Improving the evaluation of new cancer treatments: challenges and opportunities

Targeted acidosis mediated delivery of antigenic MHC-binding peptides

Cytotoxic T lymphocytes are the primary effector immune cells responsible for protection against cancer, as they target peptide neoantigens presented through the major histocompatibility complex (MHC) on cancer cells, leading to cell death. Targeting peptide-MHC (pMHC) complex offers a promising strategy for immunotherapy due to their specificity and effectiveness against cancer. In this work, we exploit the acidic tumor micro-environment to selectively deliver antigenic peptides to cancer using pH(low) insertion peptides (pHLIP). We demonstrated the delivery of MHC binding peptides directly to the cytoplasm of melanoma cells resulted in the presentation of antigenic peptides on MHC, and activation of T cells. This work highlights the potential of pHLIP as a vehicle for the targeted delivery of antigenic peptides and its presentation via MHC-bound complexes on cancer cell surface for activation of T cells with implications for enhancing anti-cancer immunotherapy.

Ligilactobacillus salivarius CCFM 1266 modulates gut microbiota and GPR109a-mediated immune suppression to attenuate immune checkpoint blockade-induced colitis

The wide application of immune checkpoint blockade (ICB) therapy is impeded by the development of ICB-induced colitis, a condition intricately linked to alterations in the gut microbiota. In our previous study, Ligilactobacillus salivarius CCFM 1266 and Bacteroides fragilis HCK-B3 exhibited anti-inflammatory properties. In this research, treatment with both L. salivarius CCFM 1266 and B. fragilis HCK-B3 significantly ameliorated body weight loss and colonic inflammation in murine colitis models induced by intravenous ipilimumab injection, with L. salivarius CCFM 1266 demonstrating superior effectiveness. This amelioration was characterized by an augmented ratio of Treg cells and M2 macrophages, a diminishment in pro-inflammatory cytokines (IL-1β, TNF-α, IFN-γ, IL-23), and an elevation in the anti-inflammatory cytokine IL-10. The ingestion of L. salivarius CCFM 1266 exerted a discernible influence on the composition of the gut microbiota. Untargeted metabolomics revealed an increase in colonic nicotinic acid levels following the administration of L. salivarius CCFM 1266, potentially initiating the activation of the colonic GPR109a pathway. This mechanism likely serves as the fundamental basis for the protective capacity of L. salivarius CCFM 1266 against ICB-induced colitis. Importantly, L. salivarius CCFM 1266 did not interfere with the anti-tumor immune response elicited by ipilimumab. Probiotic intervention thus emerges as a promising approach for alleviating ICB-induced colitis.

Targeted Acidosis Mediated Delivery of Antigenic MHC-Binding Peptides

Cytotoxic T lymphocytes are the primary effector immune cells responsible for protection against cancer, as they target peptide neoantigens presented through the major histocompatibility complex (MHC) on cancer cells, leading to cell death. Targeting peptide-MHC (pMHC) complexes offers a promising strategy for immunotherapy due to its specificity and effectiveness against cancer. In this work, we exploit the acidic tumor micro-environment to selectively deliver antigenic peptides to cancer cells using pH(low) insertion peptides (pHLIP). We demonstrated that the delivery of MHC binding peptides directly to the cytoplasm of melanoma cells resulted in the presentation of antigenic peptides on MHC, and subsequent activation of T cells. This work highlights the potential of pHLIP as a vehicle for targeted delivery of antigenic peptides and their presentation via MHC-bound complexes on cancer cell surfaces for activation of T cells with implications for enhancing anti-cancer immunotherapy.

Dominant negative TGF-β receptor type II in T lymphocytes promotes anti-tumor immunity by modulating T cell subsets and enhancing CTL responses

Transforming growth factor-β (TGF-β) is a multifunctional regulatory cytokine that maintains tolerance in the immune system by regulating the proliferation, differentiation and survival of lymphocytes. TGF-β blockade therapy for cancer has achieved some results but shows limited efficacy and side effects because these drugs are not selective and act on various types of cells throughout the body. We demonstrate here that dominant negative TGF-β receptor type II specifically targeting T cells decreases tumor load in tumor-bearing mice. In addition, the dominant negative TGF-β receptor type II promotes the proliferation and differentiation of T cells and increases the expression of T-bet, which in turn promotes the secretion of granzyme A, granzyme B, perforin and IFN-γ secreted by T cells, and enhances the cytotoxicity and anti-tumor effects of T cells. Moreover, we also found that dominant negative TGF-β receptor type II reduces the proportion of regulatory T cells (Tregs) in tumor tissue and spleen of tumor-bearing mice. Co-culture experiments with T cells and tumor cells revealed that dominant negative TGF-β receptor type II inhibited tumor cell proliferation and increased apoptosis. Our results indicate that specifically inhibiting TGF-β receptor type II in T cells increases anti-tumor immunity and has a strong therapeutic potential.

Progress and Principle of Drug Nanocrystals for Tumor Targeted Delivery

Drugs are referred to as drug nanocrystals when they exist as nanoscale crystal structures. This kind of nanocarrier has been widely utilized to increase the solubility and absorption for poorly aqueous soluble drugs after oral administration, or prolong the drug circulation when intravenous administration. The systemic cytotoxicity caused by antitumor drugs usually come from the nonspecific drug distribution. To solve the disadvantage of poor targetability, drug nanocrystals for tumor targeted delivery have been developed in recent years. In this review, the targeting mechanisms of various surface modified drug nanocrystals are introduced with the focus on passive targeting, active targeting and stimuli-responsive targeting in details. Function and application of common surface modified materials are also discussed.

Innovative trial approaches in immune-mediated inflammatory diseases: current use and future potential

BACKGROUND

Despite progress that has been made in the treatment of many immune-mediated inflammatory diseases (IMIDs), there remains a need for improved treatments. Randomised controlled trials (RCTs) provide the highest form of evidence on the effectiveness of a potential new treatment regimen, but they are extremely expensive and time consuming to conduct. Consequently, much focus has been given in recent years to innovative design and analysis methods that could improve the efficiency of RCTs. In this article, we review the current use and future potential of these methods within the context of IMID trials.

