Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis

Artificial intelligence for chimeric antigen receptor-based therapies: a comprehensive review of current applications and future perspectives

Using artificial intelligence (AI) to enhance chimeric antigen receptor (CAR)-based therapies' design, production, and delivery is a novel and promising approach. This review provides an overview of the current applications and challenges of AI for CAR-based therapies and suggests some directions for future research and development. This paper examines some of the recent advances of AI for CAR-based therapies, for example, using deep learning (DL) to design CARs that target multiple antigens and avoid antigen escape; using natural language processing to extract relevant information from clinical reports and literature; using computer vision to analyze the morphology and phenotype of CAR cells; using reinforcement learning to optimize the dose and schedule of CAR infusion; and using AI to predict the efficacy and toxicity of CAR-based therapies. These applications demonstrate the potential of AI to improve the quality and efficiency of CAR-based therapies and to provide personalized and precise treatments for cancer patients. However, there are also some challenges and limitations of using AI for CAR-based therapies, for example, the lack of high-quality and standardized data; the need for validation and verification of AI models; the risk of bias and error in AI outputs; the ethical, legal, and social issues of using AI for health care; and the possible impact of AI on the human role and responsibility in cancer immunotherapy. It is important to establish a multidisciplinary collaboration among researchers, clinicians, regulators, and patients to address these challenges and to ensure the safe and responsible use of AI for CAR-based therapies.

Recent Advances in Artificial Intelligence to Improve Immunotherapy and the Use of Digital Twins to Identify Prognosis of Patients with Solid Tumors

To date, the public health system has been impacted by the increasing costs of many diagnostic and therapeutic pathways due to limited resources. At the same time, we are constantly seeking to improve these paths through approaches aimed at personalized medicine. To achieve the required levels of diagnostic and therapeutic precision, it is necessary to integrate data from different sources and simulation platforms. Today, artificial intelligence (AI), machine learning (ML), and predictive computer models are more efficient at guiding decisions regarding better therapies and medical procedures. The evolution of these multiparametric and multimodal systems has led to the creation of digital twins (DTs). The goal of our review is to summarize AI applications in discovering new immunotherapies and developing predictive models for more precise immunotherapeutic decision-making. The findings from this literature review highlight that DTs, particularly predictive mathematical models, will be pivotal in advancing healthcare outcomes. Over time, DTs will indeed bring the benefits of diagnostic precision and personalized treatment to a broader spectrum of patients.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

The role of intestinal flora on tumor immunotherapy: recent progress and treatment implications

Immunotherapy, specifically immune checkpoint inhibitors, has emerged as a promising approach for treating malignant tumors. The gut, housing approximately 70 % of the body's immune cells, is abundantly populated with gut bacteria that actively interact with the host's immune system. Different bacterial species within the intestinal flora are in a delicate equilibrium and mutually regulate each other. However, when this balance is disrupted, pathogenic microorganisms can dominate, adversely affecting the host's metabolism and immunity, ultimately promoting the development of disease. Emerging researches highlight the potential of interventions such as fecal microflora transplantation (FMT) to improve antitumor immune response and reduce the toxicity of immunotherapy. These remarkable findings suggest the major role of intestinal flora in the development of cancer immunotherapy and led us to the hypothesis that intestinal flora transplantation may be a new breakthrough in modifying immunotherapy side effects.

Predictive Biomarkers for Immunotherapy in Gastric Cancer: Current Status and Emerging Prospects

