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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., (©2024 The Authors; Published by the American Association for Cancer Research.)

Broad-spectrum RAS inhibition has the potential to benefit roughly a quarter of human patients with cancer whose tumours are driven by RAS mutations 1,2 . RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS and NRAS, with affinity for both mutant and wild-type variants 3 . More than 90% of cases of human pancreatic ductal adenocarcinoma (PDAC) are driven by activating mutations in KRAS 4 . Here we assessed the therapeutic potential of RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumour activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo. Pharmacological analyses revealed divergent responses to RMC-7977 in tumour versus normal tissues. Treated tumours exhibited waves of apoptosis along with sustained proliferative arrest, whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC mouse model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumours identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro. Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance., (© 2024. The Author(s).)

RAS oncogenes (collectively NRAS, HRAS and especially KRAS) are among the most frequently mutated genes in cancer, with common driver mutations occurring at codons 12, 13 and 61 1 . Small molecule inhibitors of the KRAS(G12C) oncoprotein have demonstrated clinical efficacy in patients with multiple cancer types and have led to regulatory approvals for the treatment of non-small cell lung cancer 2,3 . Nevertheless, KRAS G12C mutations account for only around 15% of KRAS-mutated cancers 4,5 , and there are no approved KRAS inhibitors for the majority of patients with tumours containing other common KRAS mutations. Here we describe RMC-7977, a reversible, tri-complex RAS inhibitor with broad-spectrum activity for the active state of both mutant and wild-type KRAS, NRAS and HRAS variants (a RAS(ON) multi-selective inhibitor). Preclinically, RMC-7977 demonstrated potent activity against RAS-addicted tumours carrying various RAS genotypes, particularly against cancer models with KRAS codon 12 mutations (KRAS G12X ). Treatment with RMC-7977 led to tumour regression and was well tolerated in diverse RAS-addicted preclinical cancer models. Additionally, RMC-7977 inhibited the growth of KRAS G12C cancer models that are resistant to KRAS(G12C) inhibitors owing to restoration of RAS pathway signalling. Thus, RAS(ON) multi-selective inhibitors can target multiple oncogenic and wild-type RAS isoforms and have the potential to treat a wide range of RAS-addicted cancers with high unmet clinical need. A related RAS(ON) multi-selective inhibitor, RMC-6236, is currently under clinical evaluation in patients with KRAS-mutant solid tumours (ClinicalTrials.gov identifier: NCT05379985)., (© 2024. The Author(s).)

