Search Results (4)

We previously demonstrated that patients with metastatic unresectable stage IIIb–IV melanoma receiving cetirizine (a second-generation H1 antagonist antihistamine) premedication with immunotherapy had better outcomes than those not receiving cetirizine. In this retrospective study, we searched for a gene signature potentially predictive of the response to the addition of cetirizine to checkpoint inhibition (nivolumab or pembrolizumab with or without previous ipilimumab). Transcriptomic analysis showed that inducible T cell costimulator ligand (ICOSLG) expression directly correlated with the disease control rate (DCR) when detected with a loading value > 0.3. A multivariable logistic regression model showed a positive association between the DCR and ICOSLG expression for progression-free survival and overall survival. ICOSLG expression was associated with CD64, a specific marker of M1 macrophages, at baseline in the patient samples who received cetirizine concomitantly with checkpoint inhibitors, but this association was not present in subjects who had not received cetirizine. In conclusion, our results show that the clinical advantage of concomitant treatment with cetirizine during checkpoint inhibition in patients with malignant melanoma is associated with high ICOSLG expression, which could predict the response to immune checkpoint inhibitor blockade. Full article

Resistance biomarkers are needed to identify patients with advanced melanoma obtaining a response to ICI treatment and developing resistance later. We searched a combination of molecular signatures of response to ICIs in patients with metastatic melanoma. In a retrospective study on patients with metastatic melanoma treated with an anti-PD-1 agent carried out at Istituto Nazionale Tumori—IRCCS—Fondazione “G. Pascale”, Naples, Italy. We integrated a whole proteome profiling of metastatic tissue with targeted transcriptomics. To assess the prognosis of patients according to groups of low and high risk, we used PFS and OS as outcomes. To identify the proteins and mRNAs gene signatures associated with the patient’s response groups, the discriminant analysis for sparse data performed via partial least squares procedure was performed. Tissue samples from 22 patients were analyzed. A combined protein and gene signature associated with poorer response to ICI immunotherapy in terms of PFS and OS was identified. The PFS and OS Kaplan–Meier curves were significantly better for patients with high expression of the protein signature compared to patients with low expression of the protein signature and who were high-risk (Protein: HR = 0.023, 95% CI: 0.003–0.213; p < 0.0001. Gene: HR = 0.053, 95% CI: 0.011–0.260; p < 0.0001). The Kaplan–Meier curves showed that patients with low-risk gene signatures had better PFS (HR = 0 0.221, 95% CI: 0.071–0.68; p = 0.007) and OS (HR = 0.186, 95% CI: 0.05–0.695; p = 0.005). The proteomic and transcriptomic combined analysis was significantly associated with the outcomes of the anti-PD-1 treatment with a better predictive value compared to a single signature. All the patients with low expression of protein and gene signatures had progression within 6 months of treatment (median PFS = 3 months, 95% CI: 2–3), with a significant difference vs. the low-risk group (median PFS = not reached; p < 0.0001), and significantly poorer survival (OS = 9 months, 95% CI: 5–9) compared to patients with high expression of protein and gene signatures (median OS = not reached; p < 0.0001). We propose a combined proteomic and transcriptomic signature, including genes involved in pro-tumorigenic pathways, thereby identifying patients with reduced probability of response to immunotherapy with ICIs for metastatic melanoma. Full article

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity. Full article

The recent successes in the use of immunotherapy to treat cancer have led to a multiplicity of new compounds in development. Novel clinical-grade biomarkers are needed to guide the choice of these agents to obtain the maximal likelihood of patient benefit. Predictive biomarkers for immunotherapy differ from the traditional biomarkers used for targeted therapies: the complexity of the immune response and tumour biology requires a more holistic approach than the use of a single analyte biomarker. This paper reviews novel biomarker approaches for the effective development of immune-oncology therapies, highlighting the promise of the advances in next-generation gene expression profiling that allow biologic information to be efficiently organized and interpreted for a maximum predictive value at the individual patient level. Full article