Gastric cancer presents substantial management challenges, and the advent of immunotherapy has ignited renewed hope among patients. Nevertheless, a significant proportion of patients do not respond to immunotherapy, and adverse events associated with immunotherapy also occur on occasion, underscoring the imperative to identify suitable candidates for treatment. Several biomarkers, including programmed death ligand-1 expression, tumor mutation burden, mismatch repair status, Epstein-Barr Virus infection, circulating tumor DNA, and tumor-infiltrating lymphocytes, have demonstrated potential in predicting the effectiveness of immunotherapy in gastric cancer. However, the quest for the optimal predictive biomarker for gastric cancer immunotherapy remains challenging, as each biomarker carries its own limitations. Recently, multi-omics technologies have emerged as promising platforms for discovering novel biomarkers that may help in selecting gastric cancer patients likely to respond to immunotherapy. The identification of reliable predictive biomarkers for immunotherapy in gastric cancer holds the promise of enhancing patient selection and improving treatment outcomes. In this review, we aim to provide an overview of clinically established biomarkers of immunotherapy in gastric cancer. Additionally, we introduce newly reported biomarkers based on multi-omics studies in the context of gastric cancer immunotherapy, thereby contributing to the ongoing efforts to refine patient stratification and treatment strategies.

The ageing immune system as a potential target of senolytics

Ageing leads to a sharp decline in immune function, precipitating the development of inflammatory conditions. The combined impact of these processes renders older individuals at greater risk of inflammatory and immune-related diseases, such as cancer and infections. This is compounded by reduced efficacy in interventions aiming to limit disease impact, for instance vaccines being less effective in elderly populations. This state of diminished cellular function is driven by cellular senescence, a process where cells undergo stable growth arrest following exposure to stressful stimuli, and the associated pro-inflammatory secretory phenotype. Removing harmful senescent cells (SnCs) using senolytic therapies is an emerging field holding promise for patient benefit. Current senolytics have been developed either to specifically target SnCs, or repurposed from cancer therapies or vaccination protocols. Herein, we discuss recent developments in senolytic therapies, focusing on how senolytics could be used to combat the age-associated diminution of the immune system. In particular, exploring how these drugs may be used to promote immunity in the elderly, and highlighting recent trials of senolytics in idiopathic pulmonary fibrosis and diabetic kidney disease. Novel immunotherapeutic approaches including chimeric antigen receptor T-cells or monoclonal antibodies targeting SnCs are being investigated to combat the shortcomings of current senolytics and their adverse effects. The flexible nature of senolytic treatment modalities and their efficacy in safely removing harmful SnCs could have great potential to promote healthy immune function in ageing populations.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Immune checkpoint inhibitors: immune-related adverse events, healthcare utilization, and costs among commercial and Medicare Advantage patients

Background

Immune checkpoint inhibitors (ICI) are increasingly used across multiple cancer types and stages and little is known about real-world outcomes. This study sought to determine healthcare utilization, costs, immune-related adverse events (irAEs), and all-cause mortality of single-agent versus combination ICI in the USA.

Materials and methods

This is a retrospective study conducted with 2016–2018 data from the HealthCore Integrated Research Database, consisting of commercial and Medicare-insured adult patients with a cancer diagnosis using ICI in the USA. Outcomes were healthcare utilization, costs, and irAEs (FDA-recognized and others) up to 1-year post-index between patients using ICI monotherapy (mono, PD-1/PD-L1 inhibitor) and combination therapy (combo, PD-1/PD-L1 with CTLA-4 inhibitors).

Results

In total, 9084 patients received monotherapy and 904 patients received combo therapy. Mean age 65 years for mono and 58 years for combo. Overall, the combo arm had higher rates of FDA-recognized irAEs (67.4% vs. 45.9%), especially endocrinopathies (27.7% vs 14.7%) and dermatitis (25.9% vs. 12.4%). All-cause mortality over 1-year follow-up was similar, 30.7% in mono vs 30.8% in combo arms. The combo group had higher rates of all-cause inpatient hospitalizations (55.4% mono vs 65.6% combo) and emergency department (ED) visits (33.7% mono vs 41.4% combo). IrAE-related hospitalizations were higher in combo (55.2% vs 42.1%). IrAE-related ED visits were 15.7% mono vs 22.7% combo. This increased toxicity and health care utilization was reflected in significant differences in healthcare costs. Stark differences were seen in all-cause medical costs as well as costs related to inpatient and ED utilization and costs attributed to irAEs.

