Relationship between Steroid Receptors (As Continuous Variables) and Response to Adjuvant Treatments in Postmenopausal Women with Node-Positive Breast Cancer

In current clinical practice for breast cancer patients, estrogen (ER) and progesterone receptor (PgR) concentrations, quantified by the dextran-coated charcoal assay, are categorized by an arbitrary cutoff into a negative or positive status. However, although the results obtained with this approach are easy to interpret, such a representation could oversimplify the relationship between ER and PgR content and patient outcome and imply an assumption of monotonicity, which is generally expected but rarely proven. We evaluated the relationship between ER and PgR content (considered on a continuous scale) and clinical outcome, using a flexible statistical model, in a group of postmenopausal patients with N-positive operable tumors who were submitted to surgery and different adjuvant treatments (tamoxifen or CMF). Univariate analysis indicated that in the tamoxifen-treated group, ER level, number of metastatic nodes (pN) and age, but not PgR, were significant indicators of clinical outcome (p=0.032, p=0.021 and p=0.029, respectively). Multivariate analysis indicated that in this group of patients there was no interaction between variables, and in the final model for disease-free survival (DFS) only ER and pN were retained with an overall predictive ability of the regression model of 0.723, as evaluated by Harrell's c. However, pN markedly contributed to the predictive ability of the model with respect to ER, since a marked decrease in Harrell's c statistic (c=0.582) was observed when pN was removed from the model. In the CMF-treated group, only pN affected clinical outcome. When the estimated DFS curves obtained from the final Cox regression models were plotted according to four values of ER (in the tamoxifen-treated group) or three values of pN (in the CMF-treated group) we observed that in the tamoxifen-treated group patients with an ER concentration equal to 0 fmol/mg cytosol protein had the worst prognosis, whereas a marked improvement of the expected DFS was observed for patients with a low but detectable ER level (generally classified as ER-negative because falling below the conventional cutoff value of 10 fmol/mg cytosol protein). Our results seem to suggest that the use of steroid receptor concentrations on a continuous scale, instead of dichotomous “status”, is to be preferred in the choice of adequate therapeutic strategies.

Medullary Breast Carcinoma: Prognostic Implications of P53 Expression

Medullary breast carcinoma (MBC) is a rare pathological type of breast cancer. The rate of p53 protein accumulation is higher in MBC than in common invasive ductal carcinoma. Whether this particular feature of MBC influences the outcome after treatment is unknown. We retrospectively analyzed the characteristics, treatment and outcome of 71 patients with MBC treated between 1981 and 1996. The median age was 51 years (range 27–81) and the median clinical tumor size was 25 mm (range 0-70 mm). Breast-conserving treatment was offered when possible: 55 patients had undergone a tumorectomy and radiotherapy while 16 patients had undergone a mastectomy. p53 protein accumulation was determined by immunohistochemistry on paraffin-embedded tumor specimens from 58/71 samples available for this study. The median follow-up for the 56 survivors was 113 months (range 30–241). The 10-year survival and metastasis-free survival rates were 81% and 81.4%, respectively. The local recurrence rate was 16.4%. The two factors predicting outcome were pathological axillary node involvement in the 60 patients who underwent axillary dissection and adjuvant chemotherapy. p53 accumulation was found in 33/58 patients (57%). p53 status was not predictive of survival nor of distant or local recurrences. We confirm that medullary breast carcinoma has a favorable prognosis despite its aggressive pathological features. p53 protein accumulation, found in the majority of MBCs, was not related to outcome.

