Pharmacological profiling of kinase dependency in cell lines across triple-negative breast cancer subtypes

Tinengotinib (TT-00420), a Novel Spectrum-Selective Small-Molecule Kinase Inhibitor, Is Highly Active Against Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous cancer lacking actionable targets. Using a phenotypic screen of TNBC cells, we discovered a novel multiple kinase inhibitor tinengotinib (TT-00420) that strongly inhibited Aurora A/B, FGFR1/2/3, VEGFRs, JAK1/2, and CSF1R in biochemical assays. Exposure to tinengotinib specifically inhibited proliferation across all subtypes of TNBC in vitro and in vivo, while leaving luminal breast cancer cells intact. Incubation of HCC1806 with tinengotinib led to dose-dependent downregulation of genes essential for TNBC cell growth and proliferation. Studies revealed that the potential mechanism of action of tinengotinib involved, predominantly, inhibition of Aurora A or B kinase activity, while inhibition of other pathways contributed to suppression of potency and activity. In vitro treatment of TNBC cell lines or in vivo administration in a syngeneic model with tinengotinib resulted in up-regulation of CXCL10 and 11 or diminished tumor-associated macrophage (TAM) infiltration. Tinengotinib represents a novel combinatorial inhibitory mechanism to treat TNBC. The phase I trial of tinengotinib was completed (ClinicalTrials.gov identifier: NCT03654547).

Differential responses to kinase inhibition in FGFR2-addicted triple negative breast cancer cells: a quantitative phosphoproteomics study

Fibroblast Growth Factor (FGF) dependent signalling is frequently activated in cancer by a variety of different mechanisms. However, the downstream signal transduction pathways involved are poorly characterised. Here a quantitative differential phosphoproteomics approach, SILAC, is applied to identify FGF-regulated phosphorylation events in two triple- negative breast tumour cell lines, MFM223 and SUM52, that exhibit amplified expression of FGF receptor 2 (FGFR2) and are dependent on continued FGFR2 signalling for cell viability. Comparative Gene Ontology proteome analysis revealed that SUM52 cells were enriched in proteins associated with cell metabolism and MFM223 cells enriched in proteins associated with cell adhesion and migration. FGFR2 inhibition by SU5402 impacts a significant fraction of the observed phosphoproteome of these cells. This study expands the known landscape of FGF signalling and identifies many new targets for functional investigation. FGF signalling pathways are found to be flexible in architecture as both shared, and divergent, responses to inhibition of FGFR2 kinase activity in the canonical RAF/MAPK/ERK/RSK and PI3K/AKT/PDK/mTOR/S6K pathways are identified. Inhibition of phosphorylation-dependent negative-feedback pathways is observed, defining mechanisms of intrinsic resistance to FGFR2 inhibition. These findings have implications for the therapeutic application of FGFR inhibitors as they identify both common and divergent responses in cells harbouring the same genetic lesion and pathways of drug resistance.

Phenotypic Screening Combined with Machine Learning for Efficient Identification of Breast Cancer-Selective Therapeutic Targets

The lack of functional understanding of most mutations in cancer, combined with the non-druggability of most proteins, challenge genomics-based identification of oncology drug targets. We implemented a machine-learning-based approach (idTRAX), which relates cell-based screening of small-molecule compounds to their kinase inhibition data, to directly identify effective and readily druggable targets. We applied idTRAX to triple-negative breast cancer cell lines and efficiently identified cancer-selective targets. For example, we found that inhibiting AKT selectively kills MFM-223 and CAL148 cells, while inhibiting FGFR2 only kills MFM-223. Since the effects of catalytically inhibiting a protein can diverge from those of reducing its levels, targets identified by idTRAX frequently differ from those identified through gene knockout/knockdown methods. This is critical if the purpose is to identify targets specifically for small-molecule drug development, whereby idTRAX may produce fewer false-positives. The rapid nature of the approach suggests that it may be applicable in personalizing therapy.

