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| html | 3c28cab | Shashikant Koul | 2025-03-28 | Fix the S16b output figure path |
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Carolyn Hruban, Daniel C. Bruhm, Inna M. Chen, Shashikant Koul, Akshaya V. Annapragada, Nicholas A. Vulpescu, Sarah Short, Susann Theile, Kavya Boyapati, Bahar Alipanahi, Zachary L Skidmore, Alessandro Leal, Stephen Cristiano, Vilmos Adleff, Julia S. Johannsen, Robert B. Scharpf, Zachariah H. Foda, Jillian Phallen, and Victor E. Velculescu
Determining response to therapy for patients with pancreatic cancer can be challenging. We evaluated methods for assessing therapeutic response using cell-free DNA (cfDNA) in plasma samples from 40 patients with metastatic pancreatic cancer as part of the CheckPAC trial (NCT02866383). Patients were evaluated before and after initiation of therapy using tumor-informed plasma whole-genome sequencing (WGMAF), and genome-wide cfDNA fragmentation profiles and repeat landscapes (ARTEMIS-DELFI). Of those assessed with WGMAF, molecular responders had a median overall survival (OS) of 319 days compared to 126 days for non-responders (HR=0.29, 95% CI=0.11–0.79, p=0.011). For ARTEMIS-DELFI, patients with low scores after therapy initiation had a longer median OS than patients with high scores (233 days versus 172 days, HR=0.12, 95% CI=0.046-0.31, p<0.0001). We validated ARTEMIS-DELFI in a separate cohort of 40 patients with pancreatic cancer who were part of the PACTO trial (NCT02767557). These analyses suggest that non-invasive mutation and fragmentation-based cfDNA approaches can identify therapeutic response of individuals with pancreatic cancer.
1 Overview of study design and samples. A[Code], B[Code]
2 WGMAF approach predicts survival for patients in CheckPAC trial. A[Code], B[Code], C1, C2[Code]
3 cfDNA fragmentation features reflect underlying tumor biology in pancreatic cancer. A[Code], B[Code]
4 Genome-wide cfDNA fragmentation profiles comprise chromatin structure from peripheral blood cells and pancreatic cancer. Fig[Code]
5 Heat map of clinical features and cfDNA fragmentation and genomic repeat features. Fig[Code]
6 ARTEMIS-DELFI scores predict survival for patients in CheckPAC trial. A[Code], B1, B2[Code], C1, C2[Code]
7 Multivariate hazard analyses demonstrate on-treatment ARTEMIS-DELFI scores as independent predictors of overall survival for patients in the CheckPAC trial. A[Code], B[Code]
8 Example of a molecular responder and non-responder to treatment with different methodologies. A, B[Code]
9 Multivariate hazard analyses demonstrate on-treatment ARTEMIS-DELFI scores as independent predictors of overall survival for patients in the PACTO trial. A[Code], B[Code]
1 Flowchart of sample selection for WGMAF and ARTEMIS-DELFI analyses. Fig[Code]
2 Selection of post-treatment liquid biopsy timepoint for molecular analyses. A[Code], B[Code]
3 Overview of WGMAF method. Fig[Code]
4 Genome-wide mutational landscape of patients with pancreatic cancer in CheckPAC study. Fig[Code]
5 WGMAF values correlate with targeted MAF analyses. Fig[Code]
6 WGMAF stratifies progression free and overall survival for patients in the CheckPAC trial with stable disease. Fig[Code]
7 Multivariate hazard analyses demonstrate on-treatment WGMAF values as independent predictors of overall survival for patients in the CheckPAC trial. Fig[Code]
8 Fragmentation patterns for patients in CheckPAC trial with partial response or stable disease are more closely correlated to healthy plasma. Fig[Code]
9 Feature importance for locked ARTEMIS-DELFI machine learning model. Fig[Code]
10 ARTEMIS-DELFI scores on-treatment and best overall response in CheckPAC trial. Fig[Code]
11 ARTEMIS-DELFI stratifies progression free and overall survival for patients in CheckPAC trial with stable disease. Fig[Code]
12 ARTEMIS-DELFI score and WGMAF value are closely correlated. Fig[Code]
13 ARTEMIS-DELFI samples detect 100% of samples with WGMAF>0.01. Fig[Code]
14 Survival analyses for RECIST scoring at first follow up scan and for BOR RECIST in CheckPAC trial. Fig[Code]
15 Timepoints selected for CA19-9 and CT for patients in CheckPAC trial. Fig[Code]
16 Landmark CA19-9 levels show limited correlation with response for patients in CheckPAC trial. A[Code], B[Code]
17 ichorCNA shows limited correlation with clinical response and does not stratify progression free and overall survival. Fig[Code]
18 CA19-9 was correlated with ARTEMIS-DELFI and WGMAF for patients in CheckPAC trial who were secretors. Fig[Code]
19 Longitudinal evaluation of patient response to treatment in CheckPAC trial using imaging and liquid biopsies. Fig[Code]
20 Change in WGMAF value stratifies progression free but not overall survival at 8-week timepoint in CheckPAC trial. Fig[Code]
21 Change in DELFI-TF scores between baseline and 8 week time point scores are predictive of progression free and overall survival in CheckPAC trial. A[Code], B[Code]
22 DELFI features correlate with clinical response of patients in PACTO cohort. A[Code], B[Code]
23 ARTEMIS-DELFI scores predict survival for patients in PACTO trial. A[Code], B[Code]
24 RECIST 1.1 scoring at first follow-up scan does not stratify overall survival in the PACTO trial. Fig[Code]
