Identification of potential synthetic lethal genes to p53 using a computational biology approach

The genomic landscape of breast and non-breast cancers from individuals with germline CHEK2 deficiency

CHEK2 is considered to be involved in homologous recombination repair (HRR). Individuals who have germline pathogenic variants (gPVs) in CHEK2 are at increased risk to develop breast cancer and likely other primary cancers. PARP inhibitors (PARPi) have been shown to be effective in the treatment of cancers that present with HRR deficiency-for example, caused by inactivation of BRCA1/2. However, clinical trials have shown little to no efficacy of PARPi in patients with CHEK2 gPVs. Here, we show that both breast and non-breast cancers from individuals who have biallelic gPVs in CHEK2 (germline CHEK2 deficiency) do not present with molecular profiles that fit with HRR deficiency. This finding provides a likely explanation why PARPi therapy is not successful in the treatment of CHEK2-deficient cancers.

BUB1 inhibition sensitizes lung cancer cell lines to radiotherapy and chemoradiotherapy

Background

Lung cancer is a major public health concern, with high incidence and mortality. Despite advances in targeted therapy and immunotherapy, microtubule stabilizers (paclitaxel, docetaxel), DNA intercalating platinum drugs (cisplatin) and radiation therapy continue to play a critical role in the management of locally advanced and metastatic lung cancer. Novel molecular targets would provide opportunities for improving the efficacies of radiotherapy and chemotherapy.

Hypothesis

We hypothesize that BUB1 (Ser/Thr kinase) is over-expressed in lung cancers and that its inhibition will sensitize lung cancers to chemoradiation.

Methods

BUB1 inhibitor (BAY1816032) was combined with platinum (cisplatin), microtubule poison (paclitaxel), a PARP inhibitor (olaparib) and radiation in cell proliferation and radiation sensitization assays. Biochemical and molecular assays were used to evaluate their impact on DNA damage signaling and cell death mechanisms.

Results

BUB1 expression assessed by immunostaining of lung tumor microarrays (TMAs) confirmed higher BUB1 expression in NSCLC and SCLC compared to that of normal tissues. BUB1 overexpression in lung cancer tissues correlated directly with expression of TP53 mutations in non-small cell lung cancer (NSCLC). Elevated BUB1 levels correlated with poorer overall survival in NSCLC and small cell lung cancer (SCLC) patients. A BUB1 inhibitor (BAY1816032) synergistically sensitized lung cancer cell lines to paclitaxel and olaparib. Additionally, BAY1816032 enhanced cell killing by radiation in both NSCLC and SCLC. Molecular changes following BUB1 inhibition suggest a shift towards pro-apoptotic and anti-proliferative states, indicated by altered expression of BAX, BCL2, PCNA, and Caspases 9 and 3.

Conclusion

A direct correlation between BUB1 protein expression and overall survival was shown. BUB1 inhibition sensitized both NSCLC and SCLC to various chemotherapies (cisplatin, paclitaxel) and targeted therapy (PARPi). Furthermore, we present the novel finding that BUB1 inhibition sensitized both NSCLC and SCLC to radiotherapy and chemoradiation. Our results demonstrate BUB1 inhibition as a promising strategy to sensitize lung cancers to radiation and chemoradiation therapies.

Translating p53-based therapies for cancer into the clinic

Inactivation of the most important tumour suppressor gene TP53 occurs in most, if not all, human cancers. Loss of functional wild-type p53 is achieved via two main mechanisms: mutation of the gene leading to an absence of tumour suppressor activity and, in some cases, gain-of-oncogenic function; or inhibition of the wild-type p53 protein mediated by overexpression of its negative regulators MDM2 and MDMX. Because of its high potency as a tumour suppressor and the dependence of at least some established tumours on its inactivation, p53 appears to be a highly attractive target for the development of new anticancer drugs. However, p53 is a transcription factor and therefore has long been considered undruggable. Nevertheless, several innovative strategies have been pursued for targeting dysfunctional p53 for cancer treatment. In mutant p53-expressing tumours, the predominant strategy is to restore tumour suppressor function with compounds acting either in a generic manner or otherwise selective for one or a few specific p53 mutations. In addition, approaches to deplete mutant p53 or to target vulnerabilities created by mutant p53 expression are currently under development. In wild-type p53 tumours, the major approach is to protect p53 from the actions of MDM2 and MDMX by targeting these negative regulators with inhibitors. Although the results of at least some clinical trials of MDM2 inhibitors and mutant p53-restoring compounds are promising, none of the agents has yet been approved by the FDA. Alternative strategies, based on a better understanding of p53 biology, the mechanisms of action of compounds and treatment regimens as well as the development of new technologies are gaining interest, such as proteolysis-targeting chimeras for MDM2 degradation. Other approaches are taking advantage of the progress made in immune-based therapies for cancer. In this Review, we present these ongoing clinical trials and emerging approaches to re-evaluate the current state of knowledge of p53-based therapies for cancer.

Cell fate regulation governed by p53: Friends or reversible foes in cancer therapy

Cancer is a leading cause of death worldwide. Targeted therapies aimed at key oncogenic driver mutations in combination with chemotherapy and radiotherapy as well as immunotherapy have benefited cancer patients considerably. Tumor protein p53 (TP53), a crucial tumor suppressor gene encoding p53, regulates numerous downstream genes and cellular phenotypes in response to various stressors. The affected genes are involved in diverse processes, including cell cycle arrest, DNA repair, cellular senescence, metabolic homeostasis, apoptosis, and autophagy. However, accumulating recent studies have continued to reveal novel and unexpected functions of p53 in governing the fate of tumors, for example, functions in ferroptosis, immunity, the tumor microenvironment and microbiome metabolism. Among the possibilities, the evolutionary plasticity of p53 is the most controversial, partially due to the dizzying array of biological functions that have been attributed to different regulatory mechanisms of p53 signaling. Nearly 40 years after its discovery, this key tumor suppressor remains somewhat enigmatic. The intricate and diverse functions of p53 in regulating cell fate during cancer treatment are only the tip of the iceberg with respect to its equally complicated structural biology, which has been painstakingly revealed. Additionally, TP53 mutation is one of the most significant genetic alterations in cancer, contributing to rapid cancer cell growth and tumor progression. Here, we summarized recent advances that implicate altered p53 in modulating the response to various cancer therapies, including chemotherapy, radiotherapy, and immunotherapy. Furthermore, we also discussed potential strategies for targeting p53 as a therapeutic option for cancer.

Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients

Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.

Drugs Targeting p53 Mutations with FDA Approval and in Clinical Trials

Mutations in the tumor suppressor p53 (p53) promote cancer progression. This is mainly due to loss of function (LOS) as a tumor suppressor, dominant-negative (DN) activities of missense mutant p53 (mutp53) over wild-type p53 (wtp53), and wtp53-independent oncogenic activities of missense mutp53 by interacting with other tumor suppressors or oncogenes (gain of function: GOF). Since p53 mutations occur in ~50% of human cancers and rarely occur in normal tissues, p53 mutations are cancer-specific and ideal therapeutic targets. Approaches to target p53 mutations include (1) restoration or stabilization of wtp53 conformation from missense mutp53, (2) rescue of p53 nonsense mutations, (3) depletion or degradation of mutp53 proteins, and (4) induction of p53 synthetic lethality or targeting of vulnerabilities imposed by p53 mutations (enhanced YAP/TAZ activities) or deletions (hyperactivated retrotransposons). This review article focuses on clinically available FDA-approved drugs and drugs in clinical trials that target p53 mutations and summarizes their mechanisms of action and activities to suppress cancer progression.

Inhibitors of the PLK1 polo-box domain: drug design strategies and therapeutic opportunities in cancer

INTRODUCTION

Polo Like Kinase 1 (PLK1) is a key regulator of mitosis and its overexpression is frequently observed in a wide variety of human cancers, while often being associated with poor survival rates. Therefore, it is considered a potential and attractive target for cancer therapeutic development. The Polo like kinase family is characterized by the presence of a unique C terminal polobox domain (PBD) involved in regulating kinase activity and subcellular localization. Among the two functionally essential, druggable sites with distinct properties that PLK1 offers, targeting the PBD presents an alternative approach for therapeutic development.

AREAS COVERED

Significant progress has been made in progressing from the peptidic PBD inhibitors first identified, to peptidomimetic and recently drug-like small molecules. In this review, the rationale for targeting the PBD over the ATP binding site is discussed, along with recent progress, challenges, and outlook.

EXPERT OPINION

The PBD has emerged as a viable alternative target for the inhibition of PLK1, and progress has been made in using compounds to elucidate mechanistic aspects of activity regulation and in determining roles of the PBD. Studies have resulted in proof of concept of in vivo efficacy suggesting promise for PBD binders in clinical development.

TP-0903 Is Active in Preclinical Models of Acute Myeloid Leukemia with TP53 Mutation/Deletion

Acute myeloid leukemia (AML) with mutations in the tumor suppressor gene TP53 confers a dismal prognosis with 3-year overall survival of <5%. While inhibition of kinases involved in cell cycle regulation induces synthetic lethality in a variety of TP53 mutant cancers, this strategy has not been evaluated in mutant TP53 AML. Previously, we demonstrated that TP-0903 is a novel multikinase inhibitor with low nM activity against AURKA/B, Chk1/2, and other cell cycle regulators. Here, we evaluated the preclinical activity of TP-0903 in TP53 mutant AML cell lines, including a single-cell clone of MV4-11 containing a TP53 mutation (R248W), Kasumi-1 (R248Q), and HL-60 (TP 53 null). TP-0903 inhibited cell viability (IC50, 12−32 nM) and induced apoptosis at 50 nM. By immunoblot, 50 nM TP-0903 upregulated pChk1/2 and pH2AX, suggesting induction of DNA damage. The combination of TP-0903 and decitabine was additive in vitro, and in vivo significantly prolonged median survival compared to single-agent treatments in mice xenografted with HL-60 (vehicle, 46 days; decitabine, 55 days; TP-0903, 63 days; combination, 75 days) or MV4-11 (R248W) (51 days; 62 days; 81 days; 89 days) (p < 0.001). Together, these results provide scientific premise for the clinical evaluation of TP-0903 in combination with decitabine in TP53 mutant AML.

p53-Mediated Indirect Regulation on Cellular Metabolism: From the Mechanism of Pathogenesis to the Development of Cancer Therapeutics

The transcription factor p53 is the most well-characterized tumor suppressor involved in multiple cellular processes, which has expanded to the regulation of metabolism in recent decades. Accumulating evidence reinforces the link between the disturbance of p53-relevant metabolic activities and tumor development. However, a full-fledged understanding of the metabolic roles of p53 and the underlying detailed molecular mechanisms in human normal and cancer cells remain elusive, and persistent endeavor is required to foster the entry of drugs targeting p53 into clinical use. This mini-review summarizes the indirect regulation of cellular metabolism by wild-type p53 as well as mutant p53, in which mechanisms are categorized into three major groups: through modulating downstream transcriptional targets, protein-protein interaction with other transcription factors, and affecting signaling pathways. Indirect mechanisms expand the p53 regulatory networks of cellular metabolism, making p53 a master regulator of metabolism and a key metabolic sensor. Moreover, we provide a brief overview of recent achievements and potential developments in the therapeutic strategies targeting mutant p53, emphasizing synthetic lethal methods targeting mutant p53 with metabolism. Then, we delineate synthetic lethality targeting mutant p53 with its indirect regulation on metabolism, which expands the synthetic lethal networks of mutant p53 and broadens the horizon of developing novel therapeutic strategies for p53 mutated cancers, providing more opportunities for cancer patients with mutant p53. Finally, the limitations and current research gaps in studies of metabolic networks controlled by p53 and challenges of research on p53-mediated indirect regulation on metabolism are further discussed.