METHODS

We provide a review of several innovative methods that would provide utility in IMID research. These include novel study designs (adaptive trials, Sequential Multi-Assignment Randomised Trials, basket, and umbrella trials) and data analysis methodologies (augmented analyses of composite responder endpoints, using high-dimensional biomarker information to stratify patients, and emulation of RCTs from routinely collected data). IMID trials are now well-placed to embrace innovative methods. For example, well-developed statistical frameworks for adaptive trial design are ready for implementation, whilst the growing availability of historical datasets makes the use of Bayesian methods particularly applicable. To assess whether and how these innovative methods have been used in practice, we conducted a review via PubMed of clinical trials pertaining to any of 51 IMIDs that were published between 2018 and 20 in five high impact factor clinical journals.

RESULTS

Amongst 97 articles included in the review, 19 (19.6%) used an innovative design method, but most of these were relatively straightforward examples of innovative approaches. Only two (2.1%) reported the use of evidence from routinely collected data, cohorts, or biobanks. Eight (9.2%) collected high-dimensional data.

CONCLUSIONS

Application of innovative statistical methodology to IMID trials has the potential to greatly improve efficiency, to generalise and extrapolate trial results, and to further personalise treatment strategies. Currently, such methods are infrequently utilised in practice. New research is required to ensure that IMID trials can benefit from the most suitable methods.

Developing a predictive signature for two trial endpoints using the cross-validated risk scores method

The existing cross-validated risk scores (CVRS) design has been proposed for developing and testing the efficacy of a treatment in a high-efficacy patient group (the sensitive group) using high-dimensional data (such as genetic data). The design is based on computing a risk score for each patient and dividing them into clusters using a nonparametric clustering procedure. In some settings, it is desirable to consider the tradeoff between two outcomes, such as efficacy and toxicity, or cost and effectiveness. With this motivation, we extend the CVRS design (CVRS2) to consider two outcomes. The design employs bivariate risk scores that are divided into clusters. We assess the properties of the CVRS2 using simulated data and illustrate its application on a randomized psychiatry trial. We show that CVRS2 is able to reliably identify the sensitive group (the group for which the new treatment provides benefit on both outcomes) in the simulated data. We apply the CVRS2 design to a psychology clinical trial that had offender status and substance use status as two outcomes and collected a large number of baseline covariates. The CVRS2 design yields a significant treatment effect for both outcomes, while the CVRS approach identified a significant effect for the offender status only after prefiltering the covariates.

Piano-Stool Ruthenium(II) Complexes with Delayed Cytotoxic Activity: Origin of the Lag Time

We have recently reported a series of piano-stool ruthenium(II) complexes of the general formula [RuCl₂(η⁶-arene)(P(1-pyrenyl)R²R³)] showing excellent cytotoxic activities (particularly when R² = R³ = methyl). In the present study, new members of this family of compounds have been prepared with the objective to investigate the effect of the steric hindrance of a bulky phosphane ligand, namely diisopropyl(1-pyrenyl)phosphane (L), on exchange reactions involving the coordinated halides (X = Cl, I). Two η⁶-arene rings were used, i.e. η⁶-methyl benzoate (mba) and η⁶-p-cymene (p-cym), and four complexes were synthesized, namely [RuCl₂(mba)(L)] (1_Cl2^iPr), [RuI₂(mba)(L)] (1_I2^iPr), [RuCl₂(p-cym)(L)] (2_Cl2^iPr), and [RuI₂(p-cym)(L)] (2_I2^iPr). Unexpectedly, all of the complexes exhibited poor cytotoxic activities after 24 h of incubation with cells, in contrast to the related compounds previously reported. However, it was observed that aged DMSO solutions of 2_I2^iPr (from 2 to 7 days) exhibited better activities in comparison to freshly prepared solutions and that the activity improved over “aging” time. Thorough studies were therefore performed to uncover the origin of this lag time in the cytotoxicity efficiency. The data achieved clearly demonstrated that compounds 2_I2^iPr and 2_Cl2^iPr were undergoing a series of transformation reactions in DMSO (with higher rates for the iodido complex 2_I2^iPr), ultimately generating cyclometalated species through a mechanism involving DMSO as a coordinated proton abstractor. The cyclometalated complexes detected in solution were subsequently prepared; hence, pure [RuCl(p-cym)(κ²C-diisopropyl(1-pyrenyl)phosphane)] (3_Cl^iPr), [RuI(p-cym)(κ²C-diisopropyl(1-pyrenyl)phosphane)] (3_I^iPr), and [Ru(p-cym)(κS-dmso)(κ²C-diisopropyl(1-pyrenyl)phosphane)]PF₆ (3_dmso^iPr) were synthesized and fully characterized. Remarkably, 3_Cl^iPr, 3_I^iPr, and 3_dmso^iPr are all very efficient cytotoxic agents, exhibiting slightly better activities in comparison to the chlorido noncyclometalated complexes [RuCl₂(η⁶-arene)(P(1-pyrenyl)R²R³)] described in an earlier report. For comparison purposes, the iodido compounds [RuI₂(mba)(dimethyl(1-pyrenyl)phosphane)] (1_I2^Me) and [RuI₂(p-cym)(dimethyl(1-pyrenyl)phosphane)] (2_I2^Me), bearing the less hindered dimethyl(1-pyrenyl)phosphane ligand, have also been prepared. The cytotoxic and chemical behaviors of 1_I2^Me and 1_I2^Me were comparable to those of their chlorido counterparts reported previously.

DMSO gradually converts half-sandwich, 1-pyrenyl-containing ruthenium(II) complexes into cyclometalated species showing notable cytotoxic properties.

