BACKGROUND
a major challenge in oncology drug discovery is the identification of tumor vulnerabilities that can lead to novel therapeutic targets, and linking these vulnerabilities to specific patient populations that are likely to benefit from pharmacological inhibition of these targets.
while historically drug targets have originated from in-depth dissection of cancer biology, more recently tumor genome sequencing efforts such as the cancer genome atlas   and international cancer genome consortium   have defined the genomic landscape and complexity for an ever growing number of tumor types and subtypes.
however, with these approaches, it is becoming increasingly difficult to identify novel oncogenic drivers that are both pharmacologically accessible and applicable to a substantial number of patients.
functional genomics offers an alternative means for target identification that is complementary to in-depth biology and sequencing.
gene silencing through sequence-specific targeting of mrnas by rna interference  takes advantage of an endogenous cellular pathway  <cit>  and has become a powerful research tool by enabling high-throughput and systematic loss of function screens in cultured cells and model organisms  <cit> .
one of the largest screens to date is project achilles at the broad institute, a pioneering effort that has utilized a lentivirally delivered short hairpin rna  library to catalog the dependency of  <dig> cancer cell lines on  <dig>  genes  <cit> .
the analysis of genome-scale rnai screens typically has focused on a priori partition of cell lines based on known biological or genetic contexts, such as the mutation of an established oncogene or tumor suppressor, followed by a comparison of the sensitivity patterns of the two groups to identify genes that, when knocked down, confer preferential sensitivity in one group over the other.
this analytical approach has led, for example, to the discovery of arid1b and smarca <dig> as specific vulnerabilities for arid1a and smarca4-mutant cancers, respectively  <cit> .
however, the need to pre-define the groups for interrogation represents an inherent limitation due to the incompleteness of our knowledge  as well as biological complexities .
predefining groups, furthermore, calls for a separate analysis for each biological or genetic context, making it impractical to query all contexts of potential interest.
to address these limitations, we have developed a complementary data mining strategy based on patterns of sensitivity in functional genomics screens that requires no a priori assumptions about the underlying biology of dependency.
oncogene addiction or synthetic lethality usually results in exceptional response in a subset of tumors or cell lines that are exquisitely vulnerable to knockdown or inhibition of the gene being interrogated  <cit> .
the responder subsets are, by definition, outliers relative to the rest of the population or cell line panel.
taking advantage of this observation, our strategy adapts and extends outlier analysis methodologies to identify genes with a subset of exceptional responders among the screened cell lines.
such a data-driven approach in principle makes it possible to identify vulnerabilities in any biological or genetic context in a single analysis, and also allows for the discovery of novel or complex contexts in which inhibition of specific genes represents a vulnerability that would not have been considered in a pre-defined class comparison analysis.
outlier analysis has been widely applied to gene expression data for the discovery of cancer-associated genes  <cit> .
it was first described in the identification of a gene fusion in prostate cancer involving two transcription factors, erg and etv <dig>  <cit> , which led to the cancer outlier profile analysis  method  <cit> .
many technically more sophisticated approaches have followed, including model-based pattern recognition for deviation from uni-modality  and numerical detection for marked high expression in a subset of tumors that is distant from the majority .
outlier detection has also been useful in finding drugs with rare but exceptional response in clinical trials  <cit> .
while highly informative, exceptional responder studies in the clinic are constrained by the relatively modest number of biological mechanisms currently targeted by drugs as well as the challenge of following up hypotheses in patients.
large-scale functional genomic studies relieve these restrictions and enable investigating thousands of genes in parallel.
here we apply an outlier analysis based strategy to functional genomic profiles for systematic oncology target discovery.
the utility of such approach is illustrated by the observation that genes with outlier patterns are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no molecular profiling or any other information being used to drive the analysis.
we show that it may enable the identification of novel candidate therapeutic targets, and that the characteristics of the exceptional responder lines could further point to tumor indications and biomarkers of response to guide precision medicine strategies.
RESULTS
identification of genes with outlier sensitivity patterns
to identify genes with an exceptional responder pattern, we used the union of the results output by three diverse methods.
they each focus on different features  to detect outliers and therefore are considered complementary.
application of these approaches is not intended as a comprehensive comparison of various outlier methodologies; rather we reasoned that together they would provide a more complete set of outliers and outlier genes than any single algorithm.
the first two methods were originally developed for outlier analysis of gene expression data: profile analysis using clustering and kurtosis   <cit> , and outlier sum   <cit> .
pack is a model-based pattern recognition algorithm for discovering bimodal distribution, which first determines the number of clusters in the dataset for each gene and then computes a measure of how much the distribution differs from gaussianity  for those gene profiles with two clusters.
positive kurtosis indicates clusters of unequal relative size, while negative kurtosis indicates clusters of approximately equal representation.
the os algorithm uses the “outlier-sum” statistic, which is defined using values outside a variability-based numerical limit.
it was recently assessed to have the best performance among six closely related outlier techniques  <cit> .
for the third method, inspired by the q  statistic utilized in dixon’s test for outlier detection  <cit> , we devised an intuitive nonparametric approach   to explicitly identify genes with dependency patterns where groups of sensitive cell lines are separated by a major “gap” from the bulk population .
all three methods were applied to the achilles  ataris gene level scores across a diverse panel of  <dig> cell lines derived from consensus clustering of individual hairpin patterns for on-target effects  <cit> .
