Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Francoise Derouane
 -- 3341 2022-10-27 13:33:49 |
2 Reference format revised.
Lindsay Dong
 -10 word(s) 3331 2022-10-28 03:10:30 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Derouane, F.;  Marcke, C.V.;  Berlière, M.;  Gerday, A.;  Fellah, L.;  Leconte, I.;  Bockstal, M.R.V.;  Galant, C.;  Corbet, C.;  Duhoux, F.P. Predictive Biomarkers of Neoadjuvant Chemotherapy in Breast Cancer. Encyclopedia. Available online: https://encyclopedia.pub/entry/31650 (accessed on 17 November 2024).
Derouane F,  Marcke CV,  Berlière M,  Gerday A,  Fellah L,  Leconte I, et al. Predictive Biomarkers of Neoadjuvant Chemotherapy in Breast Cancer. Encyclopedia. Available at: https://encyclopedia.pub/entry/31650. Accessed November 17, 2024.
Derouane, Françoise, Cédric Van Marcke, Martine Berlière, Amandine Gerday, Latifa Fellah, Isabelle Leconte, Mieke R. Van Bockstal, Christine Galant, Cyril Corbet, Francois P. Duhoux. "Predictive Biomarkers of Neoadjuvant Chemotherapy in Breast Cancer" Encyclopedia, https://encyclopedia.pub/entry/31650 (accessed November 17, 2024).
Derouane, F.,  Marcke, C.V.,  Berlière, M.,  Gerday, A.,  Fellah, L.,  Leconte, I.,  Bockstal, M.R.V.,  Galant, C.,  Corbet, C., & Duhoux, F.P. (2022, October 27). Predictive Biomarkers of Neoadjuvant Chemotherapy in Breast Cancer. In Encyclopedia. https://encyclopedia.pub/entry/31650
Derouane, Françoise, et al. "Predictive Biomarkers of Neoadjuvant Chemotherapy in Breast Cancer." Encyclopedia. Web. 27 October, 2022.
Predictive Biomarkers of Neoadjuvant Chemotherapy in Breast Cancer
Edit
This entry is adapted from the peer-reviewed paper 10.3390/cancers14163876

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. 

breast cancer neoadjuvant chemotherapy biomarkers predictive factors

1. Introduction

Breast cancer remains one of the most prevalent cancers in women, with 2,261,419 new cases and 684,996 deaths worldwide in 2020, despite major improvements in terms of prevention, diagnosis and treatment [1]. In current clinical practice, breast cancers are classified in five subtypes based on the expression of hormone receptors (estrogen and/or progesterone receptors (ER and/or PgR)), the overexpression of epidermal growth factor receptor 2 (HER2/Neu) and the percentage of tumor cells expressing Ki-67. These subtypes comprise luminal A, luminal B, HR+ HER2-positive, HR- HER2-positive and triple-negative breast cancers (TNBC) [2]. The majority of patients are diagnosed with early-stage disease, while 3–10% of patients are diagnosed with de novo metastatic breast cancer [3]. Although most early breast cancers are curable with the current treatment options, up to 20% of patients will relapse within 10 years. At present, treatment decisions in both the early and the metastatic settings depend on the immunohistopathological classification, with adaptation of the chemotherapy regimen based on the surrogate molecular subtype (e.g., the addition of anti-HER2 monoclonal antibodies in the HER2+ subtype, the addition of carboplatin in the early triple negative subtype, adjuvant hormonotherapy in the HR+ subtypes, etc.) [2][4]. The implementation of neoadjuvant chemotherapy (NAC) as the current standard of care for patients with high-risk early-stage or locally advanced breast cancer is one of the major changes in the evolving breast cancer landscape [2]. The high-risk breast cancers concerned by this change in the treatment paradigm are mainly TNBC and HER2-positive tumors but also include hormone-receptor-positive (HR+) cancers larger than 2 cm and/or with axillary lymph node involvement. While providing the same overall survival (OS) and disease-free survival (DFS) as adjuvant chemotherapy, NAC has several advantages, such as: allowing for more conservative surgeries by reducing the tumor size and down-staging the lymph node status, assessing the sensitivity of the tumor to chemotherapeutic agents and eradicating micro-metastases but also adding the possibility of escalating treatment with adjuvant drugs in case of residual disease, a feature of worse prognosis [5][6][7]. Despite this, NAC also has disadvantages, including: drug-related side effects, the postponement of surgery in some cases (e.g., the postponement of NAC due to side effects resulting in a longer delay before surgery), difficulties in healing after surgery and disease progression that may occur during treatment [8] (Figure 1).
/media/item_content/202210/635b25462a97dcancers-14-03876-g001.png
Figure 1. Overview of current indications, regimens, advantages and disadvantages of NAC.

2. Breast Cancer Subtypes and Intratumoral Heterogeneity

2.1. Molecular Classification and Intrinsic Subtypes

Over the past 20 years, several molecular classifications have been determined by genomic and transcriptomic clustering in order to better understand the intratumoral heterogeneity in breast cancer. However, these intrinsic subtypes have not yet supplanted the surrogate molecular subtype (determined by immunohistochemistry) in clinical practice for therapeutic decision making. In 2000, Perou et al. analyzed 65 surgical pieces of breast cancers from 42 individuals and identified 4 intrinsic subtypes by gene expression analysis: luminal-like, basal-like, normal-like and HER2-enriched [9]. Later, the PAM50 classification distinguished the luminal A and luminal B categories within the luminal-like group. Studies have shown that these subgroups differ in both their clinical characteristics and their response to treatment. Prognosis also differs between the intrinsic subtypes, regardless of the immunohistopathological subtype [10][11][12][13][14]. Within the immunohistochemical triple negative subtype, Prat et Perou later highlighted two intrinsic subtypes: the claudin-low and the basal-like subtypes, the claudin-low being associated with poorer prognosis [15][16]. The heterogeneity of the triple negative disease is nevertheless more complex, and in 2011, Lehmann et al. described seven subtypes (TNBCtype): basal-like 1 (BL1), basal-like (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL), luminal androgen receptor (LAR) and unstable (UNS) [17][18]. In a retrospective study, the response to NAC containing anthracyclines and cyclophosphamide was different among subgroups, with a better pCR rate in the BL1 subgroup (52%) in comparison to the BL2 and LAR subgroups (0% and 10%) [17]. After refining this TNBCtype classification by considering the transcript of normal stroma and immune cells, four subtypes have been largely studied (TNBCtype-4): BL1, BL2, M and LAR [17] (Figure 2).
/media/item_content/202210/635b256422749cancers-14-03876-g002.png
Figure 2. Breast cancer subtypes and intrinsic classification. (A) Comparison between immunohistochemical classification and molecular classification [9][10][16][17]. (B) Distribution of immunohistochemical subtypes in the molecular classification, as defined by Prat and Perou [16]. HR+: hormone receptor-positive; TNBC: triple negative breast cancer; BL1: basal-like 1; BL2: basal-like 2; M: mesenchymal; LAR: luminal androgen receptor.

