The tumor immune microenvironment in resected treatment-naive pancreatic cancer patients with long-term survival.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide. Presently, only a fraction of patients undergo successful surgical resection, the most effective treatment. Enhancing treatment strategies necessitates a deep comprehension of the factors underlying extended survival after surgical resection in patients. METHODS: This study aims to identify the important factors of PDAC patients' long-term survival with metatranscriptomics and multiplex immunofluorescence (IF) staining analyses. Specifically, differences in tumor immune microenvironment (TIME) were investigated between treatment-naïve PDAC short-term survivors (STS, overall survival <6 months) and long-term survivors (LTS, overall survival >5 years). RESULTS: As a result, we detected 589 over-expressed genes, including HOXB9, CDA, and HOXB8, and 507 under-expressed genes, including AMY2B, SCARA5, and SLC2A2 in LTS. Most of the Reactome overbiological pathways enriched in our data were over-expressed in LTS, such as RHO GTPase Effectors and Cell Cycle Checkpoints. Eleven microbiomes significantly differed between LTS and STS, including Sphingopyxis and Capnocytophaga. Importantly, we demonstrate that the TIME profile with an increased abundance of memory B cells and the reduction of M0 and pro-tumoral M2 macrophages are associated with a good prognosis in PDAC. CONCLUSIONS: In this study, we delved into the TIME with metatranscriptomics and IF staining analyses to understand the prerequisite of prolonged survival in PDAC patients. In LTS, several biological pathways were overexpressed, and specific microbiomes were identified. Furthermore, apparent differences in driven immune factors were found that provide valuable insights into developing new treatment strategies.

Comparing Sequence-Based and Literature-Based Pathogenicity Scoring Methods for Human Variants.

Assessing the pathogenicity of genetic variants is a critical aspect of genomic medicine and precision healthcare. Over the last decades, the identification of genetic variants and their characterization has become simpler (advent of high-throughput sequencing technologies, analysis, and visualization support tools, etc.). However, the quality of assessments to distinguish benign from pathogenic variants is critical to inform clinical decision-making and improve patient outcomes. In this article, we investigate the relationships using correlation tests between the characterization of genetic variants in the literature and their pathogenicity scores computed by two state-of-the-art assessment tools (SIFT and PolyPhen-2).

Prediction of molecular subclasses of uveal melanoma by deep learning using routine haematoxylin-eosin-stained tissue slides.

AIMS: Uveal melanoma has a high propensity to metastasize. Prognosis is associated with specific driver mutations and copy number variations, and these can only be obtained after genetic testing. In this study we evaluated the efficacy of patient outcome prediction using deep learning on haematoxylin and eosin (HE)-stained primary uveal melanoma slides in comparison to molecular testing. METHODS: In this retrospective study of patients with uveal melanoma, 113 patients from the Erasmus Medical Centre who underwent enucleation had tumour tissue analysed for molecular classification between 1993 and 2020. Routine HE-stained slides were scanned to obtain whole-slide images (WSI). After annotation of regions of interest, tiles of 1024 × 1024 pixels were extracted at a magnification of 40×. An ablation study to select the best-performing deep-learning model was carried out using three state-of-the-art deep-learning models (EfficientNet, Vision Transformer, and Swin Transformer). RESULTS: Deep-learning models were subjected to a training cohort (n = 40), followed by a validation cohort (n = 20), and finally underwent a test cohort (n = 48). A k-fold cross-validation (k = 3) of validation and test cohorts (n = 113 of three classes: BAP1, SF3B1, EIF1AX) demonstrated Swin Transformer as the best-performing deep-learning model to predict molecular subclasses based on HE stains. The model achieved an accuracy of 0.83 ± 0.09 on the validation cohort and 0.75 ± 0.04 on the test cohort. Within the subclasses, this model correctly predicted 70% BAP1-mutated, 61% SF3B1-mutated and 80% EIF1AX-mutated UM in the test set. CONCLUSIONS: This study showcases the potential of the deep-learning methodology for predicting molecular subclasses in a multiclass manner using HE-stained WSI. This development holds promise for advanced prognostication of UM patients without the need of molecular or immunohistochemical testing. Additionally, this study suggests there are distinct histopathological features per subclass; mainly utilizing epithelioid cellular morphology for BAP1-classification, but an unknown feature distinguishes EIF1AX and SF3B1.