NOTCH3 inactivation increases triple negative breast cancer sensitivity to gefitinib by promoting EGFR tyrosine dephosphorylation and its intracellular arrest

Notch dysregulation has been implicated in numerous tumors, including triple-negative breast cancer (TNBC), which is the breast cancer subtype with the worst clinical outcome. However, the importance of individual receptors in TNBC and their specific mechanism of action remain to be elucidated, even if recent findings suggested a specific role of activated-Notch3 in a subset of TNBCs. Epidermal growth factor receptor (EGFR) is overexpressed in TNBCs but the use of anti-EGFR agents (including tyrosine kinase inhibitors, TKIs) has not been approved for the treatment of these patients, as clinical trials have shown disappointing results. Resistance to EGFR blockers is commonly reported. Here we show that Notch3-specific inhibition increases TNBC sensitivity to the TKI-gefitinib in TNBC-resistant cells. Mechanistically, we demonstrate that Notch3 is able to regulate the activated EGFR membrane localization into lipid rafts microdomains, as Notch3 inhibition, such as rafts depletion, induces the EGFR internalization and its intracellular arrest, without involving receptor degradation. Interestingly, these events are associated with the EGFR tyrosine dephosphorylation at Y1173 residue (but not at Y1068) by the protein tyrosine phosphatase H1 (PTPH1), thus suggesting its possible involvement in the observed Notch3-dependent TNBC sensitivity response to gefitinib. Consistent with this notion, a nuclear localization defect of phospho-EGFR is observed after combined blockade of EGFR and Notch3, which results in a decreased TNBC cell survival. Notably, we observed a significant correlation between EGFR and NOTCH3 expression levels by in silico gene expression and immunohistochemical analysis of human TNBC primary samples. Our findings strongly suggest that combined therapies of TKI-gefitinib with Notch3-specific suppression may be exploited as a drug combination advantage in TNBC treatment.

Network modeling of kinase inhibitor polypharmacology reveals pathways targeted in chemical screens

Small molecule screens are widely used to prioritize pharmaceutical development. However, determining the pathways targeted by these molecules is challenging, since the compounds are often promiscuous. We present a network strategy that takes into account the polypharmacology of small molecules in order to generate hypotheses for their broader mode of action. We report a screen for kinase inhibitors that increase the efficacy of gemcitabine, the first-line chemotherapy for pancreatic cancer. Eight kinase inhibitors emerge that are known to affect 201 kinases, of which only three kinases have been previously identified as modifiers of gemcitabine toxicity. In this work, we use the SAMNet algorithm to identify pathways linking these kinases and genetic modifiers of gemcitabine toxicity with transcriptional and epigenetic changes induced by gemcitabine that we measure using DNaseI-seq and RNA-seq. SAMNet uses a constrained optimization algorithm to connect genes from these complementary datasets through a small set of protein-protein and protein-DNA interactions. The resulting network recapitulates known pathways including DNA repair, cell proliferation and the epithelial-to-mesenchymal transition. We use the network to predict genes with important roles in the gemcitabine response, including six that have already been shown to modify gemcitabine efficacy in pancreatic cancer and ten novel candidates. Our work reveals the important role of polypharmacology in the activity of these chemosensitizing agents.

Metabolic advantages and vulnerabilities in brain metastases

Metabolic adaptations permit tumor cells to metastasize to and thrive in the brain. Brain metastases continue to present clinical challenges due to rising incidence and resistance to current treatments. Therefore, elucidating altered metabolic pathways in brain metastases may provide new therapeutic targets for the treatment of aggressive disease. Due to the high demand for glucose in the brain, increased glycolytic activity is favored for energy production. Primary tumors that undergo Warburg-like metabolic reprogramming become suited to growth in the brain microenvironment. Indeed, elevated metabolism is a predictor of metastasis in many cancer subtypes. Specifically, metabolic alterations are seen in primary tumors that are associated with the formation of brain metastases, namely breast cancer, lung cancer, and melanoma. Because of this selective pressure, inhibitors of key metabolic factors may reduce tumor cell viability, thus exploiting metabolic pathways for cancer therapeutics. This review summarizes the metabolic advantages and vulnerabilities of brain metastases.

Progress towards a public chemogenomic set for protein kinases and a call for contributions

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases.

Trop-2 Induces Tumor Growth Through AKT and Determines Sensitivity to AKT Inhibitors

PURPOSE

Inhibition of AKT is a key target area for personalized cancer medicine. However, predictive markers of response to AKT inhibitors are lacking. Correspondingly, the AKT-dependent chain of command for tumor growth, which will mediate AKT-dependent therapeutic responses, remains unclear.

EXPERIMENTAL DESIGN

Proteomic profiling was utilized to identify nodal hubs of the Trop-2 cancer growth-driving network. Kinase-specific inhibitors were used to dissect Trop-2-dependent from Trop-2-independent pathways. In vitro assays, in vivo preclinical models, and case series of primary human breast cancers were utilized to define the mechanisms of Trop-2-driven growth and the mode of action of Trop-2-predicted AKT inhibitors.

