An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets

Tissue-specific RNA methylation prediction from gene expression data using sparse regression models

N6-methyladenosine (m6A) is a highly prevalent and conserved post-transcriptional modification observed in mRNA and long non-coding RNA (lncRNA). Identifying potential m6A sites within RNA sequences is crucial for unraveling the potential influence of the epitranscriptome on biological processes. In this study, we introduce Exp2RM, a novel approach that formulates single-site-based tissue-specific elastic net models for predicting tissue-specific methylation levels utilizing gene expression data. The resulting ensemble model demonstrates robust predictive performance for tissue-specific methylation levels, with an average R-squared value of 0.496 and a median R-squared value of 0.482 across all 22 human tissues. Since methylation distribution varies among tissues, we trained the model to incorporate similar patterns, significantly improves accuracy with the median R-squared value increasing to 0.728. Additonally, functional analysis reveals Exp2RM's ability to capture coefficient genes in relevant biological processes. This study emphasizes the importance of tissue-specific methylation distribution in enhancing prediction accuracy and provides insights into the functional implications of methylation sites.

MAPK inhibitor sensitivity scores predict sensitivity driven by the immune infiltration in pediatric low-grade gliomas

Pediatric low-grade gliomas (pLGG) show heterogeneous responses to MAPK inhibitors (MAPKi) in clinical trials. Thus, more complex stratification biomarkers are needed to identify patients likely to benefit from MAPKi therapy. Here, we identify MAPK-related genes enriched in MAPKi-sensitive cell lines using the GDSC dataset and apply them to calculate class-specific MAPKi sensitivity scores (MSSs) via single-sample gene set enrichment analysis. The MSSs discriminate MAPKi-sensitive and non-sensitive cells in the GDSC dataset and significantly correlate with response to MAPKi in an independent PDX dataset. The MSSs discern gliomas with varying MAPK alterations and are higher in pLGG compared to other pediatric CNS tumors. Heterogenous MSSs within pLGGs with the same MAPK alteration identify proportions of potentially sensitive patients. The MEKi MSS predicts treatment response in a small set of pLGG patients treated with trametinib. High MSSs correlate with a higher immune cell infiltration, with high expression in the microglia compartment in single-cell RNA sequencing data, while low MSSs correlate with low immune infiltration and increased neuronal score. The MSSs represent predictive tools for the stratification of pLGG patients and should be prospectively validated in clinical trials. Our data supports a role for microglia in the response to MAPKi.

FTY720 in immuno-regenerative and wound healing technologies for muscle, epithelial and bone regeneration

In 2010, the FDA approved the administration of FTY720, S1P lipid mediator, as a therapy to treat relapsing forms of multiple sclerosis. FTY720 was found to sequester pro-inflammatory lymphocytes within the lymph node, preventing them from causing injury to the central nervous system due to inflammation. Studies harnessing the anti-inflammatory properties of FTY720 as a pro-regenerative strategy in wound healing of muscle, bone and mucosal injuries are currently being performed. This in-depth review discusses the current regenerative impact of FTY720 due to its anti-inflammatory effect stratified into an assessment of wound regeneration in the muscular, skeletal, and epithelial systems. The regenerative effect of FTY720 in vivo was characterized in three animal models, with differing delivery mechanisms emerging in the last 20 years. In these studies, local delivery of FTY720 was found to increase pro-regenerative immune cell phenotypes (neutrophils, macrophages, monocytes), vascularization, cell proliferation and collagen deposition. Delivery of FTY720 to a localized wound environment demonstrated increased bone, muscle, and mucosal regeneration through changes in gene and cytokine production primarily by controlling the local immune cell phenotypes. These changes in gene and cytokine production reduced the inflammatory component of wound healing and increased the migration of pro-regenerative cells (neutrophils and macrophages) to the wound site. The application of FTY720 delivery using a biomaterial has demonstrated the ability of local delivery of FTY720 to promote local wound healing leveraging an immunomodulatory mechanism.

Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy

X-linked chronic granulomatous disease (CGD) is associated with defective phagocytosis, life-threatening infections, and inflammatory complications. We performed a clinical trial of lentivirus-based gene therapy in four patients (NCT02757911). Two patients show stable engraftment and clinical benefits, whereas the other two have progressively lost gene-corrected cells. Single-cell transcriptomic analysis reveals a significantly lower frequency of hematopoietic stem cells (HSCs) in CGD patients, especially in the two patients with defective engraftment. These two present a profound change in HSC status, a high interferon score, and elevated myeloid progenitor frequency. We use elastic-net logistic regression to identify a set of 51 interferon genes and transcription factors that predict the failure of HSC engraftment. In one patient, an aberrant HSC state with elevated CEBPβ expression drives HSC exhaustion, as demonstrated by low repopulation in a xenotransplantation model. Targeted treatments to protect HSCs, coupled to targeted gene expression screening, might improve clinical outcomes in CGD.

