Multi-omics prediction of immune-related adverse events during checkpoint immunotherapy

Biomarkers associated with immune-related adverse events induced by immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) constitute a pivotal class of immunotherapeutic drugs in cancer treatment. However, their widespread clinical application has led to a notable surge in immune-related adverse events (irAEs), significantly affecting the efficacy and survival rates of patients undergoing ICI therapy. While conventional hematological and imaging tests are adept at detecting organ-specific toxicities, distinguishing adverse reactions from those induced by viruses, bacteria, or immune diseases remains a formidable challenge. Consequently, there exists an urgent imperative for reliable biomarkers capable of accurately predicting or diagnosing irAEs. Thus, a thorough review of existing studies on irAEs biomarkers is indispensable. Our review commences by providing a succinct overview of major irAEs, followed by a comprehensive summary of irAEs biomarkers across various dimensions. Furthermore, we delve into innovative methodologies such as machine learning, single-cell RNA sequencing, multiomics analysis, and gut microbiota profiling to identify novel, robust biomarkers that can facilitate precise irAEs diagnosis or prediction. Lastly, this review furnishes a concise exposition of irAEs mechanisms to augment understanding of irAEs prediction, diagnosis, and treatment strategies.

Prophylactic IL-23 blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy

BACKGROUND

Immune-related adverse events (irAEs), characterized by targeted inflammation, occur in up to 60% of patients with melanoma treated with immune checkpoint inhibitors (ICIs). Evidence proved that the baseline peripheral blood profiles of patients at risk for severe irAEs development paralleled clinical autoimmunity. Interleukin (IL)-23 blockade with risankizumab is recommended for cases that are suffering from autoimmune disease, such as autoimmune colitis. However, currently, the role of IL-23 in irAEs onset and severity remains poorly understood.

METHODS

The pro-inflammatory cytokines most associated with severe irAEs onset were identified by retrospective analysis based on GSE186143 data set. To investigate the efficacy of prophylactic IL-23 blockade administration to prevent irAEs, refer to a previous study, we constructed two irAEs murine models, including dextran sulfate sodium salt (DSS)-induced colitis murine model and a combined-ICIs-induced irAEs murine model. To further explore the applicability of our findings, murine models with graft-versus-host disease were established, in which Rag2-/-Il2rg-/- mice were transferred with human peripheral blood mononuclear cells and received combined cytotoxic T-lymphocyte associated antigen 4 (CTLA-4) and programmed cell death protein-1 (PD-1) treatment. Human melanoma cells were xenografted into these mice concomitantly.

RESULTS

Here we show that IL-23 was upregulated in the serum of patients suffering from irAEs after dual anti-CTLA-4 and anti-PD-1 treatment, and increased as a function of irAEs severity. Additionally, Augmented CD4+ Tems may preferentially underlie irAEs onset. Treating mice with anti-mouse IL-23 antibody concomitantly with combined CTLA-4 and PD-1 immunotherapy ameliorates colitis and, in addition, preserves antitumor efficacy. Moreover, in xenografted murine models with irAEs, prophylactic blockade of human IL-23 using clinically available IL-23 inhibitor (risankizumab) ameliorated colitis, hepatitis and lung inflammation, and moreover, immunotherapeutic control of tumors was retained. Finally, we also provided a novel machine learning-based computational framework based on two blood-based features-IL-23 and CD4+ Tems-that may have predictive potential for severe irAEs and ICIs response.

CONCLUSIONS

Our study not only provides clinically feasible strategies to dissociate efficacy and toxicity in the use of combined ICIs for cancer immunotherapy, but also develops a blood-based biomarker that makes it possible to achieve a straightforward and non-invasive, detection assay for early prediction of irAEs onset.

Peripheral blood immune parameters, response, and adverse events after neoadjuvant chemotherapy plus durvalumab in early-stage triple-negative breast cancer

PURPOSE

We evaluated T- and B-cell receptor (TCR and BCR) repertoire diversity and 38 serum cytokines in pre- and post-treatment peripheral blood of 66 patients with triple-negative breast cancer (TNBC) who received neoadjuvant chemotherapy plus durvalumab and assessed associations with pathologic response and immune-related adverse events (irAEs) during treatment.

METHODS

Genomic DNA was isolated from buffy coat for TCR and BCR clonotype profiling using the Immunoseq platform and diversity was quantified with Pielou's evenness index. MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panel was used to measure serum cytokine levels, which were compared between groups using moderated t-statistic with Benjamini-Hochberg correction for multiple testing.

RESULTS

TCR and BCR diversity was high (Pielou's index > 0.75) in all samples. Baseline receptor diversities and change in diversity pre- and post-treatment were not associated with pathologic response or irAE status, except for BCR diversity that was significantly lower post-treatment in patients who developed irAE (unadjusted p = 0.0321). Five cytokines increased after treatment in patients with pathologic complete response (pCR) but decreased in patients with RD, most prominently IL-8. IFNγ, IL-7, and GM-CSF levels were higher in pre-treatment than in post-treatment samples of patients who developed irAEs but were lower in those without irAEs.

CONCLUSION

Baseline peripheral blood cytokine levels may predict irAEs in patients treated with immune checkpoint inhibitors and chemotherapy, and increased post-treatment B-cell clonal expansion might mediate irAEs.

Cerebral 18F-FDG PET/CT Metabolism as Diagnostic Signature for Central Nervous System Toxicity After Immune Checkpoint Blockade Cancer Treatment

Our aim was to investigate probable biomarkers specific to immune-related central nervous system toxicity (CNST) in cancer patients treated with immune checkpoint inhibitors (ICI) by analysis of 18F-FDG PET/CT images. Methods: Cancer patients receiving ICI treatment were enrolled in a multicenter observational study that analyzed regional metabolic changes before and during CNST onset from January 2020 to February 2022. In 1:1 propensity score-matched pairs, the regional SUVmean of each bilateral brain lobe of CNST patients (CNST+) was compared with that of patients who had central nervous system infections (CNSIs) and patients without CNST or CNSI (CNST-). In a validation cohort, patients were recruited from February 2022 to July 2023 and followed up for 24 wk after the start of ICI. Early changes in regional SUVmean at 5-6 wk after therapy initiation were evaluated for ability to predict later CNST onset. Results: Of 6,395 ICI-treated patients, 2,387 underwent prognostic 18F-FDG PET/CT and 125 of the scanned patients had CNST (median time from ICI treatment to onset, 9 wk; quartile range, 2-23 wk). Regional 18F-FDG PET/CT SUVmean changes were higher in CNST+ than in CNST- patients (117 patient pairs) but were lower than in CNSI patients (50 pairs). Differentiating analysis reached an area under the curve (AUC) of 0.83 (95% CI, 0.78-0.88) for CNST+ versus CNST- and of 0.80 (95% CI, 0.72-0.89) for CNST+ versus CNSI. Changes in SUVmean were also higher before CNST onset than for CNST- (60 pairs; AUC, 0.74; 95% CI, 0.66-0.83). In a validation cohort of 2,878 patients, preonset changes in SUVmean reached an AUC of 0.86 (95% CI, 0.79-0.94) in predicting later CNST incidence. Conclusion: Brain regional hypermetabolism could be detected during and before CNST clinical onset. CNST may be a distinct pathologic entity versus brain infections defined by 18F-FDG PET/CT brain scans. Regional SUV differences may be translated into early diagnostic tools based on moderate differentiating accuracy in our study.

Management and outcome of oncological patients under immune checkpoint inhibitors presenting at the emergency department

INTRODUCTION

With the rising use of immune checkpoint inhibitors (ICIs) in oncology, emergency physicians are increasingly confronted with their immune-related adverse events (irAEs). We described the types of irAEs presenting to the ED of a Belgian cancer centre and determined associations with the development of an irAE and other patient's characteristics. Secondary objectives describe the therapeutic management and determine 7 and 30-day mortality.

METHODS

A retrospective chart review of ED visits of patients on ICI from 15 December 2016 to 6 December 2020 was performed. Clinical presentation, cancer characteristics and type of ICI were extracted by a single abstractor. We recorded any suspicion of irAE in the ED and confirmation of an irAE was based on the patient's oncologist report. Outcome was based on mortality at date of last follow-up.