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., Competing Interests: Declaration of competing interest No conflicts of interests relevant to the topic of this study., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tavabi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Broad-spectrum RAS inhibition holds the potential to benefit roughly a quarter of human cancer patients whose tumors are driven by RAS mutations. However, the impact of inhibiting RAS functions in normal tissues is not known. RMC-7977 is a highly selective inhibitor of the active (GTP-bound) forms of KRAS, HRAS, and NRAS, with affinity for both mutant and wild type (WT) variants. As >90% of human pancreatic ductal adenocarcinoma (PDAC) cases are driven by activating mutations in KRAS , we assessed the therapeutic potential of RMC-7977 in a comprehensive range of PDAC models, including human and murine cell lines, human patient-derived organoids, human PDAC explants, subcutaneous and orthotopic cell-line or patient derived xenografts, syngeneic allografts, and genetically engineered mouse models. We observed broad and pronounced anti-tumor activity across these models following direct RAS inhibition at doses and concentrations that were well-tolerated in vivo . Pharmacological analyses revealed divergent responses to RMC-7977 in tumor versus normal tissues. Treated tumors exhibited waves of apoptosis along with sustained proliferative arrest whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS inhibition in the setting of PDAC., Competing Interests: Competing interests J.J., Y.W., B.L., M.M., S.C., L.J., X.W., Y.C.Y., C.H., H.C., Y.G., R.Z., E.Q., Z.W., J.A.M.S., M.H., D.W., and M.S. are employees and stockholders of Revolution Medicines. R.H.V., B.Z.S., A.J.A., C.J.D., and K.P.O. received research funding from Revolution Medicines. A.J.A. consults for Anji Pharmaceuticals, Affini-T Therapeutics, Arrakis Therapeutics, AstraZeneca, Boehringer Ingelheim, Oncorus, Inc., Merck & Co. Inc., Mirati Therapeutics, Nimbus Therapeutics, Plexium, Revolution Medicines, Reactive Biosciences, Riva Therapeutics, Servier Pharmaceuticals, Syros Pharmaceuticals, T-knife Therapeutics, Third Rock Ventures, and Ventus Therapeutics. A.J.A. holds equity in Riva Therapeutics. A.J.A. has research funding from Bristol Myers Squibb, Deerfield, Inc., Eli Lilly, Mirati Therapeutics, Novartis, Novo Ventures, and Syros Pharmaceuticals. C.J.D. is a consultant/advisory board member for Cullgen, Deciphera Pharmaceuticals, Eli Lilly, Mirati Therapeutics, Reactive Biosciences, Revolution Medicines, Ribometrics, Sanofi, and SHY Therapeutics. C.J.D. has received research funding support from Deciphera Pharmaceuticals, Mirati Therapeutics, Reactive Biosciences, and SpringWorks Therapeutics. R.H.V. has received consulting fees from BMS, is an inventor on patents relating to cancer cellular immunotherapy, cancer vaccines, and KRAS immune epitopes, and receives royalties from Children’s Hospital Boston for a licensed research-only monoclonal antibody.

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.

The discovery of small-molecule inhibitors requires suitable binding pockets on protein surfaces. Proteins that lack this feature are considered undruggable and require innovative strategies for therapeutic targeting. KRAS is the most frequently activated oncogene in cancer, and the active state of mutant KRAS is such a recalcitrant target. We designed a natural product-inspired small molecule that remodels the surface of cyclophilin A (CYPA) to create a neomorphic interface with high affinity and selectivity for the active state of KRAS G12C (in which glycine-12 is mutated to cysteine). The resulting CYPA:drug:KRAS G12C tricomplex inactivated oncogenic signaling and led to tumor regressions in multiple human cancer models. This inhibitory strategy can be used to target additional KRAS mutants and other undruggable cancer drivers. Tricomplex inhibitors that selectively target active KRAS G12C or multiple RAS mutants are in clinical trials now (NCT05462717 and NCT05379985).

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., (© 2023. Springer Nature Limited.)

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.

Hyperactivation of mTOR kinase by mutations in the PI3K/mTOR pathway or by crosstalk with other mutant cancer drivers, such as RAS, is a feature of many tumors. Multiple allosteric inhibitors of mTORC1 and orthosteric dual inhibitors of mTORC1 and mTORC2 have been developed as anticancer drugs, but their clinical utility has been limited. To address these limitations, we have developed a novel class of "bi-steric inhibitors" that interact with both the orthosteric and the allosteric binding sites in order to deepen the inhibition of mTORC1 while also preserving selectivity for mTORC1 over mTORC2. In this report, we describe the discovery and preclinical profile of the development candidate RMC-5552 and the in vivo preclinical tool compound RMC-6272. We also present evidence that selective inhibition of mTORC1 in combination with covalent inhibition of KRAS G12C shows increased antitumor activity in a preclinical model of KRAS G12C mutant NSCLC that exhibits resistance to KRAS G12C inhibitor monotherapy.

