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.