Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form

BACKGROUND AND OBJECTIVE

Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.

METHODS

In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Disease Event at both 5-year and 10-year follow-ups of 696 female patients with a first invasive breast cancer diagnosis enrolled at IRCCS "Giovanni Paolo II" in Bari, Italy. After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. Then, we classified breast cancer patients in the two classes, namely, Invasive Disease Event and non-Invasive Disease Event, via a Support Vector Machine classifier trained on a subset of significative features previously identified.

RESULTS

Both 5-year and 10-year models resulted particularly accurate in predicting breast cancer recurrence event, achieving an AUC value of 92.07% and 92.84%, an accuracy of 88.71% and 88.82%, a sensitivity of 86.83% and 88.06%, a specificity of 89.55% and 89.3%, a precision of 71.93% and 84.82%, respectively.

CONCLUSIONS

This is the first study proposing an approach which converts clinical information into an image-form to develop a decision support system for identifying patients at high risk of occurrence of an Invasive Disease Event, and then defining personalized oncological therapeutic treatments for breast cancer patients.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

Validation of PREDICT Version 2.2 in a Retrospective Cohort of Indian Women With Operable Breast Cancer

PURPOSE

Online prediction models that use known prognostic factors in breast cancer (BC) are routinely used to assist in decisions for adjuvant therapy. PREDICT Version 2.2 (P2.2) is one such online tool, which uses tumor size, lymph node involvement, grade, age, hormone receptor status, human epidermal growth factor receptor 2 (HER2) status, and Ki67. We performed an external validation in a retrospective cohort of patients treated at a tertiary center in India.

METHODS

Women with operable BC between 2008 and 2016 with nonmetastatic, T1-T2 invasive, and HER2 receptor-negative BC and with available 5-year overall survival (OS) data were selected. Median predicted 5-year OS rates were used to calculate predicted events for the whole cohort and subgroups. The chi-square test was used to evaluate the goodness of fit of the tool.

RESULTS

Of 11,760 cases registered between 2008 and 2016, 2,783 (23.66%) eligible patients with a median age of 50 (26-70) years and a median pT size of 2.5 (0.1-5) cm, 2,037 (73.19%) with grade 3 tumors, 1,172 (42.11%) with node-positive disease, 817 (29.35%) with triple-negative breast cancer, and 1,966 (70.64%) with HR-positive BC were included in the analysis. The observed 5-year OS and predicted 5-year OS in the whole cohort were 94.8% and 90.00%, respectively, with an absolute difference of 4.8% (95% CI, 3.417 to 6.198, P < .001). The observed 5-year OS and predicted 5-year OS were also different in various subgroups.

CONCLUSION

PREDICT version 2.2 overestimated the number of deaths, with lower predicted 5-year OS compared with the observed value, in this retrospective Indian cohort. The reasons for this discrepancy could be differing biologic characteristics and possible selection bias in our cohort. We recommend a prospective validation of PREDICT in Indian patients and advocate caution in its use until such validation is achieved.

Adjuvant Docetaxel in Node-Negative Breast Cancer Patients: A Randomized Trial of AGO-Breast Study Group, German Breast Group, and EORTC-Pathobiology Group

BACKGROUND

In node-negative breast cancer (NNBC), a high risk of recurrence is determined by clinico-pathological or tumor-biological assessment. Taxanes may improve adjuvant chemotherapy.

METHODS

NNBC 3-Europe, the first randomized phase-3 trial in node-negative breast cancer (BC) with tumor-biological risk assessment, recruited 4146 node-negative breast cancer patients from 2002 to 2009 in 153 centers. Risk assessment was performed by clinico-pathological factors (43%) or biomarkers (uPA/PAI-1, urokinase-type plasminogen activator/its inhibitor PAI-1). High-risk patients received six courses 5-fluorouracil (500 mg/m²), epirubicin (100 mg/m²), cyclophosphamide (500 mg/m²) (FEC), or three courses FEC followed by three courses docetaxel 100 mg/m² (FEC-Doc). Primary endpoint was disease-free survival (DFS).

RESULTS

In the intent-to-treat population, 1286 patients had received FEC-Doc, and 1255 received FEC. Median follow-up was 45 months. Tumor characteristics were equally distributed; 90.6% of tested tumors had high uPA/PAI-1-concentrations. Planned courses were given in 84.4% (FEC-Doc) and 91.5% (FEC). Five-year-DFS was 93.2% (95% C.I. 91.1-94.8) with FEC-Doc and 93.7% (91.7-95.3) with FEC. Five-year-overall survival was 97.0% (95.4-98.0) for FEC-Doc and 96.6% % (94.9-97.8) for FEC.

CONCLUSIONS

With adequate adjuvant chemotherapy, even high-risk node-negative breast cancer patients have an excellent prognosis. Docetaxel did not further reduce the rate of early recurrences and led to significantly more treatment discontinuations.

Long-term prognostic significance of HER2-low and HER2-zero in node-negative breast cancer

BACKGROUND

Recently, novel antibody--drug conjugates (ADCs) showed clinical activity in a subset of advanced human epidermal growth factor receptor 2 (HER2)-negative patients. We investigated the prognostic significance of HER2-low and HER2-zero tumours.

PATIENTS AND METHODS

The retrospective cohort study included 410 consecutive node-negative breast cancer patients without adjuvant systemic therapy treated between 1985 and 2000 (median follow-up: 16.73 [IQR 8.58-23.45] years). 351 (85.6%) were HER-2 negative and subdivided into HER2-zero (immunohistochemistry [IHC] score 0) and HER2-low (IHC score 1+ or 2+/in situ hybridisation [ISH]-negative). HER2 gene expression was available in 170 (48.4%) patients. Differences in HER2 status for immunohistochemistry, gene expression and clinico-pathologic parameters were assessed using Fisher's exact test, Pearson's correlation and Mann-Whitney test. Prognosis was investigated using the Kaplan-Meier method and Cox regression analyses.

RESULTS

Of the 351 HER2-negative patients, 198 (56.4%) had HER2-low tumours and 153 (43.6%) were HER2-zero. Significant differences between HER2-zero and HER2-low tumours were found in histologic grading (P = 0.001), Ki-67 (P = 0.013) and HER2 gene expression (P = 0.002). HER2-low patients had significantly longer disease-free survival (DFS) (15-year rate: 67.5% [95% CI 61.0-74.7] vs. 47.3% [95% CI 39.9-56.1], P < 0.001) and overall survival (OS) (15-year rate: 75.4% [95% CI 69.4-81.9] vs. 66.8% [95% CI 59.5-74.9], P = 0.009). The OS difference was observed in hormone receptor (HR)-positive (P = 0.039) but not HR-negative (P = 0.086) tumours. The results of multivariable analyses confirmed the independent prognostic significance of HER2 status (DFS: HR, 0.546; 95% CI, 0.402-0.743; P < 0.001; OS: HR, 0.653; 95% CI, 0.458-0.932; P = 0.019).

CONCLUSION

HER2-low patients had a better survival than HER2-zero patients.

SCUBE3 serves as an independent poor prognostic factor in breast cancer

BACKGROUND

Accumulating evidences indicate that the signal peptide-CUB-EGF-like domain-containing protein 3 (SCUBE3) plays a key role in the development and progression of many human cancers. However, the underlying mechanism and prognosis value of SCUBE3 in breast cancer are still unclear.

METHODS

The clinical data of 137 patients with breast cancer who underwent surgical resection in Taizhou Hospital of Zhejiang Province were retrospectively analyzed. We first conducted a comprehensive study on the expression pattern of SCUBE3 using the Tumor Immune Estimation Resource (TIMER) and UALCAN databases. In addition, the expression of SCUBE3 in breast tumor tissues was confirmed by immunohistochemistry. The protein-protein interaction analysis and functional enrichment analysis of SCUBE3 were analyzed using the STRING and Enrichr databases. Moreover, tissue microarray (TMA) was used to analyze the relationship between SCUBE3 expression levels and clinical-pathological parameters, such as histological type, grade, the status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2). We further supplemented and identified the above results using the UALCAN and bc-GenExMiner v4.4 databases from TCGA data. The correlation between the expression of SCUBE3 and survival was calculated by multivariate Cox regression analysis to investigate whether SCUBE3 expression may be an independent prognostic factor of breast cancer.

