Patent Application
20160019374
The invention relates to methods for predicting computationally the sensitivity of cells to combination of compound molecules that modulate a set of endogenous target molecules to produce a beneficial biomedical or biological effect.
Statistical inference and regression methods in conjunction with gene expression or mutations have been used to identify specific biomarkers associated with an increased sensitivity/resistance to drugs and to predict drug sensitivity. For instance, the sensitivity to PARP inhibitors of Ewing's sarcoma cells with mutations in the EWS gene and to MEK inhibitors in NRAS-mutant cell lines with AHR expression have been predicted using analysis of variance and the elastic net method1 and then experimentally validated.2,3 In these analyses, the statistical variable associated to drugs was represented by the half maximal inhibitory concentration (IC50) in different cell lines. However, besides the IC50, there are many other types of information that characterize chemical compounds. These types of information can enhance the statistical analyses and improve the accuracy of predictions. For instance, a method to predict drugs sensitivity in cell lines based on the integration of genomic data with molecular physico-chemical descriptors of the drugs has been recently proposed.4 Another useful type of information is the residual activity of drug target proteins after interacting with a compound. This information is often available for drugs belonging to the class of kinase inhibitors. The important role of kinases in cancer biology5 has spurred a considerable effort towards the synthesis of libraries of fully profiled kinase inhibitors, providing a map of the strength of each compound on a large number of its drug targets.6-8 A recently published dataset has profiled several hundred kinase inhibitors using a panel of more than 300 kinases.8 Kinase profiling, patient genetic profiles, and sensitivity of primary leukemia patient samples to kinase inhibitors were recently used by Tyner et al.9 to identify functionally important kinase targets and clarify kinase pathway dependence in cancer.
The invention provides a computational method that incorporates profiled drug libraries and in vitro measurements to predict the response of cells to previously untested drug combinations containing two or more compounds. Besides making prediction about the cellular response to drugs, the method identifies critical drug targets and pathways that are statistically associated to drug sensitivity in a given cell line. To this end the invention also provides drug combinations containing two or more compounds identified using the methods herein together with a pharmaceutically acceptable carrier to form a pharmaceutical for the treatment of disease.
The above is accomplished through a method of quantitatively predicting effectiveness of drug combinations with two or more compounds for use as a therapy for a disease. In Some embodiments the disease is a cancer, such as breast cancer, lung cancer, colon cancer, prostate cancer, melanoma, or other cancer identified by the National Cancer Institute (NCI). The method includes providing a biological sample from a patient suffering from a disease, optionally a cancer, the sample comprising cells from the patient; providing a library of drugs in which each drug i has known residual activity Ak,i of each drug-target protein k under the effect of the drug i; assaying the cells from the biological sample with each drug i to obtain a viability parameter vi for each drug, wherein the assay is an endpoint assay that determines growth, survival, or death of a living cell; performing a regression analysis using results from the assays to determine a training set and to identify residual activity parameters as predictors of the viability, wherein the results are modeled assuming a dependence as Equation (I):
and the regression analysis provides a set of coefficients β0, . . . , βp, where p is a number of drug-target proteins; predicting the viability of a combination COMB with N drugs and dosages d=(d1, d2, . . . , dN) as Equation (II)
where residual activity Ak,COMB of the drug target k under the effect of a combination with N drugs and dosages d=(d1, d2, . . . , dN) is calculated according to Equation (III)
and searching within the space of dosages d=(d1, d2, . . . , dN)to find a corresponding optimal value of the predicted combination viability vCOMB; and selecting the identified optimal combination of drugs as a combinatorial therapy for a patient having a same cancer or same disease.
In some embodiments, the Equation (I)
is reduced to a linear form using a logarithmic transformation and the coefficients (β0, β1, . . . , βp) are obtained using a linear regression procedure including the lasso, ridge, or elastic net methods.
In some embodiments the biological sample is a bodily fluid, such as blood or serum; however, in other embodiments the biological sample is a tumor or bodily tissue, such as a biopsied tissue from any organ. The skilled artisan will appreciate that cells, such as cancer cells, may be harvested from the biological sample using any suitable approach. Examples of cancer cells and thus cancers that can be treated using the methods of the invention include breast cancer cells, lung cancer cells, prostate cancer cells, melanoma cancer cells and other cancer cells identified by the National Cancer Institute (NCI). In other embodiments the cells may be non-cancerous cells from different tissues for predicting toxicity of a combination of two or more compounds.
