Chemokine expression predicts T cell-inflammation and improved survival with checkpoint inhibition across solid cancers

Comprehensive analysis of CXCL10 and MIP-3a reveals their potential clinical application in hepatocellular carcinoma

Chemokines play a crucial role in the pathogenesis of patients with hepatocellular carcinoma (HCC). The expression levels of interferon-γ-induced protein-10 (CXCL10) and macrophage inflammatory protein-3α (MIP-3a) were investigated to clarify their clinical significance in HCC. The protein levels of CXCL10 and MIP-3a in the serum of 105 HBV-associated HCC patients, 50 patients with liver cirrhosis (LC), 50 patients with chronic hepatitis B (CHB) and 50 healthy donors (HC) were detected by liquid chip technology (Luminex) or ELISA. In addition, their mRNA levels were also determined in liver cancer and adjacent cancer tissue (paracancer; ParaCa) from 65 HCC patients. The online database UALCAN was used to analyze the association between CXCL10 and pathological manifestations of liver cancer. In addition, the diagnostic value of CXCL10/MIP-3a and AFP in HCC patients was determined by analyzing the Receiver Operating Characteristic Curve (ROC). The protein concentrations of CXCL10 and MIP-3a were significantly higher in the HCC group than in the LC, CHB and HC groups. CXCL10 in sera and liver cancer tissues is significantly positively correlated with ALT, but no significance between CXCL10 in ParaCa tissues and sera-ALT. Their mRNA is significantly higher in cancer tissues than in ParaCa tissues. The areas under the ROC curve of CXCL10, MIP-3a, CXCL10 and MIP-3a combined and AFP were 0.9169, 0.9261, 0.9299 and 0.7880, respectively. Elevated chemokines CXCL10 and MIP-3a in HCC patients may be associated with the clinical manifestation of HCC and could be a potential molecular marker for prognostic evaluation or a therapeutic target for HCC.

Therapeutic Anti-Tumor Efficacy of DC-Based Vaccines Targeting TME-Associated Antigens Is Improved When Combined with a Chemokine-Modulating Regimen and/or Anti-PD-L1

We previously reported that dendritic cell (DC)-based vaccines targeting antigens expressed by tumor-associated vascular endothelial cells (VECs) and pericytes effectively control tumor growth in translational mouse tumor models. In the current report, we examined whether the therapeutic benefits of such tumor blood vessel antigen (TBVA)-targeted vaccines could be improved by the cotargeting of tumor antigens in the s.c. B16 melanoma model. We also evaluated whether combination vaccines incorporating anti-PD-L1 checkpoint blockade and/or a chemokine-modulating (CKM; IFNα + TLR3-L [rintatolimod] + Celecoxib) regimen would improve T cell infiltration/functionality in tumors yielding enhanced treatment benefits. We report that DC-peptide or DC-tumor lysate vaccines coordinately targeting melanoma antigens and TBVAs were effective in slowing B16 growth in vivo and extending survival, with superior outcomes observed for DC-peptide-based vaccines. Peptide-based vaccines that selectively target either melanoma antigens or TBVAs elicited a CD8+ T cell repertoire recognizing both tumor cells and tumor-associated VECs and pericytes in vitro, consistent with a treatment-induced epitope spreading mechanism. Notably, combination vaccines including anti-PD-L1 + CKM yielded superior therapeutic effects on tumor growth and animal survival, in association with the potentiation of polyfunctional CD8+ T cell reactivity against both tumor cells and tumor-associated vascular cells and a pro-inflammatory TME.

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.

Challenges and Future Directions in the Management of Tumor Mutational Burden-High (TMB-H) Advanced Solid Malignancies

A standardized assessment of Tumor Mutational Burden (TMB) poses challenges across diverse tumor histologies, treatment modalities, and testing platforms, requiring careful consideration to ensure consistency and reproducibility. Despite clinical trials demonstrating favorable responses to immune checkpoint inhibitors (ICIs), not all patients with elevated TMB exhibit benefits, and certain tumors with a normal TMB may respond to ICIs. Therefore, a comprehensive understanding of the intricate interplay between TMB and the tumor microenvironment, as well as genomic features, is crucial to refine its predictive value. Bioinformatics advancements hold potential to improve the precision and cost-effectiveness of TMB assessments, addressing existing challenges. Similarly, integrating TMB with other biomarkers and employing comprehensive, multiomics approaches could further enhance its predictive value. Ongoing collaborative endeavors in research, standardization, and clinical validation are pivotal in harnessing the full potential of TMB as a biomarker in the clinic settings.