2.2. Intratumoral Heterogeneity in Breast Cancers

Intratumoral heterogeneity (ITH) in breast cancer refers to the diversity found within tumors, existing at several levels [19][20]. ITH includes multiple concepts and principles such as clonal heterogeneity and cell state heterogeneity [19][21][22]. Clonal heterogeneity concerns the phenotypic variability between cells within a tumor depending on spatial and temporal factors, leading to different clones of cells with different sensitivities to treatment. Many spatial factors can influence the clones of cells: hypoxia variance from the center of the tumor to the periphery, angiogenesis, pH variation inside the tumor and interactions with cells from the tumor microenvironment (TME). Depending on their exposure to hypoxia, or other factors, cells will acquire different metabolisms and different sensitivities to treatment. Temporal factors are more related to the sequential exposure of the tumor to different lines of treatment that could lead to a selection of resistant clones within the tumors [19][21]. Cell state heterogeneity is the fact that people can observe cells at different states in a single tumor, with some cells exhibiting stem cell properties (CSC) and others with differentiated properties or progenitor’s properties, altogether forming tumors with different levels of mechanisms of resistance to treatment [19][22]. The heterogeneity found inside each breast cancer has been described as a strong mechanism of resistance to chemotherapy, with the evolution of cell-to-cell interactions but also genetic modifications under treatment pressure [23]. In breast cancer, several molecular subtypes can also be found within a single tumor. Indeed, molecular subtypes are not a static state, and interconversion between subtypes can occur and lead to tumor progression, metastasis or resistance to chemotherapy [22][24].

3. Resistance to Neoadjuvant Chemotherapy

3.1. Drug-Associated Resistance

It is well known that the metabolism of most chemotherapy drugs involves cytochrome P450 enzymes (CYP) [25]. Polymorphisms in the CYP1B1 gene appear to correlate with resistance to taxanes, while CYP2B6 is involved in the metabolism of cyclophosphamide and doxorubicin [26][27]. In one study, CYP2C9*2 heterozygote breast cancer patients had a decrease in the efficacy in neoadjuvant chemotherapy compared to patients with wild-type alleles [28]. Many other CYP enzymes were reported to be associated with the efficacy of neoadjuvant chemotherapy in breast cancer, and this could explain the clinical resistance to certain drugs [25].
Chemotherapy drug concentration is also regulated by the efflux of drugs out of the cells via transmembrane proteins. These proteins include the ATP-binding cassette (ABC) transporter family, in which the P-glycoprotein (P-gp) has already been associated with drug resistance in breast cancer [29]. In TNBC, several related genes are more expressed, such as ABCC1, ABCC11 or ABCG2, and those could be involved in chemoresistance to commonly used drugs [30].

3.2. Cancer Cell-Associated Resistance

CSCs are a population of cells with self-renewal properties present in breast cancer tumors, and they have been found in residual tumors after NAC, indicating that these cells are resistant to conventional treatment [31][32]. Moreover, CSCs are found more often in TNBC than in other subtypes and could be involved in the poor survival of this subtype [33][34]. Changes in the genes involved in the DDR system have also been pointed out as a cause of resistance to chemotherapy. Among the incriminated genes, HORMAD1 could play a role in chemoresistance in TNBC [35]. EMT plays an important role in breast cancer, which could lead to chemotherapy resistance and metastasis [36]. Cells that undergo EMT have common characteristics with CSCs, explaining part of their resistance to chemotherapy. The evasion of apoptosis is another mechanism leading to the resistance to several drugs such as doxorubicin, cyclophosphamide and paclitaxel. The overexpression of factors such as Bcl2, MCL1 or NF-KB has been shown to decrease the sensitivity to chemotherapy [37][38][39].

4. Current Biomarkers Used for the Clinical Decision Making of Breast Cancer Patients

4.1. Ki-67 before NAC

Ki-67 is a marker of cell proliferation used in clinical practice to assess the aggressiveness of the tumor at the time of diagnosis [40]. Ki-67 is expressed in all the cell cycle phases, with the exception of the G0 phase, and high Ki-67 expression is related to high tumor proliferation and thus a large number of dividing cells [41]. Ki-67 has been evaluated in several studies for its predictive potential, but its use in that indication is still controversial [42]. Nevertheless, in a meta-analysis, Chen et al. [43] analyzed 44 studies and concluded that high Ki-67 expression at diagnosis was associated with increased pCR rates in breast cancer patients treated with anthracycline- and/or taxane-containing NACs. This finding concerned all subtypes of breast cancer and remained significant using different thresholds of Ki-67 (e.g., >15%, >20%, >50%). Even though Ki-67 has not been validated as a predictive marker of pCR, its prognostic value has been largely studied at the moment of diagnosis but also in residual tumors after NAC [40].

4.2. Tumor Size

Tumor size plays a key role in the response to chemotherapy. Livingston-Rosanoff et al. included 38,864 patients between 2010 to 2013 in a retrospective study. These patients underwent NAC and surgery for unifocal lesions ranging in size from cT1 to cT3. Tumors with a size > 5 cm have a lower chance to achieve pCR, regardless of their immunohistological subtype [44]. This could be explained by the fact that larger tumors have a higher probability of displaying increased heterogeneity, with different populations of cells susceptible to having a variable sensitivity to treatment. Tumor size is therefore a relevant predictive factor of non-response to NAC, but it is not sufficient to predict whether patients will achieve a pCR or not.

4.3. Surrogate Molecular Subtypes as Determined by Immunohistochemistry

Tumor subtype is defined by hormone receptor and HER2 status, as well as by Ki-67 immunoreactivity, and has extensively been described as a feature that could influence response to NAC [7][45][46][47][48][49].
The CTNeoBC study pooled data from 12 international trials that included 11,955 early BC patients treated with NAC. The more aggressive subtypes were associated with pCR and better long-term outcomes. Those aggressive subtypes were TNBC, HER2-enriched and high-grade HR-positive tumors. These results are similar to the ones obtained by the pooled analysis of the German neo-adjuvant chemotherapy trials conducted by von Minckwitz et al. [49].

