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.

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 fluorescence microscopy and coupled this to computational model predictions.

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.

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.

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.

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 , and -oncogenic alterations commonly found in non-small cell lung cancer (NSCLC).

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-β) or oncogenetic (MYC) factors.

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.