Archive for the ‘Kinome’ Category

New Kinome Database Promises Wealth of Unexpected Cancer Drug Targets

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Posted 03 Aug 2011 — by James Street
Category Kinase Subfamily, Kinome

By Lois Wingerson | January 5, 2011

 

A word for oncologists to know this year (if they don’t already) is “kinome.” Analagous to “genome,” it’s the complete set of kinases in an organism. The human kinases, of course, are the class of enzymes whose inhibitors have proven so promising and so frustrating in recent clinical cancer trials.

Over the past year or so, study of the kinome has revealed molecules that some tumors are relying on to survive, many of which until now we had no idea were involved in cancer or which we hardly knew at all. Numerous reports have suggested novel kinases that might be useful drug targets in particular malignancies.

In September, for instance, a team from Leiden in the Netherlands reported in Molecular Cancer that they had used kinome analysis to identify two kinases (Src and nuclear factor kappaB) involved in myxoid liposarcoma. Earlier last year, two different teams in the United States found new kinases involved in the development of ovarian cancer and in its response to cis-platin treatment.

From tidbits to a feast

Other teams have been serving up tidbits about new kinases, piecemeal. Now a team from Finland has offered drug developers a feast: A “functional taxonomy” of the kinome, revealing which of 459 kinase proteins show either significant gains or losses of activity in human tumors compared to normal tissues. The data are based on analysis of 5,681 human tissue samples.

As an appetizer, the report by Sami Kilpinen et al in PLoS One  last month provides the identities of several hitherto obscure kinases that appear to be involved in prostate and gynecological cancers as well as some lymphomas. It identifies altogether 22 kinases not previously associated with prostate cancer. Far more is certain to come, because the database is published openly on the Internet.

More than 10,000 patent applications for kinase inhibitors have been filed in the last decade in the United States, because many of the 518 human kinases and other molecules in their pathways are among the most frequently mutated in cancer. Although kinase inhibitors have revolutionized the treatment of a few cancers such as chronic myeloid leukemia (CML) and gastrointestinal stromal tumor (GIST), “clinical progress has been uneven,” Zachary Knight of Rockefeller University and coauthors observed in a review about “kinomics” and cancer in Nature Reviews Cancer last February.

Kinase inhibitors have achieved significant survival gains for only a few cancers, because relatively few malignancies are “addicted” to only one kinase mutation for their survival. Many develop resistance to a particular kinase inhibitor by mutating, reverting to a different kinase signaling pathway, or making molecular adjustments downstream of the inhibitor. Rationally designed combinations of kinase inhibitors have failed in malignancy after malignancy, at least in part because of insufficient understanding of the kinome.

Expression patterns match side effects

The mass of information from Kilpinen et al also promises to offer insights into the side effects that have hindered widespread use of some kinase inhibitors, because it shows kinase expression patterns for a wide range of normal tissues. The skin rash associated with epidermal growth factor receptor (EGFR) is echoed in the discovery of elevated transcriptional activity in hair follicles in the Finnish study. ERBB2/HER-2 was shown to be active in healthy tissues including heart, lung, and colorectal tissues, corresponding to the most common side effects of anti-ERBB2 drugs: cardiovascular and respiratory problems and diarrhea.

The dataset also contributes important information by revealing co-expression of different kinases in similar tumors, offering insights for combination therapies. As Knight et al observed in their review of the field, “There are clearly clusters of kinases that tend to be inhibited by similar drugs, but … there are also many target combinations that should be accessible but remain undiscovered.”

They speculate that some pharmaceutical companies already try to optimize drug profiles against complex patterns of kinases, rather than individually, in order to maximize their specificity. The public availability of the Finnish data can only speed this process.

Studies Suggest Single Protein May Control Inflammation-Related Tumor Growth

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Posted 14 Jun 2011 — by James Street
Category Kinase, Kinome, Molecular, Proteomics

    Scientists say that they have discovered that a single protein may control tumor inflammation and progression. They claim that tumor- derived chemoattractants that stimulate myeloid cell receptor tyrosine kinases (RTKs), toll-like/IL-1 receptors (TLR/IL1Rs), and GPCRs unexpectedly activate a single PI3-kinase isoform known as p110γ and a single integrin called α4β1 to promote myeloid cell recruitment to tumors and tumor progression.

    The team, based at the University of California, San Diego (UCSD) and University of Torino in Italy, reports their findings in Cancer Cell. The paper is titled “Receptor Tyrosine Kinases and TLR/IL1Rs Unexpectedly Activate Myeloid Cell PI3Kg, A Single Convergent Point Promoting Tumor Inflammation and Progression.” Their work overturns the widely held tenet that p110γ is only activated by GPCRs, says USCD’s Judith A. Varner, Ph.D., and colleagues.

    The team first confirmed that CD11b+ myeloid cells are recruited to and extensively populate a range of human and murine tumors, including murine and human breast, pancreatic, and lung carcinomas but not corresponding normal tissues. These cells persistently invaded growing tumors over time until as much as 25% of a tumor’s mass comprised myeloid cells.

    Furthermore, tumor inflammation was directly proportional to angiogenesis throughout the growth of the tumor. Additional studies showed that inflammatory factors in the tumor microenvironment were derived from both tumor and inflammatory cells.

    As most tumors produce multiple chemoattractants, the researchers then looked to see whether a common mechanism was involved in regulating myeloid cell recruitment to tumors. They tested the ability of chemoattractants to promote myeloid cell adhesion to endothelial cells (ECs) in vivo and in vitro.

    They took this approach because in order to exit the blood stream in response to signals released from diseased tissues, immune cells transiently adhere to and transmigrate through vascular endothelium, a process that depends on adhesion to EC receptors. A diverse range of tumor-derived chemoattractants was able to tigger EC adhesion by myeloid cells.

    Previous studies have shown that the myeloid cell receptors α4β1 and αMβ2 are involved in recognizing the EC surface adhesion proteins VCAM-1 and ICAM-1 and that blocking α4β1 but not αMβ2 suppresses murine and human myeloid cell adhesion to ECs. Dr. Varner’s team confirmed that murine myeloid cells in which integrin α4 expression was ablated by siRNA also failed to adhere to EC and the EC surface adhesion protein VCAM-1.

    “As chemoattractants had no effect on EC expression of α4 ligands during these assays, these results indicate that diverse chemoattractants stimulate myeloid cell adhesion by selectively increasing integrin α4β1 but not αMβ2,” the authors note.

