Better prognostic markers for nonmuscle invasive papillary urothelial carcinomas

Bladder cancer is a common type of cancer, especially among men in developed countries. Most cancers in the urinary bladder are papillary urothelial carcinomas. They are characterized by a high recurrence frequency (up to 70 %) after local resection. It is crucial for prognosis to discover these recurrent tumours at an early stage, especially before they become muscle-invasive. Reliable prognostic biomarkers for tumour recurrence and stage progression are lacking. This is why patients diagnosed with a non-muscle invasive bladder cancer follow extensive follow-up regimens with possible serious side effects and with high costs for the healthcare systems. WHO grade and tumour stage are two central biomarkers currently having great impact on both treatment decisions and follow-up regimens. However, there are concerns regarding the reproducibility of WHO grading, and stage classification is challenging in small and fragmented tumour material. In Paper I, we examined the reproducibility and the prognostic value of all the individual microscopic features making up the WHO grading system. Among thirteen extracted features there was considerable variation in both reproducibility and prognostic value. The only feature being both reasonably reproducible and statistically significant prognostic was cell polarity. We concluded that further validation studies are needed on these features, and that future grading systems should be based on well-defined features with true prognostic value. With the implementation of immunotherapy, there is increasing interest in tumour immune response and the tumour microenvironment. In a search for better prognostic biomarkers for tumour recurrence and stage progression, in Paper II, we investigated the prognostic value of tumour infiltrating immune cells (CD4, CD8, CD25 and CD138) and previously investigated cell proliferation markers (Ki-67, PPH3 and MAI). Low Ki 67 and tumour multifocality were associated with increased recurrence risk. Recurrence risk was not affected by the composition of immune cells. For stage progression, the only prognostic immune cell marker was CD25. High values for MAI was also strongly associated with stage progression. However, in a multivariate analysis, the most prognostic feature was a combination of MAI and CD25. BCG-instillations in the bladder are indicated in intermediate and high-risk non-muscle invasive bladder cancer patients. This old-fashion immunotherapy has proved to reduce both recurrence- and progression-risk, although it is frequently followed by unpleasant side-effects. As many as 30-50% of high-risk patients receiving BCG instillations, fail by develop high-grade recurrences. They do not only suffer from unnecessary side-effects, but will also have a delay in further treatment. Together with colleagues at three different Dutch hospitals, in Paper III, we looked at the prognostic and predictive value of T1-substaging. A T1-tumour invades the lamina propria, and we wanted to separate those with micro- from those with extensive invasion. We found that BCG-failure was more common among patients with extensive invasion. Furthermore, T1-substaging was associated with both high-grade recurrence-free and progression-free survival. Finally, in Paper IV, we wanted to investigate the prognostic value of two classical immunohistochemical markers, p53 and CK20, and compare them with previously investigated proliferation markers. p53 is a surrogate marker for mutations in the gene TP53, considered to be a main characteristic for muscle-invasive tumours. CK20 is a surrogate marker for luminal tumours in the molecular classification of bladder cancer, and is frequently used to distinguish reactive urothelial changes from urothelial carcinoma in situ. We found both positivity for p53 and CK20 to be significantly associated with stage progression, although not performing better than WHO grade and stage. The proliferation marker MAI, had the highest prognostic value in our study. Any combination of variables did not perform better in a multivariate analysis than MAI alone

Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes

BACKGROUND: In the clinical context, samples assayed by microarray are often classified by cell line or tumour type and it is of interest to discover a set of genes that can be used as class predictors. The leukemia dataset of Golub et al. [1] and the NCI60 dataset of Ross et al. [2] present multiclass classification problems where three tumour types and nine cell lines respectively must be identified. We apply an evolutionary algorithm to identify the near-optimal set of predictive genes that classify the data. We also examine the initial gene selection step whereby the most informative genes are selected from the genes assayed. RESULTS: In the absence of feature selection, classification accuracy on the training data is typically good, but not replicated on the testing data. Gene selection using the RankGene software [3] is shown to significantly improve performance on the testing data. Further, we show that the choice of feature selection criteria can have a significant effect on accuracy. The evolutionary algorithm is shown to perform stably across the space of possible parameter settings – indicating the robustness of the approach. We assess performance using a low variance estimation technique, and present an analysis of the genes most often selected as predictors. CONCLUSION: The computational methods we have developed perform robustly and accurately, and yield results in accord with clinical knowledge: A Z-score analysis of the genes most frequently selected identifies genes known to discriminate AML and Pre-T ALL leukemia. This study also confirms that significantly different sets of genes are found to be most discriminatory as the sample classes are refined

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues