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Cancer is one of the leading causes of death and chromosomal instability (CIN) is a hallmark feature of cancer. CIN, a source of genetic variation in either altered chromosome number or structure contributes to tumor heterogeneity and has become a hot topic in recent years prominently for its role in therapeutic responses. Synthetic lethality and synthetic rescue based approaches, for example, advancing CRISPR-Cas9 platform, are emerging as a powerful strategy to identify new potential targets to selectively eradicate cancer cells. Unfortunately, only few of them are further explored therapeutically due to the difficulty in linking these targets to small molecules for pharmacological intervention. This, however, can be alleviated by the efforts to bring chemical, bioactivity, and genomic data together, as well as established computational approaches. In this chapter, we will discuss some of these advances, including established databases and in silico target-ligand prediction, with the aim to navigate through the synthetically available chemical space to the biologically targetable landscape, and eventually, to the chemical modeling of synthetic lethality and synthetic rescue interactions, that are of great clinical and pharmaceutical relevance and significance.

Despite the success of targeted therapies including immunotherapies in cancer treatments, tumor resistance to targeted therapies remains a fundamental challenge. Tumors can evolve resistance to a therapy that targets one gene by acquiring compensatory alterations in another gene, such compensatory interaction between two genes is referred to as synthetic rescue (SR) interactions. To identify SRs, here we describe an algorithm, INCISOR, that leverages tumor transcriptomics and clinical information from 10,000 patients as well as data from experimental screens. INCISOR can identify SRs that are common across several cancer-types in genome-wide fashion by sifting through half a billion possible gene-gene combinations and provide a framework to design therapies to tackle resistance.

Inherited or acquired mutations can lead to pathological outcomes. However, in a process defined as synthetic rescue, phenotypic outcome created by primary mutation is alleviated by suppressor mutations. An exhaustive characterization of these mutations in humans is extremely valuable to better comprehend why patients carrying the same detrimental mutation exhibit different pathological outcomes or different responses to treatment. Here, we first review all known suppressor mutations’ mechanisms characterized by genetic screens on model species like yeast or flies. However, human suppressor mutations are scarce, despite some being discovered based on orthologue genes. Because of recent advances in high-throughput screening, developing an inventory of human suppressor mutations for pathological processes seems achievable. In addition, we review several screening methods for suppressor mutations in cultured human cells through knock-out, knock-down or random mutagenesis screens on large scale. We provide examples of studies published over the past years that opened new therapeutic avenues, particularly in oncology.

Engineering the redox cofactor metabolism is known to be a key challenge in developing a platform strain for biosynthesis of valuable products. Hence, general strategies for manipulation of co-factor metabolism in industrially relevant hosts are of significance. Here, we demonstrate an improvement in α-ketoglutarate (AKG) production in S. cerevisiae using a novel approach based on synthetic rescue. Here, we first perturb the cytosolic NADPH metabolism via deletion of glucose-6-phosphate dehydrogenase (ZWF1). In parallel, we used a strain design algorithm to identify strategies for further improvement in AKG production. Implementation of the identified genetic targets, including disruption of succinyl-CoA Ligase (LSC2) and constitutive expression of NADP+-specific isocitrate dehydrogenases (IDP1 and IDP2) resulted in more than 3 fold improvement in AKG production as compared to the wild type. Our results demonstrate this improvement is due to a synthetic rescue mechanism in which the metabolic flux was redirected towards AKG production through the manipulation of redox cofactors. Disrupting lsc2 in zwf1 mutant improved specific growth rate more than 15% as compared to the zwf1 mutant. In addition, our result suggests that cytosolic isocitrate dehydrogenase (IDP2) may be regulated by isocitrate pools. Together, these results suggest the ability to improve metabolite production via a model guided synthetic rescue mechanism in S. cerevisiae and the potential for using IDP2 expression as a generalized strategy to effectively meet NADPH requirements in engineered strains.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome‐wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients’ response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involvesynthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (therescuer). Here we perform a genome-wide prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients’ response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

