Abstract
Synthetic lethality (SL) provides a conceptual framework for tackling targets that are not classically ‘druggable,’ including loss-of-function mutations in tumor suppressor genes required for carcinogenesis. Recent technological advances have led to an inflection point in our understanding of genetic interaction networks and ability to identify a wide array of novel SL drug targets. Here, we review concepts and lessons emerging from first-generation trials aimed at testing SL drugs, discuss how the nature of the targeted lesion can influence therapeutic outcomes, and highlight the need to develop clinical biomarkers distinct from those based on the paradigms developed to target activated oncogenes.
Keywords: synthetic lethality, PARP, BRCA, loss-of-function, gain-of-function
INTRODUCTION
The implementation of the genetic concept of synthetic lethality (SL) in cancer therapy holds great promise but remains in its infancy (1,2). Recent technological advances in high-throughput forward genetic and chemogenetic screening in human cells have led to a rapid increase in our understanding of genetic interaction networks and ability to identify novel SL drug targets. Successive waves of focused and large-scale CRISPR-based screens are producing a profound increase in the number of candidate SL targets (3–11), many of which are currently in drug development pipelines. As such targets reach the later stages of preclinical validation and therapeutic development, clinical trials aimed at testing this new generation of SL therapeutics are poised to undergo a dramatic increase in number and scope within coming years.
Here, we review concepts and lessons emerging from first-generation clinical trials aimed at testing SL drugs, including inhibitors of the poly (ADP-ribose) polymerase (PARP) in the treatment of BRCA1/2-deficient tumors, an archetypal example of SL anticancer therapy, and highlight how the nature of the targeted lesion and its genetic context can influence efficacy and durability of response. Furthermore, we discuss the need to depart from existing paradigms for biomarker development, which were primarily developed for drugs targeting activated oncogenes, and emphasize additional factors likely to influence the success of future efforts to apply SL in cancer therapy.
The concept of SL can be traced back to initial observations made almost 100 years ago by the American geneticist Calvin Bridges, who noted that when crossing fruit flies (Drosophila melanogaster), inheritance of specific mutant gene pairs were lethal in combination, despite inheritance of either mutant gene alone resulting in perfectly normal viability. Further contemporary development of knockout libraries and systematic genetic interaction studies in yeast model systems led to refinement of SL, as defined by cellular or organismal lethality caused by combined alterations of gene pairs that are otherwise individually viable (Fig 1a). With respect to cancer therapy, the definition of SL has since been expanded to encompass pharmacologic inhibition of one gene product with inactivation of the other in cancer cells (12). If one considers chemical inhibition of a gene product as akin to genetic loss of function (LOF), drugs acting through an SL mechanism can be thought to target genetic alterations through the modulation of the product of a second unaltered gene. It should be noted, however, that the term SL has also been employed in the cancer therapeutics arena for the targeting of gain-of-function (GOF) alterations in cancer cells that are not amenable to direct targeting (e.g. targeting of mutant KRAS (1), or amplification of CCNE1 (13)).
Genetic Interaction Networks Underpin the Molecular Basis for SL Therapeutics
Initial efforts to develop personalized targeted anticancer therapies largely focused on the inhibition of proteins encoded by GOF alterations in driver oncogenes. In contrast, restoring the function of proteins encoded by inactivated tumor suppressor genes has proven to be markedly more challenging, providing a rationale to search for genes displaying SL with tumor suppressor genes. The prospective identification of targets that can lead to development of SL-based drugs has been enabled by large-scale tumor sequencing efforts that have now catalogued the somatic genetic alterations of cancer, technological advances in forward genetic and chemogenetic screens in human cells (Fig 1b, (14–16)), as well as large-scale cooperative efforts to map genetic dependencies across cancer cell types (Fig 1c). In particular, the emergence of CRISPR-based tools and the increasingly varied ways in which they can facilitate functional genomics has greatly accelerated the rate and robustness of SL target discovery (1).
Several large-scale functional genomic screening initiatives aimed at identifying genetic dependencies across hundreds of cancer cell lines have identified hundreds of potentially novel drug targets (3,17). These approaches have taken advantage of large-scale cancer cell line profiling to interrogate genetic interaction networks across intentionally diverse genomic contexts and uncover genetic essentialities conferred by specific genetic alterations (15). Notably, prior analyses of context-specific genetic dependencies identified in pan-cancer cell line LOF genomic screens have shown them to be highly enriched for SL interactions (18,19). Specific efforts include Project Achilles (Broad Institute, (20)), Project DRIVE (Novartis (18)), and Project Score (Sanger Institute (3,21)), each of which has contributed data to the Cancer Dependency Map (DepMap) (22), a collaborative project aimed at creating a comprehensive database of potential novel drug targets and biomarkers (Fig 1c). The resources generated by such large-scale approaches for target discovery are enabling the characterization of genetic interaction networks and the identification of potentially druggable SL cancer targets such as WRN in microsatellite-unstable tumors (11,23).
Despite the size of the cancer cell line panels used in large-scale genomic screening initiatives, this approach nevertheless has some limitations. For example, available cell panels often lack a corresponding normal ‘control’ cell line or have poor representation of certain genetic alterations or of certain tumor types. The identification of SL interactions in cell line panel screens therefore relies on comparing the growth of cells with a given (and represented) genetic alteration with those that do not. The attribution of a cellular phenotype to a specific SL pair can also be difficult to establish when statistical power is low due to underrepresentation of a genetic alteration in the cell line panel (15). Isogenic models, which vary only in the genetic alteration of interest, provide an attractive alternative as it is much easier to infer synthetic lethality relationships from screens in isogenic cell line pairs, and they can be used to probe any genetic alteration of interest (24). Isogenic models are of course not devoid of limitations; engineering specific genetic alterations outside of the typical cellular contexts in which they occur may lead to phenotypes that diverge from the human tumors they seek to model. Furthermore, these models do not take into account the potential impact of the chronology of the acquisition of specific genetic alterations on the dependency of a cell on a specific molecular pathway (25); hence, the biological impact of the genetic alteration edited into a cell may not be entirely reflective of its naturally occurring counterpart. As no model system is perfect, the combined use of sequenced cell line panels, isogenic cell line pairs, and animal models provide complementary and orthogonal validation of candidate genetic interactions.
The continued development of functional genomic technologies has leveraged the flexibility and scalability of CRISPR-based forward genetics to precisely edit DNA or RNA and to modify sequence-specific regulatory processes (26). Engineering of CRISPR-Cas proteins or guide RNAs (gRNAs) has allowed for the development of Cas variants capable of recruiting additional enzymatic activities to specific genomic sequences in scalable fashion, and led to CRISPR-Cas platforms for base editing, direct RNA editing, epigenetic modification, and inhibition (CRISPRi) or activation (CRISPRa) of transcription, among other functions (27). This increased flexibility has enabled a diverse array of methods for dissection of genetic interaction networks and SL target discovery. For example, the use of orthologous CRISPR-Cas nucleases and gRNAs has been shown to facilitate ‘one-by-all’ genetic interaction screens in which a uniform ‘anchor’ gRNA is coupled with a variable ‘query’ gRNA to systematically identify alterations that are SL with the ‘anchor’ of interest (28,29). This approach can be particularly useful when the ‘anchor’ genetic alteration cannot be modeled using single cell-derived isogenic clones due to cellular essentiality (29). Although originally developed to study regulatory elements and long non-coding RNAs (lncRNAs) through generation of kilobase-long deletions (30,31), the development of dual- or multi-gRNA libraries has additionally facilitated multiplexing of ‘query’ gRNAs to uncover genetic interactions that would otherwise be obscured due to functional redundancy between gene paralogs (32,33). Moreover, the integration of tools for both GOF (CRISPRa) and LOF (CRISPRi or CRISPR-mediated knockout) perturbations has facilitated the development of combined bidirectional screens for the concurrent identification of genetic interactions involving LOF and/or GOF alterations (28,34,35).
