Synthetic lethality: general principles, utility and detection using genetic screens in human cells

Genome engineering on size reduction and complexity simplification: A review

BACKGROUND

Genome simplification is an important topic in the field of life sciences that has attracted attention from its conception to the present day. It can help uncover the essential components of the genome and, in turn, shed light on the underlying operating principles of complex biological systems. This has made it a central focus of both basic and applied research in the life sciences. With the recent advancements in related technologies and our increasing knowledge of the genome, now is an opportune time to delve into this topic.

AIM OF REVIEW

Our review investigates the progress of genome simplification from two perspectives: genome size reduction and complexity simplification. In addition, we provide insights into the future development trends of genome simplification.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Reducing genome size requires eliminating non-essential elements as much as possible. This process has been facilitated by advances in genome manipulation and synthesis techniques. However, we still need a better and clearer understanding of living systems to reduce genome complexity. As there is a lack of quantitative and clearly defined standards for this task, we have opted to approach the topic from various perspectives and present our findings accordingly.

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

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

SL-Miner: a web server for mining evidence and prioritization of cancer-specific synthetic lethality

SUMMARY

Synthetic lethality (SL) refers to a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect cell viability. It significantly expands the range of potential therapeutic targets for anti-cancer treatments. SL interactions are primarily identified through experimental screening and computational prediction. Although various computational methods have been proposed, they tend to ignore providing evidence to support their predictions of SL. Besides, they are rarely user-friendly for biologists who likely have limited programming skills. Moreover, the genetic context specificity of SL interactions is often not taken into consideration. Here, we introduce a web server called SL-Miner, which is designed to mine the evidence of SL relationships between a primary gene and a few candidate SL partner genes in a specific type of cancer, and to prioritize these candidate genes by integrating various types of evidence. For intuitive data visualization, SL-Miner provides a range of charts (e.g. volcano plot and box plot) to help users get insights from the data.

AVAILABILITY AND IMPLEMENTATION

SL-Miner is available at https://slminer.sist.shanghaitech.edu.cn.

Synthetic Lethality by Co-Inhibition of Androgen Receptor and Polyadenosine Diphosphate-Ribose in Metastatic Prostate Cancer

Androgen receptor pathway inhibitors (ARPI) and polyadenosine diphosphate-ribose inhibitors (PARPi) are part of the standard of care in patients with metastatic castration-resistant prostate cancer (mCRPC). There is biological evidence that the association of ARPI and PARPi could have a synergistic effect; therefore, several ongoing clinical trials are investigating the efficacy of this combination with preliminary results that are not perfectly concordant in identifying patients who can obtain the most benefit from this therapeutic option. The purpose of this review is to describe the PARPi mechanisms of action and to analyze the biological mechanisms behind the interplay between the androgen receptor and the PARPi system to better understand the rationale of the ARPI + PARPi combinations. Furthermore, we will summarize the preliminary results of the ongoing studies on these combinations, trying to understand in which patients to apply. Finally, we will discuss the clinical implications of this combination and its possible future perspectives.

The Role of the AT-Rich Interaction Domain 1A Gene (ARID1A) in Human Carcinogenesis

The switch/sucrose non-fermentable (SWI/SNF) (SWI/SNF) complex uses energy from ATP hydrolysis to mobilise nucleosomes on chromatin. Components of SWI/SNF are mutated in 20% of all human cancers, of which mutations in AT-rich binding domain protein 1A (ARID1A) are the most common. ARID1A is mutated in nearly half of ovarian clear cell carcinoma and around one-third of endometrial and ovarian carcinomas of the endometrioid type. This review will examine in detail the molecular functions of ARID1A, including its role in cell cycle control, enhancer regulation, and the prevention of telomerase activity. ARID1A has key roles in the maintenance of genomic integrity, including DNA double-stranded break repair, DNA decatenation, integrity of the cohesin complex, and reduction in replication stress, and is also involved in mismatch repair. The role of ARID1A loss in the pathogenesis of some of the most common human cancers is discussed, with a particular emphasis on gynaecological cancers. Finally, several promising synthetic lethal strategies, which exploit the specific vulnerabilities of ARID1A-deficient cancer cells, are briefly mentioned.

Synthetic Lethality Screening with Recursive Feature Machines

Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively targeting cancer cells based on genetic vulnerabilities. The rise of large-scale gene perturbation screens such as the Cancer Dependency Map (DepMap) offers the opportunity to identify SL pairs automatically using machine learning. We build on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train RFMs to predict viability scores for a given CRISPR gene knockout from cell line embeddings consisting of gene expression and mutation features. After training, RFMs use a statistical operator known as average gradient outer product to provide weights for each feature indicating the importance of each feature in predicting cellular viability. We subsequently apply correlation-based filters to re-weight RFM feature importances and identify those features that are most indicative of low cellular viability. Our resulting pipeline is computationally efficient, taking under 3 minutes for analyzing all 17, 453 knockouts from DepMap for candidate SL pairs. We show that our pipeline more accurately recovers experimentally verified SL pairs than prior approaches. Moreover, our pipeline finds new candidate SL pairs, thereby opening novel avenues for identifying genetic vulnerabilities in cancer.

Genome mining yields putative disease-associated ROMK variants with distinct defects

Bartter syndrome is a group of rare genetic disorders that compromise kidney function by impairing electrolyte reabsorption. Left untreated, the resulting hyponatremia, hypokalemia, and dehydration can be fatal, and there is currently no cure. Bartter syndrome type II specifically arises from mutations in KCNJ1, which encodes the renal outer medullary potassium channel, ROMK. Over 40 Bartter syndrome-associated mutations in KCNJ1 have been identified, yet their molecular defects are mostly uncharacterized. Nevertheless, a subset of disease-linked mutations compromise ROMK folding in the endoplasmic reticulum (ER), which in turn results in premature degradation via the ER associated degradation (ERAD) pathway. To identify uncharacterized human variants that might similarly lead to premature degradation and thus disease, we mined three genomic databases. First, phenotypic data in the UK Biobank were analyzed using a recently developed computational platform to identify individuals carrying KCNJ1 variants with clinical features consistent with Bartter syndrome type II. In parallel, we examined genomic data in both the NIH TOPMed and ClinVar databases with the aid of Rhapsody, a verified computational algorithm that predicts mutation pathogenicity and disease severity. Subsequent phenotypic studies using a yeast screen to assess ROMK function-and analyses of ROMK biogenesis in yeast and human cells-identified four previously uncharacterized mutations. Among these, one mutation uncovered from the two parallel approaches (G228E) destabilized ROMK and targeted it for ERAD, resulting in reduced cell surface expression. Another mutation (T300R) was ERAD-resistant, but defects in channel activity were apparent based on two-electrode voltage clamp measurements in X. laevis oocytes. Together, our results outline a new computational and experimental pipeline that can be applied to identify disease-associated alleles linked to a range of other potassium channels, and further our understanding of the ROMK structure-function relationship that may aid future therapeutic strategies to advance precision medicine.

Unleashing the Power of Synthetic Lethality: Augmenting Treatment Efficacy through Synergistic Integration with Chemotherapy Drugs

Cancer is the second leading cause of death in the world, and chemotherapy is one of the main methods of cancer treatment. However, the resistance of cancer cells to chemotherapeutic drugs has always been the main reason affecting the therapeutic effect. Synthetic lethality has emerged as a promising approach to augment the sensitivity of cancer cells to chemotherapy agents. Synthetic lethality (SL) refers to the specific cell death resulting from the simultaneous mutation of two non-lethal genes, which individually allow cell survival. This comprehensive review explores the classification of SL, screening methods, and research advancements in SL inhibitors, including Poly (ADP-ribose) polymerase (PARP) inhibitors, Ataxia telangiectasia and Rad3-related (ATR) inhibitors, WEE1 G2 checkpoint kinase (WEE1) inhibitors, and protein arginine methyltransferase 5 (PRMT5) inhibitors. Emphasizing their combined use with chemotherapy drugs, we aim to unveil more effective treatment strategies for cancer patients.

Overexpression of XRCC1 is Associated with Poor Survival in Patients with Head and Neck Squamous Carcinoma and Has Potential to Be Used as Targeted Therapy by Synthetic Lethality

Background: Head neck squamous cell carcinoma (HNSC) is globally prevalent cancer attributed to tobacco habit. Despite the significant advances in early diagnosis and treatment of HNSC chemo-radio resistance are routinely observed in patients. Aberrant DNA repair mechanisms mainly microhomology mediated DNA end joining (MMEJ) pathway causing deleterious mutations and is implicated in treatment resistance. X-ray cross complimenting group 1 (XRCC1) has recently been shown to play an essential role in MMEJ making XRCC1 a potential therapeutic target to render tumors chemo-radiosensitive. This study analyzes the correlation between the expression level of XRCC1 gene with survival, regulation by miRNA and synthetic lethality partners in HNSCC. Materials and Methods: XRCC1 gene expression was evaluated in 520 HNSC patients and 44 of normal tissues using the UALCAN (TCGA) database and its correlation with survival outcome of HNSC patients was analyzed by Kaplan-Meier plot. Infiltration of immune cells in tumors was analyzed by “Tumor-Infiltrating Immune Estimation Resource (TIMER) and promoter methylation status of XRCC1 in samples was analysed by UALCAN. STRING was used to find gene interacting partners of XRCC1.  Results: XRCC1 was significantly overexpressed in primary tumor of HNSCC and significantly increased with tumor stages and grade and associated with poor survival rate. High XRCC1 expression in HNSC was positively correlated with infiltration level of B cells naïsve, CD4+ and macrophages. Conclusion: These results indicate that XRCC1 is a prognostic marker for predicting survival in HNSC patients. Understanding how XRCC1 leads to treatment resistance and modulate immune response can lead to development of targeted therapy.

Mslar: Microbial synthetic lethal and rescue database

Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.

Multi-Omic Analysis of CIC's Functional Networks Reveals Novel Interaction Partners and a Potential Role in Mitotic Fidelity

CIC encodes a transcriptional repressor and MAPK signalling effector that is inactivated by loss-of-function mutations in several cancer types, consistent with a role as a tumour suppressor. Here, we used bioinformatic, genomic, and proteomic approaches to investigate CIC's interaction networks. We observed both previously identified and novel candidate interactions between CIC and SWI/SNF complex members, as well as novel interactions between CIC and cell cycle regulators and RNA processing factors. We found that CIC loss is associated with an increased frequency of mitotic defects in human cell lines and an in vivo mouse model and with dysregulated expression of mitotic regulators. We also observed aberrant splicing in CIC-deficient cell lines, predominantly at 3' and 5' untranslated regions of genes, including genes involved in MAPK signalling, DNA repair, and cell cycle regulation. Our study thus characterises the complexity of CIC's functional network and describes the effect of its loss on cell cycle regulation, mitotic integrity, and transcriptional splicing, thereby expanding our understanding of CIC's potential roles in cancer. In addition, our work exemplifies how multi-omic, network-based analyses can be used to uncover novel insights into the interconnected functions of pleiotropic genes/proteins across cellular contexts.

Genome mining yields new disease-associated ROMK variants with distinct defects

Bartter syndrome is a group of rare genetic disorders that compromise kidney function by impairing electrolyte reabsorption. Left untreated, the resulting hyponatremia, hypokalemia, and dehydration can be fatal. Although there is no cure for this disease, specific genes that lead to different Bartter syndrome subtypes have been identified. Bartter syndrome type II specifically arises from mutations in the KCNJ1 gene, which encodes the renal outer medullary potassium channel, ROMK. To date, over 40 Bartter syndrome-associated mutations in KCNJ1 have been identified. Yet, their molecular defects are mostly uncharacterized. Nevertheless, a subset of disease-linked mutations compromise ROMK folding in the endoplasmic reticulum (ER), which in turn results in premature degradation via the ER associated degradation (ERAD) pathway. To identify uncharacterized human variants that might similarly lead to premature degradation and thus disease, we mined three genomic databases. First, phenotypic data in the UK Biobank were analyzed using a recently developed computational platform to identify individuals carrying KCNJ1 variants with clinical features consistent with Bartter syndrome type II. In parallel, we examined ROMK genomic data in both the NIH TOPMed and ClinVar databases with the aid of a computational algorithm that predicts protein misfolding and disease severity. Subsequent phenotypic studies using a high throughput yeast screen to assess ROMK function-and analyses of ROMK biogenesis in yeast and human cells-identified four previously uncharacterized mutations. Among these, one mutation uncovered from the two parallel approaches (G228E) destabilized ROMK and targeted it for ERAD, resulting in reduced protein expression at the cell surface. Another ERAD-targeted ROMK mutant (L320P) was found in only one of the screens. In contrast, another mutation (T300R) was ERAD-resistant, but defects in ROMK activity were apparent after expression and two-electrode voltage clamp measurements in Xenopus oocytes. Together, our results outline a new computational and experimental pipeline that can be applied to identify disease-associated alleles linked to a range of other potassium channels, and further our understanding of the ROMK structure-function relationship that may aid future therapeutic strategies.

