Artificial intelligence-assisted immunohistochemical (IHC) evaluation of tumor amphiregulin (AREG) and epiregulin (EREG) expression as a combined predictive biomarker for panitumumab (Pan) therapy benefit in RAS wild-type (wt) metastatic colorectal cancer (mCRC): Analysis within the phase III PICCOLO trial.

2021 ◽  
Vol 39 (3_suppl) ◽  
pp. 111-111
Author(s):  
Christopher Williams ◽  
Jenny F. Seligmann ◽  
Christoph Guetter ◽  
Liping Zhang ◽  
Dongyao Yan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Tumor Cells ◽  
Risk Ratio ◽  
Tumor Tissue ◽  
Tumor Location ◽  
Phase Iii ◽  
Routine Practice ◽  
Mrna Levels ◽  
Ligand Expression ◽  
High Ligand

111 Background: High tumor mRNA levels of the EGFR ligands, AREG and EREG are associated with anti-EGFR agent response in patients (pts) with RAS-wt mCRC, regardless of tumor location. However, ligand RNA assays have not been adopted into routine clinical practice due to issues with analytical precision and practicality. Here we test whether AREG and EREG expression assessed by IHC can predict benefit from Pan. Methods: A retrospective biomarker study within the PICCOLO trial (NCT00389870; irinotecan [Ir] ± Pan in fluoropyrimidine-resistant RAS-wt mCRC). AREG and EREG positive tumor cells were assessed by IHC in all RAS-wt patients with available tumor tissue. Pathologists annotated tumor areas on digital images of glass slides. Artificial intelligence (AI) algorithms calculated the percentage of tumor cells staining positive for AREG and EREG within the tumor areas. More than 50% AREG and/or EREG tumor cell positivity was regarded as high ligand expression. The primary endpoint was progression-free survival (PFS) and secondary endpoints were RECIST response rate (RR) and overall survival (OS). Results: 274 RAS-wt pts had available tumor tissue. High ligand expression (n = 132) was associated with significant PFS benefit from IrPan compared with Ir (8.0 vs 3.2 months; HR 0.54 [0.37-0.79]; p = 0.001); whereas low ligand expression (n = 142) was not (3.4 vs 4.4 months; HR 1.05 [95% CI, 0.74-1.49]; p = 0.78). The ligand-treatment interaction was significant (p = 0.02) and independent of BRAF-mutation status and primary tumor location. Likewise RR was significantly improved in pts with high ligand expression (IrPan vs Ir: 48% vs 6%; risk ratio, 7.8 [2.90-20.69]; p < 0.0001) but not those with low ligand expression (IrPan vs Ir: 25% vs 14%; risk ratio, 1.8 [95% CI, 0.89-3.65]; p = 0.10) (interaction p = 0.01). Lesser effect was seen on OS. Conclusions: IHC assessment of AREG and EREG identified pts who did or did not benefit from Pan, as has been previously demonstrated through mRNA quantification. IHC represents a more practicable technique as it can be provided at the point of care and is associated with shorter turn-around times. AREG and EREG IHC may be of use in routine practice to identify patients who would benefit from anti-EGFR therapy and those for whom alternative treatment strategies should be explored.

AKN-028, a FLT-3 Kinase Inhibitor In Preclinical Development, Induces Significant Gene Regulation That Differs From PKC-412

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1837-1837
Author(s):  
Vendela C Parrow ◽  
Anna Eriksson ◽  
Hanna Göransson ◽  
Linda Rickardson ◽  
Fredrik Lehmann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Cells ◽  
Cell Lines ◽  
Mechanism Of Action ◽  
Primary Tumor ◽  
Kinase Inhibitor ◽  
Global Gene Expression ◽  
Vehicle Control ◽  
Phase Iii ◽  
Mrna Levels

Abstract Abstract 1837 AKN-028 (formerly BVT-II, abstract presented at EHA 2008, Eriksson A et al) is a tyrosine kinase inhibitor originally identified in a screen targeting FLT-3. In vitro testing by fluorometric microculture cytotoxicity assay (FMCA, Lindhagen E. et al, Nat Protoc 2008; 3:1364-9) on primary tumor cells from 29 patients with different haematological malignancies showed that the cytotoxic activity was most pronounced in acute myeloic leukaemia (AML). In vivo, efficacy was demonstrated in a hollow fiber mouse model using MV4-11 cells and primary tumor cells from AML patients. Kinase inhibitors interacting with the ATP-binding pocket usually targets more than one kinase. PKC-412, presently in phase III trials in AML, is a staurosporine analogue targeting many kinases. In this study we show the kinase inhibition profile of AKN-028 when characterised on a broad panel of 320 different kinases. AKN-028 is relatively specific compared to the staurosporine analogues. To further analyse the difference in mechanism of action between the multi-targeting inhibitor PKC-412 and the more selective AKN-028, we have performed global gene expression analysis by Affymetrix arrays using two different leukaemia cell-lines, HL-60 and MV4-11 (expressing FLT3-ITD), and tumour cells from a patient with FLT3-ITDpos AML. The cells were treated with 10 μM of either compound or vehicle control for 6 h. Principal component (PC) analysis was used to visualise the global gene expression pattern, see fig 1. Searching for differential expression of mRNA levels showed that treatment with AKN-028 resulted in significantly altered gene expression in all three cell types tested, compared to vehicle control treated cells. 430 mRNAs were down-regulated, and 280 were up-regulated. By contrast, treatment with PKC-412 caused very few significant alterations of mRNA levels when compared to vehicle treated cells. Further analysis of gene expression patterns to elucidate the mechanism of action of AKN-028 is ongoing. In conclusion, even though AKN-028 is a relatively selective kinase inhibitor targeting FLT-3, it has more profound effects altering gene expression both in cultured AML cell-lines and a primary AML tumor sample than the multikinase inhibitor PKC-412. Disclosures: Parrow: Akinion Pharmaceuticals: Employment, Equity Ownership. Lehmann:Akinion Pharmaceuticals: Consultancy. Larsson:Akinion Pharmaceuticals: Consultancy.

scholarly journals The Caspase-1/IL-18 Axis of the Inflammasome in Tumor Cells: A Modulator of the Th1/Tc1 Response of Tumor-Infiltrating T Lymphocytes in Colorectal Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 189
Author(s):  
Linda Bilonda Mutala ◽  
Cécile Deleine ◽  
Matilde Karakachoff ◽  
Delphine Dansette ◽  
Kathleen Ducoin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
T Lymphocytes ◽  
Tumor Cells ◽  
Ex Vivo ◽  
Explant Culture ◽  
T Cell Response ◽  
Mrna Levels ◽  
Innate And Adaptive Immunity ◽  
Caspase 1 ◽  
Culture Model

In colorectal cancer (CRC), a high density of T lymphocytes represents a strong prognostic marker in subtypes of CRC. Optimized immunotherapy strategies to boost this T-cell response are still needed. A good candidate is the inflammasome pathway, an emerging player in cancer immunology that bridges innate and adaptive immunity. Its effector protein caspase-1 matures IL-18 that can promote a T-helper/cytotoxic (Th1/Tc1) response. It is still unknown whether tumor cells from CRC possess a functional caspase-1/IL-18 axis that could modulate the Th1/Tc1 response. We used two independent cohorts of CRC patients to assess IL-18 and caspase-1 expression by tumor cells in relation to the density of TILs and the microsatellite status of CRC. Functional and multiparametric approaches at the protein and mRNA levels were performed on an ex vivo CRC explant culture model. We show that, in the majority of CRCs, tumor cells display an activated and functional caspase-1/IL-18 axis that contributes to drive a Th1/Tc1 response elicited by TILs expressing IL-18Rα. Furthermore, unsupervised clustering identified three clusters of CRCs according to the caspase-1/IL-18/TIL density/interferon gamma (IFNγ) axis and microsatellite status. Together, our results strongly suggest that targeting the caspase-1/IL-18 axis can improve the anti-tumor immune response in subgroups of CRC.