The heterogeneous sensitivity of pediatric brain tumors to different oncolytic viruses is predicted by unique gene expression profiles.

Despite decades of research, the prognosis of high-grade pediatric brain tumors (PBTs) remains dismal; however, recent cases of favorable clinical responses were documented in clinical trials using oncolytic viruses (OVs). In the current study, we employed four different species of OVs: adenovirus Delta24-RGD, herpes simplex virus rQNestin34.5v1, reovirus R124, and the non-virulent Newcastle disease virus rNDV-F0-GFP against three entities of PBTs (high-grade gliomas, atypical teratoid/rhabdoid tumors, and ependymomas) to determine their in vitro efficacy. These four OVs were screened on 14 patient-derived PBT cell cultures and the degree of oncolysis was assessed using an ATP-based assay. Subsequently, the observed viral efficacies were correlated to whole transcriptome data and Gene Ontology analysis was performed. Although no significant tumor type-specific OV efficacy was observed, the analysis revealed the intrinsic biological processes that associated with OV efficacy. The predictive power of the identified expression profiles was further validated in vitro by screening additional PBTs. In summary, our results demonstrate OV susceptibility of multiple patient-derived PBT entities and the ability to predict in vitro responses to OVs using unique expression profiles. Such profiles may hold promise for future OV preselection with effective oncolytic potency in a specific tumor, therewith potentially improving OV responses.

Multiomic profiling of transplant glomerulopathy reveals a novel T-cell dominant subclass.

Kidney transplant (KTx) biopsies showing transplant glomerulopathy (TG) (glomerular basement membrane double contours (cg) > 0) and microvascular inflammation (MVI) in the absence of C4d staining and donor-specific antibodies (DSAs) do not fulfill the criteria for chronic active antibody-mediated rejection (CA-AMR) diagnosis and do not fit into any other Banff category. To investigate this, we initiated a multicenter intercontinental study encompassing 36 cases, comparing the immunomic and transcriptomic profiles of 14 KTx biopsies classified as cg+MVI DSA-/C4d- with 22 classified as CA-AMR DSA+/C4d+ through novel transcriptomic analysis using the NanoString Banff-Human Organ Transplant (B-HOT) panel and subsequent orthogonal subset analysis using two innovative 5-marker multiplex immunofluorescent panels. Nineteen genes were differentially expressed between the two study groups. Samples diagnosed with CA-AMR DSA+/C4d+ showed a higher glomerular abundance of natural killer cells and higher transcriptomic cell type scores for macrophages in an environment characterized by increased expression of complement-related genes (i.e., C5AR1) and higher activity of angiogenesis, interstitial fibrosis tubular atrophy, CA-AMR, and DSA-related pathways when compared to samples diagnosed with cg+MVI DSA-/C4d-. Samples diagnosed with cg+MVI DSA-/C4d- displayed a higher glomerular abundance and activity of T cells (CD3+, CD3+CD8+, and CD3+CD8-). Thus, we show that using novel multiomic techniques, KTx biopsies with cg+MVI DSA-/C4d- have a prominent T-cell presence and activity, putting forward the possibility that these represent a more T-cell dominant phenotype.

Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review.

