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

A population-based study exploring phenotypic clusters and clinical outcomes in stroke using unsupervised machine learning approach

Individuals developing stroke have varying clinical characteristics, demographic, and biochemical profiles. This heterogeneity in phenotypic characteristics can impact on cardiovascular disease (CVD) morbidity and mortality outcomes. This study uses a novel clustering approach to stratify individuals with incident stroke into phenotypic clusters and evaluates the differential burden of recurrent stroke and other cardiovascular outcomes. We used linked clinical data from primary care, hospitalisations, and death records in the UK. A data-driven clustering analysis (kamila algorithm) was used in 48,114 patients aged ≥ 18 years with incident stroke, from 1-Jan-1998 to 31-Dec-2017 and no prior history of serious vascular events. Cox proportional hazards regression was used to estimate hazard ratios (HRs) for subsequent adverse outcomes, for each of the generated clusters. Adverse outcomes included coronary heart disease (CHD), recurrent stroke, peripheral vascular disease (PVD), heart failure, CVD-related and all-cause mortality. Four distinct phenotypes with varying underlying clinical characteristics were identified in patients with incident stroke. Compared with cluster 1 (n = 5,201, 10.8%), the risk of composite recurrent stroke and CVD-related mortality was higher in the other 3 clusters (cluster 2 [n = 18,655, 38.8%]: hazard ratio [HR], 1.07; 95% CI, 1.02-1.12; cluster 3 [n = 10,244, 21.3%]: HR, 1.20; 95% CI, 1.14-1.26; and cluster 4 [n = 14,014, 29.1%]: HR, 1.44; 95% CI: 1.37-1.50). Similar trends in risk were observed for composite recurrent stroke and all-cause mortality outcome, and subsequent recurrent stroke outcome. However, results were not consistent for subsequent risk in CHD, PVD, heart failure, CVD-related mortality, and all-cause mortality. In this proof of principle study, we demonstrated how a heterogenous population of patients with incident stroke can be stratified into four relatively homogenous phenotypes with differential risk of recurrent and major cardiovascular outcomes. This offers an opportunity to revisit the stratification of care for patients with incident stroke to improve patient outcomes.

Clinical characteristics and prognosis in systemic lupus erythematosus-associated pulmonary arterial hypertension based on consensus clustering and risk prediction model

BACKGROUND

Pulmonary arterial hypertension (PAH) is a severe complication of systemic lupus erythematosus (SLE). This study aims to explore the clinical characteristics and prognosis in SLE-PAH based on consensus clustering and risk prediction model.

METHODS

A total of 205 PAH (including 163 SLE-PAH and 42 idiopathic PAH) patients were enrolled retrospectively based on medical records at the First Affiliated Hospital of Zhengzhou University from July 2014 to June 2021. Unsupervised consensus clustering was used to identify SLE-PAH subtypes that best represent the data pattern. The Kaplan-Meier survival was analyzed in different subtypes. Besides, the least absolute shrinkage and selection operator combined with Cox proportional hazards regression model were performed to construct the SLE-PAH risk prediction model.

RESULTS

Clustering analysis defined two subtypes, cluster 1 (n = 134) and cluster 2 (n = 29). Compared with cluster 1, SLE-PAH patients in cluster 2 had less favorable levels of poor cardiac, kidney, and coagulation function markers, with higher SLE disease activity, less frequency of PAH medications, and lower survival rate within 2 years (86.2% vs. 92.8%) (P < 0.05). The risk prediction model was also constructed, including older age at diagnosis (≥ 38 years), anti-dsDNA antibody, neuropsychiatric lupus, and platelet distribution width (PDW).

CONCLUSIONS

Consensus clustering identified two distinct SLE-PAH subtypes which were associated with survival outcomes. Four prognostic factors for death were discovered to construct the SLE-PAH risk prediction model.

A Novel Data Analytics-derived Metric (Nearest Cluster Distance) Is Easily Implemented in Routine Practice and Correctly Identifies Breast Cancer Cases for Quality Review

Background

Errors in breast cancer grading and predictive testing are clinically important and can be difficult to detect in routine practice. A quality metric able to identify a subset of breast cancer cases which are high yield on quality review would be of practical clinical benefit.

