Correlation between Gene Variants, Signaling Pathways, and Efficacy of Chemotherapy Drugs against Colon Cancers

Evidence That β1-Integrin Is Required for the Anti-Viability and Anti-Proliferative Effect of Resveratrol in CRC Cells

The β1-integrin receptor is broadly expressed on tumor and other cells in the tumor microenvironment (TME), and is an unfavorable prognostic factor for cancers. Nature-derived resveratrol has preventive and apoptotic effects on tumors, but whether resveratrol can exert its suppressive actions on TME-induced tumorigenesis through β1-integrin on the surface of CRC cells is still unknown. HCT116 or SW480 cells were exposed to inhibitory antibodies against β1-integrin, bacitracin (selective β1-integrin inhibitor), integrin-binding RGD (Arg-Gly-Asp) peptide, and/or resveratrol. We evaluated the anti-tumor actions and signaling impacts of resveratrol in colorectal cancer (CRC)-TME. We found that resveratrol completely altered the β1-integrin distribution pattern and expression on the surface of CRC cells in TME. Moreover, resveratrol down-regulated CRC cell proliferation, colony formation, viability, and up-regulated apoptosis in a concentration-dependent way. These actions of resveratrol were antagonized mainly by inhibitory antibodies against β1-integrin but not β5-integrin, and by an integrin-binding RGD peptide but not by RGE peptide, and by bacitracin in TME. Similarly, resveratrol-blocked TME-induced p65-NF-kB and its promoted gene markers linked to proliferation (cyclin D1), invasion (focal adhesion kinase, FAK), or apoptosis (caspase-3), were largely abrogated by anti-β1-integrin or RGD peptide, suggesting that β1-integrin is a potential transmission pathway for resveratrol/integrin down-stream signaling in CRC cells. The current results highlight, for the first time, the important gateway role of β1-integrins as signal carriers for resveratrol on the surfaces of HCT116 and SW480 cells, and their functional cooperation for the modulatory effects of resveratrol on TME-promoted tumorigenesis.

Predicting Cancer Drug Response In Vivo by Learning an Optimal Feature Selection of Tumour Molecular Profiles

(1) Background: Inter-tumour heterogeneity is one of cancer’s most fundamental features. Patient stratification based on drug response prediction is hence needed for effective anti-cancer therapy. However, single-gene markers of response are rare and/or may fail to achieve a significant impact in the clinic. Machine Learning (ML) is emerging as a particularly promising complementary approach to precision oncology. (2) Methods: Here we leverage comprehensive Patient-Derived Xenograft (PDX) pharmacogenomic data sets with dimensionality-reducing ML algorithms with this purpose. (3) Results: Combining multiple gene alterations via ML leads to better discrimination between sensitive and resistant PDXs in 19 of the 26 analysed cases. Highly predictive ML models employing concise gene lists were found for three cases: paclitaxel (breast cancer), binimetinib (breast cancer) and cetuximab (colorectal cancer). Interestingly, each of these multi-gene ML models identifies some treatment-responsive PDXs not harbouring the best actionable mutation for that case. Thus, ML multi-gene predictors generally have much fewer false negatives than the corresponding single-gene marker. (4) Conclusions: As PDXs often recapitulate clinical outcomes, these results suggest that many more patients could benefit from precision oncology if ML algorithms were also applied to existing clinical pharmacogenomics data, especially those algorithms generating classifiers combining data-selected gene alterations.

Profiling Colorectal Cancer in the Landscape Personalized Testing-Advantages of Liquid Biopsy

Drug-specific therapeutic approaches for colorectal cancer (CRC) have contributed to significant improvements in patient health. Nevertheless, there is still a great need to improve the personalization of treatments based on genetic and epigenetic tumor profiles to maximize the quality and efficacy while limiting cytotoxicity. Currently, CEA and CA 19-9 are the only validated blood biomarkers in clinical practice. For this reason, laboratories are trying to identify new specific prognostics and, more importantly, predictive biomarkers for CRC patient profiling. Thus, the unique landscape of personalized biomarker data should have a clinical impact on CRC treatment strategies and molecular genetic screening tests should become the standard method for diagnosing CRC. This review concentrates on recent molecular testing in CRC and discusses the potential modifications in CRC assay methodology with the upcoming clinical application of novel genomic approaches. While mechanisms for analyzing circulating tumor DNA have been proven too inaccurate, detecting and analyzing circulating tumor cells and protein analysis of exosomes represent more promising options. Blood liquid biopsy offers good prospects for the future if the results align with pathologists’ tissue analyses. Overall, early detection, accurate diagnosis and treatment monitoring for CRC with specific markers and targeted molecular testing may benefit many patients.

Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data

To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute’s Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.

