N-cadherin: A diagnostic marker to help discriminate primary liver carcinomas from extrahepatic carcinomas.

Distinguishing primary liver cancer (PLC), namely hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA), from liver metastases is of crucial clinical importance. Histopathology remains the gold standard, but differential diagnosis may be challenging. While absent in most epithelial, the expression of the adherens junction glycoprotein N-cadherin is commonly restricted to neural and mesenchymal cells, or carcinoma cells that undergo the phenomenon of epithelial-to-mesenchymal transition (EMT). However, we recently established N- and E-cadherin expression as hallmarks of normal hepatocytes and cholangiocytes, which are also preserved in HCC and iCCA. Therefore, we hypothesized that E- and/or N-cadherin may distinguish between carcinoma derived from the liver vs carcinoma of other origins. We comprehensively evaluated E- and N-cadherin in 3359 different tumors in a multicenter study using immunohistochemistry and compared our results with previously published 882 cases of PLC, including 570 HCC and 312 iCCA. Most carcinomas showed strong positivity for E-cadherin. Strong N-cadherin positivity was present in HCC and iCCA. However, except for clear cell renal cell carcinoma (23.6% of cases) and thyroid cancer (29.2%), N-cadherin was only in some instances faintly expressed in adenocarcinomas of the gastrointestinal tract (0%-0.5%), lung (7.1%), pancreas (3.9%), gynecological organs (0%-7.4%), breast (2.2%) as well as in urothelial (9.4%) and squamous cell carcinoma (0%-5.6%). As expected, N-cadherin was detected in neuroendocrine tumors (25%-75%), malignant melanoma (46.2%) and malignant mesothelioma (41%). In conclusion, N-cadherin is a useful marker for the distinction of PLC vs liver metastases of extrahepatic carcinomas (P < .01).

Deep Learning-Enabled Diagnosis of Liver Adenocarcinoma.

BACKGROUND & AIMS: Diagnosis of adenocarcinoma in the liver is a frequent scenario in routine pathology and has a critical impact on clinical decision making. However, rendering a correct diagnosis can be challenging, and often requires the integration of clinical, radiologic, and immunohistochemical information. We present a deep learning model (HEPNET) to distinguish intrahepatic cholangiocarcinoma from colorectal liver metastasis, as the most frequent primary and secondary forms of liver adenocarcinoma, with clinical grade accuracy using H&E-stained whole-slide images. METHODS: HEPNET was trained on 714,589 image tiles from 456 patients who were randomly selected in a stratified manner from a pool of 571 patients who underwent surgical resection or biopsy at Heidelberg University Hospital. Model performance was evaluated on a hold-out internal test set comprising 115 patients and externally validated on 159 patients recruited at Mainz University Hospital. RESULTS: On the hold-out internal test set, HEPNET achieved an area under the receiver operating characteristic curve of 0.994 (95% CI, 0.989-1.000) and an accuracy of 96.522% (95% CI, 94.521%-98.694%) at the patient level. Validation on the external test set yielded an area under the receiver operating characteristic curve of 0.997 (95% CI, 0.995-1.000), corresponding to an accuracy of 98.113% (95% CI, 96.907%-100.000%). HEPNET surpassed the performance of 6 pathology experts with different levels of experience in a reader study of 50 patients (P = .0005), boosted the performance of resident pathologists to the level of senior pathologists, and reduced potential downstream analyses. CONCLUSIONS: We provided a ready-to-use tool with clinical grade performance that may facilitate routine pathology by rendering a definitive diagnosis and guiding ancillary testing. The incorporation of HEPNET into pathology laboratories may optimize the diagnostic workflow, complemented by test-related labor and cost savings.

PITX2 as a Sensitive and Specific Marker of Midgut Neuroendocrine Tumors: Results from a Cohort of 1157 Primary Neuroendocrine Neoplasms.