RESULTS

We found that the expression level of SCUBE3 was significantly upregulated in breast cancer tissue compared with adjacent normal tissues. The results showed that the distribution of breast cancer patients in the high expression group and the low expression group was significantly different in ER, PR, HER2, E-cadherin, and survival state (p < 0.05), but there was no significant difference in histologic grade, histologic type, tumor size, lymph node metastasis, TMN stage, subtypes, or recurrence (p > 0.05). In addition, the high expression of SCUBE3 was associated with relatively poor prognosis of ER- (p = 0.012), PR- (p = 0.029), HER2 + (p = 0.007). The multivariate Cox regression analysis showed that the hazard ratio (HR) was 2.80 (95% CI 1.20-6.51, p = 0.0168) in individuals with high SCUBE3 expression, and HR was increased by 1.86 (95% CI 1.06-3.25, p = 0.0300) for per 1-point increase of SCUBE3 expression.

CONCLUSIONS

These findings demonstrate that the high expression of SCUBE3 indicates poor prognosis in breast cancer. SCUBE3 expression may serve as a potential diagnostic indicator of breast cancer.

A Comparison Between the Online Prediction Models CancerMath and PREDICT as Prognostic Tools in Thai Breast Cancer Patients

Background and Purpose

Web-based prognostic calculators have been developed to inform about the use of adjuvant systemic treatments in breast cancer. CancerMath and PREDICT are two examples of web-based prognostic tools that predict patient survival up to 15 years after an initial diagnosis of breast cancer. The aim of this study is to validate the use of CancerMath and PREDICT as prognostic tools in Thai breast cancer patients.

Patients and Methods

A total of 615 patients who underwent surgical treatment for stage I to III breast cancer from 2003 to 2011 at the Division of Head Neck and Breast Surgery, Department of Surgery, Siriraj Hospital, Mahidol University, Thailand were recruited. A model-predicted overall survival rate (OS) and the actual OS of the patients were compared. The efficacy of the model was evaluated using receiver-operating characteristic (ROC) analysis.

Results

For CancerMath, the predicted 5-year OS was 88.9% and the predicted 10-year OS was 78.3% (p<0.001). For PREDICT, the predicted 5-year OS was 83.1% and the predicted 10-year OS was 72.0% (p<0.001). The actual observed 5-year OS was 90.8% and the observed 10-year OS was 82.6% (p<0.001). CancerMath demonstrated better predictive performance than PREDICT in all subgroups for both 5- and 10-year OS. In addition, there was a marked difference between CancerMath and observed survival rates in patients who were older as well as patients who were stage N3. The area under the ROC curve for 5-year OS in CancerMath and 10-year OS was 0.74 (95% CI; 0.65-0.82) and 0.75 (95% CI; 0.68-0.82). In the PREDICT group, the area under the ROC curve for 5-year OS was 0.78 (95% CI; 0.71-0.85) and for 10-year OS, it was 0.78 (95% CI; 0.71-0.84).

Conclusion

CancerMath and PREDICT models both underestimated the OS in Thai breast cancer patients. Thus, a novel prognostic model for Thai breast cancer patients is required.

PTHrP, A Biomarker for CNS Metastasis in Triple-Negative Breast Cancer and Selection for Adjuvant Chemotherapy in Node-Negative Disease

Background

Triple-negative breast cancer (TNBC) is characterized by poor prognosis and lack of targeted therapies and biomarkers to guide decisions on adjuvant chemotherapy. Parathyroid hormone-related protein (PTHrP) is frequently overexpressed in breast cancer and involved in proliferation and metastasis, two hallmarks of poor prognosis for node-negative breast cancer. We investigated the prognostic value of PTHrP with respect to organ-specific metastasis and nodal status in TNBC.

Methods

We assessed PTHrP expression using immunohistochemistry in a clinically annotated tissue microarray for a population-based study of 314 patients newly diagnosed with TNBC, then analyzed its correlation to progression and survival using Kaplan-Meier and Cox regression analyses. The Cancer Genome Atlas (TCGA) validation analysis was performed through Bioconductor. All statistical tests were two-sided.

Results

PTHrP overexpression (160 of 290 scorable cases, 55.2%) was statistically significantly associated in univariate analysis with decreased overall survival (OS) in our cohort (P = .0055) and The Cancer Genome Atlas (P = .0018) and decreased central nervous system (CNS)-progression-free survival (P = .0029). In multivariate analysis, PTHrP was a statistically significant independent prognostic factor for CNS-progression-free survival in TNBC (hazard ratio [HR] = 5.014, 95% confidence interval [CI] = 1.421 to 17.692, P = .0122) and for OS selectively in node-negative TNBC (HR = 2.423, 95% CI = 1.129 to 5.197, P = .0231). Strikingly, PTHrP emerged as the only statistically significant prognostic factor (HR = 2.576, 95% CI = 1.019 to 6.513, P = .0456) for OS of low-clinical risk node-negative patients who did not receive adjuvant chemotherapy.

Conclusions

PTHrP is a novel independent prognostic factor for CNS metastasis and adjuvant chemotherapy selection of low-clinical risk node-negative TNBC. Its predictive value needs to be prospectively assessed in clinical trials.

Prognostic Significance of Focal Adhesion Kinase in Node-Negative Breast Cancer

Background

Focal adhesion kinase (FAK) is a cytoplasmic tyrosine kinase that plays an important role as a mediator of cell migration, invasion, proliferation and survival. Conflicting results for the prognostic role of FAK in breast cancer (BC) prompted us to determine its impact.

Methods

Patients with node-negative BC entered this retrospective study. FAK expression was determined by immunohistochemistry (n = 335). The prognostic impact of FAK was examined with Cox regression analyses and Kaplan-Meier estimation in the whole cohort as well as in different molecular subtypes.

Results

151 (45.1%) had a FAK-positive BC. In univariate analyses, FAK expression showed a significant impact for shorter disease-free survival (DFS) (hazard ratio (HR) 1.54, 95% confidence interval (CI) 1.04-2.28, p = 0.030) but not for metastasis-free survival and overall survival. Significant prognostic relevance for DFS (HR 1.76, 95% CI 1.05-2.97, p = 0.033) was observed in particular in estrogen receptor-positive HER2-negative BC patients, most notably in luminal B-like tumors (HR 2.32, CI 1.20-4.48, p = 0.012). However, FAK lost its prognostic impact in multivariate Cox regression analysis.

Conclusion

FAK was associated with impaired DFS in univariate analysis. Prognostic relevance for DFS was most pronounced in luminal B-like BC. However, FAK expression was not associated with an independent impact on survival for BC in multivariate analysis.

Limited influence of germline genetic variation on all-cause mortality in women with early onset breast cancer: evidence from gene-based tests, single-marker regression, and whole-genome prediction

PURPOSE

Women diagnosed with breast cancer have heterogeneous survival outcomes that cannot be fully explained by known prognostic factors, and germline variation is a plausible but unconfirmed risk factor.

METHODS

We used three approaches to test the hypothesis that germline variation drives some differences in survival: mortality loci identification, tumor aggressiveness loci identification, and whole-genome prediction. The 2954 study participants were women diagnosed with breast cancer before age 50, with a median follow-up of 15 years who were genotyped on an exome array. We first searched for loci in gene regions that were associated with all-cause mortality. We next searched for loci in gene regions associated with five histopathological characteristics related to tumor aggressiveness. Last, we also predicted 10-year all-cause mortality on a subset of 1903 participants (3,245,343 variants after imputation) using whole-genome prediction methods.

RESULTS

No risk loci for mortality or tumor aggressiveness were identified. This null result persisted when restricting to women with estrogen receptor-positive tumors, when examining suggestive loci in an independent study, and when restricting to previously published risk loci. Additionally, the whole-genome prediction model also found no evidence to support an association.

CONCLUSION

Despite multiple complementary approaches, our study found no evidence that mortality in women with early onset breast cancer is influenced by germline variation.