4.4. Tumor-Infiltrating Lymphocytes (TILs)

TILs are evaluated on hematoxylin and eosin slides and can be assessed in the stroma and in the intratumoral area. Stromal TILs are present in the tumor microenvironment without contact with the tumor cells, whereas intratumoral TILs are defined as TILs found in the tumor zone or in the peritumoral area in contact with tumor cells. In breast cancer, stromal TILs evaluation is considered the most reproducible parameter since stromal TILS are more abundant than intratumoral TILs [50][51][52].
The correlation between the levels of TILs and pCR in the neoadjuvant setting has been evaluated in several studies and in all immunohistological subtypes (Table 2). Luminal breast cancer presents fewer TILs than the HER2-enriched and TNBC subtypes.

4.5. PD-L1 Expression

Breast cancer is considered less immunogenic than other cancer types. Nevertheless, TNBC has been highlighted as the subtype with the highest expression of PD-L1 due to the genomic instability found in this particular subtype [53]. Several studies have evaluated PD-L1 expression in breast cancer, especially in TNBC, with conflicting results concerning the correlation between PD-L1 expression and its predictive value in the neoadjuvant setting [54][55]. These reported conflicting results could be explained by several factors: the heterogeneity of breast cancer itself, the biopsy type (surgical piece vs. needle), the use of different FDA-approved PD-L1 antibodies, and the different methodologies used across studies to evaluate PD-L1 (the consideration of the tumor cells and/or immune cells, the calculation of the combined positive score (CPS) or tumor proportion score (TPS), the use of different cut-offs) [54][56].

5. Predictive Biomarkers under Investigation

5.1. Imaging and Radiomics Biomarkers

5.1.1. MRI

Chamming and colleagues analyzed texture features on MRI data before NAC and found that some of them were associated with pCR in TNBC [57]. Another study suggested that, with the parameters from intratumoral and peri-tumoral texture, molecular subtypes could be identified by radiomics [58]. Liu et al. developed a radiomics signature with a combination of images from T2-weighted imaging, diffusion-weighted imaging and contrast-enhanced T1-weighted imaging. The signature itself had an accuracy of predicting pCR of 0.79, while the addition of clinical information (e.g., age, molecular classification, Ki-67 status, stage) to this signature improved the accuracy to an AUC of 0.86. They furthermore validated their models on an external dataset [59].

5.1.2. Quantitative Ultrasound

Compared to MRI, ultrasound imaging has several advantages such as its lower cost, the absence of the injection of exogenous contrast agents and the fact that it is transportable. It is therefore more accessible for the screening and evaluation of all patients. QUS is a technique that extracts characteristics of the physical properties of tissues (e.g., elastography) both in intratumoral and marginal regions. Different studies have evaluated the evolution in the structure of the tumor tissue after treatment by QUS. This technique can detect tumor cell death in response to chemotherapy and, in addition, could predict response to NAC after one-to-four weeks of chemotherapy [60][61][62][63][64][65].

5.1.3. 18F-FDG PET/CT

18F-FDG PET/CT is a molecular imaging technique used in clinical practice in oncology [66]. In breast cancer, PET/CT is essentially used to screen for distant metastases, but numerous studies from the past decade have described a potential role of PET/CT as an instrument for predicting response to NAC [4][67][68][69][70]. Higher glycolytic activities at diagnosis and significant reductions in the standardized uptake value (SUVmax) of the tracer during NAC have been described as predictive factors of response to NAC, but they are still controversial [66].

5.2. Plasmatic Biomarkers

5.2.1. Peripheral Blood Cells and Ratios

Systemic inflammation at the time of cancer diagnosis is of interest, as it may reflect tumor-associated inflammation. Moreover, neutrophil and lymphocyte counts have been described as predictors of survival and response to therapy in multiple cancer types [71][72]. The neutrophil-to-lymphocyte ratio (NLR), which is the ratio between the absolute numbers of neutrophils and lymphocytes, has been evaluated in several studies in breast cancer, but its use in clinical practice has not yet been implemented because of contradictory findings [71][72][73][74][75][76]. In 2021, Zhu et al. performed a retrospective study of NLR in 346 patients with BC and concluded that NLR could be an independent predictor of pCR after NAC [77]. A higher NLR was indeed associated with lower pCR. Patients were rigorously selected, and patients with a recent surgery or biopsy or with an autoimmune disease or recent infection were excluded. All selected patients received the same NAC regimen, which was not always the case in previous studies. The threshold value was 1.695 and was determined by ROC curve analyses, which is consistent with previous studies using cut-off values ranging from 1.7–4 [72].

5.2.2. Liquid Biopsies

Liquid biopsies offer a minimally invasive technique for diagnosis, disease monitoring and the evaluation of the response to treatment. Several components of the tumor can be analyzed with liquid biopsy samples, such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and tumor-educated platelets (TEPs) and exosomes. While TEPs and exosomes are currently studied primarily as diagnostic tools, ctDNA and CTCs show promising results in assessing response to treatment and predicting resistance in early breast cancer [78][79][80][81][82][83].

5.3. Gene Signatures

Some of them have already been validated in clinical practice, essentially in HR-positive and HER2-negative tumors: EndoPredict, Oncotype DX, MammaPrint and PAM50. Their utility in daily routines consists in providing an individual risk assessment of disease recurrence and prognostic information in order to better guide adjuvant therapy selection in early disease. The potential value of these well-known multigene profiles as predictive biomarkers of response to NAC has also been evaluated, with interesting results. Nevertheless, their indication in this setting has not yet been validated in clinical practice.

5.3.1. EndoPredict—Molecular Score (MS)

EndoPredict is a 12-gene signature measuring the expression of 8 cancer-related genes, 3 reference genes and 1 control gene. The prognostic value of this signature has been validated, stratifying patients treated with adjuvant endocrine treatment (tamoxifen) into a low or a high risk of recurrence at 10 years [84]. Moreover, the addition of clinical features such as nodal status and tumor size to the EndoPredict score is also a good indicator of late recurrence (EPclin) and can help clinicians to decide if additional treatments are needed in case of high-risk scores. In a comparative, non-randomized analysis of two prospective studies of HR-positive and HER2-negative early breast cancer, this multigene score could predict the chemotherapy benefit [85]. Regarding the NAC setting, only a few studies have shown the feasibility of using the MS score in this indication [86][87][88][89].

