WO2025106967A1 - Gene signature for breast cancer margin status after surgery - Google Patents

Abstract

Positive surgical margins after surgery for breast cancer are defined as malignant cells identified at the edge of the resection specimen and have been associated with at least two-fold increase in ipsilateral breast cancer recurrence, higher distant recurrence rates, and shorter survival. While there have been several clinical factors associated with the risk of positive margins, there is a paucity of data considering both clinical variables and genomic profiles associated with positive margins. In this disclosure was explored the association of clinicopathologic and molecular factors with the occurrence of positive margins after first surgery in breast cancer. As a result, it was found that the occurrence of positive margins after surgery was associated with various clinical factors. Further, as the first effort to pursue molecular understanding of the margin status, a gene panel of 29 genes including 17 protein-coding genes was also identified for potential prediction of the margin status.

Description

Attorney Docket no.15969-055PC0 Gene Signature for Breast Cancer Margin Status After Surgery FIELD [0001] The present disclosure is directed to a prognostic method for predicting surgical margin status in a breast cancer patient after surgery based on clinicopathologic and molecular factors in biopsy samples. BACKGROUND [0002] Breast cancer remains a major health concern in the United States with an estimate of over 297,000 new cases for 2023 (SEER Cancer Statistics Factsheets: Breast Cancer. National Cancer Institute). While survival rates have improved for breast cancer patients with advances in multimodality therapies, surgical resection with negative margins remains the standard of care for most patients. Most early-stage breast cancer patients are candidates for breast- conserving therapy (lumpectomy) or mastectomy for surgical resection given that multiple randomized controlled trials have demonstrated equivalent long-term survival outcomes (Fisher et al., 2002; Clarke et al., 2005; and Veronesi et al., 2002). Regardless of surgical strategy, margin status of the resected specimen remains one of the most important factors associated with recurrence after breast cancer surgery (Houssami et al., 2010). [0003] Positive surgical margins are defined as malignant cells identified at the edge of the resection specimen and have been associated with at least two-fold increase in ipsilateral breast cancer recurrence (Moran et al., 2014; and Bundred et al., 2022), higher distant recurrence rates, and shorter survival (Maishman et al., 2017). Further, patients with positive margins are candidates for re-excision of the concerned margin (Moran et al., 2014; and Morrow et al., 2016), and these subsequent surgeries are associated with a significant burden to both the patients and healthcare system. While there have been several clinical factors associated with the risk of positive margins including higher stage, higher grade, non-ductal histology, HER2 amplification, and suspicion of multifocality, there is a paucity of data considering both clinical variables and genomic profiles associated with positive margins (Wj et al., 2021; and Pan et al., 2018). SUMMARY [0004] In this disclosure, the clinical and pathologic factors associated with breast cancer surgical margins were evaluated using the data for breast cancer (BRCA) available in The Attorney Docket no.15969-055PC0 Cancer Genome Atlas (TCGA) repository (portal.gdc.cancer.gov). In addition to clinical data analysis, exploration of molecular data was also performed in order to identify the genes potentially associated with positive margins. [0005] It is the purpose of this disclosure to explore the association of clinicopathologic and molecular factors with the occurrence of positive margins after first surgery in breast cancer. [0006] Briefly, the clinical and RNA-Seq data for 951 (75 positive and 876 negative margins) primary breast cancer patients from The Cancer Genome Atlas (TCGA) were used. The role of each clinicopathologic factor for margin prediction and also their impact on survival were evaluated using logistic regression, Fisher’s exact test, and Cox proportional hazards regression models. In addition, differential expression analysis on a matched dataset (71 positive and 71 negative margins) was performed using Deseq2 and LASSO regression. [0007] As results, association studies showed that higher stage, larger tumor size (T), positive lymph nodes (N), and presence of distant metastasis (M) significantly contributed (p ≤ 0.05) to positive surgical margins. In the case of surgery, lumpectomy was significantly associated with positive margin compared with mastectomy. Moreover, PAM50 Luminal A subtype had higher chance of positive margin resection compared with Basal-like subtype. Survival models demonstrated that positive margin status along with higher stage, higher TNM, and negative hormone receptor status was significant for disease progression. It was also found that margin status might be a surrogate of tumor stage. In addition, 29 genes that could be potential positive margin predictors and 8 pathways were identified from molecular data analysis. [0008] In conclusion, the occurrence of positive margins after surgery was associated with various clinical factors, similar to the findings reported in earlier studies. In addition, it was found here that the PAM50 intrinsic subtype Luminal A has more chance of obtaining positive margins compared with Basal type. As the first effort to pursue molecular understanding of the margin status, a gene panel of 29 genes including 17 protein-coding genes, which were significantly differentially expressed between breast tumors with positive and negative margin status, was also identified. These genes may serve as potential predictors of margin status prior to surgery, for example by assessing gene expression in the biopsy samples. [0009] In one aspect, after validation of the 29 genes using a larger dataset, a nomogram for surgery margin status in breast cancer may be developed using these genes along with selected Attorney Docket no.15969-055PC0 clinicopathological features. This nomogram would help doctors to segregate patients who have higher chance of attaining positive margins after tumor removal surgery. This can aid in preparing better treatment strategies, like performing more aggressive tumor removal surgery. [0010] A prior nomogram model was published in 2018 by Pan et al., however, this model used only clinicopathological characteristics. A similar margin status study by Barentsz at al, 2015 was also done for non-palpable breast cancer using logistic regression models (Barentsz et al., 2015). However, such prior models do not measure the likelihood of attaining positive margins at the molecular level. The present methodologies include both clinicopathologic features and the set of the biomarkers identified in the present research for development of a novel margin status nomogram. [0011] Generally, lumpectomy has higher chance of attaining positive margins compared with mastectomy. Patients who have positive surgical margins after surgery undergo re-excisions (sometimes multiple) to attain negative margins. Repeated re-excisions can be traumatic to the patients, and also a burden for the health care system. Hence, the nomogram based on the data obtained in this disclosure will aid in correctly identifying patients that have higher likelihood of positive margins, which in turn will help in reducing unnecessary mastectomies. [0012] Based on the results described above, a new method is provided for predicting positive or negative margin status in a breast cancer patient who has undergone lumpectomy or mastectomy surgery. The method comprises steps of: collecting a breast cancer tissue sample from the patient; analyzing clinicopathologic and molecular factors of the patient; based on the analysis result, determining whether to perform further gene analysis with a panel of genes; performing gene analysis by measuring the gene expression levels of each gene, normalizing the gene expression levels of each gene against one or more housekeeping genes, and determining an increase or decrease in the gene expression levels of each gene; and determining positive or negative margin status. [0013] As the clinicopathologic factors, in one embodiment, one or more of the following are considered: age grouped into old (60+ years), middle Age (40-59 ), young (<40 ), menopausal status (postmenopausal, perimenopausal, premenopausal, and indeterminate), race (white, African American, Asians and American Indians, and others), type of surgery (lumpectomy, simple mastectomy, modified radical mastectomy, and other types of surgeries including partial mastectomy, Patey's surgery, excision with needle wire localization), tumor size (T1, T2, T3, Attorney Docket no.15969-055PC0 and T4), lymph node status (N0, N1, N2, and N3), distant metastasis (M0 and M1), and cancer stage (Stage I, Stage II, Stage III, and Stage IV), and histology (ductal, lobular, mixed, mucinous, medullary carcinoma, metaplastic carcinoma and other types of histology); and optionally type of surgery, tumor size, lymph node status, distant metastasis, and cancer stage. [0014] As the molecular factors, PAM50 subtypes (Luminal A, Luminal B, basal, Her2, and normal), and positive, negative or equivocal status of immunohistochemistry (IHC) markers including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor subtype 2 (HER2) are considered. [0015] As the clinicopathologic and molecular factors for further gene analysis, at least one of lumpectomy, high tumor stage, i.e., Stage III and Stage IV, larger tumor size (T4), positive lymph nodes (N2, N3), presence of distant metastasis (M1), and PAM50 subtypes of Luminal A, Her2-enriched (Her2), and basal subtypes is considered. [0016] The panel of genes aforementioned comprises at least two of the following genes: ARC, AMBP, C1orf167, CNGA3, EEF1A2, EPHA6, FOXN4, DIO1, KRT75, SPRR1B, SOX15, STUM, AC084880.1, AC008663.2, AC099329.2, AC004947.1, ANO3, KCNJ6, PTGDR, PCP4L1, SPINK1, BEND3P1, CPHL1P, AP002001.2, LINC01344, LINC00589, AC114296.1, AF015262.1, and SLC26A4-AS1. [0017] Optionally, the panel of genes comprises at least one of the following protein-coding genes, comprising ARC, AMBP, C1orf167, CNGA3, EEF1A2, EPHA6, FOXN4, DIO1, KRT75, SPRR1B, SOX15, STUM, ANO3, KCNJ6, PTGDR, PCP4L1, and SPINK1 [0018] Optionally, the panel of genes comprises at least one of the following well-known tumor marker genes upregulated in positive margin breast cancer, comprising EEF1A2, EPHA6, FOXN4, SOX15. [0019] Optionally, the panel of genes comprises alpha-1-microglobulin/Bikunin precursor (AMBP) gene and/or iodothyronine deiodinase 1 (DIO1) gene, whose expressions are downregulated in other cancers but upregulated in positive margin breast cancer. [0020] Optionally, the panel of genes comprises at least one of the following genes, ARC, C1orf167, CNGA3, KRT75, SPRR1B, STUM. which are not typically associated with cancer but identified in positive margin breast cancer. Attorney Docket no.15969-055PC0 [0021] Optionally, the panel of genes comprises at least one of the following genes, SPINK1, potassium inwardly rectifying channel subfamily J member 6 (KCNJ6), anoctamin 3 (ANO3), prostaglandin D2 receptor (PTGDR, also