Identifying Novel Therapeutic Targets for Overcoming TNBC Chemo Resistance Through Comprehensive CRISPR-Cas9 Genome Screening

Triple-negative breast cancer (TNBC) represents 15 -20% of cases but disproportionately contributes to 35% of breast cancer deaths. Chemotherapy resistance remains a significant challenge in TNBC treatment. In this study, we identified the MDA -MB-231 cell line as the most representative model for TNBC chemotherapy -poor responders by comparing genomic profiles from TNBC cell lines and patient samples. We performed a genome-wide CRISPR-Cas9 screen and RNAseq analysis in MDA -MB-231 cells to uncover potential synthetic lethal targets for cisplatin/doxorubicin treatment. Our analysis confirmed the involvement of known essential genes in DNA damage repair and regulation of DNA replication pathways, such as BCL2L1, ATM, CDC25B, and NBN, in sensitizing cells to cisplatin/doxorubicin. Additionally, We identified hundreds of previously unrecognized genes and pathways related to DNA repair, G2/M DNA damage checkpoint, AMPK signaling, and mTOR signaling. The observed differences between transcriptomic responses and essential pathways from the CRISPR screen suggest a complex regulatory system in cellular response to DNA damage drugs. By combining .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint various data analysis methods and biological experimental approaches, we have pinpointed several promising genes, such as MCM9 and NEPPS, which could serve as potential drug targets to overcome chemoresistance. Overall, our approach efficiently identified essential genes with potential synthetic lethal interactions with cisplatin/doxorubicin, offering new possibilities for combination therapies in chemo resistant TNBC patients.

Triple-negative breast cancer (TNBC) represents a breast cancer subtype distinguished by the absence of three pivotal receptors: estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) 1. These receptors substantially influence the proliferation and dissemination of breast cancer cells. In most breast cancer cases, therapeutic interventions target one or more of these receptors to impede or halt cancer progression 2. Nevertheless, the lack of these receptors in TNBC renders it a particularly formidable challenge for treatment3. A combination of surgical intervention, radiotherapy, and chemotherapy constitutes the conventional treatment regimen for TNBC 4. Cisplatin and doxorubicin, two frontline chemotherapeutic agents, are employed in managing triple -negative breast cancer. Cisplatin, a platinum -based chemotherapy compound, functions by forming covalent bonds with DNA, potentially resulting in DNA damage and subsequent cellular demise 5,6. Doxorubicin, another DNA -damaging drug, is an anthracycline chemotherapeutic agent that primarily operates by inflicting damage upon the DNA of cancer cells. Its mechanism .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint encompasses DNA double helix intercalation, topoisomerase II inhibition, and free radical generation, culminating in DNA damage and eventual cell death 7. While these chemotherapeutic drugs effectively eradicate tumor cells, resistance has been observed in TNBC patients 8,9. In large clinical trials, it has been observed that approximately half of the patients with triple -negative breast cancer (TNBC) have residual cancer after undergoing neoadjuvant chemotherapy (NACT) 10,11. Furthermore, around 40% of residual disease patients will eventually develop distant metastasis 12. These findings underscore the importance of identifying more effective treatment strategies for TNBC poor chemo drug responders. In recent years, advances in understanding TNBC's molecular characteristics have led to new therapeutic options. Immunotherapy, such as atezolizumab 13, has shown promise when combined with nab-paclitaxel for advanced TNBC. PARP inhibitors (e.g., olaparib, niraparib, talazoparib) 14 15 have emerged as valuable, particularly when combined with chemotherapy agents for metastatic TNBC patients. Antibody -drug conjugates, like sacituzumab govitecan16, selectively deliver cytotoxic agents to cancer cells, minimizing damage to healthy cells. Moreover, targeted therapies, including PI3K and mTOR inhibitors, offer new TNBC treatment avenues17. These advances underscore the potential for novel, personalized therapies based on TNBC's molecular landscape. These emerging treatments capitalize on the concept of synergistic lethality, which occurs when the simultaneous inhibition or disruption of two or more genes or pathways results in cell death 18.The combination of PARP inhibitors and HRR deficiency successfully .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint applies this concept in cancer therapy 19. PARP inhibitors are promising as combined drug therapy with DNA -damaging drugs for treating BRCA1 or BRCA2 mutated, HER2 - negative breast cancers. Olaparib and talazoparib have received FDA approval for this purpose 20. Poly (ADP-ribose) polymerase (PARP) is an enzyme related with DNA repair, explicitly repairing single -strand DNA breaks through the base excision repair (BER) pathway 21. BRCA1 and BRCA2 genes play an important role in another type of DNA repair called homologous recombination repair (HRR) 22. BRCA1 or BRCA2 mutations cells rely on alternative repair pathways like BER to maintain genomic integrity. Inhibiting PARP leads to unrepaired single -strand breaks converting into double -strand during replication. Without functional HRR, cells cannot repair these breaks, resulting in genomic instability and cell death23 . The CRISPR/Cas9 system is a versatile and powerful gene editing tool that disrupts or modifies specific genes 24,25. Genome -wide CRISPR screening allows researchers to systematically examine the entire genome to identify genes whose knockout or modification can lead to particular phenotypes, such as increased sensitivity to chemotherapy or synthetic lethality combined with another genetic disruption 26,27 28,29. In our study, we conduct a genome -wide CRISPR screening using the TKOV3 library to identify potential druggable targets that could help overcome resistance to DNA-damaging chemotherapeutic agents, such as cisplatin and doxorubicin, in TNBC patients. Our approach focuses on investigating synthetic lethality and potential drug combinations to enhance treatment efficacy and counteract resistance mechanisms. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Before carrying out our CRISPR screening experiment, it is crucial to select an appropriate and representative cell line model for the study. The similarity between the cell model and patients with drug-resistant tumors directly determines whether the drug targets we identify can genuinely progress from preclinical to clinical stages. In Chapter 2, 21 TNBC cell lines from the CCLE database are evaluated by comparing their genomic profiles with those of TNBC poor responders. Among these cell lines, MDA-MB-231 was selected as one of the most representative models for conducting a genome-wide CRISPR screening.