    Indeed, further investigation indicated that chemokines, cytokines, and growth factors that activate RTKs, TLR/IL1Rs, and GPCRs all activate integrin α4β1, thereby promoting murine and human myeloid cell adhesion to ECs. Importantly, knocking out integrin α4 in tumors implanted in mice provide evidence that integrin α4 activation is required for trafficking and infiltration of myeloid cells into tumors in vivo.

    The observation that the chemoattractants stimulating structurally diverse GPCRs, RTKs, TLR/IL1Rs, and type I cytokine receptors all activate myeloid cell integrin α4β1 indicates that a common downstream signaling pathway might link these receptors, the authors write. When they evaluated inhibitors of various signaling pathways using myeloid cell adhesion assays, they found that selective inhibitors of PI3Kγ (p110γ) and Ras GTPases blocked myeloid cell adhesion to endothelium, while inhibitors of other PI3K isoforms and signaling proteins had no effect on adhesion.

    The class IB PI3K p110γ has been well-characterized as a GPCR-activated lipid kinase that promotes chemokine-stimulated chemotaxis and polarization of neutrophils, lymphocytes, and thymocytes in vitro and in vivo. p110γ-knockout mice and animals that express an inactivated form of p110γ have been shown to exhibit defects in granulocyte responses to chemokines.

    Previous studies have in addition shown that p110γ is essential for GPCR- but not RTK-mediated activation of PI3K activity, which makes the new observation that p110γ inhibitors blocked adhesion in response to GPCR as well as RTK ligands particularly unexpected, the team stresses.

    Using p110γ-specific knockout or inhibition methods, the researchers went on to demonstrate that growth factors, interleukins, and chemokines, which are ligands for RTKs, TLR/IL-1Rs, and GPCRs, promoted adhesion of wild-type, but not of P110γ-knockout myeloid cells or those with inactive P110γ to ECs or VCAM-1. Given that the knockdown cells still expressed normal levels of α4 integrin, these results indicated that p110γ is necessary for integrin adhesive activity. siRNA knockdown of p110γ but not other PI3K catalytic subunits also suppressed adhesion to ECs or VCAM-1, regardless of the stimulus, the authors add.

    “Although prior studies have indicated that only GPCR ligands activate p110γ, our results indicate that ligands for RTKs and TLR/IL1Rs promote p110γ activity and p110γ-dependent integrin α4β1 activation,” they claim. “Importantly, p110γ is also sufficient for integrin α4β1 activation as cells from p110γCAAX mice, which express membrane-targeted, constitutively activated p110γ adhered strongly to ECs even in the absence of stimulation.”

    In a further set of studies the team went on to confirm the pivotal role of p110γ but not other PI3K isoforms in promoting α4β1-mediated myeloid cell trafficking to tumors in vivo. They surprisingly found that p110γ was the major PI3K catalytic isoform expressed in primary myeloid cells, whereas lymphocytes express large amounts of p110δ, while LLC cells express large amounts of p110α and β and little γ or δ. Additional in vitro studies demonstrated that growth factors and cytokines activate p110γ directly, rather than indirectly through GPCRs.

    The combined collection of findings made particular sense when viewed in a clinical context. The researchers tested  the efficacy of a selective p110γ inhibitor or a pan-PI3K inhibitor, in tumor-bearing mice. While both inhibitors suppressed lung cancer inflammation angiogenesis and tumor growth  in vivo, activity of the P110γ inhibitor was related to the inhibition of tumor inflammation and angiogenesis, without directly affecting the tumor cells themselves.

    While a number of clinical trials are already under way to test pan-PI3K inhibitors in cancer patients, “our studies indicate that p110γ is an excellent target for a relatively nontoxic cancer therapeutic, as this isoform is primarily expressed by myeloid cells and is a convergence point of diverse chemoattractant signaling pathways that are required for tumor inflammation and tumor progression,” the authors conclude.

PX-866 Targets the Phosphatidylinositol-3-kinase (PI-3K) Pathway

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Posted 13 Jun 2011 — by James Street
Category Drugs, Kinase, Kinome, Proteomics, PTEN

PX-866 is a small molecule drug that irreversibly inhibits phosphatidylinositol-3-kinase (PI-3K), a family of intracellular signaling molecules that play essential roles in multiple cellular functions. In normal cells the PI-3K pathway is tightly controlled, whereas the inappropriate activation of PI-3K is important in the pathogenesis of multiple human cancers, making it a rational and attractive target for drug development.

There are 3 classes of PI-3K (I, II and III) categorized based on structure and substrate specificity. The class I PI-3Ks are most tightly associated with human disease and are the targets of PX-866. Four class I PI-3K isoforms have been identified: alpha (α), beta (β), delta (δ) and gamma (γ). The α and β PI-3Ks are broadly expressed in human tissues and deregulation of these two PI-3K family members occurs in many solid tumors. The δ and γ PI-3Ks are primarily expressed in cells that comprise the human immune system and the δ PI-3K is important for lymphomas and leukemia tumor cell growth.

In normal cells the class I PI-3Ks are activated by the stimulation of signaling receptors found on the cell surface that respond to soluble growth factors or cell-cell contact. Once activated, class I PI-3K chemically modifies a membrane-associated lipid molecule called phosphatidylinositol 4,5-bisphosphate (PIP2) to form the product phosphatidylinositol 3, 4, 5-trisphosphate (PIP3). PIP3 serves as a “second messenger” that activates downstream targets, including AKT, the principal mediator of PI-3K signal transduction. The activation of AKT triggers the subsequent activation of additional downstream signaling molecules including the mammalian target of rapamycin (mTORC1), as well as other proteins that control cell growth, metabolism, survival, and proliferation.

In non-diseased cells the PI-3K pathway is tightly controlled by a molecule called PTEN, which counteracts the activity of class I PI-3K by converting PIP3 back to PIP2. Many cancers contain mutations that result in the deregulated activation of the PI-3K pathway. These mutations may function by inhibiting the activity of PTEN, or by overcoming PTEN activity through enhancement of PI-3K signaling. This enhancement can occur by mutations within the PI-3K gene that result in increased enzymatic activity, or by changes to the growth factor receptors that are upstream of PI-3K. These receptors include EGFR, cMet, IGF1R and Her2, which have been shown to be important for multiple cancers. The deregulated activation of PI-3K drives multiple biological activities that are critical for cancer cells, including processes that enable tumors to metastasize and invade normal tissues, continue to grow under conditions of low nutrients or low oxygen, and to resist chemotherapy and radiation therapy (Engelman 2009; Liu, Cheng et al. 2009; Sarker, Reid et al. 2009; Ghayad and Cohen 2010).