RESULTS

227 patients on ICI presented to the ED, with a total of 451 visits. 54 (12%) of the visits resulted in a diagnosis of irAE. Four clinical features were associated with an irAE: gastrointestinal complaints (p=0.01), skin rashes (p=0.02), acute renal failure (p=0.002) and abnormal liver function (p=0.04). An irAE was also associated with three different factors: a cancer status in remission (OR=5.33, 95% CI 2.57 to 11.04), a combination of two ICIs (OR=4.43, 95% CI 2.09 to 9.42) and a medical history of irAE (OR=2.44, 95% CI 1.27 to 4.68). 30-day mortality was lower in the irAE group (0%) than in the non-irAE group (13%, 95% CI 9% to 19%).

CONCLUSIONS

Oncological patients under ICI presenting in the ED are more likely to have an irAE if they present with gastrointestinal and dermatological complaints, acute renal failure and abnormal liver function. This is also true for patients with any history of irAE, a concomitant use of two ICIs and with a cancer status in remission.

Unveiling the future: precision pharmacovigilance in the era of personalized medicine

In the era of personalized medicine, pharmacovigilance faces new challenges and opportunities, demanding a shift from traditional approaches. This article delves into the evolving landscape of drug safety monitoring in the context of personalized treatments. We aim to provide a succinct reflection on the intersection of tailored therapeutic strategies and vigilant pharmacovigilance practices. We discuss the integration of pharmacogenetics in enhancing drug safety, illustrating how genetic profiling aids in predicting drug responses and adverse reactions. Emphasizing the importance of phase IV-post-marketing surveillance, we explore the limitations of pre-marketing trials and the necessity for a comprehensive approach to drug safety. The article discusses the pivotal role of pharmacogenetics in pre-exposure risk management and the redefinition of pharmacoepidemiological methods for post-exposure surveillance. We highlight the significance of integrating patient-specific genetic profiles in creating personalized medication leaflets and the use of advanced computational methods in data analysis. Additionally, we examine the ethical, privacy, and data security challenges inherent in precision medicine, emphasizing their implications for patient consent and data management.

Biomarkers for immune-related adverse events in cancer patients treated with immune checkpoint inhibitors

Although immune checkpoint inhibitors have greatly improved cancer therapy, they also cause immune-related adverse events, including a wide range of inflammatory side effects resulting from excessive immune activation. Types of immune-related adverse events are diverse and can occur in almost any organ, with different frequencies and severities. Furthermore, immune-related adverse events may occur within the first few weeks after treatment or even several months after treatment discontinuation. Predictive biomarkers include blood cell counts and cell surface markers, serum proteins, autoantibodies, cytokines/chemokines, germline genetic variations and gene expression profiles, human leukocyte antigen genotype, microRNAs and the gut microbiome. Given the inconsistencies in research results and limited practical utility, there is to date no established biomarker that can be used in routine clinical practice, and additional investigations are essential to demonstrate efficacy and subsequently facilitate integration into routine clinical use.

Clinical and translational attributes of immune-related adverse events

With immune checkpoint inhibitors (ICIs) becoming the mainstay of treatment for many cancers, managing their immune-related adverse events (irAEs) has become an important part of oncological care. This Review covers the clinical presentation of irAEs and crucial aspects of reversibility, fatality and long-term sequelae, with special attention to irAEs in specific patient populations, such as those with autoimmune diseases. In addition, the genetic basis of irAEs, along with cellular and humoral responses to ICI therapy, are discussed. Detrimental effects of empirically used high-dose steroids and second-line immunosuppression, including impaired ICI effectiveness, call for more tailored irAE-treatment strategies. We discuss open therapeutic challenges and propose potential avenues to accelerate personalized management strategies and optimize outcomes.

Current status and progress of research on the ADP-dependent glucokinase gene

ADP-dependent glucokinase (ADPGK) produces glucose-6-phosphate with adenosine diphosphate (ADP) as the phosphate group donor, in contrast to ATP-dependent hexokinases (HKs). Originally found in archaea, ADPGK is involved in glycolysis. However, its biological function in most eukaryotic organisms is still unclear, and the molecular mechanism of action requires further investigation. This paper provides a concise overview of ADPGK's origin, biological function and clinical application. It aims to furnish scientific information for the diagnosis and treatment of human metabolic diseases, neurological disorders, and malignant tumours, and to suggest new strategies for the development of targeted drugs.

Nivolumab + Tacrolimus + Prednisone ± Ipilimumab for Kidney Transplant Recipients With Advanced Cutaneous Cancers

PURPOSE

Cancer-related mortality rates among kidney transplant recipients (KTR) are high, but these patients have largely been excluded from trials of immune checkpoint inhibitors because of immunosuppression and risk of treatment-related allograft loss (TRAL). We conducted a prospective clinical trial testing nivolumab (NIVO) + tacrolimus (TACRO) + prednisone (PRED) ± ipilimumab (IPI) in KTR with advanced cutaneous cancers.

METHODS

Adult KTR with advanced melanoma or basal, cutaneous squamous, or Merkel cell carcinomas were eligible. Immunosuppression was standardized to TACRO (serum trough 2-5 ng/mL) + PRED 5 mg once daily. Patients then received NIVO 480 mg IV once every 4 weeks. The primary composite end point was partial or complete (tumor) response (CR) or stable disease per RECIST v1.1 without allograft loss at 16W. Patients with progressive disease (PD) could receive IPI 1 mg/kg IV + NIVO 3 mg/kg once every 3 weeks × 4 followed by NIVO. Donor-derived cell-free DNA (dd-cfDNA) levels were measured approximately once every 2 weeks as a potential predictor of allograft rejection.

RESULTS

Among eight evaluable patients, none met the trial's primary end point. All eight patients experienced PD on NIVO + TACRO + PRED; TRAL occurred in one patient. Six patients then received IPI + NIVO + TACRO + PRED. Best overall responses: two CR (one with TRAL) and four PD (one with TRAL). In total, 7 of 8 pre-NIVO tumor biopsies contained a paucity of infiltrating immune cells. In total, 2 of 5 on-NIVO biopsies demonstrated moderate immune infiltrates; both patients later experienced a CR to IPI + NIVO. In 2 of 3 patients with TRAL, dd-cfDNA elevations occurred 10 and 15 days before increases in serum creatinine.

CONCLUSION

In most KTR with advanced skin cancer, TACRO + PRED provides insufficient allograft protection and compromises immune-mediated tumor regression after administration of NIVO ± IPI. Elevated dd-cfDNA levels can signal treatment-related allograft rejection earlier than rises in serum creatinine.

Neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio: Markers predicting immune-checkpoint inhibitor efficacy and immune-related adverse events

We conducted a comprehensive review of existing prediction models pertaining to the efficacy of immune-checkpoint inhibitor (ICI) and the occurrence of immune-related adverse events (irAEs). The predictive potential of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in determining ICI effectiveness has been extensively investigated, while limited research has been conducted on predicting irAEs. Furthermore, the combined model incorporating NLR and PLR, either with each other or in conjunction with additional markers such as carcinoembryonic antigen, exhibits superior predictive capabilities compared to individual markers alone. NLR and PLR are promising markers for clinical applications. Forthcoming models ought to incorporate established efficacious models and newly identified ones, thereby constituting a multifactor composite model. Furthermore, efforts should be made to explore effective clinical application approaches that enhance the predictive accuracy and efficiency.