Construction of Ginsenoside Nanoparticles with pH/Reduction Dual Response for Enhancement of Their Cytotoxicity Toward HepG2 Cells

The aim of this study is to construct a pH- and reduction-responsive nanodrug delivery system to effectively deliver a ginsenoside (Rh₂) and enhance its cytotoxicity against human hepatocarcinoma cells (HepG2). Here, pullulan polysaccharide was grafted by urocanic acid and α-lipoic acid (α-LA) to obtain a copolymer, α-LA-conjugated N-urocanyl pullulan (LA-URPA), which was expected to have pH and redox dual response. Then, the copolymer LA-URPA was used to encapsulate ginsenoside Rh₂ to form Rh₂ nanoparticles (Rh₂ NPs). The results showed that Rh₂ NPs exhibited an average size of 119.87 nm with a uniform spherical morphology. Of note, Rh₂ NPs showed a high encapsulation efficiency of 86.00%. Moreover, Rh₂ NPs possessed excellent pH/reduction dual-responsive drug release under acidic conditions (pH 5.5) and glutathione (GSH) stimulation with a low drug leakage of 14.8% within 96 h. Furthermore, Rh₂ NPs with pH/reduction dual response had higher cytotoxicity than Rh₂ after incubation with HepG2 cells for 72 h, indicating that Rh₂ NPs had a longer circulation time. After the treatment with Rh₂ NPs, the excessive increase of reactive oxygen species and the decrease of superoxide dismutase, glutathione (GSH), and mitochondrial membrane potential suggested that the mitochondrial pathway mediated by oxidative stress played a role in this Rh₂ NP-induced apoptosis. In conclusion, this study provides a new strategy for improving the application of ginsenoside Rh₂ in the food and pharmaceutical fields.

An approach using Caenorhabditis elegans screening novel targets to suppress tumour cell proliferation

OBJECTIVES

Tumour cell proliferation requires high metabolism to meet the bioenergetics and biosynthetic needs. Dauer in Caenorhabditis elegans is characterized by lower metabolism, and we established an approach with C elegans to find potential tumour therapy targets.

MATERIALS AND METHODS

RNAi screening was used to find dauer-related genes, and these genes were further analysed in glp-1(-) mutants for tumour-suppressing testing. The identified tumour-related genes were verified in clinical tumour tissues.

RESULTS

The lifespan of glp-1(-) mutants was found to be extended by classical dauer formation signalling. Then, 61 of 287 kinase-coding genes in Caenorhabditis elegans were identified as dauer-related genes, of which 27 were found to be homologous to human oncogenes. Furthermore, 12 dauer-related genes were randomly selected for tumour-suppressing test, and six genes significantly extended the lifespan of glp-1(-) mutants. Of these six genes, F47D12.9, W02B12.12 and gcy-21 were newly linked to dauer formation. These three new dauer-related genes significantly suppressed tumour cell proliferation and thus extended the lifespan of glp-1(-) mutants in a longevity- or dauer-independent manner. The mRNA expression profiles indicated that these dauer-related genes trigged similar low metabolism pattern in glp-1(-) mutants. Notably, the expression of homolog gene DCAF4L2/F47D12.9, TSSK6/W02B12.12 and NPR1/gcy-21 was found to be higher in glioma compared with adjacent normal tissue. In addition, the high expression of TSSK6/W02B12.12 and NPR1/gcy-21 correlated with a worse survival in glioma patients.

CONCLUSIONS

Dauer gene screening in combination with tumour-suppressing test in glp-1(-) mutants provided a useful approach to find potential targets for tumour therapy via suppressing tumour cell proliferation and rewiring tumour cell metabolism.

Improving the Time to Activation of New Clinical Trials at a National Cancer Institute–Designated Comprehensive Cancer Center

PURPOSE:

The time it takes a performing site to activate a clinical trial can directly affect the ability to provide innovative and state-of-the-art care to patients. We sought to understand the process of activating an oncology clinical trial at a matrix National Cancer Institute–designated comprehensive cancer center.

METHODS:

A multidisciplinary team of stakeholders within the cancer center, university, and affiliate hospitals held a retreat to map out the process of activating a clinical trial. We applied classical quality improvement and Six Sigma methodology to determine bottlenecks and non–value-added time in activating a clinical trial. During this process, attention was paid to time to pass through each step, and perceived barriers and bottlenecks were identified through group discussions.

RESULTS:

The process map identified 66 steps with 12 decision points to activate a new clinical trial. The following two steps were instituted first: allow parallel scientific committee and institutional review board (IRB) review and allow the clinical research coordination committee, a group that determines university interest and feasibility, to review protocols independent of the IRB and scientific committee approval. The clinical research coordination committee continues to track the activation time, and this framework is used to identify additional improvement steps.

CONCLUSION:

By applying quality improvement methodologies and Six Sigma principles, we were able to identify redundancies in the process to activate a clinical trial. This allowed us to redesign the process of activating a clinical trial at a matrix comprehensive cancer center. More importantly, the process map provides a framework to maintain these gains and implement additional changes and serves as an example to deploy across the campus and at other similar institutions.

Melanin-Like Nanomaterials for Advanced Biomedical Applications: A Versatile Platform with Extraordinary Promise

Developing efficient, sustainable, and biocompatible high-tech nanoplatforms derived from naturally existing components in living organisms is highly beneficial for diverse advanced biomedical applications. Melanins are nontoxic natural biopolymers owning widespread distribution in various biosystems, possessing fascinating physicochemical properties and playing significant physiological roles. The multifunctionality together with intrinsic biocompatibility renders bioinspired melanin-like nanomaterials considerably promising as a versatile and powerful nanoplatform with broad bioapplication prospects. This panoramic Review starts with an overview of the fundamental physicochemical properties, preparation methods, and polymerization mechanisms of melanins. A systematical and well-bedded description of recent advancements of melanin-like nanomaterials regarding diverse biomedical applications is then given, mainly focusing on biological imaging, photothermal therapy, drug delivery for tumor treatment, and other emerging biomedicine-related implementations. Finally, current challenges toward clinical translation with an emphasis on innovative design strategies and future striving directions are rationally discussed. This comprehensive and detailed Review provides a deep understanding of the current research status of melanin-like nanomaterials and is expected to motivate further optimization of the design of novel tailorable and marketable multifunctional nanoplatforms in biomedicine.

