Multi‐omics analysis of intra‐tumoural and inter‐tumoural heterogeneity in pancreatic ductal adenocarcinoma

Abstract

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is associated with the tumour heterogeneity. To explore intra-and inter-tumoural heterogeneity in PDAC, we analysed the multi-omics profiles of 61 PDAC lesion samples, along with the matched pancreatic normal tissue samples, from 19 PDAC patients. Haematoxylin and Eosin (H&E) staining revealed that diversely differentiated lesions coexisted both within and across individual tumours. Whole exome sequencing (WES) of samples from multi-region revealed diverse types of mutations in diverse genes between cancer cells within a tumour and between tumours from different individuals. The copy number variation (CNV) analysis also showed that PDAC exhibited intra-and inter-tumoural heterogeneity in CNV and that high average CNV burden was associated poor prognosis of the patients. Phylogenetic tree analysis and clonality/timing analysis of mutations displayed diverse evolutionary pathways and spatiotemporal characteristics of genomic alterations between different lesions from the same or different tumours. Hierarchical clustering analysis illustrated higher inter-tumoural heterogeneity than intra-tumoural heterogeneity of PDAC at the transcriptional levels as lesions from the same patients are grouped into a single cluster. Immune marker genes are differentially expressed in different regions and tumour samples as shown by tumour microenvironment (TME) analysis. TME appeared toThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Open-source curation of a pancreatic ductal adenocarcinoma gene expression analysis platform (pdacR) supports a two-subtype model

“…Furthermore, others have indicated that data that is readily accessible through online repositories has, in some cases, been either poorly formatted or poorly annotated for subsequent analysis 43,44 . In certain instances, this may lead to misguided or misinformed conclusions in well-meaning investigations [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] . Clearly, these challenges pose a major hurdle in the development of novel hypotheses, particularly for those without first-hand familiarity with how the data were generated.…”

“…First, validation is frequently only performed on one publicly available dataset. Second, there are multiple examples of inappropriate use of The Cancer Genome Atlas (TCGA) Program PDAC dataset [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] . The mRNA dataset includes 182 samples, but only 150 have been confirmed histologically as PDAC.…”

Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells

“…Gemcitabine is considered the first choice for chemotherapeutic treatment of PDAC but nevertheless has a very poor response rate . Previous research has investigated the lipid response of PDAC models to gemcitabine, giving insight into treatment resistance in cancer, − so this serves as an interesting biological system by which to test the single-cell lipidomics approach developed here.…”

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

“…In clinical practice, the decision-making, therapeutic management, and follow-up still rely on the traditional anatomy-based TNM staging system ( Katz et al, 2008 ). Although this provides a relatively trustworthy reference for determining whether patients will undergo surgical resection, the high inter- and intra-tumoral heterogeneity of PACA results in a wide range of outcomes even among patients at the same stage ( Liu et al, 2022c ). With the advancement of high-throughput sequencing and evidence-based medicine, molecular biomarkers such as BRCA1/2 mutations, NTRK fusion, DNA mismatch repair deficiency (dMMR), and microsatellite instability-high (MSI-H) have been gradually brought into clinical guideline ( Wattenberg et al, 2020 ; Doebele et al, 2020 ; Le et al, 2017 ).…”