Methods

Data analytic techniques were used to generate consensus tumor signature centers from a dataset over 500 breast cancer cases from a single practice. Cases were assigned a novel metric, Nearest Cluster Distance, corresponding to their distances from the nearest tumor signature center. The subset of tumors exceeding a cutoff for this metric were flagged, and then reviewed and rescored in a blinded fashion together with matched controls. A simplified version of this metric was created using universally accessible methods.

Results

Flagged cases showed statistically significant movement toward consensus tumor signature centers compared with controls, consistent with identification of cases which could benefit from review and possible rescoring. The simplified metric performs identically.

Conclusion

This method can be readily applied in routine practice and is promising as a real time quality check for breast cancer diagnosis and reporting.

Cluster Analysis According to Immunohistochemistry is a Robust Tool for Non-Small Cell Lung Cancer and Reveals a Distinct, Immune Signature-defined Subgroup

Clustering in medicine is the subgrouping of a cohort according to specific phenotypical or genotypical traits. For breast cancer and lymphomas, clustering by gene expression profiles has already resulted in important prognostic and predictive subgroups. For non-small cell lung cancer (NSCLC), however, little is known. We performed a cluster analysis on a cohort of 365 surgically resected, well-documented NSCLC patients, which was followed-up for a median of 62 months, incorporating 70 expressed proteins and several genes. Our data reveal that tumor grading by architecture is significant, that large cell carcinoma is likely not a separate entity, and that an immune signature cluster exists. For squamous cell carcinomas, a prognostically relevant cluster with poorer outcome was found, defined by a high CD4/CD8 ratio and lower presence of granzyme B+ tumor-infiltrating lymphocytes (TIL). This study shows that clustering analysis is a useful tool for verifying established characteristics and generating new insights for NSCLC. Importantly, for one "immune signature" cluster, the signature of the TIL (especially the amount of CD8+ TIL) was more crucial than the histologic or any other phenotypical aspect. This may be an important finding toward explaining why only a fraction of eligible patients respond to immunomodulating anticancer therapies.

Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study

BACKGROUND

CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Subtyping CKD with multidimensional patient data holds the key to precision medicine. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes.

METHODS

We used unsupervised consensus clustering on 72 baseline characteristics among 2696 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) study to identify novel CKD subgroups that best represent the data pattern. Calculation of the standardized difference of each parameter used the cutoff of ±0.3 to show subgroup features. CKD subgroup associations were examined with the clinical end points of kidney failure, the composite outcome of cardiovascular diseases, and death.

RESULTS

The algorithm revealed three unique CKD subgroups that best represented patients' baseline characteristics. Patients with relatively favorable levels of bone density and cardiac and kidney function markers, with lower prevalence of diabetes and obesity, and who used fewer medications formed cluster 1 (n=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (n=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (n=395). These three subgroups, when linked with future clinical end points, were associated with different risks of CKD progression, cardiovascular disease, and death. Furthermore, patient heterogeneity among predefined subgroups with similar baseline kidney function emerged.

CONCLUSIONS

Consensus clustering synthesized the patterns of baseline clinical and laboratory measures and revealed distinct CKD subgroups, which were associated with markedly different risks of important clinical outcomes. Further examination of patient subgroups and associated biomarkers may provide next steps toward precision medicine.

Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP

Our cluster analysis of the Cancer Genome Atlas for co-expression of HSP27 and CRYAB in breast cancer patients identified three patient groups based on their expression level combination (high HSP27 + low CRYAB; low HSP27 + high CRYAB; similar HSP27 + CRYAB). Our analyses also suggest that there is a statistically significant inverse relationship between HSP27 and CRYAB and known clinicopathological markers in breast cancer. Screening an unbiased 248 breast cancer patient tissue microarray (TMA) for the protein expression of HSP27 and phosphorylated HSP27 (HSP27-82pS) with CRYAB also identified three patient groups based on HSP27 and CRYAB expression levels. TMA24 also had recorded clinical-pathological parameters, such as ER and PR receptor status, patient survival, and TP53 mutation status. High HSP27 protein levels were significant with ER and PR expression. HSP27-82pS associated with the best patient survival (Log Rank test). High CRYAB expression in combination with wild-type TP53 was significant for patient survival, but a different patient outcome was observed when mutant TP53 was combined with high CRYAB expression. Our data suggest that HSP27 and CRYAB have different epichaperome influences in breast cancer, but more importantly evidence the value of a cluster analysis that considers their coexpression. Our approach can deliver convergence for archival datasets as well as those from recent treatment and patient cohorts and can align HSP27 and CRYAB expression to important clinical-pathological features of breast cancer.

