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RESEARCH 

Notch signaling pathway: An emerging 

therapeutic target for African-American 

triple negative breast cancer patients 
Nikita Wright1&, Shristi Bhattarai1&, Bikram Sahoo1, Mishal Imaan Syed1, Padmashree Rida1, Ritu Aneja1,2* 

1Department of Biology, Georgia State University, Atlanta, GA 30303 
2International Consortium for Advancing Research on Triple Negative Breast Cancer, Georgia State 

University, Atlanta, GA 30303 

&Co-first authors 

*Corresponding author email: raneja@gsu.edu 

ABSTRACT 
The most fatal form of breast cancer, triple negative breast cancer (TNBC), continues to challenge 

clinicians worldwide with its lack of reliable prognostic biomarkers and pharmacologically actionable 

treatment targets. In the US, this aggressive disease disproportionately afflicts African-American women 

at a rate 2-3 times higher than European-American (EA) women, thereby contributing to the observed 

higher mortality rates of AA BC patients. In order to address the unmet clinical need for new and 

effective treatments for AA TNBCs, we describe herein a potentially actionable pathway that appears to 

be in overdrive in TNBCs of AA patients compared to EA TNBCs: the Notch signaling pathway. Notch 

signaling is implicated in multiple aspects of carcinogenesis and tumor progression including the 

regulation of proliferation, apoptosis, the biology of cancer stem cells, tumor angiogenesis and 

epithelial-to-mesenchymal transition. Our gene expression analyses have uncovered significant 

upregulation of Notch signaling as well as gene ontologies reflecting dysregulation of key processes 

regulated by Notch signaling among AA compared to EA TNBC patients. Furthermore, we present 

evidence suggesting that upregulated Notch signaling may predict poor prognosis in TNBC. Our 

findings thus suggest differences in Notch signaling among racially-distinct TNBC patients that may 

contribute to the more aggressive clinical behavior of TNBC in AAs. These observations also suggest 

that Notch signaling may be an attractive therapeutic target for high-risk AA TNBC patients. 

KEYWORDS: Triple negative breast cancer, racial disparity, Notch signaling pathway, African-

American 

Citation: Wright N et al (2019) Notch signaling pathway: An emerging therapeutic target for African-

American triple negative breast cancer patients. Cancer Health Disparities 3:e1-e22. 

doi:10.9777/chd.2019.1013.

 



 
 
 
 
 

 

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The tip of the iceberg: Racial disparities 

in triple negative breast cancer 

Triple negative breast cancer (TNBC) is a 

significant public health concern in the US as it 

afflicts about one-fifth of the ~2.8 million women 

in the country with breast cancer (BC) (Criscitiello 

et al., 2012; Howlader N, 2014). Worldwide, the 

disease accounts for approximately 20% of BC 

cases. TNBC is the most clinically challenging 

subtype of BC as it lacks expression of the 

pharmacologically-targetable estrogen receptor 

(ER), progesterone receptor (PR) and human 

epidermal growth factor receptor 2 (HER2) 

(Bianchini et al., 2016b; Lehmann et al., 2011; Shah 

et al., 2012). Furthermore, the disease is 

characterized by high rates of recurrence and 

metastases, especially within the first five years 

post diagnosis (Bianchini et al., 2016a; Kassam et 

al., 2009; Lehmann et al., 2011; Shah et al., 2012). 

TNBCs exhibit high interpatient heterogeneity and 

many classification schemes have emerged that 

categorize TNBCs into transcriptomically-distinct 

molecular subtypes and with unique DNA copy 

number variations (Burstein et al., 2015; Lehmann 

et al., 2016). TNBCs also exhibit intratumoral 

heterogeneity which, together with the lack of 

clinically facile methods of determining molecular 

subtypes, thwarts the development of novel 

targeted treatments. Thus, despite advances in 

treatments for other BC subtypes, chemotherapy 

remains the cornerstone of treatment for TNBC. 

TNBC disproportionately afflicts African-American 

(Raab et al.) women, especially younger 

premenopausal AAs (Brewster et al., 2014; 

Danforth, 2013; Jiagge et al., 2018; Keenan et al., 

2015; Newman and Kaljee, 2017; Wu et al., 2017). 

After adjusting for potentially confounding factors, 

including tumor stage and grade (which tend to 

be higher in AA women) and age at diagnosis and 

socioeconomic status (which tend to be lower in 

AA women), AA women exhibit ~2 times the 

likelihood of presenting with TNBC (Dietze et al., 

2015), which partially explains their worse 

outcomes relative to EA patients. The 

predisposition towards developing TNBC appears 

to be deeply rooted in biogeographic ancestry. 

For example, ~50% of Nigerian (Agboola et al., 

2012) and Malian (Ly et al., 2012) and ~80% of 

Ghanaian (Stark et al., 2010a; Stark et al., 2010b) 

women with BC have triple-negative breast 

tumors, in contrast with ~15% of EA (Carey et al., 

2006; Stark et al., 2010a) or white British women 

(Agboola et al., 2012; Bowen et al., 2008). Stark et 

al. found that 83% of African women presented 

with TNBC compared to only 41.9% of AAs and 

15.4% of EAs (Stark et al., 2010b). 

Although controversial, racial disparities in disease 

course and outcomes have been reported within 

the TNBC subtype. Accumulating evidence suggest 

that AAs present with more unfavorable clinico-

pathological characteristics such as larger tumor 

size, higher proliferation, more extensive lymph 

node involvement, as well as present at a younger 

age than EAs among TNBC patients (Dietze et al., 

2015; Lund et al., 2009; Sullivan et al., 2014). 

Furthermore, AAs have been reported to harbor 

more aggressive TNBC subtypes such as basal-like 

1 and mesenchymal stem-like as well as greater 

intratumoral heterogeneity than EAs (Keenan et al., 

2015; Lindner et al., 2013). As a result, AAs have 

been reported to experience shorter overall 

survival (OS) and progression-free survival (PFS) 

than EAs among TNBC patients (Dietze et al., 2015; 

Lund et al., 2009; Sullivan et al., 2014). Newman 

and colleagues observed 30% higher mortality 

rate among AA compared to EA TNBC patients 

(Newman et al., 2006). These recent findings have 



 
 
 
 
 

 

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sparked investigations into distinctions in inherent 

tumor biology between AA and EA TNBCs to 

elucidate the molecular underpinnings driving the 

racially disparate burden in TNBC. Currently, there 

are no reliable methods to identify AA TNBC 

patients at high risk of poor outcomes, which 

would indicate the need to offer more aggressive 

treatment. Identification of biomarkers that can 

risk-stratify AA TNBC patients and predict 

responsiveness to targeted and cytotoxic agents 

could improve prognosis of this high-risk patient 

population. 