Encapsulation of a Ru(η6-p-cymene) complex of the antibacterial drug trimethoprim into a polydiacetylene-phospholipid assembly to enhance its in vitro anticancer and antibacterial activities

The first report of a Ru(arene)–liposome nanoaggregate to enhance the in vitro anticancer activity of a Ru–arene complex in liver cancer cells.

Nanoparticles based on carboxymethylcellulose-modified rice protein for efficient delivery of lutein

Lutein, as a natural nutrient, has many benefits for human health.

Adaptive Signature Design- review of the biomarker guided adaptive phase -III controlled design

Genomics having a profound impact on oncology drug development necessitates the use of genomic signatures for therapeutic strategy and emerging medicine proposals. Since its advent in the arena of clinical trials biomarker-related predictive methods for the identification and selection of patient subgroups, with optimal treatment response, are widely used. Genetic signatures which are accountable for the differential response to treatments are experimentally recognizable and analytically validated in phase II stage of clinical trials. The availability of robust and validated biomarkers in phase III is limited. Hence, the development of a clinical trial design without the availability of biomarker identity for treatment-sensitive patients becomes indispensable. Adaptive Signature Design (ASD) is a design procedure of developing and validating a predictive classifier (diagnostic testing strategy) when the signature of subjects responding differentially to treatment is remote in the context of the study. This review provides a detailed methodology and statistical background of this pioneering design developed by Freidlin and Simon (2005). In addition, it concentrates on the advances in ASD regarding statistical issues such as predictive assay identification, classification techniques, statistical methods, subgroup search, choice of differentially expressed genes, and multiplicity correction. The statistical methodology behind the design is explained with the intent of building the ground steps for future research approachable, especially for beginning researchers. Most of the existing research articles give a microcosmic view of the design and lack in describing the details behind the methodology. This study covers those details and marks the novelty of our research.

Combination Therapy of TGF-β Blockade and Commensal-derived Probiotics Provides Enhanced Antitumor Immune Response and Tumor Suppression

Galunisertib (Gal) is a transforming growth factor (TGF-β) blockade which is being investigated as a potential tumor immunotherapy candidate drug in clinical trials. However, primary or acquired resistance is often found in the recruited cancer patients, which limits its clinical application. Tumor immune microenvironment can be regulated by intestinal microbiota, leading to different therapeutic outcomes. It is hypothesized that manipulation of cancer patients' intestinal microbiome in the early stage of therapy may be a promising strategy to improve the therapeutic efficacy of Gal. Methods: 4T1 and H22 subcutaneous tumor bearing mice were used to evaluate the therapeutic effect. Escherichia coli strain Nissle 1917 (EcN), a widely used probiotic bacteria, was orally delivered to the tumor bearing mice daily along with Gal treatment. Antitumor effect of the combination therapy was evaluated by tumor volume, histological staining of tumor tissues. Furthermore, flow cytometry was performed to analyze the alteration of immune microenvironment in tumor bed after treatment. The suppressing effect of the combination therapy on tumor invasiveness and metastasis was evaluated in both mice and zebrafish xenografts models. Fecal sample 16S rRNA gene sequencing was conducted to analyze changes of intestinal microbial diversity. The effect of intestinal microbiota on tumor suppression after receiving EcN was further tested by fecal transplant. Results: The therapeutic outcomes in tumor growth inhibition and metastasis suppression of Gal were significantly potentiated by EcN, resulting from the strengthened antitumor immunity. EcN was able to relieve the immunosuppressive tumor microenvironment, which was evidenced by enhanced tumor-specific effector T cells infiltration and dendritic cells activation. Intestinal microbiota was modulated by EcN, illustrated by a shift of gut microbiome toward certain beneficial bacteria. Conclusion: These results suggested that Gal combined with EcN might be a novel therapeutic approach with great potential of clinical implications for cancer prevention or treatment.

Combination Therapy of TGF-β Blockade and Commensal-derived Probiotics Provides Enhanced Antitumor Immune Response and Tumor Suppression

Galunisertib (Gal) is a transforming growth factor (TGF-β) blockade which is being investigated as a potential tumor immunotherapy candidate drug in clinical trials. However, primary or acquired resistance is often found in the recruited cancer patients, which limits its clinical application. Tumor immune microenvironment can be regulated by intestinal microbiota, leading to different therapeutic outcomes. It is hypothesized that manipulation of cancer patients' intestinal microbiome in the early stage of therapy may be a promising strategy to improve the therapeutic efficacy of Gal. Methods: 4T1 and H22 subcutaneous tumor bearing mice were used to evaluate the therapeutic effect. Escherichia coli strain Nissle 1917 (EcN), a widely used probiotic bacteria, was orally delivered to the tumor bearing mice daily along with Gal treatment. Antitumor effect of the combination therapy was evaluated by tumor volume, histological staining of tumor tissues. Furthermore, flow cytometry was performed to analyze the alteration of immune microenvironment in tumor bed after treatment. The suppressing effect of the combination therapy on tumor invasiveness and metastasis was evaluated in both mice and zebrafish xenografts models. Fecal sample 16S rRNA gene sequencing was conducted to analyze changes of intestinal microbial diversity. The effect of intestinal microbiota on tumor suppression after receiving EcN was further tested by fecal transplant. Results: The therapeutic outcomes in tumor growth inhibition and metastasis suppression of Gal were significantly potentiated by EcN, resulting from the strengthened antitumor immunity. EcN was able to relieve the immunosuppressive tumor microenvironment, which was evidenced by enhanced tumor-specific effector T cells infiltration and dendritic cells activation. Intestinal microbiota was modulated by EcN, illustrated by a shift of gut microbiome toward certain beneficial bacteria. Conclusion: These results suggested that Gal combined with EcN might be a novel therapeutic approach with great potential of clinical implications for cancer prevention or treatment.