the achilles dataset includes  <dig> genes that yielded similarity solution and thereby consensus scores  <cit> .
the pack algorithm identified  <dig> genes with bimodal distribution, of which  <dig>  had positive kurtosis with one of the two clusters representing a small “outlier” subgroup.
as the outlier subgroup could have higher or lower shrna score than the rest of the panel, we focused on the  <dig> genes for which the outlier group is more vulnerable to knockdown.
the os and gap methods led to the identification of genes with a non-random outlier pattern  where statistical significance was estimated using permutation.
we also required that the outlier group consist of at least five cell lines to avoid spurious one-off observations, yielding  <dig> genes from the pack algorithm,  <dig> from the os method, and  <dig> from the gap approach.
in all, there were  <dig> unique outlier genes, including  <dig> that emerged from all three methods .fig.
<dig> outlier genes identified with profiling analysis using clustering & kurtosis , outlier sum  and gap analysis procedure  methodologies.
a a summary venn diagram including statistical significance of pairwise overlap , with detailed gene list and relevant results included in additional file 4: table s <dig>  b the ataris gene level score distribution for the  <dig> genes identified by all three outlier methods.
a probability density estimate is computed by gaussian kernel smoothing
enrichment for curated cancer genes and oncogenic pathways
we evaluated the biological relevance of the identified outlier genes by comparing them to two well-known cancer gene collections.
the cancer gene census  from the sanger institute catalogues genes for which mutations have been causally implicated in cancer  <cit>  while the msk-impact™  panel is a curated collection of key cancer genes used for diagnostic genomic testing with next-generation sequencing technology .
although determined independent of any genetic or other molecular profiling information, the genes with outlier patterns are significantly over-represented by those genetically linked to cancer from both cgc and impact .
each of the three outlier analysis approaches enriched for established cancer genes, and their union had greater statistical significance than any of them alone , supporting the complementarity of these methodologies.fig.
<dig> assessment of genes with significant outlier pattern through comparison with two well-known cancer gene collections.
the cancer gene census  from the sanger institute catalogues genes for which mutations have been causally implicated in cancer; the msk-integrated mutation profiling of actionable cancer targets  is a curated panel of key cancer genes used for diagnostic genomic testing.
“union” corresponds to  <dig> combined genes resulting from the three outlier methods
we further assessed the validity of our outlier analysis strategy through unbiased signature comparison.
among  <dig> gene sets from the broad institute’s msigdb canonical pathway  library , which collects canonical representations of biological processes compiled by domain experts from pathway databases, the outlier genes are strongly and specifically enriched in those associated with various tumor types including the prominent oncogenes and therapeutic targets braf, nras, kras, pik3ca, cdk <dig> and ctnnb <dig>  as well as important cellular pathways and processes involved in cancer such as the wnt, pi3k-mtor, p <dig> and rb-e2f pathways, cell cycle and apoptosis .
cdk <dig> , like the targets of the recently approved breast cancer drug ibrance, is a member of the cyclin-dependent kinase family of ser/thr protein kinases whose de-regulation occurs frequently in certain types of cancer  <cit> .
in addition to established cancer genes, our outlier analysis also reveals potentially novel therapeutic opportunities.
for instance, asparagine synthetase  , an enzyme that catalyzes the conversion of aspartate and glutamine to asparagine and glutamate in an atp-dependent manner, has been shown to increase the chemotherapy sensitivity of leukemia cells resistant to l-asparaginase when inhibited  <cit> .
collectively these results demonstrate that outlier analysis can serve as a useful strategy to identify cancer driver genes from rnai sensitivity patterns.table  <dig> enriched signatures from the broad canonical pathway library among outlier genes
enabling tumor indication and predictive biomarker discoveries
with the identification of genes whose knockdown confers exceptional response, we next asked whether the cell lines making up the outlier groups are enriched in a particular tumor type or subtype, or have molecular features which may be linked to susceptibility to target interference.
such insights would not only guide the precision medicine strategy in oncology drug development for selecting patients who are most likely to benefit from targeted therapies, but also help to further prioritize the most biologically compelling and clinically translatable outlier patterns.
the  <dig> cell lines from the achilles dataset were mapped to both general tumor types and common histological subtypes .
in the case of breast cancer, both clinical marker  and gene expression  based classifications  <cit>  were incorporated.
significant tumor type associations  were found for more than half  of the genes with outlier patterns.
these include well recognized relationships such as kras in pancreatic and colorectal cancers , cdk <dig> in luminal/er+ breast cancer , braf in skin cancer , pik3ca in breast and gastric cancers  and apc in colorectal cancer , as well as mechanistically supported relationships like cdk <dig> in ovarian cancer  where ccne <dig>  the cyclin that interacts with cdk <dig>  is frequently amplified  <cit> .table  <dig> outlier genes whose predictive genetic biomarkers are the gene itself  or from the same gene family/protein complex, suggesting potential oncogenic addiction or a synthetic lethal relationship.