Bladder cancer (BC) diagnosis and prediction of prognosis are hindered by subjective pathological evaluation, which may cause misdiagnosis and under-/over-treatment. Computational pathology (CPATH) can identify clinical outcome predictors, offering an objective approach to improve prognosis. However, a systematic review of CPATH in BC literature is lacking. Therefore, we present a comprehensive overview of studies that used CPATH in BC, analyzing 33 out of 2285 identified studies. Most studies analyzed regions of interest to distinguish normal versus tumor tissue and identify tumor grade/stage and tissue types (e.g., urothelium, stroma, and muscle). The cell's nuclear area, shape irregularity, and roundness were the most promising markers to predict recurrence and survival based on selected regions of interest, with >80% accuracy. CPATH identified molecular subtypes by detecting features, e.g., papillary structures, hyperchromatic, and pleomorphic nuclei. Combining clinicopathological and image-derived features improved recurrence and survival prediction. However, due to the lack of outcome interpretability and independent test datasets, robustness and clinical applicability could not be ensured. The current literature demonstrates that CPATH holds the potential to improve BC diagnosis and prediction of prognosis. However, more robust, interpretable, accurate models and larger datasets-representative of clinical scenarios-are needed to address artificial intelligence's reliability, robustness, and black box challenge.

Ex vivo drug sensitivity screening predicts response to temozolomide in glioblastoma patients and identifies candidate biomarkers.

BACKGROUND: Patient-derived glioma stem-like cells (GSCs) have become the gold-standard in neuro-oncological research; however, it remains to be established whether loss of in situ microenvironment affects the clinically-predictive value of this model. We implemented a GSC monolayer system to investigate in situ-in vitro molecular correspondence and the relationship between in vitro and patient response to temozolomide (TMZ). METHODS: DNA/RNA-sequencing was performed on 56 glioblastoma tissues and 19 derived GSC cultures. Sensitivity to TMZ was screened across 66 GSC cultures. Viability readouts were related to clinical parameters of corresponding patients and whole-transcriptome data. RESULTS: Tumour DNA and RNA sequences revealed strong similarity to corresponding GSCs despite loss of neuronal and immune interactions. In vitro TMZ screening yielded three response categories which significantly correlated with patient survival, therewith providing more specific prediction than the binary MGMT marker. Transcriptome analysis identified 121 genes related to TMZ sensitivity of which 21were validated in external datasets. CONCLUSION: GSCs retain patient-unique hallmark gene expressions despite loss of their natural environment. Drug screening using GSCs predicted patient response to TMZ more specifically than MGMT status, while transcriptome analysis identified potential biomarkers for this response. GSC drug screening therefore provides a tool to improve drug development and precision medicine for glioblastoma.

Feasibility and Potential of Transcriptomic Analysis Using the NanoString nCounter Technology to Aid the Classification of Rejection in Kidney Transplant Biopsies.

BACKGROUND: Transcriptome analysis could be an additional diagnostic parameter in diagnosing kidney transplant (KTx) rejection. Here, we assessed feasibility and potential of NanoString nCounter analysis of KTx biopsies to aid the classification of rejection in clinical practice using both the Banff-Human Organ Transplant (B-HOT) panel and a customized antibody-mediated rejection (AMR)-specific NanoString nCounter Elements (Elements) panel. Additionally, we explored the potential for the classification of KTx rejection building and testing a classifier within our dataset. METHODS: Ninety-six formalin-fixed paraffin-embedded KTx biopsies were retrieved from the archives of the ErasmusMC Rotterdam and the University Hospital Cologne. Biopsies with AMR, borderline or T cell-mediated rejections (BLorTCMR), and no rejection were compared using the B-HOT and Elements panels. RESULTS: High correlation between gene expression levels was found when comparing the 2 chemistries pairwise (r = 0.76-0.88). Differential gene expression (false discovery rate; P < 0.05) was identified in biopsies diagnosed with AMR (B-HOT: 294; Elements: 76) and BLorTCMR (B-HOT: 353; Elements: 57) compared with no rejection. Using the most predictive genes from the B-HOT analysis and the Element analysis, 2 least absolute shrinkage and selection operators-based regression models to classify biopsies as AMR versus no AMR (BLorTCMR or no rejection) were developed achieving an receiver-operating-characteristic curve of 0.994 and 0.894, sensitivity of 0.821 and 0.480, and specificity of 1.00 and 0.979, respectively, during cross-validation. CONCLUSIONS: Transcriptomic analysis is feasible on KTx biopsies previously used for diagnostic purposes. The B-HOT panel has the potential to differentiate AMR from BLorTCMR or no rejection and could prove valuable in aiding kidney transplant rejection classification.