5.3.2. Oncotype DX—Recurrence Score (RS)

The Oncotype DX recurrence score is the result of the relative expression quantification of 21 genes (16 cancer-related genes and 5 reference genes). This score allows for the classification of patients into three categories: low risk, intermediate risk and high risk. The prognostic value of RS was validated in the prospective TAILORx and RxPONDER studies, demonstrating that patients with intermediate risk could be spared adjuvant chemotherapy in addition to endocrine therapy [90][91]. Later, the potential predictive value of the RS was evaluated in several retrospective and prospective studies.

5.3.3. Mammaprint

The Mammaprint assay is a 70-gene signature used in post-menopausal early breast cancer patients. This signature classifies tumors in two groups that are associated with good or poor prognosis based on the recurrence risk at 5 and 10 years. In the prospective MINDACT study, patients with ER-positive and HER2-negative early breast cancer and a low Mammaprint score who received endocrine therapy could safely be spared adjuvant chemotherapy [92]. The use of Mammaprint as a predictive marker of response to NAC has only been evaluated in small exploratory studies.

5.3.4. PAM50—Prosigna Assay

PAM50 is a 50-gene signature used and validated to identify intrinsic molecular subtypes of breast cancer (luminal A, luminal B, HER2-enriched, basal-like) but also to estimate a Risk of Recurrence (ROR) score capable of classifying tumors into low, intermediate or high risk of distant recurrence [93]. This gene signature was developed in order to evaluate the risk of relapse in patients with HR+ and HER2-negative breast cancer and to evaluate the indication of adjuvant chemotherapy in high-risk cases. In the neoadjuvant setting, Prat et al. studied this assay in core needle biopsy samples to evaluate if it was suitable for core biopsies [94]. They found that the Prosigna assay performed on core needle biopsies was reliable in terms of ROR score and intrinsic subtypes classification.