Top notch distinctions: Shedding light 

on the Notch signaling pathway in triple 

negative breast cancer 

The Notch signaling pathway has recently 

emerged as a novel therapeutic target of interest 

in TNBC. The pathway is present in most 

multicellular organisms and is highly conserved. It 

is essential for cell proliferation, differentiation and 

development and plays keys roles in cell fate 

determination (Ranganathan et al., 2011). There are 

four different Notch receptors (Notch1, Notch2, 

Notch3 and Notch4) expressed in mammals 

(Dontu et al., 2004). The Notch receptor is a 

hetero-oligomer transmembrane receptor protein 

composed of an extracellular protein domain, a 

single transmembrane pass and a small 

intracellular region. Notch receptors on the cell 

surface engage with Notch ligands- Delta-like 

(DLL1, DLL3, DLL4) and Jagged (JAG1, JAG2) to 

initiate the signaling cascade (Bray et al., 2008; 

Brou et al., 2000; Fiuza and Arias, 2007). After the 

interaction of a notch ligand with its receptor, a 

metalloprotease protein from the ADAM-family 

(ADAM10) cleaves the Notch receptor outside the 

membrane (van Tetering et al., 2009); the 

extracellular portion of the Notch receptor 

attached to its ligand is thus released and gets 

endocytosed by ligand-expressing cells. The 

remaining part of the Notch receptor inside the 

inner leaflet of the cell membrane undergoes a 

cleavage by an enzyme called gamma-secretase, 

releasing the intracellular domain of the Notch 

protein (Borggrefe and Liefke, 2012). The Notch 

intracellular domain then forms a trimeric core 

transactivation complex with the sequence-specific 

DNA binding protein, CSL (CBF-1/Su(H)/Lag-1), 

and the transcriptional co-activator, Mastermind, 

to activate transcription of target genes (Nam et 

al., 2006; Wilson and Kovall, 2006). Transcriptional 

targets include transcription factors (NF‐κB2 and c‐

Myc), cell‐cycle regulators (cyclin D1 and p21), 

growth factor receptors and regulators of 

angiogenesis and apoptosis. Dysregulated Notch 

signaling is associated with various malignancies. 

Expression of Notch receptors and ligand protein 

have been reported to be upregulated in breast 

tumors compared to normal breast tissues (Mittal 

et al., 2009; Rizzo et al., 2008; Zardawi et al., 2010). 

Parr et al. observed an aberrant level of Notch-1 

and Notch-2 expression in breast tumor tissue via 

immunohistochemistry and quantitative RT-PCR. 

The study demonstrated that high expression level 

of Notch ligands and/or receptors correlated 

significantly with poor clinical outcomes (Parr et al., 

2004). Moreover, several studies suggest that 

increased expression of Notch is associated with 

oncogene expression, maintaining stemness of BC 

stem cells and deregulated cell cycle progression 

(Harrison et al., 2010; Ronchini and Capobianco, 

2001; Sharma et al., 2006; Weng et al., 2006). 

Notch signaling plays a critical role in TNBC. 

Dickson et al. were the first to uncover an 

association of Notch expression with TNBC. The 

study revealed a significant correlation of 

overexpression of JAG-1 and Notch-1 with poor 



 
 
 
 
 

 

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prognosis among TNBC patients (Dickson et al., 

2007). Furthermore, Notch-1 and Notch-4 

receptors have been discovered to be 

overexpressed in TNBC vascular endothelial cells 

(Speiser et al., 2012). Recent studies also 

uncovered a Jagged1-Notch1-CyclinD1 axis playing 

a key role in maintaining proliferation of TNBC, as 

opposed to other types of BC (Cohen et al., 2010). 

Notch3 signaling controls survival of hypoxic TNBC 

cells and Notch4 is involved in self-renewal of BC 

stem cells. (Harrison et al., 2010; Sansone et al., 

2007) Evidence reinforcing the notion that 

dysregulation of Notch signaling is 

pathogenetically relevant in TNBC came from a 

study wherein chromosomal rearrangements 

producing constitutively active versions of Notch1 

or Notch2 were detected almost exclusively in 

TNBC cell lines and tumors (Robinson et al., 2011). 

When the Notch signaling pathway gets activated, 

the Notch intracellular domain (ICD), generated by 

the enzyme gamma-secretase, translocates from 

the cytoplasm to the nucleus where it binds to the 

CSL complex to initiate the transcription of 

downstream targets (Shih Ie and Wang, 2007). The 

nuclear localization of Notch has been reported to 

occur more frequently in TNBC compared to 

hormone receptor-positive tumors (Touplikioti, 

2012). Moreover, Speiser et al. discovered more 

positive nuclear and cytoplasmic staining of 

Notch-1 and Notch-4 in TNBC samples and more 

positive membrane staining of these proteins in 

hormone receptor-positive breast tumor 

specimens (Speiser et al., 2012). Many studies on 

Notch signaling have postulated that its cellular 

localization can be a morphological illustration of 

its function (Bray et al., 2008). As previously 

described, upon interaction of the transmembrane 

Notch receptor with its ligand, the receptor is 

proteolytically cleaved and the NICD is released 

into the nucleus. Inside the nucleus, NICD 

modulates transcription of target genes via 

interaction with CSL (Schroeter et al., 1998; 