Preclinical studies have shown that PX-866 is efficacious in numerous mouse xenograft models of human cancers as a single agent and in combination with chemotherapy, radiation and targeted cancer drugs, such as EGFR inhibitors. Oncothyreon is currently evaluating PX-866 in Phase 1 / 2 and Phase 2 clinical studies in solid tumor diseases.

Promising new target for stifling the growth and spread of cancer

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Posted 13 Jun 2011 — by James Street
Category Inflamation, Kinase, Kinome, metastases, Proteomics

UCSD researchers find inhibiting single protein blocks the inflammation that fuels tumors

Cancer and chronic inflammation are partners in peril, with the latter increasing the likelihood that malignant tumors will develop, grow and spread. Researchers at the University of California, San Diego School of Medicine say they’ve identified a tumor inflammation trigger that is common to most, if not all, cancers. And using existing inhibitory drugs, the scientists were able to dramatically decrease primary tumor growth in animal studies and, more importantly, halt tumor progression and metastasis.

The findings appear in the June 14 issue of the journal Cancer Cell, authored by Judith A. Varner, PhD, professor of medicine at the UC San Diego Moores Cancer Center, and colleagues in the UCSD School of Medicine and at the University of Torino, Italy.

When cancer cells appear in the body, they often provoke an immune system response. Under some circumstances, this is a good thing. But Varner and colleagues were able to show that when responding myeloid or white blood cells called macrophages are drawn to invasive cancer cells, the result can be considerable trouble for patients. Rather than suppressing the cancer, the myeloid cells are tricked by the tumor into aiding and abetting its growth and spread. Scientists have long recognized that myeloid cells can invade and promote tumor growth. But until now it was not fully appreciated how this hijacking occurs and whether there are ways to disrupt this process by suppressing the trigger that leads to myeloid cell recruitment into tumors.

Probing more deeply into the tumor inflammation process, the UCSD research team identified a range of tumor-produced molecules that attract these dangerous myeloid cells. They also pinpointed the specific trigger on myeloid cells enabling them to invade the tumor environment and accelerate tumor growth and metastasis. It is an enzyme called PI-3 kinase gamma on myeloid cells that turns on an adhesion receptor allowing the cells to enter tumors.

When researchers blocked the activity of PI-3-kinase-gamma, either genetically or through the use of a drug designed for this purpose, myeloid cells were blocked access into tumors, resulting in reduced tumor growth and a dramatic decrease in metastasis. Without the recruitment of myeloid cells, Varner said, the capability of a cancer tumor to grow is largely stifled.

“Most strategies targeting the role of myeloid cells in cancer have focused on reducing their expression of inflammatory molecules,” Varner explained. “We’ve found a single convergent point – the PI-3 kinase-gamma enzyme – that, when blocked, appears to result in significant suppression of tumor inflammation and growth regardless of the initiating event. It could be a very important therapeutic target for future cancer treatments and could impact most, if not all, types of solid cancer.”

Michael Karin, PhD, distinguished professor of pharmacology in UCSD’s Laboratory of Gene Regulation and Signal Transduction and a pioneer in inflammation research, agreed: “I think that the inhibition of PI-3K activity represents a very interesting and promising approach for inhibition of tumor-associated inflammation. It seems to fully normalize the tumor microenvironment and provide a new addition to our armamentum of anti-cancer drugs.”

Varner said a number of biotechnology companies are pursuing potential drugs using PI-3-kinase inhibitors to treat diseases from cancer to heart disease to arthritis. The PI-3-kinase-gamma protein may be a particularly promising therapeutic target, because it is not widely expressed in the body, and its inhibition would likely produce fewer side effects than many therapeutics.

###

 

Co-authors of the research are Michael C. Schmid, Christie J. Avraamides, Philippe Foubert, Joan R.E. Manglicmot, Xiaodan Song and Wolfgang Wrasidlo of the UCSD Moores Cancer Center; Holly C. Dippold and Mark H. Ginsberg, UCSD Department of Medicine; Irene Franco and Emilio Hirsch, Department of Genetics, Biology and Biochemistry, Molecular Biotechnology Center, School of Medicine, University of Torino, Italy; Lesley G. Ellies, UCSD Department of Pathology; Sara L. Blair, UCSD Department of Surgery; and Lissette M. Acevedo and David A. Cheresh, UCSD Moores Cancer Center and UCSD Department of Pathology.

Funding for this research came, in part, from grants from the National Institutes of Health and the California Tobacco Related Disease Research Program.

Contact: Scott LaFee
slafee@ucsd.edu
619-543-6163 begin_of_the_skype_highlighting 619-543-6163 end_of_the_skype_highlighting
University of California – San Diego