Targeting Intracellular Calcium Signaling ([Ca2+]i) to Overcome Acquired Multidrug Resistance of Cancer Cells: A Mini-Overview

Cancer is a main public health problem all over the world. It affects millions of humans no matter their age, gender, education, or social status. Although chemotherapy is the main strategy for the treatment of cancer, a major problem limiting its success is the intrinsic or acquired drug resistance. Therefore, cancer drug resistance is a major impediment in medical oncology resulting in a failure of a successful cancer treatment. This mini-overview focuses on the interdependent relationship between intracellular calcium ([Ca²⁺]_i) signaling and multidrug resistance of cancer cells, acquired upon treatment of tumors with anticancer drugs. We propose that [Ca²⁺]_i signaling modulates gene expression of multidrug resistant (MDR) genes which in turn can be modulated by epigenetic factors which in turn leads to modified protein expression in drug resistant tumor cells. A precise knowledge of these mechanisms will help to develop new therapeutic strategies for drug resistant tumors and will improve current chemotherapy.

Prognostic contribution of mammographic breast density and HER2 overexpression to the Nottingham Prognostic Index in patients with invasive breast cancer

Background

To investigate whether very low mammographic breast density (VLD), HER2, and hormone receptor status holds any prognostic significance within the different prognostic categories of the widely used Nottingham Prognostic Index (NPI). We also aimed to see whether these factors could be incorporated into the NPI in an effort to enhance its performance.

Methods

This study included 270 patients with newly diagnosed invasive breast cancer. Patients with mammographic breast density of <10 % were considered as VLD. In this study, we compared the performance of NPI with and without VLD, HER2, ER and PR. Cox multivariate analysis, time-dependent receiver operating characteristic curve (tdROC), concordance index (c-index) and prediction error (0.632+ bootstrap estimator) were used to derive an updated version of NPI.

Results

Both mammographic breast density (VLD) (p < 0.001) and HER2 status (p = 0.049) had a clinically significant effect on the disease free survival of patients in the intermediate and high risk groups of the original NPI classification. The incorporation of both factors (VLD and HER2 status) into the NPI provided improved patient outcome stratification by decreasing the percentage of patients in the intermediate prognostic groups, moving a substantial percentage towards the low and high risk prognostic groups.

Conclusions

Very low density (VLD) and HER2 positivity were prognostically significant factors independent of the NPI. Furthermore, the incorporation of VLD and HER2 to the NPI served to enhance its accuracy, thus offering a readily available and more accurate method for the evaluation of patient prognosis.

Multimodal Assessment of Estrogen Receptor mRNA Profiles to Quantify Estrogen Pathway Activity in Breast Tumors

BACKGROUND

Molecular markers have transformed our understanding of the heterogeneity of breast cancer and have allowed the identification of genomic profiles of estrogen receptor (ER)-α signaling. However, our understanding of the transcriptional profiles of ER signaling remains inadequate. Therefore, we sought to identify the genomic indicators of ER pathway activity that could supplement traditional immunohistochemical (IHC) assessments of ER status to better understand ER signaling in the breast tumors of individual patients.

MATERIALS AND METHODS

We reduced ESR1 (gene encoding the ER-α protein) mRNA levels using small interfering RNA in ER⁺ MCF7 breast cancer cells and assayed for transcriptional changes using Affymetrix HG U133 Plus 2.0 arrays. We also compared 1034 ER⁺ and ER^- breast tumors from publicly available microarray data. The principal components of ER activity generated from these analyses and from other published estrogen signatures were compared with ESR1 expression, ER-α IHC, and patient survival.

RESULTS

Genes differentially expressed in both analyses were associated with ER-α IHC and ESR1 mRNA expression. They were also significantly enriched for estrogen-driven molecular pathways associated with ESR1, cyclin D1 (CCND1), MYC (v-myc avian myelocytomatosis viral oncogene homolog), and NFKB (nuclear factor kappa B). Despite their differing constituent genes, the principal components generated from these new analyses and from previously published ER-associated gene lists were all associated with each other and with the survival of patients with breast cancer treated with endocrine therapies.

CONCLUSION

A biomarker of ER-α pathway activity, generated using ESR1-responsive mRNAs in MCF7 cells, when used alongside ER-α IHC and ESR1 mRNA expression, could provide a method for further stratification of patients and add insight into ER pathway activity in these patients.

Use of implicit persuasion in decision making about adjuvant cancer treatment: A potential barrier to shared decision making

BACKGROUND

Shared decision making (SDM) is widely advocated, especially for preference-sensitive decisions like those on adjuvant treatment for early-stage cancer. Here, decision making involves a subjective trade-off between benefits and side-effects, and therefore, patients' informed preferences should be taken into account. If clinicians consciously or unconsciously steer patients towards the option they think is in their patients' best interest (i.e. implicit persuasion), they may be unwittingly subverting their own efforts to implement SDM. We assessed the frequency of use of implicit persuasion during consultations and whether the use of implicit persuasion was associated with expected treatment benefit and/or decision making.

METHODS

Observational study design in which consecutive consultations about adjuvant systemic therapy with stage I-II breast cancer patients treated at oncology outpatient clinics of general teaching hospitals and university medical centres were audiotaped, transcribed and coded by two researchers independently.

RESULTS

In total, 105 patients (median age = 59; range: 35-87 years) were included. A median of five (range: 2-10) implicitly persuasive behaviours were employed per consultation. The number of behaviours used did not differ by disease stage (P = 0.07), but did differ by treatment option presented (P = 0.002) and nodal status (P = 0.01). About 50% of patients with stage I or node-negative disease were steered towards undergoing chemotherapy, whereas 96% of patients were steered towards undergoing endocrine therapy, irrespective of expected treatment benefit. Decisions were less often postponed if more implicit persuasion was used (P = 0.03).

INTERPRETATION

Oncologists frequently use implicit persuasion, steering patients towards the treatment option that they think is in their patients' best interest. Expected treatment benefit does not always seem to be the driving force behind implicit persuasion. Awareness of one's use of these steering behaviours during decision making is a first step to help overcome the performance gap between advocating and implementing SDM.

SPAG5 as a prognostic biomarker and chemotherapy sensitivity predictor in breast cancer: a retrospective, integrated genomic, transcriptomic, and protein analysis

BACKGROUND

Proliferation markers and profiles have been recommended for guiding the choice of systemic treatments for breast cancer. However, the best molecular marker or test to use has not yet been identified. We did this study to identify factors that drive proliferation and its associated features in breast cancer and assess their association with clinical outcomes and response to chemotherapy.

METHODS

We applied an artificial neural network-based integrative data mining approach to data from three cohorts of patients with breast cancer (the Nottingham discovery cohort (n=171), Uppsala cohort (n=249), and Molecular Taxonomy of Breast Cancer International Consortium [METABRIC] cohort; n=1980). We then identified the genes with the most effect on other genes in the resulting interactome map. Sperm-associated antigen 5 (SPAG5) featured prominently in our interactome map of proliferation and we chose to take it forward in our analysis on the basis of its fundamental role in the function and dynamic regulation of mitotic spindles, mitotic progression, and chromosome segregation fidelity. We investigated the clinicopathological relevance of SPAG5 gene copy number aberrations, mRNA transcript expression, and protein expression and analysed the associations of SPAG5 copy number aberrations, transcript expression, and protein expression with breast cancer-specific survival, disease-free survival, distant relapse-free survival, pathological complete response, and residual cancer burden in the Nottingham discovery cohort, Uppsala cohort, METABRIC cohort, a pooled untreated lymph node-negative cohort (n=684), a multicentre combined cohort (n=5439), the Nottingham historical early stage breast cancer cohort (Nottingham-HES; n=1650), Nottingham early stage oestrogen receptor-negative breast cancer adjuvant chemotherapy cohort (Nottingham-oestrogen receptor-negative-ACT; n=697), the Nottingham anthracycline neoadjuvant chemotherapy cohort (Nottingham-NeoACT; n=200), the MD Anderson taxane plus anthracycline-based neoadjuvant chemotherapy cohort (MD Anderson-NeoACT; n=508), and the multicentre phase 2 neoadjuvant clinical trial cohort (phase 2 NeoACT; NCT00455533; n=253).