In some embodiments the predictions of viability and toxicity are combined in a prediction or form a therapeutic index of a drug combination with two or more compounds. The therapeutic index of different drug combinations can then be compared and desired combinations chosen for combination with a pharmaceutically acceptable carrier to form a pharmaceutical as known in the medical arts. In some embodiments the predictions of viability are combined to predict synergistic and antagonistic properties of drug combinations for potential therapy. Once the pharmaceutical is formed it can be administered to the same patient from which the biological sample was taken or can be administered to a different patient suffering from the same disease or cancer. In some instances a pharmaceutical formed from a combination of two or more compounds selected from assays performed on cells obtained from a first cancer sample is selected for the treatment of a patient suffering from a different cancer.
In some embodiments the magnitude of the coefficients β1, . . . , βp is used to identify drug-target proteins whose inhibition or stimulation is associated to a positive therapeutic response.
In some embodiments the library of drugs consists of compounds in the drug class of kinase inhibitors.
For clarity of disclosure, and not by way of limitation, the invention is discussed according to different detailed embodiments; however, the skilled artisan would recognize that features of one embodiment can be combined with other embodiments and is therefore within the intended scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of ordinary skill in the art to which this invention belongs. If a definition set forth in this document is contrary to or otherwise inconsistent with a well accepted definition set forth in the art, the definition set forth in this document prevails over a contradictory definition.
The term “residual activity” as used herein refers to the activity of a protein after inhibition by one or more drugs, e. g, the activity of a kinase after inhibition by one or more kinase inhibitors.
The term “viability parameter” as used herein refers to a measure of viability of a cell population, usually expressed as a percentage of a control untreated population of cells.
The term “optimal value of a predicted combination viability” as used herein refers to either a maximum or a minimum of the viability parameter, depending on the specific therapeutic application.
The term “therapeutic index” or “therapeutic index of a drug” as used herein refers to ratio of the viability of the disease cells, e.g. cancer cells, over the viability of normal control cells treated with the same drug or drug combination.
The term “endpoint assay” as used herein refers to a measurement of growth, survival, or death of a cell at a specific therapeutically relevant time-point.
As an introduction to the invention we have introduced an integrated experimental and computational methodology that identifies the role of specific kinases in the drug response of a given cell line. The key element of our KIEN methodology is a multiple regression procedure that uses in vitro screen data as a training set. If a new library of kinase inhibitor compounds were to be synthetized and profiled, then our model would be able to immediately estimate the effect of these drugs on in vitro experiments on a given cell line. In support of our approach we show an application to a lung cancer cell line in Example 3, but the skilled artisan will appreciate that our method can be extended to different cancerous and non-cancerous cell lines. The method also facilitates the design of new kinase inhibitors and the development of therapeutic interventions with combinations of many inhibitors.30 While in some embodiments at least two drugs or compounds for the combination are used, in other embodiments the procedure could be extended to three drug combinations, or more than three drug combinations. Finally, the method could be extended to regression models that are specific of cancer cells with the same set of mutations, or it could be directly used with patient-derived primary cells to identify a personalized treatment.
Drug-kinase profiling represents a controller-target network20 that when combined with in vitro testing, can be used in regression models to predict drug response and to identify pathways statistically associated to drug sensitivity. Network methods in biology are often based on the analysis of large datasets from high-throughput experiments. An example is given by gene regulatory networks, which presents many challenges either when restricted to a homogeneous set of data21,22 or when it includes different classes of data.23-26 In our method, information from the drug-target network and experimental query of the biological system are integrated. The goal is not a reconstruction of a regulatory network, but to identify a set of kinases linked to a therapeutic response in a given cell line. In order to establish associations, the system has to be perturbed by the use of kinase inhibitor drugs. The response to these single drugs or drug combinations becomes a training set that when combined with the kinase profiling, can lead to predictions.