Conclusions

Higher rates of irAEs, healthcare utilization, and costs occur with combination immunotherapy. As further indications are approved for combination ICI, our study highlights the real-world tradeoffs involved with combination therapy regarding burdens of toxicity and increased healthcare utilization.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00520-022-06826-9.

Chemokine Receptor-Targeted Therapies: Special Case for CCR8

Immune checkpoint blockade inhibitors (CBIs) targeting cytotoxic T lymphocyte associated protein-4 (CTLA-4) and program death receptor-1 (PD-1) or its ligand-1 (PD-L1) have transformed the outlook of many patients with cancer. This remarkable progress has highlighted, from the translational point of view, the importance of immune cells in the control of tumor progression. There is still room for improvement, since current CBI therapies benefit a minority of patients. Moreover, interference with immune checkpoint receptors frequently causes immune related adverse events (irAEs) with life-threatening consequences in some of the patients. Immunosuppressive cells in the tumor microenvironment (TME), including intratumoral regulatory T (Treg) cells, tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs), contribute to tumor progression and correlate with a negative disease outlook. Recent reports revealed the selective expression of the chemokine receptor CCR8 on tumor Treg cells, making CCR8 a promising target in translational research. In this review, I summarize our current knowledge about the cellular distribution and function of CCR8 in physiological and pathophysiological processes. The discussion includes an assessment of how the removal of CCR8-expressing cells might affect both anti-tumor immunity as well as immune homeostasis at remote sites. Based on these considerations, CCR8 appears to be a promising novel target to be considered in future translational research.

The role of immunotherapy in treating lung cancer: current status and future perspective

Lung cancers have the worst incident and mortality rates. Cancers such as advanced non-small-cell lung carcinomas are inoperable and often the only treatment available is chemo-radiotherapy. There has been little improvement in long-term survival recently, prompting research into novel treatments. Immune checkpoint inhibitors (ICIs) are a form of immunotherapy used in lung cancer. The efficacy of ICIs is dependent on: the part of the pathway affected; the presence of prognostic biomarkers; the method of efficacy assessment; the stage of the disease and other drugs involved. Monoclonal antibodies, Toll-like receptor agonists and cancer vaccines have shown modest effects on survival. Refinement of treatment regimens and prognostic biomarkers will help improve the survival of patients in the future.

Dl-3-N-Butylphthalide Presents Anti-Cancer Activity in Lung Cancer by Targeting PD-1/PD-L1 Signaling

Introduction

Lung cancer serves as one of the most malignant cancer types. Immunotherapy targeting PD-1/PD-L1 axis is a promising strategy for cancer treatment. Dl-3-N-butylphthalide (NBP), a small molecule compound extracted from the seeds of Apium graveolens, possesses a large range of biological effects and demonstrates anti-cancer activities. However, the role of NBP in the modulation of lung cancer remains obscure.

Methods

In this study, we aimed to explore the effect of NBP on PD-L1 signaling and the progression of lung cancer.

Results

Significantly, the treatment of NBP repressed the proliferation of lung cancer cells in vitro. Tumorigenicity analysis in nude mice showed that the tumor volume and tumor weight were attenuated by the treatment of NBP in the mice. Meanwhile, the levels of Ki-67 and PD-L1 were reduced by the treatment of NBP in the tumor tissues of the mice. NBP suppressed IFN-γ-induced PD-L1 enhancement in lung cancer cells. The treatment of NBP inhibited PD-L1 expression in lung cancer cells co-cultured with unstimulated PBMCs or activated T cell. NBP inhibited PD-1 expression in activated T cells co-cultured with lung cancer cells. Conditioned medium from activated T cells increased PD-L1 expression, and NBP reversed this effect. Co-culture with A549 and H1975 cells reduced T cell proliferation and activity, while the treatment of NBP reversed the reduction. Consistently, the treatment of NBP caused notably decreased apoptosis of co-cultured T cells. Mechanically, KAT7 was able to bind to PD-L1 promoter and epigenetically induce PD-L1 expression by promoting the enrichment of histone H3 lysine 14 acetylation (H3K14ac) and RNA polymerase II on PD-L1 promoter.