Biology of Oestrogen-Receptor Positive Primary Breast Cancer in Older Women with Utilisation of Core Needle Biopsy Samples and Correlation with Clinical Outcome

The majority of biological profiling studies use surgical excision (SE) samples, excluding patients receiving nonsurgical and neoadjuvant therapy. We propose using core needle biopsy (CNB) for biological profiling in older women. Over 37 years (1973–2010), 1 758 older (≥70 years) women with operable primary breast cancer attended a dedicated clinic. Of these, 693 had sufficient quality CNB to construct tissue microarray (TMA). The pattern of biomarkers was analysed in oestrogen receptor (ER)-positive cases, using immunohistochemistry and partitional clustering analysis. The biomarkers measured were: progesterone receptor (PgR), Ki67, Epidermal Growth Factor Receptor (EGFR), Human Epidermal Growth Factor Receptor (HER)-2, HER3, HER4, p53, cytokeratins CK5/6 and CK7/8, Mucin (MUC)1, liver kinase B1 (LKB1), Breast Cancer Associated gene (BRCA) 1, B-Cell Lymphoma (BCL)-2, phosphate and tensin homolog (PTEN), vascular endothelial growth factor (VEGF), and Amplified in breast cancer 1 (AIB1). CNB TMA construction was possible in 536 ER-positive cases. Multivariate analysis showed progesterone receptor (PgR) (p = 0.015), Ki67 (p = 0.001), and mucin (MUC)1 (p = 0.033) as independent predictors for breast-cancer-specific survival (BCSS). Cluster analysis revealed three biological clusters, which were consistent with luminal A, luminal B, and low-ER luminal. The low-ER luminal cluster had lower BCSS compared to luminal A and B. The presence of the low-ER luminal cluster unique to older women, identified in a previous study in SE TMAs in the same cohort, is confirmed. This present study is novel in its use of core needle biopsy tissue microarrays to profile the biology of breast cancer in older women.

Independent Prognostic Value of Intratumoral Heterogeneity and Immune Response Features by Automated Digital Immunohistochemistry Analysis in Early Hormone Receptor-Positive Breast Carcinoma

Immunohistochemistry (IHC) for ER, PR, HER2, and Ki67 is used to predict outcome and therapy response in breast cancer patients. The current IHC assessment, visual or digital, is based mostly on global biomarker expression levels in the tissue sample. In our study, we explored the prognostic value of digital image analysis of conventional breast cancer IHC biomarkers supplemented with their intratumoral heterogeneity and tissue immune response indicators. Surgically excised tumor samples from 101 female patients with hormone receptor-positive breast cancer (HRBC) were stained for ER, PR, HER2, Ki67, SATB1, CD8, and scanned at 20x. Digital image analysis was performed using the HALO™ platform. Subsequently, hexagonal tiling was used to compute intratumoral heterogeneity indicators for ER, PR and Ki67 expression. Multiple Cox regression analysis revealed three independent predictors of the patient's overall survival: Haralick's texture entropy of PR (HR = 0.19, p = 0.0005), Ki67 Ashman's D bimodality (HR = 3.0, p = 0.01), and CD8+SATB1+ cell density in tumor tissue (HR = 0.32, p = 0.02). Remarkably, the PR and Ki67 intratumoral heterogeneity indicators were prognostically more informative than the rates of their expression. In particular, a distinct non-linear relationship between the rate of PR expression and its intratumoral heterogeneity was observed and revealed a non-linear prognostic effect of PR expression. The independent prognostic significance of CD8+SATB1+ cells infiltrating the tumor could indicate their role in anti-tumor immunity. In conclusion, we suggest that prognostic modeling, based entirely on the computational image-based IHC biomarkers, is possible in HRBC patients. The intratumoral heterogeneity and immune response indicators outperformed both conventional breast cancer IHC and clinicopathological variables while markedly increasing the power of the model.

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.

Prognostic indices predictive of short-term disease-free survival of breast carcinoma patients receiving primary surgical treatment in Sri Lanka

Background

Breast carcinoma (BCa) is the commonest malignancy among women worldwide and in Sri Lanka. Several prognostic indices are described for BCa.