Emerging data have also begun to demonstrate the promise of CRISPR-based perturbations when combined with multi-omic readouts, as exemplified by Kelly et al. (36), who demonstrated the promise of coupling broad discovery screens with more focused orthogonal validation screens. By combining protein-protein interaction mapping (using affinity purification/mass spectrometry) with a focused custom CRISPR knockout screen, the authors dissected functional interactions within the RAS pathway, and in doing so, identified novel genetic dependencies mediated by both direct protein-protein interactions and indirect functional genetic interactions (36). The development of single-cell technologies to assay the transcriptome of individual cells while simultaneously retrieving guide RNA sequences present in each cell has also allowed investigators to link CRISPR-mediated perturbations with transcriptional phenotypes (37,38). Additional recent work has highlighted the use of 3D culture systems in genetic interaction screens in improving the recapitulation of in vivo phenotypes as compared to traditional 2D screen, as illustrated by the their greater accuracy in the assessment of growth advantage resulting from the knocking-out of tumor suppressor genes (39). As evidenced by these studies, the development of appropriate workflows can leverage the flexibility and scalability of CRISPR-based forward genetics to systematically connect an increasing variety of perturbations with specific phenotypes in 2D, 3D and in vivo model systems (40,41).
The effectiveness of CRISPR-based approaches to SL target discovery has led to burgeoning interest in the adaptation of such technology to screen for non-cell autonomous therapeutic vulnerabilities. High-throughput in vivo CRISPR screens in primary CD8+ T-cells have been shown to allow for the discovery of negative regulators of anti-tumor T-cell immunity, as evidenced by their ability to recover known immunotherapy targets such as PD-1 and Tim-3 (42). Conversely, in vivo application of CRISPR-based screening tools may also prove to be useful in identifying cancer cell targets that stimulate host immunity and/or synergize with checkpoint blockade; in addition to recovering PD-L1, a recent in vivo genetic screening approach was used to identify unanticipated candidate genes with potential roles in mediating immune evasion (43). The continued development of such approaches has the potential to expand existing definitions of SL to encompass inter-cell genetic interactions and non-cell autonomous mechanisms.
Biomarkers for SL: When the Presence of a Mutation May Not Be Enough
The development of poly (ADP-ribose) polymerase (PARP) inhibitors as anticancer therapy for BRCA1/2-deficient tumors has provided an archetypal example of efforts to harness SL for therapeutic gain (44). Highlighting the substantial promise of SL therapeutics, recent data have demonstrated dramatic improvements in overall survival afforded by the use of PARP inhibition for BRCA1/2-deficient ovarian cancer (45). At the same time, PARP inhibitors have produced limited effects on survival in late-stage clinical trials of patients with metastatic breast, prostate, and pancreatic cancers (46–48). The explanation of such disparate and seemingly conflicting results, in combination with first principles gleaned from key preclinical studies, has begun to reveal factors potentially critical to the success of future efforts to apply SL in the clinic (Fig 2).
A very important variable to consider for successful application of SL-based therapies is the zygosity of the target genetic alteration, as exemplified by prior experience with PARP inhibitors. Whilst GOF mutations affecting proto-oncogenes typically act dominantly, LOF alterations affecting tumor suppressor genes tend to be recessive, requiring biallelic inactivation for promotion of tumorigenesis. Homozygous or biallelic inactivation of a tumor suppressor most commonly involves a deleterious mutation of one allele followed by inactivation of the wild-type (WT) allele via loss of heterozygosity (LOH), a second somatic mutation or structural rearrangement; a homozygous deletion (i.e., physical genetic loss of both alleles) or epigenetic silencing. As the majority of tumor suppressors require biallelic LOF to promote tumorigenesis and therapeutically relevant phenotypes (49), the optimal development of SL therapies targeting such genes will likely require biomarkers that accurately assess LOH and zygosity. For example, the vast majority of trials examining PARP inhibition in genotypically selected patients have not included prospective assessment of biallelic gene inactivation (46,48,50–55), resulting in the likely inclusion of patients with monoallelic mutations who are less likely to respond. In a retrospective ad hoc analysis of one such trial (TRITON2 phase II study of rucaparib in metastatic prostate cancer), the rate of PSA response among patients with mono- vs biallelic BRCA1/2 inactivation was found to be 11% vs 81%, respectively (56).
This finding is consistent with multiple other lines of evidence suggesting that the presence of a single wild-type BRCA1/2 allele appears to be sufficient for most therapeutically-relevant phenotypes, including DSB repair. Although haploinsufficiency has been described for a subset of BRCA1- and BRCA2-related functions—namely suppression of aneuploidy and replication stress (57,58)—evidence to support a lack of functional HR deficiency in tumors with heterozygous inactivation of BRCA1 or BRCA2 includes preclinical functional data derived from both in vitro (59,60) and in vivo models (60–63). The presence of a single functional allele also appears to be sufficient for proficient DSB repair in human patients, as evidenced by i) the observation that HR-deficient genomic scarring is almost exclusively limited to tumors harboring biallelic BRCA1/2 inactivation (64–67), ii) PARP inhibitor and platinum resistance in tumors harboring heterozygous reversion mutations (68–70), and iii) the aforementioned TRITON2 analysis indicating a markedly poor response rate among patients with mono-allelic BRCA1/2 inactivation. (55). Notably, the above concept is not solely pertinent to HR-deficiency. Further examples of SL therapies thought to specifically target biallelic genetic inactivation include AURKA/B inhibition in RB1-mutant tumors (71,72), WRN inhibition in tumors with microsatellite instability induced by biallelic inactivation of mismatch repair genes (11,73), and PRMT5/MAT2A inhibition in tumors with biallelic collateral loss of MTAP (74).
Although these data highlight the importance of LOH assessment in targeting genes that require biallelic inactivation for loss of function, it is important to note that a subset of tumor suppressors are known to be haploinsufficient and can produce targetable phenotypes with loss of only a single allele (75). The complexity of genotype-phenotype relationships is further illustrated by the observation that some genes may be haploinsufficient for only a subset of related phenotypes (41), whereas others (e.g. TP53) can harbor alleles with either LOF or dominant negative activity, depending on the specific mutation (50). With this in mind, we suggest that, apart from the context of SL therapies that target haploinsufficient genes and genetic alterations producing a dominant-negative phenotype, a robust assessment of biallelic gene inactivation as a biomarker will be necessary for the successful development of many, if not most, SL-based therapeutic strategies.
Acquired resistance is another phenomenon that affects PARPi efficacy in a major way. Among multiple resistance mechanisms that have been identified to date, the most frequently detected mechanism involves the restoration of BRCA1/2 function via secondary reversion mutations or intragenic deletions that restore the open reading frame. Under the selection pressure imposed by PARP inhibition, reversion mutations that restore functional homologous recombination (HR) provide tumor cells with a marked fitness advantage upon PARP inhibitor treatment, which in turn leads to acquired resistance and tumor progression. Reversion mutations have similarly been identified in patients with prior exposure to platinum-based chemotherapy, suggesting convergent resistance mechanisms for therapies that target defective HR (69,76). A recent study reported by Pettitt et al. demonstrated the existence of reversion ‘hotspots’ and ‘deserts’ in specific regions encoding BRCA2, suggesting that the likelihood of reversion is affected by the position of the original mutation (77). Interestingly, as reversion mutations that do not restore the same codon as the original mutation frequently result in short stretches of frameshifted neopeptides, the authors of this study postulated that some revertant alleles encode neoantigens that may increase immunogenicity and vulnerability to immune checkpoint blockade, although this possibility is yet to be tested experimentally (77). Of note, homozygous deletion, at variance with combined mutation and LOH, prevents the possibility of secondary mutations that restore the reading frame (unless only a portion of the gene is deleted). Unsurprisingly, long-term responders to PARP inhibition appear to be enriched in homozygous deletions or rearrangements in BRCA1/2 that prevent the clonal outgrowth of resistant clones harboring somatic reversion mutations (78,79). Deletions occurring at tumor suppressor loci can also extend beyond the bona fide driver genes, which may allow identification of genetic or pharmacological sensitivities caused by gene loss that is collateral to the actual cancer-driving event (80,81).
