The subject matter described herein mainly relates to bioinformatics, genomic processing arts, proteomic processing arts, and related arts.

Genomic and proteomic analyses have substantial promise, both realized and potential, for clinical application in medical fields such as oncology, where various cancers are known to be associated with specific combinations of genomic mutations/variations and/or high or low expression levels for specific genes, which play a role in growth and evolution of cancer, e.g. cell proliferation and metastasis. For example, the Wnt signaling pathway affects regulation of cell proliferation, and is highly regulated. High Wnt pathway activity due to loss of regulation has been correlated to cancer, including malignant colon tumors. While not being limited to any particular theory of operation, it is believed that deregulation of the Wnt pathway in malignant colon cells leads to high Wnt pathway activity that in turn causes cell proliferation of the malignant colon cells, i.e. spread of colon cancer. On the other hand, abnormally low pathway activity might also be of interest, for example in the case of osteoporosis.

Technologies for acquiring genomic and proteomic data have become readily available in clinical settings. For example, measurements by microarrays are routinely employed to assess gene expression levels, protein levels, methylation, and so forth. Automated gene sequencing enables cost-effective identification of genetic variations in DNA and mRNA. Quantitative assessment of mRNA levels during gene sequencing holds promise as yet another clinical tool for assessing gene expression levels.

In spite of (or, perhaps, because of) these advances, clinical application of genomic and proteomic analyses faces a substantial hurdle—data overload. For example, the number of identifiable mutations in a single clinical sample can number in the hundreds of thousands or more. Most of these mutations are so called bystander mutations without specific contribution to cancer growth, and only a few do contribute to cancer growth and functional evolution, and these present the targets for effective treatment. Processing these large quantities of data to identify clinically useful information, like for example in the application of choosing the right therapy, is difficult.

One approach is to limit the analysis to a few canonical or standardized tests, such as tests approved by the U.S. Food and Drug Administration (FDA). In such an approach, a specific indicator or combination of indicators (e.g., mutations and/or specified high or low gene expression levels) is detected in order to test “positive” for the indicated disease condition (e.g., a particular type of cancer). The canonical test is supported by clinical studies that have shown strong correlation with the disease condition or with treatment efficacy. This approach is useful only for those clinical conditions for which a canonical test has been developed, e.g. specific diagnosis of a disease, or predicting response to a drug in a specific cancer type at a specific stage, and is also rigid as it is only applicable for the canonical conditions.

Another approach is based on identification of functionally related groups of genomic or proteomic indicators. For example, the Wnt pathway comprises a cascade of proteomic reactions. Major components of this chain include (but are not limited to) binding of the Wnt signaling protein to a frizzled surface receptor of the cell which causes activation of proteins of the disheveled family of proteins which in turn impact the level of transcription agents such as β-catenin/TCF4 based protein complexes in the cell nucleus. These transcription agents, in turn, control transcription of target mRNA molecules that in turn are translated into target proteins of the Wnt pathway. Clinical studies have shown some correlations between regulatory proteins of the Wnt pathway and the activity of the Wnt pathway.

However, applying such clinical study results to the diagnosis and clinical evaluation of a specific patient is difficult due to the complexity of signaling pathways, e.g. the Wnt pathway. As a simple example, measurement of the expression level of a protein that is “upstream” in the Wnt pathway may fail to detect abnormal behavior of a protein that is “downstream” in the Wnt pathway. It is believed that the Wnt pathway includes numerous feedback mechanisms and the simplified concept of “upstream” and “downstream” may be inapplicable for a substantial portion of the Wnt pathway; more generally, abnormal behavior in one portion of the protein cascade comprising the Wnt pathway may have more or less effect on other portions of the protein cascade, and on the activity of the Wnt pathway as a whole. Still further, in some clinical studies protein expression levels for regulatory proteins of the signaling cascade are assessed by measuring mRNA expression levels of the genes that encode for the regulatory proteins. This is an indirect measurement that may not accurately assess the regulatory protein expression level, and hardly ever reflects the amount of active proteins (after a specific post-translational modification like phosphorylation).

The main problem underlying the present invention was thus to provide suitable methods and means for performing genomic and, respectively, proteomic analyses. Specific aspects of the underlying problem as well as further objections in connection with the present invention become apparent when studying the description, the examples provided herein and, in particular, when studying the attached claims.