Aims

To assess clinicopathological features and prognostic indices derived from routine clinical, histopathological and immunohistochemical (IHC) data, in a cohort of patients undergoing primary surgery for BCa and to determine their prognostic impact on short-term disease free survival.

Setting and Design

: This is a bidirectional cohort study of 208 women undergoing primary surgery for BCa at the National Hospital of Sri Lanka, from 2012-2014, excluding post-neoadjuvant chemotherapy cases.

Material and Methods

Clinical details, tumor size and nodal status were obtained from histopathology reports. Histopathology and estrogen/progesterone receptor and HER2 status were reviewed. Molecular subtype based on IHC was determined. Nodal ratio (number of positive nodes/total number retrieved) and Nottingham prognostic index were calculated. Follow up information was obtained by patient interviews and record review.

Statistical Analysis

Data was analyzed by univariate and multivariate Cox regression using SPSS19.0.

Results

Mean follow-up duration was 27.16 months (0.5-52 months, s = 9.35 months). 174 (82.9%) remained disease free with 19 (9%) deaths. Thirteen (6.2%) survived with metastasis and 4 (1.9%) with recurrences. On univariate Cox regression, tumor, nodal and TNM stages, nodal ratio and lymphovascular invasion (LVI) were predictive of disease free survival (DFS) (P = 0.001, P = 0.021, P = 0.022, P = 0.002, P = 0.018). On multivariate analysis TNM stage and LVI were predictive of DFS.

Conclusion

TNM stage and LVI were the most important predictors of short-term disease free survival in this study population, confirming that early detection of BCa at a lower stage has a significant impact on short-term outcomes.

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.

Expression of CD74 in invasive breast carcinoma: its relation to Nottingham Prognostic Index, hormone receptors, and HER2 immunoprofile

PURPOSE

To study the immunohistochemical expression of CD74 in series of invasive breast carcinomas classified according to their estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) immunoprofile and explore its correlation to Nottingham Prognostic Index (NPI) and tumor pathologic stage to determine if it has a prognostic value.

METHODS

A total of 160 cases of mammary carcinoma were classified broadly according to their ER, PR, and HER2 expression into luminal, HER2-positive, and triple-negative groups. The NPI was calculated and pathologic stage was recorded for each individual case and cases were classified into different prognostic groups. The CD74 expression was evaluated immunohistochemically and correlated to different prognostic variables.

RESULTS

The CD74 immunohistochemical expression in invasive breast carcinoma was significantly higher in triple-negative tumors, higher tumor grades, presence of lymph nodal metastasis, higher tumor stages, and higher NPI scores.

CONCLUSIONS

The CD74 might be a useful prognostic indicator predicting poor outcome of patients with breast carcinoma. Its consistent expression in triple-negative breast carcinomas points to the need of further studies to test the possibility if it can be targeted in treatment of breast carcinoma, especially in such groups.

Ki67/SATB1 ratio is an independent prognostic factor of overall survival in patients with early hormone receptor-positive invasive ductal breast carcinoma

Biological diversity of breast cancer presents challenges for personalized therapy and necessitates multiparametric approaches to understand and manage the disease. Multiple protein biomarkers tested by immunohistochemistry (IHC), followed by digital image analysis and multivariate statistics of the data, have been shown to be effective in exploring latent profiles of tumor tissue immunophenotype. In this study, based on tissue microarrays of 107 patients with hormone receptor (HR) positive invasive ductal breast carcinoma, we investigated the prognostic value of the integrated immunophenotype to predict overall survival (OS) of the patients. A set of 10 IHC markers (ER, PR, HER2, Ki67, AR, BCL2, HIF-1α, SATB1, p53, and p16) was used. The main factor of the variance was characterized by opposite loadings of ER/PR/AR/BCL2 and Ki67/HIF-1α; it was associated with histological grade but did not predict OS. The second factor was driven by SATB1 expression along with moderate positive HIF-1α and weak negative Ki67 loadings. Importantly, this factor did not correlate with any clinicopathologic parameters, but was an independent predictor of better OS. Ki67 and SATB1 did not reach statistical significance as single predictors; however, high Ki67/SATB1 ratio was an independent predictor of worse OS. In addition, our data indicate potential double prognostic meaning of HIF-1α expression in breast cancer and necessitate focused studies, taking into account the immunophenotype interactions and tissue heterogeneity aspects.

Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer

The Nottingham prognostic index plus (NPI+) is based on the assessment of biological class combined with established clinicopathologic prognostic variables providing improved patient outcome stratification for breast cancer superior to the traditional NPI. This study aimed to determine prognostic capability of the NPI+ in predicting risk of development of distant disease. A well-characterised series of 1073 primary early-stage BC cases treated in Nottingham and 251 cases from Budapest were immunohistochemically assessed for cytokeratin (Ck)5/6, Ck18, EGFR, oestrogen receptor (ER), progesterone receptor, HER2, HER3, HER4, Mucin 1 and p53 expression. NPI+ biological class and prognostic scores were assigned using individual algorithms for each biological class incorporating clinicopathologic parameters and investigated in terms of prediction of distant metastases-free survival (MFS). The NPI+ identified distinct prognostic groups (PG) within each molecular class which were predictive of MFS providing improved patient outcome stratification superior to the traditional NPI. NPI+ PGs, between series, were comparable in predicting patient outcome between series in luminal A, basal p53 altered and HER2+/ER+ (p > 0.01) tumours. The low-risk groups were similarly validated in luminal B, luminal N, basal p53 normal tumours (p > 0.01). Due to small patient numbers the remaining PGs could not be validated. NPI+ was additionally able to predict a higher risk of metastases at certain distant sites. This study may indicate the NPI+ as a useful tool in predicting the risk of metastases. The NPI+ provides accurate risk stratification allowing improved individualised clinical decision making for breast cancer.

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.

The role of primary endocrine therapy in older women with operable breast cancer

ABSTRACT 

A Cochrane review of randomized trials shows no difference in overall survival between surgery and primary endocrine therapy (PET) in older women with operable primary breast cancer. Most of these trials were small and unselected for estrogen receptor (ER) status. Evidence exists showing a significant correlation between the degree of ER-positivity and response and outcome in patients receiving PET. Although surgery remains the treatment of choice, patients with ER-rich tumors tend to do equally well on PET. When deciding optimal therapies, co-morbidities and frailty (which impact on the likelihood of death due to competing causes), patient choice, agent of choice (notably the third-generation aromatase inhibitors) and biology (more than just being ER-positive) should all be taken into account.

Classification of patients with breast cancer according to Nottingham prognostic index highlights significant differences in immunohistochemical marker expression

BACKGROUND

Prognosis and treatment of patients with breast carcinoma of no special type (NST) is dependent on a few established parameters, such as tumor size, histological grade, lymph node stage, expression of estrogen receptor, progesterone receptor, and HER-2/neu, and proliferation index. The original Nottingham Prognostic Index (NPI) employs a three-tiered classification system that stratifies patients with breast cancer into good, moderate, and poor prognostic groups. The aim of our study was to use robust immunohistochemical methodology for determination of ER, PR, HER-2/neu, Ki-67, p53, and Bcl-2, and to observe differences in the expression of these markers when patients are stratified according to the original, three-tiered Nottingham Prognostic Index.

METHODS

Paraffin blocks from 120 patients diagnosed with breast carcinoma, NST, were retrieved from our archive. Cases included in the study were female patients previously treated with modified radical mastectomy and axillary dissection.

RESULTS

Our study demonstrates that expression of markers of good prognosis, such as ER, PR, and Bcl-2, is seen with higher frequency in good and moderate NPI groups. In contrast, overexpression of HER-2/neu, a marker of adverse prognosis, is more frequent in moderate and poor NPI groups. High proliferation index, as measured by Ki-67, is seen in moderate and poor NPI groups, whereas low proliferation index is seen in good NPI groups.

CONCLUSIONS

These data confirm that the original, three-tiered NPI statistically correlates with the expression of prognostic immunohistochemical markers in breast carcinoma NST.

