Circumventing cancer drug resistance in the era of personalized medicine

Not Everyone Fits the Mold: Intratumor and Intertumor Heterogeneity and Innovative Cancer Drug Design and Development

Disruptive innovations in medicine are game-changing in nature and bring about radical shifts in the way we understand human diseases, their treatment, and/or prevention. Yet, disruptive innovations in cancer drug design and development are still limited. Therapies that cure all cancer patients are in short supply or do not exist at all. Chief among the causes of this predicament is drug resistance, a mechanism that is much more dynamic than previously understood. Drug resistance has limited the initial success experienced with biomarker-guided targeted therapies as well. A major contributor to drug resistance is intratumor heterogeneity. For example, within solid tumors, there are distinct subclones of cancer cells, presenting profound complexity to cancer treatment. Well-known contributors to intratumor heterogeneity are genomic instability, the microenvironment, cellular genotype, cell plasticity, and stochastic processes. This expert review explains that for oncology drug design and development to be more innovative, we need to take into account intratumor heterogeneity. Initially thought to be the preserve of cancer cells, recent evidence points to the highly heterogeneous nature and diverse locations of stromal cells, such as cancer-associated fibroblasts (CAFs) and cancer-associated macrophages (CAMs). Distinct subpopulations of CAFs and CAMs are now known to be located immediately adjacent and distant from cancer cells, with different subpopulations exerting different effects on cancer cells. Disruptive innovation and precision medicine in clinical oncology do not have to be a distant reality, but can potentially be achieved by targeting these spatially separated and exclusive cancer cell subclones and CAF subtypes. Finally, we emphasize that disruptive innovations in drug discovery and development will likely come from drugs whose effect is not necessarily tumor shrinkage.

Monitoring of Response and Resistance in Plasma of EGFR-Mutant Lung Cancer Using Droplet Digital PCR

The identification of oncogenic driver mutations has led to the rapid rise of genotype-directed treatments. However, genetic analysis of tumors remains cumbersome and a morbid experience for patients. Noninvasive assessment of tumor genotype, so-called "liquid biopsy," such as plasma genotyping represents a potentially transformative tool. Here we describe a genotyping protocol of cell-free plasma DNA (cfDNA) using Droplet Digital™ PCR (ddPCR™). ddPCR emulsifies DNA into ~20,000 droplets in which PCR is performed to endpoint in each droplet for both mutant and wild-type DNA. Droplets are run through a modified flow cytometer where mutant and wild-type DNA emit different colored signals. The count of these signals upon Poisson distribution analysis allows sensitive quantification of allelic prevalence.

Dynamic Targeting in Cancer Treatment

With the advent of personalized medicine, design and development of anti-cancer drugs that are specifically targeted to individual or sets of genes or proteins has been an active research area in both academia and industry. The underlying motivation for this approach is to interfere with several pathological crosstalk pathways in order to inhibit or at the very least control the proliferation of cancer cells. However, after initially conferring beneficial effects, if sub-lethal, these artificial perturbations in cell function pathways can inadvertently activate drug-induced up- and down-regulation of feedback loops, resulting in dynamic changes over time in the molecular network structure and potentially causing drug resistance as seen in clinics. Hence, the targets or their combined signatures should also change in accordance with the evolution of the network (reflected by changes to the structure and/or functional output of the network) over the course of treatment. This suggests the need for a "dynamic targeting" strategy aimed at optimizing tumor control by interfering with different molecular targets, at varying stages. Understanding the dynamic changes of this complex network under various perturbed conditions due to drug treatment is extremely challenging under experimental conditions let alone in clinical settings. However, mathematical modeling can facilitate studying these effects at the network level and beyond, and also accelerate comparison of the impact of different dosage regimens and therapeutic modalities prior to sizeable investment in risky and expensive clinical trials. A dynamic targeting strategy based on the use of mathematical modeling can be a new, exciting research avenue in the discovery and development of therapeutic drugs.

Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning

Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation. This approach can be potentially applied towards prediction of drug-resistant phenotypes prior to chemotherapy.

New insights into RAS biology reinvigorate interest in mathematical modeling of RAS signaling

RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.

Cancer Immunotherapy: The Dawn of Antibody Cocktails

Since the approval of the first monoclonal antibody (mAb), rituximab, for hematological malignancies, almost 30 additional mAbs have been approved in oncology. Despite remarkable advances, relatively weak responses and resistance to antibody monotherapy remain major open issue. Overcoming resistance might require combinations of drugs blocking both the major target and the emerging secondary target. We review clinically approved combinations of antibodies and either cytotoxic regimens (chemotherapy and irradiation) or kinase inhibitors. Thereafter, we focus on the most promising and currently very active arena that combines mAbs inhibiting immune checkpoints or growth factor receptors. Clinically approved and experimental oligoclonal mixtures of mAbs targeting different antigens (hetero-combinations) or different epitopes of the same antigen (homo-combinations) are described. Effective oligoclonal mixtures of antibodies that mimic the polyclonal immune response will likely become a mainstay of cancer therapy.

A near infrared light-triggered human serum albumin drug delivery system with coordination bonding of indocyanine green and cisplatin for targeting photochemistry therapy against oral squamous cell cancer

To ensure site–specific drug release in tumor cells and cancer-associated fibroblasts and reduce the systemic toxicity of chemotherapy, a novel drug delivery system called human serum albumin-indocyanine green-cisplatin nanoparticles was developed.

Patient-derived xenograft mouse models: A high fidelity tool for individualized medicine

Patient-derived xenograft (PDX) mouse models involve the direct transfer of fresh human tumor samples into immunodeficient mice following surgical resection or other medical operations. Gene expression in tumors may be maintained by serial passages of tumors from mouse to mouse. These models aid research into tumor biology and pharmacology without manual manipulation of cell cultures in vitro. and are widely used in individualized cancer therapy/translational medicine, drug development and coclinical trials. PDX models exhibit higher predictive values for clinical outcomes than cell line-derived xenograft models and genetically engineered mouse models. However, PDX models are associated with certain challenges in clinical application. The present study reviewed current collections of PDX models and assessed the challenges and future directions of this field.

Prolonged MEK inhibition leads to acquired resistance and increased invasiveness in KRAS mutant gastric cancer

Gastric cancer (GC) is one of the most common causes of cancer-associated death. However, traditional therapeutic strategies have failed to significantly improve the survival of patient with advanced GC. While KRAS mutations have been found in some patients with gastric cancer, an effective therapy to treat KRAS-driven gastric cancer has not been established yet. To provide a rationale for clinical application of kinase inhibitors targeting RAS pathways, we first determined the sensitivity of GC cell lines harboring KRAS mutations or amplification to RAS pathway inhibitors. We found that MAPK pathway inhibitors (MEKi and ERKi) were more effective than AKT inhibitor, suggesting that KRAS-driven gastric cancer cells are dependent on MAPK pathway for survival. Further, we established a KRAS mutant GC cell line with acquired resistance to MEK inhibitors in order to mimic clinical situation of kinase inhibitor resistance. A comprehensive analysis of tyrosine phosphorylation in receptor tyrosine kinases in combination with small molecule chemical library screening revealed upregulated c-MET phosphorylation in this resistance cell line with elevated sensitivity to c-MET TKI (crizotinib) and PI3K/mTOR dual inhibitor (BEZ235). We also showed that migration and invasion of resistant cells were promoted, and crizotinib and BEZ235 could inhibit this malignant phenotype. Overall, our results indicate that prolonged MAPK pathway inhibition could result in acquired resistance which is associated with increased malignant phenotype in KRAS mutant GC and pharmacological targeting c-MET and PI3K/mTOR could overcome this problem.

