Selective inhibitors of mTORC1 activate 4EBP1 and suppress tumor growth.

The clinical benefits of pan-mTOR active-site inhibitors are limited by toxicity and relief of feedback inhibition of receptor expression. To address these limitations, we designed a series of compounds that selectively inhibit mTORC1 and not mTORC2. These 'bi-steric inhibitors' comprise a rapamycin-like core moiety covalently linked to an mTOR active-site inhibitor. Structural modification of these components modulated their affinities for their binding sites on mTOR and the selectivity of the bi-steric compound. mTORC1-selective compounds potently inhibited 4EBP1 phosphorylation and caused regressions of breast cancer xenografts. Inhibition of 4EBP1 phosphorylation was sufficient to block cancer cell growth and was necessary for maximal antitumor activity. At mTORC1-selective doses, these compounds do not alter glucose tolerance, nor do they relieve AKT-dependent feedback inhibition of HER3. Thus, in preclinical models, selective inhibitors of mTORC1 potently inhibit tumor growth while causing less toxicity and receptor reactivation as compared to pan-mTOR inhibitors.

An integrative analysis of colon cancer identifies an essential function for PRPF6 in tumor growth.

The spliceosome machinery is composed of multimeric protein complexes that generate a diverse repertoire of mRNA through coordinated splicing of heteronuclear RNAs. While somatic mutations in spliceosome components have been discovered in several cancer types, the molecular bases and consequences of spliceosome aberrations in cancer are poorly understood. Here we report for the first time that PRPF6, a member of the tri-snRNP (small ribonucleoprotein) spliceosome complex, drives cancer proliferation by preferential splicing of genes associated with growth regulation. Inhibition of PRPF6 and other tri-snRNP complex proteins, but not other snRNP spliceosome complexes, selectively abrogated growth in cancer cells with high tri-snRNP levels. High-resolution transcriptome analyses revealed that reduced PRPF6 alters the constitutive and alternative splicing of a discrete number of genes, including an oncogenic isoform of the ZAK kinase. These findings implicate an essential role for PRPF6 in cancer via splicing of distinct growth-related gene products.

Barriers to point-of-care ultrasound utilization during cardiac arrest in the emergency department: a regional survey of emergency physicians.

INTRODUCTION: Though point-of-care ultrasound (POCUS) is recognized as a useful diagnostic and prognostic intervention during cardiac arrest (CA), critics advise caution. The purpose of this survey study was to determine the barriers to POCUS during CA in the Emergency Department (ED). METHODS: Two survey instruments were distributed to emergency medicine (EM) attending and resident physicians at three academic centers in the South Florida. The surveys assessed demographics, experience, proficiency, attitudes and barriers. Descriptive and inferential statistics along with Item Response Theory Logistic Model and the Friedman Test with Wilcoxon Signed Rank tests were used to profile responses and rank barriers. RESULTS: 206 EM physicians were invited to participate in the survey, and 187 (91%) responded. 59% of attending physicians and 47% of resident physicians reported that POCUS is performed in all their cases of CA. 5% of attending physicians and 0% of resident physicians reported never performing POCUS during CA. The top-ranked departmental barrier for attending physicians was "No structured curriculum to educate physicians on POCUS." The top-ranked personal barriers were "I do not feel comfortable with my POCUS skills" and "I do not have sufficient time to dedicate to learning POCUS." The top-ranked barriers for resident physicians were "Time to retrieve and operate the machine" and "Chaotic milieu." CONCLUSIONS: While our study demonstrates that most attending and resident physicians utilize POCUS in CA, barriers to high-quality implementation exist. Top attending physician barriers relate to POCUS education, while the top resident physician barriers relate to logistics and the machines. Interventions to overcome these barriers might lead to optimization of POCUS performance during CA in the ED.

Kras mutant genetically engineered mouse models of human cancers are genomically heterogeneous.

