Molecularly targeted cancer therapy: some lessons from the past decade

Downregulation of PIK3CA via antibody-esiRNA-complexes suppresses human xenograft tumor growth

Precision cancer therapy requires on the one hand detailed knowledge about a tumor’s driver oncogenes and on the other hand an effective targeted therapy that specifically inhibits these oncogenes. While the determination of genomic landscape of a tumor has reached a very precise level, the respective therapy options are scarce. The application of small inhibitory (si) RNAs is a promising field of investigation. Here, we present the effective in vivo-treatment of colorectal cancer (CRC) xenograft tumors with antibody-complexed, endoribonuclease-prepared small inhibitory (esi)RNAs. We chose heterogeneous endoribonuclease-prepared siRNA pools (esiRNAs) against the frequently mutated genes PIK3CA and KRAS and coupled them to the anti-EGFR antibody cetuximab, which was internalized specifically into the tumor cells. esiRNA pools have been shown to exhibit superior specificity in target gene knockdown compared to classic siRNAs. We identified a significant decrease in tumor growth upon this treatment due to decreased tumor cell proliferation. The ex vivo-analysis of the xenograft CRC tumors revealed the expected downregulation of the intended direct targets PIK3CA and KRAS on protein level. Moreover, known downstream targets for EGFR signaling such as p-ERK, p-AKT, and c-MYC were decreased as well. We therefore propose the use of antibody-esiRNA complexes as a novel experimental treatment option against key components of the EGFR signaling cascade.

Label-Free Quantitative Proteomics Combined with Biological Validation Reveals Activation of Wnt/β-Catenin Pathway Contributing to Trastuzumab Resistance in Gastric Cancer

Resistance to trastuzumab, which specifically target HER2-positive breast and gastric cancer, can develop ultimately in cancer patients. However, the underlying mechanisms of resistance in gastric cancer have not been fully elucidated. Here, we established trastuzumab-resistant MKN45 and NCI N87 gastric cancer sublines from their parental cells. The resistant cells exhibited characteristics of epithelial-mesenchymal transition (EMT) and acquired higher migratory and invasive capacities. To exploit the activated pathways and develop new strategies to overcome trastuzumab resistance, we investigated MKN45 and MKN45/R cells via label-free quantitative proteomics, and found pathways that were altered significantly in MKN45/R cells, with the Wnt/β-catenin pathway being the most significant. We further confirmed the activation of this pathway by detecting its key molecules in MKN45/R and NCI N87/R cells via Western blot, in which Wnt3A, FZD6, and CTNNB1 increased, whereas GSK-3β decreased, manifesting the activation of the Wnt/β-catenin pathway. Correspondingly, inhibition of Wnt/β-catenin pathway by ICG-001, a specific Wnt/β-catenin inhibitor, preferentially reduced proliferation and invasion of trastuzumab-resistant cells and reversed EMT. Concurringly, CTNNB1 knockdown in stable cell lines potently sensitized cells to trastuzumab and induced more apoptosis. Taken together, our study demonstrates that the Wnt/β-catenin pathway mediates trastuzumab resistance, and the combination of Wnt/β-catenin inhibitors with trastuzumab may be an effective treatment option.

Detection of the Cell Cycle-Regulated Negative Feedback Phosphorylation of Mitogen-Activated Protein Kinases in Breast Carcinoma using Nanofluidic Proteomics

Mitogen-activated protein kinases (MAPKs) play an important role in the regulation of cell proliferation, oncogenic transformation, and drug resistance. This study examined the capability of nanofluidic proteomics to identify aberrations in the MAPK signaling cascade, monitor its drug response, and guide the rational design of intervention strategies. Specifically, the protein post-translational modification (PTM) profiles of MEK1, MEK2, and ERK1/2 were measured in breast carcinoma and breast cancer cell lines. Nanofluidic proteomics revealed hyper-phosphorylation of MAPKs in breast carcinoma and breast cancer cells treated with kinase inhibitors that interfere with cell cycle regulation, such as dinaciclib, an inhibitor of cyclin-dependent kinases, and rigosertib, an inhibitor of polo-like kinase 1. A pMEK1 (Thr286) phosphor-isoform, which serves as a biomarker of cell cycle-regulated negative feedback phosphorylation in breast cancer cells, was detected in breast carcinoma. Inhibition of the MAPK pathway with dabrafenib, a B-Raf inhibitor, or trametinib, a MEK1/2 inhibitor, suppressed both the positively regulated phosphorylation of MAPKs and the negatively regulated phosphorylation of MEK1. Interestingly, the combinations of dabrafenib and rigosertib or trametinib and rigosertib permitted the suppression of positively regulated MAPK phosphorylation together with the promotion of negatively regulated MEK1 phosphorylation. The effectiveness of protein PTM-guided drug combinations for inhibition of the MAPK pathway remains to be experimentally tested. Via protein PTM profiling, nanofluidic proteomics provides a robust means to detect anomalies in the MAPK signaling cascade, monitor its drug response, and guide the possible design of drug combinations for MAPK pathway-focused targeting.

Somatic mutation of the cohesin complex subunit confers therapeutic vulnerabilities in cancer

A synthetic lethality-based strategy has been developed to identify therapeutic targets in cancer harboring tumor-suppressor gene mutations, as exemplified by the effectiveness of poly ADP-ribose polymerase (PARP) inhibitors in BRCA1/2-mutated tumors. However, many synthetic lethal interactors are less reliable due to the fact that such genes usually do not perform fundamental or indispensable functions in the cell. Here, we developed an approach to identifying the "essential lethality" arising from these mutated/deleted essential genes, which are largely tolerated in cancer cells due to genetic redundancy. We uncovered the cohesion subunit SA1 as a putative synthetic-essential target in cancers carrying inactivating mutations of its paralog, SA2. In SA2-deficient Ewing sarcoma and bladder cancer, further depletion of SA1 profoundly and specifically suppressed cancer cell proliferation, survival, and tumorigenic potential. Mechanistically, inhibition of SA1 in the SA2-mutated cells led to premature chromatid separation, dramatic extension of mitotic duration, and consequently, lethal failure of cell division. More importantly, depletion of SA1 rendered those SA2-mutated cells more susceptible to DNA damage, especially double-strand breaks (DSBs), due to reduced functionality of DNA repair. Furthermore, inhibition of SA1 sensitized the SA2-deficient cancer cells to PARP inhibitors in vitro and in vivo, providing a potential therapeutic strategy for patients with SA2-deficient tumors.