genetic biomarkers and tumor types are listed by decreasing statistical significance of association
apc
apc
crc
braf
braf
crc;melanoma
kras
kras
pancreatic;crc
nras
nras
pik3ca
pik3ca
breast;gastric;breast lum_subtype;gastric_adenocarcinoma; crc;breast her2+;breast tnbc;prostate_adenocarcinoma; breast her2_subtype;breast er+
zmiz1
zmiz1
leukemia/lymphoma;breast;aml
to uncover predictive genetic biomarkers, we focused on the lesions that are most likely to be functional, as tumor genomes are often unstable and thus the vast majority of genetic changes are generally passengers  <cit> .
for somatic mutations, we selected those in hotspot positions as well as nonsense and frameshift events.
hotspots were identified systematically using patient-derived genomic profiles from  <dig> tcga tumor types , which were subsequently employed to filter cell line mutation data compiled from the cosmic  <cit>  and ccle  <cit>  databases.
for copy number alterations, we restricted our analysis to high-level amplifications  and deletions .
six  of the  <dig> genes with outlier patterns are significantly  characterized by their own genetic lesions , suggesting oncogenic addiction as the underlying mechanism of their exquisite vulnerability.
all of these with the exception of zmiz <dig> represent prominent oncogenes or tumor suppressors.
zmiz <dig> has previously been identified as a candidate oncogene in multiple murine transposon and insertional mutagenesis screens .
for  <dig> additional genes with outlier patterns, the associated predictive genetic biomarkers are either members of the same gene family or components of the same protein complex , revealing possible synthetic lethal relationships where lesions in functionally related gene confer special dependency.
these include well-documented relationships such as the vulnerability for ctnnb <dig> knockdown in the context of apc lesions and e2f <dig> vulnerability in the context of rb <dig> lesions as well as the recently discovered dependency on arid1b in the context of arid1a lesions  <cit> .
in the case of cdk <dig>  ccne <dig> amplification is specifically over-represented among its associated exceptional responder cell lines , consistent with the tumor type enrichment of ovarian cancer described above.
novel relationships of potential interest were also observed such as top2a vulnerability with parp <dig> lesions, hnrnpa <dig> vulnerability with rpl <dig> lesions and psmd <dig> vulnerability for psmc <dig> lesions.
distinct dependency and coherence between solid and hematological malignancies
to obtain a global view of tumor cell dependency, we performed unsupervised hierarchical clustering of functional genomic profiles using the genes with outlier dependency patterns .
the vast majority of hematological cell lines cluster together by functional data , in contrast to those of solid origin that are more scattered and heterogeneous, despite hematological cell lines representing only 14 % of the total, with tumor types like central nervous system  and ovarian cancer being equally or more heavily covered by the panel .
even functionally related genes, such as those in the wnt pathway, differ in points of liability: while cell lines derived from liquid tumors tend to be vulnerable to tcf <dig> knockdown, those of solid origin are susceptible to other pathway genes including gsk3a/b, ctnnb <dig> and apc .
potentially context-dependent oncogenic roles for some tumor suppressor genes
several outlier genes whose knockdown confers striking vulnerability are tumor suppressors including apc  <cit> , phlpp <dig>  <cit>  and spen  <cit> , while a number of other candidates have been reported to harbor anti-oncogenic activities such as the pro-apoptotic gene mtch <dig>  <cit>  and the anti-metastatic rna chaperone rbm <dig>  <cit> .
among the five lines most responsive to adenomatous polyposis coli  knockdown, all contain loss of function mutation in the apc gene itself and four are of colorectal  origin .
furthermore, they strongly overlap with those dependent on ctnnb <dig> .
truncating apc mutations have been reported to have a dominant negative effect on proliferation, spindle checkpoint control, survival and chromosomal stability  <cit> .
the exceptional responders for phlpp <dig> and mtch <dig>  are over-represented by cells from leukemia/lymphoma and colorectal cancer respectively .
the results therefore support and may further generalize the notion that in certain contexts, tumor suppressor genes can become oncogenic and create specific liabilities.
discussion
the analysis of large-scale cell based functional genomics datasets has predominantly focused on identifying vulnerabilities in known biological contexts, such as mutated oncogenes or tumor suppressors.
a limitation of this approach is that it requires knowledge of the biology to be interrogated and can generally be described as starting with known biology, then looking for patterns in the data that support it.
we have reversed this paradigm and now describe an alternative data mining strategy that starts by looking for profiles indicative of potential dependencies of interest, with no assumptions about the underlying biology of the dependency.
it is based on identifying genes with subgroups of exceptionally sensitive cell lines.
by definition, these exceptional responders are statistical outliers.
we therefore hypothesized that outlier analysis, whose previous application in genomic studies has been limited to gene expression data, would also be useful in investigating rna interference response patterns.
we tested the hypothesis by applying outlier analysis to genome-scale shrna screen results from project achilles at the broad institute, and found genes with outlier patterns are significantly and specifically enriched with those that have been causally or genetically linked to cancer as well as related pathways and processes, demonstrating the effectiveness of our novel approach.
as we intentionally selected three diverse methods for outlier analysis, it is not surprising that in addition to common predictions, each algorithm also identifies unique outlier genes .
the significant over-representation of known cancer genes from each method  suggests the general utility of the outlier approach is unlikely tied to a specific algorithm, and other outlier analysis methodologies can be similarly employed to decipher functional genomic profiles.
the complementarity of the diverse approaches is manifested in the superior enrichment of their output union , highlighting the heterogeneous pattern of outlier distribution among cancer genes.
the gap method  in principle is related to bimodal type of approach in that both detect major separation between outlier and non-outlier groups.