Discussion

Thus, we concluded that NBP repressed PD-L1 expression by targeting KAT7 and attenuated PD-1/PD-L1 axis to relieve lung cancer progression. NBP may be applied as the potential therapeutic strategy in immunotherapy of lung cancer.

Applying artificial intelligence for cancer immunotherapy

Artificial intelligence (AI) is a general term that refers to the use of a machine to imitate intelligent behavior for performing complex tasks with minimal human intervention, such as machine learning; this technology is revolutionizing and reshaping medicine. AI has considerable potential to perfect health-care systems in areas such as diagnostics, risk analysis, health information administration, lifestyle supervision, and virtual health assistance. In terms of immunotherapy, AI has been applied to the prediction of immunotherapy responses based on immune signatures, medical imaging and histological analysis. These features could also be highly useful in the management of cancer immunotherapy given their ever-increasing performance in improving diagnostic accuracy, optimizing treatment planning, predicting outcomes of care and reducing human resource costs. In this review, we present the details of AI and the current progression and state of the art in employing AI for cancer immunotherapy. Furthermore, we discuss the challenges, opportunities and corresponding strategies in applying the technology for widespread clinical deployment. Finally, we summarize the impact of AI on cancer immunotherapy and provide our perspectives about underlying applications of AI in the future.

Risk factors for adverse events induced by immune checkpoint inhibitors in patients with non-small-cell lung cancer: a systematic review and meta-analysis

Background

Immune checkpoint inhibitors (ICIs) can cause serious immune-related adverse events (irAEs). This study aimed to identify risk factors for all types of irAEs induced by ICIs in patients with non-small-cell lung cancer (NSCLC), by systematic review and meta-analyses.

Methods

A systematic search was performed in Pubmed, Embase and Web of Science by two independent reviewers. Studies were selected that included patients with NSCLC and evaluated characteristics of patients with and without irAEs induced by ICIs. Quality and risk of bias of the selected studies were assessed. Random effects meta-analyses were conducted to estimate pooled odds ratios (ORs) for risk factors of developing all type of irAEs, and separately for pneumonitis, interstitial lung disease and severe irAEs. With the objective of exploring sources of heterogeneity, stratified analyses were performed by quality and region.

Results

25 studies met the inclusion criteria. In total, the data of 6696 patients were pooled. 33 different risk factors for irAEs were reported. irAEs of interest were reported for 1653 (25%) of the patients. Risk factors related to the development of irAEs were: C-reactive protein, neutrophil lymphocyte ratio (NLR), use of PD-1 inhibitor, high PD-L1 expression, an active or former smoking status, ground glass attenuation, and a better treatment response.

Conclusion

The identified risk factors for the development of these irAEs are mostly related to the alteration of the immune system, proinflammatory states and loss of immunological self-tolerance. Patients identified as having a higher risk for irAEs should be monitored more closely.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00262-021-02996-3.

Response to immune checkpoint inhibitor rechallenge after high-grade immune related adverse events in patients with advanced melanoma

Twenty to sixty percent of patients receiving immune checkpoint inhibitors (ICIs) experience high-grade immune-related adverse events (irAEs) which may prevent the continuation of treatment. Limited clinical evidence is available to guide treatment for these patients. Patients with stage IV or unresectable stage III melanoma at NYU Langone Health were reviewed from 1 January 2014 to 1 July 2019. Patients with first-line ICI systemic therapy, a high-grade irAE and a rechallenge with ICI therapy were included. Postrechallenge irAE recurrence, response rate, overall survival (OS) and progression-free survival (PFS) were evaluated. Postrechallenge irAEs recurred in 71.9% (n = 23/32) of patients at a median of 5.1 weeks from rechallenge, with 46.9% (15/32) recurring as high-grade events. Clinical response was achieved in 46.9% (15/32) of patients, including 40.6% (13/32) with a complete response and 6.3% (2/32) with partial response. Median OS from first ICI initiation was 85.4 weeks (45.7-140.7; 25th-75th percentile) and median PFS was 42.9 weeks (29.2-114.2; 25th-75th percentile). Patients with a shorter time to initial irAE and shorter time to postrechallenge irAE were at greater risk for disease progression [hazard ratio 7.80; 95% confidence interval (CI), 1.91-32.83; P = 0.004; hazard ratio 7.45, 95% CI, 1.57-35.35; P = 0.012). Those with greater duration to rechallenge (>10 weeks) were at lower risk for disease progression (hazard ratio 0.15, 0.03-0.68; P = 0.015). ICI rechallenge can be considered in patients with advanced melanoma, as the risk-benefit profile appears favorable. Treatment toxicity should be appropriately managed, as longer durations to rechallenge may lower the risk of disease progression.