Author Summary

Bartter syndrome is a rare genetic disorder characterized by defective renal electrolyte handing, leading to debilitating symptoms and, in some patients, death in infancy. Currently, there is no cure for this disease. Bartter syndrome is divided into five types based on the causative gene. Bartter syndrome type II results from genetic variants in the gene encoding the ROMK protein, which is expressed in the kidney and assists in regulating sodium, potassium, and water homeostasis. Prior work established that some disease-associated ROMK mutants misfold and are destroyed soon after their synthesis in the endoplasmic reticulum (ER). Because a growing number of drugs have been identified that correct defective protein folding, we wished to identify an expanded cohort of similarly misshapen and unstable disease-associated ROMK variants. To this end, we developed a pipeline that employs computational analyses of human genome databases with genetic and biochemical assays. Next, we both confirmed the identity of known variants and uncovered previously uncharacterized ROMK variants associated with Bartter syndrome type II. Further analyses indicated that select mutants are targeted for ER-associated degradation, while another mutant compromises ROMK function. This work sets-the-stage for continued mining for ROMK loss of function alleles as well as other potassium channels, and positions select Bartter syndrome mutations for correction using emerging pharmaceuticals.

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

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

Racial differences in RAD51 expression are regulated by miRNA-214-5P and its inhibition synergizes with olaparib in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) affects young women and is the most aggressive subtype of breast cancer (BC). TNBCs disproportionally affect women of African-American (AA) descent compared to other ethnicities. We have identified DNA repair gene RAD51 as a poor prognosis marker in TNBC and its posttranscriptional regulation through microRNAs (miRNAs). This study aims to delineate the mechanisms leading to RAD51 upregulation and develop novel therapeutic combinations to effectively treat TNBCs and reduce disparity in clinical outcomes.

METHODS

Analysis of TCGA data for BC cohorts using the UALCAN portal and PrognoScan identified the overexpression of RAD51 in TNBCs. miRNA sequencing identified significant downregulation of RAD51-targeting miRNAs miR-214-5P and miR-142-3P. RT-PCR assays were used to validate the levels of miRNAs and RAD51, and immunohistochemical and immunoblotting techniques were used similarly for RAD51 protein levels in TNBC tissues and cell lines. Luciferase assays were performed under the control of RAD51 3'-UTR to confirm that miR-214-5P regulates RAD51 expression. To examine the effect of miR-214-5P-mediated downregulation of RAD51 on homologous recombination (HR) in TNBC cells, Dr-GFP reporter assays were performed. To assess the levels of olaparib-induced DNA damage responses in miR-214-5P, transfected cells, immunoblots, and immunofluorescence assays were used. Furthermore, COMET assays were used to measure DNA lesions and colony assays were performed to assess the sensitivity of BRCA-proficient TNBC cells to olaparib.

RESULTS

In-silico analysis identified upregulation of RAD51 as a poor prognostic marker in TNBCs. miRNA-seq data showed significant downregulation of miR-214-5P and miR-142-3P in TNBC cell lines derived from AA women compared to Caucasian-American (CA) women. miR-214-5P mimics downregulated RAD51 expression and induces HR deficiency as measured by Dr-GFP assays in these cell lines. Based on these results, we designed a combination treatment of miR-214-5P and olaparib in HR-proficient AA TNBC cell lines using clonogenic survival assays. The combination of miR-214-5P and olaparib showed synergistic lethality compared to individual treatments in these cell lines.

CONCLUSIONS

Our studies identified a novel epigenetic regulation of RAD51 in TNBCs by miR-214-5P suggesting a novel combination therapies involving miR-214-5P and olaparib to treat HR-proficient TNBCs and to reduce racial disparity in therapeutic outcomes.

Okazaki fragment maturation: DNA flap dynamics for cell proliferation and survival

Unsuccessful processing of Okazaki fragments leads to the accumulation of DNA breaks which are associated with many human diseases including cancer and neurodegenerative disorders. Recently, Okazaki fragment maturation (OFM) has received renewed attention regarding how unprocessed Okazaki fragments are sensed and repaired, and how inappropriate OFM impacts on genome stability and cell viability, especially in cancer cells. We provide an overview of the highly efficient and faithful canonical OFM pathways and their regulation of genomic integrity and cell survival. We also discuss how cells induce alternative error-prone OFM processes to promote cell survival in response to environmental stresses. Such stress-induced OFM processes may be important mechanisms driving mutagenesis, cellular evolution, and resistance to radio/chemotherapy and targeted therapeutics in human cancers.

KRAS-Mutant Lung Cancer: Targeting Molecular and Immunologic Pathways, Therapeutic Advantages and Restrictions

RAS mutations are among the most common oncogenic mutations in human cancers. Among RAS mutations, KRAS has the highest frequency and is present in almost 30% of non-small-cell lung cancer (NSCLC) patients. Lung cancer is the number one cause of mortality among cancers as a consequence of outrageous aggressiveness and late diagnosis. High mortality rates have been the reason behind numerous investigations and clinical trials to discover proper therapeutic agents targeting KRAS. These approaches include the following: direct KRAS targeting; synthetic lethality partner inhibitors; targeting of KRAS membrane association and associated metabolic rewiring; autophagy inhibitors; downstream inhibitors; and immunotherapies and other immune-modalities such as modulating inflammatory signaling transcription factors (e.g., STAT3). The majority of these have unfortunately encountered limited therapeutic outcomes due to multiple restrictive mechanisms including the presence of co-mutations. In this review we plan to summarize the past and most recent therapies under investigation, along with their therapeutic success rate and potential restrictions. This will provide useful information to improve the design of novel agents for treatment of this deadly disease.

Loss of tRNA methyltransferase 9 and DNA damage response genes in yeast confers sensitivity to aminoglycosides

tRNA methyltransferase 9 (Trm9)-catalysed tRNA modifications have been shown to translationally enhance the DNA damage response (DDR). Here, we show that Saccharomyces cerevisiae trm9Δ, distinct DNA repair and spindle assembly checkpoint (SAC) mutants are differentially sensitive to the aminoglycosides tobramycin, gentamicin and amikacin, indicating DDR and SAC activation might rely on translation fidelity, under aminoglycoside stress. Further, we report that the DNA damage induced by aminoglycosides in the base excision repair mutants ogg1Δ and apn1Δ is mediated by reactive oxygen species, which induce the DNA adduct 8-hydroxy deoxyguanosine. Finally, the synergistic effect of tobramycin and the DNA-damaging agent bleomycin to sensitize trm9Δ and the DDR mutants mlh1Δ, rad51Δ, mre11Δ and sgs1Δ at significantly lower concentrations compared with wild-type suggests that cells with tRNA modification dysregulation and DNA repair gene defects can be selectively sensitized using a combination of translation inhibitors and DNA-damaging agents.