Uncovering perturbations in human hematopoiesis associated with healthy aging and myeloid malignancies at single-cell resolution

Early hematopoiesis is a continuous process in which hematopoietic stem and progenitor cells (HSPCs) gradually differentiate toward specific lineages. Aging and myeloid malignant transformation are characterized by changes in the composition and regulation of HSPCs. In this study, we used single-cell RNA sequencing (scRNA-seq) to characterize an enriched population of human HSPCs obtained from young and elderly healthy individuals.

Based on their transcriptional profile, we identified changes in the proportions of progenitor compartments during aging, and differences in their functionality, as evidenced by gene set enrichment analysis. Trajectory inference revealed that altered gene expression dynamics accompanied cell differentiation, which could explain aging-associated changes in hematopoiesis. Next, we focused on key regulators of transcription by constructing gene regulatory networks (GRNs) and detected regulons that were specifically active in elderly individuals. Using previous findings in healthy cells as a reference, we analyzed scRNA-seq data obtained from patients with myelodysplastic syndrome (MDS) and detected specific alterations of the expression dynamics of genes involved in erythroid differentiation in all patients with MDS such as TRIB2. In addition, the comparison between transcriptional programs and GRNs regulating normal HSPCs and MDS HSPCs allowed identification of regulons that were specifically active in MDS cases such as SMAD1, HOXA6, POU2F2, and RUNX1 suggesting a role of these transcription factors (TFs) in the pathogenesis of the disease.

In summary, we demonstrate that the combination of single-cell technologies with computational analysis tools enable the study of a variety of cellular mechanisms involved in complex biological systems such as early hematopoiesis and can be used to dissect perturbed differentiation trajectories associated with perturbations such as aging and malignant transformation. Furthermore, the identification of abnormal regulatory mechanisms associated with myeloid malignancies could be exploited for personalized therapeutic approaches in individual patients.

Artificial intelligence assists precision medicine in cancer treatment

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

Cytomegalovirus infection disrupts the influence of short-chain fatty acid producers on Treg/Th17 balance

BACKGROUND

Both the gut microbiota and chronic viral infections have profound effects on host immunity, but interactions between these influences have been only superficially explored. Cytomegalovirus (CMV), for example, infects approximately 80% of people globally and drives significant changes in immune cells. Similarly, certain gut-resident bacteria affect T-cell development in mice and nonhuman primates. It is unknown if changes imposed by CMV on the intestinal microbiome contribute to immunologic effects of the infection.

RESULTS

We show that rhesus cytomegalovirus (RhCMV) infection is associated with specific differences in gut microbiota composition, including decreased abundance of Firmicutes, and that the extent of microbial change was associated with immunologic changes including the proliferation, differentiation, and cytokine production of CD8+ T cells. Furthermore, RhCMV infection disrupted the relationship between short-chain fatty acid producers and Treg/Th17 balance observed in seronegative animals, showing that some immunologic effects of CMV are due to disruption of previously existing host-microbe relationships.

CONCLUSIONS

Gut microbes have an important influence on health and disease. Diet is known to shape the microbiota, but the influence of concomitant chronic viral infections is unclear. We found that CMV influences gut microbiota composition to an extent that is correlated with immunologic changes in the host. Additionally, pre-existing correlations between immunophenotypes and gut microbes can be subverted by CMV infection. Immunologic effects of CMV infection on the host may therefore be mediated by two different mechanisms involving gut microbiota. Video Abstract.

Genomic Effect of DNA Methylation on Gene Expression in Colorectal Cancer

Simple Summary

Abnormal DNA methylation is known to regulate gene expression, and its features have been frequently observed in colorectal cancer (CRC) patients. In addition, alterations in DNA methylation can be proposed as biomarkers for cancer prognosis, as they occur in the early stage of carcinogenesis. Although numerous studies have attempted to shed light on the impacts of DNA methylation on gene expression, it is still unclear which specific regions regulate gene expression and how they are associated with patient survival. In this study, we elucidated the intricate relationship between DNA methylation and gene expression. Furthermore, we found genes that were influenced by DNA methylation and were associated with survival; these genes were mainly enriched in immune-related pathways.