Weijzen et al., 2002). Thus, the subcellular 

localization of the Notch protein reflects the 

functional activity of the receptor. Membrane 

localization of the Notch protein represents a 

mature receptor that has not yet been activated; 

cytoplasmic localization of the protein represents a 

newly synthesized receptor which is on its way to 

plasma membrane; and nuclear localization of 

Notch protein reflects the activated state of the 

receptor (Speiser et al., 2012). However, the 

correlation between the amount of Notch staining 

and Notch pathway activity, especially if the 

staining is distributed to the nucleus or cytoplasm, 

remains unknown. Furthermore, in a study by Yao 

et al., there was significantly more membranous 

staining in the ER-positive compared to the ER-

negative BC cases (Yao et al., 2011). This finding 

suggests that estrogen increases Notch-1 protein 

levels and causes it to accumulate at the cell 

membrane [which represents a mature but non-

activated form of Notch-1], but not in the nucleus 

(Rizzo et al., 2008). Thus, nuclear localized Notch-1 

and Notch-4 may serve as potential therapeutic 

targets for TNBC patients. Moreover, Rizzo et al. 

observed sensitivity of TNBC cells to Notch 

inhibitors. The authors demonstrated arrest of 

TNBC cells in G2 phase of cell cycle upon 

knockdown of Notch-1 and Notch-4 with siRNA or 

pharmacological inhibition of gamma-secretase 

(GSI) (Rizzo et al., 2008). GSI inhibitors are 

currently in clinical trials to reduce Notch signaling 

in patients with recurrent TNBC (Olsauskas-Kuprys 

et al., 2013). A recent clinical trial demonstrated 

that the administration of a GSI inhibitor in 

combination with Docetaxel elicited anti-tumor 

activity in patients with locally advanced/metastatic 



 
 
 
 
 

 

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RESEARCH 

TNBC (Locatelli and Curigliano, 2017). Thus, Notch 

signaling, which is highly upregulated in TNBC, 

may be a potential therapeutic target for TNBC 

patients, who lack good targeted therapy options. 

Uncharted territory: Investigating 

disparities in the Notch signaling 

pathway among racially-distinct triple 

negative breast cancer patients 

The emergence of Notch signaling as a 

therapeutic target of interest in TNBC has spurred 

interest in this pathway as a potential suspect in 

the racially disparate burden in TNBC. However, 

literature evidence supporting this speculation 

remains sparse. A genome-wide association study 

conducted by Adriano and colleagues revealed 

that a SNP in the NOTCH4 locus was significantly 

associated with AA but not EA sarcoidosis patients 

and this finding remained consistent in multivariate 

models (Adrianto et al., 2012). Sarcoidosis 

disproportionately afflicts AAs suggesting that this 

genetic variant may play a role in the disease’s 

disparate burden (Cozier et al., 2011; James and 

Sherlock, 1994). Furthermore, Stewart et al. 

discovered that the Notch 2 N-terminal-like 

(NOTCH2NL) gene was significantly upregulated 

among AA compared to EA BC patients in the 

TCGA dataset (Stewart et al., 2013). However, 

ethnic differences in Notch signaling and its role in 

the higher incidence of and poorer outcomes from 

TNBC remain understudied. 

We recently queried TCGA breast dataset for AA 

and EA TNBC patients and exploited 

bioinformatics tools to determine differentially-

expressed genes, biological pathways, and gene 

ontologies between the racially-distinct TNBC 

patients. According to our DESeq2 or differential 

gene expression analyses, we observed significant 

upregulation of genes encoding key components 

of the Notch signaling network such as NOTCH-

Regulated Ankyrin Repeat Protein (NRARP), 

NOTCH2NL, Delta/Notch-like EGFR Repeat 

containing (DNER), Jagged 1 (JAG1), Jagged 2 

(JAG2), Hess family belch transcription factor 4 

(HES4), and Matrix Metalloproteinase-9 (MMP9) 

among AA compared to EA TNBC samples 

(p<0.05) (Table 1). We employed the GAGE and 

Pathview packages to identify differentially-

expressed biological pathways or experimentally-

derived differential expression sets (Table 2) and 

gene ontologies (Table 3) between RNA 

sequenced AA and EA TNBC samples. 

Interestingly, we discovered that the Notch 

signaling pathway expression set was more 

upregulated in AA compared to EA TNBC samples 

(p=0.054). We also observed significant 

downregulation of key processes that are normally 

repressed by Notch signaling such as focal 

adhesion, extracellular matrix (ECM)-receptor 

interaction, and adherents junction expression sets 

as well as cell junction assembly, cell-cell junction 

organization, cell-cell adhesion, epithelial cell 

development, negative regulation of endothelial 

cell proliferation, double-strand break repair, and 

regulation of DNA repair gene ontologies 

(p<0.05). Furthermore, we observed significant 

upregulation of gene ontologies reflecting T cell 

antitumoral immunity (which is enhanced by Notch 

signaling) such as T cell differentiation, T cell 

activation, adaptive immune response, and 

interferon-gamma production (p<0.05). Moreover, 

Kaplan-Meier survival analyses revealed that 

overexpression of DNER predicted significantly 

poorer relapse-free survival (RFS) in TNBCs 

(HR=2.4; p=0.0012); JAG2 expression also 



 
 
 
 
 

 

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Table 1. Genes significantly upregulated among AA compared to EA TNBC patients in the TCGA dataset. 

Gene Symbol Entrez Name Base Mean log2FoldChange p value 

ENSG00000198435 NRARP 441478 NOTCH-regulated ankyrin repeat protein 516.3894051 5.051550368 4.38E-07 

ENSG00000167244 IGF2 3481 insulin like growth factor 2 2619.306453 -1.233367377 1.02E-05 

ENSG00000157764 BRAF 673 B-Raf proto-oncogene, serine/threonine kinase 214.9862772 -0.502496583 2.33E-05 

ENSG00000165105 RASEF 158158 RAS and EF-hand domain containing 162.6660192 -0.958748689 7.76E-05 

ENSG00000184916 JAG2 3714 jagged 2 836.7242091 3.853121028 0.0001166 

ENSG00000188290 HES4 57801 hes family bHLH transcription factor 4 137.2454011 3.84511881 0.0001205 

ENSG00000171408 PDE7B 27115 phosphodiesterase 7B 70.86301288 -0.806853305 0.0001551 

ENSG00000073921 PICALM 8301 phosphatidylinositol binding clathrin assembly protein 4146.086807 -0.328030427 0.0001577 

ENSG00000264343 NOTCH2NL 388677 notch 2 N-terminal like 1130.083257 3.692675235 0.0002219 

