BACKGROUND
in the last decade, cancer treatment has seen a shift from a "one size fits all" philosophy to a more personalized approach.
technical advances allow for the assessment of complex genetic defects and pathway aberrations, enabling refined cancer classification and treatment prediction based on molecular rather than histological features.
gene expression profiling has been used to classify patient samples as being benign or malignant or to classify them into cancer subclasses  <cit> .
lately, molecular profiling of cancer samples has been applied to the prediction of drug sensitivity  <cit> .
drug-sensitive samples have been compared to drug-resistant samples to build "drug sensitivity profiles", statistical models built from a defined set of genes whose differential expression in a cell line may confer sensitivity to a drug.
thus, cancer cell lines whose gene expression patterns are similar to the genes in the sensitivity profile will have a higher probability of responding to the drug.
it has been demonstrated that gene expression profiling can refine the prediction of drug response to targeted drug therapies.
for example, harris et al.
used gene expression profiling to identify lists of genes that are potential predictors of response to herceptin and vinorelbine in her2-positive breast cancers  <cit> .
another study used the coxen  algorithm to build sensitivity profiles for the drugs cisplatin and paclitaxel  <cit> .
the profiles were subsequently used to predict drug response in bladder and breast cancer samples.
of particular interest is the fact that these samples were not part of the original anti-cancer cancer panel, showing that drug sensitivity profiles generated from the screen can be generalized to any tumor sample.
approaches such as coxen make use of comprehensive pharmacogenomic resources to derive drug sensitivity profiles.
coxen was applied to the nci- <dig>  anti-cancer screen, which tested > <dig>  compounds on  <dig> cancer cell lines, all of which have been profiled  for genome-wide gene expression  <cit> .
sensitivity profiles for the compounds in the screen can be generated by comparing gene expression values of sensitive and resistant cell lines.
the coxen algorithm  can only generate profiles for compounds that are part of an anti-cancer drug screen  <cit> .
if a drug was not tested as part of the nci- <dig> dataset, its sensitivity profile cannot be generated.
updating an existing cancer screen with the latest available or experimental drugs is a non-trivial issue, and requires the same expertise, infrastructure and conditions as when the screen was established the first time around.
in this work, we present a method to predict responses to drugs that are not part of available anti-cancer screens.
we propose to find substitute sensitivity profiles for these untested drugs by using structural similarity.
it has been known that the structure of a compound is related to its activity .
shi et al.
performed an analysis where  <dig> compounds  were clustered based on activity across cancer cell lines  <cit> .
they found that compounds with similar structures clustered together.
several other studies have been performed to show that specific classes of compounds  have similar activity patterns across cancer cell lines  <cit> .
therefore, it can be hypothesized that structurally similar compounds have similar activities and might affect the same pathways.
in other words, the sensitivity pattern for drug a  might be similar to that of drug b  if drug a and drug b are structurally similar.
we are interested in extending this idea to test whether the drug sensitivity profiles of a structural analogue of a drug can be used as a substitute when predicting its response in cancer cell lines.
RESULTS
to analyze whether a structural analogue of a drug can be used to predict its response, two questions need to be answered.
the first question is whether two structurally similar drugs have the same sensitivity pattern across the nci- <dig> cancer cell lines.
secondly, even if we can show that there is a strong association between structural similarity and drug sensitivity, we need to test whether the correlation is strong enough to be of any practicality.
the second question we would like to ask is whether the sensitivity profile of drug a be used to predict activity of drug b without a significant loss of accuracy, if drug a and drug b are structurally similar.
we investigated whether this proposition holds only for compounds in the same chemical family, or also for compounds that are similar but from different families.
structural similarity and response similarity in nci- <dig> cancer cell lines
the first question we set out to address was whether two structurally similar drugs have the same sensitivity pattern across cancer cell lines in an anti-cancer screen, even when the mechanisms of action or the classes of the compounds might not be known.
we used the tanimoto coefficient between pairs of compounds as the structural similarity measure .
in figure  <dig>  we show pairs of compounds ordered by their tanimoto structural similarity coefficients , and the similarity between their responses across the  <dig> cancer cell lines .
the similarity between responses was measured as the percentage of cell lines that elicited the same response to both the compounds.
as the tanimoto coefficient increases , the similarity between the cell line responses increases too .
this supports the hypothesis that if two compounds are structurally similar, their sensitivity patterns in the nci- <dig> data are similar too.