FINDINGS

In the METABRIC cohort, we detected SPAG5 gene gain or amplification at the Ch17q11.2 locus in 206 (10%) of 1980 patients overall, 46 (19%) of 237 patients with a PAM50-HER2 phenotype, and 87 (18%) of 488 patients with PAM50-LumB phenotype. Copy number aberration leading to SPAG5 gain or amplification and high SPAG5 transcript and SPAG5 protein concentrations were associated with shorter overall breast cancer-specific survival (METABRIC cohort [copy number aberration]: hazard ratio [HR] 1·50, 95% CI 1·18-1·92, p=0·00010; METABRIC cohort [transcript]: 1·68, 1·40-2·01, p<0·0001; and Nottingham-HES-breast cancer cohort [protein]: 1·68, 1·32-2·12, p<0·0001). In multivariable analysis, high SPAG5 transcript and SPAG5 protein expression were associated with reduced breast cancer-specific survival at 10 years compared with lower concentrations (Uppsala: HR 1·62, 95% CI 1·03-2·53, p=0·036; METABRIC: 1·27, 1·02-1·58, p=0·034; untreated lymph node-negative cohort: 2·34, 1·24-4·42, p=0·0090; and Nottingham-HES: 1·73, 1·23-2·46, p=0·0020). In patients with oestrogen receptor-negative breast cancer with high SPAG5 protein expression, anthracycline-based adjuvant chemotherapy increased breast cancer-specific survival overall compared with that for patients who did not receive chemotherapy (Nottingham-oestrogen receptor-negative-ACT cohort: HR 0·37, 95% CI 0·20-0·60, p=0·0010). Multivariable analysis showed high SPAG5 transcript concentrations to be independently associated with longer distant relapse-free survival after receiving taxane plus anthracycline neoadjuvant chemotherapy (MD Anderson-NeoACT: HR 0·68, 95% CI 0·48-0·97, p=0·031). In multivariable analysis, both high SPAG5 transcript and high SPAG5 protein concentrations were independent predictors for a higher proportion of patients achieving a pathological complete response after combination cytotoxic chemotherapy (MD Anderson-NeoACT: OR 1·71, 95% CI, 1·07-2·74, p=0·024; Nottingham-ACT: 8·75, 2·42-31·62, p=0·0010).

INTERPRETATION

SPAG5 is a novel amplified gene on Ch17q11.2 in breast cancer. The transcript and protein products of SPAG5 are independent prognostic and predictive biomarkers that might have clinical utility as biomarkers for combination cytotoxic chemotherapy sensitivity, especially in oestrogen receptor-negative breast cancer.

FUNDING

Nottingham Hospitals Charity and the John and Lucille van Geest Foundation.

Adjuvant treatment delay in breast cancer patients

BACKGROUND

to evaluate if time between surgery and the first adjuvant treatment (chemotherapy, radiotherapy or hormone therapy) in patients with breast cancer is a risk factor for lower overall survival (OS).

METHOD

data from a five-year retrospective cohort study of all women diagnosed with invasive breast cancer at an academic oncology service were collected and analyzed.

RESULTS

three hundred forty-eight consecutive women were included. Time between surgery and the first adjuvant treatment was a risk factor for shorter overall survival (HR=1.3, 95CI 1.06-1.71, p=0.015), along with negative estrogen receptor, the presence of lymphovascular invasion and greater tumor size. A delay longer than 4 months between surgery and the first adjuvant treatment was also associated with shorter overall survival (cumulative survival of 80.9% for delays ≤ 4 months vs. 72.6% for delays > 4 months; p=0.041, log rank test).

CONCLUSION

each month of delay between surgery and the first adjuvant treatment in women with invasive breast cancer increases the risk of death in 1.3-fold, and this effect is independent of all other well-established risk factors. Based on these results, we recommend further public strategies to decrease this interval.

High EGFR protein expression and exon 9 PIK3CA mutations are independent prognostic factors in triple negative breast cancers

BACKGROUND

Triple negative breast cancers (TNBC) are a more aggressive subset of breast cancer. A better understanding of its biology could allow the rational development of targeted therapies.

METHODS

We extensively analyzed the EGFR/PI3K/PTEN axis in a large, homogeneous population of TNBC to help defining the putative role of anti-EGFR and -PI3K targeted therapies in this setting. EGFR gene amplification, EGFR protein expression, PIK3CA and PTEN gene alterations (two members of EGFR downstream pathways) and their clinicopathological and prognostic implications were analyzed in 204 TNBC samples from European patients.

RESULTS

EGFR amplification was detected in 18 of the 204 TNBC specimens (8.9 %) and was significantly associated with higher EGFR protein levels. Fourteen PIK3CA mutations were identified in exon 9 (6.7 %), and 17 in exon 20 (8.3 %). PIK3CA mutations, especially in exon 9, were significantly associated with grade I-II tumors. PTEN deletions were detected in 43 samples (21.50 %) and were significantly associated with grade III tumors (p < 0.001). Univariate analysis showed a significant association between relapse-free survival (RFS), T and N stage and exon 9 PIK3CA mutations. Overall survival was significantly associated with T stage, N stage and adjuvant chemotherapy, which was administered to 70.3 % of patients. In multivariate analyses, T stage, N stage, presence of exon 9 PIK3CA mutations and high EGFR protein level were independent poor prognostic factors for RFS, while adjuvant chemotherapy was associated with a better outcome.

CONCLUSIONS

High EGFR protein expression and exon 9 PIK3CA activating mutations are independent prognostic factors in TNBC. The efficacy of anti-PI3K targeted therapies needs to be evaluated in this setting.

The Performance of the Nottingham Prognosis Index and the Adjuvant Online Decision Making Tool for Prognosis in Early-stage Breast Cancer Patients

Background:

Prognostic tools are widely used in the practice of Oncology and have been developed to help stratify patients into specific risk-related grouping. We sought to apply of two such tools used for patients with early-stage breast cancer and to correlate them with actual outcomes.

Methods:

A retrospective study was designed to include early-stage breast cancer cases seen from 1994 to 2014 at the Seyedoshohada Hospital in Isfahan, Iran. Information was derived from the patients’ records, and indices were derived from prognostic tools. Information was analyzed using descriptive statistics and one sample t-test.

Results:

In 233 patients, the difference between the predicted overall survival (OS) by the Adjuvant Online (AO) prognosis tools (69.28) and the observed OS (71.2) was not statistically significant (P = 0.52), and the AO prognosis tools had predicted the patients’ OS correctly. In the Nottingham prognosis index (NPI), this difference in all groups except the very poor prognosis group was not statistically significant.

Conclusions:

Adjuvant Online prognosis tools were capable of predicting the 10-year OS rate although not in all of the subgroups. The NPI was capable of distinguishing good, moderate, and poor survival rates, but this ability was not visible in more specific groups with moderate and poor prognosis.

Loss of circadian clock gene expression is associated with tumor progression in breast cancer

Several studies suggest a link between circadian rhythm disturbances and tumorigenesis. However, the association between circadian clock genes and prognosis in breast cancer has not been systematically studied. Therefore, we examined the expression of 17 clock components in tumors from 766 node-negative breast cancer patients that were untreated in both neoadjuvant and adjuvant settings. In addition, their association with metastasis-free survival (MFS) and correlation to clinicopathological parameters were investigated. Aiming to estimate functionality of the clockwork, we studied clock gene expression relationships by correlation analysis. Higher expression of several clock genes (e.g., CLOCK, PER1, PER2, PER3, CRY2, NPAS2 and RORC) was found to be associated with longer MFS in univariate Cox regression analyses (HR<1 and FDR-adjusted P < 0.05). Stratification according to molecular subtype revealed prognostic relevance for PER1, PER3, CRY2 and NFIL3 in the ER+/HER2- subgroup, CLOCK and NPAS2 in the ER-/HER2- subtype, and ARNTL2 in HER2+ breast cancer. In the multivariate Cox model, only PER3 (HR = 0.66; P = 0.016) and RORC (HR = 0.42; P = 0.003) were found to be associated with survival outcome independent of established clinicopathological parameters. Pairwise correlations between functionally-related clock genes (e.g., PER2-PER3 and CRY2-PER3) were stronger in ER+, HER2- and low-grade carcinomas; whereas, weaker correlation coefficients were observed in ER- and HER2+ tumors, high-grade tumors and tumors that progressed to metastatic disease. In conclusion, loss of clock genes is associated with worse prognosis in breast cancer. Coordinated co-expression of clock genes, indicative of a functional circadian clock, is maintained in ER+, HER2-, low grade and non-metastasizing tumors but is compromised in more aggressive carcinomas.