A Prediction Model for Breast Cancer Recurrence after Adjuvant Hormone Therapy

Hormone therapy with tamoxifen has long been the established adjuvant treatment for node-positive, estrogen–receptor-positive breast cancer in postmenopausal women. Since 30–40% of these patients fail to respond, reliable outcome prediction is necessary for successful treatment allocation. Using pathobiological variables (available in most clinical records: tumor size, nodal involvement, estrogen and progesterone receptor content) from 596 patients recruited at a comprehensive cancer center, we developed a prediction model which we validated in an independent cohort of 175 patients recruited at a general hospital. Calculated at 3 and 4 years of follow-up, the discrimination indices were 0.716 [confidence limits (CL) 0.641, 0.752] and 0.714 (CL 0.650, 0.750) for the training data, and 0.726 (CL 0.591, 0.769) and 0.677 (CL 0.580, 0.745) for the testing data. Waiting for more effective approaches from genomic and proteomic studies, a model based on consolidated pathobiological variables routinely assessed at relatively low costs may be considered as the reference for assessing the gain of new markers over traditional ones, thus substantially improving the conventional use of prognostic criteria.

p53, cathepsin D, Bcl-2 are joint prognostic indicators of breast cancer metastatic spreading

Background

Traditional prognostic indicators of breast cancer, i.e. lymph node diffusion, tumor size, grading and estrogen receptor expression, are inadequate predictors of metastatic relapse. Thus, additional prognostic parameters appear urgently needed. Individual oncogenic determinants have largely failed in this endeavour. Only a few individual tumor growth drivers, e.g. mutated p53, Her-2, E-cadherin, Trops, did reach some prognostic/predictive power in clinical settings. As multiple factors are required to drive solid tumor progression, clusters of such determinants were expected to become stronger indicators of tumor aggressiveness and malignant progression than individual parameters. To identify such prognostic clusters, we went on to coordinately analyse molecular and histopathological determinants of tumor progression of post-menopausal breast cancers in the framework of a multi-institutional case series/case-control study.

Methods

A multi-institutional series of 217 breast cancer cases was analyzed. Twenty six cases (12 %) showed disease relapse during follow-up. Relapsed cases were matched with a set of control patients by tumor diameter, pathological stage, tumor histotype, age, hormone receptors and grading. Histopathological and molecular determinants of tumor development and aggressiveness were then analyzed in relapsed versus non-relapsed cases. Stepwise analyses and model structure fitness assessments were carried out to identify clusters of molecular alterations with differential impact on metastatic relapse.

Results

p53, Bcl-2 and cathepsin D were shown to be coordinately associated with unique levels of relative risk for disease relapse. As many Ras downstream targets, among them matrix metalloproteases, are synergistically upregulated by mutated p53, whole-exon sequence analyses were performed for TP53, Ki-RAS and Ha-RAS, and findings were correlated with clinical phenotypes. Notably, TP53 insertion/deletion mutations were only detected in relapsed cases. Correspondingly, Ha-RAS missense oncogenic mutations were only found in a subgroup of relapsing tumors.

Conclusions

We have identified clusters of specific molecular alterations that greatly improve prognostic assessment with respect to singularly-analysed indicators. The combined analysis of these multiple tumor-relapse risk factors promises to become a powerful approach to identify patients subgroups with unfavourable disease outcome.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2713-3) contains supplementary material, which is available to authorized users.

Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers

The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy.

Circulating biomarkers from tumour bulk to tumour machinery: promises and pitfalls

In this paper, we provide a working classification for circulating biomarkers according to their potential clinical application. We broadly divided biomarkers into four groups: (i) biomarkers of cancer risk, (ii) biomarkers of tumour-host interactions, (iii) biomarker of tumour burden, and (iv) function-related biomarkers. We hope this classification will provide a framework to which the results of future studies can be added. We also discuss the promises and pitfalls in the optional use of biomarkers in oncology.

