Novel molecular classification of colorectal cancer and correlation with survival.

Introduction: Colorectal cancer (CRC) is one of the most common cancers worldwide. This study was designed to evaluate biological patterns, explore molecular classification and correlate with survival outcome in treatment naïve CRC patients. Methods: Over 11 years consecutive series of 435 CRC patients were operated on as primary surgical therapy. A total of 201 CRC patients were included, whose complete set of clinical information was available, and their good quality tumour blocks were retrieved. Immunohistochemistry was used for tumour analysis, and partitional clustering was performed using R software for cluster analysis. Results: The median age was 43 (range 10-85) years; adenocarcinoma was the most commonly seen histological type. The great majority had positive CK20, CEA, E-Cadherin, Ki67, CDX2, and p53 expression. There were four distinct molecular classes found, whereas Ki67, CDX2, and p53 play the main role in partitioning. Younger age negatively impacted survival; overall and disease-specific survival was 26 months only with 50 months' longest survival. Conclusion: Colorectal cancer is a biologically heterogeneous disease with at least four distinct molecular patterns, where cell proliferation and gene repair mechanisms appear to play the key role.

Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort.

Purpose: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials: Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results: One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the "hero" model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions: ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

BACKGROUND: Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified. METHODS: The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system. RESULTS: The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible. CONCLUSION: ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy.

The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.

Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles.

Breast Cancer is one of the most common causes of cancer death in women, representing a very complex disease with varied molecular alterations. To assist breast cancer prognosis, the classification of patients into biological groups is of great significance for treatment strategies. Recent studies have used an ensemble of multiple clustering algorithms to elucidate the most characteristic biological groups of breast cancer. However, the combination of various clustering methods resulted in a number of patients remaining unclustered. Therefore, a framework still needs to be developed which can assign as many unclustered (i.e. biologically diverse) patients to one of the identified groups in order to improve classification. Therefore, in this paper we develop a novel classification framework which introduces a new ensemble classification stage after the ensemble clustering stage to target the unclustered patients. Thus, a step-by-step pipeline is introduced which couples ensemble clustering with ensemble classification for the identification of core groups, data distribution in them and improvement in final classification results by targeting the unclustered data. The proposed pipeline is employed on a novel real world breast cancer dataset and subsequently its robustness and stability are examined by testing it on standard datasets. The results show that by using the presented framework, an improved classification is obtained. Finally, the results have been verified using statistical tests, visualisation techniques, cluster quality assessment and interpretation from clinical experts.

The combined expression of solute carriers is associated with a poor prognosis in highly proliferative ER+ breast cancer.

PURPOSE: Breast cancer (BC) is a heterogeneous disease characterised by variant biology, metabolic activity, and patient outcome. Glutamine availability for growth and progression of BC is important in several BC subtypes. This study aimed to evaluate the biological and prognostic role of the combined expression of key glutamine transporters, SLC1A5, SLC7A5, and SLC3A2 in BC with emphasis on the intrinsic molecular subtypes. METHODS: SLC1A5, SLC7A5, and SLC3A2 were assessed at the protein level, using immunohistochemistry on tissue microarrays constructed from a large well-characterised BC cohort (n = 2248). Patients were stratified into accredited clusters based on protein expression and correlated with clinicopathological parameters, molecular subtypes, and patient outcome. RESULTS: Clustering analysis of SLC1A5, SLC7A5, and SLC3A2 identified three clusters low SLCs (SLC1A5-/SLC7A5-/SLC3A2-), high SLC1A5 (SLC1A5+/SLC7A5-/SLC3A2-), and high SLCs (SLC1A5+/SLC7A5+/SLC3A2+) which had distinct correlations to known prognostic factors and patient outcome (p < 0.001). The key regulator of tumour cell metabolism, c-MYC, was significantly expressed in tumours in the high SLC cluster (p < 0.001). When different BC subtypes were considered, the association with the poor outcome was observed in the ER+ high proliferation/luminal B class only (p = 0.003). In multivariate analysis, SLC clusters were independent risk factor for shorter BC-specific survival (p = 0.001). CONCLUSION: The co-operative expression of SLC1A5, SLC7A5, and SLC3A2 appears to play a role in the aggressive subclass of ER+ high proliferation/luminal BC, driven by c-MYC, and therefore have the potential to act as therapeutic targets, particularly in synergism.

An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA.

