A Novel Approach to Classifying Chemotherapeutic Agents Based on Their Impact on Canonical Pathways: Implications for Overcoming Multidrug Resistance in Breast Cancer

A Novel Approach to Classifying Chemotherapeutic Agents Based on Their Impact on Canonical Pathways: Implications for Overcoming Multidrug Resistance in Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Novel Approach to Classifying Chemotherapeutic Agents Based on Their Impact on Canonical Pathways: Implications for Overcoming Multidrug Resistance in Breast Cancer Lin Batha, Mahmoud Zhra, Mariam Ahmed Galal, Jasmine Holail, Fai Aldossari, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4204722/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Breast cancer faces a significant challenge in the form of multidrug resistance (MDR), which requires the development of innovative therapeutic approaches. This study investigates the molecular mechanisms underlying MDR in MCF7 breast cancer cells to identify common signaling pathways that contribute to resistance. Additionally, this research aims to propose a novel classification system for chemotherapeutic agents based on their influence on these pathways. Methodology: To create drug-resistant MCF7 sublines, MCF7 cells were subjected to chemotherapeutic drugs for 12 months, with concentrations gradually increasing over time. The transcriptome of these sublines was then analyzed using next-generation RNA sequencing through Ion Torrent technology. Differentially expressed genes (DEGs) and their associated canonical pathways, molecular functions, and upstream regulators were identified using Ingenuity Pathway Analysis (IPA). Results: Unique mRNA expression patterns were associated with chemoresistance, indicating notable up- and downregulation patterns of DEGs in MCF7 chemoresistant subline cells. The use of IPA allowed for the categorization of chemotherapeutic drugs into three groups based on their effects on canonical pathways: Group I (Arabinosylcytosine, Methotrexate), Group II (Paclitaxel, Cyclophosphamide, Nocodazole), and Group III (5-Fluorouracil, Etoposide, Doxorubicin, Cisplatin). Conclusion: A new approach to enhance the effectiveness of breast cancer chemotherapy is proposed. This approach involves categorizing chemotherapeutic agents based on their impact on canonical pathways. The innovative classification system has the potential to guide the development of combination therapies, predict drug resistance mechanisms, and ultimately improve patient outcomes. However, it is essential to conduct extensive research and validate these findings in other breast cancer cell lines and clinical settings. Biological sciences/Biochemistry Biological sciences/Biological techniques Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Drug discovery Health sciences/Molecular medicine Breast Cancer Multidrug Resistance RNA Seq Analysis Chemoresistance Transcriptome Analysis Canonical Pathways Figures Figure 1 Introduction Cancer remains a global health concern and is a leading cause of mortality worldwide (1). Current treatment options include surgery, radiation therapy, chemotherapy, targeted therapy, endocrine therapy, and immunotherapy (2). While significant progress has been made in cancer therapy, multidrug resistance (MDR) continues to present a significant challenge in chemotherapy. MDR is implicated in over 90% of chemotherapy-related patient deaths . The inherent heterogeneity and adaptability of cancer cells present a significant obstacle, severely impacting the efficacy of cancer therapy (3). Drug resistance can be either intrinsic or acquired. Intrinsic resistance exists before drug administration, usually due to (a) pre-existing inherited genetic mutations; (b) intra-tumoral genetic heterogeneity; or (c) activation of intrinsic pathways responsible for detoxification as a defense mechanism against anti-tumor agents (4-7). Acquired resistance, on the other hand, is gradually acquired by tumor cells throughout treatment, either against a specific chemotherapeutic agent or multiple agents. Acquired resistance can result from various cellular and molecular responses, including (a) activation of secondary proto-oncogenes as new driver genes; (b) alterations in drug target expression levels; (c) drug metabolism within the tumor; (d) ATP-binding cassette transporters that pump out chemotherapeutics; (e) impaired cellular responses affecting cell cycle arrest, apoptosis, and DNA repair; (f) induction of signaling pathways promoting cancer progression; (g) epigenetic alterations caused by errors in DNA methylation, acetylation, histone modification, and altered micro-RNAs, leading to upstream and downstream receptor changes; (h) changes in the tumor microenvironment (TME) after treatment. These mechanisms can act independently or synergistically to promote MDR (4-7). MDR remains a major challenge in oncology, particularly in breast cancer, due to cross-resistance to a range of structurally and functionally different anti-breast cancer agents. This significantly impacts patient prognosis. One study estimated that one in two breast cancer patients fails to respond to treatment due to intrinsic or acquired drug resistance (8). To understand the mechanism of MDR development against various chemotherapeutic agents, it is crucial to examine their mechanisms of action and common signaling pathways. While multiple signaling pathways and genes have been associated with MDR, the exact mechanism remains unclear. Transcriptomics analysis has been used to characterize intra-tumoral heterogeneity, revealing the molecular basis for MDR, tumor growth dynamics, and relapse potential (9, 10). Notably, miRNAs have been shown to play a key regulatory role in MDR, modulating multiple mechanisms to provide a more comprehensive understanding of cross-resistance through shared pathways among different drugs (11). This project aims to explore common signaling pathways that contribute to MDR in chemo-resistant MCF7 breast cancer cell sublines. By using transcriptomic analysis, we aim to improve our understanding of MDR mechanisms in breast cancer. This approach involves carefully combining distinct drug classes with minimal overlap in signaling pathways and toxicities, offering a promising strategy to address the complexities associated with drug resistance. Materials And Methods 3.1. Materials Used The materials used in this study were as follows: MCF7 cells were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). The cell culture medium consisted of Dulbecco's Modified Eagle Medium 1X (DMEM) (Gibco, USA) supplemented with 10% Fetal Bovine Serum (FBS) (Gibco, USA), 1X Pen Strip (Gibco, USA), 1% Glutamax (Gibco, USA), and 1% Non-Essential Amino Acid (NEAA) (Gibco, USA). The chemotherapeutic drugs used were camptothecin (Cat. No.: C9911, Sigma-Aldrich, USA), methotrexate (Cat. No.: 45412, Sigma-Aldrich), doxorubicin (Cat. No.: D1515, Sigma-Aldrich), fluorouracil (Cat. No.: F6627, Sigma-Aldrich), paclitaxel (Cat. No.: T7191, Sigma-Aldrich), arabinofuranosylcytosine (Cat. No.: C1768, Sigma-Aldrich), cisplatin (Cat. No.: 232120, Sigma-Aldrich), cyclophosphamide (Cat. No.: 218707, Sigma-Aldrich), etoposide (Cat. No.: E1383, Sigma-Aldrich), and nocodazole (Cat. No.: M1404, Sigma-Aldrich). Other reagents used were dimethyl sulfoxide (DMSO) (Sigma-Aldrich, USA) and 0.05% trypsin (Gibco, USA). 3.2. Cell Culture MCF7 parental cells and their chemoresistant counterparts were cultured under sterile conditions in DMEM supplemented with 10% FBS, 1X Pen Strip, 2mM Glutamax, and 1% NEAA at 37°C in a humidified incubator with 95% air and 5% CO2. Cells were treated with chemotherapeutic drugs dissolved in DMSO at an initial concentration of 10-16 M. At 80-90% confluency, cells were passaged using 0.05% trypsin at a ratio of 1:3. To generate drug-resistant cells, the parental cell line was subjected to continuous incubation with increasing drug concentrations over a period of 6 -12 months until the final drug concentration in the cultured cells reached 10 μM (Methotrexate, fluorouracil, and paclitaxel), or 100 μM (Doxorubicin, arabinofuranosylcytosine, cisplatin, cyclophosphamide, etoposide, and nocodazole). 3.3. In vitro drug sensitivity assay In vitro drug cytotoxicity on the MCF7/Chemotherapy-Resistant sublines was assessed using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. This colorimetric assay measures cellular viability by quantifying the amount of formazan produced by metabolically active cells. The assay was performed in triplicate wells per treatment condition. In brief, 2x103 cells were seeded in a 96-well plate and incubated for 24 hours to allow for cell attachment. Subsequently, the chemotherapeutic agents were diluted in 100 μL of growth medium and added to the respective wells. The cells were then incubated for an additional 48 hours to allow for drug exposure. After the 48-hour incubation period, 10 μL of MTT solution (5 mg/mL in PBS) was added to each well, followed by 100 μL of phenol-free media. The plate was then incubated for 4 hours at 37°C in a 5% CO2 atmosphere to allow for formazan synthesis. Following the formazan synthesis incubation, 100 μL of 10% SDS-HCl solution were added to each well to lyse the cells and solubilize the formazan crystals. The plate was then incubated for 18 hours at room temperature to allow for complete cell lysis and formazan solubilization. Cell viability was determined by measuring the absorbance of the formazan solution at 570 nm using a spectrophotometer (Agilent BioTek Epoch Microplate Spectrophotometer, Agilent Technologies Inc., California, USA) equipped with Gen5 software. The absorbance values were directly proportional to the number of viable cells in each well. The cell viability percentage was calculated by comparing the absorbance values of treated cells to those of untreated control cells. 3.4. Ion Torrent Analysis 3.4.1 RNA Extraction and Quality Control The Ion Torrent platform was used for next-generation RNA sequencing (RNA-seq) to examine the transcriptional landscape of chemoresistant MCF7 cells in comparison to their parental counterparts. Ion torrent analysis was conducted as described in our previous work (12). The RNA extraction process involved the use of the Ambion® Aqueous kit from Thermo Fisher Scientific, following the manufacturer's instructions. The extracted RNA was then evaluated for quantity and integrity using the Bio-Rad Experion® automated electrophoresis system from Bio-Rad Laboratories. Subsequently, the isolated RNA was purified using the RiboMinus™ Eukaryote System v2 from Invitrogen to eliminate ribosomal RNA. The mRNA concentration was determined using the Qubit™ RNA HS Assay kit from Thermo Fisher Scientific. 3.4.2 Library construction for Next-Generation Sequencing (NGS) For the construction of RNA-seq libraries, the Ion Total RNA-Seq Kit v2, Ion Total RNA-Seq Primer Set v2, and Ion Express™ RNA-Seq Barcode 01-16 Kit from Thermo Fisher Scientific were employed. Initially, cDNA libraries were generated from 2 μg of total RNA using the Ion Total RNA-Seq Kit v2. These cDNA libraries were subsequently fragmented and barcoded using the Ion Express™ RNA-Seq Barcode 01-16 Kit. Finally, the barcoded libraries were amplified using the Ion Total RNA-Seq Primer Set v2. Sequencing of the prepared RNA-Seq libraries was conducted using the Ion Proton Semiconductor Sequencer from Thermo Fisher Scientific. 3.4.3. Data Analysis The sequencing data was extracted using Agilent Feature Extraction Software from Agilent Technologies and imported into GeneSpring GX software, also from Agilent Technologies, for further analysis. The data preprocessing in this study involved two steps: log2 transformation and normalization of the gene expression values using the percentile shift algorithm. To identify DEGs, the gene expression levels between chemoresistant MCF7 cells and control MCF7 cells were compared using a two-tailed t-test. DEGs were defined as genes that exhibited a fold change (FC) of 2.5 or greater and a p-value of ≤ 0.05. This analysis helped to identify genes that were significantly differentially expressed between the two cell types. 