Background: Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Here we demonstrate that many of these molecular events involve synthetic rescue (SR) genetic interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Methods: Analyzing recently published large scale in vitro functional screens we identify 100,000s candidate SR interactions that show evidence of rescue events in cancer cell lines. We then analyze tumor transcriptomic, genomic, and genetic profiles together with survival and clinical characteristics of 10,000 TCGA cancer patients to identify the few thousand SR interactions that have strong evidence that they are mediators of emerging resistance in patient tumors. Results: We identify the first rescue network, composed of SR interactions common to many cancer types. The identified SRs successfully match the resistance mediators identified in recently published clinical studies. We perform multiple in vitro analyses in head and neck and lung cancer, showing that targeting predicted rescuer genes successfully re-sensitizes resistant cancer cells, providing specific leads for targeting resistance proactively. We further demonstrate that SR-based combination therapy can improve the progression free survival in mouse xenografts models. Notably, we show that SR interactions successfully predict cancer patients' response in the TCGA compendium, showing performance superior to existing machine learning based predictive models. Finally, we show that SR analysis of melanoma patients successfully identifies known mediators of resistance to checkpoint immunotherapy (reported previously in mice) and suggests new combination therapies that counteract the resistance. Conclusions: This work presents a new paradigm identifying and harnessing synthetic rescue interactions to counteract resistance to both targeted- and immuno-therapies. Future implementations of this approach will have two broad implications in the precision oncology era: first for determining the most effective treatment regimen based on the molecular characteristics of individual patient’s tumor; second for identifying conjunct drugs to counteract resistance to existing primary therapies, for both targeted and immune checkpoint therapies. Citation Format: Avinash Das, Joo Sang Lee, Gao Zhang, Zhiyong Wang, Arnaud Amzallag, Genevieve Boland, Sridhar Hannenhalli, Meenhard Herlyn, Cyril Benes, J. Silvio Gutkind, Keity Flaherty, Eytan Ruppin. Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-149.

Introduction: Numerous anti-cancer drugs that show promising initial response but eventually develop resistance. Adaptive response is one of the mechanisms of the emergence of resistance, as in the case of Lapatinib, where kinase reprogramming overcomes the ERBB2 inhibition. Such adaptive response belongs to a class of cellular reprogramming events called Synthetic Rescues (SR), where changes in the activity state of one (vulnerable) gene are lethal to the cell and they evade the lethality by reprogramming the activity state of other gene(s) (rescuer). Aim: We aim to identify the pan cancer genome-scale SR network and demonstrate that such rescue reprogramming is widespread in cancer and is crucial for predicting drug response and deciphering the emergence of drug resistance. Results: We develop a novel data driven approach, termed “IdeNtification of ClinIcal Synthetic Rescues in cancer”(INCISOR), to identify genome-scale SR network. Analyzing gene-expression, copy number variation and clinical data of 7,550 patients across 23 different cancer types from the TCGA collection, we identify four major classes of SR reprogramming, which involve: two inactive genes (DD), an inactive vulnerable gene and an overactive rescuer (DU), an overactive vulnerable gene and an inactive rescuer (UD), and two overactive genes (UU). Many of these are prevalent across many cancer types, however some are specific to individual cancer types. We further identified SR networks specific to breast cancer. Using an independent patient dataset, we show that patient's survival is highly determined by the number of functionally activated SRs belonging to each of four SR classes, with DU class showing the highest clinical significance among them. Analysis of somatic mutation in TCGA revealed the preferential over-activation of (DU) rescuers as a response to the inactivation of their vulnerable partners by somatic mutations, providing an independent validation of our predictions. We successfully predicted, for the first time, patient response to 72% of (26 out of 36) major breast cancer drugs using the pan cancer (DU) SR network. We find that as cancer progresses, the number of inactivated vulnerable genes that are rescued increases, suggesting a correlation between the level of SR reprogramming and tumor's aggressiveness. Importantly, using multiple patient data we show that the pan cancer SR networks are predictive of emergence of resistance to anthracyclines, taxanes, fluorouracil, olarip, carbolplatin and cyclophasmide in breast, gastric and ovarian cancer. DU-rescuers of the drug targets are markedly over-expressed in post-treatment samples from patients that acquired resistance. Conclusions: We introduce and rigorously define a new concept of synthetic rescue reprogramming occurring in cancer and present its first genome-wide SR networks. Our analysis shows that molecular reprogramming due to SR is widespread and of considerable clinical importance. The SR networks uncovered provide new insights into the specific molecular mechanisms underlying the emerging resistance to cancer drugs and are predictive of the variable response among patients. Last and not least, our study identifies key rescuer hub genes whose targeting can mitigate the emergence of resistance, offering a new class of candidate drug targets for adjuvant cancer therapies aimed at counteracting resistance. Citation Format: Avinash D. Sahu, Joo Sang Lee, Sridhar Hannenhalli, Eytan Ruppin. Tracing synthetic rescue reprogramming to counteract cancer resistance. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B56.

Inherited or acquired mutations can lead to pathological outcomes. However, in a process defined as synthetic rescue, phenotypic outcome created by primary mutation is alleviated by suppressor mutations. An exhaustive characterization of these mutations in humans is extremely valuable to better comprehend why patients carrying the same detrimental mutation exhibit different pathological outcomes or different responses to treatment. Here, we first review all known suppressor mutations’ mechanisms characterized by genetic screens on model species like yeast or flies. However, human suppressor mutations are scarce, despite some being discovered based on orthologue genes. Because of recent advances in high-throughput screening, developing an inventory of human suppressor mutations for pathological processes seems achievable. In addition, we review several screening methods for suppressor mutations in cultured human cells through knock-out, knock-down or random mutagenesis screens on large scale. We provide examples of studies published over the past years that opened new therapeutic avenues, particularly in oncology.