Abstract

The aberrant expression of cancer-related genes can lead to colorectal cancer (CRC) carcinogenesis, and DNA methylation is one of the causes of abnormal expression. Although many studies have been conducted to reveal how DNA methylation affects transcription regulation, the ways in which it modulates gene expression and the regions that significantly affect DNA methylation-mediated gene regulation remain unclear. In this study, we investigated how DNA methylation in specific genomic areas can influence gene expression. Several regression models were constructed for gene expression prediction based on DNA methylation. Among these models, ElasticNet, which had the best performance, was chosen for further analysis. DNA methylation near transcription start sites (TSS), especially from 2 kb upstream to 7 kb downstream of TSS, had an essential regulatory role in gene expression. Moreover, methylation-affected and survival-associated genes were compiled and found to be mainly enriched in immune-related pathways. This study investigated genomic regions in which methylation changes can affect gene expression. In addition, this study proposed that aberrantly expressed genes due to DNA methylation can lead to CRC pathogenesis by the immune system.

Genome-wide profiling of circulatory microRNAs associated with cognition and dementia

INTRODUCTION

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression. Their role in the pathophysiology of dementia and potential as biomarkers remains undetermined.

METHODS

We conducted a single- (one-by-one) and multi-marker (joint) analysis to identify well-expressed circulating miRNAs in plasma (total = 591) associated with general cognition and incident dementia, for 1615 participants of the population-based Rotterdam Study.

RESULTS

During single-marker analysis, 47 miRNAs were nominally (P ≤ .05) associated with cognition and 18 miRNAs were nominally associated with incident dementia, after adjustment for potential confounders. Three miRNAs were common between cognition and dementia (miR-4539, miR-372-3p, and miR-566), with multi-marker analysis revealing another common miRNA (miR-7106-5p). In silico analysis of these four common miRNAs led to several putative target genes expressed in the brain, highlighting the mitogen-activated protein kinase signaling pathway.

DISCUSSION

We provide population-based evidence on the relationship between circulatory miRNAs with cognition and dementia, including four common miRNAs that may elucidate downstream mechanisms.

HIGHLIGHTS

MicroRNAs (miRNAs) are involved in the (dys)function of the central nervous system. Four circulating miRNAs in plasma are associated with cognition and incident dementia. Several predicted target genes of these four miRNAs are expressed in the brain. These four miRNAs may be linked to pathways underlying dementia. Although miRNAs are promising biomarkers, experimental validation remains essential.

Causal Path of COPD Progression-Associated Genes in Different Biological Samples

Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease with pulmonary and extra-pulmonary complications. Due to the disease's systemic nature, many investigations investigated the genetic alterations in various biological samples. We aimed to infer causal genes in COPD's pathogenesis in different biological samples using elastic-net logistic regression and the Structural Equation Model. Samples of small airway epithelial cells, bronchoalveolar lavage macrophages, lung tissue biopsy, sputum, and blood samples were selected (135, 70, 235, 143, and 226 samples, respectively). Elastic-net Logistic Regression analysis was implemented to identify the most important genes involved in COPD progression. Thirty-three candidate genes were identified as essential factors in the pathogenesis of COPD and regulation of lung function. Recognized candidate genes in small airway epithelial (SAE) cells have the highest area under the ROC curve (AUC = 97%, SD = 3.9%). Our analysis indicates that macrophages and epithelial cells are more influential in COPD progression at the transcriptome level.

Single-cell multiomics reveals persistence of HIV-1 in expanded cytotoxic T cell clones

Understanding the drivers and markers of clonally expanding HIV-1-infected CD4⁺ T cells is essential for HIV-1 eradication. We used single-cell ECCITE-seq, which captures surface protein expression, cellular transcriptome, HIV-1 RNA, and TCR sequences within the same single cell to track clonal expansion dynamics in longitudinally archived samples from six HIV-1-infected individuals (during viremia and after suppressive antiretroviral therapy) and two uninfected individuals, in unstimulated conditions and after CMV and HIV-1 antigen stimulation. Despite antiretroviral therapy, persistent antigen and TNF responses shaped T cell clonal expansion. HIV-1 resided in Th1-polarized, antigen-responding T cells expressing BCL2 and SERPINB9 that may resist cell death. HIV-1 RNA⁺ T cell clones were larger in clone size, established during viremia, persistent after viral suppression, and enriched in GZMB⁺ cytotoxic effector memory Th1 cells. Targeting HIV-1-infected cytotoxic CD4⁺ T cells and drivers of clonal expansion provides another direction for HIV-1 eradication.

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment

Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results.

While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.

Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data

BACKGROUND

Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions - approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection,L 0 L 1 penalisation andL 0 L 2 penalisation in real genomic data and (3) compare algorithmic preselection according to performance in our benchmark datasets to algorithmic selection by internal cross-validation.

METHODS

Five large ( n 4000 ) genomic datasets were extracted from Gene Expression Omnibus. 'Gold-standard' regression models were trained on subspaces of these datasets ( n 4000 , p = 500 ). Penalised regression models were trained on small samples from these subspaces ( n ∈ { 25 , 75 , 150 } , p = 500 ) and validated against the gold-standard models. Variable selection performance and out-of-sample prediction were assessed. Penalty 'preselection' according to test performance in the other 4 datasets was compared to selection internal cross-validation error minimisation.