A recent study examining the therapeutic relevance of mutations in BRCA1/2 has suggested that tumor lineage may also be a relevant factor in influencing the penetrance of HR-deficient phenotypes (82). Examination of genomic scarring and responses to PARP inhibition revealed a significant interaction between cancer type and the phenotypic penetrance of deleterious mutations in BRCA1/2 (82). Although tumors with mutant BRCA1/2 arising from the breast, ovary, prostate, or pancreas (i.e., BRCA-associated cancer types) and non-BRCA associated cancer types both displayed higher levels of genomic signatures associated with HR deficiency, in a limited number of patients only BRCA-associated cancer types appeared to derive clinical benefit from PARP inhibition (82). Similar lack of efficacy for PARP inhibition was observed in a phase II randomized trial of rucaparib for genetically unselected patients with recurrent or metastatic bladder cancer (83). As other studies have demonstrated evidence to suggest that biallelic HR gene inactivation is penetrant in non-BRCA associated cancer types (5,61,62,65,84), these data have led to controversy surrounding the role of lineage in BRCA1/2-associated phenotypes (82,85). Nevertheless, a parsimonious interpretation of the lack of clinical benefit seen with PARP inhibition in non-BRCA associated cancer is that such results are confounded by higher rates of LOH in cancers originating from BRCA-associated cancer types than in other malignancies, given that BRCA-associated tumors are more than twice as likely to display biallelic inactivation than non-BRCA-associated cancers (90% and 44% for BRCA-associated tissue types and non-BRCA associated cancer types, respectively) (86).
Taken together, findings discussed in this section suggest that the convenience of enrolling patients for trials testing SL-based therapies merely on the basis of the presence of a pathogenic mutation affecting the target gene, in a way akin to how the enrollment of patients in studies testing small molecule inhibitors or antibodies targeting oncogenes activated by a given hotspot mutation, would be expected to result in modest therapeutic benefit. Patient populations defined on this basis would be heterogeneous and the probability of a targetable phenotype would likely vary according to the frequency of loss of the WT allele in a given lineage. The inability to assess the zygosity of LOF alterations is a clear limitation for many current biomarker platforms, including those approved as companion diagnostics for PARP inhibitors (87,88).
Additional promising approaches to identify patient populations likely to benefit from SL therapies include the use of functional or phenotypic biomarkers. Such measures may be particularly valuable in addressing the large number of variants in tumor genomes that are of undetermined functional significance, while additionally facilitating the identification of tumors that become therapeutically vulnerable through epigenetic mechanisms. In the well-studied example of HR-deficiency, phenotypic biomarkers include genomic ‘scars’ detected by sequencing or single nucleotide polymorphism array (i.e., large-scale state transitions, COSMIC signature 3, HRDetect) (64,66,89,90), and ex vivo assays of HR proficiency that rely on detection of damage-induced nuclear foci (i.e., RAD51 focus formation assay) (64,91). Although these approaches have shown promising results in specific scenarios, each has important trade-offs and limitations. For example, markers that quantify genomic ‘scarring’ have shown to be sensitive for the detection of tumors harboring biallelic HR gene inactivation (65,66), but have nevertheless been limited by modest positive-predictive value and the inability to provide a ‘real-time’ readout of phenotype, as the emergence of drug resistance does not remove the existing ‘scars’ of prior HR-deficiency. In contrast to genomic approaches, functional assays of HRD have the potential to provide a dynamic, real-time readout of DNA repair capacity. The most promising functional markers involve the measurement of downstream changes in protein localization that reflect the fidelity of multiple upstream components. For example, the accumulation of RAD51 into nuclear foci (a marker of intact HR) in response to DNA damage has long been considered a gold standard for evaluating HR function in preclinical models. We and others have demonstrated that assessment of RAD51 foci can provide a robust binary readout of HR proficiency versus deficiency (64,91) that accurately reflects both drug response and acquired resistance (92,93), highlighting its potential as a dynamic biomarker of HR integrity. Nevertheless, the clinical development and broader adoption of such assays has to date been hampered by the need for fresh tissue and ex vivo manipulation of cancer cells. The development of assays that obviate such logistical hurdles will be key to the clinical implementation of functional biomarkers aimed at providing a dynamic readout of therapeutic vulnerability.
LOF Alterations Required for Tumor Initiation Versus Maintenance
The application of evolutionary theory to cancer has provided a theoretical framework for understanding the molecular processes responsible for the malignant state and how such processes are constrained by selection (25). Moreover, evolutionary theory has provided important lessons relevant to the identification of robust SL interactions and strategies to pair exploitable target alterations with tractable drug targets. For example, the effect of PARP inhibition on the clonal evolution of BRCA1/2-deficient tumors suggests that loss of HR proficiency may be critical to promoting early stages of tumorigenesis, but dispensable for maintenance of the malignant phenotype (94). Reversion mutations that restore the function of BRCA1/2 do not appear to impair tumorigenesis at later stages of tumor evolution. Such observations indicate that BRCA1/2 inactivation causes genomic instability that promotes the acquisition of additional genetic alterations, rather than being essential for the transformed cell phenotype. This is in contrast to genetic alterations required for tumor maintenance that may conversely maximize the fitness penalty incurred by resistance mechanisms to SL anticancer therapies.
Genetic alterations required for tumor maintenance were initially identified using in vitro and in vivo models of GOF tumorigenesis (95,96). In seminal studies, acute inactivation of transforming oncogenes, including MYC, KRAS, and ABL (among others) was found to result in differentiation, apoptosis, or cell cycle arrest, depending on tissue context (97–99). This reversal of malignant phenotype led to the concept of ‘oncogene addiction,’ which forms the basis for most targeted anticancer therapies. It has since been demonstrated that genetic alterations required for tumor maintenance are not limited to GOF alterations. Inactivation of APC, for example, is required for the maintenance of colorectal cancer, as evidenced by murine models with established colorectal tumors in which APC restoration drives rapid tumor cell differentiation and sustained regression without evidence of relapse (100). LOF alterations required for maintenance of tumor progression extend beyond those that inactivate APC; inducible restoration of WT p53 protein in established tumors caused by loss of p53 function has been shown to lead to tumor regression in most genetic contexts (101–103).
Growing evidence suggests that inactivation of ataxia telangiectasia mutated (ATM) as a tumor suppressor gene is required not only for initiation but also for maintenance of malignant phenotypes. In addition to its role in DNA damage signaling, ATM has a key gatekeeper role via regulation of p53 stabilization in response to cellular stress (104); inactivation of ATM appears to suppress p53 signaling to a degree that is sufficient for tumorigenesis, evidenced by the observation that LOF alterations in ATM and TP53 are mutually exclusive across cancer types (105,106). Furthermore, a recent study using a conditional murine model has shown that ATM reactivation in initially ATM-deficient B- and T-cell lymphoma results in sustained tumor regression (107). Consistent with this notion, early-phase clinical data examining synthetic lethal strategies based on ATR inhibition in ATM-deficient tumors have revealed long and durable responses in heavily pretreated cancer patients (108,109), a clinical observation distinct from those seen with PARP inhibition in BRCA1/2-mutated tumors. Albeit limited, these data are consistent with the notion that targeting LOF alterations required for tumor maintenance can enhance the likelihood of durable tumor control. We thus anticipate that the pattern of ATR inhibitor efficacy in patients with tumors with ATM LOF will be different from that observed with PARP inhibitors, reinforcing the biological differences in these two pairs.
