Exploiting Synthetic Lethality and Network Biology to Overcome EGFR Inhibitor Resistance in Lung Cancer

In situ single-cell therapeutic response imaging facilitated by the TRIPODD fluorescence imaging platform

Purpose: Small molecule drugs such as tyrosine kinase inhibitors (TKIs) targeting tumoral molecular dependencies have become standard of care for numerous cancer types. Notably, epidermal growth factor receptor (EGFR) TKIs (e.g., erlotinib, afatinib, osimertinib) are the current first-line treatment for non-small cell lung cancer (NSCLC) due to their improved therapeutic outcomes for EGFR mutated and overexpressing disease over traditional platinum-based chemotherapy. However, many NSCLC tumors develop resistance to EGFR TKI therapy causing disease progression. Currently, the relationship between in situ drug target availability (DTA), local protein expression and therapeutic response cannot be accurately assessed using existing analytical tools despite being crucial to understanding the mechanism of therapeutic efficacy. Procedure: We have previously reported development of our fluorescence imaging platform termed TRIPODD (Therapeutic Response Imaging through Proteomic and Optical Drug Distribution) that is capable of simultaneous quantification of single-cell DTA and protein expression with preserved spatial context within a tumor. TRIPODD combines two complementary fluorescence imaging techniques: intracellular paired agent imaging (iPAI) to measure DTA and cyclic immunofluorescence (cyCIF), which utilizes oligonucleotide conjugated antibodies (Ab-oligos) for spatial proteomic expression profiling on tissue samples. Herein, TRIPODD was modified and optimized to provide a downstream analysis of therapeutic response through single-cell DTA and proteomic response imaging. Results: We successfully performed sequential imaging of iPAI and cyCIF resulting in high dimensional imaging and biomarker assessment to quantify single-cell DTA and local protein expression on erlotinib treated NSCLC models. Pharmacodynamic and pharmacokinetic studies of the erlotinib iPAI probes revealed that administration of 2.5 mg/kg each of the targeted and untargeted probe 4 h prior to tumor collection enabled calculation of DTA values with high Pearson correlation to EGFR, the erlotinib molecular target, expression in the tumors. Analysis of single-cell biomarker expression revealed that a single erlotinib dose was insufficient to enact a measurable decrease in the EGFR signaling cascade protein expression, where only the DTA metric detected the presence of bound erlotinib. Conclusion: We demonstrated the capability of TRIPODD to evaluate therapeutic response imaging to erlotinib treatment as it relates to signaling inhibition, DTA, proliferation, and apoptosis with preserved spatial context.

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.

Molecular Pathology of Lung Cancer

This overview of the molecular pathology of lung cancer includes a review of the most salient molecular alterations of the genome, transcriptome, and the epigenome. The insights provided by the growing use of next-generation sequencing (NGS) in lung cancer will be discussed, and interrelated concepts such as intertumor heterogeneity, intratumor heterogeneity, tumor mutational burden, and the advent of liquid biopsy will be explored. Moreover, this work describes how the evolving field of molecular pathology refines the understanding of different histologic phenotypes of non-small-cell lung cancer (NSCLC) and the underlying biology of small-cell lung cancer. This review will provide an appreciation for how ongoing scientific findings and technologic advances in molecular pathology are crucial for development of biomarkers, therapeutic agents, clinical trials, and ultimately improved patient care.

Characterization of a small molecule inhibitor of disulfide reductases that induces oxidative stress and lethality in lung cancer cells

Phenotype-based screening can identify small molecules that elicit a desired cellular response, but additional approaches are required to characterize their targets and mechanisms of action. Here, we show that a compound termed LCS3, which selectively impairs the growth of human lung adenocarcinoma (LUAD) cells, induces oxidative stress. To identify the target that mediates this effect, we use thermal proteome profiling (TPP) and uncover the disulfide reductases GSR and TXNRD1 as targets. We confirm through enzymatic assays that LCS3 inhibits disulfide reductase activity through a reversible, uncompetitive mechanism. Further, we demonstrate that LCS3-sensitive LUAD cells are sensitive to the synergistic inhibition of glutathione and thioredoxin pathways. Lastly, a genome-wide CRISPR knockout screen identifies NQO1 loss as a mechanism of LCS3 resistance. This work highlights the ability of TPP to uncover targets of small molecules identified by high-throughput screens and demonstrates the potential therapeutic utility of inhibiting disulfide reductases in LUAD.