Automated Identification of Patients With Immune-Related Adverse Events From Clinical Notes Using Word Embedding and Machine Learning

PURPOSE

Although immune checkpoint inhibitors (ICIs) have substantially improved survival in patients with advanced malignancies, they are associated with a unique spectrum of side effects termed immune-related adverse events (irAEs). To ensure treatment safety, research efforts are needed to comprehensively detect and understand irAEs. Retrospective analysis of data from electronic health records can provide knowledge to characterize these toxicities. However, such information is not captured in a structured format within the electronic health record and requires manual chart review.

MATERIALS AND METHODS

In this work, we propose a natural language processing pipeline that can automatically annotate clinical notes and determine whether there is evidence that a patient developed an irAE. Seven hundred eighty-one cases were manually reviewed by clinicians and annotated for irAEs at the patient level. A dictionary of irAEs keywords was used to perform text reduction on clinical notes belonging to each patient; only sentences with relevant expressions were kept. Word embeddings were then used to generate vector representations over the reduced text, which served as input for the machine learning classifiers. The output of the models was presence or absence of any irAEs. Additional models were built to classify skin-related toxicities, endocrine toxicities, and colitis.

RESULTS

The model for any irAE achieved an average F1-score = 0.75 and area under the receiver operating characteristic curve = 0.85. This outperformed a basic keyword filtering approach. Although the classifier of any irAEs achieved good accuracy, individual irAE classification still has room for improvement.

CONCLUSION

We demonstrate that patient-level annotations combined with a machine learning approach using keywords filtering and word embeddings can achieve promising accuracy in classifying irAEs in clinical notes. This model may facilitate annotation and analysis of large irAEs data sets.

Immune checkpoint inhibitors in the treatment of cancer

BACKGROUND

Immunotherapy drugs, known as immune checkpoint inhibitors (ICIs), work by blocking checkpoint proteins from binding with their partner proteins. The two main pathways that are specifically targeted in clinical practice are cytotoxic T-lymphocyte antigen-4 (CTLA-4) and programmed cell death protein 1 (PD-1) that showed potent immune-modulatory effects through their function as negative regulators of T cell activation.

METHODS

In view of the rapid and extensive development of this research field, we conducted a comprehensive review of the literature and update on the use of CTLA-4, PD-1 and PD-L1 targeted therapy in the treatment of several types of cancer including melanoma, non-small-cell lung carcinoma, breast cancer, hepatocellular carcinoma, hodgkin lymphoma, cervical cancer, head and neck squamous cell carcinoma.

RESULTS

Based on the last updated list released on March 2019, seven ICIs are approved by the FDA including ipilimumab, pembrolizumab, nivolumab, atezolizumab, avelumab, durvalumab, and cemiplimab.

CONCLUSION

This review also highlighted the most common adverse effects caused by ICIs and which affect people in different ways.

