2039 - Association of Serum Metabolomic Signatures with Survival in Pancreatic Cancer Patients Undergoing Neoadjuvant Therapy

03:00pm - 04:00pm ET

Poster Hall - Exhibit Hall A

Screen: 30

POSTER

Presenter(s)

Diya Choudhary, BS - University of Texas MD Anderson Cancer Center, Houston, TX

D. Choudhary¹, S. Pudakalakatti², S. Liu³, P. Bhattacharya², and E. J. Koay¹; ¹Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, ²Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, ³Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX

Purpose/Objective(s): Pancreatic ductal adenocarcinoma (PDAC) remains a leading cause of cancer-related mortality in the United States and is characterized by poor therapeutic response. There remain limited biomarkers for early treatment response and real-time therapeutic response assessment. Conventional imaging detects anatomical changes only after substantial tumor evolution and CA19-9 lacks specificity for PDAC. We hypothesize that serial serum-based metabolomics can be associated with clinical outcomes in near real-time and provide early, minimally invasive prediction of therapeutic efficacy.

Materials/Methods: Serum from 80 PDAC patients at one institution (median age 64.5 years; 46M/34F; neoadjuvant chemotherapy alone, without RT) was collected pre-therapy and post-therapy (prior to surgery). One-dimensional ¹H NMR metabolomic profiling quantified 15 serum metabolites using a low-volume longitudinal platform (500 MHz, cryo broadband observe (BBO) probe), with spectra processed in TopSpin 4.5.0 (Bruker) and metabolites assigned by chemical shifts validated against Human Metabolome Database (HMDB).

For each metabolite, baseline levels and treatment-induced measurements (?=post–pre) were log₂-transformed, standardized to z-scores, and adjusted for multiple testing. Associations with progression-free survival (PFS) and overall survival (OS) were assessed using univariable and multivariable Cox regression models adjusted for age, sex, cancer stage, treatment and CA19-9 levels.

Results: Cox regression analyses identified significant metabolite–survival associations across baseline and delta models; given its clinical relevance, the baseline lactate/alanine ratio was also evaluated (Table 1).

Conclusion: Serum metabolomic profiling identifies metabolomic signatures associated with outcomes in PDAC patients undergoing neoadjuvant therapy. Further validation of our biomarker associations and deeper understandings of the mechanistic underpinnings of these findings is currently ongoing in our laboratories.

Table 1:Top performing metabolites; CI=confidence interval; MV=multivariable; UV=univariable; BL=baseline; ?=change (post-pre)

Outcome	Model	Metabolite	HR	95% CI	p
OS	UV-BL	Alanine	0.74	0.56-0.99	0.04
OS	UV-BL	Lactate	0.75	0.56-1.02	0.06
OS	UV-?	Phenylalanine	0.79	0.62-1.00	0.05
OS	UV-?	Glycine	1.45	0.99-2.11	0.05
PFS	UV-?	Phenylalanine	0.78	0.61-0.99	0.04
PFS	MV-BL	Lactate	0.51	0.26-0.98	0.04
PFS	MV-BL	Methylxanthine	0.22	0.01-0.84	0.02
PFS	UV-BL	Lactate/Alanine	0.84	0.75-0.93	0.001
OS	UB-BL	Lactate/Alanine	0.81	0.72-0.92	0.001