NTRC is a Precision Medicine company Precision Medicine Services with a focus on Oncology and Cancer Immunotherapy
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How Advanced Bioinformatics Can Overcome Data Overwhelm in Biomarker Discovery for Patient Stratification
NTRC is a Precision Medicine company with a focus on Oncology and Cancer Immunotherapy NTRC is a Precision Medicine company Precision Medicine Services with a focus on Oncology and Cancer Immunotherapy
As many drug developers will know, a robust biomarker strategy can provide an early indication of whether a drug candidate has the potential to progress along the drug discovery pipeline. Identifying biomarkers that predict which patient populations are most likely to respond to a particular treatment can increase the chances of a new investigational drug ultimately entering the market. Moreover, these predictive biomarkers can help drug developers differentiate their molecules from competitors and make informed decisions during preclinical and clinical stages.
Predictive drug response biomarker identification The burden of data overload can often result in labs involves parallel testing of compounds on a panel inadvertently neglecting or overlooking important of over 100 cell lines. Cancer cell line panel profiling data trends as they are forced to review data through has become a trustworthy tool to examine and a narrow lens. Without a dedicated bioinformatics characterize in vitro drug response on diverse cells team or expert training, it is difficult for researchers across different tissue types. The resulting data can to thoroughly examine all data points, uncover identify predictive biomarkers to stratify patients for a trends and find relevant correlations in a timely and therapeutic response or identify potential resistance cost-effective manner. Incomplete or unreliable data to the drug. analyses of the cell panel profiling, in turn, makes it challenging to determine the right route or immediate These cell panel profiling studies, however, require next steps in the development pipeline. logistical planning to test different compounds on hundreds of cell lines, specialized methods to gather When in-house bioinformatics teams are in place, and process data, as well as bioinformatics analysis they are often under significant pressure to examine tools to visualize data insights (See box insert below and analyze large quantities of data within very strict for an example). Many laboratories may not have timelines (which can lead to an increased risk of access to the niche expertise or capacity required to unconscious bias relating to which areas of the data manage and evaluate all data generated. Below, we are prioritized for analysis). When data is reviewed discuss common challenges with predictive biomarker under these conditions to look for answers to pre- discovery faced by drug developers and approaches determined research questions, inconspicuous data to resolve them. trends can be ignored. This lack of a comprehensive approach can leave an enormous untapped potential Biomarker Discovery Challenges in Drug in cell panel profiling data that needs to be explored. Development In addition, many smaller biotechs or industry Predictive biomarkers can provide a wealth of start-ups only have a single (or a small group of) information about how effective a drug will be compounds in development. It is not logistically in a particular patient population. Finding these feasible for them to set up the infrastructure required biomarkers can help researchers make educated to perform large-scale panel profiling assays or recruit decisions about choosing biological models during bioinformatics teams for analyses. Moreover, for drug the preclinical phase as well as avoid timeline delays candidates to successfully enter the clinical phase, and unnecessary costs associated with a trial-and- biotech and pharma companies need to demonstrate error approach. However, many laboratories can independent and unbiased confirmation of their attest to the fact that cell panel profiling studies concepts. Therefore, there is a need to find third-party result in vast amounts of data points, and parsing collaborators to test and validate drug candidate through large volumes of data can be tedious and compounds and glean reliable insights from panel overwhelming. profiling assays. NTRC is a Precision Medicine company Precision Medicine Services with a focus on Oncology and Cancer Immunotherapy
Approaches to Expedite Biomarker Discovery from a competitor. Furthermore, these tools – alongside the specialist expertise of the provider – can To address data overload, laboratories tend to employ help automate and execute analyses in such a way as an internal bioinformatics team or train existing to help elucidate trends in the data that might have scientists. However, setting up a dedicated team or otherwise been overlooked. training staff on bioinformatics analysis requires a considerable investment of time and resources. Virtual biotech collaborators provide mechanistic hypotheses by consolidating panel profiling data, Moreover, smaller biotechs and start-ups do not performing advanced bioinformatics analysis and have the budget for such teams. Outsourcing then presenting the results in user-friendly and to expert bioinformatics service providers can interactive reports. Rather than having to spot alleviate the burden of tasks such as data analysis trends buried within the numbers, the interactive and provide extra bandwidth for smaller teams in a reporting enables researchers to promptly determine cost-effective manner. These service providers use key insights, perform focused searches through the modern bioinformatics tools that perform proprietary data, sort and filter by desired variables and toggle filtering of genetic information to provide robust between different graphs to examine the underlying genomic biomarker analysis. With comparative biology. Interactive data also facilitates effective profiling methods, it is possible to predict whether communication to funders and investors to fully a compound of interest is likely to be differentiated demonstrate the potential of the biomarker. NTRC is a Precision Medicine company Precision Medicine Services with a focus on Oncology and Cancer Immunotherapy
Case Study: Using cancer cell panel profiling and bioinformatics to investigate the mechanistic hypothesis for AMG-510, an experimental cancer drug
Cancer cell panel profiling is a powerful approach to identify novel predictive drug response biomarkers. Through panel profiling, it is possible to determine the activity of drug candidate compounds in cell proliferation assays with 100+ human cancer cell lines. Precision medicine services offered by NTRC provides comprehensive cancer cell panel profiling with detailed and interactive bioinformatics analysis on IC50 data. Their mission is to help drug developers find mechanistic hypotheses to progress to the clinic in a well-informed manner.
Using known genetic information about the cell lines in the panel, their sensitivity to the compound can be correlated to the presence or absence of specific cancer gene mutations to provide genomic biomarker analysis. With this method, a mutation in the Wnt pathway regulator β-catenin, was identified as a potential patient stratification marker for TTK kinase inhibitors [1].
Additionally, comparison of the IC50 fingerprints of compounds in proliferation assays, such as Oncolines™ offered by NTRC, can reveal similarities or differences in the biochemical mechanisms underlying the anti-proliferative activity of other anti-cancer compounds [2]. Such comparative profiling was used to identify the biochemical cross-reactivity between the irreversible BTK inhibitor ibrutinib, and EGFR [3, 4]. Furthermore, it was shown that this cross-reactivity was absent in the second-generation BTK inhibitor, acalabrutinib, which is biochemically more selective [4].
In the case study below, AMG-510, an experimental cancer drug, was tested on a panel of 103 cancer cell lines derived from diverse tissue tumor origins.
Cell proliferation assays (OncolinesTM)
Panel profiling of AMG-510 resulted in dose-response curves for each individual cell line. With the corresponding IC50 values, the cell lines were ranked based on their sensitivity to AMG-510 in a waterfall plot.
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The waterfall plot shown above details the sensitivity of AMG-510 with respect to all cancer cell lines tested in the Oncolines™ profiling study. Cell lines are ranked from sensitive (left) to insensitive (right). Red bars indicate cell lines harboring the G12C mutation in the KRAS oncogene. Blue bars are cell lines that do not express this mutation.
Bioinformatics analysis
Gene mutation analysis: The cancer cell line sensitivity was then correlated with the genetic information of each cell line, i.e., the presence or absence of mutations in oncogenes or tumor suppressor genes.
AMG-510 1e-18 KRAS G12C
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In the volcano plot above, cell lines with the same cancer gene mutation are grouped together and displayed as circles. The dashed vertical line displays the average sensitivity of the cancer cell lines. Cell lines harboring the KRAS-G12C mutation are 1000 times more sensitive to AMG-510 than cell lines that do not express this mutation, which is visualized by the leftward shift of the green circle representing the KRAS-G12C mutant lines in the panel by 3 logarithmic units.
Comparative analysis via OncolinesProfilerTM: The drug sensitivity fingerprint generated in Oncolines™ was then used for comparative analyses to test for similarities to a database of 178 pre- profiled anti-cancer drugs. Hierarchical clustering and network tree analyses revealed that AMG- 510 is closely related to compounds such as dinaciclib and KU-60019. NTRC is a Precision Medicine company Precision Medicine Services with a focus on Oncology and Cancer
Immunotherapy
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fulvestrant GSK461364 vincristine
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afatinib 508 zanubrutinib neratinib
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osimertinib pictilisib bortezomib Herceptin apitolisib prednisolone
acalabrutinib
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vecabrutinib
trans retinoic acid retinoic trans
− all
The above figure shows a hierarchical clustering tree based on the Oncolines™ profiles of 178 reference anti-cancer agents. Each compound is assigned to one of 21 clusters and is colored accordingly. Compounds that belong to the same cluster often act using the same mechanism.
AMG-510
dinaciclib
KU−60019
The network tree shows the similarity of the Oncolines™ profiles of AMG-510 with other compounds in the reference library. Compounds are displayed as circles. Colors indicate the hierarchical clustering. Compounds whose Oncolines™ profiles show significant similarity to AMG- 510 (Pearson correlation > 0.5) are connected with a line. NTRC is a Precision Medicine company Precision Medicine Services with a focus on Oncology and Cancer Immunotherapy
Summary:
Coupling panel profiling assays with advanced bioinformatics analyses can reveal unique characteristics of the candidate compound, its likely mechanism of action and correlation with other existing drugs. Advanced bioinformatics analyses can highlight hidden trends that would have otherwise gone unnoticed in basic assays. Interactive reports such as those provided by NTRC make it easier to filter out the most relevant findings and eliminate data overwhelm. For instance, rather than examining the dose-response curves of 103 cell lines and individually plotting the numbers, the interactive report makes it easier to quickly determine which cell lines are most sensitive to the compound, what tissue types are impacted by it and what mechanism of action the compound most likely uses in vivo.
Take an Informed Route to the Clinic Gain New Insights and Identify Relevant Biomarkers with Confidence Panel profiling and bioinformatics analyses performed in collaboration with service providers such as NTRC To find out how NTRC can help you maximize the can reliably predict drug response biomarkers. It can results of cancer cell panel profiling, accelerate help drug developers take an informed route to the timelines and make more informed decisions with clinic by stratifying the right patient populations advanced bioinformatics analysis, contact early on, reducing risk of failure at later stages, [email protected] or visit www.oncolines.com and ultimately, increasing the probability that the compound successfully enters the market as an approved therapeutic.
References
[1] Zaman et al., TTK inhibitors as a targeted therapy for CTNNB1 (β-catenin) mutant cancers. Mol Cancer Ther 2017;16:2609-17.
[2] Uitdehaag et al., Cell panel profiling reveals conserved therapeutic clusters and differentiates the mechanism of action of different PI3K/mTOR, Aurora kinase and EZH2 inhibitors. Mol Cancer Ther 2016;15:3097-109.
[3] Willemsen-Seegers et al., Compound selectivity and target residence time of kinase inhibitors studied with surface plasmon resonance. J Mol Biol 2016;429:574-86.
[4] Uitdehaag et al., Combined cellular and biochemical profiling to identify drug response biomarkers for kinase inhibitors approved for clinical use between 2013 and 2017. Mol Cancer Ther 2019;18:470-81.