Targeting the cancer kinome through polypharmacology

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Posted 07 Jun 2011 — by James Street
Category Breast Cancer, Chronic myeloid leukaemia (CML), GIST, Kinome, Lung Cancer, Oncogenes
Zachary A. Knight, Henry Lin, and Kevan M. Shokat
Zachary A. Knight, The Rockefeller University, New York, New York 10065, USA.
Correspondence to Z.A.K. and K.M.S. Email: zknight@rockefeller.edu; Email: shokat@cmp.ucsf.edu
Small right arrow pointing to: The publisher’s final edited version of this article is available at Nat Rev Cancer
Small right arrow pointing to: See other articles in PMC that cite the published article.
Abstract
Kinase inhibitors are the largest class of new cancer drugs. However, it is already apparent that most tumours can escape from the inhibition of any single kinase. If it is necessary to inhibit multiple kinases, how do we choose which ones? In this Opinion article, we discuss some of the strategies that are currently being used to identify new therapeutic combinations of kinase targets.
More than 10,000 patent applications for kinase inhibitors have been filed since 2001 in the United States alone1. This massive investment has been fuelled by the realization that kinases are intimately involved in cancer cell growth, proliferation and survival. Indeed, kinases and their direct regulators are among the most frequently mutated oncogenes and tumour suppressors2–4. Well known examples include the oncogenic kinases PIK3CA (the p110α subunit of PI3K), epidermal growth factor receptor (EGFR) and BRAF; the Ras family of oncogenes, which activate both PI3Ks and Raf; and the PTEN tumour suppressor, which inhibits PI3K signalling.
Despite the excitement surrounding these targets, clinical progress has been uneven. Kinase inhibitors have revolutionized the treatment of a select group of diseases, such as chronic myeloid leukaemia (CML) and gastrointestinal stromal tumours (GIST), which are driven by a single oncogenic kinase; for these conditions, kinase inhibitors have achieved multi-year increases in survival5–7. Smaller but significant responses have been observed for some cancers that are highly dependent on angiogenesis, and therefore sensitive to inhibitors of vascular endothelial growth factor (VEGF) signalling, such as renal cell carcinoma8–11.
Kinase inhibitors have been least effective in treating the types of cancer that have the highest mortality rates, such as lung, breast, colorectal, pancreatic and prostate cancer. Clinical trials show that the most effective kinase inhibitors prolong survival by only a few months for these cancers12–17. Results have been improved by identifying markers for patients that are more likely to respond to kinase inhibitor therapy — such as EGFR mutations in lung cancer18, ERBB2 overexpression in breast cancer19, and wild-type KRAS in lung and colorectal cancer20,21 — but even among these subgroups, relapse is inevitable for patients with disseminated disease.
Why has clinical progress been so challenging? One reason is that most tumours can escape from the inhibition of any single kinase (FIG. 1). This first became clear when resistance mutations in BCR–ABL were discovered in patients with CML who were resistant to imatinib22; similar mutations have now been detected in other kinases following treatment with kinase inhibitors23–26. Alternatively, tumours can acquire drug resistance through mechanisms that do not involve mutation of the target (FIG. 1a). These mechanisms include the activation of surrogate kinases that substitute for the drug target27 and the inactivation of phosphatases to amplify the residual kinase activity that persists during drug treatment28. It is also clear that many tumours possess intrinsic resistance to kinase inhibitors at the time of initial therapy (FIG. 1b). This can result from the activation of multiple, redundant kinase signalling pathways29 or the presence of activating mutations in downstream pathway components, such as KRAS or PTEN, which enable the tumour to bypass the drug target20,21,30.
Figure 1

Resistance to kinase inhibitors
Overcoming these resistance mechanisms will require targeting tumour cells at multiple levels, through either single drugs that bind to multiple proteins31 or cocktails of highly selective inhibitors32. The challenge for the cancer research community is to learn how to predict the best combinations of targets and then prioritize those combinations for clinical testing. This is a daunting task, because the number of possible target combinations is almost limitless, but clinical trials are slow and expensive.
Targeting one kinase with multiple drugs
If a tumour depends on the activity of a single kinase, then using multiple drugs to target that kinase can be effective. This was first demonstrated in CML, in which early clinical trials showed that more than 90% of patients with chronic phase disease responded to the BCR–ABL inhibitor imatinib5 (TABLE 1), but that a subset of those patients relapsed while on the drug. Disease progression was associated with the emergence of leukaemic cells bearing mutations in BCR–ABL that block imatinib binding22, suggesting that drugs targeting these BCR–ABL mutants would be effective. Two second-generation BCR–ABL inhibitors were developed (dasatinib and nilotinib) that retain activity against most of the more than 50 clinically observed BCR–ABL resistance mutations, and these drugs are highly effective against imatinib-resistant disease33,34. However, a common BCR–ABL mutation (T315I) prevents the binding of all three drugs, and this has emerged as the default allele for many patients on long-term inhibitor therapy22,35. To address this problem, third-generation drugs have been developed that potently inhibit BCR–ABL T315I. These agents are effective in preclinical models of drug-resistant CML36–39, and four such compounds are currently in clinical trials. Some patients have now survived more than 10 years since starting treatment by undergoing sequential therapy with three generations of BCR–ABL inhibitors40, proving that it is possible to extend the therapeutic response in CML by repeatedly targeting the same kinase.
Table 1
Table 1

US FDA-approved kinase inhibitors
A similar approach has been used to target the ERBB2 receptor tyrosine kinase in breast cancer. Trastuzumab is a monoclonal antibody that binds to the extracellular domain of ERBB2, thereby both inhibiting ERBB2 signalling and recruiting immune cells to the tumour19; however, patients with metastatic cancer who are treated with trastuzumab invariably relapse. The mechanism of trastuzumab resistance is not understood, but it is clear that resistant tumours remain dependent on ERBB2 signalling. This is because patients with breast cancer who have progressed on trastuzumab therapy nonetheless respond to lapatinib14, a small molecule inhibitor of the tyrosine kinase domain of ERBB2. Therefore, it is possible to induce a second response in these patients by targeting ERBB2 with a drug that binds to a different site on the protein. Unlike CML, however, the clinical response to lapatinib in metastatic breast cancer is brief, and disease progression typically occurs within a few months14.
These examples show that in certain cases sequential targeting of a single kinase with multiple drugs can prolong the therapeutic response. It is unclear how broadly this model applies, because it is unclear how many tumours are truly dependent on a single oncogene (a state referred to as ‘oncogene addiction’ (REF. 41)). The strongest evidence in favour of this hypothesis is the discovery of resistance mutations after kinase inhibitor treatment in CML, GIST, lung cancer and a myeloproliferative disorder known as hypereosinophilic syndrome22–26. Such mutations are definitive proof that the mutated kinase was required for the survival of that tumour. The oncogene addiction model is also supported by many preclinical studies showing that tumour cell lines containing an activating mutation or amplification of a kinase can be more sensitive to inhibitors of that kinase in vitro42–44.
Conversely, the detection of an oncogenic kinase mutation does not guarantee sensitivity to the corresponding kinase inhibitor. For example, mutations in PIK3CA or PTEN are poor predictors of the sensitivity of tumour cell lines to PI3K inhibitors31,45. Mutations in KRAS do not, in general, sensitize tumour cells to inhibitors of Raf or Mek43,46. Indeed, the response of most tumours to inhibition of an oncogene is much less dramatic than the response in CML, in which even transient inhibition of BCR–ABL irreversibly commits cells to apoptosis44 (FIG. 2). In this respect, it is worth noting that the term oncogene addiction gained widespread use because it describes a paradox: inhibiting an oncogene would be predicted to reverse the gain of function caused by the oncogene, not kill all the tumour cells. It is only recently that this term has been conflated with the idea that oncogenes should be expected to be required for tumour survival. For this reason, there is clearly a need to identify additional vulnerabilities in tumours beyond the genes that are directly mutated.
Figure 2

