| 1. |
- Johansson, Ida, et al.
(författare)
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High-resolution genomic profiling of male breast cancer reveals differences hidden behind the similarities with female breast cancer
- 2011
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Ingår i: Breast Cancer Research and Treatment. - : Springer Science and Business Media LLC. - 1573-7217 .- 0167-6806. ; 129:3, s. 747-760
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Tidskriftsartikel (refereegranskat)abstract
- Male breast cancer (MBC) is extremely rare and poorly characterized on the molecular level. Using high-resolution genomic data, we aimed to characterize MBC by genomic imbalances and to compare it with female breast cancer (FBC), and further to investigate whether the genomic profiles hold any prognostic information. Fifty-six fresh frozen MBC tumors were analyzed using high-resolution tiling BAC arrays. Significant regions in common between cases were assessed using Genomic Identification of Significant Targets in Cancer (GISTIC) analysis. A publicly available genomic data set of 359 FBC tumors was used for reference purposes. The data revealed a broad pattern of aberrations, confirming that MBC is a heterogeneous tumor type. Genomic gains were more common in MBC than in FBC and often involved whole chromosome arms, while losses of genomic material were less frequent. The most common aberrations were similar between the genders, but high-level amplifications were more common in FBC. We identified two genomic subgroups among MBCs; male-complex and male-simple. The male-complex subgroup displayed striking similarities with the previously reported luminal-complex FBC subgroup, while the male-simple subgroup seems to represent a new subgroup of breast cancer occurring only in men. There are many similarities between FBC and MBC with respect to genomic imbalances, but there are also distinct differences as revealed by high-resolution genomic profiling. MBC can be divided into two comprehensive genomic subgroups, which may be of prognostic value. The male-simple subgroup appears notably different from any genomic subgroup so far defined in FBC.
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| 2. |
- Klintman, Marie, et al.
(författare)
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The Prognostic Value of Mitotic Activity Index (MAI), Phosphohistone H3 (PPH3), Cyclin B1, Cyclin A, and Ki67, Alone and in Combinations, in Node-Negative Premenopausal Breast Cancer
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:12
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Tidskriftsartikel (refereegranskat)abstract
- Proliferation, either as the main common denominator in genetic profiles, or in the form of single factors such as Ki67, is recommended for clinical use especially in estrogen receptor-positive (ER) patients. However, due to high costs of genetic profiles and lack of reproducibility for Ki67, studies on other proliferation factors are warranted. The aim of the present study was to evaluate the prognostic value of the proliferation factors mitotic activity index (MAI), phosphohistone H3 (PPH3), cyclin B1, cyclin A and Ki67, alone and in combinations. In 222 consecutive premenopausal node-negative breast cancer patients (87% without adjuvant medical treatment), MAI was assessed on whole tissue sections (predefined cut-off >= 10 mitoses), and PPH3, cyclin B1, cyclin A, and Ki67 on tissue microarray (predefined cut-offs 7th decile). In univariable analysis (high versus low) the strongest prognostic proliferation factor for 10-year distant disease-free survival was MAI (Hazard Ratio (HR)=3.3, 95% Confidence Interval (CI): 1.8-6.1), followed by PPH3, cyclin A, Ki67, and cyclin B1. A combination variable, with patients with MAI and/or cyclin A high defined as high-risk, had even stronger prognostic value (HR=4.2, 95% CI: 2.2-7). When stratifying for ER-status, MAI was a significant prognostic factor in ER-positive patients only (HR=7.0, 95% CI: 3.1-16). Stratified for histological grade, MAI added prognostic value in grade 2 (HR=7.2, 95% CI: 3.1-38) and grade 1 patients. In multivariable analysis including HER2, age, adjuvant medical treatment, ER, and one proliferation factor at a time, only MAI (HR=2.7, 95% CI: 1.1-6.7), and cyclin A (HR=2.7, 95% CI: 1.2-6.0) remained independently prognostic. In conclusion this study confirms the strong prognostic value of all proliferation factors, especially MAI and cyclin A, in all patients, and more specifically in ER-positive patients, and patients with histological grade 2 and 1. Additionally, by combining two proliferation factors, an even stronger prognostic value may be found.