The present invention provides new and improved methods and apparatuses as disclosed herein.

In accordance with a main aspect of the present invention, the above problem is solved by a specific method for assessing cellular signaling pathway activity using probabilistic modeling of target gene expression, namely a method comprising: inferring activity of one or more cellular signaling pathway(s) in tissue of a medical subject based at least on the expression level(s) (in particular on mRNA and/or protein level) of one or more target gene(s) of the cellular signaling pathway(s) measured in an extracted sample of the tissue of the medical subject, wherein the inferring comprises: inferring activity of the cellular signaling pathway(s) in the tissue of the medical subject by evaluating at least a portion of a probabilistic model, preferably a Bayesian network, representing the cellular signaling pathway(s) for a set of inputs including at least the expression level(s) of the one or more target genes of the cellular signaling pathway(s) measured in the extracted sample of the tissue of the medical subject; estimating a level in the tissue of the medical subject of at least one transcription factor (TF) element, the at least one TF element controlling transcription of the one or more target gene(s) of the cellular signaling pathway(s), the estimating being based at least in part on conditional probabilities relating the at least one TF element and the expression level(s) of the one or more target gene(s) of the cellular signaling pathway(s) measured in the extracted sample of the tissue of the medical subject; and inferring activity of the cellular signaling pathway(s) based on the estimated level in the tissue sample of the transcription factor; and determining whether the cellular signaling pathway(s) is/are operating abnormally in the tissue of the medical subject based on the inferred activity of the cellular signaling pathway(s) in the tissue of the medical subject; wherein the inferring is performed by a digital processing device using the probabilistic model of the cellular signaling pathway(s).

The “target gene(s)” may be “direct target genes” and/or “indirect target genes” (as described herein).

Preferably the inferring comprises estimating a level in the tissue of the medical subject of at least one transcription factor (TF) element represented by a TF node of the probabilistic model, the TF element controlling transcription of the one or more target gene(s) of the cellular signaling pathway(s), the estimating being based at least in part on conditional probabilities of the probabilistic model relating the TF node and nodes in the probabilistic model representing the one or more target gene(s) of the cellular signaling pathway(s) measured in the extracted sample of the tissue of the medical subject.

The probabilistic model may be a Bayesian network model. Thus, according to a preferred embodiment the inferring is performed by using a Bayesian network comprising nodes representing information about the signaling pathway(s) and conditional probability relationships between connected nodes of the Bayesian network.

The cellular signaling pathway(s) may be a Wnt pathway, an ER (Estrogen Receptor) pathway, an AR (Androgen Receptor) pathway and/or a Hedgehog pathway. Thus, according to a preferred embodiment the cellular signaling pathway(s) comprise(s) a Wnt pathway, an ER pathway, an AR pathway and/or a Hedgehog pathway.

Particularly suitable target genes are described in the following text passages as well as the examples below (see e.g. Tables 1-9).

Thus, according to a preferred embodiment the target gene(s) is/are selected from the group comprising or consisting of target genes listed in Table 1 or Table 6 (for Wnt pathway), target genes listed in Table 2, Table 5 or Table 7 (for ER pathway), target genes listed in Table 3 or Table 8 (for Hedgehog pathway) and target genes listed in Table 4 or Table 9 (for AR pathway).

Particularly preferred is a method wherein the inferring comprises: inferring activity of a Wnt pathway in the tissue of the medical subject based at least on expression levels of one or more, preferably at least three, target gene(s) of the Wnt pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: KIAA1199, AXIN2, RNF43, TBX3, TDGF1, SOX9, ASCL2, IL8, SP5, ZNRF3, KLF6, CCND1, DEFA6 and FZD7.

Further preferred is a method, wherein the inferring is further based on expression levels of at least one target gene of the Wnt pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: NKD1, OAT, FAT1, LEF1, GLUL, REG1B, TCF7L2, COL18A1, BMP7, SLC1A2, ADRA2C, PPARG, DKK1, HNF1A and LECT2.

Particularly preferred is a method wherein the inferring (also) comprises: inferring activity of an ER pathway in the tissue of the medical subject based at least on expression levels of one or more, preferably at least three, target gene(s) of the ER pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: CDH26, SGK3, PGR, GREB1, CA12, XBP1, CELSR2, WISP2, DSCAM, ERBB2, CTSD, TFF1 and NRIP1.