Degrees of oncogene addiction
Targeting nodes in a signalling network
Three sets of targets collectively account for a large proportion of current efforts in kinase inhibitor drug discovery. These are the receptor tyrosine kinases (for example, EGFR, ERBB2, platelet-derived growth factor receptor (PDGFR) and VEGF receptor 2 (VEGFR2)), the kinases in the MAPK pathway (for example, BRAF, MEK1 and MEK2) and the kinases in the PI3K pathway (for example, PIK3CA, Akt and mTOR). These three groups of targets are mechanistically linked because most receptor tyrosine kinases activate the MAPK and PI3K pathways as their primary signalling function (FIG. 3).
Figure 3

Strategies for multi-targeted kinase inhibition
There is a compelling biological rationale for targeting each of these groups in combination. For example, clinical resistance to tyrosine kinase inhibitors is often associated with reactivation of PI3K signalling28. Therefore, the effectiveness of tyrosine kinase inhibitors might be increased by combination with an inhibitor of the PI3K pathway. This combination has been shown to be effective in animal models and is undergoing extensive clinical testing: at least 21 clinical trials are currently evaluating the combination of a tyrosine kinase inhibitor and an mTOR inhibitor in several types of cancer. There has been a particular emphasis on the use of PI3K inhibitors to sensitize tumours to inhibitors of EGFR or ERBB2 (such as erlotinib47, lapatinib48 and trastuzumab49). This is because the anti-tumour activity of EGFR and/or ERBB2 inhibitors has been correlated with their ability to inhibit the phosphorylation of ERBB3, a kinase-inactive receptor that primarily functions to activate the PI3K pathway28.
Other target combinations are suggested by the connectivity of the signalling network. For example, mTOR activates a well-characterized negative feedback loop that inhibits the activity of PI3K (FIG. 3a). mTOR inhibitors such as rapamycin block this negative feedback loop, resulting in hyper-activation of PI3K that may counteract the anti-proliferative effect of mTOR inhibition. For this reason, it has been proposed that the dual inhibition of PI3K and mTOR may be more effective than inhibiting either target alone. Preclinical experiments support this idea45, and drugs such as PP121 that target multiple steps in this pathway have been designed31 (FIG. 3a). Several dual PI3K and mTOR inhibitors are currently being evaluated in clinical trials (for example, NVP-BEZ235, BGT226 and XL765) alongside agents that selectively target either PI3Ks (for example, XL147 and GDC-0941) or mTOR (for example, OSI-027, AZD8055 and rapamycin analogues). As there are practical challenges associated with developing both multi-targeted single agents and multi-drug cocktails50, it will be interesting to see which approach emerges as the most successful from these clinical trials.
Combination therapy can be used in other cases to target an otherwise undruggable protein. For example, KRAS is one of the most commonly mutated oncogenes, but efforts to find Ras inhibitors have been unsuccessful. It was long believed that the MAPK pathway was the primary Ras effector in most tumours51, but Raf and Mek inhibitors have inconsistent activity against tumour cells with Ras mutations43,46. Ras also directly binds to and activates PI3K52, and the disruption of this interaction prevents KRAS-driven tumorigenesis in the mouse53. For this reason, it may be necessary to inhibit both the MAPK and PI3K pathways to block the growth of tumours with Ras mutations. This conclusion is supported by data showing that resistance to Mek inhibitors in some KRAS-mutant cells is caused by mutations in PIK3CA or PTEN, and that this resistance is reversed by PI3K inhibition54. Moreover, the combination of PI3K and Mek inhibitors is active in a mouse model of KRAS-driven lung cancer55. The rationale for this combination is so compelling that Merck and AstraZeneca recently announced a joint Phase I clinical trial that will test the combination of an Akt inhibitor (MK-2206) and a Mek inhibitor (AZD6244) against solid tumours (FIG. 3b).
Limitations of rational drug combinations
The challenge associated with developing these types of rationally designed drug cocktails is that preclinical experiments do not predict their efficacy in humans. This is true even when the individual agents have already shown clinical anticancer activity. For example, preclinical experiments supported the combination of gefitinib and trastuzumab in breast cancer56,57, erlotinib and bevacizumab in renal cell carcinoma58, and cetuximab and bevacizumab in colorectal cancer59, but all of these failed in clinical trials58,60,61. In the case of cetuximab and bevacizumab, the drug combination reduced survival compared with the single agents60.
In some cases, these discrepancies may be due to misinterpretation of the preclinical data, rather than a failure of the preclinical model itself. For example, careful studies have shown that the addition of gefitinib to trastuzumab therapy in xenograft models of breast cancer results in only modest additional efficacy62, and that this additional benefit requires gefitinib concentrations that may be toxic in humans63. In other cases, subtle changes in the dosing regimen can have a large effect on the activity of the combination. For example, preclinical studies of the cyclin-dependent kinase inhibitor flavopiridol and the topoisomerase inhibitor irinotecan showed that this combination can effectively induce apoptosis in colon cancer cells when administered in a specific sequence (irinotecan followed by flavopiridol, resulting in apoptosis in 43% of the cells)64. The reverse sequence of drugs (15% apoptosis) and concurrent therapy (30% apoptosis) were both less effective. This finding was rationalized by a model in which pretreatment with flavopiridol arrested cells in the G1 phase of the cell cycle and thereby reduced the number of cells progressing through S phase and therefore irinotecan sensitivity64. A subsequent clinical trial validated the safety and preliminary efficacy of this sequential dosing regimen65.
Preclinical studies of drug combinations are probably biased towards validating the targets that are already believed to be important, and this bias limits their ability to prioritize new drug combinations for clinical testing. For example, all kinase inhibitors have some toxicity to cells, and for this reason two kinase inhibitors can usually be shown to be more toxic than either compound alone. For these comparisons it is often unclear what should be used as the normal cell to measure therapeutic index66, and in many cases the survival of the mouse in a xenograft experiment is the only evidence of differential toxicity. This can be addressed to some degree by correlating lethality with genotype across many tumour cell lines42 or by using pairs of isogenic cell lines that differ at a single locus66, but this becomes challenging when comparing drug combinations.
For a small group of kinase targets with an undisputed role in cancer — such as the oncogenic receptor tyrosine kinases and the core components of the PI3K and MAPK pathways — numerous clinical trials of drug combinations are planned or underway. It is uncertain, however, that these kinases are the best cancer drug targets67, and the route to clinical testing for combinations of drugs that target other kinases is less straight-forward. One major obstacle is that it is difficult to conduct clinical trials combining two investigational drugs, and even more difficult if the two drugs originate from different pharmaceutical companies68,69. Companies are reluctant to conduct joint clinical trials of early-stage compounds because of fears about loss of intellectual property and the possibility of an unforeseen side effect from the combination68,69. This creates a Catch-22 scenario: many kinase inhibitors are likely to be effective only as part of a combination therapy, but it will be difficult to test those combinations until after the drugs are approved as single agents. Indeed, the joint venture mentioned above between AstraZeneca and Merck to test Akt and Mek inhibitors in combination was reported in national media, such as The Wall Street Journal, partly because such early stage collaborations are so rare70. In the field of AIDS research, this problem was addressed in 1993 by the formation of the Inter-Company Collaboration for AIDS Drug Development that coordinated the testing of drug cocktails by 15 pharmaceutical companies71. However, there is not yet a comparable mechanism for companies to collaborate to test new combinations of investigational drugs in oncology, where there is arguably the greatest need and opportunity.
Using RNAi to discover new targets
The development of RNA interference (RNAi) has made it possible to directly screen for the genes required for tumour proliferation in mammalian cells. These screens have two advantages. First, they can identify new drug targets, as any gene that selectively blocks tumour growth when knocked down by RNAi is a candidate. Second, these screens provide an unbiased test of models of tumour signalling, because they directly examine which genes are most important to the tumour. This perspective is valuable, because most combination therapies are based on simple models of tumour signalling; however, there is little evidence that such models capture the most crucial interactions in the tumour cell, which could be highly indirect and inaccessible to simple reasoning.
Three recent papers illustrate the power of large-scale RNAi screens to address this problem by looking for genes that are selectively required for the growth of tumour cells expressing an activated KRAS mutant72–74. Luo et al.72 screened ~75,000 short hairpin RNAs (shRNAs) and found 83 shRNAs targeting 77 genes that preferentially impaired the growth of KRASG13D cells compared with control cells in which the KRASG13D allele had been disrupted by homologous recombination72. Analysis of these hits revealed an unexpected enrichment of a network of genes involved in mitosis. A small molecule inhibitor of the mitotic kinase polo-like kinase 1 (PLK1) had increased cytotoxicity to KRAS-mutant cells in vitro and in vivo72.
Scholl et al.73 screened a smaller set of shRNAs (5,024 targeting 1,011 genes) against a broader panel of cells that included 8 tumour cell lines (4 KRASG13D mutant and 4 KRAS wild-type) and 2 control cell lines73. The top hit was STK33, a serine threonine kinase in the calmodulin kinase family with no previous connection to Ras signalling or cancer. shRNAs targeting STK33 induced KRAS mutation-dependent toxicity in a broad panel of tumour cell lines, through a mechanism that may involve modulation of S6K1 kinase activity73.
Barbie et al.74 screened a panel of shRNAs targeting kinases, phosphatases and oncogenes against a panel of 19 tumour cell lines and then extracted from these data the genes selectively required for the survival of KRAS-mutant cells74. The top hit from this screen was TBK1, a protein kinase that activates nuclear factor-κB (NF-κB) signalling by phosphorylating the NF-κB inhibitory protein IκBα. A companion paper showed that genetic inhibition of NF-κB signalling was sufficient to block tumour development in a mouse model of KRAS-driven lung adenocarcinoma75.