Computational methods, databases and tools for synthetic lethality prediction

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Functional buffering via cell-specific gene expression promotes tissue homeostasis and cancer robustness

Functional buffering that ensures biological robustness is critical for maintaining tissue homeostasis, organismal survival, and evolution of novelty. However, the mechanism underlying functional buffering, particularly in multicellular organisms, remains largely elusive. Here, we proposed that functional buffering can be mediated via expression of buffering genes in specific cells and tissues, by which we named Cell-specific Expression-BUffering (CEBU). We developed an inference index (C-score) for CEBU by computing C-scores across 684 human cell lines using genome-wide CRISPR screens and transcriptomic RNA-seq. We report that C-score-identified putative buffering gene pairs are enriched for members of the same duplicated gene family, pathway, and protein complex. Furthermore, CEBU is especially prevalent in tissues of low regenerative capacity (e.g., bone and neuronal tissues) and is weakest in highly regenerative blood cells, linking functional buffering to tissue regeneration. Clinically, the buffering capacity enabled by CEBU can help predict patient survival for multiple cancers. Our results suggest CEBU as a potential buffering mechanism contributing to tissue homeostasis and cancer robustness in humans.

Evaluation of the Role of p53 Tumour Suppressor Posttranslational Modifications and TTC5 Cofactor in Lung Cancer

Mutations in the p53 tumor suppressor are found in over 50% of cancers. p53 function is controlled through posttranslational modifications and cofactor interactions. In this study, we investigated the posttranslationally modified p53, including p53 acetylated at lysine 382 (K382), p53 phosphorylated at serine 46 (S46), and the p53 cofactor TTC5/STRAP (Tetratricopeptide repeat domain 5/ Stress-responsive activator of p300-TTC5) proteins in lung cancer. Immunohistochemical (IHC) analysis of lung cancer tissues from 250 patients was carried out and the results were correlated with clinicopathological features. Significant associations between total or modified p53 with a higher grade of the tumour and shorter overall survival (OS) probability were detected, suggesting that mutant and/or modified p53 acts as an oncoprotein in these patients. Acetylated at K382 p53 was predominantly nuclear in some samples and cytoplasmic in others. The localization of the K382 acetylated p53 was significantly associated with the gender and grade of the disease. The TTC5 protein levels were significantly associated with the grade, tumor size, and node involvement in a complex manner. SIRT1 expression was evaluated in 50 lung cancer patients and significant positive correlation was found with p53 S46 intensity, whereas negative TTC5 staining was associated with SIRT1 expression. Furthermore, p53 protein levels showed positive association with poor OS, whereas TTC5 protein levels showed positive association with better OS outcome. Overall, our results indicate that an analysis of p53 modified versions together with TTC5 expression, upon testing on a larger sample size of patients, could serve as useful prognostic factors or drug targets for lung cancer treatment.

Synthetic Lethality in Ovarian Cancer

Ovarian cancers include several distinct malignancies which differ with respect to clinicopathologic features and prognosis. High-grade serous cancer is the most common histologic subtype and accounts for most ovarian cancer-related deaths. High-grade serous ovarian cancer (HGSOC) is treated with surgery and platinum-based chemotherapy, but most patients relapse and succumb to chemoresistant disease. The genetic concept of synthetic lethality, in which the synergy of mutations in multiple genes results in cell death, provides a framework to design novel therapeutic approaches to overcome chemoresistance in ovarian cancer. Recent progress in understanding the genomic architecture and hereditary drivers of ovarian cancer has shown potential for synthetic lethality strategies designed around homologous DNA repair. Clinical trials have validated high response rates for PARP inhibitors in patients with BRCA1 or BRCA2 mutations. Here we discuss the biological rationale behind targeting BRCA-PARP synthetic lethality based on genetic context in ovarian cancer and how this approach is being assessed in the clinic. Applying the concept of synthetic lethality to target non-BRCA-mutant cancers is an ongoing challenge, and we discuss novel approaches to target ovarian cancer using synthetic lethality in combination with and beyond PARP inhibitors. This review will also describe obstacles for synthetic lethality in ovarian cancer and new opportunities to develop potent targeted drugs for patients with ovarian cancer.

Mapping the landscape of synthetic lethal interactions in liver cancer

Almost all the current therapies against liver cancer are based on the "one size fits all" principle and offer only limited survival benefit. Fortunately, synthetic lethality (SL) may provide an alternate route towards individualized therapy in liver cancer. The concept that simultaneous losses of two genes are lethal to a cell while a single loss is non-lethal can be utilized to selectively eliminate tumors with genetic aberrations. Methods: To infer liver cancer-specific SL interactions, we propose a computational pipeline termed SiLi (statistical inference-based synthetic lethality identification) that incorporates five inference procedures. Based on large-scale sequencing datasets, SiLi analysis was performed to identify SL interactions in liver cancer. Results: By SiLi analysis, a total of 272 SL pairs were discerned, which included 209 unique target candidates. Among these, polo-like kinase 1 (PLK1) was considered to have considerable therapeutic potential. Further computational and experimental validation of the SL pair TP53-PLK1 demonstrated that inhibition of PLK1 could be a novel therapeutic strategy specifically targeting those patients with TP53-mutant liver tumors. Conclusions: In this study, we report a comprehensive analysis of synthetic lethal interactions of liver cancer. Our findings may open new possibilities for patient-tailored therapeutic interventions in liver cancer.

The chromatin-binding domain of Ki-67 together with p53 protects human chromosomes from mitotic damage

Vertebrate mammals express a protein called Ki-67 which is most widely known as a clinically useful marker of highly proliferative cells. Previous studies of human cells indicated that acute depletion of Ki-67 can elicit a delay at the G1/S boundary of the cell cycle, dependent on induction of the checkpoint protein p21. Consistent with those observations, we show here that acute Ki-67 depletion causes hallmarks of DNA damage, and the damage occurs even in the absence of checkpoint signaling. This damage is not observed in cells traversing S phase but is instead robustly detected in mitotic cells. The C-terminal chromatin-binding domain of Ki-67 is necessary and sufficient to protect cells from this damage. We also observe synergistic effects when Ki-67 and p53 are simultaneously depleted, resulting in increased levels of chromosome bridges at anaphase, followed by the appearance of micronuclei. Therefore, these studies identify the C terminus of Ki-67 as an important module for genome stability.

Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

Polo-like Kinase 1 as an emerging drug target: structure, function and therapeutic implications

Polo-like kinase 1 (PLK1) is a conserved mitotic serine-threonine protein kinase, functions as a regulatory protein, and is involved in the progression of the mitotic cycle. It plays important roles in the regulation of cell division, maintenance of genome stability, in spindle assembly, mitosis, and DNA-damage response. PLK1 is consist of a N-terminal serine-threonine kinase domain, and a C-terminal Polo-box domain (regulatory site). The expression of PLK1 is controlled by transcription repressor in the G1 stage and transcription activators in the G2 stage of the cell cycle. Overexpression of PLK1 results in undermining of checkpoints causes excessive cellular division resulting in abnormal cell growth, leading to the development of cancer. Blocking the expression of PLK1 by an antibody, RNA interference, or kinase inhibitors, causes a subsequent reduction in the proliferation of tumour cells and induction of apoptosis in tumour cells without affecting the healthy cells, suggesting an attractive target for drug development. In this review, we discuss detailed information on expression, gene and protein structures, role in different diseases, and progress in the design and development of PLK1 inhibitors. We have performed an in-depth analysis of the PLK1 inhibitors and their therapeutic implications with special focus to the cancer therapeutics.

Interplay between HMGA and TP53 in cell cycle control along tumor progression

The high mobility group A (HMGA) proteins are found to be aberrantly expressed in several tumors. Studies (in vitro and in vivo) have shown that HMGA protein overexpression has a causative role in carcinogenesis process. HMGA proteins regulate cell cycle progression through distinct mechanisms which strongly influence its normal dynamics along malignant transformation. Tumor protein p53 (TP53) is the most frequently altered gene in cancer. The loss of its activity is recognized as the fall of a barrier that enables neoplastic transformation. Among the different functions, TP53 signaling pathway is tightly involved in control of cell cycle, with cell cycle arrest being the main biological outcome observed upon p53 activation, which prevents accumulation of damaged DNA, as well as genomic instability. Therefore, the interaction and opposing effects of HMGA and p53 proteins on regulation of cell cycle in normal and tumor cells are discussed in this review. HMGA proteins and p53 may reciprocally regulate the expression and/or activity of each other, leading to the counteraction of their regulation mechanisms at different stages of the cell cycle. The existence of a functional crosstalk between these proteins in the control of cell cycle could open the possibility of targeting HMGA and p53 in combination with other therapeutic strategies, particularly those that target cell cycle regulation, to improve the management and prognosis of cancer patients.

ATM Inhibitor Suppresses Gemcitabine-Resistant BTC Growth in a Polymerase θ Deficiency-Dependent Manner

Patients with advanced biliary tract cancer (BTC) inevitably experience progression after first-line, gemcitabine-based chemotherapy, due to chemo-resistance. The genetic alterations of DNA damage repair (DDR) genes are usually determined in BTC tumors. In this study, we found that the POLQ mRNA levels are downregulated and the ataxia-telangiectasia mutated (ATM) inhibitor AZD0156 was more sensitive in gemcitabine-resistant BTC sublines than in the parental cell lines. The knockdown of DNA polymerase θ does not affect cell proliferation, but its combination with the ATM inhibitor facilitated cell death in gemcitabine-resistant and gemcitabine-intensive BTC cells. Moreover, in the DNA damage caused by photon, hydrogen peroxide, or chemotherapy drugs, synthetic lethal interactions were found in combination with ATM inhibition by AZD0156 and DNA polymerase θ depletion, resulting in increased DNA damage accumulation and micronucleus formation, as well as reduced cell survival and colony formation. Collectively, our results reveal that ATM acts as a potential target in gemcitabine-resistant and DNA polymerase θ-deficient BTC.

Current developments of targeting the p53 signaling pathway for cancer treatment

p53 is one of the most well-studied tumor suppressors. It is mutated or deleted in half of all cancers. In the other half carrying wild type p53, the p53 signaling pathway is disrupted by abnormalities of other components in the pathway. Due to its paramount role in tumor suppression, p53 has attracted great interest in drug development as any clinically successful therapeutic agent to target the p53 pathway will save millions of lives. However, designing therapeutics targeting the pathway has been extremely challenging, despite more than forty years of research. This review will summarize past and current efforts of developing p53-based gene therapy and targeted therapies for cancer treatment. In addition, the current efforts of exploiting the immunogenicity of p53 protein for cancer immunotherapy will be reviewed. Challenges and future directions for targeting the p53 pathway will be discussed.

Development of synthetic lethality in cancer: molecular and cellular classification

Recently, genetically targeted cancer therapies have been a topic of great interest. Synthetic lethality provides a new approach for the treatment of mutated genes that were previously considered unable to be targeted in traditional genotype-targeted treatments. The increasing researches and applications in the clinical setting made synthetic lethality a promising anticancer treatment option. However, the current understandings on different conditions of synthetic lethality have not been systematically assessed and the application of synthetic lethality in clinical practice still faces many challenges. Here, we propose a novel and systematic classification of synthetic lethality divided into gene level, pathway level, organelle level, and conditional synthetic lethality, according to the degree of specificity into its biological mechanism. Multiple preclinical findings of synthetic lethality in recent years will be reviewed and classified under these different categories. Moreover, synthetic lethality targeted drugs in clinical practice will be briefly discussed. Finally, we will explore the essential implications of this classification as well as its prospects in eliminating existing challenges and the future directions of synthetic lethality.

Analyses of expressions and prognostic values of Polo-like kinases in non-small cell lung cancer

BACKGROUND

Despite great advances in its early diagnosis and treatment, lung cancer is still an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. The family of Polo-like kinases (PLKs) consists of five serine/threonine kinases, which have been reported to participate in various human diseases. However, the expression and prognostic value of each PLK in human lung cancer have not been fully understood. This study analyzed mRNA expression and prognostic value of different PLKs in human non-small cell lung cancer (NSCLC).