An integrated biomarker-based strategy for ecotoxicological evaluation of risk in environmental management

Environmental impacts by both natural events and man-made interventions are a fact of life; and developing the capacity to minimise these impacts and their harmful consequences for biological resources, ecosystems and human health is a daunting task for environmental legislators and regulators. A major challenge in impact and risk assessment, as part of integrated environmental management (IEM), is to link harmful effects of pollution (including toxic chemicals) in individual sentinel animals to their ecological consequences. This obstacle has resulted in a knowledge-gap for those seeking to develop effective policies for sustainable use of resources and environmental protection. Part of the solution to this problem may lie with the use of diagnostic clinical-type laboratory-based ecotoxicological tests or biomarkers, utilising sentinel animals as integrators of pollution, coupled with direct immunochemical tests for contaminants. These rapid and cost-effective ecotoxicological tools can provide information on the health status of individuals and populations based on relatively small samples of individuals. In the context of ecosystem status or health of the environment, biomarkers are also being used to link processes of molecular and cellular damage through to higher levels (i.e., prognostic capability), where they can result in pathology with reduced physiological performance and reproductive success. Complex issues are involved in evaluating environmental risk, such as the effects of the physico-chemical environment on the speciation and uptake of pollutant chemicals and inherent inter-individual and inter-species differences in vulnerability to toxicity; and the toxicity of complex mixtures. Effectively linking the impact of pollutants through the various hierarchical levels of biological organisation to ecosystem and human health requires a pragmatic integrated approach based on existing information that either links or correlates processes of pollutant uptake, detoxication and pathology with each other and higher level effects. It is further proposed here that this process will be facilitated by pursuing a holistic or whole systems approach with the development of computational simulation models of cells, organs and animals in tandem with empirical data (i.e., the middle-out approach). In conclusion, an effective integrated environmental management strategy to secure resource sustainability requires an integrated capability for risk assessment and prediction. Furthermore, if such a strategy is to influence and help in the formulation of environmental policy decisions, then it is crucial to demonstrate scientific robustness of predictions concerning the long-term consequences of pollution to politicians, industrialists and environmental managers; and also increase stakeholder awareness of environmental problems.

Prognostic and predictive indicators in operable breast cancer

Because of its biological heterogeneity and wide spectrum of responsiveness to different treatments, breast cancer is a complex disease of difficult clinical management. Over the past several years, knowledge of the molecular mechanisms regulating normal and aberrant cell growth leading to cancer has been enhanced. These advances have enabled the identification of an increasing number of surrogate biomarkers, which have been correlated with prognosis or used as predictors of response to specific treatments. Axillary nodal status, age, tumor size, pathologic grade, and hormone receptor status are the established prognostic and/or predictive factors for selection of adjuvant treatments. The role of new biomarkers, such as p53, HER2/neu, angiogenesis, and the proliferation index value, is promising; however, the clinical value of their determination must be provided by prospective clinical studies.

Biocomplexity: the post-genome challenge in ecotoxicology

There are four crucial challenges for the environmental toxicologists in the next decade: (1) understanding the mechanisms of molecular and subcellular interactions with pollutant chemicals, including genomic and proteomic aspects; (2) the development of predictive simulation models of toxic effects on complex cellular and physiological processes; (3) linking molecular, cellular and patho-physiological 'endpoints' with higher level ecological consequences; and (4) precautionary anticipation of possible harmful impacts of novel developments in industrial processes, including biotechnology and nanotechnology. One of the major difficulties in ecotoxicology is to link harmful effects of chemical pollutants in individual animals and plants with the ecological consequences. Consequently, this obstacle has resulted in a 'knowledge-gap' for those seeking to develop policies for sustainable use of resources and environmental protection. The overall problem is: how to develop effective procedures for environmental/ecological impact and risk assessment? However, the use of diagnostic 'clinical-type' tests or 'biomarkers' has started to provide information on the health-status of populations based on relatively small samples of individuals. Also, biomarkers can now be used to begin to link processes of molecular and cellular damage through to the higher levels (i.e. prognostic capability), where they can result in reduced performance and reproductive success. Research effort to meet this challenge must be inter-disciplinary in character, since the key questions mainly involve complex interfacial problems. These include effects of physico-chemical speciation on uptake and toxicity, the toxicity of complex mixtures; and linking the impact of pollutants through the various hierarchical levels of biological organisation to ecosystem and human health. Finally, the development and use of process-based computational simulation models (i.e. 'virtual' cells, organs and animals), illustrated using an endosomal/lysosomal uptake and cell injury model, will facilitate the development of a predictive capacity for estimating risk associated with the possibility of future environmental events.

A computational model of the digestive gland epithelial cell of marine mussels and its simulated responses to oil-derived aromatic hydrocarbons

This paper describes a computational model of digestive gland epithelial cells (digestive cells) of marine mussels. These cells are the major environmental interface for uptake of contaminants, particularly those associated with natural particulates that are filtered from seawater by mussels. Digestive cells show well characterised reactions to exposure to lipophilic xenobiotics, such as oil-derived aromatic hydrocarbons (AHs), which accumulate in these cells with minimal biotransformation. The simulation model is based on processes associated with the flux of carbon through the cell. Physiological parameters such as fluctuating food concentration, cell volume, respiration, secretion/excretion, storage of glycogen and lipid, protein/organelle turnover (autophagy/resynthesis) and export of carbon to other tissues of the mussel are all included in the model. The major response to AHs is induction of increased autophagy in these cells. Simulations indicate that the reactions to AHs and food deprivation correspond well with responses measured in vivo.

Double-Peaked Time Distribution of Mortality for Breast Cancer Patients Undergoing Mastectomy

PURPOSE

To gather information on the natural history of breast cancer from the time-distribution of deaths of patients undergoing mastectomy alone.

PATIENTS AND METHODS

A total of 1173 patients, who entered controlled clinical trials carried out at the Milan Cancer Institute and underwent radical or modified radical mastectomy without any adjuvant therapy for operable breast cancer, were examined. The risk of death at a given time after surgery was studied utilizing the death-specific hazard rate. The risk distribution was assessed relative to tumor size, axillary lymph node involvement, and menopausal status.

RESULTS

The hazard rate for death presented an early peak at about the 3rd-4th year after surgery and a second late peak near the 8th year. The double-peaked pattern was almost completely generated by N+ patients, while N- patients did not show relevant structures. Pre-menopausal patients showed an initial mortality wave covering about 6 years, with maximum height at the 4th year, followed by a peak 8 years after surgery, while post-menopausal patients showed an early high mortality surge peaking at the 3rd year, followed by a modest increase at the 8th year. Detailed analysis revealed that post-menopausal patients with early mortality had significantly larger tumors and higher nodal involvement, while no special trait characterized the corresponding pre-menopausal patients. Moreover, patients of the late mortality peak were more likely to have suffered early local-regional or contra-lateral recurrence or to be pre-menopausal patients recurring anywhere at the second recurrence peak.

CONCLUSION

The double-peaked hazard curve confirmed the occurrence of discontinuous features in the natural history of breast cancer for patients undergoing mastectomy. Indeed, the mortality pattern maintained definite signs of the previous double-peaked structure of recurrences. However, death events did not parallel the corresponding recurrence events and, moreover, pre and post-menopausal patients revealed dissimilar survival after recurrence, at least for early deaths. These findings, showing disconnection of mortality pattern from recurrence pattern for subsets of patients, suggest that parameters other than those influencing the recurrence risk may determine the survival of recurred patients.

Prognostic role of serum CA15.3 in 362 node-negative breast cancers. An old player for a new game

The aims of the present investigation were to evaluate the association between serum CA15.3 levels and other biological and clinical variables and its prognostic role in patients with node-negative breast cancer. We evaluated 362 patients operated upon primary breast cancer from 1982 to 1992 (median follow-up 69 months). Serum CA15.3 was measured by an immunoradiometric assay. The association between variables was investigated by a Principal Component Analysis (PCA) and the prognostic role of CA15.3 on relapse-free survival (RFS) was investigated by Cox regression models adjusting for age, oestrogen receptor (ER), tumour stage, and ER x age interaction, with both the likelihood ratio test and Harrell's c statistic. The prognostic contribution of CA 15.3 was highly significant. Log relative hazard of relapse was constant until approximately 10 (U/ml) of CA15.3 and increased thereafter with increasing marker levels. CA15.3 showed a significant contribution using as a cut-off point a value of 31 U/ml. However, the contribution to the model of the marker as a continuous variable is much greater. From these findings, we can conclude that: (i) CA15.3 is a prognostic marker in node-negative breast cancer; (ii) its relationship with prognosis is continuous, with the risk of relapse increasing progressively from approximately 10 U/ml.

Time-dependent relevance of steroid receptors in breast cancer

PURPOSE

To analyze the time-dependent prognostic role of the investigated variables, considered, when appropriate, on a continuous scale, for the purpose of evaluating and describing the interrelationships between clinically relevant patient and tumor characteristics (age, size and histology, and estrogen receptor [ER] and progesterone receptor content) and the risk of new disease manifestation.

PATIENTS AND METHODS

We applied a flexible statistical model to a case series of 1,793 patients with axillary lymph node-negative breast cancer with a minimal potential follow-up of 10 years. To avoid a potential confounding effect of adjuvant treatment, only patients given local-regional therapy until relapse were considered.

RESULTS

ER content and tumor size (adjusted for all the other covariates) showed a time-dependent relationship with the risk of new disease manifestations. In particular, ER content failed to show a prognostic effect within the first years of follow-up; thereafter, a positive association with risk of relapse was observed. For tumor size, within the first years of follow-up, the risk of relapse was directly related to size for only tumors up to 2.5 cm in diameter; thereafter, the impact on prognosis progressively decreased.

CONCLUSION

The availability of a long follow-up on a large breast cancer series, as well as the use of innovative statistical approaches, allowed us to explore the functional relation between steroid receptors and clinical outcome and to generate a hypothesis on the involvement of ER in favoring long-term metastasis development.

Quantitative measurement of soluble cytokeratin fragments in tissue cytosol of 599 node negative breast cancer patients: a prognostic marker possibly associated with apoptosis

Apoptosis is associated with caspase-mediated proteolysis of Type I (K18 and K19) cytokeratins. We previously showed a positive association between the levels of tissue polypeptide antigen (TPA), that recognizes cytokeratins K8, K18, and K19 fragments, and induced apoptosis in breast cancer cell lines. The aim of the present study was to evaluate the interrelationships between TPA, steroid receptors, and p53, and their joint prognostic role in node-negative breast cancer patients not treated with adjuvant therapies. Age and pT were also considered since they are known prognostic factors. Five hundred and ninety-nine cases with N- breast cancer were evaluated (median follow-up: 60 months). TPA was measured by an immunoradiometric assay and p53 by an immunochemiluminescent assay in tumor cytosol. Multiple correspondence analysis was used to study the associations among variables. Their prognostic role (univariate analysis) and their joint effect (multivariate analysis) on RFS were investigated with Cox regression models. TPA showed a direct association with ER and PgR. Higher p53 values were weakly associated to low values of ER, PgR, and TPA. Younger age was related to low and intermediate values of ER and PgR and to low p53 values, while older age was related to high values of ER. Multivariate analysis showed a significant prognostic impact for pT, age, ER, and TPA. Among the interactions considered clinically relevant, only that between ER and age was found. RFS estimated values were poorer in cases with lower than in those with higher TPA values, both in patients expected to have a poor (pT2, young age, low ER) and a better prognosis (pT1, older age, high ER). From the findings of the present study we can draw the following conclusions: The relationship of TPA with prognosis gives an additional contribution to pT, age, and steroid receptors in N- breast cancer; TPA may be considered the first marker of apoptosis measured with a fully standardized quantitative method in tumor cytosol and could be evaluated in prognostic indexes including markers related to different biological mechanisms.