Deciding about (neo-)adjuvant rectal and breast cancer treatment: Missed opportunities for shared decision making

BACKGROUND

The first step in shared decision making (SDM) is creating choice awareness. This is particularly relevant in consultations concerning preference-sensitive treatment decisions, e.g. those addressing (neo-)adjuvant therapy. Awareness can be achieved by explicitly stating, as the 'reason for encounter', that a treatment decision needs to be made. It is unknown whether oncologists express such reason for encounter. This study aims to establish: 1) if 'making a treatment decision' is stated as a reason for the encounter and if not, what other reason for encounter is provided; and 2) whether mentioning that a treatment decision needs to be made is associated with enhanced patient involvement in decision making.

MATERIAL AND METHODS

Consecutive first consultations with: 1) radiation oncologists and rectal cancer patients; or 2) medical oncologists and breast cancer patients, facing a preference-sensitive treatment decision, were audiotaped. The tapes were transcribed and coded using an instrument developed for the study. Oncologists' involvement of patients in decision making was coded using the OPTION-scale.

RESULTS

Oncologists (N = 33) gave a reason for encounter in 70/100 consultations, usually (N = 52/70, 74%) at the start of the consultation. The reason for encounter stated was 'making a treatment decision' in 3/100 consultations, and 'explaining treatment details' in 44/100 consultations. The option of foregoing adjuvant treatment was not explicitly presented in any consultation. Oncologist' involvement of patients in decision making was below baseline (Md OPTION-score = 10). Given the small number of consultations in which the need to make a treatment decision was stated, we could not investigate the impact thereof on patient involvement.

CONCLUSION

This study suggests that oncologists rarely express that a treatment decision needs to be made in consultations concerning preference-sensitive treatment decisions. Therefore, patients might not realize that foregoing (neo-)adjuvant treatment is a viable choice. Oncologists miss a crucial opportunity to facilitate SDM.

Post-operative nomogram for predicting freedom from recurrence after surgery in localised breast cancer receiving adjuvant hormone therapy

PURPOSE

To develop a prognostic nomogram to predict freedom from recurrence for patients treated with adjuvant hormonal therapy (HT) for localised breast cancer (BC).

METHODS

We performed a retrospective analysis of 142 patients treated with adjuvant HT between 1996 and 2000. Clinical and pathological parameters were analysed.

RESULTS

A nomogram that predicts the probability of remaining free of recurrence for 5 years after surgery with adjuvant HT was developed using a Cox proportional hazards regression model. The progesterone receptor status (p < 0.001), nodal status (p = 0.008) and cathepsin-D (p < 0.001) were retained to construct the nomogram (C-index 0.734).

CONCLUSIONS

The nomogram we developed may be useful for estimating the probability of successful treatment 5 years after surgery for localised BC.

Long non-coding RNA expression profiles predict metastasis in lymph node-negative breast cancer independently of traditional prognostic markers

INTRODUCTION

Patients with clinically and pathologically similar breast tumors often have very different outcomes and treatment responses. Current prognostic markers allocate the majority of breast cancer patients to the high-risk group, yielding high sensitivities in expense of specificities below 20%, leading to considerable overtreatment, especially in lymph node-negative patients. Seventy percent would be cured by surgery and radiotherapy alone in this group. Thus, precise and early indicators of metastasis are highly desirable to reduce overtreatment. Previous prognostic RNA-profiling studies have only focused on the protein-coding part of the genome, however the human genome contains thousands of long non-coding RNAs (lncRNAs) and this unexplored field possesses large potential for identification of novel prognostic markers.

METHODS

We evaluated lncRNA microarray data from 164 primary breast tumors from adjuvant naïve patients with a mean follow-up of 18 years. Eighty two patients who developed detectable distant metastasis were compared to 82 patients where no metastases were diagnosed. For validation, we determined the prognostic value of the lncRNA profiles by comparing the ability of the profiles to predict metastasis in two additional, previously-published, cohorts.

RESULTS

We showed that lncRNA profiles could distinguish metastatic patients from non-metastatic patients with sensitivities above 90% and specificities of 64-65%. Furthermore; classifications were independent of traditional prognostic markers and time to metastasis.

CONCLUSIONS

To our knowledge, this is the first study investigating the prognostic potential of lncRNA profiles. Our study suggest that lncRNA profiles provide additional prognostic information and may contribute to the identification of early breast cancer patients eligible for adjuvant therapy, as well as early breast cancer patients that could avoid unnecessary systemic adjuvant therapy. This study emphasizes the potential role of lncRNAs in breast cancer prognosis.

Microarrays as Research Tools and Diagnostic Devices

Molecular diagnostics comprises a main analytical division in clinical laboratory diagnostics. The analysis of RNA or DNA helps to diagnose infectious diseases and identify genetic determined disorders or even cancer. Starting from mono-parametric tests within the last years, technologies have evolved that allow for the detection of many parameters in parallel, e.g., by using multiplex nucleic acid amplification techniques, microarrays, or next-generation sequencing technologies. The introduction of closed-tube systems as well as lab-on-a-chip devices further resulted in a higher automation degree with a reduced contamination risk. These applications complement or even stepwise replace classical methods in clinical microbiology like virus cultures, resistance determination, microscopic and metabolic analyses, as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel biomarkers.

This article provides an overview of microarrays as diagnostics devices and research tools. Introduced in 1995 for transcription analysis, microarrays are used today to detect several different biomolecules like DNA, RNA, miRNA, and proteins among others. Mainly used in research, some microarrays also found their way to clinical diagnostics. Further, closed lab-on-a-chip devices that use DNA microarrays as detection tools are discussed, and additionally, an outlook toward applications of next-generation sequencing tools in diagnostics will be given.

Are we able to predict survival in ER-positive HER2-negative breast cancer? A comparison of web-based models

Background:

Several prognostic models have been proposed and demonstrated to be predictive of survival outcomes in breast cancer. In the present article, we assessed whether three of these models are comparable at an individual level.

Methods:

We used a large data set (n=965) of women with hormone receptor-positive and HER2-negative early breast cancer from the public data set of the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. We compared the overall performance of three validated web-based models: Adjuvant!, CancerMath.net and PREDICT, and we assessed concordance of these models in 10-year survival prediction.

Results:

Discrimination performances of the three calculators to predict 10-year survival were similar for the Adjuvant! Model, 0.74 (95% CI 0.71–0.77) for the Cancermath.net model and 0.72 (95% CI 0.69–0.75) for the PREDICT model). Calibration performances, assessed graphically, were satisfactory. Predictions were concordant and stable in the subgroup, with a predicted survival higher than 90% with a median score dispersion at 0.08 (range 0.06–0.10). Dispersion, however, reached 30% for the subgroups with a predicted survival between 10 and 50%.

Conclusion:

This study revealed that the three web-based predictors equally perform well at the population level, but exhibit a high degree of discordance in the intermediate and poor prognosis groups.

Prognostic influence of cyclooxygenase-2 protein and mRNA expression in node-negative breast cancer patients

Background

Cyclooxygenases (COX) play a key role in prostaglandin metabolism and are important for tumor development and progression. The aim of this study was to analyze the prognostic impact of COX-2 expression in a cohort of lymph node-negative breast cancer patients not treated in the adjuvant setting.

Methods

COX-2 expression was determined by immunohistochemistry (IHC) in tumor tissue of 193 node-negative breast cancer patients. Additionally, mRNA expression was determined in corresponding tumor samples using microarray based gene-expression data. Univariate and multivariate Cox regression analyses adjusted for age at diagnosis, tumor size, histological grade, human epithelial growth factor receptor 2 (HER2), estrogen receptor (ER) and progesterone receptor (PR) were performed to evaluate the association of both COX-2 protein and mRNA expression with survival. Survival rates were determined by the Kaplan-Meier method. Correlations between COX-2 expression and established prognostic factors were analyzed using the Chi-square test. A potential correlation between COX-2 protein expression and COX-2 mRNA expression was assessed utilizing the Kruscal-Wallis-H-test.