Staurosporine: new lease of life for parent compound of today's novel and highly successful anti-cancer drugs

Staurosporine, together with such examples as penicillin, aspirin, ivermectin and sildenafil, exemplifies the role that serendipity has in drug discovery and why 'finding things without actually searching for them' retains a prominent role in drug discovery. Hitherto not clinically useful, due to its potency and promiscuity, new delivery technology is opening up new horizons for what was previously just the parent compound of innovative, highly-successful anti-cancer agents.

Plasmodial Kinase Inhibitors: License to Cure?

Advances in the genetics, function, and stage-specificity of Plasmodium kinases has driven robust efforts to identify targets for the design of antimalarial therapies. Reverse genomics following phenotypic screening against Plasmodia or related parasites has uncovered vulnerable kinase targets including PI4K, PKG, and GSK-3, an approach bolstered by access to human disease-directed kinase libraries. Alternatively, screening compound libraries against Plasmodium kinases has successfully led to inhibitors with antiplasmodial activity. As with other therapeutic areas, optimizing compound ADMET and PK properties in parallel with target inhibitory potency and whole cell activity becomes paramount toward advancing compounds as clinical candidates. These and other considerations will be discussed in the context of progress achieved toward deriving important, novel mode-of-action kinase-inhibiting antimalarial medicines.

Neutral Theory and the Somatic Evolution of Cancer

Kimura's neutral theory argued that positive selection was not responsible for an appreciable fraction of molecular substitutions. Correspondingly, quantitative analysis reveals that the vast majority of substitutions in cancer genomes are not detectably under selection. Insights from the somatic evolution of cancer reveal that beneficial substitutions in cancer constitute a small but important fraction of the molecular variants. The molecular evolution of cancer community will benefit by incorporating the neutral theory of molecular evolution into their understanding and analysis of cancer evolution-and accepting the use of tractable, predictive models, even when there is some evidence that they are not perfect.

The renaissance of anti-neoplastic immunity from tumor cell demise

Cancer therapies can temporarily reduce tumor burdens by inducing malignant cell death. However, cancer cure is still far from realization because tumors often gain resistance to current treatment and eventually relapse. Accumulating evidence suggests that successful cancer interventions require anti-tumor immunity. Therapy-induced cell stress responses ultimately result in one or more cell death modalities, including apoptosis, autophagy, necroptosis, and pyroptosis. These irreversible dying processes are accompanied by active or passive release of cell death-associated molecular patterns (CDAMPs), which can be sensed by corresponding pattern recognition receptors (PRR) on tumor-infiltrating immune cells. This crosstalk with the immune system can reawaken immune surveillance in the tumor microenvironment (TME). This review focuses on immune-modulatory properties of anti-cancer regimens and CDAMP-mediated communications between cell stress responses and the immune contexture of TME. In addition, we describe how immunogenic cell death can elicit strong and durable anti-tumor immune responses.

Targeted Tumor Therapy Remixed-An Update on the Use of Small-Molecule Drugs in Combination Therapies

Over the last decade, the treatment of tumor patients has been revolutionized by the highly successful introduction of novel targeted therapies, in particular small-molecule kinase inhibitors and monoclonal antibodies, as well as by immunotherapies. Depending on the mutational status, BRAF and MEK inhibitor combinations or immune checkpoint inhibitors are current first-line treatments for metastatic melanoma. However, despite great improvements of survival rates limitations due to tumor heterogeneity, primary and acquired therapy resistance, immune evasion, and economical considerations will need to be overcome. Accordingly, ongoing clinical trials explore the individualized use of small-molecule drugs in new targeted therapy combinations based on patient parameters and tumor biopsies. With focus on melanoma therapy this review aims at providing a comprehensive overview of such novel alternative and combinational therapy strategies currently emerging from basic research. The molecular principles and drug classes that may hold promise for improved tumor therapy combination regimens including kinase inhibition, induction of apoptosis, DNA-damage response inhibition, epigenetic reprogramming, telomerase inhibition, redox modulation, metabolic reprogramming, proteasome inhibition, cancer stem cell transdifferentiation, immune cell signaling modulation, and others, are explained in brief. In addition, relevant targeted therapy combinations in current clinical trials and individualized treatment strategies are highlighted.

Fn3 proteins engineered to recognize tumor biomarker mesothelin internalize upon binding

Mesothelin is a cell surface protein that is overexpressed in numerous cancers, including breast, ovarian, lung, liver, and pancreatic tumors. Aberrant expression of mesothelin has been shown to promote tumor progression and metastasis through interaction with established tumor biomarker CA125. Therefore, molecules that specifically bind to mesothelin have potential therapeutic and diagnostic applications. However, no mesothelin-targeting molecules are currently approved for routine clinical use. While antibodies that target mesothelin are in development, some clinical applications may require a targeting molecule with an alternative protein fold. For example, non-antibody proteins are more suitable for molecular imaging and may facilitate diverse chemical conjugation strategies to create drug delivery complexes. In this work, we engineered variants of the fibronectin type III domain (Fn3) non-antibody protein scaffold to bind to mesothelin with high affinity, using directed evolution and yeast surface display. Lead engineered Fn3 variants were solubly produced and purified from bacterial culture at high yield. Upon specific binding to mesothelin on human cancer cell lines, the engineered Fn3 proteins internalized and co-localized to early endosomes. To our knowledge, this is the first report of non-antibody proteins engineered to bind mesothelin. The results validate that non-antibody proteins can be engineered to bind to tumor biomarker mesothelin, and encourage the continued development of engineered variants for applications such as targeted diagnostics and therapeutics.

