Human epidermal growth factor 2 (HER2/ERBB2) is frequently amplified/mutated in cancer. The tyrosine kinase inhibitors (TKIs) lapatinib, neratinib, and tucatinib are FDA-approved for the treatment of ...HER2-positive breast cancer. Direct comparisons of the preclinical efficacy of the TKIs have been limited to small-scale studies. Novel biomarkers are required to define beneficial patient populations.
In this study, the anti-proliferative effects of the three TKIs were directly compared using a 115 cancer cell line panel. Novel TKI response/resistance markers were identified through cross-analysis of drug response profiles with mutation, gene copy number and expression data.
All three TKIs were effective against HER2-amplified breast cancer models; neratinib showing the most potent activity, followed by tucatinib then lapatinib. Neratinib displayed the greatest activity in HER2-mutant and EGFR-mutant cells. High expression of HER2, VTCN1, CDK12, and RAC1 correlated with response to all three TKIs. DNA damage repair genes were associated with TKI resistance. BRCA2 mutations were correlated with neratinib and tucatinib response, and high expression of ATM, BRCA2, and BRCA1 were associated with neratinib resistance.
Neratinib was the most effective HER2-targeted TKI against HER2-amplified, -mutant, and EGFR-mutant cell lines. This analysis revealed novel resistance mechanisms that may be exploited using combinatorial strategies.
Kinase inhibitors form the largest class of precision medicine. From 2013 to 2017, 17 have been approved, with 8 different mechanisms. We present a comprehensive profiling study of all 17 inhibitors ...on a biochemical assay panel of 280 kinases and proliferation assays of 108 cancer cell lines. Drug responses of the cell lines were related to the presence of frequently recurring point mutations, insertions, deletions, and amplifications in 15 well-known oncogenes and tumor-suppressor genes. In addition, drug responses were correlated with basal gene expression levels with a focus on 383 clinically actionable genes. Cell lines harboring actionable mutations defined in the FDA labels, such as mutant BRAF(V600E) for cobimetinib, or
gene translocation for ALK inhibitors, are generally 10 times more sensitive compared with wild-type cell lines. This sensitivity window is more narrow for markers that failed to meet endpoints in clinical trials, for instance
loss for CDK4/6 inhibitors (2.7-fold) and
mutation for cobimetinib (2.3-fold). Our data underscore the rationale of a number of recently opened clinical trials, such as ibrutinib in
- or
-expressing cancers. We propose and validate new response biomarkers, such as mutation in
or
for EGFR and HER2 inhibitors,
and
expression for MEK inhibitors, and
expression for ALK inhibitors. Potentially, these new markers could be combined to improve response rates. This comprehensive overview of biochemical and cellular selectivities of approved kinase inhibitor drugs provides a rich resource for drug repurposing, basket trial design, and basic cancer research.
In heat processing, microbial inactivation is traditionally described as log-linear. As a general rule, the relation between rate of inactivation and temperature is also described as a log-linear ...relation. The model is also sometimes applied in pressure and in pulsed electric field (PEF) processing. The model has proven its value by the excellent safety record of the last 80 years, but there are many deviations from log-linearity. This could lead to either over-processing or under-processing resulting in safety problems or, more likely, spoilage problems. As there is a need for minimal processing, accurate information of the inactivation kinetics is badly needed. To predict inactivation more precisely, models have been developed that can cope with deviations of linearity. As extremely low probabilities of survival must be predicted, extrapolation is almost always necessary. However, extrapolation is hardly possible without knowledge of the nature of nonlinearity. Therefore, knowledge of the physiology of inactivation is necessary. This paper discusses the physiology of denaturation by heat, high pressure and pulse electric field. After discussion of the physiological aspects, the various aspects of the development of inactivation models will be addressed. Both general and more specific aspects are discussed such as choice of test strains, effect of the culture conditions, conditions during processing and recovery conditions and mathematical modelling of inactivation. In addition to lethal inactivation, attention will be paid to sublethal inactivation because of its relevance to food preservation. Finally, the principles of quantitative microbiological risk assessment are briefly mentioned to show how appropriate inactivation criteria can be set.
