Plasma profiling can non-invasively identify biomarkers associated with treatment outcomes and reveal mechanisms underlying drug resistance. Loncastuximab tesirine (loncastuximab tesirine-lpyl; Lonca), an antibody-drug conjugate comprising a humanized anti-CD19 antibody and a pyrrolobenzodiazepine dimer cytotoxin, is indicated for relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) after ≥2 systemic treatments. Here, we explored biological signatures associated with response to lonca using plasma-derived cell-free DNA (cfDNA) samples from 145 R/R DLBCL patients in a phase 2 trial (NCT3589469, LOTIS-2). cfDNA was extracted from plasma from 100 patients at baseline and 80 patients on treatment (cycle 2 day 1; C2D1). Low pass WGS was performed to characterize cfDNA fragments, reflecting nucleosome protection and chromatin state. Transcriptional activation for protein-coding genes was inferred by modeling fragment distribution around each transcription start site (TSS-GAP scores1). Binding activity of 504 cancer-associated transcription factors (TFs) was inferred by measuring chromatin accessibility from ~1000 binding sites per factor across the genome (TFBA scores2). Class comparison statistics were performed using the Wilcoxon's rank sum test, with a paired test for longitudinal comparisons. Gene Set Enrichment Analysis (GSEA) was performed using Molecular Signatures Database (MSigDB) sets. A multivariate classifier for RECIST response was constructed by applying L1-regularized logistic regression on both TSS-GAP and TFBA features. We evaluated molecular changes occurring during treatment with lonca by comparing TSS-GAP scores from C2D1 against baseline. Estimated levels of CD19, a proxy for B-cell abundance, significantly decreased after treatment (p = 6.08E-3), which supports the hypothesis that lonca is actively reducing B-cells. Markers of two other immune populations showed no significant change (CD3E p = 0.55; CD14 p = 0.88). GSEA identified 157 significantly enriched sets (152 reduced after treatment; False Discovery Rate (FDR) < 5%), including B-cell gene set signatures decreasing from baseline. To construct a multivariate classifier for response using baseline data, we focused on TSS-GAP scores of genes encoding for all 2751 TFs across the genome. Our final model integrated both TSS-GAP TF scores and clinical lab data to distinguish patients based on predicted response groups (AUC = 0.76). Using GSEA we characterized molecular differences between responders and non-responders and identified 3145 significant gene sets (FDR < 5%), with the majority being enriched with resistance to lonca (3070). Identified biological signatures were enriched for deregulated DNA damage repair response, cell-cycle checkpoints, elevated MYC target levels, and higher metabolic activity. Overall, our results showcase the potential of our approach to identify markers associated with response to lonca and suggest mechanisms of resistance, potentially informing rational drug combination research. Our modeling efforts integrating TSS-GAP and clinical data indicate the potential of this platform to predict lonca efficacy, pending additional validation.