Natural scaffolds in anticancer therapy and precision medicine

The diversity of natural compounds is essential for their mechanism of action. The source, structures and structure activity relationship of natural compounds contributed to the development of new classes of chemotherapy agents for over 40 years. The availability of combinatorial chemistry and high-throughput screening has fueled the challenge to identify novel compounds that mimic nature's chemistry and to predict their macromolecular targets. Combining conventional and targeted therapies helped to successfully overcome drug resistance and prolong disease-free survival. Here, we aim to provide an overview of preclinical investigated natural compounds alone and in combination to further improve personalization of cancer treatment.

131I-Evans blue: evaluation of necrosis targeting property and preliminary assessment of the mechanism in animal models

Necrosis is a form of cell death, which is related to various serious diseases such as cardiovascular disease, cancer, and neurodegeneration. Necrosis-avid agents (NAAs) selectively accumulated in the necrotic tissues can be used for imaging and/or therapy of related diseases. The aim of this study was to preliminarily investigate necrosis avidity of ¹³¹I-evans blue (¹³¹I-EB) and its mechanism. The biodistribution of ¹³¹I-EB at 24 h after intravenous administration showed that the radioactivity ratio of necrotic to viable tissue was 3.41 in the liver and 11.82 in the muscle as determined by γ counting in model rats. Autoradiography and histological staining displayed preferential uptake of ¹³¹I-EB in necrotic tissues. In vitro nuclear extracts from necrotic cells exhibited 82.3% of the uptake in nuclei at 15 min, as well as 79.2% of the uptake at 2 h after ¹³¹I-EB incubation. The DNA binding study demonstrated that evans blue (EB) has strong binding affinity with calf-thymus DNA (CT-DNA) (K_sv=5.08×10⁵ L/(mol/L)). Furthermore, the accumulation of ¹³¹I-EB in necrotic muscle was efficiently blocked by an excess amount of unlabeled EB. In conclusion, ¹³¹I-EB can not only detect necrosis by binding the DNA released from necrotic cells, but also image necrotic tissues generated from the disease clinically.

Analysis of mutation, selection, and epistasis: an informed approach to cancer clinical trials

Currently, drug development efforts and clinical trials to test them are often prioritized by targeting genes with high frequencies of somatic variants among tumors. However, differences in oncogenic mutation rate-not necessarily the effect the variant has on tumor growth-contribute enormously to somatic variant frequency. We argue that decoupling the contributions of mutation and cancer lineage selection to the frequency of somatic variants among tumors is critical to understanding-and predicting-the therapeutic potential of different interventions. To provide an indicator of that strength of selection and therapeutic potential, the frequency at which we observe a given variant across patients must be modulated by our expectation given the mutation rate and target size to provide an indicator of that strength of selection and therapeutic potential. Additionally, antagonistic and synergistic epistasis among mutations also impacts the potential therapeutic benefit of targeted drug development. Quantitative approaches should be fostered that use the known genetic architectures of cancer types, decouple mutation rate, and provide rigorous guidance regarding investment in targeted drug development. By integrating evolutionary principles and detailed mechanistic knowledge into those approaches, we can maximize our ability to identify those targeted therapies most likely to yield substantial clinical benefit.

Targeting Ligand Specificity Linked to Tumor Tissue Topological Heterogeneity via Single-Cell Micro-Pharmacological Modeling

Targeted therapy has held promise to be a successful anticancer treatment due to its specificity towards tumor cells that express the target receptors. However, not all targeting drugs used in the clinic are equally effective in tumor eradication. To examine which biochemical and biophysical properties of targeted agents are pivotal for their effective distribution inside the tumor and their efficient cellular uptake, we combine mathematical micro-pharmacological modeling with in vivo imaging of targeted human xenograft tumors in SCID mice. The mathematical model calibrated to experimental data was used to explore properties of the targeting ligand (diffusion and affinity) and ligand release schemes (rates and concentrations) with a goal to identify the properties of cells and ligands that enable high receptor saturation. By accounting for heterogeneities typical of in vivo tumors, our model was able to identify cell- and tissue-level barriers to efficient drug uptake. This work provides a base for utilizing experimentally measurable properties of a ligand-targeted agent and patient-specific attributes of the tumor tissue to support the development of novel targeted imaging agents and for improvement in their delivery to individual tumor cells.

Regulation of brachyury by fibroblast growth factor receptor 1 in lung cancer

Recent evidence suggests that T-box transcription factor brachyury plays an important role in lung cancer development and progression. However, the mechanisms underlying brachyury-driven cellular processes remain unclear. Here we found that fibroblast growth factor receptor 1/mitogen-activated protein kinase (FGFR1/MAPK) signaling regulated brachyury in lung cancer. Analysis of FGFR1-4 and brachyury expression in human lung tumor tissue and cell lines found that only expression of FGFR1 was positively correlated with brachyury expression. Specific knockdown of FGFR1 by siRNA suppressed brachyury expression and epithelial-mesenchymal transition (EMT) (upregulation of E-cadherin and β-catenin and downregulation of Snail and fibronectin), whereas forced overexpression of FGFR1 induced brachyury expression and promoted EMT in lung cancer cells. Activation of fibroblast growth factor (FGF)/FGFR1 signaling promoted phosphorylated MAPK extracellular signal-regulated kinase (ERK) 1/2 translocation from cytoplasm to nucleus, upregulated brachyury expression, and increased cell growth and invasion. In addition, human lung cancer cells with higher brachyury expression were more sensitive to inhibitors targeting FGFR1/MAPK pathway. These findings suggest that FGFR1/MAPK may be important for brachyury activation in lung cancer, and this pathway may be an appealing therapeutic target for a subset of brachyury-driven lung cancer.

