Introduction To establish the impact of the number of lymph node metastases (nLNM) and the lymph node ratio (LNR) on survival in patients with early‐stage cervical cancer after surgery. Material and methods In this nationwide historical cohort study, all women diagnosed between 1995 and 2020 with International Federation of Gynecology and Obstetrics (FIGO) 2009 stage IA2–IIA1 cervical cancer and nodal metastases after radical hysterectomy and pelvic lymphadenectomy from the Netherlands Cancer Registry were selected. Optimal cut‐offs for prognostic stratification by nLNM and LNR were calculated to categorize patients into low‐risk or high‐risk groups. Kaplan–Meier overall survival analysis and flexible parametric relative survival analysis were used to determine the impact of nLNM and LNR on survival. Missing data were imputed. Results The optimal cut‐off point was ≥4 for nLNM and ≥0.177 for LNR. Of the 593 women included, 500 and 501 (both 84%) were categorized into the low‐risk and 93 and 92 (both 16%) into the high‐risk groups for nLNM and LNR, respectively. Both high‐risk groups had a worse 5‐year overall survival (p < 0.001) compared with the low‐risk groups. Being classified into the high‐risk groups is an independent risk factor for relative survival, with excess hazard ratios of 2.4 (95% confidence interval 1.6–3.5) for nLNM and 2.5 (95% confidence interval 1.7–3.8) for LNR. Conclusions Presenting a patient's nodal status postoperatively by the number of positive nodes, or by the nodal ratio, can support further risk stratification regarding survival in the case of node‐positive early‐stage cervical cancer.

A renal core biopsy for histological evaluation is the gold standard for diagnosing renal transplant pathology. However, renal biopsy interpretation is subjective and can render insufficient precision, making it difficult to apply a targeted therapeutic regimen for the individual patient. This warrants a need for additional methods assessing disease state in the renal transplant. Significant research activity has been focused on the role of molecular analysis in the diagnosis of renal allograft rejection. The identification of specific molecular expression patterns in allograft biopsies related to different types of allograft injury could provide valuable information about the processes underlying renal transplant dysfunction and can be used for the development of molecular classifier scores, which could improve our diagnostic and prognostic ability and could guide treatment. Molecular profiling has the potential to be more precise and objective than histological evaluation and may identify injury even before it becomes visible on histology, making it possible to start treatment at the earliest time possible. Combining conventional diagnostics (histology, serology, and clinical data) and molecular evaluation will most likely offer the best diagnostic approach. We believe that the use of state-of-the-art molecular analysis will have a significant impact in diagnostics after renal transplantation. In this review, we elaborate on the molecular phenotype of both acute and chronic T cell-mediated rejection and antibody-mediated rejection and discuss the additive value of molecular profiling in the setting of diagnosing renal allograft rejection and how this will improve transplant patient care.

Background Calcium oxalate (CaOx) deposition in the kidney may lead to loss of native renal function but little is known about the prevalence and role of CaOx deposition in transplanted kidneys. Methods In patients transplanted in 2014 and 2015, all for-cause renal allograft biopsies obtained within 3 months post-transplantation were retrospectively investigated for CaOx deposition. Additionally, all preimplantation renal biopsies obtained in 2000 and 2001 were studied. Results In 2014 and 2015, 388 patients were transplanted, of whom 149 had at least one for-cause renal biopsy. Twenty-six (17%) patients had CaOx deposition. In the population with CaOx deposition: Patients had significantly more often been treated with dialysis before transplantation (89 vs. 64%; p = 0.011); delayed graft function occurred more frequently (42 vs. 23%; p = 0.038); and the eGFR at the time of first biopsy was significantly worse (21 vs. 29 ml/min/1.73m 2 ; p = 0.037). In a multivariate logistic regression analysis, eGFR at the time of first biopsy (OR 0.958, 95%-Cl: 0.924–0.993, p = 0.019), dialysis before transplantation (OR 4.868, 95%-Cl: 1.128–21.003, p = 0.034) and the time of first biopsy after transplantation (OR 1.037, 95%-Cl: 1.013–1.062, p = 0.002) were independently associated with CaOx deposition. Graft survival censored for death was significantly worse in patients with CaOx deposition (p = 0.018). In only 1 of 106 preimplantation biopsies CaOx deposition was found (0.94%). Conclusion CaOx deposition appears to be primarily recipient-derived and is frequently observed in for-cause renal allograft biopsies obtained within 3 months post-transplantation. It is associated with inferior renal function at the time of biopsy and worse graft survival.

Complement- and apoptosis-related gene expression is elevated in deceased donor transplants before transplantation. High BAX:BCL2 ratio and TLR4 expression during AR may reflect enhanced intragraft cell death and immunogenic danger signals, and pose a risk factor for adverse graft outcome.

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.