Pooled genome-wide CRISPR screening on MDA-MB-231 cell lines We performed a genome -wide CRISPR/cas9 pooled screen to identify genes related to cisplatin or doxorubicin resistance in the most representative cell line, MDA -MB-231. TKOv3sgRNA library, which contains 7,0948 sgRNA targeting 18053 genes, was used for this screening. After lentiviral pooled sgRNA virus and puromycin selection with lower MOI (0.3), the baseline sample was harvested. Additionally, on day 28, samples after cisplatin or doxorubicin, or DMSO treatment with three triplicates were collected (Figu re 1). After deep-depth sequencing, the MAGeCKFlute algorithm was used for sequence data analysis. The number of missed genes for all samples is from 200~400 sgRNA among a total of 7,0948 sgRNA ( Figure 2A). With passage time increase, the Gini index was increased from 0.04 to 0.06, which is reasonable because longer treatment will cause the unevenness in pooled sgRNA. From the 3D PCA plot ( Figure 2C) and pairwise sample correlation plot (Figure 2D), samples in the same treatment group show a clear pattern and higher correlation value, which indicates that triplicates samples have a constant result. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Overall, the data analysis result indicated that our approach successfully produced a CRISPR-based screening of one MDA-MB-231 cell line. Essential gene identification and pathway enrichment analysis Using the MAGeCK MLE algorithm, gene essentiality scores (β scores) were calculated correspondingly in each group. Lower β scores significantly depleted sgRNAs in the final time point. The distribution of normalized sgRNA β scores (Figure 3A for CIS, 6A for DOX) for each group is similar, which indicates the good quality of our negative screening. The square plot for cisplatin treatment (Figure 3C) and doxorubicin treatment (Figure 4C) was generated, the which 96 essential genes ( Table 3) and 93 essential g enes (Table 4) were identified in cisplatin and doxorubicin correspondingly. KEGG pathways analysis are performed by using essential genes list detected on cisplatin treatment and doxorubicin treatment correspondingly, top 15 highly enriched pathways for each analysis are visualized (Figure 3D and Figure 3D). In both drug treatment studies, the enriched pathways are highly associated with DNA damage/DNA repair signaling pathways. Deficiency of essential genes contribute to increased chemosensitivity in TNBC In our study, a cell survival assay using siRNA-mediated gene silencing was performed to validate the novel genes identified from our CRISPR essential gene list. The results, as illustrated in the Figure 6A-6C, demonstrate that knocking down the expression of specific genes, namely ATR, NEPPS, and MCM9, increases the sensitivity of triple-negative breast cancer (TNBC) cell lines to the chemotherapeutic drug cisplatin. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Relationship between essential gene expression and TNBC patient survival outcomes In the essential gene lists identified through our CRISPR screening, we performed an overall survival analysis using TCGA data for those genes (Figure 5)that were not well - studied previously. The TCGA data unveiled notable differences in overall survival between patients exhibiting high and low expression levels of the following genes (Figure 3D-E): MCM9 (Hazard Ratio = 4.15, 95% CI: 1.48 -11.68, p -value = 0.006), SNAPC1 (Hazard Ratio = 3.23, 95% CI: 1.14-9.16, p-value = 0.02), SLC44A3 (Hazard Ratio = 2.91, 95% CI: 1.04 -8.16, p-value = 0.039), and OPRD1 (Hazard Ratio = 6.21, 95% CI: 2.2 - 17.53, p-value = 0.001). The Kaplan -Meier curves showed a distinct separation between the high and low expression groups for each gene, indicating that gene expression levels are correlated with patient outcomes in the cancer type under investigation.

Upon analyzing the data from our CRISPR screening, we found that both the read level QC and sample level QC demonstrated high data quality. Missed sgRNAs are not detected in the final sequencing data after performing a CRISPR -Cas9 screen. This can occur fo r various reasons, such as the low initial representation of the sgRNA in the library, poor experimental conditions, or technical issues during sequencing 30. For all our samples, the number of missed sgRNAs is below 500, a tiny proportion compared to the 70,948 sgRNAs in the tkov3 library. This indicates that our experimental steps were successful and without significant issues. The Gini index, also known as the Gini coefficient, is a statistical measure that quantifies the inequality or dispersion of a distribution. In the context of CRISPR-Cas9 screens, the Gini index assesses the unevenness of sgRNA representation in .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint the initial library or after selection 31. The overall Gini index for all samples is low, consistent with the expected Gini index in negative screening. Furthermore, the normalization results of the three datasets are consistent, indicating that our results are comparable across samples, allowing for more accurate identification of essential genes. PCA (Principal Component Analysis) plots are valuable for visualizing high -dimensional data in a lower -dimensional space—the first three principal components in our analysis account for over 80% of the sample variance. Through the 3D PCA plot, a clear patt ern can be observed, with triplicate samples from each group clustering together. The pairwise sample correlation results show that the correlation value within each group is greater than 0.9, while the correlation value between groups does not exceed 0.82 . This demonstrates good reproducibility in our experiments, and the data is trustworthy and also There are significant differences between groups under different experimental conditions. In our study, we used genome -wide CRISPR screening to identify essential genes that could help overcome cisplatin/doxorubicin resistance in triple-negative breast cancer. Our

were supported by a gene table (Table 1 for CIS and Table 2 for DOX treatment group) including summary of well-studied genes from previous research. For cisplatin treatment, the well-studied genes included in our gene list are DNMT1, PPIA, RUNX, BCL2L1, RUNX2, NBN, GTF2H5, USP22, HSP90AB1, CDC25B, NCF1, FANCA, FANCG, and ERCC1. Some of these genes have known drug targets or inhibitors. For example, DNM T1, involved in the DNA methylation pathway, is targeted by .