The elastic net method is one of the most widely used regularization techniques. Regularization techniques are used in statistical and machine learning models to achieve an optimal tradeoff between accuracy and simplicity. Simplicity makes a model less prone to overfitting and more likely to generalize. In our analysis, we found that the elastic net regressions based on single drug responses were not successful, while drug pair data provided statistically significant predictions. A possible explanation for this finding is the following: single drugs might be less able to overcome the robustness of biological networks.20 The phenotypic signal is therefore blunted and not easily measured. If a second drug is added, any compensatory capacity is already stretched and the effects from the inhibition of each kinase can be seen more clearly. Using data from drug pairs, we found that noise can be better filtered out and stronger statistical associations between kinases and therapeutic response are revealed. Clearly, if a different training set with higher variance in efficacy measures were used in the primary screen, it is likely that also single drug in vitro response would have given a significant predictive model.
We identified several kinases that are implicated in lung cancer that gives biological significance to our KIEN method. In particular, TGFBR2 appears as a top hit both in the correlation and in the elastic net methods. This finding is consistent with recent siRNA experiments on A549 cell lines,10 which demonstrated that silencing of this receptor reduces cell proliferation, invasion, and metastasis. The Cyclin-dependent kinase 4 (CDK4) appears as a second top target in the correlation analysis, and is also highly significant in the KIEN analysis. Experiments using lentiviral-mediated shRNA to inhibit CDK4 in A549 have shown inhibited cell cycle progression, suppressed cell proliferation, colony formation, and migration,11 and there is an ongoing clinical trial using a CDK4/6 inhibitor in lung cancer.27 The KIEN analysis identified EGFR, which is known to be overexpressed in the majority of non-small cell lung cancers.12 Furthermore, RNAi experiments targeting EGFR demonstrated cancer growth suppression in A549 xenograft in mice.13 The third kinase in Table 2 (
The pathway-based enrichment provides a broader view on the role of the kinases identified by our method in Table 2 (
Materials
The primary screening of a kinase inhibitor (KI) library comprised of 244 KIs was purchased from EMD Chemicals, and diluted with DMSO to 2 mM concentrations for high-throughput screening purposes. The KI library was stored at −80° C. Additionally, PDK1/Akt1/Flt3 Dual Pathway Inhibitor (CAS #331253-86-2) was ordered from EMD. Only 140 out of 244 were used in the drug-target network reconstruction because the drug profiling information was available only for these compounds. One kinase inhibitor known to affect the kinase targets indirectly was excluded.
Cell Culture
Cell lines IMR-90 (normal lung fibroblast) and A549 (lung adenocarcinoma) were cultured in RPMI 1640 (Hyclone) supplemented with 10% Canadian characterized fetal bovine serum (Hyclone), 1% 200 mM L-glutamine (Omega), and 1% penicillin/streptomycin (Omega). The media for the cells were renewed every 3 days and kept at 80-90% confluency. Cells were maintained in a humidified environment at 37° C. and 5% CO2.
Kinase Inhibitor Experiments
IMR-90 (1500 cells/well) and A549 (750 cells/well) were seeded on 384-well microplates (Grenier Bio-One) and incubated for 3 hours before the addition of kinase inhibitor(s). The reason that IMR-90 was seeded at double the cell density of A549 is due to the difference in cell division. IMR-90's doubling time is 36-48 hours whereas A549's is 22 hours. We wanted to make sure that the cells have divided at least once during the 72 hr drug treatment. Furthermore, both A549's and IMR-90's final confluency at 72 hrs is 90-95% and within the range of the ATPlite 1 step assay. ECHO 555 Liquid Handler (Labcyte) was used to dispense nanoliter volumes of each KI to 384-well plates with cells attached (wet dispense). The final volume in the plate is 40 uL and cells were incubated for 72 hours with KI treatment.
ATP Measurements
ATPlite 1 Step (Perkin Elmer) was used to evaluate the cell number and cytotoxicity. ATP measurements were done by dispensing 20 uL of the ATPlite 1 Step solution to each well to a final volume of 60 uL. The plate was placed on a shaker at 1100 rpm and the luminescence activity was detected by Analyst GT Plate Reader. The percent (%) of control is the quantity of ATPlite 1 step measurement of the treated versus the untreated wells of each individual cell type.
