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An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading. We digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa. The AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73). An AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist. Swedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health.

The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a group-level, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.

ALK rearranged renal cell carcinoma (ALK-RCC) has recently been included in 2016 WHO classification as a provisional entity. In this study, we describe 12 ALK-RCCs from 8 institutions, with detailed clinical, pathological, immunohistochemical (IHC), fluorescence in situ hybridization (FISH), and next generation sequencing (NGS) analyses. Patients' age ranged from 25 to 68 years (mean, 46.3 years). Seven patients were females and five were males (M:F = 1:1.4). Tumor size ranged from 17 to 70 mm (mean 31.5, median 25 mm). The pTNM stage included: pT1a (n = 7), pT1b (n = 1), and pT3a (n = 4). Follow-up was available for 9/12 patients (range: 2 to 153 months; mean 37.6 months); 8 patients were alive without disease and one was alive with distant metastases. The tumors demonstrated heterogeneous, ‘difficult to classify' morphology in 10/12 cases, typically showing diverse architectural and cellular morphologies, including papillary, tubular, tubulocystic, solid, sarcomatoid (spindle cell), rhabdoid, signet-ring cell, and intracytoplasmic vacuoles, often set in a mucinous background. Of the remaining two tumors, one showed morphology resembling mucinous tubular and spindle cell renal cell carcinoma (MTSC RCC-like) and one was indistinguishable from metanephric adenoma. One additional case also showed a focal metanephric adenoma-like area, in an otherwise heterogeneous tumor. By IHC, all tumors were diffusely positive for ALK and PAX8. In both cases with metanephric adenoma-like features, WT1 and ALK were coexpressed. ALK rearrangement was identified in 9/11 tumors by FISH. ALK fusion partners were identified by NGS in all 12 cases, including the previously reported: STRN (n = 3), TPM3 (n = 3), EML4 (n = 2), and PLEKHA7 (n = 1), and also three novel fusion partners: CLIP1 (n = 1), KIF5B (n = 1), and KIAA1217 (n = 1). ALK-RCC represents a genetically distinct entity showing a heterogeneous histomorphology, expanded herein to include unreported metanephric adenoma-like and MTSC RCC-like variants. We advocate a routine ALK IHC screening for “unclassifiable RCCs” with heterogeneous features.

Sclerosing pneumocytoma is a unique benign neoplasm of the lungs. The molecular alterations in sclerosing pneumocytoma are not well understood. In a previous whole-exome sequencing study, recurrent AKT1 point mutation was observed in about half of the cases of sclerosing pneumocytoma. However, in the remaining half, cancer-related mutations have still not been identified. In this study, we first analyzed the raw sequence data from the previous whole-exome sequencing study (PRJNA297066 cohort). Using Genomon-ITDetector, a special software for detection of internal tandem duplications, we identified recurrent internal tandem duplications in the AKT1 gene in 22 of the 44 tumor samples (50%). All the cases positive for AKT1 internal tandem duplications lacked AKT1 point mutations. Next, we performed targeted next-generation sequencing in an independent cohort of sclerosing pneumocytoma from our hospital (VGH-TPE cohort), and again identified recurrent AKT1 internal tandem duplications in 20 of the 40 (50%) tumor samples analyzed. The internal tandem duplications resulted in duplications of 7 to 16 amino acids in a narrow region of the Pleckstrin homology domain of the AKT1 protein. This region contains the interaction interface between the Pleckstrin homology and kinase domains, which is known to play a critical role in the activation of the AKT1 protein. Moreover, we found that AKT1 internal tandem duplications were mutually exclusive of other forms of AKT1 mutations, including point mutations and short indels. Taking all forms of AKT1 mutations together, we detected AKT1 mutations in almost all the sclerosing pneumocytomas in our study (PRJNA297066 cohort: 41 out of 44 cases, 93%; VGH-TPE cohort: 40 out of 40 cases, 100%). Our results suggest that AKT1 mutation is the genetic hallmark of sclerosing pneumocytoma. These results would help in better understanding of the pathogenesis of sclerosing pneumocytoma.

ALK-rearranged renal cell carcinoma is a provisional entity in the 2016 WHO Classification of Tumors of the Urinary System and Male Genital Organs. The reported fusion partners included VCL, TPM3, EML4, STRN, and HOOK1. Herein, we present a peculiar renal cell carcinoma morphologically resembling metanephric adenoma and harboring a novel PLEKHA7-ALK fusion. Microscopically, the tumor is composed of bland epithelial cells with scant to moderate amount of amphophilic cytoplasm, round and uniform nuclei, delicate chromatin, and inconspicuous nucleoli, arranged in tightly packed small acini and angulated tubules. Papillary formation, intraluminal glomeruloid tufts, microcysts, and solid nests were focally observed. Psammomatous calcifications were evident. The tumor cells were diffusely reactive for CK7, AMACR, PAX8, and ALK, while non-reactive for WT1, BRAF V600E, CD57, carbonic anhydrase IX, TFE3, and cathepsin K. Fluorescence in situ hybridization showed breaking apart of ALK. A novel PLEKHA7exon18-ALKexon20 fusion was detected using ArcherDX FusionPlex next-generation sequencing panel and was further confirmed with reverse-transcriptase PCR. Our case demonstrates that in contrast to prior cases showing high-grade tumor cells, ALK-rearranged renal cell carcinoma may also present as a low-grade renal tumor mimicking metanephric adenoma. Immunohistochemistry and molecular testing are helpful to identify this tumor, which may be eligible for ALK inhibitor-targeted therapy.