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham prognostic index plus, which uses breast cancer relevant biomarkers to stain tumor tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumor, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time-consuming, imprecise, and subjective process, which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system, which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumor and non-tumor), a second FCN to extract tumor nuclei region, and a multi-column convolutional neural network, which takes the outputs of the first two FCNs and the stain intensity description image as an input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as the input and directly outputs a clinical score. We will present experimental results, which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.

Development of a pre-operative scoring system for predicting risk of post-operative paediatric cerebellar mutism syndrome.

BACKGROUND: Despite previous identification of pre-operative clinical and radiological predictors of post-operative paediatric cerebellar mutism syndrome (CMS), a unifying pre-operative risk stratification model for use during surgical consent is currently lacking. The aim of the project is to develop a simple imaging-based pre-operative risk scoring scheme to stratify patients in terms of post-operative CMS risk. METHODS: Pre-operative radiological features were recorded for a retrospectively assembled cohort of 89 posterior fossa tumour patients from two major UK treatment centers (age 2-23yrs; gender 28 M, 61 F; diagnosis: 38 pilocytic astrocytoma, 32 medulloblastoma, 12 ependymoma, 1 high grade glioma, 1 pilomyxoid astrocytoma, 1 atypical teratoid rhabdoid tumour, 1 hemangioma, 1 neurilemmoma, 2 oligodendroglioma). Twenty-six (29%) developed post-operative CMS. Based upon results from univariate analysis and C4.5 decision tree, stepwise logistic regression was used to develop the optimal model and generate risk scores. RESULTS: Univariate analysis identified five significant risk factors and C4.5 decision tree analysis identified six predictors. Variables included in the final model are MRI primary location, bilateral middle cerebellar peduncle involvement (invasion and/or compression), dentate nucleus invasion and age at imaging >12.4 years. This model has an accuracy of 88.8% (79/89). Using risk score cut-off of 203 and 238, respectively, allowed discrimination into low (38/89, predicted CMS probability <3%), intermediate (17/89, predicted CMS probability 3-52%) and high-risk (34/89, predicted CMS probability ≥52%). CONCLUSIONS: A risk stratification model for post-operative paediatric CMS could flag patients at increased or reduced risk pre-operatively which may influence strategies for surgical treatment of cerebellar tumours. Following future testing and prospective validation, this risk scoring scheme will be proposed for use during the surgical consenting process.

A multicentre integration of a computer-led follow-up of prostate cancer is valid and safe.

OBJECTIVE: To test a computer-led follow-up service for prostate cancer in two UK hospitals; the testing aimed to validate the computer expert system in making clinical decisions according to the individual patient's clinical need with a valid model accurately identify patients with disease recurrence or treatment failure based on their blood test and clinical picture. PATIENTS AND METHODS: A clinical-decision support system (CDSS) was developed from European (European Association of Urology) and national (National Institute for Health and Care Excellence) guidelines along with knowledge acquired from Urologists. This model was then applied in two UK hospitals to review patients after prostate cancer treatment. These patients' data (n = 200) were then reviewed by two independent urology consultants (blinded from the CDSS and the other consultant's rating) and the agreement was calculated by kappa statistics for validation. The second endpoint was to verify the system by estimating the system reliability. RESULTS: The two individual urology consultants identified 12% and 15% of the patients to have potential disease progression and recommended their referral to urology care. The kappa coefficient for the agreement between the CDSS and the two consultants was 0.81 (P < 0.001) and 0.84 (P < 0.001). The agreement amongst both specialist was also high with k = 0.83 (P < 0.001). The system reliability was estimated on all cases and this demonstrated 100% repeatability of the decisions. CONCLUSION: A CDSS follow-up is a valid model for providing safe follow-up for prostate cancer.

MYC regulation of glutamine-proline regulatory axis is key in luminal B breast cancer.

BACKGROUND: Altered cellular metabolism is a hallmark of cancer and some are reliant on glutamine for sustained proliferation and survival. We hypothesise that the glutamine-proline regulatory axis has a key role in breast cancer (BC) in the highly proliferative classes. METHODS: Glutaminase (GLS), pyrroline-5-carboxylate synthetase (ALDH18A1), and pyrroline-5-carboxylate reductase 1 (PYCR1) were assessed at DNA/mRNA/protein levels in large, well-characterised cohorts. RESULTS: Gain of PYCR1 copy number and high PYCR1 mRNA was associated with Luminal B tumours. High ALDH18A1 and high GLS protein expression was observed in the oestrogen receptor (ER)+/human epidermal growth factor receptor (HER2)- high proliferation class (Luminal B) compared with ER+/HER2- low proliferation class (Luminal A) (P=0.030 and P=0.022 respectively), however this was not observed with mRNA. Cluster analysis of the glutamine-proline regulatory axis genes revealed significant associations with molecular subtypes of BC and patient outcome independent of standard clinicopathological parameters (P=0.012). High protein expression of the glutamine-proline enzymes were all associated with high MYC protein in Luminal B tumours only (P<0.001). CONCLUSIONS: We provide comprehensive clinical data indicating that the glutamine-proline regulatory axis plays an important role in the aggressive subclass of luminal BC and is therefore a potential therapeutic target.

