A Comprehensive Benchmark of Transcriptomic Biomarkers for Immune Checkpoint Blockades

Developing transcriptomic biomarkers for TAVO412 utilizing next generation sequencing analyses of preclinical tumor models

Introduction

TAVO412, a multi-specific antibody targeting epidermal growth factor receptor (EGFR), mesenchymal epithelial transition factor (c-Met), and vascular endothelial growth factor A (VEGF-A), is undergoing clinical development for the treatment of solid tumors. TAVO412 has multiple mechanisms of action for tumor growth inhibition that include shutting down the EGFR, c-Met, and VEGF signaling pathways, having enhanced Fc effector functions, addressing drug resistance that can be mediated by the crosstalk amongst these three targets, as well as inhibiting angiogenesis. TAVO412 demonstrated strong in vivo tumor growth inhibition in 23 cell-line derived xenograft (CDX) models representing diverse cancer types, as well as in 9 patient-derived xenograft (PDX) lung tumor models.

Methods

Using preclinical CDX data, we established transcriptomic biomarkers based on gene expression profiles that were correlated with anti-tumor response or distinguished between responders and non-responders. Together with specific driver mutation that associated with efficacy and the targets of TAVO412, a set of 21-gene biomarker was identified to predict the efficacy. A biomarker predictor was formulated based on the Linear Prediction Score (LPS) to estimate the probability of patients or tumor model response to TAVO412 treatment.

Results

This efficacy predictor for TAVO412 demonstrated 78% accuracy in the CDX training models. The biomarker model was further validated in the PDX data set and resulted in comparable accuracy.

Conclusions

In implementing precision medicine by leveraging preclinical model data, a predictive transcriptomic biomarker empowered by next-generation sequencing was identified that could optimize the selection of patients that may benefit most from TAVO412 treatment.

Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer

Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information. Testing multiple integration strategies, most of them yield multimodal models surpassing both the best unimodal models and established univariate biomarkers, such as PD-L1 expression. Additionally, several multimodal combinations demonstrate improved patient risk stratification compared to models built with routine clinical features only. Our study thus provides evidence of the superiority of multimodal over unimodal approaches, advocating for the collection of large multimodal NSCLC datasets to develop and validate robust and powerful immunotherapy biomarkers.

Novel Computational and Artificial Intelligence Models in Cancer Research

The ICIBM 2023 marked the 11th annual conference of its kind, with the ICIBM recently becoming the official conference of the International Association for Intelligent Biology and Medicine (IAIBM), showcasing cutting-edge advancements at the intersection of computation and biomedical research [...].

Current transcriptome database and biomarker discovery for immunotherapy by immune checkpoint blockade

Immune checkpoint blockade (ICB) has revolutionized the current immuno-oncology and significantly improved clinical outcome for cancer treatment. Despite the advancement in clinics, only a small subset of patients derives immune response to the ICB therapy. Therefore, a robust predictive biomarker that identifies potential candidate becomes increasingly crucial in delivering this technology to the public. In this review, we first discuss the biomarkers that focus on tumor genome, tumor microenvironment and tumor-host interaction. Then, we compare existing databases for biomarker discovery for ICB response. We also present IOhub - an interactive web portal that incorporates 36 bulk and 10 single-cell transcriptome datasets for benchmark analysis of the current biomarkers. Finally, we highlight the trending interest in antibody drug conjugate and combination treatment and their use in precision immuno-oncology.

Examining Transcriptomic Alterations in Rat Models of Intracerebral Hemorrhage and Severe Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a life-threatening condition associated with significant morbidity and mortality. This study investigates transcriptomic alterations in rodent models of ICH and severe ICH to shed light on the genetic pathways involved in hemorrhagic brain injury. We performed principal component analysis, revealing distinct principal component segments of normal rats compared to ICH and severe ICH rats. We employed heatmaps and volcano plots to identify differentially expressed genes and utilized bar plots and KEGG pathway analysis to elucidate the molecular pathways involved. We identified a multitude of differentially expressed genes in both the ICH and severe ICH models. Our results revealed 5679 common genes among the normal, ICH, and severe ICH groups in the upregulated genes group, and 1196 common genes in the downregulated genes, respectively. A volcano plot comparing these groups further highlighted common genes, including PDPN, TIMP1, SERPINE1, TUBB6, and CD44. These findings underscore the complex interplay of genes involved in inflammation, oxidative stress, and neuronal damage. Furthermore, pathway enrichment analysis uncovered key signaling pathways, including the TNF signaling pathway, protein processing in the endoplasmic reticulum, MAPK signaling pathway, and Fc gamma R-mediated phagocytosis, implicated in the pathogenesis of ICH.