Concomitant inhibition of receptor tyrosine kinases and downstream AKT synergistically inhibited growth of KRAS/BRAF mutant colorectal cancer cells

Receptor tyrosine kinase (RTK) signaling pathways are frequently activated in cancer cells due to mutations of RTKs and/or their downstream signaling proteins such as KRAS and BRAF. About 40% colorectal cancers (CRCs) contain KRAS or BRAF mutant genes and are resistant to treatments with individual inhibitors of RTKs, AKT, MEK, or BRAF. Therefore, an understanding of the molecular mechanisms of the drug resistance is necessary for developing effective strategies to treat the diseases. Here we report the discovery of an AKT/ERK reactivation mechanism that account for the cancer cell resistance to the AKT and MEK inhibitors treatments. The reactivations of AKT and ERK after the AKT or MEK inhibitor treatment were caused by a relief of an AKT or ERK-mediated feedback inhibition of the RTKs and/or their downstream pathways. A combination of RTK inhibitors, based on the RTK activation/phosphorylation profile, synergized with the AKT inhibitor, but not the MEK inhibitor, to completely inhibit the AKT phosphorylation and to block the growth of KRAS/BRAF mutant CRC cells. These results underscored the importance of AKT and the AKT feedback signaling to cancer cell growth and offered a novel therapeutic approach for the treatment of KRAS/BRAF mutant CRC cells.

From Chemotherapy to Combined Targeted Therapeutics: In Vitro and in Vivo Models to Decipher Intra-tumor Heterogeneity

Recent advances in next-generation sequencing and other omics technologies capable to map cell fate provide increasing evidence on the crucial role of intra-tumor heterogeneity (ITH) for cancer progression. The different facets of ITH, from genomic to microenvironmental heterogeneity and the hierarchical cellular architecture originating from the cancer stem cell compartment, contribute to the range of tumor phenotypes. Decoding these complex data resulting from the analysis of tumor tissue complexity poses a challenge for developing novel therapeutic strategies that can counteract tumor evolution and cellular plasticity. To achieve this aim, the development of in vitro and in vivo cancer models that resemble the complexity of ITH is crucial in understanding the interplay of cells and their (micro)environment and, consequently, in testing the efficacy of new targeted treatments and novel strategies of tailoring combinations of treatments to the individual composition of the tumor. This challenging approach may be an important cornerstone in overcoming the development of pharmaco-resistances during multiple lines of treatment. In this paper, we report the latest advances in patient-derived 3D (PD3D) cell cultures and patient-derived tumor xenografts (PDX) as in vitro and in vivo models that can retain the genetic and phenotypic heterogeneity of the tumor tissue.

Micropharmacology: An In Silico Approach for Assessing Drug Efficacy Within a Tumor Tissue

Systemic chemotherapy is one of the main anticancer treatments used for most kinds of clinically diagnosed tumors. However, the efficacy of these drugs can be hampered by the physical attributes of the tumor tissue, such as tortuous vasculature, dense and fibrous extracellular matrix, irregular cellular architecture, tumor metabolic gradients, and non-uniform expression of the cell membrane receptors. This can impede the transport of therapeutic agents to tumor cells in sufficient quantities. In addition, tumor microenvironments undergo dynamic spatio-temporal changes during tumor progression and treatment, which can also obstruct drug efficacy. To examine ways to improve drug delivery on a cell-to-tissue scale (single-cell pharmacology), we developed the microscale pharmacokinetics/pharmacodynamics (microPKPD) modeling framework. Our model is modular and can be adjusted to include only the mathematical equations that are crucial for a biological problem under consideration. This modularity makes the model applicable to a broad range of pharmacological cases. As an illustration, we present two specific applications of the microPKPD methodology that help to identify optimal drug properties. The hypoxia-activated drugs example uses continuous drug concentrations, diffusive-advective transport through the tumor interstitium, and passive transmembrane drug uptake. The targeted therapy example represents drug molecules as discrete particles that move by diffusion and actively bind to cell receptors. The proposed modeling approach takes into account the explicit tumor tissue morphology, its metabolic landscape and/or specific receptor distribution. All these tumor attributes can be assessed from patients' diagnostic biopsies; thus, the proposed methodology can be developed into a tool suitable for personalized medicine, such as neoadjuvant chemotherapy.

Unearthing new genomic markers of drug response by improved measurement of discriminative power

Background

Oncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs. Screening these drugs against a large panel of cancer cell lines has led to the discovery of new genomic markers of in vitro drug response. However, while the identification of such markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.

Methods

Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers.

Results

The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.

Conclusions

Discovering markers using more than one statistical test and testing them on independent data is unusual. We found this helpful to discard statistically significant drug-gene associations that were actually spurious correlations. This approach also revealed new, independently validated, in vitro markers of drug response such as Temsirolimus-CDKN2A (resistance) and Gemcitabine-EWS_FLI1 (sensitivity).

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0336-z) contains supplementary material, which is available to authorized users.

Molecular Tumor Boards: Ethical Issues in the New Era of Data Medicine

The practice and development of modern medicine requires large amounts of data, particularly in the domain of cancer. The future of personalized medicine lies neither with "genomic medicine" nor with "precision medicine", but with "data medicine" (DM) (big data, data mining). The establishment of this DM has required far-reaching changes, to establish four essential elements connecting patients and doctors: biobanks, databases, bioinformatic platforms and genomic platforms. The "transformation" of scientific research areas, such as genetics, bioinformatics and biostatistics, into clinical specialties has generated a new vision of care. Molecular tumor boards (MTB) are one response to these changes and are now providing better access to next-generation sequencing (NGS) and new cancer treatments to patients with inoperable or metastatic cancers, and those for whom the usual treatment has failed. However, MTB face a crucial ethical challenge: maintaining and improving the trust of patients, clinicians, researchers and industry in academic medical centers supported by private or public funding rather than providing genetic data directly to private companies. We believe that, in this era of DM, appropriate modern digital communication networks will be required to maintain this trust and to improve the organization and effectiveness of the system. There is, therefore, a need to reconsider the form and content of informed consent (IC) documents at all academic medical centers and to introduce dynamic and electronic informed consent (e-IC).