3.4.4. Ingenuity Pathway Analysis (IPA) To gain insights into the functional implications of the identified DEGs, IPA software was employed. This software suite, provided by Qiagen in Hilden, Germany, was used to analyze the DEGs and identify critical pathways, networks, and molecular and cellular functions associated with them. To filter for statistically significant pathways and functions, a cutoff of -log(p-value) >3 was applied. This ensured that only pathways and functions with a high level of statistical significance were considered. The significance of the IPA core analysis was evaluated using either a Fisher's exact test or a Z-score. These statistical tests were used to generate predictions of activation, inactivation, or no effect for the identified pathways and functions. This analysis helped to further understand the potential impact of the DEGs on various biological processes. Results 4.1. Establishment of drug resistance MCF7 sublines To investigate the mechanisms of drug resistance in MCF7 breast cancer cells, we generated drug-resistant sublines by continuously exposing parental MCF7 cells to increasing concentrations of various chemotherapeutic drugs. The final drug concentrations in the cultured cells ranged from 10 μM for methotrexate, fluorouracil, and paclitaxel to 100 μM for doxorubicin, arabinofuranosylcytosine, cisplatin, cyclophosphamide, etoposide, and nocodazole. This gradual increase in drug concentration ensured that only cells with the ability to tolerate the drug survived and proliferated, leading to the enrichment of resistant cells in the population. The drug-resistant MCF7 sublines exhibited significantly higher resistance to their respective chemotherapeutic drugs compared to the parental cells. 4.2 Activation/Inactivation of Pathways and Upstream Regulators in chemoresistance IPA analysis revealed several statistically significant pathways and molecular and cellular functions associated with DEGs in chemoresistant MCF7 subline cells. Table 1 highlights notable alterations in these functions, including cell death and survival, gene expression, and protein synthesis, suggesting modifications in signaling pathways that regulate these processes in chemoresistant MCF7 cells. These changes include increased protein synthesis, altered apoptosis regulation, altered gene expression patterns, enhanced DNA/RNA repair mechanisms, altered RNA post-transcriptional modification, altered cellular development, and increased cellular growth, proliferation, and movement. Overall, the findings suggest that chemoresistant MCF7 cells undergo significant molecular and cellular alterations, enabling their survival and growth in the presence of chemotherapeutic drugs. IPA analysis identified also several canonical pathways that were significantly associated with these differentially expressed genes (Supplementary Table 1). A comparative analysis was conducted to determine the top canonical pathways that were linked to the significant DEGs. Among these top pathways were EIF2 signaling, elF4 and p70S6K regulation signaling, and the Sirtuin Signaling Pathway, as indicated in Table 2. The IPA Canonical pathways, which are intricately linked to alterations in mRNA expression within MCF7 cells exhibiting resistance to nine distinct chemotherapeutic drugs, were thoroughly explored in Figure 1. By employing Fisher's exact test with a significance threshold of -log(P-value) > 1.301 (p < 0.05), the transcriptional shifts were carefully examined. These findings are visually represented in a heatmap, where pathways are organized based on their Z-scores. A Z-score ≥ 1 indicates a significant increase in pathway activity (depicted in orange), while a Z-score ≤ -1 signifies a substantial decrease (rendered in blue). Pathways with undetermined predictions are depicted in gray. Upon careful examination of the heatmap in Figure 1, a distinct pattern of mRNA expression changes becomes apparent in MCF7 cells resistant to chemotherapy. Several pathways show significant activation in chemoresistant cells compared to their parental counterparts, indicating their potential role in conferring resistance to chemotherapy. Conversely, several pathways demonstrate noteworthy inactivation in chemoresistant cells, indicating their involvement in maintaining sensitivity to chemotherapy. The identification of these differentially regulated pathways provides crucial insights into the molecular intricacies underlying chemoresistance in MCF7 cells. Moreover, a thorough examination of the primary IPA Canonical pathways allows for the classification of the nine chemotherapeutic medications into three distinct groups based on their activation or inhibition patterns. The first group, Group I, consists of Arabinosylcytosine and Methotrexate. Group II encompasses Paclitaxel, Cyclophosphamide, and Nocodazole. Lastly, Group III comprises 5-fluorouracil, Etoposide, Doxorubicin, and Cisplatin. This categorization offers valuable insights into the various mechanisms by which these drugs exert their effects and how these effects intricately intertwine with chemoresistance mechanisms. By thoroughly exploring the IPA Canonical pathways, we can enhance our understanding of cellular responses to chemotherapy and establish a strong foundation for tailored therapeutic interventions aimed at mitigating specific resistance profiles. IPA upstream regulator analysis was also conducted using the Ingenuity Knowledge Base (IKB). The IKB is a curated database of molecular interactions and biological processes. It can be used to identify potential upstream regulators of gene expression changes. The IPA upstream regulator analysis identified several potential upstream regulators of the DEGs (Supplementary Table 2). These regulators included MYC, CD437, LARP1, ST1926, TP53, and HNF4A (Table 3). The activation Z-score and p-value of overlap for each regulator were calculated to assess the likelihood that the regulator was activated or inhibited in the chemoresistant cells. The drugs used in this study were grouped into three categories based on the patterns of drug resistance observed in MCF7 cells and were consistent with our categorization based on the canonical pathway activation/inactivation. Group I drugs (Arabinosylcytosine, Methotrexate) induced a similar pattern of gene expression changes, with upregulation of MYC, CD437, and LARP1, and downregulation of HNF4A. Group II drugs (Paclitaxel, Cyclophosphamide, Nocodazole) induced a different pattern of gene expression changes, with downregulation of MYC, CD437, and LARP1, and upregulation of TP53. Group III drugs (5-fluorouracil, Etoposide, Doxorubicin, Cisplatin) induced a third pattern of gene expression changes, with downregulation of MYC, CD437, and LARP1, and upregulation of TP53. Discussion Chemoresistance in cancer is a complex and multifaceted phenomenon, with both inherent and acquired resistance pathways contributing to treatment failure (13). This complexity is further compounded by the heterogeneous nature of cancer, making it difficult to fully understand the mechanisms of chemoresistance (14). One significant factor in chemoresistance is the presence of cancer stem cells, which possess intrinsic resistance to chemotherapy (9). Despite these challenges, several strategies have been proposed to overcome chemoresistance, including the use of novel agents, combination treatments, and targeted therapies (9, 10). However, the effectiveness of these strategies in clinical settings remains a major hurdle (13). Our findings reveal that the effects of the nine chemotherapeutic drugs tested can be classified into three distinct groups based on their impact on canonical pathways. These groups are: Group I (Arabinosylcytosine, Methotrexate), Group II (Paclitaxel, Cyclophosphamide, Nocodazole), and Group III (5-fluorouracil, Etoposide, Doxorubicin, Cisplatin). We propose a combination therapy strategy that utilizes chemotherapeutic drugs from different groups. In the event of a relapse, the treatment should involve drugs from a different group than those used in the initial treatment. This approach needs further validation through in vitro and in vivo studies. Continuous exposure to increasing concentrations of chemotherapeutic drugs leads to the emergence of drug-resistant cells, a process that reflects the selective pressures experienced by cancer cells in vivo (15). This approach leads to the enrichment of a drug-resistant cell population through genetic and epigenetic alterations (16). These alterations include the overexpression of drug efflux pumps, changes in drug metabolism, enhanced DNA repair capacity, and modifications in cell signaling pathways (15). The development of drug resistance is further complicated by the evolution of multifactorial mechanisms, such as gene amplification and increased enzyme activity (16). These mechanisms are associated with altered sequestration and efflux of chemotherapeutic drugs by multidrug-resistant cells (17). Despite the identification of these mechanisms, strategies to overcome drug resistance have largely failed due to the complexity of the resistance phenotype. Several studies have reported the development of MCF7 resistant sublines through stepwise increases in different chemotherapeutic drugs. The establishment of MCF7 sublines, including MCF7/VP (Vincristine), MCF7/Pac (Paclitaxel), MCF7/TopoisomeraseII (Etoposide), MCF7/MTX (Methotrexate), MCF7/5-FU (5-fluorouracil), MCF7/Cis (Cisplatin), MCF7/Cyclo (Cyclophosphamide), and MCF7/AraC (Arabinosylcytosine), involves the selection and expansion of inherently resistant cells (18-21). These sublines display various characteristics of drug resistance, including altered gene expression patterns and increased expression of drug resistance-related proteins (18, 20-22). The resistance mechanisms include increased expression of multidrug resistance protein (MRP) and glutathione S-transferase P1-1 (GSTP1-1) (18), reduced drug sensitivity of topoisomerase II (22), enhanced energy-dependent drug efflux (20), and upregulation of MDR1 gene and detoxifying enzymes (21). These studies collectively highlight the complex and multifaceted nature of drug resistance in MCF7 cells. The use of a singular cell line in cancer research offers several advantages, including the elimination of cellular heterogeneity, unifying genetic background, facilitating comparative studies, and ensuring reproducibility and consistency (23). However, the presence of tumor heterogeneity poses a challenge to the effectiveness of drug delivery and treatment methods. Therefore, while the use of a singular cell line can provide valuable insights, it is crucial to consider the limitations and utilize multiple cell lines. Thus, our proposed classification for chemotherapeutic drugs based on canonical pathway activation/inactivation should be validated using a diverse panel of breast cancer cell lines representing the heterogeneity of tumor cells. Despite the success of established combination regimens, there exists an opportunity to optimize treatment strategies by exploring novel combinations and integrating additional highly effective oncology drugs. This rationale is grounded in understanding the shared and distinct signaling pathways activated and inactivated by various chemotherapeutic agents commonly used in breast cancer treatment. To address this, we systematically grouped nine chemotherapeutic agents based on their activation scores and intersections within various signaling pathways in our MCF7 chemoresistant sublines. The resulting three groups provide a framework for comparing newly assembled groups with traditional breast cancer regimens. This comparative analysis, considering indications, mechanisms of action, resistance, and relapse rates, aims to deepen our understanding of the molecular mechanisms governing MDR across these diverse drug classes. The ultimate goal is to provide a foundation for developing innovative regimens that integrate additional drugs