Collagen modifying enzyme P4HA1 is overexpressed and plays a role in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related deaths globally and is histologically defined as either small cell lung cancer (SCLC) or non-small cell lung cancer (NSCLC), with the latter accounting for 80% of all lung cancers. The 5-year overall survival rate for lung cancer patients is low as it is often discovered at advanced stages when potential cure by surgical resection is no longer an option. To identify a biomarker and target for lung cancer, we performed analysis of multiple datasets of lung cancer gene expression data. Our analyses indicated that the collagen-modifying enzyme Prolyl 4-Hydroxylase Subunit Alpha 1 (P4HA1) is overexpressed in NSCLC. Furthermore, our investigation found that overexpression of enzymes involved in this pathway predicts poor outcome for patients with lung adenocarcinoma. Our functional studies using knockdown strategies in lung cancer cell lines in vitro indicated that P4HA1 is critical for lung cancer growth, migration, and invasion. Additionally, diethyl pythiDC (PythiDC), a small molecule inhibitor, decreased the malignant phenotypes of lung cancer cells. Moreover, we found that miR-124 regulates and targets P4HA1 in lung cancer cells. Thus, our study suggests that collagen-modifying enzymes play an important role in lung cancer aggressiveness. Furthermore, our studies showed that P4HA1 is required for lung cancer cell growth and invasion, suggesting its potential as a valid therapeutic target in lung adenocarcinoma.

Rare epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer

Epidermal growth factor receptor (EGFR) mutations are the second most common oncogenic driver event in non-small cell lung cancer (NSCLC). Classical activating mutations (exon 19 deletions and the L858R point mutation) comprise the vast majority of EGFR mutations and are well defined as strong predictors for good clinical response to EGFR tyrosine kinase inhibitors (EGFRi). However, low frequency mutations including point mutations, deletions, insertions and duplications occur within exons 18-25 of the EGFR gene in NSCLC and are associated with poorer responses to EGFRi. Despite an increased uptake of more sensitive detection methods to identify rare EGFR mutations in patients, our understanding of the biology of these rare EGFR mutations is poor compared to classical mutations. In particular, clinical data focused on these mutations is lacking due to their rarity and challenges in trial recruitment, resulting in an absence of effective treatment strategies for many low frequency EGFR mutations. In this review, we describe the structural and mechanistic features of rare EGFR mutations in NSCLC and discuss the preclinical and clinical evidence for EGFRi response for individual rare EGFR mutations. We also discuss EGFRi sensitivity for complex EGFR mutations, and conclude by offering a perspective on the outstanding questions and future steps required to make advances in the treatment of NSCLC patients that harbour rare EGFR mutations.

Epigenetic synthetic lethality approaches in cancer therapy

The onset and development of malignant tumors are closely related to epigenetic modifications, and this has become a research hotspot. In recent years, a variety of epigenetic regulators have been discovered, and corresponding small molecule inhibitors have been developed, but their efficacy in solid tumors is generally poor. With the introduction of the first synthetic lethal drug (the PARP inhibitor olaparib in ovarian cancer with BRCA1 mutation), research into synthetic lethality has also become a hotspot. High-throughput screening with CRISPR-Cas9 and shRNA technology has revealed a large number of synthetic lethal pairs involving epigenetic-related synthetic lethal genes, such as those encoding SWI/SNF complex subunits, PRC2 complex subunits, SETD2, KMT2C, and MLL fusion proteins. In this review, we focus on epigenetic-related synthetic lethal mechanisms, including synthetic lethality between epigenetic mutations and epigenetic inhibitors, epigenetic mutations and non-epigenetic inhibitors, and oncogene mutations and epigenetic inhibitors.

Targeting EGFR exon 20 insertion mutations in non-small cell lung cancer

Inframe insertions of three or more base pairs in exon 20 of the epidermal growth factor receptor (EGFR) gene were among the first EGFR mutations to be identified as oncogenic drivers in non-small cell lung cancer (NSCLC). However, unlike the classical EGFR L858R point mutation or exon 19 deletions, which represent the majority of EGFR mutations in NSCLC, low frequency EGFR exon 20 insertion mutations are associated with de novo resistance to targeted EGFR inhibitors and correlate with a poor patient prognosis. Here, we review the developments over the last 5 years in which pre-clinical studies, including elucidation of the crystal structure of an EGFR exon 20 insertion mutant kinase, have revealed a unique mechanism of kinase activation and steric conformation that define the lack of response of these EGFR mutations to clinically approved EGFR inhibitors. The recent development of several novel small molecule compounds that selectively inhibit EGFR exon 20 insertions holds promise for future therapeutic options that will be effective for patients with this molecular subtype of NSCLC.

Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance

Drug resistance remains one of the greatest challenges facing precision oncology today. Despite the vast array of resistance mechanisms that cancer cells employ to subvert the effects of targeted therapy, a deep understanding of cancer signalling networks has led to the development of novel strategies to tackle resistance both in the first-line and salvage therapy settings. In this review, we provide a brief overview of the major classes of resistance mechanisms to targeted therapy, including signalling reprogramming and tumour evolution; our discussion also focuses on the use of different forms of polytherapies (such as inhibitor combinations, multi-target kinase inhibitors and HSP90 inhibitors) as a means of combating resistance. The promise and challenges facing each of these polytherapies are elaborated with a perspective on how to effectively deploy such therapies in patients. We highlight efforts to harness computational approaches to predict effective polytherapies and the emerging view that exceptional responders may hold the key to better understanding drug resistance. This review underscores the importance of polytherapies as an effective means of targeting resistance signalling networks and achieving durable clinical responses in the era of personalised cancer medicine.