Further preferred is a method, wherein the inferring is further based on expression levels of at least one target gene of the ER pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: AP1B1, ATPSJ, COL18A1, COX7A2L, EBAG9, ESR1, HSPB1, IGFBP4, KRT19, MYC, NDUFV3, PISD, PREDM15, PTMA, RARA, SOD1 and TRIM25.

A method wherein the inferring (also) comprises inferring activity of a Hedgehog pathway in the tissue of the medical subject based at least on expression levels of one or more, preferably at least three, target gene(s) of the Hedgehog pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: GLI1, PTCH1, PTCH2, IGFBP6, SPP1, CCND2, FST, FOXL1, CFLAR, TSC22D1, RAB34, S100A9, S100A7, MYCN, FOXM1, GLI3, TCEA2, FYN and CTSL1, is also preferred.

Further preferred is a method, wherein the inferring is further based on expression levels of at least one target gene of the Hedgehog pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: BCL2, FOXA2, FOXF1, G19, HHIP, IL1R2, JAG2, JUP, MIF, MYLK, NKX2.2, NKX2.8, PITRM1 and TOM1.

Also preferred is a method wherein the inferring (also) comprises inferring activity of an AR pathway in the tissue of the medical subject based at least on expression levels of one or more, preferably at least three, target gene(s) of the AR pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: KLK2, PMEPA1, TMPRSS2, NKX3_1, ABCC4, KLK3, FKBPS, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR and EAF2, is also preferred.

Further preferred is a method, wherein the inferring is further based on expression levels of at least one target gene of the AR pathway measured in the extracted sample of the tissue of the medical subject selected from the group comprising or consisting of: APP, NTS, PLAU, CDKN1A, DRG1, FGF8, IGF1, PRKACB, PTPN1, SGK1 and TACC2.

Another aspect of the present invention relates to a method (as described herein), further comprising: recommending prescribing a drug for the medical subject that corrects for abnormal operation of the cellular signaling pathway(s); wherein the recommending is performed only if the cellular signaling pathway(s) is/are determined to be operating abnormally in the tissue of the medical subject based on the inferred activity of the cellular signaling pathway(s).

The present invention also relates to a method (as described herein) comprising: inferring activity of a Wnt pathway in tissue of a medical subject based at least on expression levels of two, three or more target genes of a set of target genes of the Wnt pathway measured in an extracted sample of the tissue of the medical subject and/or inferring activity of an ER pathway in tissue of a medical subject based at least on expression levels of two, three or more target genes of a set of target genes of the ER pathway measured in an extracted sample of the tissue of the medical subject and/or inferring activity of a Hedgehog pathway in tissue of a medical subject based at least on expression levels of two, three or more target genes of a set of target genes of the Hedgehog pathway measured in an extracted sample of the tissue of the medical subject, and/or inferring activity of an AR pathway in tissue of a medical subject based at least on expression levels of two, three or more target genes of a set of target genes of the AR pathway measured in an extracted sample of the tissue of the medical subject.

Preferably, the set of target genes of the Wnt pathway includes at least nine, preferably all target genes selected from the group comprising or consisting of: KIAA1199, AXIN2, RNF43, TBX3, TDGF1, SOX9, ASCL2, IL8, SP5, ZNRF3, KLF6, CCND1, DEFA6 and FZD7, and/or the set of target genes of the ER pathway includes at least nine, preferably all target genes selected from the group comprising or consisting of: CDH26, SGK3, PGR, GREB1, CA12, XBP1, CELSR2, WISP2, DSCAM, ERBB2, CTSD, TFF1 and NRIP1, and/or the set of target genes of the Hedgehog pathway includes at least nine, preferably all target genes selected from the group comprising or consisting of: GLI1, PTCH1, PTCH2, IGFBP6, SPP1, CCND2, FST, FOXL1, CFLAR, TSC22D1, RAB34, S100A9, S100A7, MYCN, FOXM1, GLI3, TCEA2, FYN and CTSL1, and/or the set of target genes of the AR pathway includes at least nine, preferably all target genes selected from the group comprising or consisting of: KLK2, PMEPA1, TMPRSS2, NKX3_1, ABCC4, KLK3, FKBPS, ELL2, UGT2B15, DHCR24, PPAP2A, NDRG1, LRIG1, CREB3L4, LCP1, GUCY1A3, AR and EAF2.