this is manifested in the degree of overlap significance between outlier genes identified where gap shows a stronger agreement with pack over os .
however, unlike bimodal which looks for two normal distributions with distinct means, gap does not require the outlier group to be gaussian.
given that the outlier group is often relatively small in size, we believe it is advantageous to circumvent modeling it explicitly in consideration of limited statistical power.
the method’s utility is highlighted by some well-known cancer genes such as cdk <dig>  apc and ezh <dig> are only captured by gap .
the identification of outliers from functional genomic data also helps to uncover potential indications and predictive biomarkers associated with candidate targets  that may guide the development of precision medicine strategies.
the national cancer institute  has recently launched the exceptional responders initiative to investigate the molecular factors of tumors associated with exceptional treatment responders of cancer patients to drug therapies  <cit> .
outlier analysis of functional genomics data from large-scale gene silencing provides an opportunity to address similar questions for thousands of genes in parallel using pre-clinical models.
the set of genes with outlier dependency patterns may also provide a useful framework for a global view of tumor cell dependency.
hematological lines have a unique vulnerability pattern that appears more homogenous than their solid tumor counterparts.
this may reflect their evolutionary history where leukemias and lymphomas likely require fewer rounds of clonal expansion  as their precursor cells are already mobile and invasive  <cit> .
it is also possible that some technical factors  may contribute to the observed difference.
with experimental validation, the grouping of tumor cells by functional dependency could lead to important insights on the design of more sophisticated molecular biomarker strategies of both positive and negative selections for basket trials.
our apparently counter-intuitive observation that the knockdown of several tumor suppressor genes resulted in striking vulnerability in a subset of tumor cells suggested they could be oncogenic in specific circumstances.
this is analogous to the recent finding in the arid <dig> family whereby inactivation of arid1a creates a special dependence on the related tumor suppressor gene arid1b  <cit>  and the “abnormal”  swi/snf complex is pro-oncogenic.
the presence of additional tumor suppressors like arid1b and apc among outlier genes suggests that these are unlikely to be isolated cases and more may have context-dependent dual properties, presenting a challenge to the simple binary classification of cancer genes as either oncogenes or tumor suppressors, and bringing up the possibility of expanding the druggable genome to include tumor suppressors when coupled with an appropriate precision medicine strategy.
while outlier vulnerability analysis of achilles rnai sensitivity pattern has successfully uncovered many well-known cancer genes along with their tumor type and genetic biomarker relationships, it also has a few notable misses.
egfr and erbb <dig>  two established oncology therapeutic targets, do not appear to harbor a significant exceptional responder group distinct from the population .
we should note that the outlier approach, as any analytical strategy, is contingent upon the quality of the input: technological limitations such as hairpin off-target seed effects  have an inevitable negative impact on analysis results.
even though we used ataris gene-level solutions which have on-target signals greatly amplified as input, our analysis is still not completely immune from this complexity and novel findings in particular need to be experimentally confirmed for proper interpretation.
we have described here the application of outlier analysis to only a single large functional genomics dataset; however, the identification of many known cancer genes and relationships provides confidence and proof-of-principle for the general approach.
whereas we have used achilles shrna patterns as an example, outlier analysis should be equally relevant to datasets from alternative functional genomic technologies including sirna and crispr , as well as to other shrna screen data.
since outliers are rare events by definition, their detection requires a sufficiently large population as with genome-wide gene expression datasets.
furthermore, for most of the outlier techniques to work effectively, the overall input data  should follow a symmetric and preferentially normal distribution without the confounding of excessive technical outliers as discussed above.
the outlier analysis approach is not only useful for mining of gene-level values but also lower-level data such as those from individual hairpins where the consistency between outlier responders could provide another means to select for on-target effects.
CONCLUSIONS
with the rapid evolution of functional genomic technologies, there is an ever growing demand for analytical strategies to maximize discoveries from the large amounts of data being generated.
the current analyses typically focus on genes whose knockdown enhances response in pre-defined molecular contexts and thus are inherently limited in their ability to reveal new disease-relevant biology.
here we tackle this important conceptual problem and demonstrate one solution by introducing a novel strategy to identify tumor vulnerabilities from functional genomic profiles based on patterns of responsiveness alone.
it takes advantage of the observation that oncogene addiction or synthetic lethality generally manifests itself in the exquisite sensitivity of a subset of tumors or cell lines, and therefore is built upon the identification of genes with outlier drop-out pattern.
we thus expand the utility of outlier analysis, whose application in genomics thus far has been restricted to gene expression data, towards the mining of functional genomic profiles.
we show that genes with outlier vulnerability pattern are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, demonstrating its utility for the identification of therapeutic targets.
the characteristics of the outlier lines can further reveal tumor indications and biomarkers of response associated with candidate targets to guide the development of precision medicine strategies.
in addition, it provides a useful framework for a global view of tumor cell dependency, which led to the observation of distinct sensitivity and coherence between solid and hematological malignancies.
the counter-intuitive finding of several tumor suppressors with outlier sensitivity patterns challenges the simple binary classification of cancer genes as either oncogenes or tumor suppressors, and generalizes the notion that tumor suppressors could play context-dependent oncogenic roles.
therefore, our novel analytical approach described here offers a valuable alternative means to mine fast-growing functional genomic data in an unbiased manner for discoveries that may lead to the next generation of oncology medicines.