TdIF1: a putative oncogene in NSCLC tumor progression

TdT-interacting factor 1 (TdIF1) is a ubiquitously expressed DNA- and protein-binding protein that directly binds to terminal deoxynucleotidyl transferase (TdT) polymerase. Little is known about the functional role of TdIF1 in cancer cellular signaling, nor has it previously been identified as aberrant in any type of cancer. We report here for the first time that TdIF1 is abundantly expressed in clinical lung cancer patients and that high expression of TdIF1 is associated with poor patient prognosis. We further established that TdIF1 is highly expressed in human non-small cell lung cancer (NSCLC) cell lines compared to a normal lung cell line. shRNA-mediated gene silencing of TdIF1 resulted in the suppression of proliferation and anchorage-independent colony formation of the A549 adenocarcinoma cell line. Moreover, when these TdIF1-silenced cells were used to establish a mouse xenograft model of human NSCLC, tumor size was greatly reduced. These data suggest that TdIF1 is a potent regulator of lung tumor development. Several cell cycle-related and tumor growth signaling pathways, including the p53 and HDAC1/2 pathways, were identified as participating in the TdIF1 signaling network by in silico analysis. Microarray, transcriptome and protein-level analyses validated p53 and HDAC1/2 modulation upon TdIF1 downregulation in an NSCLC cellular model. Moreover, several other cell cycle regulators were affected at the transcript level by TdIF1 silencing, including an increase in CDKN1A/p21 transcripts. Taken together, these results indicate that TdIF1 is a bona fide tumor-promoting factor in NSCLC and a potential target for therapy.

A protein involved in the immune system also plays a role in the most common type of lung cancer. Weiping Min, of the University of Western Ontario in Canada, and international colleagues found, for the first time, that the protein TdIF1 is significantly upregulated in non-small cell lung cancer (NSCLC) tissues in patients. High expression levels of this protein were correlated with poor prognosis. NSCLC tumor tissues grown in mice where TdIF1 expression was ‘knocked down’ were significantly smaller than in those without TdIF1 knockdown. Further analyses showed the protein was involved in known cell signaling pathways with roles in NSCLC progression. The findings indicate TdIF1 should be further investigated as a biomarker of NSCLC or as a molecular target for its treatment.

Predicting Tumor Sensitivity to Chemotherapeutic Drugs in Oral Squamous Cell Carcinoma Patients

Oral Squamous Cell Carcinoma (OSCC) patients respond poorly to chemotherapy. We analyzed the expression of 11 drug response-related genes in 31 OSCC biopsies, collected prior to any treatment, using custom-designed PCR array. Further, we investigated the drug response pattern of selected anticancer drugs by BH3 (Bcl2 Homology-3) profiling in the primary cells isolated from OSCC tissues. Then, we correlated the results of drug-response gene expression pattern with apoptotic priming to predict tumor response to chemotherapy. The best performing drug (BPD) and response differences (RD) between the drugs were identified using statistical methods to select the best choice of drug in a personalized manner. Based on the correlation, we classified OSCC tumors as sensitive (13 tumors), moderately responsive (16 tumors) or resistant (2 tumors) to chemotherapy. We found that up-regulation of genes linked with drug resistance facilitates survival of tumor samples, which was revealed by the percentage of apoptotic priming. Moreover, we found that paclitaxel-induced 40–45% apoptotic priming compared to other drugs. Average response difference (RD) analysis showed that 80% of tumors responded well to paclitaxel as compared to other drugs studied. Therefore, gene expression analysis with BH3 profiling reveals drug sensitivity that could be translated for drug selection before treatment.

Is adaptive therapy natural?

Research suggests that progression-free survival can be prolonged by integrating evolutionary principles into clinical cancer treatment protocols. The goal is to prevent or slow the proliferation of resistant malignant cell populations. The logic behind this therapy relies on ecological and evolutionary processes. These same processes would be available to natural selection in decreasing the probability of an organism's death due to cancer. We propose that organisms' anticancer adaptions include not only ones for preventing cancer but also ones for directing and retarding the evolution of life-threatening cancer cells. We term this last strategy natural adaptive therapy (NAT). The body's NAT might include a lower than otherwise possible immune response. A restrained immune response might forego maximum short-term kill rates. Restraint would forestall immune-resistant cancer cells and produce long-term durable control of the cancer population. Here, we define, develop, and explore the possibility of NAT. The discovery of NAT mechanisms could identify new strategies in tumor prevention and treatments. Furthermore, we discuss the potential risks of immunotherapies that force the immune system to ramp up the short-term kill rates of malignant cancer cells in a manner that undermines the body's NAT and accelerates the evolution of immune resistance.

Deciphering the metabolic role of AMPK in cancer multi-drug resistance

Multi-drug resistance (MDR) is a curious bottleneck in cancer research and chemotherapy, whereby some cells rapidly adapt to the tumor microenvironment via a myriad of heterogeneous metabolic activities. Despite being a major impediment to treatment, there is a silver lining: control over metabolic regulation could be an effective approach to overcome or correct resistance pathways. In this critical review, we comprehensively and carefully curated and analyzed large networks of previously identified proteins associated with metabolic adaptation in MDR. We employed data and text mining to study and categorize more than 600 studies in PubMed, with particular focus on AMPK, a central and fundamental modulator in the energy metabolism network that has been specifically implicated in cancer MDR pathways. We have identified one protein set of metabolic adaptations with 137 members closely related to cancer MDR processes, and a second protein set with 165 members derived from AMPK-based networks, with 28 proteins found at the intersection between the two sets. Furthermore, according to genomics analysis of the cancer genome atlas (TCGA) provisional data, the highest alteration frequency (80.0%) of the genes encoding the intersected proteins (28 proteins), ranked three cancer types with quite remarkable significance across 166 studies. The hierarchical relationships of the entire identified gene and protein networks indicate broad correlations in AMPK-mediated metabolic regulation pathways, which we use decipher and depict the metabolic roles of AMPK and demonstrate the potential of metabolic control for therapeutic intervention in MDR.

An insight into the emerging role of cyclin-dependent kinase inhibitors as potential therapeutic agents for the treatment of advanced cancers

Cancer denotes a pathological manifestation that is characterized by hyperproliferation of cells. It has anticipated that a better understanding of disease pathogenesis and the role of cell-cycle regulators may provide an opportunity to develop an effective cancer therapeutic agents. Specifically, the cyclin-dependent kinases (CDKs) which regulate the transition of cell-cycle through different phases; have been identified as fundamental targets for therapeutic advances. It is an evident from experimental studies that several events leading to tumor growth occur by exacerbation of CDK4/CDK6 in G1-phase of cell division cycle. Additionally, the characteristics of S- and G2/M-phase regulated by CDK1/CDK2 are pivotal events that may lead to abrupt the cell division. Although, previously reported CDK inhibitors have shown remarkable results in pre-clinical studies, but have not yielded appreciable clinical results yet. Therefore, the development of clinically potent CDK inhibitors has remained to be a challenging task. However, continuous efforts has led to the development of some novel CDKs inhibitors that have emerged as a potent strategy for the treatment of advanced cancers. In this article, we have summarized the role of CDKs in cell-cycle regulation and tumorigenesis and recent advances in the development of CDKs inhibitors as a promising therapy for the treatment of advanced cancer. In addition, we have also performed a comparison of crystallographic studies to get valuable insight into the interaction mode differences of inhibitors, binding to CDK isoforms with apparently similar binding sites. The knowledge of ligand-specific recognition towards a particular CDK isoform may be applied as a key tool in future for the designing of isoform-specific inhibitors.