Anticancer activity of synthetic (±)-kusunokinin and its derivative (±)-bursehernin on human cancer cell lines

Kusunokinin is a potent lignan compound with a several biological properties including antitrypanosomal and anticancer. In this study, (±)-kusunokinin and its derivative, (±)-bursehernin, were synthesized and investigated for their anticancer activities on cell viability, cell cycle arrest and apoptosis in cancer cell lines including breast cancer (MCF-7, MDA-MB-468 and MDA-MB-231), colon cancer (HT-29) and cholangiocarcinoma (KKU-K100, KKU-M213 and KKU-M055) cells. The result showed that (±)-kusunokinin and (±)-bursehernin represented the strongest growth inhibition against breast cancer (MCF-7) and cholangiocarcinoma (KKU-M213) cells with the IC₅₀ values of 4.30 ± 0.65 μM and 3.70 ± 0.79 μM, respectively, both of which were lower than IC₅₀ of normal fibroblast cells (L929). Etoposide was used as a positive control since this chemotherapeutic drug is in the lignan group same as (±)-kusunokinin. Surprisingly, etoposide showed less cytotoxicity than (±)-kusunokinin and its derivative on MCF-7, HT-29, KKU-M213 and KKU-K100. Moreover, (±)-bursehernin induced cell cycle arrest at G2/M phase, meanwhile (±)-kusunokinin tended to increased cell population at G2/M phase but did not show the significant difference compared with non-treated cells. Interestingly, protein levels related to cell proliferation pathway (topoisomerase II, STAT3, cyclin D1, and p21) were significantly decreased at 72 h. Both compounds induced apoptotic cell in time-dependent manner as confirmed by MultiCaspase assay. In conclusion, synthetic compound, (±)-kusunokinin and (±)-bursehernin, showed anticancer effects via the reduction of cell proliferation proteins and induction of apoptosis.

DPS-2: A Novel Dual MEK/ERK and PI3K/AKT Pathway Inhibitor with Powerful Ex Vivo and In Vivo Anticancer Properties

Development of novel bioactive compounds against KRAS and/or BRAF mutant colorectal cancer (CRC) is currently an urgent need in oncology. In addition, single or multitarget kinase inhibitors against MEK/ERK and PI3K/AKT pathways are of potential therapeutic advantage. A new compound based on the benzothiophene nucleus was synthesized, based on previous important outcomes on other pharmaceutical preparations, to be tested as potential anticancer agent. Treatments by 2-5 μM DPS-2 of several CRC and melanoma cell lines bearing either BRAF or KRAS mutations have shown a remarkable effect on cell viability in 2D and 3D cultures. More detailed analysis has shown that DPS-2 can kill cancer cells by apoptosis, reducing at the same time their autophagy properties. After testing activities of several signaling pathways, the compound was found to have a dual inhibition of two major proliferative/survival pathways, MEK/ERK and PI3K/AKT, in both CRC and melanoma, thus providing a mechanistic evidence for its potent anticancer activity. Antitumor activity of DPS-2 was further validated in vivo, as DPS-2 treatment of mouse xenografts of Colo-205 colorectal cancer cells remarkably reduced their tumor formation properties. Our findings suggest that DPS-2 has significant anti-KRAS/ anti-BRAF mutant CRC activity in preclinical models, potentially providing a novel treatment strategy for these difficult-to-treat tumors, which needs to be further exploited.

Novel platinum compounds and nanoparticles as anticancer agents

Since the approval of cisplatin in 1979, platinum-based drugs have been regularly used in cancer chemotherapy as a first-line treatment or with the combination of other nonplatinum drugs. Subsequent approval of second- and third-generation drugs such as carboplatin and oxaliplatin respectively, has widened the therapeutic achievement of platinum compounds. There are few other platinum drugs approved recently and many other new drugs as well as the formulations of the old ones are going through clinical trials now. Considering the astonishing achievement of these drugs, analyses on the overall scenario of the patent applications on platinum compounds have become the priority to the scientific community. This review summarizes the published patent applications on the novel platinum anticancer compounds from 2012 to 2017 (August).

Enhanced Sensitivity of Cancer Stem Cells to Chemotherapy Using Functionalized Mesoporous Silica Nanoparticles

Cancer stem cells (CSCs) are responsible for cancer drug resistance with high expression of ABCG2, which pumps the internalized chemotherapeutic out to escape drug-induced cytotoxicity. Here, we established a functionalized mesoporous silica nanoparticle (MSN) system to deliver shABCG2 and doxorubicin (Dox) synergistically. With excellent cell uptake and endosomal escape capacities, the dual-delivery carriers internalized shABCG2 and Dox into CSCs efficiently. ABCG2 depletion increased intracellular and intranuclear Dox enrichment, drove vigorous Dox-induced cell death, and impaired the self-renewal of CSCs. Additionally, the nanoparticles eliminated tumors efficiently and reduced tumor initiation by CSCs in vivo, with negligible side effects. Our findings suggest that well-designed delivery systems for conventional chemotherapeutic agents are promising for CSC therapy.