ENSG00000185737 NRG3 10718 neuregulin 3 18.2400562 -1.093851375 0.0002292 

ENSG00000046889 PREX2 80243 
phosphatidylinositol-3,4,5-trisphosphate dependent Rac 

exchange factor 2 
35.37360263 -0.965093716 0.0003995 

ENSG00000151689 INPP1 3628 inositol polyphosphate-1-phosphatase 257.8331804 -0.464334443 0.0007263 

ENSG00000151151 IPMK 253430 inositol polyphosphate multikinase 66.09782388 -0.677173589 0.0007599 

ENSG00000101384 JAG1 182 jagged 1 2029.134072 3.352573762 0.0008006 

ENSG00000169435 RASSF6 166824 Ras association domain family member 6 87.45601131 -0.732054428 0.0010291 

ENSG00000041353 RAB27B 5874 RAB27B, member RAS oncogene family 67.49077359 -0.735088474 0.0011336 

ENSG00000175985 PLEKHD1 400224 pleckstrin homology and coiled-coil domain containing D1 3.551807893 -0.997599044 0.0012151 

ENSG00000169220 RGS14 10636 regulator of G-protein signaling 14 356.4357972 0.493690701 0.0013154 

ENSG00000154678 PDE1C 5137 phosphodiesterase 1C 63.21150224 -0.857678049 0.0014838 

ENSG00000151491 EPS8 2059 epidermal growth factor receptor pathway substrate 8 895.8248723 -0.525349111 0.0016848 

ENSG00000064932 SBNO2 22904 strawberry notch homolog 2 1823.609706 3.135072772 0.0017181 

ENSG00000011405 PIK3C2A 5286 
phosphatidylinositol-4-phosphate 3-kinase catalytic subunit 

type 2 alpha 
1239.790208 -0.396160165 0.0017905 

ENSG00000109452 INPP4B 8821 inositol polyphosphate-4-phosphatase type II B 152.8627179 -0.735904178 0.0018282 

ENSG00000178568 ERBB4 2066 erb-b2 receptor tyrosine kinase 4 74.66419079 -0.896585238 0.0024244 

ENSG00000187957 DNER 92737 delta/notch like EGF repeat containing 173.0818588 2.870907184 0.004093 

ENSG00000078142 PIK3C3 5289 phosphatidylinositol 3-kinase catalytic subunit type 3 551.5572258 -0.281487717 0.0055186 



 
 
 
 
 

 

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ENSG00000085832 EPS15 2060 epidermal growth factor receptor pathway substrate 15 1725.645419 -0.21533493 0.0055454 

ENSG00000137825 ITPKA 3706 inositol-trisphosphate 3-kinase A 11.85034496 -0.688848495 0.0061921 

ENSG00000137142 IGFBPL1 347252 insulin like growth factor binding protein like 1 12.11877278 -0.790048534 0.0062746 

ENSG00000073536 NLE1 54475 notchless homolog 1 347.5008396 2.711514433 0.0066977 

ENSG00000163964 PIGX 54965 phosphatidylinositol glycan anchor biosynthesis class X 740.0513792 -0.330509569 0.0084688 

ENSG00000097033 SH3GLB1 51100 SH3 domain containing GRB2 like endophilin B1 2960.384131 -0.219544393 0.0084951 

ENSG00000169752 NRG4 145957 neuregulin 4 7.692847298 -0.718554497 0.0086447 

ENSG00000161896 IP6K3 117283 inositol hexakisphosphate kinase 3 11.780405 -0.794971219 0.0092705 

ENSG00000162434 JAK1 3716 Janus kinase 1 3697.004587 -0.265820839 0.0099595 

ENSG00000197563 PIGN 23556 phosphatidylinositol glycan anchor biosynthesis class N 628.3454755 -0.361073745 0.0100388 

ENSG00000146648 ERBB1 1956 epidermal growth factor receptor 788.1655218 -0.514822969 0.0106547 

ENSG00000186479 RGS7BP 401190 regulator of G-protein signaling 7 binding protein 9.793117489 -0.738439155 0.0107012 

ENSG00000197081 IGF2R 3482 insulin like growth factor 2 receptor 3893.882883 -0.353086769 0.0109115 

ENSG00000100985 MMP9 4318 matrix metallopeptidase 9 2820.554471 0.550370107 0.0117042 

ENSG00000106780 MEGF9 1955 multiple EGF like domains 9 788.1489691 -0.382312117 0.0121799 

ENSG00000182836 PLCXD3 345557 
phosphatidylinositol specific phospholipase C X domain 

containing 3 
16.82951336 -0.73024397 0.0140536 

ENSG00000122126 OCRL 4952 OCRL, inositol polyphosphate-5-phosphatase 1231.48057 -0.285100271 0.0151527 

ENSG00000184588 PDE4B 5142 phosphodiesterase 4B 1333.079513 -0.509410864 0.0163107 

ENSG00000114302 PRKAR2A 5576 
protein kinase cAMP-dependent type II regulatory subunit 

alpha 
311.0446898 -0.458155081 0.0166052 

ENSG00000116711 PLA2G4A 5321 phospholipase A2 group IVA 336.2022255 -0.582605068 0.0171755 

ENSG00000171105 INSR 3643 insulin receptor 1909.416792 -0.295867756 0.0173053 

ENSG00000164318 EGFLAM 133584 EGF like, fibronectin type III and laminin G domains 229.0134131 -0.469580197 0.0176762 

ENSG00000132554 RGS22 26166 regulator of G-protein signaling 22 4.574242623 -0.636004928 0.0192316 

ENSG00000184613 NELL2 4753 neural EGFL like 2 196.5001129 -0.572676436 0.0232947 

ENSG00000142892 PIGK 10026 phosphatidylinositol glycan anchor biosynthesis class K 781.7799581 -0.208806421 0.0234037 

ENSG00000141639 MAPK4 5596 mitogen-activated protein kinase 4 208.7727368 -0.569541107 0.0325864 

ENSG00000107643 MAPK8 5599 mitogen-activated protein kinase 8 324.7787941 -0.300393202 0.033222 

ENSG00000100078 PLA2G3 50487 phospholipase A2 group III 6.341731975 -0.631494614 0.0378273 

