METHODS AND SYSTEMS FOR DETERMINING HER2 STATUS USING MOLECULAR DATA
A computer-implemented method, computing system and computer-readable medium for determining HER2-low status of a patient using molecular data of the patient includes: (a) receiving digital biological data; (b) processing the digital biological data using a trained multi-stage machine learning architecture; (c) generating a digital HER2-low status report corresponding to the patient; and (d) causing the digital HER2-low status report to be displayed. A computer-implemented method, computing system and computer-readable medium for training a model architecture to determine HER2-low status of a patient using molecular data of the patient includes: (a) receiving training digital biological data; (b) initializing a machine learning model; (c) processing the plurality of molecular signatures using the machine learning model to generate a trained machine learning model; and (d) storing the trained machine learning.
1 . A computer-implemented method for determining HER2-low status of a patient using molecular data of the patient, comprising:
receiving, via one or more processors, digital biological data;
processing, via one or more processors, the digital biological data corresponding to the patient using a trained multi-stage machine learning architecture, wherein the processing includes:
(i) processing the digital biological data using a trained HER2-positive model to determine whether the digital biological data indicates that a HER2 status of the patient is HER2-positive;
(ii) when the HER2 status of the patient is not HER2-positive, processing the digital biological data using a trained HER2-low model to identify whether the HER2 status of the patient is HER2-low; and
(iii) when the HER2 status of the patient is not HER2-positive or HER2-low, designating the HER2 status of the patient as HER2-negative;
generating, via one or more processors, a digital HER2-low status report corresponding to the patient; and
causing, via a display device, the digital HER2-low status report to be displayed.
2 . The computer-implemented method of claim 1 , wherein the digital biological data includes RNA data.
3 . The computer-implemented method of claim 1 , wherein the digital biological data includes at least some transcriptomic data.
4 . The computer-implemented method of claim 3 , wherein the at least some of the transcriptomic data includes at least some data generated via RNA seq.
5 . The computer-implemented method of claim 1 , wherein the digital biological data includes at least one of DNA data or copy number variant data.
6 . The computer-implemented method of claim 1 , wherein receiving the digital biological data includes receiving the digital biological data from a next-generation sequencing platform.
7 . The computer-implemented method of claim 1 , wherein the trained HER2-positive model is a random forest model.
8 . The computer-implemented method of claim 1 , wherein the trained HER2-low model is a random forest model.
9 . The computer-implemented method of claim 1 , wherein the trained HER2-positive model is a binary classifier trained on molecular signature data labeled according to (HER2-positive, NOT-HER2-positive) labels.
10 . The computer-implemented method of claim 1 , wherein the trained HER2-low model is a binary classifier trained on molecular signature data labeled according to (HER2-low, NOT-HER2-low) labels.
11 . The computer-implemented method of claim 1 , further comprising:
generating a prediction as to the HER2-low, HER2-positive and/or HER2-negative status of a given sample based on the trained multi-stage machine learning architecture.
12 . The computer-implemented method of claim 1 , further comprising:
identifying at least one patient from a population of patients by processing the data of the patient using trained multi-stage machine learning architecture; and
matching the identified patient for treatment using a targeted therapy.
13 . The computer-implemented method of claim 12 , wherein the targeted therapy is a HER2 targeted therapy.
14 . The computer-implemented method of claim 13 , wherein the targeted therapy is trastuzumab deruxtecan.
15 . A computing system comprising:
one or more processors; and
one or more memories having stored thereon computer-readable instructions that, when executed, cause the computing system to:
receive digital biological data;
process the digital biological data corresponding to a patient using a trained multi-stage machine learning architecture, wherein the processing includes:
(i) processing the digital data using a trained HER2-positive model to determine whether the digital biological data indicates that a HER2 status of the patient is HER2-positive;
(ii) when the patient is not HER2-positive, processing the digital biological data using a trained HER2-low model to identify whether the HER2 status of the patient is HER2-low; and
(iii) when the patient is not HER2-positive or HER2-low, designating the HER2 status of the patient as HER2-negative;
generate, via one or more processors, a digital HER2-low status report corresponding to the patient; and
causing, via a display device, the digital HER2-low status report to be displayed.
16 . The computing system of claim 15 , wherein the digital biological data includes one or both of (i) RNA data, and (ii) at least some transcriptomic data.
17 . The computing system of claim 15 , wherein the at least some of the transcriptomic data includes at least some data generated via RNA seq.
18 . A computer-readable medium having stored thereon computer-executable instructions that, when executed, cause a computer to:
receive digital biological data;
process the digital biological data corresponding to a patient using a trained multi-stage machine learning architecture, wherein the processing includes:
(i) processing the digital data using a trained HER2-positive model to determine whether the digital biological data indicates that a HER2 status of the patient is HER2-positive;
(ii) when the patient is not HER2-positive, processing the digital biological data using a trained HER2-low model to identify whether a HER2 status of the patient is HER2-low; and
(ii) when the patient is not HER2-positive or HER2-low, designating the HER2 status of the patient as HER2-negative;
generate, via one or more processors, a digital HER2-low status report corresponding to the patient; and
cause, via a display device, the digital HER2-low status report to be displayed.
19 . The computer-readable medium of claim 18 , wherein the digital biological data includes one or both of (i) RNA data, and (ii) at least some transcriptomic data.
20 . The computer-readable medium of claim 18 , wherein the at least some of the transcriptomic data includes at least some data generated via RNA seq.