 a major challenge in oncology drug discovery is the identification of tumor vulnerabilities that can lead to novel therapeutic targets, and linking these vulnerabilities to specific patient populations that are likely to benefit from pharmacological inhibition of these targets. while historically drug targets have originated from in-depth dissection of cancer biology, more recently tumor genome sequencing efforts such as the cancer genome atlas and international cancer genome consortium have defined the genomic landscape and complexity for an ever growing number of tumor types and subtypes. however, with these approaches, it is becoming increasingly difficult to identify novel oncogenic drivers that are both pharmacologically accessible and applicable to a substantial number of patients. functional genomics offers an alternative means for target identification that is complementary to in-depth biology and sequencing. gene silencing through sequence-specific targeting of mrnas by rna interference takes advantage of an endogenous cellular pathway and has become a powerful research tool by enabling high-throughput and systematic loss of function screens in cultured cells and model organisms. one of the largest screens to date is project achilles at the broad institute, a pioneering effort that has utilized a lentivirally delivered short hairpin rna library to catalog the dependency of cancer cell lines on genes. the analysis of genome-scale rnai screens typically has focused on a priori partition of cell lines based on known biological or genetic contexts, such as the mutation of an established oncogene or tumor suppressor, followed by a comparison of the sensitivity patterns of the two groups to identify genes that, when knocked down, confer preferential sensitivity in one group over the other. this analytical approach has led, for example, to the discovery of arid1b and smarca as specific vulnerabilities for arid1a and smarca4-mutant cancers, respectively. however, the need to pre-define the groups for interrogation represents an inherent limitation due to the incompleteness of our knowledge as well as biological complexities. predefining groups, furthermore, calls for a separate analysis for each biological or genetic context, making it impractical to query all contexts of potential interest. to address these limitations, we have developed a complementary data mining strategy based on patterns of sensitivity in functional genomics screens that requires no a priori assumptions about the underlying biology of dependency. oncogene addiction or synthetic lethality usually results in exceptional response in a subset of tumors or cell lines that are exquisitely vulnerable to knockdown or inhibition of the gene being interrogated. the responder subsets are, by definition, outliers relative to the rest of the population or cell line panel. taking advantage of this observation, our strategy adapts and extends outlier analysis methodologies to identify genes with a subset of exceptional responders among the screened cell lines. such a data-driven approach in principle makes it possible to identify vulnerabilities in any biological or genetic context in a single analysis, and also allows for the discovery of novel or complex contexts in which inhibition of specific genes represents a vulnerability that would not have been considered in a pre-defined class comparison analysis. outlier analysis has been widely applied to gene expression data for the discovery of cancer-associated genes. it was first described in the identification of a gene fusion in prostate cancer involving two transcription factors, erg and etv, which led to the cancer outlier profile analysis method. many technically more sophisticated approaches have followed, including model-based pattern recognition for deviation from uni-modality and numerical detection for marked high expression in a subset of tumors that is distant from the majority. outlier detection has also been useful in finding drugs with rare but exceptional response in clinical trials. while highly informative, exceptional responder studies in the clinic are constrained by the relatively modest number of biological mechanisms currently targeted by drugs as well as the challenge of following up hypotheses in patients. large-scale functional genomic studies relieve these restrictions and enable investigating thousands of genes in parallel. here we apply an outlier analysis based strategy to functional genomic profiles for systematic oncology target discovery. the utility of such approach is illustrated by the observation that genes with outlier patterns are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no molecular profiling or any other information being used to drive the analysis. we show that it may enable the identification of novel candidate therapeutic targets, and that the characteristics of the exceptional responder lines could further point to tumor indications and biomarkers of response to guide precision medicine strategies.  identification of genes with outlier sensitivity patterns to identify genes with an exceptional responder pattern, we used the union of the results output by three diverse methods. they each focus on different features to detect outliers and therefore are considered complementary. application of these approaches is not intended as a comprehensive comparison of various outlier methodologies; rather we reasoned that together they would provide a more complete set of outliers and outlier genes than any single algorithm. the first two methods were originally developed for outlier analysis of gene expression data: profile analysis using clustering and kurtosis, and outlier sum. pack is a model-based pattern recognition algorithm for discovering bimodal distribution, which first determines the number of clusters in the dataset for each gene and then computes a measure of how much the distribution differs from gaussianity for those gene profiles with two clusters. positive kurtosis indicates clusters of unequal relative size, while negative kurtosis indicates clusters of approximately equal representation. the os algorithm uses the “outlier-sum” statistic, which is defined using values outside a variability-based numerical limit. it was recently assessed to have the best performance among six closely related outlier techniques. for the third method, inspired by the q statistic utilized in dixon’s test for outlier detection, we devised an intuitive nonparametric approach to explicitly identify genes with dependency patterns where groups of sensitive cell lines are separated by a major “gap” from the bulk population. all three methods were applied to the achilles ataris gene level scores across a diverse panel of cell lines derived from consensus clustering of individual hairpin patterns for on-target effects. the achilles dataset includes genes that yielded similarity solution and thereby consensus scores. the pack algorithm identified genes with bimodal distribution, of which had positive kurtosis with one of the two clusters representing a small “outlier” subgroup. as the outlier subgroup could have higher or lower shrna score than the rest of the panel, we focused on the genes for which the outlier group is more vulnerable to knockdown. the os and gap methods led to the identification of genes with a non-random outlier pattern where statistical significance was estimated using permutation. we also required that the outlier group consist of at least five cell lines to avoid spurious one-off observations, yielding genes from the pack algorithm, from the os method, and from the gap approach. in all, there were unique outlier genes, including that emerged from all three methods.fig. outlier genes identified with profiling analysis using clustering & kurtosis, outlier sum and gap analysis procedure methodologies. a a summary venn diagram including statistical significance of pairwise overlap, with detailed gene list and relevant results included in additional file 4: table s b the ataris gene level score distribution for the genes identified by all three outlier methods. a probability density estimate is computed by gaussian kernel smoothing enrichment for curated cancer genes and oncogenic pathways we evaluated the biological relevance of the identified outlier genes by comparing them to two well-known cancer gene collections. the cancer gene census from the sanger institute catalogues genes for which mutations have been causally implicated in cancer while the msk-impact™ panel is a curated collection of key cancer genes used for diagnostic genomic testing with next-generation sequencing technology. although determined independent of any genetic or other molecular profiling information, the genes with outlier patterns are significantly over-represented by those genetically linked to cancer from both cgc and impact. each of the three outlier analysis approaches enriched for established cancer genes, and their union had greater statistical significance than any of them alone, supporting the complementarity of these methodologies.fig. assessment of genes with significant outlier pattern through comparison with two well-known cancer gene collections. the cancer gene census from the sanger institute catalogues genes for which mutations have been causally implicated in cancer; the msk-integrated mutation profiling of actionable cancer targets is a curated panel of key cancer genes used for diagnostic genomic testing. “union” corresponds to combined genes resulting from the three outlier methods we further assessed the validity of our outlier analysis strategy through unbiased signature comparison. among gene sets from the broad institute’s msigdb canonical pathway library, which collects canonical representations of biological processes compiled by domain experts from pathway databases, the outlier genes are strongly and specifically enriched in those associated with various tumor types including the prominent oncogenes and therapeutic targets braf, nras, kras, pik3ca, cdk and ctnnb as well as important cellular pathways and processes involved in cancer such as the wnt, pi3k-mtor, p and rb-e2f pathways, cell cycle and apoptosis. cdk, like the targets of the recently approved breast cancer drug ibrance, is a member of the cyclin-dependent kinase family of ser/thr protein kinases whose de-regulation occurs frequently in certain types of cancer. in addition to established cancer genes, our outlier analysis also reveals potentially novel therapeutic opportunities. for instance, asparagine synthetase, an enzyme that catalyzes the conversion of aspartate and glutamine to asparagine and glutamate in an atp-dependent manner, has been shown to increase the chemotherapy sensitivity of leukemia cells resistant to l-asparaginase when inhibited. collectively these results demonstrate that outlier analysis can serve as a useful strategy to identify cancer driver genes from rnai sensitivity patterns.table enriched signatures from the broad canonical pathway library among outlier genes enabling tumor indication and predictive biomarker discoveries with the identification of genes whose knockdown confers exceptional response, we next asked whether the cell lines making up the outlier groups are enriched in a particular tumor type or subtype, or have molecular features which may be linked to susceptibility to target interference. such insights would not only guide the precision medicine strategy in oncology drug development for selecting patients who are most likely to benefit from targeted therapies, but also help to further prioritize the most biologically compelling and clinically translatable outlier patterns. the cell lines from the achilles dataset were mapped to both general tumor types and common histological subtypes. in the case of breast cancer, both clinical marker and gene expression based classifications were incorporated. significant tumor type associations were found for more than half of the genes with outlier patterns. these include well recognized relationships such as kras in pancreatic and colorectal cancers, cdk in luminal/er+ breast cancer, braf in skin cancer, pik3ca in breast and gastric cancers and apc in colorectal cancer, as well as mechanistically supported relationships like cdk in ovarian cancer where ccne the cyclin that interacts with cdk is frequently amplified.table outlier genes whose predictive genetic biomarkers are the gene itself or from the same gene family/protein complex, suggesting potential oncogenic addiction or a synthetic lethal relationship. genetic biomarkers and tumor types are listed by decreasing statistical significance of association apc apc crc braf braf crc;melanoma kras kras pancreatic;crc nras nras pik3ca pik3ca breast;gastric;breast lum_subtype;gastric_adenocarcinoma; crc;breast her2+;breast tnbc;prostate_adenocarcinoma; breast her2_subtype;breast er+ zmiz1 zmiz1 leukemia/lymphoma;breast;aml to uncover predictive genetic biomarkers, we focused on the lesions that are most likely to be functional, as tumor genomes are often unstable and thus the vast majority of genetic changes are generally passengers. for somatic mutations, we selected those in hotspot positions as well as nonsense and frameshift events. hotspots were identified systematically using patient-derived genomic profiles from tcga tumor types, which were subsequently employed to filter cell line mutation data compiled from the cosmic and ccle databases. for copy number alterations, we restricted our analysis to high-level amplifications and deletions. six of the genes with outlier patterns are significantly characterized by their own genetic lesions, suggesting oncogenic addiction as the underlying mechanism of their exquisite vulnerability. all of these with the exception of zmiz represent prominent oncogenes or tumor suppressors. zmiz has previously been identified as a candidate oncogene in multiple murine transposon and insertional mutagenesis screens. for additional genes with outlier patterns, the associated predictive genetic biomarkers are either members of the same gene family or components of the same protein complex, revealing possible synthetic lethal