Dead-Seq: Discovering Synthetic Lethal Interactions from Dead Cells Genomics

Pooled genetic screens have revolutionized the field of functional genomics, yet perturbations that decrease fitness, such as those leading to synthetic lethality, have remained difficult to quantify at the genomic level. We and colleagues previously developed "death screening," a protocol based on the purification of dead cells in genetic screens, and used it to identify a set of genes necessary for mitochondrial gene expression, translation, and oxidative phosphorylation (OXPHOS), thus offering new possibilities for the diagnosis of mitochondrial disorders. Here, we describe Dead-Seq, a refined protocol for death screening that is compatible with most pooled screening protocols, including genome-wide CRISPR/Cas9 screening. Dead-Seq converts negative-selection screens into positive-selection screens and generates high-quality data directly from dead cells, at limited sequencing costs.

The potential of PARP inhibitors in targeted cancer therapy and immunotherapy

DNA damage response (DDR) deficiencies result in genome instability, which is one of the hallmarks of cancer. Poly (ADP-ribose) polymerase (PARP) enzymes take part in various DDR pathways, determining cell fate in the wake of DNA damage. PARPs are readily druggable and PARP inhibitors (PARPi) against the main DDR-associated PARPs, PARP1 and PARP2, are currently approved for the treatment of a range of tumor types. Inhibition of efficient PARP1/2-dependent DDR is fatal for tumor cells with homologous recombination deficiencies (HRD), especially defects in breast cancer type 1 susceptibility protein 1 or 2 (BRCA1/2)-dependent pathway, while allowing healthy cells to survive. Moreover, PARPi indirectly influence the tumor microenvironment by increasing genomic instability, immune pathway activation and PD-L1 expression on cancer cells. For this reason, PARPi might enhance sensitivity to immune checkpoint inhibitors (ICIs), such as anti-PD-(L)1 or anti-CTLA4, providing a rationale for PARPi-ICI combination therapies. In this review, we discuss the complex background of the different roles of PARP1/2 in the cell and summarize the basics of how PARPi work from bench to bedside. Furthermore, we detail the early data of ongoing clinical trials indicating the synergistic effect of PARPi and ICIs. We also introduce the diagnostic tools for therapy development and discuss the future perspectives and limitations of this approach.

Pre-Existing and Acquired Resistance to PARP Inhibitor-Induced Synthetic Lethality

The advanced development of synthetic lethality has opened the doors for specific anti-cancer medications of personalized medicine and efficient therapies against cancers. One of the most popular approaches being investigated is targeting DNA repair pathways as the implementation of the PARP inhibitor (PARPi) into individual or combinational therapeutic schemes. Such treatment has been effectively employed against homologous recombination-defective solid tumors as well as hematopoietic malignancies. However, the resistance to PARPi has been observed in both preclinical research and clinical treatment. Therefore, elucidating the mechanisms responsible for the resistance to PARPi is pivotal for the further success of this intervention. Apart from mechanisms of acquired resistance, the bone marrow microenvironment provides a pre-existing mechanism to induce the inefficiency of PARPi in leukemic cells. Here, we describe the pre-existing and acquired mechanisms of the resistance to PARPi-induced synthetic lethality. We also discuss the potential rationales for developing effective therapies to prevent/repress the PARPi resistance in cancer cells.

Robust linear DNA degradation supports replication-initiation-defective mutants in Escherichia coli

RecBCD helicase/nuclease supports replication fork progress via recombinational repair or linear DNA degradation, explaining recBC mutant synthetic lethality with replication elongation defects. Since replication initiation defects leave chromosomes without replication forks, these should be insensitive to the recBCD status. Surprisingly, we found that both Escherichia coli dnaA46(Ts) and dnaC2(Ts) initiation mutants at semi-permissive temperatures are also recBC-colethal. Interestingly, dnaA46 recBC lethality suppressors suggest underinitiation as the problem, while dnaC2 recBC suppressors signal overintiation. Using genetic and physical approaches, we studied the dnaA46 recBC synthetic lethality, for the possibility that RecBCD participates in replication initiation. Overproduced DnaA46 mutant protein interferes with growth of dnaA+ cells, while the residual viability of the dnaA46 recBC mutant depends on the auxiliary replicative helicase Rep, suggesting replication fork inhibition by the DnaA46 mutant protein. The dnaA46 mutant depends on linear DNA degradation by RecBCD, rather than on recombinational repair. At the same time, the dnaA46 defect also interacts with Holliday junction-moving defects, suggesting reversal of inhibited forks. However, in contrast to all known recBC-colethals, which fragment their chromosomes, the dnaA46 recBC mutant develops no chromosome fragmentation, indicating that its inhibited replication forks are stable. Physical measurements confirm replication inhibition in the dnaA46 mutant shifted to semi-permissive temperatures, both at the level of elongation and initiation, while RecBCD gradually restores elongation and then initiation. We propose that RecBCD-catalyzed resetting of inhibited replication forks allows replication to displace the "sticky" DnaA46(Ts) protein from the chromosomal DNA, mustering enough DnaA for new initiations.

Clinical Pharmacokinetics and Pharmacodynamics of Rucaparib

Rucaparib is an oral small-molecule poly(ADP-ribose) polymerase inhibitor indicated for patients with recurrent ovarian cancer in the maintenance and treatment settings and for patients with metastatic castration-resistant prostate cancer associated with a deleterious BRCA1 or BRCA2 mutation. Rucaparib has a manageable safety profile; the most common adverse events reported were fatigue and nausea in both indications. Accumulation in plasma exposure occurred after repeated administration of the approved 600-mg twice-daily dosage. Steady state was achieved after continuous twice-daily dosing for a week. Rucaparib has moderate oral bioavailability and can be dosed with or without food. Although a high-fat meal weakly increased maximum concentration and area under the curve, the effect was not clinically significant. A mass balance analysis indicated almost a complete dose recovery of rucaparib over 12 days, with metabolism, renal, and hepatic excretion as the elimination routes. A population pharmacokinetic analysis of rucaparib revealed no effect of age, sex, race, or body weight. No starting dose adjustments were necessary for patients with mild-to-moderate hepatic or renal impairment; the effect of severe organ impairment on rucaparib exposure has not been evaluated. In patients, rucaparib moderately inhibited cytochrome P450 (CYP) 1A2 and weakly inhibited CYP3As, CYP2C9, and CYP2C19. Rucaparib weakly increased systemic exposures of oral contraceptives and oral rosuvastatin and marginally increased the exposure of oral digoxin (a P-glycoprotein substrate). In vitro studies suggested that rucaparib inhibits transporters MATE1, MATE2-K, OCT1, and OCT2. No clinically meaningful drug interactions with rucaparib as a perpetrator were observed. An exposure-response analysis revealed dose-dependent changes in selected clinical efficacy and safety endpoints. Overall, this article provides a comprehensive review of the clinical pharmacokinetics, pharmacodynamics, drug-drug interactions, effects of intrinsic and extrinsic factors, and exposure-response relationships of rucaparib.