with minimal overlap in signaling pathways and toxicities. This strategic approach has the potential to significantly enhance chemotherapy effectiveness and reduce relapse rates in the future (24). Based on the proposed classifications, it becomes evident that certain drugs share similar groupings concerning both their activation scores in the canonical pathways and their pharmacokinetics (24). Notably, 5-Fluorouracil and Arabinosylcytosine, classified as antimetabolites, function as inactivators in various signaling pathways. Similarly, Paclitaxel and Nocodazole, which primarily act as mitotic spindle inhibitors, show no difference in terms of their roles as activators or inactivators in the analyzed signaling pathways illustrated in Figure 1. Lastly, Etoposide, Doxorubicin, and Cisplatin—classified as genotoxic agents based on their chemotherapeutic mechanisms primarily serve as activators in a diverse array of signaling pathways detailed in Figure 1. These classifications hold particular significance in endeavors aimed at comprehending the formation of multidrug resistance and determining suitable chemotherapeutic agents in the event of relapse. By considering the activation/inhibition of canonical pathways and pharmacokinetic properties of these drugs, researchers can develop more effective combination therapies and predict potential drug resistance mechanisms. Thus, the grouping of anti-cancer drugs based on their canonical signaling pathways and pharmacokinetics offers a novel approach to understanding and optimizing chemotherapy regimens, ultimately leading to improved patient outcomes in breast cancer treatment. The strategy of combination therapy, involving the synergistic combination of multiple anti-cancer agents, has emerged as a promising avenue to enhance the efficacy of breast cancer treatment compared to monotherapy (25). This approach seeks to overcome the limitations associated with single-agent therapies by mitigating resistance, inhibiting metastasis, and reducing the number of mitotically active cells. Clinical trials consistently highlight the effectiveness of combination chemotherapy regimens in improving patient outcomes in breast cancer. A notable example is the cyclophosphamide, methotrexate, and 5-fluorouracil (CMF) combination adjuvant chemotherapy regimen pioneered by the Istituto Nazionale Tumori in Milan. This regimen demonstrates improved overall survival among breast cancer patients, with no significant difference in relapse rates between pre-menopausal and post-menopausal women (12, 13). The US Breast Cancer Intergroup reported the efficacy of the CMF regimen in reducing recurrence and enhancing survival, particularly in patients with axillary node-negative disease (12, 13). Supporting the efficacy of combination therapy, the NSABP B-20 trial demonstrated that adding tamoxifen to the CMF regimen significantly decreased the relapse rate in patients with ER-positive, lymph node-negative disease. This effect aligns with the findings of the EBCTCG meta-analysis, indicating that CMF contributed to a 30% reduction in the recurrence rate over a 10-year period (26). Our proposed classification system, which assigns cyclophosphamide to group II, methotrexate to group I, and 5-fluorouracil to group III, further supports the potential of this categorization approach. In addition to CMF, other combination regimens exhibit promise in breast cancer treatment. The AC combination therapy consisting of doxorubicin (group III) and cyclophosphamide (group II), administered every three weeks for four cycles, proves to be an effective and shorter alternative to the CMF regimen for patients with node-negative disease (27). The FAC regimen, comprising 5-fluorouracil (group III), doxorubicin (group III), and cyclophosphamide (group II), demonstrates superior efficacy compared to AC and CMF in postmenopausal women with hormone-receptor-positive, node-positive breast cancer (28, 29). More patient-customized regimens, such as doxorubicin (group III), cyclophosphamide (group II), followed by paclitaxel (group II) (AC-T), have been developed by Memorial Sloan Kettering Cancer Center for the treatment of aggressive early-stage breast cancer in young patients (30-32). The efficacy of breast cancer chemotherapeutic regimens varies in their ability to prevent relapse and prolong disease-free survival (DFS) among patients. Bang et al. (2000) compared six cycles of AC to six cycles of oral CMF and found a DFS of 64% for AC-treated patients compared to 78% for CMF-treated patients (33). Another study by CALGB 9344 compared four cycles of AC to four cycles of AC followed by paclitaxel (AC-P) and reported a DFS rate of 65% for AC alone versus 70% for AC-P at five years (34). Additionally, Martin et al. (2003) compared six cycles of FAC to six cycles of CMF and found a DFS of 58% for FAC compared to 50% for CMF at five years (35). Finally, a study by MD Anderson Cancer Center compared four cycles of paclitaxel followed by four cycles of FAC to four cycles of FAC alone and reported a DFS of 86% for paclitaxel followed by FAC (pac/FAC) and 83% for FAC alone at 48 months (36). These studies demonstrate that contemporary regimens incorporating anthracyclines, such as doxorubicin, appear to be more effective in improving overall survival than CMF. However, they are associated with increased long-term toxicities, particularly an increased risk of acute myeloid leukemia (AML), compared to CMF. Therefore, it is crucial to establish an individualized selection process for the treatment of breast cancer patients. CMF may remain suitable for those with a low risk of recurrence, whereas patients at a high risk of recurrence may benefit from more effective treatments, such as regimens including anthracyclines (37). To further advance evidence-based regimen selection, it is essential to understand how these drugs interact in terms of their activated and inactivated biochemical pathways and how they overcome relapse. This knowledge can guide the development of more personalized and effective treatment strategies for breast cancer patients. The increasing prevalence of MDR in cancer treatment has prompted the exploration of alternative strategies to improve patient outcomes. Our approach is to utilize drugs with minimally overlapping signaling pathways and different pharmacokinetic profiles to minimize the risk of MDR development. This strategy is particularly relevant in the context of relapse treatment, where cancer cells have already demonstrated resistance to initial treatment regimens. A notable study conducted in 1979 examined the efficacy of methotrexate (group I) and vincristine in patients with advanced breast cancer who had failed treatment with FAC (5-fluorouracil (group III), doxorubicin (group III), and cyclophosphamide (group II)) or experienced relapse. Among the 17 patients enrolled, four achieved partial remission lasting six months, with a median survival of ten months (38). Additionally, seven patients exhibited stable disease with minimal toxicity. This study is particularly intriguing in light of the distinct pharmacological profiles of methotrexate and vincristine compared to FAC drugs. As suggested by this study, methotrexate belongs to a different group based on its general function within the listed signaling pathways. Unfortunately, we did not examine vincristine canonical pathways in this study. These findings suggest that employing drugs with minimal or no overlapping signaling pathways may hold promise in reducing the likelihood of MDR development, particularly in the context of relapse treatment. However, further research, including additional data on multiple breast cancer cell lines and clinical trials, is warranted to fully elucidate the potential benefits of this approach. Conclusion Our newly proposed classification of chemotherapeutic drugs based on their activation scores and intersections within signaling pathways offers a new perspective for optimizing breast cancer treatment. The systematic grouping of nine agents into three distinct categories provides a framework for comparing traditional regimens and newly assembled groups. This classification, may deepen our understanding of the molecular mechanisms governing MDR. The potential of this classification extends beyond theoretical insights, offering a practical foundation for developing innovative regimens that integrate additional drugs with minimal overlap in signaling pathways and toxicities. This strategic approach holds the promise of significantly enhancing chemotherapy effectiveness and reducing relapse rates in the future. By considering the canonical activation/inactivation pathways of these drugs, researchers can tailor more effective combination therapies, predict potential drug resistance mechanisms, and ultimately improve patient outcomes in breast cancer treatment. However, while our classification shows great promise, further research on other drugs and multiple breast cancer cell lines are warranted to validate its efficacy and determine its applicability across diverse patient populations. The evolving landscape of breast cancer treatment demands continued exploration and refinement of treatment strategies, and our proposed classification opens up new avenues for such advancements. Declarations ACKNOWLEDGEMENT The funding for this project was provided by the Alfaisal University-Office of Research and Innovation (ORI). CONFLICT OF INTEREST The authors state that the research was carried out without any commercial or financial associations that could be perceived as a possible conflict of interest. AUTHOR CONTRIBUTION STATEMENT LB, MG & JH: Responsible for Data Analysis, and Writing the Original Draft; MAZ, MZ & MA: Involved in Methodology and Data Analysis; HF: Responsible for Writing Review and Funding Acquisition; AA: Oversaw Conceptualization, Supervision, and contributed to Writing Review and Editing. 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Two months of doxorubicin-cyclophosphamide with and without interval reinduction therapy compared with 6 months of cyclophosphamide, methotrexate, and fluorouracil in positive-node breast cancer patients with tamoxifen-nonresponsive tumors: results from the National Surgical Adjuvant Breast and Bowel Project B-15. J Clin Oncol. 1990;8(9):1483-96. Albain KS, Barlow WE, Ravdin PM, Farrar WB, Burton GV, Ketchel SJ, et al. Adjuvant chemotherapy and timing of tamoxifen in postmenopausal patients with endocrine-responsive, node-positive breast cancer: a phase 3, open-label, randomised controlled trial. Lancet. 2009;374(9707):2055-63. Peto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet. 2012;379(9814):432-44. Norton L. Theoretical concepts and the emerging role of taxanes in adjuvant therapy. 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Doxorubicin in combination with fluorouracil and cyclophosphamide (i.v. FAC regimen, day 1, 21) versus methotrexate in combination with fluorouracil and cyclophosphamide (i.v. CMF regimen, day 1, 21) as adjuvant chemotherapy for operable breast cancer: a study by the GEICAM group. Ann Oncol. 2003;14(6):833-42. Ramanathan B, Jan K-Y, Chen C-H, Hour T-C, Yu H-J, Pu Y-S. Resistance to Paclitaxel Is Proportional to Cellular Total Antioxidant Capacity. Cancer Research. 2005;65(18):8455-60. Espinosa E, Zamora P, Feliu J, González Barón M. Classification of anticancer drugs—a new system based on therapeutic targets. Cancer Treatment Reviews. 2003;29(6):515-23. Yap HY, Blumenschein GR, Tashima CK, Hortobagyi GN, Buzdar AU, Wiseman CL. Combination chemotherapy with vincristine and methotrexate for advanced refractory breast cancer. Cancer. 