Functional genomics approaches to improve pre‐clinical drug screening and biomarker discovery
2021; Springer Nature; Volume: 13; Issue: 9 Linguagem: Inglês
10.15252/emmm.202013189
ISSN1757-4684
Autores Tópico(s)Advanced biosensing and bioanalysis techniques
ResumoReview13 July 2021Open Access Functional genomics approaches to improve pre-clinical drug screening and biomarker discovery Long V Nguyen orcid.org/0000-0002-3347-438X Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Cambridge Cancer Centre, Cambridge, UK Search for more papers by this author Carlos Caldas Corresponding Author [email protected] orcid.org/0000-0003-3547-1489 Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Cambridge Cancer Centre, Cambridge, UK Search for more papers by this author Long V Nguyen orcid.org/0000-0002-3347-438X Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Cambridge Cancer Centre, Cambridge, UK Search for more papers by this author Carlos Caldas Corresponding Author [email protected] orcid.org/0000-0003-3547-1489 Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK Cancer Research UK Cambridge Cancer Centre, Cambridge, UK Search for more papers by this author Author Information Long V Nguyen1,2 and Carlos Caldas *,1,2 1Department of Oncology and Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK 2Cancer Research UK Cambridge Cancer Centre, Cambridge, UK **Corresponding author. Tel: +44 01223 769650; E-mail: [email protected] EMBO Mol Med (2021)13:e13189https://doi.org/10.15252/emmm.202013189 See the Glossary for abbreviations used in this article. PDFDownload PDF of article text and main figures. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Advances in sequencing technology have enabled the genomic and transcriptomic characterization of human malignancies with unprecedented detail. However, this wealth of information has been slow to translate into clinically meaningful outcomes. Different models to study human cancers have been established and extensively characterized. Using these models, functional genomic screens and pre-clinical drug screening platforms have identified genetic dependencies that can be exploited with drug therapy. These genetic dependencies can also be used as biomarkers to predict response to treatment. For many cancers, the identification of such biomarkers remains elusive. In this review, we discuss the development and characterization of models used to study human cancers, RNA interference and CRISPR screens to identify genetic dependencies, large-scale pharmacogenomics studies and drug screening approaches to improve pre-clinical drug screening and biomarker discovery. Glossary Genotype–phenotype relationships The association between a specific genotype (i.e., genetic alteration) and the resulting phenotype (i.e., observable characteristic). In cancer biology, this refers to how specific genetic alterations produce changes in the properties of tumour cells. Patient avatars Patient-derived tumour xenografts that are co-treated with the same therapies as the patient to mimic changes that would occur in the patient in order to evaluate response to therapy and changes in clonal heterogeneity. Predictive biomarker A marker (e.g., genetic abnormality or protein) that can be used to predict the response of a tumour to a specific therapy. Spatiotemporal heterogeneity This refers to the different clones (that harbour distinct genomic aberrations) present in different areas of a tumour and that appeared at different time points in disease progression. Targeted therapy Drug therapy directed at a specific gene product or pathway, as opposed to chemotherapy, which is less specific and targets all proliferating cells. Tumour microenvironment Interactions between tumour cells and the cells and growth factors within its surrounding environment, such as stromal cells, blood vessels and infiltrating inflammatory cells. Introduction Rapidly developing sequencing technology, in conjunction with advances in computational analysis, has culminated in the genomic and transcriptomic characterization of individual patients' tumours, bringing the possibility of precision-medicine to the clinic. However, a remaining challenge is how to use this information to identify therapeutic targets and predictive biomarkers to guide clinical decision-making about whether a particular drug is likely to be effective for the treatment of a specific patient's disease. There have been a number of notable successes in the development of targeted therapy. Some of these include the use of tyrosine kinase inhibitors such as Imatinib for chronic myelogenous leukaemia harbouring the BCR-ABL translocation (Annunziata et al, 2020), BRAF/MEK inhibitors for BRAF mutated cancers (Zaman et al, 2019), HER2-targeted therapies in breast cancer (Wang & Xu, 2019), and inhibitors of EGFR or ALK kinases in lung adenocarcinomas driven by EGFR mutation or ALK fusions (Bernicker et