Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.

BACKGROUND: Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. METHODS: In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. RESULTS: Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). CONCLUSIONS: We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Overcoming resistance to EGFR monotherapy in HNSCC by identification and inhibition of individualized cancer processes.

Therapeutic strategies for advanced head and neck squamous carcinoma (HNSCC) consist of multimodal treatment, including Epidermal Growth Factor Receptor (EGFR) inhibition, immune-checkpoint inhibition, and radio (chemo) therapy. Although over 90% of HNSCC tumors overexpress EGFR, attempts to replace cytotoxic treatments with anti-EGFR agents have failed due to alternative signaling pathways and inter-tumor heterogeneity. Methods: Using protein expression data obtained from hundreds of HNSCC tissues and cell lines we compute individualized signaling signatures using an information-theoretic approach. The approach maps each HNSCC malignancy according to the protein-protein network reorganization in every tumor. We show that each patient-specific signaling signature (PaSSS) includes several distinct altered signaling subnetworks. Based on the resolved PaSSSs we design personalized drug combinations. Results: We show that simultaneous targeting of central hub proteins from each altered subnetwork is essential to selectively enhance the response of HNSCC tumors to anti-EGFR therapy and inhibit tumor growth. Furthermore, we demonstrate that the PaSSS-based drug combinations lead to induced expression of T cell markers and IFN-γ secretion, pointing to higher efficiency of the immune response. Conclusion: The PaSSS-based approach advances our understanding of how individualized therapies should be tailored to HNSCC tumors.

Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures.

Despite huge investments and major efforts to develop remedies for Alzheimer's disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1-3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

Decoding cancer heterogeneity: studying patient-specific signaling signatures towards personalized cancer therapy.

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.

Personalized disease signatures through information-theoretic compaction of big cancer data.

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.

Impact of the Anticancer Drug NT157 on Tyrosine Kinase Signaling Networks.

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. Mol Cancer Ther; 17(5); 931-42. ©2018 AACR.

PSMA-targeted polyinosine/polycytosine vector induces prostate tumor regression and invokes an antitumor immune response in mice.

There is an urgent need for an effective treatment for metastatic prostate cancer (PC). Prostate tumors invariably overexpress prostate surface membrane antigen (PSMA). We designed a nonviral vector, PEI-PEG-DUPA (PPD), comprising polyethylenimine-polyethyleneglycol (PEI-PEG) tethered to the PSMA ligand, 2-[3-(1, 3-dicarboxy propyl)ureido] pentanedioic acid (DUPA), to treat PC. The purpose of PEI is to bind polyinosinic/polycytosinic acid (polyIC) and allow endosomal release, while DUPA targets PC cells. PolyIC activates multiple pathways that lead to tumor cell death and to the activation of bystander effects that harness the immune system against the tumor, attacking nontargeted neighboring tumor cells and reducing the probability of acquired resistance and disease recurrence. Targeting polyIC directly to tumor cells avoids the toxicity associated with systemic delivery. PPD selectively delivered polyIC into PSMA-overexpressing PC cells, inducing apoptosis, cytokine secretion, and the recruitment of human peripheral blood mononuclear cells (PBMCs). PSMA-overexpressing tumors in nonobese diabetic/severe combined immunodeficiency (NOD/SCID) mice with partially reconstituted immune systems were significantly shrunken following PPD/polyIC treatment, in all cases. Half of the tumors showed complete regression. PPD/polyIC invokes antitumor immunity, but unlike many immunotherapies does not need to be personalized for each patient. The potent antitumor effects of PPD/polyIC should spur its development for clinical use.

PSMA-homing dsRNA chimeric protein vector kills prostate cancer cells and activates anti-tumor bystander responses.

The treatment of metastatic androgen-resistant prostate cancer remains a challenge. We describe a protein vector that selectively delivers synthetic dsRNA, polyinosinic/polycytidylic acid (polyIC), to prostate tumors by targeting prostate specific membrane antigen (PSMA), which is overexpressed on the surface of prostate cancer cells.The chimeric protein is built from the double stranded RNA (dsRNA) binding domain of PKR tethered to a single chain anti-PSMA antibody. When complexed with polyIC, the chimera demonstrates selective and efficient killing of prostate cancer cells. The treatment causes the targeted cancer cells to undergo apoptosis and to secrete toxic cytokines. In a "bystander effect", these cytokines kill neighboring cancer cells that do not necessarily overexpress PSMA, and activate immune cells that enhance the killing effect. The strong effects of the targeted polyIC are demonstrated on both 2D cell cultures and 3D tumor spheroids.

Therapeutic destruction of insulin receptor substrates for cancer treatment.

Insulin receptor substrates 1 and 2 (IRS1/2) mediate mitogenic and antiapoptotic signaling from insulin-like growth factor 1 receptor (IGF-IR), 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 upregulation mediates resistance to anticancer drugs. IRS2 is associated with cancer cell motility and metastasis. Currently, there are no anticancer agents that target IRS1/2. We present new IGF-IR/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 shown while unraveling a novel mechanism of resistance to B-RAF(V600E/K) inhibitors. We found that IRS1 is upregulated in PLX4032-resistant melanoma cells and in cell lines derived from patients whose tumors developed PLX4032 resistance. In both settings, NT compounds led to the 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 antitumor effects in ovarian and prostate cancers. Our findings offer preclinical proof-of-concept for IRS1/2 inhibitors as cancer therapeutics including 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.