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| 3. |
- Strand, Carina, et al.
(författare)
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Combination of the proliferation marker cyclin A, histological grade, and estrogen receptor status in a new variable with high prognostic impact in breast cancer
- 2012
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Ingår i: Breast Cancer Research and Treatment. - : Springer Science and Business Media LLC. - 0167-6806 .- 1573-7217. ; 131:1, s. 33-40
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Tidskriftsartikel (refereegranskat)abstract
- Global gene expression profiles, consisting mainly of genes associated with proliferation, have been shown to subdivide histological grade 2 breast cancers into groups with different prognosis. We raised the question whether this subdivision could be done using a single proliferation marker, cyclin A. Furthermore, we combined cyclin A (CA), histological grade (G), and estrogen receptor-ER (E) into a new variable, CAGE. Our aim was to investigate not only the prognostic importance of cyclin A alone but also the value of the combination variable CAGE. In 219 premenopausal node-negative patients, cyclin A was assessed using immunohistochemistry on tissue microarrays. High cyclin A was defined as above the seventh decile of positive cells. Only 13% of the patients received adjuvant systemic therapy. Cox proportional hazards regression was used to model the impact of the factors on distant disease-free survival (DDFS). Cyclin A divided histological grade 2 tumors into two groups with significantly different DDFS (hazard ratio [HR]: 15, P < 0.001). When stratifying for ER status, cyclin A was a prognostic factor only in the ER positive subgroup. We found that CAGE was an independent prognostic factor for DDFS in multivariate analysis (HR: 4.1, P = 0.002), together with HER2. CAGE and HER2 identified 53% as low-risk patients with a 5-year DDFS of 95%. A new prognostic variable was created by combining cyclin A, histological grade, and ER (CAGE). CAGE together with HER2 identified a large low-risk group for whom adjuvant chemotherapy will have limited efficacy and may be avoided.
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| 4. |
- Strand, Carina, et al.
(författare)
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The combination of Ki67, histological grade and estrogen receptor status identifies a low-risk group among 1,854 chemo-naive women with N0/N1 primary breast cancer
- 2013
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Ingår i: SpringerPlus. - : Springer Science and Business Media LLC. - 2193-1801. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Background: The aim was to confirm a previously defined prognostic index, combining a proliferation marker, histological grade, and estrogen receptor (ER) in different subsets of primary N0/N1 chemo-naive breast cancer patients. Methods/design: In the present study, including 1,854 patients, Ki67 was used in the index (KiGE), since it is the generally accepted proliferation marker in clinical routine. The low KiGE-group was defined as histological grade 1 patients and grade 2 patients which were ER-positive and had low Ki67 expression. All other patients made up the high KiGE-group. The KiGE-index separated patients into two groups with different prognosis. In multivariate analysis, KiGE was significantly associated with disease-free survival, when adjusted for age at diagnosis, tumor size and adjuvant endocrine treatment (hazard ratio: 3.5, 95% confidence interval: 2.6-4.7, P<0.0001). Discussion: We have confirmed a prognostic index based on a proliferation marker (Ki67), histological grade, and ER for identification of a low-risk group of patients with N0/N1 primary breast cancer. For this low-risk group constituting 57% of the patients, with a five-year distant disease-free survival of 92%, adjuvant chemotherapy will have limited effect and may be avoided.
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| 5. |
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| 6. |
- Jönsson, Göran B, et al.