The IGF pathway is activated in insulinomas but downregulated in metastatic disease

Clinical and molecular studies have implicated epidermal growth factor receptor (EGFR), insulin-like growth factor (IGF) and target of rapamycin (mTOR) signaling pathways in the regulation of pancreatic neuroendocrine tumor (PanNET) growth. Interpretation and comparison of these studies is complex due to clinical and molecular tumor heterogeneity. We therefore focused in this study on insulinomas, which we examined for mRNA and protein expression of EGFR, IGF and mTOR signaling pathway components by quantitative real-time PCR (n=48) and immunohistochemistry (n=86). Findings were compared with normal pancreatic islets and correlated with histopathological data and clinical outcome. Insulinomas showed low EGFR and high IGF2 expression. IGFBP2, IGFBP3 and IGFBP6 mRNA levels were 2-4 folds higher than in islets. High protein expression of IGF2, IGF1R and INSR (in 51-92% of the tumors) and low to moderate expression of mTORC1 pathway proteins p-PS6k and p-4EBP1 (7-28% of the tumors) were observed. Correlations were found between 1) ERK1 mRNA expression and that of numerous IGF pathway genes, 2) p-ERK and IGF1R protein expression and 3) decrease of IGF pathway components and both metastatic disease and shorter 10 years disease free survival. In conclusion, our observations suggest that high expression of IGF signaling pathway components is a hallmark of insulinomas, but does not necessarily lead to increased mTOR signaling. Reduced expression of IGF pathway components may be an adverse prognostic factor in insulinomas.

Prosopis juliflora (Sw.), DC induces apoptosis and cell cycle arrest in triple negative breast cancer cells: in vitro and in vivo investigations

Plant originated drugs/formulations are extensively prescribed by the physicians as a complementary therapy for treating various human ailments including cancer. In this study Prosopis juliflora leaves methanol extract was prepared and exposed to human breast cancer cell lines i.e. MDA-MB-231 and MCF-7 and human keratinocytes HaCaT as a representative of normal cells. Initially, a series of in vitro experiments like cell proliferation, migration, colony formation, cell cycle arrest and inhibition of angiogenesis. After confirmation of the efficient and selective activity against triple negative breast cancer cell line, we further evaluated the possible mechanism of inducing cell death and experiments like detection of reactive oxygen species, caspases and poly (ADP-ribose) polymerase cleavage study and Annexin V assay were performed. We also evaluated in vivo anti tumorigenic activity of the P. juliflora leaves by using 4T1 cells (a triple negative mouse origin breast cancer cell line) and BALB/c xenograft mouse model. In vitro experiments revealed that methanol extract of Prosopis juliflora leaves possess impressive anti-breast cancer activity more specifically against triple negative breast cancer cells, while the in vivo studies demonstrated that P. juliflora leaves extract significantly suppressed the 4T1 induced tumor growth. Present investigations clearly focus the significance of P. juliflora as an important resource for finding novel leads against triple negative breast cancer. The results may also act as a ready reference towards developing P. juliflora based formulation as an alternative and complementary medicine for the management of breast cancer.

A Combination of Approved Antibodies Overcomes Resistance of Lung Cancer to Osimertinib by Blocking Bypass Pathways

Purpose: Because of emergence of resistance to osimertinib, a third-generation EGFR tyrosine kinase inhibitor (TKI), no targeted treatments are available for patients with lung cancer who lose sensitivity due to new mutations or bypass mechanisms. We examined in animals and in vitro an alternative therapeutic approach making use of antibodies.Experimental Design: An osimertinib-sensitive animal model of lung cancer, which rapidly develops drug resistance, has been employed. To overcome compensatory hyperactivation of ERK, which we previously reported, an anti-EGFR antibody (cetuximab) was combined with other antibodies, as well as with a subtherapeutic dose of osimertinib, and cancer cell apoptosis was assayed.Results: Our animal studies identified a combination of three clinically approved drugs, cetuximab, trastuzumab (an anti-HER2 mAb), and osimertinib (low dose), as an effective and long-lasting treatment that is able to prevent onset of resistance to osimertinib. A continuous schedule of concurrent treatment was sufficient for effective tumor inhibition and for prevention of relapses. Studies employing cultured cells and analyses of tumor extracts indicated that the combination of two mAbs and a subtherapeutic TKI dose sorted EGFR and HER2 for degradation; cooperatively enhanced apoptosis; inhibited activation of ERK; and reduced abundance of several bypass proteins, namely MET, AXL, and HER3.Conclusions: Our in vitro assays and animal studies identified an effective combination of clinically approved drugs that might overcome resistance to irreversible TKIs in clinical settings. The results we present attribute the long-lasting effect of the drug combination to simultaneous blockade of several well-characterized mechanisms of drug resistance. Clin Cancer Res; 24(22); 5610-21. ©2018 AACR See related commentary by Fan and Yu, p. 5499.

Personalized medicine in non-small cell lung cancer: a review from a pharmacogenomics perspective

Non-small cell lung cancer is a prevalent and rapidly-expanding challenge to modern medicine. While generalized medicine with traditional chemotherapy yielded comparatively poor response rates and treatment results, the cornerstone of personalized medicine using genetic profiling to direct treatment has exalted the successes seen in the field and raised the standard for patient treatment in lung and other cancers. Here, we discuss the current state and advances in the field of personalized medicine for lung cancer, reviewing several of the mutation-targeting strategies that are approved for clinical use and how they are guided by patient genetic information. These classes include inhibitors of tyrosine kinase (TKI), anaplastic lymphoma kinase (ALK), and monoclonal antibodies. Selecting from these treatment plans and determining the optimal dosage requires in-depth genetic guidance with consideration towards not only the underlying target genes but also other factors such as individual metabolic capability and presence of resistance-conferring mutations both directly on the target gene and along its cascade(s). Finally, we provide our viewpoints on the future of personalized medicine in lung cancer, including target-based drug combination, mutation-guided drug design and the necessity for data of population genetics, to provide rough guidance on treating patients who are unable to get genetic testing.