Risk groups in children under six months of age using self-organizing maps

Fetal and infant growth tends to follow irregular patterns and, particularly in developing countries, these patterns are greatly influenced by unfavorable living conditions and interactions with complications during pregnancy. The aim of this study was to identify groups of children with different risk profiles for growth development. The study sample comprised 496 girls and 508 boys under six months of age from 27 pediatric primary health care units in the city of Rio de Janeiro, Brazil. Data were obtained through interviews with the mothers and by reviewing each child's health card. An unsupervised learning, know as a self-organizing map (SOM) and a K-means algorithm were used for cluster analysis to identify groups of children. Four groups of infants were identified. The first (139) consisted of infants born exclusively by cesarean delivery, and their mothers were exclusively multiparous; the highest prevalences of prematurity and low birthweight, a high prevalence of exclusive breastfeeding and a low proportion of hospitalization were observed for this group. The second (247 infants) and the third (298 infants) groups had the best and worst perinatal and infant health indicators, respectively. The infants of the fourth group (318) were born heavier, had a low prevalence of exclusive breastfeeding, and had a higher rate of hospitalization. Using a SOM, it was possible to identify children with common features, although no differences between groups were found with respect to the adequacy of postnatal weight. Pregnant women and children with characteristics similar to those of group 3 require early intervention and more attention in public policy.

Nottingham Prognostic Index Plus (NPI+): a modern clinical decision making tool in breast cancer

BACKGROUND

Current management of breast cancer (BC) relies on risk stratification based on well-defined clinicopathologic factors. Global gene expression profiling studies have demonstrated that BC comprises distinct molecular classes with clinical relevance. In this study, we hypothesised that molecular features of BC are a key driver of tumour behaviour and when coupled with a novel and bespoke application of established clinicopathologic prognostic variables can predict both clinical outcome and relevant therapeutic options more accurately than existing methods.

METHODS

In the current study, a comprehensive panel of biomarkers with relevance to BC was applied to a large and well-characterised series of BC, using immunohistochemistry and different multivariate clustering techniques, to identify the key molecular classes. Subsequently, each class was further stratified using a set of well-defined prognostic clinicopathologic variables. These variables were combined in formulae to prognostically stratify different molecular classes, collectively known as the Nottingham Prognostic Index Plus (NPI+). The NPI+ was then used to predict outcome in the different molecular classes.

RESULTS

Seven core molecular classes were identified using a selective panel of 10 biomarkers. Incorporation of clinicopathologic variables in a second-stage analysis resulted in identification of distinct prognostic groups within each molecular class (NPI+). Outcome analysis showed that using the bespoke NPI formulae for each biological BC class provides improved patient outcome stratification superior to the traditional NPI.

CONCLUSION

This study provides proof-of-principle evidence for the use of NPI+ in supporting improved individualised clinical decision making.

Identification of key clinical phenotypes of breast cancer using a reduced panel of protein biomarkers

Background:

Breast cancer is a heterogeneous disease characterised by complex molecular alterations underlying the varied behaviour and response to therapy. However, translation of cancer genetic profiling for use in routine clinical practice remains elusive or prohibitively expensive. As an alternative, immunohistochemical analysis applied to routinely processed tissue samples could be used to identify distinct biological classes of breast cancer.

Methods:

In this study, 1073 archival breast tumours previously assessed for 25 key breast cancer biomarkers using immunohistochemistry and classified using clustering algorithms were further refined using naïve Bayes classification performance. Criteria for class membership were defined using the expression of a reduced panel of 10 proteins able to identify key molecular classes. We examined the association between these breast cancer classes with clinicopathological factors and patient outcome.

Results:

We confirm patient classification similar to established genotypic biological classes of breast cancer in addition to novel sub-divisions of luminal and basal tumours. Correlations between classes and clinicopathological parameters were in line with expectations and showed highly significant association with patient outcome. Furthermore, our novel biological class stratification provides additional prognostic information to the Nottingham Prognostic Index.

Conclusion:

This study confirms that distinct molecular phenotypes of breast cancer can be identified using robust and routinely available techniques and both the luminal and basal breast cancer phenotypes are heterogeneous and contain distinct subgroups.

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.

Biology of primary breast cancer in older women treated by surgery: with correlation with long-term clinical outcome and comparison with their younger counterparts

Background:

As age advances breast cancer appears to change its biological characteristics, however, very limited data are available to define the precise differences between older and younger patients.

Methods:

Over 36 years (1973–2009), 1758 older (⩾70 years) women with early operable primary breast cancer were managed in a dedicated clinic. In all, 813 underwent primary surgery and 575 good quality tumour samples were available for biological analysis. The pattern of biomarkers was analysed using indirect immunohistochemistry on tissue microarrays. Comparison was made with a previously characterised series of younger (<70 years) patients.