MYC and therapy resistance in cancer: risks and opportunities

The MYC transcription factor, encoded by the c-MYC proto-oncogene, is activated by growth-promoting signals, and is a key regulator of biosynthetic and metabolic pathways driving cell growth and proliferation. These same processes are deregulated in MYC-driven tumors, where they become critical for cancer cell proliferation and survival. As other oncogenic insults, overexpressed MYC induces a series of cellular stresses (metabolic, oxidative, replicative, etc.) collectively known as oncogenic stress, which impact not only on tumor progression, but also on the response to therapy, with profound, multifaceted consequences on clinical outcome. On one hand, recent evidence uncovered a widespread role for MYC in therapy resistance in multiple cancer types, with either standard chemotherapeutic or targeted regimens. Reciprocally, oncogenic MYC imparts a series of molecular and metabolic dependencies to cells, thus giving rise to cancer-specific vulnerabilities that may be exploited to obtain synthetic-lethal interactions with novel anticancer drugs. Here we will review the current knowledge on the links between MYC and therapeutic responses, and will discuss possible strategies to overcome resistance through new, targeted interventions.

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

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

A genetic map of the chromatin regulators to drug response in cancer cells

BACKGROUND

Diverse drug vulnerabilities owing to the Chromatin regulators (CRs) genetic interaction across various cancers, but the identification of CRs genetic interaction remains challenging.

METHODS

In order to provide a global view of the CRs genetic interaction in cancer cells, we developed a method to identify potential drug response-related CRs genetic interactions for specific cancer types by integrating the screen of CRISPR-Cas9 and pharmacogenomic response datasets.

RESULTS

Totally, 625 drug response-related CRs synthetic lethality (CSL) interactions and 288 CRs synthetic viability (CSV) interactions were detected. Systematically network analysis presented CRs genetic interactions have biological function relationship. Furthermore, we validated CRs genetic interactions induce multiple omics deregulation in The Cancer Genome Atlas. We revealed the colon adenocarcinoma patients (COAD) with mutations of a CRs set (EP300, MSH6, NSD2 and TRRAP) mediate a better survival with low expression of MAP2 and could benefit from taxnes. While the COAD patients carrying at least one of the CSV interactions in Vorinostat CSV module confer a poor prognosis and may be resistant to Vorinostat treatment.

CONCLUSIONS

The CRs genetic interaction map provides a rich resource to investigate cancer-associated CRs genetic interaction and proposes a powerful strategy of biomarker discovery to guide the rational use of agents in cancer therapy.

Targeting Homologous Recombination Deficiency in Ovarian Cancer with PARP Inhibitors: Synthetic Lethal Strategies That Impact Overall Survival

Simple Summary

Synthetic lethality approaches to cancer therapy involves combining events to cause cancer cell death. Using this strategy, major advances have occurred in the treatment of women with ovarian cancer who have defects in the Homologous Recombination Repair (HRR) pathway. When the HRR pathway is defective, due to mutations or epigenetic changes in genes such as BRCA1 or BRCA2, cells can no longer accurately repair double strand breaks (DSBs). Capitalising on this weakness, pharmacological inhibition of poly (ADP-ribose) polymerase (PARP) that function to repair single strand breaks (SSBs) leads to synthetic lethality in cells with defective HRR. PARP inhibitors (PARPis) including olaparib, niraparib and rucaparib are approved for the clinical management of women with ovarian cancer. Understanding and overcoming issues of acquired resistance to PARPis, extending these strategies to benefit more patients and combining PARPis with other drugs, including immunotherapies, are of high priority in the field today.

Abstract

The advent of molecular targeted therapies has made a significant impact on survival of women with ovarian cancer who have defects in homologous recombination repair (HRR). High-grade serous ovarian cancer (HGSOC) is the most common histological subtype of ovarian cancer, with over 50% displaying defective HRR. Poly ADP ribose polymerases (PARPs) are a family of enzymes that catalyse the transfer of ADP-ribose to target proteins, functioning in fundamental cellular processes including transcription, chromatin remodelling and DNA repair. In cells with deficient HRR, PARP inhibitors (PARPis) cause synthetic lethality leading to cell death. Despite the major advances that PARPis have heralded for women with ovarian cancer, questions and challenges remain, including: can the benefits of PARPis be brought to a wider range of women with ovarian cancer; can other drugs in clinical use function in a similar way or with greater efficacy than currently clinically approved PARPis; what can we learn from long-term responders to PARPis; can PARPis sensitise ovarian cancer cells to immunotherapy; and can synthetic lethal strategies be employed more broadly to develop new therapies for women with ovarian cancer. We examine these, and other, questions with focus on improving outcomes for women with ovarian cancer.

NSF4SL: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers

MOTIVATION

Detecting synthetic lethality (SL) is a promising strategy for identifying anti-cancer drug targets. Targeting SL partners of a primary gene mutated in cancer is selectively lethal to cancer cells. Due to high cost of wet-lab experiments and availability of gold standard SL data, supervised machine learning for SL prediction has been popular. However, most of the methods are based on binary classification and thus limited by the lack of reliable negative data. Contrastive learning can train models without any negative sample and is thus promising for finding novel SLs.

RESULTS

We propose NSF4SL, a negative-sample-free SL prediction model based on a contrastive learning framework. It captures the characteristics of positive SL samples by using two branches of neural networks that interact with each other to learn SL-related gene representations. Moreover, a feature-wise data augmentation strategy is used to mitigate the sparsity of SL data. NSF4SL significantly outperforms all baselines which require negative samples, even in challenging experimental settings. To the best of our knowledge, this is the first time that SL prediction is formulated as a gene ranking problem, which is more practical than the current formulation as binary classification. NSF4SL is the first contrastive learning method for SL prediction and its success points to a new direction of machine-learning methods for identifying novel SLs.

AVAILABILITY AND IMPLEMENTATION

Our source code is available at https://github.com/JieZheng-ShanghaiTech/NSF4SL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Overcoming selection bias in synthetic lethality prediction

MOTIVATION

Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data.

RESULTS

We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples.

AVAILABILITY AND IMPLEMENTATION

https://github.com/joanagoncalveslab/sbsl.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Lethal Inhibitors and Inhibitor Combinations for Mono-Driver versus Multi-Driver Triple-Negative Breast Cancer Cells

There are no signaling-based targeted therapies for triple-negative breast cancer. The development of targeted cancer therapy relies on identifying oncogenic signaling drivers, understanding their contributions to oncogenesis and developing inhibitors to block such drivers. In this study, we determine that DU-4475 is a mono-driver cancer cell line relying on BRAF and the mitogen-activated protein kinase pathway for viability and proliferation. It is fully and lethally inhibited by BRAF or Mek inhibitors at low nM concentrations, but it is resistant to inhibitors targeting other signaling pathways. The inhibitory lethality caused by blocking Mek or BRAF is through apoptosis. In contrast, MDA-MB-231 is a multi-driver triple-negative breast cancer cell line dependent on both Src and the KRAS-activated mitogen-activated kinase pathway for proliferation and viability. Blocking each pathway alone only partially inhibits cell proliferation without killing them, but the combination of dasatinib, an Src inhibitor, and trametinib, a Mek inhibitor, achieves synthetic lethality. The combination is highly potent, with an IC50 of 8.2 nM each, and strikingly synergistic, with a combination index of less than 0.003 for 70% inhibition. The synthetic lethality of the drug combination is achieved by apoptosis. These results reveal a crucial difference between mono-driver and multi-driver cancer cells and suggest that pharmacological synthetic lethality may provide a basis for effectively inhibiting multi-driver cancers.

Splicing dysregulation in human hematologic malignancies: beyond splicing mutations

Splicing is a fundamental process in pre-mRNA maturation. Whereas alternative splicing (AS) enriches the diversity of the proteome, its aberrant regulation can drive oncogenesis. So far, most attention has been given to spliceosome mutations (SMs) in the context of splicing dysregulation in hematologic diseases. However, in recent years, post-translational modifications (PTMs) and transcriptional alterations of splicing factors (SFs), just as epigenetic signatures, have all been shown to contribute to global splicing dysregulation as well. In addition, the contribution of aberrant splicing to the neoantigen repertoire of cancers has been recognized. With the pressing need for novel therapeutics to combat blood cancers, this article provides an overview of emerging mechanisms that contribute to aberrant splicing, as well as their clinical potential.

Triple-Negative Breast Cancer: A Review of Current Curative Intent Therapies

Breast cancer is the most commonly diagnosed malignancy in women, with triple-negative breast cancer (TNBC) accounting for 10–20% of cases. Historically, fewer treatment options have existed for this subtype of breast cancer, with cytotoxic chemotherapy playing a predominant role. This article aims to review the current treatment paradigm for curative-intent TNBC, while also reviewing potential future developments in this landscape. In addition to chemotherapy, recent advances in the understanding of the molecular biology of TNBC have led to promising new studies of targeted and immune checkpoint inhibitor therapies in the curative-intent setting. The appropriate selection of TNBC patient subgroups with a higher likelihood of benefit from treatment is critical to identify the best treatment approach.

Targeting PARP1 to Enhance Anticancer Checkpoint Immunotherapy Response: Rationale and Clinical Implications

Reinvigorating the antitumor immune response using immune checkpoint inhibitors (ICIs) has revolutionized the treatment of several malignancies. However, extended use of ICIs has resulted in a cancer-specific response. In tumors considered to be less immunogenic, the response rates were low or null. To overcome resistance and improve the beneficial effects of ICIs, novel strategies focused on ICI-combined therapies have been tested. In particular, poly ADP-ribose polymerase inhibitors (PARPi) are a class of agents with potential for ICI combined therapy. PARPi impairs single-strand break DNA repair; this mechanism involves synthetic lethality in tumor cells with deficient homologous recombination. More recently, novel evidence indicated that PAPRi has the potential to modulate the antitumor immune response by activating antigen-presenting cells, infiltrating effector lymphocytes, and upregulating programmed death ligand-1 in tumors. This review covers the current advances in the immune effects of PARPi, explores the potential rationale for combined therapy with ICIs, and discusses ongoing clinical trials.

ASO Author Reflections: Broaden the Targeted Population of Synthetic Lethality for Taiwanese Breast Cancer

Poly(ADP-ribose) polymerase (PARP) inhibitors selectively cause the failure of DNA single-stranded break (SSB) repair but do not affect double-stranded break (DSB) repair. Furthermore, antitumor activities have been reported for breast cancer, with germline BRCA1/2 mutations. The prevalence of germline BRCA1/2 mutations never exceed one-tenth; beyond BRCA1/2, genes recurrently altered (more than 5%) in the homologous repair pathway are ARID1A, PALB2, and PTEN. Altered homologous recombination repair genes can total up to one-quarter based on different definitions, and the potential of PARP inhibitors will be elucidated when further studies unraveling the impact of individual homologous recombination genes are conducted.

Molecularly Targeted Therapy towards Genetic Alterations in Advanced Bladder Cancer

Despite the introduction of immune checkpoint inhibitors and antibody-drug conjugates to the management of advanced urothelial carcinoma, the disease is generally incurable. The increasing incorporation of next-generation sequencing of tumor tissue into the characterization of bladder cancer has led to a better understanding of the somatic genetic aberrations potentially involved in its pathogenesis. Genetic alterations have been observed in kinases, such as FGFRs, ErbBs, PI3K/Akt/mTOR, and Ras-MAPK, and genetic alterations in critical cellular processes, such as chromatin remodeling, cell cycle regulation, and DNA damage repair. However, activating mutations or fusions of FGFR2 and FGFR3 remains the only validated therapeutically actionable alteration, with erdafitinib as the only targeted agent currently approved for this group. Bladder cancer is characterized by genomic heterogeneity and a high tumor mutation burden. This review highlights the potential relevance of aberrations and discusses the current status of targeted therapies directed at them.