Improved detection of synthetic lethal interactions in Drosophila cells using variable dose analysis (VDA)

Synthetic sick or synthetic lethal (SS/L) screens are a powerful way to identify candidate drug targets to specifically kill tumor cells, but this approach generally suffers from low consistency between screens. We found that many SS/L interactions involve essential genes and are therefore detectable within a limited range of knockdown efficiency. Such interactions are often missed by overly efficient RNAi reagents. We therefore developed an assay that measures viability over a range of knockdown efficiency within a cell population. This method, called Variable Dose Analysis (VDA), is highly sensitive to viability phenotypes and reproducibly detects SS/L interactions. We applied the VDA method to search for SS/L interactions with TSC1 and TSC2, the two tumor suppressors underlying tuberous sclerosis complex (TSC), and generated a SS/L network for TSC. Using this network, we identified four Food and Drug Administration-approved drugs that selectively affect viability of TSC-deficient cells, representing promising candidates for repurposing to treat TSC-related tumors.

Candidate synthetic lethality partners to PARP inhibitors in the treatment of ovarian clear cell cancer

Inhibitors of poly(ADP-ribose) polymerase (PARP) are new types of personalized treatment of relapsed platinum-sensitive ovarian cancer harboring BRCA1/2 mutations. Ovarian clear cell cancer (CCC), a subset of ovarian cancer, often appears as low-stage disease with a higher incidence among Japanese. Advanced CCC is highly aggressive with poor patient outcome. The aim of the present study was to determine the potential synthetic lethality gene pairs for PARP inhibitions in patients with CCC through virtual and biological screenings as well as clinical studies. We conducted a literature review for putative PARP sensitivity genes that are associated with the CCC pathophysiology. Previous studies identified a variety of putative target genes from several pathways associated with DNA damage repair, chromatin remodeling complex, PI3K-AKT-mTOR signaling, Notch signaling, cell cycle checkpoint signaling, BRCA-associated complex and Fanconi's anemia susceptibility genes that could be used as biomarkers or therapeutic targets for PARP inhibition. BRCA1/2, ATM, ATR, BARD1, CCNE1, CHEK1, CKS1B, DNMT1, ERBB2, FGFR2, MRE11A, MYC, NOTCH1 and PTEN were considered as candidate genes for synthetic lethality gene partners for PARP interactions. When considering the biological background underlying PARP inhibition, we hypothesized that PARP inhibitors would be a novel synthetic lethal therapeutic approach for CCC tumors harboring homologous recombination deficiency and activating oncogene mutations. The results showed that the majority of CCC tumors appear to have indicators of DNA repair dysfunction similar to those in BRCA-mutation carriers, suggesting the possible utility of PARP inhibitors in a subset of CCC.

Quantitative phosphoproteomic analysis of acquired cancer drug resistance to pazopanib and dasatinib

Acquired drug resistance impacts the majority of patients being treated with tyrosine kinase inhibitors (TKIs) and remains a key challenge in modern anti-cancer therapy. The lack of clinically effective therapies to overcome resistance represents an unmet need. Understanding the signalling that drives drug resistance will facilitate the development of new salvage therapies to treat patients with secondary TKI resistance. In this study, we utilise mass spectrometry to characterise the global phosphoproteomic alterations that accompany the acquisition of resistance to two FDA-approved TKIs, pazopanib and dasatinib, in the A204 rhabdoid tumour cell line. Our analysis finds that only 6% and 9.7% of the quantified phosphoproteome is altered upon the acquisition of pazopanib and dasatinib resistance, respectively. Pazopanib resistant cells display elevated phosphorylation in cytoskeletal regulatory pathways while dasatinib resistant cells show an upregulation of the insulin receptor/IGF-1R signalling pathway. Drug response profiling rediscovers several previously reported vulnerabilities associated with pazopanib and dasatinib resistance and identifies a new dependency to the second generation HSP90 inhibitor NVP-AUY-922. This study provides a useful resource detailing the candidate signalling determinants of acquired TKI resistance; and reveals a therapeutic approach of inhibiting HSP90 function as a means of salvage therapy to overcome pazopanib and dasatinib resistance.

Significance

Pazopanib and dasatinib are tyrosine kinase inhibitors (TKIs) approved for the treatment of multiple cancer types. Patients who are treated with these drugs are prone to the development of drug resistance and consequently tumour relapse. Here we use quantitative phosphoproteomics to characterise the signalling pathways which are enriched in cells that have acquired resistance to these two drugs. Furthermore, targeted drug screens were used to identify salvage therapies capable of overcoming pazopanib and dasatinib resistance. This data advances our understanding of the mechanisms of TKI resistance and highlights candidate targets for cancer therapy.

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Pazopanib resistant cells display elevated phosphorylation in cytoskeletal regulatory pathways.

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Phosphoproteins in the insulin and IGF-1R pathways are upregulated in dasatinib resistant cells.

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Less than 10% of the phosphoproteome is altered in acquired drug-resistant A204 cells.

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Both dasatinib and pazopanib resistant A204 cells are vulnerable to HSP90 inhibition.