RAS wild-type (RASw/t) tumours have been associated with better outcomes in patients with metastatic colorectal cancer (mCRC) treated with anti-EGFR monoclonal antibodies (mAb). We investigated the ...expression of EGFR downstream proteins under their active phosphorylated forms as potential markers in response to these patients.
One-hundred tumour samples were collected from patients with mCRC refractory to FOLFOX and/or FOLFIRI and treated by a combination of chemotherapy with anti-EGFR mAb. The outcomes were measured on response evaluation criteria in solid tumour (RECIST), progression-free survival (PFS) and overall survival (OS). All samples were assessed for RAS and BRAF mutations, and the key phosphorylated proteins of EGFR downstream signalling were quantitatively analysed using the BioPlex Protein array.
Among the 60 RASw/t patients, 45.0% achieved a complete or partial response when treated with anti-EGFR mAb. Expression of pAKT, pERK1/2 and pMEK1 was significantly lower in RASw/t patients (P=0.0246; P=0.004; P=0.0110, respectively). The response rate was significantly higher for RASw/t patients who express pEGFR and pAKT (P=0.0258; P=0.0277, respectively).
Overexpression of pEGFR and pAKT may predict the response rate in RASw/t patients treated with anti-EGFR mAb. On the basis of our results, we hypothesise that the association of anti-EGFR mAb and anti-AKT therapies could be of interest.
After surgical resection of pancreatic ductal adenocarcinoma (PDAC), patients are predominantly treated with adjuvant chemotherapy, commonly consisting of gemcitabine (GEM)-based regimens or the ...modified FOLFIRINOX (mFFX) regimen. While mFFX regimen has been shown to be more effective than GEM-based regimens, it is also associated with higher toxicity. Current treatment decisions are based on patient performance status rather than on the molecular characteristics of the tumor. To address this gap, the goal of this study was to develop drug-specific transcriptomic signatures for personalized chemotherapy treatment.
We used PDAC datasets from preclinical models, encompassing chemotherapy response profiles for the mFFX regimen components. From them we identified specific gene transcripts associated with chemotherapy response. Three transcriptomic artificial intelligence signatures were obtained by combining independent component analysis and the least absolute shrinkage and selection operator-random forest approach. We integrated a previously developed GEM signature with three newly developed ones. The machine learning strategy employed to enhance these signatures incorporates transcriptomic features from the tumor microenvironment, leading to the development of the ‘Pancreas-View’ tool ultimately clinically validated in a cohort of 343 patients from the PRODIGE-24/CCTG PA6 trial.
Patients who were predicted to be sensitive to the administered drugs (n = 164; 47.8%) had longer disease-free survival (DFS) than the other patients. The median DFS in the mFFX-sensitive group treated with mFFX was 50.0 months stratified hazard ratio (HR) 0.31, 95% confidence interval (CI) 0.21-0.44, P < 0.001 and 33.7 months (stratified HR 0.40, 95% CI 0.17-0.59, P < 0.001) in the GEM-sensitive group when treated with GEM. Comparatively patients with signature predictions unmatched with the treatments (n = 86; 25.1%) or those resistant to all drugs (n = 93; 27.1%) had shorter DFS (10.6 and 10.8 months, respectively).
This study presents a transcriptome-based tool that was developed using preclinical models and machine learning to accurately predict sensitivity to mFFX and GEM.
•Transcriptomic signatures were developed for key pancreatic cancer drugs to enable personalized treatment.•The Pancreas-View tool integrates four drug signatures to assist informed therapeutic decisions.•Signatures accurately identify high responder patients, indicative of improved DFS and cancer-specific survival.•Clinical validation involving a cohort of 343 patients confirms the efficacy of this signature approach.•Transcriptomic signatures that integrate predictors from preclinical models and machine learning offer a rationalized treatment strategy.