ENSG00000198759 EGFL6 25975 EGF like domain multiple 6 182.8515839 -0.479870257 0.0381221 



 
 
 
 
 

 

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ENSG00000107175 CREB3 10488 cAMP responsive element binding protein 3 1217.85437 -0.203621813 0.0407954 

ENSG00000145725 PPIP5K2 23262 diphosphoinositol pentakisphosphate kinase 2 756.5355326 -0.222844677 0.0425919 

ENSG00000154217 PITPNC1 26207 phosphatidylinositol transfer protein, cytoplasmic 1 348.007567 -0.291090967 0.0466811 

ENSG00000158786 PLA2G2F 64600 phospholipase A2 group IIF 1.839617956 -0.585514584 0.0480319 

ENSG00000166997 CNPY4 245812 canopy FGF signaling regulator 4 378.1533882 -0.251460656 0.0527973 

ENSG00000109339 MAPK10 5602 mitogen-activated protein kinase 10 137.1476556 -0.515298729 0.0555319 

ENSG00000138698 RAP1GDS1 5910 Rap1 GTPase-GDP dissociation stimulator 1 1021.906809 -0.195978894 0.0566303 

ENSG00000138798 EGF 1950 epidermal growth factor 142.104671 -0.531883076 0.0619204 

ENSG00000070193 FGF10 2255 fibroblast growth factor 10 3.462625673 -0.562797728 0.0638813 

ENSG00000113070 HBEGF 1839 heparin binding EGF like growth factor 277.4721214 -0.313566424 0.0768535 

ENSG00000065361 ERBB3 2065 erb-b2 receptor tyrosine kinase 3 3949.329412 -0.056599349 0.7418933 

 

Table 2. Biological pathways significantly upregulated and downregulated among AA compared to EA TNBC patients in TCGA dataset. 

Upregulated KEGG pathways p-value 

hsa03010 Ribosome  0.005533158 

hsa04672 Intestinal immune network for IgA production  0.006164042 

hsa04640 Hematopoietic cell lineage  0.028001619 

hsa04330 Notch signaling pathway  0.053768917 

Downregulated KEGG pathways 

hsa00600 Sphingolipid metabolism  0.01816452 

hsa00512 Mucin type O-Glycan biosynthesis  0.02316771 

hsa04510 Focal adhesion  0.02628723 

hsa00982 Drug metabolism - cytochrome P450  0.02880007 

hsa04520 Adherens junction  0.03365944 

hsa00500 Starch and sucrose metabolism  0.03901639 

hsa00983 Drug metabolism - other enzymes  0.04328416 

hsa04512 ECM-receptor interaction  0.04359347 

 



 
 
 
 
 

 

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Table 3. Gene ontologies significantly upregulated and downregulated among AA compared to EA TNBC patients in TCGA dataset. 

Upregulated  p value 

GO:0048534 hematopoietic or lymphoid organ development  0.000429373 

GO:0002521 leukocyte differentiation  0.000467453 

GO:0030097 hemopoiesis  0.000595111 

GO:0002252 immune effector process  0.000606896 

GO:0030098 lymphocyte differentiation  0.000668007 

GO:0002520 immune system development  0.000752091 

GO:0001816 cytokine production  0.001262715 

GO:0001817 regulation of cytokine production  0.001304632 

GO:0019080 viral genome expression  0.002009214 

GO:0019083 viral transcription  0.002009214 

GO:0060337 type I interferon-mediated signaling pathway  0.002260808 

GO:0071357 cellular response to type I interferon  0.002260808 

GO:0001818 negative regulation of cytokine production  0.002341488 

GO:0034340 response to type I interferon  0.002360857 

GO:0045087 innate immune response  0.002660009 

GO:0050776 regulation of immune response  0.003381564 

GO:0006415 translational termination  0.003994976 

GO:0019058 viral infectious cycle  0.004289754 

GO:0045321 leukocyte activation  0.005368308 

GO:0046649 lymphocyte activation  0.006021313 

GO:0002443 leukocyte mediated immunity  0.006737807 

GO:0043241 protein complex disassembly  0.006915896 

GO:0045619 regulation of lymphocyte differentiation  0.00711599 

GO:0043624 cellular protein complex disassembly  0.009113965 

GO:0032609 interferon-gamma production  0.009394054 



 
 
 
 
 

 

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GO:0030217 T cell differentiation  0.009403061 

GO:0071356 cellular response to tumor necrosis factor  0.009678773 

GO:0009615 response to virus  0.009744867 

GO:0051250 negative regulation of lymphocyte activation  0.009818976 

GO:0050778 positive regulation of immune response  0.009903295 

GO:2000106 regulation of leukocyte apoptotic process  0.010825095 

GO:0071887 leukocyte apoptotic process  0.011246013 

GO:0042089 cytokine biosynthetic process  0.011366604 

GO:0002695 negative regulation of leukocyte activation  0.011891576 

GO:0002684 positive regulation of immune system process  0.012690724 

GO:0050866 negative regulation of cell activation  0.012821361 

GO:0034113 heterotypic cell-cell adhesion  0.012850245 

GO:0006414 translational elongation  0.013939004 

GO:0002253 activation of immune response  0.014337616 

GO:0051607 defense response to virus  0.01448594 

GO:0034341 response to interferon-gamma  0.014747388 

GO:0032984 macromolecular complex disassembly  0.01485217 

GO:0032649 regulation of interferon-gamma production  0.015069142 

GO:0051707 response to other organism  0.017081494 

GO:0002263 cell activation involved in immune response  0.017140132 

GO:0002366 leukocyte activation involved in immune response  0.017140132 

GO:0045580 regulation of T cell differentiation  0.017360252 

GO:0000184 nuclear-transcribed mRNA catabolic process, nonsense-mediated decay  0.017466425 

GO:0071346 cellular response to interferon-gamma  0.017582795 

GO:0021903 rostrocaudal neural tube patterning  0.01828075 

GO:0071706 tumor necrosis factor superfamily cytokine production  0.018894657 

GO:0008544 epidermis development  0.019826968 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e11                                              Cancer Health Disparities 

RESEARCH 

GO:0050701 interleukin-1 secretion  0.01990773 

GO:0032088 negative regulation of NF-kappaB transcription factor activity  0.020009424 