Integrating network pharmacology and drug side-effect data to explore mechanism of liver injury-induced by tyrosine kinase inhibitors

Tyrosine kinase inhibitors (TKIs) are highly efficient small-molecule anticancer drugs. Despite the specificity and efficacy of TKIs, they can produce off-target effects, leading to severe liver toxicity, and even some of them are labeled as black box hepatotoxicity. Thus, we focused on representative TKIs associated with severe hepatic adverse events, namely lapatinib, pazopanib, regorafenib, and sunitinib as objections of study, then integrated drug side-effect data from United State Food and Drug Administration (U.S. FDA) and network pharmacology to elucidate mechanism underlying TKI-induced liver injury. Based on network pharmacology, we constructed a specific comorbidity module of high risk of serious adverse effects and created drug-disease networks. Enrichment analysis of the networks revealed the depletion of all-trans-retinoic acid and the involvement of down-regulation of the HSP70 family-mediated endoplasmic reticulum (ER) stress as key factors in TKI-induced liver injury. These results were further verified by transcription data. Based on the target prediction results of drugs and reactive metabolites, we also shed light on the association between toxic metabolites and severe hepatic adverse reactions, and thinking HSPA8, HSPA1A, CYP1A1, CYP1A2 and CYP3A4 were potential therapeutic or preventive targets against TKI-induced liver injury. In conclusion, our research provides comprehensive insights into the mechanism underlying severe liver injury caused by TKIs, offering a better understanding of how to enhance patient safety and treatment efficacy.

Exploration of the potential association between GLP-1 receptor agonists and suicidal or self-injurious behaviors: a pharmacovigilance study based on the FDA Adverse Event Reporting System database

BACKGROUND

Establishing whether there is a potential relationship between glucagon-like peptide 1 receptor agonists (GLP-1RAs) and suicidal or self-injurious behaviors (SSIBs) is crucial for public safety. This study investigated the potential association between GLP-1RAs and SSIBs by exploring the FDA Adverse Event Reporting System (FAERS) database.

METHODS

A disproportionality analysis was conducted using post-marketing data from the FAERS repository (2018 Q1 to 2022 Q4). SSIB cases associated with GLP-1RAs were identified and analyzed through disproportionality analysis using the information component. The parametric distribution with a goodness-of-fit test was employed to analyze the time-to-onset, and the Ω shrinkage was used to evaluate the potential effect of co-medication on the occurrence of SSIBs.

RESULTS

In total, 204 cases of SSIBs associated with GLP-1RAs, including semaglutide, liraglutide, dulaglutide, exenatide, and albiglutide, were identified in the FAERS database. Time-of-onset analysis revealed no consistent mechanism for the latency of SSIBs in patients receiving GLP-1RAs. The disproportionality analysis did not indicate an association between GLP-1RAs and SSIBs. Co-medication analysis revealed 81 cases with antidepressants, antipsychotics, and benzodiazepines, which may be proxies of mental health comorbidities.

CONCLUSIONS

We found no signal of disproportionate reporting of an association between GLP-1RA use and SSIBs. Clinicians need to maintain heightened vigilance on patients premedicated with neuropsychotropic drugs. This contributes to the greater acceptance of GLP-1RAs in patients with type 2 diabetes mellitus or obesity.

From genomic spectrum of NTRK genes to adverse effects of its inhibitors, a comprehensive genome-based and real-world pharmacovigilance analysis

Introduction: The discovery of neurotrophic tyrosine receptor kinase (NTRK) gene fusions has facilitated the development of precision oncology. Two first-generation NTRK inhibitors (larotrectinib and entrectinib) are currently approved for the treatment of patients with solid tumors harboring NTRK gene fusions. Nevertheless, comprehensive NTRK profiling at the pan-cancer genomic level and real-world studies pertaining to the adverse events of NTRK inhibitors are lacking. Methods: We characterize the genome of NTRK at the pan-cancer level through multi-omics databases such as The Cancer Genome Atlas (TCGA). Through the FDA Adverse Event Reporting System (FAERS) database, we collect reports of entrectinib and larotrectinib-induced adverse events and perform a pharmacovigilance analysis using various disproportionality methods. Results: NTRK1/2/3 expression is lower in most tumor tissues, while they have higher methylation levels. NTRK gene expression has prognostic value in some cancer types, such as breast invasive carcinoma (BRCA). The cancer type with highest NTRK alteration frequency is skin cutaneous melanoma (SKCM) (31.98%). Thyroid carcinoma (THCA) has the largest number of NTRK fusion cases, and the most common fusion pair is ETV6-NTRK3. Adverse drug events (ADEs) obtained from the FAERS database for larotrectinib and entrectinib are 524 and 563, respectively. At the System Organ Class (SOC) level, both drugs have positive signal value for "nervous system disorder". Other positive signals for entrectinib include "cardiac disorders", "metabolism and nutrition disorders", while for larotrectinib, it is "hepatobiliary disorders". The unexpected signals are also listed in detail. ADEs of the two NTRK inhibitors mainly occur in the first month. The median onset time of ADEs for entrectinib and larotrectinib was 16 days (interquartile range [IQR] 6-86.5) and 44 days ([IQR] 7-136), respectively. Conclusion: Our analysis provides a broad molecular view of the NTRK family. The real-world adverse drug event analysis of entrectinib and larotrectinib contributes to more refined medication management.

Predictive biomarkers for immune-related adverse events in cancer patients treated with immune-checkpoint inhibitors

PURPOSE

The objective of this study was to identify potential predictors of immune-related adverse events (irAEs) in cancer patients receiving immune checkpoint inhibitor therapy among serum indexes, case data, and liquid biopsy results.

METHODS

We retrospectively analyzed 418 patients treated with anti-programmed cell death 1(PD-1)/PD-1 ligand (PD-L1) inhibitors from January 2018 to May 2022 in our cancer center. We identified factors that correlated with the occurrence of irAEs and evaluated associations between irAEs and anti-PD-1/PD-L1 inhibitor responses.

RESULTS

The incidence of irAEs was 42.1%, and pneumonitis (9.1%), thyroid toxicity (9.1%), cardiotoxicity (8.1%), and dermatologic toxicity (6.9%) were the four most common irAEs. Multivariate logistic analysis identified female sex, antibiotic use, higher post-treatment neutrophil-to-lymphocyte ratio (NLR), and higher baseline circulating tumor cell (CTC) level, as predictive biomarkers for the occurrence of irAEs. A lower baseline prognostic nutritional index (PNI), body mass index (BMI) ≥ 25 kg/m2, and higher post-treatment lactate dehydrogenase (LDH) level were predictive factors for more severe irAEs (higher severity grade). Patients without irAEs had better overall survival than those with irAEs. Specifically, pneumonitis and cardiotoxicity were found to be significant predictors of poor prognosis in the irAE subgroup with different organ-related irAEs. Low-dose steroid (dexamethasone 10 mg) treatment had no significant effect on outcomes.

CONCLUSIONS

Gender, antibiotic use, post-treatment NLR, and baseline CTC level are potential predictive biomarkers of irAEs, while baseline PNI, BMI, and post-treatment LDH may predict the severity of irAEs. The predictive effect of irAE occurrence on survival benefit may depend on the type of irAE.

Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges

Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the approach to cancer research. Applications of NGS include the identification of tumor specific alterations that can influence tumor pathobiology and also impact diagnosis, prognosis and therapeutic options. Pharmacogenomics (PGx) studies the role of inheritance of individual genetic patterns in drug response and has taken advantage of NGS technology as it provides access to high-throughput data that can, however, be difficult to manage. Machine learning (ML) has recently been used in the life sciences to discover hidden patterns from complex NGS data and to solve various PGx problems. In this review, we provide a comprehensive overview of the NGS approaches that can be employed and the different PGx studies implicating the use of NGS data. We also provide an excursus of the ML algorithms that can exert a role as fundamental strategies in the PGx field to improve personalized medicine in cancer.

Pathogenesis, pathological characteristics and individualized therapy for immune-related adverse effects

Immune checkpoint inhibitors (ICIs) are a class of antitumor medications that target immune checkpoints, which induce the activation of lymphocytes. These treatments effectively prolong the survival of patients with advanced tumors, especially lung cancer. However, in addition to tumor killing effects, ICIs may also cause an imbalance between immune tolerance and immunity. Over-activated lymphocytes may cause various types of damage to multiple organs throughout the body, called immune-related adverse events. In this review, we summarize the pathogenesis, pathological characteristics, biomarkers, and therapeutic agents for immune-related adverse events.

