Biomarkers of radioresistance in head and neck squamous cell carcinomas

PURPOSE

Head and neck squamous cell carcinoma (HNSCC) is a major cause of morbidity and mortality. Although HNSCC is mainly caused by tobacco and alcohol consumption, infection by Human Papilloma Virus (HPV) has been also associated with the increasing incidence of oropharyngeal squamous cell carcinomas (OPSCC) during the past decades. HPV-positive HNSCC is characterized by a higher radiosensitivity compared to HPV-negative tumor. While several clinical trials are evaluating de-escaladed radiation doses strategies in HPV-positive HNSCC, molecular mechanisms associated with relative radioresistance in HPV-negative HNSCC are still broadly unknown. Our goal was to review recently proposed biomarkers of radioresistance in this setting, which may be useful for stratifying tumor's patient according to predicted level of radioresistance.

CONCLUSIONS

most of biomarkers of radioresistance in HPV-negative HNSCC are identified using a hypothesis-driven approach, based on molecular mechanisms known to play a key role during carcinogenesis, compared to an unsupervised data-driven approach regardless the biological rational. DNA repair and hypoxia are the two most widely investigated biological and targetable pathways related to radioresistance in HNSCC. The better understanding of molecular mechanisms and biomarkers of radioresistance in HPV-negative HNSCC could help for the development of radiosensitization strategies, based on targetable biomarkers, in radioresistant tumors as well as de-escalation radiation dose strategies, based on biological level of radioresistance, in radiosensitive tumors.

Evaluation of the EdgeSeq Precision Immuno-Oncology Panel for Gene Expression Profiling From Clinical Formalin-Fixed Paraffin-Embedded Tumor Specimens

Characterizing the tumor microenvironment (TME) of archived clinical tissues requires reliable gene expression profiling (GEP) of formalin-fixed paraffin-embedded (FFPE) samples. The EdgeSeq Precision Immuno-oncology Panel (PIP) is a targeted GEP assay designed for TME characterization but lacks widespread technical validation on a large cohort of clinical samples. Here, we evaluated its performance by exploring its concordance with multiple orthogonal platforms using 1,220 FFPE samples across various cancer types. Quantitative comparisons with RNA-seq and NanoString showed strong correlations at the sample level (median ρ = 0.73 and 0.81) and moderate correlations at the single-gene level (median ρ = 0.49 and 0.57). Gene signature analysis revealed high concordance with RNA-seq on widely used signatures for TME characterization and immune checkpoint inhibitor (ICI) efficacy prediction, though some genes in these signatures are not targeted by EdgeSeq PIP. From a histopathological viewpoint, the tumor/immune abundances derived from hematoxylin and eosin (H & E) staining were well recapitulated by the transcriptomic profiles assessed by EdgeSeq PIP. Furthermore, the mRNA level of PD-L1 assessed by EdgeSeq PIP was moderately correlated with the PD-L1 score (ρ = 0.65) estimated by immunohistochemistry (IHC); the mRNA level of CD8A aligned well (ρ = 0.55) with the IHC-derived abundance of CD8⁺ T cells. Overall, our results showed that EdgeSeq PIP generated well-correlated data with independent approaches at mRNA, protein, and histological levels, thus providing strong technical support for further using EdgeSeq PIP in biomarker studies and companion diagnostic (CDx) development.

A refined cell-of-origin classifier with targeted NGS and artificial intelligence shows robust predictive value in DLBCL

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous entity of B-cell lymphoma. Cell-of-origin (COO) classification of DLBCL is required in routine practice by the World Health Organization classification for biological and therapeutic insights. Genetic subtypes uncovered recently are based on distinct genetic alterations in DLBCL, which are different from the COO subtypes defined by gene expression signatures of normal B cells retained in DLBCL. We hypothesize that classifiers incorporating both genome-wide gene-expression and pathogenetic variables can improve the therapeutic significance of DLBCL classification. To develop such refined classifiers, we performed targeted RNA sequencing (RNA-Seq) with a commercially available next-generation sequencing (NGS) platform in a large cohort of 418 DLBCLs. Genetic and transcriptional data obtained by RNA-Seq in a single run were explored by state-of-the-art artificial intelligence (AI) to develop a NGS-COO classifier for COO assignment and NGS survival models for clinical outcome prediction. The NGS-COO model built through applying AI in the training set was robust, showing high concordance with COO classification by either Affymetrix GeneChip microarray or the NanoString Lymph2Cx assay in 2 validation sets. Although the NGS-COO model was not trained for clinical outcome, the activated B-cell-like compared with the germinal-center B-cell-like subtype had significantly poorer survival. The NGS survival models stratified 30% high-risk patients in the validation set with poor survival as in the training set. These results demonstrate that targeted RNA-Seq coupled with AI deep learning techniques provides reproducible, efficient, and affordable assays for clinical application. The clinical grade assays and NGS models integrating both genetic and transcriptional factors developed in this study may eventually support precision medicine in DLBCL.

A DNA damage repair gene-associated signature predicts responses of patients with advanced soft-tissue sarcoma to treatment with trabectedin

Predictive biomarkers of trabectedin represent an unmet need in advanced soft‐tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5, and BRCA1) and did not evaluate several other DDR‐related genes that could have a relevant role for trabectedin efficacy. In this retrospective translational study, 118 genes involved in DDR were evaluated to determine, by transcriptomics, a predictive gene signature of trabectedin efficacy. A six‐gene predictive signature of trabectedin efficacy was built in a series of 139 tumor samples from patients with advanced STS. Patients in the high‐risk gene signature group showed a significantly worse progression‐free survival compared with patients in the low‐risk group (2.1 vs 6.0 months, respectively). Differential gene expression analysis defined new potential predictive biomarkers of trabectedin sensitivity (PARP3 and CCNH) or resistance (DNAJB11 and PARP1). Our study identified a new gene signature that significantly predicts patients with higher probability to respond to treatment with trabectedin. Targeting some genes of this signature emerges as a potential strategy to enhance trabectedin efficacy.