Results:

There was high expression of oestrogen receptor (ER), PgR, Bcl2, Muc1, BRCA1 and 2, E-cadherin, luminal cytokeratins, HER3, HER4, MDM2 and 4 and low expression of human epidermal growth factor receptor (HER)-2, Ki67, p53, EGFR and CK17. Oestrogen receptor and axillary stage appeared as independent prognostic factors. Unsupervised partitional clustering showed six biological clusters in older patients, five of which were common in the younger patients, whereas the low ER luminal cluster was distinct in the older series. The luminal phenotype showed better breast cancer-specific survival, whereas basal and HER2-overexpressing tumours were associated with poor outcome.

Conclusion:

Early operable primary breast cancer in older women appears as a distinct biological entity, with existence of a novel cluster. Overall older women showed less aggressive tumour biology and ER appeared as an independent prognostic factor alongside the time-dependent axillary stage. These biological characteristics may explain the differences in clinical outcome and should be considered in making therapeutic decisions.

Pattern discovery from patient controlled analgesia demand behavior

Unlike previous research on patient controlled analgesia, this study explores patient demand behavior over time. We apply clustering methods to disclose demand patterns among patients over the first 24h of analgesic medication after surgery. We consider demographic, biomedical, and surgery-related data in statistical analyses to determine predictors for patient demand behavior, and use stepwise regression and Bayes risk analysis to evaluate the influence of demand pattern on analgesic requirements. We identify three demand patterns from 1655 patient controlled analgesia request log files. Statistical tests show correlations of gender (p=.0022), diastolic blood pressure (p=.025), surgery type (p=.0028), and surgical duration (p<.0095) with demand patterns. Stepwise regression and Bayes risk analysis show demand pattern plays the most important role in analgesic consumption prediction (p=0.E+0). This study suggests analgesia request patterns over time exist among patients, and clustering can disclose demand behavioral patterns.

Immunohistochemistry profiles of breast ductal carcinoma: factor analysis of digital image analysis data

BACKGROUND

Molecular studies of breast cancer revealed biological heterogeneity of the disease and opened new perspectives for personalized therapy. While multiple gene expression-based systems have been developed, current clinical practice is largely based upon conventional clinical and pathologic criteria. This gap may be filled by development of combined multi-IHC indices to characterize biological and clinical behaviour of the tumours. Digital image analysis (DA) with multivariate statistics of the data opens new opportunities in this field.

METHODS

Tissue microarrays of 109 patients with breast ductal carcinoma were stained for a set of 10 IHC markers (ER, PR, HER2, Ki67, AR, BCL2, HIF-1α, SATB1, p53, and p16). Aperio imaging platform with the Genie, Nuclear and Membrane algorithms were used for the DA. Factor analysis of the DA data was performed in the whole group and hormone receptor (HR) positive subgroup of the patients (n = 85).

RESULTS

Major factor potentially reflecting aggressive disease behaviour (i-Grade) was extracted, characterized by opposite loadings of ER/PR/AR/BCL2 and Ki67/HIF-1α. The i-Grade factor scores revealed bimodal distribution and were strongly associated with higher Nottingham histological grade (G) and more aggressive intrinsic subtypes. In HR-positive tumours, the aggressiveness of the tumour was best defined by positive Ki67 and negative ER loadings. High Ki67/ER factor scores were strongly associated with the higher G and Luminal B types, but also were detected in a set of G1 and Luminal A cases, potentially indicating high risk patients in these categories. Inverse relation between HER2 and PR expression was found in the HR-positive tumours pointing at differential information conveyed by the ER and PR expression. SATB1 along with HIF-1α reflected the second major factor of variation in our patients; in the HR-positive group they were inversely associated with the HR and BCL2 expression and represented the major factor of variation. Finally, we confirmed high expression levels of p16 in Triple-negative tumours.

CONCLUSION

Factor analysis of multiple IHC biomarkers measured by automated DA is an efficient exploratory tool clarifying complex interdependencies in the breast ductal carcinoma IHC profiles and informative value of single IHC markers. Integrated IHC indices may provide additional risk stratifications for the currently used grading systems and prove to be useful in clinical outcome studies.

VIRTUAL SLIDES

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1512077125668949.