CONCLUSIONS & FUTURE DIRECTIONS
Recent technological advancements have greatly accelerated the process of identifying tumor-specific genetic dependencies and candidate SL targets. Despite a marked increase in the rate of target discovery, relatively few SL drugs have been tested in the clinic, and the field remains largely in its infancy. As the number of novel SL drugs entering clinical trials continues to grow, we review concepts and lessons emerging from the first wave of clinical trials aimed at testing SL drugs, including inhibitors of the poly (ADP-ribose) polymerase (PARP) for BRCA1/2-deficient tumors, an archetypal example of SL anticancer therapy. The continued maturation of clinical data in this space has begun to provide important insights into how the nature of the targeted lesion and its genetic context can influence clinical outcomes, and highlights the need to develop clinical biomarkers distinct from existing platforms developed for the targeting of activated oncogenes. In particular, the development of companion diagnostics capable of assessing biallelic gene inactivation, and/or clinically relevant phenotypes, will be key to further progress in this arena. The next few years are likely to provide a roadmap for how the next generation of synthetic lethal interactions can be exploited therapeutically in the context of combinatorial immuno-oncology treatments. Emerging data examining how distinct elements of the DDR influence tumor immunity (110,111), and how SL interactions may result in immunogenic cell death (112), suggest the possibility of strong synergy between these two classes of therapeutics. In combination with ongoing efforts to expand the known spectrum of SL genetic interactions, a strong foundation is being laid for the development of effective SL therapeutics and novel combinatorial regimens exploiting SL interactions.
STATEMENT OF SIGNIFICANCE.
Synthetic lethality, as applied to cancer therapy, targets tumor-specific alterations not amenable to direct targeting, including selected oncogenes, such as those targeted by gene amplification or protein overexpression that results in inactivation of the same pathways controlled by tumor suppressors, or genes whose tumor-specific loss of function alterations result in greater vulnerability to perturbation of a second pathway. Identifying and targeting synthetic lethal interactions has been accelerated by high-throughput CRISPR technology, opening up a next generation of approaches with a greater opportunity for selective tumor cell killing that can translate into drug development and novel biomarkers for cancer clinical trials.
Acknowledgements:
Figures created with BioRender.com
Financial support: SNP and JSR-F are funded in part by the Breast Cancer Research Foundation and a National Cancer Institute SPORE in Breast Cancer Genomic Instability at Memorial Sloan Kettering Cancer Center (1P50CA247749-01). Research reported in this publication was funded in part by a Cancer Center Support Grant (P30CA008748) of the National Institutes of Health/National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Conflicts of interest: MZ, MZ and MK are employees and shareholders of Repare Therapeutics. DD is a shareholder in Repare Therapeutics. JSR-F reports receiving personal/consultancy fees from Goldman Sachs, Paige.AI and Repare Therapeutics, membership of the scientific advisory boards of VolitionRx, Repare Therapeutics and Paige.AI, membership of the Board of Directors of Grupo Oncoclinicas, and ad hoc membership of the scientific advisory boards of Roche Tissue Diagnostics, Ventana Medical Systems, Novartis, Genentech and InVicro. SNP is a medical board advisor to Varian, Philips, AstraZeneca, and XRAD therapeutics. All other authors declare no conflicts of interest.
REFERENCES
- 1.Huang A, Garraway LA, Ashworth A, Weber B. Synthetic lethality as an engine for cancer drug target discovery. Nat Rev Drug Discov 2020;19(1):23–38 doi 10.1038/s41573-019-0046-z. [DOI] [PubMed] [Google Scholar]
- 2.Lord CJ, Ashworth A. PARP inhibitors: Synthetic lethality in the clinic. Science 2017;355(6330):1152–8 doi 10.1126/science.aam7344. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Behan FM, Iorio F, Picco G, Goncalves E, Beaver CM, Migliardi G, et al. Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 2019;568(7753):511–6 doi 10.1038/s41586-019-1103-9. [DOI] [PubMed] [Google Scholar]
- 4.Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, et al. High-Resolution CRISPR Screens Reveal Fitness Genes and Genotype-Specific Cancer Liabilities. Cell 2015;163(6):1515–26 doi 10.1016/j.cell.2015.11.015. [DOI] [PubMed] [Google Scholar]
- 5.Mengwasser KE, Adeyemi RO, Leng Y, Choi MY, Clairmont C, D’Andrea AD, et al. Genetic Screens Reveal FEN1 and APEX2 as BRCA2 Synthetic Lethal Targets. Mol Cell 2019;73(5):885–99 e6 doi 10.1016/j.molcel.2018.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Alvarez-Quilon A, Wojtaszek JL, Mathieu MC, Patel T, Appel CD, Hustedt N, et al. Endogenous DNA 3’ Blocks Are Vulnerabilities for BRCA1 and BRCA2 Deficiency and Are Reversed by the APE2 Nuclease. Mol Cell 2020;78(6):1152–65 e8 doi 10.1016/j.molcel.2020.05.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Lawson KA, Sousa CM, Zhang X, Kim E, Akthar R, Caumanns JJ, et al. Functional genomic landscape of cancer-intrinsic evasion of killing by T cells. Nature 2020;586(7827):120–6 doi 10.1038/s41586-020-2746-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Cortez JT, Montauti E, Shifrut E, Gatchalian J, Zhang Y, Shaked O, et al. CRISPR screen in regulatory T cells reveals modulators of Foxp3. Nature 2020;582(7812):416–20 doi 10.1038/s41586-020-2246-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Olivieri M, Cho T, Alvarez-Quilon A, Li K, Schellenberg MJ, Zimmermann M, et al. A Genetic Map of the Response to DNA Damage in Human Cells. Cell 2020;182(2):481–96 e21 doi 10.1016/j.cell.2020.05.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Steinhart Z, Pavlovic Z, Chandrashekhar M, Hart T, Wang X, Zhang X, et al. Genome-wide CRISPR screens reveal a Wnt-FZD5 signaling circuit as a druggable vulnerability of RNF43-mutant pancreatic tumors. Nat Med 2017;23(1):60–8 doi 10.1038/nm.4219. [DOI] [PubMed] [Google Scholar]
- 11.Chan EM, Shibue T, McFarland JM, Gaeta B, Ghandi M, Dumont N, et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 2019;568(7753):551–6 doi 10.1038/s41586-019-1102-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hartwell LH, Szankasi P, Roberts CJ, Murray AW, Friend SH. Integrating genetic approaches into the discovery of anticancer drugs. Science 1997;278(5340):1064–8 doi 10.1126/science.278.5340.1064. [DOI] [PubMed] [Google Scholar]