As neuroendocrine tumors (NETs) often present as metastatic lesions, immunohistochemical assignment to a site of origin is one of the most important tasks in their pathologic assessment. Because a fraction of NETs eludes the typical expression profiles of their primary localization, additional sensitive and specific markers are required to improve diagnostic certainty. We investigated the expression of the transcription factor Pituitary Homeobox 2 (PITX2) in a large-scale cohort of 909 NET and 248 neuroendocrine carcinomas (NEC) according to the immunoreactive score (IRS) and correlated PITX2 expression groups with general tumor groups and primary localization. PITX2 expression (all expression groups) was highly sensitive (98.1%) for midgut-derived NET, but not perfectly specific, as non-midgut NET (especially pulmonary/duodenal) were quite frequently weak or moderately positive. The specificity rose to 99.5% for a midgut origin of NET if only a strong PITX2 expression was considered, which was found in only 0.5% (one pancreatic/one pulmonary) of non-midgut NET. In metastases of midgut-derived NET, PITX2 was expressed in all cases (87.5% strong, 12.5% moderate), whereas CDX2 was negative or only weakly expressed in 31.3% of the metastases. In NEC, a fraction of cases (14%) showed a weak or moderate PITX2 expression, which was not associated with a specific tumor localization. Our study independently validates PITX2 as a very sensitive and specific immunohistochemical marker of midgut-derived NET in a very large collective of neuroendocrine neoplasms. Therefore, our data argue toward implementation into diagnostic panels applied for NET as a firstline midgut marker.

Automated curation of large-scale cancer histopathology image datasets using deep learning.

BACKGROUND: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION: Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.

Differential Immunoexpression of Inhibitory Immune Checkpoint Molecules and Clinicopathological Correlates in Keratoacanthoma, Primary Cutaneous Squamous Cell Carcinoma and Metastases.

Beyond established anti-programmed cell death protein 1/programmed cell death ligand 1 immunotherapy, T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domain (TIGIT) and its ligand CD155 are promising novel inhibitory immune checkpoint targets in human malignancies. Yet, in cutaneous squamous cell carcinoma, evidence on the collective expression patterns of these inhibitory immune checkpoints is scarce. Complete tumour sections of 36 cutaneous squamous cell carcinoma, 5 cutaneous metastases and 9 keratoacanthomas, a highly-differentiated, squamoproliferative tumour, with disparately benign biologic behaviour, were evaluated by immunohistochemistry for expression of programmed cell death ligand 1 (Tumor Proportion Score, Immune Cell Score), TIGIT, CD155 and CD8+ immune infiltrates. Unlike keratoacanthomas, cutaneous squamous cell carcinoma displayed a strong positive correlation of programmed cell death ligand 1 Tumor Proportion Score and CD115 expression (p < 0.001) with significantly higher programmed cell death ligand 1 Tumor Proportion Score (p < 0.001) and CD155 expression (p < 0.01) in poorly differentiated G3-cutaneous squamous cell carcinoma compared with keratoacanthomas. TIGIT+ infiltrates were significantly increased in programmed cell death ligand 1 Immune Cell Score positive primary tumours (p = 0.05). Yet, a strong positive correlation of TIGIT expression with CD8+ infiltrates was only detected in cutaneous squamous cell carcinoma (p < 0.01), but not keratoacanthomas. Providing a comprehensive overview on the collective landscape of inhibitory immune checkpoint expression, this study reveals associations of novel inhibitory immune checkpoint with CD8+ immune infiltrates and tumour differentiation and highlights the TIGIT/CD155 axis as a potential new target for cutaneous squamous cell carcinoma immunotherapy.

Analysis of chronic inflammatory lesions of the colon for BMMF Rep antigen expression and CD68 macrophage interactions.

Consumption of Eurasian bovine meat and milk has been associated with cancer development, in particular with colorectal cancer (CRC). In addition, zoonotic infectious agents from bovine products were proposed to cause colon cancer (zur Hausen et al., 2009). Bovine meat and milk factors (BMMF) are small episomal DNA molecules frequently isolated from bovine sera and milk products, and recently, also from colon cancer (de Villiers et al., 2019). BMMF are bioactive in human cells and were proposed to induce chronic inflammation in precancerous tissue leading to increased radical formation: for example, reactive oxygen and reactive nitrogen species and elevated levels of DNA mutations in replicating cells, such as cancer progenitor cells (zur Hausen et al., 2018). Mouse monoclonal antibodies against the replication (Rep) protein of H1MSB.1 (BMMF1) were used to analyze BMMF presence in different cohorts of CRC peritumor and tumor tissues and cancer-free individuals by immunohistochemistry and Western blot. BMMF DNA was isolated by laser microdissection from immunohistochemistry-positive tissue regions. We found BMMF Rep protein present specifically in close vicinity of CD68+ macrophages in the interstitial lamina propria adjacent to CRC tissues, suggesting the presence of local chronic inflammation. BMMF1 (modified H1MSB.1) DNA was isolated from the same tissue regions. Rep and CD68+ detection increased significantly in peritumor cancer tissues when compared to tissues of cancer-free individuals. This strengthens previous postulations that BMMF function as indirect carcinogens by inducing chronic inflammation and DNA damage in replicating cells, which represent progress to progenitor cells for adenoma (polyps) formation and cancer.