relationships where lesions in functionally related gene confer special dependency. these include well-documented relationships such as the vulnerability for ctnnb knockdown in the context of apc lesions and e2f vulnerability in the context of rb lesions as well as the recently discovered dependency on arid1b in the context of arid1a lesions. in the case of cdk ccne amplification is specifically over-represented among its associated exceptional responder cell lines, consistent with the tumor type enrichment of ovarian cancer described above. novel relationships of potential interest were also observed such as top2a vulnerability with parp lesions, hnrnpa vulnerability with rpl lesions and psmd vulnerability for psmc lesions. distinct dependency and coherence between solid and hematological malignancies to obtain a global view of tumor cell dependency, we performed unsupervised hierarchical clustering of functional genomic profiles using the genes with outlier dependency patterns. the vast majority of hematological cell lines cluster together by functional data, in contrast to those of solid origin that are more scattered and heterogeneous, despite hematological cell lines representing only 14 % of the total, with tumor types like central nervous system and ovarian cancer being equally or more heavily covered by the panel. even functionally related genes, such as those in the wnt pathway, differ in points of liability: while cell lines derived from liquid tumors tend to be vulnerable to tcf knockdown, those of solid origin are susceptible to other pathway genes including gsk3a/b, ctnnb and apc. potentially context-dependent oncogenic roles for some tumor suppressor genes several outlier genes whose knockdown confers striking vulnerability are tumor suppressors including apc, phlpp and spen, while a number of other candidates have been reported to harbor anti-oncogenic activities such as the pro-apoptotic gene mtch and the anti-metastatic rna chaperone rbm. among the five lines most responsive to adenomatous polyposis coli knockdown, all contain loss of function mutation in the apc gene itself and four are of colorectal origin. furthermore, they strongly overlap with those dependent on ctnnb. truncating apc mutations have been reported to have a dominant negative effect on proliferation, spindle checkpoint control, survival and chromosomal stability. the exceptional responders for phlpp and mtch are over-represented by cells from leukemia/lymphoma and colorectal cancer respectively. the results therefore support and may further generalize the notion that in certain contexts, tumor suppressor genes can become oncogenic and create specific liabilities. discussion the analysis of large-scale cell based functional genomics datasets has predominantly focused on identifying vulnerabilities in known biological contexts, such as mutated oncogenes or tumor suppressors. a limitation of this approach is that it requires knowledge of the biology to be interrogated and can generally be described as starting with known biology, then looking for patterns in the data that support it. we have reversed this paradigm and now describe an alternative data mining strategy that starts by looking for profiles indicative of potential dependencies of interest, with no assumptions about the underlying biology of the dependency. it is based on identifying genes with subgroups of exceptionally sensitive cell lines. by definition, these exceptional responders are statistical outliers. we therefore hypothesized that outlier analysis, whose previous application in genomic studies has been limited to gene expression data, would also be useful in investigating rna interference response patterns. we tested the hypothesis by applying outlier analysis to genome-scale shrna screen results from project achilles at the broad institute, and found genes with outlier patterns are significantly and specifically enriched with those that have been causally or genetically linked to cancer as well as related pathways and processes, demonstrating the effectiveness of our novel approach. as we intentionally selected three diverse methods for outlier analysis, it is not surprising that in addition to common predictions, each algorithm also identifies unique outlier genes. the significant over-representation of known cancer genes from each method suggests the general utility of the outlier approach is unlikely tied to a specific algorithm, and other outlier analysis methodologies can be similarly employed to decipher functional genomic profiles. the complementarity of the diverse approaches is manifested in the superior enrichment of their output union, highlighting the heterogeneous pattern of outlier distribution among cancer genes. the gap method in principle is related to bimodal type of approach in that both detect major separation between outlier and non-outlier groups. this is manifested in the degree of overlap significance between outlier genes identified where gap shows a stronger agreement with pack over os. however, unlike bimodal which looks for two normal distributions with distinct means, gap does not require the outlier group to be gaussian. given that the outlier group is often relatively small in size, we believe it is advantageous to circumvent modeling it explicitly in consideration of limited statistical power. the method’s utility is highlighted by some well-known cancer genes such as cdk apc and ezh are only captured by gap. the identification of outliers from functional genomic data also helps to uncover potential indications and predictive biomarkers associated with candidate targets that may guide the development of precision medicine strategies. the national cancer institute has recently launched the exceptional responders initiative to investigate the molecular factors of tumors associated with exceptional treatment responders of cancer patients to drug therapies. outlier analysis of functional genomics data from large-scale gene silencing provides an opportunity to address similar questions for thousands of genes in parallel using pre-clinical models. the set of genes with outlier dependency patterns may also provide a useful framework for a global view of tumor cell dependency. hematological lines have a unique vulnerability pattern that appears more homogenous than their solid tumor counterparts. this may reflect their evolutionary history where leukemias and lymphomas likely require fewer rounds of clonal expansion as their precursor cells are already mobile and invasive. it is also possible that some technical factors may contribute to the observed difference. with experimental validation, the grouping of tumor cells by functional dependency could lead to important insights on the design of more sophisticated molecular biomarker strategies of both positive and negative selections for basket trials. our apparently counter-intuitive observation that the knockdown of several tumor suppressor genes resulted in striking vulnerability in a subset of tumor cells suggested they could be oncogenic in specific circumstances. this is analogous