Progenitor-like exhausted SPRY1+CD8+ T cells potentiate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cell carcinoma

Neoadjuvant immune checkpoint blockade (ICB) demonstrates promise in operable esophageal squamous cell carcinoma (ESCC), but lacks available efficacy biomarkers. Here, we perform single-cell RNA-sequencing of tumors from patients with ESCC undergoing neoadjuvant ICB, revealing a subset of exhausted CD8+ T cells expressing SPRY1 (CD8+ Tex-SPRY1) that displays a progenitor exhausted T cell (Tpex) phenotype and correlates with complete response to ICB. We validate CD8+ Tex-SPRY1 cells as an ICB-specific predictor of improved response and survival using independent ICB-/non-ICB cohorts and demonstrate that expression of SPRY1 in CD8+ T cells enforces Tpex phenotype and enhances ICB efficacy. Additionally, CD8+ Tex-SPRY1 cells contribute to proinflammatory phenotype of macrophages and functional state of B cells, which thereby promotes antitumor immunity by enhancing CD8+ T cell effector functions. Overall, our findings unravel progenitor-like CD8+ Tex-SPRY1 cells' role in effective responses to ICB for ESCC and inform mechanistic biomarkers for future individualized immunotherapy.

Effect of age on the risk of immune-related adverse events in patients receiving immune checkpoint inhibitors

Identifying patients at increased risk of immune-related adverse events (irAEs) facilitates safe application of immune checkpoint inhibitors (ICIs). This retrospective study aimed to determine the effect of age on the risk of irAEs in patients receiving ICIs and to identify potential mechanisms underlying age-related irAE risk differences. We analyzed reports of FDA Adverse Event Reporting System from July 1, 2014, to September 30, 2021. The information component ratio (ICΔ) was used to compare the irAE risk between older adults (> 65 years) and younger adults (25-65 years), of which the 95% confidential interval lower limit (ICΔ025) exceeding zero indicated significantly increased risk. We found that older adults had a significantly higher overall irAE risk than younger adults (ICΔ025 0.38), which was observed in almost all organ systems. We further analyzed the correlation between age-related irAE risks and age-related transcriptional changes to identify potential genes and pathways underlying age-related irAE risk differences. We found that genes significantly correlated with ICΔ were enriched in processes including extracellular matrix organization, regulation of myeloid leukocyte mediated immunity, and regulation of c-Jun N-terminal kinase (JNK) cascade. In addition, single-cell RNA sequencing analysis confirmed that genes involved in collagen-containing extracellular matrix and JNK cascade were significantly upregulated in myeloid cells from ICI-associated colitis tissues compared with ICI-treated colon tissues without colitis. In conclusion, older adults receiving ICIs have higher irAE risks than younger adults. Upregulation of genes involved in JNK cascade and collagen-containing extracellular matrix in myeloid cells may contribute to increased irAE risks in older adults.

Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy

Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.

Deciphering transcriptomic determinants of the divergent link between PD-L1 and immunotherapy efficacy

Programmed cell death ligand 1 (PD-L1) expression remains the most widely used biomarker for predicting response to immune checkpoint inhibitors (ICI), but its predictiveness varies considerably. Identification of factors accounting for the varying PD-L1 performance is urgently needed. Here, using data from three independent trials comprising 1239 patients, we have identified subsets of cancer with distinct PD-L1 predictiveness based on tumor transcriptome. In the Predictiveness-High (PH) group, PD-L1+ tumors show better overall survival, progression-free survival, and objective response rate with ICI than PD-L1- tumors across three trials. However, the Predictiveness-Low (PL) group demonstrates an opposite trend towards better outcomes for PD-L1- tumors. PD-L1+ tumors from the PH group demonstrate the superiority of ICI over chemotherapy, whereas PD-L1+ tumors from the PL group show comparable efficacy between two treatments or exhibit an opposite trend favoring chemotherapy. This observation of context-dependent predictiveness remains strong regardless of immune subtype (Immune-Enriched or Non-Immune), PD-L1 regulation mechanism (adaptative or constitutive), tumor mutation burden, or neoantigen load. This work illuminates avenues for optimizing the use of PD-L1 expression in clinical decision-making and trial design, although this exploratory concept should be further confirmed in large trials.

Stocking the toolbox-Using preclinical models to understand the development and treatment of immune checkpoint inhibitor-induced immune-related adverse events

Cancer patients treated with immune checkpoint inhibitors (ICIs) are susceptible to a broad and variable array of immune-related adverse events (irAEs). With increasing clinical use of ICIs, defining the mechanism for irAE development is more critical than ever. However, it currently remains challenging to predict when these irAEs occur and which organ may be affected, and for many of the more severe irAEs, inaccessibility to the tissue site hampers mechanistic insight. This lack of understanding of irAE development in the clinical setting emphasizes the need for greater use of preclinical models that allow for improved prediction of biomarkers for ICI-initiated irAEs or that validate treatment options that inhibit irAEs without hampering the anti-tumor immune response. Here, we discuss the utility of preclinical models, ranging from exploring databases to in vivo animal models, focusing on where they are most useful and where they could be improved.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Immune checkpoint therapy for solid tumours: clinical dilemmas and future trends

Immune-checkpoint inhibitors (ICBs), in addition to targeting CTLA-4, PD-1, and PD-L1, novel targeting LAG-3 drugs have also been approved in clinical application. With the widespread use of the drug, we must deeply analyze the dilemma of the agents and seek a breakthrough in the treatment prospect. Over the past decades, these agents have demonstrated dramatic efficacy, especially in patients with melanoma and non-small cell lung cancer (NSCLC). Nonetheless, in the field of a broad concept of solid tumours, non-specific indications, inseparable immune response and side effects, unconfirmed progressive disease, and complex regulatory networks of immune resistance are four barriers that limit its widespread application. Fortunately, the successful clinical trials of novel ICB agents and combination therapies, the advent of the era of oncolytic virus gene editing, and the breakthrough of the technical barriers of mRNA vaccines and nano-delivery systems have made remarkable breakthroughs currently. In this review, we enumerate the mechanisms of each immune checkpoint targets, associations between ICB with tumour mutation burden, key immune regulatory or resistance signalling pathways, the specific clinical evidence of the efficacy of classical targets and new targets among different tumour types and put forward dialectical thoughts on drug safety. Finally, we discuss the importance of accurate triage of ICB based on recent advances in predictive biomarkers and diagnostic testing techniques.

Case Report: A Presentation of Early-Onset Immune-Mediated Bullous Pemphigoid in a Patient with Urothelial Cancer

Cutaneous immune-related adverse events (cirAEs) are the most common side effects of immune checkpoint inhibitor (ICI) therapy (30-50% for all grades). The vast majority of them are low or mild and can be treated without ICI interruption. Autoimmune blistering disorders, such as immune-mediated bullous pemphigoid (IBP), are rare (<1%) but potentially serious conditions that must be early detected. The onset generally occurs within the first months of the treatment, and it appears to be more common with antiprogrammed death-1 or antiprogrammed ligand 1 (anti-PD1/PDL1) than with anticytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4). We present a case of a three-day severe IBP onset after receiving the first cycle of atezolizumab. This exceptional early presentation could suggest the presence of some predisposing condition and demonstrates the need to better understand predictive toxicity-related biomarkers in candidate patients for immunotherapy.

Clonally expanded CD38hi cytotoxic CD8 T cells define the T cell infiltrate in checkpoint inhibitor-associated arthritis

Immune checkpoint inhibitor (ICI) therapies used to treat cancer, such as anti-PD-1 antibodies, can induce autoimmune conditions in some individuals. The T cell mechanisms mediating such iatrogenic autoimmunity and their overlap with spontaneous autoimmune diseases remain unclear. Here, we compared T cells from the joints of 20 patients with an inflammatory arthritis induced by ICI therapy (ICI-arthritis) with two archetypal autoimmune arthritides, rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Single-cell transcriptomic and antigen receptor repertoire analyses highlighted clonal expansion of an activated effector CD8 T cell population in the joints and blood of patients with ICI-arthritis. These cells were identified as CD38hiCD127- CD8 T cells and were uniquely enriched in ICI-arthritis joints compared with RA and PsA and also displayed an elevated interferon signature. In vitro, type I interferon induced CD8 T cells to acquire the ICI-associated CD38hi phenotype and enhanced cytotoxic function. In a cohort of patients with advanced melanoma, ICI therapy markedly expanded circulating CD38hiCD127- T cells, which were frequently bound by the therapeutic anti-PD-1 drug. In patients with ICI-arthritis, drug-bound CD8 T cells in circulation showed marked clonal overlap with drug-bound CD8 T cells from synovial fluid. These results suggest that ICI therapy directly targets CD8 T cells in patients who develop ICI-arthritis and induces an autoimmune pathology that is distinct from prototypical spontaneous autoimmune arthritides.

Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

An updated review of gastrointestinal toxicity induced by PD-1 inhibitors: from mechanisms to management

PD-1 inhibitors, as one of commonly used immune checkpoint inhibitors, enable T-cell activation and prevent immune escape by blocking the PD-1/PD-L1 signaling pathway. They have transformed the treatment landscape for cancer in recent years, due to the advantages of significantly prolonging patients' survival and improving their life quality. However, the ensuing unpredictable immune-related adverse effects (irAEs) plague clinicians, such as colitis and even potentially fatal events like intestinal perforation and obstruction. Therefore, understanding the clinical manifestations and grading criteria, underlying mechanisms, available diverse therapies, accessible biomarkers, and basis for risk stratification is of great importance for the management. Current evidence suggests that irAEs may be a marker of clinical benefit to immunotherapy in patients, so whether to discontinue PD-1 inhibitors after the onset of irAEs and rechallenge after remission of irAEs requires further evaluation of potential risk-reward ratios as well as more data from large-scale prospective studies to fully validate. At the end, the rare gastrointestinal toxicity events caused by PD-1 inhibitors are also sorted out. This review provides a summary of available data on the gastrointestinal toxicity profile caused by PD-1 inhibitors, with the aim of raising clinicians' awareness in daily practice, so that patients can safely benefit from therapy.

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

Integrative analysis of risk factors for immune-related adverse events of checkpoint blockade therapy in cancer

Immune-related adverse events (irAEs) induced by checkpoint inhibitors involve a multitude of different risk factors. Here, to interrogate the multifaceted underlying mechanisms, we compiled germline exomes and blood transcriptomes with clinical data, before and after checkpoint inhibitor treatment, from 672 patients with cancer. Overall, irAE samples showed a substantially lower contribution of neutrophils in terms of baseline and on-therapy cell counts and gene expression markers related to neutrophil function. Allelic variation of HLA-B correlated with overall irAE risk. Analysis of germline coding variants identified a nonsense mutation in an immunoglobulin superfamily protein, TMEM162. In our cohort and the Cancer Genome Atlas (TCGA) data, TMEM162 alteration was associated with higher peripheral and tumor-infiltrating B cell counts and suppression of regulatory T cells in response to therapy. We developed machine learning models for irAE prediction, validated using additional data from 169 patients. Our results provide valuable insights into risk factors of irAE and their clinical utility.

Predicting immune-related adverse events using a simplified frailty score in cancer patients treated with checkpoint inhibitors: A retrospective cohort study

OBJECTIVE

Checkpoint inhibitors (CPIs) are in widespread clinical use. Little is known about which patients are at risk for developing toxicity. It is essential being able to identify patients with higher risk of experiencing immune-related adverse events (IRAEs) before initiation of CPI treatment to optimize treatment decisions and follow-up strategy. The aim of this study was to investigate whether a simplified frailty score based on performance status (PS), age, and comorbidity expressed as Charlson comorbidity index (CCI) could predict development of IRAEs.

METHODS

We performed a retrospective cohort study at three Swedish centers. All patients (n = 596) treated with PD-L1 or PD-1 inhibitor for advanced cancer between January 2017 and December 2021 were included.

RESULTS

In total, 361 patients (60.6%) were classified as nonfrail and 235 (39.4%) as frail. The most common cancer type was non-small cell lung cancer (n = 203; 34.1%) followed by malignant melanoma (n = 195; 32.7%). Any grade of IRAE occurred in 138 (58.7%) frail and in 155 (42.9%) non-frail patients (OR: 1.58; 95% CI: 1.09-2.28). Age, CCI, and PS did not independently predict the occurrence of IRAEs. Multiple IRAEs occurred in 53 (22.6%) frail and in 45 (12.5%) nonfrail patients (OR: 1.62; 95% CI: 1.00-2.64).

DISCUSSION

In conclusion, the simplified frailty score predicted all grade IRAEs and multiple IRAEs in multivariate analyses whereas age, CCI, or PS did not separately predict development of IRAEs suggesting that this easy-to-use score may be of value in clinical decision making but a large prospective study is needed to assess its true value.

Checkpoint Inhibitor-Induced Colitis: An Update

Immunotherapy with immune checkpoint inhibitors (ICIs) nowadays has indications for several solid tumors. The current targets for ICIs are CTLA-4, PD-1, and PD-L1 receptors. Despite the clinical advantages derived from ICIs, a variety of side effects are linked to overstimulation of the immune system. Among these, ICI-related colitis is one of the most common, with a disabling impact on the patient. Diarrhea, abdominal pain, abdominal distension, cramping, and hematochezia are the most common ICI enterocolitis presenting symptoms. The most frequently used grading system for assessment of the severity of ICI enterocolitis is called the Common Terminology Criteria for Adverse Events (CTCAE) grading. With regard to the histological picture, there is no specific feature; however, microscopic damage can be classified into five types: (1) acute active colitis, (2) chronic active colitis, (3) microscopic colitis-like, (4) graft-versus-host disease-like, and (5) other types. Supportive therapy (oral hydration, a bland diet without lactose or caffeine, and anti-diarrheal agents) is indicated in mild colitis. Symptomatic treatment alone or with loperamide, a low-fiber diet, and spasmolytics are recommended for low-grade diarrhea. In more severe cases, corticosteroid treatment is mandatory. In refractory cases, off-label use of biological therapies (infliximab or vedolizumab) was proposed.

Psychiatric disorders associated with immune checkpoint inhibitors: a pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) database

Background

With the increasing use of immune checkpoint inhibitors (ICIs) for tumour immunotherapy, the immune-related adverse events (irAEs) caused by their collateral effect on the immune system pose a key challenge for the clinical application of ICIs. Psychiatric adverse events are a class of adverse events associated with ICIs that are realistically observed in the real world. We aim to provide a comprehensive study and summary of psychiatric adverse events associated with ICIs.

Methods

We obtained ICI adverse reaction reports during January 2012-December 2021 from the FDA Adverse Event Reporting System (FAERS) database. ICI reports underwent screening to minimize the influence of other adverse reactions, concomitant medications, and indications for medication use that may also contribute to psychiatric disorders. Disproportionality analysis was performed to find psychiatric adverse events associated with ICIs by comparing ICIs with the full FAERS database using the reporting odds ratio (ROR). Influencing factors were explored based on univariate logistic regression analysis. Finally, the Cancer Genome Atlas (TCGA) pan-cancer transcriptome data were combined to explore the potential biological mechanisms associated with ICI-related pAEs.

Findings

Reports of psychiatric adverse events accounted for 2.71% of the overall ICI adverse event reports in the FAERS database. Five categories of psychiatric adverse events were defined as ICI-related psychiatric adverse events (pAEs). The median age of reports with ICI-related pAEs was 70 (interquartile range [IQR] 24-95), with 21.54% of reports having a fatal outcome. Cases with indications for lung cancer, skin cancer and kidney site cancer accounted for the majority. The odds of ICI-related pAEs increased in older patients (65-74: OR = 1.44 [1.22-1.70], P < 0.0001: ≥75: OR = 1.84 [1.54-2.20], P < 0.0001). The occurrence of ICI-related pAEs may be related to NOTCH signalling and dysregulation of synapse-associated pathways.

Interpretation

This study investigated psychiatric adverse events highly associated with ICI treatment, their influencing factors and potential biological mechanisms, which provides a reliable basis for further in-depth study of ICI-related pAEs. However, as an exploratory study, our findings need to be further confirmed in a large-scale prospective study.