PD-L1 silencing inhibits triple-negative breast cancer development and upregulates T-cell-induced pro-inflammatory cytokines

Triple-negative breast cancer (TNBC) is an invasive tumor with a high incidence of distant metastasis and poor prognosis. In TNBC cells, high PD-L1 expression can induce an immunosuppressive tumor microenvironment, repressing the anti-tumoral immune responses. Although FDA-approved agents targeting the PD-1/PD-L1 axis are potent to eliminate tumoral cells, their immune-related adverse events have become worrisome. As the regulator of gene expression, siRNAs can directly target PD-L1 in breast cancer cells. The gene modification of tumoral PD-L1 can reduce our reliance on the current method of targeting the PD-L1/PD-1 axis. We initiated the study with bioinformatics analysis; the results indicated that TNBC and the MDA-MB-231 cells significantly overexpressed PD-L1 compared to other breast cancer subtypes and cell lines. Our results demonstrated that PD-L1 silencing substantially reduced PD-L1 expression at mRNA and protein levels in MDA-MB-231 cells. Moreover, our results demonstrated that PD-L1 knockdown reduced cancer cell proliferation and induced apoptosis via intrinsic and extrinsic apoptosis pathways. We observed that PD-L1 silencing effectively inhibited the migration of TNBC cells. Further investigation also displayed that silencing of PD-L1 in breast cancer cells induced T-cell cytotoxic function by upregulating the gene expression of pro-inflammatory cytokines, i.e., IL-2, IFN-γ, and TNF-α, and downregulating the gene expression of anti-inflammatory cytokines, i.e., IL-10, and TGF-β, in a co-culture system.

Clinical Trial Design and Drug Approval in Oncology: A Primer for the Advanced Practitioner in Oncology

Evidenced-based practice requires timely and accurate integration of scientific advances. This presents a challenge for the oncology clinician given the robust pace of scientific discovery and the increasing number of new drug approvals and expanded indications for previously approved drugs. All currently available antineoplastic therapies have been developed through the clinical trials process. Advanced practitioners (APs) in oncology are often involved in the conduct of clinical trials as primary investigators, sub-investigators, study coordinators, or in the delivery and monitoring of care to patients enrolled in these trials. A prerequisite to evidenced-based practice is understanding how clinical trials are conducted and how to critically analyze published results of studies leading to U.S. Food & Drug Administration approval. Any AP involved in the clinical management and supportive care of patients receiving antineoplastic therapies should be able to critically review published data to glean findings that warrant a change in practice. The goals of this manuscript are to summarize key elements of the clinical trial process for oncology drug development and approval in the United States and to provide a primer for the interpretation of clinical data.

CoMNRank: An integrated approach to extract and prioritize human microbial metabolites from MEDLINE records

MOTIVATION

Trillions of bacteria in human body (human microbiota) affect human health and diseases by controlling host functions through small molecule metabolites.An accurate and comprehensive catalog of the metabolic output from human microbiota is critical for our deep understanding of how microbial metabolism contributes to human health.The large number of published biomedical research articles is a rich resource of microbiome studies.However, automatically extracting microbial metabolites from free-text documents and differentiating them from other human metabolites is a challenging task.Here we developed an integrated approach called Co-occurrence Metabolite Network Ranking (CoMNRank) by combining named entity extraction, network construction and topic sensitive network-based prioritization to extract and prioritize microbial metabolites from biomedical articles.

METHODS

The text data included 28,851,232 MEDLINE records.CoMNRank consists of three steps: (1) extraction of human metabolites from MEDLINE records; (2) construction of a weighted co-occurrence metabolite network (CoMN); (3) prioritization and differentiation of microbial metabolites from other human metabolites.

RESULTS

For the first step of CoMNRank, we extracted 11,846 human metabolites from MEDLINE articles, with a baseline performance of precision of 0.014, recall of 0.959 and F1 of 0.028.We then constructed a weighted CoMN of 6,996 nodes and 986,186 edges.CoMNRank effectively prioritized microbial metabolites: the precision of top ranked metabolites is 0.45, a 31-fold enrichment as compared to the overall precision of 0.014.Manual curation of top 100 metabolites showed a true precision of 0.67, among which 48% true positives are not captured by existing databases.

CONCLUSION

Our study sets the foundation for future tasks of microbial entity and relationship extractions as well as data-driven studies of how microbial metabolism contributes to human health and diseases.

Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study

Background

Immune checkpoint inhibitors are associated with unique immune-related adverse events (irAEs). As most of the immune checkpoint inhibitors are new to the market, it is important to conduct studies using real-world data sources to investigate their safety profiles.