Immunomodulatory Effects of Stereotactic Body Radiotherapy and Vaccination with Heat-Killed Mycobacterium Obuense (IMM-101) in Patients with Locally Advanced Pancreatic Cancer.

BACKGROUND: Patients with locally advanced pancreatic cancer (LAPC) are treated with chemotherapy. In selected cases, stereotactic body radiotherapy (SBRT) can be added to the regimen. We hypothesized that adding an adjuvant containing a heat-killed mycobacterium (IMM-101) to SBRT may lead to beneficial immuno-modulatory effects, thereby improving survival. This study aims to investigate the safety of adding IMM-101 to SBRT and to investigate the immuno-modulatory effects of the combination treatment in the peripheral blood of LAPC patients. METHODS: LAPC patients were treated with SBRT (40 Gy) and six intradermal vaccinations of one milligram IMM-101. The primary endpoint was an observed toxicity rate of grade 4 or higher. Targeted gene-expression profiling and multicolor flow cytometry were performed for longitudinal immune-monitoring of the peripheral blood. RESULTS: Twenty patients received study treatment. No treatment-related adverse events of grade 4 or higher occurred. SBRT/IMM-101 treatment induced a transient decrease in different lymphocyte subsets and an increase in CD14+CD16-CD11b+HLA-DRlow myeloid-derived suppressor cells. Importantly, treatment significantly increased activated ICOS+, HLA-DR+ and Ki67+PD1+ T and NK cell frequencies. This was not accompanied by increased levels of most inhibitory markers, such as TIM-3 and LAG-3. CONCLUSIONS: Combination therapy with SBRT and a heat-killed mycobacterium vaccine was safe and had an immune-stimulatory effect.

A Decentralized Kidney Transplant Biopsy Classifier for Transplant Rejection Developed Using Genes of the Banff-Human Organ Transplant Panel.

Introduction: A decentralized and multi-platform-compatible molecular diagnostic tool for kidney transplant biopsies could improve the dissemination and exploitation of this technology, increasing its clinical impact. As a first step towards this molecular diagnostic tool, we developed and validated a classifier using the genes of the Banff-Human Organ Transplant (B-HOT) panel extracted from a historical Molecular Microscope® Diagnostic system microarray dataset. Furthermore, we evaluated the discriminative power of the B-HOT panel in a clinical scenario. Materials and Methods: Gene expression data from 1,181 kidney transplant biopsies were used as training data for three random forest models to predict kidney transplant biopsy Banff categories, including non-rejection (NR), antibody-mediated rejection (ABMR), and T-cell-mediated rejection (TCMR). Performance was evaluated using nested cross-validation. The three models used different sets of input features: the first model (B-HOT Model) was trained on only the genes included in the B-HOT panel, the second model (Feature Selection Model) was based on sequential forward feature selection from all available genes, and the third model (B-HOT+ Model) was based on the combination of the two models, i.e. B-HOT panel genes plus highly predictive genes from the sequential forward feature selection. After performance assessment on cross-validation, the best-performing model was validated on an external independent dataset based on a different microarray version. Results: The best performances were achieved by the B-HOT+ Model, a multilabel random forest model trained on B-HOT panel genes with the addition of the 6 most predictive genes of the Feature Selection Model (ST7, KLRC4-KLRK1, TRBC1, TRBV6-5, TRBV19, and ZFX), with a mean accuracy of 92.1% during cross-validation. On the validation set, the same model achieved Area Under the ROC Curve (AUC) of 0.965 and 0.982 for NR and ABMR respectively. Discussion: This kidney transplant biopsy classifier is one step closer to the development of a decentralized kidney transplant biopsy classifier that is effective on data derived from different gene expression platforms. The B-HOT panel proved to be a reliable highly-predictive panel for kidney transplant rejection classification. Furthermore, we propose to include the aforementioned 6 genes in the B-HOT panel for further optimization of this commercially available panel.