Synthesis and in vitro anticancer evaluation of some fused indazoles, quinazolines and quinolines as potential EGFR inhibitors

derivatives of benzo[g]indazole 5a, b, benzo[h]quinazoline 7, 12a-c, 13a-c and 15a-c and benzo[h]quinoline 17a-c and 19a-c were synthesized from 6-methoxy-3,4-dihydronaphthalen-1(2H)-one (1). Anticancer activity of all the synthesized compounds was evaluated against four cancerous cell lines; HepG2, MCF-7, HCT116 and Caco-2. MCF-7 cells emerged as the most sensitive cell line against the target compounds. All the examined compounds, except 5a and 5b, displayed potent to moderate anticancer activity against MCF-7 cells with an IC₅₀ values ranging from 7.21 to 21.55 µM. In particular, compounds 15c and 19b emerged as the most potent derivatives against EGFR-expressing MCF-7 cells with IC₅₀ values = 7.70 ± 0.39 and 7.21 ± 0.43 μM, respectively. Additionally, both compounds did not display any significant cytotoxicity towards normal BHK-21 fibroblast cells (IC₅₀ value > 200 µM), thereby providing a good safety profile as anticancer agents. Furthermore, compounds 15c and 19b displayed potent inhibitory activity towards EGFR in the sub-micromolar range (IC₅₀ = 0.13 ± 0.01 and 0.14 ± 0.01 μM, respectively), compared to that of Erlotinib (IC₅₀ = 0.11 ± 0.01 μM). Docking studies for 15c and 19b into EGFR active site was carried out to explore their potential binding modes. Therefore, compounds 15c and 19b can be considered as interesting candidates for further development of more potent anticancer agents.

Targeting the mTOR Signaling Pathway Utilizing Nanoparticles: A Critical Overview

Proteins of the mammalian target of rapamycin (mTOR) signaling axis are overexpressed or mutated in cancers. However, clinical inhibition of mTOR signaling as a therapeutic strategy in oncology shows rather limited progress. Nanoparticle-based mTOR targeted therapy proposes an attractive therapeutic option for various types of cancers. Along with the progress in the biomedical applications of nanoparticles, we start to realize the challenges and opportunities that lie ahead. Here, we critically analyze the current literature on the modulation of mTOR activity by nanoparticles, demonstrate the complexity of cellular responses to functionalized nanoparticles, and underline challenges lying in the identification of the molecular mechanisms of mTOR signaling affected by nanoparticles. We propose the idea that subcytotoxic doses of nanoparticles could be relevant for the induction of subcellular structural changes with possible involvement of mTORC1 signaling. The evaluation of the mechanisms and therapeutic effects of nanoparticle-based mTOR modulation will provide fundamental knowledge which could help in developing safe and efficient nano-therapeutics.

Application of polydopamine in tumor targeted drug delivery system and its drug release behavior

Inspired by the bionics of marine mussels, polydopamine (PDA), a new polymer with unique physicochemical properties was discovered. Due to its simple preparation, good biocompatibility, unique drug-loading methods, PDA has attracted tremendous attentions in field of drug delivery and imaging, and the combination of chemotherapy and other therapies or diagnostic methods, such as photothermotherapy (PTT), photoacoustic imaging (PAI), magnetic resonance imaging (MRI), etc. As an excellent drug carrier in tumor targeted drug delivery system, the drug release behavior of drug-loaded PDA-based nanoparticles is also an important factor to be considered in the establishment of drug delivery systems. Therefore, the purpose of this review is to provide a comprehensive overview of the various applications of PDA in tumor targeted drug delivery systems and to gain insight into the release behavior of the drug-loaded PDA-based nanocarriers. A sufficient understanding and discussion of these aspects is expected to provide a better way to design more rational and effective PDA-based tumor nano-targeted delivery systems. Apart from this, the prospects for the future application of PDA in this field and some unique insights are listed at the end of the article.

Prognostic and predictive signatures for treatment decisions

AIM

We develop a subgroup selection procedure using both prognostic and predictive biomarkers to identify four patient subpopulations: low- and high-risk responders, and low- and high-risk nonresponders.

METHODS

We utilize three regression models to identify three sets of biomarkers: S, prognostic biomarkers; T, predictive biomarkers; and U, prognostic and predictive biomarkers. The prognostic signature C(S) combines with a predictive signature, either C(T) or C(U), to develop two procedures C(S,T) and C(S,U) for identification of four subgroups.

RESULTS

Simulation experiment showed that proposed models for identifying the biomarker sets S and U performed well, as did the procedure C(S,U) for subgroup identification.

CONCLUSION

The proposed model provides more comprehensive characterization of patient subpopulations, and better accuracy in patient treatment assignment.

Pro-guest and acyclic cucurbit[n]uril conjugated polymers for the controlled release of anti-tumor drugs

We report “pro-guest” and acyclic cucurbit[n]uril conjugated polymers as supramolecular drug delivery systems (DDSs).

Nanoparticle core stability and surface functionalization drive the mTOR signaling pathway in hepatocellular cell lines

Specifically designed and functionalized nanoparticles hold great promise for biomedical applications. Yet, the applicability of nanoparticles is critically predetermined by their surface functionalization and biodegradability. Here we demonstrate that amino-functionalized polystyrene nanoparticles (PS-NH2), but not amino- or hydroxyl-functionalized silica particles, trigger cell death in hepatocellular carcinoma Huh7 cells. Importantly, biodegradability of nanoparticles plays a crucial role in regulation of essential cellular processes. Thus, biodegradable silica nanoparticles having the same shape, size and surface functionalization showed opposite cellular effects in comparison with similar polystyrene nanoparticles. At the molecular level, PS-NH2 obstruct and amino-functionalized silica nanoparticles (Si-NH2) activate the mTOR signalling in Huh7 and HepG2 cells. PS-NH2 induced time-dependent lysosomal destabilization associated with damage of the mitochondrial membrane. Solely in PS-NH2-treated cells, permeabilization of lysosomes preceded cell death. Contrary, Si-NH2 nanoparticles enhanced proliferation of HuH7 and HepG2 cells. Our findings demonstrate complex cellular responses to functionalized nanoparticles and suggest that nanoparticles can be used to control activation of mTOR signaling with subsequent influence on proliferation and viability of HuH7 cells. The data provide fundamental knowledge which could help in developing safe and efficient nano-therapeutics.