1979;44(1):32-4. Tables Table 1: Molecular and cellular functions identified using QIAGEN's Ingenuity Pathway Analysis. Numbers represent differentially expressed genes (DEGs) associated with functions in MCF7 chemoresistant subline cells. Molecular and cellular function Arabinosylcytosine Methotrexate Paclitaxel Cyclophosphamide Nocodazole Fluorouracil Etoposide Doxorubicin Cisplatin Protein Synthesis 394 435 316 509 491 539 458 334 334 Cell Death and Survival 757 798 711 792 872 1066 826 779 706 Gene Expression 558 583 544 750 596 528 RNA Damage and Repair 139 160 170 119 130 RNA Post-Transcriptional Modification 186 190 214 185 Cellular Development 616 520 637 719 617 552 Cellular Growth and Proliferation 579 630 519 Cellular Movement 505 Cell Cycle 489 407 Table 2: Top 5 IPA Canonical pathways associated with mRNA expression concordant with chemoresistance induced in MCF7 to 9 different chemotherapeutic drugs. The overlap of top canonical pathways in IPA is evaluated using a statistical assessment of gene or molecule overlap. The overlap p-value, determined using a hypergeometric or Fisher's exact test, compares the observed overlap to an anticipated overlap generated from a random model. A lower overlap p-value indicates a more significant pathway similarity, suggesting potential functional connections. Significance was determined at a predetermined threshold (< 0.05). %: refers to the proportion of overlapping genes or molecules relative to the total number involved in the pathways. Arabinosylcytosine Methotrexate Paclitaxel Cyclophosphamide Nocodazole 5-fluorourasil Etoposide Doxorubicin Cisplatin EIF2 Signaling 46.9 % (105/224) P = 1.09E-58 51.3 % (115/224) P = 3.84E-69 38.4 % (86/224) P = 4.02E-42 54.9 % (123/224) P = 1.31E-78 55.4 % (124/224) P = 1.54E-74 41.5 % (93/224) P = 1.47E-31 54.0 % (121/224) P = 2.06E-72 32.1 % (72/224) P = 1.10E-25 25.4 % (57/224) P = 6.34E-17 Regulation of eIF4 and p70S6K Signaling 31.8 % (57/179) P = 4.39E-22 32.4 % (58/179) P = 1.40E-22 29.1 % (52/179) P = 1.37E-19 39.1 % (70/179) P = 7.15E-33 39.7 % (71/179) P = 5.25E-31 36.9 % (66/179) P = 1.77E-19 35.8 % (64/179) P = 9.82E-26 23.5 %42/179) P = 1.56E-10 21.2 % (38/179) P = 2.65E-09 Sirtuin Signaling Pathway 24.7 % (72/292) P = 2.91E-20 29.8 % (87/292) P = 3.33E-30 24.7 % (72/292) P = 5.90E-22 34.6 % (101/292) P = 2.49E-41 29.5 % (86/292) P = 2.19E-26 28.1 % (82/292) P = 1.27E-15 29.5 % (86/292) P = 3.43E-27 Oxidative Phosphorylation 36.4 % (40/110) P = 3.17E-18 47.3 % (52/110) P = 1.44E-29 65.5 % (72/110) P = 5.28E-54 44.5 % (49/110) P = 4.97E-25 Mitochondrial Dysfunction 26.3 % (45/171) P = 2.77E-14 34.5 % (59/171) P = 1.53E-24 49.1 % (84/171) P = 9.53E-49 31.6 % (54/171) P = 1.43E-18 35.7 % (61/171) P = 1.57E-24 mTOR Signaling 26.4 % (56/212) P = 7.43E-19 34.9 % (74/212) P = 4.77E-28 34.0 % (72/212) P = 8.70E-19 Estrogen Receptor Signaling 19.6 % (79/404) P = 1.88E-17 Huntington's Disease Signaling 29.5 % (83/281) P = 3.22E-17 22.8 % (64/281) P = 1.18E-14 BAG2 Signaling Pathway 35.7 % (30/84) P = 7.23E-13 Inhibition of ARE-Mediated mRNA Degradation Pathway 24.8 % (40/161) P = 6.60E-11 Unfolded protein response 30.0 % (27/90) P = 1.67E-10 Coronavirus Pathogenesis Pathway 20.7 % (42/203) P = 9.04E-10 Autophagy 20.2 % (43/213) P = 1.27E-09 Table 3: Top upstream regulators activated/inhibited in MCF7 subline chemoresistant cells. This table summarizes the results of an upstream regulator analysis that was conducted using a significance threshold of p-value < 0.05 and activation Z-score (≥ 2 or ≤ -2). The activation Z-score is a statistical measure that indicates the likelihood that a given upstream regulator is activated or inhibited. The p-value of overlap is a statistical measure that indicates the likelihood that there is a significant overlap between the genes in the dataset and the known target genes of a given upstream regulator. The numbers in the boxes represent the activation Z-score (top) and p-value of overlap (bottom). Arabinosylcytosine Methotrexate Paclitaxel Cyclophosphamide Nocodazole Fluorouracil Etoposide Doxorubicin Cisplatin MYC -6.35 1.59E-66 -10.97 2.4E-78 -0.72 4.25E-71 -1.02 1.71E-81 -4.29 7.89E-79 6.80 3.54E-35 9.11 2.51E-64 6.20 3.64E-31 4.57 4.74E-37 CD 437 5.58 8.67E-49 8.21 3.96E-59 -1.94 2.73E-46 -0.64 2.05E-90 1.07 3.13E-61 -6.47 9.3E-45 -7.31 5.85E-59 -5.98 6.94E-28 -2.79 1.24E-18 LARP1 3.95 2.11E-46 7.12 2.06E-77 -1.41 5.65E-37 -0.93 3.53E-77 3.47 6.71E-74 -4.87 1.04E-27 -8.43 1.21E-72 -5.07 2.29E-22 -1.00 1.15E-17 ST1926 6.19 1.58E-44 8.758 3.61E-59 -1.07 2.8E-38 -0.50 6.88E-87 2.02 1.5E-57 -6.91 1.34E-39 -8.13 4.43E-56 -4.93 5.49E-21 -2.70 7.92E-16 TP53 2.93 7.91E-45 1.75 3.13E-54 0.85 2.57E-58 -0.86 2.31E-56 4.42 1.41E-69 2.87 4.24E-37 2.62 3.66E-46 -0.18 4.67E-32 2.96 9.48E-36 HNF4A 1.83 5.49E-26 1.29 1.54E-38 0.41 1.32E-29 0.97 1E-36 1.23 4.32E-32 1.10 5.88E-29 -0.56 6.87E-27 0.49 2.93E-31 0.71 8.98E-26 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1CanonicalPathwaysAnalysis.xls SupplementaryTable2UpstreamRegulatorsAnalysis.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4204722","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":296127752,"identity":"9f3384f5-15b9-463f-9901-8ecaf79f607a","order_by":0,"name":"Lin Batha","email":"","orcid":"","institution":"Alfaisal University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Batha","suffix":""},{"id":296127753,"identity":"5e8a8f27-e0a7-49c3-bb76-07b1189b408a","order_by":1,"name":"Mahmoud Zhra","email":"","orcid":"","institution":"Alfaisal 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07:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4204722/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4204722/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55765278,"identity":"e210452e-39cd-44c8-8550-727ce342c990","added_by":"auto","created_at":"2024-05-02 20:06:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":735443,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A-B) Exploration of IPA Canonical Pathways in MCF7 Cells Reveals Distinct mRNA Expression Patterns in Chemoresistance to Nine Chemotherapeutic Drugs.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4204722/v1/f1055eee61aeb34d6c8503cf.png"},{"id":72579043,"identity":"66e9ec73-00da-42cf-b843-855a023226e7","added_by":"auto","created_at":"2024-12-30 04:53:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1473259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4204722/v1/3035f092-c9f5-4d0c-93ac-3bfa2a113f5f.pdf"},{"id":55765277,"identity":"bc4f1824-726e-4984-b6d2-d493690fe224","added_by":"auto","created_at":"2024-05-02 20:06:38","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":86528,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1CanonicalPathwaysAnalysis.xls","url":"https://assets-eu.researchsquare.com/files/rs-4204722/v1/17abd34c43f60f16a8b688f0.xls"},{"id":55765279,"identity":"75d0457f-e24d-46c1-9410-da290af1f45d","added_by":"auto","created_at":"2024-05-02 20:06:39","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8288768,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2UpstreamRegulatorsAnalysis.xls","url":"https://assets-eu.researchsquare.com/files/rs-4204722/v1/59820cfa27e5f324d55fd980.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Approach to Classifying Chemotherapeutic Agents Based on Their Impact on Canonical Pathways: Implications for Overcoming Multidrug Resistance in Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer remains a global health concern and is a leading cause of mortality worldwide (1). Current treatment options include surgery, radiation therapy, chemotherapy, targeted therapy, endocrine therapy, and immunotherapy (2). While significant progress has been made in cancer therapy, multidrug resistance (MDR) continues to present a significant challenge in chemotherapy. MDR is implicated in over 90% of chemotherapy-related patient deaths . The inherent heterogeneity and adaptability of cancer cells present a significant obstacle, severely impacting the efficacy of cancer therapy (3).\u003c/p\u003e\n\u003cp\u003eDrug resistance can be either intrinsic or acquired. Intrinsic resistance exists before drug administration, usually due to (a) pre-existing inherited genetic mutations; (b) intra-tumoral genetic heterogeneity; or (c) activation of intrinsic pathways responsible for detoxification as a defense mechanism against anti-tumor agents (4-7). Acquired resistance, on the other hand, is gradually acquired by tumor cells throughout treatment, either against a specific chemotherapeutic agent or multiple agents. Acquired resistance can result from various cellular and molecular responses, including (a) activation of secondary proto-oncogenes as new driver genes; (b) alterations in drug target expression levels; (c) drug metabolism within the tumor; (d) ATP-binding cassette transporters that pump out chemotherapeutics; (e) impaired cellular responses affecting cell cycle arrest, apoptosis, and DNA repair; (f) induction of signaling pathways promoting cancer progression; (g) epigenetic alterations caused by errors in DNA methylation, acetylation, histone modification, and altered micro-RNAs, leading to upstream and downstream receptor changes; (h) changes in the tumor microenvironment (TME) after treatment. These mechanisms can act independently or synergistically to promote MDR (4-7).\u003c/p\u003e\n\u003cp\u003eMDR remains a major challenge in oncology, particularly in breast cancer, due to cross-resistance to a range of structurally and functionally different anti-breast cancer agents. This significantly impacts patient prognosis. One study estimated that one in two breast cancer patients fails to respond to treatment due to intrinsic or acquired drug resistance (8). To understand the mechanism of MDR development against various chemotherapeutic agents, it is crucial to examine their mechanisms of action and common signaling pathways.\u003c/p\u003e\n\u003cp\u003eWhile multiple signaling pathways and genes have been associated with MDR, the exact mechanism remains unclear. Transcriptomics analysis has been used to characterize intra-tumoral heterogeneity, revealing the molecular basis for MDR, tumor growth dynamics, and relapse potential (9, 10). Notably, miRNAs have been shown to play a key regulatory role in MDR, modulating multiple mechanisms to provide a more comprehensive understanding of cross-resistance through shared pathways among different drugs (11). This project aims to explore common signaling pathways that contribute to MDR in chemo-resistant MCF7 breast cancer cell sublines. By using transcriptomic analysis, we aim to improve our understanding of MDR mechanisms in breast cancer. This approach involves carefully combining distinct drug classes with minimal overlap in signaling pathways and toxicities, offering a promising strategy to address the complexities associated with drug resistance.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e3.1. Materials Used\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe materials used in this study were as follows: MCF7 cells were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). The cell culture medium consisted of Dulbecco\u0026apos;s Modified Eagle Medium 1X (DMEM) (Gibco, USA) supplemented with 10% Fetal Bovine Serum (FBS) (Gibco, USA), 1X Pen Strip (Gibco, USA), 1% Glutamax (Gibco, USA), and 1% Non-Essential Amino Acid (NEAA) (Gibco, USA). The chemotherapeutic drugs used were camptothecin (Cat. No.: C9911, Sigma-Aldrich, USA), methotrexate (Cat. No.: 45412, Sigma-Aldrich), doxorubicin (Cat. No.: D1515, Sigma-Aldrich), fluorouracil (Cat. No.: F6627, Sigma-Aldrich), paclitaxel (Cat. No.: T7191, Sigma-Aldrich), arabinofuranosylcytosine (Cat. No.: C1768, Sigma-Aldrich), cisplatin (Cat. No.: 232120, Sigma-Aldrich), cyclophosphamide (Cat. No.: 218707, Sigma-Aldrich), etoposide (Cat. No.: E1383, Sigma-Aldrich), and nocodazole (Cat. No.: M1404, Sigma-Aldrich). Other reagents used were dimethyl sulfoxide (DMSO) (Sigma-Aldrich, USA) and 0.05% trypsin (Gibco, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Cell Culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMCF7 parental cells and their chemoresistant counterparts were cultured under sterile conditions in DMEM supplemented with 10% FBS, 1X Pen Strip, 2mM Glutamax, and 1% NEAA at 37\u0026deg;C in a humidified incubator with 95% air and 5% CO2. Cells were treated with chemotherapeutic drugs dissolved in DMSO at an initial concentration of 10-16 M. At 80-90% confluency, cells were passaged using 0.05% trypsin at a ratio of 1:3. To generate drug-resistant cells, the parental cell line was subjected to continuous incubation with increasing drug concentrations over a period of 6 -12 months until the final drug concentration in the cultured cells reached 10 \u0026mu;M (Methotrexate, fluorouracil, and paclitaxel), or 100 \u0026mu;M (Doxorubicin, arabinofuranosylcytosine, cisplatin, cyclophosphamide, etoposide, and nocodazole).