A common finding from all three papers was that, although many genes were required for the survival of KRAS-mutant cells, few of those genes could have been predicted in advance on the basis of known biochemical interactions or models of Ras signalling. Among the three kinases (PLK1, STK33 and TBK1) that were the focus of follow-up experiments, only TBK1 had been previously linked to Ras (through a pathway involving the exocyst complex, RALB and RALGDS), and this protein could hardly be described as a well-known Ras effector. This is even more remarkable when we consider that Ras and its downstream targets are among the most intensely studied proteins in biology.
Similar results were described in a series of papers that attempted to define the ‘essential kinome’ that is required for cell proliferation and survival67,76,77. This was done by carrying out kinome-wide shRNA screens on a large panel of tumour cell lines, primary cells and pairs of isogenic cells that differed in the expression of a single gene. The primary conclusion from these papers was that there was little overlap in the kinases that are required for cell proliferation across many different cell lines. Indeed, there was no correlation between the number of PubMed citations for a kinase and the likelihood that the kinase was important for tumour cell proliferation. In the words of the authors77: “Although the regulation of cell proliferation and survival are heavily studied areas, we did not see a bias in these screens toward the identification of previously known and well studied kinases, suggesting that our knowledge of the molecular events in these areas is still meager.” (D. A. Grueneberg et al, 2008).
Given the unpredictable sensitivities of tumour cells to shRNAs targeting a single kinase, it may be possible to identify new pairs of targets by screening shRNAs in the presence of a drug. An early experiment in this area looked for shRNAs that synergistically killed cancer cells in the presence of A-443654, a small molecule inhibitor of Akt78. This was motivated by the surprisingly weak anti-tumour activity of A-443654 as a single agent in preclinical models79. Two kinases were identified in this screen: casein kinase 1, γ3 (CSNK1G3) and inositol polyphosphate multikinase (IPMK). Neither of these kinases had previously been linked to Akt signalling or cancer. However, knock down of both genes potentiated the inhibition of phosphorylation of Akt and ribosomal protein S6, suggesting that these kinases may have a cryptic role in regulating signalling through the PI3K pathway.
Barriers to translating RNAi into drugs
RNAi screens can help challenge our assumptions about the genes that are most important in cancer. However, there are considerable obstacles to translating any hit from one of these screens into a new drug. Most RNAi screens measure only cell proliferation in vitro, which ignores most of the capabilities of a tumour. Therefore, it will be necessary to validate the large number of genes that emerge from these screens in more complex and time-consuming models. Once these hits are validated, they become subject to the same caveats that accompany potential drug targets identified in any other way.
In this respect, it is important to emphasize that there is not a direct correlation between RNAi knockdown of a gene and the identification of a potential drug target. Most drugs cannot be replicated by an shRNA because, for example, the drug interferes with multiple targets or inhibits a single domain of a multidomain protein only. Likewise, most shRNAs cannot be replicated by a drug, because most proteins are undruggable. Indeed, there are many examples in which an shRNA (or gene knockout) and a drug targeting the same protein give different phenotypes, and the reasons for these differences have been extensively documented (for a review of this topic see REF. 80). As a result, RNAi screens may be more likely to expose the gaps in our knowledge of cancer biology than to directly point the way to new therapeutic approaches.
Using drugs to discover kinase targets
Historically, most drugs were discovered because they possessed activity in cells or animals, and their targets and mechanism of action were elucidated only later. This is sometimes called ‘phenotype-based’ drug discovery because the phenotype was discovered before the target. By contrast, almost all modern drug discovery is ‘target-based’, meaning that the target is selected first, on the basis of a hypothesis about its role in disease.
Nonetheless, there are instances in which phenotype-based drug discovery has contributed to the development of kinase inhibitors for cancer, albeit unintentionally. One example is sorafenib, which was originally designed as an inhibitor of Raf based on the logic that Raf inhibition might be effective for Ras-mutant tumours81. Sorafenib has yet to show clinical benefit for tumours that contain frequent Ras mutations, such as lung and pancreatic cancer, and has also failed in clinical trials for the treatment of melanoma82, a disease that has a high rate of BRAF mutations43,83. However, in early clinical trials of sorafenib (which were designed to establish safety and therefore contained a diverse patient population) responses were observed in two unexpected tumour types84: renal cell (kidney) and hepatocellular (liver) cancer. One patient with kidney cancer in an early Phase I trial achieved stable disease for 2 years84, leading to the broader testing and approval of sorafenib for kidney cancer (and more recently liver cancer). The efficacy of sorafenib in kidney cancer is now attributed to the inhibition of VEGFR2 in endothelial cells, which blocks angiogenesis, rather than the inhibition of Raf in the tumour. Preclinical studies have shown that the inhibition of an additional target, PDGFR in pericytes, may be important85. Therefore, sorafenib probably blocks tumour growth through the inhibition of two kinases, expressed in different tissues, neither of which was the intended target of the drug.
Imatinib provides a second example of serendipitious target discovery. After its initial approval for the treatment of CML, imatinib was tested in five patients with hypereosinophilic syndrome, a disease of unknown molecular origin, based on the reasoning that treatments that are effective in CML are sometimes also effective in patients with hypereosinophilia86 (even though the mechanism of action of those other treatments, such as hydroxyurea and interferon-α, is unrelated to the mechanism of imatinib). Remarkably, four of the five patients treated with imatinib showed a complete haematological response (normalized eosinophil counts), such that they were able to discontinue other therapies. Analysis of DNA from the leukocytes of these patients led to the discovery of a chromosomal rearrangement that generated a fusion between PDGFRA and FIP1L1, producing a constitutively active PDGFR kinase24. As PDGFR is one of a small number of kinases inhibited by imatinib, this suggested that PDGFR activation was probably the cause of the disease. This was confirmed by the discovery of a T674I resistance mutation in PDGFR in a patient who had relapsed from imatinib therapy24.
As these examples show, the advantage of using drugs to identify cancer targets is that they can reveal in an unbiased way the proteins most essential to the tumour. The major limitation of this approach is that it is difficult to identify the targets of a molecule that has an unknown mechanism of action87. If the target is unknown, then it is difficult to increase the potency of the compound by medicinal chemistry. It can also be challenging to determine whether the efficacy and toxicity of the drug are linked (because they reside in the same target) or separable (because they reside in different targets). For these reasons, it is often impossible to improve compounds that are identified in a screen but have an unknown mechanism of action.
Targeted polypharmacology
In the case of sorafenib and imatinib, it was straightforward to identify the relevant targets of those drugs, because the targets were almost certain to be kinases. As these two drugs have a relatively small number of high-affinity targets in the human kinome (fewer than 20), the possibilities could be rapidly tested. Could this approach be generalized, so that kinase inhibitors could be used to search in an unbiased way for new combinations of therapeutic targets?
A unique feature of kinase inhibitors is that they have the potential for greater target promiscuity than almost any other type of drug. This is because the kinase superfamily (including the structurally related protein, lipid and small molecule kinases) is the largest family of druggable genes that binds to a common substrate (ATP). Kinases differ in this respect from other large gene families, such as G protein-coupled receptors, which interact in their druggable site with a wide range of structurally diverse ligands, including both peptides and small molecules. This fact has been emphasized50 by noting that the kinase inhibitor sunitinib inhibits at least 79 kinases at low micromolar concentrations, whereas all the other approved drugs combined target only 320 proteins. Therefore, individual kinase inhibitors have an enormous potential for unpredicted target combinations and so new biological activities.
Despite this potential for promiscuity, it is increasingly feasible to enumerate the targets of kinase inhibitors in a systematic way. This is because most kinases can be heterologously expressed, either as a soluble kinase domain or on the surface of phage, and assayed for drug binding in a purified format. Although there are exceptions, the activity of most kinase inhibitors in cells correlates with biochemical parameters that can be measured in vitro, such as the dissociation constant (KD) of the drug and the Michaelis–Menten constant for ATP binding (KM,ATP) of the kinase88. As kinases have become increasingly important drug targets, the measurement of these biochemical parameters has been industrialized, and there are now many vendors that offer to screen compounds against panels of kinases that approach or exceed half of the kinome (FIG. 4a). As the cost of assaying compounds against these panels has decreased, it has transformed the types of experiments that are feasible (FIG. 4b). For example, a widely cited paper from 2000 reported the specificity of 24 commonly used kinase inhibitors against 28 kinases (approximately 700 kinase–drug pairs)89. In 2007, the same group published a follow-up paper that analysed the specificity of 65 common kinase inhibitors against 70 kinases (approximately 4,500 kinase–drug pairs)90. In 2008, scientists from GlaxoSmithKline reported the testing of a panel of 577 diverse kinase inhibitors against 203 kinases (more than 117,000 unique kinase–drug pairs)91; in this case, the aim was not to evaluate any specific compound but to characterize the selectivity properties of kinase inhibitors as a drug class.
Figure 4