Also preferred is a method, wherein the set of target genes of the Wnt pathway further includes at least one target gene selected from the group comprising or consisting of: NKD1, OAT, FAT1, LEF1, GLUL, REG1B, TCF7L2, COL18A1, BMP7, SLC1A2, ADRA2C, PPARG, DKK1, HNF1A and LECT2, and/or the set of target genes of the ER pathway further includes at least one target gene selected from the group comprising or consisting of: AP1B1, ATPSJ, COL18A1, COX7A2L, EBAG9, ESR1, HSPB1, IGFBP4, KRT19, MYC, NDUFV3, PISD, PREDM15, PTMA, RARA, SOD1 and TRIM25, and/or the set of target genes of the Hedgehog pathway further includes at least one target gene selected from the group comprising or consisting of: BCL2, FOXA2, FOXF1, G19, HHIP, IL1R2, JAG2, JUP, MIF, MYLK, NKX2.2, NKX2.8, PITRM1 and TOM1, and/or the set of target genes of the AR pathway further includes at least one target gene selected from the group comprising or consisting of: APP, NTS, PLAU, CDKN1A, DRG1, FGF8, IGF1, PRKACB, PTPN1, SGK1 and TACC2, is particularly preferred.

The sample(s) to be used in accordance with the present invention can be, e.g., a sample obtained from a breast lesion, or from a colon of a medical subject known or suspected of having colon cancer, or from a liver of a medical subject known or suspected of having liver cancer, or so forth, preferably via a biopsy procedure or other sample extraction procedure. The tissue of which a sample is extracted may also be metastatic tissue, e.g. (suspected) malignant tissue originating from the colon, breast, liver, or other organ that has spread outside of the colon, breast, liver, or other organ. In some cases, the tissue sample may be circulating tumor cells, that is, tumor cells that have entered the bloodstream and may be extracted as the extracted tissue sample using suitable isolation techniques.

Another disclosed aspect of the present invention pertain the use of a non-transitory storage medium as described herein or a computer program as described herein for specific diagnosis of a disease or predicting response to a drug in a specific cancer type at a specific stage.

In accordance with another disclosed aspect, an apparatus comprising a digital processor configured to perform a method according to the invention as described herein.

In accordance with another disclosed aspect, a non-transitory storage medium stores instructions that are executable by a digital processing device to perform a method according to the invention as described herein. The non-transitory storage medium may be a computer-readable storage medium, such as a hard drive or other magnetic storage medium, an optical disk or other optical storage medium, a random access memory (RAM), read-only memory (ROM), flash memory, or other electronic storage medium, a network server, or so forth. The digital processing device may be a handheld device (e.g., a personal data assistant or smartphone), a notebook computer, a desktop computer, a tablet computer or device, a remote network server, or so forth.

In accordance with another disclosed aspect, a computer program comprises program code means for causing a digital processing device to perform a method according to the invention as described herein. The digital processing device may be a handheld device (e.g., a personal data assistant or smartphone), a notebook computer, a desktop computer, a tablet computer or device, a remote network server, or so forth.

One advantage resides in a clinical decision support (CDS) system providing clinical recommendations based on probabilistic analysis of one or more cellular signaling pathway(s), for example using a Bayesian network model of a Wnt pathway, an ER pathway, an AR pathway and/or a Hedgehog pathway.

Another advantage resides in improved assessment of cellular signaling pathway activity that is less susceptible to error.

Another advantage resides in providing a CDS system recommending targeted treatment for loss of regulation of a cellular signaling pathway.

Another advantage resides in providing a CDS system that is designed to detect loss of regulation for a particular cellular signaling pathway, such as a Wnt pathway, an ER pathway, an AR pathway or a Hedgehog pathway, and is readily adapted to provide recommendations for different types of cancer sourced by that particular cellular signaling pathway.

The present invention as described herein can, e.g., also advantageously be used in connection with: diagnosis based on predicted (inferred) activity; prognosis based on predicted (inferred) activity; drug prescription based on predicted (inferred) activity; prediction of drug efficacy based on predicted (inferred) activity; prediction of adverse effects based on predicted (inferred) activity; monitoring of drug efficacy; drug development; assay development; pathway research; cancer staging; enrollment of subject in a clinical trial based on predicted (inferred) activity; selection of subsequent test to be performed, and/or; selection of companion diagnostics tests.