al, 2019). In many other cancer types, discoveries of predictive biomarkers for clinical use have remained elusive. There are a number of reasons for this. The detection of a specific mutation does not necessarily mean it is a driver mutation, the mutation may not be present in a majority of the cells that drive disease progression, and rapid development of resistance may occur from the selection or de novo generation of drug-resistant clones. To overcome these challenges, efforts have been taken to model cancer in different systems, study how specific genomic alterations result in changes to tumour growth, and test different therapies in these model systems as a surrogate readout for how the patient's cancer will respond to treatment. These approaches offer the opportunity to uncover genetic dependencies, identify novel therapeutic targets and define mechanisms of drug resistance. In this review, we will discuss the advantages and limitations of different models of human cancer, including human cancer cell lines, patient-derived tumour organoids (PDTOs), and patient-derived tumour xenografts (PDTXs). We will also review RNA interference (RNAi) and CRISPR technologies applied to functional genomic screens for the discovery of novel therapeutic targets, drug screens, and combination therapy screens using different cancer models in order to identify novel molecular biomarkers that can predict response to drug therapy. Models to study human cancers Human cancer cell lines Hundreds of human cancer cell lines have been established and are the most widely used to study cancer biology and drug screening. Resources with comprehensive genomic information about these cell lines include the Cancer Cell Line Encyclopedia (CCLE; Barretina et al, 2012), the Genomics of Drug Sensitivity in Cancer (GDSC; Yang et al, 2013; Iorio et al, 2016), the Cell Model Passports (van der Meer et al, 2019), the Cellosaurus (Bairoch, 2018), and the COSMIC catalogue of somatic mutations (Tate et al, 2019). There are also gene expression, single nucleotide polymorphism, gene fusion (Klijn et al, 2015) and proteomic data on these cell lines, from the MD Anderson Cell Lines Project (MCLP) (Li et al, 2017) and the NCI-60 cell lines (Nishizuka et al, 2003; Gholami et al, 2013). The main advantage of human cancer cell lines is that they can be easily maintained in vitro through serial passaging and expansion, thus representing an unlimited resource (Fig 1). Cell lines are generally perceived to be homogeneous, offering a simplistic model for studying the functional consequences of different genomic perturbations, making them ideal for high-throughput drug screening and functional assays (Sachs & Clevers, 2014). The lack of phenotypic and genetic heterogeneity (compared to the original tumour) can be attributed to the high selection pressures in early passages when the cells are forced to grow in 2D culture. There may also be genetic deviation over time whereby the cell line is no longer representative of the original patient's disease (Stein et al, 2004). This is demonstrated when gene expression profiles of tumours more closely resemble normal tissues than cancer cell lines (van Staveren et al, 2009). However, recent whole-exome sequencing of 106 human cancer cell lines showed they are in fact highly heterogeneous. This was attributed to the presence of pre-existing subclones and the emergence of new genetic variants, which contribute to genetic instability and may ultimately affect drug sensitivity screens (Ben-David et al, 2018). Figure 1. Models to study human cancers The main models to study human cancers are human cancer cell lines, 3-dimensional in vitro culture systems such as those used to grow PDTOs, PDTXs and PDTCs as depicted in the diagram above. PDTCs are short-term ex vivo cultures of cells dissociated from PDTXs and can be used for drug screening. The outer circles represent different characteristics of model systems that may be required to study different aspects of cancer biology. For example, human cancer cell lines have been immortalized and thus have infinite replicative capacity in vitro (Sachs & Clevers, 2014). PDTOs are more representative of the full spectrum of human cancers, given the high rate at which they can be established (Sachs et al, 2018). Both PDTOs and PDTXs retain spatial architecture (Clevers, 2016; McGranahan & Swanton, 2017), PDTXs retain tumour-stromal interactions important for studying the role of the microenvironment in tumour biology (Julien et al, 2012; Peng et al, 2013). Adaptations of PDTO models can be made to study tumour-stromal interactions (Aboulkheyr Es et al, 2018), and interactions between the tumour and immune cells (Neal et al, 2018), as opposed to PDTXs which are grown in mice typically lacking a functional immune system (Shultz et al, 2014; Byrne et al, 2017). Download figure Download PowerPoint Human cancer cell lines are also difficult