Targeted therapy in the treatment of solid tumors: practice contradicts theory

Implications of IL-21 in solid tumor therapy

Cancer, the most deadly disease, is known as a recent dilemma worldwide. Presently different treatments are used for curing cancers, especially solid cancers. Because of the immune-enhancing functions of cytokine, IL-21 as a cytokine may have new possibilities to manipulate the immune system in disease conditions, as it stimulates NK and CTL functions and drives IgG antibody production. Indeed, IL-21 has been revealed to elicit antitumor-immune responses in several tumor models. Combining IL-21 with other agents, which target tumor cells, immune-regulatory circuits, or other immune-enhancing molecules enhances this activity. The exciting breakthrough in the results obtained in pre-clinical situations has led to the early outset of present developing clinical trials in cancer patients. In the paper, we have reviewed the function of IL-21 in solid tumor immunotherapy.

Application of Nanoparticles in Tumour Targeted Drug Delivery and Vaccine

Cancer is a major cause of death worldwide, and nearly 1 in 6 deaths each year is caused by cancer. Traditional cancer treatment strategies cannot completely solve cancer recurrence and metastasis. With the development of nanotechnology, the study of nanoparticles (NPs) has gradually become a hotspot of medical research. NPs have various advantages. NPs exploit the enhanced permeability and retention (EPR) of tumour cells to achieve targeted drug delivery and can be retained in tumours long-term. NPs can be used as a powerful design platform for vaccines as well as immunization enhancers. Liposomes, as organic nanomaterials, are widely used in the preparation of nanodrugs and vaccines. Currently, most of the anticancer drugs that have been approved and entered clinical practice are prepared from lipid materials. However, the current clinical conversion rate of NPs is still extremely low, and the transition of NPs from the laboratory to clinical practice is still a substantial challenge. In this paper, we review the in vivo targeted delivery methods, material characteristics of NPs and the application of NPs in vaccine preparation. The application of nanoliposomes is also emphasized. Furthermore, the challenges and limitations of NPs are briefly discussed.

Experimental Study of Antitumor Activity of Pefagtal Addressed to αvβ3 Integrins

We studied the effect of a new targeted drug Pefagtal that represents a conjugate in which the MS2 phage filled with a substance toxic to cells (thallium salts) is covalently linked to peptides containing the RGD motif. The antitumor and pronounced antimetastatic effects of Pefagtal were demonstrated on transplanted mouse tumors differing in histological type and status of metastasis: Krebs-2 ascites adenocarcinoma of the mammary gland, Lewis lung adenocarcinoma, hepatoma-29, and lung adenocarcinoma. It is assumed that the RGD motif mediates primary binding of the construct to αvβ3 and αvβ5 integrins that are predominantly overexpressed in the endothelial cells of tumor blood vessels and in tumor and metastatic cells.

Anti-CD44-Conjugated Olive Oil Liquid Nanocapsules for Targeting Pancreatic Cancer Stem Cells

The latest trends in cancer research and nanomedicine focus on using nanocarriers to target cancer stem cells (CSCs). Specifically, lipid liquid nanocapsules are usually developed as nanocarriers for lipophilic drug delivery. Here, we developed olive oil liquid NCs (O²LNCs) functionalized by covalent coupling of an anti-CD44-fluorescein isothiocyanate antibody (αCD44). First, O²LNCs are formed by a core of olive oil surrounded by a shell containing phospholipids, a nonionic surfactant, and deoxycholic acid molecules. Then, O²LNCs were coated with an αCD44 antibody (αCD44-O²LNC). The optimization of an αCD44 coating procedure, a complete physicochemical characterization, as well as clear evidence of their efficacy in vitro and in vivo were demonstrated. Our results indicate the high targeted uptake of these αCD44-O²LNCs, and the increased antitumor efficacy (up to four times) of paclitaxel-loaded-αCD44-O²LNC compared to free paclitaxel in pancreatic CSCs (PCSCs). Also, αCD44-O²LNCs were able to selectively target PCSCs in an orthotopic xenotransplant in vivo model.

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

Background

Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.

Methods

We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.

Results

We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.

Conclusions

We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

Potential applications of nanoparticles for tumor microenvironment remodeling to ameliorate cancer immunotherapy

In recent years, researchers have made significant innovations in the field of tumor immunotherapy based on the knowledge of biology, oncology, and immunology. Tumor immunotherapy involves the use of immune checkpoint inhibitors and CAR (chimeric antigen receptor)-T cell therapy. As compared with conventional chemotherapy, immunotherapy is a potential approach to induce a more powerful immune response against tumor in the patient suffering from the advanced stage malignancy. Regardless of the developments made, a large number of clinical studies have confirmed that a substantial number of cancer patients still demonstrate non-responsiveness to immunotherapy, mainly due to the immunomodulating interactions of tumor cells with the immunosuppressive tumor microenvironment (iTME). It leads to immune tolerance of tumors and influences the efficacy of immunotherapy. This immune failure could be attributed to a complex immunosuppressive network comprising stromal and inflammatory cells, vessel system, ECM (extracellular matrix) and the cytokines released in tumor microenvironment (TME). The antitumor immune activity can be enhanced at different stages of tumor development by selective suppression of inhibitory pathways in the TME. This specific task can be achieved by using nano-sized drug delivery tools which are specific in their action and biocompatible in nature. Several recent studies have described the use of nanoparticles for iTME remodeling through the specific elimination of immunosuppressive cells, obstructing immune checkpoints, promotion of inflammatory cytokines, and amending the regulatory cells of the immune system. The efficacy of current immunotherapy can be improved by nanoparticle-mediated remodeling of iTME.