Cationic Polymeric Nanoparticle Delivering CCR2 siRNA to Inflammatory Monocytes for Tumor Microenvironment Modification and Cancer Therapy

Accumulating evidence has confirmed that malignant tumors have a complex microenvironment, which consists of a heterogeneous collection of tumor cells and other cell subsets (including the full gamut of immune cells). Tumor-associated macrophages (TAMs), derived from circulating Ly6C^hi monocytes, constitute the most substantial fraction of tumor-infiltrating immune cells in nearly all cancer types and contribute to tumor progression, vascularization, metastasis, immunosuppression, and therapeutic resistance. Interrupting monocyte recruitment to tumor tissues by disturbing pivotal signaling pathways (such as CCL2-CCR2) is viewed as one of the most promising avenues for tumor microenvironment manipulation and cancer therapy. One critical issue for monocyte-based therapy is to deliver therapeutic agents into monocytes efficiently. In the present study, we systematically investigated the relationship between the surface potential and the biodistribution of polymeric nanoparticles in monocytes in vivo, aiming to screen and identify an appropriate delivery system for monocyte targeting, and we found that cationic nanoparticles have a higher propensity to accumulate in monocytes compared with their neutral counterparts. We further demonstrated that siCCR2-encapsulated cationic nanoparticle (CNP/siCCR2) could modify immunosuppressive tumor microenvironment more efficiently and exhibit superior antitumor effect in an orthotopic murine breast cancer model.

Inhibiting β-Catenin by β-Carboline-Type MDM2 Inhibitor for Pancreatic Cancer Therapy

The β-catenin and MDM2 oncoproteins are overexpressed and constitutively activated in human pancreatic cancer and contribute to its initiation, progression, and metastasis. The Wnt/β-catenin signaling pathway strongly interacts with the MDM2-p53 signaling pathway, accelerating the tumorigenesis and its development. Therefore, therapies inhibiting both β-catenin and MDM2 are suggested to be ideal treatments for patients with advanced pancreatic cancer. We have recently identified a novel class of β-carboline compounds as the specific and potent MDM2 inhibitors, including a lead compound SP141. In the present study, we utilized SP141 as an exemplary β-carboline compound to characterize β-catenin as a molecular target of the β-carboline compounds and to demonstrate an important role of β-catenin in the anticancer activity of β-carboline. We found that the silencing of either β-catenin or MDM2 largely reduced the anticancer activity of SP141 while the double silencing of both genes almost completely blocked SP141’s activity. SP141 directly bound to β-catenin and inhibited its expression and activity in pancreatic cancer cells in vitro and in vivo. The inhibitory effects of SP141 on β-catenin were mediated by the ubiquitin–proteasome system in an MDM2-independent manner. In conclusion, these results suggest that SP141 exerts its anticancer activity by dually inhibiting β-catenin and MDM2. We envision that β-carboline derivatives can be developed as promising dual inhibitors of β-catenin and MDM2 for the treatment of advanced pancreatic cancer.

Expression of HNF4G and its potential functions in lung cancer

The hepatocyte nuclear factor 4 gamma (HNF4G), a member of orphan nuclear receptors, is up-regulated and functions as an oncoprotein in bladder cancer. In the present study, we observed that HNF4G expression was elevated in lung cancer tissues as compared to adjacent normal lung tissues. The expression of HNF4G protein was correlated with the tumor size and the prognosis of patients. Transfection with a small interference RNA (siRNA) targeting HNF4G in two lung cancer cell lines (H358 and H292 cells) significantly inhibited cell proliferation via arresting cells at G1 phase and inducing cell apoptosis. In addition, HNF4G siRNA reduced cell proliferation in a xenograft tumor-bearing model. Moreover, A549 cells, which had relative lower level of HNF4G, were ectopic expressed with HNF4G and treated with an AKT inhibitor (MK-2206). MK-2206 exposure not only attenuated the promoting effects of HNF4G overexpression on cell proliferation and cell cycle progression, but also suppressed the inhibitory effects of HNF4G overexpression on cell apoptosis. These data suggested that AKT signaling pathway was a potential upstream mediator of HNF4G. Collectively, our data indicate that HNF4G exerts as an oncogenic role in lung cancer by promoting cell proliferation and that HNF4G expression is a potential prognosis factor for lung cancer.

Bad phosphorylation as a target of inhibition in oncology

Bcl-2 agonist of cell death (BAD) is a BH3-only member of the Bcl-2 family which possesses important regulatory function in apoptosis. BAD has also been shown to possess many non-apoptotic functions closely linked to cancer including regulation of glycolysis, autophagy, cell cycle progression and immune system development. Interestingly, BAD can be either pro-apoptotic or pro-survival depending on the phosphorylation state of three specific serine residues (human S75, S99 and S118). Expression of BAD and BAD phosphorylation patterns have been shown to influence tumor initiation and progression and play a predictive role in disease prognosis, drug response and chemosensitivity in various cancers. This review aims to summarize the current evidence on the functional role of BAD phosphorylation in human cancer and evaluate the potential utility of modulating BAD phosphorylation in cancer.