Synthesis, docking and anticancer activity of azo-linked hybrids of 1,3,4-thia-/oxadiazoles with cyclic imides

A series of novel analogues based on a diazole-imide pharmacophore were synthesized by diazotizing substituted 1,3,4-thia-/oxadiazol-2-amines and subsequently coupling the resulting diazonium salts with N-substituted cyclic imides. The resulting compounds C1 to C28 were characterized by various spectral methods, viz. IR, NMR and mass spectroscopy. All the synthesized compounds were tested against two human cancer cell lines: human breast adenocarcinoma cell line MCF-7 and colorectal adenocarcinoma cell line HT-29. Among the synthesized compounds, C14 (2-(4-chloro-3-((5-(4-nitrophenyl)-1,3,4-thiadiazol-2-yl)diazenyl)phenyl)-4,5,6,7-tetrahydro-1H-isoindole-1,3(2H)-dione) emerged as a potential candidate against both MCF-7 and HT-29 with [Formula: see text] values of 0.09 ± 0.02 [Formula: see text]M and 0.11 ± 0.03 [Formula: see text]M, respectively. Similarly, compound C16 displayed highest anticancer activity against MCF-7 cell line with [Formula: see text] = 0.07 ± 0.02 [Formula: see text]M. Target fishing (inverse docking) using ChemMapper server identified EGFR tyrosine and CDK2 kinases as high priority targets for this pharmacophore. Computational docking (AutoDock 4.2) was used to analyse the interactions between the target proteins and active compounds.

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

•

Cancer is rooted in incorrect cellular decisions caused by genetic alterations.

•

Dynamic models of signaling networks can map the relevant repertoire of alterations.

•

Discrete dynamic network models can predict therapeutic interventions.

•

Progress in personalized medicine needs integration of multiple data and model types.

A Convergence-Based Framework for Cancer Drug Resistance

Despite advances in cancer biology and therapeutics, drug resistance remains problematic. Resistance is often multifactorial, heterogeneous, and prone to undersampling. Nonetheless, many individual mechanisms of targeted therapy resistance may coalesce into a smaller number of convergences, including pathway reactivation (downstream re-engagement of original effectors), pathway bypass (recruitment of a parallel pathway converging on the same downstream output), and pathway indifference (development of a cellular state independent of the initial therapeutic target). Similar convergences may also underpin immunotherapy resistance. Such parsimonious, convergence-based frameworks may help explain resistance across tumor types and therapeutic categories and may also suggest strategies to overcome it.

Cancer driver mutation prediction through Bayesian integration of multi-omic data

Identification of cancer driver mutations is critical for advancing cancer research and personalized medicine. Due to inter-tumor genetic heterogeneity, many driver mutations occur at low frequencies, which make it challenging to distinguish them from passenger mutations. Here, we show that a novel Bayesian hierarchical modeling approach, named rDriver can achieve enhanced prediction accuracy by identifying mutations that not only have high functional impact scores but also are associated with systemic variation in gene expression levels. In examining 3,080 tumor samples from 8 cancer types in The Cancer Genome Atlas, rDriver predicted 1,389 driver mutations. Compared with existing tools, rDriver identified more low frequency mutations associated with lineage specific functional properties, timing of occurrence and patient survival. Evaluation of rDriver predictions using engineered cell-line models resulted in a positive predictive value of 0.94 in PIK3CA genes. Our study highlights the importance of integrating multi-omic data in predicting cancer driver mutations and provides a statistically rigorous solution for cancer target discovery and development.

The clinical impact of using complex molecular profiling strategies in routine oncology practice

Molecular profiling and functional assessment of signalling pathways of advanced solid tumours are becoming increasingly available. However, their clinical utility in guiding patients’ treatment remains unknown. Here, we assessed whether molecular profiling helps physicians in therapeutic decision making by analysing the molecular profiles of 1057 advanced cancer patient samples after failing at least one standard of care treatment using a combination of next-generation sequencing (NGS), immunohistochemistry (IHC) and other specific tests. The resulting information was interpreted and personalized treatments for each patient were suggested. Our data showed that NGS alone provided the oncologist with useful information in 10–50% of cases (depending on cancer type), whereas the addition of IHC/other tests increased extensively the usefulness of the information provided. Using internet surveys, we investigated how therapy recommendations influenced treatment choice of the oncologist. For patients who were still alive after the provision of the molecular information (76.8%), 60.4% of their oncologists followed report recommendations. Most treatment decisions (93.4%) were made based on the combination of NGS and IHC/other tests, and an approved drug- rather than clinical trial enrolment- was the main treatment choice. Most common reasons given by physicians to explain the non-adherence to recommendations were drug availability and cost, which remain barriers to personalised precision medicine. Finally, we observed that 27% of patients treated with the suggested therapies had an overall survival > 12 months. Our study demonstrates that the combination of NGS and IHC/other tests provides the most useful information in aiding treatment decisions by oncologists in routine clinical practice.

S55746 is a novel orally active BCL-2 selective and potent inhibitor that impairs hematological tumor growth

Escape from apoptosis is one of the major hallmarks of cancer cells. The B-cell Lymphoma 2 (BCL-2) gene family encodes pro-apoptotic and anti-apoptotic proteins that are key regulators of the apoptotic process. Overexpression of the pro-survival member BCL-2 is a well-established mechanism contributing to oncogenesis and chemoresistance in several cancers, including lymphoma and leukemia. Thus, BCL-2 has become an attractive target for therapeutic strategy in cancer, as demonstrated by the recent approval of ABT-199 (Venclexta™) in relapsed or refractory Chronic Lymphocytic Leukemia with 17p deletion. Here, we describe a novel orally bioavailable BCL-2 selective and potent inhibitor called S55746 (also known as BCL201). S55746 occupies the hydrophobic groove of BCL-2. Its selectivity profile demonstrates no significant binding to MCL-1, BFL-1 (BCL2A1/A1) and poor affinity for BCL-XL. Accordingly, S55746 has no cytotoxic activity on BCL-XL-dependent cells, such as platelets. In a panel of hematological cell lines, S55746 induces hallmarks of apoptosis including externalization of phosphatidylserine, caspase-3 activation and PARP cleavage. Ex vivo, S55746 induces apoptosis in the low nanomolar range in primary Chronic Lymphocytic Leukemia and Mantle Cell Lymphoma patient samples. Finally, S55746 administered by oral route daily in mice demonstrated robust anti-tumor efficacy in two hematological xenograft models with no weight lost and no change in behavior. Taken together, these data demonstrate that S55746 is a novel, well-tolerated BH3-mimetic targeting selectively and potently the BCL-2 protein.

Performance comparison of three DNA extraction kits on human whole-exome data from formalin-fixed paraffin-embedded normal and tumor samples

Next-generation sequencing (NGS) studies are becoming routinely used for the detection of novel and clinically actionable DNA variants at a pangenomic scale. Such analyses are now used in the clinical practice to enable precision medicine. Formalin-fixed paraffin-embedded (FFPE) tissues are still one of the most abundant source of cancer clinical specimen, unfortunately this method of preparation is known to degrade DNA and therefore compromise subsequent analysis. Some studies have reported that variant detection can be performed on FFPE samples sequenced with NGS techniques, but few or none have done an in-depth coverage analysis and compared the influence of different state-of-the-art FFPE DNA extraction kits on the quality of the variant calling. Here, we generated 42 human whole-exome sequencing data sets from fresh-frozen (FF) and FFPE samples. These samples include normal and tumor tissues from two different organs (liver and colon), that we extracted with three different FFPE extraction kits (QIAamp DNA FFPE Tissue kit and GeneRead DNA FFPE kit from Qiagen, Maxwell™ RSC DNA FFPE Kit from Promega). We determined the rate of concordance of called variants between matched FF and FFPE samples on all common variants (representing at least 86% of the total number of variants for SNVs). The concordance rate is very high between all matched FF / FFPE pairs, with equivalent values for the three kits we analyzed. On the other hand, when looking at the difference between the total number of variants in FF and FFPE, we find a significant variation for the three different FFPE DNA extraction kits. Coverage analysis shows that FFPE samples have less good indicators than FF samples, yet the coverage quality remains above accepted thresholds. We detect limited but statistically significant variations in coverage indicator values between the three FFPE extraction kits. Globally, the GeneRead and QIAamp kits have better variant calling and coverage indicators than the Maxwell kit on the samples used in this study, although this kit performs better on some indicators and has advantages in terms of practical usage. Taken together, our results confirm the potential of FFPE samples analysis for clinical genomic studies, but also indicate that the choice of a FFPE DNA extraction kit should be done with careful testing and analysis beforehand in order to maximize the accuracy of the results.

