Journal of Pathology Clinical Research最新文献

The androgen receptor (AR) plays a crucial role in the development and homeostasis of the prostate and is a key therapeutic target in prostate cancer (PCa). The gold standard therapy for advanced PCa is androgen deprivation therapy (ADT), which targets androgen production and AR signaling. However, resistance to ADT develops via AR-dependent and AR-independent mechanisms. As reports on AR expression patterns in PCa have been conflicting, we performed cell-by-cell AR quantification by immunohistochemistry in the benign and malignant prostate to monitor changes with disease development, progression, and hormonal treatment. Prostates from radical prostatectomy (RP) cases, both hormone-naïve and hormone-treated, prostate tissues from patients on palliative ADT, and bone metastases were included. In the normal prostate, AR is expressed in >99% of luminal cells, 51% of basal cells, and 61% of fibroblasts. An increase in the percentage of AR negative (%AR−) cancer cells along with a gradual loss of fibroblastic AR were observed with increasing Gleason grade and hormonal treatment. This was accompanied by a parallel increase in staining intensity of AR positive (AR+) cells under ADT. Staining AR with N- and C-terminal antibodies yielded similar results. The combination of %AR− cancer cells, %AR− fibroblasts, and AR intensity score led to the definition of an AR index, which was predictive of biochemical recurrence in the RP cohort and further stratified patients of intermediate risk. Lastly, androgen receptor variant 7 (ARV7)+ cells and AR− cells expressing neuroendocrine and stem markers were interspersed among a majority of AR+ cells in ADT cases. Altogether, the comprehensive quantification of AR expression in the prostate reveals concomitant changes in tumor cell subtypes and fibroblasts, emphasizing the significance of AR− cells with disease progression and palliative ADT.

The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains – even to their developers – often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive ‘what-if’-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.

ADP-ribosylation (ADPR) of proteins is catalyzed by ADP-ribosyltransferases, which are targeted by inhibitors (i.e. poly(ADP-ribose) polymerase inhibitors [PARPi]). Although renal cell carcinoma (RCC) cells are sensitive in vitro to PARPi, studies on the association between ADPR levels and somatic loss of function mutations in DNA damage repair genes are currently missing. Here we observed, in two clear cell RCC (ccRCC) patient cohorts (n = 257 and n = 241) stained with an engineered ADP-ribose binding macrodomain (eAf1521), that decreased cytoplasmic ADPR (cyADPR) levels significantly correlated with late tumor stage, high-ISUP (the International Society of Urological Pathology) grade, presence of necrosis, dense lymphocyte infiltration, and worse patient survival (p < 0.01 each). cyADPR proved to be an independent prognostic factor (p = 0.001). Comparably, absence of nuclear ADPR staining in ccRCC correlated with absence of PARP1 staining (p < 0.01) and worse patient outcome (p < 0.05). In papillary RCC the absence of cyADPR was also significantly associated with tumor progression and worse patient outcome (p < 0.05 each). To interrogate whether the ADPR status could be associated with genetic alterations in DNA repair, chromatin remodeling, and histone modulation, we performed DNA sequence analysis and identified a significant association of increased ARID1A mutations in ccRCC^{cyADPR+++/PARP1+} compared with ccRCC^{cyADPR−/PARP1−} (31% versus 4%; p < 0.05). Collectively, our data suggest the prognostic value of nuclear and cytoplasmic ADPR levels in RCC that might be further influenced by genetic alterations.

The clinical phenotype of somatic mutations in endometriosis is unknown. The objective was to determine whether somatic KRAS mutations were associated with greater disease burden in endometriosis (i.e. more severe subtypes and higher stage). This prospective longitudinal cohort study included 122 subjects undergoing endometriosis surgery at a tertiary referral center between 2013 and 2017, with 5–9 years of follow-up. Somatic activating KRAS codon 12 mutations were detected in endometriosis lesions using droplet digital PCR. KRAS mutation status for each subject was coded as present (KRAS mutation in at least one endometriosis sample in a subject) or absent. Standardized clinical phenotyping for each subject was carried out via linkage to a prospective registry. Primary outcome was anatomic disease burden, based on distribution of subtypes (deep infiltrating endometriosis, ovarian endometrioma, and superficial peritoneal endometriosis) and surgical staging (Stages I–IV). Secondary outcomes were markers of surgical difficulty, demographics, pain scores, and risk of re-operation. KRAS mutation presence was higher in subjects with deep infiltrating endometriosis or endometrioma lesions only (57.9%; 11/19) and subjects with mixed subtypes (60.6%; 40/66), compared with those with superficial endometriosis only (35.1%; 13/37) (p = 0.04). KRAS mutation was present in 27.6% (8/29) of Stage I cases, in comparison to 65.0% (13/20) of Stage II, 63.0% (17/27) of Stage III, and 58.1% (25/43) of Stage IV cases (p = 0.02). KRAS mutation was also associated with greater surgical difficulty (ureterolysis) (relative risk [RR] = 1.47, 95% CI: 1.02–2.11) and non-Caucasian ethnicity (RR = 0.64, 95% CI: 0.47–0.89). Pain severities did not differ based on KRAS mutation status, at either baseline or follow-up. Re-operation rates were low overall, occurring in 17.2% with KRAS mutation compared with 10.3% without (RR = 1.66, 95% CI: 0.66–4.21). In conclusion, KRAS mutations were associated with greater anatomic severity of endometriosis, resulting in increased surgical difficulty. Somatic cancer-driver mutations may inform a future molecular classification of endometriosis.

Oesophageal adenocarcinomas may show different histopathological patterns, including excessive acellular mucin pools, signet-ring cells (SRCs), and poorly cohesive cells (PCCs). These components have been suggested to correlate with poor outcomes after neoadjuvant chemoradiotherapy (nCRT), which might influence patient management. However, these factors have not been studied independently of each other with adjustment for tumour differentiation grade (i.e. the presence of well-formed glands), which is a possible confounder. We studied the pre- and post-treatment presence of extracellular mucin, SRCs, and/or PCCs in relation to pathological response and prognosis after nCRT in patients with oesophageal or oesophagogastric junction adenocarcinoma. A total of 325 patients were retrospectively identified from institutional databases of two university hospitals. All patients were scheduled for ChemoRadiotherapy for Oesophageal cancer followed by Surgery Study (CROSS) nCRT and oesophagectomy between 2001 and 2019. Percentages of well-formed glands, extracellular mucin, SRCs, and PCCs were scored in pre-treatment biopsies and post-treatment resection specimens. The association between histopathological factors (≥1 and >10%) and tumour regression grade 3–4 (i.e. >10% residual tumour), overall survival, and disease-free survival (DFS) was evaluated, adjusted for tumour differentiation grade amongst other clinicopathological variables. In pre-treatment biopsies, ≥1% extracellular mucin was present in 66 of 325 patients (20%); ≥1% SRCs in 43 of 325 (13%), and ≥1% PCCs in 126 of 325 (39%). We show that pre-treatment histopathological factors were unrelated to tumour regression grade. Pre-treatment presence of >10% PCCs was associated with lower DFS (hazard ratio [HR] 1.73, 95% CI 1.19–2.53). Patients with post-treatment presence of ≥1% SRCs had higher risk of death (HR 1.81, 95% CI 1.10–2.99). In conclusion, pre-treatment presence of extracellular mucin, SRCs, and/or PCCs is unrelated to pathological response. The presence of these factors should not be an argument to refrain from CROSS. At least 10% PCCs pre-treatment and any SRCs post-treatment, irrespective of the tumour differentiation grade, seem indicative of inferior prognosis, but require further validation in larger cohorts.

Our objective was to test whether p53 expression status is associated with survival for women diagnosed with the most common ovarian carcinoma histotypes (high-grade serous carcinoma [HGSC], endometrioid carcinoma [EC], and clear cell carcinoma [CCC]) using a large multi-institutional cohort from the Ovarian Tumor Tissue Analysis (OTTA) consortium. p53 expression was assessed on 6,678 cases represented on tissue microarrays from 25 participating OTTA study sites using a previously validated immunohistochemical (IHC) assay as a surrogate for the presence and functional effect of TP53 mutations. Three abnormal expression patterns (overexpression, complete absence, and cytoplasmic) and the normal (wild type) pattern were recorded. Survival analyses were performed by histotype. The frequency of abnormal p53 expression was 93.4% (4,630/4,957) in HGSC compared to 11.9% (116/973) in EC and 11.5% (86/748) in CCC. In HGSC, there were no differences in overall survival across the abnormal p53 expression patterns. However, in EC and CCC, abnormal p53 expression was associated with an increased risk of death for women diagnosed with EC in multivariate analysis compared to normal p53 as the reference (hazard ratio [HR] = 2.18, 95% confidence interval [CI] 1.36–3.47, p = 0.0011) and with CCC (HR = 1.57, 95% CI 1.11–2.22, p = 0.012). Abnormal p53 was also associated with shorter overall survival in The International Federation of Gynecology and Obstetrics stage I/II EC and CCC. Our study provides further evidence that functional groups of TP53 mutations assessed by abnormal surrogate p53 IHC patterns are not associated with survival in HGSC. In contrast, we validate that abnormal p53 IHC is a strong independent prognostic marker for EC and demonstrate for the first time an independent prognostic association of abnormal p53 IHC with overall survival in patients with CCC.

Prognostic tools are an essential component of the clinical management of patients with renal cell carcinoma (RCC). Although tumour stage and grade can provide important information, they fail to consider patient- and tumour-specific biology. In this study, we set out to find a novel molecular marker of RCC by using hepatocyte nuclear factor 4A (HNF4A), a transcription factor implicated in RCC progression and malignancy, as a blueprint. Through transcriptomic analyses, we show that the nuclear factor I A (NFIA)-driven transcription network is active in primary RCC and that higher levels of NFIA confer a survival benefit. We validate our findings using immunohistochemical staining and analysis of a 363-patient tissue microarray (TMA), showing for the first time that NFIA can independently predict poor cancer-specific survival in clear cell RCC (ccRCC) patients (hazard ratio = 0.46, 95% CI = 0.24–0.85, p value = 0.014). Furthermore, we confirm the association of HNF4A with higher grades and stages in ccRCC in our TMA cohort. We present novel data that show HNF4A protein expression does not confer favourable prognosis in papillary RCC, confirming our survival analysis with publicly available HNF4A RNA expression data. Further work is required to elucidate the functional role of NFIA in RCC as well as the testing of these markers on patient material from diverse multi-centre cohorts, to establish their value for the prognostication of RCC.