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3. \u003cem\u003eIn vitro\u003c/em\u003e drug sensitivity assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn vitro drug cytotoxicity on the MCF7/Chemotherapy-Resistant sublines was assessed using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. This colorimetric assay measures cellular viability by quantifying the amount of formazan produced by metabolically active cells. The assay was performed in triplicate wells per treatment condition. In brief, 2x103 cells were seeded in a 96-well plate and incubated for 24 hours to allow for cell attachment. Subsequently, the chemotherapeutic agents were diluted in 100 \u0026mu;L of growth medium and added to the respective wells. The cells were then incubated for an additional 48 hours to allow for drug exposure. After the 48-hour incubation period, 10 \u0026mu;L of MTT solution (5 mg/mL in PBS) was added to each well, followed by 100 \u0026mu;L of phenol-free media. The plate was then incubated for 4 hours at 37\u0026deg;C in a 5% CO2 atmosphere to allow for formazan synthesis. Following the formazan synthesis incubation, 100 \u0026mu;L of 10% SDS-HCl solution were added to each well to lyse the cells and solubilize the formazan crystals. The plate was then incubated for 18 hours at room temperature to allow for complete cell lysis and formazan solubilization. Cell viability was determined by measuring the absorbance of the formazan solution at 570 nm using a spectrophotometer (Agilent BioTek Epoch Microplate Spectrophotometer, Agilent Technologies Inc., California, USA) equipped with Gen5 software. The absorbance values were directly proportional to the number of viable cells in each well. The cell viability percentage was calculated by comparing the absorbance values of treated cells to those of untreated control cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4. Ion Torrent Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1 RNA Extraction and Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ion Torrent platform was used for next-generation RNA sequencing (RNA-seq) to examine the transcriptional landscape of chemoresistant MCF7 cells in comparison to their parental counterparts. Ion torrent analysis was conducted as described in our previous work (12). The RNA extraction process involved the use of the Ambion\u0026reg; Aqueous kit from Thermo Fisher Scientific, following the manufacturer\u0026apos;s instructions. The extracted RNA was then evaluated for quantity and integrity using the Bio-Rad Experion\u0026reg; automated electrophoresis system from Bio-Rad Laboratories. Subsequently, the isolated RNA was purified using the RiboMinus\u0026trade; Eukaryote System v2 from Invitrogen to eliminate ribosomal RNA. The mRNA concentration was determined using the Qubit\u0026trade; RNA HS Assay kit from Thermo Fisher Scientific.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Library construction for Next-Generation Sequencing (NGS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the construction of RNA-seq libraries, the Ion Total RNA-Seq Kit v2, Ion Total RNA-Seq Primer Set v2, and Ion Express\u0026trade; RNA-Seq Barcode 01-16 Kit from Thermo Fisher Scientific were employed. Initially, cDNA libraries were generated from 2 \u0026mu;g of total RNA using the Ion Total RNA-Seq Kit v2. These cDNA libraries were subsequently fragmented and barcoded using the Ion Express\u0026trade; RNA-Seq Barcode 01-16 Kit. Finally, the barcoded libraries were amplified using the Ion Total RNA-Seq Primer Set v2. Sequencing of the prepared RNA-Seq libraries was conducted using the Ion Proton Semiconductor Sequencer from Thermo Fisher Scientific.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.3. Data Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sequencing data was extracted using Agilent Feature Extraction Software from Agilent Technologies and imported into GeneSpring GX software, also from Agilent Technologies, for further analysis. The data preprocessing in this study involved two steps: log2 transformation and normalization of the gene expression values using the percentile shift algorithm. To identify DEGs, the gene expression levels between chemoresistant MCF7 cells and control MCF7 cells were compared using a two-tailed t-test. DEGs were defined as genes that exhibited a fold change (FC) of 2.5 or greater and a p-value of \u0026le; 0.05. This analysis helped to identify genes that were significantly differentially expressed between the two cell types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.4. Ingenuity Pathway Analysis (IPA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo gain insights into the functional implications of the identified DEGs, IPA software was employed. This software suite, provided by Qiagen in Hilden, Germany, was used to analyze the DEGs and identify critical pathways, networks, and molecular and cellular functions associated with them. To filter for statistically significant pathways and functions, a cutoff of -log(p-value) \u0026gt;3 was applied. This ensured that only pathways and functions with a high level of statistical significance were considered. The significance of the IPA core analysis was evaluated using either a Fisher\u0026apos;s exact test or a Z-score. These statistical tests were used to generate predictions of activation, inactivation, or no effect for the identified pathways and functions. This analysis helped to further understand the potential impact of the DEGs on various biological processes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e4.1. Establishment of drug resistance MCF7 sublines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the mechanisms of drug resistance in MCF7 breast cancer cells, we generated drug-resistant sublines by continuously exposing parental MCF7 cells to increasing concentrations of various chemotherapeutic drugs. The final drug concentrations in the cultured cells ranged from 10 \u0026mu;M for methotrexate, fluorouracil, and paclitaxel to 100 \u0026mu;M for doxorubicin, arabinofuranosylcytosine, cisplatin, cyclophosphamide, etoposide, and nocodazole. This gradual increase in drug concentration ensured that only cells with the ability to tolerate the drug survived and proliferated, leading to the enrichment of resistant cells in the population. The drug-resistant MCF7 sublines exhibited significantly higher resistance to their respective chemotherapeutic drugs compared to the parental cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Activation/Inactivation of Pathways and Upstream Regulators in chemoresistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIPA analysis revealed several statistically significant pathways and molecular and cellular functions associated with DEGs in chemoresistant MCF7 subline cells. Table 1 highlights notable alterations in these functions, including cell death and survival, gene expression, and protein synthesis, suggesting modifications in signaling pathways that regulate these processes in chemoresistant MCF7 cells. These changes include increased protein synthesis, altered apoptosis regulation, altered gene expression patterns, enhanced DNA/RNA repair mechanisms, altered RNA post-transcriptional modification, altered cellular development, and increased cellular growth, proliferation, and movement. Overall, the findings suggest that chemoresistant MCF7 cells undergo significant molecular and cellular alterations, enabling their survival and growth in the presence of chemotherapeutic drugs.\u003c/p\u003e\n\u003cp\u003eIPA analysis identified also several canonical pathways that were significantly associated with these differentially expressed genes (Supplementary Table 1). A comparative analysis was conducted to determine the top canonical pathways that were linked to the significant DEGs. Among these top pathways were EIF2 signaling, elF4 and p70S6K regulation signaling, and the Sirtuin Signaling Pathway, as indicated in Table 2. The IPA Canonical pathways, which are intricately linked to alterations in mRNA expression within MCF7 cells exhibiting resistance to nine distinct chemotherapeutic drugs, were thoroughly explored in Figure 1. By employing Fisher\u0026apos;s exact test with a significance threshold of -log(P-value) \u0026gt; 1.301 (p \u0026lt; 0.05), the transcriptional shifts were carefully examined. These findings are visually represented in a heatmap, where pathways are organized based on their Z-scores. A Z-score \u0026ge; 1 indicates a significant increase in pathway activity (depicted in orange), while a Z-score \u0026le; -1 signifies a substantial decrease (rendered in blue). Pathways with undetermined predictions are depicted in gray.\u003c/p\u003e\n\u003cp\u003eUpon careful examination of the heatmap in Figure 1, a distinct pattern of mRNA expression changes becomes apparent in MCF7 cells resistant to chemotherapy. Several pathways show significant activation in chemoresistant cells compared to their parental counterparts, indicating their potential role in conferring resistance to chemotherapy. Conversely, several pathways demonstrate noteworthy inactivation in chemoresistant cells, indicating their involvement in maintaining sensitivity to chemotherapy. The identification of these differentially regulated pathways provides crucial insights into the molecular intricacies underlying chemoresistance in MCF7 cells. Moreover, a thorough examination of the primary IPA Canonical pathways allows for the classification of the nine chemotherapeutic medications into three distinct groups based on their activation or inhibition patterns. The first group, Group I, consists of Arabinosylcytosine and Methotrexate. Group II encompasses Paclitaxel, Cyclophosphamide, and Nocodazole. Lastly, Group III comprises 5-fluorouracil, Etoposide, Doxorubicin, and Cisplatin. This categorization offers valuable insights into the various mechanisms by which these drugs exert their effects and how these effects intricately intertwine with chemoresistance mechanisms. By thoroughly exploring the IPA Canonical pathways, we can enhance our understanding of cellular responses to chemotherapy and establish a strong foundation for tailored therapeutic interventions aimed at mitigating specific resistance profiles.\u003c/p\u003e\n\u003cp\u003eIPA upstream regulator analysis was also conducted using the Ingenuity Knowledge Base (IKB). The IKB is a curated database of molecular interactions and biological processes. It can be used to identify potential upstream regulators of gene expression changes. The IPA upstream regulator analysis identified several potential upstream regulators of the DEGs (Supplementary Table 2). These regulators included MYC, CD437, LARP1, ST1926, TP53, and HNF4A (Table 3). The activation Z-score and p-value of overlap for each regulator were calculated to assess the likelihood that the regulator was activated or inhibited in the chemoresistant cells. The drugs used in this study were grouped into three categories based on the patterns of drug resistance observed in MCF7 cells and were consistent with our categorization based on the canonical pathway activation/inactivation. Group I drugs (Arabinosylcytosine, Methotrexate) induced a similar pattern of gene expression changes, with upregulation of MYC, CD437, and LARP1, and downregulation of HNF4A. Group II drugs (Paclitaxel, Cyclophosphamide, Nocodazole) induced a different pattern of gene expression changes, with downregulation of MYC, CD437, and LARP1, and upregulation of TP53. Group III drugs (5-fluorouracil, Etoposide, Doxorubicin, Cisplatin) induced a third pattern of gene expression changes, with downregulation of MYC, CD437, and LARP1, and upregulation of TP53.