KRAS receives and relays signals at the plasma membrane (PM) where it transmits extracellular growth factor signals to downstream effectors. SNORD50A/B were recently found to bind KRAS and inhibit its tumorigenic action by unknown mechanisms. KRAS proximity protein labeling was therefore undertaken in SNORD50A/B wild-type and knockout cells, revealing that SNORD50A/B RNAs shape the composition of proteins proximal to KRAS, notably by inhibiting KRAS proximity to the SNARE vesicular transport proteins SNAP23, SNAP29, and VAMP3. To remain enriched on the PM, KRAS undergoes cycles of endocytosis, solubilization, and vesicular transport to the PM. Here we report that SNAREs are essential for the final step of this process, with KRAS localization to the PM facilitated by SNAREs but antagonized by SNORD50A/B. Antagonism between SNORD50A/B RNAs and specific SNARE proteins thus controls KRAS localization, signaling, and tumorigenesis, and disrupting SNARE-enabled KRAS function represents a potential therapeutic opportunity in KRAS-driven cancer.

Objective The chronification of pain is heterogeneous in rheumatology. Chronic overlapping pain conditions (COPCs) such as fibromyalgia, endometriosis, migraine, and back pain may co‐occur with one another and in rheumatic diseases. We describe the sociodemographic and clinical profiles associated with concomitant COPCs among patients with rheumatic diseases. Methods We retrospectively identified patients visiting rheumatology clinics at a single institution from 2010 to 2020 for five common rheumatic conditions: psoriatic arthritis (PsA), rheumatoid arthritis (RA), Sjögren syndrome (SjS), systemic lupus erythematosus (SLE), and systemic sclerosis (SSc). We compared sociodemographic, clinical, and lifestyle factors by rheumatic condition and by COPC status. We also report sex‐stratified diagnosis of COPCs. The primary outcome was diagnostic validation of one or more COPCs. Results We identified 5992 rheumatology patients: 846 with PsA, 2605 with RA, 956 with SjS, 975 with SLE, and 610 with SSc. Approximately 36–62% of patients had a concomitant COPC diagnosis. Patients with SjS had the highest prevalence (62%). Diagnosis of one or more COPCs was highest among Black patients and lowest among Asian patients. Patients using public insurance had a higher prevalence of one or more COPCs compared with those with private insurance. Patients with one or more COPCs had more depression and anxiety and more frequent emergency department visits, surgeries, and hospitalizations. Conclusion Our findings suggest that COPCs are strikingly common among patients with rheumatic disease and are associated with lower quality of life and greater health care needs. Future research may elucidate drivers of chronic pain and how to best address the unique analgesic needs of this multimorbid population.

Epithelial squamous cell carcinomas (SCC) most commonly originate in the skin, where they display disruptions in the normally tightly regulated homeostatic balance between keratinocyte proliferation and terminal differentiation. We performed a transcriptome-wide screen for genes of unknown function that possess inverse expression patterns in differentiating keratinocytes compared with cutaneous SCC (cSCC), leading to the identification of MAB21L4 (C2ORF54) as an enforcer of terminal differentiation that suppresses carcinogenesis. Loss of MAB21L4 in human cSCC organoids increased expression of RET to enable malignant progression. In addition to transcriptional upregulation of RET, deletion of MAB21L4 preempted recruitment of the CacyBP-Siah1 E3 ligase complex to RET and reduced its ubiquitylation. In SCC organoids and in vivo tumor models, genetic disruption of RET or selective inhibition of RET with BLU-667 (pralsetinib) suppressed SCC growth while inducing concomitant differentiation. Overall, loss of MAB21L4 early during SCC development blocks differentiation by increasing RET expression. These results suggest that targeting RET activation is a potential therapeutic strategy for treating SCC. Significance: Downregulation of RET mediated by MAB21L4–CacyBP interaction is required to induce epidermal differentiation and suppress carcinogenesis, suggesting RET inhibition as a potential therapeutic approach in squamous cell carcinoma.

Supplementary Data from MAB21L4 Deficiency Drives Squamous Cell Carcinoma via Activation of RET

An unprecedented amount of access to data, “big data (or high dimensional data),” cloud computing, and innovative technology have increased applications of artificial intelligence in finance and numerous other industries. Machine learning is used in process automation, security, underwriting and credit scoring, algorithmic trading and robo-advisory. In fact, machine learning AI applications are purported to save banks an estimated $447 billion by 2023. Given the advantages that AI brings to finance, we focused on applying supervised machine learning to an investment problem. 10-K SEC filings are routinely used by investors to determine the worth and status of a company–Warren Buffett is frequently cited to read a 10-K a day. We sought to answer–“Can machine learning analyze more than thousands of companies and spot patterns? Can machine learning automate the process of human analysis in predicting whether a company is fit to merge? Can machine learning spot something that humans cannot?” In the advent of rising antitrust discussion of growing market concentrations and the concern for decrease in competition, we analyzed merger activity using text as a data set. Merger activity has been traditionally hard to predict in the past. We took advantage of the large amount of publicly available filings through the Securities Exchange Commission that give a comprehensive summary of a company, and used text, and an innovative way to analyze a company. In order to verify existing theory and measure harder to observe variables, we look to use a text document and examined a firm’s 10-K SEC filing. To minimize over-fitting, the L2 LASSO regularization technique is used. We came up with a model that has 85% accuracy compared to a 35% accuracy using the “bag-of-words” method to predict a company’s likelihood of merging from words alone on the same period’s test data set. These steps are the beginnings of tackling more complicated questions, such as “Which section or topic of words is the most predictive?” and “What is the difference between being acquired and acquiring?” Using product descriptions to characterize mergers further into horizontal and vertical mergers could eventually assist with the causal estimates that are of interest to economists. More importantly, using language and words to categorize companies could be useful in predicting counterfactual scenarios and answering policy questions, and could have different applications ranging from detecting fraud to better trading.