Design of combination therapy for engineered bacterial therapeutics in non-small cell lung cancer.

Synthetic biology enables the engineering of bacteria to safely deliver potent payloads to tumors for effective anti-cancer therapies. However, a central challenge for translation is determining ideal bacterial therapy candidates for specific cancers and integrating them with other drug treatment strategies to maximize efficacy. To address this, we designed a screening and evaluation pipeline for characterization of bacterial therapies in lung cancer models. We screened 10 engineered bacterial toxins across 6 non-small cell lung cancer patient-derived cell lines and identified theta toxin as a promising therapeutic candidate. Using a bacteria-spheroid co-culture system (BSCC), analysis of differentially expressed transcripts and gene set enrichment revealed significant changes in at least 10 signaling pathways with bacteria-producing theta toxin. We assessed combinatorial treatment of small molecule pharmaceutical inhibitors targeting 5 signaling molecules and of 2 chemotherapy drugs along with bacterially-produced theta toxin and showed improved dose-dependent response. This combination strategy was further tested and confirmed, with AKT signaling as an example, in a mouse model of lung cancer. In summary, we developed a pipeline to rapidly characterize bacterial therapies and integrate them with current targeted therapies for lung cancer.

Assessing chemotherapy dosing strategies in a spatial cell culture model.

Predicting patient responses to chemotherapy regimens is a major challenge in cancer treatment. Experimental model systems coupled with quantitative mathematical models to calculate optimal dose and frequency of drugs can enable improved chemotherapy regimens. Here we developed a simple approach to track two-dimensional cell colonies composed of chemo-sensitive and resistant cell populations via fluorescence microscopy and coupled this to computational model predictions. Specifically, we first developed multiple 4T1 breast cancer cell lines resistant to varying concentrations of doxorubicin, and demonstrated how heterogeneous populations expand in a two-dimensional colony. We subjected cell populations to varied dose and frequency of chemotherapy and measured colony growth. We then built a mathematical model to describe the dynamics of both chemosensitive and chemoresistant populations, where we determined which number of doses can produce the smallest tumor size based on parameters in the system. Finally, using an in vitro model we demonstrated multiple doses can decrease overall colony growth as compared to a single dose at the same total dose. In the future, this system can be adapted to optimize dosing strategies in the setting of heterogeneous cell types or patient derived cells with varied chemoresistance.

A rapid screening platform to coculture bacteria within tumor spheroids.

The prevalence of tumor-colonizing bacteria along with advances in synthetic biology are leading to a new generation of living microbial cancer therapies. Because many bacterial systems can be engineered to recombinantly produce therapeutics within tumors, simple and high-throughput experimental platforms are needed to screen the large collections of bacteria candidates and characterize their interactions with cancer cells. Here, we describe a protocol to selectively grow bacteria within the core of tumor spheroids, allowing for their continuous and parallel profiling in physiologically relevant conditions. Specifically, tumor spheroids are incubated with bacteria in a 96-well low-adhesion plate followed by a series of washing steps and an antibiotic selection protocol to confine bacterial growth within the hypoxic and necrotic core of tumor spheroids. This bacteria spheroid coculture (BSCC) system is stable for over 2 weeks, does not require specialized equipment and is compatible with time-lapse microscopy, commercial staining assays and histology that uniquely enable analysis of growth kinetics, viability and spatial distribution of both cellular populations, respectively. We show that the procedure is applicable to multiple tumor cell types and bacterial species by varying protocol parameters and is validated by using animal models. The BSCC platform will allow the study of bacteria-tumor interactions in a continuous manner and facilitate the rapid development of engineered microbial therapies.

The 2021 Global Lung Cancer Therapy Landscape.

INTRODUCTION: The lung cancer treatment landscape has substantially evolved over the past decade. However, a systematic analysis of the current global drug development landscape has not been conducted. METHODS: We curated and analyzed a comprehensive list of therapeutic entities (TEs) in preclinical development and clinical trials for lung cancer. RESULTS: On the basis of our analysis of 707 TEs, we found a consistent forward trajectory in the development pipeline for both NSCLC and SCLC. Most of the TEs were in the advanced stages of clinical trials. Targeted therapies continue to dominate in the non-immuno-oncology space. Immuno-oncology targets are expanding beyond inhibitors of the programmed death-ligand 1 axis. CONCLUSIONS: Our analysis highlights a robust portfolio of both preclinical and clinical TEs and suggests that lung cancer treatment is going to become even more biomarker-driven.

Cell-autonomous immune gene expression is repressed in pulmonary neuroendocrine cells and small cell lung cancer.