to establish from primary patient material. Faster growing cancer cells are more likely to adapt to in vitro growth conditions compared to slow-growing cells, thus resulting in an over-representation of advanced/metastatic cancers as cell lines (Masters, 2000). Another limitation of human cancer cell lines is that there have been significant differences found in their transcriptomic profiles compared to primary human tumour samples (Najgebauer et al, 2020). These differences include an upregulation of cell-cycle-related pathways and downregulation of immune pathways in cell lines compared to primary tumours (Yu et al, 2019). The other drawback of cell lines is the lack of tumour architecture and microenvironment, which may be a reason why drug therapies identified from work with cell lines do not always translate into clinical efficacy. Nevertheless, human cancer cell lines are still widely used to model disease and test drug sensitivity due to their ease for incorporation into high-throughput screens and also hold promise for unveiling novel drug targets by repurposing already known drug therapies (Pushpakom et al, 2019). Patient-derived tumour organoids Organoids were initially established from normal tissues, generated from either pluripotent stem cells, or adult organ-restricted stem cells, and self-organize into 3D structures that contain the same diversity of cell types and architecture of the original organ tissue (Clevers, 2016). Recently, organoids have also been generated from numerous human tumours, including breast, prostate, lung, colorectal, renal, bladder, pancreatic, oesophageal, gastric, liver, and ovarian cancers (Bleijs et al, 2019). The main advantage of PDTOs is that they appear to regenerate the heterogeneity seen in human cancers (Fig 1). For example, colorectal PDTOs derived from single cells have demonstrated extensive mutational diversification and a differential response to anti-cancer drugs (Roerink et al, 2018). Weeber and colleagues also showed that colorectal PDTOs retain 90% of somatic mutations and DNA copy number alterations (CNAs) compared to original biopsy specimens (Weeber et al, 2015). Modelling the tumour microenvironment is crucial to investigating the anti-tumour effects of immune checkpoint inhibitors. However, similar to cell lines, PDTO culture systems do not preserve the tumour microenvironment. To circumvent this limitation, several groups have developed microfluidics platforms to co-culture PDTOs with other cell types, such as adipocytes, lymphocytes, macrophages and myofibroblasts, in an attempt to simulate the interactions between the tumour and cellular components of the microenvironment (Aboulkheyr Es et al, 2018). Alternatively, Neal and colleagues cultured PDTOs with an air–liquid interface to preserve stromal architecture and functional tumour-infiltrating lymphocytes. They validate this system on colorectal, pancreas, lung, biliary and primary CNS cancers and show that tumour-infiltrating lymphocytes constituted the full T-cell receptor spectrum of the original tumour, and further that treatment with anti-PD-1 and anti-PD-L1 immune checkpoint inhibitors resulted in the expected tumour cytotoxicity (Neal et al, 2018). Organoid biobanks have been established in numerous tissue systems, including colorectal (van de Wetering et al, 2015), and breast (Sachs et al, 2018), some of which have been extensively characterized. For example, Sachs and colleagues established a biobank of human breast organoids with a > 80% success rate of PDTO formation and showed the organoids closely resemble patients' tumours in terms of histopathology, hormone and HER2 receptor status, CNA and mutational profiles, and gene expression, which were preserved even after extended passaging in vitro. The 'Cell Model Passports' is a helpful resource that compiles clinical information and sequencing data from PDTOs derived from different tumour types (van der Meer et al, 2019). Patient-derived tumour xenografts In general, PDTXs are established by taking freshly obtained tumour cells from a patient and engrafting these cells into immunodeficient mice (Fig 1). While this model is thought to most closely resemble the original patient's tumour, since cellular and genetic heterogeneity, tumour architecture and microenvironment are preserved, these models can be both time consuming and expensive. Generation of a PDTX model can require up to 4–8 months, limiting its usefulness in the clinic for real-time testing of response to treatment (Hidalgo et al, 2014). Tumour engraftment is largely dependent on the use of mice with deficient immune systems. Recently, highly immune-deficient NSG mice, which lack mature T, B and natural killer cells, have allowed higher engraftment rates, as compared to earlier models of immune-deficient mice, such as NOD-SCID, and athymic mice (Shultz et al, 2014; Byrne et al, 2017). One concern in this situation is