Treating ALK-positive non-small cell lung cancer

Targeting genomic alterations, such as epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) gene rearrangements, have radically changed the treatment of patients with non-small cell lung cancer (NSCLC). In the case of ALK-rearranged gene, subsequent rapid development of effective genotype-directed therapies with ALK tyrosine kinase inhibitors (TKIs) triggered major advances in the personalized molecularly based approach of NSCLC. Crizotinib was the first-in-class ALK TKI with proven superiority over standard platinum-based chemotherapy for the 1st-line therapy of ALK-rearranged NSCLC patients. However, the acquired resistance to crizotinib and its diminished efficacy to the central nervous system (CNS) relapse led to the development of several novel ALK inhibitors, more potent and with different selectivity compared to crizotinib. To date, four ALK TKIs, crizotinib, ceritinib, alectinib and brigatinib have received approval from the Food and Drug Administration (FDA) and/or the European Medicines Agency (EMA) and even more agents are currently under investigation for the treatment of ALK-rearranged NSCLC. However, the optimal frontline approach and the exact sequence of ALK inhibitors are still under consideration. Recently announced results of phase III trials recognized higher efficacy of alectinib compared to crizotinib in first-line setting, even in patients with CNS involvement. In this review, we will discuss the current knowledge regarding the biology of the ALK-positive NSCLC, the available therapeutic inhibitors and we will focus on the raised issues from their use in clinical practise.

Cell signaling heterogeneity is modulated by both cell-intrinsic and -extrinsic mechanisms: An integrated approach to understanding targeted therapy

During the last decade, our understanding of cancer cell signaling networks has significantly improved, leading to the development of various targeted therapies that have elicited profound but, unfortunately, short-lived responses. This is, in part, due to the fact that these targeted therapies ignore context and average out heterogeneity. Here, we present a mathematical framework that addresses the impact of signaling heterogeneity on targeted therapy outcomes. We employ a simplified oncogenic rat sarcoma (RAS)-driven mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase-protein kinase B (PI3K-AKT) signaling pathway in lung cancer as an experimental model system and develop a network model of the pathway. We measure how inhibition of the pathway modulates protein phosphorylation as well as cell viability under different microenvironmental conditions. Training the model on this data using Monte Carlo simulation results in a suite of in silico cells whose relative protein activities and cell viability match experimental observation. The calibrated model predicts distributional responses to kinase inhibitors and suggests drug resistance mechanisms that can be exploited in drug combination strategies. The suggested combination strategies are validated using in vitro experimental data. The validated in silico cells are further interrogated through an unsupervised clustering analysis and then integrated into a mathematical model of tumor growth in a homogeneous and resource-limited microenvironment. We assess posttreatment heterogeneity and predict vast differences across treatments with similar efficacy, further emphasizing that heterogeneity should modulate treatment strategies. The signaling model is also integrated into a hybrid cellular automata (HCA) model of tumor growth in a spatially heterogeneous microenvironment. As a proof of concept, we simulate tumor responses to targeted therapies in a spatially segregated tissue structure containing tumor and stroma (derived from patient tissue) and predict complex cell signaling responses that suggest a novel combination treatment strategy.

A signaling pathway is a network of molecules in a cell that is typically initiated by stimuli (e.g., microenvironmental cues) acting on receptors and internal signaling molecules to determine cell fate. Signaling pathways in cancer cells are different from those in normal cells, and this difference helps cancer cells to grow and thrive indefinitely. Drugs that target the aberrant signaling pathways in cancer cells (often referred to as targeted therapy) are promising for improving treatment outcomes of many different cancers in patients. However, most patients eventually develop resistance to these drugs. Resistance may already be present in the tumor or may emerge via mutation or via microenvironmental mediation. Tumor heterogeneity, which is characterized by subtle or dramatic differences among tumor cells, plays a key role in the development of drug resistance. Some tumor cells respond well to therapy, while others may adapt to the stress induced by the drug within the microenvironment. Moreover, removal of drug-sensitive cells may result in the competitive release of drug-resistant cells. Here, we present mathematical models to assess the impact of heterogeneity in signaling pathways within tumor cells on the outcomes of targeted therapy. We consider a simplified version of two well-known signaling pathways that modulate the growth of lung cancer cells. By using different targeted therapies, we quantify the effect of pathway inhibition on protein activity and cell viability and developed a mathematical model of the network, which is trained to reproduce these data and to develop a panel of heterogeneous in silico cells. The model predicts potential mechanisms of drug resistance and proposes combination therapies that are effective across the panel. We validate these combination therapies experimentally using the lung cancer cells and integrated the in silico cells into a computational lung tissue model that explicitly captures the microenvironment of lung cancer. Our results suggest that heterogeneity in both the tumor and microenvironment impacts treatment response in different ways and suggest a novel combination therapy for a better response.

Prospective identification of resistance mechanisms to HSP90 inhibition in KRAS mutant cancer cells

Inhibition of the HSP90 chaperone results in depletion of many signaling proteins that drive tumorigenesis, such as downstream effectors of KRAS, the most commonly mutated human oncogene. As a consequence, several small-molecule HSP90 inhibitors are being evaluated in clinical trials as anticancer agents. To prospectively identify mechanisms through which HSP90-dependent cancer cells evade pharmacologic HSP90 blockade, we generated multiple mutant KRAS-driven cancer cell lines with acquired resistance to the purine-scaffold HSP90 inhibitor PU-H71. All cell lines retained dependence on HSP90 function, as evidenced by sensitivity to short hairpin RNA-mediated suppression of HSP90AA1 or HSP90AB1 (also called HSP90α and HSP90β, respectively), and exhibited two types of genomic alterations that interfere with the effects of PU-H71 on cell viability and proliferation: (i) a Y142N missense mutation in the ATP-binding domain of HSP90α that co-occurred with amplification of the HSP90AA1 locus, (ii) genomic amplification and overexpression of the ABCB1 gene encoding the MDR1 drug efflux pump. In support of a functional role for these alterations, exogenous expression of HSP90α Y142N conferred PU-H71 resistance to HSP90-dependent cells, and pharmacologic MDR1 inhibition with tariquidar or lowering ABCB1 expression restored sensitivity to PU-H71 in ABCB1-amplified cells. Finally, comparison with structurally distinct HSP90 inhibitors currently in clinical development revealed that PU-H71 resistance could be overcome, in part, by ganetespib (also known as STA9090) but not tanespimycin (also known as 17-AAG). Together, these data identify potential mechanisms of acquired resistance to small molecules targeting HSP90 that may warrant proactive screening for additional HSP90 inhibitors or rational combination therapies.