The ATP standard was prepared with culture media to final volume of 40 uL, and 20 uL of ATPlite 1 step reagent was added.
Computational Methods
Correlations between selectivity/viability and kinase activity were calculated using the python scipy linregress function, which also provide p-values. Ranking the p-values and directly applying the Benjamini-Hochberg procedure gave us the FDR values. The elastic net regression was carried out using the Scikit-learn package31 which finds the coefficients β that minimize the function according to Equation (IV):
where v is the vector of the observed viabilities and A is the matrix containing the residual activity of the kinases from the profiling, and M is the total number of drugs or drug combinations used. The parameters α and β determine the relative weights of the lasso and ridge penalties quantified using Li (∥·∥1) and L2 (∥·∥2) norm, respectively. We used α=0.15 and p=0.01 in the results of
Pathway-Based Enrichment
Reactome pathways were downloaded using a newer build of the ‘biomaRt’ library (v2.12.0) in Bioconductor/R (v2.15.0). Gene symbols from the kinase list were converted to Entrez gene identifier numbers (‘entrezgene’) and mapped against the gene ids in each Reactome pathway. For each pathway, the set of significant genes enriched within any given pathway was computed using a Fisher exact test. The procedure computes the significance (p-value) of observing significant kinases, as deemed significant by our method, within the selected pathway. These pathways are identified from 518 Reactome pathways. Given that our gene set consists entirely of kinases and would be generalized towards kinase-specific effects, the set of all kinases (˜300) were selected for background adjustment and more sensitive enrichment of the pathways. This procedure was repeated for each pathway to generate p-values and pathway rankings. False discovery rate [FDR] values were later generated to further restrict significance.
We used our method to predict synergistic effects between four kinase inhibitors: G13, O20, I15, and K10. These kinase inhibitors were selected for their ability to protect primary myoblasts against 0.1% hypoxia. Out of 244 drugs from the EMD library of kinase inhibitors, we first selected 58 drugs that have a positive effect on cell survivability against hypoxia. Single-drug screening results with these drugs were used as a training set to build a regression model that uses the kinase catalytic activity as predictor of the viability. The model assumed a dependence of the viability Vii of the form of Equation (V)
v
i
=e
β
*(A1,i)β
where we indicate as Ak,i the residual activity of the kinase k under the effect of drug i. The residual activities were obtained from a published dataset containing catalytic activities of kinase inhibitor targets8. P17 was also found to protect primary myoblasts, but was excluded in our analysis since the kinase profiling information was not available in this dataset.
Equation (V) can be reduced to a linear form using a logarithmic transformation, and the coefficients (β0, β1, . . . , βp) can obtained using a linear regression procedure. The resulting coefficients βk can be interpreted as a measure of the protective impact of kinase k on the cell survival against hypoxia. A higher value of the coefficient of a kinase indicates a higher protective effect of that kinase on the survivability.
The coefficients βk were then used to predict the viability of four-drug combinations. We assumed that the residual activity Ak,c of the kinase k under the effect of a four-drug combination c with dosage d=(d1, d2, d3, d4) is
A
k,0
=A
k,1
d
*A
k,2
d
*A
k,3
d
*A
k,4
d
. (VI)
The predicted viability of a four-drug combination, va can then be obtained using a modified form of Equation (V) as
v
c
=e
β
*A
1,c
β
* . . . *A
p,c
↑
, (VI)
where the coefficients βk are determined using the linear regression on the single-drug data. In the prediction we only considered kinases with positive coefficients since we are modeling a protective effect on cell survival against hypoxia.