that the process whereby PDTXs engraft and propagate may not resemble human cancers that usually initiate in an immune-competent host. Moreover, PDTXs in immune-deficient hosts may represent a poor model to study the effects of immunotherapy on the tumour microenvironment. In PDTXs, the microenvironment is largely thought to be preserved on initial engraftment as human stromal components are also engrafted, though over time, human stromal cells are replaced by murine stromal cells (Julien et al, 2012; Peng et al, 2013). This can be associated with downregulation of genes corresponding to cell adhesion and immune response (Morgan et al, 2017). There may also be metabolic differences in PDTXs established orthotopically versus subcutaneously, a finding that was attributed to differences in the microenvironment (Zhan et al, 2017). Conversely, recent proteomic analyses demonstrated in a PDTX model of colorectal cancer that infiltrating murine stromal cells adopted metabolic profiles that became "human-like" and recapitulated what was found in original patient tumours (Blomme et al, 2018). Due to spatiotemporal heterogeneity observed in different tumours, establishing PDTXs from a single small sample of the original tumour will likely not capture the full mutational diversity present in a patient's disease (McGranahan & Swanton, 2017). This has been illustrated in PDTX models of melanoma (Rabbie et al, 2020), where the spatial heterogeneity of different genetic clones results in variable responses to treatment (Kemper et al, 2015; Wellbrock, 2015). This suggests that spatial clonal heterogeneity can have a substantial impact on studies using PDTXs to study tumour biology and response to therapy. Numerous efforts have been made to establish PDTXs as a resource to study tumour heterogeneity and responses to therapy (Bertotti et al, 2011; Byrne et al, 2017; Woo et al, 2021). These studies have demonstrated that PDTXs largely retain the same histologic and genetic characteristics of the original patient's tumour (Hidalgo et al, 2014). This appears to be true in numerous tumour types, including breast (DeRose et al, 2011; Bruna et al, 2016a), lung (Fichtner et al, 2008), colon (Dalerba et al, 2011; Julien et al, 2012), prostate (Palanisamy et al, 2020), melanoma (Krepler et al, 2017), bladder (Kim et al, 2020) and pancreatic cancers (Loukopoulos et al, 2004), among others. However, PDTXs are also generated with higher efficiency from invasive and metastatic tumours compared to slow-growing or non-metastatic tumours. For example, triple-negative breast cancers have shown a higher engraftment efficiency compared to hormone-receptor-positive breast cancers, that are generally thought to be less aggressive clinically (DeRose et al, 2011; Zhang et al, 2013). This has led to the establishment of PDTX biobanks that are over-representative of more aggressive tumours. One major concern has been that clonal selection pressures on initial engraftment and subsequent passaging of PDTXs may alter the clonal composition of the PDTXs, such that it does not entirely mimic the original patient tumour (Ben-David et al, 2017). Eirew and colleagues analysed the clonal dynamics of breast tumour xeno-engraftment into immunodeficient mice and found variable clonal selection pressures between samples (Eirew et al, 2015). However, an international consortium analysed the CNA profiles of over 500 PDTX models at high-resolution and found a strong conservation of CNAs between patient tumours and late passage PDTXs (PDXNET Consortium et al, 2021). Together, these studies suggest that clonal selection can be variable between PDTX models, though in many cases are relatively conserved. Transcriptomic analyses of established PDTXs have also demonstrated PDTXs mirror the gene expression patterns observed in original patient tumours (Dalerba et al, 2011), and they appear to maintain clonal intra-tumour heterogeneity and tumour architecture, even with serial passaging (Bruna et al, 2016a). Furthermore, short-term cultures can be generated from PDTXs, called PDTX-derived tumour cells (PDTCs, Fig 1), and these can be used for pre-clinical high-throughput drug screens (Bruna et al, 2016b; Georgopoulou et al, 2021). While establishing PDTXs can be resource intensive, they most closely resemble the original patient tumour and microenvironment, and thus hold the most promise for validating targeted therapies that will have meaningful clinical outcomes. Identifying novel predictive biomarkers for clinical use For the purposes of cancer treatment, an ideal predictive biomarker is one that can be easily assessed from a patient's biopsy specimen and has a high likelihood of predicting response to a particular line of therapy. Tissue on which the biomarker can be assessed can be obtained by biopsy of the primary tumour or sites of metastases, or liquid biopsy in the form of circulating tumour cells or circulating