Small cell lung cancer (SCLC), accounting for around 13% of all lung cancers, often results in rapid tumor growth, early metastasis, and acquired therapeutic resistance. The POU class 2 homeobox 3 (POU2F3) is a master regulator of tuft cell identity and defines the SCLC-P subtype that lacks the neuroendocrine markers. Here, we have identified a previously uncharacterized protein, C11orf53, which is coexpressed with POU2F3 in both SCLC cell lines and patient samples. Mechanistically, C11orf53 directly interacts with POU2F3 and is recruited to chromatin by POU2F3. Depletion of C11orf53 reduced enhancer H3K27ac levels and chromatin accessibility, resulting in a reduction of POU2F3-dependent gene expression. On the basis of the molecular function of C11orf53, we renamed it as “POU Class 2 Homeobox Associating Factor 2” (POU2AF2). In summary, our study has identified a new coactivator of POU2F3 and sheds light on the therapeutic potential of targeting POU2AF2/POU2F3 heterodimer in human SCLC.

Small cell lung cancer (SCLC) is an aggressive disease, with patients diagnosed with either early-stage, limited stage, or extensive stage of SCLC tumor progression. Discovering and targeting the functional biomarkers for SCLC will be crucial in understanding the molecular basis underlying SCLC tumorigenesis to better assist in improving clinical treatment. Emerging studies have demonstrated that dysregulations in BAP1 histone H2A deubiquitinase complex are collectively associated with pathogenesis in human SCLC. Here, we investigated the function of the oncogenic BAP1/ASXL3/BRD4 epigenetic axis in SCLC by developing a next-generation BAP1 inhibitor, iBAP-II, and focusing on the epigenetic balance established between BAP1 and non-canonical PRC1 complexes in regulating SCLC-specific transcriptional programming. We further demonstrated that pharmacologic inhibition of BAP1’s catalytic activity disrupted BAP1/ASXL3/BRD4 epigenetic axis by inducing protein degradation of the ASXL3 scaffold protein, which bridges BRD4 and BAP1 at active enhancers. Furthermore, treatment of iBAP-II represses neuroendocrine lineage-specific ASCL1/MYCL/E2F signaling in SCLC cell lines, and dramatically inhibits SCLC cell viability and tumor growth in vivo. In summary, this study has provided mechanistic insight into the oncogenic function of BAP1 in SCLC and highlighted the potential of targeting BAP1’s activity as a novel SCLC therapy.

Abnormalities in genetic and epigenetic modifications can lead to drastic changes in gene expression profiles that are associated with various cancer types. Small cell lung cancer (SCLC) is an aggressive and deadly form of lung cancer with limited effective therapies currently available. By utilizing a genome-wide CRISPR-Cas9 dropout screen in SCLC cells, we identified paired box protein 9 (PAX9) as an essential factor that is overexpressed in human malignant SCLC tumor samples and is transcriptionally driven by the BAP1/ASXL3/BRD4 epigenetic axis. Genome-wide studies revealed that PAX9 occupies distal enhancer elements and represses gene expression by restricting enhancer activity. In multiple SCLC cell lines, genetic depletion of PAX9 led to significant induction of a primed-active enhancer transition, resulting in increased expression of a large number of neural differentiation and tumor-suppressive genes.Mechanistically, PAX9 interacted and co-functioned with the nucleosome remodeling and deacetylase (NuRD) complex at enhancers to repress nearby gene expression, which was reversed by pharmacological HDAC inhibition. Overall, this study provides mechanistic insight into the oncogenic function of the PAX9/NuRD complex epigenetic axis in human SCLC and suggests that re-activation of primed enhancers may have potential therapeutic efficacy in treating SCLC expressing high levels of PAX9.

In managing COVID-19 with non-pharmaceutical interventions, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model for the United States city of Chicago using daily COVID-19 ICU and hospital census data between March and August 2020. We projected ICU occupancies from September to May 2021 under two possible levels of transmission increase. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10-60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.

In non-pharmaceutical management of COVID-19, occupancy of intensive care units (ICU) is often used as an indicator to inform when to intensify mitigation and thus reduce SARS-CoV-2 transmission, strain on ICUs, and deaths. However, ICU occupancy thresholds at which action should be taken are often selected arbitrarily. We propose a quantitative approach using mathematical modeling to identify ICU occupancy thresholds at which mitigation should be triggered to avoid exceeding the ICU capacity available for COVID-19 patients and demonstrate this approach for the United States city of Chicago. We used a stochastic compartmental model to simulate SARS-CoV-2 transmission and disease progression, including critical cases that would require intensive care. We calibrated the model using daily COVID-19 ICU and hospital census data between March and August 2020. We projected various possible ICU occupancy trajectories from September 2020 to May 2021 with two possible levels of transmission increase and uncertainty in core model parameters. The effect of combined mitigation measures was modeled as a decrease in the transmission rate that took effect when projected ICU occupancy reached a specified threshold. We found that mitigation did not immediately eliminate the risk of exceeding ICU capacity. Delaying action by 7 days increased the probability of exceeding ICU capacity by 10–60% and this increase could not be counteracted by stronger mitigation. Even under modest transmission increase, a threshold occupancy no higher than 60% was required when mitigation reduced the reproductive number Rt to just below 1. At higher transmission increase, a threshold of at most 40% was required with mitigation that reduced Rt below 0.75 within the first two weeks after mitigation. Our analysis demonstrates a quantitative approach for the selection of ICU occupancy thresholds that considers parameter uncertainty and compares relevant mitigation and transmission scenarios. An appropriate threshold will depend on the location, number of ICU beds available for COVID-19, available mitigation options, feasible mitigation strengths, and tolerated durations of intensified mitigation.