this can be interpreted in the following manner: two structurally similar drugs may bind similar cellular structures, and activate or inhibit similar cellular pathways, and therefore share a common cause of resistance.
we next set out to investigate the practical implications of this finding.
predictive power of sensitivity profiles of structurally similar drugs
the second question we wanted to ask is whether we can use one drug as a substitute to perform gene-expression-based sensitivity prediction for another drug.
in this scenario, we take advantage of an existing anti-cancer screen, such as the nci- <dig>  to construct a sensitivity profile for a drug a, and use it as a substitute profile for another drug b. specifically, we wanted to see what the loss of accuracy in response prediction would be if the sensitivity profile was constructed from a drug with high structural similarity.
using a cross-validation setup within the nci- <dig> data set, we could measure the change in prediction accuracy when using drug b's sensitivity profile compared to using a substitute profile generated from drugs a with various levels of structural similarity to b.
families of compounds in our dataset
to ensure that not all structurally similar pairs of compounds in our dataset belonged to the same family, we identified  <dig> pairs of compounds with tanimoto coefficients higher than  <dig>  we were able to extract several pairs of interest where the compounds did not belong to the same family.
figure  <dig> shows derivatives of camptothecin  along with amino acid derivatives clustered together in a visualization of the  <dig> compounds in our dataset.
comparing their activity patterns in nci- <dig>  we found that both classes exhibited exactly the same activity across the cell lines.
both camptothecins and amino-acid derivatives have been known to have anti-neoplastic activities, but we couldn't find any evidence in the literature of the amino acid derivatives inhibiting topoisomerase i. the above example shows that while the pairs of compounds might belong to different functional or mechanistic families, structural similarities  highly correlate with their cellular responses.
CONCLUSIONS
cancer patients need access to the latest decision criteria given an increasing number of highly specific and targeted anti-cancer drugs.
it is believed that the pre-treatment cellular state is predictive of whether a cancer cell is going to respond to a particular drug.
functional anti-cancer drug screens give access to pre-treatment gene expression states from sensitive and resistant cancer cells, and are useful in deriving drug sensitivity profiles that can be applied to independent cancer samples.
one major limitation of this approach is that profiles can only be generated for drugs that have been tested in anti-cancer screens.
in our work, we addressed the issue of deriving sensitivity profiles for untested drugs for which we do not  have response information across a number of cell lines.
for those drugs, we examined the possibility of using substitute drug sensitivity profiles from structurally similar drugs that have been tested in the screen.
we established that it is indeed true that structural similarity is highly correlated with sensitivity across cancer cell lines.
we have also shown that for structurally related drugs, the use of substitute sensitivity profiles is likely to result in response predictions similar to using a drug's own profile in the first place.
our study may thus be helpful in increasing the relevance of existing screens, by expanding their use to other drugs  with high structural similarity.
since not all new cancer drugs are immediately tested in anti-cancer screens, our approach ultimately increases the number of drugs for which patient response can be predicted.
a drawback of the current study is the small number of drugs that we could include in our analysis, i.e.
drugs that showed sufficient variation across cell lines.
this introduces some bias that may favor pairs of compounds in the same class and with high similarity in activity patterns.
nonetheless, the above experiments demonstrate that there is a strong correlation between structural similarity and drug sensitivity in the nci- <dig> cancer cell lines.
in terms of future work, we want to explore the following issues.
first, we want to fine-tune our sensitivity profile generating algorithm, in order to increase the prediction accuracy when using a compound's own profile .
second, we want to explore other structural similarity measures to ensure that we indeed identify the closest structural pairs among the nci- <dig> compounds.
third, we want to investigate other similarity measures  that can identify an analogue for sensitivity profile substitution.