to the recent finding in the arid family whereby inactivation of arid1a creates a special dependence on the related tumor suppressor gene arid1b and the “abnormal” swi/snf complex is pro-oncogenic. the presence of additional tumor suppressors like arid1b and apc among outlier genes suggests that these are unlikely to be isolated cases and more may have context-dependent dual properties, presenting a challenge to the simple binary classification of cancer genes as either oncogenes or tumor suppressors, and bringing up the possibility of expanding the druggable genome to include tumor suppressors when coupled with an appropriate precision medicine strategy. while outlier vulnerability analysis of achilles rnai sensitivity pattern has successfully uncovered many well-known cancer genes along with their tumor type and genetic biomarker relationships, it also has a few notable misses. egfr and erbb two established oncology therapeutic targets, do not appear to harbor a significant exceptional responder group distinct from the population. we should note that the outlier approach, as any analytical strategy, is contingent upon the quality of the input: technological limitations such as hairpin off-target seed effects have an inevitable negative impact on analysis results. even though we used ataris gene-level solutions which have on-target signals greatly amplified as input, our analysis is still not completely immune from this complexity and novel findings in particular need to be experimentally confirmed for proper interpretation. we have described here the application of outlier analysis to only a single large functional genomics dataset; however, the identification of many known cancer genes and relationships provides confidence and proof-of-principle for the general approach. whereas we have used achilles shrna patterns as an example, outlier analysis should be equally relevant to datasets from alternative functional genomic technologies including sirna and crispr, as well as to other shrna screen data. since outliers are rare events by definition, their detection requires a sufficiently large population as with genome-wide gene expression datasets. furthermore, for most of the outlier techniques to work effectively, the overall input data should follow a symmetric and preferentially normal distribution without the confounding of excessive technical outliers as discussed above. the outlier analysis approach is not only useful for mining of gene-level values but also lower-level data such as those from individual hairpins where the consistency between outlier responders could provide another means to select for on-target effects.  with the rapid evolution of functional genomic technologies, there is an ever growing demand for analytical strategies to maximize discoveries from the large amounts of data being generated. the current analyses typically focus on genes whose knockdown enhances response in pre-defined molecular contexts and thus are inherently limited in their ability to reveal new disease-relevant biology. here we tackle this important conceptual problem and demonstrate one solution by introducing a novel strategy to identify tumor vulnerabilities from functional genomic profiles based on patterns of responsiveness alone. it takes advantage of the observation that oncogene addiction or synthetic lethality generally manifests itself in the exquisite sensitivity of a subset of tumors or cell lines, and therefore is built upon the identification of genes with outlier drop-out pattern. we thus expand the utility of outlier analysis, whose application in genomics thus far has been restricted to gene expression data, towards the mining of functional genomic profiles. we show that genes with outlier vulnerability pattern are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, demonstrating its utility for the identification of therapeutic targets. the characteristics of the outlier lines can further reveal tumor indications and biomarkers of response associated with candidate targets to guide the development of precision medicine strategies. in addition, it provides a useful framework for a global view of tumor cell dependency, which led to the observation of distinct sensitivity and coherence between solid and hematological malignancies. the counter-intuitive finding of several tumor suppressors with outlier sensitivity patterns challenges the simple binary classification of cancer genes as either oncogenes or tumor suppressors, and generalizes the notion that tumor suppressors could play context-dependent oncogenic roles. therefore, our novel analytical approach described here offers a valuable alternative means to mine fast-growing functional genomic data in an unbiased manner for discoveries that may lead to the next generation of oncology medicines. 
several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles. genome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. we thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency. identification of exceptional responders may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. genes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. the novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries. outlier analysisfunctional genomicsoncologycancertarget identificationprecision medicineoncogene addictionsynthetic lethalityissue-copyright-statement© the author 2016
 genome-scale functional genomic screens across large cell line panels provide a rich resource for discovering tumor vulnerabilities that can lead to the next generation of targeted therapies. their data analysis typically has focused on identifying genes whose knockdown enhances response in various pre-defined genetic contexts, which are limited by biological complexities as well as the incompleteness of our knowledge. we thus introduce a complementary data mining strategy to identify genes with exceptional sensitivity in subsets, or outlier groups, of cell lines, allowing an unbiased analysis without any a priori assumption about the underlying biology of dependency.  genes with outlier features are strongly and specifically enriched with those known to be associated with cancer and relevant biological processes, despite no a priori knowledge being used to drive the analysis. identification of exceptional responders may not lead only to new candidates for therapeutic intervention, but also tumor indications and response biomarkers for companion precision medicine strategies. several tumor suppressors have an outlier sensitivity pattern, supporting and generalizing the notion that tumor suppressors can play context-dependent oncogenic roles.  the novel application of outlier analysis described here demonstrates a systematic and data-driven analytical strategy to decipher large-scale functional genomic data for oncology target and precision medicine discoveries. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords outlier analysisfunctional genomicsoncologycancertarget identificationprecision medicineoncogene addictionsynthetic lethalityissue-copyright-statement© the author 2016 