KRAS mutant tumors are largely recalcitrant to targeted therapies. Genetically engineered mouse models (GEMMs) of Kras mutant cancer recapitulate critical aspects of this disease and are widely used for preclinical validation of targets and therapies. Through comprehensive profiling of exomes and matched transcriptomes of >200 KrasG12D-initiated GEMM tumors from one lung and two pancreatic cancer models, we discover that significant intratumoral and intertumoral genomic heterogeneity evolves during tumorigenesis. Known oncogenes and tumor suppressor genes, beyond those engineered, are mutated, amplified, and deleted. Unlike human tumors, the GEMM genomic landscapes are dominated by copy number alterations, while protein-altering mutations are rare. However, interspecies comparative analyses of the genomic landscapes demonstrate fidelity between genes altered in KRAS mutant human and murine tumors. Genes that are spontaneously altered during murine tumorigenesis are also among the most prevalent found in human indications. Using targeted therapies, we also demonstrate that this inherent tumor heterogeneity can be exploited preclinically to discover cancer-specific and genotype-specific therapeutic vulnerabilities. Focusing on Kras allelic imbalance, a feature shared by all three models, we discover that MAPK pathway inhibition impinges uniquely on this event, indicating distinct susceptibility and fitness advantage of Kras-mutant cells. These data reveal previously unknown genomic diversity among KrasG12D-initiated GEMM tumors, places them in context of human patients, and demonstrates how to exploit this inherent tumor heterogeneity to discover therapeutic vulnerabilities.

Racial and gender disparities in violent trauma: Results from the NEMSIS database.

BACKGROUND: Barriers to EMS care can result in suboptimal outcomes and preventable morbidity and mortality. Large EMS databases such as the National Emergency Medical Services Information System (NEMSIS) dataset provide valuable data on the relative incidence of such barriers to care. METHODS: A retrospective cross-sectional analysis was performed using the NEMSIS database. Cases of violent trauma were collected based on gender and racial group. Each group was analyzed for the ratio of cases that involved an EMS barrier to care. Chi-square testing was used to assess associations, and the relative risk was used as the measure of strength of association. For all tests, statistical significance was set at the 0.05 level. RESULTS: 719,812 cases of violent trauma were analyzed using the NEMSIS dataset. EMS encountered barriers to care for white and non-white patients was found to be 4.9% and 4.0% respectively. The difference between groups was found to be 0.9% (95% CI [0.7%, 1.1%] p < 0.0001). RR was 1.23 for white patients (95% CI [1.19, 1.26]), and 0.82 (95% CI [0.79, 0.84]) for non-white. EMS barriers to care for male and female patients was found to be 6.03% and 3.34%, respectively. The difference between groups was found to be 2.7% (95% CI [2.6%, 2.8%] p < 0.0001). RR for male patients was 1.80 (95% Cl [1.76, 1.84]) while RR for female patients was 0.55 (95% CI [0.54, 0.57]). CONCLUSIONS: Racially white patients and male patients have a statistically significant higher risk of encountering an EMS barrier to care in cases of violent trauma.

Mouse tumour models to guide drug development and identify resistance mechanisms.

We need improved, translatable and predictive tumour models for the evaluation of response and the evolution of resistance to targeted therapeutics. We provide a review of the use of different types of preclinical tumour models to evaluate novel anticancer agents, and model the rapidly evolving landscape of resistance to targeted therapy. We focus on describing the various preclinical models available for candidate drug development and design considerations for preclinical experiments, depending on the aspect of drug action being interrogated. We discuss selected examples of how experimental findings have translated into clinical outcomes for targeted agents, predicted mechanisms that drive resistance and strategies to overcome the evolution thereof. We discuss challenges in preclinical experimental design and interpretation and possible improvements in animal models of therapeutic response and resistance, with an emphasis on improved translation of experimental research into clinical practice.

Mutator suppression and escape from replication error-induced extinction in yeast.

Cells rely on a network of conserved pathways to govern DNA replication fidelity. Loss of polymerase proofreading or mismatch repair elevates spontaneous mutation and facilitates cellular adaptation. However, double mutants are inviable, suggesting that extreme mutation rates exceed an error threshold. Here we combine alleles that affect DNA polymerase δ (Pol δ) proofreading and mismatch repair to define the maximal error rate in haploid yeast and to characterize genetic suppressors of mutator phenotypes. We show that populations tolerate mutation rates 1,000-fold above wild-type levels but collapse when the rate exceeds 10⁻³ inactivating mutations per gene per cell division. Variants that escape this error-induced extinction (eex) rapidly emerge from mutator clones. One-third of the escape mutants result from second-site changes in Pol δ that suppress the proofreading-deficient phenotype, while two-thirds are extragenic. The structural locations of the Pol δ changes suggest multiple antimutator mechanisms. Our studies reveal the transient nature of eukaryotic mutators and show that mutator phenotypes are readily suppressed by genetic adaptation. This has implications for the role of mutator phenotypes in cancer.