6. Conclusions

Predicting the response to NAC in early breast cancer still needs dedicated investigations, since most of the studies performed until now only considered one parameter, limiting their performances. This field remains an area of unmet clinical need, as exemplified by triple negative early breast cancer, where neoadjuvant escalation strategies have recently changed the treatment landscape. In the recently published Keynote-522 trial, the NAC backbone contained carboplatin in both treatment arms, and the addition of pembrolizumab led to a significantly higher pCR rate (64.8 vs. 51.2%) compared to placebo. Nevertheless, in the patients achieving pCR, recurrence rates were not significantly different between the treatment groups [95]. Thus, 50% of the patients do not require the addition of immunotherapy to chemotherapy. As treatment side effects were more pronounced in the more heavily treated patient population, finding a biomarker predictive of response to chemotherapy would be clinically and economically useful. At the same time, a better selection of patients for NAC would avoid directly ruling out promising new agents but also avoid the emergence of resistant clones due to prolonged drug exposure [8]. For a better selection of patients, for developing new drugs and avoiding the residual disease, it is therefore essential to explore and develop new predictive biomarkers with high sensitivity and specificity.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249.
  2. Loi, S. The ESMO clinical practise guidelines for early breast cancer: Diagnosis, treatment and follow-up: On the winding road to personalized medicine. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2019, 30, 1183–1184.
  3. Cardoso, F.; Kyriakides, S.; Ohno, S.; Penault-Llorca, F.; Poortmans, P.; Rubio, I.T.; Zackrisson, S.; Senkus, E. Early breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2019, 30, 1194–1220.
  4. Cardoso, F.; Paluch-Shimon, S.; Senkus, E.; Curigliano, G.; Aapro, M.S.; André, F.; Barrios, C.H.; Bergh, J.; Bhattacharyya, G.S.; Biganzoli, L.; et al. 5th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 5). Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2020, 31, 1623–1649.
  5. Mauri, D.; Pavlidis, N.; Ioannidis, J.P. Neoadjuvant versus adjuvant systemic treatment in breast cancer: A meta-analysis. J. Natl. Cancer Inst. 2005, 97, 188–194.
  6. Charfare, H.; Limongelli, S.; Purushotham, A.D. Neoadjuvant chemotherapy in breast cancer. Br. J. Surg. 2005, 92, 14–23.
  7. von Minckwitz, G.; Untch, M.; Blohmer, J.U.; Costa, S.D.; Eidtmann, H.; Fasching, P.A.; Gerber, B.; Eiermann, W.; Hilfrich, J.; Huober, J.; et al. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J. Clin. Oncol. 2012, 30, 1796–1804.
  8. Asaoka, M.; Gandhi, S.; Ishikawa, T.; Takabe, K. Neoadjuvant Chemotherapy for Breast Cancer: Past, Present, and Future. Breast Cancer Basic Clin. Res. 2020, 14, 1178223420980377.
  9. Perou, C.M.; Sørlie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; Rees, C.A.; Pollack, J.R.; Ross, D.T.; Johnsen, H.; Akslen, L.A.; et al. Molecular portraits of human breast tumours. Nature 2000, 406, 747–752.
  10. Parker, J.S.; Mullins, M.; Cheang, M.C.; Leung, S.; Voduc, D.; Vickery, T.; Davies, S.; Fauron, C.; He, X.; Hu, Z.; et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 2009, 27, 1160–1167.
  11. Sørlie, T.; Perou, C.M.; Tibshirani, R.; Aas, T.; Geisler, S.; Johnsen, H.; Hastie, T.; Eisen, M.B.; van de Rijn, M.; Jeffrey, S.S.; et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 2001, 98, 10869–10874.
  12. Hugh, J.; Hanson, J.; Cheang, M.C.; Nielsen, T.O.; Perou, C.M.; Dumontet, C.; Reed, J.; Krajewska, M.; Treilleux, I.; Rupin, M.; et al. Breast cancer subtypes and response to docetaxel in node-positive breast cancer: Use of an immunohistochemical definition in the BCIRG 001 trial. J. Clin. Oncol. 2009, 27, 1168–1176.
  13. Carey, L.A.; Berry, D.A.; Cirrincione, C.T.; Barry, W.T.; Pitcher, B.N.; Harris, L.N.; Ollila, D.W.; Krop, I.E.; Henry, N.L.; Weckstein, D.J.; et al. Molecular Heterogeneity and Response to Neoadjuvant Human Epidermal Growth Factor Receptor 2 Targeting in CALGB 40601, a Randomized Phase III Trial of Paclitaxel Plus Trastuzumab With or Without Lapatinib. J. Clin. Oncol. 2016, 34, 542–549.
  14. Rouzier, R.; Perou, C.M.; Symmans, W.F.; Ibrahim, N.; Cristofanilli, M.; Anderson, K.; Hess, K.R.; Stec, J.; Ayers, M.; Wagner, P.; et al. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2005, 11, 5678–5685.
  15. Marra, A.; Trapani, D.; Viale, G.; Criscitiello, C.; Curigliano, G. Practical classification of triple-negative breast cancer: Intratumoral heterogeneity, mechanisms of drug resistance, and novel therapies. NPJ Breast Cancer 2020, 6, 54.
  16. Prat, A.; Perou, C.M. Deconstructing the molecular portraits of breast cancer. Mol. Oncol. 2011, 5, 5–23.
  17. Lehmann, B.D.; Jovanović, B.; Chen, X.; Estrada, M.V.; Johnson, K.N.; Shyr, Y.; Moses, H.L.; Sanders, M.E.; Pietenpol, J.A. Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection. PLoS ONE 2016, 11, e0157368.
  18. Lehmann, B.D.; Bauer, J.A.; Chen, X.; Sanders, M.E.; Chakravarthy, A.B.; Shyr, Y.; Pietenpol, J.A. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Investig. 2011, 121, 2750–2767.
  19. Haynes, B.; Sarma, A.; Nangia-Makker, P.; Shekhar, M.P. Breast cancer complexity: Implications of intratumoral heterogeneity in clinical management. Cancer Metastasis Rev. 2017, 36, 547–555.
  20. Zardavas, D.; Irrthum, A.; Swanton, C.; Piccart, M. Clinical management of breast cancer heterogeneity. Nat. Rev. Clin. Oncol. 2015, 12, 381–394.
  21. Lüönd, F.; Tiede, S.; Christofori, G. Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression. Br. J. Cancer 2021, 125, 164–175.
  22. Turner, K.M.; Yeo, S.K.; Holm, T.M.; Shaughnessy, E.; Guan, J.L. Heterogeneity within molecular subtypes of breast cancer. Am. J. Physiol. Cell Physiol. 2021, 321, C343–C354.
  23. Tuasha, N.; Petros, B. Heterogeneity of Tumors in Breast Cancer: Implications and Prospects for Prognosis and Therapeutics. Scientifica 2020, 2020, 4736091.
