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Identifying Biomarkers to Pair with Targeting Treatments within Triple Negative Breast Cancer for Improved Patient Stratification.

ABSTRACT: The concept of precision medicine has been around for many years and recent advances in high-throughput sequencing techniques are enabling this to become reality. Within the field of breast cancer, a number of signatures have been developed to molecularly sub-classify tumours. Notable examples recently approved by National Institute for Health and Care Excellence in the UK to guide treatment decisions for oestrogen receptors (ER)+ human epidermal growth factor receptor 2 (HER2)- patients include Prosigna test, EndoPredict, and Oncotype DX. However, a population of still unmet need are those with triple negative breast cancer (TNBC). Accounting for 15-20% of patients, this population has comparatively poor prognosis and as yet no targeted treatment options. Studies have shown that some patients with TNBC respond favourably to DNA damaging drugs (carboplatin) or agents which inhibit DNA damage response (poly ADP ribose polymerase (PARP) inhibitors). Known to be a heterogeneous population, there is a need to identify further TNBC patients who may benefit from these treatments. A number of signatures have been identified based on association with treatment response or specific genetic features/pathways however many of these were not restricted to TNBC patients and as of yet are not common practice in the clinic.

SUBMITTER: Tovey H

PROVIDER: S-EPMC6966447 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Identifying Biomarkers to Pair with Targeting Treatments within Triple Negative Breast Cancer for Improved Patient Stratification.

Tovey Holly H Cheang Maggie Chon U MCU

Cancers 20191125 12

The concept of precision medicine has been around for many years and recent advances in high-throughput sequencing techniques are enabling this to become reality. Within the field of breast cancer, a number of signatures have been developed to molecularly sub-classify tumours. Notable examples recently approved by National Institute for Health and Care Excellence in the UK to guide treatment decisions for oestrogen receptors (ER)+ human epidermal growth factor receptor 2 (HER2)- patients include ...[more]

PMID: 31769425

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Project description:BackgroundA major challenge of bioinformatics in the era of precision medicine is to identify the molecular biomarkers for complex diseases. It is a general expectation that these biomarkers or signatures have not only strong discrimination ability, but also readable interpretations in a biological sense. Generally, the conventional expression-based or network-based methods mainly capture differential genes or differential networks as biomarkers, however, such biomarkers only focus on phenotypic discrimination and usually have less biological or functional interpretation. Meanwhile, the conventional function-based methods could consider the biomarkers corresponding to certain biological functions or pathways, but ignore the differential information of genes, i.e., disregard the active degree of particular genes involved in particular functions, thereby resulting in less discriminative ability on phenotypes. Hence, it is strongly demanded to develop elaborate computational methods to directly identify functional network biomarkers with both discriminative power on disease states and readable interpretation on biological functions.ResultsIn this paper, we present a new computational framework based on an integer programming model, named as Comparative Network Stratification (CNS), to extract functional or interpretable network biomarkers, which are of strongly discriminative power on disease states and also readable interpretation on biological functions. In addition, CNS can not only recognize the pathogen biological functions disregarded by traditional Expression-based/Network-based methods, but also uncover the active network-structures underlying such dysregulated functions underestimated by traditional Function-based methods. To validate the effectiveness, we have compared CNS with five state-of-the-art methods, i.e. GSVA, Pathifier, stSVM, frSVM and AEP on four datasets of different complex diseases. The results show that CNS can enhance the discriminative power of network biomarkers, and further provide biologically interpretable information or disease pathogenic mechanism of these biomarkers. A case study on type 1 diabetes (T1D) demonstrates that CNS can identify many dysfunctional genes and networks previously disregarded by conventional approaches.ConclusionTherefore, CNS is actually a powerful bioinformatics tool, which can identify functional or interpretable network biomarkers with both discriminative power on disease states and readable interpretation on biological functions. CNS was implemented as a Matlab package, which is available at http://www.sysbio.ac.cn/cb/chenlab/images/CNSpackage_0.1.rar .

Project description:Breast cancer (BC) is the most common cancer globally, accounting for 685,000 deaths in 2020. Triple-negative breast cancers (TNBC) lack oestrogen (ER) and progesterone (PR) hormone receptor expression and HER2 overexpression. TNBC represent 10-15% of all BC with high incidence in women under 50-years old that have BRCA mutations, and have a dismal prognosis. African American and Hispanic women are at higher risk partly due to the common occurrence of BRCA mutations. The standard treatment for TNBC includes surgery, radiotherapy, and chemotherapy although, resistance to all standard-of-care therapies eventually develops. It is crucial to identify and develop more efficacious therapeutics with different mechanisms of action to improve on survival in these women. Recent findings with oncolytic adenoviruses (OAds) may generate a new strategy to improve on the outcomes for women afflicted by TNBC and other types of BC. OAds are genetically engineered to selectively lyse, eliminate and recruit the host antitumour immune responses, leaving normal cells unharmed. The most common modifications are deletions in the early gene products including the E1B55 KDa protein, specific regions of the E1A protein, or insertion of tumour-specific promoters. Clinical trials using OAds for various adenocarcinomas have not yet been sufficiently evaluated in BC patients. Preclinical studies demonstrated efficacy in BC cell lines, including TNBC cells, with promising novel adenoviral mutants. Here we review the results reported for the most promising OAds in preclinical studies and clinical trials administered alone and in combination with current standard of care or with novel therapeutics. Combinations of OAds with small molecule drugs targeting the epidermal growth factor receptor (EGFR), androgen receptor (AR), and DNA damage repair by the novel PARP inhibitors are currently under investigation with reported enhanced efficacy. The combination of the PARP-inhibitor Olaparib with OAds showed an impressive anti-tumour effect. The most promising findings to date are with OAds in combination with antibodies towards the immune checkpoints or expression of cytokines from the viral backbone. Although safety and efficacy have been demonstrated in numerous clinical trials and preclinical studies with cancer-selective OAds, further developments are needed to eliminate metastatic lesions, increase immune activation and intratumoural viral spread. We discuss shortcomings of the OAds and potential solutions for improving on patient outcomes.

Dataset Information

Identifying Biomarkers to Pair with Targeting Treatments within Triple Negative Breast Cancer for Improved Patient Stratification.

Publications

Identifying Biomarkers to Pair with Targeting Treatments within Triple Negative Breast Cancer for Improved Patient Stratification.

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