Marketing Authorization Procedures for Advanced Cancer Drugs: Exploring the Views of Patients, Oncologists, Healthcare Decision Makers, and Citizens in France

BACKGROUND

The past decades have seen advances in cancer treatments in terms of toxicity and side effects but progress in the treatment of advanced cancer has been modest. New drugs have emerged improving progression free survival but with little impact on overall survival, raising questions about the criteria on which to base decisions to grant marketing authorizations and about the authorization procedure itself. For decisions to be fair, transparent and accountable, it is necessary to consider the views of those with relevant expertise and experience.

METHODS

We conducted a Q-study to explore the views of a range of stakeholders in France, involving: 54 patients (18 months after diagnosis); 50 members of the general population; 27 oncologists; 19 healthcare decision makers; and 2 individuals from the pharmaceutical industry.

RESULTS

Three viewpoints emerged, focussing on different dimensions entitled: 1) 'Quality of life (QoL), opportunity cost and participative democracy'; 2)'QoL and patient-centeredness'; and 3) 'Length of life'. Respondents from all groups were associated with each viewpoint, except for healthcare decision makers, who were only associated with the first one.

CONCLUSION

Our results highlight plurality in the views of stakeholders, emphasize the need for transparency in decision making processes, and illustrate the importance of a re-evaluation of treatments for all 3 viewpoints. In the context of advanced cancer, our results suggest that QoL should be more prominent amongst authorization criteria, as it is a concern for 2 of the 3 viewpoints.

pH-Driven preparation of two related platinum(ii) complexes exhibiting distinct cytotoxic properties

A distinct cytotoxic/DNA interaction behaviour has been observed for a pair of aqua/hydroxido/chlorido equilibrium related Pt^II complex structures. The equilibrium is shifted between {Pt^II(NN)} and {Pt^II(NNO)} cores as a function of pCl and pH.

Neoadjuvant Treatment Approach: The Rosetta Stone for Breast Cancer?

Breast cancer represents a heterogeneous group of diseases with varied biological features, behavior, and response to therapy; thus, management of breast cancer relies on the availability of robust predictive and prognostic factors to support therapy decision-making. Traditionally, neoadjuvant treatment for breast cancer was preserved for locally advanced, converting an inoperable to a surgical resectable cancer. Neoadjuvant trials, additionally, offer: 1) the opportunity to evaluate new treatment options in a faster way and with fewer patients than large adjuvant trials; 2) to identify and validate the prognostic and predictive value of a marker with its association with clinical outcome in relation to the administered treatment. In this setting, thanks to new, affordable technologies which help to detail the molecular profiles of tumors, new trial designs based on new target therapies, like window-of-opportunity, are also suggested, as they represent the chance to identify tumor sensitivity or to overcome tumor resistance to the treatment used, based on its interaction with tumor biology in early tumor stages. However, clinicians and researchers should pay particular attention: In this setting, the safety of patients is paramount, given the exposure of potentially curable patients to investigational agents with limited safety experience, the definition of the study population and the study design, such as adaptive strategies, should limit patient exposure to ineffective agents, and intensify safety monitoring in the course of the treatment. Here, issues related to outcome determination in breast cancer, including some critical points of view, are presented.

Mechanisms and biomaterials in pH-responsive tumour targeted drug delivery: A review

As the mainstay in the treatment of various cancers, chemotherapy plays a vital role, but still faces many challenges, such as poor tumour selectivity and multidrug resistance (MDR). Targeted drug delivery using nanotechnology has provided a new strategy for addressing the limitations of the conventional chemotherapy. In the last decade, the volume of research published in this area has increased tremendously, especially with functional nano drug delivery systems (nanocarriers). Coupling a specific stimuli-triggered drug release mechanism with these delivery systems is one of the most prevalent approaches for improving therapeutic outcomes. Among the various stimuli, pH triggered delivery is regarded as the most general strategy, targeting the acidic extracellular microenvironment and intracellular organelles of solid tumours. In this review, we discuss recent advances in the development of pH-sensitive nanocarriers for tumour-targeted drug delivery. The review focuses on the chemical design of pH-sensitive biomaterials, which are used to fabricate nanocarriers for extracellular and/or intracellular tumour site-specific drug release. The pH-responsive biomaterials bring forth conformational changes in these nanocarriers through various mechanisms such as protonation, charge reversal or cleavage of a chemical bond, facilitating tumour specific cell uptake or drug release. A greater understanding of these mechanisms will help to design more efficient drug delivery systems to address the challenges encountered in conventional chemotherapy.

Subgroup identification for treatment selection in biomarker adaptive design

BACKGROUND

Advances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects.

METHODS

The diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets.

RESULTS

The DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error.

CONCLUSION

Accuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis.

A visualization method measuring the performance of biomarkers for guiding treatment decisions

Biomarkers that predict efficacy and safety for a given drug therapy become increasingly important for treatment strategy and drug evaluation in personalized medicine. Methodology for appropriately identifying and validating such biomarkers is critically needed, although it is very challenging to develop, especially in trials of terminal diseases with survival endpoints. The marker-by-treatment predictiveness curve serves this need by visualizing the treatment effect on survival as a function of biomarker for each treatment. In this article, we propose the weighted predictiveness curve (WPC). Based on the nature of the data, it generates predictiveness curves by utilizing either parametric or nonparametric approaches. Especially for nonparametric predictiveness curves, by incorporating local assessment techniques, it requires minimum model assumptions and provides great flexibility to visualize the marker-by-treatment relationship. WPC can be used to compare biomarkers and identify the one with the highest potential impact. Equally important, by simultaneously viewing several treatment-specific predictiveness curves across the biomarker range, WPC can also guide the biomarker-based treatment regimens. Simulations representing various scenarios are employed to evaluate the performance of WPC. Application on a well-known liver cirrhosis trial sheds new light on the data and leads to discovery of novel patterns of treatment biomarker interactions.

