37 research outputs found
Drug-screening and genomic analyses of HER2-positive breast cancer cell lines reveal predictors for treatment response
No full textSandra Jernström,1,2 Vesa Hongisto,3 Suvi-Katri Leivonen,1,2 Eldri Undlien Due,1 Dagim Shiferaw Tadele,1 Henrik Edgren,4,5 Olli Kallioniemi,4 Merja Perälä,6 Gunhild Mari Mælandsmo,2,7,8 Kristine Kleivi Sahlberg,1,9 1Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 2KG Jebsen Centre for Breast Cancer Research, Institute for Clinical Medicine, University of Oslo, Oslo, Norway; 3Misvik Biology Oy, Turku, 4Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, 5Medisapiens, Helsinki, Finland, 6VTT Technical Research Centre of Finland, Turku, Finland; 7Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; 8Institute of Pharmacy, Faculty of Health Sciences, University of Tromsø, Tromsø, 9Department of Research, Vestre Viken Hospital Trust, Drammen, Norway Background: Approximately 15%–20% of all diagnosed breast cancers are characterized by amplified and overexpressed HER2 (= ErbB2). These breast cancers are aggressive and have a poor prognosis. Although improvements in treatment have been achieved after the introduction of trastuzumab and lapatinib, many patients do not benefit from these drugs. Therefore, in-depth understanding of the mechanisms behind the treatment responses is essential to find alternative therapeutic strategies. Materials and methods: Thirteen HER2 positive breast cancer cell lines were screened with 22 commercially available compounds, mainly targeting proteins in the ErbB2-signaling pathway, and molecular mechanisms related to treatment sensitivity were sought. Cell viability was measured, and treatment responses between the cell lines were compared. To search for response predictors and genomic and transcriptomic profiling, PIK3CA mutations and PTEN status were explored and molecular features associated with drug sensitivity sought. Results: The cell lines were divided into three groups according to the growth-retarding effect induced by trastuzumab and lapatinib. Interestingly, two cell lines insensitive to trastuzumab (KPL4 and SUM190PT) showed sensitivity to an Akt1/2 kinase inhibitor. These cell lines had mutation in PIK3CA and loss of PTEN, suggesting an activated and druggable Akt-signaling pathway. Expression levels of five genes (CDC42, MAPK8, PLCG1, PTK6, and PAK6) were suggested as predictors for the Akt1/2 kinase-inhibitor response. Conclusion: Targeting the Akt-signaling pathway shows promise in cell lines that do not respond to trastuzumab. In addition, our results indicate that several molecular features determine the growth-retarding effects induced by the drugs, suggesting that parameters other than HER2 amplification/expression should be included as markers for therapy decisions. Keywords: ErbB2, drug screening, gene expression, pharmacogenomics, predictor
Spatial cumulant models enable spatially informed treatment strategies and analysis of local interactions in cancer systems
No full textTheoretical and applied cancer studies that use individual-based models (IBMs) have been limited by the lack of a mathematical formulation that enables rigorous analysis of these models. However, spatial cumulant models (SCMs), which have arisen from theoretical ecology, describe population dynamics generated by a specific family of IBMs, namely spatio-temporal point processes (STPPs). SCMs are spatially resolved population models formulated by a system of differential equations that approximate the dynamics of two STPP-generated summary statistics: first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances). We exemplify how SCMs can be used in mathematical oncology by modelling theoretical cancer cell populations comprising interacting growth factor-producing and non-producing cells. To formulate model equations, we use computational tools that enable the generation of STPPs, SCMs and mean-field population models (MFPMs) from user-defined model descriptions (Cornell et al. Nat Commun 10:4716, 2019). To calculate and compare STPP, SCM and MFPM-generated summary statistics, we develop an application-agnostic computational pipeline. Our results demonstrate that SCMs can capture STPP-generated population density dynamics, even when MFPMs fail to do so. From both MFPM and SCM equations, we derive treatment-induced death rates required to achieve non-growing cell populations. When testing these treatment strategies in STPP-generated cell populations, our results demonstrate that SCM-informed strategies outperform MFPM-informed strategies in terms of inhibiting population growths. We thus demonstrate that SCMs provide a new framework in which to study cell-cell interactions, and can be used to describe and perturb STPP-generated cell population dynamics. We, therefore, argue that SCMs can be used to increase IBMs’ applicability in cancer research.peerReviewe
Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.
