Abstract 1169: Genomic evolution of pancreatic cancer at single-cell resolution

As the breadth of genomic sequencing datasets increases, we can engage more directly with the evolutionary principles governing cancer progression. But as clonal evolution happens at the single-cell level, one must rely on strong assumptions when making conclusions with bulk-sequencing results, which fails in capturing clonal heterogeneity to its fullest.

Herein we have developed and optimized a high-throughput, high-depth targeted single-nucleus DNA-seq (snDNA-seq) technique (doi.org/10.1101/2022.03.06.483206) for archival primary solid tumor samples. We focused on pancreatic ductal adenocarcinoma (PDAC), well known as one of the most lethal cancers, and sequenced over 200,000 single nuclei from 80 archival primary samples of 25 PDAC patients. The samples included both early- and late-stage diagnoses and multiregional sampling from primary tumors and metastasis to capture clonal heterogeneity on both temporal and spatial scales.

With significant increase in sensitivity than bulk (down to mutations in 0.1% cells), we discovered thousands of novel mutations per sample on our 120,000 base-pair-long panel regions, suggesting a mutation rate higher than previously estimated. A small fraction of these mutations is in 1-10% single cells and are enriched in early-stage samples. They form mutually exclusive clones which functionally target key pathways including TGF-β, homologous recombination, suggesting subclonal evolution under positive selection at the early stage of cancer. Most novel mutations are in <1% single cells sampled and enabled us to measure convergent evolution and positive/negative selection within each tumor.

We next revisited PDAC’s genomic evolution model established by bulk studies. It posits that PDAC often arises when KRAS hotspot mutation-bearing precursors acquire TP53 and/or CDKN2A inactivation through stepwise and punctuated evolution. While it assumes that TGF-β inactivating mutations are present in all cancer cells (clonal), we found that they are targeted in a highly subclonal manner; moreover, short mutations and focal copy number variations occur in a stepwise manner over time leading to the most “fit” genotype. In many PDACs whose bulk results show no alteration to the TGF-β pathway, snDNA-seq shows focal deletions that are likely below bulk’s sensitivity.

Ongoing studies have begun to extend these analyses to longitudinal samples to study treatment response; normal pancreas tissues to study pancreas cells’ clonal evolution in aging and chronic disease conditions; blood samples to investigate circulating tumor cells in PDAC patients. Computational pipelines and analysis tools are being built as platform for more in-depth analyses. Overall, the high-throughput snDNA-seq technique brings genomic study of PDAC to a much higher resolution and holds the promise to not only inform precision medicine but also shed light on many fundamental questions on cancer evolution.

Citation Format: Haochen Zhang, Palash Sashittal, Elias-Ramzey Karnoub, Benjamin J. Raphael, Christine A. Iacobuzio-Donahue. Genomic evolution of pancreatic cancer at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1169.

Abstract 6421: Molecular profiles and single cell analysis identify immunogenic pancreatic ductal adenocarcinoma (iPDAC)

Most pancreatic ductal adenocarcinomas (PDAC) are lethal and resistant to immunotherapy. Thus, identifying the immunogenic subgroup (iPDAC) and therapeutic targets can save lives. Herein, we present molecular features of iPDAC. 3 cohorts (A, B, C) from 288 patients whose sequenced tumors (MSK-IMPACT) were classified by homologous recombination deficiency groups. MSI-H were excluded. Survival, tumor mutation burden, genomic instability score, and enriched pathways for each cohort are included in Table 1. Patients in A (BRCA1/2/PALB2) had longer survivals vs B/C. 61 samples were selected for bulk RNAseq analysis for A vs C. Gene Ontology was enriched for upregulated humoral, T cell, and neutrophil immunity. CIBERSORT suggested higher infiltration of gamma delta T (Tgd) cells (p=0.039) and neutrophils (p=0.012), but lower Treg (p=0.001). Multidimensional insights in cellular components of cancer, immune, stroma, and neural genes were obtained by single nuclear RNA (snRNAseq) analysis from 30 biopsies for A vs C. 10x Genomics Chromium platform for library and Scanpy for computational analysis after Cell Ranger pipelines were used. 61,868 nuclei were profiled from 18 (13 baseline and 5 matched longitudinal) samples after quality evaluation. UMAP accurately clustered cells from each patient. Long-term survivors (LTS) had heterogenous baseline immune cell infiltrates of plasma cells, neutrophils, and CD8 (+) cytotoxic T cells. In matched samples of LTS, evolution of more prominent CD8 (+) T cells, macrophage, plasma cell, and neutrophil were observed. Single nucleus T-Cell Receptor sequencing for clonal trajectory inference will be done to determine the associated single cell molecular features contributing to iPDAC and identify novel targets for future intervention.

Table 1. Cohort (Total: N=288) A: core HRD (BRCA1/2/PALB2) B: non-core HRD (ATM, BARD1, BLM, CHEK2, RAD50, RAD51C, RTEL1, MUTYH) C: others without HR-gene alterations Number (%) 48 (16.6) 19 (6.5) 221 (76) Median overall survival (95% confidence Interval) 33 months (3.6-64) 16 (11- not reached) 16 (14-18) Tumor Mutation Burden (TMB) 4.4 3.5 3.9 Genomic Instability Score (GIS, HRD score) 26 12 13 Gene Ongology term, enrichment score, adjusted p-value Adaptive immune response, GO:0002250, 0.49, 1.69e-10 Not included Reference to cohort A Humoral immune response, GO:0006959, 0.58, 1.67e-9 T cell activation, GO:0042110, 0.44, 2.75e-8 Neutrophil chemotaxis, GO:0030593, 0.73, 4.3e-10

Citation Format: Wungki Park, Catherine O'Connor, Shigeaki Umeda, Roshan Sharma, Yingjie Zhu, Elias-Ramzey Karnoub, Anna Varghese, Kevin C. Soares, Alejandro Jimemez, Asli Yavas, Kenneth H. Yu, Balachandran P. Vinod, Joanne F. Chou, Danny N. Khalil, Kelsen David, Hulya Sahin Ozkan, Olca Basturk, Marinela Capanu, Tal Nawy, Michael F. Berger, Ghassan K. Abou-Alfa, Jorge S. Reis-Filho, Ronan Chaligne, Nadeem Riaz, Dana Pe'er, Christine Iacobuzio-Donahue, Eileen M. O'Reilly. Molecular profiles and single cell analysis identify immunogenic pancreatic ductal adenocarcinoma (iPDAC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6421.

Application of high-throughput single-nucleus DNA sequencing in pancreatic cancer

Despite insights gained by bulk DNA sequencing of cancer it remains challenging to resolve the admixture of normal and tumor cells, and/or of distinct tumor subclones; high-throughput single-cell DNA sequencing circumvents these and brings cancer genomic studies to higher resolution. However, its application has been limited to liquid tumors or a small batch of solid tumors, mainly because of the lack of a scalable workflow to process solid tumor samples. Here we optimize a highly automated nuclei extraction workflow that achieves fast and reliable targeted single-nucleus DNA library preparation of 38 samples from 16 pancreatic ductal adenocarcinoma patients, with an average library yield per sample of 2867 single nuclei. We demonstrate that this workflow not only performs well using low cellularity or low tumor purity samples but reveals genomic evolution patterns of pancreatic ductal adenocarcinoma as well.