- 13.Noack S, Raab M, Matthess Y, Sanhaji M, Kramer A, Gyorffy B, et al. Synthetic lethality in CCNE1-amplified high grade serous ovarian cancer through combined inhibition of Polo-like kinase 1 and microtubule dynamics. Oncotarget 2018;9(40):25842–59 doi 10.18632/oncotarget.25386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Haley B, Roudnicky F. Functional Genomics for Cancer Drug Target Discovery. Cancer Cell 2020. doi 10.1016/j.ccell.2020.04.006. [DOI] [PubMed] [Google Scholar]
- 15.Ghandi M, Huang FW, Jane-Valbuena J, Kryukov GV, Lo CC, McDonald ER, 3rd, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 2019;569(7757):503–8 doi 10.1038/s41586-019-1186-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cancer Genome Atlas Research N, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013;45(10):1113–20 doi 10.1038/ng.2764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lin A, Sheltzer JM. Discovering and validating cancer genetic dependencies: approaches and pitfalls. Nat Rev Genet 2020. doi 10.1038/s41576-020-0247-7. [DOI] [PubMed] [Google Scholar]
- 18.McDonald ER 3rd, de Weck A, Schlabach MR, Billy E, Mavrakis KJ, Hoffman GR, et al. Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening. Cell 2017;170(3):577–92 e10 doi 10.1016/j.cell.2017.07.005. [DOI] [PubMed] [Google Scholar]
- 19.Blomen VA, Majek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, et al. Gene essentiality and synthetic lethality in haploid human cells. Science 2015;350(6264):1092–6 doi 10.1126/science.aac7557. [DOI] [PubMed] [Google Scholar]
- 20.Aguirre AJ, Meyers RM, Weir BA, Vazquez F, Zhang CZ, Ben-David U, et al. Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting. Cancer Discov 2016;6(8):914–29 doi 10.1158/2159-8290.CD-16-0154. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Dempster JM, Pacini C, Pantel S, Behan FM, Green T, Krill-Burger J, et al. Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets. Nat Commun 2019;10(1):5817 doi 10.1038/s41467-019-13805-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, et al. Defining a Cancer Dependency Map. Cell 2017;170(3):564–76 e16 doi 10.1016/j.cell.2017.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Lazzari L, Corti G, Picco G, Isella C, Montone M, Arcella P, et al. Patient-Derived Xenografts and Matched Cell Lines Identify Pharmacogenomic Vulnerabilities in Colorectal Cancer. Clin Cancer Res 2019;25(20):6243–59 doi 10.1158/1078-0432.CCR-18-3440. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Torrance CJ, Agrawal V, Vogelstein B, Kinzler KW. Use of isogenic human cancer cells for high-throughput screening and drug discovery. Nat Biotechnol 2001;19(10):940–5 doi 10.1038/nbt1001-940. [DOI] [PubMed] [Google Scholar]
- 25.Ashworth A, Lord CJ, Reis-Filho JS. Genetic interactions in cancer progression and treatment. Cell 2011;145(1):30–8 doi 10.1016/j.cell.2011.03.020. [DOI] [PubMed] [Google Scholar]
- 26.Anzalone AV, Koblan LW, Liu DR. Genome editing with CRISPR-Cas nucleases, base editors, transposases and prime editors. Nat Biotechnol 2020;38(7):824–44 doi 10.1038/s41587-020-0561-9. [DOI] [PubMed] [Google Scholar]
- 27.Engreitz J, Abudayyeh O, Gootenberg J, Zhang F. CRISPR Tools for Systematic Studies of RNA Regulation. Cold Spring Harb Perspect Biol 2019;11(8) doi 10.1101/cshperspect.a035386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Najm FJ, Strand C, Donovan KF, Hegde M, Sanson KR, Vaimberg EW, et al. Orthologous CRISPR-Cas9 enzymes for combinatorial genetic screens. Nat Biotechnol 2018;36(2):179–89 doi 10.1038/nbt.4048. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.DeWeirdt PC, Sangree AK, Hanna RE, Sanson KR, Hegde M, Strand C, et al. Genetic screens in isogenic mammalian cell lines without single cell cloning. Nat Commun 2020;11(1):752 doi 10.1038/s41467-020-14620-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Diao Y, Fang R, Li B, Meng Z, Yu J, Qiu Y, et al. A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells. Nat Methods 2017;14(6):629–35 doi 10.1038/nmeth.4264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Han K, Jeng EE, Hess GT, Morgens DW, Li A, Bassik MC. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat Biotechnol 2017;35(5):463–74 doi 10.1038/nbt.3834. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Andersson-Rolf A, Merenda A, Mustata RC, Li T, Dietmann S, Koo BK. Simultaneous paralogue knockout using a CRISPR-concatemer in mouse small intestinal organoids. Dev Biol 2016;420(2):271–7 doi 10.1016/j.ydbio.2016.10.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Dede M, McLaughlin M, Kim E, Hart T. Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens. Genome Biol 2020;21(1):262 doi 10.1186/s13059-020-02173-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.le Sage C, Lawo S, Panicker P, Scales TME, Rahman SA, Little AS, et al. Dual direction CRISPR transcriptional regulation screening uncovers gene networks driving drug resistance. Sci Rep 2017;7(1):17693 doi 10.1038/s41598-017-18172-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Bowman EK, Deaner M, Cheng JF, Evans R, Oberortner E, Yoshikuni Y, et al. Bidirectional titration of yeast gene expression using a pooled CRISPR guide RNA approach. Proc Natl Acad Sci U S A 2020;117(31):18424–30 doi 10.1073/pnas.2007413117. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Kelly MR, Kostyrko K, Han K, Mooney NA, Jeng EE, Spees K, et al. Combined Proteomic and Genetic Interaction Mapping Reveals New RAS Effector Pathways and Susceptibilities. Cancer Discov 2020;10(12):1950–67 doi 10.1158/2159-8290.CD-19-1274. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Replogle JM, Norman TM, Xu A, Hussmann JA, Chen J, Cogan JZ, et al. Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing. Nat Biotechnol 2020;38(8):954–61 doi 10.1038/s41587-020-0470-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P, Klughammer J, et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods 2017;14(3):297–301 doi 10.1038/nmeth.4177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Han K, Pierce SE, Li A, Spees K, Anderson GR, Seoane JA, et al. CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities. Nature 2020;580(7801):136–41 doi 10.1038/s41586-020-2099-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Noorani I, Bradley A, de la Rosa J. CRISPR and transposon in vivo screens for cancer drivers and therapeutic targets. Genome Biol 2020;21(1):204 doi 10.1186/s13059-020-02118-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Ringel T, Frey N, Ringnalda F, Janjuha S, Cherkaoui S, Butz S, et al. Genome-Scale CRISPR Screening in Human Intestinal Organoids Identifies Drivers of TGF-beta Resistance. Cell Stem Cell 2020;26(3):431–40 e8 doi 10.1016/j.stem.2020.02.007. [DOI] [PubMed] [Google Scholar]