Systematic evaluation of common natural language processing techniques to codify clinical notes.

Proper codification of medical diagnoses and procedures is essential for optimized health care management, quality improvement, research, and reimbursement tasks within large healthcare systems. Assignment of diagnostic or procedure codes is a tedious manual process, often prone to human error. Natural Language Processing (NLP) has been suggested to facilitate this manual codification process. Yet, little is known on best practices to utilize NLP for such applications. With Large Language Models (LLMs) becoming more ubiquitous in daily life, it is critical to remember, not every task requires that level of resource and effort. Here we comprehensively assessed the performance of common NLP techniques to predict current procedural terminology (CPT) from operative notes. CPT codes are commonly used to track surgical procedures and interventions and are the primary means for reimbursement. Our analysis of 100 most common musculoskeletal CPT codes suggest that traditional approaches can outperform more resource intensive approaches like BERT significantly (P-value = 4.4e-17) with average AUROC of 0.96 and accuracy of 0.97, in addition to providing interpretability which can be very helpful and even crucial in the clinical domain. We also proposed a complexity measure to quantify the complexity of a classification task and how this measure could influence the effect of dataset size on model's performance. Finally, we provide preliminary evidence that NLP can help minimize the codification error, including mislabeling due to human error.

Computed tomography-based automated 3D measurement of femoral version: Validation against standard 2D measurements in symptomatic patients.

To validate 3D methods for femoral version measurement, we asked: (1) Can a fully automated segmentation of the entire femur and 3D measurement of femoral version using a neck based method and a head-shaft based method be performed? (2) How do automatic 3D-based computed tomography (CT) measurements of femoral version compare to the most commonly used 2D-based measurements utilizing four different landmarks? Retrospective study (May 2017 to June 2018) evaluating 45 symptomatic patients (57 hips, mean age 18.7 ± 5.1 years) undergoing pelvic and femoral CT. Femoral version was assessed using four previously described methods (Lee, Reikeras, Tomczak, and Murphy). Fully-automated segmentation yielded 3D femur models used to measure femoral version via femoral neck- and head-shaft approaches. Mean femoral version with 95% confidence intervals, and intraclass correlation coefficients were calculated, and Bland-Altman analysis was performed. Automatic 3D segmentation was highly accurate, with mean dice coefficients of 0.98 ± 0.03 and 0.97 ± 0.02 for femur/pelvis, respectively. Mean difference between 3D head-shaft- (27.4 ± 16.6°) and 3D neck methods (12.9 ± 13.7°) was 14.5 ± 10.7° (p < 0.001). The 3D neck method was closer to the proximal Lee (-2.4 ± 5.9°, -4.4 to 0.5°, p = 0.009) and Reikeras (2 ± 5.6°, 95% CI: 0.2 to 3.8°, p = 0.03) methods. The 3D head-shaft method was closer to the distal Tomczak (-1.3 ± 7.5°, 95% CI: -3.8 to 1.1°, p = 0.57) and Murphy (1.5 ± 5.4°, -0.3 to 3.3°, p = 0.12) methods. Automatic 3D neck-based-/head-shaft methods yielded femoral version angles comparable to the proximal/distal 2D-based methods, when applying fully-automated segmentations.

Building large-scale registries from unstructured clinical notes using a low-resource natural language processing pipeline.