Objective

The aim of the study was to develop a framework for signal detection and filtration of novel irAEs for 6 Food and Drug Administration–approved immune checkpoint inhibitors.

Methods

In our framework, we first used the Food and Drug Administration’s Adverse Event Reporting System (FAERS) standardized in an Observational Health Data Sciences and Informatics (OHDSI) common data model (CDM) to collect immune checkpoint inhibitor-related event data and conducted irAE signal detection. OHDSI CDM is a standard-driven data model that focuses on transforming different databases into a common format and standardizing medical terms to a common representation. We then filtered those already known irAEs from drug labels and literature by using a customized text-mining pipeline based on clinical text analysis and knowledge extraction system with Medical Dictionary for Regulatory Activities (MedDRA) as a dictionary. Finally, we classified the irAE detection results into three different categories to discover potentially new irAE signals.

Results

By our text-mining pipeline, 490 irAE terms were identified from drug labels, and 918 terms were identified from the literature. In addition, of the 94 positive signals detected using CDM-based FAERS, 53 signals (56%) were labeled signals, 10 (11%) were unlabeled published signals, and 31 (33%) were potentially new signals.

Conclusions

We demonstrated that our approach is effective for irAE signal detection and filtration. Moreover, our CDM-based framework could facilitate adverse drug events detection and filtration toward the goal of next-generation pharmacovigilance that seamlessly integrates electronic health record data for improved signal detection.

Intestinal microbiota: a new force in cancer immunotherapy

Cancer displays high levels of heterogeneity and mutation potential, and curing cancer remains a challenge that clinicians and researchers are eager to overcome. In recent years, the emergence of cancer immunotherapy has brought hope to many patients with cancer. Cancer immunotherapy reactivates the immune function of immune cells by blocking immune checkpoints, thereby restoring the anti-tumor activity of immune cells. However, immune-related adverse events are a common complication of checkpoint blockade, which might be caused by the physiological role of checkpoint pathways in regulating adaptive immunity and preventing autoimmunity. In this context, the intestinal microbiota has shown great potential in the immunotherapy of cancer. The intestinal microbiota not only regulates the immune function of the body, but also optimizes the therapeutic effect of immune checkpoint inhibitors, thus reducing the occurrence of complications. Therefore, manipulating the intestinal microbiota is expected to enhance the effectiveness of immune checkpoint inhibitors and reduce adverse reactions, which will lead to new breakthroughs in immunotherapy and cancer management. Video abstract.

Colitis following the use of immune checkpoint inhibitors: A real-world analysis of spontaneous reports submitted to the FDA adverse event reporting system

BACKGROUND

Although colitis has been reported in patients treated with immune checkpoint inhibitors (ICIs), associations between colitis and ICIs had not been thoroughly assessed in real-world studies. Here, we identified and characterized significant colitis-associated with ICIs.

METHODS

Based on the Food and Drug Administration Adverse Event Reporting System (FAERS) from January 2004 to December 2019, the disproportionality analysis and Bayesian analysis, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were adopted to data mining of the suspected adverse events of colitis after ICIs administrating. Clinical characteristics of patients with ICIs-associated colitis and the time to onset of colitis following different ICI regimens were collected.

RESULTS

A total of 3786 reports of colitis adverse events were identified with ICIs. Seven ICI monotherapies were associated with the reporting of colitis. Statistically significant ROR, PRR, information component (IC), and empirical Bayesian geometric mean (EBGM) emerged for all ICI monotherapies and combination therapies. ICIs-associated colitis affected mostly male (53.51%), with a wide mean age range (60.65 to 72 years). Colitis adverse events were commonly reported in patients with melanoma and lung cancer. Adverse outcomes of colitis concerning ICI were mainly outcomes of hospitalization-initiated or prolonged and other serious. Among colitis cases, 17.43% cases of colitis concerning ICI lead to death. The adverse event ofcolitis occurred earliest in ipilimumab monotherapy with a median time to onset of 64.21 days (IQR: 27-69 days) among all monotherapies.

CONCLUSIONS

ICI may lead to severe and disabling ICIs-associated colitis during therapy. Analysis of FAERS data identified signals for adverse events of colitis with ICI regimens. Practitioners should consider the factors that may increase the likelihood of colitis. The findings support a continued surveillance and risk factor identification studies.

BRD4 inhibition suppresses PD-L1 expression in triple-negative breast cancer

Programmed death-ligand 1 (PD-L1) expression on the surface of tumour cells can cause tumour immune evasion. Benefits of combining anti-PD-L1 therapy with nab-paclitaxel in patients with advanced triple-negative breast cancer (TNBC) have been reported. However, some patients cannot tolerate the immune-related adverse effects (irAEs) caused by antibody-based immunotherapy. BRD4 is a member of the bromodomain and extra-terminal domain (BET) family. BRD4 inhibition has shown antitumour effects in many tumours, but its role in TNBC has not been definitively concluded. In particular, the immune regulation of BRD4 in TNBC has been rarely studied. In this study, we used JQ1, a BET inhibitor, and small interfering RNAs (siRNAs) targeting BRD4 to explore the influence of BRD4 on PD-L1 expression in TNBC. The results indicated that BRD4 inhibition suppressed PD-L1 expression and the PD-L1 upregulation induced by interferon-γ (IFN-γ). In the in vivo experiments, we found that JQ1 not only reduced the PD-L1 expression level but also changed the proportions of T lymphocyte subsets in the spleens of tumour-bearing mice, which helped to relieve immunosuppression. Briefly, our study reveals that BRD4 regulates PD-L1 expression and may provide a potential method for blocking the programmed death 1 (PD-1)/PD-L1 immune checkpoint in TNBC.

[Immune-related adverse events after immune checkpoints inhibitors in 2019: An update]

Use of checkpoint inhibitors to treat cancer was one of the most important revolution these last years and an increasing number of new types of tumors is currently under investigation with these new treatments. However, immune-related adverse events associated with these agents frequently affect various organs, mimicking auto-immune or inflammatory diseases. Some of these effects can be severe, often requiring hospitalization and specialized treatment (immunosuppression). Most known agents are ipilimumab (anti-CTLA-4 antibody) nivolumab and pembrolizumab (anti-PD-1 antibodies). New molecules are now approved or in development as anti-PD-L1 antibodies, anti-LAG-3 or anti-TIM-3 antibodies, increasing the probability and new description of immune-related adverse events. With his experience in auto-immune diseases, the immunologist/internal medicine specialist has an important role in the management of these toxicities. The goal of this review is to focus on the incidence, diagnostic assessment and recommended management of the most relevant immune-related adverse events.

Hypophysitis induced by immune checkpoint inhibitors: a 10-year assessment

Introduction: Hypophysitis caused by immune checkpoint inhibitors (ICIs) has risen to the medical attention during the past decade. ICIs are monoclonal antibodies that block the interaction between molecules that normally inhibit the function of effector T cells, ultimately increasing their ability to destroy cancer cells but also causing immune-related adverse events, such as hypophysitis. Ipilimumab, a CTLA-4 blocker, was the first ICI approved from the Food and Drug Administration for advanced melanoma patients in 2011. Several additional ICIs targeting CTLA-4, PD-1, or PD-L1 are now used in many clinical trials, making it important for physicians to recognize and treat hypophysitis adequately.Areas covered: This review will provide insights into the mechanisms of pituitary toxicity, highlight the complexity of clinical phenotypes of ICI hypophysitis, and offer practical recommendations.Expert opinion: ICI hypophysitis differs in many respects from primary hypophysitis, and also according to the type of ICI that caused it. Its pathogenesis remains unknown, although the expression of CTLA-4 and PD-1 on pituitary cells could play a role. The diagnosis is mainly clinical since there are no specific serological markers and MRI findings are subtle. The treatment is based on long-term hormone replacement and does not typically require discontinuation of immunotherapy.