called PGD2) and PCP4L1, which are downregulated protein-coding genes identified in the positive margin breast cancer. [0022] The house keeping genes for gene expression normalization comprise at least one of the following genes:_LRP (Large ribosomal protein), BACT (β-actin), CYC (Cyclophilin A), GADPH (Glyceraldehyde-3-phosphate dehydrogenase, PGK (Phosphoglycerokinase 1), B2M (β-2-microglobulin), _BGUS (β-glucuronidase), HPRT (Hypoxanthine ribosyltransferase), TBP (TATA-box-binding protein), TfR (Transferrin receptor), PBGD (Porphobilinogen deaminase), ATP6 (ATP synthase 6), and rRNA (18S ribosomal RNA) (de Kok et al., 2005). [0023] In addition, a kit for a prognostic assay for margin status in a tissue sample from a breast cancer patient is provided, and the kit comprises at least two pairs of PCR primer oligonucleotides for determining the gene expression levels of at least two genes selected from: ARC, AMBP, C1orf167, CNGA3, EEF1A2, EPHA6, FOXN4, DIO1, KRT75, SPRR1B, SOX15, STUM, AC084880.1, AC008663.2, AC099329.2, AC004947.1, ANO3, KCNJ6, PTGDR, PCP4L1, SPINK1, BEND3P1, CPHL1P, AP002001.2, LINC01344, LINC00589, AC114296.1, AF015262.1, SLC26A4-AS1, and at least one pair of PCR primer oligonucleotides targeting at least one house keeping gene selected from LRP, BACT, CYC, GADPH, PGK, B2M, BGUS, HPRT, TBP, TfR, PBGD, ATP6, rRNA. The kit may further comprise necessary reagents for RT-PCR or qRT-PCR reactions. BRIEF DESCRIPTION OF THE DRAWINGS [0024] Figure 1. Surgical margins: The rim of tissue after cancer surgery is called surgical margin or margin of resection. Margin status remains an important risk factor for local recurrence. Margins are checked after surgical biopsy, lumpectomy, and mastectomy. (A) Definitions of positive and negative margin. (B) Illustration of positive and negative margins. [0025] Figure 2. Flowchart of the method for developing a margin status decision model. [0026] Figure 3. The Kaplan–Meier (K-M) curves for cumulative survival in years for margin status for two end points: progression-free interval (PFI) (a) and overall survival (OS) (b). P value, Hazard ratio (HR), and the number of events ‘/’ number of cases are given in the legends of plots. Attorney Docket no.15969-055PC0 [0027] Figure 4. The Kaplan–Meier curves for cumulative survival in years for the cohorts as defined by Tumor Size, Lymph Node Status, Distant Metastasis, AJCC Stage, PAM50, ER status, PR status, and histology for a recommended end point in the TCGA-BRCA cohort: progression-free interval (PFI). P-value (p), Hazard ratio (HR) and the number of events ‘/’ number of cases are given in the legends of plots. [0028] Figure 5. Principal Component Analysis (PCA) plot for 142 matched samples. [0029] Figure 6. Unsupervised clustering for 29 significant genes for prediction of margin status derived using LASSO regression from Deseq2 analysis for TCGA RNA-Seq data. Molecular data analysis using TCGA-BRCA RNA-Seq data matched (subtype and stage) dataset was used: n = 142; 71 positive vs. 71 negative margin samples, 103 DEGs (53 upregulated and 50 downregulated) identified using DESeq2 analysis. [0030] Figure 7. 29 DEGs identified. *FDR, False Discovery Rate, ** FC, Fold Change DETAILED DESCRIPTION [0031] A. Overview [0032] A1. Summary [0033] Positive surgical margins following surgery for breast cancer are defined as malignant cells identified at the edge of the resection specimen and have been associated with at least two-fold increase in ipsilateral breast cancer recurrence, higher distant recurrence rates, and shorter survival. Further, patients with positive margins are candidates for re-excision of the concerned margin and these subsequent surgeries are associated with a significant burden to both the patients and healthcare system. While there have been several clinical factors associated with the risk of positive margins including higher stage, higher grade, non-ductal histology, HER2 amplification, and suspicion of multifocality, there is a paucity of data considering both clinical variables and genomic profiles associated with positive margins. To explore the association of clinicopathologic and molecular factors with the occurrence of positive margins after first surgery in breast cancer, the clinical and pathologic factors associated with breast cancer surgical margins were evaluated using the data for breast cancer (BRCA) from the public resource, The Cancer Genome Atlas (TCGA). In addition to clinical Attorney Docket no.15969-055PC0 data analysis, exploration of molecular data was also performed in order to identify the genes potentially associated with positive margins. [0034] As a result, it was found that the occurrence of positive margins after surgery was associated with various clinical factors, similar to the findings reported in earlier studies. In addition, they found that the PAM50 intrinsic subtype Luminal A has more chance of obtaining positive margins compared to Basal type. As the first effort to pursue molecular understanding of the margin status, a gene panel of 29 genes including 17 protein-coding genes was also identified for potential prediction of the margin status which needs to be validated using a larger sample set. [0035] Using the above mentioned 29 genes from a larger dataset, a nomogram could be developed along with selected clinicopathological features. This nomogram would help doctors to segregate patients who have higher chance of attaining positive margins after tumor removal surgery. This can aid in preparing better treatment strategy, like performing a more aggressive tumor removal surgery. [0036] A.2. Characterization study to determine effects of factors on margin status [0037] Here was performed the analysis of both clinicopathologic and molecular factors with the occurrence of positive margins in breast cancer. The incidence rate of positive margins (7.8%; 75/951) in TCGA-BRCA was comparable to other studies (Behm et al., 2013). It was observed that the risk of positive margins increases with higher tumor stage, larger tumor size, positive lymph nodes, and presence of distant metastasis consistent with prior studies (Behm et al., 2013; Lombardi et al., 2019; Lovrics et al., 2009.; Park et al., 2000). Conversely, age which has been previously reported to be associated with margin status was not significant in this analysis (Lombardi et al., 2019). This discordance could be attributed to the low number of young patients in the TCGA-BRCA cohort. [0038] At the molecular level, the study in this disclosure demonstrated that immunohistochemistry (IHC) markers ER, PR, and HER2 did not impact margin status, and this is in concordance with the findings described by Horattas et al., (Horattas et al., 2022). Evaluation of PAM50 intrinsic subtypes in the study, however, demonstrated that Luminal A subtype had a higher risk of positive margins compared with Basal subtype. There is a paucity of literature considering the impact of PAM50 subtypes on margin status, and this novel finding Attorney Docket no.15969-055PC0 appears to be counter intuitive, given that Basal subtype is associated with higher recurrence rates (Bertucci et al., 2012). The higher incidence of positive margins in the Luminal A subtype may be attributed to morphologic characteristics noted in radiomic studies which attribute spiculated features more commonly to Luminal subtypes and circumscribed features more commonly to Basal subtypes (Cho, 2021). [0039] The survival analysis demonstrated that positive margin status, larger tumor size, positive lymph nodes, distant metastasis, hormone receptor status (ER-negative, PR-negative), higher tumor stage, and two of the PAM50 subtypes (Basal and Her2) significantly contributed to disease progression. This conclusion agrees with previous studies including those using TCGA data, even though margin status was not evaluated in prior TCGA-BRCA data studies (Carter et al., 1989; Nguyen et al., 2020; and Liu et al., 2018). The bi-variable survival models indicated that margin status acted independently from PAM50 or hormone receptor status. It also suggested that margin status might be a surrogate to tumor stage. [0040] A.3. Mastectomy does not guarantee negative margins. [0041] Per current guidelines, mastectomy is typically indicated for breast cancer patients with larger tumor size relative to breast size, inflammatory breast cancer, multicentric disease, and patient preference as well as in patients with a contraindication to breast-conserving therapy. Patients may prefer a mastectomy over a lumpectomy for a variety of reasons including a decreased risk of positive margins. Our findings are consistent with the literature in regard to the higher risk of positive margins in patients undergoing lumpectomy (Moran et al., 2014). Interestingly, Hewitt, et al. reported that in patients with large invasive lobular carcinoma (ILC) tumors, mastectomy fails to obtain clear margins (Hewitt et al., 2022). Out of the 23 patients with positive margins after mastectomy (simple/radical) in the cohort here, 15 of them had invasive ductal carcinoma (IDC) and 7 had ILC. It is important to note that 21 out of these 23 tumors belonged to the higher stage group (Stage III or IV). This again emphasizes higher stage patients have, higher chance of undergoing re-excisions irrespective of type of first surgery or histology. [0042] A.4. Biomarkers for margin status [0043] To our knowledge, this is the first study to evaluate the impact of gene expression on margin status. The 29 genes obtained after molecular data analysis included many well-known Attorney Docket no.15969-055PC0 cancer markers. The 12 protein-coding genes upregulated in positive margin group included several well-known tumor markers for breast cancer (EEF1A2, EPHA6, FOXN4, SOX15) (Tomlinson et al., 2005; Fox and Kandpal, 2004; Itkonen and Stenman, 2014; Wang et al., 2020; and Mehta et al., 2019). In addition, there were genes that were reported in other cancers but were not much explored in breast cancer. The low expression of Alpha-1- Microglobulin/Bikunin Precursor (AMBP) has been reported to increase tumor progression in prostate cancer (Intasqui, et al., 2018) and oral squamous carcinoma (Sekikawa et al., 2018) but is relatively understudied in breast cancer. A study exploring expression of this gene across different cancers reported it to be downregulated in breast cancer (Lepedda et al., 2023). However, this gene was found to be overexpressed in the positive margin cohort in this study. Similarly, Iodothyronine Deiodinase 1 (DIO1), a gene involved in the activation and inactivation of thyroid hormone whose low expression is said to promote tumor progression was also upregulated in patients with positive margins (Popławskiet al., 2017). These findings with AMBP and DIO1 genes may merit further studies. In addition, 6 upregulated genes not typically associated with cancer (ARC, C1orf167, CNGA3, KRT75, SPRR1B, STUM) were also identified. [0044] There were 5 downregulated protein-coding genes identified in the positive margin group including SPINK1, a well-known tumor marker (Soon et al., 2011; Lin 2021). Potassium Inwardly Rectifying Channel Subfamily J Member 6 (KCNJ6) was also downregulated in positive margin cases. Potassium channel-driven signaling is known to regulate metastasis in triple negative cancer (Payne et al., 2022). Anoctamin 3 (ANO3) was another downregulated genes whose paralogue ANO1 is a known cancer