ROR1 and ROR2 expression in pancreatic cancer

Background: The Wnt receptors ROR1 and ROR2 are generating increased interest as cancer therapeutic targets but remain understudied in pancreatic ductal adenocarcinoma (PDAC). Compared to canonical Wnt/ β-catenin signalling, the role of noncanonical Wnt signalling in PDAC remains largely unknown. Only one study has investigated the prognostic significance of the noncanonical Wnt signalling receptor, ROR2 in PDAC. No studies have investigated the prognostic role of ROR1 in PDAC. Methods: Here, we performed analysis of ROR1 and ROR2 mRNA expression in three publicly available datasets ICGC-PACA-AU (n = 81), TCGA-PAAD (n = 150) and CPTAC-PDAC (n = 137). ROR1 and ROR2 protein expression from the CPTAC-PDAC discovery cohort were also analysed. Immunohistochemistry (IHC) using the validated anti ROR1 monoclonal antibody (4A5) was performed on the Australian Pancreatic Cancer Genome Initiative (APGI) cohort of PDAC samples (n = 152). Association between ROR1 cytoplasmic staining intensity and clinicopathological parameters including stage, grade and overall survival (OS) was investigated. Results: High ROR1 mRNA expression levels correlated with a favourable OS outcome in all of the ICGC-PACA-AU, TCGA-PAAD and CPTAC-PDAC cohorts. ROR1 protein expression was not associated with stage, grade or OS in the APGI cohort. Conclusion: ROR1 and ROR2 have potential as prognostic markers when measured at the mRNA level in PDAC. Our IHC cohort did not support ROR1 protein expression in predicting OS, and highlighted the discrepancy of prognostic biomarkers when measured by MS, IHC and RNAseq

DNA methylation patterns identify subgroups of pancreatic neuroendocrine tumors with clinical association

Here we report the DNA methylation profile of 84 sporadic pancreatic neuroendocrine tumors (PanNETs) with associated clinical and genomic information. We identified three subgroups of PanNETs, termed T1, T2 and T3, with distinct patterns of methylation. The T1 subgroup was enriched for functional tumors and ATRX, DAXX and MEN1 wild-type genotypes. The T2 subgroup contained tumors with mutations in ATRX, DAXX and MEN1 and recurrent patterns of chromosomal losses in half of the genome with no association between regions with recurrent loss and methylation levels. T2 tumors were larger and had lower methylation in the MGMT gene body, which showed positive correlation with gene expression. The T3 subgroup harboured mutations in MEN1 with recurrent loss of chromosome 11, was enriched for grade G1 tumors and showed histological parameters associated with better prognosis. Our results suggest a role for methylation in both driving tumorigenesis and potentially stratifying prognosis in PanNETs

Whole-genome sequencing of acral melanoma reveals genomic complexity and diversity

To increase understanding of the genomic landscape of acral melanoma, a rare form of melanoma occurring on palms, soles or nail beds, whole genome sequencing of 87 tumors with matching transcriptome sequencing for 63 tumors was performed. Here we report that mutational signature analysis reveals a subset of tumors, mostly subungual, with an ultraviolet radiation signature. Significantly mutated genes are BRAF, NRAS, NF1, NOTCH2, PTEN and TYRP1. Mutations and amplification of KIT are also common. Structural rearrangement and copy number signatures show that whole genome duplication, aneuploidy and complex rearrangements are common. Complex rearrangements occur recurrently and are associated with amplification of TERT, CDK4, MDM2, CCND1, PAK1 and GAB2, indicating potential therapeutic options.This work was supported by a National Health and Medical Research Council of Australia (NHMRC) Program Grant (1093017, G.J.M., R.A.S., N.H., G.V.L., J.F.T.), an NHMRC project grant (APP1123217) and NHMRC Fellowship grants (R.A.S., N.K.H. - APP1139071, G.VL.). G.V.L is supported by an NHMRC Practitioner Fellowship and the University of Sydney Medical Foundation. R.A.S is supported by an NHMRC Practitioner Fellowship. J.S.W. is supported by a NHMRC early career fellowship (1111678). N.W. is supported by an NHMRC Senior Research Fellowship (1139071). N.K.H. is supported by an NHMRC Senior Principal Research Fellowship (1117663). P.M.F. was supported by the Deborah and John McMurtrie MIA Pathology Fellowship. T.J.D. was supported by the Jani Haenke Melanoma Pathology Fellowship. Support from Melanoma Institute Australia, the Royal Prince Alfred Hospital and New South Wales Health Pathology is also gratefully acknowledged

Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal cancer with fewer than 7% of patients surviving past 5 years. T-cell immunity has been linked to the exceptional outcome of the few long-term survivors1,2, yet the relevant antigens remain unknown. Here we use genetic, immunohistochemical and transcriptional immunoprofiling, computational biophysics, and functional assays to identify T-cell antigens in long-term survivors of pancreatic cancer. Using whole-exome sequencing and in silico neoantigen prediction, we found that tumours with both the highest neoantigen number and the most abundant CD8+ T-cell infiltrates, but neither alone, stratified patients with the longest survival. Investigating the specific neoantigen qualities promoting T-cell activation in long-term survivors, we discovered that these individuals were enriched in neoantigen qualities defined by a fitness model, and neoantigens in the tumour antigen MUC16 (also known as CA125). A neoantigen quality fitness model conferring greater immunogenicity to neoantigens with differential presentation and homology to infectious disease-derived peptides identified long-term survivors in two independent datasets, whereas a neoantigen quantity model ascribing greater immunogenicity to increasing neoantigen number alone did not. We detected intratumoural and lasting circulating T-cell reactivity to both high-quality and MUC16 neoantigens in long-term survivors of pancreatic cancer, including clones with specificity to both high-quality neoantigens and predicted cross-reactive microbial epitopes, consistent with neoantigen molecular mimicry. Notably, we observed selective loss of high-quality and MUC16 neoantigenic clones on metastatic progression, suggesting neoantigen immunoediting. Our results identify neoantigens with unique qualities as T-cell targets in pancreatic ductal adenocarcinoma. More broadly, we identify neoantigen quality as a biomarker for immunogenic tumours that may guide the application of immunotherapies

DNA methylation patterns identify subgroups of pancreatic neuroendocrine tumors with clinical association

Here we report the DNA methylation profile of 84 sporadic pancreatic neuroendocrine tumors (PanNETs) with associated clinical and genomic information. We identified three subgroups of PanNETs, termed T1, T2 and T3, with distinct patterns of methylation. The T1 subgroup was enriched for functional tumors and ATRX, DAXX and MEN1 wild-type genotypes. The T2 subgroup contained tumors with mutations in ATRX, DAXX and MEN1 and recurrent patterns of chromosomal losses in half of the genome with no association between regions with recurrent loss and methylation levels. T2 tumors were larger and had lower methylation in the MGMT gene body, which showed positive correlation with gene expression. The T3 subgroup harboured mutations in MEN1 with recurrent loss of chromosome 11, was enriched for grade G1 tumors and showed histological parameters associated with better prognosis. Our results suggest a role for methylation in both driving tumorigenesis and potentially stratifying prognosis in PanNETs

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Abstract Uncertainty estimation is crucial for understanding the reliability of deep learning (DL) predictions, and critical for deploying DL in the clinic. Differences between training and production datasets can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models for predicting cancer of unknown primary, using three RNA-seq datasets with 10,968 samples across 57 cancer types. Our results highlight that simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation. Moreover, we designed a prototypical metric—the area between development and production curve (ADP), which evaluates the accuracy loss when deploying models from development to production. Using ADP, we demonstrate that Bayesian DL improves accuracy under data distributional shifts when utilising ‘uncertainty thresholding’. In summary, Bayesian DL is a promising approach for generalising uncertainty, improving performance, transparency, and safety of DL models for deployment in the real world

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.</p

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Abstract Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME

CD155 loss enhances tumor suppression via combined host and tumor-intrinsic mechanisms

Critical immune-suppressive pathways beyond programmed death 1 (PD-1) and programmed death ligand 1 (PD-L1) require greater attention. Nectins and nectin-like molecules might be promising targets for immunotherapy, since they play critical roles in cell proliferation and migration and exert immunomodulatory functions in pathophysiological conditions. Here, we show CD155 expression in both malignant cells and tumor-infiltrating myeloid cells in humans and mice. Cd155-/- mice displayed reduced tumor growth and metastasis via DNAM-1 upregulation and enhanced effector function of CD8+ T and NK cells, respectively. CD155-deleted tumor cells also displayed slower tumor growth and reduced metastases, demonstrating the importance of a tumor-intrinsic role of CD155. CD155 absence on host and tumor cells exerted an even greater inhibition of tumor growth and metastasis. Blockade of PD-1 or both PD-1 and CTLA4 was more effective in settings in which CD155 was limiting, suggesting the clinical potential of cotargeting PD-L1 and CD155 function