Nottingham Prognostic Index Plus: Validation of a clinical decision making tool in breast cancer in an independent series.

The Nottingham Prognostic Index Plus (NPI+) is a clinical decision making tool in breast cancer (BC) that aims to provide improved patient outcome stratification superior to the traditional NPI. This study aimed to validate the NPI+ in an independent series of BC. Eight hundred and eighty five primary early stage BC cases from Edinburgh were semi-quantitatively assessed for 10 biomarkers [Estrogen Receptor (ER), Progesterone Receptor (PgR), cytokeratin (CK) 5/6, CK7/8, epidermal growth factor receptor (EGFR), HER2, HER3, HER4, p53, and Mucin 1] using immunohistochemistry and classified into biological classes by fuzzy logic-derived algorithms previously developed in the Nottingham series. Subsequently, NPI+ Prognostic Groups (PGs) were assigned for each class using bespoke NPI-like formulae, previously developed in each NPI+ biological class of the Nottingham series, utilising clinicopathological parameters: number of positive nodes, pathological tumour size, stage, tubule formation, nuclear pleomorphism and mitotic counts. Biological classes and PGs were compared between the Edinburgh and Nottingham series using Cramer's V and their role in patient outcome prediction using Kaplan-Meier curves and tested using Log Rank. The NPI+ biomarker panel classified the Edinburgh series into seven biological classes similar to the Nottingham series (p > 0.01). The biological classes were significantly associated with patient outcome (p < 0.001). PGs were comparable in predicting patient outcome between series in Luminal A, Basal p53 altered, HER2+/ER+ tumours (p > 0.01). The good PGs were similarly validated in Luminal B, Basal p53 normal, HER2+/ER- tumours and the poor PG in the Luminal N class (p > 0.01). Due to small patient numbers assigned to the remaining PGs, Luminal N, Luminal B, Basal p53 normal and HER2+/ER- classes could not be validated. This study demonstrates the reproducibility of NPI+ and confirmed its prognostic value in an independent cohort of primary BC. Further validation in large randomised controlled trial material is warranted.

A quantifier-based fuzzy classification system for breast cancer patients.

OBJECTIVES: Recent studies of breast cancer data have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a range of different clustering techniques. Consensus between unsupervised classification algorithms has been successfully used to categorise patients into these specific groups, but often at the expenses of not classifying the whole set. It is known that fuzzy methodologies can provide linguistic based classification rules. The objective of this study was to investigate the use of fuzzy methodologies to create an easy to interpret set of classification rules, capable of placing the large majority of patients into one of the specified groups. MATERIALS AND METHODS: In this paper, we extend a data-driven fuzzy rule-based system for classification purposes (called 'fuzzy quantification subsethood-based algorithm') and combine it with a novel class assignment procedure. The whole approach is then applied to a well characterised breast cancer dataset consisting of ten protein markers for over 1000 patients to refine previously identified groups and to present clinicians with a linguistic ruleset. A range of statistical approaches was used to compare the obtained classes to previously obtained groupings and to assess the proportion of unclassified patients. RESULTS: A rule set was obtained from the algorithm which features one classification rule per class, using labels of High, Low or Omit for each biomarker, to determine the most appropriate class for each patient. When applied to the whole set of patients, the distribution of the obtained classes had an agreement of 0.9 when assessed using Kendall's Tau with the original reference class distribution. In doing so, only 38 patients out of 1073 remain unclassified, representing a more clinically usable class assignment algorithm. CONCLUSION: The fuzzy algorithm provides a simple to interpret, linguistic rule set which classifies over 95% of breast cancer patients into one of seven clinical groups.

Characteristics of basal cytokeratin expression in breast cancer.