METHODS

First, mRNA expression of PLKs in patients with NSCLC from the Oncomine and the Gene Expression Profiling Interactive Analysis (GEPIA) database was investigated. Then, a Kaplan-Meier plotter was employed for survival analysis. The sequence alteration for PLKs was analyzed using The Cancer Genome Atlas (TCGA) and the cBioPortal database. Additionally, we analyzed the association among different PLKs using the LinkedOmics database. Finally, the enrichment analysis of PLKs was achieved using the DAVID database.

RESULTS

The mRNA expression levels of PLK1 and PLK4 were significantly overexpressed, while mRNA expression level of PLK3 was underexpressed in patients with NSCLC. mRNA expressions of PLK1 and PLK4 were significantly and positively related to the tumor stage of NSCLC. Increased expressions of PLK1, PLK4, and PLK5 and decreased expression of PLK2 were attributed to limited overall survival time in NSCLC. PLK1 was positively correlated with PLK4 via the LinkedOmics database.

CONCLUSIONS

PLKs are relevant targets for NSCLC treatment, especially PLK1 and PLK4.

Analysis, identification and visualization of subgroups in genomics

Motivation

Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients’ samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches.

Results

Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar (‘analysis and visualization of alteration data’) that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection.

Availability

AVAtar is available at: https://github.com/sysbio-bioinf/avatar

Contact

hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502

Systematic Analysis of Aberrant Biochemical Networks and Potential Drug Vulnerabilities Induced by Tumor Suppressor Loss in Malignant Pleural Mesothelioma

Background: Malignant pleural mesothelioma (MPM) is driven by the inactivation of tumor suppressor genes (TSGs). An unmet need in the field is the translation of the genomic landscape into effective TSG-specific therapies. Methods: We correlated genomes against transcriptomes of patients' MPM tumors, by weighted gene co-expression network analysis (WGCNA). The identified aberrant biochemical networks and potential drug targets induced by tumor suppressor loss were validated by integrative data analysis and functional interrogation. Results: CDKN2A/2B loss activates G2/M checkpoint and PI3K/AKT, prioritizing a co-targeting strategy for CDKN2A/2B-null MPM. CDKN2A deficiency significantly co-occurs with deletions of anti-viral type I interferon (IFN-I) genes and BAP1 mutations, that enriches the IFN-I signature, stratifying a unique subset, with deficient IFN-I, but proficient BAP1 for oncolytic viral immunotherapies. Aberrant p53 attenuates differentiation and SETD2 loss acquires the dependency on EGFRs, highlighting the potential of differentiation therapy and pan-EGFR inhibitors for these subpopulations, respectively. LATS2 deficiency is linked with dysregulated immunoregulation, suggesting a rationale for immune checkpoint blockade. Finally, multiple lines of evidence support Dasatinib as a promising therapeutic for LATS2-mutant MPM. Conclusions: Systematic identification of abnormal cellular processes and potential drug vulnerabilities specified by TSG alterations provide a framework for precision oncology in MPM.

Pharmacogenomic Analysis Reveals CCNA2 as a Predictive Biomarker of Sensitivity to Polo-Like Kinase I Inhibitor in Gastric Cancer

Despite recent innovations and advances in early diagnosis, the prognosis of advanced gastric cancer remains poor due to a limited number of available therapeutics. Here, we employed pharmacogenomic analysis of 37 gastric cancer cell lines and 1345 small-molecule pharmacological compounds to investigate biomarkers predictive of cytotoxicity among gastric cancer cells to the tested drugs. We discovered that expression of CCNA2, encoding cyclin A2, was commonly associated with responses to polo-like kinase 1 (PLK1) inhibitors (BI-2536 and volasertib). We also found that elevated CCNA2 expression is required to confer sensitivity to PLK1 inhibitors through increased mitotic catastrophe and apoptosis. Further, we demonstrated that CCNA2 expression is elevated in KRAS mutant gastric cancer cell lines and primary tumors, resulting in an increased sensitivity to PLK1 inhibitors. Our study suggests that CCNA2 is a novel biomarker predictive of sensitivity to PLK1 inhibitors for the treatment of advanced gastric cancer, particularly cases carrying KRAS mutation.

Link synthetic lethality to drug sensitivity of cancer cells

Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.

A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations

Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein-protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.

Prognosis, Biology, and Targeting of TP53 Dysregulation in Multiple Myeloma

Multiple myeloma (MM) is the second most common hematological cancer and is characterized by genetic features including translocations, chromosomal copy number aberrations, and mutations in key oncogene and tumor suppressor genes. Dysregulation of the tumor suppressor TP53 is important in the pathogenesis of many cancers, including MM. In newly-diagnosed MM patients, TP53 dysregulation occurs in three subsets: monoallelic deletion as part of deletion of chromosome 17p (del17p) (~8%), monoallelic mutations (~6%), and biallelic inactivation (~4%). Del17p is an established high-risk feature in MM and is included in current disease staging criteria. Biallelic inactivation and mutation have also been reported in MM patients but are not yet included in disease staging criteria for high-risk disease. Emerging clinical and genomics data suggest that the biology of high-risk disease is complex, and so far, traditional drug development efforts to target dysregulated TP53 have not been successful. Here we review the TP53 dysregulation literature in cancer and in MM, including the three segments of TP53 dysregulation observed in MM patients. We propose a reverse translational approach to identify novel targets and disease drivers from TP53 dysregulated patients to address the unmet medical need in this setting.

TP53 in bone and soft tissue sarcomas

Genomic and functional study of existing and emerging sarcoma targets, such as fusion proteins, chromosomal aberrations, reduced tumor suppressor activity, and oncogenic drivers, is broadening our understanding of sarcomagenesis. Among these mechanisms, the tumor suppressor p53 (TP53) plays significant roles in the suppression of bone and soft tissue sarcoma progression. Although mutations in TP53 were thought to be relatively low in sarcomas, modern techniques including whole-genome sequencing have recently illuminated unappreciated alterations in TP53 in osteosarcoma. In addition, oncogenic gain-of-function activities of missense mutant p53 (mutp53) have been reported in sarcomas. Moreover, new targeting strategies for TP53 have been discovered: restoration of wild-type p53 (wtp53) activity through inhibition of TP53 negative regulators, reactivation of the wtp53 activity from mutp53, depletion of mutp53, and targeting of vulnerabilities in cells with TP53 deletions or mutations. These discoveries enable development of novel therapeutic strategies for therapy-resistant sarcomas. We have outlined nine bone and soft tissue sarcomas for which TP53 plays a crucial tumor suppressive role. These include osteosarcoma, Ewing sarcoma, chondrosarcoma, rhabdomyosarcoma (RMS), leiomyosarcoma (LMS), synovial sarcoma, liposarcoma (LPS), angiosarcoma, and undifferentiated pleomorphic sarcoma (UPS).