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChemoresistance in cancer is a complex and multifaceted phenomenon, with both inherent and acquired resistance pathways contributing to treatment failure (13). This complexity is further compounded by the heterogeneous nature of cancer, making it difficult to fully understand the mechanisms of chemoresistance (14). One significant factor in chemoresistance is the presence of cancer stem cells, which possess intrinsic resistance to chemotherapy (9). Despite these challenges, several strategies have been proposed to overcome chemoresistance, including the use of novel agents, combination treatments, and targeted therapies (9, 10). However, the effectiveness of these strategies in clinical settings remains a major hurdle (13). Our findings reveal that the effects of the nine chemotherapeutic drugs tested can be classified into three distinct groups based on their impact on canonical pathways. These groups are: Group I (Arabinosylcytosine, Methotrexate), Group II (Paclitaxel, Cyclophosphamide, Nocodazole), and Group III (5-fluorouracil, Etoposide, Doxorubicin, Cisplatin). We propose a combination therapy strategy that utilizes chemotherapeutic drugs from different groups. In the event of a relapse, the treatment should involve drugs from a different group than those used in the initial treatment. This approach needs further validation through in vitro and in vivo studies.\u003c/p\u003e\n\u003cp\u003eContinuous exposure to increasing concentrations of chemotherapeutic drugs leads to the emergence of drug-resistant cells, a process that reflects the selective pressures experienced by cancer cells in vivo (15). This approach leads to the enrichment of a drug-resistant cell population through genetic and epigenetic alterations (16). These alterations include the overexpression of drug efflux pumps, changes in drug metabolism, enhanced DNA repair capacity, and modifications in cell signaling pathways (15). The development of drug resistance is further complicated by the evolution of multifactorial mechanisms, such as gene amplification and increased enzyme activity (16). These mechanisms are associated with altered sequestration and efflux of chemotherapeutic drugs by multidrug-resistant cells (17). Despite the identification of these mechanisms, strategies to overcome drug resistance have largely failed due to the complexity of the resistance phenotype.\u003c/p\u003e\n\u003cp\u003eSeveral studies have reported the development of MCF7 resistant sublines through stepwise increases in different chemotherapeutic drugs. The establishment of MCF7 sublines, including MCF7/VP (Vincristine), MCF7/Pac (Paclitaxel), MCF7/TopoisomeraseII (Etoposide), MCF7/MTX (Methotrexate), MCF7/5-FU (5-fluorouracil), MCF7/Cis (Cisplatin), MCF7/Cyclo (Cyclophosphamide), and MCF7/AraC (Arabinosylcytosine), involves the selection and expansion of inherently resistant cells (18-21). These sublines display various characteristics of drug resistance, including altered gene expression patterns and increased expression of drug resistance-related proteins (18, 20-22). The resistance mechanisms include increased expression of multidrug resistance protein (MRP) and glutathione S-transferase P1-1 (GSTP1-1) (18), reduced drug sensitivity of topoisomerase II (22), enhanced energy-dependent drug efflux (20), and upregulation of MDR1 gene and detoxifying enzymes (21). These studies collectively highlight the complex and multifaceted nature of drug resistance in MCF7 cells.\u003c/p\u003e\n\u003cp\u003eThe use of a singular cell line in cancer research offers several advantages, including the elimination of cellular heterogeneity, unifying genetic background, facilitating comparative studies, and ensuring reproducibility and consistency (23). However, the presence of tumor heterogeneity poses a challenge to the effectiveness of drug delivery and treatment methods. Therefore, while the use of a singular cell line can provide valuable insights, it is crucial to consider the limitations and utilize multiple cell lines. Thus, our proposed classification for chemotherapeutic drugs based on canonical pathway activation/inactivation should be validated using a diverse panel of breast cancer cell lines representing the heterogeneity of tumor cells.\u003c/p\u003e\n\u003cp\u003eDespite the success of established combination regimens, there exists an opportunity to optimize treatment strategies by exploring novel combinations and integrating additional highly effective oncology drugs. This rationale is grounded in understanding the shared and distinct signaling pathways activated and inactivated by various chemotherapeutic agents commonly used in breast cancer treatment. To address this, we systematically grouped nine chemotherapeutic agents based on their activation scores and intersections within various signaling pathways in our MCF7 chemoresistant sublines. The resulting three groups provide a framework for comparing newly assembled groups with traditional breast cancer regimens. This comparative analysis, considering indications, mechanisms of action, resistance, and relapse rates, aims to deepen our understanding of the molecular mechanisms governing MDR across these diverse drug classes. The ultimate goal is to provide a foundation for developing innovative regimens that integrate additional drugs with minimal overlap in signaling pathways and toxicities. This strategic approach has the potential to significantly enhance chemotherapy effectiveness and reduce relapse rates in the future (24). Based on the proposed classifications, it becomes evident that certain drugs share similar groupings concerning both their activation scores in the canonical pathways and their pharmacokinetics (24). Notably, 5-Fluorouracil and Arabinosylcytosine, classified as antimetabolites, function as inactivators in various signaling pathways. Similarly, Paclitaxel and Nocodazole, which primarily act as mitotic spindle inhibitors, show no difference in terms of their roles as activators or inactivators in the analyzed signaling pathways illustrated in Figure 1. Lastly, Etoposide, Doxorubicin, and Cisplatin\u0026mdash;classified as genotoxic agents based on their chemotherapeutic mechanisms primarily serve as activators in a diverse array of signaling pathways detailed in Figure 1. These classifications hold particular significance in endeavors aimed at comprehending the formation of multidrug resistance and determining suitable chemotherapeutic agents in the event of relapse. By considering the activation/inhibition of canonical pathways and pharmacokinetic properties of these drugs, researchers can develop more effective combination therapies and predict potential drug resistance mechanisms. Thus, the grouping of anti-cancer drugs based on their canonical signaling pathways and pharmacokinetics offers a novel approach to understanding and optimizing chemotherapy regimens, ultimately leading to improved patient outcomes in breast cancer treatment.\u003c/p\u003e\n\u003cp\u003eThe strategy of combination therapy, involving the synergistic combination of multiple anti-cancer agents, has emerged as a promising avenue to enhance the efficacy of breast cancer treatment compared to monotherapy (25). This approach seeks to overcome the limitations associated with single-agent therapies by mitigating resistance, inhibiting metastasis, and reducing the number of mitotically active cells. Clinical trials consistently highlight the effectiveness of combination chemotherapy regimens in improving patient outcomes in breast cancer. A notable example is the cyclophosphamide, methotrexate, and 5-fluorouracil (CMF) combination adjuvant chemotherapy regimen pioneered by the Istituto Nazionale Tumori in Milan. This regimen demonstrates improved overall survival among breast cancer patients, with no significant difference in relapse rates between pre-menopausal and post-menopausal women (12, 13). The US Breast Cancer Intergroup reported the efficacy of the CMF regimen in reducing recurrence and enhancing survival, particularly in patients with axillary node-negative disease (12, 13). Supporting the efficacy of combination therapy, the NSABP B-20 trial demonstrated that adding tamoxifen to the CMF regimen significantly decreased the relapse rate in patients with ER-positive, lymph node-negative disease. This effect aligns with the findings of the EBCTCG meta-analysis, indicating that CMF contributed to a 30% reduction in the recurrence rate over a 10-year period (26). Our proposed classification system, which assigns cyclophosphamide to group II, methotrexate to group I, and 5-fluorouracil to group III, further supports the potential of this categorization approach.\u003c/p\u003e\n\u003cp\u003eIn addition to CMF, other combination regimens exhibit promise in breast cancer treatment. The AC combination therapy consisting of doxorubicin (group III) and cyclophosphamide (group II), administered every three weeks for four cycles, proves to be an effective and shorter alternative to the CMF regimen for patients with node-negative disease (27). The FAC regimen, comprising 5-fluorouracil (group III), doxorubicin (group III), and cyclophosphamide (group II), demonstrates superior efficacy compared to AC and CMF in postmenopausal women with hormone-receptor-positive, node-positive breast cancer (28, 29). More patient-customized regimens, such as doxorubicin (group III), cyclophosphamide (group II), followed by paclitaxel (group II) (AC-T), have been developed by Memorial Sloan Kettering Cancer Center for the treatment of aggressive early-stage breast cancer in young patients (30-32).\u003c/p\u003e\n\u003cp\u003eThe efficacy of breast cancer chemotherapeutic regimens varies in their ability to prevent relapse and prolong disease-free survival (DFS) among patients. Bang et al. (2000) compared six cycles of AC to six cycles of oral CMF and found a DFS of 64% for AC-treated patients compared to 78% for CMF-treated patients (33). Another study by CALGB 9344 compared four cycles of AC to four cycles of AC followed by paclitaxel (AC-P) and reported a DFS rate of 65% for AC alone versus 70% for AC-P at five years (34). Additionally, Martin et al. (2003) compared six cycles of FAC to six cycles of CMF and found a DFS of 58% for FAC compared to 50% for CMF at five years (35). Finally, a study by MD Anderson Cancer Center compared four cycles of paclitaxel followed by four cycles of FAC to four cycles of FAC alone and reported a DFS of 86% for paclitaxel followed by FAC (pac/FAC) and 83% for FAC alone at 48 months (36). These studies demonstrate that contemporary regimens incorporating anthracyclines, such as doxorubicin, appear to be more effective in improving overall survival than CMF. However, they are associated with increased long-term toxicities, particularly an increased risk of acute myeloid leukemia (AML), compared to CMF. Therefore, it is crucial to establish an individualized selection process for the treatment of breast cancer patients. CMF may remain suitable for those with a low risk of recurrence, whereas patients at a high risk of recurrence may benefit from more effective treatments, such as regimens including anthracyclines (37). To further advance evidence-based regimen selection, it is essential to understand how these drugs interact in terms of their activated and inactivated biochemical pathways and how they overcome relapse. This knowledge can guide the development of more personalized and effective treatment strategies for breast cancer patients.