CRISPRs in the human genome are differentially expressed between malignant and normal adjacent to tumor tissue.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) have been identified in bacteria, archaea and mitochondria of plants, but not in eukaryotes. Here, we report the discovery of 12,572 putative CRISPRs randomly distributed across the human chromosomes, which we termed hCRISPRs. By using available transcriptome datasets, we demonstrate that hCRISPRs are distinctively expressed as small non-coding RNAs (sncRNAs) in cell lines and human tissues. Moreover, expression patterns thereof enabled us to distinguish normal from malignant tissues. In prostate cancer, we confirmed the differential hCRISPR expression between normal adjacent and malignant primary prostate tissue by RT-qPCR and demonstrate that the SHERLOCK and DETECTR dipstick tools are suitable to detect these sncRNAs. We anticipate that the discovery of CRISPRs in the human genome can be further exploited for diagnostic purposes in cancer and other medical conditions, which certainly will lead to the development of point-of-care tests based on the differential expression of the hCRISPRs.

Deciphering Tumour Microenvironment of Liver Cancer through Deconvolution of Bulk RNA-Seq Data with Single-Cell Atlas.

Liver cancers give rise to a heavy burden on healthcare worldwide. Understanding the tumour microenvironment (TME) underpins the development of precision therapy. Single-cell RNA sequencing (scRNA-seq) technology has generated high-quality cell atlases of the TME, but its wider application faces enormous costs for various clinical circumstances. Fortunately, a variety of deconvolution algorithms can instead repurpose bulk RNA-seq data, alleviating the need for generating scRNA-seq datasets. In this study, we reviewed major public omics databases for relevance in this study and utilised eight RNA-seqs and one microarray dataset from clinical studies. To decipher the TME of liver cancer, we estimated the fractions of liver cell components by deconvoluting the samples with Cibersortx using three reference scRNA-seq atlases. We also confirmed that Cibersortx can accurately deconvolute cell types/subtypes of interest. Compared with non-tumorous liver, liver cancers showed multiple decreased cell types forming normal liver microarchitecture, as well as elevated cell types involved in fibrogenesis, abnormal angiogenesis, and disturbed immune responses. Survival analysis shows that the fractions of five cell types/subtypes significantly correlated with patient outcomes, indicating potential therapeutic targets. Therefore, deconvolution of bulk RNA-seq data with scRNA-seq atlas references can be a useful tool to help understand the TME.

Spatial genomics reveals a high number and specific location of B cells in the pancreatic ductal adenocarcinoma microenvironment of long-term survivors.

Background and aim: Only 10% of pancreatic ductal adenocarcinoma (PDAC) patients survive longer than five years. Factors underlining long-term survivorship in PDAC are not well understood. Therefore, we aimed to identify the key players in the tumor immune microenvironment (TIME) associated with long-term survivorship in PDAC patients. Methods: The immune-related gene expression profiles of resected PDAC tumors of patients who survived and remained recurrence-free of disease for ≥36 months (long-term survivors, n=10) were compared to patients who had survived ≤6 months (short-term survivors, n=10) due to tumor recurrence. Validation was performed by the spatial protein expression profile of immune cells using the GeoMx™ Digital Spatial Profiler. An independent cohort of samples consisting of 12 long-term survivors and 10 short-term survivors, was used for additional validation. The independent validation was performed by combining qualitative immunohistochemistry and quantitative protein expression profiling. Results: B cells were found to be significantly increased in the TIME of long-term survivors by gene expression profiling (p=0.018). The high tumor infiltration of B cells was confirmed by spatial protein profiling in the discovery and the validation cohorts (p=0.002 and p=0.01, respectively). The higher number of infiltrated B cells was found mainly in the stromal compartments of PDAC samples and was exclusively found within tumor cells in long-term survivors. Conclusion: This is the first comprehensive study that connects the immune landscape of gene expression profiles and protein spatial infiltration with the survivorship of PDAC patients. We found a higher number and a specific location of B cells in TIME of long-term survivors which emphasizes the importance of B cells and B cell-based therapy for future personalized immunotherapy in PDAC patients.

