| CPC G16H 50/20 (2018.01) [G16B 5/00 (2019.02); G16B 40/00 (2019.02); G16H 50/30 (2018.01); G16H 50/50 (2018.01)] | 17 Claims |
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1. A method for determining a survival prognosis of a patient suffering from a breast tumor, using processing carried out by electronic processing and/or calculation means, said method comprising the steps of:
(a) defining a biological network representative of a particular biological process associated with the breast tumor,
said biological network comprising a plurality of nodes, a set of directional relationships between said nodes and a set of genes associated with said nodes,
wherein each node represents a gene and/or a protein and/or a complex of multiple proteins and/or another molecule and/or an ion present in a cell or in contact therewith and/or a particular external condition to which the cell is subjected and/or states in which the cell can be found,
wherein each directional relationship is defined by a source node, a target node and an interaction type, and in which the interaction type comprises inhibition interaction, in an inhibition relationship, or excitation interaction, in an excitation relationship, or absence of interaction, said directional relationship being determined from the source node to the target node;
(b) accessing a patient-related data set, said data set comprising expressions of genes in a biological sample of said tumor isolated from the patient;
(c) calculating a continuous expression value for said nodes of the biological network, wherein said calculation step comprises:
if the node is associated with only one gene and if, based on said patient data set, it is found that the gene is present in the biological sample, calculating the continuous expression value of the node as the expression of the associated gene detected in the biological sample;
if the node is associated with multiple genes, and if, based on said patient data set, it is found that at least one of said genes is present in the biological sample, calculating the continuous expression value of the node based on the expressions of the associated genes, which are present in the biological sample;
if the node is not associated with any gene, or if, based on said patient data set, the associated gene is not found in the biological sample, marking the node as a node not associated with a continuous expression value;
(d) carrying out, by said electronic processing and/or calculation means, a binarization of the data set of continuous expression values calculated for each node of the biological network with which a continuous expression value is associated, based on a comparison of the continuous expression value to a respective threshold, in order to obtain a first binarized data set of the nodes, obtained on the basis of said calculating step (c);
(e) calculating, by said electronic processing and/or calculation means, an aggressiveness score, derivable from the first binarized data set of the nodes, based on said first binarized data set of the nodes; and
(f) determining a result of the survival prognosis based on said calculated aggressiveness score,
wherein step (e) of calculating an aggressiveness score comprises calculating the aggressiveness score as the energy of the patient's biological sample and/or the fraction of the out-of-balance nodes of the patient's biological sample and/or based on a projection of the data set on the main component thereof through a Principal Component Analysis methodology and/or based on a projection of the data set on a simulated binarized data set first principal component, through a Principal Component Analysis methodology,
wherein the step (e) of calculating an aggressiveness score further comprises:
simulating, through computational simulation, the biological network, through simulated samples which represent possible cellular states described by the biological network, in order to obtain a simulated binarized data set; and
defining the aggressiveness score based on a projection of the data set on the simulated binary data set first principal component, through a Principal Component Analysis methodology, and
wherein said step of simulating the biological network comprises:
numerically calculating the state of the biological network from a certain initial condition chosen randomly;
updating, by means of a simulation algorithm, the binarized value of each node, in a sequential manner, changing the state thereof so that there is an increase in the number of satisfied directional relationships among the directional relationships of which said node is the target; and
iterating said updating step, until a steady state of the biological network is achieved, in which each node is in a binary state which satisfies the majority of the directional relationships of which said node is the target, wherein said steady state corresponds to a possible state of the cell.
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