No full textTumor heterogeneity is a complex and widely recognized trait that poses significant challenges in developing effective cancer therapies. In particular, many tumors harbor a variety of subpopulations with distinct therapeutic response characteristics. Characterizing this heterogeneity by determining the subpopulation structure within a tumor enables more precise and successful treatment strategies. In our prior work, we developed PhenoPop, a computational framework for unravelling the drug-response subpopulation structure within a tumor from bulk high-throughput drug screening data. However, the deterministic nature of the underlying models driving PhenoPop restricts the model fit and the information it can extract from the data. As an advancement, we propose a stochastic model based on the linear birth-death process to address this limitation. Our model can formulate a dynamic variance along the horizon of the experiment so that the model uses more information from the data to provide a more robust estimation. In addition, the newly proposed model can be readily adapted to situations where the experimental data exhibits a positive time correlation. We test our model on simulated data (in silico) and experimental data (in vitro), which supports our argument about its advantages
Abstract 2107: Identification of alternative compounds by drug screening of HER2 positive breast cancer cell lines.
No full textAuthor Correction: Early response evaluation by single cell signaling profiling in acute myeloid leukemia
No full textDrug-screening and genomic analyses of HER2-positive breast cancer cell lines reveal predictors for treatment response
No full textBackground
Approximately 15%–20% of all diagnosed breast cancers are characterized by amplified and overexpressed HER2 (= ErbB2). These breast cancers are aggressive and have a poor prognosis. Although improvements in treatment have been achieved after the introduction of trastuzumab and lapatinib, many patients do not benefit from these drugs. Therefore, in-depth understanding of the mechanisms behind the treatment responses is essential to find alternative therapeutic strategies.
Materials and methods
Thirteen HER2 positive breast cancer cell lines were screened with 22 commercially available compounds, mainly targeting proteins in the ErbB2-signaling pathway, and molecular mechanisms related to treatment sensitivity were sought. Cell viability was measured, and treatment responses between the cell lines were compared. To search for response predictors and genomic and transcriptomic profiling, PIK3CA mutations and PTEN status were explored and molecular features associated with drug sensitivity sought.
Results
The cell lines were divided into three groups according to the growth-retarding effect induced by trastuzumab and lapatinib. Interestingly, two cell lines insensitive to trastuzumab (KPL4 and SUM190PT) showed sensitivity to an Akt1/2 kinase inhibitor. These cell lines had mutation in PIK3CA and loss of PTEN, suggesting an activated and druggable Akt-signaling pathway. Expression levels of five genes (CDC42, MAPK8, PLCG1, PTK6, and PAK6) were suggested as predictors for the Akt1/2 kinase-inhibitor response.
Conclusion
Targeting the Akt-signaling pathway shows promise in cell lines that do not respond to trastuzumab. In addition, our results indicate that several molecular features determine the growth-retarding effects induced by the drugs, suggesting that parameters other than HER2 amplification/expression should be included as markers for therapy decisions
Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data
No full textInternational audienceTumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug -tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug -response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteris-tics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response
Table of parameters used in the end-points and live cell image methods.
No full textTable of parameters used in the end-points and live cell image methods.</p
Range for parameter generation of experiments with 2 subpopulations.
No full textRange for parameter generation of experiments with 2 subpopulations.</p
Estimation of the initial proportion and <i>GR</i><sub>50</sub> for 2 subpopulations from the mixture (BF21), and the monoclonal (SENSITIVE500, RESISTANT250) datasets using all three methods (PhenoPop, End-points, Live cell image).
No full textTrue initial proportion is obtained from the initial setting of the BF21 dataset, with a 2: 1 between sensitive and resistant cells. The estimated initial proportions are labeled according to their respective methods. True GR50 are estimated from monoclonal data and denoted as ‘Monoclonal’ from all three methods, while the estimated GR50 are labeled as ‘Mixture’. Each shape in the GR50 estimation corresponds to a method, circle for PhenoPop, square for End-points, and diamond for Live cell image. Each color in the plot represents a distinct subpopulation: orange for sensitive and blue for resistant.</p
