Complex synthetic lethality in cancer.

The concept of synthetic lethality has been widely applied to identify therapeutic targets in cancer, with varying degrees of success. The standard approach normally involves identifying genetic interactions between two genes, a driver and a target. In reality, however, most cancer synthetic lethal effects are likely complex and also polygenic, being influenced by the environment in addition to involving contributions from multiple genes.

Gene essentiality in cancer is better predicted by mRNA abundance than by gene regulatory network-inferred activity.

Gene regulatory networks (GRNs) are often deregulated in tumor cells, resulting in altered transcriptional programs that facilitate tumor growth. These altered networks may make tumor cells vulnerable to the inhibition of specific regulatory proteins. Consequently, the reconstruction of GRNs in tumors is often proposed as a means to identify therapeutic targets.

Exploiting Cancer Synthetic Lethality in Cancer-Lessons Learnt from PARP Inhibitors.

PARP inhibitors now have proven utility in the treatment of homologous recombination (HR) defective cancers. These drugs, and the synthetic lethality effect they exploit, have not only taught us how to approach the treatment of HR defective cancers but have also illuminated how resistance to a synthetic lethal approach can occur, how cancer-associated synthetic lethal effects are perhaps more complex than we imagine, how the better use of biomarkers could improve the success of treatment and even how drug resistance might be targeted. Here, we discuss some of the lessons learnt from the study of PARP inhibitor synthetic lethality and how these lessons might have wider application.

Paralog dispensability shapes homozygous deletion patterns in tumor genomes.

Genomic instability is a hallmark of cancer, resulting in tumor genomes having large numbers of genetic aberrations, including homozygous deletions of protein coding genes. That tumor cells remain viable in the presence of such gene loss suggests high robustness to genetic perturbation. In model organisms and cancer cell lines, paralogs have been shown to contribute substantially to genetic robustness-they are generally more dispensable for growth than singletons.

Genetic interactions under the microscope.

Traditional genetic interaction screens profile phenotypes at aggregate level, missing interactions that may influence the distribution of single cells in specific states. Here, Heigwer and colleagues use an imaging approach to generate a large-scale high-resolution genetic interaction map in Drosophila cells and demonstrate its utility for understanding gene function.

Antibody reliability influences observed mRNA-protein correlations in tumour samples.

Reverse phase protein arrays (RPPA) have been used to quantify the abundance of hundreds of proteins across thousands of tumour samples in the Cancer Genome Atlas. By number of samples, this is the largest tumour proteomic dataset available and it provides an opportunity to systematically assess the correlation between mRNA and protein abundances. However, the RPPA approach is highly dependent on antibody reliability and approximately one-quarter of the antibodies used in the the Cancer Genome Atlas are deemed to be somewhat less reliable.

Targeting synthetic lethal paralogs in cancer.

Synthetic lethal interactions, where mutation of one gene renders cells sensitive to inhibition of another gene, can be exploited for the development of targeted therapeutics in cancer. Pairs of duplicate genes (paralogs) often share common functionality and hence are a potentially rich source of synthetic lethal interactions. Because the majority of human genes have paralogs, exploiting such interactions could be a widely applicable approach for targeting gene loss in cancer.

Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles.

Large-scale studies of human proteomes have revealed only a moderate correlation between mRNA and protein abundances. It is unclear to what extent this moderate correlation reflects post-transcriptional regulation and to what extent it reflects measurement error. Here, by analyzing replicate profiles of tumors and cell lines, we show that there is considerable variation in the reproducibility of measurements of transcripts and proteins from individual genes.

Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines.

Pairs of paralogs may share common functionality and, hence, display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However, only a biased subset of human paralog pairs has been tested for synthetic lethality to date.

Proteomic signatures of radioresistance: Alteration of inflammation, angiogenesis and metabolism-related factors in radioresistant oesophageal adenocarcinoma.

The clinical management of locally advanced oesophageal adenocarcinoma (OAC) involves neoadjuvant chemoradiotherapy (CRT), but as radioresistance remains a major clinical challenge, complete pathological response to CRT only occurs in 20-30% of patients. In this study we used an established isogenic cell line model of radioresistant OAC to detect proteomic signatures of radioresistance to identify novel molecular and cellular targets of radioresistance in OAC. A total of 5785 proteins were identified of which 251 were significantly modulated in OE33R cells, when compared to OE33P.

Cross-species identification of PIP5K1-, splicing- and ubiquitin-related pathways as potential targets for RB1-deficient cells.

The RB1 tumor suppressor is recurrently mutated in a variety of cancers including retinoblastomas, small cell lung cancers, triple-negative breast cancers, prostate cancers, and osteosarcomas. Finding new synthetic lethal (SL) interactions with RB1 could lead to new approaches to treating cancers with inactivated RB1. We identified 95 SL partners of RB1 based on a Drosophila screen for genetic modifiers of the eye phenotype caused by defects in the RB1 ortholog, Rbf1.