Robust deep learning model for prognostic stratification of pancreatic ductal adenocarcinoma patients.

A major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of new patients. MODEL-P was trained on autoencoder integrated multi-omics of 146 patients with PDAC together with their survival outcome. Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes (median survival 10.1 and 22.7 months, respectively, log rank p = 1 × 10-6), which correspond to DNA damage repair and immune response. We rigorously validated MODEL-P by stratifying patients in five independent datasets into these two survival groups and achieved significant survival difference, which is superior to current practice and other subtyping schemas. We believe the subtype-specific signatures would facilitate PDAC pathogenesis discovery, and MODEL-P can provide clinicians the prognoses information in the treatment decision-making to better gauge the benefits versus the risks.

Fusion transcripts and their genomic breakpoints in polyadenylated and ribosomal RNA-minus RNA sequencing data.

BACKGROUND: Fusion genes are typically identified by RNA sequencing (RNA-seq) without elucidating the causal genomic breakpoints. However, non-poly(A)-enriched RNA-seq contains large proportions of intronic reads that also span genomic breakpoints. RESULTS: We have developed an algorithm, Dr. Disco, that searches for fusion transcripts by taking an entire reference genome into account as search space. This includes exons but also introns, intergenic regions, and sequences that do not meet splice junction motifs. Using 1,275 RNA-seq samples, we investigated to what extent genomic breakpoints can be extracted from RNA-seq data and their implications regarding poly(A)-enriched and ribosomal RNA-minus RNA-seq data. Comparison with whole-genome sequencing data revealed that most genomic breakpoints are not, or minimally, transcribed while, in contrast, the genomic breakpoints of all 32 TMPRSS2-ERG-positive tumours were present at RNA level. We also revealed tumours in which the ERG breakpoint was located before ERG, which co-existed with additional deletions and messenger RNA that incorporated intergenic cryptic exons. In breast cancer we identified rearrangement hot spots near CCND1 and in glioma near CDK4 and MDM2 and could directly associate this with increased expression. Furthermore, in all datasets we find fusions to intergenic regions, often spanning multiple cryptic exons that potentially encode neo-antigens. Thus, fusion transcripts other than classical gene-to-gene fusions are prominently present and can be identified using RNA-seq. CONCLUSION: By using the full potential of non-poly(A)-enriched RNA-seq data, sophisticated analysis can reliably identify expressed genomic breakpoints and their transcriptional effects.

The Role of the Respiratory Microbiome and Viral Presence in Lower Respiratory Tract Infection Severity in the First Five Years of Life.