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint decitabine, which is clinically used for other cancer types 32. RUNX, which regulates apoptosis and cell proliferation, has a small molecule inhibitor (AI -10-49) in preclinical development 33. BCL2L1, involved in the intrinsic apoptotic pathway, can be targeted by BikDD and lapatinib, both in preclinical stages 32. RUNX2, which also plays a role in regulating the cell cycle and apoptosis, is targeted by BET inhibitors JQ1 and I -BET762, currently in phase I/II clinical trials 34. Furthermore, several genes in our list have been previously implicated in chemoresistance. For example, PPIA is involved in miRNA regulation and impacts breast cancer cell sensitivity to doxorubicin 35. RUNX is involved in the YAP signaling pathway, and its knockdown enhances sensitivity to doxorubicin in breast cancer cells 32. NBN is involved in DNA repair and homologous recombination, playing a role in doxorubicin, paclitaxel, and carboplatin resistance in HER2 - and MDM2 -enriched breast cancer subtypes 36. GTF2H5 is involved in nucleotide. For doxorubicin treatment, the well -studied genes included in our gene list are ABCC1, HIST1H2BJ, ZEB2, ATM, FANCL, CDC25B, XRCC1, ACTG1, IRS1, NBN, NFE2L2, NDUFB9, CDK5, and CDCA3. Several genes in this list have previously been reported to play a role in chemoresistance. For instance, ABCC1 is a drug efflux transporter that has been implicated in resistance to doxorubicin, paclitaxel, and cisplatin in TNBC 37. HIST1H2BJ is involved in glutathione synthesis and copper chelation, promoting resistance to doxorubicin, paclitaxel, and cisplatin in TNBC 38. ZEB2, a transcription factor, is associated with drug resistance in breast cancer cells by regulating the epithelial- .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint mesenchymal transition (EMT) 39. ATM, a kinase involved in DNA damage response, is known to contribute to doxorubicin resistance in breast cancer cells .FANCL is part of the Fanconi anemia DNA repair pathway, and its inhibition has been shown to enhance the sensitivity of breast cancer cells to cisplatin and olaparib. CDC25B, a cell cycle regulator, has been implicated in doxorubicin and paclitaxel resistance in breast cancer cells 40. XRCC1, a key protein in base excision repair, has been reported to contribute to resistance against doxorubicin in breast cancer cells . Some of these genes have potential drug targets or inhibitors. The ATM kinase inhibitor KU-55933, which targets ATM involved in the DNA damage response pathway, is in preclinical development 41. FANCL, part of the Fanconi anemia DNA repair pathway, has been targeted by small molecule inhibitors such as curcumin in preclinical studies 42. CDK5 inhibitors, like roscovitine and dinaciclib, have shown promise in preclinical studies and are in clinical trials for various cancer types 43. CDK5 is involved in cell cycle regulation and the DNA damage response, which contribute to chemoresistance. CDC25B and NBN are the overlapping genes between the cisplatin and doxorubicin essential gene lists. CDC25B is involved in cell cycle regulation and DNA damage response and has been targeted by Thiostrepton, FDI -6, and Siomycin A in preclinical studies for platinum-resistant ovarian cancer treatment 44. NBN, on the other hand, is involved in DNA repair and homologous recombination and has been studied in vitro using siRNA. While no drug targets or inhibitors have been identified for NBN, its role in DNA repair suggests it may be a potential therapeutic target in the future. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint In our study, the KEGG enrichment analysis of the essential gene list revealed distinct, yet overlapping, pathways enriched in the cisplatin and doxorubicin treatment groups. Both groups showed a strong connection to DNA damage repair, highlighting the importance of these pathways in response to chemotherapeutic agents. In the cisplatin treatment group, the top 15 enriched pathways were primarily associated with double -strand break repair, response to radiation, DNA replication, regulation of DNA metabolic process, and nucleotide-excision repair. These results suggest that cisplatin -induced DNA damage triggers a range of cellular responses, including the activation of DNA repair mechanisms and changes in DNA conformation, which may contribute to chemoresistance. In contrast, the doxorubicin treatment group's top 15 enriched pathways were mainly involved in double -strand break repair via nonhomologous end joining, non - recombinational repair, regulation of response to DNA damage stimulus, cell cycle G2/M phase trans ition, and positive regulation of DNA metabolic process. These findings indicate that doxorubicin -induced DNA damage elicits different cellular responses, primarily focusing on cell cycle regulation and checkpoint activation. This disparity in pathway enrichment between the two treatment groups underscores the differences in the mechanisms of action of cisplatin and doxorubicin and the cellular responses they induce. Our study has provided valuable insights into the distinct yet overlapping pathways involved in the cellular response to cisplatin and doxorubicin treatment. These results emphasize the importance of understanding the molecular mechanisms underlying DNA .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint damage repair and response to chemotherapeutic agents to develop more effective therapies and overcome chemoresistance in cancer treatment. Further studies are warranted to investigate the potential therapeutic targets identified in these enriched pathways and to explore the crosstalk between them to develop novel strategies for overcoming chemoresistance in cancer cells. Our cell survival assay demonstrates that knocking down the expression of specific genes, namely ATR, NEPPS, and MCM9, would increase the sensitivity of triple-negative breast cancer (TNBC) cell lines to the chemotherapeutic drug cisplatin. We employed not only the MDA-MB-231 cell line, which was used for our initial screening, but also other TNBC cell lines, such as MDA -MB-436 and HS578T, to strengthen our hypothesis. Our results showed a significant reduction in cell viability in all three cell lines foll owing the knockdown of ATR, NEPPS, and MCM9, indicating an increased sensitivity to cisplatin treatment. In our survival analysis, we observed a significant association between the expression levels of MCM9, SNAPC1, SLC44A3, and OPRD1 genes and overall survival in the patient population analyzed using TCGA data. Specifically, patients with lower expression levels of these genes experienced better survival outcomes, suggesting a potential therapeutic strategy that involves downregulating these genes to enhance patients' response to chemotherapy. These findings are consistent with the results of our CRISPR screening. A Venn diagram (Figure 5H) can be created by overlapping different essential gene validation methods, such as genome wide CRISPR screening, siRNA knockdown .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint validation, differential gene expression analysis, and survival analysis. The intersection of these methods helps identify the core set of essential genes, such as MCM9, with a higher level of confidence as a potential gene target for overcoming triple-negative breast cancer. In conclusion, our study strongly supports the notion that utilizing CRISPR screening for essential cancer genes is an efficient and practical approach to identify potential drug targets to overcome chemotherapeutic drug resistance. By integrating multiple data analysis techniques and biological experimental analyses, we have identified several genes that hold promise as potential drug targets for combating chemoresistance. Our findings underscore the importance of leveraging advanced genetic screening tools and data-driven

to better understand the molecular mechanisms underlying drug resistance and to develop more effective therapeutic strategies for cancer treatment.

