Novel Blood Biomarkers for Response Prediction and Monitoring of Stereotactic Ablative Radiotherapy and Immunotherapy in Metastatic Oligoprogressive Lung Cancer

Up to 80% of patients under immune checkpoint inhibitors (ICI) face resistance. In this context, stereotactic ablative radiotherapy (SABR) can induce an immune or abscopal response. However, its molecular determinants remain unknown. We present early results of a translational study assessing biomarkers of response to combined ICI and SABR (I-SABR) in liquid biopsy from oligoprogressive patients in a prospective observational multicenter study. Cohort A includes metastatic patients in oligoprogression to ICI maintaining the same ICI due to clinical benefit and who receive concomitant SABR. B is a comparative group of oligometastatic patients receiving only SABR. Blood samples are extracted at baseline (T1), after the first (T2) and last (T3) fraction, two months post-SABR (T4) and at further progression (TP). Response is evaluated by iRECIST and defined by the objective response rate (ORR)-complete and partial responses. We assess peripheral blood mononuclear cells (PBMCs), circulating cell-free DNA (cfDNA) and small RNA from extracellular vesicles. Twenty-seven patients could be analyzed (cohort A: n = 19; B: n = 8). Most were males with non-small cell lung cancer and one progressing lesion. With a median follow-up of 6 months, the last ORR was 63% (26% complete and 37% partial response). A decrease in cfDNA from T2 to T3 correlated with a good response. At T2, CD8+PD1+ and CD8+PDL1+ cells were increased in non-responders and responders, respectively. At T2, 27 microRNAs were differentially expressed. These are potential biomarkers of response to I-SABR in oligoprogressive disease.

Combined Dietary Administration of Chlorella fusca and Ethanol-Inactivated Vibrio proteolyticus Modulates Intestinal Microbiota and Gene Expression in Chelon labrosus

The use of functional feeds in aquaculture is currently increasing. This study aimed to assess the combined impact of dietary green microalgae Chlorella fusca and ethanol-inactivated Vibrio proteolyticus DCF12.2 (CVP diet) on thick-lipped grey mullet (Chelon labrosus) juvenile fish. The effects on intestinal microbiota and the transcription of genes related to metabolism, stress, and the immune system were investigated after 90 days of feeding. Additionally, the fish were challenged with Aeromonas hydrophila and polyinosinic-polycytidylic acid (poly I:C) to evaluate the immune response. Microbiota analysis revealed no significant differences in alpha and beta diversity between the anterior and posterior intestinal sections of fish fed the control (CT) and CVP diets. The dominant genera varied between the groups; Pseudomonas and Brevinema were most abundant in the CVP group, whereas Brevinema, Cetobacterium, and Pseudomonas were predominant in the CT group. However, microbial functionality remained unaltered. Gene expression analysis indicated notable changes in hif3α, mhcII, abcb1, mx, and tnfα genes in different fish organs on the CVP diet. In the head kidney, gene expression variations were observed following challenges with A. hydrophila or poly I:C, with higher peak values seen in fish injected with poly I:C. Moreover, c3 mRNA levels were significantly up-regulated in the CVP group 72 h post-A. hydrophila challenge. To conclude, incorporating C. fusca with V. proteolyticus in C. labrosus diet affected the microbial species composition in the intestine while preserving its functionality. In terms of gene expression, the combined diet effectively regulated the transcription of stress and immune-related genes, suggesting potential enhancement of fish resistance against stress and infections.

Fluorometric Quantification of Total Cell-Free DNA as a Prognostic Biomarker in Non-Small-Cell Lung Cancer Patients Treated with Immune Checkpoint Blockade

The present study aimed to investigate the potential of basal cell-free fluorometric DNA (cfDNA) quantification as a prognostic biomarker in advanced non-small cell lung cancer (NSCLC) patients treated with an Immune Checkpoint Blockade (ICB). A discovery and validation cohort of 61 and 31 advanced lung cancer patients treated with ICB were included in this study. Quantification of cfDNA concentration was performed before the start of the treatment and patients were followed up for a median of 34 (30-40) months. The prognostic predicted value of cfDNA was evaluated based on ROC, and Cox regression was conducted via univariate and multivariate analyses to estimate the hazard ratio. We observed that a cfDNA cut-off of 0.55 ng/µL before the ICB determines the overall survival of patients with a log rank p-value of 3.3 × 10^-4. That represents median survivals of 3.8 vs. 17.5 months. Similar results were obtained in the validation cohort being the log rank p-value 3.8 × 10^-2 with median survivals of 5.9 vs. 24.3. The univariate and multivariate analysis revealed that the cut-off of 0.55 ng/µL before ICB treatment was an independent predictive factor and was significantly associated with a better survival outcome. High cfDNA concentrations identify patients with advanced NSCLC who do not benefit from the ICB. The determination of cfDNA is a simple test that could select a group of patients in whom new therapeutic strategies are needed.

Metabolomics unravels subtype-specific characteristics related to neoadjuvant therapy response in breast cancer patients

INTRODUCTION

Breast cancer is the most diagnosed tumor and the leading cause of cancer death in women worldwide. Metabolomics allows the quantification of the entire set of metabolites in blood samples, making it possible to study differential metabolomics patterns related to neoadjuvant treatment in the breast cancer neoadjuvant setting.

OBJECTIVES

Characterizing metabolic differences in breast cancer blood samples according to their response to neoadjuvant treatment.

METHODS

One hundred and three plasma samples of breast cancer patients, before receiving neoadjuvant treatment, were analyzed through UPLC-MS/MS metabolomics. Then, metabolomics data were analyzed using probabilistic graphical models and biostatistics methods.

RESULTS

Metabolomics data allowed the identification of differences between groups according to response to neoadjuvant treatment. These differences were specific to each breast cancer subtype. Patients with HER2+ tumors showed differences in metabolites related to amino acids and carbohydrates pathways between the two pathological response groups. However, patients with triple-negative tumors showed differences in metabolites related to the long-chain fatty acids pathway. Patients with Luminal B tumors showed differences in metabolites related to acylcarnitine pathways.

CONCLUSIONS

It is possible to identify differential metabolomics patterns between complete and partial responses to neoadjuvant therapy, being this metabolomic profile specific for each breast cancer subtype.