Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.
Background Phosphorylation is an important regulatory mechanism of protein activity in cells. Studies in various cancers have reported perturbations in kinases resulting in aberrant phosphorylation of oncoproteins and tumor suppressor proteins. Methods In this study, we carried out quantitative phosphoproteomic analysis of gastric cancer tissues and corresponding xenograft samples. Using these data, we employed bioinformatics analysis to identify aberrant signaling pathways. We further performed molecular inhibition and silencing of the upstream regulatory kinase in gastric cancer cell lines and validated its effect on cellular phenotype. Through an ex vivo technology utilizing patient tumor and blood sample, we sought to understand the therapeutic potential of the kinase by recreating the tumor microenvironment. Results Using mass spectrometry-based high-throughput analysis, we identified 1,344 phosphosites and 848 phosphoproteins, including differential phosphorylation of 177 proteins (fold change cut-off ≥ 1.5). Our data showed that a subset of differentially phosphorylated proteins belonged to splicing machinery. Pathway analysis highlighted Cdc2-like kinase (CLK1) as upstream kinase. Inhibition of CLK1 using TG003 and CLK1 siRNA resulted in a decreased cell viability, proliferation, invasion and migration as well as modulation in the phosphorylation of SRSF2. Ex vivo experiments which utilizes patient's own tumor and blood to recreate the tumor microenvironment validated the use of CLK1 as a potential target for gastric cancer treatment. Conclusions Our data indicates that CLK1 plays a crucial role in the regulation of splicing process in gastric cancer and that CLK1 can act as a novel therapeutic target in gastric cancer. Keywords Phosphoserine/threonine • Spliceosome complex • Targeted therapy • Biomarker • PDX in vivo models List of abbreviations CLK Cdc2-like kinase PDX Patient-derived xenografts IHC Immunohistochemistry TMT Tandem Mass Tag bRPLC Basic pH reverse phase chromatography HCD Higher energy collision dissociation IPA Ingenuity pathway analysis FBS Fetal bovine serum Electronic supplementary material The online version of this article (
KRAS mutation status can distinguish between metastatic colorectal carcinoma (mCRC) patients who may benefit from therapies that target the epidermal growth factor receptor (EGFR), such as cetuximab. However, patients whose tumors harbor mutant KRAS (codons 12/13, 61 and 146) are often excluded from EGFR-targeted regimens, while other patients with wild type KRAS will sometimes respond favorably to these same drugs. These conflicting observations suggest that a more robust approach to individualize therapy may enable greater frequency of positive clinical outcome for mCRC patients. Here, we utilized alive tumor tissues in ex-vivo platform termed CANscript, which preserves the native tumor heterogeneity, in order to interrogate the antitumor effects of EGFRtargeted drugs in mCRC (n = 40). We demonstrated that, irrespective of KRAS status, cetuximab did not induce an antitumor response in a majority of patient tumors. In the subset of non-responsive tumors, data showed that expression levels of EGFR ligands contributed to a mechanism of resistance. Transcriptomic and phosphoproteomic profiling revealed deregulation of multiple pathways, significantly the Notch and Erbb2. Targeting these nodes concurrently resulted in antitumor efficacy in a majority of cetuximab-resistant tumors. These findings highlight the importance of integrating molecular profile and functional testing tools for optimization of alternate strategies in resistant population.Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide with a 5-year survival rate of less than 10% 1 . An important molecular target implicated in disease progression is Epidermal Growth Factor Receptor (EGFR) signaling, which after ligand binding triggers two main pathways: the RAS-RAF-MAPK cascade leading to cell proliferation, survival, invasion and metastasis; and the PI3K-PTEN-AKT pathway which controls cell survival, motility and neo-angiogenesis 2 . Notably, EGFR is overexpressed in 60-80% of colorectal tumors 3 . Current chemotherapeutic options include 5FU + leucovorin, XELOX, XELIRI, FOLFOX and FOLFIRI which are combinations of capecitabine, 5-fluorouracil, leucovorin and oxaliplatin or irinotecan. Two classes of anti-EGFR monoclonal antibodies (mAbs) are at present prescribed in combination with conventional chemotherapy for the treatment of CRC. However the underlying problem of using cetuximab (a chimeric-IgG1mAb) is that it has only 8.8% efficacy when used in monotherapy, and 22.9% when used in combination therapy for
