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Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling.

ABSTRACT: For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and formation of metastases. A prediction of the outcome of adjuvant therapy tailored for the patient is hard, due to the heterogeneous nature of the disease. We devised a methodology for predicting 5-years survival based on the new machine learning paradigm of coherent voting networks, with improved accuracy over state-of-the-art prediction methods. The 'coherent voting communities' metaphor provides a certificate justifying the survival prediction for an individual patient, thus facilitating its acceptability in practice, in the vein of explainable Artificial Intelligence. The method we propose is quite flexible and applicable to other types of cancer.

SUBMITTER: Pellegrini M

PROVIDER: S-EPMC8289832 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling.

Pellegrini Marco M

Scientific reports 20210719 1

For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and formation of metastases. A prediction of the outcome of adjuvant therapy tailored for the patient is hard, due to the heterogeneous nature of the disease. We devised a methodology for predicting 5-years survival based on the new machine learning paradigm of coherent voting networks, with improved accuracy over state-of-the-art prediction methods. The ...[more]

PMID: 34282236

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Project description:PurposeBreast cancer is the leading cause of cancer death worldwide in women. The molecular mechanism for human breast cancer is unknown. Gene microarray has been widely used in breast cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis survival. So far, the valuable multigene signatures in clinical practice are unclear, and the biological importance of individual genes is difficult to detect, as the described signatures virtually do not overlap. Early prognosis of this disease, breast invasive ductal carcinoma (IDC) and breast ductal carcinoma in situ (DCIS), is vital in breast surgery.MethodsThus, this study reports gene expression profiling in large breast cancer cohorts from Gene Expression Omnibus, including GSE29044 (N=138) and GSE10780 (N=185) test series and four independent validation series GSE21653 (N=266), GSE20685 (N=327), GSE26971 (N=276), and GSE12776 (N=204). Significantly differentially expressed genes in human breast IDC and breast DCIS were detected by transcriptome microarray analysis.ResultsWe created a set of three genes (MAMDC2, TSHZ2, and CLDN11) that were significantly correlated with disease-free survival of breast cancer patients using a univariate Cox regression model (significance level P<0.01) in a meta-analysis. Based on the risk score of the three genes, the test series patients could be separated into low-risk and high-risk groups with significantly different survival times. This signature was validated in the other three cohorts. The prognostic value of this three-gene signature was confirmed in the internal validation series and another four independent breast cancer data sets. The prognostic impact of one of the three genes, CLDN11, was confirmed by immunohistochemistry. CLDN11 was significantly overexpressed in human breast IDC as compared with normal breast tissues and breast DCIS.ConclusionUsing novel gene expression profiling together with a meta-analysis validation approach, we have identified a three-gene signature with independent prognostic impact. Furthermore, CLDN11 may offer a biomarker to predict prognosis as well as a new target for prognostic and therapeutic intervention for human breast IDC.

Project description:Purpose Aromatase inhibitors (AIs) have an established role in breast cancer treatment. Response rates are only 50-70% in the neoadjuvant setting and lower in advanced disease. Accurate biomarkers are urgently needed to predict response in these settings and to determine which individuals will benefit from adjuvant AI therapy. Participants and Methods Pre- and on-treatment (after 2 weeks and 3 months) biopsies were obtained from 89 post-menopausal women with ER+ breast cancer receiving neoadjuvant letrozole for transcript profiling. Dynamic clinical response was assessed by three-dimensional ultrasound measurements. Results The molecular response to letrozole was characterised and a four gene classifier of clinical response was established (accuracy of 96%) based upon the level of two genes prior to treatment (one associated with immune signalling, IL6ST and the other with apoptosis, NGFRAP1) and two proliferation genes (ASPM, MCM4) at 2 weeks of therapy. The four gene signature was found to be 91% accurate in a blinded, completely independent validation dataset of patients treated with anastrozole. Matched 2 week on-treatment biopsies improved predictive power over pre-treatment biopsies alone. This signature also significantly predicted recurrence free survival (p=0.029) and breast cancer specific survival (p=0.009). We demonstrate that the test can also be performed using quantitative PCR or immunohistochemistry. Conclusion A four gene predictive model of clinical response to AIs by two weeks has been generated and validated. Deregulated immune and apoptotic responses before treatment and a failure to reduce proliferation by 2 weeks are functional characteristics of breast tumours that do not respond to AIs. 25 Pre treatment, 25 two week and 25 three month on-treatment primary breast tumour samples from the same patients. Data was analysed with two previously generated datasets, GSE55374 which was also processed on Illumina HT-12v4 BeadChips (GPL10558) and GSE20181 on Affymetrix U133A GeneChips (GPL96)

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Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling.

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Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling.

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