Neuro-ophthalmic side effects of molecularly targeted cancer drugs

The past two decades has been an amazing time in the advancement of cancer treatment. Molecularly targeted therapy is a concept in which specific cellular molecules (overexpressed, mutationally activated, or selectively expressed proteins) are manipulated in an advantageous manner to decrease the transformation, proliferation, and/or survival of cancer cells. In addition, increased knowledge of the role of the immune system in carcinogenesis has led to the development of immune checkpoint inhibitors to restore and enhance cellular-mediated antitumor immunity. The United States Food and Drug Administration approval of the chimeric monoclonal antibody (mAb) rituximab in 1997 for the treatment of B cell non-Hodgkin lymphoma ushered in a new era of targeted therapy for cancer. A year later, trastuzumab, a humanized mAb, was approved for patients with breast cancer. In 2001, imatinib was the first small-molecule kinase inhibitor approved. The approval of ipilimumab-the first in class immune checkpoint inhibitor-in 2011 serves as a landmark period of time in the resurgence of immunotherapy for cancer. Despite the notion that increased tumor specificity results in decreased complications, toxicity remains a major hurdle in the development and implementation of many of the targeted anticancer drugs. This article will provide an overview of the current cellular and immunological understanding of cancer pathogenesis-the foundation upon which molecularly targeted therapies were developed-and a description of the ocular and neuro-ophthalmic toxicity profile of mAbs, immune checkpoint inhibitors, and small-molecule kinase inhibitors.

Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours

Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models.

Short-term spheroid culture of primary colorectal cancer cells as an in vitro model for personalizing cancer medicine

Chemotherapy treatment of cancer remains a challenge due to the molecular and functional heterogeneity displayed by tumours originating from the same cell type. The pronounced heterogeneity makes it difficult for oncologists to devise an effective therapeutic strategy for the patient. One approach for increasing treatment efficacy is to test the chemosensitivity of cancer cells obtained from the patient’s tumour. 3D culture represents a promising method for modelling patient tumours in vitro. The aim of this study was therefore to evaluate how closely short-term spheroid cultures of primary colorectal cancer cells resemble the original tumour. Colorectal cancer cells were isolated from human tumour tissue and cultured as spheroids. Spheroid cultures were established with a high success rate and remained viable for at least 10 days. The spheroids exhibited significant growth over a period of 7 days and no difference in growth rate was observed for spheroids of different sizes. Comparison of spheroids with the original tumour revealed that spheroid culture generally preserved adenocarcinoma histology and expression patterns of cytokeratin 20 and carcinoembryonic antigen. Interestingly, spheroids had a tendency to resemble tumour protein expression more closely after 10 days of culture compared to 3 days. Chemosensitivity screening using spheroids from five patients demonstrated individual response profiles. This indicates that the spheroids maintained patient-to-patient differences in sensitivity towards the drugs and combinations most commonly used for treatment of colorectal cancer. In summary, short-term spheroid culture of primary colorectal adenocarcinoma cells represents a promising in vitro model for use in personalized medicine.

The Role of Kinase Modulators in Cellular Senescence for Use in Cancer Treatment

Recently, more than 30 small molecules and eight monoclonal antibodies that modulate kinase signaling have been approved for the treatment of several pathological conditions, including cancer, idiopathic pulmonary fibrosis, and rheumatoid arthritis. Among them, kinase modulators have been a primary focus for use in cancer treatment. Cellular senescence is believed to protect cells from tumorigenesis by irreversibly halting cell cycle progression and avoiding the growth of damaged cells and tissues. Senescence can also contribute to tumor suppression and be utilized as a mechanism by anti-cancer agents. Although the role of kinase modulators in cancer treatment and their effects on senescence in tumor development have been extensively studied, the relationship between kinase modulators for cancer treatment and senescence has not been fully discussed. In this review, we discuss the pro- and anti-tumorigenesis functions of senescence and summarize the key roles of kinase modulators in the regulation of senescence against tumors.

Topography-Guided Control of Local Migratory Behaviors and Protein Expression of Cancer Cells

In vivo cancer cell migration and invasion are directed by biophysical guidance mechanisms such as pre-existing microtracks and basement membrane extracellular matrices. Here, this paper reports the correlation of the local migratory behavior of cancer cells and the biochemical signal expression using the topography that can guide or inhibit cell behaviors. To this end, the local apparent migration and the protein expression level are investigated with respect to the topographical feature size (flat, nanoline, and microline) and orientation (microline, microconcentric, and microradial) with the collectively migrating (A431) and individually migrating (MDA-MB-231 and U-87-MG) cancer cells. The results show that the migration and the protein expression of focal adhesion kinase, rho-associated protein kinase, and extracellular signal-regulated kinase are localized in the periphery of cell colony. Furthermore, the inhibition of migratory behavior at the periphery recues the protein expression, while the guidance of migration enhances the aforementioned protein expression. The results may imply the employ of biophysical inhibitory factors can help to control invasiveness of cancer cells during the progression state.

FGFR Family Members Protein Expression as Prognostic Markers in Oral Cavity and Oropharyngeal Squamous Cell Carcinoma

Introduction

Fibroblast growth factor receptor family member proteins (FGFR1–4) have been identified as promising novel therapeutic targets and prognostic markers in a wide spectrum of solid tumors. The present study investigates the expression and prognostic value of four FGFR family member proteins in a large multicenter oral cavity squamous cell carcinoma (OCSCC) and oropharyngeal squamous cell carcinoma (OPSCC) cohort.

Methods

Protein expression of FGFR1–4 was determined by immunohistochemistry on tissue microarrays containing 951 formalin-fixed paraffin embedded OCSCC and OPSCC tissues from the University Medical Center Utrecht and University Medical Center Groningen. Protein expression was correlated to overall survival using Cox regression models, and bootstrapping was performed as internal validation.