Structural expression of bovine milk and meat factors in tissues of colorectal, lung and pancreatic cancer patients.

Chronic inflammation, linked to the presence of bovine milk and meat factors (BMMFs) and specific subsets of macrophages, results in oxygen radical synthesis and induction of mutations in DNA of actively replicating cells and replicating single stranded DNA. Cancers arising from this process have been characterized as indirect carcinogenesis by infectious agents (without persistence of genes of the agent in premalignant or cancers cells). Here, we investigate structural properties of pleomorphic vesicles, regularly identified by staining peritumor tissues of colorectal, lung and pancreatic cancer for expression of BMMF Rep. The latter represents a subgroup of BMMF1 proteins involved in replication of small single-stranded circular plasmids of BMMF, but most likely also contributing to pleomorphic vesicular structures found in the periphery of colorectal, lung and pancreatic cancers. Structurally dense regions are demonstrated in preselected areas of colorectal cancer, after staining with monoclonal antibodies against BMMF1 Rep. Similar structures were observed in human embryonic cells (HEK293TT) overexpressing Rep. These data suggest that Rep or Rep isoforms contribute to the structural formation of vesicles.

Residual apoptotic activity of a tumorigenic p53 mutant improves cancer therapy responses.

Engineered p53 mutant mice are valuable tools for delineating p53 functions in tumor suppression and cancer therapy. Here, we have introduced the R178E mutation into the Trp53 gene of mice to specifically ablate the cooperative nature of p53 DNA binding. Trp53R178E mice show no detectable target gene regulation and, at first sight, are largely indistinguishable from Trp53-/- mice. Surprisingly, stabilization of p53R178E in Mdm2-/- mice nevertheless triggers extensive apoptosis, indicative of residual wild-type activities. Although this apoptotic activity suffices to trigger lethality of Trp53R178E ;Mdm2-/- embryos, it proves insufficient for suppression of spontaneous and oncogene-driven tumorigenesis. Trp53R178E mice develop tumors indistinguishably from Trp53-/- mice and tumors retain and even stabilize the p53R178E protein, further attesting to the lack of significant tumor suppressor activity. However, Trp53R178E tumors exhibit remarkably better chemotherapy responses than Trp53-/- ones, resulting in enhanced eradication of p53-mutated tumor cells. Together, this provides genetic proof-of-principle evidence that a p53 mutant can be highly tumorigenic and yet retain apoptotic activity which provides a survival benefit in the context of cancer therapy.

Deep-learning based classification of a tumor marker for prognosis on Hodgkin's disease.

PURPOSE: Hodgkin's disease is a common malignant disorder in adolescent patients. Although most patients are cured, approximately 10%-15% of patients experience a relapse or have resistant disease. Furthermore, there are no definitive molecular predictors for early identification of patients at high risk of treatment failure to first line therapy. The aim of this study was to evaluate the deep learning-based classifier model of medical image classification to predict clinical outcome that may help in appropriate therapeutic decisions. METHODS: Eighty-three FFPE biopsy specimens from patients with Hodgkin's disease were stratified according to the patient's qPET scores, stained with picrosirius red dye and digitalized by whole slide image scanning. The resulting whole slide images were cut into tiles and annotated by two classes based on the collagen fibers' degree of coloring with picrosirius red. The neural network (YOLOv4) was then trained with the annotated data. Training was performed with 30 cases. Prognostic power of the weakly stained picrosirius red fibers was evaluated with 53 cases. The same neural network was trained with MMP9 stained tissue slides from the same cases and the quantification results were compared with the variant from the picrosirius red cases. RESULTS: There was a weak monotonically increasing relationship by parametric ANOVA between the qPET groups and the percentages of weakly stained fibers (p = .0185). The qPET-positive cases showed an average of 18% of weakly stained fibers, and the qPET-negative cases 10%-14%. Detection performance showed an AUC of 0.79. CONCLUSIONS: Picrosirius red shows distinct associations as a prognostic metric candidate of disease progression in Hodgkin's disease cases using whole slide images but not sufficiently as a prognostic device.