The results in
In Vitro Screen of the Kinase Inhibitor Library
Our methodology begins with the high-throughput screening of single drug and drug pair experiments. The 244 kinase inhibitors (KIs) of the EMD drug library were screened at 1000 nM individually and the treatment lasted for 72 hours. To quantify a selective response of a cancer cell line with respect to a control normal cell line, we define the selectivity S of a single drug or drug combination as
where vN indicates the viability of normal cells (IMR90) after treatment, and vc the viability of cancer cells (A549) after treatment. From the screening of the 244 KIs, the top hit was PDK1/Akt1/Flt3 Dual Pathway Inhibitor (CAS #331253-86-2) as ranked by selectivity (
Analysis of Correlations
In our second step, we analyzed the Pearson's correlation of the primary and secondary screening with a published dataset15 containing target profiles for 140 kinase inhibitors. Therefore, even though we had a library of 244 KIs in the experimental screening, we were limited to utilizing 140 KIs for the analysis. For each inhibitor, the dataset provides the residual activity (0≦A≦1) of 291 kinases after drug treatment. This quantity is a measure of the strength of inhibition of a drug on each kinase.
For each kinase k, we calculate the Pearson's correlation, Ck, between the selectivity Si and the activities Ak,i, with i ε {1, . . . , M} indicating the single drug or drug pair in the set. For drug pairs, the activity is estimated as a product of the residual activities of the two drugs. The kinases are then ranked based on the p-value of their correlation with selectivity, and we calculate the False Discovery Rate (FDR) adjusted p value.16 The list of kinases mostly correlated to the selectivity from the primary and secondary screen are listed in Table 1 (FIG.7). We also calculated the correlation between the normal or cancer cell viability and the activities.
Elastic Net Regression
Next, we built a regression model that predicts the response of a cell line to a drug or drug combination i. The response we predict is the normal and cancer cell viability, from which the selectivity can be derived. For this purpose, we define a regression problem in which we use the residual activity of the kinase k under the effect of drug i, which we indicate as Ak,i, as predictors of the viability. The response can be written as Equation (VIII)
v
i=β0+β1A1,i+ . . . +βpAp,i. (VIII)
A fitting procedure based on a training set of measurements produces the coefficients (β0, β1, . . . , βp). Equation (VIII) can then be used to predict the viability of a new drug that has not been tested, but of which the profiling information is available. Note that we are integrating two different types of data: kinase profiling data is obtained through enzymatic assays that probe directly the interaction between drug and kinases, while the in vitro cell response data is the result of complex signaling that involves many pathways downstream of the affected kinases. The coefficients βk can be seen as a measure of the sensitivity of a given cell line due to alterations in the activity of kinase k.
It is well known that the least square method does not perform well in the case of linear regression with many predictors. In our case, we would like to use a database of drugs that have been profiled on about 300 kinases. However, it would be desirable to select and keep in the final model a minimal set of the kinases that provide a simple model, useful to gain biological insight. The lasso technique17 is a powerful method to reduce the number of predictors by imposing a penalty on the regression coefficients. However, in the presence of a group of kinase predictors with strong mutual correlation, the lasso could select only one kinase predictor from the group while missing the others. To prevent this problem, our method uses the elastic net approach. This method incorporates the lasso penalty as well as a ridge penalty to keep the regression coefficients small without completely removing them.18 The weights of the ridge and lasso penalties in the least square procedure can be optimized for best performance of the method.
We show in
Data transformations can represent a powerful strategy to improve regression. We applied a logarithmic transformation, which is consistent with the hypothesis of an independent action on the different kinases on the total viability. In this case we assume that the viability can be rewritten in the form of Equation (IX)
v
1
=e
β
(A1,1)β
By applying a log transformation on both sides of Eq. (IX) we reduce the problem to a linear regression, to which the elastic net strategy can be applied. We show in
In addition to a regression model that can be used to predict the efficacy of drugs that have not been tested, the βi can be used to rank kinases in terms of their relevance in the regression. Therefore, these coefficients identify the kinases whose inhibition is associated to a decrease in the cell viability. A ranking based on the differential βiC−βiN, where the index N and C identify the regression model of the cancer and normal cells, gives insight on specific pathways important for a selective response of cancer cells. Table 2 (
In order to test whether selected pathways were significantly enriched for the identified kinase genes in Table 2 (
The invention described herein may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The specific embodiments previously described are therefor to be considered as illustrative of, and not limiting, the scope of the invention.
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This invention claims benefit of priority to U.S. provisional patent application Ser. No. 62/026,110 filed Jul. 18, 2014; the entire content of which is herein incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 62026110 | Jul 2014 | US |
