The value of molecular markers in classification and prediction of progression in non-muscle-invasive bladder cancer

Rapid Detection of Recurrent Non-Muscle Invasive Bladder Cancer in Urine Using ATR-FTIR Technology

Non-muscle Invasive Bladder Cancer (NMIBC) accounts for 80% of all bladder cancers. Although it is mostly low-grade tumors, its high recurrence rate necessitates three-times-monthly follow-ups and cystoscopy examinations to detect and prevent its progression. A rapid liquid biopsy-based assay is needed to improve detection and reduce complications from invasive cystoscopy. Here, we present a rapid spectroscopic method to detect the recurrence of NMIBC in urine. Urine samples from previously-diagnosed NMIBC patients (n = 62) were collected during their follow-up visits before cystoscopy examination. Cystoscopy results were recorded (41 cancer-free and 21 recurrence) and attenuated total refraction Fourier transform infrared (ATR-FTIR) spectra were acquired from urine samples using direct application. Spectral processing and normalization were optimized using parameter grid searching. We assessed their technical variability through multivariate analysis and principal component analysis (PCA). We assessed 35 machine learning models on a training set (70%), and the performance was evaluated on a held-out test set (30%). A Regularized Random Forests (RRF) model achieved a 0.92 area under the receiver operating characteristic (AUROC) with 86% sensitivity and 77% specificity. In conclusion, our spectroscopic liquid biopsy approach provides a promising technique for the early identification of NMIBC with a less invasive examination.

Computational Analysis Identifies Novel Biomarkers for High-Risk Bladder Cancer Patients

In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result, we obtain a small set of seven of the most-useful biomarkers for diagnostics. They can also be used to build tests that avoid the costly and time-consuming existing methods. We summarize the current biological knowledge of the chosen biomarkers and contrast it with our findings.

The diagnostic applicability of A-type Lamin in non-muscle invasive bladder cancer

PURPOSE

Lamin A is a major component of the nuclear lamina maintaining nuclear integrity, regulation of gene expression, cell proliferation, and apoptosis. Its deregulation in cancer has been recently reported to be associated with its prognosis. However, its clinical significance in non-muscle invasive bladder cancer (NMIBC) remains to be defined.

MATERIAL/METHODS

Immunohistochemical staining and RT-qPCR were performed to screen the expression patterns of Lamin A/C protein and Lamin A mRNA respectively in 58 high and low grade NMIBC specimens.

RESULTS

Lamin A/C protein was expressed only in the nucleus and less exhibited in NMIBC tissues compared to non-tumoral ones. On the other side, Lamin A mRNA was up-regulated in NMIBC compared to controls. Nevertheless, both expression patterns (protein and mRNA) were not correlated to clinical prognosis factors and were not able to predict the overall survival of patients with high-grade NMIBC.

CONCLUSIONS

The deregulation of A-type Lamin is not associated with the prognosis of NMIBC, but it could serve as a diagnostic biomarker distinguishing NMIBC patients from healthy subjects suggesting its involvement as an initiator event of tumorigenesis in our cohort.

Nomograms in Urologic Oncology: Lights and Shadows

Decision-making in urologic oncology involves integrating multiple clinical data to provide an answer to the needs of a single patient. Although the practice of medicine has always been an “art” involving experience, clinical data, scientific evidence and judgment, the creation of specialties and subspecialties has multiplied the challenges faced every day by physicians. In the last decades, with the field of urologic oncology becoming more and more complex, there has been a rise in tools capable of compounding several pieces of information and supporting clinical judgment and experience when approaching a difficult decision. The vast majority of these tools provide a risk of a certain event based on various information integrated in a mathematical model. Specifically, most decision-making tools in the field of urologic focus on the preoperative or postoperative phase and provide a prognostic or predictive risk assessment based on the available clinical and pathological data. More recently, imaging and genomic features started to be incorporated in these models in order to improve their accuracy. Genomic classifiers, look-up tables, regression trees, risk-stratification tools and nomograms are all examples of this effort. Nomograms are by far the most frequently used in clinical practice, but are also among the most controversial of these tools. This critical, narrative review will focus on the use, diffusion and limitations of nomograms in the field of urologic oncology.

The therapeutic and prognostic implications of molecular biomarkers in urothelial carcinoma

Urothelial cell carcinoma (UCC) of the bladder and upper urinary tract is a heterogeneous disease with distinct biologic features resulting in different clinical behaviors. Bladder cancer (BC) is classified into non-muscle invasive BC (NMIBC) and muscle invasive BC (MIBC). NMIBC is associated with high recurrence rates and risk of progression to invasive disease, whereas MIBC is complicated by systemic recurrence after radical cystectomy because of the limited efficacy of available therapies. UCC of the upper urinary tract (UUT-UCC) is a rare but aggressive urologic cancer characterized by multifocality, local recurrence, and metastasis. Conventional histopathologic evaluation of UCC, including tumor stage and grade, cannot accurately predict the behavior of BC and UUT-UCC. Recent clinical and preclinical studies aimed at understanding the molecular landscape of UCC have provided insight into molecular subtyping, inter- or intratumoral heterogeneity, and potential therapeutic targets. Combined analysis of molecular markers and standard pathological features may improve risk stratification and help monitor tumor progression and treatment response, ultimately improving patient outcomes. This review discusses prognostic and therapeutic biomarkers for BC and UUT-UCC, and describes recent advances in molecular stratification that may guide prognosis, patient stratification, and treatment selection.