Building clinical registries is an important step in clinical research and improvement of patient care quality. Natural Language Processing (NLP) methods have shown promising results in extracting valuable information from unstructured clinical notes. However, the structure and nature of clinical notes are very different from regular text that state-of-the-art NLP models are trained and tested on, and they have their own set of challenges. In this study, we propose Sentence Extractor with Keywords (SE-K), an efficient and interpretable classification approach for extracting information from clinical notes and show that it outperforms more computationally expensive methods in text classification. Following the Institutional Review Board (IRB) approval, we used SE-K and two embedding based NLP approaches (Sentence Extractor with Embeddings (SE-E) and Bidirectional Encoder Representations from Transformers (BERT)) to develop comprehensive registry of anterior cruciate ligament surgeries from 20 years of unstructured clinical data at a multi-site tertiary-care regional children's hospital. The low-resource approach (SE-K) had better performance (average AUROC of 0.94 ± 0.04) than the embedding-based approaches (SE-E: 0.93 ± 0.04 and BERT: 0.87 ± 0.09) for out of sample validation, in addition to minimum performance drop between test and out-of-sample validation. Moreover, the SE-K approach was at least six times faster (on CPU) than SE-E (on CPU) and BERT (on GPU) and provides interpretability. Our proposed approach, SE-K, can be effectively used to extract relevant variables from clinic notes to build large-scale registries, with consistently better performance compared to the more resource-intensive approaches (e.g., BERT). Such approaches can facilitate information extraction from unstructured notes for registry building, quality improvement and adverse event monitoring.

Comprehensive evaluation of magnetic resonance imaging sequences for signal intensity based assessment of anterior cruciate ligament healing following surgical treatment.

Normalized signal intensity (SI) obtained from magnetic resonance imaging (MRI) has been used to track anterior cruciate ligament (ACL) postoperative remodeling. We aimed to assess the effect of MRI sequence (PD: proton density-weighted; T2: T2-weighted; CISS: constructive interference in steady state) on postoperative changes in healing ACLs/grafts. We hypothesized that CISS is better at detecting longitudinal SI and texture changes of the healing ACL/graft compared to the common clinical sequences (PD and T2). MR images of patients who underwent ACL surgery were evaluated and separated into groups based on surgical procedure (Bridge-Enhanced ACL Repair (BEAR; n = 50) versus ACL reconstruction (ACLR; n = 24)). CISS images showed decreasing SI across all timepoints in both the BEAR and ACLR groups (p < 0.01), PD and T2 images showed decreasing SI in the 6-to-12- and 12-to-24-month postoperative timeframes in the BEAR group (p < 0.02), and PD images additionally showed decreasing SI between 6- and 24-months postoperation in the ACLR group (p = 0.02). CISS images showed texture changes in both the BEAR and ACLR groups, showing increases in energy and decreases in entropy in the 6-to-12- and 6-to-24-month postoperative timeframes in the BEAR group (p < $\lt $ 0.04), and increases in energy, decreases in entropy, and increases in homogeneity between 6 and 24 months postoperation in the ACLR group (p < 0.04). PD images showed increases in energy and decreases in entropy between 6- and 24-months postoperation in the ACLR group (p < 0.008). Finally, CISS was estimated to require a smaller sample size than PD and T2 to detect SI differences related to postoperative remodeling.

Mechanisms of response and tolerance to active RAS inhibition in KRAS-mutant NSCLC.

Resistance to inactive state-selective RASG12C inhibitors frequently entails accumulation of RASGTP, rendering effective inhibition of active RAS potentially desirable. Here, we evaluated the anti-tumor activity of the RAS(ON) multi-selective tri-complex inhibitor RMC-7977 and dissected mechanisms of response and tolerance in KRASG12C-mutant NSCLC. Broad-spectrum, reversible RASGTP inhibition with or without concurrent covalent targeting of active RASG12C yielded superior and differentiated antitumor activity across diverse co-mutational KRASG12C-mutant NSCLC mouse models of primary or acquired RASG12C(ON) or (OFF) inhibitor resistance. Interrogation of time-resolved single cell transcriptional responses established an in vivo atlas of multi-modal acute and chronic RAS pathway inhibition in the NSCLC ecosystem and uncovered a regenerative mucinous transcriptional program that supports long-term tumor cell persistence. In patients with advanced KRASG12C-mutant NSCLC, the presence of mucinous histological features portended poor response to sotorasib or adagrasib. Our results have potential implications for personalized medicine and the development of rational RAS inhibitor-anchored therapeutic strategies.

