Integrative immune transcriptomic classification improves patient selection for precision immunotherapy in advanced gastro-oesophageal adenocarcinoma.

BACKGROUND: Advanced gastro-oesophageal cancer (GEA) treatment has been improved by the introduction of immune checkpoint inhibitors (CPIs), yet identifying predictive biomarkers remains a priority, particularly in patients with a combined positive score (CPS) < 5, where the benefit is less clear. Our study assesses certain immune microenvironment features related to sensitivity or resistance to CPIs with the aim of implementing a personalised approach across CPS < 5 GEA. DESIGN: Through integrative transcriptomic and clinicopathological analyses, we studied in both a retrospective and a prospective cohort, the immune tumour microenvironment features. We analysed the cell types composing the immune infiltrate highlighting their functional activity. RESULTS: This integrative study allowed the identification of four different groups across our patients. Among them, we identified a cluster whose tumours expressed the most gene signatures related to immunomodulatory pathways and immunotherapy response. These tumours presented an enriched immune infiltrate showing high immune function activity that could potentially achieve the best benefit from CPIs. Finally, our findings were proven in an external CPI-exposed population, where the use of our transcriptomic results combined with CPS helped better identify those patients who could benefit from immunotherapy than using CPS alone (p = 0.043). CONCLUSIONS: This transcriptomic classification could improve precision immunotherapy for GEA.

Molecular profiling of advanced solid tumours. The impact of experimental molecular-matched therapies on cancer patient outcomes in early-phase trials: the MAST study.

INTRODUCTION: Molecular-matched therapies have revolutionized cancer treatment. We evaluated the improvement in clinical outcomes of applying an in-house customized Next Generation Sequencing panel in a single institution. METHODS: Patients with advanced solid tumors were molecularly selected to receive a molecular-matched treatment into early phase clinical trials versus best investigators choice, according to the evaluation of a multidisciplinary molecular tumor board. The primary endpoint was progression-free survival (PFS) assessed by the ratio of patients presenting 1.3-fold longer PFS on matched therapy (PFS2) than with prior therapy (PFS1). RESULTS: Of a total of 231 molecularly screened patients, 87 were eligible for analysis. Patients who received matched therapy had a higher median PFS2 (6.47 months; 95% CI, 2.24-14.43) compared to those who received standard therapy (2.76 months; 95% CI, 2.14-3.91, Log-rank p = 0.022). The proportion of patients with a PFS2/PFS1 ratio over 1.3 was significantly higher in the experimental arm (0.33 vs 0.08; p = 0.008). DISCUSSION: We demonstrate the pivotal role of the institutional molecular tumor board in evaluating the results of a customized NGS panel. This process optimizes the selection of available therapies, improving disease control. Prospective randomized trials are needed to confirm this approach and open the door to expanded drug access.

"Proteotranscriptomic analysis of advanced colorectal cancer patient derived organoids for drug sensitivity prediction".

BACKGROUND: Patient-derived organoids (PDOs) from advanced colorectal cancer (CRC) patients could be a key platform to predict drug response and discover new biomarkers. We aimed to integrate PDO drug response with multi-omics characterization beyond genomics. METHODS: We generated 29 PDO lines from 22 advanced CRC patients and provided a morphologic, genomic, and transcriptomic characterization. We performed drug sensitivity assays with a panel of both standard and non-standard agents in five long-term cultures, and integrated drug response with a baseline proteomic and transcriptomic characterization by SWATH-MS and RNA-seq analysis, respectively. RESULTS: PDOs were successfully generated from heavily pre-treated patients, including a paired model of advanced MSI high CRC deriving from pre- and post-chemotherapy liver metastasis. Our PDOs faithfully reproduced genomic and phenotypic features of original tissue. Drug panel testing identified differential response among PDOs, particularly to oxaliplatin and palbociclib. Proteotranscriptomic analyses revealed that oxaliplatin non-responder PDOs present enrichment of the t-RNA aminoacylation process and showed a shift towards oxidative phosphorylation pathway dependence, while an exceptional response to palbociclib was detected in a PDO with activation of MYC and enrichment of chaperonin T-complex protein Ring Complex (TRiC), involved in proteome integrity. Proteotranscriptomic data fusion confirmed these results within a highly integrated network of functional processes involved in differential response to drugs. CONCLUSIONS: Our strategy of integrating PDOs drug sensitivity with SWATH-mass spectrometry and RNA-seq allowed us to identify different baseline proteins and gene expression profiles with the potential to predict treatment response/resistance and to help in the development of effective and personalized cancer therapeutics.

Solid Tumor Opioid Receptor Expression and Oncologic Outcomes: Analysis of the Cancer Genome Atlas and Genotype Tissue Expression Project.

Background: Opioid receptors are expressed not only by neural cells in the central nervous system, but also by many solid tumor cancer cells. Whether perioperative opioids given for analgesia after tumor resection surgery might inadvertently activate tumor cells, promoting recurrence or metastasis, remains controversial. We analysed large public gene repositories of solid tumors to investigate differences in opioid receptor expression between normal and tumor tissues and their association with long-term oncologic outcomes. Methods: We investigated the normalized gene expression of µ, κ, δ opioid receptors (MOR, KOR, DOR), Opioid Growth Factor (OGFR), and Toll-Like 4 (TLR4) receptors in normal and tumor samples from twelve solid tumor types. We carried out mixed multivariable logistic and Cox regression analysis on whether there was an association between these receptors' gene expression and the tissue where found, i.e., tumor or normal tissue. We also evaluated the association between tumor opioid receptor gene expression and patient disease-free interval (DFI) and overall survival (OS). Results: We retrieved 8,780 tissue samples, 5,852 from tumor and 2,928 from normal tissue, of which 2,252 were from the Genotype Tissue Expression Project (GTEx) and 672 from the Cancer Genome Atlas (TCGA) repository. The Odds Ratio (OR) [95%CI] for gene expression of the specific opioid receptors in the examined tumors varied: MOR: 0.74 [0.63-0.87], KOR: 1.27 [1.17-1.37], DOR: 1.66 [1.48-1.87], TLR4: 0.29 [0.26-0.32], OGFR: 2.39 [2.05-2.78]. After controlling all confounding variables, including age and cancer stage, there was no association between tumor opioid receptor expression and long-term oncologic outcomes. Conclusion: Opioid receptor gene expression varies between different solid tumor types. There was no association between tumor opioid receptor expression and recurrence. Understanding the significance of opioid receptor expression on tumor cells remains elusive.