Funding

This work was supported by the Natural Science Foundation of Guangdong Province (2018A030313846 and 2021A1515012593), the Science and Technology Planning Project of Guangdong Province (2019A030317020) and the National Natural Science Foundation of China (81802257, 81871859, 81772457, 82172750 and 82172811). Guangdong Basic and Applied Basic Research Foundation (Guangdong - Guangzhou Joint Fouds) (2022A1515111212). This work was supported by Key Research and Development Projects of Sichuan Science and Technology (2022YFS0221, 2022YFS0074, 2022YFS0156 and 2022YFS0378). Sichuan Provincial People's Hospital Hospital Young Talent Fund (2021QN08).

Dynamic immune signatures as biomarkers for irAEs

Identifying predictive biomarkers of immune-related adverse events (irAEs) is crucial to maximize the benefits for patients with cancer treated with immune checkpoint inhibitors (ICIs). In a recent study published in Med,Nuñez et al. utilized multi-omics approaches and identified blood immune signatures that have the potential to predict the development of autoimmune toxicity.

Pre-treatment comorbidities, C-reactive protein and eosinophil count, and immune-related adverse events as predictors of survival with checkpoint inhibition for multiple tumour entities

BACKGROUND

The development of immune-related adverse events (irAEs) may be associated with clinical efficacy of checkpoint inhibitors (CPIs) in patients with cancer. We therefore investigated the effect of irAEs and pre-treatment parameters on outcome in a large, real-life patient cohort.

METHODS

We performed a single-centre, retrospective, observational study including patients who received CPIs from 2011 to 2018 and followed until 2021. The primary outcome was overall survival, and the secondary outcome was the development of irAEs.

RESULTS

In total, 229 patients with different tumour entities (41% non-small cell lung cancer [NSCLC], 29% melanoma) received a total of 282 CPI treatment courses (ipilimumab, nivolumab, pembrolizumab or atezolizumab). Thirty-four percent of patients developed irAEs (of these 17% had CTCAE Grade ≥3). Factors independently associated with mortality were pre-treatment CRP ≥10 mg/L (hazard ratio [HR] 2.064, p = 0.0003), comorbidity measured by Charlson comorbidity index (HR 1.149, p = 0.014) and irAEs (HR 0.644, p = 0.036) (age-adjusted, n = 216). Baseline eosinophil count ≤0.2 × 10⁹ /L was a further independent predictor of mortality (age-, CRP-, CCI- and irAE-adjusted HR = 2.252, p = 0.002, n = 166). Anti-CTLA-4 use (p < 0.001), and pre-treatment CRP <10 mg/L were independently associated with irAE occurrence (p = 0.037).

CONCLUSIONS

We found an independent association between irAE occurrence and improved overall survival in a real-life cohort spanning multiple tumour entities and treatment regimens. Pre-treatment comorbidities, CRP and eosinophil count represent potential markers for predicting treatment response.

T follicular helper cells in cancer

T follicular helper (Tfh) cells provide essential help to B cells for effective antibody-mediated immune responses. Although the crucial function of these CD4⁺ T cells in infection and vaccination is well established, their involvement in cancer is only beginning to emerge. Increased numbers of Tfh cells in Tfh cell-derived or B cell-associated malignancies are often associated with an unfavorable outcome, whereas in various solid organ tumor types of non-lymphocytic origin, their presence frequently coincides with a better prognosis. We discuss recent advances in understanding how Tfh cell crosstalk with B cells and CD8⁺ T cells in secondary and tertiary lymphoid structures (TLS) enhances antitumor immunity, but may also exacerbate immune-related adverse events (irAEs) such as autoimmunity during immune checkpoint blockade (ICB) and cancer immunotherapy.

Construction of a prognostic model for HCC based on ferroptosis-related lncRNAs expression and its potential to predict the response and irAEs of immunotherapy

Background: Ferroptosis is an iron-dependent programmed cell death process, and studies have confirmed that it plays an important regulatory role in the occurrence and development of various malignancies including hepatocellular carcinoma (HCC). In addition, the role of abnormally expressed long non-coding RNAs (lncRNAs) in regulating and driving the occurrence and development of HCC has attracted more and more attention. However, there is still a lack of research on the role of ferroptosis-related lncRNAs in the prognosis prediction of HCC patients.

Method: In this study, we used the Pearson test method to analyze the association between differentially expressed lncRNAs and ferroptosis-related genes in HCC and normal tissues obtained from The Cancer Genome Atlas (TCGA), and found 68 aberrantly expressed and prognosis-related ferroptosis-related lncRNAs. Based on this, we established an HCC prognostic model composed of 12 ferroptosis-related lncRNAs. In addition, HCC patients were divided into a high-risk group and a low-risk group according to the risk score of this 12 ferroptosis-related lncRNAs prognostic model. Gene enrichment analysis indicated that ferroptosis-related lncRNA-based expression signatures may regulate HCC immune microenvironment signaling pathways through ferroptosis, chemical carcinogenesis-reactive oxygen species, and NK cell-mediated cytotoxicity pathways. In addition, immune cell correlation analysis showed that there were significant differences in immune infiltrating cell subtypes, such as Th cells, macrophages, monocytes, and Treg cells between the two groups. In addition, the expression of multiple immune checkpoint molecules was found to be significantly increased in the high-risk group (eg, PD1, CTLA-4, CD86, etc.).

Results: Our research provides a new method for predicting prognosis using a ferroptosis-related lncRNA expression signature prognostic model in hepatocellular carcinoma. And it provides new tools for predicting patient response and adverse effects of immunotherapy.

Conclusion: In conclusion, ferroptosis-related lncRNA expression signatures can be used to construct a prognostic prediction model to predict the overall survival of HCC patients, and can be used as an independent influencing factor for prognosis. Further analysis showed that ferroptosis-related lncRNAs may affect the efficacy of immunotherapy in patients with HCC by altering the tumor microenvironment, so this model may serve as a new indicator of the response and irAEs of HCC to immunotherapy.

Predictive Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Events

Immune-checkpoint inhibitors (ICIs) are antagonists of inhibitory receptors in the immune system, such as the cytotoxic T-lymphocyte-associated antigen-4, the programmed cell death protein-1 and its ligand PD-L1, and they are increasingly used in cancer treatment. By blocking certain suppressive pathways, ICIs promote T-cell activation and antitumor activity but may induce so-called immune-related adverse events (irAEs), which mimic traditional autoimmune disorders. With the approval of more ICIs, irAE prediction has become a key factor in improving patient survival and quality of life. Several biomarkers have been described as potential irAE predictors, some of them are already available for clinical use and others are under development; examples include circulating blood cell counts and ratios, T-cell expansion and diversification, cytokines, autoantibodies and autoantigens, serum and other biological fluid proteins, human leucocyte antigen genotypes, genetic variations and gene profiles, microRNAs, and the gastrointestinal microbiome. Nevertheless, it is difficult to generalize the application of irAE biomarkers based on the current evidence because most studies have been retrospective, time-limited and restricted to a specific type of cancer, irAE or ICI. Long-term prospective cohorts and real-life studies are needed to assess the predictive capacity of different potential irAE biomarkers, regardless of the ICI type, organ involved or cancer site.

Nivolumab-induced pneumonitis and cardiopathy in a patient with relapsed Hodgkin's lymphoma

INTRODUCTION

Nivolumab, the monoclonal antibody inhibitor of programmed cell death protein 1, enhances the T-cell response, including anti-tumour responses, by blocking the attachment of programmed death-ligand 1 and programmed death-ligand 2 ligands to the programmed cell death protein 1 receptor, which in turn leads to a reduction in tumour growth. Nivolumab has been approved in relapsed or refractory classic Hodgkin's lymphoma after autologous transplantation of haematopoietic stem cell and treatment with brentuximab as monotherapy.