Abstract
Introduction: Human epidermal growth factor 2 (HER2/ERBB2) is frequently amplified or mutated across various cancer types. The tyrosine kinase inhibitors (TKIs) lapatinib, neratinib, and ...tucatinib are FDA-approved for the treatment of HER2-positive breast cancer. All three TKIs bind and inhibit the kinase domain of HER2 but differ both in the mechanism of binding and in specificity for other HER family members. Direct comparisons to differentiate the pre-clinical efficacy of the three TKIs have been limited to small-scale studies and novel biomarkers of response to further define appropriate patient populations are required. Methods: In this study, the anti-proliferative effects of the three TKIs were compared using a 115-cancer cell line panel, including 12 breast cancer cell lines and 22 cell lines harbouring point mutations or amplifications of EGFR, HER2, or HER3. Hierarchical clustering analysis was carried out to compare the IC50 “fingerprint” of the three TKIs to 168 other anti-cancer agents. Novel markers of TKI sensitivity and resistance were identified through cross-analysis of each drug response profile with mutation, copy number variation, and gene expression data. Results: All three TKIs were effective against HER2-positive breast cancer models; neratinib showed the most potent activity, followed by tucatinib and lapatinib respectively (Table 1). Neratinib displayed the greatest anti-proliferative activity in HER2-mutant and EGFR-mutant cell lines. Clustering analysis revealed that the anti-proliferative profile of tucatinib was most similar to trastuzumab, while neratinib and lapatinib were most like other HER family inhibitors. Mutation and gene expression analysis identified potential markers of response for each TKI. High expression of four genes (HER2, VTCN1, CDK12, and RAC1) correlated with response to all three TKIs. DNA damage repair genes were significantly associated with resistance to the HER2-targeted TKIs. BRCA2 mutation was correlated with neratinib and tucatinib response, and high gene expression of ATM, BRCA2, and BRCA1 were all associated with neratinib resistance. Conclusions: Neratinib was the most effective HER2-targeted TKI against HER2-amplified, -mutant, and EGFR-mutant cell lines. This analysis revealed possible mechanisms that may be exploited using combinatorial strategies involving CDK inhibitors, immunotherapies, and targeting DNA repair pathways.
Table: IC50 values for neratinib, lapatinib, and tucatinib in the HER2+ breast cancer cell linesIC50 values (nM)Cell linesNeratinibLapatinibTucatinibAU-56520294125BT-4745926229HCC195413814262122MDA-MB-453306228445928SKBR3715222
Citation Format: Neil T Conlon, Jeffrey J Kooijman, Suzanne JC van Gerwen, Winfried R Mulder, Guido JR Zaman, Irmina Diala, Lisa D Eli, Alshad S Lalani, John Crown, Denis Collins. Comparative analysis of anti-proliferative effects and gene profiling of lapatinib, neratinib, and tucatinib abstract. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS10-06.
Food safety control is a matter for concern for all parts of the food supply chain, including governments that develop food safety policy, food industries that must control potential hazards, and ...consumers who need to keep to the intended use of the food. In the future, food safety policy may be set using the framework of risk analysis, part of which is the development of (inter)national microbiological risk assessment (MRA) studies. MRA studies increase our understanding of the impact of risk management interventions and of the relationships among subsequent parts of food supply chains with regard to the safety of the food when it reaches the consumer. Application of aspects of MRA in the development of new food concepts has potential benefits for the food industry. A tiered approach to applying MRA can best realize these benefits. The tiered MRA approach involves calculation of microbial fate for a product and process design on the basis of experimental data (e.g., monitoring data on prevalence) and predictive microbiological models. Calculations on new product formulations and novel processing technologies provide improved understanding of microbial fate beyond currently known boundaries, which enables identification of new opportunities in process design. The outcome of the tiered approach focuses on developing benchmarks of potential consumer exposure to hazards associated with new products by comparison with exposure associated with products that are already on the market and have a safe history of use. The tiered prototype is a tool to be used by experienced microbiologists as a basis for advice to product developers and can help to make safety assurance for new food concepts transparent to food inspection services.