(författare)
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Genomic subtypes of breast cancer identified by array-comparative genomic hybridization display distinct molecular and clinical characteristics
- 2010
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Ingår i: Breast Cancer Research. - : Springer Science and Business Media LLC. - 1465-5411 .- 1465-542X. ; 12:3
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Breast cancer is a profoundly heterogeneous disease with respect to biologic and clinical behavior. Gene-expression profiling has been used to dissect this complexity and to stratify tumors into intrinsic gene-expression subtypes, associated with distinct biology, patient outcome, and genomic alterations. Additionally, breast tumors occurring in individuals with germline BRCA1 or BRCA2 mutations typically fall into distinct subtypes. Methods: We applied global DNA copy number and gene-expression profiling in 359 breast tumors. All tumors were classified according to intrinsic gene-expression subtypes and included cases from genetically predisposed women. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was used to identify significant DNA copy-number aberrations and genomic subgroups of breast cancer. Results: We identified 31 genomic regions that were highly amplified in > 1% of the 359 breast tumors. Several amplicons were found to co-occur, the 8p12 and 11q13.3 regions being the most frequent combination besides amplicons on the same chromosomal arm. Unsupervised hierarchical clustering with 133 significant GISTIC regions revealed six genomic subtypes, termed 17q12, basal-complex, luminal-simple, luminal-complex, amplifier, and mixed subtypes. Four of them had striking similarity to intrinsic gene-expression subtypes and showed associations to conventional tumor biomarkers and clinical outcome. However, luminal A-classified tumors were distributed in two main genomic subtypes, luminal-simple and luminal-complex, the former group having a better prognosis, whereas the latter group included also luminal B and the majority of BRCA2-mutated tumors. The basal-complex subtype displayed extensive genomic homogeneity and harbored the majority of BRCA1-mutated tumors. The 17q12 subtype comprised mostly HER2-amplified and HER2-enriched subtype tumors and had the worst prognosis. The amplifier and mixed subtypes contained tumors from all gene-expression subtypes, the former being enriched for 8p12-amplified cases, whereas the mixed subtype included many tumors with predominantly DNA copy-number losses and poor prognosis. Conclusions: Global DNA copy-number analysis integrated with gene-expression data can be used to dissect the complexity of breast cancer. This revealed six genomic subtypes with different clinical behavior and a striking concordance to the intrinsic subtypes. These genomic subtypes may prove useful for understanding the mechanisms of tumor development and for prognostic and treatment prediction purposes.
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| 7. |
- Kalderstam, Jonas, et al.
(författare)
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Training artificial neural networks directly on the concordance index for censored data using genetic algorithms.
- 2013
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 1873-2860 .- 0933-3657. ; 58:2, s. 125-132
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: The concordance index (c-index) is the standard way of evaluating the performance of prognostic models in the presence of censored data. Constructing prognostic models using artificial neural networks (ANNs) is commonly done by training on error functions which are modified versions of the c-index. Our objective was to demonstrate the capability of training directly on the c-index and to evaluate our approach compared to the Cox proportional hazards model. METHOD: We constructed a prognostic model using an ensemble of ANNs which were trained using a genetic algorithm. The individual networks were trained on a non-linear artificial data set divided into a training and test set both of size 2000, where 50% of the data was censored. The ANNs were also trained on a data set consisting of 4042 patients treated for breast cancer spread over five different medical studies, 2/3 used for training and 1/3 used as a test set. A Cox model was also constructed on the same data in both cases. The two models' c-indices on the test sets were then compared. The ranking performance of the models is additionally presented visually using modified scatter plots. RESULTS: Cross validation on the cancer training set did not indicate any non-linear effects between the covariates. An ensemble of 30 ANNs with one hidden neuron was therefore used. The ANN model had almost the same c-index score as the Cox model (c-index=0.70 and 0.71, respectively) on the cancer test set. Both models identified similarly sized low risk groups with at most 10% false positives, 49 for the ANN model and 60 for the Cox model, but repeated bootstrap runs indicate that the difference was not significant. A significant difference could however be seen when applied on the non-linear synthetic data set. In that case the ANN ensemble managed to achieve a c-index score of 0.90 whereas the Cox model failed to distinguish itself from the random case (c-index=0.49). CONCLUSIONS: We have found empirical evidence that ensembles of ANN models can be optimized directly on the c-index. Comparison with a Cox model indicates that near identical performance is achieved on a real cancer data set while on a non-linear data set the ANN model is clearly superior.
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