Cell Culture Human triple-negative breast cancer cell lines MDA -MB-231, MDA-MB-436, and HS - 578T, along with the Homo sapiens embryonic kidney cell line 293T, were obtained from the American Type Culture Collection (Manassas, VA, USA) for this study. All cell lines were cultured in Ham's F -12K (Kaighn's) medium, supplemented with 10% fetal bovine serum (VWR, Radnor, PA, USA), 1% GlutMax, 1% sodium pyruvate, and penicillin - streptomycin (Gibco, Waltham, MA, USA). The cell lines were incubated at 37 °C in a 5% CO2 atmosphere. All cell lines underwent authentication via STR profiling and were tested for mycoplasma contamination every three months. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint TKOv3 library Construction The Toronto Knock-Out CRISPR (TKOv3) library, containing 71,090 sgRNAs targeting 18,049 protein-coding genes, was acquired from Addgene (Watertown, MA, USA) and expanded 1000-fold using the electroporation method. For lentivirus production, 7.5×10^6 293T c ells were seeded in 15 cm plates and prepared for transfection. Following the manufacturer's guidelines , packaging vectors psPAX2, pMD2.G (Addgene), TKOv3 library plasmid, and Lipofectamine (Thermo Fisher Scientific) were mixed in OptiMEM (Thermo Fisher Scientific, Waltham, MA, USA). After 48 h of incubation, the lentivirus - containing medium was collected and stored at -80°C. Pooled sgRNA screens MDA-MB-231 cells were transduced with the TKOv3 lentivirus library at a low multiplicity of infection (MOI) of 0.3. After 72 h of puromycin (2 μg/mL) selection, surviving cells were considered baseline samples (T0), and 3×10^7 cells were harvested and stored at -80°C. The remaining cells were divided into three groups (control, cisplatin treatment, and doxorubicin treatment), each performed in triplicate. Following four weeks of treatment, 3×10^7 cells were harvested from each group. Genomic DNA was extracted using the QIAamp Blood Maxi Kit (Qiagen, Hilden, Germany). Two polymerase chain reactions (PCRs) were carried out to enrich the sgRNA -targeted genomic regions and amplify the sgRNA. The resulting libraries were sequenced on a NovaSeq 6000 system (Illumina, San Diego, CA, USA), producing nearly 80 million reads per sample to achieve 600x coverage of the CRISPR library. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Crispr Data analysis CRISPR pooled CRISPR/Cas9 knockout screening data was analyzed using the MAGeCK (Model-based Analysis of Genome -wide CRISPR-Cas9 Knockout) algorithm. Data were first normalized using a list of non -targeting control sgRNAs. Gene essentiality scores (beta-scores) were then determined for each group using the MAGeCK MLE (Maximum Likelihood Estimation) method . Principal component analysis (PCA) and Pearson correlation analysis were performed using the R packages "stats" and "corrplot" , respectively. Cell survival assay using siRNA-mediated gene silencing Small interfering RNAs (siRNAs) were used to validate the essential genes identified in our study. These siRNAs, which were specifically designed to target the essential genes, were purchased from Thermo Fisher Scientific (MA, USA). Detailed information on the siRNAs is provided in the Supplementary SI Gene list. We transfected MDA -MB-231, MDA-MB-436, and HS578T cells with siRNAs using the Lipofectamine RNAiMAX Transfection Reagent kit (#13778150, Thermo Fisher Scientific, MA, USA) according to the manufacturer's protocol. The cells were then seeded into 96-well plates at a density of 2.5 x 10^3 cells per well. After 24 hours, the medium was replaced, and the cells were treated with cisplatin and doxorubicin. Following 120 hours of incubation, we assessed cell viability using the alamarBlue HS Cell Viability Reagent (#A50100, Thermo Fisher Scientific, MA, USA). The absorbance of each well was measured using a microplate reader. We determined the half -maximal inhibitory concentration (IC50) values with the help of GraphPad Prism 7 software. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint 3.5.6 Survival analysis In this study, we utilized The Cancer Genome Atlas (TCGA) database to analyze the overall survival of TNBC patients with varying gene expression levels. The gene expression values were obtained from the TCGA database, and patients were divided into two gro ups based on the median expression value: high expression group and low expression group. Kaplan-Meier curves were generated to compare the overall survival of the two groups. Hazard ratios and p-values were calculated using the log-rank test to assess the statistical significance of the differences in survival.

.CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Figure 1. A schematic diagram for genome wide CRISPR by the TKVO3 library. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Figure 2. Genome-wide CRISPR-Cas9 negative screens in the MDA-MB-231 cell line. (A) The missed sgRNAs were tested on days 0 (T0), and days 28. (B) The Gini index of sgRNAs on days 0 (T0), and days 28. (C) 3D PCA plot of baseline, control, and two drug treatment groups. PC1 to PC3 can explain more than 80% of total information. (D) Correlation plot between baseline, control, and two drug treatment group, each group containing triplicate samples. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Figure 3. Genome-wide CRISPR knockout screen in TNBC with Cisplatin treatment . (A) Box plot for sgRNA β scores for each group. (B) Normalized distribution of β scores for each group. (C) The gene essentiality scores reported from MAGeCK -MLE in cisplatin treatment. (D) Essential gene enrichment pathways of by KEGG analysis in cispla tin treatment. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Figure 4. Genome-wide CRISPR knockout screen in TNBC with doxorubicin treatment (A) Box plot for sgRNA β scores for each group. (B) Normalized distribution of β scores for each group. (C) The gene essentiality scores reported from MAGeCK-MLE in cisplatin treatment. (D) Essential gene enrichment pathways of by KEGG analysis in doxorub icin treatment. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Figure 5. Kaplan-Meier overall survival analysis of essential genes based on TCGA database. Overall survival analysis for essential genes MCM9, SNAPC1,SLC44A3,OPRD1 (A-D) TNBC. .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Figure 6. siRNA Knockdown cell viability assay. Cell growth inhibition of MDA-MB-231, MDA-MB-436 and HS578T transfected with siRNA ATR, siRNA NEPPS or siRNA MCM9, followed by treatment with 4 -serial-diluted cisplatin doses for 120 h (A-C). A Venn diagram can be created by overlapping different essential gene validation methods (D). .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Table 1. Well studied gene in essential gene list for cisplatin treatment Continued Gene Pathway Validation Method Inhibitor Drug Development Status Chemo Drug Cancer Type CDC25B 45 Cell cycle regulation, DNA damage response In vitro (siRNA), in vivo (xenograft model) Thiostrepton, FDI-6, Siomycin A Preclinical Paclitaxel, Cisplatin Platinum-resistant ovarian cancer NCF1 46 Autophagy, ROS production In vitro (siRNA) Ginsenoside Ro Preclinical 5-Fluorouracil Esophageal cancer USP22, HSP90AB1 47 HSP90 regulation and ubiquitin pathway In vitro (siRNA), in vivo (xenograft model) Ganetespib, AT13387 Phase II trials Irinotecan Mammary and colorectal cancer DNMT1 48 DNA methylation In vitro (siRNA), In vivo (mice model, xenograft with gene knockdown) Decitabine Clinical (used for other cancer types) Decitabine Triple-negative breast cancer BCL2L1 49 Apoptosis In vitro (cell lines), In vivo (mice model) BikDD, Lapatinib Preclinical Doxorubicin Breast Cancer RUNX2 50 BET inhibition In vitro (siRNA), in vivo (xenograft model, CRISPR knockout) BET inhibitors: JQ1, I- BET762 Preclinical, Phase I/II Cisplatin, Taxanes Triple-negative breast cancer HSP90 51 Chaperone protein function In vitro (siRNA), in vivo (xenograft model) 17-AAG, PU-H71 Phase II/III trials Doxorubicin HER2-negative breast cancer .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Table 1. Continued Gene Pathway Validation Method Inhibitor Drug Development Status Chemo Drug Cancer Type PPIA 52 miRNA regulation In vitro (miRNA-192-5p mimic) - - Doxorubicin Breast cancer RUNX1 53 YAP signaling pathway In vitro (shRNA knockdown), In vivo (xenograft) - - Doxorubicin Breast cancer NBN 54 DNA repair, homologous recombination In vitro (siRNA) - - Doxorubicin, Paclitaxel, Carboplatin HER2- and MDM2-enriched breast cancer subtypes GTF2H5 55 Nucleotide excision repair (NER) In vitro - - Carboplatin, Paclitaxel High-grade serous ovarian cancer FANCA, FANCG 56 DNA damage repair, Fanconi anemia/BRCA pathway In vitro (siRNA) - - Cisplatin Drug-resistant lung cancer ERCC1 57 Nucleotide excision repair In vitro (siRNA), in vivo (xenograft model) - - Cisplatin Various cancer types .