Background: Lung cancer (LC) tissue for immunological characterization is often scarce. We explored and compared T cell characteristics between broncho-alveolar lavage from tumor affected (t-BAL) and contralateral lung (cl-BAL), with matched peripheral blood (PB). Methods: BAL and PB were collected during bronchoscopy for diagnostic and/or therapeutic purposes in patients with monolateral primary lesion. Results: Of 33 patients undergoing BAL and PB sampling, 21 had histologically-confirmed LC. Most cases were locally-advanced or metastatic non-small cell LC. T cell characteristics were not significantly different in t-BAL vs. cl-BAL. Compared to PB, CD8 T cells in BAL presented features of immune activation and exhaustion (high PD-1, low IFN-g production). Accordingly, regulatory CD4 T cells were also higher in BAL vs. PB. When dichotomizing T cell density in t-BAL in high and low, we found that PD-L1 expression in LC was associated with T cell density in t-BAL. T-BAL with high T cell density had higher %IFN-g+CD8 T cells and lower %T-regs. Conclusion: In BAL from advanced LC patients, T cells present features of exhaustion. T cells in t-BAL could be the best surrogate of tumor-infiltrating T cell, and future studies should evaluate T cell phenotype and density as potential biomarkers for cancer immunotherapy outcome.
Background: High-throughput sequencing and ctDNA are driving the precision medicine paradigm shift. However, the analysis and the interpretation of the resulting data is an open challenge. By an integrated and multidisciplinary work among wet and dry lab experts, we have explored the feasibility of an automated BAM data solution with built-in variant calling.Methods: A cohort of 49 women with metastatic breast cancer was prospectively enrolled within the CRO-2018-56 clinical trial and characterized for ESR1 and PIK3CA genes by NGS of ctDNA. The resulting BAM files were analyzed using FreeBayes, GATK, Miseq Reporter, LoFreq, Mutect2, SAMtools and SNVer variant callers. Their concordance was evaluated by Cohen's kappa (k) with respect to manual annotation, both before and after filtering for clinical significance, based on the OncoKB database.
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