marker for head and neck squamous carcinoma (Ruiz et al., 2012). The Prostaglandin D2 Receptor (PTGDR) also called PGD2 has been associated with different types of cancers (Jara-Gutiérrez and Baladrón, 2021) even though its role in breast cancer has not been well described. However, it has been reported that high expression of PGD2 resulted in reduced tumor proliferation (Pan, et al., 2021). This might explain the reason for PGD2 being significantly downregulated in positive margin cases in the cohort in this study. There is limited knowledge of PCP4L1 in malignancies. [0045] The unsupervised clustering of these 29 genes grouped the samples primarily by subtype, although different enrichment of margin positive samples was observed from the two main clusters. A larger sample size of patients with positive margins may further validate these Attorney Docket no.15969-055PC0 genes as predictors of margin status. Since positive margin is a strong indicator for breast cancer recurrence, these genes in turn may be considered as potential markers of recurrence. [0046] Furthermore, the pathway analysis revealed prominent pathways like MYC_TARGETS, E2F_TARGETS that have been reported by previous studies to be associated with breast cancer recurrence (Table 13) (Xu, et al, 2010). Estrogen response related pathways were upregulated in margin positive samples further emphasizing our previous observation of Luminal A having higher chance of positive margin compared with basal subtype (Table 4). [0047] A.5. Relevance [0048] The clinical data analysis results using TCGA-BRCA data show that higher stage, larger tumor size, positive lymph nodes, presence of distant metastasis, and Luminal A subtypes have higher chance of obtaining positive margins after first surgery. Also, it was observed that mastectomy for tumor removal reduced the chance of positive margins compared with lumpectomy. This agrees with the previously reported studies. However, it was also found that margin status likely was a surrogate to tumor stage and hence patients diagnosed at higher stage, regardless of type of surgery had higher chance of obtaining positive margins. Additionally, it was also observed that patients belonging to Luminal A intrinsic subtype had higher chance of obtaining positive margins compared with Basal subtype. Based on these findings, patients with Luminal A or higher stage tumors should be counseled on their increased risk of positive margins. Clinical indications for wider margin resection for these patients would require further rigorous examination in a clinical trial prior to definitely altering guidelines. Also identified 29 genes and 8 pathways significantly differential expressed between positive and negative margins were identified. These 29 genes, some of which had not been reported to be associated with breast cancer previously, could serve as potential predictors of margin status. However, additional studies need to be performed on a larger sample size to validate these findings. On-going studies to further identify risk factors associated with positive margins will help physicians in determining treatment strategy and counseling their patients. [0049] All identified publications mentioned herein are hereby incorporated by reference to the same extent as if each such publication was specifically and individually indicated to be incorporated by reference in its entirety. While the disclosure has been described in connection Attorney Docket no.15969-055PC0 with exemplary embodiments, it will be understood that it is capable of further modifications and this application covers any variations, uses, or adaptations following, in general, the principles of the disclosure and including such departures as come within known or customary practice within the art to which the disclosure pertains. [0050] B. Definitions [0051] Embodiments of materials and methods are described herein; any methods and materials similar or equivalent to those described herein can be used in the practice of or testing of the invention. Unless defined otherwise, all technical and scientific terms herein have the same meaning as commonly understood by one of ordinary skill in the art to which the invention pertains. In describing and claiming the present invention, the following terminology will be used. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and it is not intended to be limiting. Also, the entire teachings of any patents, patent applications or other publications referred to herein are incorporated by reference herein as if fully set forth herein. [0052] Other than in the operating examples, or where otherwise indicated, all numbers expressing quantities of ingredients or reaction conditions used herein should be understood as modified in all instances by the term “about.” The term “about” generally refers to a range of numbers that one of skill in the art would consider equivalent to the recited value (i.e., having the same function or result). In a specific embodiment, the term “about” includes a stated numerical value as well as a value that is +/- 15% of the stated numerical value. For example, about 5.75 M includes 5.75 molar as well as 6.61 M and 4.89 M, and all 1/10 values in between. In many instances, the term “about” may include numbers that are rounded to the nearest significant figure. [0053] The present invention can “comprise” (open ended) the components of the present invention (e.g., genes or oligonucleotide probes) as well as other ingredients or elements described herein. As used herein, “comprising” means the elements recited, or their equivalent in structure or function, plus any other element or elements which are not recited. The terms “having” and “including” are also to be construed as open ended unless the context suggests otherwise. Attorney Docket no.15969-055PC0 [0054] All ranges recited herein include the endpoints, including those that recite a range “between” two values. Terms such as “about,” “generally,” “substantially,” “approximately” and the like are to be construed as modifying a term or value such that it is not an absolute, but does not read on the prior art. Such terms will be defined by the circumstances and the terms that they modify as those terms are understood by those of skill in the art. This includes, at very least, the degree of expected experimental error, technique error and instrument error for a given technique used to measure a value. Unless otherwise indicated, as used herein, “a” and “an” include the plural, such that, e.g., “a gene” can mean at least one gene, as well as a plurality of genes, i.e., more than one gene. As understood by one of skill in the art, the term “gene” can be used to refer to a single gene or more than one gene. [0055] Where used herein, the term “and/or” when used in a list of two or more items means that any one of the listed characteristics can be present, or any combination of two or more of the listed characteristics can be present. For example, if a composition of the instant invention is described as containing characteristics A, B, and/or C, the composition can contain A feature alone; B alone; C alone; A and B in combination; A and C in combination; B and C in combination; or A, B, and C in combination. [0056] As used herein, the term “subject” or “patient” refers to a mammal, in particular a primate, and in more particular a human individual, who has undergone lumpectomy or mastectomy surgery for the removal of cancer tissues/cells and/or reducing metastasis to lymph nodes and other organ tissues. Preferably, the subject can be a female patient (e.g., adult female and adolescent female) who suffers from primary or metastatic breast cancer, however a male breast cancer patient can also be considered for the application of the invention. [0057] As used herein, the term "treatment" or “treat” in the context of medical meaning refers to intervention of disease, disorder, condition or one or more symptoms thereof to obtain a desired physiological and/or clinical effect. "Treatment" or “treat” includes, but is not limited to, performing a surgical intervention and/or administering one or more drugs or agents (e.g., chemo drugs) for purposes such as: inhibiting the disease, disorder, condition, or one or more symptoms thereof; slowing or delaying the progress of the disease, disorder, condition, or one or more symptoms thereof; stabilizing (i.e., not worsening) a state of the disease, disorder, condition, or one or more symptoms thereof; and relieving, palliating, alleviating, or ameliorating the severity of the disease, disorder, condition, or one or more symptoms thereof; Attorney Docket no.15969-055PC0 or preventing remission, whether partial or total and whether detectable or undetectable. “Treatment,” or “treat” may not necessarily indicate complete eradication or cure of breast cancer or associated symptoms thereof. [0058] As used herein, the term “PAM50 (Prediction Analysis of Microarray 50)” refers to a which is a prognostic tool that can be used to guide treatment and predict patient outcomes. The PAM50 assay measures the transcriptional patterns of 50 genes in a breast tumor sample. The assay can be performed determining gene expression levels derived from microarrays, RNASeq, or qRT-PCR, in order to classify breast cancer into 5 subtypes The 5 subtypes are (1) Luminal A: typically ER+ with lower proliferation; (2) Luminal B: typically ER+, but has higher proliferation; (3) HER2-enriched: majority Her2+, but can be ER+ or ER-; (4) Basal- like: typically ER-/Her2-; and (5) Normal-like. Each subtype has different biological properties and prognoses. The PAM50 assay can also be used to generate Risk of Recurrence (ROR) scores to predict the risk of recurrence of disease in ER+ breast cancer. The 50 PAM genes are: ACTR3B, ANLN, BAG1, BCL2, BIRC5, BLVRA, CCNB1, CCNE1, CDC20, CDC6, CDH3, CENPF, CEP55, CXXC5, EGFR, ERBB2, ESR1, EXO1, FGFR4, FOXA1, FOXC1, GPR160, GRB7, KIF2C, KRT14, KRT17, KRT5, MAPT, MDM2, MELK, MIA, MKI67, MLPH, MMP11, MYBL2, MYC, NAT1, NDC80, NUF2, ORC6L, PGR, PHGDH, PTTG1, RRM2, SFRP1, SLC39A6, TMEM45B, TYMS, UBE2C, UBE2T. and their corresponding Acc numbers are available at https://www.biostars.org/p/77590/#91724. [0059] The term “nucleotide” as used herein refers to a sub-unit of a nucleic acid (whether DNA or RNA or an analogue thereof) which may comprise, but is not limited to, a phosphate group, a 5-carbon sugar group and a nitrogen containing base, as well as analogs of such sub- units. Other groups (e.g., protecting groups) can be attached to the sugar group and nitrogen containing base group. It will be appreciated that, as used herein, the terms “nucleotide” will include those moieties which contain not only the naturally occurring purine and pyrimidine bases, e.g., adenine (A), thymine (T), cytosine (C), guanine (G), or uracil (U), but also modified purine and pyrimidine bases and other heterocyclic bases which have been modified (these moieties are sometimes referred to herein, collectively, as “purine and pyrimidine bases and analogs thereof”). As used herein, the term “oligonucleotide” refers to any fragment of polyribonucleotide or polydeoxyribonucleotide that may be unmodified RNA or DNA or modified RNA or DNA, among others, single- and double-stranded DNA, DNA that is a mixture of single- and double-stranded regions, single- and double-stranded RNA, and RNA Attorney Docket no.15969-055PC0 that is mixture of