PHLDA1 Mediates Drug Resistance in Receptor Tyrosine Kinase-Driven Cancer

Development of resistance causes failure of drugs targeting receptor tyrosine kinase (RTK) networks and represents a critical challenge for precision medicine. Here, we show that PHLDA1 downregulation is critical to acquisition and maintenance of drug resistance in RTK-driven cancer. Using fibroblast growth factor receptor (FGFR) inhibition in endometrial cancer cells, we identify an Akt-driven compensatory mechanism underpinned by downregulation of PHLDA1. We demonstrate broad clinical relevance of our findings, showing that PHLDA1 downregulation also occurs in response to RTK-targeted therapy in breast and renal cancer patients, as well as following trastuzumab treatment in HER2+ breast cancer cells. Crucially, knockdown of PHLDA1 alone was sufficient to confer de novo resistance to RTK inhibitors and induction of PHLDA1 expression re-sensitized drug-resistant cancer cells to targeted therapies, identifying PHLDA1 as a biomarker for drug response and highlighting the potential of PHLDA1 reactivation as a means of circumventing drug resistance.

Oncoprotein HBXIP enhances HOXB13 acetylation and co-activates HOXB13 to confer tamoxifen resistance in breast cancer

Background

Resistance to tamoxifen (TAM) frequently occurs in the treatment of estrogen receptor positive (ER+) breast cancer. Accumulating evidences indicate that transcription factor HOXB13 is of great significance in TAM resistance. However, the regulation of HOXB13 in TAM-resistant breast cancer remains largely unexplored. Here, we were interested in the potential effect of HBXIP, an oncoprotein involved in the acceleration of cancer progression, on the modulation of HOXB13 in TAM resistance of breast cancer.

Methods

The Kaplan-Meier plotter cancer database and GEO dataset were used to analyze the association between HBXIP expression and relapse-free survival. The correlation of HBXIP and HOXB13 in ER+ breast cancer was assessed by human tissue microarray. Immunoblotting analysis, qRT-PCR assay, immunofluorescence staining, Co-IP assay, ChIP assay, luciferase reporter gene assay, cell viability assay, and colony formation assay were performed to explore the possible molecular mechanism by which HBXIP modulates HOXB13. Cell viability assay, xenograft assay, and immunohistochemistry staining analysis were utilized to evaluate the effect of the HBXIP/HOXB13 axis on the facilitation of TAM resistance in vitro and in vivo.

Results

The analysis of the Kaplan-Meier plotter and the GEO dataset showed that mono-TAM-treated breast cancer patients with higher HBXIP expression levels had shorter relapse-free survivals than patients with lower HBXIP expression levels. Overexpression of HBXIP induced TAM resistance in ER+ breast cancer cells. The tissue microarray analysis revealed a positive association between the expression levels of HBXIP and HOXB13 in ER+ breast cancer patients. HBXIP elevated HOXB13 protein level in breast cancer cells. Mechanistically, HBXIP prevented chaperone-mediated autophagy (CMA)-dependent degradation of HOXB13 via enhancement of HOXB13 acetylation at the lysine 277 residue, causing the accumulation of HOXB13. Moreover, HBXIP was able to act as a co-activator of HOXB13 to stimulate interleukin (IL)-6 transcription in the promotion of TAM resistance. Interestingly, aspirin (ASA) suppressed the HBXIP/HOXB13 axis by decreasing HBXIP expression, overcoming TAM resistance in vitro and in vivo.

Conclusions

Our study highlights that HBXIP enhances HOXB13 acetylation to prevent HOXB13 degradation and co-activates HOXB13 in the promotion of TAM resistance of breast cancer. Therapeutically, ASA can serve as a potential candidate for reversing TAM resistance by inhibiting HBXIP expression.

Electronic supplementary material

The online version of this article (10.1186/s13045-018-0577-5) contains supplementary material, which is available to authorized users.

Epigenetic reprogramming: A key mechanism driving therapeutic resistance

Prostate cancer initiation, development and progression is driven by androgen receptor (AR) signaling. Androgen deprivation therapy is the primary treatment for patients that present with locally advanced or metastatic disease. However, androgen deprivation therapy is not curative, and patients will progress to castrate-resistant disease (CRPC). Although most patient's progress to CRPC via restoration of AR signaling (CRPC-Ad), approximately a quarter of patients will progress via mechanisms independent of AR signaling. This highly lethal phenotype is termed aggressive variant prostate cancer (AVPC). Data from clinical and preclinical studies demonstrate that AVPC involves combinatorial loss-of-function mutations in key tumor suppressor genes, low to absent AR levels, and re-expression of reprogramming, stem, and neuroendocrine related gene signatures. Further, AVPC is shown to evolve from a CRPC-Ad phenotype. Overall, lineage plasticity underlying progression to AVPC is thought to be provoked by genome-wide chromatin remodeling. Here, we will discuss an emerging focus on key drivers of chromatin remodeling in AVPC, and how their identification could provide noninvasive biomarkers to predict or detect AVPC emergence, and therapeutic targets to prevent or reverse progression to AVPC.

Computational drugs repositioning identifies inhibitors of oncogenic PI3K/AKT/P70S6K-dependent pathways among FDA-approved compounds

The discovery of inhibitors for oncogenic signalling pathways remains a key focus in modern oncology, based on personalized and targeted therapeutics. Computational drug repurposing via the analysis of FDA-approved drug network is becoming a very effective approach to identify therapeutic opportunities in cancer and other human diseases. Given that gene expression signatures can be associated with specific oncogenic mutations, we tested whether a "reverse" oncogene-specific signature might assist in the computational repositioning of inhibitors of oncogenic pathways. As a proof of principle, we focused on oncogenic PI3K-dependent signalling, a molecular pathway frequently driving cancer progression as well as raising resistance to anticancer-targeted therapies. We show that implementation of "reverse" oncogenic PI3K-dependent transcriptional signatures combined with interrogation of drug networks identified inhibitors of PI3K-dependent signalling among FDA-approved compounds. This led to repositioning of Niclosamide (Niclo) and Pyrvinium Pamoate (PP), two anthelmintic drugs, as inhibitors of oncogenic PI3K-dependent signalling. Niclo inhibited phosphorylation of P70S6K, while PP inhibited phosphorylation of AKT and P70S6K, which are downstream targets of PI3K. Anthelmintics inhibited oncogenic PI3K-dependent gene expression and showed a cytostatic effect in vitro and in mouse mammary gland. Lastly, PP inhibited the growth of breast cancer cells harbouring PI3K mutations. Our data indicate that drug repositioning by network analysis of oncogene-specific transcriptional signatures is an efficient strategy for identifying oncogenic pathway inhibitors among FDA-approved compounds. We propose that PP and Niclo should be further investigated as potential therapeutics for the treatment of tumors or diseases carrying the constitutive activation of the PI3K/P70S6K signalling axis.