Results

FGFR proteins were highly expressed in 39–64 % of OCSCC and 63–79 % of OPSCC. Seventy-three percent (299/412) of OCSCC and 85 % (305/357) of OPSCC highly co-expressed two or more FGFR family member proteins. FGFR1 protein was more frequently highly expressed in human papillomavirus (HPV)-negative OPSCC than HPV-positive OPSCC (82 vs. 65 %; p = 0.008). Furthermore, protein expression of FGFR family members was not related to overall survival in OCSCC or OPSCC (p > 0.05).

Conclusion

FGFR family members are frequently highly expressed in OCSCC and OPSCC. These FGFR family member proteins are therefore potential targets for novel therapies that are urgently required to improve survival of OCSCC and OPSCC patients.

Electronic supplementary material

The online version of this article (doi:10.1007/s40291-016-0204-5) contains supplementary material, which is available to authorized users.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug's efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

Clonal cooperativity in heterogenous cancers

Tumor heterogeneity is a major obstacle to the development of effective therapies and is thus an important focus of cancer research. Genetic and epigenetic alterations, as well as altered tumor microenvironments, result in tumors made up of diverse subclones with different genetic and phenotypic characteristics. Intratumor heterogeneity enables competition, but also supports clonal cooperation via cell-cell contact or secretion of factors, resulting in enhanced tumor progression. Here, we summarize recent findings related to interclonal interactions within a tumor and the therapeutic implications of such interactions, with an emphasis on how different subclones collaborate with each other to promote proliferation, metastasis and therapy-resistance. Furthermore, we propose that disruption of clonal cooperation by targeting key factors (such as Wnt and Hedgehog, amongst others) can be an alternative approach to improving clinical outcomes.

Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes

Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target mutated genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly mutated genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.

Bisacodyl and its cytotoxic activity on human glioblastoma stem-like cells. Implication of inositol 1,4,5-triphosphate receptor dependent calcium signaling

Glioblastoma is the most common malignant brain tumor. The heterogeneity at the cellular level, metabolic specificities and plasticity of the cancer cells are a challenge for glioblastoma treatment. Identification of cancer cells endowed with stem properties and able to propagate the tumor in animal xenografts has opened a new paradigm in cancer therapy. Thus, to increase efficacy and avoid tumor recurrence, therapies need to target not only the differentiated cells of the tumor mass, but also the cancer stem-like cells. These therapies need to be effective on cells present in the hypoxic, slightly acidic microenvironment found within tumors. Such a microenvironment is known to favor more aggressive undifferentiated phenotypes and a slow-growing "quiescent state" that preserves the cells from chemotherapeutic agents, which mostly target proliferating cells. Based on these considerations, we performed a differential screening of the Prestwick Chemical Library of approved drugs on both proliferating and quiescent glioblastoma stem-like cells and identified bisacodyl as a cytotoxic agent with selectivity for quiescent glioblastoma stem-like cells. In the present study we further characterize bisacodyl activity and show its efficacy in vitro on clonal macro-tumorospheres, as well as in vivo in glioblastoma mouse models. Our work further suggests that bisacodyl acts through inhibition of Ca²⁺ release from the InsP3 receptors.

Are sigma modulators an effective opportunity for cancer treatment? A patent overview (1996-2016)

INTRODUCTION

Although several molecular targets against cancer have been identified, there is a continuous need for new therapeutic strategies. Sigma Receptors (SRs) overexpression has been recently associated with different cancer conditions. Therefore, novel anticancer agents targeting SRs may increase the specificity of therapies, overcoming some of the common drawbacks of conventional chemotherapy. Areas covered: The present review focuses on patent documents disclosing SR modulators with possible application in cancer therapy and diagnosis. The analysis reviews patents of the last two decades (1996-2016); patents were grouped according to target subtypes (S1R, S2R, pan-SRs) and relevant Applicants. The literature was searched through Espacenet, ISI Web, PatentScope and PubMed databases. Expert opinion: The number of patents related to SRs and cancer has increased in the last twenty years, confirming the importance of this receptor family as valuable target against neoplasias. Despite their short history in the cancer scenario, many SR modulators are at pre-clinical stage and one is undergoing a phase II clinical trial. SRs ligands may represent a powerful source of innovative antitumor therapeutics. Further investigation is needed for validating SR modulators as anti-cancer drugs. We strongly hope that this review could stimulate the interest of both Academia and pharmaceutical companies.

A METHYLATION-TO-EXPRESSION FEATURE MODEL FOR GENERATING ACCURATE PROGNOSTIC RISK SCORES AND IDENTIFYING DISEASE TARGETS IN CLEAR CELL KIDNEY CANCER

Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration captures relationships in the data that represent highly disease-relevant functions.

Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data. Methods: Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC ₅₀ measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than standard k-fold cross-validation. Results and Discussion: Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by the multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG. Conclusions: Thanks to this unbiased validation, we now know that this type of models can predict in vitro tumour response to some of these drugs. These models can thus be further investigated on in vivo tumour models. R code to facilitate the construction of alternative machine learning models and their validation in the presented benchmark is available at http://ballester.marseille.inserm.fr/gdsc.transcriptomicDatav2.tar.gz.

Systematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data

Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data. Methods: Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC ₅₀ measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than standard k-fold cross-validation. Results and Discussion: Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by the multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG. Conclusions: Thanks to this unbiased validation, we now know that this type of models can predict in vitro tumour response to some of these drugs. These models can thus be further investigated on in vivo tumour models. R code to facilitate the construction of alternative machine learning models and their validation in the presented benchmark is available at http://ballester.marseille.inserm.fr/gdsc.transcriptomicDatav2.tar.gz.

