , Henrik Hjorth-Hansen 7, 8 , Brian J. Druker 2, 3 , Jorrit M. Enserink 4 , Jeffrey W. Tyner 2, 9 , Satu Mustjoki 1, 10, * , Kimmo Porkka 1, * 1 Hematology Research Unit Helsinki, University of Helsinki and Department of Hematology, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland 2 Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA 3 Howard Hughes Medical Institute, Portland, OR, USA 4 Oslo University Hospital, University of Oslo, Oslo, Norway 5 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland 6 Research Center for Molecular Medicine (CeMM) of the Austrian Academy of Sciences, Vienna, Austria 7 Department of Hematology, St Olavs Hospital, Trondheim, Norway 8 Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway 9 Department of Cell, Developmental and Cancer Biology, Oregon Health and Science University, Portland, OR, USA 10 Department of Clinical Chemistry, University of Helsinki, Helsinki, Finland * These authors contributed equally to this work Correspondence to: Kimmo Porkka, email: Kimmo.porkka@helsinki.fi Keywords: chronic myeloid leukemia, high-throughput drug screening, ex vivo, tyrosine kinase inhibitors, CD34 Received: September 20, 2016 Accepted: January 24, 2017 Published: February 07, 2017 ABSTRACT 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.