Halfway between 2D and Animal Models: Are 3D Cultures the Ideal Tool to Study Cancer-Microenvironment Interactions?

An area that has come to be of tremendous interest in tumor research in the last decade is the role of the microenvironment in the biology of neoplastic diseases. The tumor microenvironment (TME) comprises various cells that are collectively important for normal tissue homeostasis as well as tumor progression or regression. Seminal studies have demonstrated the role of the dialogue between cancer cells (at many sites) and the cellular component of the microenvironment in tumor progression, metastasis, and resistance to treatment. Using an appropriate system of microenvironment and tumor culture is the first step towards a better understanding of the complex interaction between cancer cells and their surroundings. Three-dimensional (3D) models have been widely described recently. However, while it is claimed that they can bridge the gap between in vitro and in vivo, it is sometimes hard to decipher their advantage or limitation compared to classical two-dimensional (2D) cultures, especially given the broad number of techniques used. We present here a comprehensive review of the different 3D methods developed recently, and, secondly, we discuss the pros and cons of 3D culture compared to 2D when studying interactions between cancer cells and their microenvironment.

A novel compound which sensitizes BRAF wild-type melanoma cells to vemurafenib in a TRIM16-dependent manner

There is an urgent need for better therapeutic options for advanced melanoma patients, particularly those without the BRAFV600E/K mutation. In melanoma cells, loss of TRIM16 expression is a marker of cell migration and metastasis, while the BRAF inhibitor, vemurafenib, induces melanoma cell growth arrest in a TRIM16-dependent manner. Here we identify a novel small molecule compound which sensitized BRAF wild-type melanoma cells to vemurafenib. High throughput, cell-based, chemical library screening identified a compound (C012) which significantly reduced melanoma cell viability, with limited toxicity for normal human fibroblasts. When combined with the BRAFV600E/K inhibitor, vemurafenib, C012 synergistically increased vemurafenib potency in 5 BRAFWT and 4 out of 5 BRAFV600E human melanoma cell lines (Combination Index: CI < 1), and, dramatically reduced colony forming ability. In addition, this drug combination was significantly anti-tumorigenic in vivo in a melanoma xenograft mouse model. The combination of vemurafenib and C012 markedly increased expression of TRIM16 protein, and knockdown of TRIM16 significantly reduced the growth inhibitory effects of the vemurafenib and C012 combination. These findings suggest that the combination of C012 and vemurafenib may have therapeutic potential for the treatment of melanoma, and, that reactivation of TRIM16 may be an effective strategy for patients with this disease.

Clinically Observed Estrogen Receptor Alpha Mutations within the Ligand-Binding Domain Confer Distinguishable Phenotypes

OBJECTIVE

Twenty to fifty percent of estrogen receptor-positive (ER+) metastatic breast cancers express mutations within the ER ligand-binding domain. While most studies focused on the constitutive ER signaling activity commonly engendered by these mutations selected during estrogen deprivation therapy, our study was aimed at investigating distinctive phenotypes conferred by different mutations within this class.

METHODS

We examined the two most prevalent mutations, D538G and Y537S, employing corroborative genome-edited and lentiviral-transduced ER+ T47D cell models. We used a luciferase-based reporter and endogenous phospho-ER immunoblot analysis to characterize the estrogen response of ER mutants and determined their resistance to known ER antagonists.

RESULTS

Consistent with their selection during estrogen deprivation therapy, these mutants conferred constitutive ER activity. While Y537S mutants showed no estrogen dependence, D538G mutants demonstrated an enhanced estrogen-dependent response. Both mutations conferred resistance to ER antagonists that was overcome at higher doses acting specifically through their ER target.

CONCLUSIONS

These observations provide a tenable hypothesis for how D538G ESR1-expressing clones can contribute to shorter progression-free survival observed in the exemestane arm of the BOLERO-2 study. Thus, in those patients with dominant D538G-expressing clones, longitudinal analysis for this mutation in circulating free DNA may prove beneficial for informing more optimal therapeutic regimens.

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

Background

Mechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network.

Results

Here we present a comprehensive network, and discrete dynamic model, of signal transduction in ER+ breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone.

Conclusions

The use of a logic-based, discrete dynamic model enables the identification of results that are mainly due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.

Electronic supplementary material

The online version of this article (10.1186/s41236-017-0007-6) contains supplementary material, which is available to authorized users.

The impact of intermittent versus continuous exposure to EGFR tyrosine kinase inhibitor on selection of EGFR T790M-mutant drug-resistant clones in a lung cancer cell line carrying activating EGFR mutation

Drug-resistant cell lines are essential tools for investigating the mechanisms of resistance to molecular-targeted anti-cancer drugs. However, little is known about how to establish clinically relevant drug-resistant cell lines. Our study examined the impact of a drug-free period on the establishment of a cell line with clinically relevant resistance to molecular-targeted drugs. We used PC9 cells, a lung cancer cell line carrying EGFR mutation, because this is a validated target for EGFR tyrosine kinase inhibitors (TKI). PC9 cells were intermittently or continuously exposed to increasing concentrations of gefitinib (0.01 μM to 1.0 μM) and the emergence of the most common acquired resistance mutation in EGFR, T790M, was determined. T790M was detected at a 25-fold lower drug concentration in cells continuously exposed to gefitinib (PC9/GRc) than in cells intermittently exposed to gefitinib (PC9/GRi) (0.04 μM vs 1.0 μM, respectively). The mutation frequencies at those drug concentrations were 19.8% and 8.0% in PC9/GRc and PC9/GRi cells, respectively. After drug-free culture for 8 weeks, resistance to gefitinib decreased in the PC9/GRi cells but not in the PC9/GRc cells. In the PC9/GRc cells, the frequency of the T790M mutation was consistently about 20% from 0.04 μM to 1.0 μM of gefitinib. In the PC9/GRc cells, the T790M mutation was detected in all single-cell clones, at frequencies ranging from 7.0% to 37.0%, with a median of 19.5% (95% confidence interval, 17.3%–20.9%). In conclusion, compared with intermittent drug exposure, continuous exposure might select better minor drug-resistant clones when creating cell lines resistant to molecular-targeted drugs.

In vitro characterization of alkylaminophenols-induced cell death

Alkylaminophenols are synthetic derivatives well known for their anticancer activity. In the previous studies, we described the activity of the series of Alkylaminophenols derivatives and their ability to induce cell death for many cancer cell lines. However, temporal heterogeneity in cell death induced by lead compounds, N-(2-hydroxy-5-nitrophenyl (4'-methylphenyl) methyl) indoline (Compound I) and 2-((3,4-dihydroquinolin-1(2H)-yl) (4-methoxyphenyl) methyl) phenol (Compound II), has never been tested on osteosarcoma cells (U2OS). Here, we address the level of cell-to-cell heterogeneity by examine whether differences in the type of compounds could influence its effects on cell death of U2OS. Here, we applied imaging, computational methods and biochemical methods to study heterogeneity, apoptosis, reactive oxygen species and caspase. Our results demonstrate that the Hill coefficient of dose-response curve of Compound II is greater than compound I in treated U2OS cells. Both Compounds trigger not only apoptotic cell death but also necro-apoptotic and necrotic cell death. The percentage of these sub-populations varies depending on compounds in which greater variance is induced by compound II than Compound I. We also identified the accumulation of compounds-induced reactive oxygen species during the treatment. This resulted in caspase 3/7 activation in turn induced apoptosis. In summary, the screening of Compound I and II molecules for heterogeneity, apoptosis, reactive oxygen species and caspase has identified compound II as promising anti-osteosarcoma cancer agent. Compound II could be a promising lead compound for future antitumor agent development.