The PI3K/AKT/mTOR pathway is an important signaling axis that is perturbed in majority of cancers. Biomarkers such as pS6RP, GLUT1, and tumor FDG uptake are being evaluated in patient stratification for mTOR pathway inhibitors. In the absence of a clear understanding of the underlying mechanisms in tumor signaling, the biomarker strategy for patient stratification is of limited use. Here, we show that no discernible correlation exists between FDG uptake and the corresponding Ki67, GLUT1, pS6RP expression in tumor biopsies from patients with head and neck cancer. Correlation between GLUT1 and pS6RP levels in tumors was observed but elevated pS6RP was noticed even in the absence of concomitant AKT activation, suggesting that other downstream molecules of PI3K/AKT and/or other pathways upstream of mTOR are active in these tumors. Using an ex vivo platform, we identified putative responders to rapamycin, an mTOR inhibitor in these tumors. However, rapamycin did not induce antitumor effect in the majority of tumors with activated mTOR, potentially attributable to the observation that rapamycin induces feedback activation of AKT. Accordingly, treatment of these tumors with an AKT inhibitor and rapamycin uniformly resulted in abrogation of mTOR inhibition-induced AKT activation in all tumors but failed to induce antitumor response in a subset. Phosphoproteomic profiling of tumors resistant to dual AKT/mTOR inhibitors revealed differential activation of multiple pathways involved in proliferation and survival. Collectively, our results suggest that, in addition to biomarker-based segregation, functional assessment of a patient's tumor before treatment with mTOR/AKT inhibitors may be useful for patient stratification. Cancer Res; 73(3); 1118-27. Ó2013 AACR.
Histone deacetylases (HDAC) are deregulated in many human cancers and a few HDAC inhibitors (HDACi) have been shown to be effective in patients with rare leukemia. However, clinical responses are not very promising for many HDACi in solid cancers. There is an urgent need to develop best in class HDACi with higher therapeutic index in solid cancers. At present less than 10% of anti-cancer drugs entering phase 1 clinical trials are successful in getting market approval. There are many factors for this poor clinical success for oncology therapeutics, and one of them is the lack of robust preclinical model driving clinical development. Cancer is a very heterogeneous disease and many of the present preclinical models do not represent this heterogeneity. As a result, while a candidate drug may appear good in present preclinical models, they often fail to make the cut in clinical development. To circumvent this critical problem, we have developed a robust TE (Tumor Ecosystem) platform technology using patient tumors, autologous ligands, and immune compartments from the same patients.Our TE platform preserves patient tumor heterogeneity along with other functional and genomic markers and immunogenic cytokines. Efficacy of drugs exposed to the patient's tumor tissue in our TE platform is assessed by as many as 17 orthogonal assays within a week. The results from these assays are converted into a single predictive score called the “M-Score,” and it has been clinically validated using FDA approved drugs in > 1,800 patients across multiple solid and hematological cancers. Our data shows that TE platform predicts treatment outcome of FDA approved drugs in clinical setting with very high sensitivity and specificity. We have the opportunity to use this platform technology to identify cancer indications and appropriate combinations for MIT-1102, a novel HDACi that exhibited potent histone deacetylase inhibitory activity. Our data further indicates that the anti-tumor efficacy of MIT-1102 is superior to Vorinostat and comparable with Pracinostat (SB-939) while being more tolerable than Pracinostat. Results indicated the superiority of MIT-1102 when combined with SOCs in a subset of patients with gastric and pancreatic cancers. We are also using different omics to identify markers associated with response in those tumor types. MIT-1102 has the potential to be more effective in a defined patient population than other molecules in this class. In summary, we have identified a novel, potent HDACi with drug-like properties, and identified the correct cancer indications and most effective combinations using TE platform Technology. Citation Format: Mallikarjun Sundaram, Baraneedharan Ulaganathan, Muthu Dhandapani, Allen Thayakumar, Pragnashree Mukhopadhyay, Saravanan Thiyagarajan, Biswanath Majumder, Prasad Shivarudriah, H Jagadheshan, Ganesh Sambasivam, Steve Birnbuam, Padhma Radhakrishnan, Pradip K. Majumder. Application of patient tumors-derived tumor ecosystem platform for the development of novel HDAC inhibitor in solid cancers. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3736. doi:10.1158/1538-7445.AM2014-3736
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