Predictive biomarkers for treatment selection: statistical considerations

Predictive biomarkers are developed for treatment selection to identify patients who are likely to benefit from a particular therapy. This review describes statistical methods and discusses issues in the development of predictive biomarkers to enhance study efficiency for detection of treatment effect on the selected responder patients in clinical studies. The statistical procedure for treatment selection consists of three components: biomarker identification, subgroup selection and clinical utility assessment. Major statistical issues discussed include biomarker designs, procedures to identify predictive biomarkers, classification models for subgroup selection, subgroup analysis and multiple testing for clinical utility assessment and evaluation.

Modified response evaluation criteria in solid tumors is superior to response evaluation criteria in solid tumors for assessment of responses to sorafenib in patients with advanced hepatocellular carcinoma

Background

Modified response evaluation criteria in solid tumors (mRECIST) and RECIST are used to assess the effect of treatment with targeted agents for hepatocellular carcinoma (HCC). The aim of this study was to determine which set of criteria is superior in patients with advanced HCC treated with sorafenib.

Methods

A multicenter retrospective study to assess the tumor response and patient prognosis of 191 patients with HCC who had been treated with sorafenib from May 2009 through December 2011. We analyzed tumor responses as shown by contrast-enhanced computed tomography scan images according to RECIST 1.1 and mRECIST and compared the findings.

Results

The median duration of follow-up was 9.7 months and median overall survival was 10.8 months. Twenty-five patients (13.1 %) were assessed as responders by mRECIST and 15 (7.8 %) by RECIST 1.1. There was a significant difference in overall survival (OS) between responders and non-responders according to mRECIST (P = 0.0117), but no significant difference in OS between responders and non-responders according to RECIST 1.1 (P = 0.0722). Sixteen patients (8.4 %) had no measurable enhanced target lesions that could be assessed as required by mRECIST; however, these patients could be assessed by RECIST 1.1. According to RECIST 1.1, eight of them had stable disease (SD) and eight had progressive disease (PD). There was a significant difference in OS between these SD and PD patients (P = 0.0312).

Conclusions

Patients treated with sorafenib for HCC should be evaluated by mRECIST; RECIST 1.1 is preferable only for assessment of patients with lesions that are non-measurable according to mRESIST.

Pharmacologic down-regulation of EZH2 suppresses bladder cancer in vitro and in vivo

The polycomb group gene, EZH2, is highly expressed in advanced bladder cancer. Here we demonstrated that down-regulation of EZH2 in tumor tissues after neo-adjuvant chemotherapy correlated with good therapeutic response in advanced bladder cancer. We next developed a small molecule, NSC745885, derived from natural anthraquinone emodin, which down-regulated EZH2 via proteasome-mediated degradation. NSC745885 showed potent selective toxicity against multiple cancer cell lines but not normal cells. NSC745885 treatment overcame multiple-drug resistance and inhibited growth of resistant cancer cells. Over-expression of EZH2 in cancer cells attenuated effects of NSC745885, suggesting that down-regulation of EZH2 was responsible for growth inhibition of NSC745885. NSC745885 also suppressed tumor growth and down-regulated EZH2 in vivo. These results indicate that NSC7455889 suppresses bladder cancer by targeting EZH2.

A composite model for subgroup identification and prediction via bicluster analysis

Background

A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen serotypes of microorganisms, different tumor subtypes in cancer patients, or different genetic makeups of patients related to treatment response.

Methods

This paper proposes a composite model for subgroup identification and prediction using biclusters. A biclustering technique is first used to identify a set of biclusters from the sampled data. For each bicluster, a subgroup-specific binary classifier is built to determine if a particular sample is either inside or outside the bicluster. A composite model, which consists of all binary classifiers, is constructed to classify samples into several disjoint subgroups. The proposed composite model neither depends on any specific biclustering algorithm or patterns of biclusters, nor on any classification algorithms.

Results

The composite model was shown to have an overall accuracy of 97.4% for a synthetic dataset consisting of four subgroups. The model was applied to two datasets where the sample’s subgroup memberships were known. The procedure showed 83.7% accuracy in discriminating lung cancer adenocarcinoma and squamous carcinoma subtypes, and was able to identify 5 serotypes and several subtypes with about 94% accuracy in a pathogen dataset.

Conclusion

The composite model presents a novel approach to developing a biclustering-based classification model from unlabeled sampled data. The proposed approach combines unsupervised biclustering and supervised classification techniques to classify samples into disjoint subgroups based on their associated attributes, such as genotypic factors, phenotypic outcomes, efficacy/safety measures, or responses to treatments. The procedure is useful for identification of unknown species or new biomarkers for targeted therapy.

Comparison of statistical analysis plans in randomize-all phase III trials with a predictive biomarker

When there are no compelling biologic or early trial data for a candidate predictive biomarker with regard to its ability to predict the effect of an anticancer treatment at the initiation of definitive phase III trials, it is generally reasonable to include all patients as eligible for randomization but to plan for a prospective subgroup analysis based on the biomarker. We assessed such statistical analysis plans, fixed-sequence, fallback, and treatment-by-biomarker interaction approaches, in terms of the probability of asserting treatment efficacy for either the overall patient population or a biomarker-positive subpopulation of patients. If there was some evidence that the treatment would work better in the biomarker-positive subgroup than the biomarker-negative subgroup, then the fixed-sequence approaches would be favored, whereas if evidence was weak that there would be much difference in responsiveness between the two subgroups, then the fallback approach would be favored. If there was substantial uncertainty in the difference in treatment effects between the two subgroups, the treatment-by-biomarker interaction approach could be a reasonable choice as this approach generally provided a high probability of asserting treatment efficacy for the right patient population under homogeneous treatment effects and a qualitative interaction over biomarker-based subgroups. Clin Cancer Res; 20(11); 2820-30. ©2014 AACR.

Shifting patterns in the interpretation of phase III clinical trial outcomes in advanced non-small-cell lung cancer: the bar is dropping

PURPOSE

Despite multiple trials of new agents in advanced non-small-cell lung cancer (NSCLC), outcomes remain poor. This study explores how the design and interpretation of randomized trials in advanced NSCLC has changed over time.

