The Role of Genomics and Transcriptomics in Characterizing and Predicting Patient Response to Treatment in Triple Negative Breast Cancer (TNBC)

Simple Summary

Triple negative breast cancer (TNBC) is the most aggressive and is associated with adverse outcomes. In locally advanced and metastatic TNBC, there are various therapeutic options. However, the outcomes, more specifically in the metastatic settings, have been suboptimal. TNBC is an active area of interest to identify the most effective and tolerable treatment strategies in TNBC. This review seeks to summarize current research that utilizes identification of genes and levels of gene expression to therapeutic decisions in the use of different therapies to treat TNBC.

Abstract

Breast cancer is a complex disease that is one of the leading causes of cancer-related mortality in women worldwide. Of the subtypes of breast cancer, the most aggressive subtype is triple negative breast cancer (TNBC) due to its lack of targets that could be leveraged for treatment in other subtypes. Current treatment options for both local and metastatic TNBC include radiation therapy, chemotherapy, surgery, targeted therapy, and immunotherapy, which has been gaining popularity in recent years. The role of targeted therapy in TNBC is somewhat limited due to the paucity of therapeutic personalized targets, and, due to the heterogeneity of the disease, the effectiveness of these different modalities varies from patient to patient. These unique elements are the foundation of personalized medicine where genomics and transcriptomics play a critical role in increasing granularity in patients’ disease and treatment. The purpose of these molecular tools is to identify biomarkers that could be used to further characterize each patient’s unique disease features and to predict how certain treatment modalities will affect patient survival and prognosis. The interplay between these biomarkers and molecular pathways involved in treatment response with disease progression and aggressiveness is a complex phenomenon. In this review, we describe the current state of the literature in regard to biomarkers that show promise in the clinical setting to predict response to treatments such as chemotherapy, radiation, and surgery in locally advanced and metastatic TNBC.

1. Introduction

Breast cancer is the most common type of cancer in females with an estimated over 310,000 new cases and more than 42,000 deaths in 2024 per the Surveillance, Epidemiology, and End Results (SEER) database [1]. The incidence of breast cancer has been on a steady upward trend, rising around 1% every year from 2012 to 2020 [2,3]. Breast cancer is divided into three major subtypes depending on the specific molecular characteristics displayed by the cells: hormone-receptor positive (HR+), human epidermal growth factor receptor 2 positive (HER2+), and triple negative breast cancer (TNBC). Of these classes, TNBC is the most aggressive and difficult to treat largely due to the lack of molecular targets that could be leveraged for treatment. This class is further subdivided into the basal-like immunosuppressed (BLIS which includes BL1 and BL2), mesenchymal/mesenchymal stem-like (MES/MSL), luminal androgen receptor (LAR), and immunomodulatory (IM) subtypes [4,5]. Figure 1 illustrates the division and subdivisions of breast cancer. Each subtype of TNBC has distinctly different gene mutation, protein expression, and cell population profiles that also has a role in treatment decision. The subtypes are defined by the commonalities not just in genome but also tumor microenvironment (TME), yet the genomic instability and inherent tumor heterogeneity of the disease can make defining the differences within these subtypes more vague than absolute [5]. Depending on the subtype of TNBC, certain treatment modalities are more effective than others. As an example, platinum-based chemotherapy and PARP inhibitors seem to be more effective at treating the BLIS subtype versus radiation therapy in combination with immunotherapy for the IM subtype. Yet, regardless of disease stage, taxane and anthracycline chemotherapeutic agents remain part of the treatment regimen both in the neoadjuvant and adjuvant setting [6]. Several studies have reported the utility of modalities such as immunotherapy (pembrolizumab) in the adjuvant and neoadjuvant setting, intraoperative radiation therapy, and many more [7,8]. The side effects of chemotherapy are well-known with hair loss, gastrointestinal upset, myalgias, and fatigue, and surgery and radiation therapy have their own well-known effects like lymphedema and radiation dermatitis [9]. Immunotherapy has shown promise in TNBC and comes with its own set of adverse effects such as fatigue, diarrhea, rash, colitis, pruritus, etc., to name the few immune mediated adverse reactions [10]. Thus, investigation into how to optimize patient outcomes while minimizing disease and treatment burden are of the utmost importance and fit into the larger schema of personalized medicine. Personalized medicine, as it is defined, is the prescription of optimal treatment based on the molecular, genetic, and other inherent characteristics specific to the patient [11]. To this end, multiple advances utilizing molecular assays such as genomics and transcriptomics, referred to here as omics processes, have begun elucidating biomarkers that could be used to guide treatment in multiple different areas within medicine. In this review, we discuss the current studies that identify biomarkers through genomics and transcriptomics that could predict the response to specific treatment modalities such as chemotherapy, immunotherapy, and targeted therapies.

2. Molecular Subtypes of TNBC

Different TNBC subtypes have been elucidated by omics processes. In a study performed by Li et al., 71 samples of chemotherapy-naive TNBC underwent genomic processing to compare mutational profiles among each of the samples. They reported that within TNBC, mutations in TP53 and PIK3CA are most prevalent and that mutations in AKT1 and BRCA1 are independent prognostic factors. The study also sought to further characterize the subtypes of TNBC by finding common mutations within each subtype. They found that the LAR subtype had high levels of PIK3CA mutations, the basal-like subtype had many copy number variants (CNVs) suggesting genomic instability, and that the IM subtype had multiple BRCA2 and MLL3 mutations. They also found that the MES subtype could have a possible sensitivity to the RTK-pathway–pointing to a potential use for RTK inhibitors in this subtype.

Table 1. Most Common Mutations in Each TNBC Subtype and Potential Targets for Therapy [12,13].

TNBC Subtype	Most Common Mutations	Potential Targets
Basal-Like Immunosuppressed (BLIS) (BL1 and BL2)	ABL1, AKT1, ALK, ARAF, ATM, BRAF, BRCA1, BRD4, CCNE1, CDKN2A, CDKN2B, CTNNB1, DDR2, EGFR, EPHA5, ERBB2, ESR1, EZH2, FBXW7, FGFR2, FGFR3, IDH1, IGF1R, JAK2, MCL1, MLL, NF1, PDGFRA, PIK3CA *, PTCH1, PTEN, RB1 *, STK11, TP53 *, TSC1, XPO1	Inhibitors: SRC, novel ephrin, JAK2, IGFR1-R, IDH, FGFR, FAK, EZH2, endocrine, CDK4/6, CDK2, bromodomain, AKT, WNT, RAF, PI3K **, PARP **, MTOR **, MEK **, HDAC **, cell cycle **, CDK **, ALK Targeted therapy: dasatinib, lapatinib ** Monoclonal antibodies: trastuzumab ** Others: TKIs, selective inhibitors of nuclear export, p53 specific gene therapy **, immunotherapy **, anti-tubulin chemotherapy, anti-RTK therapy **, anti-HER2 therapy, anti-EGFR TKIs **
Mesenchymal and Mesenchymal Stem-Like (MES/MSL)	PIK3CA *, RB1 *, ROS1, SMARCA4, TP53 *, XPO1	Inhibitors: anti-EGFR therapy, FGFR, AKT, PI3K **, PARP **, Notch, MTOR **, MEK **, HDAC **, cell cycle **, CDK ** Targeted therapy: crizotinib, lapatinib ** Monoclonal antibodies: trastuzumab ** Others: selective inhibitors of nuclear export, p53 specific gene therapy **, immunotherapy **, anti-tubulin chemotherapy, anti-RTK therapy **, anti-EGFR TKIs **
Luminal Androgen Receptor (LAR)	AKT1, ALK, ATM, BAP1, BRAF, BRCA1, BRCA2, CTNNB1, EGFR, ERBB2, ERBB3, MCL1, NF1, NOTCH1, NOTCH2, PIK3CA *, PIK3R1, PTEN, RB1 *, SMO, TP53 *, TSC2, XPO1	Inhibitors: WNT, RAF, PI3K **, PARP **, Notch, MTOR **, MEK **, HER3, hedgehog, HDAC **, cell cycle **, CDK ** Targeted therapy: lapatinib ** Monoclonal antibodies: trastuzumab ** Others: TKIs, selective inhibitors of nuclear export, p53 specific gene therapy **, immunotherapy **, anti-tubulin chemotherapy, anti-RTK therapy **, anti-HER2 therapy, anti-EGFR TKIs **
Immunomodulatory (IM)	AKT3, ALK, ATM, BRCA1, BRCA2, BRD4, CDK6, CDKN2A, CTNNB1, EGFR, ERBB2, ERBB4, FGFR3, FLT3, IDH1, KRAS, MCL1, MLL, NF1, NOTCH1, NOTCH2, PDGFRA, PIK3CA *, PTEN, RB1 *, RET, TP53 *, TSC1	Inhibitors: RET, FLT3, anti-EGFR therapy, IDH, FGFR, CDK4/6, bromodomain, AKT, WNT, RAF, PI3K **, PARP **, Notch, MTOR **, MEK **, HDAC **, cell cycle **, CDK **, ALK Targeted therapy: lapatinib ** Monoclonal antibodies: trastuzumab ** Others: p53 specific gene therapy **, immunotherapy **, anti-RTK therapy **, anti-HER2 therapy, anti-EGFR TKIs **

* Mutations common across all TNBC subtypes. ** Therapies common across all TNBC subtypes.

summarizes the most common mutations in each TNBC subtype as well as the predicted likely effective treatment therapies discussed in the paper [12,13]. Looking at transcriptional differences, one study investigated how transcription is altered in TNBC when looking at the most common mutations in breast cancer at large. This study found various mutational profile differences between TNBC and non-TNBC, such as 70% of the most frequently mutated genes in breast cancer having decreased expression in TNBC when compared to non-TNBC or BRCA2 expression being high in TNBC. In discussion, these differences were hypothesized to be due to loss of tumor-suppressor genes, such as SETDB1, or augmentation of oncogenes, like GATA3 and PTEN, which contributed to the aggressiveness and tendency towards metastasis in TNBC [14]. While these transcriptomic differences alone do not guide clinical standards, it could flesh out the possible use of these biomarkers. Other smaller studies have looked at HER2 expression and age as other possible sources of granularity for TNBC [15,16]. Again, none of these have reached a use in clinical practice yet.

3. Genomics and Transcriptomics in TNBC

The process of identifying mutations within the genome and comparing them across different cell lines and samples through next-generation sequencing is genomics. Alongside genomics are the processes that seek to quantify mutational gene products to compare them to a wild-type control, the processes of transcriptomics. These processes are essential for understanding the underlying pathophysiology of any disease and have had a large impact on medicine as a result. Not only do these technologies allow the full characterization of disease processes but also innate host factors and the resulting uniqueness between how these two entities interact, which in turn is the basis for personalized medicine. As a result, genomics and transcriptomics are invaluable in the setting of genetically variable disease processes like cancer. Cancer genetics is difficult to elucidate because tumor heterogeneity makes each cancer cell almost completely genetically unique from its counterparts. However, being able to compare the genomes of these cells at a tumor-wide scale and seeing the patterns of expression that these genes induce can give us a glimpse into the underlying mechanisms that can then be used as possible targets for cancer treatment [17,18]. Genomics and transcriptomics have been involved in multiple recent and different cancer studies, including TNBC. A Phase 3 clinical trial investigated the incorporation of genomics into a precision medicine intervention utilizing patient-derived xenograft mice and organoids to guide treatment by a molecular tumor board and compared that to the standard-of-care treatment in patients with pancreatic ductal adenocarcinoma (PDAC). The main patient outcome measured was mdian overall survival when compared to standard-of-care treatment [19]. In cervical cancer, genomics and transcriptomics are only some of the methods used to try and find biomarkers that would predict sensitivity to immunotherapy along with metabolomics, epigenomics, radiomics, and more [20].

This is the primary utilization of these processes in cancer, to find particular markers at the molecular level that could inform the clinical response. At large and within breast cancer specifically, there have been multiple biomarkers that have been elucidated and subsequently utilized to predict response to various treatment regimens [21]. Due to the complexity of triple-negative breast cancer genetically and transcriptionally, as has been discussed prior, this makes these processes a pillar of investigation to further tailor therapy and improve patient outcomes. We specifically are observing the most recent advances that genomics and transcriptomics have contributed to identifying biomarkers that would be predictive and prognostic in various treatments for triple-negative breast cancer. Immune cells such as B- and T-cells are the cornerstone of response to immunotherapy in TNBC. More specifically, T-cell signatures, an 18-gene T-cell gene-expression profile, have been indicated to predict response to the immunotherapy [21,22]. The genomic signature of the STAT1/chemokine 12 and dendritic cells pathway is also reported to be associated with better response to the immunotherapy [23]. In the GeparNuevo Phase 2 trial, the GeparSixto immune gene-expression signature (GSIS), tumor mutational burden (TMB), and interferon signatures predicted response to durvalumab therapy [24]. A GSIS signature comprises 12 immune genes, including both immune hot and immune cold genes. These genes are both immune-suppressive genes such as PDCD1, coding for PD-1, CD274, coding for PD-L1, CTLA4, FOXP3, and IDO1, as well as immune-activating genes such as CCL5, CXCL9, CXCL13, CD80, CD21, CD8A, and IGKC [25]. The NeoTRIPaPDL1 trial also demonstrated the predictive value of a 27-gene-based score and B-cell memory signature that can predict the response to atezolizumab in combination with chemotherapy [26]. Despite the promise offered by these genomic biomarker-defining assays, their clinical utility is limited by the lack of clinical validation, inter-assay variations, and the cost associated with these assays.

4. Chemotherapy and Antibody Drug Conjugates

4.1. Chemotherapy

The primary anti-cancer therapy for localized/locally advanced TNBC is traditional cytotoxic neoadjuvant and adjuvant chemotherapy. Currently, standards of treatment designate that patients with clinically node-positive and/or at minimum T1c disease should receive a anthracycline- and taxane-containing regimen in combination with immunotherapy [27]. Figure 2 demonstrates the various mechanisms of action of the therapeutic agents that will be studied in this review. These treatments, while effective, also have a myriad of secondary adverse effects as previously discussed. As such, being able to predict the effectiveness of these toxic agents can further help both patient and physician make informed decisions and tailor the treatment. One promising study observed the changes in the expression of the oncoprotein epithelial membrane protein 2 (EMP2) in aggressive TNBC after docetaxel treatment. EMP2 was used as a biomarker since genomic analysis had found that it was expressed in >75% of invasive breast tumors and over 95% of TNBC tumors showing some expression of the protein, despite TNBC’s tumor heterogeneity. They found that the patients that failed to achieve pathological complete response (pCR) were correlated with increased EMP2 mRNA levels. High expression of EMP2 occurred in almost 70% of patients and was correlated with poor overall survival (OS) and relapse-free survival (RFS). Whether this is because docetaxel induces EMP2 transcription and translation or because docetaxel selectively kills EMP2-poor cells has yet to be elucidated, and there are thoughts that it could be involved with the activated HIF-1a pathway [28]. Similarly, the NeoPACT study observed that the combination of pembrolizumab and carboplatin plus docetaxel demonstrated improved PCR (57%) compared to the current five-drug standard when treating Stage II and III TNBC. This Phase 2 clinical trial investigated that pembrolizumab could be used in combination with alkylating chemotherapy and spare the patients from an anthracycline-based agent to reduce potential adverse effects. The study looked at multiple biomarkers including stromal tumor-infiltrating lymphocytes (TILs), programmed cell death-ligand 1 (PD-L1), 44-gene DNA damage immune response (DDIR) signature, and 27-gene tumor immune microenvironment (TIM). The results showed that there were pCR rates exceeding 70% in subgroups that were PD-L1 positive (combined positive score [CPS] ≥ 10). These results were also seen in the setting of high percentage of sTILs or TIM-positivity, which ultimately supports the hypothesis that immunologically ‘hot’ tumors will be more responsive to immunotherapy [29].

A study performed in Shandong, China looked at genomic and transcriptomic analysis of TNBC samples pre- and post-NAC and described the changes they saw. The study used whole exome sequencing (WES) and RNA sequencing analyses to characterize these differences. The most frequently mutated genes were TP53, TTN, and MUC16 within the pre- and post-treatment cohorts. Some of the predictive changes they found included a certain CDKAL1 variant causing decreased BC cell sensitivity to docetaxel and ADGRA2 or ADRB3 gene amplification associated with poor NAC response and poor prognosis [30]. Another study elucidated TNBC chemoresistance by utilizing single-cell RNA-sequencing data of TNBC patients that were responsive and resistant to chemotherapy. What they were able to accomplish was the isolation of 20 genes that were predictive of achieving residual disease (RD) or pCR in a chemotherapy-naive patient. The study also looked into which transcription factors, such as super enhancers, could be involved in the upregulation of these genes and also further divided these results to be unique for certain subtypes of TNBC. Namely, the transcription factors that seem to be involved with the regulation of all 20 genes isolated by the team are SP1, TFAP2C, and TFAP2A. Additionally, the study looked at epigenomic changes that could be driving differential transcription of some of these genes [31]. There are several studies exploring how these data/omic processes could be utilized in clinical settings. Akshatha et al. proposed the window of opportunity trial in neoadjuvant settings, in which these biomarkers could be used. They observed the levels of Ki-67, TILs, FDG-PET/CT standardized uptake values (SUVs), and gene expression in a patient given one dose of paclitaxel to create a standardized score that could be used to predict if chemotherapy would be useful, risk stratify patients, and, ultimately, better informed consent. These particular biomarkers were used to create the predictive score that has validated its use in the clinical setting [32]. Another study observed the integration of a risk score that takes into account TNBC stage and particular biomarkers. The biomarkers used are based on two different gene signatures, the CIG module, which is a 10-gene panel, and the four-gene tumor cell proliferation signature. The entire evaluation utilized the novel 27-gene HER2DX score, which similarly uses gene panels with biomarkers that are predictive of pCR and risk in HER2+ breast cancer as a reference. The novel test, TNBC-DX, was found to be significantly associated with predicting pCR and long-term complications with its two scores. The utility of the score in the setting of chemotherapy is developing. However, it should be noted that the score is not validated for immunotherapy. When the data from the NeoPACT trial was used with the TNBC-DX score, it underestimated the pCR rates reported in the trial, emphasizing the need for modulation of the score to fit immunotherapy and possibly other treatment regimens where possible [33]. Still, there are other chemotherapeutic regimens that could be investigated further, for example in combination with monoclonal antibody delivery systems.

4.2. Antibody-Drug Conjugates

An antibody-drug conjugate combines the specificity of monoclonal antibodies as a delivery system for the cytotoxic chemotherapeutic agent to synergize therapy and attempt to reduce the adverse effects. Trastuzumab deruxtecan drug-antibody conjugate is approved for TNBC with HER2-low expression. HER2-low tumors are classified molecularly as having a score of 1+ on immunohistochemical (IHC) analysis or an IHC of 2+ with a negative result on in situ hybridization (ISH). A Phase 3 trial comparing patients with metastatic breast cancer, both hormone receptor-positive and negative, treated with trastuzumab deruxtecan compared to standard treatment demonstrated that median progression-free and overall survival significantly increased in both groups. Side effect profile was better in the trastuzumab deruxtecan group compared to traditional chemotherapy regimens. Despite its effectiveness, there are concerning side effects associated, such as interstitial lung disease [34,35]. Another study, the ASCENT trial, looked at the effectiveness of sacituzumab, an antitrophoblast cell-surface antigen 2 (Trop-2) antibody, conjugated with govitecan, a topoisomerase I inhibitor in relapsed, refractory metastatic TNBC. This trial also saw a significant increase in progression-free and overall survival with a decreased rate of adverse events, especially in patients with tumors that exhibited high Trop-2 expression. These antibody–drug conjugates are proving useful even in metastatic, refractory TNBC patients. The next step then would be to increase granularity and utilize biomarkers to assess the effectiveness at the level of the individual patient to see what type of patient would benefit more and what type of patient would benefit less from these types of treatments [36]. These drug conjugates are forging a new path for treatment of not just breast cancer, but many other cancers [37].

5. Immunotherapy

5.1. Pembrolizumab

PD-1/PD-L1 inhibitors such as pembrolizumab or atezolizumab are the immunotherapies utilized in triple negative breast cancer [38]. Pembrolizumab is a monoclonal antibody that binds to PD-1 acting similarly to PD-L1, a ligand on the surfaces of cancer to deactivate the T-cell response against cancer itself thereby remaining non-targeted by the immune system. By binding to PD-1, pembrolizumab prevents the endogenous and abundant PD-L1 from evading detection by the immune system and launching a host immune defense against cancer cells. Pembrolizumab has been found to be efficient in multiple cancers including metastatic melanoma and non-small cell lung cancer with only recently being approved to be used for TNBC adjuvantly in combination with chemotherapy as well as in metastatic settings [39]. Though, given that we know the molecular mechanism of action, it would seem likely that PD-1/PD-L1 levels would be an adequate biomarker for sensitivity to the drug. In metastatic settings, combined positive score (CPS) indicating the overall expression of PD-1 is associated with response to immunotherapy and is the requirement to choose immunotherapy in such patients. This relationship seems almost linear, as if the expression of PD-L1 increases, then the effectiveness of the addition of the drug increases as well. However, the question remains as to whether this is the only biomarker available [40]. We have already discussed the effectiveness of pembrolizumab in replacing/modulating the chemotherapy regimen above [34]. As such, this does pose the question of which other biomarkers could be predictive of sensitivity or resistance to immunotherapy, specifically pembrolizumab. A study in Belgium used tumor samples from 11 patients with a history of chemotherapy-resistant, metastatic TNBC that were subsequently treated with pembrolizumab. The patients were divided into excellent responders where patients lived > 24 months after treatment, non-responders with stable disease, and rapid progressors where patients died within 2 months. Records showed that within the excellent responders, two patients developed severe immune-related adverse effects. One patient developed bronchiolitis obliterans organizing pneumonia (BOOP), confirmed on lung biopsy, which was treated with systemic steroids. A second patient had developed aseptic lymphocytic meningitis, which led to interruptions to treatment. Interestingly, treatment with pembrolizumab was continued after the meningitis resolved and the patient had developed high-grade B-cell lymphoma as well as a partial response. Blood chemistries and radiological studies yielded no prognostic biomarkers. Genomic and transcriptomic analysis revealed only GARP overexpression as a possible biomarker for resistance. In the discussion, the authors noted that GARP is a surface molecule on T-regulatory cells whose overexpression may be contributing to immunosuppression, thus explaining the lack of effect of pembrolizumab in the poor responders. Additionally, they did also find that tumor-mutational burden was also a portent of poor prognosis. However, it is known that tumor heterogeneity and mutational burden contribute to a poor prognosis to patients with TNBC, so its utility as a general biomarker is not high [41]. Another study used biomarker expression to calculate a patient risk score. This study isolated a group of 2008 genes that had differential expression between the IM subtype and all other subtypes. From there, 33 genes were identified as survival-related, from which 12 immune-related genes were isolated using a LASSO Cox regression. These biomarkers specifically included both predictors of resistance and sensitivity to immunotherapy. Using this score on two groups, dubbed high- and low-risk, as well as a validation group, the score demonstrated clinical use in predicting patient response. Specifically, a set of four genes (TDO2, CHIT1, CARMIL2, and HLA-C) and the rest of the eight (ADIRF, C19orf33, CA8, AHNAK2, RHOV, OPLAH, THEM6, and NEBL) were associated with favorable and unfavorable prognosis at high expression, respectively. Of note, these 12 genes were frequently expressed in tumor cells, though some were more prevalent in specific populations such as tumor stem cells and macrophages.

Table 2. Summary of Differently Expressed Genes and Associated Prognosis in TNBC [42].

Biomarker	Prognostic Association	Justification
TDO2	Protective	Tryptophan metabolism role: catalysis of kynurenine. Kynurenine prevents detection of the tumor by the host immune system
CHIT1	Protective	Chitotriosidase, which is part of the glycosyl hydrolase family 18 (GH18), which in a previous study, increased levels were detected in patients with primary breast cancer
CARMIL2	Protective	A characteristic of CARMIL2 is impaired T-cell activation
HLA-C	Protective	Part of the major histocompatibility complex (MHC) family, this protein is the subject of many investigations not just in cancer but also in autoimmune disorders
ADIRF	Unfavorable	No justification given by authors
C19orf33	Unfavorable	Multiple studies have shown that mutations of C19orf116 are present in ovarian carcinoma and nonmuscle-invasive bladder cancer. Deficiency of C19orf116 has shown to be a poor prognostic indicator in prostate cancer. Abnormal expression of C19orf116 has been found in pancreatic and many other cancers.
CA8	Unfavorable	Excessive CA8 was found to enhance proliferation and migration of cells in renal cell carcinoma
AHNAK2	Unfavorable	Increased levels of AHNAK2 have been found in thyroid carcinoma stimulating the NF-κB, advancing progression
RHOV	Unfavorable	Within the JNK/c-Jun pathway, increased RHOV enhances growth and spread of lung adenocarcinoma
OPLAH	Unfavorable	OPLAH has been found to be a prognostic factor in gastric cancer and squamous cell carcinoma in previous studies
THEM6	Unfavorable	Part of the thioesterase superfamily and an indicator of resistance to ADT in prostate cancer
NEBL	Unfavorable	Previous studies have found that NEBL plays a crucial role in ovarian cancer advancement

reviews the genes, their association, and justification as to why they might be differentially expressed. Larger clinical trials would be needed in order to validate this score, but preliminary data shows that it could very well be predictive of pCR and tumor progression [42].

5.2. Atezolizumab

Atezolizumab binds to PD-L1 on cancer cells to interfere with the PD-1/PD-L1 binding. Atezolizumab in combination with the chemotherapeutic drug nab paclitaxel demonstrated some effectiveness of immunotherapy in TNBC. Dugo et al. utilized a 27-gene signature using RT-qPCR, named DetermaIO, to predict response to neoadjuvant chemoimmunotherapy, including atezolizumab, in TNBC. This study also used the DetermaIO assay on the neoadjuvant immunotherapy (pembrolizumab) arm of the I-SPY2 trial. The study compared patients treated with carboplatin and nab-paclitaxel alone or in combination with atezolizumab. Similar to the ImPredict score, the investigators used the assay to assign patients into one of two designations: immuno-oncology (IO) positive or negative based on the 27-gene panel of interest. Results showed that in the patients treated with atezolizumab in combination with chemotherapy, being designated IO+ is positively associated with PD-L1 status and stromal TILs. Further statistical analysis revealed that the DetermaIO score was not predictive of pCR independently from PD-L1 and sTILs in the combined therapy arm, there was a significant positive association with pCR in the combined therapy arm, and that there was a marginally significant negative association with pCR in the chemotherapy arm. All in all, this demonstrates that the DetermaIO positive designation is useful only in the setting of immune checkpoint modulation therapy. The study also reported that while the IO+ designation was associated with higher pCR rates in the combined therapy arm, the proportion of IO+ scores between TNBC subtypes varied significantly with BL1 and M having the highest and lowest number of IO+ samples, respectively. How the specific genes used in the DetermaIO assay correlate with the genetic markers that separate TNBC subtypes is of interest within this study and could mean that this test is more useful in some subtypes versus others [43].

5.3. Molecular Predictors of Resistance to Prembrolizumab

Another study that looked at creating a model using machine learning algorithms predicting a response to pembrolizumab immunotherapy is named ImPredict (IP) and included HER2 patients. Additionally, the study evaluated the relationship between the IP score and the tumor microenvironment of TNBC to observe any utility in this setting. As laid out in the study, over 7000 genes were discovered to be related to pCR by four separate machine learning algorithms (Boruta, LASSO, SVM-RFE, and eXtreme Gradient Boosting) from the patients in the pembrolizumab arm of the I-SPY2 clinical trial. Utilizing the expression profiles from those patients, the algorithms then used median expression values of five molecular signatures as a cutoff value to separate cohorts into high and low groups. Once the groups were separated, they were used to guide characterization of each patient’s disease, response to treatment, and clinical outcomes. The study found that a high IP score predicts a positive immunotherapy response, abundant immune infiltration, and an inflammatory phenotype for patients with TNBC. A low IP score was related to immunosuppression and poor prognosis. Various genes were found that seemed to predict response to immunotherapy positively (TSPAN33, DAPP1, GZMB, GBP1, IL12RB2, MCOLN2, CCL13, LAMP3, HLA-E) and negatively (GPRC5C, RADIL, RGS22, CCDC74B, KIF3A, FAM179B, NQO1, DHRS2, UGDH, CA12, VEZF1). The authors in this study also reported that in order to be validated for clinical use, the study should be repeated with a larger sample size [44].

6. Targeted Therapy

6.1. PARP Inhibitors

Poly (ADP-ribose) polymerase (PARP) enzymes play an important role in DNA damage repair, which is only amplified in the setting of cancers with defective homologous recombination repair pathways such as BRCA-deficient cancers. BRCA mutation is prevalent in around 20% of TNBC, making them particularly useful in combination with other agents in these settings. Multiple studies, both ongoing and completed, have been investigating its use as part of neoadjuvant chemotherapy and in combination with immunotherapy, such as the OlympiA, I-SPY2, DORA, and MEDIOLA trials [45,46]. One study looked at the genomic alterations in breast cancer brain metastases (BCBM) compared to local BC or non-CNS metastases that could be targetable by immunotherapy or PARP inhibitors in a cohort of 822 patients with confirmed BCBMs. The commonly mutated genes in the BCBM cohort were TP53, PIK3A, and MYC. The FGF/FGFR pathway was not significantly altered in TNBC. In general, the study found that a significant proportion of patients with BCBM could be eligible for targeted therapy and/or immunotherapy either as a single agent or in conjunction with each other using this genomic data. The study found a high prevalence of homologous recombination repair mutations such as alterations in BRCA1, RAD51B, BARD1, and PALB2, which makes PARP inhibition ideal for this cohort. The cohort also had amplification of PD-L1/2, making immunotherapy also effective [47]. Another study looked at hypermethylation at the BRCA promoter TNBC in xenograft models as a marker for PARP inhibitors. The purpose of this study was to continue to support the use of PARP inhibitors in TNBC with a deficiency homologous recombination repair pathway, whether that may be due to a BRCA mutation or BRCA promoter epigenetic silencing. In this study, tissue from 29 TNBC patients were compared to 24 TNBC xenograft models. Two patients were found to have a pleural metastasis or primary tumor that had a hypermethylated BRCA promoter to compare to a xenografted model. When compared to the xenograft model, the patient with the pleural metastasis had similar CNVs, phenotypes, and responses to PARP inhibition. The investigators suggest that BRCA 1/2 promoter hypermethylation could be a biomarker for PARP inhibition along with BRCA 1/2 mutation [48]. A different trial looked at combining immunotherapy with PARP inhibition in response to the host’s natural adaptive response. Named the Adaptive Molecular Therapy of Evolving Cancers (AMTEC) trial, it observed biomarker changes through multi-omic analysis of patients undergoing olaparib monotherapy followed by olaparib/durvalumab combination therapy to observe biomarkers. Clinical outcomes varied from stable disease, partial response, and disease progression with the median PFS for the entire cohort being 5.52 months with a median OS of 12.88 months. No significant difference in OS or PFS was seen between patients with stable disease or partial response both around 9 and 17 months for PFS and OS, respectively. However, as expected, their outcomes were significantly better than those with disease progression with 2 months for PFS and 3 months for OS. Concerning the multi-omic analysis of tumors, while not all samples were able to be processed, data showed statistically significant correlations with participant outcomes such as TP53 mutations, TMB, high expression of PD-L1 or Ki-67, and androgen receptor positivity associated with disease progression. Clinical biomarker changes of significance included an increase in PD-L1 expression as well as PI3K/AKT pathway mutations, thus suggesting a possible role in adding immune checkpoint inhibitor therapy or other targeted therapies to PARP inhibitor monotherapy. Additionally, as previously discussed, damage to the homologous recombination repair pathway seems to be a possible biomarker to the effectiveness of PARP inhibition [49]. Voulgarelis et al. sought to take all genomic information available for a patient’s disease and fit it into a mathematical model that can predict not only sensitivity but also resistance to PARP inhibitors. That way, this model could be utilized with the utmost granularity and specificity to a patient at all points in time, not just at predetermined checkpoints. The study specifically looked at the viability of this model on mouse TNBC xenograft models. The study found 36 possible genes that were found to be related to olaparib resistance, seven of which are associated with the homologous recombination repair pathway. Of note, utilizing Kaplan–Meier curve comparison and different doses of olaparib, WEE1 stood out as a strong indicator for olaparib resistance. On further investigation, combining olaparib with adavosertib, a WEE1 kinase inhibitor, showed increased sensitivity to Olaparib, which could lead to further studies investigating their relationship [50].

6.2. PIK3/AKT/mTOR Inhibitors

The PIK3/AKT/mTOR molecular pathway is active in TNBC as well. The phosphatidylinositol 3-kinase (PI3K) enzyme phosphorylates the phosphatidylinositol 3 ring, which is involved in a number of biologically significant processes, and their mutations can cause unchecked proliferation of cancer cells. Current and previous trials looking into the effectiveness of these inhibitors include the PAKT, FAIRLINE, LOTUS, and IPATunity130 studies [45,46]. Another study looked at the biomarker results and clinical outcomes of patients with metastatic TNBC treated with the triple therapy of ipatasertib (a PIK3 inhibitor), atezolizumab, and a taxane. The total cohort had 317 patients, and among them median PFS was 5.4–7.4 months with a median duration of response of 5.6–11.1 months and median OS of 15.7–28.3 months. For patients with a PFS > 10 months, the biomarkers isolated such as NF1, CCND3, and PIK3CA mutations involved an increase in immune pathway activity. A PFS < 5 months showed mutations in CDKN2A/CDKN2B/MTAP. This data was an aggregate of data collected from three clinical trials, including study CO40151, IPATunity 130 cohort C, and IPATunity 170 [51]. While there are more studies that look at biomarkers within the PIK3 pathway and its inhibitors, they observe BC broadly and not specifically TNBC. For example, a study in Xi’an, China observed PIK3CA mutations as a prognostic marker and had marginal TNBC data, thus emphasizing a niche that could be filled with future investigations [52].

6.3. Other Targeted Therapies

Another study looked at palbociclib, a CDK4/6 inhibitor. This TKI is typically used in HR+ and HER2− breast cancer along with ribociclib and abemaciclib. It is thought that these targeted inhibitors would not be effective in TNBC tumors due to their Rb deletions. However, only 35% of TNBC tumors are Rb-deficient, making the agent class ripe for investigating effectiveness and biomarkers to predict sensitivity and/or resistance within the other 65%. The study uniquely uses an in vivo genome-wide CRISPR loss-of-function screen in TNBC to identify the genes that could indicate sensitivity to palbociclib. Of 47 genes found through this method, the most prominent candidate identified was TGFB3, which seemed to potentiate palbociclib activity when overexpressed. Indeed, a lower dose of palbociclib induced equivalent or better tumor killing in tumors overexpressing TGFB3 through CRISPR genomic alteration. Synergistic analysis confirms this augmentation of effect, and it is hypothesized by the authors that TGFβ3 induction of the INK4 family of CDK inhibitors could be a possible molecular explanation for this potentiation [53]. Another class of targeted therapies approved as tumor agonistic agents and in breast cancers including TNBC includes the neurotrophic tyrosine receptor kinase (NTRK) inhibitors entrectinib and larotrectinib. One case study saw improvement of TNBC metastatic to the lungs and supraclavicular lymph nodes. The patient had failed neoadjuvant chemotherapy, surgery, post-surgical radiation, and sacituzumab govitecan. It was found that the patients exhibited ETV6–NTRK3 and CRTC3–NTRK3 fusions based on whole-exome sequencing and RNA-seq, which is an indication to start larotrectinib. The patient had a dramatic improvement of metastases as well as a decrease of her CA 15-3. She had to stop due to fractures unrelated to her therapy [54]. Another case study reveals a patient with a similar clinical progression: primary TNBC that was treated with NACT, surgery, and radiation with recurrence that was refractory to multiple medications and had a marked response to larotrectinib. It is important to note that this patient also had the ETV6–NTRK3 fusion, which indicates its importance as a biomarker that indicates use of larotrectinib [55]. Thus far, there have been no other targeted therapies approved for TNBC with recent studies investigating patient biomarkers indicating use such as selpercatinib or repotrectinib. The investigation of these new agents in the treatment of TNBC could yield exciting new discoveries that could affect not just targeted therapies, but other treatment modalities discussed.

7. Discussion

The landscape of treatment for TNBC is evolving rapidly with targeted therapy, immunotherapy, and antibody-drug conjugates that seek to decrease the adverse effects associated with traditional chemotherapy while not sacrificing effectiveness. Given all of these different treatment modalities, many studies and tools as outlined within this review are currently being developed ranging from identifying specific genomic and transcriptomic biomarkers for treatment, prognosis, and treatment effectiveness. In the age of personalized medicine, these studies are crucial not just for the clinician to guide therapy, but for the patient to be able to make the most informed decision on their care as possible.

Utilizing genomics and transcriptomics within realms of personalized medicine is a rapidly evolving field. It affords physicians the opportunity to precisely guide therapy to the most efficacious agents and minimize side effect profiles. However, due to the growing interest and rapid pace of literature dissemination in this field, there is only so much information that could have been captured by the time this review is published and to stay relevant to this topic. Multiple clinical trials around the globe are being conducted to advance the field, as is demonstrated in

Table 3. International Actively Recruiting Clinical Trials Investigating Biomarkers of TNBC Prognosis and Treatment Efficacy Through Genomics and/or Transcriptomics. [Source: clinicaltrials.gov].

NCT#	Actively Recruiting Studies	Intervention	Methods and Biomarkers	Early, Locally Advanced, or Metastatic	Location(s)
NCT05916755	Predictive Biomarkers of Response to Checkpoint Inhibitors in Triple Negative Breast Cancer: A Multiomics Platform (PORTRAIT)	NACT with and without ICI	WGS and RNA-seq, ctDNA, TCR-β, PD-L1, and TILs (B- and T-lymphocytes)	Locally advanced	Spain
NCT06355037	Dasatinib Combined with Quercetin to Reverse Chemo Resistance in Triple Negative Breast Cancer	Dasatinib and quercetin with NACT	Age-related secretory factors, IHC of senescent fibroblasts, and number and area of neutrophil extracellular traps	Metastatic	China
NCT06709651	Microbiome Immunotherapy Neoadjuvant Assessment (MINA)	Neoadjuvant chemo and immunotherapy	Microbiota present in breast tissue	Locally advanced	Ireland
NCT06182306	Prospective Evaluation of AI R&D Tool for Patient Stratification—MoA Evaluation in Triple Negative Breast Cancer (PEAR-MET)	PearBio, a novel AI tool that uses biomarker data, recommendations v. standard of care-guided medical oncologists	Various including RNAseq, MSI testing, TMB, and more	Metastatic	United Kingdom
NCT06418126	Prediction of Radiotherapy Efficacy in Patients with Triple-negative Breast Cancer (TNBC-RT2023)	NACT/ACT with radiotherapy	IL-1β, Il-5, and IL-6	Locally advanced	France
NCT05552001	A2-ESO-1 TCR-Engineered T Cells for Relapsed/Refractory Advanced or Metastatic NY-ESO-1 Overexpression Positive Triple Negative Breast Cancer	anti-HLA-A2/NY-ESO-1 TCR-transduced autologous T lymphocytes	PD-1, NY-ESO-1-specific T cell, T regulatory cells	Locally advanced and metastatic	United states of America
NCT05192798	Albumin-Bound Paclitaxel Combined with Antiangiogenic Agents in First-line Treatment of Relapsed or Metastatic TNBC	Nab paclitaxel, nab paclitaxel and apatinib mesylate, nab paclitaxel and bevacizumab	Serum VEGF-A	Metastatic	China
NCT04877821	The Efficacy and Safety of Sintilimab Plus Anlotinib Combined with Chemotherapy as Neoadjuvant Therapy in TNBC (NeoSACT)	Sintilimab and anlotinib with NACT or NACT followed by surgery	Immune biomarkers (PDL1, CD8, TILs, HRD)	Locally advanced	China
NCT05949021	OCTANE: Adjuvant Liposomal Doxorubicin and Carboplatin for Early-stage Triple-negative Breast Cancer	Liposomal doxorubicin and carboplatin	ctDNA	Early	United States of America
NCT03740893	PHOENIX DDR/Anti-PD-L1 Trial: A Pre-surgical Window of Opportunity and Post-surgical Adjuvant Biomarker Study of DNA Damage Response Inhibition and/or Anti-PD-L1 Immunotherapy in Patients with Neoadjuvant Chemotherapy Resistant Residual Triple Negative Breast Cancer (PHOENIX)	AZD6738, olaparib, and durvalumab followed by surgery	DDR biomarkers (53BP1, RAD51, RPA, RPA32, pRPA, BRCA1/2, PARP, immune checkpoint ligands and receptors	Locally advanced	United Kingdom
NCT04947189	Seviteronel in Combination with Chemotherapy in Androgen-receptor Positive Metastatic Triple-negative Breast Cancer (4CAST)	Seviteronel and dexamethasone (SEVI-D) with and without NACT	RNAseq, androgen receptor, ZEB1, ctDNA analysis	Metastatic	Australia
NCT05174832	Induction of Cisplatin/Nab-paclitaxel/Pembrolizumab Followed by Olaparib/Pembrolizumab Maintenance in mTNBC Patients	Cisplatin/Nab-paclitaxel/Pembrolizumab followed by olaparib/pembrolizumab	Unspecified	Metastatic	China
NCT05556200	A Phase II Trial of Camrelizumab in Combination with Apatinib for Neoadjuvant Treatment of Early-stage TNBC With a High Proportion of TILs	Camrelizumab with apatinib	Tumor and stromal PD-L1, and TILs (B and T lymphocytes)	Early	China
NCT05914961	Immunotherapy-related CRP Kinetics in Early and Metastatic Triple-negative Breast Cancer	Immunotherapy	CRP	Early, locally advanced, or metastatic	Germany
NCT06246786	Breast/Cyclosporin A/TNBC (Triple Negative Breast Cancer)	Cyclosporin A prior to surgery	g-H2Ax, apoptosis markers	Early and locally advanced	United States of America
NCT05831553	TIP in Patients Affected by Metastatic TNBC (TIP)	Atezolizumab plus Nab-paclitaxel	Tissue Immune Profile (TILs, PD-L1, CD73)	Metastatic	Italy
NCT04986852	Olinvacimab with Pembrolizumab in Patients with mTNBC	Olinvacimab with pembrolizumab	Tumor exome sequencing, MDSC, other unspecified biomarkers	Metastatic	Australia
NCT06162351	A Study to Evaluate the Efficacy and Toxicities of PLX038, in Patients with Locally Advanced or Metastatic Triple-negative Breast Cancer (TOPOLOGY)	PLX038 with prior NACT exposure	Replication stress-related biomarkers (SLFN11, RB1)	Locally advanced or metastatic	France
NCT03213041	Pembrolizumab and Carboplatin in Treating Patients with Circulating Tumor Cells Positive Metastatic Breast Cancer	Carboplatin with pembrolizumab	PD-L1, CAMLs, ctDNA, circulating tumor cells	Metastatic	United States of America
NCT06134375	A Study of Tetrathiomolybdate (TM) Plus Capecitabine	Capecitabine and pembrolizumab with and without tetrathiomolybdate	VEGFR2+ EPCs, LOXL-2, ctDNA, and other unspecified	Locally advanced or metastatic	United States of America
NCT05422794	Testing the Addition of Anti-Cancer Drug, ZEN003694 (ZEN-3694) and PD-1 Inhibitor (Pembrolizumab), to Standard Chemotherapy (Nab-Paclitaxel) Treatment in Patients with Advanced Triple-Negative Breast Cancer	ZEN003694 with and without pembrolizumab and nab-paclitaxel	Pancytokeratin, CD8, PD-1, PD-L1, and CD31	Locally advanced	United States of America
NCT06240195	Biomarkers of Efficacy and Tolerability of Sacituzumab-Govitecan in the Treatment of Patients with Triple-negative Breast Cancer in the Metastatic Phase: Prospective Multicenter Real-world Study (BIO-PROSA)	Sacituzumab govecitan	Unspecified	Metastatic	Italy
NCT05082259	ASTEROID: A Trial of ASTX660 in Combination with Pembrolizumab (ASTEROID)	ASTX660 with pembrolizumab	PD-L1, cytokines, immune transcriptome changes	Metastatic	United Kingdom
NCT04360941	PAveMenT: Palbociclib and Avelumab in Metastatic AR+ Triple Negative Breast Cancer (PAveMenT)	Palbociclib and avelumab	ctDNA, RB1, PIK3CA, PTEN, T-cell and T-cell receptor clonality	Locally advanced or metastatic	United Kingdom
NCT06649331	Platform Study of ADC Rechallenge in ADC-treated Metastatic Breast Cancer	SHR-A1811, SHR-A1921, SHR-A2009, SHR-A2102	HER2, TROP2, HER3, Nectin4	Locally advanced or metastatic	China
NCT06230185	ctDNA Based MRD Testing for NAC Monitoring in TNBC (B-STRONGER-I)	NACT	ctDNA	Early to locally advanced	United States of America
NCT01042379	I-SPY TRIAL: Neoadjuvant and Personalized Adaptive Novel Agents to Treat Breast Cancer (I-SPY)	Various treatment regimens in combination with NACT	Unspecified	Locally advanced or metastatic	United States of America
NCT03606967	Testing the Addition of an Individualized Vaccine to Durvalumab and Tremelimumab and Chemotherapy in Patients with Metastatic Triple Negative Breast Cancer	Nab-paclitaxel, durvalumab, tremelimumab with and without synthetic long peptide vaccine	TIL percentage, PD-L1 on TILs and tumor, genomic and transcriptomic evaluation	Metastatic	United States of America
NCT05037825	The Gut Microbiome and Immune Checkpoint Inhibitor Therapy in Solid Tumors (PARADIGM)	Immunotherapy, diet, prebiotics and probiotics	Microbiome and metabolite analysis	Early, locally advanced, and metastatic	United States of America
NCT02276443	Molecular Testing and Imaging in Improving Response in Patients with Stage I-III Triple-Negative Breast Cancer Receiving Chemotherapy MDACC Breast Moonshot Initiative	Personalized regimen versus standard NACT	Genomic signature	Early to locally advanced	United States of America
NCT06261918	Transcriptional and Epimetabolic Profile of Breast Carcinoma with Luminal or HER2+ or Locally Advanced Triple-negative Histotype in Patients With/Without Previous Clinical History of Metabolic Syndrome (PROMETA)	NACT	Unspecified	Locally advanced	Italy
NCT04348747	Dendritic Cell Vaccines Against Her2/Her3 and Pembrolizumab for the Treatment of Brain Metastasis from Triple Negative Breast Cancer or HER2+ Breast Cancer	Dendritic cell vaccines against HER2/HER3 with pembrolizumab	CTLs, PD-L1, cytokine expression	Metastatic	United States of America
NCT02945579	Multicenter Trial for Eliminating Breast Cancer Surgery or Radiotherapy in Exceptional Responders to Neoadjuvant Systemic Therapy	Radiation therapy after systemic NACT with and without surgery	CTC and cDNA	Early	United States of America
NCT02993068	Stand up to Cancer: MAGENTA (Making Genetic Testing Accessible)	Online module versus phone call genetics evaluation and education	Various genetic panels	Early, locally advanced, or metastatic	United States of America
NCT05180006	Impact of Neoadjuvant Immunotherapy in Early-Stage Breast Cancer Before Standard Therapy (BIS-Program)	Atezolizumab with and without ipatasertib/bevacizumab/trastuzumab/pertuzumab	GzmB+ CD8+ T lymphocytes, GzmB/CD8, CD8/FoxP3, CD8/CD68, PD-L1, MHC-I, RNA-Seq	Early	France
NCT05253053	To Evaluate Efficacy and Safety of TT-00420 (Tinengotinib) as Monotherapy and Combination Therapy in Patients with Advanced Solid Tumors	T-00420 with and without atezolizumab or nab-paclitaxel	FGFR2, PD-L1, dMMR, MSI, TNBC subtype, TMB	Locally advanced or metastatic	China
NCT05955105	A Study of ILB2109 and Toripalimab in Patients with Advanced Solid Malignancies	ILB2109 and toripalimab	Adenosine Signature gene panel, TMB, MSI status, PD-L1, CD68, A2aR, CD8	Locally advanced or metastatic	China
NCT05958199	A Study of NPX267 for Subjects with Solid Tumors Known to Express HHLA2/B7-H7	NPX267	Unspecified	Unspecified	United States of America
NCT05076760	MEM-288 Oncolytic Virus Alone and in Combination with Nivolumab in Solid Tumors Including Non-Small Cell Lung Cancer	MEM-288 with and without nivolumab	CD40L, type 1 IFN, and other biomarkers of anti-tumor activity, immunogenicity and immune activation	Locally advanced or metastatic	United States of America
NCT05565417	Study of the Monoclonal Antibody IMT-009 in Patients with Advanced Solid Tumors or Lymphomas	IMT-009 post treatment with NACT, sacituzumab govitecan, a PD-L1 inhibitor, or PARP inhibitor	Unspecified	Unspecified	United States of America
NCT03475953	A Phase I/II Study of Regorafenib Plus Avelumab in Solid Tumors (REGOMUNE)	Regorafenib with and without avelumab	Cytokines, lymphocytes, and other angiogenic and immunologic biomarkers	Locally advanced and metastatic	France
NCT05107674	A Study of NX-1607 in Adults with Advanced Malignancies	NX-1607 with and without paclitaxel after standard NACT	Inflammatory cytokines, tumor-infiltrating immune cells	Locally advanced and metastatic	United States of America and United Kingdom

NACT—neoadjuvant chemotherapy; ICI—immune checkpoint Inhibitor; WGS—whole genome sequencing; ctDNA—circulating tumor DNA; TILs—tumor-infiltrating lymphocytes; IHC—immunohistochemistry; MSI—microsatellite instability; DDR—DNA damage repair; CRP—C-reactive protein; TMB—tumor mutational burden.

, and there are many clinical trials underway to incorporate personalized therapy and improve patient care outcomes in TNBC. This abundance of data further justifies the necessity of this review article to succinctly and efficiently summarize the data thus far to visualize where to go from here.

Multiple actively recruiting and closed clinical trials around the world are seeking to find these biomarkers, which can range from genomic and transcriptomic analysis, as discussed here, to tumor-infiltrating lymphocytes (TILs), cytokine levels, and circulating tumor DNA (ctDNA). The MINA study uniquely seeks to correlate the diversity of the microbiome and their metabolites within TNBC tumors as biomarkers that indicate sensitivity or resistance [56]. Many clinical trials are also Phase 1 or 2 that are looking at the safety and effectiveness of new chemotherapeutic, antibody-drug conjugate, immunotherapy, and targeted drug therapies for TNBC. Table 3 displays a collection of the actively recruiting clinical trials that are looking for biomarkers in TNBC that could be used to predict response to treatment or recurrence of disease. The future of these investigations into these biomarkers could reveal the next clinical tool that could help physicians and patients alike in making the best decision for their care.

8. Conclusions

This review looks at the state of personalized medicine in TNBC and the role of current studies that use genomics and transcriptomics to elucidate biomarkers that could be used to guide patient care and improve patient outcomes in patients with TNBC. It is important to reiterate that in TNBC settings, there are limited molecular targets (that too in metastatic settings) to leverage for effective therapy, which is why there is a huge interest in finding new therapeutic targets and potential biomarkers. As we continue to further investigate these biomarkers, we believe the face of TNBC treatment paradigm would be vastly different in the coming era.