Targeting the chromatin remodeling enzyme BRG1 increases the efficacy of chemotherapy drugs in breast cancer cells

Brahma related gene product 1 (BRG1) is an ATPase that drives the catalytic activity of a subset of the mammalian SWI/SNF chromatin remodeling enzymes. BRG1 is overexpressed in most human breast cancer tumors without evidence of mutation and is required for breast cancer cell proliferation. We demonstrate that knockdown of BRG1 sensitized triple negative breast cancer cells to chemotherapeutic drugs used to treat breast cancer. An inhibitor of the BRG1 bromodomain had no effect on breast cancer cell viability, but an inhibitory molecule that targets the BRG1 ATPase activity recapitulated the increased drug efficacy observed in the presence of BRG1 knockdown. We further demonstrate that inhibition of BRG1 ATPase activity blocks the induction of ABC transporter genes by these chemotherapeutic drugs and that BRG1 binds to ABC transporter gene promoters. This inhibition increased intracellular concentrations of the drugs, providing a likely mechanism for the increased chemosensitivity. Since ABC transporters and their induction by chemotherapy drugs are a major cause of chemoresistance and treatment failure, these results support the idea that targeting the enzymatic activity of BRG1 would be an effective adjuvant therapy for breast cancer.

Determining therapeutic susceptibility in multiple myeloma by single-cell mass accumulation

Multiple myeloma (MM) has benefited from significant advancements in treatment that have improved outcomes and reduced morbidity. However, the disease remains incurable and is characterized by high rates of drug resistance and relapse. Consequently, methods to select the most efficacious therapy are of great interest. Here we utilize a functional assay to assess the ex vivo drug sensitivity of single multiple myeloma cells based on measuring their mass accumulation rate (MAR). We show that MAR accurately and rapidly defines therapeutic susceptibility across human multiple myeloma cell lines to a gamut of standard-of-care therapies. Finally, we demonstrate that our MAR assay, without the need for extended culture ex vivo, correctly defines the response of nine patients to standard-of-care drugs according to their clinical diagnoses. This data highlights the MAR assay in both research and clinical applications as a promising tool for predicting therapeutic response using clinical samples.

Multiple myeloma is characterized by high rates of drug resistance and relapse. Here the authors utilize a functional assay to assess the ex vivo drug sensitivity of single multiple myeloma cells based on measuring the mass accumulation rate of individual cells.

YAP-Dependent AXL Overexpression Mediates Resistance to EGFR Inhibitors in NSCLC

The Yes-associated protein (YAP) is a transcriptional co-activator upregulating genes that promote cell growth and inhibit apoptosis. The main dysregulation of the Hippo pathway in tumors is due to YAP overexpression, promoting epithelial to mesenchymal transition, cell transformation, and increased metastatic ability. Moreover, it has recently been shown that YAP plays a role in sustaining resistance to targeted therapies as well. In our work, we evaluated the role of YAP in acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors in lung cancer. In EGFR-addicted lung cancer cell lines (HCC4006 and HCC827) rendered resistant to several EGFR inhibitors, we observed that resistance was associated to YAP activation. Indeed, YAP silencing impaired the maintenance of resistance, while YAP overexpression decreased the responsiveness to EGFR inhibitors in sensitive parental cells. In our models, we identified the AXL tyrosine kinase receptor as the main YAP downstream effector responsible for sustaining YAP-driven resistance: in fact, AXL expression was YAP dependent, and pharmacological or genetic AXL inhibition restored the sensitivity of resistant cells to the anti-EGFR drugs. Notably, YAP overactivation and AXL overexpression were identified in a lung cancer patient upon acquisition of resistance to EGFR TKIs, highlighting the clinical relevance of our in vitro results. The reported data demonstrate that YAP and its downstream target AXL play a crucial role in resistance to EGFR TKIs and suggest that a combined inhibition of EGFR and the YAP/AXL axis could be a good therapeutic option in selected NSCLC patients.

From bench to clinical trials the EORTC experience in biology-based clinical cancer research

For over 50years the European Organization for Research and Treatment of Cancer (EORTC) has delivered major advances in cancer clinical research and cancer therapeutics. The introduction of molecularly targeted agents has led to significant improvements in outcome for patients with specific tumor types; however conventional chemotherapy remains the mainstay of treatment for the majority of patients. Due to increasing knowledge about the diversity of molecular pathways driving malignant progression, strategies to integrate biology into clinical research and development are continuously evolving. The challenges and the experience of the EORTC regarding how translational research is to be an indispensable component of the clinical research environment, which aims to deliver more sophisticated treatment approaches will be discussed in this perspective article.

MicroRNA pharmacogenomics based integrated model of miR-17-92 cluster in sorafenib resistant HCC cells reveals a strategy to forestall drug resistance

Among solid tumors, hepatocellular carcinoma (HCC) emerges as a prototypical therapy-resistant tumor. Considering the emerging sorafenib resistance crisis in HCC, future studies are urgently required to overcome resistance. Recently noncoding RNAs (ncRNAs) have emerged as significant regulators in signalling pathways involved in cancer drug resistance and pharmacologically targeting these ncRNAs might be a novel stratagem to reverse drug resistance. In the current study, using a hybrid Petri net based computational model, we have investigated the harmonious effect of miR-17-92 cluster inhibitors/mimics and circular RNAs on sorafenib resistant HCC cells in order to explore potential resistance mechanisms and to identify putative targets for sorafenib-resistant HCC cells. An integrated model was developed that incorporates seven miRNAs belonging to miR-17-92 cluster (hsa-miR-17-5p, hsa-miR-17-3p, hsa-miR-19a, hsa-miR-19b, hsa-miR-18a, hsa-miR-20a and hsa-miR-92) and crosstalk of two signaling pathways (EGFR and IL-6) that are differentially regulated by these miRNAs. The mechanistic connection was proposed by the correlation between members belonging to miR-17-92 cluster and corresponding changes in the protein levels of their targets in HCC, specifically those targets that have verified importance in sorafenib resistance. Current findings uncovered potential pathway features, underlining the significance of developing modulators of this cluster to combat drug resistance in HCC.

A framework for understanding and targeting residual disease in oncogene-driven solid cancers

Molecular targeted therapy has the potential to dramatically improve survival in patients with cancer. However, complete and durable responses to targeted therapy are rare in individuals with advanced-stage solid cancers. Even the most effective targeted therapies generally do not induce a complete tumor response, resulting in residual disease and tumor progression that limits patient survival. We discuss the emerging need to more fully understand the molecular basis of residual disease as a prelude to designing therapeutic strategies to minimize or eliminate residual disease so that we can move from temporary to chronic control of disease, or a cure, for patients with advanced-stage solid cancers. Ultimately, we propose a shift from the current reactive paradigm of analyzing and treating acquired drug resistance to a pre-emptive paradigm of defining the mechanisms that result in residual disease, to target and limit this disease reservoir.