  24. Zhou, S.; Huang, Y.-E.; Liu, H.; Zhou, X.; Yuan, M.; Hou, F.; Wang, L.; Jiang, W. Single-cell RNA-seq dissects the intratumoral heterogeneity of triple-negative breast cancer based on gene regulatory networks. Mol. Ther.-Nucleic Acids 2021, 23, 682–690.
  25. Luo, B.; Yan, D.; Yan, H.; Yuan, J. Cytochrome P450: Implications for human breast cancer (Review). Oncol. Lett. 2021, 22, 548.
  26. Bray, J.; Sludden, J.; Griffin, M.J.; Cole, M.; Verrill, M.; Jamieson, D.; Boddy, A.V. Influence of pharmacogenetics on response and toxicity in breast cancer patients treated with doxorubicin and cyclophosphamide. Br. J. Cancer 2010, 102, 1003–1009.
  27. Marsh, S.; Somlo, G.; Li, X.; Frankel, P.; King, C.R.; Shannon, W.D.; McLeod, H.L.; Synold, T.W. Pharmacogenetic analysis of paclitaxel transport and metabolism genes in breast cancer. Pharm. J. 2007, 7, 362–365.
  28. Seredina, T.A.; Goreva, O.B.; Talaban, V.O.; Grishanova, A.Y.; Lyakhovich, V.V. Association of cytochrome P450 genetic polymorphisms with neoadjuvant chemotherapy efficacy in breast cancer patients. BMC Med. Genet. 2012, 13, 45.
  29. An, J.; Peng, C.; Tang, H.; Liu, X.; Peng, F. New Advances in the Research of Resistance to Neoadjuvant Chemotherapy in Breast Cancer. Int. J. Mol. Sci. 2021, 22, 9644.
  30. Ferrari, P.; Scatena, C.; Ghilli, M.; Bargagna, I.; Lorenzini, G.; Nicolini, A. Molecular Mechanisms, Biomarkers and Emerging Therapies for Chemotherapy Resistant TNBC. Int. J. Mol. Sci. 2022, 23, 1665.
  31. Lee, H.E.; Kim, J.H.; Kim, Y.J.; Choi, S.Y.; Kim, S.W.; Kang, E.; Chung, I.Y.; Kim, I.A.; Kim, E.J.; Choi, Y.; et al. An increase in cancer stem cell population after primary systemic therapy is a poor prognostic factor in breast cancer. Br. J. Cancer 2011, 104, 1730–1738.
  32. Park, S.Y.; Lee, H.E.; Li, H.; Shipitsin, M.; Gelman, R.; Polyak, K. Heterogeneity for stem cell-related markers according to tumor subtype and histologic stage in breast cancer. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2010, 16, 876–887.
  33. Ma, F.; Li, H.; Wang, H.; Shi, X.; Fan, Y.; Ding, X.; Lin, C.; Zhan, Q.; Qian, H.; Xu, B. Enriched CD44+/CD24− population drives the aggressive phenotypes presented in triple-negative breast cancer (TNBC). Cancer Lett. 2014, 353, 153–159.
  34. Wang, H.; Wang, L.; Song, Y.; Wang, S.; Huang, X.; Xuan, Q.; Kang, X.; Zhang, Q. CD44(+)/CD24(-) phenotype predicts a poor prognosis in triple-negative breast cancer. Oncol. Lett. 2017, 14, 5890–5898.
  35. Zong, B.; Sun, L.; Peng, Y.; Wang, Y.; Yu, Y.; Lei, J.; Zhang, Y.; Guo, S.; Li, K.; Liu, S. HORMAD1 promotes docetaxel resistance in triple negative breast cancer by enhancing DNA damage tolerance Corrigendum in /10.3892/or.2021.8146. Oncol. Rep. 2021, 46, 138.
  36. Smith, B.N.; Bhowmick, N.A. Role of EMT in Metastasis and Therapy Resistance. J. Clin. Med. 2016, 5, 17.
  37. Wang, Y.; Wang, X.; Zhao, H.; Liang, B.; Du, Q. Clusterin confers resistance to TNF-alpha-induced apoptosis in breast cancer cells through NF-kappaB activation and Bcl-2 overexpression. J. Chemother. 2012, 24, 348–357.
  38. Ozretic, P.; Alvir, I.; Sarcevic, B.; Vujaskovic, Z.; Rendic-Miocevic, Z.; Roguljic, A.; Beketic-Oreskovic, L. Apoptosis regulator Bcl-2 is an independent prognostic marker for worse overall survival in triple-negative breast cancer patients. Int. J. Biol. Markers 2018, 33, 109–115.
  39. Campbell, K.J.; Dhayade, S.; Ferrari, N.; Sims, A.H.; Johnson, E.; Mason, S.M.; Dickson, A.; Ryan, K.M.; Kalna, G.; Edwards, J.; et al. MCL-1 is a prognostic indicator and drug target in breast cancer. Cell Death Dis. 2018, 9, 19.
  40. Li, L.; Han, D.; Wang, X.; Wang, Q.; Tian, J.; Yao, J.; Yuan, L.; Qian, K.; Zou, Q.; Yi, W.; et al. Prognostic values of Ki-67 in neoadjuvant setting for breast cancer: A systematic review and meta-analysis. Future Oncol. 2017, 13, 1021–1034.
  41. Scholzen, T.; Gerdes, J. The Ki-67 protein: From the known and the unknown. J. Cell. Physiol. 2000, 182, 311–322.
  42. Nielsen, T.O.; Leung, S.C.Y.; Rimm, D.L.; Dodson, A.; Acs, B.; Badve, S.; Denkert, C.; Ellis, M.J.; Fineberg, S.; Flowers, M.; et al. Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group. J. Natl. Cancer Inst. 2021, 113, 808–819.
  43. Chen, X.; He, C.; Han, D.; Zhou, M.; Wang, Q.; Tian, J.; Li, L.; Xu, F.; Zhou, E.; Yang, K. The predictive value of Ki-67 before neoadjuvant chemotherapy for breast cancer: A systematic review and meta-analysis. Future Oncol. 2017, 13, 843–857.
  44. Livingston-Rosanoff, D.; Schumacher, J.; Vande Walle, K.; Stankowski-Drengler, T.; Greenberg, C.C.; Neuman, H.; Wilke, L.G. Does Tumor Size Predict Response to Neoadjuvant Chemotherapy in the Modern Era of Biologically Driven Treatment? A Nationwide Study of US Breast Cancer Patients. Clin. Breast Cancer 2019, 19, e741–e747.
  45. Cortazar, P.; Zhang, L.; Untch, M.; Mehta, K.; Costantino, J.P.; Wolmark, N.; Bonnefoi, H.; Cameron, D.; Gianni, L.; Valagussa, P.; et al. Pathological complete response and long-term clinical benefit in breast cancer: The CTNeoBC pooled analysis. Lancet 2014, 384, 164–172.
  46. Haque, W.; Verma, V.; Hatch, S.; Suzanne Klimberg, V.; Brian Butler, E.; Teh, B.S. Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy. Breast Cancer Res. Treat. 2018, 170, 559–567.
  47. Cortazar, P.; Geyer, C.E., Jr. Pathological complete response in neoadjuvant treatment of breast cancer. Ann. Surg. Oncol. 2015, 22, 1441–1446.
  48. Shen, G.; Zhao, F.; Huo, X.; Ren, D.; Du, F.; Zheng, F.; Zhao, J. Meta-Analysis of HER2-Enriched Subtype Predicting the Pathological Complete Response within HER2-Positive Breast Cancer in Patients Who Received Neoadjuvant Treatment. Front. Oncol. 2021, 11, 632357.
  49. von Minckwitz, G.; Untch, M.; Nüesch, E.; Loibl, S.; Kaufmann, M.; Kümmel, S.; Fasching, P.A.; Eiermann, W.; Blohmer, J.U.; Costa, S.D.; et al. Impact of treatment characteristics on response of different breast cancer phenotypes: Pooled analysis of the German neo-adjuvant chemotherapy trials. Breast Cancer Res. Treat. 2011, 125, 145–156.