Recontamination of food products can cause foodborne illnesses or spoilage of foods. It is therefore useful to quantify this recontamination so that it can be incorporated in microbiological risk ...assessments (MRA). This paper describes a first attempt to quantify one of the recontamination routes: via the air. Data on the number of airborne microorganisms were collected from literature and industries. The settling velocities of different microorganisms were calculated for different products by combining the data on aerial concentrations with sedimentation counts assuming that settling is under the influence of gravity only. Air movement is not explicitly considered in this study.
Statistical analyses were performed to clarify the effect of different products and seasons on the number of airborne microorganisms and the settling velocity. For both bacteria and moulds, three significantly different product categories with regard to the level of airborne organisms were identified. The statistical distribution in these categories was described by a lognormal distribution. The settling velocity did not depend on the product, the season of sampling or the type of microorganism, and had a geometrical mean value of 2.7 mm/s. The statistical distribution of the settling velocity was described by a lognormal distribution as well. The probability of recontamination via the air was estimated by the product of the number of bacteria in the air, the settling velocity, and the exposed area and time of the product. For three example products, the contamination level as a result of airborne recontamination was estimated using Monte Carlo simulations. What-if scenarios were used to exemplify determination of design criteria to control a specified contamination level.
In past years many models describing growth and inactivation of microorganisms have been developed. This study is a discussion of the growth and inactivation models that can be used in a stepwise ...procedure for quantitative risk assessment. First, rough risk assessments are performed in which orders of magnitude for microbial processes are estimated by the use of simple models. This method provides an efficient way to find the main determinants of risk. Second, the main determinants of risk are studied more accurately and quantitatively. It is best to compare several models at this level, as no model is expected to be able accurately to predict microbial responses under all circumstances. By comparing various models the main determinants of risk are studied from several points of view, and risks can be assessed on a broad basis. If, however, process variations have a more profound effect on risk than the differences between models, it is most efficient to use the simplest model available. If relevant, the process variations can be stochastically described in the third level of detail. Stochastic description of the process parameters will however not change the conclusion on the usefulness of simple models in quantitative risk assessments. The proposed stepwise procedure that starts simply before going into detail provides a structured method of risk assessment and prevents the researcher from getting caught in too much complexity. This simplicity is necessary because of the complex nature of food safety. The principal aspects are highlighted during the procedure and many factors can be omitted since their quantitative effect is negligible.
Abstract
Kinases are the major anticancer drug target class of the 21st century with nearly sixty small molecule kinase inhibitors approved for clinical use in the first two decades. While there are ...more than 500 kinases encoded by the human genome, currently approved inhibitors act primarily through approximately twenty different targets. Key to the success of kinase inhibitor therapy has been the simultaneous development of biomarker assays to enable the selection of patients most likely to respond. Predictive drug response biomarkers can be identified with cancer cell panel profiling, which is the parallel testing of compounds on a large panel of cancer cell lines. By correlating drug sensitivity with genomic information of the cell lines, strategies for patient stratification or drug repurposing have been developed 1-3. In two earlier studies 2,3, we compared the kinase selectivity and the cellular inhibition profiles of all kinase inhibitors approved by the FDA until May 2018. Here, we will present the cancer cell panel profiling of all twenty small molecule kinase inhibitors approved since then. Several of these inhibitors act through well-known targets, such as ALK (lorlatinib), BRAF (encorafenib), EGFR (dacomitinib) and MEK1 (binimetinib, selumetinib). Others act through kinases for which no small molecule inhibitors have been approved before, such as CSF1R (pexidartinib), FGFR (erdafitinib, pemigatinib), c-MET (capmatinib), RET (selpercatinib, pralsetinib) and TRK (larotrectinib, entrectinib). All compounds were profiled on a panel of 102 cancer cell