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Karacosta, L. G.

Publications and source records attributed to Karacosta, L. G..

4 recordsLinked to original sources

DLL3 expression predicts response and long-term outcome in small cell lung cancer patients treated with tarlatamab

Background: Small cell lung cancer (SCLC) is an aggressive, high-grade neuroendocrine carcinoma (hgNEC) with poor prognosis. Tarlatamab, a DLL3-targeting T cell engager was approved in 2024 for relapsed SCLC, with almost doubled overall survival benefit compared to chemotherapy. However, over half of patients do not respond to tarlatamab and others develop resistance within months. With multiple DLL3-targeting therapies in clinical trials, there are currently no validated predictive biomarkers to identify patients most likely to benefit from tarlatamab. In this study, we evaluate baseline DLL3 IHC level in patients as a predictive biomarker to tarlatamab clinical outcome. Methods: We assembled a cohort of 138 patients within IRB-approved MD Anderson GEMINI database (PA13-0589) with DLL3 expression by CLIA-validated immunohistochemistry (IHC), and those treated with tarlatamab from 7/1/2024 to 3/30/2026. Only those with DLL3 IHC results and treated with tarlatamab were included in this predictive biomarker study. All DLL3 levels were reported as percentage cells with cytoplasmic or surface DLL3 expression, including 24 with additional intensity score and H-score calculation. demographic, clinical, and outcome data were collected. Systemic and intracranial response data were assessed by RECIST and mRANO BM criteria respectively. Longitudinal liquid biopsies were taken from a subset of patients for circulating tumor DNA (ctDNA) and (Cytometry by Time-of-Flight) CyTOF analyses. Results: In the predictive biomarker cohort of 54 patients with SCLC treated with tarlatamab, Median DLL3% level in tarlatamab treated cohort was 75%. Median follow-up time was 9.9 months, median time on tarlatamab treatment (ToT) was 5.8 months. At the data cut-off, 61% (33 out of 54) patients were alive. Kaplan-Meier curve stratified by DLL3 % of <=75% vs. >75 showed significant longer ToT and a trend toward longer median OS (mOS). In DLL3>75% compared to DLL3 <=75%: mToT was 10.1 months vs. 2.9 months (p=0.036), mOS: not reached vs. 9.5 months (p= 0.066). Among 41 patients who completed one cycle of tarlatamab treatment and were eligible for systemic (extra-cranial) response per RECIST criteria; we observed partial response (PR) in 51% (21) patients, stable disease (SD) in 22% (9) patients; and progressive disease (PD) in 27% (11) patients. The median DLL3 % in those with PR vs. PD were 80% vs. 40% (p=0.006); median DLL3% comparing PR and SD were 80% vs. 60% (p=0.051). Among 35 patients evaluable for intracranial response, best objective response prior to any brain radiation were 5 (14%) CR,11(31%) PR, 8 (23%) SD and 11 (31%) PD. 9 received brain radiation (8 SRS, 1 WBRT) during tarlatamab to achieve better intracranial control while systemic control is maintained. DLL3% does not appear to be correlated with intracranial response. Longitudinal ctDNA and CyTOF analyses showed persistent ctDNA positivity, as well as increasing NEUROD1 subtype cells in circulating tumor cells in post-tarlatamab progression samples. We additionally profiled DLL3 expression by IHC in 138 patients with SCLC and found heterogenous DLL3 expression, with Median DLL3% of 70% across different biopsy sites. Conclusions: Our data suggests DLL3 expression in patients with SCLC is heterogeneous and not ubiquitous, with median DLL3 IHC of 70%. DLL3 IHC predicts objective response and long-term outcome with tarlatamab. In addition, dynamic monitoring of ctDNA level and CTCs by CyTOF are suggestive of development of tarlatamab resistance, however further studies with larger cohort of paired longitudinal samples are needed to validate the novel blood-based biomarkers. To our knowledge, this study is the first to validate DLL3 IHC as a predictive biomarker for tarlatamab in SCLC, which leads the way for optimizing treatment selection and combinatorial therapies for the subset of patients less likely to respond to tarlatamab.

cancer biology↗

CyTOF-based profiling of circulating tumor cells predicts aggressiveness and therapy response in SCLC liquid biopsies at a personalized level

Small-cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma characterized by high numbers of circulating tumor cells (CTCs). We applied CyTOF and a 20-marker antibody panel to detect and phenotype CTCs directly in liquid biopsies of 51 SCLC patients (treatment-naive, chemotherapy and immunotherapy-treated, and tarlatamab-treated), of which a subset were longitudinally tracked. Unsupervised clustering revealed distinct cell populations enriched in patient liquid biopsies compared to those from healthy donors. Further analysis identified CTC populations of the three established SCLC subtypes driven by the high expression of ASCL1, NeuroD1, and POU2F3 transcription factors respectively. Significant differences in CTC EMT markers, established therapeutic targets (e.g. DLL3), and subtype heterogeneity were observed between naive versus treated samples. Changes in subtype proportions were observed in longitudinally tracked samples in both treatment modalities. Our study demonstrates the utility of CyTOF for high-resolution CTC profiling, offering dynamic insights into CTC heterogeneity, treatment response, and resistance mechanisms. HighlightsO_LICTCs can be detected, subtyped and phenotyped in SCLC liquid biopsies using CyTOF C_LIO_LICTC subtypes and EMT states are differentially associated with treatment modality C_LIO_LICTC DLL3 levels and epithelial features increase following anti-DLL3 BiTE therapy C_LIO_LICyTOF CTC subtyping can predict disease aggressiveness C_LIO_LILongitudinal tracking reveals CTC plasticity and therapy response correlations C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/694669v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@384cbcorg.highwire.dtl.DTLVardef@35dd37org.highwire.dtl.DTLVardef@4e7d61org.highwire.dtl.DTLVardef@1098e4f_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Multi-Omics Analysis of Fibroblasts from the Invasive Tumor Edge Reveals that Tumor-Stroma Crosstalk Induces O-glycosylation of the CDK4-pRB Axis

The invasive leading edge represents a potential gateway for tumor invasion. We hypothesize that crosstalk between tumor and stromal cells within the tumor microenvironment (TME) results in the activation of key biological pathways depending on their location in the tumor (edge vs core). Here, we highlight phenotypic differences between Tumor-Adjacent-Fibroblasts (TAFs) from the invasive edge and Cancer-Associated Fibroblasts (CAFs) from the tumor core, established from human lung adenocarcinomas. We use an innovative multi-omics approach that includes genomics, proteomics and, O-glycoproteomics to characterize crosstalk between TAFs and cancer cells. Our analysis shows that O-glycosylation, an essential post-translational modification resulting from sugar metabolism, alters key biological pathways including the CDK4-pRB axis in the stroma, and indirectly modulates pro-invasive features of cancer cells. In summary, aside from improving the efficacy of CDK4 inhibitors anti-cancer agents, the O-glycoproteome poses a new consideration for important biological processes involved in tumor-stroma crosstalk.

cancer biology↗

Mapping Lung Cancer Epithelial-Mesenchymal Transition States and Trajectories with Single-Cell Resolution

Elucidating a continuum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises new insights in cancer progression and drug response. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGF{beta}-treatment and identify through TGF{beta}-withdrawal, an MET state previously unrealized. We demonstrate significant differences between EMT and MET trajectories using a novel computational tool (TRACER) for reconstructing trajectories between cell states. Additionally, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET STAte MaP (STAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET STAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework that can be extended to phenotypically characterize clinical samples in the context of in vitro studies showing differential EMT-MET traits related to metastasis and drug sensitivity.

systems biology↗