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology.

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Spatiotemporal analysis of tumour-infiltrating immune cells in biliary carcinogenesis.

BACKGROUND: Intraductal papillary neoplasms (IPN) and biliary epithelial neoplasia (BilIN) are well-defined precursor lesions of biliary tract carcinoma (BTC). The aim of this study was to provide a comprehensive characterisation of the inflammatory microenvironment in BTC precursor lesions. METHODS: Immunohistochemistry was employed to assess tumour-infiltrating immune cells in tissue samples from patients, for whom precursor lesions were identified alongside invasive BTC. The spatiotemporal evolution of the immune microenvironment during IPN-associated carcinogenesis was comprehensively analysed using triplet sample sets of non-neoplastic epithelium, precursor lesion and invasive BTC. Immune-cell dynamics during IPN- and BilIN-associated carcinogenesis were subsequently compared. RESULTS: Stromal CD3+ (P = 0.002), CD4+ (P = 0.007) and CD8+ (P < 0.001) T cells, CD20+ B cells (P = 0.008), MUM1+ plasma cells (P = 0.012) and CD163+ M2-like macrophages (P = 0.008) significantly decreased in IPN compared to non-tumorous biliary epithelium. Upon transition from IPN to invasive BTC, stromal CD68+ (P = 0.001) and CD163+ (P < 0.001) macrophages significantly increased. In contrast, BilIN-driven carcinogenesis was characterised by significant reduction of intraepithelial CD8+ T-lymphocytic infiltration from non-tumorous epithelium via BilIN (P = 0.008) to BTC (P = 0.004). CONCLUSION: IPN and BilIN are immunologically distinct entities that undergo different immune-cell variations during biliary carcinogenesis. Intraepithelial CD8+ T-lymphocytic infiltration of biliary tissue decreased already at the IPN-precursor stage, whereas BilIN-associated carcinogenesis showed a slowly progressing reduction towards invasive carcinoma.

iPLA2ß-Null Mice Show HCC Protection by an Induction of Cell-Cycle Arrest after Diethylnitrosamine Treatment.

Group VIA phospholipase A2 (iPLA2ß) play diverse biological functions in epithelial cells and macrophages. Global deletion in iPLA2ß-null (KO) mice leads to protection against hepatic steatosis in non-alcoholic fatty liver disease, in part, due to the replenishment of the loss of hepatocellular phospholipids. As the loss of phospholipids also occurs in hepatocellular carcinoma (HCC), we hypothesized that global deletion in KO mice may lead to protection against HCC. Here, HCC induced by diethylnitrosamine (DEN) was chosen because DEN causes direct injury to the hepatocytes. Male wild-type (WT) and KO mice at 3-5 weeks of age (12-13 mice/group) were subjected to a single intraperitoneal treatment with 10 mg/kg DEN, and mice were killed 12 months later. Analyses of histology, plasma cytokines, and gene expression were performed. Due to the low-dose DEN used, we observed a liver nodule in 3 of 13 WT and 2 of 12 KO mice. Only one DEN-treated WT mouse was confirmed to have HCC. DEN-treated KO mice did not show any HCC but showed suppressed hepatic expression of cell-cycle cyclinD2 and BCL2 as well as inflammatory markers IL-1ß, IL-10, and VCAM-1. Notably, DEN-treated KO mice showed increased hepatic necrosis and elevated levels of plasma lactate dehydrogenase suggesting an exacerbation of liver injury. Thus, global iPLA2ß deficiency in DEN-treated mice rendered HCC protection by an induction of cell-cycle arrest. Our results suggest the role of iPLA2ß inhibition in HCC treatment.

Cutibacterium acnes is an intracellular and intra-articular commensal of the human shoulder joint.