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Table 2. Well studied gene in essential gene list for doxorubicin treatment Continued Gene Pathway Validation Method Inhibitor Drug Development Status Chemo Drug Cancer Type XRCC1 58 DNA repair In vitro (siRNA) Triptolide preclinical Cisplatin Triple-negative breast cancer XRCC1 59 Base excision repair In vitro (siRNA) Berberine preclinical Epirubicin, Doxorubicin, Cyclophosphamide, 5- fluorouracil, Docetaxel, Cisplatin Breast cancer IRS1 60 PI3K-AKT-mTOR signaling In vitro (miRNA and inhibitor) Y-29794 preclinical Paclitaxel, Carboplatin, Gemcitabine, Doxorubicin, Cisplatin Triple-negative breast cancer Cdk5 61 Cell cycle regulation, carboplatin- induced cell death In vitro (siRNA) - - Carboplatin Breast cancer FANCL 56 anconi anemia/BRCA pathway In vitro (siRNA) - - Cisplatin Lung cancer NFE2L2 62 Chemotherapy resistance, hypoxia response In vitro (siRNA, hypoxia exposure) - - Cisplatin, doxorubicin, and etoposide Breast cancer NBN 54 Homologous recombination DNA repair In vitro (immunofluorescence, western blot) - - Docetaxel, doxorubicin, and cyclophosphamide Breast cancer .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Table 2. Continued Gene Pathway Validation Method Inhibitor Drug Development Status Chemo Drug Cancer Type HIST1H2BJ 63,64 Glutathione synthesis, copper chelation In vitro (siRNA), In vivo (mice) - - Doxorubicin, paclitaxel, 5- fluorouracil Breast cancer ABCC1 65 Drug efflux transporters In vitro (siRNA) - - Doxorubicin, paclitaxel, cisplatin Triple-negative breast cancer ZEB2 66 ATM activation In vitro (siRNA) - - Doxorubicin, paclitaxel, cisplatin Breast cancer CDK5 67 drug resistance- related pathways In vitro (siRNA) - - Paclitaxel, cisplatin, and doxorubicin Triple-negative breast cancer CDCA3 68 Cell proliferation, metastasis, chemoresistance In vitro (siRNA, RT- qPCR) - - Paclitaxel, cisplatin, and doxorubicin Triple-negative breast cancer CDC25B 69 Cell cycle regulation In vitro (siRNA) - - Paclitaxel, Cisplatinum Platinum- resistant ovarian cancer ATM 70 Cell cycle regulation In vitro (siRNA), In vivo (xenograft mice) - - Taxanes Breast cancer .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Table 3. CRISPR essential gene list for Cisplatin treatment Gene Rank DMSO Treat EntrezID Symbol TAS2R30 1 -0.1351332 -1.2364965 259293 TAS2R30 NPEPPS 2 -0.3645906 -1.0243701 9520 NPEPPS ERCC1 3 -0.3319763 -0.8988315 2067 ERCC1 FANCL 4 -0.3693193 -0.8376133 55120 FANCL SPEN 5 -0.2590529 -0.8298921 23013 SPEN CBFB 6 -0.3430779 -0.8049361 865 CBFB FANCB 7 -0.2717461 -0.7982489 2187 FANCB PAF1 8 -0.3518974 -0.7937679 54623 PAF1 ERCC5 9 -0.1182243 -0.7790149 2073 ERCC5 KLF16 10 -0.3753239 -0.7752921 83855 KLF16 SLX4 11 -0.3773561 -0.7297921 84464 SLX4 BRIP1 12 -0.2607826 -0.706077 83990 BRIP1 NSUN5 13 -0.2347978 -0.6740478 55695 NSUN5 RPL41 14 -0.3529563 -0.6725725 6171 RPL41 NCF1 15 -0.3553765 -0.6480094 653361 NCF1 COX7B 16 -0.2704965 -0.6417152 1349 COX7B WDR24 17 -0.3000855 -0.6362276 84219 WDR24 PDZK1 18 -0.3135153 -0.6358209 5174 PDZK1 POLR3D 19 -0.3431437 -0.6336769 661 POLR3D ATAD5 20 -0.3696284 -0.6306091 79915 ATAD5 HIST1H2BJ 21 -0.2860178 -0.6299128 8970 HIST1H2BJ CDC25B 22 -0.3087405 -0.6292165 994 CDC25B FAF2 23 -0.3324104 -0.6192134 23197 FAF2 ESCO2 24 -0.2286287 -0.6162214 157570 ESCO2 ACTG1 25 -0.3610852 -0.6144497 71 ACTG1 VCL 26 -0.3727195 -0.6131192 7414 VCL FAM120A 27 -0.3285104 -0.6090931 23196 FAM120A .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Gene Rank DMSO Treat EntrezID Symbol HSP90AB1 28 -0.3253732 -0.605722 3326 HSP90AB1 GTF2H5 29 -0.1308977 -0.6055634 404672 GTF2H5 APOLD1 30 -0.3387373 -0.6045638 81575 APOLD1 PPP1R15B 31 -0.2944295 -0.6036814 84919 PPP1R15B FAM133B 32 -0.1080237 -0.6026955 257415 FAM133B UVSSA 33 -0.2945939 -0.6026266 57654 UVSSA NBN 34 -0.2980664 -0.602537 4683 NBN RAD18 35 -0.0184755 -0.5978491 56852 RAD18 ERCC8 36 -0.223341 -0.5904588 1161 ERCC8 ETAA1 37 -0.2938704 -0.5789183 54465 ETAA1 SPRTN 38 -0.3473528 -0.5750508 83932 SPRTN FLII 39 -0.3271424 -0.5702527 2314 FLII SRP54 40 -0.3080039 -0.5686671 6729 SRP54 GSS 41 -0.2006774 -0.5637586 2937 GSS SLC44A3 42 -0.1966919 -0.5589121 126969 SLC44A3 GTF3C1 43 -0.2859651 -0.5576919 2975 GTF3C1 BARD1 44 -0.2596777 -0.5570577 580 BARD1 UBE2K 45 -0.1413154 -0.5570508 3093 UBE2K SLC7A1 46 -0.3216968 -0.555865 6541 SLC7A1 SUCO 47 -0.2802828 -0.5547689 51430 SUCO RUNX2 48 -0.0624873 -0.5533901 860 RUNX2 SECISBP2 49 -0.3153765 -0.5495571 79048 SECISBP2 RNF113A 50 -0.3141335 -0.5487987 7737 RNF113A PSMF1 51 -0.3008089 -0.5485437 9491 PSMF1 CNPY2 52 -0.3117067 -0.5441522 10330 CNPY2 SCAF4 53 -0.2546991 -0.54176 57466 SCAF4 DUSP10 54 -0.2936929 -0.5357761 11221 DUSP10 CCS 55 -0.1684249 -0.5313915 9973 CCS OTUB1 56 -0.2843012 -0.5311571 55611 OTUB1 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Gene Rank DMSO Treat EntrezID Symbol MCM9 57 -0.1977179 -0.5297439 254394 MCM9 POLR2A 58 -0.0658533 -0.52467 5430 POLR2A TMEM53 59 -0.2582045 -0.519789 