Targeting Therapies for the p53 Protein in Cancer Treatments

Half of all human cancers contain TP53 mutations, and in many other cancers, the function of the p53 protein is compromised. The diversity of these mutations and phenotypes presents a challenge to the development of drugs that target p53 mutant cancer cells. This review describes the rationale for many different approaches in the development of p53 targeted therapies: ( a) viruses and gene therapies, ( b) increased levels and activity of wild-type p53 proteins in cancer cells, ( c) p53 protein gain-of-function inhibitors, ( d) p53 protein loss-of-function structural correctors, ( e) mutant p53 protein synthetic lethal drugs interfering with the p53 pathway, and ( f) cellular immune responses to mutant p53 protein antigens. As these types of therapies are developed, tested, and evaluated, the best of them will have a significant impact upon cancer treatments and possibly prevention.

Lipidomics as a tool of predicting progression from non-alcoholic fatty pancreas disease to type 2 diabetes mellitus

There are three subclasses of PC (phosphatidylcholine, dPC; pPC; and plasmanylcholine, aPC). Several species of pPC decreased significantly in NDM and DM patients and especially in DM patients, while dPC and aPC showed no significant change.

The Cancer Omics Atlas: an integrative resource for cancer omics annotations

Background

The Cancer Genome Atlas (TCGA) is an important data resource for cancer biologists and oncologists. However, a lack of bioinformatics expertise often hinders experimental cancer biologists and oncologists from exploring the TCGA resource. Although a number of tools have been developed for facilitating cancer researchers to utilize the TCGA data, these existing tools cannot fully satisfy the large community of experimental cancer biologists and oncologists without bioinformatics expertise.

Methods

We developed a new web-based tool The Cancer Omics Atlas (TCOA, http://tcoa.cpu.edu.cn) for fast and straightforward querying of TCGA “omics” data.

Results

TCOA provides the querying of gene expression, somatic mutations, microRNA (miRNA) expression, protein expression data based on a single molecule or cancer type. TCOA also provides the querying of expression correlation between gene pairs, miRNA pairs, gene and miRNA, and gene and protein. Moreover, TCOA provides the querying of the associations between gene, miRNA, or protein expression and survival prognosis in cancers. In addition, TCOA displays transcriptional profiles across various human cancer types based on the pan-cancer analysis. Finally, TCOA provides the querying of molecular profiles for 2877 immune-related genes in human cancers. These immune-related genes include those that are established or promising targets for cancer immunotherapy such as CTLA4, PD1, PD-L1, PD-L2, IDO1, LAG3, and TIGIT.

Conclusions

TCOA is a useful tool that supplies a number of unique and new functions complementary to the existing tools to facilitate exploration of the TCGA resource.

Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network

Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future.

The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms

The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.

TP53 mutations, expression and interaction networks in human cancers

Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

Ranking novel cancer driving synthetic lethal gene pairs using TCGA data

Synthetic lethality (SL) has emerged as a promising approach to cancer therapy. In contrast to the costly and labour-intensive genome-wide siRNA or CRISPR-based human cell line screening approaches, computational approaches to prioritize potential synthetic lethality pairs for further experimental validation represent an attractive alternative. In this study, we propose an efficient and comprehensive in-silico pipeline to rank novel SL gene pairs by mining vast amounts of accumulated tumor high-throughput sequencing data in The Cancer Genome Atlas (TCGA), coupled with other protein interaction networks and cell line information. Our pipeline integrates three significant features, including mutation coverage in TCGA, driver mutation probability and the quantified cancer network information centrality, into a ranking model for SL gene pair identification, which is presented as the first learning-based method for SL identification. As a result, 107 potential SL gene pairs were obtained from the top 10 results covering 11 cancers. Functional analysis of these genes indicated that several promising pathways were identified, including the DNA repair related Fanconi Anemia pathway and HIF-1 signaling pathway. In addition, 4 SL pairs, mTOR-TP53, VEGFR2-TP53, EGFR-TP53, ATM-PRKCA, were validated using drug sensitivity information in the cancer cell line databases CCLE or NCI60. Interestingly, significant differences in the cell growth of mTOR siRNA or EGFR siRNA knock-down were detected between cancer cells with wild type TP53 and mutant TP53. Our study indicates that the pre-screening of potential SL gene pairs based on the large genomics data repertoire of tumor tissues and cancer cell lines could substantially expedite the identification of synthetic lethal gene pairs for cancer therapy.

Sensitivity to PRIMA-1MET is associated with decreased MGMT in human glioblastoma cells and glioblastoma stem cells irrespective of p53 status