  50. Solinas, C.; Ceppi, M.; Lambertini, M.; Scartozzi, M.; Buisseret, L.; Garaud, S.; Fumagalli, D.; de Azambuja, E.; Salgado, R.; Sotiriou, C.; et al. Tumor-infiltrating lymphocytes in patients with HER2-positive breast cancer treated with neoadjuvant chemotherapy plus trastuzumab, lapatinib or their combination: A meta-analysis of randomized controlled trials. Cancer Treat. Rev. 2017, 57, 8–15.
  51. Salgado, R.; Denkert, C.; Demaria, S.; Sirtaine, N.; Klauschen, F.; Pruneri, G.; Wienert, S.; Van den Eynden, G.; Baehner, F.L.; Penault-Llorca, F.; et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: Recommendations by an International TILs Working Group 2014. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2015, 26, 259–271.
  52. Hendry, S.; Salgado, R.; Gevaert, T.; Russell, P.A.; John, T.; Thapa, B.; Christie, M.; van de Vijver, K.; Estrada, M.V.; Gonzalez-Ericsson, P.I.; et al. Assessing Tumor-infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method from the International Immunooncology Biomarkers Working Group: Part 1: Assessing the Host Immune Response, TILs in Invasive Breast Carcinoma and Ductal Carcinoma In Situ, Metastatic Tumor Deposits and Areas for Further Research. Adv. Anat. Pathol. 2017, 24, 235–251.
  53. Denkert, C.; Liedtke, C.; Tutt, A.; von Minckwitz, G. Molecular alterations in triple-negative breast cancer-the road to new treatment strategies. Lancet 2017, 389, 2430–2442.
  54. Vranic, S.; Cyprian, F.S.; Gatalica, Z.; Palazzo, J. PD-L1 status in breast cancer: Current view and perspectives. Semin. Cancer Biol. 2021, 72, 146–154.
  55. Marinelli, D.; Mazzotta, M.; Pizzuti, L.; Krasniqi, E.; Gamucci, T.; Natoli, C.; Grassadonia, A.; Tinari, N.; Tomao, S.; Sperduti, I.; et al. Neoadjuvant Immune-Checkpoint Blockade in Triple-Negative Breast Cancer: Current Evidence and Literature-Based Meta-Analysis of Randomized Trials. Cancers 2020, 12, 2497.
  56. Zhang, L.; Wang, X.I.; Ding, J.; Sun, Q.; Zhang, S. The predictive and prognostic value of Foxp3+/CD25+ regulatory T cells and PD-L1 expression in triple negative breast cancer. Ann. Diagn. Pathol. 2019, 40, 143–151.
  57. Chamming’s, F.; Ueno, Y.; Ferré, R.; Kao, E.; Jannot, A.S.; Chong, J.; Omeroglu, A.; Mesurolle, B.; Reinhold, C.; Gallix, B. Features from Computerized Texture Analysis of Breast Cancers at Pretreatment MR Imaging Are Associated with Response to Neoadjuvant Chemotherapy. Radiology 2018, 286, 412–420.
  58. Braman, N.; Prasanna, P.; Whitney, J.; Singh, S.; Beig, N.; Etesami, M.; Bates, D.D.B.; Gallagher, K.; Bloch, B.N.; Vulchi, M.; et al. Association of Peritumoral Radiomics with Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer. JAMA Netw. Open 2019, 2, e192561.
  59. Liu, Z.; Li, Z.; Qu, J.; Zhang, R.; Zhou, X.; Li, L.; Sun, K.; Tang, Z.; Jiang, H.; Li, H.; et al. Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2019, 25, 3538–3547.
  60. Quiaoit, K.; DiCenzo, D.; Fatima, K.; Bhardwaj, D.; Sannachi, L.; Gangeh, M.; Sadeghi-Naini, A.; Dasgupta, A.; Kolios, M.C.; Trudeau, M.; et al. Quantitative ultrasound radiomics for therapy response monitoring in patients with locally advanced breast cancer: Multi-institutional study results. PLoS ONE 2020, 15, e0236182.
  61. Sadeghi-Naini, A.; Sannachi, L.; Pritchard, K.; Trudeau, M.; Gandhi, S.; Wright, F.C.; Zubovits, J.; Yaffe, M.J.; Kolios, M.C.; Czarnota, G.J. Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture. Oncotarget 2014, 5, 3497–3511.
  62. Tadayyon, H.; Sannachi, L.; Gangeh, M.J.; Kim, C.; Ghandi, S.; Trudeau, M.; Pritchard, K.; Tran, W.T.; Slodkowska, E.; Sadeghi-Naini, A.; et al. A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound. Sci. Rep. 2017, 7, 45733.
  63. Sannachi, L.; Gangeh, M.; Tadayyon, H.; Gandhi, S.; Wright, F.C.; Slodkowska, E.; Curpen, B.; Sadeghi-Naini, A.; Tran, W.; Czarnota, G.J. Breast Cancer Treatment Response Monitoring Using Quantitative Ultrasound and Texture Analysis: Comparative Analysis of Analytical Models. Transl. Oncol. 2019, 12, 1271–1281.
  64. Fernandes, J.; Sannachi, L.; Tran, W.T.; Koven, A.; Watkins, E.; Hadizad, F.; Gandhi, S.; Wright, F.; Curpen, B.; El Kaffas, A.; et al. Monitoring Breast Cancer Response to Neoadjuvant Chemotherapy Using Ultrasound Strain Elastography. Transl. Oncol. 2019, 12, 1177–1184.
  65. Tadayyon, H.; Sannachi, L.; Gangeh, M.; Sadeghi-Naini, A.; Tran, W.; Trudeau, M.E.; Pritchard, K.; Ghandi, S.; Verma, S.; Czarnota, G.J. Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach. Oncotarget 2016, 7, 45094–45111.
  66. Paydary, K.; Seraj, S.M.; Zadeh, M.Z.; Emamzadehfard, S.; Shamchi, S.P.; Gholami, S.; Werner, T.J.; Alavi, A. The Evolving Role of FDG-PET/CT in the Diagnosis, Staging, and Treatment of Breast Cancer. Mol. Imaging Biol. 2019, 21, 1–10.
  67. Fowler, A.M.; Mankoff, D.A.; Joe, B.N. Imaging Neoadjuvant Therapy Response in Breast Cancer. Radiology 2017, 285, 358–375.
  68. Schelling, M.; Avril, N.; Nährig, J.; Kuhn, W.; Römer, W.; Sattler, D.; Werner, M.; Dose, J.; Jänicke, F.; Graeff, H.; et al. Positron emission tomography using Fluorodeoxyglucose for monitoring primary chemotherapy in breast cancer. J. Clin. Oncol. 2000, 18, 1689–1695.
  69. Lee, H.W.; Lee, H.M.; Choi, S.E.; Yoo, H.; Ahn, S.G.; Lee, M.K.; Jeong, J.; Jung, W.H. The Prognostic Impact of Early Change in 18F-FDG PET SUV After Neoadjuvant Chemotherapy in Patients with Locally Advanced Breast Cancer. J. Nucl. Med. Off. Publ. Soc. Nucl. Med. 2016, 57, 1183–1188.
  70. Dose Schwarz, J.; Bader, M.; Jenicke, L.; Hemminger, G.; Jänicke, F.; Avril, N. Early prediction of response to chemotherapy in metastatic breast cancer using sequential 18F-FDG PET. J. Nucl. Med. Off. Publ. Soc. Nucl. Med. 2005, 46, 1144–1150.
  71. Corbeau, I.; Jacot, W.; Guiu, S. Neutrophil to Lymphocyte Ratio as Prognostic and Predictive Factor in Breast Cancer Patients: A Systematic Review. Cancers 2020, 12, 958.
  72. Li, X.; Dai, D.; Chen, B.; Tang, H.; Xie, X.; Wei, W. The value of neutrophil-to-lymphocyte ratio for response and prognostic effect of neoadjuvant chemotherapy in solid tumors: A systematic review and meta-analysis. J. Cancer 2018, 9, 861–871.
  73. Guo, W.; Lu, X.; Liu, Q.; Zhang, T.; Li, P.; Qiao, W.; Deng, M. Prognostic value of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for breast cancer patients: An updated meta-analysis of 17079 individuals. Cancer Med. 2019, 8, 4135–4148.
  74. Chae, S.; Kang, K.M.; Kim, H.J.; Kang, E.; Park, S.Y.; Kim, J.H.; Kim, S.H.; Kim, S.W.; Kim, E.K. Neutrophil-lymphocyte ratio predicts response to chemotherapy in triple-negative breast cancer. Curr. Oncol. 2018, 25, e113–e119.
  75. Xue, L.B.; Liu, Y.H.; Zhang, B.; Yang, Y.F.; Yang, D.; Zhang, L.W.; Jin, J.; Li, J. Prognostic role of high neutrophil-to-lymphocyte ratio in breast cancer patients receiving neoadjuvant chemotherapy: Meta-analysis. Medicine 2019, 98, e13842.
  76. Cullinane, C.; Creavin, B.; O’Leary, D.P.; O’Sullivan, M.J.; Kelly, L.; Redmond, H.P.; Corrigan, M.A. Can the Neutrophil to Lymphocyte Ratio Predict Complete Pathologic Response to Neoadjuvant Breast Cancer Treatment? A Systematic Review and Meta-analysis. Clin. Breast Cancer 2020, 20, e675–e681.
  77. Zhu, J.; Jiao, D.; Zhao, Y.; Guo, X.; Yang, Y.; Xiao, H.; Liu, Z. Development of a predictive model utilizing the neutrophil to lymphocyte ratio to predict neoadjuvant chemotherapy efficacy in early breast cancer patients. Sci. Rep. 2021, 11, 1350.
  78. Magbanua, M.J.M.; Swigart, L.B.; Wu, H.T.; Hirst, G.L.; Yau, C.; Wolf, D.M.; Tin, A.; Salari, R.; Shchegrova, S.; Pawar, H.; et al. Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2021, 32, 229–239.
  79. Beaver, J.A.; Jelovac, D.; Balukrishna, S.; Cochran, R.; Croessmann, S.; Zabransky, D.J.; Wong, H.Y.; Toro, P.V.; Cidado, J.; Blair, B.G.; et al. Detection of cancer DNA in plasma of patients with early-stage breast cancer. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2014, 20, 2643–2650.
  80. Alimirzaie, S.; Bagherzadeh, M.; Akbari, M.R. Liquid biopsy in breast cancer: A comprehensive review. Clin. Genet. 2019, 95, 643–660.
  81. Shoukry, M.; Broccard, S.; Kaplan, J.; Gabriel, E. The Emerging Role of Circulating Tumor DNA in the Management of Breast Cancer. Cancers 2021, 13, 3813.
  82. Honoré, N.; Galot, R.; van Marcke, C.; Limaye, N.; Machiels, J.-P. Liquid Biopsy to Detect Minimal Residual Disease: Methodology and Impact. Cancers 2021, 13, 5364.
  83. Sant, M.; Bernat-Peguera, A.; Felip, E.; Margelí, M. Role of ctDNA in Breast Cancer. Cancers 2022, 14, 310.
  84. Filipits, M.; Rudas, M.; Jakesz, R.; Dubsky, P.; Fitzal, F.; Singer, C.F.; Dietze, O.; Greil, R.; Jelen, A.; Sevelda, P.; et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2011, 17, 6012–6020.
  85. Sestak, I.; Martín, M.; Dubsky, P.; Kronenwett, R.; Rojo, F.; Cuzick, J.; Filipits, M.; Ruiz, A.; Gradishar, W.; Soliman, H.; et al. Prediction of chemotherapy benefit by EndoPredict in patients with breast cancer who received adjuvant endocrine therapy plus chemotherapy or endocrine therapy alone. Breast Cancer Res. Treat. 2019, 176, 377–386.
  86. Dubsky, P.C.; Singer, C.F.; Egle, D.; Wette, V.; Petru, E.; Balic, M.; Pichler, A.; Greil, R.; Petzer, A.L.; Bago-Horvath, Z.; et al. The EndoPredict score predicts response to neoadjuvant chemotherapy and neoendocrine therapy in hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer patients from the ABCSG-34 trial. Eur. J. Cancer 2020, 134, 99–106.
  87. Soliman, H.; Wagner, S.; Flake, D.D., II; Robson, M.; Schwartzberg, L.; Sharma, P.; Magliocco, A.; Kronenwett, R.; Lancaster, J.M.; Lanchbury, J.S.; et al. Evaluation of the 12-Gene Molecular Score and the 21-Gene Recurrence Score as Predictors of Response to Neo-adjuvant Chemotherapy in Estrogen Receptor-Positive, HER2-Negative Breast Cancer. Ann. Surg. Oncol. 2020, 27, 765–771.
  88. Bertucci, F.; Finetti, P.; Viens, P.; Birnbaum, D. EndoPredict predicts for the response to neoadjuvant chemotherapy in ER-positive, HER2-negative breast cancer. Cancer Lett. 2014, 355, 70–75.
  89. Mazo, C.; Barron, S.; Mooney, C.; Gallagher, W.M. Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-positive, HER2-negative Breast Cancer Patients. Cancers 2020, 12, 1133.
  90. Andre, F.; Ismaila, N.; Henry, N.L.; Somerfield, M.R.; Bast, R.C.; Barlow, W.; Collyar, D.E.; Hammond, M.E.; Kuderer, N.M.; Liu, M.C.; et al. Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women with Early-Stage Invasive Breast Cancer: ASCO Clinical Practice Guideline Update—Integration of Results From TAILORx. J. Clin. Oncol. 2019, 37, 1956–1964.
  91. Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E.; Dees, E.C.; Goetz, M.P.; Olson, J.A.; et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. N. Engl. J. Med. 2018, 379, 111–121.
  92. Cardoso, F.; van’t Veer, L.J.; Bogaerts, J.; Slaets, L.; Viale, G.; Delaloge, S.; Pierga, J.Y.; Brain, E.; Causeret, S.; DeLorenzi, M.; et al. 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. N. Engl. J. Med. 2016, 375, 717–729.
  93. Gnant, M.; Filipits, M.; Greil, R.; Stoeger, H.; Rudas, M.; Bago-Horvath, Z.; Mlineritsch, B.; Kwasny, W.; Knauer, M.; Singer, C.; et al. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: Using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 2014, 25, 339–345.
  94. Prat, A.; Galván, P.; Jimenez, B.; Buckingham, W.; Jeiranian, H.A.; Schaper, C.; Vidal, M.; Álvarez, M.; Díaz, S.; Ellis, C.; et al. Prediction of Response to Neoadjuvant Chemotherapy Using Core Needle Biopsy Samples with the Prosigna Assay. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2016, 22, 560–566.
  95. Schmid, P.; Cortes, J.; Dent, R.; Pusztai, L.; McArthur, H.; Kümmel, S.; Bergh, J.; Denkert, C.; Park, Y.H.; Hui, R.; et al. Event-free Survival with Pembrolizumab in Early Triple-Negative Breast Cancer. N. Engl. J. Med. 2022, 386, 556–567.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Oncology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Françoise Derouane
,
Cédric van Marcke
,
Martine Berlière
,
Amandine Gerday
,
Latifa Fellah
,
Isabelle Leconte
,
Mieke R. Van Bockstal
,
Christine Galant
,
Cyril Corbet
,
Francois P. Duhoux
View Times: 749
Revisions: 2 times (View History)
Update Date: 28 Oct 2022
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback