Insulin receptor substrates 1 and 2 (IRS1/2) mediate mitogenic and anti-apoptotic signaling from insulin-like growth factor 1 receptor (IGF1R), insulin receptor (IR) and other oncoproteins. IRS1 plays a central role in cancer cell proliferation, its expression is increased in many human malignancies and its up-regulation mediates resistance to anti-cancer drugs. IRS2 is associated with cancer cell motility and metastasis. Currently there are no anti-cancer agents that target IRS1/2. We present new IGF1R/IRS-targeted agents (NT compounds) that promote inhibitory Ser-phosphorylation and degradation of IRS1 and IRS2. Elimination of IRS1/2 results in long-term inhibition of IRS1/2-mediated signaling. The therapeutic significance of this inhibition in cancer cells was demonstrated while unraveling a novel mechanism of resistance to B-RAFV600E/K inhibitors. We found that IRS1 is up-regulated in PLX4032-resistant melanoma cells and in cell lines derived from patients whose tumors developed PLX4032 resistance. In both settings, NT compounds led to elimination of IRS proteins and evoked cell death. Treatment with NT compounds in vivo significantly inhibited the growth of PLX4032-resistant tumors, and displayed potent anti-tumor effects in ovarian and prostate cancers. Our findings offer preclinical proof of concept for IRS1/2 inhibitors as cancer therapeutics including in PLX4032-resistant melanoma. By the elimination of IRS proteins, such agents should prevent acquisition of resistance to mutated-B-RAF inhibitors and possibly restore drug sensitivity in resistant tumors.

The tumor microenvironment (TME) exerts critical pro-tumorigenic effects through cytokines and growth factors that support cancer cell proliferation, survival, motility and invasion. Insulin-like growth factor-1 (IGF-1) and Signal transducer and activator of transcription 3 (STAT3) stimulate colorectal cancer (CRC) development and progression via cell autonomous and microenvironmental effects. Using a unique inhibitor, NT157, which targets both IGF-1 receptor (IGF-1R) and STAT3, we show that these pathways regulate many TME functions associated with sporadic colonic tumorigenesis in CPC-APC mice, in which cancer development is driven by loss of the Apc tumor suppressor gene. NT157 causes a substantial reduction in tumor burden by affecting cancer cells, cancer-associated fibroblasts (CAF) and myeloid cells. Decreased cancer cell proliferation and increased apoptosis were accompanied by inhibition of CAF activation and decreased inflammation. Furthermore, NT157 inhibited expression of pro-tumorigenic cytokines, chemokines and growth factors, including IL-6, IL-11 and IL-23 as well as CCL2, CCL5, CXCL7, CXCL5, ICAM1 and TGFβ; decreased cancer cell migratory activity and reduced their proliferation in the liver. NT157 represents a new class of anti-cancer drugs that affect both the malignant cell and its supportive microenvironment.

The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets. However, tumors are still being assigned to pre-defined categories (breast luminal A, triple negative, etc.), conceptually contradicting the vast heterogeneity that is known to exist among tumors, and likely overlooking unique tumors that must be addressed and treated individually. We present herein an approach based on information theory that, rather than searches for what makes a tumor similar to other tumors, addresses tumors individually and unbiasedly, and impartially decodes the critical patient-specific molecular network reorganization in every tumor. Methods : Using a large dataset obtained from ~3500 tumors of 11 types we decipher the altered protein network structure in each tumor, namely the patient-specific signaling signature. Each signature can harbor several altered protein subnetworks. We suggest that simultaneous targeting of central proteins from every altered subnetwork is essential to efficiently disturb the altered signaling in each tumor. We experimentally validate our ability to dissect sample-specific signaling signatures and to rationally design personalized drug combinations. Results : We unraveled a surprisingly simple order that underlies the extreme apparent complexity of tumor tissues, demonstrating that only 17 altered protein subnetworks characterize ~3500 tumors of 11 types. Each tumor was described by a specific subset of 1-4 subnetworks out of 17, i.e. a tumor-specific altered signaling signature. We show that the majority of tumor-specific signaling signatures are extremely rare, and are shared by only 5 tumors or less, supporting a personalized, comprehensive study of tumors in order to design the optimal combination therapy for every patient. We validate the results by confirming that the processes identified in the 11 original cancer types characterize patients harboring a different cancer type as well. We show experimentally, using different cancer cell lines, that the individualized combination therapies predicted by us achieved higher rates of killing than the clinically prescribed treatments. Conclusions : We present a new strategy to deal with the inter-tumor heterogeneity and to break down the high complexity of cancer systems into simple, easy to crack, patient-specific signaling signatures that guide the rational design of personalized drug therapies.

Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics.

BRAFV600E melanoma patients, despite initially responding to the clinically prescribed anti-BRAFV600E therapy, often relapse, and their tumors develop drug resistance. While it is widely accepted that these tumors are originally driven by the BRAFV600E mutation, they often eventually diverge and become supported by various signaling networks. Therefore, patient-specific altered signaling signatures should be deciphered and treated individually. In this study, we design individualized melanoma combination treatments based on personalized network alterations. Using an information-theoretic approach, we compute high-resolution patient-specific altered signaling signatures. These altered signaling signatures each consist of several co-expressed subnetworks, which should all be targeted to optimally inhibit the entire altered signaling flux. Based on these data, we design smart, personalized drug combinations, often consisting of FDA-approved drugs. We validate our approach in vitro and in vivo showing that individualized drug combinations that are rationally based on patient-specific altered signaling signatures are more efficient than the clinically used anti-BRAFV600E or BRAFV600E/MEK targeted therapy. Furthermore, these drug combinations are highly selective, as a drug combination efficient for one BRAFV600E tumor is significantly less efficient for another, and vice versa. The approach presented herein can be broadly applicable to aid clinicians to rationally design patient-specific anti-melanoma drug combinations.

It is well known that specific signal transduction inhibitors rarely suffice as anti-cancer agents. In most cases, tumors possess primary drug resistance due to their inherent heterogeneity, or acquire drug resistance due to genomic instability and acquisition of mutations. Here we expand our previous study of the novel compound, NT157, and show that it acts as a dual-targeting agent that invokes the blockage of two signal transduction pathways that are central to the development and maintenance of multiple human cancers. We show that NT157 targets not only IGF1R-IRS1/2, as previously reported, but also the Stat3 signaling pathway and demonstrates remarkable anti-cancer characteristics in A375 human melanoma cells and in a metastatic melanoma model in mice.

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The small-molecule drug NT157 has demonstrated promising efficacy in preclinical models of a number of different cancer types, reflecting activity against both cancer cells and the tumor microenvironment. Two known mechanisms of action are degradation of insulin receptor substrates (IRS)-1/2 and reduced Stat3 activation, although it is possible that others exist. To interrogate the effects of this drug on cell signaling pathways in an unbiased manner, we have undertaken mass spectrometry-based global tyrosine phosphorylation profiling of NT157-treated A375 melanoma cells. Bioinformatic analysis of the resulting dataset resolved 5 different clusters of tyrosine-phosphorylated peptides that differed in the directionality and timing of response to drug treatment over time. The receptor tyrosine kinase AXL exhibited a rapid decrease in phosphorylation in response to drug treatment, followed by proteasome-dependent degradation, identifying an additional potential target for NT157 action. However, NT157 treatment also resulted in increased activation of p38 MAPK α and γ, as well as the JNKs and specific Src family kinases. Importantly, cotreatment with the p38 MAPK inhibitor SB203580 attenuated the antiproliferative effect of NT157, while synergistic inhibition of cell proliferation was observed when NT157 was combined with a Src inhibitor. These findings provide novel insights into NT157 action on cancer cells and highlight how globally profiling the impact of a specific drug on cellular signaling networks can identify effective combination treatments. .