Be Active or Not: the Relative Contribution of Active and Passive Tumor Targeting of Nanomaterials

Malignant tumor (cancer) remains as one of the deadliest diseases throughout the world, despite its overall mortality drops. Nanomaterials (NMs) have been widely studied as diagnostic and/or therapeutic agents for tumors. A feature of NMs, compared to small molecules, is that NMs can be concentrated passively in tumors through enhanced permeability and retention (EPR) effect. In the meantime, NMs can be engineered to target toward tumor specific markers in an active manner, e.g., receptor-mediated targeting. The relative contribution of the EPR effect and the receptor-mediated targeting to NM accumulation in tumor tissues has not been clearly defined yet. Here, we tackle this fundamental issue by reviewing previous studies. First, we summarize the current knowledge on these two tumor targeting strategies of NMs, and on how NMs arrive to tumors from blood circulation. We then demonstrate that contribution of the active and passive effects to total accumulation of NMs in tumors varies with time. Over time, the receptor-mediated targeting contributes more than the EPR effect with a ratio of 3 in the case of urokinase-type plasminogen activator receptor (uPAR)-mediated targeting and human serum albumin (HSA)-mediated EPR effect. Therefore, this review highlights the dynamics of active and passive targeting of NMs on their accumulation at tumor sites, and is valuable for future design of NMs in cancer diagnosis and treatment.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

Effects of 5-Amyno-4-(1,3-benzothyazol-2-yn)-1-(3-methoxyphenyl)-1,2-dihydro-3H-pyrrol-3-one Intake on Digestive System in a Rat Model of Colon Cancer

Introduction. Pyrrol derivate 5-amyno-4-(1,3-benzothyazol-2-yn)-1-(3-methoxyphenyl)-1,2-dihydro-3H-pyrrol-3-one (D1) has shown antiproliferative activities in vitro, so investigation of the impact of D1 intake on gut organs in rats that experienced colon cancer seems to be necessary. Materials and Methods. D1 at the dose of 2.3 mg/kg was administered per os daily for 27 (from the 1st day of experiment) or 7 (from the 21st week of experiment) weeks to rats that experienced 1,2-dimethylhydrazine (DMH)-induced colon cancer for 20 weeks. 5-Fluorouracil (5FU) was chosen as reference drug and was administered intraperitoneally weekly for 7 weeks (from the 21st week of experiment) at the dose of 45 mg/kg. Results. Antitumor activity of D1 comparable with the 5FU one against DMH-induced colon cancer in rats was observed (decrease of tumor number and tumor total area up to 46%). D1 attenuated the inflammation of colon, gastric and jejunal mucosa, and the liver, caused by DMH, unlike 5FU, aggravating the latter. In addition, D1 partially normalized mucosa morphometric parameters suggesting its functional restore. Conclusions. D1 possesses, comparable with 5-fluorouracil antitumor efficacy, less damaging effects on the tissues beyond cancerous areas and contributes to partial morphological and functional gut organs recovery.

An unexpected increase of toxicity of amino acid-containing ionic liquids

The influence of the structure of cations and anions on the biological activity of ionic liquids is addressed.

Pharmacokinetic and Biodistribution Assessment of a Near Infrared-Labeled PSMA-Specific Small Molecule in Tumor-Bearing Mice

Prostate cancer is the most frequently diagnosed cancer in men and often requires surgery. Use of near infrared (NIR) technologies to perform image-guided surgery may improve accurate delineation of tumor margins. To facilitate preclinical testing of such outcomes, here we developed and characterized a PSMA-targeted small molecule, YC-27. IRDye 800CW was conjugated to YC-27 or an anti-PSMA antibody used for reference. Human 22Rv1, PC3M-LN4, and/or LNCaP prostate tumor cells were exposed to the labeled compounds. In vivo targeting and clearance properties were determined in tumor-bearing mice. Organs and tumors were excised and imaged to assess probe localization. YC-27 exhibited a dose dependent increase in signal upon binding. Binding specificity and internalization were visualized by microscopy. In vitro and in vivo blocking studies confirmed YC-27 specificity. In vivo, YC-27 showed good tumor delineation and tissue contrast at doses as low as 0.25 nmole. YC-27 was cleared via the kidneys but bound the proximal tubules of the renal cortex and epididymis. Since PSMA is also broadly expressed on the neovasculature of most tumors, we expect YC-27 will have clinical utility for image-guided surgery and tumor resections.