cell-free tumour DNA (ctDNA; Wan et al, 2017). ctDNA is released into the bloodstream from the turnover of tumour cells and has been used to monitor disease burden (Dawson et al, 2013), detect the presence of clinically actionable mutations, and to analyse clonal evolution in cancer (Murtaza et al, 2015). ctDNA thus has appeal for obtaining helpful biological information from non-invasive liquid biopsy of a patient's blood. The biomarker itself can be the presence or absence of genetic alterations such as mutations, translocations, copy number alterations, epigenetic modifications or gene expression profiles indicating dependency on a specific targetable pathway. While there have been a number of disease context-specific practice-changing discoveries, such as HER2-targeted therapy in HER2 amplified breast and gastroesophageal cancers, or poly(ADP-ribose) polymerase (PARP) inhibition in ovarian cancers with homologous DNA repair deficiency, there still appear to be a range of responses to appropriately selected therapy and the eventual development of drug resistance. Hence, there is value in the discovery of predictive biomarkers not only for other disease-specific contexts where there are no reliable biomarkers to guide the choice of appropriate therapy, but also for the prediction of drug resistance, and the selection of appropriate therapy once this has developed. There are a number of approaches to discover novel biomarkers in cancer. These include genomic and transcriptomic profiling, proteomic approaches and metabolomic approaches, all of which are beyond the scope of our review but have been covered in detail elsewhere (Armitage & Barbas, 2014; Hristova & Chan, 2019). We will focus on the identification of novel therapeutic targets, in the context of genetic alterations that can predict response to specific drug therapies and thus act as useful predictive biomarkers. Functional genomic screens for discovery of novel therapeutic targets The basic premise of a functional genomic screen is that disruption of a key gene product essential for tumour growth will result in tumour regression and death. This particular gene product thus represents a potential therapeutic target. If the gene of interest represents an essential pathway in normal cells as well, the therapeutic index is expected to be narrow, and treatment associated with high levels of toxicity. Therefore, identifying genetic dependencies such as oncogenic addiction, where the cancer cells are driven by and dependent on a certain oncogene, or synthetic lethality, where the acquisition of a second genetic alteration leads to cell death, may uncover more ideal therapeutic targets (Fig 2). Figure 2. Depiction of genetic dependencies in cancer Synthetic lethality is depicted on the left, using VPS4A on chromosome 16q and VPS4B on chromosome 18q as an example. When 16q or 18q deletions occur separately, there is no effect on tumour growth. However, when VPS4B is inhibited in tumour cells with 16q deletion, or when VPS4A is inhibited in tumour cells with 18q deletion, synthetic lethality occurs and there is cell death (Neggers et al, 2020). Oncogenic addiction is depicted on the right, using BRAF as an example. In cells harbouring an oncogenic BRAF-V600E mutation, there is constitutive activation of the signalling pathway leading to cell growth and proliferation. However, these cells are particularly sensitive to knockdown or inhibition of BRAF, which leads to cell death, as the cells are dependent on this signalling pathway for continued growth (Settleman, 2012). Download figure Download PowerPoint The concept of synthetic lethality was originally described in Drosophila as recessive lethality, in which the loss of one gene has little effect on cell viability, but the additional loss of a second gene leads to cell death. Extrapolated to the context of cancer, when the cancer cells harbour a specific fixed genetic alteration of one gene, the additional loss of a second gene either by mutation, deletion or pharmacological inhibition, will result in selective cell death of cancer cells sparing normal cells that do not harbour the specific fixed genetic alteration (Kaelin, 2005). The first most clinically relevant application of synthetic lethality is the use of PARP inhibitors in BRCA1- or BRCA2-deficient tumours (Fong et al, 2009). PARP, BRCA1 and BRCA2 are components required for efficient DNA repair, and therefore, tumour cells that already harbour a BRCA1 or BRCA2 mutation will have increased sensitivity to PARP inhibitor therapy (Bryant et al, 2005; Farmer et al, 2005). Normal cells have at least one functioning copy of BRCA1 or BRCA2 and are thus largely spared, limiting toxicity from PARP inhibitor therapy. PARP inhibitors have demonstrated a progression-free survival benefit as maintenance therapy for treatment-naïve advanced/metastatic ovarian cancer in patients with a germline or somatic BRCA1 or