In recent years, the treatment of breast cancer has advanced dramatically and neoadjuvant chemotherapy (NAC) has become a common treatment method, especially for locally advanced breast cancer. However, other than the subtype of breast cancer, no clear factor indicating sensitivity to NAC has been identified. In this study, we attempted to use artificial intelligence (AI) to predict the effect of preoperative chemotherapy from hematoxylin and eosin images of pathological tissue obtained from needle biopsies prior to chemotherapy. Application of AI to pathological images typically uses a single machine-learning model such as support vector machines (SVMs) or deep convolutional neural networks (CNNs). However, cancer tissues are extremely diverse and learning with a realistic number of cases limits the prediction accuracy of a single model. In this study, we propose a novel pipeline system that uses three independent models each focusing on different characteristics of cancer atypia. Our system uses a CNN model to learn structural atypia from image patches and SVM and random forest models to learn nuclear atypia from fine-grained nuclear features extracted by image analysis methods. It was able to predict the NAC response with 95.15% accuracy on a test set of 103 unseen cases. We believe that this AI pipeline system will contribute to the adoption of personalized medicine in NAC therapy for breast cancer.

Gamma-butyrobetaine dioxygenase (BBOX1) is a catalyst for the conversion of gamma-butyrobetaine to l-carnitine, which is detected in normal renal tubules. The purpose of this study was to analyze the prognosis, immune response, and genetic alterations associated with low BBOX1 expression in patients with clear cell renal cell carcinoma (RCC). We analyzed the relative influence of BBOX1 on survival using machine learning and investigated drugs that can inhibit renal cancer cells with low BBOX1 expression. We analyzed clinicopathologic factors, survival rates, immune profiles, and gene sets according to BBOX1 expression in a total of 857 patients with kidney cancer from the Hanyang University Hospital cohort (247 cases) and The Cancer Genome Atlas (610 cases). We employed immunohistochemical staining, gene set enrichment analysis, in silico cytometry, pathway network analyses, in vitro drug screening, and gradient boosting machines. BBOX1 expression in RCC was decreased compared with that in normal tissues. Low BBOX1 expression was associated with poor prognosis, decreased CD8+ T cells, and increased neutrophils. In gene set enrichment analyses, low BBOX1 expression was related to gene sets with oncogenic activity and a weak immune response. In pathway network analysis, BBOX1 was linked to regulation of various T cells and programmed death-ligand 1. In vitro drug screening showed that midostaurin, BAY-61-3606, GSK690693, and linifanib inhibited the growth of RCC cells with low BBOX1 expression. Low BBOX1 expression in patients with RCC is related to short survival time and reduced CD8+ T cells; midostaurin, among other drugs, may have enhanced therapeutic effects in this context.

Cancer progression is influenced by junctional adhesion molecule (JAM) family members. The relationship between JAM family members and different types of cancer was examined using The Cancer Genome Atlas dataset. mRNA levels of the F11R (F11 receptor) in tumours were inversely correlated to the expression of JAM-2 and JAM-3. This relationship was unique to breast cancer (BCa) and was associated with poor prognosis (p = 0.024, hazard ratio = 1.44 [1.05–1.99]). A 50-gene molecular signature (prediction analysis of microarray 50) was used to subtype BCa. F11R mRNA expression significantly increased in human epidermal growth factor receptor 2 (HER2)-enriched (p = 0.0035) and basal-like BCa tumours (p = 0.0005). We evaluated F11R protein levels in two different compositions of BCa subtype patient tissue array cohorts to determine the relationship between BCa subtype and prognosis. Immunohistochemistry staining revealed that a high F11R protein level was associated with poor overall survival (p < 0.001; Taipei Medical University [TMU] cohort, p < 0.001; Kaohsiung Veterans General Hospital [KVGH] cohort) or disease-free survival (p < 0.001 [TMU cohort], p = 0.034 [KVGH cohort]) in patients with BCa. Comparison of F11R levels in different subtypes revealed the association of poor prognosis with high levels of F11R among luminal (p < 0.001 [TMU cohort], p = 0.027 [KVGH cohort]), HER2 positive (p = 0.018 [TMU cohort], p = 0.037 [KVGH cohort]), and triple-negative (p = 0.013 [TMU cohort], p = 0.037 [KVGH cohort]) BCa. F11R-based RNA microarray analysis and Ingenuity Pathway Analysis were successful in profiling the detailed gene ontology of triple-negative BCa cells regulated by F11R. The EP300 transcription factor was highly correlated with F11R in BCa (R = 0.51, p < 0.001). By analysing these F11R-affected molecules with the L1000CDs datasets, we were able to predict some repurposing drugs for potential application in F11R-positive BCa treatment.