Lower respiratory tract infections (LRTIs) in children are common and, although often mild, a major cause of mortality and hospitalization. Recently, the respiratory microbiome has been associated with both susceptibility and severity of LRTI. In this current study, we combined respiratory microbiome, viral, and clinical data to find associations with the severity of LRTI. Nasopharyngeal aspirates of children aged one month to five years included in the STRAP study (Study to Reduce Antibiotic prescription in childhood Pneumonia), who presented at the emergency department (ED) with fever and cough or dyspnea, were sequenced with nanopore 16S-rRNA gene sequencing and subsequently analyzed with hierarchical clustering to identify respiratory microbiome profiles. Samples were also tested using a panel of 15 respiratory viruses and Mycoplasma pneumoniae, which were analyzed in two groups, according to their reported virulence. The primary outcome was hospitalization, as measure of disease severity. Nasopharyngeal samples were isolated from a total of 167 children. After quality filtering, microbiome results were available for 54 children and virology panels for 158 children. Six distinct genus-dominant microbiome profiles were identified, with Haemophilus-, Moraxella-, and Streptococcus-dominant profiles being the most prevalent. However, these profiles were not found to be significantly associated with hospitalization. At least one virus was detected in 139 (88%) children, of whom 32.4% had co-infections with multiple viruses. Viral co-infections were common for adenovirus, bocavirus, and enterovirus, and uncommon for human metapneumovirus (hMPV) and influenza A virus. The detection of enteroviruses was negatively associated with hospitalization. Virulence groups were not significantly associated with hospitalization. Our data underlines high detection rates and co-infection of viruses in children with respiratory symptoms and confirms the predominant presence of Haemophilus-, Streptococcus-, and Moraxella-dominant profiles in a symptomatic pediatric population at the ED. However, we could not assess significant associations between microbiome profiles and disease severity measures.

Identification, Validation, and Utilization of Immune Cells in Pancreatic Ductal Adenocarcinoma Based on Marker Genes.

The immune response affects tumor biological behavior and progression. The specific immune characteristics of pancreatic ductal adenocarcinoma (PDAC) can determine the metastatic abilities of cancerous cells and the survival of patients. Therefore, it is important to characterize the specific immune landscape in PDAC tissue samples, and the effect of various types of therapy on that immune composition. Previously, a set of marker genes was identified to assess the immune cell composition in different types of cancer tissue samples. However, gene expression and subtypes of immune cells may vary across different types of cancers. The aim of this study was to provide a method to identify immune cells specifically in PDAC tissue samples. The method is based on defining a specific set of marker genes expressed by various immune cells in PDAC samples. A total of 90 marker genes were selected and tested for immune cell type-specific definition in PDAC; including 43 previously used, and 47 newly selected marker genes. The immune cell-type specificity was checked mathematically by calculating the "pairwise similarity" for all candidate genes using the PDAC RNA-sequenced dataset available at The Cancer Genome Atlas. A set of 55 marker genes that identify 22 different immune cell types for PDAC was created. To validate the method and the set of marker genes, an independent mRNA expression dataset of 24 samples of PDAC patients who received various types of (neo)adjuvant treatments was used. The results showed that by applying our method we were able to identify PDAC specific marker genes to characterize immune cell infiltration in tissue samples. The method we described enabled identifying different subtypes of immune cells that were affected by various types of therapy in PDAC patients. In addition, our method can be easily adapted and applied to identify the specific immune landscape in various types of tissue samples.

Dendritic cell vaccination and CD40-agonist combination therapy licenses T cell-dependent antitumor immunity in a pancreatic carcinoma murine model.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is notoriously resistant to treatment including checkpoint-blockade immunotherapy. We hypothesized that a bimodal treatment approach consisting of dendritic cell (DC) vaccination to prime tumor-specific T cells, and a strategy to reprogram the desmoplastic tumor microenvironment (TME) would be needed to break tolerance to these pancreatic cancers. As a proof-of-concept, we investigated the efficacy of combined DC vaccination with CD40-agonistic antibodies in a poorly immunogenic murine model of PDAC. Based on the rationale that mesothelioma and pancreatic cancer share a number of tumor associated antigens, the DCs were loaded with either pancreatic or mesothelioma tumor lysates. METHODS: Immune-competent mice with subcutaneously or orthotopically growing KrasG12D/+;Trp53R172H/+;Pdx-1-Cre (KPC) PDAC tumors were vaccinated with syngeneic bone marrow-derived DCs loaded with either pancreatic cancer (KPC) or mesothelioma (AE17) lysate and consequently treated with FGK45 (CD40 agonist). Tumor progression was monitored and immune responses in TME and lymphoid organs were analyzed using multicolor flow cytometry and NanoString analyzes. RESULTS: Mesothelioma-lysate loaded DCs generated cross-reactive tumor-antigen-specific T-cell responses to pancreatic cancer and induced delayed tumor outgrowth when provided as prophylactic vaccine. In established disease, combination with stimulating CD40 antibody was necessary to improve survival, while anti-CD40 alone was ineffective. Extensive analysis of the TME showed that anti-CD40 monotherapy did improve CD8 +T cell infiltration, but these essential effector cells displayed hallmarks of exhaustion, including PD-1, TIM-3 and NKG2A. Combination therapy induced a strong change in tumor transcriptome and mitigated the expression of inhibitory markers on CD8 +T cells. CONCLUSION: These results demonstrate the potency of DC therapy in combination with CD40-stimulation for the treatment of pancreatic cancer and provide directions for near future clinical trials.