RESULTS

Trop-2 and AKT expression was shown to be tightly coordinated in human breast cancers, with virtual overlap with AKT activation profiles at T308 and S473, consistent with functional interaction in vivo AKT allosteric inhibitors were shown to only block the growth of Trop-2-expressing tumor cells, both in vitro and in preclinical models, being ineffective on Trop-2-null cells. Consistently, AKT-targeted siRNA only impacted on Trop-2-expressing cells. Lentiviral downregulation of endogenous Trop-2 abolished tumor response to AKT blockade, indicating Trop-2 as a mandatory activator of AKT.

CONCLUSIONS

Our findings indicate that the expression of Trop-2 is a stringent predictor of tumor response to AKT inhibitors. They also support the identification of target-activatory pathways, as efficient predictors of response in precision cancer therapy. Clin Cancer Res; 22(16); 4197-205. ©2016 AACR.

An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer

BACKGROUND

Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis.

RESULTS

We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. We validated our predictions using published and new experimental data.

CONCLUSIONS

In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.

Analysis of PIK3CA Mutations and Activation Pathways in Triple Negative Breast Cancer

Background

Triple Negative Breast Cancer (TNBC) accounts for 12–24% of all breast carcinomas, and shows worse prognosis compared to other breast cancer subtypes. Molecular studies demonstrated that TNBCs are a heterogeneous group of tumors with different clinical and pathologic features, prognosis, genetic-molecular alterations and treatment responsivity. The PI3K/AKT is a major pathway involved in the regulation of cell survival and proliferation, and is the most frequently altered pathway in breast cancer, apparently with different biologic impact on specific cancer subtypes. The most common genetic abnormality is represented by PIK3CA gene activating mutations, with an overall frequency of 20–40%. The aims of our study were to investigate PIK3CA gene mutations on a large series of TNBC, to perform a wider analysis on genetic alterations involving PI3K/AKT and BRAF/RAS/MAPK pathways and to correlate the results with clinical-pathologic data.

Materials and Methods

PIK3CA mutation analysis was performed by using cobas® PIK3CA Mutation Test. EGFR, AKT1, BRAF, and KRAS genes were analyzed by sequencing. Immunohistochemistry was carried out to identify PTEN loss and to investigate for PI3K/AKT pathways components.

Results

PIK3CA mutations were detected in 23.7% of TNBC, whereas no mutations were identified in EGFR, AKT1, BRAF, and KRAS genes. Moreover, we observed PTEN loss in 11.3% of tumors. Deregulation of PI3K/AKT pathways was revealed by consistent activation of pAKT and p-p44/42 MAPK in all PIK3CA mutated TNBC.

Conclusions

Our data shows that PIK3CA mutations and PI3K/AKT pathway activation are common events in TNBC. A deeper investigation on specific TNBC genomic abnormalities might be helpful in order to select patients who would benefit from current targeted therapy strategies.

Down-regulation of PAR1 activity with a pHLIP-based allosteric antagonist induces cancer cell death

Even though abnormal expression of G protein-coupled receptors (GPCRs) and of their ligands is observed in many cancer cells of various origins, only a few anti-cancer compounds directly act on their signalling. One promising approach to modulate their activity consists of targeting the receptor cytoplasmic surfaces interacting with the associated G-proteins using peptides mimicking the intracellular loops of the receptor. Thus, to be fully effective, the peptide mimics must be selectively targeted to the tumour while sparing healthy tissues, translocated across the cell membrane and stay anchored to the cytoplasmic leaflet of the plasma membrane. In the present study, we introduce a novel way to selectively target and inhibit the activity of a GPCR in cancer cells under acidic conditions, such as those found in solid tumours. We find that the conjugation of a peptide fragment derived from the third intracellular loop (i3) of the protease-activated receptor 1 (PAR1) to a peptide that can selectively target tumours solely based on their acidity [pH(Low) Insertion Peptide (pHLIP)], produces a construct capable of effectively down-regulating PAR1 activity in a concentration- and pH-dependent manner and of inducing a potent cytotoxic effect in a panel of cancer cells that is proportional to the relative level of receptor expression at the cell surface. This strategy not only allows for a more selective targeting and specific intracellular delivery than current approaches, but also offers new possibilities for developing novel anti-cancer drugs targeting GPCRs.