79639 TMEM53 HACE1 60 -0.2943966 -0.5181 57531 HACE1 RXFP4 61 -0.2558961 -0.5165972 339403 RXFP4 EME1 62 -0.215416 -0.5140326 146956 EME1 BCL2L1 63 -0.1415258 -0.509979 598 BCL2L1 CDC25A 64 -0.1635515 -0.5080418 993 CDC25A UPF1 65 -0.2318316 -0.5075868 5976 UPF1 HSPB3 66 -0.2446037 -0.507311 8988 HSPB3 MTERFD2 67 -0.2726669 -0.5065803 130916 MTERFD2 UPF3A 68 -0.1999803 -0.5050429 65110 UPF3A FAM175A 69 -0.2782374 -0.5034297 84142 FAM175A ADCY8 70 -0.2545478 -0.4966254 114 ADCY8 ZFAND3 71 -0.214094 -0.4924959 60685 ZFAND3 DNAJA1 72 -0.2530878 -0.4921719 3301 DNAJA1 XPC 73 -0.1525156 -0.4877805 7508 XPC RUNX1 74 -0.1905557 -0.4869877 861 RUNX1 CYB5R4 75 -0.2497534 -0.4851677 51167 CYB5R4 GPA33 76 -0.2143834 -0.4832167 10223 GPA33 ADAMTSL4 77 -0.2383558 -0.4788046 54507 ADAMTSL4 PPIA 78 -0.1914239 -0.4696081 5478 PPIA CHD2 79 -0.2106807 -0.4691807 1106 CHD2 MKS1 80 -0.1291878 -0.4590328 54903 MKS1 ROPN1L 81 -0.1204538 -0.4581986 83853 ROPN1L DUSP28 82 -0.0917856 -0.4569094 285193 DUSP28 DNMT1 83 -0.1663992 -0.4556341 1786 DNMT1 MNT 84 -0.0885038 -0.4551584 4335 MNT PAN2 85 -0.187149 -0.4540898 9924 PAN2 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Gene Rank DMSO Treat EntrezID Symbol SERP1 86 -0.2148306 -0.4492985 27230 SERP1 ACER2 87 -0.1507793 -0.4476095 340485 ACER2 HIST1H3B 88 -0.2083854 -0.4475957 8358 HIST1H3B C1orf52 89 -0.1876751 -0.4466513 148423 C1orf52 SMARCC1 90 -0.0559303 -0.4463617 6599 SMARCC1 MUSTN1 91 -0.1604998 -0.4447141 389125 MUSTN1 C1orf112 92 -0.0500467 -0.4446658 55732 C1orf112 PKN3 93 -0.0802105 -0.4379304 29941 PKN3 UIMC1 94 -0.1941138 -0.4334976 51720 UIMC1 FAM178A 95 -0.1695429 -0.4299542 55719 FAM178A FBXO7 96 -0.1673397 -0.4282031 25793 FBXO7 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Table 4. CRISPR essential gene list for Doxorubicin treatment Gene Rank DMSO Treat EntrezID Symbol ABCC1 1 -0.0742760 -1.5151284 4363 ABCC1 TDP2 2 -0.3092421 -1.1145884 51567 TDP2 GCLM 3 -0.3146230 -0.8633248 2730 GCLM ZNF451 4 0.0425952 -0.8365990 26036 ZNF451 TAS2R30 5 -0.1359444 -0.7913176 259293 TAS2R30 UIMC1 6 -0.2276415 -0.7744236 51720 UIMC1 FAM175A 7 -0.2786786 -0.7236031 84142 FAM175A ATM 8 -0.3411623 -0.6845115 472 ATM HNRNPR 9 -0.3712627 -0.6511459 10236 HNRNPR HIST1H2BJ 10 -0.2855531 -0.6429758 8970 HIST1H2BJ BABAM1 11 -0.2803874 -0.6309492 29086 BABAM1 DYNLL1 12 -0.2048135 -0.6206121 8655 DYNLL1 NHEJ1 13 -0.3072919 -0.6050336 79840 NHEJ1 OSGEP 14 -0.3534633 -0.5985460 55644 OSGEP POLR3D 15 -0.3618902 -0.5802603 661 POLR3D SUMO2 16 -0.3706170 -0.5712525 6613 SUMO2 ZEB2 17 -0.3295330 -0.5706086 9839 ZEB2 SPTLC2 18 -0.3430146 -0.5687876 9517 SPTLC2 PPP2R5D 19 -0.3761414 -0.5667521 5528 PPP2R5D POLQ 20 -0.3550222 -0.5592744 10721 POLQ FANCL 21 -0.3718562 -0.5590113 55120 FANCL BRE 22 -0.2885273 -0.5556048 9577 BRE CRAMP1L 23 0.1005674 -0.5450668 57585 CRAMP1L PSMF1 24 -0.3135403 -0.5438067 9491 PSMF1 ANKRD35 25 -0.3452322 -0.5349789 148741 ANKRD35 SNAPC1 26 -0.3400404 -0.5343073 6617 SNAPC1 POLR2J 27 -0.3702257 -0.5341826 5439 POLR2J .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Gene Rank DMSO Treat EntrezID Symbol DENR 28 -0.3627315 -0.5307000 8562 DENR CDKN2AIP 29 -0.3488456 -0.5294883 55602 CDKN2AIP BRCC3 30 -0.1904970 -0.5288721 79184 BRCC3 ZMAT2 31 -0.3605987 -0.5260403 153527 ZMAT2 CDC25B 32 -0.3444430 -0.5220522 994 CDC25B FBLIM1 33 -0.3246935 -0.5216645 54751 FBLIM1 TMEM230 34 -0.3671732 -0.5203005 29058 TMEM230 HNRNPH1 35 -0.3185821 -0.5189365 3187 HNRNPH1 SFR1 36 -0.1113358 -0.5187288 119392 SFR1 XRCC1 37 -0.3541678 -0.5175725 7515 XRCC1 ACTG1 38 -0.3619945 -0.5153015 71 ACTG1 ATP13A1 39 -0.3335899 -0.5134113 57130 ATP13A1 WDR24 40 -0.3290373 -0.5119574 84219 WDR24 OPRD1 41 -0.3228020 -0.5072284 4985 OPRD1 C10orf76 42 -0.2693973 -0.5047012 79591 C10orf76 IRS1 43 -0.1707344 -0.5039396 3667 IRS1 TRIT1 44 -0.3028437 -0.5015371 54802 TRIT1 SECISBP2 45 -0.3226454 -0.4992730 79048 SECISBP2 OR5K2 46 -0.2687973 -0.4988507 402135 OR5K2 ETAA1 47 -0.3146426 -0.4977775 54465 ETAA1 FAF2 48 -0.3267154 -0.4976390 23197 FAF2 RAB14 49 -0.3079703 -0.4974105 51552 RAB14 GSS 50 -0.2274002 -0.4970020 2937 GSS RHOT2 51 -0.3358727 -0.4920861 89941 RHOT2 TP53BP1 52 -0.2810918 -0.4902860 7158 TP53BP1 SCAF4 53 -0.2746217 -0.4841169 57466 SCAF4 NBN 54 -0.3041417 -0.4821990 4683 NBN ANKRD17 55 -0.3014153 -0.4800042 26057 ANKRD17 NFE2L2 56 -0.2954866 -0.4797411 4780 NFE2L2 .