CASE REPORT

We herewith report a case of 65-year-old woman who developed an interstitial pneumonitis and a global cardiac hypokinesis following a treatment with Nivolumab for a refractory Hodgkin's Lymphoma. Nivolumab was administered as the fifth line of therapy. Some concomitant patient treatments include drug with known autoimmune toxicities. Although the patient had a persistent complete remission following the sixth infusion, it was discontinued as she developed dyspnea of NYHA stage IV and orthopnea. The chest tomography revealed a bilateral micronodular pattern of organizing pneumonia with bilateral pleural effusion. The forced expiratory volume was decreased to 50%. In parallel her transthoracic echocardiography revealed a global hypokinesis with a left ventricular ejection fraction of 20%.

MANAGEMENT AND OUTCOME

The patient was treated with empiric antibiotics although the microbial assessments were negative. She was also treated with beta-blocker and angiotensin-converting enzyme inhibitors. The cardiac magnetic resonance imaging performed after 4 months confirmed the hypokinetic cardiopathy with an ejection fraction of 48%. The patient had a significant clinical improvement. The tomography emission positron scan conducted 8 months after interruption of Nivolumab showed complete remission with some moderate activation of residual lesion basal posterior lobe of left lung field.

DISCUSSION

Early and effective diagnosis of immune-related adverse events through the search for predictive biomarkers like drug factors and individual risk factors will allow targeted surveillance leading to a better tolerance.

Machine Learning Models Using Routinely Collected Clinical Data Offer Robust and Interpretable Predictions of 90-Day Unplanned Acute Care Use for Cancer Immunotherapy Patients

PURPOSE

Clinical management of patients receiving immune checkpoint inhibitors (ICIs) could be informed using accurate predictive tools to identify patients at risk of short-term acute care utilization (ACU). We used routinely collected data to develop and assess machine learning (ML) algorithms to predict unplanned ACU within 90 days of ICI treatment initiation.

METHODS

We used aggregated electronic health record data from 7,960 patients receiving ICI treatments to train and assess eight ML algorithms. We developed the models using pre-SARS-COV-19 COVID-19 data generated between January 2016 and February 2020. We validated our algorithms using data collected between March 2020 and June 2022 (peri-COVID-19 sample). We assessed performance using area under the receiver operating characteristic curves (AUROC), sensitivity, specificity, and calibration plots. We derived intuitive explanations of predictions using variable importance and Shapley additive explanation analyses. We assessed the marginal performance of ML models compared with that of univariate and multivariate logistic regression (LR) models.

RESULTS

Most algorithms significantly outperformed the univariate and multivariate LR models. The extreme gradient boosting trees (XGBT) algorithm demonstrated the best overall performance (AUROC, 0.70; sensitivity, 0.53; specificity, 0.74) on the peri-COVID-19 sample. The algorithm performance was stable across both pre- and peri-COVID-19 samples, as well as ICI regimen and cancer groups. Type of ICI agents, oxygen saturation, diastolic blood pressure, albumin level, platelet count, immature granulocytes, absolute monocyte, chloride level, red cell distribution width, and alcohol intake were the top 10 key predictors used by the XGBT algorithm.

CONCLUSION

Machine learning algorithms trained using routinely collected data outperformed traditional statistical models when predicting 90-day ACU. The XGBT algorithm has the potential to identify high-ACU risk patients and enable preventive interventions to avoid ACU.

Integrated analysis reveals the microenvironment of non-small cell lung cancer and a macrophage-related prognostic model

Background

In the treatment of non-small cell lung cancer (NSCLC), recent advances in immunotherapy have heralded a new era. Despite the success of immune therapy, a subset of patients persistently fails to respond. Therefore, to better improve the efficacy of immunotherapy and achieve the purpose of precision therapy, the research and exploration of tumor immunotherapy biomarkers have received much attention.

Methods

Single-cell transcriptomic profiling was used to reveal tumor heterogeneity and the microenvironment in NSCLC. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was utilized to speculate the relative fractions of 22 infiltration immunocyte types in NSCLC. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used for the construction of risk prognostic models and predictive nomograms of NSCLC. Spearman's correlation analysis was employed to explore the relationship between risk score and tumor mutation burden (TMB) and immune checkpoint inhibitors (ICIs). Screening of chemotherapeutic agents in the high- and low-risk groups was performed with the "pRRophetic" package in R. Intercellular communication analysis was conducted using the "CellChat" package.

Results

We found that most tumor-infiltrating immune cells were T cells and monocytes. We also found that there was a significant difference in the tumor-infiltrating immune cells and ICIs across different molecular subtypes. Further analysis showed that M0 and M1 mononuclear macrophages were significantly different in different molecular subtypes. The risk prediction model was shown to have to ability to accurately predict the prognosis, immune cell infiltration, and chemotherapy efficacy of patients in the high and low-risk groups. Finally, we found that the carcinogenic effect of migration inhibitory factor (MIF) is mediated by binding to CD74, CXCR4, and CD44 receptors involved in MIF cell signaling.

Conclusions

We have revealed the tumor microenvironment (TME) of NSCLC through single-cell data analysis and constructed a prognosis model of macrophage-related genes. These results could provide new therapeutic targets for NSCLC.

4-1BB-Based CAR T Cells Effectively Reverse Exhaustion and Enhance the Anti-Tumor Immune Response through Autocrine PD-L1 scFv Antibody

Exhaustion of chimeric antigen receptor (CAR) T cells is one of the limitations for CAR T efficacy in solid tumors and for tumor recurrence after initial CAR T treatment. Tumor treatment with a combination of programmed cell death receptor-1 (PD-1)/programmed cell death ligand-1 (PD-L1) blockage and CD28-based CAR T cells has been intensively studied. However, it remains largely unclear whether autocrine single-chain variable fragments (scFv) PD-L1 antibody can improve 4-1BB-based CAR T cell anti-tumor activity and revert CAR T cell exhaustion. Here, we studied T cells engineered with autocrine PD-L1 scFv and 4-1BB-containing CAR. The antitumor activity and exhaustion of CAR T cells were investigated in vitro and in a xenograft cancer model using NCG mice. CAR T cells with autocrine PD-L1 scFv antibody demonstrate enhanced anti-tumor activity in solid tumors and hematologic malignancies by blocking the PD-1/PD-L1 signaling. Importantly, we found that CAR T exhaustion was largely diminished by autocrine PD-L1 scFv antibody in vivo. As such, 4-1BB CAR T with autocrine PD-L1 scFv antibody combined the power of CAR T cells and the immune checkpoint inhibitor, thereby increasing the anti-tumor immune function and CAR T persistence, providing a cell therapy solution for a better clinical outcome.

Adverse reactions and efficacy of camrelizumab in patients with lung adenocarcinoma with high PD-L1 expression: A case report

RATIONALE

Immune checkpoint inhibitors have been rapidly developed for lung cancer therapy and major clinical guidelines have recommended them as the optimal first-line treatment for PD-L1-positive advanced lung cancer. Unfortunately, there is a lack of efficient prediction tools for the occurrence of immune-related adverse events (irAEs) caused by immunotherapy, and there is a lack of real-world data on the processing of irAEs, particularly those occurring in multiple systems simultaneously.

PATIENT CONCERNS

We report a 62-year-old male with expectoration who was diagnosed with lung adenocarcinoma with brain and bone metastases.

DIAGNOSES

The results of the lung cancer tissue biopsy showed lung adenocarcinoma. Gene detection results of lung cancer tissue biopsy showed that the KRAS gene G12D was mutated and PD-L1 was positive, with a tumor proportion score of 95% (Dako 22C3 IHC platform).

INTERVENTIONS

The patient initially received 1 cycle of pemetrexed in combination with cisplatin-based chemotherapy. After the results of PD-L1 testing were reported, he received 1 cycle of camrelizumab immunotherapy in combination with pemetrexed plus cisplatin based chemotherapy.

OUTCOMES

Seventeen days after treatment, the patient presented with symptoms such as yellow staining of the sclera and skin, itching throughout the body, dry mouth, and ecchymosis of the skin of the right lower extremity, which continued to worsen. Following treatment with 2 mg/kg methylprednisolone, the patient's condition continued to deteriorate. IrAEs were controlled after dose escalation to 8 mg/kg in combination with plasma exchange therapy and treatment with multiple doses of mycophenolate ester. The patient then received no treatment for almost 2 months, but examination revealed that the tumor still had a persistent shrinkage reaction.