lines, known as Oncolines, representing a wide range of solid tumors and hematological malignancies, and harboring mutant and wild-type versions of many major cancer driver genes 3. To determine similarities in the mechanisms underlying the anti-proliferative activity of the inhibitors, their IC50 fingerprint in the cell proliferation assays were compared by hierarchical clustering 4. Compounds that act through the same primary kinase clustered together in this analysis. Exceptions were investigated further by profiling of additional cell lines, representing cancer gene alterations that were not present in the 102 cell line panel, such as FLT3 mutation and TRK-gene fusions, which occur in relatively small cancer patient populations. To compare the genomic targeting of kinase inhibitors acting on the same biochemical target, the cancer cell lines were classified as either “mutant” or “wild-type” for specific cancer gene alterations and were grouped per cancer gene. The relationship between drug sensitivity and cancer gene mutation status was examined. Detailed genomic biomarker analyses and comparative profiling results of novel BTK, EGFR, FGFR, FLT3 and TRK kinase inhibitors will be presented.1 Mol Cancer Ther 2017;26:2609-17; 2 PLoS ONE 2014;9:e92146; 3 Mol Cancer Ther 2019;18:470-81; 4 Mol Cancer Ther 2016;15:3097-109
Citation Format: Jeffrey J. Kooijman, Wilhelmina E. van Riel, Martine B. Prinsen, Jelle Dylus, Jeroen A. de Roos, Yvonne Grobben, Nicole Willemsen-Seegers, Michelle Muller, Jos de Man, Yugo Narumi, Yusuke Kawase, Rogier C. Buijsman, Suzanne J. van Gerwen, Guido J. Zaman. Comparative cancer cell panel profiling of kinase inhibitors approved for clinical use from 2018 to 2020 abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1480.
Abstract
In cancer therapy, combination drug treatment aims to improve response rate and decrease the development of drug resistance. The discovery of new effective drug combinations is constrained ...by the cost and effort of carrying out large unbiased screens and by poor translation of results towards the clinic. Here we describe how focusing on the biological mechanisms underlying the activity of drug candidates may aid a priori selection of promising synergy candidates and help in translate synergistic combinations towards a clinical situation.
We have previously shown 1 that curve shift analysis as developed by Straetemans et al. 2 is a better method than combination-matrix screening. Also a dose based score such as the isobologram or the CI-index more robustly assesses synergy than an effect-based score such as the Bliss-additivity 1. On this basis, we developed a two-step synergy screening approach, called SynergyScreen™. By distinguishing separate synergy screening and synergy confirmation stages, this setup capitalizes on insights from high throughput screening to discover robust and reproducible pharmacologically synergistic pairs.
To further improve the efficiency of synergy screening, we focused on pre-selecting compounds in our screening library according to their biological mechanism. We profiled a library of more than 160 anti-cancer agents in a cell panel of 102 cell lines from diverse tumor origins 3. Agents were clustered according to response and so-called exemplars were collected into a focused library that represents the spectrum of biological mechanisms of current cancer therapy. This synergy screening library includes many standard of care chemotherapeutic agents, approved and pre-clinical kinase inhibitors, epigenetic modulators and compounds acting by other mechanisms.
Finally, we harnassed recent insights into the biology of synergy to understand and predict synergistic pairs. A tool was developed that uses the response of a compound in a 102 cell line panel to pick potential synergistic partners from the database of preprofiled compounds. It does this by computationally assessing if pairs can pharmacologically mimick clinically validated synthetic lethal interactions 4. We optimized prediction accuracy using the results of internal and external synergy screens. The tool was applied to specifically enrich test libraries and to predict synergies at clinically relevant doses, including the results of a SynergyScreen™ with the poly-ADP ribose polymerase (PARP) inhibitor niraparib and the BET bromodomain inhibitor JQ1.
References 1 Uitdehaag et al. (2015). PLoS ONE 10(5): e0125021. 2 Straetemans et al. (2005). Biometrical J. 47, 299-308. 3 Uitdehaag et al. (2016). Mol. Cancer Ther. 15, 3097-3109. 4 Lee et al. (2018). Nature Communications 9, 2546.
Citation Format: Joost C. Uitdehaag, Derek W. van Tilborg, Martine B.W. Prinsen, Jeffrey J. Kooijman, Jelle Dylus, Jeroen A.D.M. de Roos, Suzanne J.C. van Gerwen, Jos de Man, Rogier C. Buijsman, Guido J.R. Zaman. Combining cell panel profiling and synthetic lethality data to efficiently screen for synergistic combinations 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 2158.
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