single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or double-stranded, or a mixture of single- and double- stranded regions. PCR primer oligonucleotides are short, single-stranded DNA fragments, usually 15–30 nucleotides long. Their nucleotide sequences are designed to be complementary to the beginning and end of the target sequence to be amplified by DNA polymerase. The forward and reverse primers are designed to provide a starting point for opposite strands of the DNA. For reverse transcription, a primer binds to the RNA template and provides a starting point for the synthesis of a new DNA strand by reverse transcriptase. [0060] C. Examples of Embodiments [0061] For the application of the invention disclosed herein to prognostic use for breast cancer surgery, gene expression analysis is to be performed with the 29 marker genes listed in Table 12, which are identified useful for the prediction of positive or negative margin status from breast cancer samples. From patient biopsy samples, total RNA is extracted to be analyzed using reverse transcription PCR (RT-PCR) (optionally with DNA band densitometry) or quantitative reverse transcription PCR (qRT-PCR), and for the same purpose microarray or RNA-fluorescence in situ hybridization (FISH) may be contemplated. Among those 29 genes, 17 genes are protein coding genes, and in such cases their mRNAs can be isolated after total RNA extraction, using oligo(dT) on magnetic beads or on resin packed in a column. In addition, their gene expression can be analyzed by measuring the quantity of the expressed proteins using Western blotting or dot blotting or performing their enzyme activity assay; and this can be used alone or as a parallel method with mRNA quantity analysis for more accurate measurement of gene expression. [0062] For better accuracy of the prediction, the gene expression levels of the whole panel of 29 genes can be analyzed; however it can be contemplated to perform gene expression analysis with some selection of the genes, for example 17 protein coding genes. For prognostic purpose, a kit comprising multiple pairs of primer oligonucleotides targeting multiple genes selected from the 29 genes - at least two genes, but optionally the whole 29 genes – as well as at least a pair of primer oligonucleotides targeting a housekeeping gene (e.g., HPRT) may be provided for the measurement of RNA extracted from a patient bio sample. The kit may further comprise necessary reagents for RT-PCR or qRT-PCR, including cell lysis buffer, DNase, DNase stop solution (e.g., 50 mM EDTA), reverse transcriptase, reaction buffer, individual Attorney Docket no.15969-055PC0 deoxynucleotide triphosphates (dNTPs) (dATP, dCTP, dGTP, and dTTP) or mixture stock solution thereof, RNase H, DNA polymerase as well as dsDNA-binding/intercalating dyes such as SYBR® Green I or dye-labeled, sequence-specific oligonucleotide probes with Taq DNA Polymerase, which are cleaved by Taq DNA Polymerase having 5'^>3' exonuclease activity and to emit light signal. [0063] The gene expression data can be utilized for the development of margin status nomogram. Basically, in nomogram, each variable (e.g., each gene) is listed separately, with a corresponding number of points assigned to a given magnitude of the variable (e.g., normalized gene expression level of the gene). Then, the cumulative point score (risk score) for all the variables is obtained to be matched to the probability of an event (e.g., positive margin). Patients can be divided into high-risk (for obtaining positive margins after surgery) or low-risk groups using the optimal cut-off value for risk score. The genes combined with clinicopathological features (eg. tumor stage) can be included in the construction of the nomogram models. Nomograms are a way to convert statistical predictive models according to the profile of an individual patient into a single numerical estimate of the probability of an event, e.g., death or recurrence, and they are commonly used for cancer prognosis. (Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol.2015 Apr;16(4)) [0064] As another way of the use of the 29 genes for margin status prediction, a calculation method similar to cell cycle progression (CCP) score can be contemplated. For example, the average expression of 29 positive margin marker genes, after normalized by the expression of at least one housekeeping gene, is scored, and a one-unit change in the score is correlated to the risk of the event, e.g., positive margin. Such calculation method may be applied to the genes involved in the 8 pathways listed in Table 13. [0065] The margin status determined by the methods described above may be considered along with other clinicopathologic and molecular factors, including type of previous surgery, tumor stage, tumor size, lymph nodes, distant metastasis, hormone receptor status (ER-negative, PR- negative), and PAM50 subtypes, as well as histology and age of onset, which may significantly contribute to breast cancer for further treatment of breast cancer, for further cancer treatment. For example, if determined to be likely positive, more aggressive tumor removal surgery may be performed. Otherwise, less aggressive tumor removal surgery may be considered with or Attorney Docket no.15969-055PC0 without chemotherapy, hormonal therapy, radiation therapy, immunotherapy and/or targeted therapy; or only chemotherapy, hormonal therapy, radiation therapy, immunotherapy and/or targeted therapy may be performed without further surgery. EXAMPLES [0066] Example 1. Materials and Methods [0067] 1.1. TCGA-BRCA Data [0068] The TCGA-BRCA patient data including clinical data and sample annotations were downloaded from the Genomic Data Commons (GDC) portal. The RNA-Seq data for the corresponding samples were also downloaded from GDC using the TCGA Biolinks R package (Colaprico et al., 2016). The survival data were obtained from the TCGA Pan-Cancer study and integrated into the clinical data (Liu et al., 2018). Since the number of the male patients was small and all of them had negative margin status, they were excluded to avoid the possibility of introducing additional bias. Also, the redacted samples and filtered cases were removed using sample annotations. Finally, 951 (75 positive and 876 negative margins) cases were retained for this study. The samples were categorized into positive and negative margin groups based on the margin status assigned after the first tumor removal surgery. [0069] The 951-sample cohort included primary tumors from patients diagnosed with breast cancer from 1988 to 2013 and had a median follow-up period of 2.2 years. Characteristics of the cohort were examined using frequency distributions, and attributes with low numbers were grouped together as “Other”. The diagnosis age of the patients was in the range of 26 to 90 years and was grouped into three categories: old (60+ years), middle age (40 – 59 years) and young (< 40 years). [0070] 1.2. Clinical Data Analysis [0071] Clinicopathologic factors for margin prediction were evaluated using logistic regression models. Subsequently, Fisher’s exact test was performed to test the association of each factor with margins. The impact of each clinicopathologic feature on disease progression was evaluated using univariable and multivariable Cox proportional hazards regression with the recommended endpoint progression-free interval (PFI) (Liu et al., 2018). The outcome of interest was time from date of diagnosis to local recurrence or distant metastasis or death from the disease whichever comes first. For margin status, Overall Survival (OS) was also estimated Attorney Docket no.15969-055PC0 in addition to PFI. Significant factors from the univariable analysis were subjected to multivariable analysis to explore their effect on survival. In order to get a better understanding of the correlation of each significant factor in the multivariable model with survival, a bivariable survival analysis was also performed. All analyses were carried out in R. All statistical tests were 2 sided, and P values ≤ 0.05 were considered significant. [0072] 1.3. Molecular Data Analysis [0073] A matched subset (n = 142) of the current TCGA dataset was selected for molecular analysis. All the cases with a positive margin that had tumor stage reported were included (n = 71) and the negative margin cases (n = 71) were selected by matching primarily on tumor stage and PAM50 subtype. Other features like race, age, and menopausal status were matched as much as possible. Principal component analysis (PCA) was performed to assess distribution of gene expression across PAM50 subtypes (Parker et al., 2009) and margin status. The RNA- Seq data analysis was performed using the package DESeq2 (Love et al., 2014) with adjustment for PAM50 subtype and tumor stage. A 5% False Discovery Rate (FDR) and a fold change of 2 were established as significant criteria. The significant differentially expressed genes (DEGs) from DESeq2 were further subjected to LASSO regression (Tibshirani, 1996) using caret package (Kuhn, 2008) in order to prevent multicollinearity and to extract the potential gene markers. A 10-fold cross-validation was performed to obtain the minimum lambda which was used in LASSO regression to predict the signature genes. Prediction models using Leave-One- Out Cross-Validation (LOOCV) (Trevor Hastie and Jerome, 2009) were performed to validate the gene signature. Additionally, pathway analysis was performed on the gene list from the DESeq2 result using the GSEA Preranked test tool against Hallmark gene set collection (Subramanian et al., 2005; and Mootha et al., 2003). [0074] Example 2. Positive margin is significantly associated with higher tumor stage and lumpectomy. [0075] The probability of attaining positive margins after surgery was observed to be significantly (p ≤ 0.05) associated with higher tumor stage, larger tumor size and chest wall involvement (T4), positive lymph nodes (N2, N3), and distant metastasis (M1), based on univariable logistic regression models and Fisher’s exact test (Table 1). The type of first surgery to remove tumor also influenced margin status with lumpectomy (as reference) having significantly higher chance of obtaining positive margins than mastectomy (Simple Attorney Docket no.15969-055PC0 Mastectomy: p = 0.002, Odds Ratio (OR) = 0.30, Confidence Interval (CI) = 0.13 – 0.62. Modified Radical Mastectomy: p < 0.001, OR = 0.30, CI = 0.15 – 0.57). Among PAM50 subtypes, Luminal A subtype (as reference) was observed to be significantly contributing towards positive margin in the univariable regression model compared with the Basal-like (Basal) subtype (p = 0.05, OR = 0.44, CI = 0.18 – 0.94). Her2-enriched (Her2) subtype was associated with positive margins (OR = 1.39) although it was not significant (p = 0.397). The results of Fisher’s exact test were consistent with the logistic regression results except for PAM50 subtype which did not show any association with margin status. [0076] Table 1. Summary of clinical characteristics of TCGA-BRCA data (n = 951) and their association with margin status. Clinical Feature N Margin Status Fisher's Univariable Logistic Regression No. (%)* Test N_{egative Positive p p OR 95% CI} ^{Age Group} _{Old (60+ years) 440 404 (46.1) 36 (48.0) 0.944 ref ref ref} Middle Age (40-59 ) 449 414 (47.3) 35 (46.7) 0.832 0.95 0.58 − 1.54 Young (<40 ) 62 58 (6.6) 4 (5.3) 0.638 0.77 0.23 − 2.02 Menopausal Postmenopausal

