Real-World Evidence of Diagnostic Testing and Treatment Patterns in U.S. Breast Cancer Patients with Implications for Treatment Biomarkers from RNA-Sequencing Data
Techniques for analysis of gene expression data contained in real world data and real word evidence for assessing biologic pathways for identifying molecular subtypes are provided. Systems and methods include, for a plurality of biological pathways, determining a pathway score using gene expression data and determining of summary score for the plurality of biological pathways. That summary score may be compared to one or more enrichment scores each associated with a pre-determined molecular subtype. A molecular subtype is determined based on that comparison. Various heuristics may be applied to filter pathways before summary scoring.
1 . A computer-implemented method for determining a molecular subtype of a cancer specimen, the method comprising:
for each of a plurality of pre-determined biological pathways, determining a pathway score using gene expression data of a plurality of nucleic acids associated with the specimen;
preparing a summary score for the plurality of biological pathways, based upon the pathway score for each biological pathway;
comparing the summary score to one or more enrichment scores each associated with a pre-determined molecular subtype; and
returning a determined molecular subtype based on the comparison of the summary score and the one or more enrichment scores.
2 . The computer-implemented method of claim 1 , further comprising:
receiving gene expression data corresponding to a plurality of available biological pathways; and
applying a pathway heuristic filter to identify a subset of the available biological pathways, wherein the subset being the pre-determined biological pathways.
3 . The computer-implemented method of claim 1 , wherein the pathway heuristic filter is a pathway overlap filter.
4 . The computer-implemented method of claim 3 , further comprising filtering the available biologic pathways by applying the pathway overlap filter to identify and filter out pathways having 90% or greater genes in common with a pathway to be retained in the subset.
5 . The computer-implemented method of claim 3 , further comprising filtering the available biologic pathways by applying the pathway overlap filter to identify and filter out pathways having 80% or greater genes in common with a pathway to be retained in the subset.
6 . The computer-implemented method of claim 3 , further comprising filtering the available biologic pathways by applying the pathway overlap filter to identify and filter out pathways having 50% or greater genes in common with a pathway to be retained in the subset.
7 . The computer-implemented method of claim 1 , wherein the pathway heuristic filter is a gene expression data filter.
8 . The computer-implemented method of claim 1 , wherein the pathway heuristic filter is a molecular subtype filter.
9 . The computer-implemented method of claim 1 , wherein the pathway score for each biological pathway is a z-score.
10 . The computer-implemented method of claim 9 , wherein the summary score is an average of the z-scores for the biological pathways.
11 . The computer-implemented method of claim 10 , further comprising, before preparing the average of the z-scores, scaling one or more of the z-scores.
12 . The computer-implemented method of claim 11 , wherein scaling one or more of the z-scores comprises flipping a sign of the one or more z-scores.
13 . The computer-implemented method of claim 12 , further comprising wherein scaling one or more of the z-scores comprises flipping the sign of the one or more z-scores.
14 . The computer-implemented method of claim 13 , further flipping the sign of z-scores having a mean negative z-score in a group of positive gene expression samples and having a mean positive z-score in a group of negative gene expression samples.
15 . The computer-implemented method of claim 13 , further flipping the sign of z-scores having negative score below a negative threshold or a positive score above a positive threshold.
16 . The computer-implemented method of claim 1 , further comprising scaling the pathway score for each of a plurality of biological pathways before determining the summary score.
17 . The computer-implemented method of claim 1 , wherein the gene expression data are RNA-seq gene expression data.
18 . The computer-implemented method of claim 1 , wherein the pre-determined biological pathways are one or more of the Hallmark pathways.
19 . The computer-implemented method of claim 1 , wherein the pre-determined biological pathways are all one or more of the Hallmark pathways.
20 . The computer-implemented method of claim 1 , wherein the pre-determined biological pathways are one or more pathways related to estrogen signaling.
21 . The computer-implemented method of claim 1 , wherein the pre-determined biological pathways are one or more of pathways downstream of human epidermal growth factor receptor 2 (HER2), downstream of RAS, or downstream of mTOR.
22 . The computer-implemented method of claim 1 , wherein the pre-determined biological pathways are one or more immune-related pathways.
23 . The computer-implemented method of claim 1 , wherein the pre-determined biological pathways are one or more immune-related Hallmark pathways.
24 . The computer-implemented method of claim 1 , wherein the one or more enrichment scores in (c) are determined by UMAP analysis.
25 . The computer-implemented method of claim 1 , wherein the determined molecular subtype is a HR+ subtype, a HR+/HER2+ subtype, a HR−/HER2+ subtype, or a HER2− subtype.
26 . The computer-implemented method of claim 1 , wherein the determined molecular subtype is a triple negative subtype.
27 . The computer-implemented method of claim 1 , wherein the specimen is from a patient diagnosed with breast cancer.
28 . The computer-implemented method of claim 1 , wherein the specimen is a breast cancer specimen.
29 . A system having a memory and a processor, the memory storing instructions, that when executed, cause the processor to: for each of a plurality of pre-determined biological pathways, determine a pathway score using gene expression data of a plurality of nucleic acids associated with the specimen; prepare a summary score for the plurality of biological pathways, based upon the pathway score for each biological pathway; compare the summary score to one or more enrichment scores each associated with a pre-determined molecular subtype; and
return a determined molecular subtype based on the comparison of the summary score and the one or more enrichment scores.
30 . The system of claim 29 , the memory storing further instructions, that when executed, cause the processor to:
receive gene expression data corresponding to a plurality of available biological pathways; and
apply a pathway heuristic filter to identify a subset of the available biological pathways, wherein the subset being the pre-determined biological pathways.