Alterations of the TP53 tumor suppressor gene occur in ~30% of primary glioblastoma (GBM) with a high frequency of missense mutations associated with the acquisition of oncogenic “gain-of-function” (GOF) mutant (mut)p53 activities. PRIMA-1^MET/APR-246, emerged as a promising compound to rescue wild-type (wt)p53 function in different cancer types. Previous studies suggested the role of wtp53 in the negative regulation of the DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT), a major determinant in resistance to therapy in GBM treatment. The potential role of MGMT in expression of p53 and the efficacy of PRIMA-1^MET with respect to TP53 status and expression of MGMT in GBM remain unknown. We investigated response to PRIMA-1^MET of wtp53/MGMT-negative (U87MG, A172), mutp53/MGMT-positive U138, LN-18, T98/Empty vector (T98/EV) and its isogenic MGMT/shRNA gene knockdown counterpart (T98/shRNA). We show that MGMT silencing decreased expression of mutp53/GOF in T98/shRNA. PRIMA-1^MET further cleared T98/shRNA cells of mutp53, decreased proliferation and clonogenic potential, abrogated the G₂ checkpoint control, increased susceptibility to apoptotic cell death, expression of GADD45A and sustained expression of phosphorylated Erk1/2. PRIMA-1^MET increased expression of p21 protein in U87MG and A172 and promoted senescence in U87MG cell line. Importantly, PRIMA-1^MET decreased relative cell numbers, disrupted the structure of neurospheres of patient-derived GBM stem cells (GSCs) and enabled activation of wtp53 with decreased expression of MGMT in MGMT-positive GSCs or decreased expression of mutp53. Our findings highlight the cell-context dependent effects of PRIMA-1^MET irrespective of p53 status and suggest the role of MGMT as a potential molecular target of PRIMA-1^MET in MGMT-positive GSCs.

Phospholipase Cβ1 regulates proliferation of neuronal cells

Cells have developed lineage-specific mechanisms to control proliferation and drive morphologic changes upon differentiation. A hallmark of differentiation is the assembly of signaling molecules that transduce extracellular signals, such as the production of the G protein-regulated enzyme phospholipase Cβ (PLCβ), which generates calcium signals from sensory stimuli. We found that in most cancerous cell lines there is positive correlation between PLCβ1 levels and cell proliferation. In cells of neuronal lineage, however, reducing PLCβ1 levels increases the rate of proliferation. Using a combination of biochemical and biophysical methods, we find that, in the G₁ phase, a cytosolic population of PLCβ1 associates with cyclin-dependent kinase 16 (CDK16), a neuron-specific enzyme that is activated by cyclin Y to inactivate the antioncogenic protein p27^Kip1. Binding of PLCβ1 directly inhibits CDK16 activity and in turn reduces the ability of cells to enter the S phase. Activation of Gα_q by carbachol causes movement of PLCβ from the cytosol to the plasma membrane, reducing its association with CDK16. Similarly, the overexpression of activated Gα_q moves PLCβ1 to the membrane, reverses G₁ arrest, and promotes proliferation, thereby connecting external stimuli with cell proliferation. Our results present a model in which the transient high expression of PLCβ1 that occurs at the onset of differentiation arrests cells in the G₁ phase through its association with CDK16 and allows CDK16 to transition to its postmitotic function of neurite outgrowth and trafficking of synaptic vesicles. The novel role of PLCβ1 in neuronal cell proliferation offers a unique interaction that can be manipulated to guide cells into a neuronal phenotype or to develop therapies for neuroblastomas.-Garwain, O., Valla, K., Scarlata, S. Phospholipase Cβ1 regulates proliferation of neuronal cells.

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

Changes in gene expression variability reveal a stable synthetic lethal interaction network in BRCA2-ovarian cancers

Synthetic lethal interactions (SLIs) are robust mechanisms that provide cells with the ability to remain viable despite having mutations in genes critical to the DNA damage response, a core cellular process. Studies in model organisms such as S. cerevisiae showed that thousands of genes important in maintaining DNA integrity cooperated in a SLI network. Two genes participate in a SLI when a mutation in one gene has no effect on the cell, but mutations in both interacting genes are lethal. Furthermore in C. elegans, a mutation in a critical gene that is important for development induced a change in expression variability in the synthetic lethal interactor. In cancer, targeting SLIs shows promise in selectively killing cancer cells. For example, targeting PARP1 is an effective treatment for BRCA1/2- breast and ovarian cancers. Although PARP1 is already identified as having a SLI with BRCA1/2-, computationally searching for other genes that cooperate in the SLI network could highlight genes that may have promise for being a cancer-specific drug target. Using RNA sequencing data for ovarian cancer patients with BRCA2 mutations and the R Bioconductor package pathVar, we showed that genes whose expression changes to an invariant, stable expression state are likely candidates for SLIs with BRCA2. Our results highlight the interactions between the genes with predicted SLIs and protein-coding genes that are functionally important in the DNA damage response. The method of analyzing expression variability to computationally identify genes with SLIs can be applied to query SLIs in other tumor types.

'Big data' approaches for novel anti-cancer drug discovery

INTRODUCTION

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery

Background

A computational evolution system (CES) is a knowledge discovery engine that can identify subtle, synergistic relationships in large datasets. Pareto optimization allows CESs to balance accuracy with model complexity when evolving classifiers. Using Pareto optimization, a CES is able to identify a very small number of features while maintaining high classification accuracy. A CES can be designed for various types of data, and the user can exploit expert knowledge about the classification problem in order to improve discrimination between classes. These characteristics give CES an advantage over other classification and feature selection algorithms, particularly when the goal is to identify a small number of highly relevant, non-redundant biomarkers. Previously, CESs have been developed only for binary class datasets. In this study, we developed a multi-class CES.

Results

The multi-class CES was compared to three common feature selection and classification algorithms: support vector machine (SVM), random k-nearest neighbor (RKNN), and random forest (RF). The algorithms were evaluated on three distinct multi-class RNA sequencing datasets. The comparison criteria were run-time, classification accuracy, number of selected features, and stability of selected feature set (as measured by the Tanimoto distance). The performance of each algorithm was data-dependent. CES performed best on the dataset with the smallest sample size, indicating that CES has a unique advantage since the accuracy of most classification methods suffer when sample size is small.

Conclusion

The multi-class extension of CES increases the appeal of its application to complex, multi-class datasets in order to identify important biomarkers and features.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-017-0134-8) contains supplementary material, which is available to authorized users.

PLK1, A Potential Target for Cancer Therapy

Polo-like kinase 1 (PLK1) plays an important role in the initiation, maintenance, and completion of mitosis. Dysfunction of PLK1 may promote cancerous transformation and drive its progression. PLK1 overexpression has been found in a variety of human cancers and was associated with poor prognoses in cancers. Many studies have showed that inhibition of PLK1 could lead to death of cancer cells by interfering with multiple stages of mitosis. Thus, PLK1 is expected to be a potential target for cancer therapy. In this article, we examined PLK1’s structural characteristics, its regulatory roles in cell mitosis, PLK1 expression, and its association with survival prognoses of cancer patients in a wide variety of cancer types, PLK1 interaction networks, and PLK1 inhibitors under investigation. Finally, we discussed the key issues in the development of PLK1-targeted cancer therapy.