575 (65.6) 49 (65.3) 0.269 ref ref Status

Perimenopausal 37 35 (4.0) 2 (2.7) 0.590 0.67 0.11 − 2.29 Premenopausal 209 192 (21.9) 17 (22.7) 0.896 1.04 0.57 − 1.81 Indeterminate 27 22 (2.5) 5 (6.7) 0.058 2.67 0.86 − 6.84 Not Available 54

2 (2.7) − − − Tumor Size T1 245 228 (26.0) 17 (22.7) 0.014 ref ref ref (T) T2 559 521 (59.5) 38 0.942 0.98 0.55 − 1.81 T3 119 105 (12.0)

0.126 1.79 0.84 − 3.76 T4 26 20 (2.3) 6 (8.0) 0.009 4.02 1.33 − 10.95 Not Available 2 2 (0.2) 0 (0.0) − − − Lymph Node N0 458 434 24 0.003 ref ref ref Status (N) N1 305 0.346 1.34 0.73 − 2.45 N2 109 0.013 2.45 1.17 − 4.91 N3 66 0.001 3.62 1.62 − 7.64 Not Available 13 − − −

Distant M0 796 737 (84.1) 59 (78.7) < 0.001 ref ref Metastasis (M)

M1 14 6 (0.7) 8 (10.7) < 0.001 16.66 5.61 − 52.11 Not Available 141 133 (15.2) 8 (10.7) − − − ^{AJCC Stage} _{Stage I 161 153 (17.5) 8 (10.7) < 0.001 ref ref ref} Stage II 544 515 (58.8) 29 (38.7) 0.857 1.08 0.50 −2.57 Stage III 217 191 (21.8) 26 (34.7) 0.023 2.60 1.20 − 6.30 Stage IV 12 4 (0.5) 8 (10.7) < 0.001 38.24 10.02 − 171.69 Attorney Docket no.15969-055PC0 Not Available 17 13 (1.5) 4 (5.3) − − − ^PAM50 Luminal A 488 445 (50.8) 43 (57.3) 0.112 ref ref ref Luminal B 182 170 (19.4) 12 (16.0) 0.354 0.73 0.36 − 1.38 Basal 171 164 (18.7) 7 (9.3) 0.050 0.44 0.18 − 0.94 Her2 76 67 (7.6) 9 (12.0) 0.397 1.39 0.61 − 2.86 Normal 34 30 (3.4) 4 (5.3) 0.562 1.38 0.40 − 3.70 ^Race _{White 659 612 (69.9) 47 (62.7) 0.170 ref ref ref} African American 157 141 (16.1) 16 (21.3) 0.199 1.48 0.79 − 2.62 Other 57 56 (6.4) 1 (1.3) 0.153 0.23 0.01 − 1.10 Not Available 78 67 (7.6) 11 (14.7) − − − Type of First Lumpectomy 222 189 (21.6) 33 (44.0) < 0.001 ref ref ref Surgery Simple Mastectomy 181 172 (19.6) 9 (12.0) 0.002 0.30 0.13 − 0.62 Modified Radical Mastectomy 281 267 (30.5) 14 (18.7) < 0.001 0.30 0.15 − 0.57 Other 231 212 (24.2) 19 (25.3) 0.029 0.51 0.28 − 0.92 Not Available 36 36 (4.1) 0 (0.0) − − − ^{ER Status} _{Negative 215 203 (23.2) 12 (16.0) 0.119 ref ref ref} Positive 697 634 (72.4) 63 (84.0) 0.110 1.68 0.92 − 3.33 Not Available 39 39 (4.5) 0 (0.0) − − − ^{PR Status} _{Negative 311 288 (32.9) 23 (30.7) 0.528 ref ref ref} Positive 598 546 (62.3) 52 (69.3) 0.500 1.19 0.72 − 2.02 Not Available 42 42 (4.8) 0 (0.0) − − − ^{HER2 Status} _{Negative 498 463 (52.9) 35 (46.7) 0.853 ref ref ref} Positive 140 130 (14.8) 10 (13.3) 0.963 1.02 0.47 − 2.04 Equivocal 157 144 (16.4) 13 (17.3) 0.600 1.19 0.59 − 2.27 Not Available 156 139 (15.9) 17 (22.7) − − − ^Histology _{Ductal 677 629 (71.8) 48 (64.0) 0.333 ref ref ref} Lobular 186 167 (19.1) 19 (25.3) 0.161 1.49 0.84 − 2.57 Mixed 23 20 (2.3) 3 (4.0) 0.289 1.97 0.45 − 5.99 Mucinous 16 14 (1.6) 2 (2.7) 0.416 1.87 0.29 − 6.96 Others 48 45 (5.1) 3 (4.0) 0.826 0.87 0.21 − 2.51 Not Available 1 1 (0.1) 0 (0.0) − − − ^aAmerican Joint Committee on Cancer, ^bEstrogen Receptor, ^c Progestrone Receptor,

Epidermal Growth Factor Receptor 2, ^eAsians and American Indians, ^f Other types of surgeries like partial mastectomy, Patey's surgery, excision with needle wire localization etc., ^gMedullary carcinoma, Metaplastic carcinoma and other types of histology. ^*Number of cases (percentage within each margin group), ^**Odds Ratio, ^#Confidence Interval of Odds Ratio. Bold lettering denotes p value ≤ 0.05 [0077] The significant factors in the univariable regression model (Stage, PAM50, TNM: T= Tumor size, N= Lymph Node status, M= Metastasis, Type of first surgery) were used in the multivariable model with margin status as response variable. Tumor stage, size, and lymph Attorney Docket no.15969-055PC0 node status, which were highly significant in the univariable model, were no longer significant in the multivariable model (Table 2). Further evaluation using various multivariable models proved that Stage and TNM were confounding (Table 3); hence, only Stage was used in the final multivariable model (Table 4). The final regression model, in agreement with the univariable model, showed that patients diagnosed at higher tumor stage (Stage III: p < 0.001, OR = 4.85, CI = 2.09 – 12.41. Stage IV: p < 0.001, OR = 80.83, CI = 18.65 – 411.45) were significantly associated with

Similarly, in the case of type of surgery for tumor removal, the multivariable regression model reemphasized that lumpectomy (as reference) was significantly associated with positive margin compared with simple mastectomy (p = 0.002, OR = 0.27, CI = 0.12 – 0.59) and modified radical mastectomy (p < 0.001, OR = 0.17, CI = 0.08 – 0.35). For the PAM50 subtypes, Luminal A (as reference) was significantly associated with positive margins compared with basal subtype (p = 0.042, OR = 0.41, CI = 0.16 – 0.91). [0078] Table 2. Multivariable logistic regression analysis on the association of margin status with all the significant factors from univariable analysis. Clinical Feature Multivariable Logistic Regression p ^OR* 95% CI^** Tumor Size _T1 ref ref ref (T) _T2 0.617 1.32 0.48 − 4.29 T₃ 0.163 2.53 0.71 − 9.93 T₄ 0.288 2.73 0.41 − 17.52 Lymph Node Status _N0 ref ref ref (N) _N1 0.884 1.06 0.46 − 2.42 N₂ 0.346 2.15 0.44 − 11.06 N₃ ^{0.393 1.98 0.42 − 9.85} Distant Metastasis _M0 ^{ref ref ref} (M) _M1 0.037 15.39 1.16 − 210.79 A_{JCC Stage Stage I} ^{ref ref ref} S_{tage II} ^{0.891 0.91 0.22 − 3.53} S_{tage III} ^{0.749 1.40 0.17 − 10.62} S_{tage IV} ^{NA NA NA} P_{AM50 Luminal A} ^{ref ref ref} L_{uminal B} ^{0.111 0.50 0.21 − 1.12} B_asal ^{0.104 0.48 0.18 − 1.10} H_er2 0.844 1.09 0.42 − 2.56 Attorney Docket no.15969-055PC0 N_ormal ^{0.748 0.78 0.12 − 3.01 Type of First Surgery} _{Lumpectomy ref ref ref Simple Mastectomy} ^{0.010 0.33 0.14 − 0.74} M_{odified Radical Mastectomy} ^{< 0.001 0.15 0.06 − 0.35} O_ther 0.002 0.31 0.14 − 0.63 ^* R i ^** fi I l f R i B l l i l us us

Clinical Feature Multivariable Logistic Regression p ^OR* 95% CI^{** AJCC Stage} _{Stage I ref ref ref}

Attorney Docket no.15969-055PC0 ^a to in th ge,

TNM, PAM50 subtype, and hormone receptor (Estrogen Receptor (ER), Progesterone Receptor (PR)) status were significantly associated with disease progression (Table 5). While examining the survival models based on histology, mucinous carcinoma was found to have significant survival difference compared with ductal carcinoma (Table 5). However, as the (Fig.