Selectivity profiling of kinase inhibitors
Extrapolating from these trends, it is plausible that some drug discovery programmes in the near future will profile every kinase inhibitor that is synthesized against most of the kinome. This would occur before any biological testing, as a component of routine compound characterization. The availability of selectivity data on this scale would enable medicinal chemistry to focus on optimizing drug profiles against complex patterns of kinases that gave a desired phenotype, rather than attempting to maximize specificity for a single target. It is likely that drug discovery at some pharmaceutical companies already operates in this way to some degree, although it may not be explicitly acknowledged.
What would be the advantage of this approach? The primary advantage is that it allows for target serendipity — the discovery of target combinations that could not have been predicted, but that are optimal for killing tumour cells — while allowing medicinal chemists to optimize compounds based on biochemical measurements against purified proteins. This has the potential to address the limitations of both target-based drug discovery, which often fails because the target is wrong, and phenotype-based drug discovery, which often fails because the compounds cannot be optimized.
This type of ‘targeted polypharmacology’ would represent a considerable challenge to medicinal chemists, who would be asked to carry out chemical optimization against multi-dimensional target profiles. However, there is already evidence that this is possible for certain target combinations31,92, and kinase drug discovery seems to be the ideal setting to test this model. We analysed a large database of kinase inhibitor selectivity data93 to discover whether certain combinations of kinase targets are enriched among known kinase inhibitors; whether the preference for these target combinations could be rationalized on the basis of sequence analysis; and whether this could be used to estimate the combinatorial druggability of most of the kinome that has not yet been targeted by a small molecule (FIG. 5; see Supplementary information S1 (figure)). We have found, consistent with previous analyses93, that there are clearly clusters of kinases that tend to be inhibited by similar drugs, but that there are also many target combinations that should be accessible but remain undiscovered. We interpret this to mean that there is an important opportunity to discover multi-targeted kinase inhibitors with new biological activities.
Figure 5