MACC1 is involved in the regulation of proliferation, colony formation, invasion ability, cell cycle distribution, apoptosis and tumorigenicity by altering Akt signaling pathway in human osteosarcoma

There is mounting evidence that metastasis-associated in colon cancer-1 (MACC1) plays pivotal roles in development and progression of many tumors, particularly in osteosarcoma (OS). However, its precise roles and molecular mechanisms remain to be delineated in OS. In the current study, we found that the levels of MACC1 mRNA and protein in four OS cell lines (MG-63, HOS, SaOS-2 and U2OS) were significantly higher than that in hFOB1.19 osteoblast (P < 0.05). The vector pcDNA-MACC1 contributed to the increase of MACC1 level in MG-63 cells, whereas MACC1 siRNA evoked the decrease of MACC1 level in U2OS cells. In addition, MACC1 downregualtion caused the inhibition of cell proliferation in vitro, colony formation, invasion and tumor growth in vivo, arrested cell cycle in G0/G1 phase and induced cell apoptosis in U2OS cells, and reversed effects were observed in MG-63 cells by MACC1 upregulation. Most notably, MACC1 depletion markedly inactivated Akt signaling pathway in U2OS cells, conversely, MACC1 upregulation evidently activated Akt signaling pathway in MG-63 cells. Collectively, our data presented herein suggest that biological implications triggered by MACC1 may be tightly associated with the status of Akt signaling pathway in OS.

High expression of MACC1 predicts poor prognosis in patients with osteosarcoma

Increasing evidence has demonstrated that high metastasis-associated in colon cancer-1 (MACC1) level is tightly associated with the development, progression, and poor prognosis of a variety of tumors. However, the relationship between MACC1 and the occurrence, development, and progression of osteosarcoma (OS) remains to be clarified. To facilitate and deepen the understanding of the associations of MACC1 with the development and progression of OS, in the current study, we detected the expressions of MACC1 mRNA and protein, and investigated the relationship between MACC1 expression and prognosis of the patients with OS. Our findings demonstrated that expressions of MACC1 mRNA and protein in OS tissues were significantly higher than those in paired normal bone tissues (P < 0.05). Additionally, the level of MACC1 mRNA in the patients with higher clinical stage and distant metastasis was markedly higher than those with lower clinical stage and without metastasis (P < 0.05). Furthermore, high MACC1 level was closely correlated with clinical stage and distant metastasis (P < 0.05), but not related to the patients' age, gender, tumor size, and anatomical location (P > 0.05). Stepwise investigation revealed that survival time of the patients with high MACC1 level was obviously lower than that with low MACC1 level (P < 0.05). Collectively, our data suggest that MACC1 may play important roles in the development and progression of OS, and thus may be considered as a novel molecular target for therapy of the patients with OS.

Mathematical and statistical modeling in cancer systems biology

Cancer is a major health problem with high mortality rates. In the post-genome era, investigators have access to massive amounts of rapidly accumulating high-throughput data in publicly available databases, some of which are exclusively devoted to housing Cancer data. However, data interpretation efforts have not kept pace with data collection, and gained knowledge is not necessarily translating into better diagnoses and treatments. A fundamental problem is to integrate and interpret data to further our understanding in Cancer Systems Biology. Viewing cancer as a network provides insights into the complex mechanisms underlying the disease. Mathematical and statistical models provide an avenue for cancer network modeling. In this article, we review two widely used modeling paradigms: deterministic metabolic models and statistical graphical models. The strength of these approaches lies in their flexibility and predictive power. Once a model has been validated, it can be used to make predictions and generate hypotheses. We describe a number of diverse applications to Cancer Biology, including, the system-wide effects of drug-treatments, disease prognosis, tumor classification, forecasting treatment outcomes, and survival predictions.

Effect of Sanguisorba officinalis L on breast cancer growth and angiogenesis

OBJECTIVE

Sanguisorba officinalis L. (SA) has shown anti-inflammation, hematopoiesis and immunity enhancing properties. No detailed studies have been reported on its anti-cancer effects. This study therefore was undertaken to analyze its effects on human breast cancer utilizing in vitro and in vivo methodologies.

METHODS

Human breast cancer cell lines MCF-7 and MDA-MB-231 were utilized for evaluating SA influences on tumor progression and angiogenesis processes like proliferation, the cell cycle, apoptosis, tube formation and migration abilities. Both cancer xenografts were also used to determine the herb efficacy in vivo. Bioactivity-guided fractionation was carried out to determine the bioactive compounds in SA.

RESULTS

SA inhibited proliferation, induced S phase arrest and triggered mitochondrial pathway apoptosis in both cancer cells. Angiogenesis experiments revealed that SA inhibited VEGF expression in both cancer cell lines. Meanwhile, the proliferation, tube formation and migration abilities of endothelial cells were also inhibited. In vivo experiments demonstrated that SA reduced tumor size and neoangiogenesis in both cancer xenografts. Gallic acid and ellagic acid were finally identified as bioactive compounds in SA.

CONCLUSIONS

SA might be of value as a breast cancer preventive and therapeutic agent by inducing apoptosis and inhibiting angiogenesis. Further research is needed to evaluate its metabolism and synergistic effects with chemotherapeutic drugs.