RESULTS

L 1 L 2 -penalisation achieved the highest cosine similarity between estimated coefficients and those of gold-standard models.L 0 L 2 -penalised models explained the greatest proportion of variance in test responses, though performance was unreliable in low signal:noise conditions.L 0 L 2 also attained the highest overall median variable selection F1 score. Penalty preselection significantly outperformed selection by internal cross-validation in each of 3 examined metrics.

CONCLUSIONS

This analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use ofL 0 L 2 penalisation for structural selection andL 1 L 2 penalisation for coefficient recovery in genomic data. Evaluation of learning algorithms according to observed test performance in external genomic datasets yields valuable insights into actual test performance, providing a data-driven complement to internal cross-validation in genomic regression tasks.

Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods.

Statistical methods for detecting differences in individual gene expression are indispensable for understanding cell types. However, conventional statistical methods, such as differentially expressed gene (DEG)-based analysis, have faced difficulties associated with the inflation of P-values because of both the large sample size and selection bias introduced by exploratory data analysis such as single-cell transcriptomics. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for the discrimination of a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data, and that it enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement differentially expressed gene-based methods for interpreting large data sets.

Computational Modeling of Gene-Specific Transcriptional Repression, Activation and Chromatin Interactions in Leukemogenesis by LASSO-Regularized Logistic Regression

Many physiological and pathological pathways are dependent on gene-specific on/off regulation of transcription. Some genes are repressed, while others are activated. Although many previous studies have analyzed the mechanisms of gene-specific repression and activation, these studies are mainly based on the use of candidate genes, which are either repressed or activated, without simultaneously comparing and contrasting both groups of genes. There is also insufficient consideration of gene locations. Here we describe an integrated machine learning approach, using LASSO-regularized logistic regression, to model gene-specific repression and activation and the underlying contribution of chromatin interactions. LASSO-regularized logistic regression accurately predicted gene-specific transcriptional events and robustly detected the rate-limiting factors that underlie the differences of gene activation and repression. An example was provided by the leukemogenic transcription factor AML1-ETO, which is responsible for 10-15 percent of all acute myeloid leukemia cases. The analysis of AML1-ETO has also revealed novel networks of chromatin interactions and uncovered an unexpected role for E-proteins in AML1-ETO-p300 interactions and a role for the pre-existing gene state in governing the transcriptional response. Our results show that logistic regression-based probabilistic modeling is a promising tool to decipher mechanisms that integrate gene regulation and chromatin interactions in regulated transcription.

Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a 'taxonomy-like' structure. K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics, and other '-omics', data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effective in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying immune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network analyses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical responses identified with this computational pipeline.

Bempegaldesleukin (NKTR-214) plus Nivolumab in Patients with Advanced Solid Tumors: Phase I Dose-Escalation Study of Safety, Efficacy, and Immune Activation (PIVOT-02)

This single-arm, phase I dose-escalation trial (NCT02983045) evaluated bempegaldesleukin (NKTR-214/BEMPEG), a CD122-preferential IL2 pathway agonist, plus nivolumab in 38 patients with selected immunotherapy-naïve advanced solid tumors (melanoma, renal cell carcinoma, and non-small cell lung cancer). Three dose-limiting toxicities were reported in 2 of 17 patients during dose escalation [hypotension (n = 1), hyperglycemia (n = 1), metabolic acidosis (n = 1)]. The most common treatment-related adverse events (TRAE) were flu-like symptoms (86.8%), rash (78.9%), fatigue (73.7%), and pruritus (52.6%). Eight patients (21.1%) experienced grade 3/4 TRAEs; there were no treatment-related deaths. Total objective response rate across tumor types and dose cohorts was 59.5% (22/37), with 7 complete responses (18.9%). Cellular and gene expression analysis of longitudinal tumor biopsies revealed increased infiltration, activation, and cytotoxicity of CD8⁺ T cells, without regulatory T-cell enhancement. At the recommended phase II dose, BEMPEG 0.006 mg/kg plus nivolumab 360 mg every 3 weeks, the combination was well tolerated and demonstrated encouraging clinical activity irrespective of baseline PD-L1 status. SIGNIFICANCE: These data show that BEMPEG can be successfully combined with a checkpoint inhibitor as dual immunotherapy for a range of advanced solid tumors. Efficacy was observed regardless of baseline PD-L1 status and baseline levels of tumor-infiltrating lymphocytes, suggesting therapeutic potential for patients with poor prognostic risk factors for response to PD-1/PD-L1 blockade.See related commentary by Rouanne et al., p. 1097.This article is highlighted in the In This Issue feature, p. 1079.