 in the last decade, cancer treatment has seen a shift from a "one size fits all" philosophy to a more personalized approach. technical advances allow for the assessment of complex genetic defects and pathway aberrations, enabling refined cancer classification and treatment prediction based on molecular rather than histological features. gene expression profiling has been used to classify patient samples as being benign or malignant or to classify them into cancer subclasses. lately, molecular profiling of cancer samples has been applied to the prediction of drug sensitivity. drug-sensitive samples have been compared to drug-resistant samples to build "drug sensitivity profiles", statistical models built from a defined set of genes whose differential expression in a cell line may confer sensitivity to a drug. thus, cancer cell lines whose gene expression patterns are similar to the genes in the sensitivity profile will have a higher probability of responding to the drug. it has been demonstrated that gene expression profiling can refine the prediction of drug response to targeted drug therapies. for example, harris et al. used gene expression profiling to identify lists of genes that are potential predictors of response to herceptin and vinorelbine in her2-positive breast cancers. another study used the coxen algorithm to build sensitivity profiles for the drugs cisplatin and paclitaxel. the profiles were subsequently used to predict drug response in bladder and breast cancer samples. of particular interest is the fact that these samples were not part of the original anti-cancer cancer panel, showing that drug sensitivity profiles generated from the screen can be generalized to any tumor sample. approaches such as coxen make use of comprehensive pharmacogenomic resources to derive drug sensitivity profiles. coxen was applied to the nci- anti-cancer screen, which tested > compounds on cancer cell lines, all of which have been profiled for genome-wide gene expression. sensitivity profiles for the compounds in the screen can be generated by comparing gene expression values of sensitive and resistant cell lines. the coxen algorithm can only generate profiles for compounds that are part of an anti-cancer drug screen. if a drug was not tested as part of the nci- dataset, its sensitivity profile cannot be generated. updating an existing cancer screen with the latest available or experimental drugs is a non-trivial issue, and requires the same expertise, infrastructure and conditions as when the screen was established the first time around. in this work, we present a method to predict responses to drugs that are not part of available anti-cancer screens. we propose to find substitute sensitivity profiles for these untested drugs by using structural similarity. it has been known that the structure of a compound is related to its activity. shi et al. performed an analysis where compounds were clustered based on activity across cancer cell lines. they found that compounds with similar structures clustered together. several other studies have been performed to show that specific classes of compounds have similar activity patterns across cancer cell lines. therefore, it can be hypothesized that structurally similar compounds have similar activities and might affect the same pathways. in other words, the sensitivity pattern for drug a might be similar to that of drug b if drug a and drug b are structurally similar. we are interested in extending this idea to test whether the drug sensitivity profiles of a structural analogue of a drug can be used as a substitute when predicting its response in cancer cell lines.  to analyze whether a structural analogue of a drug can be used to predict its response, two questions need to be answered. the first question is whether two structurally similar drugs have the same sensitivity pattern across the nci- cancer cell lines. secondly, even if we can show that there is a strong association between structural similarity and drug sensitivity, we need to test whether the correlation is strong enough to be of any practicality. the second question we would like to ask is whether the sensitivity profile of drug a be used to predict activity of drug b without a significant loss of accuracy, if drug a and drug b are structurally similar. we investigated whether this proposition holds only for compounds in the same chemical family, or also for compounds that are similar but from different families. structural similarity and response similarity in nci- cancer cell lines the first question we set out to address was whether two structurally similar drugs have the same sensitivity pattern across cancer cell lines in an anti-cancer screen, even when the mechanisms of action or the classes of the compounds might not be known. we used the tanimoto coefficient between pairs of compounds as the structural similarity measure. in figure we show pairs of compounds ordered by their tanimoto structural similarity coefficients, and the similarity between their responses across the cancer cell lines. the similarity between responses was measured as the percentage of cell lines that elicited the same response to both the compounds. as the tanimoto coefficient increases, the similarity between the cell line responses increases too. this supports the hypothesis that if two compounds are structurally similar, their sensitivity patterns in the nci- data are similar too. this can be interpreted in the following manner: two structurally similar drugs may bind similar cellular structures, and activate or inhibit similar cellular pathways, and therefore share a common cause of resistance. we next set out to investigate the practical implications of this finding. predictive power of sensitivity profiles of structurally similar drugs the second question we wanted to ask is whether we can use one drug as a substitute to perform gene-expression-based sensitivity prediction for another drug. in this scenario, we take advantage of an existing anti-cancer screen, such as the nci- to construct a sensitivity profile for a drug a, and use it as a substitute profile for another drug b. specifically, we wanted to see what the loss of accuracy in response prediction would be if the sensitivity profile was constructed from a drug with high structural similarity. using a cross-validation setup within the nci- data set, we could measure the change in prediction accuracy when using drug b's