ERK pathway agonism for cancer therapy: evidence, insights, and a target discovery framework

At least 40% of human cancers are associated with aberrant ERK pathway activity (ERKp). Inhibitors targeting various effectors within the ERKp have been developed and explored for over two decades. Conversely, a substantial body of evidence suggests that both normal human cells and, notably to a greater extent, cancer cells exhibit susceptibility to hyperactivation of ERKp. However, this vulnerability of cancer cells remains relatively unexplored. In this review, we reexamine the evidence on the selective lethality of highly elevated ERKp activity in human cancer cells of varying backgrounds. We synthesize the insights proposed for harnessing this vulnerability of ERK-associated cancers for therapeutical approaches and contextualize these insights within established pharmacological cancer-targeting models. Moreover, we compile the intriguing preclinical findings of ERK pathway agonism in diverse cancer models. Lastly, we present a conceptual framework for target discovery regarding ERKp agonism, emphasizing the utilization of mutual exclusivity among oncogenes to develop novel targeted therapies for precision oncology.

A systematic analysis of the landscape of synthetic lethality-driven precision oncology

BACKGROUND

Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Because more than 25 years have passed since SL was proposed as a promising way to selectively target cancer vulnerabilities, it is timely to comprehensively assess its impact so far and discuss its future.

METHODS

We systematically analyzed the literature and clinical trial data from the PubMed and Trialtrove databases to portray the preclinical and clinical landscape of SL oncology.

FINDINGS

We identified 235 preclinically validated SL pairs and found 1,207 pertinent clinical trials, and the number keeps increasing over time. About one-third of these SL clinical trials go beyond the typically studied DNA damage response (DDR) pathway, testifying to the recently broadening scope of SL applications in clinical oncology. We find that SL oncology trials have a greater success rate than non-SL-based trials. However, about 75% of the preclinically validated SL interactions have not yet been tested in clinical trials.

CONCLUSIONS

Dissecting the recent efforts harnessing SL to identify predictive biomarkers, novel therapeutic targets, and effective combination therapy, our systematic analysis reinforces the hope that SL may serve as a key driver of precision oncology going forward.

FUNDING

Funded by the Samsung Research Funding & Incubation Center of Samsung Electronics, the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Republic of Korea government (MSIT), the Kwanjeong Educational Foundation, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), and Center for Cancer Research (CCR).

Unveiling the vulnerabilities of synthetic lethality in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most invasive molecular subtype of breast cancer (BC), accounting for about nearly 15% of all BC cases reported annually. The absence of the three major BC hormone receptors, Estrogen (ER), Progesterone (PR), and Human Epidermal Growth Factor 2 (HER2) receptor, accounts for the characteristic "Triple negative" phraseology. The absence of these marked receptors makes this cancer insensitive to classical endocrine therapeutic approaches. Hence, the available treatment options remain solemnly limited to only conventional realms of chemotherapy and radiation therapy. Moreover, these therapeutic regimes are often accompanied by numerous treatment side-effects that account for early distant metastasis, relapse, and shorter overall survival in TNBC patients. The rigorous ongoing research in the field of clinical oncology has identified certain gene-based selective tumor-targeting susceptibilities, which are known to account for the molecular fallacies and mutation-based genetic alterations that develop the progression of TNBC. One such promising approach is synthetic lethality, which identifies novel drug targets of cancer, from undruggable oncogenes or tumor-suppressor genes, which cannot be otherwise clasped by the conventional approaches of mutational analysis. Herein, a holistic scientific review is presented, to undermine the mechanisms of synthetic lethal (SL) interactions in TNBC, the epigenetic crosstalks encountered, the role of Poly (ADP-ribose) polymerase inhibitors (PARPi) in inducing SL interactions, and the limitations faced by the lethal interactors. Thus, the future predicament of synthetic lethal interactions in the advancement of modern translational TNBC research is assessed with specific emphasis on patient-specific personalized medicine.