BRCA2 mutation (Moore et al, 2018), and an overall survival benefit as maintenance therapy for similar patients who experience disease recurrence (Pujade-Lauraine et al, 2017), and are now routinely recommended for patients with advanced/metastatic ovarian cancer with homologous DNA repair deficiency following standard chemotherapy. The FDA has now approved four different PARP inhibitors for clinical use: Olaparib, Rucaparib, Niraparib and Talazoparib (Huang et al, 2020). Synthetic lethality screens are designed to be high-throughput and thus offer the possibility of discovering multiple synthetic lethal pairs that may be relevant in different cancer types, though as we will discuss, they have a number of limitations. RNAi and CRISPR screens The first synthetic lethal screens were conducted using RNA interference technology. With this approach, short hairpin RNA (shRNA) and short interfering RNA (siRNA) sequences are designed to contain a short "seed sequence" that can bind to and downregulate mRNA to repress expression of the gene of interest (Sims et al, 2011). However, it was found that shRNA and siRNA could bind to and cause the downregulation of mRNAs unrelated to the gene of interest, so-called "off-target effects", that resulted in false-positive hits in functional genomic screens. In response, there have been informatics tools developed to eliminate false-positive data from off-target effects (Tsherniak et al, 2017). Recently developed CRISPR technology has proven to be much more specific, offering an efficient and robust tool for high-throughput screening. Generally, CRISPR screens work by using custom-designed guide RNAs (gRNAs) with a target sequence of 20 basepairs to direct Cas9 to sequence-specific regions of the genome, in order to induce a double-strand DNA break thus resulting in precise biallelic loss-of-function mutations (Basheer & Vassiliou, 2019). Variations of this technology have also been developed, including CRISPR interference (CRISPRi) that results in suppression of gene expression rather than excision of genomic DNA sequences, base editing, which allows for specific base pair modifications, and RNA targeting, where enzymes recognize and edit mRNA sequences rather than DNA (Huang et al, 2020). There is also a CRISPR strategy designed to target fusion oncogenes through targeting of two intronic sequences, one from each gene in the fusion oncogene, without affecting the genes in germline non-rearranged alleles (Martinez-Lage et al, 2020). Combinatorial CRISPR screens are increasingly being used to identify synthetic lethal targets (Thompson et al, 2021). Zhou and colleagues demonstrate the efficient knockout of three genes simultaneously in a human ovarian cancer cell line, to illustrate their technology can be applied to screen for synergistic anti-cancer genetic combinations (Zhou et al, 2020). Gier and colleagues developed a similar approach, but using a Cas12a-based system, to perform a double-knockout screen in murine leukaemia cells to uncover synthetic lethal interactions (Gier et al, 2020). How efficient these vector systems can be translated to PDTOs and PDTX models has not yet been explored, but these approaches offer promise in identifying synthetic lethal targets. Notably, it has recently been demonstrated that chromothripsis, a single event that results in extensive clustered chromosomal rearrangements, occurs as an on-target consequence of CRISPR-Cas9 genome editing. Therefore, such events also need to be considered and carefully monitored in experimental models that incorporate this approach (Leibowitz et al, 2021). High-throughput functional genomic screens using human cancer cell lines Project DRIVE, from Novartis (McDonald et al, 2017), and Project Achilles, from the Broad Institute (Cowley et al, 2014), both used a library of shRNA to screen hundreds of human cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) project. Both projects described oncogenic addictions whereby cell lines driven by common oncogenes, such as KRAS, NRAS and BRAF, exhibited increased dependency and thus sensitivity to suppression by shRNA. As expected, KRAS mutation dependence was observed in colon, pancreatic and lung cancer cell lines, NRAS mutation dependence in melanoma lines, and BRAF mutation dependence in colon, thyroid and melanoma lines (McDonald et al, 2017). The presence of certain oncogenes also predicted dependency on other genes, such as with PIK3CA mutated cell lines demonstrating a preferential dependency on MTOR (Cowley et al, 2014). These projects also described examples of known collateral synthetic lethality which are synthetic lethal relationships among paralog genes for which dependency on one paralog is conferred by loss of a second functionally redundant paralog gene. Examples of this include SMARCA2/SMARCA4, ARID1A/ARID1B, UBB-UBC and VPS4A/VPS4B. The latter pa
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