Polypharmacology in the protein kinome
We have focused on approaches to identify combinations of kinase targets with increased anticancer activity, but understanding the basis for kinase inhibitor-mediated toxicity to normal cells is also valuable, as this information will improve efforts to increase therapeutic index. The broad kinome profiling of clinically approved and investigational kinase inhibitors is likely to help identify such problematic kinase targets. Removing these toxicity-associated kinases from new drug candidates may allow for more complete inhibition of cancer cell targets while avoiding systemic toxicity.
Conclusions
Many different approaches will be necessary to identify the best combinations of targeted therapies for cancer. However, it is important to begin to consider the challenges that may be faced in the near future, when drugs targeting every kinase linked to cancer have been tested in clinical trials, but survival rates for most types of cancer have only marginally improved. It will not be sufficient in this case to simply pursue the next set of oncogenes, because tumour sequencing projects have already shown that such oncogenes do not exist, at least among the genes that are mutated with high frequency2–4. Therefore the burden will be on the cancer research community to think of more creative ways to target important proteins such as kinases that have already been identified.
Supplementary Material
S1

Acknowledgements
This work was partly supported by the US National Institutes of Health grants DK083531 (Z.A.K.) and EB001987 (K.M.S.).
Competing interests statement

The authors declare no competing financial interests.
DATABASES

National cancer institute Drug Dictionary: http://www.cancer.gov/drugdictionary/
FURTHER INFORMATION
SUPPLEMENTRAY INFORMATION
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Contributor Information
Zachary A. Knight, The Rockefeller University, New York, New York 10065, USA.
Henry Lin, Howard Hughes Medical Institute and Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, USA.
Kevan M. Shokat, Howard Hughes Medical Institute and Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, USA.
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New Kinome Database Promises Wealth of Unexpected Cancer Drug Targets

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Posted 07 Jun 2011 — by James Street
Category Kinase, Kinome
By Lois Wingerson | January 5, 2011

 

A word for oncologists to know this year (if they don’t already) is “kinome.” Analagous to “genome,” it’s the complete set of kinases in an organism. The human kinases, of course, are the class of enzymes whose inhibitors have proven so promising and so frustrating in recent clinical cancer trials.

Over the past year or so, study of the kinome has revealed molecules that some tumors are relying on to survive, many of which until now we had no idea were involved in cancer or which we hardly knew at all. Numerous reports have suggested novel kinases that might be useful drug targets in particular malignancies.

In September, for instance, a team from Leiden in the Netherlands reported in Molecular Cancer that they had used kinome analysis to identify two kinases (Src and nuclear factor kappaB) involved in myxoid liposarcoma. Earlier last year, two different teams in the United States found new kinases involved in the development of ovarian cancer and in its response to cis-platin treatment.

From tidbits to a feast

Other teams have been serving up tidbits about new kinases, piecemeal. Now a team from Finland has offered drug developers a feast: A “functional taxonomy” of the kinome, revealing which of 459 kinase proteins show either significant gains or losses of activity in human tumors compared to normal tissues. The data are based on analysis of 5,681 human tissue samples.

As an appetizer, the report by Sami Kilpinen et al in PLoS One last month provides the identities of several hitherto obscure kinases that appear to be involved in prostate and gynecological cancers as well as some lymphomas. It identifies altogether 22 kinases not previously associated with prostate cancer. Far more is certain to come, because the database is published openly on the Internet.

More than 10,000 patent applications for kinase inhibitors have been filed in the last decade in the United States, because many of the 518 human kinases and other molecules in their pathways are among the most frequently mutated in cancer. Although kinase inhibitors have revolutionized the treatment of a few cancers such as chronic myeloid leukemia (CML) and gastrointestinal stromal tumor (GIST), “clinical progress has been uneven,” Zachary Knight of Rockefeller University and coauthors observed in a review about “kinomics” and cancer in Nature Reviews Cancer last February.

Kinase inhibitors have achieved significant survival gains for only a few cancers, because relatively few malignancies are “addicted” to only one kinase mutation for their survival. Many develop resistance to a particular kinase inhibitor by mutating, reverting to a different kinase signaling pathway, or making molecular adjustments downstream of the inhibitor. Rationally designed combinations of kinase inhibitors have failed in malignancy after malignancy, at least in part because of insufficient understanding of the kinome.

Expression patterns match side effects

The mass of information from Kilpinen et al also promises to offer insights into the side effects that have hindered widespread use of some kinase inhibitors, because it shows kinase expression patterns for a wide range of normal tissues. The skin rash associated with epidermal growth factor receptor (EGFR) is echoed in the discovery of elevated transcriptional activity in hair follicles in the Finnish study. ERBB2/HER-2 was shown to be active in healthy tissues including heart, lung, and colorectal tissues, corresponding to the most common side effects of anti-ERBB2 drugs: cardiovascular and respiratory problems and diarrhea.

The dataset also contributes important information by revealing co-expression of different kinases in similar tumors, offering insights for combination therapies. As Knight et al observed in their review of the field, “There are clearly clusters of kinases that tend to be inhibited by similar drugs, but … there are also many target combinations that should be accessible but remain undiscovered.”

They speculate that some pharmaceutical companies already try to optimize drug profiles against complex patterns of kinases, rather than individually, in order to maximize their specificity. The public availability of the Finnish data can only speed this process.