Genetically engineered and self-assembled oncolytic protein nanoparticles for targeted cancer therapy

The integration of a targeted delivery with a tumour-selective agent has been considered an ideal platform for achieving high therapeutic efficacy and negligible side effects in cancer therapy. Here, we present engineered protein nanoparticles comprising a tumour-selective oncolytic protein and a targeting moiety as a new format for the targeted cancer therapy. Apoptin from chicken anaemia virus (CAV) was used as a tumour-selective apoptotic protein. An EGFR-specific repebody, which is composed of LRR (Leucine-rich repeat) modules, was employed to play a dual role as a tumour-targeting moiety and a fusion partner for producing apoptin nanoparticles in E. coli, respectively. The repebody was genetically fused to apoptin, and the resulting fusion protein was shown to self-assemble into supramolecular repebody-apoptin nanoparticles with high homogeneity and stability as a soluble form when expressed in E. coli. The repebody-apoptin nanoparticles showed a remarkable anti-tumour activity with negligible side effects in xenograft mice through a cooperative action of the two protein components with distinct functional roles. The repebody-apoptin nanoparticles can be developed as a systemic injectable and tumour-selective therapeutic protein for targeted cancer treatment.

Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?

Background

Cancer is first and foremost a disease of the genome. Specific genetic signatures within a tumour are prognostic of disease outcome, reflect subclonal architecture and intratumour heterogeneity, inform treatment choices and predict the emergence of resistance to targeted therapies. Minimally invasive liquid biopsies can give temporal resolution to a tumour’s genetic profile and allow the monitoring of treatment response through levels of circulating tumour DNA (ctDNA). However, the detection of ctDNA in repeated liquid biopsies is currently limited by economic and time constraints associated with targeted sequencing.

Methods

Here we bioinformatically profile the mutational and copy number spectrum of The Cancer Genome Network’s lung adenocarcinoma dataset to uncover recurrently mutated genomic loci.

Results

We build a panel of 400 hotspot mutations and show that the coverage extends to more than 80% of the dataset at a median depth of 8 mutations per patient. Additionally, we uncover several novel single-nucleotide variants present in more than 5% of patients, often in genes not commonly associated with lung adenocarcinoma.

Conclusion

With further optimisation, this hotspot panel could allow molecular diagnostics laboratories to build curated primer banks for ‘off-the-shelf’ monitoring of ctDNA by droplet-based digital PCR or similar techniques, in a time- and cost-effective manner.

Targeting Cancer Metabolism: Dietary and Pharmacologic Interventions

Most tumors display oncogene-driven reprogramming of several metabolic pathways, which are crucial to sustain their growth and proliferation. In recent years, both dietary and pharmacologic approaches that target deregulated tumor metabolism are beginning to be considered for clinical applications. Dietary interventions exploit the ability of nutrient-restricted conditions to exert broad biological effects, protecting normal cells, organs, and systems, while sensitizing a wide variety of cancer cells to cytotoxic therapies. On the other hand, drugs targeting enzymes or metabolites of crucial metabolic pathways can be highly specific and effective, but must be matched with a responsive tumor, which might rapidly adapt. In this review, we illustrate how dietary and pharmacologic therapies differ in their effect on tumor growth, proliferation, and metabolism and discuss the available preclinical and clinical evidence in favor of or against each of them. We also indicate, when appropriate, how to optimize future investigations on metabolic therapies on the basis of tumor- and patient-related characteristics.

SIGNIFICANCE

To our knowledge, this is the first review article that comprehensively analyzes the preclinical and preliminary clinical experimental foundations of both dietary and pharmacologic metabolic interventions in cancer therapy. Among several promising therapies, we propose treatment personalization on the basis of tumor genetics, tumor metabolism, and patient systemic metabolism.Cancer Discov; 6(12); 1315-33. ©2016 AACR.

CASTIN: a system for comprehensive analysis of cancer-stromal interactome

BACKGROUND

Cancer microenvironment plays a vital role in cancer development and progression, and cancer-stromal interactions have been recognized as important targets for cancer therapy. However, identifying relevant and druggable cancer-stromal interactions is challenging due to the lack of quantitative methods to analyze whole cancer-stromal interactome.

RESULTS

We present CASTIN (CAncer-STromal INteractome analysis), a novel framework for the evaluation of cancer-stromal interactome from RNA-Seq data using cancer xenograft models. For each ligand-receptor interaction which is derived from curated protein-protein interaction database, CASTIN summarizes gene expression profiles of cancer and stroma into three evaluation indices. These indices provide quantitative evaluation and comprehensive visualization of interactome, and thus enable to identify critical cancer-microenvironment interactions, which would be potential drug targets. We applied CASTIN to the dataset of pancreas ductal adenocarcinoma, and successfully characterized the individual cancer in terms of cancer-stromal relationships, and identified both well-known and less-characterized druggable interactions.

CONCLUSIONS

CASTIN provides comprehensive view of cancer-stromal interactome and is useful to identify critical interactions which may serve as potential drug targets in cancer-microenvironment. CASTIN is available at: http://github.com/tmd-gpat/CASTIN .

The challenges of tumor genetic diversity

The authors review and discuss the implications of genomic analyses documenting the diversity of tumors, both among patients and within individual tumors. Genetic diversity among solid tumors limits targeted therapies, because few mutations that drive tumors are both targetable and at high prevalence. Many more driver mutations and how they affect cellular signaling pathways must be identified if targeted therapy is to become widely useful. Genetic diversity within a tumor-intratumor genetic heterogeneity-makes the tumor a collection of subclones: related yet distinct cancers. Selection for pre-existing, resistant subclones by conventional or targeted therapies may explain many treatment failures. Immune therapy faces the same fundamental challenges. Nevertheless, the processes that generate and maintain heterogeneity might provide novel therapeutic targets. Addressing both types of diversity requires genomic tumor analyses linked to detailed clinical data. The trend toward sequencing restricted cancer gene panels, however, limits the ability to discover new driver mutations and assess intratumor heterogeneity. Clinical data currently collected with genomic analyses often lack critical information, substantially limiting their use in understanding tumor diversity. Now that diversity among and within tumors can no longer be ignored, research and clinical practice must adapt to take diversity into account. Cancer 2017;123:917-27. © 2016 American Cancer Society.