scholarly journals Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data

2021 ◽  
Vol 14 ◽  
pp. 263177452199062
Author(s):  
Benjamin Gutierrez Becker ◽  
Filippo Arcadu ◽  
Andreas Thalhammer ◽  
Citlalli Gamez Serna ◽  
Owen Feehan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ulcerative Colitis ◽  
Clinical Trials ◽  
Deep Learning ◽  
Mayo Clinic ◽  
Automated System ◽  
Phase Iii ◽  
Plain Language ◽  
Data Set ◽  
Endoscopic Videos

Introduction: The Mayo Clinic Endoscopic Subscore is a commonly used grading system to assess the severity of ulcerative colitis. Correctly grading colonoscopies using the Mayo Clinic Endoscopic Subscore is a challenging task, with suboptimal rates of interrater and intrarater variability observed even among experienced and sufficiently trained experts. In recent years, several machine learning algorithms have been proposed in an effort to improve the standardization and reproducibility of Mayo Clinic Endoscopic Subscore grading. Methods: Here we propose an end-to-end fully automated system based on deep learning to predict a binary version of the Mayo Clinic Endoscopic Subscore directly from raw colonoscopy videos. Differently from previous studies, the proposed method mimics the assessment done in practice by a gastroenterologist, that is, traversing the whole colonoscopy video, identifying visually informative regions and computing an overall Mayo Clinic Endoscopic Subscore. The proposed deep learning–based system has been trained and deployed on raw colonoscopies using Mayo Clinic Endoscopic Subscore ground truth provided only at the colon section level, without manually selecting frames driving the severity scoring of ulcerative colitis. Results and Conclusion: Our evaluation on 1672 endoscopic videos obtained from a multisite data set obtained from the etrolizumab Phase II Eucalyptus and Phase III Hickory and Laurel clinical trials, show that our proposed methodology can grade endoscopic videos with a high degree of accuracy and robustness (Area Under the Receiver Operating Characteristic Curve = 0.84 for Mayo Clinic Endoscopic Subscore ⩾ 1, 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 2 and 0.85 for Mayo Clinic Endoscopic Subscore ⩾ 3) and reduced amounts of manual annotation. Plain language summary Patient, caregiver and provider thoughts on educational materials about prescribing and medication safety Artificial intelligence can be used to automatically assess full endoscopic videos and estimate the severity of ulcerative colitis. In this work, we present an artificial intelligence algorithm for the automatic grading of ulcerative colitis in full endoscopic videos. Our artificial intelligence models were trained and evaluated on a large and diverse set of colonoscopy videos obtained from concluded clinical trials. We demonstrate not only that artificial intelligence is able to accurately grade full endoscopic videos, but also that using diverse data sets obtained from multiple sites is critical to train robust AI models that could potentially be deployed on real-world data.

scholarly journals Differentiated glioblastoma cells accelerate tumor progression by shaping the tumor microenvironment via CCN1-mediated macrophage infiltration

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Atsuhito Uneda ◽  
Kazuhiko Kurozumi ◽  
Atsushi Fujimura ◽  
Kentaro Fujii ◽  
Joji Ishida ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Tumor Progression ◽  
Tumor Cells ◽  
In Silico ◽  
Tumor Tissue ◽  
The Cancer Genome Atlas ◽  
Macrophage Infiltration ◽  
Macrophage Migration

AbstractGlioblastoma (GBM) is the most lethal primary brain tumor characterized by significant cellular heterogeneity, namely tumor cells, including GBM stem-like cells (GSCs) and differentiated GBM cells (DGCs), and non-tumor cells such as endothelial cells, vascular pericytes, macrophages, and other types of immune cells. GSCs are essential to drive tumor progression, whereas the biological roles of DGCs are largely unknown. In this study, we focused on the roles of DGCs in the tumor microenvironment. To this end, we extracted DGC-specific signature genes from transcriptomic profiles of matched pairs of in vitro GSC and DGC models. By evaluating the DGC signature using single cell data, we confirmed the presence of cell subpopulations emulated by in vitro culture models within a primary tumor. The DGC signature was correlated with the mesenchymal subtype and a poor prognosis in large GBM cohorts such as The Cancer Genome Atlas and Ivy Glioblastoma Atlas Project. In silico signaling pathway analysis suggested a role of DGCs in macrophage infiltration. Consistent with in silico findings, in vitro DGC models promoted macrophage migration. In vivo, coimplantation of DGCs and GSCs reduced the survival of tumor xenograft-bearing mice and increased macrophage infiltration into tumor tissue compared with transplantation of GSCs alone. DGCs exhibited a significant increase in YAP/TAZ/TEAD activity compared with GSCs. CCN1, a transcriptional target of YAP/TAZ, was selected from the DGC signature as a candidate secreted protein involved in macrophage recruitment. In fact, CCN1 was secreted abundantly from DGCs, but not GSCs. DGCs promoted macrophage migration in vitro and macrophage infiltration into tumor tissue in vivo through secretion of CCN1. Collectively, these results demonstrate that DGCs contribute to GSC-dependent tumor progression by shaping a mesenchymal microenvironment via CCN1-mediated macrophage infiltration. This study provides new insight into the complex GBM microenvironment consisting of heterogeneous cells.