LESSONS

Camrelizumab has been well tolerated in several studies, but in patients with high PD-L1 expression and a G12D mutation in KRAS, one should be alert to the development of serious or even multisystem immune-related adverse effects. Timely and individualized selection of the hormone dosage is essential for the treatment of immunotherapy-induced multisystem irAEs.

Enhancer RNA-based modeling of adverse events and objective responses of cancer immunotherapy reveals associated key enhancers and target genes

Immune checkpoint inhibitors (ICI) targeting PD-1/PD-L1 or CTLA-4 are emerging and effective immunotherapy strategies. However, ICI-treated patients present heterogeneous responses and adverse events, thus demanding effective ways to assess benefit over risk before treatment. Here, by integrating pan-cancer clinical and molecular data, we tried to predict immune-related adverse events (irAEs, risk) and objective response rates (ORRs, benefit) based on enhancer RNAs (eRNAs) expression among patients receiving anti-PD-1/PD-L1 therapies. We built two tri-variate (eRNAs) regression models, one (with ENSR00000326714, ENSR00000148786, and ENSR00000005553) explaining 71% variance (R=0.84) of irAEs and the other (with ENSR00000164478, ENSR00000035913, and ENSR00000167231) explaining 79% (R=0.89) of ORRs. Interestingly, target genes of irAE-related enhancers, including upstream regulators of MYC, were involved in metabolism, inflammation, and immune activation, while ORR-related enhancers target PAK2 and DLG1 which participate in T cell activation. More importantly, we found that ENSR00000148786 probably enhanced TMEM43/LUMA expression mainly in B cells to induce irAEs in ICI-treated patients. Our study provides references for the identification of immunotherapy-related biomarkers and potential therapeutic targets during immunotherapy.

Distinct T cell sub-clusters may serve as biomarkers for immune related adverse events

Identifying biomarkers of irAEs is the prerequisite for maximizing clinical benefits of patients treated by immune checkpoint inhibitors. Bukhari et al.¹ identified significant associations between different peripheral T cell sub-clusters and arthritis, pneumonitis, and thyroiditis.

A comprehensive tool for tumor precision medicine with pharmaco-omics data analysis

Background: Cancer precision medicine is an effective strategy to fight cancers by bridging genomics and drug discovery to provide specific treatment for patients with different genetic characteristics. Although some public databases and modelling frameworks have been developed through studies on drug response, most of them only considered the ramifications of the drug on the cell line and the effects on the patient still require a huge amount of work to integrate data from various databases and calculations, especially concerning precision treatment. Furthermore, not only efficacy but also the adverse effects of drugs on patients should be taken into account during cancer treatment. However, the adverse effects as essential indicators of drug safety assessment are always neglected. Method: A holistic estimation explores various drugs' efficacy levels by calculating their potency both in reversing and enhancing cancer-associated gene expression change. And a method for bridging the gap between cell culture and living tissue estimates the effectiveness of a drug on individual patients through the mappings of various cell lines to each person according to their genetic mutation similarities. Result: We predicted the efficacy of FDA-recommended drugs, taking into account both efficacy and toxicity, and obtained consistent results. We also provided an intuitive and easy-to-use web server called DBPOM (http://www.dbpom.net/, a comprehensive database of pharmaco-omics for cancer precision medicine), which not only integrates the above methods but also provides calculation results on more than 10,000 small molecule compounds and drugs. As a one-stop web server, clinicians and drug researchers can also analyze the overall effect of a drug or a drug combination on cancer patients as well as the biological functions that they target. DBPOM is now public, free to use with no login requirement, and contains all the data and code. Conclusion: Both the positive and negative effects of drugs during precision treatment are essential for practical application of drugs. DBPOM based on the two effects will become a vital resource and analysis platform for drug development, drug mechanism studies and the discovery of new therapies.

Correlation of preclinical and clinical biomarkers with efficacy and toxicity of cancer immunotherapy

Immune checkpoint inhibitors (ICIs) have revealed significant clinical values in different solid tumors and hematological malignancy, changing the landscape for the treatment of multiple types of cancer. However, only a subpopulation of patients has obvious tumor response and long-term survival after ICIs treatment, and many patients may experience other undesirable clinical features. Therefore, biomarkers are critical for patients to choose exact optimum therapy. Here, we reviewed existing preclinical and clinical biomarkers of immunotherapeutic efficacy and immune-related adverse events (irAEs). Based on efficacy prediction, pseudoprogression, hyperprogressive disease, or irAEs, these biomarkers were divided into cancer cell-derived biomarkers, tumor microenvironment-derived biomarkers, host-derived biomarkers, peripheral blood biomarkers, and multi-modal model and artificial intelligence assessment-based biomarkers. Furthermore, we describe the relation between ICIs efficacy and irAEs. This review provides the overall perspective of biomarkers of immunotherapeutic outcome and irAEs prediction during ICIs treatment.

Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians

Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.

The Predictive Model Construction for Immune-Related Adverse Events in Non-Small Cell Lung Cancer Patients Receiving Immunotherapy

INTRODUCTION

It is possible to predict immune-related adverse events (irAEs) in the treatment of immune checkpoint inhibitors (ICIs) based on clinical and hematological parameters. Nevertheless, the specific parameters which can predict irAEs are still in the exploration. The purpose of this retrospective study was to develop a predictive model for irAEs in non-small cell lung cancer (NSCLC) patients in the treatment of ICIs.

METHODS

Researchers enrolled NSCLC patients treated with at least 1 type of ICIs at Harbin Medical University Cancer Hospital between January 30, 2019 and December 31, 2021. Baseline parameters including demographic, clinicopathology, treatment information, and peripheral blood markers were selected retrospectively. Type, onset time, grade, and treatment of irAEs were also assessed. By analyzing the risk factors for irAEs, an irAEs prediction model was established using univariate and multivariate logistic regression.

RESULTS

In a total of 484 patients, 81 patients experienced 112 irAEs in which thyroid dysfunction was the most common irAE (n = 38, 33.9%) and ICI pneumonitis was the most serious irAE (n = 6, 33.3%). Finally, a prediction model based on lines and combination therapy of ICIs, ECOG performance status, neutrophils/lymphocytes ratio (NLR), platelet (PLT), and lymphocyte (LYM) was established. Multivariate logistic regression analysis showed that 2 or ≥3 lines of immunotherapy, ICIs combination therapy, and ECOG PS 1-2 were independent risk factors for irAEs. Baseline LYM was positively associated with irAEs (OR = 2.599, P = 0.048) while baseline NLR and PLT were negatively associated with irAEs (OR = 0.392, P = 0.047; OR = 0.992, P = 0.035, respectively). The model showed great prediction performance with the AUC value of 0.851 and 0.779 in the training cohort and validation cohort, respectively.

CONCLUSION

Our study identified the risk factors related to irAEs occurrence and constructed and assessed the predictive model of irAEs in patients with NSCLC treated by ICIs using clinical and hematological parameters, thus guiding clinicians to select precisely the population receiving immunotherapy and develop individualized treatment therapy.

Immune checkpoint inhibitor-associated toxicity in advanced non-small cell lung cancer: An updated understanding of risk factors

Immune checkpoint inhibitors (ICIs), such as programmed death-1 (PD-1), programmed death-ligand 1 (PD-L1), cytotoxic T lymphocyte antigen 4 (CTLA-4) antibodies, etc, have revolutionized cancer treatment strategies, including non-small cell lung cancer (NSCLC). While these immunotherapy agents have achieved durable clinical benefits in a subset of NSCLC patients, they bring in a variety of immune-related adverse events (irAEs), which involve cardiac, pulmonary, gastrointestinal, endocrine and dermatologic system damage, ranging from mild to life-threatening. Thus, there is an urgent need to better understand the occurrence of irAEs and predict patients who are susceptible to those toxicities. Herein, we provide a comprehensive review of what is updated about the clinical manifestations, mechanisms, predictive biomarkers and management of ICI-associated toxicity in NSCLC. In addition, this review also provides perspective directions for future research of NSCLC-related irAEs.