GO:0032640 tumor necrosis factor production  0.02003891 

GO:0032680 regulation of tumor necrosis factor production  0.02003891 

GO:0002460 adaptive immune response based on somatic recombination of immune receptors built from 

immunoglobulin superfamily domains  0.020144927 

GO:0042113 B cell activation  0.020274634 

GO:0035587 purinergic receptor signaling pathway  0.021137639 

GO:0042035 regulation of cytokine biosynthetic process  0.021288775 

GO:0034470 ncRNA processing  0.021369145 

GO:0002444 myeloid leukocyte mediated immunity  0.021915664 

GO:0009607 response to biotic stimulus  0.02334661 

GO:0051249 regulation of lymphocyte activation  0.02428066 

GO:0042107 cytokine metabolic process  0.025345542 

GO:0030917 midbrain-hindbrain boundary development  0.025352336 

GO:0060333 interferon-gamma-mediated signaling pathway  0.025576575 

GO:0006941 striated muscle contraction  0.026309493 

GO:0003009 skeletal muscle contraction  0.026410898 

GO:0006959 humoral immune response  0.027278157 

GO:0071345 cellular response to cytokine stimulus  0.027348302 

GO:0032715 negative regulation of interleukin-6 production  0.028648897 

GO:0031348 negative regulation of defense response  0.028778621 

GO:0002703 regulation of leukocyte mediated immunity  0.028922763 

GO:0043299 leukocyte degranulation  0.029801087 

GO:0043173 nucleotide salvage  0.029893126 

GO:0006613 cotranslational protein targeting to membrane  0.030068796 

GO:0006614 SRP-dependent cotranslational protein targeting to membrane  0.030068796 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e12                                              Cancer Health Disparities 

RESEARCH 

GO:0071219 cellular response to molecule of bacterial origin  0.030789397 

GO:0002250 adaptive immune response  0.030841904 

GO:0097028 dendritic cell differentiation  0.031317678 

GO:0002697 regulation of immune effector process  0.031523821 

GO:0071222 cellular response to lipopolysaccharide  0.031667044 

GO:0007606 sensory perception of chemical stimulus  0.032245552 

GO:0050865 regulation of cell activation  0.032311054 

GO:0051930 regulation of sensory perception of pain  0.034463637 

GO:0051931 regulation of sensory perception  0.034463637 

GO:0045047 protein targeting to ER  0.034644877 

GO:0072599 establishment of protein localization to endoplasmic reticulum  0.034644877 

GO:0043433 negative regulation of sequence-specific DNA binding transcription factor activity  0.034934546 

GO:0002764 immune response-regulating signaling pathway  0.035070643 

GO:0050848 regulation of calcium-mediated signaling  0.035464555 

GO:0051216 cartilage development  0.035996334 

GO:0070586 cell-cell adhesion involved in gastrulation  0.036025281 

GO:0010470 regulation of gastrulation  0.03613861 

GO:0006402 mRNA catabolic process  0.036140008 

GO:0042074 cell migration involved in gastrulation  0.036712677 

GO:0002757 immune response-activating signal transduction  0.036719431 

GO:0050704 regulation of interleukin-1 secretion  0.037451445 

GO:0070972 protein localization to endoplasmic reticulum  0.037618557 

GO:0000956 nuclear-transcribed mRNA catabolic process  0.037782421 

GO:0002694 regulation of leukocyte activation  0.03779717 

GO:0061383 trabecula morphogenesis  0.038343425 

GO:0032612 interleukin-1 production  0.038674489 

GO:0035809 regulation of urine volume  0.038920075 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e13                                              Cancer Health Disparities 

RESEARCH 

GO:0002449 lymphocyte mediated immunity  0.039034069 

GO:0002683 negative regulation of immune system process  0.039201076 

GO:0030183 B cell differentiation  0.039462908 

GO:0001783 B cell apoptotic process  0.040421151 

GO:0042249 establishment of planar polarity of embryonic epithelium  0.040903568 

GO:0006400 tRNA modification  0.040910207 

GO:0070228 regulation of lymphocyte apoptotic process  0.04166174 

GO:0006399 tRNA metabolic process  0.041688021 

GO:0032729 positive regulation of interferon-gamma production  0.041968537 

GO:0050909 sensory perception of taste  0.042391791 

GO:0033209 tumor necrosis factor-mediated signaling pathway  0.043478576 

GO:0031334 positive regulation of protein complex assembly  0.044230876 

GO:0050868 negative regulation of T cell activation  0.044579293 

GO:0042110 T cell activation  0.045651071 

GO:0071347 cellular response to interleukin-1  0.045684339 

GO:0003416 endochondral bone growth  0.046119963 

GO:0060026 convergent extension  0.046776142 

GO:0023021 termination of signal transduction  0.046852554 

GO:0038032 termination of G-protein coupled receptor signaling pathway  0.047117236 

GO:0010657 muscle cell apoptotic process  0.047468866 

GO:0006401 RNA catabolic process  0.047552326 

GO:0042254 ribosome biogenesis  0.047629412 

GO:0022904 respiratory electron transport chain  0.048240328 

GO:0043094 cellular metabolic compound salvage  0.048452721 

GO:0006612 protein targeting to membrane  0.048517893 

GO:0042401 cellular biogenic amine biosynthetic process  0.048738698 

GO:0051495 positive regulation of cytoskeleton organization  0.048955127 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e14                                              Cancer Health Disparities 

RESEARCH 

GO:0002285 lymphocyte activation involved in immune response  0.049073929 

GO:0050864 regulation of B cell activation  0.049545191 

Downregulated   

GO:0043687 post-translational protein modification  0.000493768 

GO:0007156 homophilic cell adhesion  0.000891909 

GO:0070085 glycosylation  0.001919505 

GO:0006486 protein glycosylation  0.002075176 

GO:0043413 macromolecule glycosylation  0.002075176 

GO:0006665 sphingolipid metabolic process  0.002745447 

GO:0007270 neuron-neuron synaptic transmission  0.002807343 

GO:0051966 regulation of synaptic transmission, glutamatergic  0.003328871 

GO:0006687 glycosphingolipid metabolic process  0.003952335 

GO:0009101 glycoprotein biosynthetic process  0.004520639 

GO:0035249 synaptic transmission, glutamatergic  0.004653915 

GO:0007158 neuron cell-cell adhesion  0.004656323 

GO:0006643 membrane lipid metabolic process  0.005041899 

GO:0018196 peptidyl-asparagine modification  0.005612873 

GO:0018279 protein N-linked glycosylation via asparagine  0.005612873 

GO:0006487 protein N-linked glycosylation  0.005970635 

GO:0031645 negative regulation of neurological system process  0.006532083 

GO:0051968 positive regulation of synaptic transmission, glutamatergic  0.006542085 

GO:0009100 glycoprotein metabolic process  0.007071823 

GO:0016266 O-glycan processing  0.00822715 

GO:0007595 lactation  0.008438853 

GO:0007610 behavior  0.009407186 

GO:0007420 brain development  0.009694387 

GO:0050808 synapse organization  0.010243263 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e15                                              Cancer Health Disparities 

RESEARCH 

GO:0036148 phosphatidylglycerol acyl-chain remodeling  0.010257756 

GO:0042391 regulation of membrane potential  0.010342608 

GO:0006805 xenobiotic metabolic process  0.010598743 

GO:0071466 cellular response to xenobiotic stimulus  0.011270151 

GO:0030879 mammary gland development  0.011361444 

GO:0001508 regulation of action potential  0.011440865 

GO:0051970 negative regulation of transmission of nerve impulse  0.011501433 

GO:0048667 cell morphogenesis involved in neuron differentiation  0.012566558 

GO:0051932 synaptic transmission, GABAergic  0.012763237 

GO:0044723 single-organism carbohydrate metabolic process  0.012821492 

GO:0006664 glycolipid metabolic process  0.01400463 

GO:0050805 negative regulation of synaptic transmission  0.015334527 

GO:0007626 locomotory behavior  0.015430043 

GO:0009410 response to xenobiotic stimulus  0.015493698 

GO:0048193 Golgi vesicle transport  0.015772984 

GO:0048609 multicellular organismal reproductive process  0.015789414 

GO:0031047 gene silencing by RNA  0.016586363 

GO:0048812 neuron projection morphogenesis  0.016607133 

GO:0044708 single-organism behavior  0.018681325 

GO:0030900 forebrain development  0.020384121 

GO:0060271 cilium morphogenesis  0.020950746 

GO:0007173 epidermal growth factor receptor signaling pathway  0.021617695 

GO:0038127 ERBB signaling pathway  0.021617695 

GO:0008610 lipid biosynthetic process  0.022799925 

GO:0034329 cell junction assembly  0.022808921 

GO:0006470 protein dephosphorylation  0.023740415 

GO:0044262 cellular carbohydrate metabolic process  0.023838856 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e16                                              Cancer Health Disparities 

RESEARCH 

GO:0006654 phosphatidic acid biosynthetic process  0.024003206 

GO:0046473 phosphatidic acid metabolic process  0.024003206 

GO:0007631 feeding behavior  0.024218683 

GO:0007215 glutamate receptor signaling pathway  0.025341571 

GO:0007169 transmembrane receptor protein tyrosine kinase signaling pathway  0.025778202 

GO:0048732 gland development  0.026371412 

GO:0007409 axonogenesis  0.028034327 

GO:0030902 hindbrain development  0.028207007 

GO:0050905 neuromuscular process  0.028868056 

GO:0035265 organ growth  0.029870897 

GO:0032228 regulation of synaptic transmission, GABAergic  0.030572469 

GO:0021533 cell differentiation in hindbrain  0.030591893 

GO:0006112 energy reserve metabolic process  0.030725257 

GO:0010165 response to X-ray  0.031143637 

GO:0043044 ATP-dependent chromatin remodeling  0.031721256 

GO:0021549 cerebellum development  0.031913742 

GO:0032787 monocarboxylic acid metabolic process  0.032020432 

GO:0006892 post-Golgi vesicle-mediated transport  0.032510822 

GO:0052646 alditol phosphate metabolic process  0.03300986 

GO:0007157 heterophilic cell-cell adhesion  0.033442154 

GO:0014812 muscle cell migration  0.033592578 

GO:0006302 double-strand break repair  0.03376071 

GO:0045216 cell-cell junction organization  0.034066295 

GO:0006457 protein folding  0.034232181 

GO:0006282 regulation of DNA repair  0.03433869 

GO:0001570 vasculogenesis  0.034798997 

GO:0046620 regulation of organ growth  0.035079657 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e17                                              Cancer Health Disparities 

RESEARCH 

GO:0016337 cell-cell adhesion  0.035209378 

GO:0060479 lung cell differentiation  0.035239581 

GO:0032941 secretion by tissue  0.036128621 

GO:0000271 polysaccharide biosynthetic process  0.036630136 

GO:0022037 metencephalon development  0.036660654 

GO:0032870 cellular response to hormone stimulus  0.036922137 

GO:0002064 epithelial cell development  0.03723918 

GO:0030728 ovulation  0.038142428 

GO:0007411 axon guidance  0.038665746 

GO:0036151 phosphatidylcholine acyl-chain remodeling  0.03867378 

GO:0060487 lung epithelial cell differentiation  0.039970158 

GO:0034330 cell junction organization  0.040342663 

GO:1901888 regulation of cell junction assembly  0.041298928 

GO:0036149 phosphatidylinositol acyl-chain remodeling  0.041658096 

GO:0030949 positive regulation of vascular endothelial growth factor receptor signaling pathway  0.041896616 

GO:0007274 neuromuscular synaptic transmission  0.042046032 

GO:0071599 otic vesicle development  0.042937278 

GO:0006493 protein O-linked glycosylation  0.043137696 

GO:0001937 negative regulation of endothelial cell proliferation  0.043387826 

GO:0048545 response to steroid hormone stimulus  0.043541689 

GO:0005977 glycogen metabolic process  0.046227756 

GO:0034508 centromere complex assembly  0.047156367 

GO:0061418 regulation of transcription from RNA polymerase II promoter in response to hypoxia  0.04743104 

GO:0044257 cellular protein catabolic process  0.047832199 

GO:0051963 regulation of synapse assembly  0.048565239 

GO:0060740 prostate gland epithelium morphogenesis  0.048742367 

 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e18                                              Cancer Health Disparities 