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Gene Rank DMSO Treat EntrezID Symbol AP1B1 57 -0.2068745 -0.4753791 162 AP1B1 SKP2 58 -0.2945930 -0.4744998 6502 SKP2 ATP5D 59 -0.3115119 -0.4717718 513 ATP5D MRPL24 60 -0.2615575 -0.4714464 79590 MRPL24 APOL4 61 -0.2976585 -0.4713148 80832 APOL4 AIP 62 -0.3046113 -0.4688430 9049 AIP NFYA 63 -0.3156535 -0.4687946 4800 NFYA AKT1S1 64 -0.2680733 -0.4663505 84335 AKT1S1 UBE2A 65 -0.3044743 -0.4652358 7319 UBE2A C17orf85 66 -0.3022698 -0.4640726 55421 C17orf85 FAM69A 67 -0.2966084 -0.4625424 388650 FAM69A SERP1 68 -0.2180994 -0.4623970 27230 SERP1 PABPC1 69 -0.1802178 -0.4594059 26986 PABPC1 NDUFB9 70 -0.1906992 -0.4572250 4715 NDUFB9 MORC2 71 -0.2659666 -0.4563733 22880 MORC2 DPY30 72 -0.2926885 -0.4551824 84661 DPY30 ESPN 73 -0.2385860 -0.4530499 83715 ESPN SUCO 74 -0.2775959 -0.4496434 51430 SUCO PCDH7 75 -0.2863749 -0.4489649 5099 PCDH7 USP24 76 -0.2407188 -0.4424704 23358 USP24 CCDC47 77 -0.1620402 -0.4409818 57003 CCDC47 CDK5 78 -0.1117336 -0.4394170 1020 CDK5 CHD8 79 -0.2042917 -0.4377138 57680 CHD8 KRTAP5-11 80 -0.1198474 -0.4373260 440051 KRTAP5-11 SLC44A3 81 -0.1998304 -0.4330541 126969 SLC44A3 MED10 82 -0.2738977 -0.4314824 84246 MED10 CDCA3 83 -0.2657057 -0.4310600 83461 CDCA3 TOMM70A 84 -0.2406601 -0.4256595 9868 TOMM70A SNAPIN 85 -0.2670167 -0.4244617 23557 SNAPIN .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint Gene Rank DMSO Treat EntrezID Symbol C17orf58 86 -0.2170297 -0.4236031 284018 C17orf58 PRKCG 87 -0.1466280 -0.4232846 5582 PRKCG NDUFA11 88 -0.2080877 -0.4187773 126328 NDUFA11 RBM28 89 -0.2656405 -0.4184172 55131 RBM28 FKBP3 90 -0.2503913 -0.4184103 2287 FKBP3 C2CD4D 91 -0.1885860 -0.4157031 100191040 C2CD4D DNM1 92 -0.0784177 -0.4098318 1759 DNM1 IQCH 93 -0.2148056 -0.4092502 64799 IQCH 1 Ades, F. et al. Luminal B breast cancer: molecular characterization, clinical management, and future perspectives. Journal of clinical oncology 32, 2794- 2803 (2014). 2 Shao, S. et al. Site-specific and hydrophilic ADCs through disulfide-bridged linker and branched PEG. Bioorganic & Medicinal Chemistry Letters 28, 1363-1370 (2018). 3 Nedeljković, M. & Damjanović, A. Mechanisms of chemotherapy resistance in triple-negative breast cancer—how we can rise to the challenge. Cells 8, 957 (2019). 4 Yagata, H., Kajiura, Y. & Yamauchi, H. Current strategy for triple-negative breast cancer: appropriate combination of surgery, radiation, and chemotherapy. Breast Cancer 18, 165-173 (2011). 5 Dasari, S. & Tchounwou, P. B. Cisplatin in cancer therapy: molecular mechanisms of action. European journal of pharmacology 740, 364-378 (2014). 6 Qi, R. et al. Sequence Dependent Repair of 1, N 6-Ethenoadenine by DNA Repair Enzymes ALKBH2, ALKBH3, and AlkB. Molecules 26, 5285 (2021). 7 Rivankar, S. An overview of doxorubicin formulations in cancer therapy. Journal of cancer research and therapeutics 10, 853-858 (2014). 8 Keam, B. et al. Prognostic impact of clinicopathologic parameters in stage II/III breast cancer treated with neoadjuvant docetaxel and doxorubicin chemotherapy: paradoxical features of the triple negative breast cancer. BMC cancer 7, 1-11 (2007). 9 Byrski, T. et al. Response to neoadjuvant therapy with cisplatin in BRCA1- positive breast cancer patients. Breast cancer research and treatment 115, 359-363 (2009). .CC-BY-NC-ND 4.0 International licenseavailable under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint (whichthis version posted May 17, 2024. ; https://doi.org/10.1101/2024.05.14.594192doi: bioRxiv preprint 10 Sikov, W. M. et al. Impact of the addition of carboplatin and/or bevacizumab to neoadjuvant once-per-week paclitaxel followed by dose-dense doxorubicin and cyclophosphamide on pathologic complete response rates in stage II to III triple-negative breast cancer: CALGB 40603 (Alliance). Journal of clinical oncology 33, 13 (2015). 11 Loibl, S. et al. Addition of the PARP inhibitor veliparib plus carboplatin or carboplatin alone to standard neoadjuvant chemotherapy in triple-negative breast cancer (BrighTNess): a randomised, phase 3 trial. The Lancet Oncology 19, 497-509 (2018). 12 Schmid, P. et al. Pembrolizumab for early triple-negative breast cancer. New England Journal of Medicine 382, 810-821 (2020). 13 Schmid, P. et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. New England Journal of Medicine 379, 2108-2121 (2018). 14 Dent, R. A. et al. Phase I trial of the oral PARP inhibitor olaparib in combination with paclitaxel for first-or second-line treatment of patients with metastatic triple-negative breast cancer. Breast cancer research 15, 1-8 (2013). 15 Geenen, J. J., Linn, S. C., Beijnen, J. H. & Schellens, J. H. PARP inhibitors in the treatment of triple-negative breast cancer. Clinical pharmacokinetics 57, 427-437 (2018). 16 Nagayama, A., Vidula, N., Ellisen, L. & Bardia, A. 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