METHODS

Phase III randomized controlled trials of systemic therapy for advanced NSCLC between 1980 and 2010 were identified, and their primary end point, outcome, statistical significance, and conclusions were recorded.

RESULTS

Of 245 trials identified, 203 were eligible for study inclusion. Although overall survival remains the most common primary end point of phase III trials, more trials from the last decade have used progression-free survival instead (none in 1980 to 1990, 13% in 2001 to 2010; P = .002). The percentage of trials meeting their primary statistical end points remained stable over time; however, the percentage of trials reporting a positive outcome without meeting that end point increased (30% in 1980 to 1990, 53% in 2001 to 2010; P < .001). A trend toward decreasing magnitude of survival gain in positive trials was seen over time (3.9 months in 1980 to 1990, 2.5 months in 2001 to 2010; P = .11), with a concomitant increase in the sample size of clinical trials over the same time period (median: 152 patients in 1980 to 1990, 413 in 2001 to 2010; P < .001). Only studies predating 1990 reported negative results as a result of insufficient magnitude of survival benefit despite statistical significance.

CONCLUSION

A significant shift has occurred over the past three decades in the design and interpretation of phase III trials in advanced NSCLC. The use of survival as the primary measure of benefit is declining, as is the magnitude of benefit deemed clinically relevant.

Differential network analysis for the identification of condition-specific pathway activity and regulation

MOTIVATION

Identification of differential expressed genes has led to countless new discoveries. However, differentially expressed genes are only a proxy for finding dysregulated pathways. The problem is to identify how the network of regulatory and physical interactions rewires in different conditions or in disease.

RESULTS

We developed a procedure named DINA (DIfferential Network Analysis), which is able to identify set of genes, whose co-regulation is condition-specific, starting from a collection of condition-specific gene expression profiles. DINA is also able to predict which transcription factors (TFs) may be responsible for the pathway condition-specific co-regulation. We derived 30 tissue-specific gene networks in human and identified several metabolic pathways as the most differentially regulated across the tissues. We correctly identified TFs such as Nuclear Receptors as their main regulators and demonstrated that a gene with unknown function (YEATS2) acts as a negative regulator of hepatocyte metabolism. Finally, we showed that DINA can be used to make hypotheses on dysregulated pathways during disease progression. By analyzing gene expression profiles across primary and transformed hepatocytes, DINA identified hepatocarcinoma-specific metabolic and transcriptional pathway dysregulation.

AVAILABILITY

We implemented an on-line web-tool http://dina.tigem.it enabling the user to apply DINA to identify tissue-specific pathways or gene signatures.

CONTACT

dibernardo@tigem.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Human xenografts are not rejected in a naturally occurring immunodeficient porcine line: a human tumor model in pigs

Animal models for cancer therapy are invaluable for preclinical testing of potential cancer treatments; however, therapies tested in such models often fail to translate into clinical settings. Therefore, a better preclinical model for cancer treatment testing is needed. Here we demonstrate that an immunodeficient line of pigs can host and support the growth of xenografted human tumors and has the potential to be an effective animal model for cancer therapy. Wild-type and immunodeficient pigs were injected subcutaneously in the left ear with human melanoma cells (A375SM cells) and in the right ear with human pancreatic carcinoma cells (PANC-1). All immunodeficient pigs developed tumors that were verified by histology and immunohistochemistry. Nonaffected littermates did not develop tumors. Immunodeficient pigs, which do not reject xenografted human tumors, have the potential to become an extremely useful animal model for cancer therapy because of their similarity in size, anatomy, and physiology to humans.

Designs and adaptive analysis plans for pivotal clinical trials of therapeutics and companion diagnostics

BACKGROUND

Developments in genomics and biotechnology provide unprecedented opportunities for the development of effective therapeutics and companion diagnostics for matching the right drug to the right patient. Effective co-development involves many new challenges with increased opportunity for success as well as delay and failure.

OBJECTIVE

Clinical trial designs and adaptive analysis plans for the prospective design of pivotal trials of new therapeutics and companion diagnostics are reviewed.

CONCLUSIONS

Effective co-development requires careful prospective planning of the design and analysis strategy for pivotal clinical trials. Randomized clinical trials continue to be important for evaluating the effectiveness of new treatments, but the target populations for analysis should be prospectively specified based on the companion diagnostic. Post hoc analyses of traditionally designed randomized clinical trials are often deeply problematic. Clear separation is generally required of the data used for developing the diagnostic test, including their threshold of positivity, from the data used for evaluating treatment effectiveness in subsets determined by the test. Adaptive analysis can be used to provide flexibility to the analysis but the use of such methods requires careful planning and prospective definition in order to assure that the pivotal trial adequately limits the chance of erroneous conclusions.

Developing and validating continuous genomic signatures in randomized clinical trials for predictive medicine

PURPOSE

It is highly challenging to develop reliable diagnostic tests to predict patients' responsiveness to anticancer treatments on clinical endpoints before commencing the definitive phase III randomized trial. Development and validation of genomic signatures in the randomized trial can be a promising solution. Such signatures are required to predict quantitatively the underlying heterogeneity in the magnitude of treatment effects.

EXPERIMENTAL DESIGN

We propose a framework for developing and validating genomic signatures in randomized trials. Codevelopment of predictive and prognostic signatures can allow prediction of patient-level survival curves as basic diagnostic tools for treating individual patients.

RESULTS

We applied our framework to gene-expression microarray data from a large-scale randomized trial to determine whether the addition of thalidomide improves survival for patients with multiple myeloma. The results indicated that approximately half of the patients were responsive to thalidomide, and the average improvement in survival for the responsive patients was statistically significant. Cross-validated patient-level survival curves were developed to predict survival distributions of individual future patients as a function of whether or not they are treated with thalidomide and with regard to their baseline prognostic and predictive signature indices.

CONCLUSION

The proposed framework represents an important step toward reliable predictive medicine. It provides an internally validated mechanism for using randomized clinical trials to assess treatment efficacy for a patient population in a manner that takes into consideration the heterogeneity in patients' responsiveness to treatment. It also provides cross-validated patient-level survival curves that can be used for selecting treatments for future patients.