Results

COX-2 protein expression was positive in 24.9% of the breast cancer samples. Univariate analysis showed that COX-2 protein expression was associated with shorter disease-free survival (DFS) (P = 0.0001), metastasis-free survival (MFS) (P = 0.002) as well as breast cancer specific overall survival (OS) (P = 0.043). In multivariate analysis COX-2 expression retained its significance independent of established prognostic factors for shorter DFS (P < 0.001, HR = 2.767, 95% CI = 1.563-4.901) and for inferior MFS (P = 0.002, HR = 2.7, 95% CI = 1.469-5.263) but not for OS (P = 0.096, HR = 1.929, 95% CI = 0.889-4.187). In contrast, COX-2 mRNA expression was not related to survival and failed to show a correlation with protein expression (P = 0.410).

Conclusions

The present findings support the hypothesis that COX-2 protein but not mRNA expression is associated with an unfavorable outcome in node-negative breast cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-952) contains supplementary material, which is available to authorized users.

The histone chaperone HJURP is a new independent prognostic marker for luminal A breast carcinoma

BACKGROUND

Breast cancer is a heterogeneous disease with different molecular subtypes that have varying responses to therapy. An ongoing challenge in breast cancer research is to distinguish high-risk patients from good prognosis patients. This is particularly difficult in the low-grade, ER-positive luminal A tumors, where robust diagnostic tools to aid clinical treatment decisions are lacking. Recent data implicating chromatin regulators in cancer initiation and progression offers a promising avenue to develop new tools to help guide clinical decisions.

METHODS

Here we exploit a published transcriptome dataset and an independent validation cohort to correlate the mRNA expression of selected chromatin regulators with respect to the four intrinsic breast cancer molecular subtypes. We then perform univariate and multivariate analyses to compare the prognostic value of a panel of chromatin regulators to Ki67, a currently utilized proliferation marker.

RESULTS

Unsupervised hierarchical clustering revealed a gene cluster containing several histone chaperones and histone variants highly-expressed in the proliferative subtypes (basal-like, HER2-positive, luminal B) but not in the luminal A subtype. Several chromatin regulators, including the histone chaperones CAF-1 (subunits p150 and p60), ASF1b, and HJURP, and the centromeric histone variant CENP-A, associated with local and metastatic relapse and poor patient outcome. Importantly, we find that HJURP can discriminate favorable and unfavorable outcome within the luminal A subtype, outperforming the currently utilized proliferation marker Ki67, as an independent prognostic marker for luminal A patients.

CONCLUSIONS

The integration of chromatin regulators as clinical biomarkers, in particular the histone chaperone HJURP, will help guide patient substratification and treatment options for low-risk luminal A breast carcinoma patients.

Immunoglobulin kappa chain as an immunologic biomarker of prognosis and chemotherapy response in solid tumors

Infiltration of plasma cells is associated with better prognosis in breast, lung and colon cancer. Immunoglobulin κ chain (IGKC) is now available as a single, robust immune marker predicting metastasis-free survival and response to chemotherapy. This will facilitate a deeper understanding of the role of the humoral immune system in cancer development.

Prognostic influence of pre-operative C-reactive protein in node-negative breast cancer patients

The importance of inflammation is increasingly noticed in cancer. The aim of this study was to analyze the prognostic influence of pre-operative serum C-reactive protein (CRP) in a cohort of 148 lymph node-negative breast cancer patients. The prognostic significance of CRP level for disease-free survival (DFS), metastasis-free survival (MFS) and overall survival (OS) was evaluated using univariate and multivariate Cox regression, also including information on age at diagnosis, tumor size, tumor grade, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status, proliferation index (Ki67) and molecular subtype, as well as an assessment of the presence of necrosis and inflammation in the tumor tissue. Univariate analysis showed that CRP, as a continuous variable, was significantly associated with DFS (P = 0.002, hazard ratio [HR] = 1.04, 95% confidence interval [CI] = 1.02-1.07) and OS (P = 0.036, HR= 1.03, 95% CI = 1.00-1.06), whereas a trend was observed for MFS (P = 0.111). In the multivariate analysis, CRP retained its significance for DFS (P = 0.033, HR= 1.01, 95% CI = 1.00-1.07) as well as OS (P = 0.023, HR= 1.03, 95% CI = 1.00-1.06), independent of established prognostic factors. Furthermore, large-scale gene expression analysis by Affymetrix HG-U133A arrays was performed for 72 (48.6%) patients. The correlations between serum CRP and gene expression levels in the corresponding carcinoma of the breast were assessed using Spearman's rank correlation, controlled for false-discovery rate. No significant correlation was observed between CRP level and gene expression indicative of an ongoing local inflammatory process. In summary, pre-operatively elevated CRP levels at the time of diagnosis were associated with shorter DFS and OS independent of established prognostic factors in node-negative breast cancer, supporting a possible link between inflammation and prognosis in breast cancer.

Individualizing life expectancy estimates for older adults using the Gompertz Law of Human Mortality

Background

Guidelines recommend incorporating life expectancy (LE) into clinical decision-making for preventive interventions such as cancer screening. Previous research focused on mortality risk (e.g. 28% at 4 years) which is more difficult to interpret than LE (e.g. 7.3 years) for both patients and clinicians. Our objective was to utilize the Gompertz Law of Human Mortality which states that mortality risk doubles in a fixed time interval to transform the Lee mortality index into a LE calculator.

Methods

We examined community-dwelling older adults age 50 and over enrolled in the nationally representative 1998 wave of the Health and Retirement Study or HRS (response rate 81%), dividing study respondents into development (n = 11701) and validation (n = 8009) cohorts. In the development cohort, we fit proportional hazards Gompertz survival functions for each of the risk groups defined by the Lee mortality index. We validated our LE estimates by comparing our predicted LE with observed survival in the HRS validation cohort and an external validation cohort from the 2004 wave of the English Longitudinal Study on Ageing or ELSA (n = 7042).

Results

The ELSA cohort had a lower 8-year mortality risk (14%) compared to our HRS development (23%) and validation cohorts (25%). Our model had good discrimination in the validation cohorts (Harrell’s c 0.78 in HRS and 0.80 in the ELSA). Our predicted LE’s were similar to observed survival in the HRS validation cohort without evidence of miscalibration (Hosmer-Lemeshow, p = 0.2 at 8 years). However, our predicted LE’s were longer than observed survival in the ELSA cohort with evidence of miscalibration (Hosmer-Lemeshow, p<0.001 at 8 years) reflecting the lower mortality rate in ELSA.

Conclusion

We transformed a previously validated mortality index into a LE calculator that incorporated patient-level risk factors. Our LE calculator may help clinicians determine which preventive interventions are most appropriate for older US adults.

Chemotherapy in early breast cancer: when, how and which one?

The efficacy of chemotherapy depends on the level of risk of the individual patient. Because of this, careful estimation of the risk level is mandatory. In addition to well-established clinicopathological factors, validated gene expression signatures might be useful in selected patients if all other criteria are inconclusive for therapeutic decision-making. If indicated, chemotherapy can be used either after surgery (adjuvant) or before surgery (neoadjuvant). Both approaches lead to comparable long-term survival. The neoadjuvant setting offers the additional opportunity for elaborate translational studies to develop and validate predictive biomarkers and to discover mechanisms of resistance to therapy. If possible, chemotherapy regimens should include both anthracyclines and taxanes. Docetaxel should be used every 3 weeks; better tolerability with equivalent efficacy favors the concurrent over the sequential approach. Paclitaxel, on the other hand, should be administered sequentially, either weekly or every 2 weeks. Especially, intense dose-dense sequential chemotherapy with granulocyte colony-stimulating factor support is very effective in high-risk breast cancer patients. In order to decrease toxicities, anthracycline-free regimens or a shortening of the duration of adjuvant chemotherapy are potential options that should be further explored.