sensitivity profile compared to using a substitute profile generated from drugs a with various levels of structural similarity to b. families of compounds in our dataset to ensure that not all structurally similar pairs of compounds in our dataset belonged to the same family, we identified pairs of compounds with tanimoto coefficients higher than we were able to extract several pairs of interest where the compounds did not belong to the same family. figure shows derivatives of camptothecin along with amino acid derivatives clustered together in a visualization of the compounds in our dataset. comparing their activity patterns in nci- we found that both classes exhibited exactly the same activity across the cell lines. both camptothecins and amino-acid derivatives have been known to have anti-neoplastic activities, but we couldn't find any evidence in the literature of the amino acid derivatives inhibiting topoisomerase i. the above example shows that while the pairs of compounds might belong to different functional or mechanistic families, structural similarities highly correlate with their cellular responses.  cancer patients need access to the latest decision criteria given an increasing number of highly specific and targeted anti-cancer drugs. it is believed that the pre-treatment cellular state is predictive of whether a cancer cell is going to respond to a particular drug. functional anti-cancer drug screens give access to pre-treatment gene expression states from sensitive and resistant cancer cells, and are useful in deriving drug sensitivity profiles that can be applied to independent cancer samples. one major limitation of this approach is that profiles can only be generated for drugs that have been tested in anti-cancer screens. in our work, we addressed the issue of deriving sensitivity profiles for untested drugs for which we do not have response information across a number of cell lines. for those drugs, we examined the possibility of using substitute drug sensitivity profiles from structurally similar drugs that have been tested in the screen. we established that it is indeed true that structural similarity is highly correlated with sensitivity across cancer cell lines. we have also shown that for structurally related drugs, the use of substitute sensitivity profiles is likely to result in response predictions similar to using a drug's own profile in the first place. our study may thus be helpful in increasing the relevance of existing screens, by expanding their use to other drugs with high structural similarity. since not all new cancer drugs are immediately tested in anti-cancer screens, our approach ultimately increases the number of drugs for which patient response can be predicted. a drawback of the current study is the small number of drugs that we could include in our analysis, i.e. drugs that showed sufficient variation across cell lines. this introduces some bias that may favor pairs of compounds in the same class and with high similarity in activity patterns. nonetheless, the above experiments demonstrate that there is a strong correlation between structural similarity and drug sensitivity in the nci- cancer cell lines. in terms of future work, we want to explore the following issues. first, we want to fine-tune our sensitivity profile generating algorithm, in order to increase the prediction accuracy when using a compound's own profile. second, we want to explore other structural similarity measures to ensure that we indeed identify the closest structural pairs among the nci- compounds. third, we want to investigate other similarity measures that can identify an analogue for sensitivity profile substitution. 
in a cross-validation experiment, we found that the use of substitute profiles is possible without a significant loss of prediction accuracy if the substitute profile was generated from a compound with high structural similarity to the untested compound. several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as nci- the general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. we first show that structural similarity between pairs of compounds in the nci- dataset highly correlates with the similarity between their activities across the cancer cell lines. this result shows that structurally similar drugs can be expected to have a similar effect on cancer cell lines. we next set out to test our hypothesis that we can use existing drug sensitivity profiles as substitute profiles for untested drugs. anti-cancer drug screens are a valuable resource for generating omics-based drug sensitivity profiles. the ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. in this work, we propose using an existing drug sensitivity profile for drug a as a substitute for an untested drug b given high structural similarities between drugs a and b. we show that it is possible to extend the usefulness of existing screens to untested drugs by deriving substitute sensitivity profiles from structurally similar drugs part of the screen.
 the ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as nci- the general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. however, one limitation is that the profiles can not be generated for untested drugs. in this work, we propose using an existing drug sensitivity profile for drug a as a substitute for an untested drug b given high structural similarities between drugs a and b.  we first show that structural similarity between pairs of compounds in the nci- dataset highly correlates with the similarity between their activities across the cancer cell lines. this result shows that structurally similar drugs can be expected to have a similar effect on cancer cell lines. we next set out to test our hypothesis that we can use existing drug sensitivity profiles as substitute profiles for untested drugs. in a cross-validation experiment, we found that the use of substitute profiles is possible without a significant loss of prediction accuracy if the substitute profile was generated from a compound with high structural similarity to the untested compound.  anti-cancer drug screens are a valuable resource for generating omics-based drug sensitivity profiles. we show that it is possible to extend the usefulness of existing screens to untested drugs by deriving substitute sensitivity profiles from structurally similar drugs part of the screen. 15– march amia summit on translational bioinformatics san francisco, ca, usa 