BACKGROUND: Cutibacterium acnes (C acnes) is a mysterious member of the shoulder microbiome and is associated with chronic postoperative complications and low-grade infections. Nevertheless, it is unclear whether it represents a contaminant or whether it accounts for true infections. Because it can persist intracellularly in macrophages at several body sites, it might in fact be an intra-articular commensal of the shoulder joint. METHODS: In 23 consecutive, otherwise healthy patients (17 male, 6 female; 58 years) who had no previous injections, multiple specimens were taken from the intra-articular tissue during first-time arthroscopic and open shoulder surgery. The samples were investigated by cultivation, genetic phylotyping, and immunohistochemistry using C acnes-specific antibodies and confocal laser scanning microscopy. RESULTS: In 10 patients (43.5%), cultures were C acnes-positive. Phylotype IA1 dominated the subcutaneous samples (71%), whereas type II dominated the deep tissue samples (57%). Sixteen of 23 patients (69.6%) were C acnes-positive by immunohistochemistry; in total, 25 of 40 samples were positive (62.5%). Overall, 56.3% of glenohumeral immunohistochemical samples, 62.5% of subacromial samples, and 75% of acromioclavicular (AC) joint samples were positive. In 62.5% of the tested patients, C acnes was detected immunohistochemically to reside intracellularly within stromal cells and macrophages. DISCUSSION: These data indicate that C acnes is a commensal of the human shoulder joint, where it persists within macrophages and stromal cells. Compared with culture-based methods, immunohistochemical staining can increase C acnes detection. Phylotype II seems to be most prevalent in the deep shoulder tissue. The high detection rate of C acnes in osteoarthritic AC joints might link its intra-articular presence to the initiation of osteoarthritis.

[Mucoepidermoidcarcinoma - Importance in molecular pathology]. / Das Mukoepidermoidkarzinom ­ Bedeutung der Molekularpathologie.

Mucoepidermoid carcinoma is the most common primary salivary gland malignancy and its tumor grading has an important prognostic significance. The 5 year overall survival rate is significantly higher for low grade mucoepidermoid carcinomas than for intermediate grade and high grade mucoepidermoid carcinomas. The translocation of t(11;19)(q21;p13) with the resulting CRTC1-MAML2 transfusion appears to be of prognostic relevance in patients with mucoepidermoid carcinoma. The translocation is detectable in 38-82 % of all mucoepidermoid carcinomas. Study results have shown a significantly better prognosis for patients with fusion-positive mucoepidermoid carcinomas than fusion-negative mucoepidermoid carcinomas. The t(11;19)(q21;p13) translocation can be found more often in low and intermediate grade mucoepidermoid carcinomas than in high grade tumors of the same entity. Moreover, fusion positive mucoepidermoid carcinoma were found more frequently in younger patients, smaller tumors, lower tumor stages and less frequently lymph node and distant metastases. Up to now, the translocation has not been of therapeutic importance. In selected cases, the lack of t(11;19)(q21;p13) translocation might facilitate the decision towards further escalation of therapy. More studies will be necessary to evaluate the individual prognostic and therapeutic value of CRTC1-MAML2 transfusion.

The Importance of the Right Framework: Mitogen-Activated Protein Kinase Pathway and the Scaffolding Protein PTPIP51.

The protein tyrosine phosphatase interacting protein 51 (PTPIP51) regulates and interconnects signaling pathways, such as the mitogen-activated protein kinase (MAPK) pathway and an abundance of different others, e.g., Akt signaling, NF-κB signaling, and the communication between different cell organelles. PTPIP51 acts as a scaffold protein for signaling proteins, e.g., Raf-1, epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (Her2), as well as for other scaffold proteins, e.g., 14-3-3 proteins. These interactions are governed by the phosphorylation of serine and tyrosine residues of PTPIP51. The phosphorylation status is finely tuned by receptor tyrosine kinases (EGFR, Her2), non-receptor tyrosine kinases (c-Src) and the phosphatase protein tyrosine phosphatase 1B (PTP1B). This review addresses various diseases which display at least one alteration in these enzymes regulating PTPIP51-interactions. The objective of this review is to summarize the knowledge of the MAPK-related interactome of PTPIP51 for several tumor entities and metabolic disorders.

Orchestrating cellular signaling pathways-the cellular "conductor" protein tyrosine phosphatase interacting protein 51 (PTPIP51).

The protein tyrosine phosphatase interacting protein 51 (PTPIP51) is thought to regulate crucial cellular functions such as mitosis, apoptosis, migration, differentiation and communication between organelles as a scaffold protein. These diverse functions are modulated by the tyrosine/serine phosphorylation status of PTPIP51. This review interconnects the insights obtained about the action of PTPIP51 in mitogen-activated protein kinase signaling, nuclear factor kB signaling, calcium homeostasis and chromosomal segregation and identifies important signaling hubs. The interference of PTPIP51 in such multiprotein complexes and their PTPIP51-modulated cross-talk makes PTPIP51 an ideal target for novel drugs such as the small molecule LDC-3. Graphical Abstract ᅟ.

