Yu, Jia Xin

Abstract Background: In Part 2 of the phase 2 RELATIVITY-104 study of first-line nivolumab (NIVO) plus relatlimab (RELA) with platinum-doublet chemotherapy (PDCT) vs NIVO + PDCT in patients with metastatic NSCLC, NIVO + RELA + PDCT demonstrated improved clinical benefit, including overall survival, in the prespecified subgroup of tumor PD-L1 expression ≥ 1%, which was further enriched in NSQ tumor histology. Baseline lymphocyte-activation gene 3 (LAG-3) expression was associated with enriched efficacy for both NIVO + RELA + PDCT and NIVO + PDCT. In a comparative transcriptomic analysis, NSQ and squamous tumors demonstrated distinct immune signatures, with NSQ tumors exhibiting higher expression of LAG-3 ligands and greater enrichment for combination therapy benefit by PD-L1 expression. To further elucidate the mechanism of action of NIVO + RELA + PDCT, we report additional exploratory translational data in the PD-L1 ≥ 1% NSQ subgroup for Part 2 of RELATIVITY-104. Methods: Patients with untreated metastatic NSCLC were randomized 1:1 to NIVO 360 mg + RELA 360 mg + PDCT or NIVO 360 mg + PDCT Q3W. Exploratory endpoints included translational analyses in the PD-L1 ≥ 1% NSQ subgroup. Gene expression data from pretreatment tumor RNA sequencing were normalized and analyzed for associations with clinical outcomes. Pre- and on-treatment peripheral blood samples were collected for high-dimensional flow cytometry immunophenotyping using a validated 24-marker LAG-3-specific panel that used drug-tolerant, noncompeting clones for LAG-3, PD-1, and CTLA-4. Statistical associations between biomarkers and clinical outcomes were assessed using logistic regression and Cox proportional hazards models. Data are from the May 2, 2025, database lock. Results: In the NIVO + RELA + PDCT arm, immune signatures demonstrated a significantly lower hazard ratio (HR) in the PD-L1 ≥ 1% NSQ subgroup, while stromal signatures demonstrated a significantly higher HR. T exhausted progenitor signature subsets, as well as ICOS+ CD4+ T cells, at baseline were shown to enrich for survival with NIVO + RELA + PDCT. Clinical benefit was observed across all baseline LAG-3 expression levels (< 1% and ≥ 1%); however, on-treatment increases in LAG-3+ T cells were associated with longer progression-free survival and a higher objective response rate with NIVO + RELA + PDCT. In the NIVO + PDCT arm, increases in LAG-3 cell populations were associated with lower response. Conclusions: In the RELATIVITY-104 study, the on-treatment immune dynamics of LAG-3+ T cells correlated with the benefit of NIVO + RELA + PDCT vs NIVO + PDCT in patients with PD-L1 expression ≥ 1% and NSQ NSCLC. Immune signatures including progenitor T exhausted signatures associated with improved outcomes with NIVO + RELA + PDCT. These data support the mechanistic rationale for adding anti-LAG-3 to anti-PD-1 + PDCT in PD-L1 ≥ 1% NSQ NSCLC. Citation Format: Jaclyn Neely, Annie Yu, Nicolas Girard, Manuel Cobo-Dols, Mauricio Burotto, Luis Paz-Ares, Vamsidhar Velcheti, Martin Reck, Srijata Samanta, Anila Qureshi, Priyanka Kasbekar, Charlie Garnett-Benson. RELATIVITY-104 Part 2: translational analyses in the programmed death ligand 1 (PD-L1) ≥ 1% and non-squamous (NSQ) non-small cell lung cancer (NSCLC) subgroup [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB418.

Abstract INTRODUCTION Next generation sequencing (NGS) of patients has significantly changed our ways to study cancer genomics as it provides precise estimates of gene expression, fusion transcripts, expressed single nucleotide variants (eSNVs), splice variants and copy number variants. The Breast Cancer Genome Guided Therapy (BEAUTY) is an ongoing clinical study in which RNA sequencing (RNAseq) and whole exome sequencing (WES) are performed prior to, during and after neoadjuvant chemotherapy. Here we report the use of these sequencing technologies to investigate gene expression levels and mutational profiles in a triple negative breast cancer (TNBC) patient enrolled in BEAUTY whose disease did not respond to neoadjuvant paclitaxel and anthracycline/cycphosphamide. METHODS Computational approaches were used to integrate WES and RNAseq data obtained before therapy (V1T), after 12 weekly paclitaxel treatments (V2T) and after anthracycline-based regimen at surgical resection (V3T) to study a single patient with persistent TNBC disease after neoadjuvant chemotherapy. RESULTS Using RNA-Seq data, we identified an inter-chromosomal fusion transcript between chromosome 20 and 22 (GNAS-TTC38) that was highly expressed at V1T, V2T and V3T. We also identified intra-chromosomal fusion transcripts that were expressed at two time points, such as fusion transcript (KANSL1-ARL17A) on chromosome 17 for V1T and V2T and fusion transcript (RBM12B-LINC00535) on chromosome 8 for V2T and V3T time points. Several gene expression changes were also observed. Gene expression analysis of V1T, V2T and V3T tumors was performed. Differential temporal gene expression profiles of 9884 genes that were significant and varying at different time points were obtained for pathway analysis. Pathway analysis of 9884 genes identified up regulation and down regulation of several transcription factors with a fold change of 2x or more. When compared to blood, DNA tumor and RNA-Seq data, we identified 81 common somatic eSNVs that were expressed in both V1T and V2T time points and we are in the process of investigating V3T data. We found alterations of key transporter domains (CD225, Coatamer_beta_C, DUF2435, Dynamitin, EI24, GLTP, LMF1, Porin_3, V-ATPase_C) in our V1T and V2T SNV data. Similar to gene expression analysis, we are in the process of obtaining the list of mutations at various time points to identify driver and passenger mutation candidate genes for this specific TNBC patient. CONCLUSIONS Our initial time-series analysis of eSNV, fusion transcripts and gene expression data demonstrate that intensive analysis for individual patients is feasible. Further investigation of drug transporters and transcription regulators may help develop personalized treatment strategies for patients with disease resistant to current regimens. Citation Format: Krishna R. Kalari, Xiaojia Tang, Kevin J. Thompson, Douglas W. Mahoney, Poulami Barman, Jason P. Sinnwell, Hugues Sicotte, Peter Vedell, Steven N. Hart, Travis J. Dockter, Katie N. Jones, Amy L. Conners, Ann M. Moyer, Daniel W. Visscher, Jia Yu, Bowen Gao, Sarah A. McLaughlin, John A. Copland, Alvaro Moreno-Aspitia, Donald W. Northfelt, Richard J. Gray, Vera J. Suman, Jeanette E. Eckel Passow, Jean-Pierre A. Kocher, Eric D. Wieben, Gianrico Farrugia, Cloann G. Schultz, James N. Ingle, Richard Weinshilboum, Matthew P. Goetz, Liewei Wang, Judy C. Boughey. Analysis of sequencing data to identify potential drug targets for an individual newly diagnosed with basal breast cancer who failed to respond to current standard neoadjuvant chemotherapy. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4185. doi:10.1158/1538-7445.AM2014-4185