Targeting key proteins involved in transcriptional regulation for cancer therapy: Current strategies and future prospective

The key proteins involved in transcriptional regulation play convergent roles in cellular homeostasis, and their dysfunction mediates aberrant gene expressions that underline the hallmarks of tumorigenesis. As tumor progression is dependent on such abnormal regulation of transcription, it is important to discover novel chemical entities as antitumor drugs that target key tumor-associated proteins involved in transcriptional regulation. Despite most key proteins (especially transcription factors) involved in transcriptional regulation are historically recognized as undruggable targets, multiple targeting approaches at diverse levels of transcriptional regulation, such as epigenetic intervention, inhibition of DNA-binding of transcriptional factors, and inhibition of the protein-protein interactions (PPIs), have been established in preclinically or clinically studies. In addition, several new approaches have recently been described, such as targeting proteasomal degradation and eliciting synthetic lethality. This review will emphasize on accentuating these developing therapeutic approaches and provide a thorough conspectus of the drug development to target key proteins involved in transcriptional regulation and their impact on future oncotherapy.

PARP Inhibitor Applicability: Detailed Assays for Homologous Recombination Repair Pathway Components

Poly(ADP-ribose) polymerase inhibitors (PARPi) have been in clinical use since 2014 for certain patients with germline BRCA1/2 mutations, but as evidence and approvals for their use in a wider range of patients grow, the question of how best to identify patients who would benefit from PARPi becomes ever more complex. Here, we discuss the development and current state of approved selection testing for PARPi therapy and the ongoing efforts to define a broader range of homologous recombination repair deficiencies that are susceptible to PARP inhibition.

Pleiotropic role of PARP1: an overview

Poly (ADP-ribose) polymerase 1 (PARP1) protein is encoded by the PARP1 gene located on chromosome 1 (1q42.12) in human cells. It plays a crucial role in post-translational modification by adding poly (ADP-ribose) (PAR) groups to various proteins and PARP1 itself by utilizing nicotinamide adenine dinucleotide (NAD +) as a substrate. Since the discovery of PARP1, its role in DNA repair and cell death has been its identity. This is evident from an overwhelmingly high number of scientific reports in this regard. However, PARP1 also plays critical roles in inflammation, metabolism, tumor development and progression, chromatin modification and transcription, mRNA stability, and alternative splicing. In the present study, we attempted to compile all the scattered scientific information about this molecule, including the structure and multifunctional role of PARP1 in cancer and non-cancer diseases, along with PARP1 inhibitors (PARPis). Furthermore, for the first time, we have classified PARP1-mediated cell death for ease of understanding its role in cell death pathways.

Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer

Synthetic lethality (SL) is an emerging therapeutic paradigm in cancer. We introduced a different approach to prioritize SL gene pairs through literature mining and RAS-mutant high-throughput screening (HTS) data. We matched essential genes from text-mining and mutant genes from the COSMIC and CCLE HTS datasets to build a prediction model of SL gene pairs. CCLE gene expression data were used to enrich the essential-mutant SL gene pairs using Spearman’s correlation coefficient and literature mining. In total, 223 essential trigger terms were extracted and ranked. The threshold of the essential gene score (Sg) was set to 10. We identified 586 genes essential for the SL prediction model of colon cancer. Seven essential RAS-mutant SL gene pairs were identified in our model, including CD82-KRAS/NRAS, PEBP1-NRAS, MT-CO2-HRAS, IFI27-NRAS/KRAS, and SUMO1-HRAS gene pairs. Using RAS-mutant HTS data validation, we identified two potential SL gene pairs, including the CD82 (essential gene)–KRAS (mutant gene) pair and CD82–NRAS pair in the DLD-1 colon cancer cell line (Spearman’s correlation p-values = 0.004786 and 0.00249, respectively). Based on further annotations by PubChem, we observed that digitonin targeted the complex comprising CD82, especially in KRAS-mutated HCT116 cancer cells. Moreover, we experimentally demonstrated that CD82 exhibited selective vulnerability in KRAS-mutant colorectal cancer. We used literature mining and HTS data to identify candidates for SL targets for RAS-mutant colon cancer.

Histone deacetylase inhibitor 2-hexyl-4-pentynoic acid enhances hydroxyurea therapeutic effect in triple-negative breast cancer cells

Triple-negative breast cancer (TNBC) treatment has only limited effect, and it causes a significant number of deaths. Histone deacetylase inhibitors (HDACis) are emerging as promising anti-tumor agents in many types of cancers. We thus hypothesized that 2-hexyl-4-pentynoic acid (HPTA), a novel HDACi, could sensitize TNBC to hydroxyurea (HU, a ribonucleotide reductase inhibitor). In the present study, we investigated the effect of HPTA, alone or in combination with HU on cell survival, DNA double-strand breaks (DSBs), key homologous recombination (HR) repair proteins and cell cycle progression in MDA-MB-468 and MDA-MB-231 human TNBC cell lines. HPTA and HU synergistically inhibited the survival of TNBC cell lines and resulted in the accumulation of DNA double-strand breaks (DSBs). HPTA can sensitize TNBC cells to HU by inhibiting replication protein A2 (RPA2) hyperphosphorylation-mediated HR repair, and lessen cell accumulation in S-phase by inhibiting ATR-CHK1 signaling pathway. Taken together, our data suggested that HPTA enhances HU therapeutic effect by blocking the HR repair and regulating cell cycle progression in TNBC.

A Methodological Workflow to Analyze Synthetic Lethality and Drug Synergism in Cancer Cells

Current concepts in treating cancer usually neglect individual tumor characteristics such as a given mutational make up. Consequently, a "one-size-fits-all" therapeutic concept may commonly fail in terms of efficacy, evolving drug resistance, and side effects. In times of omics, novel elaborated and personalized approaches emerge for efficiently eradicate cancer cells, while sparing healthy cells. Synthetic lethality-based strategies offer promising opportunities to exploit tumor-specific vulnerabilities and improve tolerability. Furthermore, taking advantage of putative synergistic interaction between synthetic lethal drugs specifically targeting a given tumor genotype, could further enhance efficacy and tolerability, thus preventing drug resistance. Mechanisms of drug resistance in cancers are manifold but critical to assess, in view of restoring drug sensibility. In this chapter, we provide a framework to investigate synthetic lethality and synergistic interactions, as well as drug resistance in cancer cells in vitro.

Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey

Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.