Ceramide kinase promotes tumor cell survival and mammary tumor recurrence

Recurrent breast cancer is typically an incurable disease and, as such, is disproportionately responsible for deaths from this disease. Recurrent breast cancers arise from the pool of disseminated tumor cells (DTC) that survive adjuvant or neoadjuvant therapy, and patients with detectable DTCs following therapy are at substantially increased risk for recurrence. Consequently, the identification of pathways that contribute to the survival of breast cancer cells following therapy could aid in the development of more effective therapies that decrease the burden of residual disease and thereby reduce the risk of breast cancer recurrence. We now report that ceramide kinase (Cerk) is required for mammary tumor recurrence following HER2/neu pathway inhibition and is spontaneously upregulated during tumor recurrence in multiple genetically engineered mouse models for breast cancer. We find that Cerk is rapidly upregulated in tumor cells following HER2/neu downregulation or treatment with Adriamycin and that Cerk is required for tumor cell survival following HER2/neu downregulation. Consistent with our observations in mouse models, analysis of gene expression profiles from more than 2,200 patients revealed that elevated CERK expression is associated with an increased risk of recurrence in women with breast cancer. In addition, although CERK expression is associated with aggressive subtypes of breast cancer, including those that are estrogen receptor-negative, HER2(+), basal-like, or high grade, its association with poor clinical outcome is independent of these clinicopathologic variables. Together, our findings identify a functional role for Cerk in breast cancer recurrence and suggest the clinical utility of agents targeted against this prosurvival pathway.

Predicting and communicating the risk of recurrence and death in women with early-stage breast cancer: a systematic review of risk prediction models

BACKGROUND

It is a challenge for oncologists to distinguish patients with breast cancer who can forego adjuvant systemic treatment without negatively affecting survival from those who cannot. Risk prediction models (RPMs) have been developed for this purpose. Oncologists seem to have embraced RPMs (particularly Adjuvant!) in clinical practice and often use them to communicate prognosis to patients. We performed a systematic review of published RPMs and provide an overview of the prognosticators incorporated and reported clinical validity. Subsequently, we selected the RPMs that are currently used in the clinic for a more in-depth assessment of clinical validity. Finally, we assessed lay comprehensibility of the reports generated by RPMs.

METHODS

Pubmed, EMBASE, and Web of Science were searched. Two reviewers independently selected relevant articles and extracted data. Agreement on article selection and data extraction was achieved in consensus meetings.

RESULTS

We identified RPMs based on clinical prognosticators (N = 6) and biomolecular features (N = 14). Generally predictions from RPMs seem to be accurate, except for patients ≤ 50 years or ≥ 75 years at diagnosis, in addition to Asian populations. RPM reports contain much medical jargon or technical details, which are seldom explained in lay terms.

CONCLUSION

The accuracy of RPMs' prognostic estimates is suboptimal in some patient subgroups. This urgently needs to be addressed. In their current format, RPM reports are not conducive to patient comprehension. Communicating survival probabilities using RPM might seem straightforward, but it is fraught with difficulties. If not done properly, it can backfire and confuse patients. Evidence to guide best communication practice is needed.

Molecular and protein markers for clinical decision making in breast cancer: today and tomorrow

In early breast cancer (eBC), established clinicopathological factors are not sufficient for clinical decision making particularly regarding adjuvant chemotherapy since substantial over- or undertreatment may occur. Thus, novel protein- and molecular markers have been put forward as decision aids. Since these potential prognosis and/or predictive tests differ substantially regarding their methodology, analytical and clinical validation, this review attempts to summarize the essential facts for clinicians. This review focuses on those markers which are the most advanced so far in their development towards routine clinical application, i.e. two protein markers (i.e. uPA/PAI-1 and IHC4) and six molecular multigene tests (i.e. Mammaprint®, Oncotype DX®, PAM50, Endopredict®, the 97-gene genomic grade, and 76 gene Rotterdam signatures). Next to methodological aspects, we summarized the clinical evidences, in particular the main prospective clinical trials which have already been fully recruited (i.e. MINDACT, TAILORx, WSG PLAN B) or are still ongoing (i.e. RxPONDER/SWOG S1007, WSG-ADAPT). Last but not least, this review points out the key elements for clinicians to select one test among the wide panel of proposed assays, for a specific population of patients in term of level of evidence, analytical and clinical validity as well as cost effectiveness.

Clinical and pathologic risk factors of tumor recurrence in patients with node-negative early breast cancer after mastectomy

BACKGROUND AND OBJECTIVES

Patients with node-negative breast cancer (NNBC) usually have a good prognosis, but tumor recurrence still compromises survival. In this study, we sought to identify clinical and pathologic factors that predict recurrence.

METHODS

A total of 716 patients who were proved with pT1-2N0M0 breast cancer between 2005 and 2009 were enrolled in this study.

RESULTS

Forty-seven of the 716 patients developed tumor recurrence during the 47.0 months of median follow-up. The significant risk factors of recurrence were lymphovascular invasion (LVI) (hazard ratio [HR] = 4.60, 95% CI. 2.32-9.10) and Nottingham grade 3 (HR = 4.99, 95% CI. 1.06-23.48); adjuvant radiotherapy (HR = 0.35, 95% CI. 0.14-0.92) prevented tumor recurrence. Furthermore, we investigate the therapeutic impact of adjuvant chemotherapy and radiotherapy on patients with LVI and Nottingham grade 3. The adverse effect of LVI and grade 3 can be abrogated by adjuvant radiotherapy in recurrence-free survival (RFS) (LVI((+)) radiotherapy((+)) , no recurrence; grade 3((+)) radiotherapy((+)) , HR = 0.82, 95% CI. 0.18-3.70). However, adjuvant chemotherapy did not.

CONCLUSIONS

LVI and Nottingham grade 3 were the independent risk factors predicting tumor recurrence for patients with NNBC. Adjuvant radiotherapy might be considered in NNBC patients with these unfavorable factors to improve the RFS.

Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes

Background

The popularity of a large number of microarray applications has in cancer research led to the development of predictive or prognostic gene expression profiles. However, the diversity of microarray platforms has made the full validation of such profiles and their related gene lists across studies difficult and, at the level of classification accuracies, rarely validated in multiple independent datasets. Frequently, while the individual genes between such lists may not match, genes with same function are included across such gene lists. Development of such lists does not take into account the fact that genes can be grouped together as metagenes (MGs) based on common characteristics such as pathways, regulation, or genomic location. Such MGs might be used as features in building a predictive model applicable for classifying independent data. It is, therefore, demanding to systematically compare independent validation of gene lists or classifiers based on metagene or individual gene (SG) features.

Methods

In this study we compared the performance of either metagene-or single gene-based feature sets and classifiers using random forest and two support vector machines for classifier building. The performance within the same dataset, feature set validation performance, and validation performance of entire classifiers in strictly independent datasets were assessed by 10 times repeated 10-fold cross validation, leave-one-out cross validation, and one-fold validation, respectively. To test the significance of the performance difference between MG- and SG-features/classifiers, we used a repeated down-sampled binomial test approach.

Results

MG- and SG-feature sets are transferable and perform well for training and testing prediction of metastasis outcome in strictly independent data sets, both between different and within similar microarray platforms, while classifiers had a poorer performance when validated in strictly independent datasets. The study showed that MG- and SG-feature sets perform equally well in classifying independent data. Furthermore, SG-classifiers significantly outperformed MG-classifier when validation is conducted between datasets using similar platforms, while no significant performance difference was found when validation was performed between different platforms.

Conclusion

Prediction of metastasis outcome in lymph node–negative patients by MG- and SG-classifiers showed that SG-classifiers performed significantly better than MG-classifiers when validated in independent data based on the same microarray platform as used for developing the classifier. However, the MG- and SG-classifiers had similar performance when conducting classifier validation in independent data based on a different microarray platform. The latter was also true when only validating sets of MG- and SG-features in independent datasets, both between and within similar and different platforms.

Lactate and glycine-potential MR biomarkers of prognosis in estrogen receptor-positive breast cancers

Breast cancer is a heterogeneous disease with a variable prognosis. Clinical factors provide some information about the prognosis of patients with breast cancer; however, there is a need for additional information to stratify patients for improved and more individualized treatment. The aim of this study was to examine the relationship between the metabolite profiles of breast cancer tissue and 5-year survival. Biopsies from breast cancer patients (n=98) were excised during surgery and analyzed by high-resolution magic angle spinning MRS. The data were analyzed by multivariate principal component analysis and partial least-squares discriminant analysis, and the findings of important metabolites were confirmed by spectral integration of the metabolite peaks. Predictions of 5-year survival using metabolite profiles were compared with predictions using clinical parameters. Based on the metabolite profiles, patients with estrogen receptor (ER)-positive breast cancer (n=71) were separated into two groups with significantly different survival rates (p=0.024). Higher levels of glycine and lactate were found to be associated with lower survival rates by both multivariate analyses and spectral integration, and are suggested as biomarkers for breast cancer prognosis. Similar metabolic differences were not observed for ER-negative patients, where survivors could not be separated from nonsurvivors. Predictions of 5-year survival of ER-positive patients using metabolite profiles gave better and more robust results than those using traditional clinical parameters. The results imply that the metabolic state of a tumor may provide additional information concerning breast cancer prognosis. Further studies should be conducted in order to evaluate the role of MR metabolomics as an additional clinical tool for determining the prognosis of patients with breast cancer.