Dose-response prediction for in-vitro drug combination datasets: a probabilistic approach

In this paper we propose PIICM, a probabilistic framework for dose-response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output Gaussian process regression, to predict dose-response surfaces in untested drug combination experiments. Coupled with an observation model that incorporates experimental uncertainty, PIICM is able to learn from noisily observed cell-viability measurements in settings where the underlying dose-response experiments are of varying quality, utilize different experimental designs, and the resulting training dataset is sparsely observed. We show that the model can accurately predict dose-response in held out experiments, and the resulting function captures relevant features indicating synergistic interaction between drugs.

Rational combinations of targeted cancer therapies: background, advances and challenges

Over the past two decades, elucidation of the genetic defects that underlie cancer has resulted in a plethora of novel targeted cancer drugs. Although these agents can initially be highly effective, resistance to single-agent therapies remains a major challenge. Combining drugs can help avoid resistance, but the number of possible drug combinations vastly exceeds what can be tested clinically, both financially and in terms of patient availability. Rational drug combinations based on a deep understanding of the underlying molecular mechanisms associated with therapy resistance are potentially powerful in the treatment of cancer. Here, we discuss the mechanisms of resistance to targeted therapies and how effective drug combinations can be identified to combat resistance. The challenges in clinically developing these combinations and future perspectives are considered.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins as viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal (SL) partners of cancer-specific mutations. Similarly, SL interactions of viral-induced hypomorphs can potentially be targeted as host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for SL partners of GBF1 revealed ARF1 as the top hit, disruption of which selectively killed cells that synthesize 3A alone or in the context of a poliovirus replicon. Thus, viral protein interactions can induce hypomorphs that render host cells selectively vulnerable to perturbations that leave uninfected cells otherwise unscathed. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

Synthetic Vulnerabilities in the KRAS Pathway

Mutations in Kristen Rat Sarcoma viral oncogene (KRAS) are among the most frequent gain-of-function genetic alterations in human cancer. Most KRAS-driven cancers depend on its sustained expression and signaling. Despite spectacular recent success in the development of inhibitors targeting specific KRAS alleles, the discovery and utilization of effective directed therapies for KRAS-mutant cancers remains a major unmet need. One potential approach is the identification of KRAS-specific synthetic lethal vulnerabilities. For example, while KRAS-driven oncogenesis requires the activation of a number of signaling pathways, it also triggers stress response pathways in cancer cells that could potentially be targeted for therapeutic benefit. This review will discuss how the latest advances in functional genomics and the development of more refined models have demonstrated the existence of molecular pathways that can be exploited to uncover synthetic lethal interactions with a promising future as potential clinical treatments in KRAS-mutant cancers.

A large-scale organoid-based screening platform to advance drug repurposing in pancreatic cancer

Hirt et al.¹ report an automated, high-throughput drug screening platform for organoid cultures to enable repurposing of previously approved drugs for pancreatic cancers harboring specific genetic alterations. The pancreatic cancer organoid biobank also represents a valuable tool to uncover new drug-gene interactions in pancreatic tumors.

Towards a CRISPeR understanding of homologous recombination with high-throughput functional genomics

CRISPR-dependent genome editing enables the study of genes and mutations on a large scale. Here we review CRISPR-based functional genomics technologies that generate gene knockouts and single nucleotide variants (SNVs) and discuss how their use has provided new important insights into the function of homologous recombination (HR) genes. In particular, we highlight discoveries from CRISPR screens that have contributed to define the response to PARP inhibition in cells deficient for the HR genes BRCA1 and BRCA2, uncover genes whose loss causes synthetic lethality in combination with BRCA1/2 deficiency, and characterize the function of BRCA1/2 SNVs of uncertain clinical significance. Further use of these approaches, combined with next-generation CRISPR-based technologies, will aid to dissect the genetic network of the HR pathway, define the impact of HR mutations on cancer etiology and treatment, and develop novel targeted therapies for HR-deficient tumors.

Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes

Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein–protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.