Drugging the addict: non-oncogene addiction as a target for cancer therapy

Historically, cancers have been treated with chemotherapeutics aimed to have profound effects on tumor cells with only limited effects on normal tissue. This approach was followed by the development of small‐molecule inhibitors that can target oncogenic pathways critical for the survival of tumor cells. The clinical targeting of these so‐called oncogene addictions, however, is in many instances hampered by the outgrowth of resistant clones. More recently, the proper functioning of non‐mutated genes has been shown to enhance the survival of many cancers, a phenomenon called non‐oncogene addiction. In the current review, we will focus on the distinct non‐oncogenic addictions found in cancer cells, including synthetic lethal interactions, the underlying stress phenotypes, and arising therapeutic opportunities.

Synthetic lethality in lung cancer and translation to clinical therapies

Lung cancer is a heterogeneous disease consisting of multiple histological subtypes each driven by unique genetic alterations. Despite the development of targeted therapies that inhibit the oncogenic mutations driving a subset of lung cancer cases, there is a paucity of effective treatments for the majority of lung cancer patients and new strategies are urgently needed. In recent years, the concept of synthetic lethality has been established as an effective approach for discovering novel cancer-specific targets as well as a method to improve the efficacy of existing drugs which provide partial but insufficient benefits for patients. In this review, we discuss the concept of synthetic lethality, the various types of synthetic lethal interactions in the context of oncology and the approaches used to identify these interactions, including recent advances that have transformed the ability to discover novel synthetic lethal combinations on a global scale. Lastly, we describe the specific synthetic lethal interactions identified in lung cancer to date and explore the pharmacological challenges and considerations in translating these discoveries to the clinic.

Integrative exploration of genomic profiles for triple negative breast cancer identifies potential drug targets

BACKGROUND

Triple negative breast cancer (TNBC) is high-risk due to its rapid drug resistance and recurrence, metastasis, and lack of targeted therapy. So far, no molecularly targeted therapeutic agents have been clinically approved for TNBC. It is imperative that we discover new targets for TNBC therapy.

OBJECTIVES

A large volume of cancer genomics data are emerging and advancing breast cancer research. We may integrate different types of TNBC genomic data to discover molecular targets for TNBC therapy.

DATA SOURCES

We used publicly available TNBC tumor tissue genomic data in the Cancer Genome Atlas database in this study.

METHODS

We integratively explored genomic profiles (gene expression, copy number, methylation, microRNA [miRNA], and gene mutation) in TNBC and identified hyperactivated genes that have higher expression, more copy numbers, lower methylation level, or are targets of miRNAs with lower expression in TNBC than in normal samples. We ranked the hyperactivated genes into different levels based on all the genomic evidence and performed functional analyses of the sets of genes identified. More importantly, we proposed potential molecular targets for TNBC therapy based on the hyperactivated genes.

RESULTS

Some of the genes we identified such as FGFR2, MAPK13, TP53, SRC family, MUC family, and BCL2 family have been suggested to be potential targets for TNBC treatment. Others such as CSF1R, EPHB3, TRIB1, and LAD1 could be promising new targets for TNBC treatment. By utilizing this integrative analysis of genomic profiles for TNBC, we hypothesized that some of the targeted treatment strategies for TNBC currently in development are more likely to be promising, such as poly (ADP-ribose) polymerase inhibitors, while the others are more likely to be discouraging, such as angiogenesis inhibitors.

LIMITATIONS

The findings in this study need to be experimentally validated in the future.

CONCLUSION

This is a systematic study that combined 5 different types of genomic data to molecularly characterize TNBC and identify potential targets for TNBC therapy. The integrative analysis of genomic profiles for TNBC could assist in identifying potential new therapeutic targets and predicting the effectiveness of a targeted treatment strategy for TNBC therapy.

Malignancy of Cancers and Synthetic Lethal Interactions Associated With Mutations of Cancer Driver Genes

The mutation status of cancer driver genes may correlate with different degrees of malignancy of cancers. The doubling time and multidrug resistance are 2 phenotypes that reflect the degree of malignancy of cancer cells. Because most of cancer driver genes are hard to target, identification of their synthetic lethal partners might be a viable approach to treatment of the cancers with the relevant mutations.The genome-wide screening for synthetic lethal partners is costly and labor intensive. Thus, a computational approach facilitating identification of candidate genes for a focus synthetic lethal RNAi screening will accelerate novel anticancer drug discovery.We used several publicly available cancer cell lines and tumor tissue genomic data in this study.We compared the doubling time and multidrug resistance between the NCI-60 cell lines with mutations in some cancer driver genes and those without the mutations. We identified some candidate synthetic lethal genes to the cancer driver genes APC, KRAS, BRAF, PIK3CA, and TP53 by comparison of their gene phenotype values in cancer cell lines with the relevant mutations and wild-type background. Further, we experimentally validated some of the synthetic lethal relationships we predicted.We reported that mutations in some cancer driver genes mutations in some cancer driver genes such as APC, KRAS, or PIK3CA might correlate with cancer proliferation or drug resistance. We identified 40, 21, 5, 43, and 18 potential synthetic lethal genes to APC, KRAS, BRAF, PIK3CA, and TP53, respectively. We found that some of the potential synthetic lethal genes show significantly higher expression in the cancers with mutations of their synthetic lethal partners and the wild-type counterparts. Further, our experiments confirmed several synthetic lethal relationships that are novel findings by our methods.We experimentally validated a part of the synthetic lethal relationships we predicted. We plan to perform further experiments to validate the other synthetic lethal relationships predicted by this study.Our computational methods achieve to identify candidate synthetic lethal partners to cancer driver genes for further experimental screening with multiple lines of evidences, and therefore contribute to development of anticancer drugs.