L’hormonorésistance acquise constitue l’un des défis majeurs dans le traitement du cancer du sein avancé exprimant le récepteur aux estrogènes (RE) et sans surexpression de HER2. Les mutations ...activatrices du gène
ESR1
affectant le domaine de liaison du ligand ont récemment été identifiées comme l’un des principaux mécanismes de résistance aux inhibiteurs de l’aromatase (IA). Ces mutations peuvent être recherchées sur des prélèvements histologiques ou sur ADN tumoral circulant, par PCR ou séquençage de nouvelle génération (NGS). Elles induisent une activation constitutionnelle du RE conduisant à une résistance acquise aux IA ; le tamoxifène, le fulvestrant et les thérapies ciblées anti-mTOR ou anti-CDK4/6 conservent leur efficacité. La place en pratique clinique de la détection des mutations du gène
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reste encore à définir.
Acquired endocrine resistance remains one of the main obstacles in the treatment of estrogen receptor (ER) positive, HER2 negative advanced breast cancer. Recently, activating
ESR1
gene mutations affecting the ligand-binding domain have been identified as a key mechanismin aromatase inhibitor (AI) resistance. These mutations can be detected on histological samples or circulating tumour DNA, using PCRbased assays or next-generation sequencing. They induce a constitutive activation of ER, leading to acquired resistance to AI; tamoxifen, fulvestrant and targeted therapies against mTOR or CDK4/6 retain their efficacy. The use of monitoring
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mutations in l practice is still to be defined.
It is well-known that there is considerable variation in the effectiveness of evidence-based treatments for psychiatric disorders, and a continued need to improve the real-world effectiveness of ...these treatments. In the last 20+ years the examination of noninvasive brain stimulation techniques for psychiatric treatment has increased dramatically. However, in order to test these techniques for effective therapeutic use, it is critical to understand (a) (what are) the key neural circuits to engage for specific disorders or clusters of symptoms, and (b) (how) can these circuits be reached effectively using neurostimulation? Here we focus on the research toward the application of transcranial direct current stimulation (tDCS) for posttraumatic stress disorder (PTSD). tDCS is a portable and inexpensive technique that lends itself well to be combined with, and thus potentially augment, exposure-based treatment for PTSD. In this review, we discuss the behavioral model of threat and safety learning and memory as it relates to PTSD, the underlying neurobiology of PTSD, as well as the current understandings of tDCS action, including its limitations and opportunities. Through this lens, we summarize the research on the application of tDCS to modulated threat and safety learning and memory to date, and propose new directions for its future research.
There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained ...language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model-GatorTron-using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve five clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og .
Purpose
The number of patients tapered from long‐term opioid therapy (LTOT) has increased in recent years in the United States. Some patients tapered from LTOT report improved quality of life, while ...others face increased risks of opioid‐related hospital use. Research has not yet established how the risk of opioid‐related hospital use changes across LTOT dose and subsequent tapering. Our objective was to examine associations between recent tapering from LTOT with odds of opioid‐related hospital use.
Methods
Case‐crossover design using 2014–2018 health information exchange data from Indiana. We defined opioid‐related hospital use as hospitalizations, and emergency department (ED) visits for a drug overdose, opioid abuse, and dependence. We defined tapering as a 15% or greater dose reduction following at least 3 months of continuous opioid therapy of 50 morphine milligram equivalents (MME)/day or more. We used conditional logistic regression to estimate odds ratios (OR) with 95% confidence intervals (CI).
Results
Recent tapering from LTOT was associated with increased odds of opioid‐related hospital use (OR: 1.50, 95%CI: 1.34–1.63), ED visit (OR: 1.52; 95%CI: 1.35–1.72), and inpatient hospitalization (OR: 1.40; 95%CI: 1.20–1.65). We found no evidence of heterogeneity of the effect of tapering on opioid‐related hospital use by gender, age, and race. Recent tapering among patients on a high baseline dose (>300 MME) was associated with increased odds of opioid‐related hospital use (OR: 2.95, 95% CI: 2.12–4.11, p < 0.001) compared to patients on a lower baseline doses.
Conclusions
Recent tapering from LTOT is associated with increased odds of opioid‐related hospital use.
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