Proteogenomic characterization of antibody-drug conjugate target expression profiles in transcriptional subtypes of small cell lung cancer.

e20113 Background: Small cell lung cancer (SCLC) is a highly lethal malignancy, and there is an urgent need for more effective therapies. Antibody-drug conjugates (ADCs), which combine the precision of monoclonal antibodies with the cytotoxic power of small molecules, represent a promising approach. Despite extensive research, the relationship between ADC target overexpression and the transcriptional profile of SCLC remains unclear. Our study aimed to elucidate how ADC target expression correlates with transcriptional subtypes of SCLC, potentially guiding the selection of ADCs based on molecular profiling. Methods: RNA-seq and tandem mass spectrometry data for 107 patients with matched SCLC tumors and normal adjacent lung tissue were obtained from the CPTAC SCLC cohort. SCLC subtypes were defined according to expression of four transcription factors: achaete-scute homologue 1 (ASCL1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3). ADC target expression for DLL3, SEZ6, B7-H3 ( CD276 ), and Trop2 ( TACSTD2 ) were assessed at the transcriptomic and proteomic level. The correlation between transcriptional subtype and ADC target expression was determined using Pearson’s correlation coefficient. Results: In 107 SCLC tumors, subtypes corresponded to 66 (61.6%) ASCL1, 21 (19.6%) NeuroD1, 3 (2.8%) YAP1, and 17 (15.9%) POU2F3. The ASCL1 subtype was positively correlated with DLL3 (R 2 =0.93, p=9E-87) and SEZ6 (R 2 =0.85, p=2.9E-57), and negatively correlated with TROP2 expression (R 2 =-0.72, p=1.5E-34). The NEUROD1 subtype showed moderate positive correlation with DLL3 (R 2 =0.52, p=4.8E-14) and SEZ6 (R 2 =0.58, p=3.6E-18), and a moderate negative correlation with TROP2 (R 2 =-0.52, p=1.6E-14). The POU2F3 subtype did not demonstrate strong positive or negative correlations with ADC targets. The YAP1 subtype was negatively correlated DLL3 (R 2 =-0.72, p=1.1E-32) and SEZ6 (R 2 =-0.71, p=9.5E-33), and positively correlated with Trop2 expression (R 2 =0.85, P=7.5E-61). Conclusions: Our findings reveal that molecular subtypes of SCLC exhibit unique patterns of ADC target expression. These profiles are crucial for further research and could lead to the development of subtype-specific treatments that improve clinical outcomes for patients with SCLC.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Oncology
Published
2025-05-28
DOI
https://doi.org/10.1200/jco.2025.43.16_suppl.e20113
Primary Topic
Lung Cancer Research Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Proteogenomic characterization of antibody-drug conjugate target expression profiles in transcriptional subtypes of small cell lung cancer.

Jessy S. Deshane, Fawzi Abu Rous, Aakash Desai, Changde Cheng et al.
Journal of Clinical Oncology
Lung Cancer Research Studies
article

Proteogenomic characterization of antibody-drug conjugate target expression profiles in transcriptional subtypes of small cell lung cancer.

Jessy S. Deshane, Fawzi Abu Rous, Aakash Desai, Changde Cheng, Rajat Thawani, Kayla F. Goliwas, Edwin Lin, Savitha Deshmukh, Abdul Rafeh Naqash, Sanad Alhushki, Mitch Singstock
article en

Abstract

e20113 Background: Small cell lung cancer (SCLC) is a highly lethal malignancy, and there is an urgent need for more effective therapies. Antibody-drug conjugates (ADCs), which combine the precision of monoclonal antibodies with the cytotoxic power of small molecules, represent a promising approach. Despite extensive research, the relationship between ADC target overexpression and the transcriptional profile of SCLC remains unclear. Our study aimed to elucidate how ADC target expression correlates with transcriptional subtypes of SCLC, potentially guiding the selection of ADCs based on molecular profiling. Methods: RNA-seq and tandem mass spectrometry data for 107 patients with matched SCLC tumors and normal adjacent lung tissue were obtained from the CPTAC SCLC cohort. SCLC subtypes were defined according to expression of four transcription factors: achaete-scute homologue 1 (ASCL1), neurogenic differentiation factor 1 (NeuroD1), yes-associated protein 1 (YAP1) and POU class 2 homeobox 3 (POU2F3). ADC target expression for DLL3, SEZ6, B7-H3 ( CD276 ), and Trop2 ( TACSTD2 ) were assessed at the transcriptomic and proteomic level. The correlation between transcriptional subtype and ADC target expression was determined using Pearson’s correlation coefficient. Results: In 107 SCLC tumors, subtypes corresponded to 66 (61.6%) ASCL1, 21 (19.6%) NeuroD1, 3 (2.8%) YAP1, and 17 (15.9%) POU2F3. The ASCL1 subtype was positively correlated with DLL3 (R 2 =0.93, p=9E-87) and SEZ6 (R 2 =0.85, p=2.9E-57), and negatively correlated with TROP2 expression (R 2 =-0.72, p=1.5E-34). The NEUROD1 subtype showed moderate positive correlation with DLL3 (R 2 =0.52, p=4.8E-14) and SEZ6 (R 2 =0.58, p=3.6E-18), and a moderate negative correlation with TROP2 (R 2 =-0.52, p=1.6E-14). The POU2F3 subtype did not demonstrate strong positive or negative correlations with ADC targets. The YAP1 subtype was negatively correlated DLL3 (R 2 =-0.72, p=1.1E-32) and SEZ6 (R 2 =-0.71, p=9.5E-33), and positively correlated with Trop2 expression (R 2 =0.85, P=7.5E-61). Conclusions: Our findings reveal that molecular subtypes of SCLC exhibit unique patterns of ADC target expression. These profiles are crucial for further research and could lead to the development of subtype-specific treatments that improve clinical outcomes for patients with SCLC.

Journal of Clinical OncologyVol. 43(16_suppl)
University of Utah (US), Huntsman Cancer Institute (US), Henry Ford Hospital (US), University of Alabama at Birmingham (US), University of Chicago (US), University of Oklahoma Health Sciences Center (US)
Openalex Percentile: Top 81%
Lung Cancer Research Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.