4), these results were regarded as unreliable. It is worth noting that the type of first surgery, though significantly associated with margin status, does not significantly impact survival. [0083] Table 5. Univariable survival analysis to assess the effect of each clinicopathologic factor on disease progression (Progression Free Interval, PFI). C_{linical Feature} ^{Univariable Survival Analysis p HR*} 95% CI^** M_{argin Status Negative} ^{ref ref ref} P_ositive ^{< 0.001 2.71 1.70 − 4.30} A_{ge Group 60+ years} ^{ref ref ref} 4_{0-59 years} ^{0.176 0.76 0.51 − 1.13} <_{40 years} ^{0.379 1.33 0.71 − 2.50 Menopausal Status} _{Postmenopausal ref ref ref Perimenopausal} ^{0.320 0.49 0.12 − 2.00} Attorney Docket no.15969-055PC0 P_remenopausal ^{0.985 1.00 0.64 − 1.56} I_ndeterminate ^{0.100 1.71 0.90 − 3.25} Tumor Size _T1 ref ref ref (T) _T2 0.150 1.43 0.88 ^{− 2.34} T₃ 0.048 1.88 1.00 − 3.50 T₄ < 0.001 7.64 3.73 − 15.64 Not Available − − − L^{ymph Node Status (N)} _{N0 ref ref ref N1} 0.069 1.55 0.97 − 2.47 N₂ 0.001 2.66 1.51 − 4.71 N₃ < 0.001 5.25 2.91 − 9.44 Not Available − − − D^{istant Metastasis (M)} _{M0 ref ref ref M1} < 0.001 8.96 4.76 − 16.87 Not Available − − − A_{JCC Stage Stage I} ^{ref ref ref} S_{tage II} ^{0.478 1.25 0.67 − 2.33}

P_{AM50 Luminal A} ^{ref ref ref} L_{uminal B} ^{0.451 1.23 0.72 − 2.09} B_asal 0.033 1.67 1.04 − 2.68 H_er2 ^{0.015 2.15 1.16 − 3.99} N_ormal ^{0.451 1.43 0.57 − 3.59} R_{ace White} ^{ref ref ref} A_{frican American} ^{0.313 1.27 0.80 − 2.02} O_ther ^{0.235 1.68 0.68 − 4.19} F_{irst Surgery Lumpectomy} ^{ref ref ref} S_{imple Mastectomy} ^{0.527 0.83 0.46 − 1.49} M_{odified Radical Mastectomy} ^{0.361 1.26 0.77 − 2.05} O_ther 0.372 0.78 0.44 − 1.36 E_{R Status Negative} ^{ref ref ref} P_ositive ^{0.006 0.58 0.39 − 0.86} P_{R Status Negative} ^{ref ref ref} P_ositive ^{0.001 0.51 0.35 − 0.75} H_{ER2 Status Negative} ^{ref ref ref} P_ositive ^{0.394 1.30 0.71 − 2.39} Attorney Docket no.15969-055PC0 E_quivocal ^{0.304 1.32 0.78 − 2.22} H_{istology Ductal} ^{ref ref ref} L_obular ^{0.951 0.98 0.60 −1.61} M_ixed 0.917 1.05 0.39 ^{− 2.89} M_ucinous ^{0.028 3.67 1.15 − 11.70} O_thers ^{0.350 1.42 0.68 − 2.94 *}Hazard Ratio. ^**Confidence Interval for hazard ratio. Bold lettering denotes p value ≤ 0.05 [0084] In order to assess the combined effect of margin status and other factors that were significant in the univariable model on survival, multivariable survival analysis was performed. TNM, though significant in the univariable model, was excluded in the multivariable models since tumor stage is derived from TNM and the inclusion of both features in the same model was observed to be confounding in the previous logistic regression model. Surprisingly, PAM50 and ER status were not significant in this model (Table 6). Further exploration using different multivariable models (Table 7 and Table 8) indicated that hormone receptor status and PAM50 were confounding to each other; hence only PAM50 was retained in the final model (Table 9). Higher tumor stages (III and IV), and the Basal and Her2 subtypes were significant (p ≤ 0.05) in contribution to disease progression in the final model, while margin status was not significant (p = 0.135, HR = 1.54, CI = 0.88 − 2.70). The bi-variable survival models (Table 10) demonstrated that margin status remained highly significant when PAM50 or either of the hormone receptor (ER/PR) status was added to the model whereas in the model with tumor stage, margin status was only close to significance (p = 0.067). [0085] Table 6. Multivariable survival model with Progression Free Interval (PFI) as the endpoint and includes the significant features identified in univariable survival model. Clinical Feature Multivariable Survival Analysis p HR^* 95% CI^** Margin Status Negative ref ref ref Positive 0.105 1.61 0.91 − 2.87 AJCC Stage Stage I ref ref ref Stage II 0.613 1.18 0.62 − 2.23 Stage III < 0.001 3.29 1.70 − 6.36 Stage IV < 0.001 13.81 5.14 − 37.12 PAM50 LumA ref ref ref LumB 0.637 0.86 0.45 − 1.63 Basal 0.898 1.05 0.48 − 2.30 Her2 0.897 1.06 0.45 − 2.48 N_{ormal 0.816 1.12 0.43} ^{− 2.94} Attorney Docket no.15969-055PC0 ER Negative ref ref ref Positive 0.440 0.77 0.39 − 1.50 PR Negative ref ref ref Positive 0.023 0.49 0.26 − 0.90 Histology Ductal ref ref ref Lobular 0.679 1.14 0.61 − 2.10 Mixed 0.984 0.99 0.29 − 3.36 Mucinous 0.003 6.21 1.83 − 21.04 Others 0.180 1.72 0.78 − 3.81 ^*Hazard Ratio, ^**Confidence Interval for hazard ratio. Bold lettering denotes p value ≤ 0.05 [0086] Table 7. Multivariable survival analysis model without PAM50 Clinical Feature Multivariable Survival Analysis p HR^* 95% CI^** 0.05 Progesterone

Attorney Docket no.15969-055PC0 0.05 AM50 were riable survival A^nalysis 5% CI^{** ref}

^{. . .88 − 2.70} A_{JCC Stage Stage I} ^{ref ref ref} S_{tage II} ^{0.771 1.10 0.59 − 2.06} S_{tage III} ^{0.001 3.05 1.59 − 5.88 0 − 30.94 ref 59 − 1.97 40 − 4.19 06 − 4.19 56 − 3.61 ref 69 − 2.22 24 − 2.74 3 − 16.91 75 − 3.67} ble models on erval (PFI) as

Attorney Docket no.15969-055PC0 M^{argin Status# 2nd variable} p HR^* 95% CI^** p HR 95% CI 0.067 1.66 0.96 − 2.86 AJCC Stage Stage I ref ref ref S_{tage II 0.482 1.25 0.67 − 2.32 Stage III 0.001 2.98 1.58 − 5.61 Stage IV < 0.001 9.56 3.78 − 24.19} < 0.001 2.83 1.77 ^{− 4.53} PAM50 Luminal A ref ref ref L_{uminal B 0.334 1.30 0.76 − 2.21 Basal 0.011 1.86 1.15 − 3.00 Her2 0.032 1.97 1.06 − 3.65 Normal 0.512 1.36 0.54 − 3.43} < 0.001 3.04 1.90 − 4.85 ER Negative ref ref ref P_{ositive 0.002 0.52 0.35} ^{− 0.78} < 0.001 2.98 1.87 − 4.75 PR Negative ref ref ref P_{ositive < 0.001 0.48 0.33 − 0.71} ^#Margin status was used as the first variable in all bi-variable models with negative margin kept as reference. ^*Hazard Ratio, ^**Confidence Interval for hazard ratio. Bold lettering denotes p value ≤ 0.05 [0090] Example 4. Association of gene expression with margin status identified 29 DEGs [0091] To address the sample imbalance between positive and negative margins, a matched dataset (n = 142; Table 11) was extracted from the cohort to perform unbiased molecular analyses. Principal component analysis (PCA) of matched samples using 2000 highly varying genes did not clearly cluster the samples by margin status but clustered them instead by PAM50 subtypes (Fig.5). Differential expression analysis between positive and negative margin cases discovered 53 upregulated and 50 downregulated DEGs and the subsequent LASSO regression selected 29 DEGs for the prediction of margin (Table 12). The unsupervised clustering for .

cluster, Fisher’s exact p value = 0.044) were observed. This shows the genes to some degree can separate the positive margin from negative margin. Leave-One-Out Cross-Validation (LOOCV) based prediction models with the 29 genes showed an accuracy of 0.7. [0092] Table 11. Summary of 142 matched samples selected for molecular data analysis. Margin Status Feature Negative (n= 71) Positive (n= 71) Attorney Docket no.15969-055PC0 Age Group Old (60+ years) 35 35 Middle Age (40-59) 34 32 Young (<40) 2 4 M^{enopausal Status} _{Postmenopausal 48 48} Perimenopausal 2 2 Premenopausal 19 16 Indeterminate 2 3 Not Available 0 2 Race White 53 45 African American 15 15 Asian 1 1 Not Available 2 10 Stage Stage I 13 8 Stage II 28 29 Stage III 28 26 Stage IV 2 8 PAM50 LumA 41 40 LumB 11 11 Basal 7 7 H_{er2 9 9} Normal 3 4 [0093] Table 12. Summary of 29 differentially expressed genes selected through LASSO regression. Gene Description Gene Type FDR^* FC^**

Attorney Docket no.15969-055PC0

. Pseudogene g . . LINC01344 Long intergenic non-protein coding RNA 1344 LncRNA 0.037 0.44 LIN Long intergenic non-protein coding RNA L RNA 2 28 ive P1, 7.1, 6.1, een ion wo TE, ays nly asis ing n

Attorney Docket no.15969-055PC0

7 MYC_TARGETS_V1 1.553 0.023 ↑ 8 EPITHELIAL_MESENCHYMAL_TRANSITION -1.836 0.003 ↓ REFERENCES 1. Behm EC, Beckmann KR, Dahlstrom JE, Zhang Y, Cho C, Stuart-Harris R, Craft P, Rezo A, Buckingham JM. Surgical margins and risk of locoregional recurrence in invasive breast cancer: an analysis of 10-year data from the Breast Cancer Treatment Quality Assurance Project. Breast.2013 Oct;22(5):839-44. 2. Bertucci F, Finetti P, Birnbaum D. Basal breast cancer: a complex and deadly molecular subtype. Curr Mol Med.2012 Jan;12(1):96-110. 3. Bundred JR, Michael S, Stuart B, Cutress RI, Beckmann K, Holleczek B, Dahlstrom JE, Gath J, Dodwell D, Bundred NJ. Margin status and survival outcomes after breast cancer conservation surgery: prospectively registered systematic review and meta- analysis. BMJ.2022 Sep 21;378:e070346. 4. Carter CL, Allen C, Henson DE. Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases. Cancer.1989 Jan 1;63(1):181-7. 5. Cho, N., Imaging features of breast cancer molecular subtypes: state of the art. J Pathol Transl Med, 2021.55(1): p.16-25. R, Y; apy nce ec alta

TM, Pagnotta SM, Castiglioni I, Ceccarelli M, Bontempi G, Noushmehr H. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res.2016 May 5;44(8):e71. 8. Fisher B, Anderson S, Bryant J, Margolese RG, Deutsch M, Fisher ER, Jeong JH, Wolmark N. Twenty-year follow-up of a randomized trial comparing total mastectomy, Attorney Docket no.15969-055PC0 lumpectomy, and lumpectomy plus irradiation for the treatment of invasive breast cancer. N Engl J Med.2002 Oct 17;347(16):1233-41. 9. Fox BP, Kandpal RP. Invasiveness of breast carcinoma cells and transcript profile: Eph receptors and ephrin ligands as molecular markers of potential diagnostic and prognostic application. Biochem Biophys Res Commun.2004 Jun 11;318(4):882-92. 10. Hewitt KC, Miller P, Piper M, Prionas N, Son JD, Alvarado M, Esserman LJ, Wong JM, Ewing C, Mukhtar RA. Positive margins after mastectomy in patients with invasive lobular carcinoma of the breast: Incidence and management strategies. Am J Surg.2022 Apr;223(4):699-704. 11. Horattas I, Fenton A, Gabra J, Mendiola A, Li F, Namm J, Solomon N, Gass J, Lum S, Murray M, Howard-McNatt M, Dupont E, Levine E, Brown E, Ollila D, Chiba A, Chagpar AB. Does Breast Cancer Subtype Impact Margin Status in Patients Undergoing Partial Mastectomy? Am Surg.2022 Jul;88(7):1607-1612. 12. Houssami N, Macaskill P, Marinovich ML, Dixon JM, Irwig L, Brennan ME, Solin LJ. Meta-analysis of the impact of surgical margins on local recurrence in women with early-stage invasive breast cancer treated with breast-conserving therapy. Eur J Cancer. 2010 Dec;46(18):3219-32. 13. Intasqui P, Bertolla RP, Sadi MV. Prostate cancer proteomics: clinically useful protein biomarkers and future perspectives. Expert Rev Proteomics.2018 Jan;15(1):65-79. 14. Itkonen O, Stenman UH. TATI as a biomarker. Clin Chim Acta.2014 Apr 20;431:260- 9. 15. Jara-Gutiérrez Á, Baladrón V. The Role of Prostaglandins in Different Types of Cancer. Cells.2021 Jun 13;10(6):1487. 16. Kuhn, M., Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 2008.28(5): p.1 - 26. 17. Lepedda AJ, Nieddu G, Cannas C, Formato M. Molecular and pathobiological insights of bikunin/UTI in cancer. Mol Biol Rep.2023 Feb;50(2):1701-1711. 18. Lin TC. Functional Roles of SPINK1 in Cancers. Int J Mol Sci.2021 Apr 7;22(8):3814. 19. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, Kovatich AJ, Benz CC, Levine DA, Lee AV, Omberg L, Wolf DM, Shriver CD, Thorsson V; Cancer Genome Atlas Research Network; Hu H. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell. 2018 Apr 5;173(2):400-416.e11. Attorney Docket no.15969-055PC0 20. Lombardi A, Pastore E, Maggi S, Stanzani G, Vitale V, Romano C, Bersigotti L, Vecchione A, Amanti C. Positive margins (R1) risk factors in breast cancer conservative surgery. Breast Cancer (Dove Med Press).2019 Jul 26;11:243-248. 21. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.2014;15(12):550. 22. Lovrics PJ, Cornacchi SD, Farrokhyar F, Garnett A, Chen V, Franic S, Simunovic M. The relationship between surgical factors and margin status after breast-conservation surgery for early stage breast cancer. Am J Surg.2009 Jun;197(6):740-6. 23. Maishman T, Cutress RI, Hernandez A, Gerty S, Copson ER, Durcan L, Eccles DM. Local Recurrence and Breast Oncological Surgery in Young Women With Breast Cancer: The POSH Observational Cohort Study. Ann Surg.2017 Jul;266(1):165-172. 24. Mehta GA, Khanna P, Gatza ML. Emerging Role of SOX Proteins in Breast Cancer Development and Maintenance. J Mammary Gland Biol Neoplasia. 2019 Sep;24(3):213-230. 25. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstråle M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet.2003 Jul;34(3):267-73. 26. Moran MS, Schnitt SJ, Giuliano AE, Harris JR, Khan SA, Horton J, Klimberg S, Chavez-MacGregor M, Freedman G, Houssami N, Johnson PL, Morrow M. Society of Surgical Oncology-American Society for Radiation Oncology consensus guideline on margins for breast-conserving surgery with whole-breast irradiation in stages I and II invasive breast cancer. Int J Radiat Oncol Biol Phys.2014 Mar 1;88(3):553-64. 27. Morrow M, Van Zee KJ, Solin LJ, Houssami N, Chavez-MacGregor M, Harris JR, Horton J, Hwang S, Johnson PL, Marinovich ML, Schnitt SJ, Wapnir I, Moran MS. Society of Surgical Oncology-American Society for Radiation Oncology-American Society of Clinical Oncology Consensus Guideline on Margins for Breast-Conserving Surgery with Whole-Breast Irradiation in Ductal Carcinoma In Situ. Ann Surg Oncol. 2016 Nov;23(12):3801-3810. 28. Nguyen D, Yu J, Reinhold WC, Yang SX. Association of Independent Prognostic Factors and Treatment Modality With Survival and Recurrence Outcomes in Breast Cancer. JAMA Netw Open.2020 Jul 1;3(7):e207213. Attorney Docket no.15969-055PC0 29. Pan Z, Zhu L, Li Q, Lai J, Peng J, Su F, Li S, Chen K. Predicting initial margin status in breast cancer patients during breast-conserving surgery. Onco Targets Ther. 2018 May 8;11:2627-2635. 30. Pan J, Zhang L, Huang J. Prostaglandin D2 synthase/prostaglandin D2/TWIST2 signaling inhibits breast cancer proliferation. Anticancer Drugs. 2021 Nov 1;32(10):1029-1037. 31. Park CC, Mitsumori M, Nixon A, Recht A, Connolly J, Gelman R, Silver B, Hetelekidis S, Abner A, Harris JR, Schnitt SJ. Outcome at 8 years after breast-conserving surgery and radiation therapy for invasive breast cancer: influence of margin status and systemic therapy on local recurrence. J Clin Oncol.2000 Apr;18(8):1668-75. 32. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol.2009 Mar 10;27(8):1160-7. 33. Payne SL, Ram P, Srinivasan DH, Le TT, Levin M, Oudin MJ. Potassium channel- driven bioelectric signalling regulates metastasis in triple-negative breast cancer. eBioMedicine.2022 Jan;75:103767. 34. Popławski P, Wiśniewski JR, Rijntjes E, Richards K, Rybicka B, Köhrle J, Piekiełko- Witkowska A. Restoration of type 1 iodothyronine deiodinase expression in renal cancer cells downregulates oncoproteins and affects key metabolic pathways as well as anti-oxidative system. PLoS One.2017 Dec 22;12(12):e0190179. 35. Ruiz C, Martins JR, Rudin F, Schneider S, Dietsche T, Fischer CA, Tornillo L, Terracciano LM, Schreiber R, Bubendorf L, Kunzelmann K. Enhanced expression of ANO1 in head and neck squamous cell carcinoma causes cell migration and correlates with poor prognosis. PLoS One.2012;7(8):e43265. 36. SEER Cancer Statistics Factsheets: Breast Cancer. National Cancer Institute. Available from: http://seer.cancer.gov/statfacts/html/breast.html. 37. Sekikawa S, Onda T, Miura N, Nomura T, Takano N, Shibahara T, Honda K. Underexpression of α-1-microglobulin/bikunin precursor predicts a poor prognosis in oral squamous cell carcinoma. Int J Oncol.2018 Dec;53(6):2605-2614. 38. Soon WW, Miller LD, Black MA, Dalmasso C, Chan XB, Pang B, Ong CW, Salto- Tellez M, Desai KV, Liu ET. Combined genomic and phenotype screening reveals Attorney Docket no.15969-055PC0 secretory factor SPINK1 as an invasion and survival factor associated with patient prognosis in breast cancer. EMBO Mol Med.2011 Aug;3(8):451-64. 39. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A.2005 Oct 25;102(43):15545-50. 40. Tibshirani, R., Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society. Series B (Methodological), 1996.58(1): p.267-288. 41. Tomlinson VA, Newbery HJ, Wray NR, Jackson J, Larionov A, Miller WR, Dixon JM, Abbott CM. Translation elongation factor eEF1A2 is a potential oncoprotein that is overexpressed in two-thirds of breast tumours. BMC Cancer.2005 Sep 12;5:113. 42. Trevor Hastie, R.T., Jerome Friedman, The Elements of Statistical Learning. Springer Series in Statistics.2009: Springer New York, NY. XXII, 745. 43. Veronesi U, Cascinelli N, Mariani L, Greco M, Saccozzi R, Luini A, Aguilar M, Marubini E. Twenty-year follow-up of a randomized study comparing breast- conserving surgery with radical mastectomy for early breast cancer. N Engl J Med.2002 Oct 17;347(16):1227-32. 44. Wang X, Su D, Qin Z, Chen Z. RETRACTED: Identification of FOXN4 as a tumor suppressor of breast carcinogenesis via the activation of TP53 and deactivation of Notch signaling. Gene.2020 Jan 5;722:144057. 45. Hotsinpiller Wj, Everett As, Richman Js, Parker C, Boggs Dh . Rates of margin positive resection with breast conservation for invasive breast cancer using the NCDB. Breast. 2021 Dec;60:86-89. 46. Xu J, Chen Y, Olopade OI. MYC and Breast Cancer. Genes Cancer.2010 Jun;1(6):629- 40. 47. Barentsz, M. W., Postma, E. L., van Dalen, T., van den Bosch, M. A., Miao, H., Gobardhan, P. D., van den Hout, L. E., Pijnappel, R. M., Witkamp, A. J., van Diest, P. J., van Hillegersberg, R., & Verkooijen, H. M. (2015). Prediction of positive resection margins in patients with non-palpable breast cancer. Eur J Surg Oncol, 41(1), 106-112. https://doi.org/10.1016/j.ejso.2014.08.474 48. de Kok, J. B., Roelofs, R. W., Giesendorf, B. A., Pennings, J. L., Waas, E. T., Feuth, T., Swinkels, D. W., & Span, P. N. (2005). Normalization of gene expression measurements Attorney Docket no.15969-055PC0 in tumor tissues: comparison of 13 endogenous control genes. Laboratory Investigation, 85(1), 154-159. https://doi.org/https://doi.org/10.1038/labinvest.3700208

Claims

Attorney Docket no.15969-055PC0 CLAIMS What is claimed is: 1. A method for predicting positive or negative margin status in a breast cancer patient who has undergone lumpectomy or mastectomy surgery, the method comprising steps of: i. collecting a breast cancer tissue sample from the patient; ii. analyzing one or more, or two or more clinicopathologic and molecular factors of the patient; iii. based on the analysis result of step ii, performing gene analysis by measuring gene expression levels of each gene, normalizing the gene expression levels of each gene against one or more housekeeping genes, and determining an increase or decrease in the gene expression levels of each gene; and iv. determining positive or negative margin status based on the gene analysis. 2. The method of claim 1, wherein the clinicopathologic factors comprise one or more selected from: type of surgery, wherein the type of surgery optionally comprises lumpectomy, simple mastectomy, modified radical mastectomy, partial mastectomy, Patey's surgery, or excision with needle wire localization, tumor size, wherein tumor size is optionally selected from T1, T2, T3, or T4, lymph node status, wherein lymph node status is optionally selected from N0, N1, N2, or N3, distant metastasis, wherein distant metastasis is selected from M0 or M1, and cancer stage, wherein cancer stage comprises Stage I, Stage II, Stage III, or Stage IV. 3. The method of claim 1, wherein the molecular factors comprise PAM50 subtypes (Luminal A, Luminal B, basal, Her2, and normal), and positive, negative or equivocal status of immunohistochemistry (IHC) markers including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor subtype 2 (HER2). 4. The method of any of claims 1-3, wherein the clinicopathologic and molecular factors for further gene analysis comprise at least one of: lumpectomy, a tumor stage of Stage III or Stage IV, a tumor size of T4, lymph node status of N2 or N3, presence of distant metastasis M1, and PAM50 subtypes of Luminal A, Her2-enriched (Her2), and/or basal subtypes. 5. The method of claim 1, wherein the panel of genes comprises at least two of the following genes: ARC, AMBP, C1orf167, CNGA3, EEF1A2, EPHA6, FOXN4, DIO1, KRT75, SPRR1B, SOX15, STUM, AC084880.1, AC008663.2, AC099329.2, AC004947.1, ANO3, Attorney Docket no.15969-055PC0 KCNJ6, PTGDR, PCP4L1, SPINK1, BEND3P1, CPHL1P, AP002001.2, LINC01344, LINC00589, AC114296.1, AF015262.1, and SLC26A4-AS1. 6. The method of claim 5, wherein the panel of genes comprises at least one of the following protein-coding genes, comprising ARC, AMBP, C1orf167, CNGA3, EEF1A2, EPHA6, FOXN4, DIO1, KRT75, SPRR1B, SOX15, STUM, ANO3, KCNJ6, PTGDR, PCP4L1, and SPINK1. 7. The method of claim 5, wherein the panel of genes comprises at least one of the following well-known tumor marker genes upregulated in positive margin breast cancer, comprising EEF1A2, EPHA6, FOXN4, SOX15. 8. The method of claim 5, wherein the panel of genes comprises alpha-1- microglobulin/Bikunin precursor (AMBP) gene and/or iodothyronine deiodinase 1 (DIO1) gene, whose expressions are downregulated in other cancers but upregulated in positive margin breast cancer. 9. The method of claim 5, wherein the panel of genes comprises at least one of the following genes, ARC, C1orf167, CNGA3, KRT75, SPRR1B, STUM, wherein said panel of genes are identified in positive margin breast cancer. 10. The method of claim 5, wherein the panel of genes comprises at least one of the following genes, SPINK1, potassium inwardly rectifying channel subfamily J member 6 (KCNJ6), anoctamin 3 (ANO3), prostaglandin D2 receptor (PTGDR, also called PGD2) and PCP4L1, which are downregulated protein-coding genes identified in the positive margin breast cancer. 11. The method of claim 1, wherein the house keeping genes comprise at least one of the following genes:_ LRP, BACT, CYC, GADPH, PGK, B2M, BGUS, HPRT, TBP, TfR, PBGD, ATP6, and rRNA. 12. The method of any of claims 1-11, further comprising conducting a follow up treatment for the patient exhibiting a breast cancer positive margin, wherein the follow up treatment optionally comprises surgical intervention. 13. A kit for a prognostic assay for margin status in a tissue sample from a breast cancer patient, the kit comprising at least two pairs of PCR primer oligonucleotides for determining the gene expression levels of at least two genes selected from: ARC, AMBP, C1orf167, CNGA3, EEF1A2, EPHA6, FOXN4, DIO1, KRT75, SPRR1B, SOX15, STUM, Attorney Docket no.15969-055PC0 AC084880.1, AC008663.2, AC099329.2, AC004947.1, ANO3, KCNJ6, PTGDR, PCP4L1, SPINK1, BEND3P1, CPHL1P, AP002001.2, LINC01344, LINC00589, AC114296.1, AF015262.1, SLC26A4-AS1, and at least one pair of PCR primer oligonucleotides targeting at least one house keeping gene selected from LRP, BACT, CYC, GADPH, PGK, B2M, BGUS, HPRT, TBP, TfR, PBGD, ATP6, and rRNA. 14. The kit of claim 13, wherein the kit further comprises necessary reagents for RT-PCR or qRT-PCR reactions.