The role of high-risk human papillomavirus infections in laryngeal squamous cell carcinoma.

The contribution of human papillomavirus (HPV) to the development and clinical outcome of oropharyngeal cancers has been well documented. The association of HPV in laryngeal squamous cell carcinoma (LSCC) has been examined in several studies, but controversy exists regarding its role in carcinogenesis, the outcome of the patients and thus, clinical significance of HPV testing in LSCC. In this review, we give an update of known associations between HPV-positive testing and carcinogenesis in laryngeal cancer. In an early study, the HPV-DNA detection rate in LSCC was documented being 24.0% with significant regional differences. Non-HPV-16 types were more often detected in LSCC when compared to the oropharynx. Later, single institution case series revealed markedly fewer amounts (<10%) of HPV DNA in LSCC and the results suggested that high-risk HPV infections seem to be biologically irrelevant in most LSCC. The significance of p16INK4a (p16) expression as a surrogate marker towards high-risk HPV infection and the outcome in LSCC is doubtful, since only few p16-positive LSCC samples are HPV RNA positive and accordingly there was poor correlation of p16-test results towards the outcome in LSCC. Recent meta-analysis (n = 2739) and large case series (n = 1042) of LSCC revealed the true rate of HPV-driven LSCC being 8.6%, respectively, <5%. In the latter the rate of DNA-, DNA/RNA-, DNA/p16, and DNA/RNA/p16 positivity was 5.7, 3.1, 1.9, and 1.5%, respectively. These results indicate relevant amounts of insignificant/transient HPV infection in LSCC specimen. However, in the same study the rate of transforming HPV infections increased since 2000, and younger patients had higher amounts of HPV-driven LSCC. Serologic testing of E6/E7 antibodies additionally revealed odds ratios between 2 and 5 as a hint for a weak contribution of high-risk HPV infection and the development of LSCC. The contribution of HPV for the development of LSCC needs future investigations, to date, routine HPV testing of LSCC specimen is not warranted.

CpG-biomarkers in tumor tissue and prediction models for the survival of colorectal cancer: A systematic review and external validation study.

The research aimed to identify previously published CpG-methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and validate them in a large external cohort. A systematic search was conducted, analyzing 298 unique CpGs and 12 CpG-based prognostic models from 28 studies. After adjustment for clinical variables, 48 CpGs and five prognostic models were confirmed to be associated with survival. However, the discrimination ability of the models was insufficient, with area under the receiver operating characteristic curves ranging from 0.53 to 0.62. Calibration accuracy was mostly poor, and no significant added prognostic value beyond traditional clinical variables was observed. All prognostic models were rated at high risk of bias. While a fraction of CpGs showed potential clinical utility and generalizability, the CpG-based prognostic models performed poorly and lacked clinical relevance.

Large-scale external validation and meta-analysis of gene methylation biomarkers in tumor tissue for colorectal cancer prognosis.

BACKGROUND: DNA methylation biomarkers in colorectal cancer (CRC) tissue hold potential as prognostic indicators. However, individual studies have yielded heterogeneous results, and external validation is largely absent. We conducted a comprehensive external validation and meta-analysis of previously suggested gene methylation biomarkers for CRC prognosis. METHODS: We performed a systematic search to identify relevant studies investigating gene methylation biomarkers for CRC prognosis until March 2024. Our external validation cohort with long-term follow-up included 2303 patients with CRC from 22 hospitals in southwest Germany. We used Cox regression analyses to assess associations between previously suggested gene methylation biomarkers and prognosis, adjusting for clinical variables. We calculated pooled hazard ratios (HRs) and their 95% confidence intervals (CIs) using random-effects models. FINDINGS: Of 151 single gene and 29 multiple gene methylation biomarkers identified from 121 studies, 37 single gene and seven multiple gene biomarkers were significantly associated with CRC prognosis after adjustment for clinical variables. Moreover, the directions of these associations with prognosis remained consistent between the original studies and our validation analyses. Seven single biomarkers and two multi-biomarker signatures were significantly associated with CRC prognosis in the meta-analysis, with a relatively strong level of evidence for CDKN2A, WNT5A, MLH1, and EVL. INTERPRETATION: In a comprehensive evaluation of the so far identified gene methylation biomarkers for CRC prognosis, we identified candidates with potential clinical relevance for further investigation. FUNDING: The German Research Council, the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, the German Federal Ministry of Education and Research.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.