Accurate prediction of kinase-substrate networks using knowledge graphs.

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy.

Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions.

Genetic interactions, including synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Here, by developing a new computational approach, we identified 220 robust driver-gene associated genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels.

Paralog buffering contributes to the variable essentiality of genes in cancer cell lines.

What makes a gene essential for cellular survival? In model organisms, such as budding yeast, systematic gene deletion studies have revealed that paralog genes are less likely to be essential than singleton genes and that this can partially be attributed to the ability of paralogs to buffer each other's loss. However, the essentiality of a gene is not a fixed property and can vary significantly across different genetic backgrounds. It is unclear to what extent paralogs contribute to this variation, as most studies have analyzed genes identified as essential in a single genetic background.

Cells Lacking the Tumor Suppressor Gene Are Hyperdependent on Aurora B Kinase for Survival.

Small cell lung cancer (SCLC) accounts for 15% of lung cancers and is almost always linked to inactivating and mutations. SCLC frequently responds, albeit briefly, to chemotherapy. The canonical function of the gene product RB1 is to repress the E2F transcription factor family.

Systems biology: old news or new stimulus for biochemistry.

In this issue of , biochemistry meets systems biology-a blind date that may hold all the promises, pitfalls and failures of a relationship where a new discipline has been sprung upon a well-established one. As the articles in this issue show, the blind date in this case has great potential to develop into a long-term relationship, where both partners share common values but can benefit from different complementary approaches. Together this partnership is well poised to address and solve some of the major challenges in modern biology.

Synthetic Lethality and Cancer - Penetrance as the Major Barrier.

Synthetic lethality has long been proposed as an approach for targeting genetic defects in tumours. Despite a decade of screening efforts, relatively few robust synthetic lethal targets have been identified. Improved genetic perturbation techniques, including CRISPR/Cas9 gene editing, have resulted in renewed enthusiasm for searching for synthetic lethal effects in cancer.

Author Correction: Chemosensitivity profiling of osteosarcoma tumour cell lines identifies a model of BRCAness.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Chemosensitivity profiling of osteosarcoma tumour cell lines identifies a model of BRCAness.

Osteosarcoma (OS) is an aggressive sarcoma, where novel treatment approaches are required. Genomic studies suggest that a subset of OS, including OS tumour cell lines (TCLs), exhibit genomic loss of heterozygosity (LOH) patterns reminiscent of BRCA1 or BRCA2 mutant tumours. This raises the possibility that PARP inhibitors (PARPi), used to treat BRCA1/2 mutant cancers, could be used to target OS.

Identification of highly penetrant Rb-related synthetic lethal interactions in triple negative breast cancer.

Although defects in the RB1 tumour suppressor are one of the more common driver alterations found in triple-negative breast cancer (TNBC), therapeutic approaches that exploit this have not been identified. By integrating molecular profiling data with data from multiple genetic perturbation screens, we identified candidate synthetic lethal (SL) interactions associated with RB1 defects in TNBC. We refined this analysis by identifying the highly penetrant effects, reasoning that these would be more robust in the face of molecular heterogeneity and would represent more promising therapeutic targets.

E-Cadherin/ROS1 Inhibitor Synthetic Lethality in Breast Cancer.

The cell adhesion glycoprotein E-cadherin (CDH1) is commonly inactivated in breast tumors. Precision medicine approaches that exploit this characteristic are not available. Using perturbation screens in breast tumor cells with CRISPR/Cas9-engineered mutations, we identified synthetic lethality between E-cadherin deficiency and inhibition of the tyrosine kinase ROS1.

Demonstration of universal parametric entangling gates on a multi-qubit lattice.

We show that parametric coupling techniques can be used to generate selective entangling interactions for multi-qubit processors. By inducing coherent population exchange between adjacent qubits under frequency modulation, we implement a universal gate set for a linear array of four superconducting qubits. An average process fidelity of ℱ = 93% is estimated for three two-qubit gates via quantum process tomography.

Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels.

Loss-of-function screening using RNA interference or CRISPR approaches can be used to identify genes that specific tumor cell lines depend upon for survival. By integrating the results from screens in multiple cell lines with molecular profiling data, it is possible to associate the dependence upon specific genes with particular molecular features (e.g.

Hardware for dynamic quantum computing.

We describe the hardware, gateware, and software developed at Raytheon BBN Technologies for dynamic quantum information processing experiments on superconducting qubits. In dynamic experiments, real-time qubit state information is fed back or fed forward within a fraction of the qubits' coherence time to dynamically change the implemented sequence. The hardware presented here covers both control and readout of superconducting qubits.