Collateral Victim or Rescue Worker?-The Role of Histone Methyltransferases in DNA Damage Repair and Their Targeting for Therapeutic Opportunities in Cancer

Disrupted DNA damage signaling greatly threatens cell integrity and plays significant roles in cancer. With recent advances in understanding the human genome and gene regulation in the context of DNA damage, chromatin biology, specifically biology of histone post-translational modifications (PTMs), has emerged as a popular field of study with great promise for cancer therapeutics. Here, we discuss how key histone methylation pathways contribute to DNA damage repair and impact tumorigenesis within this context, as well as the potential for their targeting as part of therapeutic strategies in cancer.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

POLE, POLD1, and NTHL1: the last but not the least hereditary cancer-predisposing genes

POLE, POLD1, and NTHL1 are involved in DNA replication and have recently been recognized as hereditary cancer-predisposing genes, because their alterations are associated with colorectal cancer and other tumors. POLE/POLD1-associated syndrome shows an autosomal dominant inheritance, whereas NTHL1-associated syndrome follows an autosomal recessive pattern. Although the prevalence of germline monoallelic POLE/POLD1 and biallelic NTHL1 pathogenic variants is low, they determine different phenotypes with a broad tumor spectrum overlapping that of other hereditary conditions like Lynch Syndrome or Familial Adenomatous Polyposis. Endometrial and breast cancers, and probably ovarian and brain tumors are also associated with POLE/POLD1 alterations, while breast cancer and other unusual tumors are correlated with NTHL1 pathogenic variants. POLE-mutated colorectal and endometrial cancers are associated with better prognosis and may show favorable responses to immunotherapy. Since POLE/POLD1-mutated tumors show a high tumor mutational burden producing an increase in neoantigens, the identification of POLE/POLD1 alterations could help select patients suitable for immunotherapy treatment. In this review, we will investigate the role of POLE, POLD1, and NTHL1 genetic variants in cancer predisposition, discussing the potential future therapeutic applications and assessing the utility of performing a routine genetic testing for these genes, in order to implement prevention and surveillance strategies in mutation carriers.

Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality

BACKGROUND

Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing.

METHODS

Here we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map.

RESULTS

Using PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2.

CONCLUSIONS

PARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

Overview of Ferroptosis and Synthetic Lethality Strategies

Ferroptosis, a term first proposed in 2012, is iron-dependent, non-apoptotic regulatory cell death induced by erastin. Ferroptosis was originally discovered during synthetic lethal screening for drugs sensitive to RAS mutant cells, and is closely related to synthetic lethality. Ferroptosis sensitizes cancer stem cells and tumors that undergo epithelial−mesenchymal transition and are resistant to anticancer drugs or targeted therapy. Therefore, ferroptosis-inducing molecules are attractive new research targets. In contrast, synthetic lethal strategies approach mechanisms and genetic abnormalities that cannot be directly targeted by conventional therapeutic strategies, such as RAS mutations, hypoxia, and abnormalities in the metabolic environment. They also target the environment and conditions specific to malignant cells, have a low toxicity to normal cells, and can be used in combination with known drugs to produce new ones. However, the concept of synthetic lethality has not been widely adopted with ferroptosis. In this review, we surveyed the literature on ferroptosis-related factors and synthetic lethality to examine the potential therapeutic targets in ferroptosis-related molecules, focusing on factors related to synthetic lethality, discovery methods, clinical application stages, and issues in drug discovery.

PARP Inhibitors Display Differential Efficacy in Models of BRCA Mutant High-Grade Serous Ovarian Cancer

Several poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitors are now in clinical use for tumours with defects in BReast CAncer genes BRCA1 or BRCA2 that result in deficient homologous recombination repair (HRR). Use of olaparib, niraparib or rucaparib for the treatment of high-grade serous ovarian cancer, including in the maintenance setting, has extended both progression free and overall survival for women with this malignancy. While different PARP inhibitors (PARPis) are mechanistically similar, differences are apparent in their chemical structures, toxicity profiles, PARP trapping abilities and polypharmacological landscapes. We have treated ovarian cancer cell line models of known BRCA status, including the paired cell lines PEO1 and PEO4, and UWB1.289 and UWB1.289+BRCA1, with five PARPis (olaparib, niraparib, rucaparib, talazoparib and veliparib) and observed differences between PARPis in both cell viability and cell survival. A cell line model of acquired resistance to veliparib showed increased resistance to the other four PARPis tested, suggesting that acquired resistance to one PARPi may not be able to be rescued by another. Lastly, as a proof of principle, HRR proficient ovarian cancer cells were sensitised to PARPis by depletion of BRCA1. In the future, guidelines will need to emerge to assist clinicians in matching specific PARPis to specific patients and tumours.

PARP1: Structural insights and pharmacological targets for inhibition

Poly(ADP-ribose) polymerase 1 (PARP1, also known as ADPRT1) is a multifunctional human ADP-ribosyltransferase. It plays a role in multiple DNA repair pathways, including the base excision repair (BER), non-homologous end joining (NHEJ), homologous recombination (HR), and Okazaki-fragment processing pathways. In response to DNA strand breaks, PARP1 covalently attaches ADP-ribose moieties to arginine, glutamate, aspartate, cysteine, lysine, and serine acceptor sites on both itself and other proteins. This signal recruits DNA repair proteins to the site of DNA damage. PARP1 binding to these sites enhances ADP-ribosylation via allosteric communication between the distant DNA binding and catalytic domains. In this review, we provide a general overview of PARP1 and emphasize novel potential approaches for pharmacological inhibition. Clinical PARP1 inhibitors bind the catalytic pocket, where they directly interfere with ADP-ribosylation. Some inhibitors may further enhance potency by "trapping" PARP1 on DNA via an allosteric mechanism, though this proposed mode of action remains controversial. PARP1 inhibitors are used clinically to treat some cancers, but resistance is common, so novel pharmacological approaches are urgently needed. One approach may be to design novel small molecules that bind at inter-domain interfaces that are essential for PARP1 allostery. To illustrate these points, this review also includes instructive videos showing PARP1 structures and mechanisms.

Therapeutic effect of curcumin in gastrointestinal cancers: A comprehensive review

Gastrointestinal (GI) cancers with a high global prevalence are a leading cause of morbidity and mortality. Accordingly, there is a great need to develop efficient therapeutic approaches. Curcumin, a naturally occurring agent, is a promising compound with documented safety and anticancer activities. Recent studies have demonstrated the activity of curcumin in the prevention and treatment of different cancers. According to systematic studies on curcumin use in various diseases, it can be particularly effective in GI cancers because of its high bioavailability in the gastrointestinal tract. Nevertheless, the clinical applications of curcumin are largely limited because of its low solubility and low chemical stability in water. These limitations may be addressed by the use of relevant analogues or novel delivery systems. Herein, we summarize the pharmacological effects of curcumin against GI cancers. Moreover, we highlight the application of curcumin's analogues and novel delivery systems in the treatment of GI cancers.