- 42.Dong MB, Wang G, Chow RD, Ye L, Zhu L, Dai X, et al. Systematic Immunotherapy Target Discovery Using Genome-Scale In Vivo CRISPR Screens in CD8 T Cells. Cell 2019;178(5):1189–204 e23 doi 10.1016/j.cell.2019.07.044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Manguso RT, Pope HW, Zimmer MD, Brown FD, Yates KB, Miller BC, et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 2017;547(7664):413–8 doi 10.1038/nature23270. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Pilie PG, Tang C, Mills GB, Yap TA. State-of-the-art strategies for targeting the DNA damage response in cancer. Nat Rev Clin Oncol 2019;16(2):81–104 doi 10.1038/s41571-018-0114-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Poveda A, Floquet A, Ledermann JA, Asher R, Penson RT, Oza AM, et al. Final overall survival (OS) results from SOLO2/ENGOT-ov21: A phase III trial assessing maintenance olaparib in patients (pts) with platinum-sensitive, relapsed ovarian cancer and a BRCA mutation. Journal of Clinical Oncology 2020;38(15_suppl):6002- doi 10.1200/JCO.2020.38.15_suppl.6002. [DOI] [Google Scholar]
- 46.Robson M, Im SA, Senkus E, Xu B, Domchek SM, Masuda N, et al. Olaparib for Metastatic Breast Cancer in Patients with a Germline BRCA Mutation. N Engl J Med 2017;377(6):523–33 doi 10.1056/NEJMoa1706450. [DOI] [PubMed] [Google Scholar]
- 47.de Bono J, Mateo J, Fizazi K, Saad F, Shore N, Sandhu S, et al. Olaparib for Metastatic Castration-Resistant Prostate Cancer. N Engl J Med 2020;382(22):2091–102 doi 10.1056/NEJMoa1911440. [DOI] [PubMed] [Google Scholar]
- 48.Golan T, Hammel P, Reni M, Van Cutsem E, Macarulla T, Hall MJ, et al. Maintenance Olaparib for Germline BRCA-Mutated Metastatic Pancreatic Cancer. N Engl J Med 2019;381(4):317–27 doi 10.1056/NEJMoa1903387. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Santarosa M, Ashworth A. Haploinsufficiency for tumour suppressor genes: when you don’t need to go all the way. Biochim Biophys Acta 2004;1654(2):105–22 doi 10.1016/j.bbcan.2004.01.001. [DOI] [PubMed] [Google Scholar]
- 50.Moore K, Colombo N, Scambia G, Kim BG, Oaknin A, Friedlander M, et al. Maintenance Olaparib in Patients with Newly Diagnosed Advanced Ovarian Cancer. N Engl J Med 2018;379(26):2495–505 doi 10.1056/NEJMoa1810858. [DOI] [PubMed] [Google Scholar]
- 51.Mirza MR, Monk BJ, Herrstedt J, Oza AM, Mahner S, Redondo A, et al. Niraparib Maintenance Therapy in Platinum-Sensitive, Recurrent Ovarian Cancer. N Engl J Med 2016;375(22):2154–64 doi 10.1056/NEJMoa1611310. [DOI] [PubMed] [Google Scholar]
- 52.Pujade-Lauraine E, Ledermann JA, Selle F, Gebski V, Penson RT, Oza AM, et al. Olaparib tablets as maintenance therapy in patients with platinum-sensitive, relapsed ovarian cancer and a BRCA1/2 mutation (SOLO2/ENGOT-Ov21): a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Oncol 2017;18(9):1274–84 doi 10.1016/S1470-2045(17)30469-2. [DOI] [PubMed] [Google Scholar]
- 53.Hussain M, Mateo J, Fizazi K, Saad F, Shore N, Sandhu S, et al. Survival with Olaparib in Metastatic Castration-Resistant Prostate Cancer. N Engl J Med 2020;383(24):2345–57 doi 10.1056/NEJMoa2022485. [DOI] [PubMed] [Google Scholar]
- 54.Litton JK, Rugo HS, Ettl J, Hurvitz SA, Goncalves A, Lee KH, et al. Talazoparib in Patients with Advanced Breast Cancer and a Germline BRCA Mutation. N Engl J Med 2018;379(8):753–63 doi 10.1056/NEJMoa1802905. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Abida W, Campbell D, Patnaik A, Shapiro JD, Sautois B, Vogelzang NJ, et al. Non-BRCA DNA Damage Repair Gene Alterations and Response to the PARP Inhibitor Rucaparib in Metastatic Castration-Resistant Prostate Cancer: Analysis From the Phase II TRITON2 Study. Clin Cancer Res 2020;26(11):2487–96 doi 10.1158/1078-0432.CCR-20-0394. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Abida W, Patnaik A, Campbell D, Shapiro J, Bryce AH, McDermott R, et al. Rucaparib in Men With Metastatic Castration-Resistant Prostate Cancer Harboring a BRCA1 or BRCA2 Gene Alteration. J Clin Oncol 2020;38(32):3763–72 doi 10.1200/JCO.20.01035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Pathania S, Bade S, Le Guillou M, Burke K, Reed R, Bowman-Colin C, et al. BRCA1 haploinsufficiency for replication stress suppression in primary cells. Nat Commun 2014;5:5496 doi 10.1038/ncomms6496. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Karaayvaz-Yildirim M, Silberman RE, Langenbucher A, Saladi SV, Ross KN, Zarcaro E, et al. Aneuploidy and a deregulated DNA damage response suggest haploinsufficiency in breast tissues of BRCA2 mutation carriers. Sci Adv 2020;6(5):eaay2611 doi 10.1126/sciadv.aay2611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Moynahan ME, Cui TY, Jasin M. Homology-directed dna repair, mitomycin-c resistance, and chromosome stability is restored with correction of a Brca1 mutation. Cancer Res 2001;61(12):4842–50. [PubMed] [Google Scholar]
- 60.Feng W, Jasin M. BRCA2 suppresses replication stress-induced mitotic and G1 abnormalities through homologous recombination. Nat Commun 2017;8(1):525 doi 10.1038/s41467-017-00634-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Kass EM, Helgadottir HR, Chen CC, Barbera M, Wang R, Westermark UK, et al. Double-strand break repair by homologous recombination in primary mouse somatic cells requires BRCA1 but not the ATM kinase. Proc Natl Acad Sci U S A 2013;110(14):5564–9 doi 10.1073/pnas.1216824110. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Kass EM, Lim PX, Helgadottir HR, Moynahan ME, Jasin M. Robust homology-directed repair within mouse mammary tissue is not specifically affected by Brca2 mutation. Nat Commun 2016;7:13241 doi 10.1038/ncomms13241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Wang Y, Park JYP, Pacis A, Denroche RE, Jang GH, Zhang A, et al. A Preclinical Trial and Molecularly Annotated Patient Cohort Identify Predictive Biomarkers in Homologous Recombination-deficient Pancreatic Cancer. Clin Cancer Res 2020;26(20):5462–76 doi 10.1158/1078-0432.CCR-20-1439. [DOI] [PubMed] [Google Scholar]
- 64.Mutter RW, Riaz N, Ng CK, Delsite R, Piscuoglio S, Edelweiss M, et al. Bi-allelic alterations in DNA repair genes underpin homologous recombination DNA repair defects in breast cancer. J Pathol 2017;242(2):165–77 doi 10.1002/path.4890. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Riaz N, Blecua P, Lim RS, Shen R, Higginson DS, Weinhold N, et al. Pan-cancer analysis of bi-allelic alterations in homologous recombination DNA repair genes. Nat Commun 2017;8(1):857 doi 10.1038/s41467-017-00921-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Davies H, Glodzik D, Morganella S, Yates LR, Staaf J, Zou X, et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat Med 2017;23(4):517–25 doi 10.1038/nm.4292. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Polak P, Kim J, Braunstein LZ, Karlic R, Haradhavala NJ, Tiao G, et al. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat Genet 2017;49(10):1476–86 doi 10.1038/ng.3934. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Edwards SL, Brough R, Lord CJ, Natrajan R, Vatcheva R, Levine DA, et al. Resistance to therapy caused by intragenic deletion in BRCA2. Nature 2008;451(7182):1111–5 doi 10.1038/nature06548. [DOI] [PubMed] [Google Scholar]