RESEARCH 

predicted significantly worse RFS in TNBCs 

(HR=1.75; p=0.027); NRARP expression was 

associated with a trend towards poorer RFS in 

TNBCs (HR=1.53; p=0.17); and NOTCH2NL 

expression was associated with poorer RFS in 

TNBC (HR=1.001; p=0.01). The prognostic value of 

NOTCH2NL expression level was upheld after 

adjusting for age, stage and race (AA vs. EA); in 

fact, the expression of NOTCH2NL was the only 

significant predictor of RFS in multivariable 

analyses (p=0.003). Interestingly, DNER ranked in 

the top 1% of genes most highly dysregulated in 

the Basal-like Immuno-Suppressed (BLIS) TNBC 

molecular subtype. AAs tend to harbor basal-like 

subtypes of TNBC and the BLIS molecular subtype 

is 1 of 2 basal-like TNBC molecular subtypes 

recently identified (Burstein et al., 2015) that has 

the worst disease-free survival and BC-specific 

survival among the 4 prognostically-distinct TNBC 

molecular subtypes. 

Our group’s findings collectively suggest that the 

Notch signaling pathway may be upregulated 

among TNBC patients of African compared to 

European ancestry, and upregulated Notch 

signaling may serve as a poor prognosis 

biomarker in TNBC. Targeting Notch signaling may 

therefore be a promising personalized therapeutic 

strategy for TNBC patients of African descent, 

including AAs. 

Taking it up a notch: Establishing Notch 

signaling as therapeutic target of 

interest for triple negative breast cancer 

patients of African descent 

TNBC remains the primary culprit for 

disproportionately lower survival rates of AA 

compared to EA BC patients. Thus, identifying 

novel therapeutic targets and/or risk-predictive 

biomarkers for TNBC patients of African ancestry 

will be critical to alleviating the racially disparate 

burden in BC. Emerging evidence suggest that 

inherent differences exist in tumor biology 

between AA and EA TNBC patients. Getz et al. 

discovered that dysregulated genes in the Wnt/-

catenin pathway were significantly more enriched 

among TNBC patient samples of African 

compared to European ancestry, which may 

rationalize the aggressive TNBC phenotypes 

observed among patients of African descent 

(Dietze et al., 2015). However, more work is 

warranted to examine inherent tumor biological 

differences between racially-distinct TNBC patients. 

Our group sought to investigate differences in 

tumor biology between AA and EA TNBC patients 

through analyzing the publicly-available gene 

expression dataset, TCGA. Interestingly, Notch 

signaling emerged as a pathway significantly 

upregulated among AA compared to EA TNBC 

patients. We observed upregulation of the Notch 

signaling pathway among AA compared to EA 

TNBC samples as well as significant upregulation 

of genes encoding key Notch signaling proteins in 

this pathway such as NRARP, DNER, JAG1, JAG2, 

HES4, and MMP9. NRARP is a downstream 

effector in the Notch pathway and its 

overexpression has been associated with breast 

carcinogenesis and BC cell proliferation (Imaoka et 

al., 2014). JAG1 and JAG2 encode two major 

ligands in the canonical Notch signaling pathway 

(Wang et al., 2010). The HES family of transcription 

factors represent a major family of downstream 

target genes in the Notch signaling pathway (Acar 

et al., 2016). MMP9 is implicated in the breakdown 

of the extracellular matrix to facilitate invasion and 

metastasis in TNBC (Mehner et al., 2014). 

Furthermore, we observed significant 

downregulation of biological pathways and gene 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e19                                              Cancer Health Disparities 

RESEARCH 

ontologies reflecting loss of cell-cell contacts, focal 

adhesion, and ECM-receptor interaction as well as 

reduced epithelial cell development, reduced 

endothelial cell proliferation, and DNA damage 

response among AA compared to EA patients. 

Notch signaling regulates proliferation, apoptosis, 

angiogenesis, hypoxia, EMT, and metastasis (Acar 

et al., 2016). Thus, significant downregulation of 

these processes among AA compared to EA 

samples may reflect increased proliferation, 

angiogenesis, metastasis, and reduced cell death 

among AA patients. We also observed significant 

upregulation of gene ontologies reflecting T cell-

mediated immune response, which is upregulated 

by Notch signaling (Uzhachenko and Shanker, 

2016). Hence, we have uncovered Notch signaling 

as a key biological pathway that may contribute to 

the racially disparate burden in TNBC and serve as 

a potential therapeutic target for AA patients. 

Our findings thus encourage a closer look at this 

biological pathway as a potential racial disparity 

biomarker and therapeutic target for TNBC. 

However, validation of our results in additional 

gene expression datasets as well as at the protein 

expression level among patient samples of known 

TNBC molecular subtypes will be critical to 

achieving this aim. Furthermore, investigating the 

effects of manipulating Notch signaling among 

racially-distinct TNBC patient-derived cell lines or 

in vivo will be pertinent to effectively targeting this 

pathway in patients of African ancestry. Notch 

signaling inhibitors such as GSI and aspartyl 

protease inhibitors are currently under clinical 

development and in clinical trials as investigational 

targeted therapies for TNBC patients (Jamdade et 

al., 2015). GSI inhibitors are associated with side 

effects including fatigue, myelosuppression, fever, 

rash, chills, anorexia, and hypophosphatemia. 

Improving the toxicity profile and efficacies of GSIs, 

and development of more effective inhibitors of 

Notch signaling could be crucial for improving 

outcomes among AA TNBC patients. 

Acknowledgements 

We have no individuals to acknowledge.  

Conflict of interest 
The authors declare that no competing or conflict of 

interests exists. The funders had no role in study 

design, writing of the manuscript, or decision to 

publish. 

Authors’ contributions 
Nikita Wright was involved in the data collection 

and drafting of the article. Shristi Bhattarai was 

involved in the drafting and editing of the article. 

Bikram Sahoo and Mishal Imaan Syed were 

involved in data collection/analysis. Dr. 

Padmashree Rida was involved in 

conceptualization and editing of the article. Dr. 

Ritu Aneja was involved in conceptualization, 

editing, and oversight of the study.  

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subtypes and preclinical models for selection of targeted 

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