Immunotherapy for Esophageal and Gastric Cancer

2017 ◽  
pp. 292-300 ◽  
Author(s):  
Ronan J. Kelly
Keyword(s):  
Gastric Cancer ◽  
Tumor Cells ◽  
Immune Cells ◽  
Single Agent ◽  
Phase Iii ◽  
Immune Checkpoints ◽  
Barr Virus ◽  
Mutation Burden ◽  
Heavily Pretreated ◽  
Epstein Barr

PD-L1 upregulation occurs in approximately 40% of gastroesophageal cancers. However, unlike other solid tumors, there is minimal PD-L1 expressed on the cancer cells; rather, expression occurs predominantly on infiltrating myeloid cells. Preliminary clinical data involving single-agent PD-1/PD-L1 inhibitors in metastatic gastroesophageal cancer have reported response rates of 22%–27% for patients with PD-L1+ tumors and 10%–17% for unselected patients. The phase III ONO-4538-12 (ATTRACTION 2) trial has demonstrated an improved overall survival for nivolumab compared with placebo for patients with heavily pretreated gastric cancer. In the future, we will need better biomarkers to select those most likely to respond and/or identify patients who may need combination immunotherapeutics or alternate strategies. A number of subsets of gastric cancer with different immune signatures, most notably tumors positive for Epstein-Barr virus and microsatellite instability, have been identified, with approximately 50% and 94% PD-L1+ staining seen on tumor cells and immune cells in the EBV subtype and approximately 33% and 45% PD-L1+ staining seen on tumor cells and immune cells in MSI high tumors. Both subtypes demonstrate PD-L1+ immune cells with tumor-infiltrating patterns, unlike the more commonly seen PD-L1+ immune cells at the invasive margin. PD-L2 expression has been reported in 52% of esophageal adenocarcinomas but little is known about the expression of other immune checkpoints. Additional factors that suggest gastroesophageal cancers may respond to checkpoint inhibition include the high somatic mutation burden and the link with chronic inflammation. Here we provide a comprehensive review of the checkpoint inhibitor data published to date in advanced esophagogastric cancers and rationalize how the immune microenvironment in these diverse tumors can explain response or resistance to immunotherapeutics.

Hypothalamic appetite-regulating neuropeptide mRNA levels in cachectic nude mice bearing human tumor cells

Metabolism ◽  
2001 ◽  
Vol 50 (10) ◽  
pp. 1213-1219 ◽  
Author(s):  
N. Nara-Ashizawa ◽  
T. Tsukada ◽  
K. Maruyama ◽  
Y. Akiyama ◽  
N. Kajimura ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Nude Mice ◽  
Human Tumor ◽  
Mrna Levels ◽  
Human Tumor Cells

scholarly journals A Method to Monitor mRNA Levels in Human Breast Tumor Cells Obtained by Fine-needle Aspiration

1997 ◽  
Vol 6 (6) ◽  
pp. 353-360 ◽  
Author(s):  
Majella S. de Lange ◽  
Bert Top ◽  
Caro Lambrechts ◽  
Riks A. Maas ◽  
Hans L. Peterse ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Fine Needle Aspiration ◽  
Breast Tumor ◽  
Needle Aspiration ◽  
Mrna Levels ◽  
Human Breast ◽  
Human Breast Tumor ◽  
Breast Tumor Cells ◽  
Fine Needle

