DLL3 positive

DLL3 is expressed on a majority of GEP NECs and on a subset of high-grade PanNETs marked by poor outcomes. Functional imaging suggests DLL3 as a promising therapeutic target in both GEP NECs and high-grade PanNETs.

The demonstrated tolerability and efficacy of obrixtamig regimens, administered as step-up followed by target doses of 90-1,080 μg/kg (once weekly or once every 3 weeks), in patients with heavily pretreated DLL3-positive tumors support further exploration in SCLC, epNEC, and LCNEC-L.

The DeLLpro-300 study provides preliminary evidence for the safety and antitumor activity of tarlatamab in DLL3+ NEPC.

DLL3 is expressed on a majority of GEP NECs and on a subset of high grade PanNETs marked by poor outcomes. Functional imaging suggests DLL3 as a promising therapeutic target in both GEP NECs and high grade PanNETs.

This is due to the limited sample size of the study, which requires additional investigation. When compared to the expression of TFs in regular SCLC, the expression of TFs in converted SCLC is comparable.

DLL3 was highly expressed in SCLC, and not associated with any clinico-pathological characteristics. In survival outcome, DLL3 was correlated with worse OS, which suggests the prognostic role of DLL3 in ES-SCLC.

Her tumor strongly expressed DLL3 protein and had clinical response to tarlatamab therapy. This case indicates that this novel therapy may be an efficacious option in other pulmonary neuroendocrine cancers.

Our results suggest that ICI+chemo and DLL3-targeting agents are worth further exploration in EGFR+ NSCLC undergoing SCT.

Both IHC and mRNA ISH showed significant correlation in tumors and lymph node metastasis. An observed trend suggested improved survival in patients with no DLL3 expression.

High-grade and low-grade MTC have different expression profiles. DLL3 should be explored as a predictor of aggressive disease and poor outcome in MTC.

Integrating CellSearch, DEPArray, and low-pass sequencing provides a minimally-invasive approach to compare genomic characteristics in DLL3-positive and negative CTCs from SCLC patients. Extension of this approach to a larger patient cohort and employing machine learning algorithms will facilitate identification of patterns within diverse CNA profiles, improving patient stratification and predicting treatment responses for personalized therapeutic strategies in SCLC.