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68–0.84) and 0.50 (range 0.40–0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

Castration-resistance prostate cancer (CRPC), also known as hormone-refractory prostate cancer (HRPC), requires immediate attention since it is not only resistant to androgen ablation, chemo- and radiotherapy, but also highly metastatic. Increasing evidence suggests that enrichment of neuroendocrine (NE) cells is associated with CRPC. Here, combined RNA-seq and ChIP-seq analysis reveals that REST is involved in epithelial-mesenchymal transition (EMT) and stemness acquisition in NE differentiated prostate cancer (PCa) cells via direct transcriptional repression of Twist1 and CD44. Specifically we show that short-term knockdown of REST induces NE differentiation of LNCaP cells. Long-term REST knockdown enhanced the expression of Twist1 and CD44, cell migration and sphere formation. Overexpression of REST in hormone-refractory CWR22Rv1 PCa cells significantly reduces Twist1 and CD44 expression, cell migration and sphere formation. Collectively, our study uncovers REST in regulating EMT and stemness properties of NE PCa cells and suggests that REST is a potential therapeutic target for CRPC.

Autophagy plays either a suppressing or promoting role during tumor development. Clarifying the role of autophagy in bladder tumorigenesis both in vitro and in vivo is crucial for developing novel therapeutic strategies through manipulating autophagy activity. Herein, we noninvasively monitored how autophagy affects bladder tumor formation using a murine model of orthotopic bladder tumor formation by “in vivo imaging system (IVIS) and transabdominal micro‐ultrasound imaging (MUI)” and validated the notion in cell lines and bladder cancer patients. Mimic clinical administration, all the drugs were delivered into the urinary bladder of the mice via intravesical instillation. Plasma biochemistry parameters, hemograms, blood pressure, and blood concentration of amiodarone and desethylamiodarone were analyzed in treated mice. Low autophagy activity was detected in the bladder tumors and associated with poor overall survival of bladder cancer patients. Amiodarone‐induced autophagy activity suppressed bladder tumor formation, whereas silencing Atg5 expression reversed the suppression. Notably, amiodarone showed equivalent anti‐tumor efficacy but with fewer side effects on the treated mice compared to the clinical anti‐cancer drug Mitomycin C (MMC). Furthermore, amiodarone delivered by intravesical route showed a negligible influence on the physiologic conditions of the treated mice. Our orthotopic mouse model revealed that increasing autophagy activity alleviated bladder tumor development. Similarly, low autophagy is associated with a poor overall survival rate. Furthermore, repurposing amiodarone‐induced autophagy accompanied by trivial side effects shows potential for the treatment of bladder cancer patients.

The microphthalmia transcription factor/transcription factor E (TFE)-family translocation renal cell carcinomas bear specific translocations that result in overexpression of TFE3 or TFEB. TFE3 fusion gene product overexpression occurs as consequence of different translocations involving chromosome Xp11.2, whereas TFEB overexpression is the result of the specific translocation t(6;11)(p21;q12), which fuses the Alpha gene to TFEB . Both TFE3 and TFEB are closely related members of the microphthalmia transcription factor/TFE-family, which also includes TFEC and microphthalmia transcription factor. These transcription factors have overlapping transcriptional targets. Overexpression of microphthalmia transcription factor has been shown to mediate the expression of cathepsin-K in osteoclasts. We hypothesize that the overexpression of the related TFE3 fusion proteins and TFEB in translocation renal cell carcinomas may have the same effect. We studied cathepsin-K in 17 cytogenetically confirmed microphthalmia transcription factor/TFE-family translocation renal cell carcinomas. Seven cases showed a t(6;11)(p21;q12), ten cases showed translocations involving Xp11.2; five cases t(X;1)(p11;q21) resulting in a PRCC–TFE3 gene fusion; three cases t(X;1)(p11;p34) resulting in a PSF–TFE3 gene fusion, one t(X;17)(p11;q25) resulting in an ASPL–TFE3 gene fusion, and one t(X;3)(p11;q23) with an unknown TFE3 gene fusion. As control we analyzed cathepsin-K in 210 clear cell, 40 papillary, 25 chromophobe renal cell carcinomas and 30 oncocytomas. All seven TFEB translocation renal cell carcinomas were labeled for cathepsin-K. Among the cytogenetically confirmed TFE3 translocation renal cell carcinomas, 6 out of 10 were positive. None of the other renal neoplasms expressed cathepsin-K. We conclude the following: (1) cathepsin-K is consistently and strongly expressed in TFEB translocation renal cell carcinomas and in 6 of 10 TFE3 translocation renal cell carcinomas. (2) Cathepsin-K immunolabeling in both TFE3 and TFEB translocation renal cell carcinomas distinguishes these neoplasms from the more common adult renal cell carcinomas, and may be a specific marker of these neoplasms. (3) These results further support the concept that the overexpression of TFE3 or TFEB in these neoplasms activates the expression of genes normally regulated by microphthalmia transcription factor in other cell types.