Combined analysis of albumin in situ hybridisation and C reactive protein immunohistochemistry for the diagnosis of intrahepatic cholangiocarcinoma: towards a molecular classification paradigm.

AIMS: Intrahepatic cholangiocarcinoma (iCCA) is a diagnosis of exclusion that can pose a challenge to the pathologist despite thorough clinical workup. Although several immunohistochemical markers have been proposed for iCCA, none of them reached clinical practice. We here assessed the combined usage of two promising diagnostic approaches, albumin in situ hybridisation (Alb-ISH) and C reactive protein (CRP) immunohistochemistry, for distinguishing iCCA from other adenocarcinoma primaries. METHODS: We conducted Alb-ISH and CRP immunohistochemistry in a large European iCCA cohort (n=153) and compared the results with a spectrum of other glandular adenocarcinomas of different origin (n=885). In addition, we correlated expression patterns with clinicopathological information and mutation data. RESULTS: Alb-ISH was highly specific for iCCA (specificity 98.8%) with almost complete negativity in perihilar CCA and only rare positives among other adenocarcinomas (sensitivity 69.5%). CRP identified the vast majority of iCCA cases (sensitivity 84.1%) at a lower specificity of 86.4%. Strikingly, the combination of CRP and Alb-ISH boosted the diagnostic sensitivity to 88.0% while retaining a considerable specificity of 86.1%. Alb-ISH significantly correlated with CRP expression, specific tumour morphologies and small or large duct iCCA subtypes. Neither Alb-ISH nor CRP was associated with iCCA patient survival. 16 of 17 recurrent mutations in either IDH1, IDH2 and FGFR2 affected Alb-ISH positive cases, while the only KRAS mutation corresponded to an Alb-ISH negative case. CONCLUSIONS: In conclusion, we propose a sequential diagnostic approach for iCCA, integrating CRP immunohistochemistry and Alb-ISH. This may improve the accuracy of CCA classification and pave the way towards a molecular-guided CCA classification.

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.

Asymmetric Centriole Numbers at Spindle Poles Cause Chromosome Missegregation in Cancer.

Chromosomal instability is a hallmark of cancer and correlates with the presence of extra centrosomes, which originate from centriole overduplication. Overduplicated centrioles lead to the formation of centriole rosettes, which mature into supernumerary centrosomes in the subsequent cell cycle. While extra centrosomes promote chromosome missegregation by clustering into pseudo-bipolar spindles, the contribution of centriole rosettes to chromosome missegregation is unknown. We used multi-modal imaging of cells with conditional centriole overduplication to show that mitotic rosettes in bipolar spindles frequently harbor unequal centriole numbers, leading to biased chromosome capture that favors binding to the prominent pole. This results in chromosome missegregation and aneuploidy. Rosette mitoses lead to viable offspring and significantly contribute to progeny production. We further show that centrosome abnormalities in primary human malignancies frequently consist of centriole rosettes. As asymmetric centriole rosettes generate mitotic errors that can be propagated, rosette mitoses are sufficient to cause chromosome missegregation in cancer.