Playing cancer at its own game: activating mitogenic signaling as a paradoxical intervention

In psychotherapy, paradoxical interventions are characterized by a deliberate reinforcement of the pathological behavior to improve the clinical condition. Such a counter-intuitive approach can be considered when more conventional interventions fail. The development of targeted cancer therapies has enabled the selective inhibition of activated oncogenic signaling pathways. However, in advanced cancers, such therapies, on average, deliver modest benefits due to the development of resistance. Here, we review the perspective of a 'paradoxical intervention' in cancer therapy: rather than attempting to inhibit oncogenic signaling, the proposed therapy would further activate mitogenic signaling to disrupt the labile homeostasis of cancer cells and overload stress response pathways. Such overactivation can potentially be combined with stress-targeted drugs to kill overstressed cancer cells. Although counter-intuitive, such an approach exploits intrinsic and ubiquitous differences between normal and cancer cells. We discuss the background underlying this unconventional approach and how such intervention might address some current challenges in cancer therapy.

Alternative approaches to target Myc for cancer treatment

The Myc proto-oncogene family consists of three members, C-MYC, MYCN, and MYCL, which encodes the transcription factor c-Myc (hereafter Myc), N-Myc, and L-Myc, respectively. Myc protein orchestrates diverse physiological processes, including cell proliferation, differentiation, survival, and apoptosis. Myc modulates about 15% of the global transcriptome, and its deregulation rewires the cellular signaling modules inside tumor cells, thereby acquiring selective advantages. The deregulation of Myc occurs in >70% of human cancers, and is related to poor prognosis; hence, hyperactivated Myc oncoprotein has been proposed as an ideal drug target for decades. Nevertheless, no specific drug is currently available to directly target Myc, mainly because of its "undruggable" properties: lack of enzymatic pocket for conventional small molecules to bind; inaccessibility for antibody due to the predominant nucleus localization of Myc. Although the topic of targeting Myc has actively been reviewed in the past decades, exciting new progresses in this field keep emerging. In this review, after a comprehensive summarization of valuable sources for potential druggable targets of Myc-driven cancer, we also peer into the promising future of utilizing macropinocytosis to deliver peptides like Omomyc or antibody agents to intracellular compartment for cancer treatment.

Targeting "undruggable" c-Myc protein by synthetic lethality

Synthetic lethal screening, which exploits the combination of mutations that result in cell death, is a promising method for identifying novel drug targets. This method provides a new avenue for targeting "undruggable" proteins, such as c-Myc. Here, we revisit current methods used to target c-Myc and discuss the important functional nodes related to c-Myc in non-oncogene addicted network, whose inhibition may cause a catastrophe for tumor cell destiny but not for normal cells. We further discuss strategies to identify these functional nodes in the context of synthetic lethality. We review the progress and shortcomings of this research field and look forward to opportunities offered by synthetic lethal screening to treat tumors potently.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal partners of cancer-specific mutations. Synthetic lethal interactions of viral-induced hypomorphs have the potential to be similarly targeted for the development of host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for synthetic lethal partners of GBF1 revealed ARF1 as the top hit, disruption of which, selectively killed cells that synthesize poliovirus 3A. Thus, viral protein interactions can induce hypomorphs that render host cells vulnerable to perturbations that leave uninfected cells intact. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

SUMMARY

Using a viral-induced hypomorph of GBF1, Navare et al., demonstrate that the principle of synthetic lethality is a mechanism to selectively kill virus-infected cells.

Correcting an instance of synthetic lethality with a pro-survival sequence

A human cDNA encoding the LIM domain containing 194 amino acid cysteine and glycine rich protein 3 (CSRP3) was identified as a BAX suppressor in yeast and a pro-survival sequence that abrogated copper mediated regulated cell death (RCD). Yeast lacks a CSRP3 orthologue but it has four LIM sequences, namely RGA1, RGA2, LRG1 and PXL1. These are known regulators of stress responses yet their roles in RCD remain unknown. Given that LIMs interact with other LIMs, we ruled out the possibility that overexpressed yeast LIMs alone could prevent RCD and that CSRP3 functions by acting as a dominant regulator of yeast LIMs. Of interest was the discovery that even though yeast cells lacking the LIM encoding PXL1 had no overt growth defect, it was nevertheless supersensitive to the effects of sublethal levels of copper. Heterologous expression of human CSPR3 as well as the pro-survival 14-3-3 sequence corrected this copper supersensitivity. These results show that the pxl1∆-copper synthetic lethality is likely due to the induction of RCD. This differs from the prevailing model in which synthetic lethality occurs because of specific defects generated by the combined loss of two overlapping but non-essential functions.