Molecular characterization of colorectal adenomas reveals POFUT1 as a candidate driver of tumor progression.

Removal of colorectal adenomas is an effective strategy to reduce colorectal cancer (CRC) mortality rates. However, as only a minority of adenomas progress to cancer, such strategies may lead to overtreatment. The present study aimed to characterize adenomas by in-depth molecular profiling, to obtain insights into altered biology associated with the colorectal adenoma-to-carcinoma progression. We obtained low-coverage whole genome sequencing, RNA sequencing and tandem mass spectrometry data for 30 CRCs, 30 adenomas and 18 normal adjacent colon samples. These data were used for DNA copy number aberrations profiling, differential expression, gene set enrichment and gene-dosage effect analysis. Protein expression was independently validated by immunohistochemistry on tissue microarrays and in patient-derived colorectal adenoma organoids. Stroma percentage was determined by digital image analysis of tissue sections. Twenty-four out of 30 adenomas could be unambiguously classified as high risk (n = 9) or low risk (n = 15) of progressing to cancer, based on DNA copy number profiles. Biological processes more prevalent in high-risk than low-risk adenomas were related to proliferation, tumor microenvironment and Notch, Wnt, PI3K/AKT/mTOR and Hedgehog signaling, while metabolic processes and protein secretion were enriched in low-risk adenomas. DNA copy number driven gene-dosage effect in high-risk adenomas and cancers was observed for POFUT1, RPRD1B and EIF6. Increased POFUT1 expression in high-risk adenomas was validated in tissue samples and organoids. High POFUT1 expression was also associated with Notch signaling enrichment and with decreased goblet cells differentiation. In-depth molecular characterization of colorectal adenomas revealed POFUT1 and Notch signaling as potential drivers of tumor progression.

Next-generation antigen receptor sequencing of paired diagnosis and relapse samples of B-cell acute lymphoblastic leukemia: Clonal evolution and implications for minimal residual disease target selection.

Antigen receptor gene rearrangements are frequently applied as molecular targets for detection of minimal residual disease (MRD) in B-cell precursor acute lymphoblastic leukemia patients. Since such targets may be lost at relapse, appropriate selection of antigen receptor genes as MRD-PCR target is critical. Recently, next-generation sequencing (NGS) - much more sensitive and quantitative than classical PCR-heteroduplex approaches - has been introduced for identification of MRD-PCR targets. We evaluated 42 paired diagnosis-relapse samples by NGS (IGH, IGK, TRG, TRD, and TRB) to evaluate clonal evolution patterns and to design an algorithm for selection of antigen receptor gene rearrangements most likely to remain stable at relapse. Overall, only 393 out of 1446 (27%) clonal rearrangements were stable between diagnosis and relapse. If only index clones with a frequency >5% at diagnosis were taken into account, this number increased to 65%; including only index clones with an absolute read count >10,000, indicating truly major clones, further increased the stability to 84%. Over 90% of index clones at relapse were also present as index clone at diagnosis. Our data provide detailed information about the stability of antigen receptor gene rearrangements, based on which we propose an algorithm for selecting stable MRD-PCR targets, successful in >97% of patients.