Small cell lung cancer (SCLC) is classified as a high-grade neuroendocrine (NE) tumor, but a subset of SCLC has been termed "variant" due to the loss of NE characteristics. In this study, we computed NE scores for patient-derived SCLC cell lines and xenografts, as well as human tumors. We aligned NE properties with transcription factor-defined molecular subtypes. Then we investigated the different immune phenotypes associated with high and low NE scores. We found repression of immune response genes as a shared feature between classic SCLC and pulmonary neuroendocrine cells of the healthy lung. With loss of NE fate, variant SCLC tumors regain cell-autonomous immune gene expression and exhibit higher tumor-immune interactions. Pan-cancer analysis revealed this NE lineage-specific immune phenotype in other cancers. Additionally, we observed MHC I re-expression in SCLC upon development of chemoresistance. These findings may help guide the design of treatment regimens in SCLC.

Combination Therapy Targeting BCL6 and Phospho-STAT3 Defeats Intratumor Heterogeneity in a Subset of Non-Small Cell Lung Cancers.

Oncogene-specific changes in cellular signaling have been widely observed in lung cancer. Here, we investigated how these alterations could affect signaling heterogeneity and suggest novel therapeutic strategies. We compared signaling changes across six human bronchial epithelial cell (HBEC) strains that were systematically transformed with various combinations of TP53, KRAS, and MYC-oncogenic alterations commonly found in non-small cell lung cancer (NSCLC). We interrogated at single-cell resolution how these alterations could affect classic readouts (ß-CATENIN, SMAD2/3, phospho-STAT3, P65, FOXO1, and phospho-ERK1/2) of key pathways commonly affected in NSCLC. All three oncogenic alterations were required concurrently to observe significant signaling changes, and significant heterogeneity arose in this condition. Unexpectedly, we found two mutually exclusive altered subpopulations: one with STAT3 upregulation and another with SMAD2/3 downregulation. Treatment with a STAT3 inhibitor eliminated the upregulated STAT3 subpopulation, but left a large surviving subpopulation with downregulated SMAD2/3. A bioinformatics search identified BCL6, a gene downstream of SMAD2/3, as a novel pharmacologically accessible target of our transformed HBECs. Combination treatment with STAT3 and BCL6 inhibitors across a panel of NSCLC cell lines and in xenografted tumors significantly reduced tumor cell growth. We conclude that BCL6 is a new therapeutic target in NSCLC and combination therapy that targets multiple vulnerabilities (STAT3 and BCL6) downstream of common oncogenes, and tumor suppressors may provide a potent way to defeat intratumor heterogeneity. Cancer Res; 77(11); 3070-81. ©2017 AACR.

ZEB1 drives epithelial-to-mesenchymal transition in lung cancer.

Increased expression of zinc finger E-box binding homeobox 1 (ZEB1) is associated with tumor grade and metastasis in lung cancer, likely due to its role as a transcription factor in epithelial-to-mesenchymal transition (EMT). Here, we modeled malignant transformation in human bronchial epithelial cells (HBECs) and determined that EMT and ZEB1 expression are early, critical events in lung cancer pathogenesis. Specific oncogenic mutations in TP53 and KRAS were required for HBECs to engage EMT machinery in response to microenvironmental (serum/TGF-ß) or oncogenetic (MYC) factors. Both TGF-ß- and MYC-induced EMT required ZEB1, but engaged distinct TGF-ß-dependent and vitamin D receptor-dependent (VDR-dependent) pathways, respectively. Functionally, we found that ZEB1 causally promotes malignant progression of HBECs and tumorigenicity, invasion, and metastases in non-small cell lung cancer (NSCLC) lines. Mechanistically, ZEB1 expression in HBECs directly repressed epithelial splicing regulatory protein 1 (ESRP1), leading to increased expression of a mesenchymal splice variant of CD44 and a more invasive phenotype. In addition, ZEB1 expression in early stage IB primary NSCLC correlated with tumor-node-metastasis stage. These findings indicate that ZEB1-induced EMT and associated molecular changes in ESRP1 and CD44 contribute to early pathogenesis and metastatic potential in established lung cancer. Moreover, TGF-ß and VDR signaling and CD44 splicing pathways associated with ZEB1 are potential EMT chemoprevention and therapeutic targets in NSCLC.

Understanding protein structure from a percolation perspective.

Underlying the unique structures and diverse functions of proteins are a vast range of amino-acid sequences and a highly limited number of folds taken up by the polypeptide backbone. By investigating the role of noncovalent connections at the backbone level and at the detailed side-chain level, we show that these unique structures emerge from interplay between random and selected features. Primarily, the protein structure network formed by these connections shows simple (bond) and higher order (clique) percolation behavior distinctly reminiscent of random network models. However, the clique percolation specific to the side-chain interaction network bears signatures unique to proteins characterized by a larger degree of connectivity than in random networks. These studies reflect some salient features of the manner in which amino acid sequences select the unique structure of proteins from the pool of a limited number of available folds.