Defining the Comprehensive Genomic Landscapes of Pancreatic Ductal Adenocarcinoma Using Real-World Endoscopic Aspiration Samples

Abstract

Purpose: Most patients with pancreatic ductal adenocarcinoma (PDAC) present with surgically unresectable cancer. As a result, endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the most common biospecimen source available for diagnosis in treatment-naïve patients. Unfortunately, these limited samples are often not considered adequate for genomic analysis, precluding the opportunity for enrollment on precision medicine trials.

Experimental design: Applying an epithelial cell adhesion molecule (EpCAM)-enrichment strategy, we show the feasibility of using real-world EUS-FNA for in-depth, molecular-barcoded, whole-exome sequencing (WES) and somatic copy-number alteration (SCNA) analysis in 23 patients with PDAC.

Results: Potentially actionable mutations were identified in >20% of patients. Further, an increased mutational burden and higher aneuploidy in WES data were associated with an adverse prognosis. To identify predictive biomarkers for first-line chemotherapy, we developed an SCNA-based complexity score that was associated with response to platinum-based regimens in this cohort.

Conclusions: Collectively, these results emphasize the feasibility of real-world cytology samples for in-depth genomic characterization of PDAC and show the prognostic potential of SCNA for PDAC diagnosis.

Figure 1

Detection of mutant KRAS in FNAs and core biopsies. a) and c) Fractional abundance of mutant KRAS in enriched (dark gray) and non-enriched (light gray) samples measured by ddPCR. Samples of the pilot phase derived from resected tumor specimens (n=5, marked with T) or core biopsies (n=3, marked with c) are shown in a) and b), one sequential core biopsy was not processed with ddPCR. Samples derived from FNAs in the second study phase (n=23) are summarized in c) and d). b) and d) gray lines indicate changes in KRAS MAF between non-enriched and enriched samples with a median increase of 95%. Straight lines indicate a percentage increase of >10%, connected lines show a difference <10%. Samples marked with Q represent KRAS mutations in Q61.

Figure 2

Genomic alterations identified during the pilot and subsequent study. Heatmap of genomic data for 30 patients (columns) for pilot phase including resected specimens and core biopsies (left side) and for EUS-FNA derived samples (right side). Heatmap includes SNV (classified as missense, silent, InDel, frameshift, stopgain, stoploss, and germline) as well as SCNA (classified as amplifications, deletions, and cnLOH) in selected PDAC driver genes organized by their functional classes. Germline mutations are only shown if identified in a CLIA certified clinical test (Supplementary Table 3) in addition to our calling algorithm. Patients’ clinical pathological data are shown as tracks at the top. The percentage of PDAC samples with an alteration of any type is noted at the left and the proportions of alterations per genes at the right.

Figure 3. Clinicopathological significance of mutational burden (only EUS-FNA cohort, n=23).

a) Potential actionable alterations based on literature findings (2). Alterations included are deleterious SNVs found in the COSMIC database and gene amplifications with copy number of 3 or greater. b) Sum of all somatic mutations (missense, silent, frameshift, InDel, stopgain and stoploss) per patient (column) arranged in decreasing order. TMBhigh cases are defined as > 670 mutations/patient (>10 SNVs/Mb). Samples without paired PBMC for germline correction are marked with v, all other samples are FNAs with paired PBMC. c) TMB shown as tracks at the bottom with TMBhigh cases left of vertical dotted line and mutations in DDR genes highlighted (p<0.0001). d) Kaplan Meier curves showing progression free survival (PFS) comparing TMBhigh vs. TMBlow (p=0.09).

Figure 4

SCNA(s) across all patients. Arm-level SCNA(s) with classification information (deletion, amplification, cnLOH, or undetermined) per chromosomal arm (left y-axis) and fraction of patients with events (right y-axis)

Figure 5

Prognostic significance of aneuploidy in patient samples. a) Exemplary segmentation plots intersected with HapLOHseq calls (lavender background) showing samples classifying into aneuploidy low (<=7 chromosomal arm events) and aneuploidy high (>=8). b) Kaplan Meier curves comparing progression free survival (PFS) of patients with low vs. high aneuploidy levels (p=0.009). c) Kaplan Meier curves comparing overall survival (OS) of patients with low vs. high aneuploidy levels (p=0.06). d) Kaplan Meier curves comparing progression free survival (PFS) of patients with low vs. high aneuploidy levels for patients with localized tumors only (p=0.02). e) Kaplan Meier curves comparing overall survival (OS) of patients with low vs. high aneuploidy levels in localized patients only (p=0.04). *For this and subsequent figures HapLOHseq calls shown pass a threshold of posterior probability of AI >0.85.

Figure 6. Predictive value of SCNAs in platinum-based therapy response

a) Scattered box plot showing the TMB level of responders vs. non-responders to platinum-based therapy (p=0.04). b) Scattered box plot showing the aneuploidy level of responders vs. non-responders to platinum-based therapy (p=0.73). c) Exemplary segmentation plots of two patients intersected with HapLOHseq calls (lavender background) showing samples with areas highlighted and zoomed in showing chromosomal arms rated positive for complexity score calculation. d) Scattered box plot showing the complexity score of responders vs. non-responders to platinum-based therapy (p=0.04). e) Kaplan Meier curves comparing progression free survival (PFS) of patients with low vs. high complexity score (p=0.11). f) Linear regression plotting complexity score vs. TMB (R₂=0.37, p=0.002), half-filled dots mark samples with alterations in known DDR genes.

Figure 7

Longitudinal follow-up of patient 17. a) Time course of disease plotting the total measurable tumor burden (TMTB) (right y-axis) and Ca19–9 (left y-axis) with therapy regime administered at bottom. Green areas mark time periods with stable disease or partial response defined by RECIST 1.1, whereas red areas show progression. Time points of tissue sampling (T1 and T2) are indicated by arrows. b) Venn diagram plotting overlapping and distinct (non-) synonymous SNVs at T1 (n=139, left) and T2 (n=110, right). c) Left: Segmentation plots of T1 and T2 with intersected HapLOHseq calls (lavender background). Red arrows indicate loci that might be associated with acquired resistance to immune-therapy. Right: Exemplary CTC images showing an increase of pulmonal metastasis between T1 and T2 (red arrow).