Sampling Defective Pathways in Phenotype Prediction Problems via the Fisher’s Ratio Sampler

Cernea, Ana; Fernández-Martínez, Juan Luis; deAndrés-Galiana, Enrique J.; Fernández-Ovies, Francisco Javier; Fernández-Muñiz, Zulima; Álvarez-Machancoses, Óscar; Saligan, Leorey N.; Sonis, Stephen T.

doi:10.1007/978-3-319-78759-6_2

Cited by 14 publications

(11 citation statements)

References 14 publications

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“…The LOOCV is a measure of the posterior discriminatory power of the different list of variables working in synergy and being organised by decreasing FR. Furthermore, we ran the Fisher’s sampling algorithm to find the combination of variables with the highest LOOCV predictive accuracy.…”

Section: Methodsmentioning

confidence: 99%

MMP11 expression in intratumoral inflammatory cells in breast cancer

et al. 2019

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Aims It is known that matrix metalloproteinase (MMP)‐11 has a role in tumour development and progression, and also that immune cells can influence cancer cells to increase their proliferative and invasive properties. The aim of the present study was to propose the evaluation of MMP11 expression by intratumoral mononuclear inflammatory cells (MICs) as a useful biological marker for breast cancer prognosis. Methods and results This study comprised 246 women with invasive breast carcinoma, and a long follow‐up period. Patients were stratified with regard to nodal status and to the development of metastatic disease. The median follow‐up period in patients without metastasis was 146 months and in patients with metastatic disease 31 months. MMP11 was determined by immunohistochemistry. For relapse‐free survival (RFS) and overall survival (OS) analysis we used the Cox’s univariate method. Cox’s regression model was used to examine the interactions between different prognostic factors in a multivariate analysis. Conclusions Our results showed that MMP11 expression by stromal cells was significantly associated with prognosis. MMP11 expression by cancer‐associated fibroblasts (CAFs) was associated with both shortened RFS and OS, but MMP11 expression by MICs showed a stronger association with both shortened RFS and OS, therefore being the most potent and independent factor to predict RFS and OS.

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Section: Methodsmentioning

confidence: 99%

MMP11 expression in intratumoral inflammatory cells in breast cancer

et al. 2019

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“…Regarding the most discriminatory prognostic variables of the histological grade, it is interesting to note that women in the HG2 group did not have any Oral contraceptives intake. Population studies aimed at exploring associations between oral contraceptive use and cancer risk have shown that the risks of endometrial and ovarian cancer appear to be reduced with the use of oral contraceptives, whereas the risks of breast, cervical, and liver cancer appear to be increased [30]. Other relevant values related with patients in the HG2 group with respect to the HG3 group are: higher Age at diagnosis, Lactation habits, and number of Pregnancies (an average of 2.3 children for women in HG2 group vs 1.7 in HG3 group); lower tumor size (Tsize) and Tobacco smoking; and lower values of the immuno-histochemical variables, except for the AR (Androgen Receptor) expression.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Clinical Prognostic Variables for Triple Negative Breast Cancer Histological Grading and Lymph Node Metastasis

Cernea

Fernández-Martínez

deAndrés-Galiana

et al. 2018

JMID

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Background: Triple Negative Breast Cancer (TNBC) is a type of breast cancer with very bad prognosis. Predicting the histological grade (HG) and the lymph nodes metastasis is crucial for developing more suitable treatment strategies. Methods: We present the main clinical and pathological variables to predict the histological grade and lymph nodes metastasis via novel machine learning techniques. These variables are currently being used for prognosis and treatment in medical practice. This analysis was performed using a database of 102 Caucasian women diagnosed with TNBC. The results were cross-validated using random simulations of this dataset. Results: HG was predicted with an accuracy of 93.8% using a list of 6 prognostic variables with significant implications: Ki67 expression, use of Oral contraceptives, Col11A1 expression, Col11A1 score, E-cad truncated and Tumor size. The lymph nodes metastasis was predicted with an accuracy of almost 85% using only 6 prognostic variables: Vascular invasion, Tumor size, Perineural invasion, Age at diagnosis, Ki67 expression, and Col11A1 score. This analysis also served to establish the median signatures of the groups with and without lymph node metastasis, and proved the existence of a kind of small-size tumors (around 2.15 cm) with lymph node metastasis but not showing vascular and perineural invasions and higher protein Col11A1 score. Besides, these signatures proved to be very stable. Conclusions: The additional information conveyed by the prognostic variables found in these two classification problems provides new insight about the genesis and progression of this disease and can be used in medical practice to improve decisions in patient diagnosis and further treatment.

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“…Nowadays, there are available tools that are capable of collecting a large amount of genomic data, alongside with cutting-edge data analytics for interpretation, which aid in our understanding of genomics, disease mechanisms, and treatments ( Figure 1). [8][9][10]…”

Section: Introductionmentioning

confidence: 99%

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

Álvarez-Machancoses¹,

deAndrés-Galiana²,

Cernea³

et al. 2020

PGPM

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The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

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Sampling Defective Pathways in Phenotype Prediction Problems via the Fisher’s Ratio Sampler

Cited by 14 publications

References 14 publications

MMP11 expression in intratumoral inflammatory cells in breast cancer

MMP11 expression in intratumoral inflammatory cells in breast cancer

Analysis of Clinical Prognostic Variables for Triple Negative Breast Cancer Histological Grading and Lymph Node Metastasis

<p>On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine</p>

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