The performance of artificial intelligence-driven technologies in diagnosing mental disorders: An umbrella review (Preprint)

2021 ◽  
Author(s):  
Alaa Abd-Alrazaq ◽  
Jens Schneider ◽  
Dari Alhuwail ◽  
Carla T Toro ◽  
Arfan Ahmed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mental Disorders ◽  
Systematic Reviews ◽  
Autism Spectrum ◽  
Great Promise ◽  
Routine Practice ◽  
Daily Routine ◽  
Reference List ◽  
Umbrella Review ◽  
Compulsive Disorder

BACKGROUND Diagnosing mental disorders is usually not an easy task and requires a large amount of time and effort given the complex nature of mental disorders. Artificial intelligence (AI) has been successfully exploited in diagnosing many mental disorders. Numerous systematic reviews summarize the evidence on the accuracy of AI models in diagnosing different mental disorders. OBJECTIVE This umbrella review aims to synthesize results of previous systematic reviews on the performance of AI models in diagnosing mental disorders. METHODS To identify relevant systematic reviews, we searched 11 electronic databases, checked the reference list of the included reviews, and checked the reviews that cited the included reviews. Two reviewers independently selected the relevant reviews, extracted the data from them, and appraised their quality. We synthesized the extracted data using the narrative approach. Specifically, results of the included reviews were grouped based on the target mental disorders that the AI classifiers distinguish. RESULTS We included 15 systematic reviews of 852 citations identified by searching all databases. The included reviews assessed the performance of AI models in diagnosing Alzheimer’s disease (n=7), mild cognitive impairment (n=6), schizophrenia (n=3), bipolar disease (n=2), autism spectrum disorder (n=1), obsessive-compulsive disorder (n=1), post-traumatic stress disorder (n=1), and psychotic disorders (n=1). The performance of the AI models in diagnosing these mental disorders ranged between 21% and 100%. CONCLUSIONS AI technologies offer great promise in diagnosing mental health disorders. The reported performance metrics paint a vivid picture of a bright future for AI in this field. To expedite progress towards these technologies being incorporated into routine practice, we recommend that healthcare professionals in the field cautiously and consciously begin to explore the opportunities of AI-based tools for their daily routine. It would also be encouraging to see a greater number of meta-analyses and further systematic reviews on performance of AI models in diagnosing other common mental disorders such as depression and anxiety. CLINICALTRIAL CRD42021231558

scholarly journals Self-Replicating RNA Viruses for RNA Therapeutics

Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3310 ◽  
Author(s):  
Kenneth Lundstrom
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Tumor Cells ◽  
Neutralizing Antibodies ◽  
Ebola Virus ◽  
Vaccine Development ◽  
Rna Viruses ◽  
Antibody Responses ◽  
Phase Iii ◽  
Phase I Clinical Trials

Self-replicating single-stranded RNA viruses such as alphaviruses, flaviviruses, measles viruses, and rhabdoviruses provide efficient delivery and high-level expression of therapeutic genes due to their high capacity of RNA replication. This has contributed to novel approaches for therapeutic applications including vaccine development and gene therapy-based immunotherapy. Numerous studies in animal tumor models have demonstrated that self-replicating RNA viral vectors can generate antibody responses against infectious agents and tumor cells. Moreover, protection against challenges with pathogenic Ebola virus was obtained in primates immunized with alphaviruses and flaviviruses. Similarly, vaccinated animals have been demonstrated to withstand challenges with lethal doses of tumor cells. Furthermore, clinical trials have been conducted for several indications with self-amplifying RNA viruses. In this context, alphaviruses have been subjected to phase I clinical trials for a cytomegalovirus vaccine generating neutralizing antibodies in healthy volunteers, and for antigen delivery to dendritic cells providing clinically relevant antibody responses in cancer patients, respectively. Likewise, rhabdovirus particles have been subjected to phase I/II clinical trials showing good safety and immunogenicity against Ebola virus. Rhabdoviruses have generated promising results in phase III trials against Ebola virus. The purpose of this review is to summarize the achievements of using self-replicating RNA viruses for RNA therapy based on preclinical animal studies and clinical trials in humans.

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