Immunoglobulin kappa C predicts overall survival in node-negative breast cancer

Background

Biomarkers of the immune system are currently not used as prognostic factors in breast cancer. We analyzed the association of the B cell/plasma cell marker immunoglobulin kappa C (IGKC) and survival of untreated node-negative breast cancer patients.

Material and Methods

IGKC expression was evaluated by immunostaining in a cohort of 335 node-negative breast cancer patients with a median follow-up of 152 months. The prognostic significance of IGKC for disease-free survival (DFS) and breast cancer-specific overall survival (OS) was evaluated with Kaplan-Meier survival analysis as well as univariate and multivariate Cox analysis adjusted for age at diagnosis, pT stage, histological grade, estrogen receptor (ER) status, progesterone receptor (PR) status, Ki-67 and human epidermal growth factor receptor 2 (HER-2) status.

Results

160 patients (47.7%) showed strong expression of IGKC. Univariate analysis showed that IGKC was significantly associated with DFS (P = 0.017, hazard ratio [HR] = 0.570, 95% confidence interval [CI] = 0.360–0.903) and OS (P = 0.011, HR = 0.438, 95% CI = 0.233–0.822) in the entire cohort. The significance of IGKC was especially strong in ER negative and in luminal B carcinomas. In multivariate analysis IGKC retained its significance independent of established clinical factors for DFS (P = 0.004, HR = 0.504, 95% CI = 0.315–0.804) as well as for OS (P = 0.002, HR = 0.371, 95% CI = 0.196–0.705).

Conclusion

Expression of IGKC has an independent protective impact on DFS and OS in node-negative breast cancer.

Biomarkers in Breast Cancer - An Update

The therapy of choice for breast cancer patients requiring adjuvant chemo- or radiotherapy is increasingly guided by the principle of weighing the individual effectiveness of the therapy against the associated side effects. This has only been made possible by the discovery and validation of modern biomarkers. In the last decades and in the last few years some biomarkers have been integrated in clinical practice and a number have been included in modern study concepts. The importance of biomarkers lies not merely in their prognostic value indicating the future course of disease but also in their use to predict patient response to therapy. Due to the many subgroups, mathematical models and computer-assisted analysis are increasingly being used to assess the prognostic information obtained from established clinical and histopathological factors. In addition to describing some recent computer programmes this overview will focus on established molecular markers which have already been extensively validated in clinical practice and on new molecular markers identified by genome-wide studies.

Interferon-inducible guanylate binding protein (GBP2) is associated with better prognosis in breast cancer and indicates an efficient T cell response

BACKGROUND

Recently, interferon-inducible guanylate binding protein (GBP2) has been discussed as a possible control factor in tumor development, which is controlled by p53, and inhibits NF-Kappa B and Rac protein as well as expression of matrix metalloproteinase 9. However, the potential role that GBP2 plays in tumor development and prognosis has not yet been studied.

METHODS

We analyzed whether GBP2 mRNA levels are associated with metastasis-free interval in 766 patients with node negative breast carcinomas who did not receive systemic chemotherapy. Furthermore, response to anthracycline-based chemotherapy was studied in 768 breast cancer patients.

RESULTS

High expression of GBP2 in breast carcinomas was associated with better prognosis in the univariate (P < 0.001, hazard ratio 0.763, 95 % CI 0.650-0.896) as well as in the multivariate Cox analysis (P = 0.008, hazard ratio 0.731, 95 % CI 0.580-0.920) adjusted to the established clinical factors age, pT stage, grading, hormone and ERBB2 receptor status. The association was particularly strong in subgroups with high proliferation and positive estrogen receptor status but did not reach significance in carcinomas with low expression of proliferation associated genes. Besides its prognostic capacity, GBP2 also predicted pathologically complete response to anthracycline-based chemotherapy (P = 0.0037, odds ratio 1.39, 95 % CI 1.11-1.74). Interestingly, GBP2 correlated with a recently established T cell signature, indicating tumor infiltration with T cells (R = 0.607, P < 0.001).

CONCLUSION

GBP2 is associated with better prognosis in fast proliferating tumors and probably represents a marker of an efficient T cell response.

Accuracy validation of adjuvant! online in Taiwanese breast cancer patients--a 10-year analysis

Background

Adjuvant! Online ( http://www.adjuvantonline.com) is an Internet-based software program that allows clinicians to make predictions about the benefits of adjuvant therapy and 10-year survival probability for early-stage breast cancer patients. This model has been validated in Western countries such as the United States, United Kingdom, Canada, Germany, and Holland. The aim of our study was to investigate the performance and accuracy of Adjuvant! Online in a cohort of Taiwanese breast cancer patients.

Methods

Data on the prognostic factors and clinical outcomes of 559 breast cancer patients diagnosed at the National Cheng Kung University Hospital in Tainan between 1992 and 2001 were enrolled in the study. Comprehensive demographic, clinical outcome data, and adjuvant treatment data were entered into the Adjuvant! Online program. The outcome prediction at 10 years was compared with the observed and predicted outcomes using Adjuvant! Online.

Results

Comparison between low- and high-risk breast cancer patient subgroups showed significant differences in tumor grading, tumor size, and lymph node status (p < 0.0001). The mean 10-year predicted death probability in 559 patients was 19.44%, and the observed death probability was 15.56%. Comparison with the Adjuvant! Online-predicted breast cancer-specific survival (BCSS) showed significant differences in the whole cohort (p < 0.001). In the low-risk subgroup, the predicted and observed outcomes did not differ significantly (3.69% and 3.85%, respectively). In high-risk patients, Adjuvant! Online overestimated breast cancer-specific survival (p = 0.016); the predicted and observed outcomes were 21.99% and 17.46%, respectively.

Conclusions

Adjuvant! Online accurately predicted 10-year outcomes and assisted in decision making about adjuvant treatment in low-risk breast cancer patients in our study, although the results were less accurate in the high-risk subgroup. Development of a prognostic program based on a national database should be considered, especially for high-risk breast cancer patients in Taiwan.

A nomogram to predict individual prognosis in node-negative breast carcinoma

BACKGROUND

Currently, the benefit of chemotherapy (CT) in node-negative breast carcinoma (NNBC) is discussed. The evaluation of classical clinical and histological factors is limited to assess individual outcome. A statistical model was developed to improve the prognostic accuracy of NNBC.

METHODS

A total of 305 node-negative breast carcinomas who underwent surgery (+/- radiotherapy) but no adjuvant treatment were selected. Putative prognosis factors including age, tumour size, oestrogen receptor (ER), progesterone receptor (PgR), Scarff-Bloom-Richardon (SBR) grading, urokinase plasminogen activator (uPA), plasminogen activator inhibitor 1 (PAI-1) and thymidine kinase (TK) were evaluated. The developed model was internally validated using Harrell's concordance index. A prognosis index (PI) was proposed and compared with Adjuvant! Online program.

RESULTS

Age (p < 0.001), pathological tumour size (pT) (p < 0.001), PgR (p = 0.02), and PAI-1 (p ≤ 0.001) were included in the Cox regression model predicting Breast cancer specific survival (BCSS) at 5-years. Internal validation revealed a concordance index of 0.71. A PI score was derived from our nomogram. The PI score was significantly associated with BCSS (hazard ratio (HR): 4.1 for intermediate, p=0.02, HR: 8.8, p < 0.001 for high group) as compared to Adjuvant! Online score (HR: 1.4, p=0.14).

CONCLUSION

A nomogram can be used to predict probability survival curves for individual breast cancer patients.