Translational and Therapeutic Evaluation of RAS-GTP Inhibition by RMC-6236 in RAS-Driven Cancers.

RAS-driven cancers comprise up to 30% of human cancers. RMC-6236 is a RAS(ON) multi-selective noncovalent inhibitor of the active, GTP-bound state of both mutant and wild-type variants of canonical RAS isoforms with broad therapeutic potential for the aforementioned unmet medical need. RMC-6236 exhibited potent anticancer activity across RAS-addicted cell lines, particularly those harboring mutations at codon 12 of KRAS. Notably, oral administration of RMC-6236 was tolerated in vivo and drove profound tumor regressions across multiple tumor types in a mouse clinical trial with KRASG12X xenograft models. Translational PK/efficacy and PK/PD modeling predicted that daily doses of 100 mg and 300 mg would achieve tumor control and objective responses, respectively, in patients with RAS-driven tumors. Consistent with this, we describe here objective responses in two patients (at 300 mg daily) with advanced KRASG12X lung and pancreatic adenocarcinoma, respectively, demonstrating the initial activity of RMC-6236 in an ongoing phase I/Ib clinical trial (NCT05379985). SIGNIFICANCE: The discovery of RMC-6236 enables the first-ever therapeutic evaluation of targeted and concurrent inhibition of canonical mutant and wild-type RAS-GTP in RAS-driven cancers. We demonstrate that broad-spectrum RAS-GTP inhibition is tolerable at exposures that induce profound tumor regressions in preclinical models of, and in patients with, such tumors. This article is featured in Selected Articles from This Issue, p. 897.

Anti-VEGF antibody therapy does not promote metastasis in genetically engineered mouse tumour models.

Resistance to anti-angiogenic therapy can occur via several potential mechanisms. Unexpectedly, recent studies showed that short-term inhibition of either VEGF or VEGFR enhanced tumour invasiveness and metastatic spread in preclinical models. In an effort to evaluate the translational relevance of these findings, we examined the consequences of long-term anti-VEGF monoclonal antibody therapy in several well-validated genetically engineered mouse tumour models of either neuroendocrine or epithelial origin. Anti-VEGF therapy decreased tumour burden and increased overall survival, either as a single agent or in combination with chemotherapy, in all four models examined. Importantly, neither short- nor long-term exposure to anti-VEGF therapy altered the incidence of metastasis in any of these autochthonous models, consistent with retrospective analyses of clinical trials. In contrast, we observed that sunitinib treatment recapitulated previously reported effects on tumour invasiveness and metastasis in a pancreatic neuroendocrine tumour (PNET) model. Consistent with these results, sunitinib treatment resulted in an up-regulation of the hypoxia marker GLUT1 in PNETs, whereas anti-VEGF did not. These results indicate that anti-VEGF mediates anti-tumour effects and therapeutic benefits without a paradoxical increase in metastasis. Moreover, these data underscore the concept that drugs targeting VEGF ligands and receptors may affect tumour metastasis in a context-dependent manner and are mechanistically distinct from one another.

Disparities in cannabis use and documentation in electronic health records among children and young adults.

The legalizations of medical and recreational cannabis have generated a great deal of interest in studying the health impacts of cannabis products. Despite increases in cannabis use, its documentation during clinical visits is not yet mainstream. This lack of information hampers efforts to study cannabis's effects on health outcomes. A clear and in-depth understanding of current trends in cannabis use documentation is necessary to develop proper guidelines to screen and document cannabis use. Here we have developed and used a natural language processing pipeline to evaluate the trends and disparities in cannabis documentation. The pipeline includes a screening step to identify clinical notes with cannabis use documentation which is then fed into a BERT-based classifier to confirm positive use. This pipeline is applied to more than 23 million notes from a large cohort of 370,087 patients seen in a high-volume multi-site pediatric and young adult clinic over a period of 21 years. Our findings show a very low but growing rate of cannabis use documentation (<2%) in electronic health records with significant demographic and socioeconomic disparities in both documentation and positive use, which requires further attention.

A bi-steric mTORC1-selective inhibitor overcomes drug resistance in breast cancer.