Breast cancer is recognised to be a heterogeneous disease and the second most common cause of morbidity and mortality worldwide in women. Basal-like breast cancer (BLBC) is associated with aggressive characteristics including development of recurrent disease and reduced survival. BLBC has been defined in some studies as tumours lacking both oestrogen receptor and progesterone receptor protein expression. Gene expression studies have shown that these tumours are also associated with expression of basal-type cytokeratins, the phenotypic patterns of basal cytokeratin expression in BLBC have not been widely studied. A well-characterised series of 995 invasive breast cancers with a long-term follow up were investigated using immunohistochemical staining for four basal cytokeratins (CK5, CK5/6, CK14 and CK17). The data were analysed using univariate and clustering analysis. As a result BLBC, as defined by negativity for ER and HER2 showed variable positivity for basal cytokeratin expression: 61.7 % CK5, 50.5 % CK5/6, 24.2 % CK14 and 23 % CK17. These characteristics were associated with poor outcome characteristics including high histological grade, mitosis, pleomorphism and tumour size >1.5 cm. CK5 positivity was more associated with ER(-), PgR(-), TN and double ER(-)PgR(-), than the other cytokeratins. Four different clusters of basal cytokeratin expression patterns were identified: (1) negativity for all basal cytokeratins, (2) CK5(+)/CK17(-), (3) CK5(-)/CK17(+) and (4) CK5(+)/CK17(+). These patterns of basal cytokeratin expression associated with differences in patient outcome, clusters 1 and 3 showed better outcomes than cluster 4 and 2, with cluster 2 having the poorest prognosis. In conclusion, four basal cytokeratin expression patterns were identified in human breast cancer using unsupervised clustering analysis and these patterns are associated with differences in patient outcome.

p53 status identifies two subgroups of triple-negative breast cancers with distinct biological features.

OBJECTIVE: Despite the clinical similarities triple-negative and basal-like breast cancer are not synonymous. Indeed, not all basal-like cancers are negative for estrogen receptor, progesterone receptor and HER2 expression while triple-negative also encompasses other cancer types. P53 protein appears heterogeneously expressed in triple-negative breast cancers, suggesting that it may be associated with specific biological subgroups with a different outcome. METHODS: We comparatively analyzed p53 expression in triple-negative tumors from two independent breast cancer case series (633 cases from the University of Ferrara and 1076 cases from the University of Nottingham). RESULTS: In both case series, p53 protein expression was able to subdivide the triple-negative cases into two distinct subsets consistent with a different outcome. In fact, triple-negative patients with a p53 expressing tumor showed worse overall and event-free survival. CONCLUSIONS: The immunohistochemical evaluation of p53 expression may help in taming the currently stormy relationship between pathological (triple-negative tumors) and biological (basal breast cancers) classifications and in selecting patient subgroups with different biological features providing a potentially powerful prognostic contribution in triple-negative breast cancers.

A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients.

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of 'core classes' by using a range of techniques to reach consensus across several different clustering algorithms, and to ascertain the key characteristics of these classes. We apply the methodology to immunohistochemical data from breast cancer patients. In doing so, we identify six core classes, of which several may be novel sub-groups not previously emphasised in literature.

Global histone modifications in breast cancer correlate with tumor phenotypes, prognostic factors, and patient outcome.

Post-translational histone modifications are known to be altered in cancer cells, and loss of selected histone acetylation and methylation marks has recently been shown to predict patient outcome in human carcinoma. Immunohistochemistry was used to detect a series of histone lysine acetylation (H3K9ac, H3K18ac, H4K12ac, and H4K16ac), lysine methylation (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) marks in a well-characterized series of human breast carcinomas (n = 880). Tissue staining intensities were assessed using blinded semiquantitative scoring. Validation studies were done using immunofluorescence staining and Western blotting. Our analyses revealed low or absent H4K16ac in the majority of breast cancer cases (78.9%), suggesting that this alteration may represent an early sign of breast cancer. There was a highly significant correlation between histone modifications status, tumor biomarker phenotype, and clinical outcome, where high relative levels of global histone acetylation and methylation were associated with a favorable prognosis and detected almost exclusively in luminal-like breast tumors (93%). Moderate to low levels of lysine acetylation (H3K9ac, H3K18ac, and H4K12ac), lysine (H3K4me2 and H4K20me3), and arginine methylation (H4R3me2) were observed in carcinomas of poorer prognostic subtypes, including basal carcinomas and HER-2-positive tumors. Clustering analysis identified three groups of histone displaying distinct pattern in breast cancer, which have distinct relationships to known prognostic factors and clinical outcome. This study identifies the presence of variations in global levels of histone marks in different grades, morphologic types, and phenotype classes of invasive breast cancer and shows that these differences have clinical significance.