- 69.Lin KK, Harrell MI, Oza AM, Oaknin A, Ray-Coquard I, Tinker AV, et al. BRCA Reversion Mutations in Circulating Tumor DNA Predict Primary and Acquired Resistance to the PARP Inhibitor Rucaparib in High-Grade Ovarian Carcinoma. Cancer Discov 2019;9(2):210–9 doi 10.1158/2159-8290.CD-18-0715. [DOI] [PubMed] [Google Scholar]
- 70.Sakai W, Swisher EM, Karlan BY, Agarwal MK, Higgins J, Friedman C, et al. Secondary mutations as a mechanism of cisplatin resistance in BRCA2-mutated cancers. Nature 2008;451(7182):1116–20 doi 10.1038/nature06633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Oser MG, Fonseca R, Chakraborty AA, Brough R, Spektor A, Jennings RB, et al. Cells Lacking the RB1 Tumor Suppressor Gene Are Hyperdependent on Aurora B Kinase for Survival. Cancer Discov 2019;9(2):230–47 doi 10.1158/2159-8290.CD-18-0389. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Gong X, Du J, Parsons SH, Merzoug FF, Webster Y, Iversen PW, et al. Aurora A Kinase Inhibition Is Synthetic Lethal with Loss of the RB1 Tumor Suppressor Gene. Cancer Discov 2019;9(2):248–63 doi 10.1158/2159-8290.CD-18-0469. [DOI] [PubMed] [Google Scholar]
- 73.Kim TM, Laird PW, Park PJ. The landscape of microsatellite instability in colorectal and endometrial cancer genomes. Cell 2013;155(4):858–68 doi 10.1016/j.cell.2013.10.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Fedoriw A, Rajapurkar SR, O’Brien S, Gerhart SV, Mitchell LH, Adams ND, et al. Anti-tumor Activity of the Type I PRMT Inhibitor, GSK3368715, Synergizes with PRMT5 Inhibition through MTAP Loss. Cancer Cell 2019;36(1):100–14 e25 doi 10.1016/j.ccell.2019.05.014. [DOI] [PubMed] [Google Scholar]
- 75.Inoue K, Fry EA. Haploinsufficient tumor suppressor genes. Adv Med Biol 2017;118:83–122. [PMC free article] [PubMed] [Google Scholar]
- 76.Weigelt B, Comino-Mendez I, de Bruijn I, Tian L, Meisel JL, Garcia-Murillas I, et al. Diverse BRCA1 and BRCA2 Reversion Mutations in Circulating Cell-Free DNA of Therapy-Resistant Breast or Ovarian Cancer. Clin Cancer Res 2017;23(21):6708–20 doi 10.1158/1078-0432.CCR-17-0544. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Pettitt SJ, Frankum JR, Punta M, Lise S, Alexander J, Chen Y, et al. Clinical BRCA1/2 Reversion Analysis Identifies Hotspot Mutations and Predicted Neoantigens Associated with Therapy Resistance. Cancer Discov 2020;10(10):1475–88 doi 10.1158/2159-8290.CD-19-1485. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Swisher EM, Kristeleit RS, Oza AM, Tinker A, Ray-Coquard IL, Oaknin A, et al. Characterization of patients (pts) with long-term responses to rucaparib in recurrent ovarian cancer (OC). Journal of Clinical Oncology 2020;38(15_suppl):6015- doi 10.1200/JCO.2020.38.15_suppl.6015. [DOI] [PubMed] [Google Scholar]
- 79.Antonarakis ES, Madison R, Snider J, Snow T, Sokol E, Chung J, et al. Association of BRCA alteration (alt) type with real-world (RW) outcomes to PARP inhibitors (PARPi) in patients (pts) with metastatic castrate-resistant prostate cancer (mCRPC). Journal of Clinical Oncology 2020;38(15_suppl):5527- doi 10.1200/JCO.2020.38.15_suppl.5527. [DOI] [Google Scholar]
- 80.Muller FL, Colla S, Aquilanti E, Manzo VE, Genovese G, Lee J, et al. Passenger deletions generate therapeutic vulnerabilities in cancer. Nature 2012;488(7411):337–42 doi 10.1038/nature11331. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Muller FL, Aquilanti EA, DePinho RA. Collateral Lethality: A new therapeutic strategy in oncology. Trends Cancer 2015;1(3):161–73 doi 10.1016/j.trecan.2015.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Jonsson P, Bandlamudi C, Cheng ML, Srinivasan P, Chavan SS, Friedman ND, et al. Tumour lineage shapes BRCA-mediated phenotypes. Nature 2019;571(7766):576–9 doi 10.1038/s41586-019-1382-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Grivas P, Vogelzang NJ, Alva AS, Feyerabend S, Loriot Y, Necchi A, et al. ATLAS: A phase II open-label study of rucaparib in patients with locally advanced or metastatic urothelial carcinoma (mUC). Journal of Clinical Oncology 2019;37(7_suppl):TPS496-TPS doi 10.1200/JCO.2019.37.7_suppl.TPS496. [DOI] [Google Scholar]
- 84.Evers B, Jonkers J. Mouse models of BRCA1 and BRCA2 deficiency: past lessons, current understanding and future prospects. Oncogene 2006;25(43):5885–97 doi 10.1038/sj.onc.1209871. [DOI] [PubMed] [Google Scholar]
- 85.Curtin NJ, Drew Y, Sharma-Saha S. Why BRCA mutations are not tumour-agnostic biomarkers for PARP inhibitor therapy. Nat Rev Clin Oncol 2019;16(12):725–6 doi 10.1038/s41571-019-0285-2. [DOI] [PubMed] [Google Scholar]
- 86.Sokol ES, Pavlick D, Khiabanian H, Frampton GM, Ross JS, Gregg JP, et al. Pan-Cancer Analysis of BRCA1 and BRCA2 Genomic Alterations and Their Association With Genomic Instability as Measured by Genome-Wide Loss of Heterozygosity. Journal of Clinical Oncology 2020(4):442–65 doi 10.1200/po.19.00345. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Jenner ZB, Sood AK, Coleman RL. Evaluation of rucaparib and companion diagnostics in the PARP inhibitor landscape for recurrent ovarian cancer therapy. Future Oncol 2016;12(12):1439–56 doi 10.2217/fon-2016-0002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Gunderson CC, Moore KN. BRACAnalysis CDx as a companion diagnostic tool for Lynparza. Expert Rev Mol Diagn 2015;15(9):1111–6 doi 10.1586/14737159.2015.1078238. [DOI] [PubMed] [Google Scholar]
- 89.Popova T, Manie E, Rieunier G, Caux-Moncoutier V, Tirapo C, Dubois T, et al. Ploidy and large-scale genomic instability consistently identify basal-like breast carcinomas with BRCA1/2 inactivation. Cancer Res 2012;72(21):5454–62 doi 10.1158/0008-5472.CAN-12-1470. [DOI] [PubMed] [Google Scholar]
- 90.Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, et al. Signatures of mutational processes in human cancer. Nature 2013;500(7463):415–21 doi 10.1038/nature12477. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Graeser M, McCarthy A, Lord CJ, Savage K, Hills M, Salter J, et al. A marker of homologous recombination predicts pathologic complete response to neoadjuvant chemotherapy in primary breast cancer. Clin Cancer Res 2010;16(24):6159–68 doi 10.1158/1078-0432.CCR-10-1027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Tumiati M, Hietanen S, Hynninen J, Pietila E, Farkkila A, Kaipio K, et al. A Functional Homologous Recombination Assay Predicts Primary Chemotherapy Response and Long-Term Survival in Ovarian Cancer Patients. Clin Cancer Res 2018;24(18):4482–93 doi 10.1158/1078-0432.CCR-17-3770. [DOI] [PubMed] [Google Scholar]
- 93.Cruz C, Castroviejo-Bermejo M, Gutierrez-Enriquez S, Llop-Guevara A, Ibrahim YH, Gris-Oliver A, et al. RAD51 foci as a functional biomarker of homologous recombination repair and PARP inhibitor resistance in germline BRCA-mutated breast cancer. Ann Oncol 2018;29(5):1203–10 doi 10.1093/annonc/mdy099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011;144(5):646–74 doi 10.1016/j.cell.2011.02.013. [DOI] [PubMed] [Google Scholar]
- 95.Loke SL, Stein C, Zhang X, Avigan M, Cohen J, Neckers LM. Delivery of c-myc antisense phosphorothioate oligodeoxynucleotides to hematopoietic cells in culture by liposome fusion: specific reduction in c-myc protein expression correlates with inhibition of cell growth and DNA synthesis. Curr Top Microbiol Immunol 1988;141:282–9 doi 10.1007/978-3-642-74006-0_38. [DOI] [PubMed] [Google Scholar]
- 96.Yamazaki H, Kijima H, Ohnishi Y, Abe Y, Oshika Y, Tsuchida T, et al. Inhibition of tumor growth by ribozyme-mediated suppression of aberrant epidermal growth factor receptor gene expression. J Natl Cancer Inst 1998;90(8):581–7 doi 10.1093/jnci/90.8.581. [DOI] [PubMed] [Google Scholar]
- 97.Druker BJ, Tamura S, Buchdunger E, Ohno S, Segal GM, Fanning S, et al. Effects of a selective inhibitor of the Abl tyrosine kinase on the growth of Bcr-Abl positive cells. Nat Med 1996;2(5):561–6 doi 10.1038/nm0596-561. [DOI] [PubMed] [Google Scholar]
- 98.Fisher GH, Wellen SL, Klimstra D, Lenczowski JM, Tichelaar JW, Lizak MJ, et al. Induction and apoptotic regression of lung adenocarcinomas by regulation of a K-Ras transgene in the presence and absence of tumor suppressor genes. Genes Dev 2001;15(24):3249–62 doi 10.1101/gad.947701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Felsher DW, Bishop JM. Reversible tumorigenesis by MYC in hematopoietic lineages. Mol Cell 1999;4(2):199–207 doi 10.1016/s1097-2765(00)80367-6. [DOI] [PubMed] [Google Scholar]
- 100.Dow LE, O’Rourke KP, Simon J, Tschaharganeh DF, van Es JH, Clevers H, et al. Apc Restoration Promotes Cellular Differentiation and Reestablishes Crypt Homeostasis in Colorectal Cancer. Cell 2015;161(7):1539–52 doi 10.1016/j.cell.2015.05.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Ventura A, Kirsch DG, McLaughlin ME, Tuveson DA, Grimm J, Lintault L, et al. Restoration of p53 function leads to tumour regression in vivo. Nature 2007;445(7128):661–5 doi 10.1038/nature05541. [DOI] [PubMed] [Google Scholar]
- 102.Xue W, Zender L, Miething C, Dickins RA, Hernando E, Krizhanovsky V, et al. Senescence and tumour clearance is triggered by p53 restoration in murine liver carcinomas. Nature 2007;445(7128):656–60 doi 10.1038/nature05529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 103.Martins CP, Brown-Swigart L, Evan GI. Modeling the therapeutic efficacy of p53 restoration in tumors. Cell 2006;127(7):1323–34 doi 10.1016/j.cell.2006.12.007. [DOI] [PubMed] [Google Scholar]
- 104.Kastan MB, Zhan Q, el-Deiry WS, Carrier F, Jacks T, Walsh WV, et al. A mammalian cell cycle checkpoint pathway utilizing p53 and GADD45 is defective in ataxia-telangiectasia. Cell 1992;71(4):587–97 doi 10.1016/0092-8674(92)90593-2. [DOI] [PubMed] [Google Scholar]
- 105.Weigelt B, Bi R, Kumar R, Blecua P, Mandelker DL, Geyer FC, et al. The Landscape of Somatic Genetic Alterations in Breast Cancers From ATM Germline Mutation Carriers. J Natl Cancer Inst 2018;110(9):1030–4 doi 10.1093/jnci/djy028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Mina M, Raynaud F, Tavernari D, Battistello E, Sungalee S, Saghafinia S, et al. Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies. Cancer Cell 2017;32(2):155–68 e6 doi 10.1016/j.ccell.2017.06.010. [DOI] [PubMed] [Google Scholar]
- 107.Riabinska A, Lehrmann D, Jachimowicz RD, Knittel G, Fritz C, Schmitt A, et al. ATM activity in T cells is critical for immune surveillance of lymphoma in vivo. Leukemia 2020;34(3):771–86 doi 10.1038/s41375-019-0618-2. [DOI] [PubMed] [Google Scholar]
- 108.Bono JSD, Tan DSP, Caldwell R, Terbuch A, Goh BC, Heong V, et al. First-in-human trial of the oral ataxia telangiectasia and Rad3-related (ATR) inhibitor BAY 1895344 in patients (pts) with advanced solid tumors. Journal of Clinical Oncology 2019;37(15_suppl):3007- doi 10.1200/JCO.2019.37.15_suppl.3007. [DOI] [Google Scholar]
- 109.Yap TA, Tan DS, Terbuch A, Caldwell R, Guo C, Goh BC, et al. First-in-Human Trial of the Oral Ataxia Telangiectasia and Rad3-Related Inhibitor BAY 1895344 in Patients with Advanced Solid Tumors. Cancer Discov 2020. doi 10.1158/2159-8290.CD-20-0868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Mouw KW, Goldberg MS, Konstantinopoulos PA, D’Andrea AD. DNA Damage and Repair Biomarkers of Immunotherapy Response. Cancer Discov 2017;7(7):675–93 doi 10.1158/2159-8290.CD-17-0226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Samstein RM, Riaz N. The DNA damage response in immunotherapy and radiation. Adv Radiat Oncol 2018;3(4):527–33 doi 10.1016/j.adro.2018.08.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 112.Pantelidou C, Sonzogni O, De Oliveria Taveira M, Mehta AK, Kothari A, Wang D, et al. PARP Inhibitor Efficacy Depends on CD8(+) T-cell Recruitment via Intratumoral STING Pathway Activation in BRCA-Deficient Models of Triple-Negative Breast Cancer. Cancer Discov 2019;9(6):722–37 doi 10.1158/2159-8290.CD-18-1218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin D, et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 2012;483(7387):100–3 doi 10.1038/nature10868. [DOI] [PubMed] [Google Scholar]
- 114.Zimmermann M, Murina O, Reijns MAM, Agathanggelou A, Challis R, Tarnauskaite Z, et al. CRISPR screens identify genomic ribonucleotides as a source of PARP-trapping lesions. Nature 2018;559(7713):285–9 doi 10.1038/s41586-018-0291-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 115.Hustedt N, Alvarez-Quilon A, McEwan A, Yuan JY, Cho T, Koob L, et al. A consensus set of genetic vulnerabilities to ATR inhibition. Open Biol 2019;9(9):190156 doi 10.1098/rsob.190156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 116.Wang C, Wang G, Feng X, Shepherd P, Zhang J, Tang M, et al. Genome-wide CRISPR screens reveal synthetic lethality of RNASEH2 deficiency and ATR inhibition. Oncogene 2019;38(14):2451–63 doi 10.1038/s41388-018-0606-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 117.Pettitt SJ, Krastev DB, Brandsma I, Drean A, Song F, Aleksandrov R, et al. Genome-wide and high-density CRISPR-Cas9 screens identify point mutations in PARP1 causing PARP inhibitor resistance. Nat Commun 2018;9(1):1849 doi 10.1038/s41467-018-03917-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 118.Ma L, Boucher JI, Paulsen J, Matuszewski S, Eide CA, Ou J, et al. CRISPR-Cas9-mediated saturated mutagenesis screen predicts clinical drug resistance with improved accuracy. Proc Natl Acad Sci U S A 2017;114(44):11751–6 doi 10.1073/pnas.1708268114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.Pacini C, Dempster JM, Gonçalves E, Najgebauer H, Karakoc E, van der Meer D, et al. Integrated cross-study datasets of genetic dependencies in cancer. bioRxiv 2020. doi 10.1101/2020.05.22.110247. [DOI] [PMC free article] [PubMed] [Google Scholar]