Synthetic lethality on drug discovery: an update on cancer therapy

INTRODUCTION

A novel anticancer therapy is the need of the hour due to growing incidences of resistance to first line cancer chemotherapy. Synthetic lethality (SL) is one of the new age treatment methods being explored for combating the resistance to anticancer agents. In this method, cell mutations are exploited for the development of new therapeutic agents, where, if there is loss of function of one gene, the cell mutations can still be fixed by alternative machinery but if two genes involved in DNA repair undergo loss of function, it causes lethality to the cell.

AREAS COVERED

The authors condense findings of SL-based novel anticancer regimen. The review emphasizes some of the SL based clinical and preclinical studies of novel targets and therapy.

EXPERT OPINION

SL conceptualizes a resolution against treatment resistance to anticancer regimen by recognition of therapeutic vulnerabilities in particular cancer cells. A multitude of clinical trials associated with SL and DNA repair are being conducted that will be useful in obtaining a clearer picture pertaining to the use of cancer biomarkers and effectiveness of drugs acting via target-based molecular changes. Furthermore, new anticancer regimen focused on personalized medicines will emerge basing their development upon SL.

Synthetic Lethality in Pancreatic Cancer: Discovery of a New RAD51-BRCA2 Small Molecule Disruptor That Inhibits Homologous Recombination and Synergizes with Olaparib

Synthetic lethality is an innovative framework for discovering novel anticancer drug candidates. One example is the use of PARP inhibitors (PARPi) in oncology patients with BRCA mutations. Here, we exploit a new paradigm based on the possibility of triggering synthetic lethality using only small organic molecules (dubbed “fully small-molecule-induced synthetic lethality”). We exploited this paradigm to target pancreatic cancer, one of the major unmet needs in oncology. We discovered a dihydroquinolone pyrazoline-based molecule (35d) that disrupts the RAD51-BRCA2 protein–protein interaction, thus mimicking the effect of BRCA2 mutation. 35d inhibits the homologous recombination in a human pancreatic adenocarcinoma cell line. In addition, it synergizes with olaparib (a PARPi) to trigger synthetic lethality. This strategy aims to widen the use of PARPi in BRCA-competent and olaparib-resistant cancers, making fully small-molecule-induced synthetic lethality an innovative approach toward unmet oncological needs.

PARP Inhibitors as Therapeutics: Beyond Modulation of PARylation

Poly (ADP-ribose) polymerase (PARP) 1 is an essential molecule in DNA damage response by sensing DNA damage and docking DNA repair proteins on the damaged DNA site through a type of posttranslational modification, poly (ADP-Ribosyl)ation (PARylation). PARP inhibitors, which inhibit PARylation through competitively binding to NAD+ binding site of PARP1 and PARP2, have improved clinical benefits for BRCA mutated tumors, leading to their accelerated clinical application. However, the antitumor activities of PARP inhibitors in clinical development are different, due to PARP trapping activity beyond blocking PARylation reactions. In this review, we comprehensively address the current state of knowledge regarding the mechanisms of action of PARP inhibitors. We will also discuss the different effects of PARP inhibitors in combination with cytotoxic chemotherapeutic agents regarding the mechanism of regulating PARylation.

The Mutator Phenotype: Adapting Microbial Evolution to Cancer Biology

The mutator phenotype hypothesis was postulated almost 40 years ago to reconcile the observation that while cancer cells display widespread mutational burden, acquisition of mutations in non-transformed cells is a rare event. Moreover, it also suggested that cancer evolution could be fostered by increased genome instability. Given the evolutionary conservation throughout the tree of life and the genetic tractability of model organisms, yeast and bacterial species pioneered studies to dissect the functions of genes required for genome maintenance (caretaker genes) or for cell growth control (gatekeeper genes). In this review, we first provide an overview of what we learned from model organisms about the roles of these genes and the genome instability that arises as a consequence of their dysregulation. We then discuss our current understanding of how mutator phenotypes shape the evolution of bacteria and yeast species. We end by bringing clinical evidence that lessons learned from single-cell organisms can be applied to tumor evolution.