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., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Total intracranial volume (TIV) is the volume enclosed inside the cranium, inclusive of the meninges and the brain. TIV is extensively used to correct variations in inter-subject head size for the evaluation of neurodegenerative diseases. In this work, we present an automatic method to generate a TIV mask from MR images while synthesizing a CT image to be used in subsequent analysis. In addition, we propose an alternative way to obtain ground truth TIV masks using a semi-manual approach, which results in significant time savings. We train a conditional generative adversarial network (cGAN) using 2D MR slices to realize our tasks. The quantitative evaluation showed that the model was able to synthesize CT and generate TIV masks that closely approximate the reference images. This study also provides a comparison of the described method against skull stripping tools that output a mask enclosing the cranial volume, using MRI scan. In particular, highlighting the deficiencies in using such tools to approximate the volume using MRI scan.

Oncogenic alterations in the RAS/RAF/MEK/ERK pathway drive the growth of a wide spectrum of cancers. While BRAF and MEK inhibitors are efficacious against BRAF V600E -driven cancers, effective targeted therapies are lacking for most cancers driven by other pathway alterations, including non-V600E oncogenic BRAF, RAS GTPase-activating protein (GAP) NF1 (neurofibromin 1) loss and oncogenic KRAS. Here, we show that targeting the SHP2 phosphatase (encoded by PTPN11) with RMC-4550, a small-molecule allosteric inhibitor, is effective in human cancer models bearing RAS-GTP-dependent oncogenic BRAF (for example, class 3 BRAF mutants), NF1 loss or nucleotide-cycling oncogenic RAS (for example, KRAS G12C ). SHP2 inhibitor treatment decreases oncogenic RAS/RAF/MEK/ERK signalling and cancer growth by disrupting SOS1-mediated RAS-GTP loading. Our findings illuminate a critical function for SHP2 in promoting oncogenic RAS/MAPK pathway activation in cancers with RAS-GTP-dependent oncogenic BRAF, NF1 loss and nucleotide-cycling oncogenic KRAS. SHP2 inhibition is a promising molecular therapeutic strategy for patients with cancers bearing these oncogenic drivers.

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 Kras G12D-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., Competing Interests: Conflict of interest statement: All authors were employees of Genentech when the work was performed and A.D., J.H.C., J.E.L., B.-e.W., Z.M., O.F., and M.R.J. hold Roche stock., (Copyright © 2017 the Author(s). Published by PNAS.)

Cancer stem cells (CSCs) are hypothesized to actively maintain tumors similarly to how their normal counterparts replenish differentiated cell types within tissues, making them an attractive therapeutic target for the treatment of cancer. Because most CSC markers also label normal tissue stem cells, it is unclear how to selectively target them without compromising normal tissue homeostasis. We evaluated a strategy that targets the cell surface leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5), a well-characterized tissue stem cell and CSC marker, with an antibody conjugated to distinct cytotoxic drugs. One antibody-drug conjugate (ADC) demonstrated potent tumor efficacy and safety in vivo. Furthermore, the ADC decreased tumor size and proliferation, translating to improved survival in a genetically engineered model of intestinal tumorigenesis. These data demonstrate that ADCs can be leveraged to exploit differences between normal and cancer stem cells to successfully target gastrointestinal cancers., (Copyright © 2015, American Association for the Advancement of Science.)

Artemisinin generates cytotoxic free radicals when it reacts with iron. Its toxicity is more selective toward cancer cells because cancer cells contain a higher level of intracellular-free iron. We previously reported that dihydroartemisinin (DHA), an active metabolite of artemisinin, has selective cytotoxicity toward Molt-4 human lymphoblastoid cells. A concern is whether cancer cells could develop resistance to DHA after repeated administration, thus limiting its therapeutic efficacy. In the present study, we developed a DHA-resistant Molt-4 cell line (RTN) by exposing Molt-4 cells to gradually increasing concentrations of DHA in vitro. The half-maximal inhibitory concentration (IC50) of DHA for RTN cells is 7.1-times higher than that of Molt-4 cells. RTN cells have a higher growth rate than Molt-4 cells. In addition, we investigated the toxicities of two more potent synthetic artemisinin compounds, artemisinin dimer-alcohol and artemisinin-tagged holotransferrin toward RTN cells; RTN cells showed no significant cross-resistance to these compounds., (Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.)