\u003c/p\u003e\n\u003cp\u003eThe increasing prevalence of MDR in cancer treatment has prompted the exploration of alternative strategies to improve patient outcomes. Our approach is to utilize drugs with minimally overlapping signaling pathways and different pharmacokinetic profiles to minimize the risk of MDR development. This strategy is particularly relevant in the context of relapse treatment, where cancer cells have already demonstrated resistance to initial treatment regimens. A notable study conducted in 1979 examined the efficacy of methotrexate (group I) and vincristine in patients with advanced breast cancer who had failed treatment with FAC (5-fluorouracil (group III), doxorubicin (group III), and cyclophosphamide (group II)) or experienced relapse. Among the 17 patients enrolled, four achieved partial remission lasting six months, with a median survival of ten months (38). Additionally, seven patients exhibited stable disease with minimal toxicity. This study is particularly intriguing in light of the distinct pharmacological profiles of methotrexate and vincristine compared to FAC drugs. As suggested by this study, methotrexate belongs to a different group based on its general function within the listed signaling pathways. Unfortunately, we did not examine vincristine canonical pathways in this study. These findings suggest that employing drugs with minimal or no overlapping signaling pathways may hold promise in reducing the likelihood of MDR development, particularly in the context of relapse treatment. However, further research, including additional data on multiple breast cancer cell lines and clinical trials, is warranted to fully elucidate the potential benefits of this approach.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur newly proposed classification of chemotherapeutic drugs based on their activation scores and intersections within signaling pathways offers a new perspective for optimizing breast cancer treatment. The systematic grouping of nine agents into three distinct categories provides a framework for comparing traditional regimens and newly assembled groups. This classification, may deepen our understanding of the molecular mechanisms governing MDR. The potential of this classification extends beyond theoretical insights, offering a practical foundation for developing innovative regimens that integrate additional drugs with minimal overlap in signaling pathways and toxicities. This strategic approach holds the promise of significantly enhancing chemotherapy effectiveness and reducing relapse rates in the future. By considering the canonical activation/inactivation pathways of these drugs, researchers can tailor more effective combination therapies, predict potential drug resistance mechanisms, and ultimately improve patient outcomes in breast cancer treatment. However, while our classification shows great promise, further research on other drugs and multiple breast cancer cell lines are warranted to validate its efficacy and determine its applicability across diverse patient populations. The evolving landscape of breast cancer treatment demands continued exploration and refinement of treatment strategies, and our proposed classification opens up new avenues for such advancements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding for this project was provided by the Alfaisal University-Office of Research and Innovation (ORI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that the research was carried out without any commercial or financial associations that could be perceived as a possible conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLB, MG \u0026amp;amp; JH: Responsible for Data Analysis, and Writing the Original Draft; MAZ, MZ \u0026amp;amp; MA: Involved in Methodology and Data Analysis; HF: Responsible for Writing Review and Funding Acquisition; AA: Oversaw Conceptualization, Supervision, and contributed to Writing Review and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available within the article and its supplementary materials or from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. 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Combination therapy in combating cancer. Oncotarget. 2017;8(23):38022-43.\u003c/li\u003e\n\u003cli\u003eFisher B, Dignam J, Wolmark N, DeCillis A, Emir B, Wickerham DL, et al. Tamoxifen and chemotherapy for lymph node-negative, estrogen receptor-positive breast cancer. J Natl Cancer Inst. 1997;89(22):1673-82.\u003c/li\u003e\n\u003cli\u003eFisher B, Brown AM, Dimitrov NV, Poisson R, Redmond C, Margolese RG, et al. Two months of doxorubicin-cyclophosphamide with and without interval reinduction therapy compared with 6 months of cyclophosphamide, methotrexate, and fluorouracil in positive-node breast cancer patients with tamoxifen-nonresponsive tumors: results from the National Surgical Adjuvant Breast and Bowel Project B-15. J Clin Oncol. 1990;8(9):1483-96.\u003c/li\u003e\n\u003cli\u003eAlbain KS, Barlow WE, Ravdin PM, Farrar WB, Burton GV, Ketchel SJ, et al. Adjuvant chemotherapy and timing of tamoxifen in postmenopausal patients with endocrine-responsive, node-positive breast cancer: a phase 3, open-label, randomised controlled trial. Lancet. 2009;374(9707):2055-63.\u003c/li\u003e\n\u003cli\u003ePeto R, Davies C, Godwin J, Gray R, Pan HC, Clarke M, et al. Comparisons between different polychemotherapy regimens for early breast cancer: meta-analyses of long-term outcome among 100,000 women in 123 randomised trials. Lancet. 2012;379(9814):432-44.\u003c/li\u003e\n\u003cli\u003eNorton L. Theoretical concepts and the emerging role of taxanes in adjuvant therapy. Oncologist. 2001;6 Suppl 3:30-5.\u003c/li\u003e\n\u003cli\u003eSimon R, Norton L. The Norton-Simon hypothesis: designing more effective and less toxic chemotherapeutic regimens. Nat Clin Pract Oncol. 2006;3(8):406-7.\u003c/li\u003e\n\u003cli\u003eSparano JA. Doxorubicin/taxane combinations: cardiac toxicity and pharmacokinetics. Semin Oncol. 1999;26(3 Suppl 9):14-9.\u003c/li\u003e\n\u003cli\u003eBang SM, Heo DS, Lee KH, Byun JH, Chang HM, Noh DY, et al. Adjuvant doxorubicin and cyclophosphamide versus cyclophosphamide, methotrexate, and 5-fluorouracil chemotherapy in premenopausal women with axillary lymph node positive breast carcinoma. Cancer. 2000;89(12):2521-6.\u003c/li\u003e\n\u003cli\u003eHenderson IC, Berry DA, Demetri GD, Cirrincione CT, Goldstein LJ, Martino S, et al. Improved outcomes from adding sequential Paclitaxel but not from escalating Doxorubicin dose in an adjuvant chemotherapy regimen for patients with node-positive primary breast cancer. J Clin Oncol. 2003;21(6):976-83.\u003c/li\u003e\n\u003cli\u003eMartin M, Villar A, Sole-Calvo A, Gonzalez R, Massuti B, Lizon J, et al. Doxorubicin in combination with fluorouracil and cyclophosphamide (i.v. FAC regimen, day 1, 21) versus methotrexate in combination with fluorouracil and cyclophosphamide (i.v. CMF regimen, day 1, 21) as adjuvant chemotherapy for operable breast cancer: a study by the GEICAM group. Ann Oncol. 2003;14(6):833-42.\u003c/li\u003e\n\u003cli\u003eRamanathan B, Jan K-Y, Chen C-H, Hour T-C, Yu H-J, Pu Y-S. Resistance to Paclitaxel Is Proportional to Cellular Total Antioxidant Capacity. Cancer Research. 2005;65(18):8455-60.\u003c/li\u003e\n\u003cli\u003eEspinosa E, Zamora P, Feliu J, Gonz\u0026aacute;lez Bar\u0026oacute;n M. Classification of anticancer drugs\u0026mdash;a new system based on therapeutic targets. Cancer Treatment Reviews. 2003;29(6):515-23.\u003c/li\u003e\n\u003cli\u003eYap HY, Blumenschein GR, Tashima CK, Hortobagyi GN, Buzdar AU, Wiseman CL. Combination chemotherapy with vincristine and methotrexate for advanced refractory breast cancer. Cancer. 1979;44(1):32-4.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Molecular and cellular functions identified using QIAGEN\u0026apos;s Ingenuity Pathway Analysis.\u003c/strong\u003e Numbers represent differentially expressed genes (DEGs) associated with functions in MCF7 chemoresistant subline cells.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"954\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular and cellular function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArabinosylcytosine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethotrexate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaclitaxel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCyclophosphamide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNocodazole\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFluorouracil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtoposide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoxorubicin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCisplatin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eProtein Synthesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eCell Death and Survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e1066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eGene Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eRNA Damage and Repair\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eRNA Post-Transcriptional Modification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eCellular Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eCellular Growth and Proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e519\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eCellular Movement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.20754716981132%\" valign=\"top\"\u003e\n \u003cp\u003eCell Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.80503144654088%\" valign=\"top\"\u003e\n \u003cp\u003e489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.69182389937107%\" valign=\"top\"\u003e\n \u003cp\u003e407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Top 5 IPA Canonical pathways associated with mRNA expression concordant with chemoresistance induced in MCF7 to 9 different chemotherapeutic drugs.\u0026nbsp;\u003c/strong\u003eThe overlap of top canonical pathways in IPA is evaluated using a statistical assessment of gene or molecule overlap. The overlap p-value, determined using a hypergeometric or Fisher\u0026apos;s exact test, compares the observed overlap to an anticipated overlap generated from a random model. A lower overlap p-value indicates a more significant pathway similarity, suggesting potential functional connections. Significance was determined at a predetermined threshold (\u0026lt; 0.05). %: refers to the proportion of overlapping genes or molecules relative to the total number involved in the pathways.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"910\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArabinosylcytosine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethotrexate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaclitaxel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCyclophosphamide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNocodazole\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e5-fluorourasil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEtoposide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDoxorubicin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCisplatin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eEIF2 Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e46.9 %\u0026nbsp;(105/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.09E-58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e51.3 %\u0026nbsp;(115/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 3.84E-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e38.4 % (86/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 4.02E-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e54.9 % (123/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.31E-78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e55.4 %\u0026nbsp;(124/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.54E-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e41.5 % (93/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.47E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e54.0 %\u0026nbsp;(121/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 2.06E-72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e32.1 %\u0026nbsp;(72/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.10E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e25.4 %\u0026nbsp;(57/224)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 