Activation of the PI3K-mTOR pathway is central to breast cancer pathogenesis including resistance to many targeted therapies. The mTOR kinase forms two distinct complexes, mTORC1 and mTORC2, and understanding which is required for the survival of malignant cells has been limited by tools to selectively and completely impair either subcomplex. To address this, we used RMC-6272, a bi-steric molecule with a rapamycin-like moiety linked to an mTOR active-site inhibitor that displays >25-fold selectivity for mTORC1 over mTORC2 substrates. Complete suppression of mTORC1 by RMC-6272 causes apoptosis in ER+/HER2- breast cancer cell lines, particularly in those that harbor mutations in PIK3CA or PTEN, due to inhibition of the rapamycin resistant, mTORC1 substrate 4EBP1 and reduction of the pro-survival protein MCL1. RMC-6272 reduced translation of ribosomal mRNAs, MYC target genes, and components of the CDK4/6 pathway, suggesting enhanced impairment of oncogenic pathways compared to the partial mTORC1 inhibitor everolimus. RMC-6272 maintained efficacy in hormone therapy-resistant acquired cell lines and patient-derived xenografts (PDX), showed increased efficacy in CDK4/6 inhibitor treated acquired resistant cell lines versus their parental counterparts, and was efficacious in a PDX from a patient experiencing resistance to CDK4/6 inhibition. Bi-steric mTORC1-selective inhibition may be effective in overcoming multiple forms of therapy-resistance in ER+ breast cancers.

Bi-steric mTORC1 inhibitors induce apoptotic cell death in tumor models with hyperactivated mTORC1.

The PI3K/AKT/mTOR pathway is commonly dysregulated in cancer. Rapalogs exhibit modest clinical benefit, likely owing to their lack of effects on 4EBP1. We hypothesized that bi-steric mTORC1-selective inhibitors would have greater potential for clinical benefit than rapalogs in tumors with mTORC1 dysfunction. We assessed this hypothesis in tumor models with high mTORC1 activity both in vitro and in vivo. Bi-steric inhibitors had strong growth inhibition, eliminated phosphorylated 4EBP1, and induced more apoptosis than rapamycin or MLN0128. Multiomics analysis showed extensive effects of the bi-steric inhibitors in comparison with rapamycin. De novo purine synthesis was selectively inhibited by bi-sterics through reduction in JUN and its downstream target PRPS1 and appeared to be the cause of apoptosis. Hence, bi-steric mTORC1-selective inhibitors are a therapeutic strategy to treat tumors driven by mTORC1 hyperactivation.

LigaNET: A multi-modal deep learning approach to predict the risk of subsequent anterior cruciate ligament injury after surgery.

Anterior cruciate ligament (ACL) injuries are a common cause of soft tissue injuries in young active individuals, leading to a significant risk of premature joint degeneration. Postoperative management of such injuries, in particular returning patients to athletic activities, is a challenge with immediate and long-term implications including the risk of subsequent injury. In this study, we present LigaNET, a multi-modal deep learning pipeline that predicts the risk of subsequent ACL injury following surgical treatment. Postoperative MRIs (n=1,762) obtained longitudinally between 3 to 24 months after ACL surgery from a cohort of 159 patients along with 11 non-imaging outcomes were used to train and test: 1) a 3D CNN to predict subsequent ACL injury from segmented ACLs, 2) a 3D CNN to predict injury from the whole MRI, 3) a logistic regression classifier predict injury from non-imaging data, and 4) a multi-modal pipeline by fusing the predictions of each classifier. The CNN using the segmented ACL achieved an accuracy of 77.6% and AUROC of 0.84, which was significantly better than the CNN using the whole knee MRI (accuracy: 66.6%, AUROC: 0.70; P<.001) and the non-imaging classifier (accuracy: 70.1%, AUROC: 0.75; P=.039). The fusion of all three classifiers resulted in highest classification performance (accuracy: 80.6%, AUROC: 0.89), which was significantly better than each individual classifier (P<.001). The developed multi-modal approach had similar performance in predicting the risk of subsequent ACL injury from any of the imaging sequences (P>.10). Our results demonstrate that a deep learning approach can achieve high performance in identifying patients at high risk of subsequent ACL injury after surgery and may be used in clinical decision making to improve postoperative management (e.g., safe return to sports) of ACL injured patients.