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Alicia Abalo

Publications and source records attributed to Alicia Abalo.

2 recordsLinked to original sources

Identification of candidate variants in plasma associated with early versus late disease progression under anti-PD-1 therapy in metastatic NSCLC.

BACKGROUND: Immune checkpoint inhibitors (ICIs), including anti-programmed cell death protein 1 (anti-PD-1) antibodies, have significantly improved outcomes in patients with metastatic non-small cell lung cancer (mNSCLC). However, substantial heterogeneity exists in clinical benefit, with some patients exhibiting early progression (EP) and others late progression (LP). To date, no biomarkers of EP versus LP disease have been implemented in clinical practice. Circulating tumor DNA (ctDNA) analysis represents a minimally invasive strategy for identifying such biomarkers. In this proof-of-concept study, we evaluated the performance of the TruSight Oncology 500 ctDNA (TSO500 ctDNA) panel and explored its feasibility to identify candidate variants associated with early and late disease progression under anti-PD-1 therapy. METHODS: Baseline ctDNA from eight mNSCLC patients treated with pembrolizumab was extracted and sequenced using the TSO500 ctDNA assay, a 523-gene targeted next-generation sequencing panel. Patients were classified according to their response as LP or EP. Variant calling was performed using the DRAGEN Bio-IT platform, and variants were annotated and clinically interpreted using the Clinical Genomics Workspace (CGW; PierianDx) according to Association for Molecular Pathology (AMP)/American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) guidelines. Survival outcomes were assessed using Kaplan-Meier and log-rank tests. Performance of ctDNA variants was evaluated using receiver operating characteristic (ROC) curve analysis, and multi-gene models were assessed using leave-one-out cross-validation with penalized logistic regression. RESULTS: All patients harbored detectable variants, including SNVs (100%), MNVs (87.5%), deletions (75%), and insertions (62.5%). Tier I variants were identified in 37.5% of patients, while all cases showed tier II and multiple tier III alterations. TP53 variants were associated with poorer outcomes under anti-PD-1 therapy. Individual gene alterations in TP53, ERBB3, SMC1A or LATS1 showed moderate discriminatory performance between LP and EP patients; however, combination of mutated genes improved apparent discrimination. Notably, specific two-gene combinations (SMC1A + LATS1 or ERBB3 + LATS1) showed the highest discriminatory performance between LP and EP patients in this exploratory cohort. CONCLUSIONS: This study demonstrates the feasibility and analytical performance of the TSO500 ctDNA panel and provides hypothesis-generating evidence that plasma gene variants may be useful to evaluate early versus late disease progression in patients with mNSCLC receiving immunotherapy.

TruSight Oncology 500

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Humans