6.34E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eRegulation of eIF4 and p70S6K Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e31.8 % (57/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 4.39E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e32.4 % (58/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.40E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e29.1 % (52/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.37E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e39.1 %\u0026nbsp;(70/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 7.15E-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e39.7 % (71/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 5.25E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e36.9 % (66/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.77E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e35.8 % (64/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 9.82E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e23.5 %42/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.56E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e21.2 % (38/179)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 2.65E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eSirtuin Signaling Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e24.7 % (72/292)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 2.91E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e29.8 % (87/292)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 3.33E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e24.7 % (72/292)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 5.90E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e34.6 %\u0026nbsp;(101/292)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 2.49E-41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e29.5 % (86/292)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 2.19E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e28.1 % (82/292)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.27E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e29.5 % (86/292)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 3.43E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eOxidative Phosphorylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e36.4 % (40/110)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 3.17E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e47.3 % (52/110)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.44E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e65.5 %\u0026nbsp;(72/110)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 5.28E-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e44.5 % (49/110)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 4.97E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eMitochondrial Dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e26.3 % (45/171)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 2.77E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e34.5 % (59/171)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.53E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e49.1 %\u0026nbsp;(84/171)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 9.53E-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e31.6 % (54/171)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.43E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e35.7 % (61/171)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.57E-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003emTOR Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e26.4 % (56/212)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 7.43E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e34.9 % (74/212)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 4.77E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e34.0 % (72/212)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 8.70E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eEstrogen Receptor Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e19.6 % (79/404)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.88E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eHuntington\u0026apos;s Disease Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e29.5 % (83/281)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 3.22E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e22.8 %\u0026nbsp;(64/281)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.18E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eBAG2 Signaling Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e35.7 %\u0026nbsp;(30/84)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 7.23E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eInhibition of ARE-Mediated mRNA\u003c/p\u003e\n \u003cp\u003eDegradation Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e24.8 % (40/161)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 6.60E-11\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eUnfolded protein response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e30.0 %\u0026nbsp;(27/90)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.67E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eCoronavirus Pathogenesis Pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e20.7 % (42/203)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 9.04E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003eAutophagy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.513157894736842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.18421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.868421052631579%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.210526315789474%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.429824561403509%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.100877192982455%\" valign=\"top\"\u003e\n \u003cp\u003e20.2 % (43/213)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e = 1.27E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/br\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Top upstream regulators activated/inhibited in MCF7 subline chemoresistant cells.\u0026nbsp;\u003c/strong\u003eThis table summarizes the results of an upstream regulator analysis that was conducted using a significance threshold of p-value \u0026lt; 0.05 and activation Z-score (\u0026ge; 2 or \u0026le; -2). The activation Z-score is a statistical measure that indicates the likelihood that a given upstream regulator is activated or inhibited. The p-value of overlap is a statistical measure that indicates the likelihood that there is a significant overlap between the genes in the dataset and the known target genes of a given upstream regulator. The numbers in the boxes represent the activation Z-score (top) and p-value of overlap (bottom).\u003c/p\u003e\n\u003ctable style=\"width: 6.9e+2pt;border-collapse:collapse;border:none;margin-left:6.75pt;margin-right: 6.75pt;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65.75pt;border: 1pt solid windowtext;padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92.5pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(238, 236, 225);padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eArabinosylcytosine\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68.55pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(238, 236, 225);padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eMethotrexate\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65.45pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003ePaclitaxel\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95.35pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eCyclophosphamide\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60.15pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;'\u003eNocodazole\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(217, 217, 217);padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eFluorouracil\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 0.75in;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(217, 217, 217);padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eEtoposide\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66.7pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(217, 217, 217);padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New Roman\",serif;color:black;'\u003eDoxorubicin\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59.3pt;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;background: rgb(217, 217, 217);padding: 0in 5.4pt;height: 26.5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;margin-top:6.0pt;text-align:center;'\u003e\u003cstrong\u003e\u003cspan style='font-size:13px;font-family:\"Times New 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Analysis, Canonical Pathways","lastPublishedDoi":"10.21203/rs.3.rs-4204722/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4204722/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction: \u003c/strong\u003eBreast cancer faces a significant challenge in the form of multidrug resistance (MDR), which requires the development of innovative therapeutic approaches. This study investigates the molecular mechanisms underlying MDR in MCF7 breast cancer cells to identify common signaling pathways that contribute to resistance. Additionally, this research aims to propose a novel classification system for chemotherapeutic agents based on their influence on these pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003eTo create drug-resistant MCF7 sublines, MCF7 cells were subjected to chemotherapeutic drugs for 12 months, with concentrations gradually increasing over time. The transcriptome of these sublines was then analyzed using next-generation RNA sequencing through Ion Torrent technology. Differentially expressed genes (DEGs) and their associated canonical pathways, molecular functions, and upstream regulators were identified using Ingenuity Pathway Analysis (IPA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Unique mRNA expression patterns were associated with chemoresistance, indicating notable up- and downregulation patterns of DEGs in MCF7 chemoresistant subline cells. The use of IPA allowed for the categorization of chemotherapeutic drugs into three groups based on their effects on canonical pathways: Group I (Arabinosylcytosine, Methotrexate), Group II (Paclitaxel, Cyclophosphamide, Nocodazole), and Group III (5-Fluorouracil, Etoposide, Doxorubicin, Cisplatin).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eA new approach to enhance the effectiveness of breast cancer chemotherapy is proposed. This approach involves categorizing chemotherapeutic agents based on their impact on canonical pathways. The innovative classification system has the potential to guide the development of combination therapies, predict drug resistance mechanisms, and ultimately improve patient outcomes. However, it is essential to conduct extensive research and validate these findings in other breast cancer cell lines and clinical settings.\u003c/p\u003e","manuscriptTitle":"A Novel Approach to Classifying Chemotherapeutic Agents Based on Their Impact on Canonical Pathways: Implications for Overcoming Multidrug Resistance in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-02 20:06:34","doi":"10.21203/rs.3.rs-4204722/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f524a196-22a1-4126-b607-29eb3e01a685","owner":[],"postedDate":"May 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31233294,"name":"Biological sciences/Biochemistry"},{"id":31233295,"name":"Biological sciences/Biological techniques"},{"id":31233296,"name":"Biological sciences/Cancer"},{"id":31233297,"name":"Biological sciences/Cell biology"},{"id":31233299,"name":"Biological sciences/Drug discovery"},{"id":31233301,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2024-12-30T04:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-02 20:06:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4204722","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4204722","identity":"rs-4204722","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}