METHODS OF ASSESSING BREAST CANCER USING CIRCULATING HORMONE RECEPTOR TRANSCRIPTS
The present disclosure provides systems and methods for analyzing circulating hormone receptor transcripts to provide diagnoses, prognoses, and treatment suggestions for patients afflicted with breast cancer. Circulating transcripts can be obtained from patient samples, including blood samples, without the need for invasive tissue biopsies. This may include expression transcripts obtained from extracellular vesicles. Analysis of hormone receptor expression transcripts may include comparison with expression transcripts of patients with known clinical outcomes.
1 . A method of diagnosing breast cancer, the method comprising the steps of
determining an expression level for one or more hormone receptors or hormone receptor subtypes from transcripts in circulation in a sample obtained from a patient; and
utilizing said expression level to diagnose and/or stage breast cancer when said expression level is above a predetermined threshold.
2 . The method of claim 1 , further comprising measuring or detecting one or more hormones in the sample and using the expression level and hormone information in the diagnosis.
3 . The method of claim 1 , wherein said utilizing step comprises determining a quantitative amount of said transcripts and determining a ratio of different species of said RNA.
4 . The method of claim 1 , wherein the transcripts comprise cell-free RNA in the sample.
5 . The method of claim 1 , further comprising isolating an extracellular vesicle from the sample and determining the expression level for contents of the vesicle.
6 . The method of claim 1 , wherein the determining step comprises measuring the transcripts in the sample for the hormone receptors and for one or more additional marker genes.
7 . The method of claim 6 , wherein the marker genes are selected from a panel comprising: AA555029_RC; ALDH4A1; AP2B1; AYTL2; BBC3; C16orf61; C20orf46; C9orf30; CCNE2; CDCl42BPA; CDCA7; CENPA; COL4A2; DCK; DIAPH3; DTL; EBF4; ECT2; EGLN1; ESM1; EXT1; FGF18; FLT1; GMPS; GNAZ; GPR126; GPR180; GSTM3; HRASLS; IGFBP5; JHDM1D; KNTC2; LGP2; LIN9; LOC100131053; LOC100288906; LOC730018; MCM6; MELK; MMP9; MS4 A7; MTDH; NMU; NUSAP1; ORC6L; OXCT1; PALM2; PECI; PITRM1; PRC1; QSCN6L1; RAB6B; RASSF7; RECQL5; RFC4; RTN4RL1; RUNDC1; SCUBE2; SERF1A; SLC2A3; STK32B; TGFB3; TSPYL5; UCHL5; WISP1; and ZNF533.
8 . The method of claim 7 , wherein the determining step comprises interrogating the sample with probes for substantially all of the panel, and measuring the expression levels for positive probe responses.
9 . The method of claim 7 , further comprising performing the recited steps at least several months after diagnosis of breast cancer to detect or predict a risk of recurrence of the breast cancer.
10 . The method of claim 1 , further comprising analyzing an image of tissue from the patient to support or confirm the diagnosis.
11 . The method of claim 10 , wherein the image comprises a digital scan of a stained, FFPE slide from a tumor from the patient.
12 . The method of claim 1 , wherein the utilizing step comprises providing the determined expression levels as inputs to an analysis system trained on training data comprising one or more sets of training expression level measurements associated with known outcomes.
13 . The method of claim 12 , wherein the analysis system comprises a computer system hosting a trained machine learning algorithm.
14 . The method of claim 12 , further comprising providing image data from the subject as part of the inputs to the analysis system, wherein the analysis system performs an analysis on a combination of the image data and the expression levels.
15 . The method of claim 14 , wherein the image data comprises an image of a stained, FFPE slide from a tumor from the patient or an image of a microarray.
16 . The method of claim 1 , wherein the sample is a blood sample.