Every which way? On predicting tumor evolution using cancer progression models

Successful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.

Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis

Computational approaches have previously been introduced to predict compounds with activity against multiple targets or compound combinations with synergistic functional effects. By contrast, there are no computational studies available that explore combinations of targets that might act synergistically upon small molecule treatment. Herein, we introduce an approach designed to identify synergistic target pairs on the basis of cell-based screening data and compounds with known target annotations. The targets involved in forming synergistic pairs were analyzed through a novel network propagation algorithm for rationalizing possible common synergy mechanisms. This algorithm enabled further analysis of each synergistic target pair and the identification of "interactors", i.e., proteins with higher propagation scores than would be expected by adding the individual contributions of each target in the synergistic pair. We detected 137 synergistic target pairs including 51 unique targets. A global network analysis of these 51 targets made it possible to derive a subnetwork of proteins with significant synergy. Furthermore, interactors were identified for 87 synergistic target pairs upon individual analysis of the network propagation of each pair. These interactors were associated with pathways related to cancer and apoptosis, membrane transport, and steroid metabolism and provided possible explanations of synergistic effects.

Surmounting Cytarabine-resistance in acute myeloblastic leukemia cells and specimens with a synergistic combination of hydroxyurea and azidothymidine

Acute myeloid leukemia (AML) patients display dismal prognosis due to high prevalence of refractory and relapsed disease resulting from chemoresistance. Treatment protocols, primarily based on the anchor drug Cytarabine, remained chiefly unchanged in the past 50 years with no standardized salvage regimens. Herein we aimed at exploring potential pre-clinical treatment strategies to surmount Cytarabine resistance in human AML cells. We established Cytarabine-resistant sublines derived from human leukemia K562 and Kasumi cells, and characterized the expression of Cytarabine-related genes using real-time PCR and Western blot analyses to uncover the mechanisms underlying their Cytarabine resistance. This was followed by growth inhibition assays and isobologram analyses testing the sublines’ sensitivity to the clinically approved drugs hydroxyurea (HU) and azidothymidine (AZT), compared to their parental cells. All Cytarabine-resistant sublines lost deoxycytidine kinase (dCK) expression, rendering them refractory to Cytarabine. Loss of dCK function involved dCK gene deletions and/or a novel frameshift mutation leading to dCK transcript degradation via nonsense-mediated decay. Cytarabine-resistant sublines displayed hypersensitivity to HU and AZT compared to parental cells; HU and AZT combinations exhibited a marked synergistic growth inhibition effect on leukemic cells, which was intensified upon acquisition of Cytarabine-resistance. In contrast, HU and AZT combination showed an antagonistic effect in non-malignant cells. Finally, HU and AZT synergism was demonstrated on peripheral blood specimens from AML patients. These findings identify a promising HU and AZT combination for the possible future treatment of relapsed and refractory AML, while sparing normal tissues from untoward toxicity.

RNA Interference-Based Cancer Drugs: The Roadblocks, and the "Delivery" of the Promise

Nucleic acid-based therapeutics like synthetic small interfering RNAs have been exploited to modulate gene function, taking advantage of RNA interference (RNAi), an evolutionally conserved biological process. Recently, the world's first RNAi drug was approved for a rare genetic disorder in the liver. However, there are significant challenges that need to be resolved before RNAi can be translated in other genetic diseases like cancer. Current drug delivery platforms for therapeutic silencing RNAs are tailored to hepatic targets. RNAi therapies for nonhepatic conditions are still at early clinical phases. In this study, we discuss the critical design considerations in anticancer RNAi drug development, insights gained from initial clinical trials, and new strategies that are entering clinical development, shaping the future of RNAi in cancer.

Cancer progression models and fitness landscapes: a many-to-many relationship

Motivation

The identification of constraints, due to gene interactions, in the order of accumulation of mutations during cancer progression can allow us to single out therapeutic targets. Cancer progression models (CPMs) use genotype frequency data from cross-sectional samples to identify these constraints, and return Directed Acyclic Graphs (DAGs) of restrictions where arrows indicate dependencies or constraints. On the other hand, fitness landscapes, which map genotypes to fitness, contain all possible paths of tumor progression. Thus, we expect a correspondence between DAGs from CPMs and the fitness landscapes where evolution happened. But many fitness landscapes-e.g. those with reciprocal sign epistasis-cannot be represented by CPMs.

Results

Using simulated data under 500 fitness landscapes, I show that CPMs' performance (prediction of genotypes that can exist) degrades with reciprocal sign epistasis. There is large variability in the DAGs inferred from each landscape, which is also affected by mutation rate, detection regime and fitness landscape features, in ways that depend on CPM method. Using three cancer datasets, I show that these problems strongly affect the analysis of empirical data: fitness landscapes that are widely different from each other produce data similar to the empirically observed ones and lead to DAGs that infer very different restrictions. Because reciprocal sign epistasis can be common in cancer, these results question the use and interpretation of CPMs.

Availability and implementation

Code available from Supplementary Material.

Contact

ramon.diaz@iib.uam.es.

Supplementary information

Supplementary data are available at Bioinformatics online.

Taking advantage of drug resistance, a new approach in the war on cancer

Identification of the driver mutations in cancer has resulted in the development of a new category of molecularly targeted anti-cancer drugs. However, as was the case with conventional chemotherapies, the effectiveness of these drugs is limited by the emergence of drug-resistant variants. While most cancer therapies are given in combinations that are designed to avoid drug resistance, we discuss here therapeutic approaches that take advantage of the changes in cancer cells that arise upon development of drug resistance. This approach is based on notion that drug resistance comes at a fitness cost to the cancer cell that can be exploited for therapeutic benefit.We discuss the development of sequential drug therapies in which the first therapy is not given with curative intent, but to induce a major new sensitivity that can be targeted with a second drug that selectively targets the acquired vulnerability. This concept of collateral sensitivity has hitherto not been used on a large scale in the clinic and holds great promise for future cancer therapy.

Harnessing synthetic lethality to predict the response to cancer treatment

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

Haploid genetic screens identify genetic vulnerabilities to microtubule-targeting agents

The absence of biomarkers to accurately predict anticancer therapy response remains a major obstacle in clinical oncology. We applied a genome‐wide loss‐of‐function screening approach in human haploid cells to characterize genetic vulnerabilities to classical microtubule‐targeting agents. Using docetaxel and vinorelbine, two well‐established chemotherapeutic agents, we sought to identify genetic alterations sensitizing human HAP1 cells to these drugs. Despite the fact that both drugs act on microtubules, a set of distinct genes were identified whose disruption affects drug sensitivity. For docetaxel, this included a number of genes with a function in mitosis, while for vinorelbine we identified inactivation of FBXW7,RB1, and NF2, three frequently mutated tumor suppressor genes, as sensitizing factors. We validated these genes using independent knockout clones and confirmed FBXW7 as an important regulator of the mitotic spindle assembly. Upon FBXW7 depletion, vinorelbine treatment led to decreased survival of cells due to defective mitotic progression and subsequent mitotic catastrophe. We show that haploid insertional mutagenesis screens are a useful tool to study genetic vulnerabilities to classical chemotherapeutic drugs by identifying thus far unknown sensitivity factors. These results provide a rationale for investigating patient response to vinca alkaloid‐based anticancer treatment in relation to the mutational status of these three tumor suppressor genes, and could in the future lead to the establishment of novel predictive biomarkers or suggest new drug combinations based on molecular mechanisms of drug sensitivity.

Precision Oncology: The Road Ahead

Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and nongenetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single cell analysis of tumor specimens. These may ultimately lead to the development of next-generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies.