The predictive effect of the CD155-TIGIT immune checkpoint axis complex on neoadjuvant chemotherapy efficacy in triple-negative breast cancer: A preliminary study.

BackgroundThe aim of the present study was to investigate whether the androgen receptor (AR) status affects the efficacy of neoadjuvant chemotherapy (NACT) in triple negative breast cancer (TNBC) patients, and to elucidate the predictive biomarkers and mutations associated with pathological complete response (pCR) in AR-positive TNBC patients.MethodsThe current retrospective cohort included 226 TNBC patients who underwent NACT. AR and FOXC1 were assessed by immunohistochemistry on pretreatment biopsy specimens of 226 TNBC patients from 2018 to 2022. The clinicopathological features of AR-negative, AR < 10%, and AR ≥ 10% TNBC patients were analyzed to confirm the appropriate threshold. The response was evaluated in terms of pCR and Miller-Payne (MP) grade in the subsequent mastectomy or breast conservation samples. Next-generation sequencing (NGS) was utilized to further investigate the molecular characteristics of 44 AR-positive TNBC patients.ResultsAmong the 226 TNBC patients, compared with AR-negative and AR < 10% tumors (68.58%, 155/226), AR ≥ 10% TNBC patients (31.41%, 71/226) exhibited distinct clinicopathological features, while no significant difference was detected between those with AR-negative tumors and those with AR < 10% tumors. Thus, tumors with AR ≥ 10% expression were defined as having AR positive expression. The pCR rate of AR-positive TNBCs was lower than that of AR-negative TNBC patients (12.68% vs. 34.19%, p < 0.001). In TNBC, multivariate analysis demonstrated that FOXC1 was an independent predictor of pCR (p = 0.042), whereas AR was not. The pCR rate was higher in FOXC1 positive patients than in FOXC1 negative patients (34.44% vs. 3.13%, p < 0.001). In the AR-positive TNBC subgroup, patients with FOXC1 expression had lower AR expression, higher Ki-67 expression, and higher histological grade. Compared with AR-positive TNBC patients who achieved pCR, the non-pCR patients had a greater percentage of mutations in genes involved in the PI3K/AKT/mTOR pathway.ConclusionsThe current study indicated that the AR-positive TNBC is correlated with lower rates of pCR after NACT. The expression of FOXC1 in TNBC patients and AR-positive TNBC patients could be utilized as a predictive marker for the efficacy of NACT. The present study provides a rationale for treating these non-pCR AR-positive TNBC tumors with PI3K/AKT/mTOR inhibitors.

Introduction: In triple-negative breast cancer (TNBC), both response to neoadjuvant chemotherapy (NAC) and the degree of pre-treatment (pre-tx) tumor immune infiltration as defined by tumor-infiltrating lymphocytes (TILs) are prognostic. Improving NAC response prediction in early TNBC would provide the opportunity to consider adjustments to the NAC regimen prior to initiating therapy. Breast magnetic resonance imaging (MRI) enables noninvasive whole-tumor measurement of microenvironment features. We investigated the value of pre-tx MRI metrics in addition to TILs for the prediction of NAC response in early TNBC patients. Methods: Women with Stage I-III TNBC who underwent pre-tx clinical breast MRI and NAC at our institution (2005-2019) were retrospectively identified. Response to NAC was noted, with pathologic complete response (pCR) defined as no residual invasive cancer present within the breast. When tissue was available, diagnostic biopsy was used to quantify pre-tx TILs as deciles from 10-100% by a breast pathologist. Patients underwent pre-tx breast MRI on either a 1.5T or 3T scanner including diffusion-weighted (DW-) and dynamic contrast-enhanced (DCE-) MRI. From DCE-MRI, tumor longest diameter (LD) and T stage (1-4), as well as contrast kinetics including percent enhancement (PE) at 2 minutes post-contrast and signal enhancement ratio (SER) were determined. Tumor peak PE and peak SER (representing the highest mean PE and SER, respectively, for 3×3 voxel subregions) and functional tumor volume (FTV, tumor volume exhibiting PE ≥ 50%) were calculated. Mean apparent diffusion coefficient (ADC) was calculated from DW-MRI. TIL levels and imaging features were compared between pCR and non-pCR groups by Wilcoxon rank sum test and performance for prediction of pCR was evaluated using areas under the curve (AUC) measures from receiver operating characteristic curve analysis. Results: 115 TNBC patients (median age: 49, range 26-79 years) were evaluated, of which 45 (39%) achieved pCR. The majority received an anthracycline-containing regimen. Pre-tx TILs (evaluated in N=60 with available biopsy specimens) ranged from 10% to 80% (median, 10%) and were significantly higher in pCR vs. non-pCR patients (p = 0.02, AUC = 0.63). Pre-tx lesion size on imaging was predictive of response (Table 1), with pCR patients having significantly lower LD (p &amp;lt; 0.01, AUC = 0.68) and FTV (p = 0.01, AUC = 0.67). Peak PE was also associated with response, significantly lower in pCR patients (p = 0.04, AUC = 0.62), while SER and ADC were not (p &amp;gt; 0.05). Stratifying by T stage, both peak PE (p = 0.03) and FTV (p = 0.05) were predictive of response in T1/T2 patients, while no imaging metrics reached significance in T3/T4 patients. Discussion: In a large cohort of TNBC patients undergoing NAC, measures of tumor size and immune infiltration were strongly predictive of NAC response. Preliminary results suggest that baseline peak PE and FTV are associated with NAC response, particularly in earlier stage TNBC patients. These findings support the utility of imaging and TILs assessments to predict response in TNBC and potentially guide NAC regimens for improved outcomes. Future work will extend these analyses to assess the value of changes in imaging metrics over the course of NAC to predict response. Acknowledgments: NIH P30CA015704, R01CA248192, and Roger E. Moe Fellowship, ASCO/CCF Hayden Family Foundation Young Investigator Award in Breast Cancer Summary of imaging characteristics across TNBC cohort. Values indicate mean (standard deviation).Whole cohort. N=115T1/T2. N=80T3/T4. N=35pCR. N=45non-pCR. N=70pAUCpCR. N=39non-pCR. N=41pAUCpCR. N=6non-pCR. N=29pAUCLongest dimension (mm)34 (15)50 (27)&amp;lt;0.010.6829 (11)31 (10)0.410.5561 (6)76 (20)0.060.75FTV (cm3)14.7 (17.0)31.2 (43.4)0.010.6710.4 (11.0)19.0 (20.2)0.050.6446.1 (24.5)52.0 (58.0)0.720.57Peak SER1.77 (0.26)1.78 (0.23)0.960.501.76 (0.26)1.79 (0.23)0.690.531.91 (0.19)1.78 (0.26)0.270.68Peak PE (%)234 (61)268 (69)0.040.62237 (61)275 (71)0.030.66247 (70)256 (63)1.000.5ADC(×10-3 mm2/s)1.19 (0.29)1.31 (0.36)0.180.581.16 (0.28)1.24 (0.30)0.390.561.40 (0.30)1.4 (0.43)0.900.48 Citation Format: Anum S Kazerouni, Laura C. Kennedy, Michael Hirano, Bonny Chau, Debosmita Biswas, Shaveta Vinayak, Matthew J. Nyflot, Habib Rahbar, Suzanne Dintzis, Savannah C. Partridge. Associations of baseline breast MRI metrics and immune infiltration with chemotherapy response in triple negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-08.

Background. Identification of predictive molecular markers of triple-negative breast cancer (TNBC) will enable the evaluation of the efficacy of neoadjuvant chemotherapy (NACT) and define optimum approaches for the prognosis of the disease course in TNBC patients. The aim of the study was to examine the correlation between the expression of the epidermal growth factor receptor (EGFR), its gene’s polymorphic variants and the neoadjuvant chemotherapy (NACT) efficacy in triple-negative breast cancer (TNBC) patients. Materials and methods. The study included 70 patients with triple-negative breast cancer, who had received 2-4 cycles of FAC and CAX regimens. The efficacy of the neoadjuvant chemotherapy was assessed according to the RECIST scale. The EGFR expression level in tumors before and after the NACT was evaluated with the help of immunohistochemistry. Genotypes for EGFR (rs2227983 and rs1468727) were detected by a real-time PCR. Results. It was found that NCT significantly decreases the EGFR expression level in the tumor ( p = 0.000). The research associates the objective clinical response as well as the pathological complete response with the low EGFR expression level ( p = 0.007 and p = 0.000 respectively). Patients carrying the EGFR CC mutant genotype of rs1468727 did not achieve a pathological complete response ( p = 0.042). In addition, patients with EGFRCC mutant genotype are more likely to have tumors with a high EGFR expression compared to EGFRTT wild-type genotype patients ( p = 0.047). Conclusion . The EGFR expression level in tumor tissue and the polymorphic variants of its gene in the rs1468727 locus can be considered as potential molecular markers with predictive significance in relation to the NACT efficacy in triple-negative breast cancer patients.

Background and Purpose:Early and accurate assessment ofbreast cancer response to NAST is important for patient management. In this study, we investigated the value of radiomic phenotypes derived from semi-quantitative and quantitative DCE-MRI parametric maps for early prediction of NASTresponse in TNBC patients. MATERIALS AND METHODS:This IRB approved study included 74 patients with stage I-III TNBC who were enrolled in the prospective ARTEMIS trial (NCT02276443). Pathologic complete response (pCR) and non-pCR were assessed by surgical histopathology after NAST (pCR=34; non-pCR=40).MRI scans were obtained at 3 time points during the NAST treatment with every 2-week anthracycline-based chemotherapy (AC): at baseline (BSL=74), post-2 cycles of AC (C2= 27) and post-4 cycles of AC (C4= 27). Patients went on to receive taxane-based chemotherapy prior to surgery. Tumor regions of interest (ROIs) were segmented by a breast radiologist at the early-phase subtractions of DCE-MRI scans using in-house developed software, followed by co-registration of the ROIs with quantitative (Ktrans, Veand Kep), and semi-quantitative DCE parametric maps (Maximum Slope Increase (MSI), Positive Enhancement Integral (PEI) and Peak Signal Enhancement Ratio (SER)).A total of 93 first order radiomic features were extracted from the tumor ROIs of each time point semi-quantitative DCE parametric map, while a total of 390 extracted radiomic features (first order-histogram features and second order grey-level-co-occurrence matrix) were extracted from each quantitative DCE parametric map using an in-house developed Matlab software.Radiomic features at each time point and changes between the 3 time points were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. Area under the receiver operating characteristics curve (AUC) was used to determine which features predicted pCR.Logistic regression was performed for feature selection, and used to build the radiomic phenotype model. The model performance was assessed by leave-one-out cross validation and 3-fold cross validation. RESULTS:Thirty-three radiomic features from PEI map were significantly different between pCR and non-pCR. The PEI most significant features were changesbetween BSL and C4 in skewness, mean and median (AUC=0.87, 0.85 and 0.87, p=&amp;lt;0.001, 0.001 and 0.002 respectively). Additionally, 31 MSI features were significantly different between pCR and non-pCR. The top 2 features were the interscan-change in skewness between BSL and C2 (AUC=0.80, P=0.007) and C4 standard deviation (AUC=0.80, P=0.006). Four BSL Veradiomic features were statistically significant between pCR and non-pCR with the best being range of difference variance (AUC=0.64, P=0.03). One BSL Kepfeature (Angular-Variance of Information measure of correlation-2) was able to differentiate pCR from non-pCR (AUC=0.64, P=0.04). Five C4-Ktrans features were able to differentiate pCR and non-pCR, with the most significant being mean value (AUC=0.86, P=0.001). BSL-Kepradiomic model built from 24 features (AUC=0.80, p=0.003) and combined (Ktrans, Veand Kep)C2-radiomic model consisting of 20 features (AUC=0.97, p=0.01) showed the best performance for prediction of pCR. CONCLUSIONS:Radiomic phenotypes form DCE-MRI parametric maps were useful for differentiation between pCR and non-pCR and showed promise as noninvasive imaging biomarkers for early prediction of NAST response in TNBC. Potentially, DCE-MRI radiomic features may be used for development of diagnostic predictive model for early noninvasive assessment of NAST treatment response in TNBC patients. Citation Format: Nabil Elshafeey, Beatriz E Adrada, Rosalind P Candelaria, Abeer H Abdelhafez, Benjamin C Musall, Jia Sun, Medine Boge, Rania M.M Mohamed, Hagar S Mahmoud, Jong Bum Son, Aikaterini Kotrosou, Shu Zhang, Jessica Leung, Deanna Lane, Marion Scoggins, David Spak, Elsa Arribas, Lumarie Santiago, Gary J. Whitman, Huong T Le-Petross, Tanya W Moseley, Jason B White, Elizabeth Ravenberg, Ken-Pin Hwang, Peng Wei, Jennifer K Litton, Lei Huo, Debu Tripathy, Vicente Valero, Alastair M Thompson, Stacy Moulder, Wei T Yang, Mark D Pagel, Jingfei Ma, Gaiane M Rauch. Radiomic phenotypes from dynamic contrast-enhanced MRI (DCE-MRI) parametric maps for early prediction of response to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-06.

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with limited treatment options and poor prognosis. Neoadjuvant chemotherapy (NAC) is standard for TNBC, though response rates vary widely. This study investigates high FOXP3+ and low Ki-67 expressions as potential predictors of poor NAC response in TNBC. Methods: This case-control study included 70 TNBC patients who received NAC. Based on NAC response, patients were categorized as cases (poor response) or controls (good response) per RECIST 1.1 criteria. FOXP3 and Ki-67 levels were assessed via immunohistochemistry, with FOXP3 classified as high (&gt;2) or low (≤2) and Ki-67 as high (&gt;20%) or low (≤20%). Associations between marker levels and NAC response were analyzed using chi-square and Fisher’s exact tests, with significance at p &lt; 0.05. Results: High FOXP3+ expression was significantly associated with poor NAC response (OR=4.231, p=0.004), indicating a fourfold increased risk. However, low Ki-67 expression was not a significant predictor of NAC response (p=0.710). These findings highlight FOXP3+ as a relevant factor in NAC outcomes, while Ki-67 lacks independent predictive value in TNBC. Conclusion: High FOXP3+ expression is a significant predictor of poor NAC response in TNBC, likely due to its immunosuppressive effects. FOXP3+ could serve as a biomarker for refining NAC strategies in TNBC, while Ki-67 appears less predictive.

BackgroundWe conducted this study to investigate the prevalence of potential chemo-response-related gene mutations in triple-negative breast cancer (TNBC) patients and to evaluate the potential relationship between these gene mutations and neoadjuvant chemotherapy response in TNBC patients.MethodsOne hundred sixty-two TNBC patients in Fudan University Shanghai Cancer Center who received NAC with 4 cycles of paclitaxel and carboplatin were enrolled in this study. Fifty-six pathological complete response (pCR) patients and 56 non-pCR patients were enrolled in this retrospective study for the training set. Clinical assessments of postoperative residual tumors were performed according to Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 criteria. Forty chemo-response-related genes were screened in each tumor specimen by second-generation sequencing analysis. Fifty TNBC patients who received neoadjuvant chemotherapy with paclitaxel and carboplatin were enrolled in the validation group.ResultsFifty-seven of 112 (50.9%) TNBCs contained at least one detected somatic mutation. As expected, TP53 mutation was the most common alteration, which was observed in 21.4% of patients. BRCA1, BRCA2, RET, PI3KCA, and PTEN mutations were each observed in 11.6%, 4.5%, 5.4%, 2.7% and 3.6% of all cases, respectively. No significant differences in any gene mutation frequency between pCR and non-pCR groups were identified. We found that the mutation status of 10 DNA repair genes involved in homologous recombination (HR) pathway successfully discriminated between responding and nonresponding tumors in the training group. Up to 18 patients who were mutation-positive experienced pCR compared to only 6 in the non-pCR group (P=0.006), and 75% the HR related gene mutation patients achieved pCR. In the validation group, TNBC patients with DNA repair gene mutations achieved 77.8% pCR.ConclusionsA subset of TNBC patients carry deleterious somatic mutations in 10 HR-related genes. The mutation status of this expanded gene panel is likely to effectively predict respond rate to neoadjuvant chemotherapy based on paclitaxel and carboplatin. Our findings need to be validated through follow-up studies in this and additional cohorts.

BackgroundPathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30–40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60–70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear. In this study, we developed a machine learning-based two-step pipeline to distinguish between various histological components in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of TNBC tissue biopsies and to identify histological features that can predict NAC response.MethodsH&E-stained WSIs of treatment-naïve biopsies from 85 patients (51 with pCR and 34 with RD) of the model development cohort and 79 patients (41 with pCR and 38 with RD) of the validation cohort were separated through a stratified eightfold cross-validation strategy for the first step and leave-one-out cross-validation strategy for the second step. A tile-level histology label prediction pipeline and four machine-learning classifiers were used to analyze 468,043 tiles of WSIs. The best-trained classifier used 55 texture features from each tile to produce a probability profile during testing. The predicted histology classes were used to generate a histology classification map of the spatial distributions of different tissue regions. A patient-level NAC response prediction pipeline was trained with features derived from paired histology classification maps. The top graph-based features capturing the relevant spatial information across the different histological classes were provided to the radial basis function kernel support vector machine (rbfSVM) classifier for NAC treatment response prediction.ResultsThe tile-level prediction pipeline achieved 86.72% accuracy for histology class classification, while the patient-level pipeline achieved 83.53% NAC response (pCR vs. RD) prediction accuracy of the model development cohort. The model was validated with an independent cohort with tile histology validation accuracy of 83.59% and NAC prediction accuracy of 81.01%. The histological class pairs with the strongest NAC response predictive ability were tumor and tumor tumor-infiltrating lymphocytes for pCR and microvessel density and polyploid giant cancer cells for RD.ConclusionOur machine learning pipeline can robustly identify clinically relevant histological classes that predict NAC response in TNBC patients and may help guide patient selection for NAC treatment.

Immunotherapy in combination with neoadjuvant chemotherapy is rapidly changing the therapeutic perspective for Triple-Negative Breast Cancer (TNBC) patients, as demonstrated by the KEYNOTE522 trial. Enhancing a proficient immune response to improve neoadjuvant therapy (NAT) efficacy is an urgent clinical need. We have previously shown by applying a morphology-guided digital spatial transcriptome profiling, that Natural Killer (NK) cell infiltration in diagnostic biopsies of TNBC patients was associated with increased chemotherapy success. In this work, we aim to characterize the specific NK landscape associated with NAT response in TNBC. We prospectively enrolled 35 patients with a new diagnosis of TNBC in our Institute and underwent the KEYNOTE522 neoadjuvant schedule. We collected peripheral blood at diagnosis (T0) and after NAT completion before surgery (T1). On peripheral blood mononuclear cells (PBMCs), we performed single-cell RNA sequencing (scRNA-seq) and flow cytometry analysis to profile circulating NKs and compare patients showing either pathological complete or partial response to the KEYNOTE522 schedule. We did not observe a significant difference in NK number or sub-clustering before therapy (T0) when comparing responders to partial responders. However, after the NAT conclusion (T1), NKs from partial responders displayed a prevalent immune tolerant profile, characterized by the down-regulation of NK cytotoxicity-related genes. Besides, CD16-CD56bright NKs were significantly increased in partial responders, whereas CD16+CD56dim NKs were enhanced in complete responders, confirming a divergent activation profile among circulating NKs that was detectable only after NAT. Altogether, our results pointed out the relevance of NK activation in supporting a proficient NAT response in TNBC patients. Still, the molecular mechanisms governing NK cytotoxicity toward TNBC are largely unknown, restraining their clinical exploitation. To fill this gap, we investigated the specific transcriptional landscape associated with NK effector functions in TNBC patients. We applied a predictive bioinformatic approach to reconstruct the hierarchical organization of the regulatory transcription network associated with NK activation in TNBC, identifying a list of top-scoring Transcription Factors (TFs). Among these, we found SMAD3, VEZF1, MAZ, and RXRA as putative novel master regulators of NK cytotoxicity in anti-TNBC response. We developed a CRISPRi-based system to knock down these TFs and confirm the impact of this transcriptional reprogramming on NK effector functions. Collectively, our data unveil a driving role of NKs in triggering NAT efficacy in TNBC patients and that transcriptional reprogramming can be exploited to potentiate NK-based adoptive cell therapies and further enhance immunotherapy efficacy in TNBC patients. Citation Format: Gloria Manzotti, Elisa Salviato, Veronica Manicardi, Anna Rita Redavid, Elisa Gasparini, Moira Ragazzi, Federica Torricelli, Emanuele Vitale, Giovanna Talarico, Francesco Bertolini, Carmine Pinto, Alessia Ciarrocchi, Francesca Reggiani. Natural killer activation drives immunotherapy efficacy in triple-negative breast cancer patients: Novel insights for therapeutic intervention [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6126.

Background: Triple Negative Breast Cancer (TNBC) is one of the breast cancer subtypes with an aggressive clinical course and has a limited therapeutic strategy. Until now, conventional chemotherapy is still used as standard therapy, although it still has an inadequate therapeutic response. One of the chemoresistance mechanisms is likely due to the immunosuppressive effect of CD73-derived adenosine. This study aims to evaluate the high CD73 expression as a negative predictor of clinical neoadjuvant chemotherapy response in TNBC. Methods: This study was a retrospective case-control study. A total of 46 TNBC patients who received neoadjuvant chemotherapy at Prof Dr. I.G.N.G Ngoerah's Hospital were enrolled. Twenty-three patients with positive neoadjuvant chemotherapy responded as the control group and 23 with negative neoadjuvant chemotherapy responded as the case group. Evaluation of CD73 expression was carried out by immunohistochemistry from a biopsy taken before the patient underwent neoadjuvant chemotherapy. CD73 expression was categorized into low and high based on staining intensity and percentage of the stained tumor cells. Data were analyzed using SPSS version 25.0 for Windows. Results: Analysis showed a significant relationship between CD73 expression and clinical neoadjuvant chemotherapy response (OR=4.41; 95% CI=1.26-15.41; p=0.017). Multivariate analysis showed CD73 expression (AOR=4.88; 95%CI=1.08-21.99; p=0.039) and T stage (AOR=18.82; 95%CI=1.83-193.00; p=0.013) simultaneously affect clinical neoadjuvant chemotherapy response. Conclusion: It can be concluded that high expression of CD73 and T stage is an independent predictor of negative clinical neoadjuvant chemotherapy response in TNBC patients.

Simple SummaryThis study aimed to identify genes associated with neoadjuvant chemotherapy (NAC) response and disease-free survival (DFS) of triple-negative breast cancer (TNBC) patients. We conducted a large-scale meta-analysis of gene expression data from multiple TNBC cohorts and analyzed four gene expression quadrants from an integrated analysis of NAC response and clinical outcomes. The study also highlighted the importance of incorporating both short-term and long-term clinical outcomes in NAC response evaluation. The findings provide insights into the complex molecular mechanisms underlying TNBC and may guide the development of personalized treatment strategies for TNBC patients.Triple-negative breast cancer (TNBC) is a heterogeneous disease with varying responses to neoadjuvant chemotherapy (NAC). The identification of biomarkers to predict NAC response and inform personalized treatment strategies is essential. In this study, we conducted large-scale gene expression meta-analyses to identify genes associated with NAC response and survival outcomes. The results showed that immune, cell cycle/mitotic, and RNA splicing-related pathways were significantly associated with favorable clinical outcomes. Furthermore, we integrated and divided the gene association results from NAC response and survival outcomes into four quadrants, which provided more insights into potential NAC response mechanisms and biomarker discovery.

Objective To explore the clinical and pathological characteristics of triple-negative breast cancer(TNBC) and to compare the response to neoadjuvant chemotherapy and survival in patients with TNBC and non-TNBC. Methods Five hundred and thirty-five patients were included in this retrospective study. 75 patients were TNBC and 460 were non-TNBC. The clinical and pathological characteristics, 5-year disease free survival (DFS) and overall survival (OS) were analyzed. 88 patients were treated with neoadjuvant chemotherapy in which 26 patients were TNBC, the other were non-TNBC. Their responses to neoadjuvant chemotherapy, and the relations of response and survival were analyzed. Results The patients with TNBC were younger than those with non-TNBC (35 vs 44), and most of the patients with TNBC were premenopausal at diagnosis (88.0 % vs 67.2 %, P =0.009). The frequency of invasive ductal carcinoma was higher in patients with TNBC than those with non-TNBC (92.0 % vs 80.4 %, P =0.020). Generally patients with TNBC had higher grade tumors (grade Ⅱ ) than patients with non-TNBC (56.0 % vs 17.2 %, P = 0.000). Lower rate of lymph node metastasis were observed in patients with TNBC than those with non-TNBC (33.3 % vs 53.9 %, P = 0.001). Patients with TNBC had worse 5-year DFS (66.67 %) and OS (80.0 %) than those with non-TNBC (74.78 %, 90.00 %). In this study. 88 patients were treated with neoadjuvant chemotherapy. The overall response rate(OR) of patients with TNBC was 88.46 %, including 65.38 % clinical complete response (cCR)and 23.08 % clinical partial response (cPR). It was significantly higher than patients with non-TNBC respectively (82.26 %, 37.10 %, 45.16 %) (P 0.05). In contrast, TNBC patients with residual disease after neoadjuvant chemotherapy had worse 5-year DFS and OS compared with non-TNBC (P <0.05). Conclusion TNBC is common in young premenopausal women. Its main pathological style is nonspecial type of invasive ductal carcinoma with high grade, with low lymph node metastasis rate. Patients with TNBC are more sensitive to neoadjuvant chemotherapy than those with non-TNBC. Patients with TNBC have increased cCR rates compared with non-TNBC, and those with cCR have a good prognosis. TNBC patients in whom cCR are not achieved have significantly worse survival rates compared with that of non-TNBC patients. Key words: Breast neoplasms; Drug therapy, combination; Triple negative breast neoplasms; Prognosis

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Simple SummaryOnly 20–50% of patients with triple negative breast cancer achieve a pathological complete response from neoadjuvant chemotherapy, a strong indicator of patient survival. Therefore, there is an urgent need for a reliable predictive model of the patient’s pathological complete response prior to actual treatment. The purpose of this study was to develop such a model based on random forest recursive feature elimination and to benchmark the performance of the proposed model against existing predictive models. Our study suggests that an 86-gene-based random forest model associated to DNA repair and cell cycle mechanisms can provide reliable predictions of neoadjuvant chemotherapy response in patients with triple negative breast cancer.Neoadjuvant chemotherapy (NAC) response is an important indicator of patient survival in triple negative breast cancer (TNBC), but predicting chemosensitivity remains a challenge in clinical practice. We developed an 86-gene-based random forest (RF) classifier capable of predicting neoadjuvant chemotherapy response (pathological Complete Response (pCR) or Residual Disease (RD)) in TNBC patients. The performance of pCR classification of the proposed model was evaluated by Receiver Operating Characteristic (ROC) curve and Precision Recall (PR) curve. The AUROC and AUPRC of the proposed model on the test set were 0.891 and 0.829, respectively. At a predefined specificity (>90%), the proposed model shows a superior sensitivity compared to the best performing reported NAC response prediction model (69.2% vs. 36.9%). Moreover, the predicted pCR status by the model well explains the distance recurrence free survival (DRFS) of TNBC patients. In addition, the pCR probabilities of the proposed model using the expression profiles of the CCLE TNBC cell lines show a high Spearman rank correlation with cyclophosphamide sensitivity in the TNBC cell lines (SRCC , p-value ). Associations between the 86 genes and DNA repair/cell cycle mechanisms were provided through function enrichment analysis. Our study suggests that the random forest-based prediction model provides a reliable prediction of the clinical response to neoadjuvant chemotherapy and may explain chemosensitivity in TNBC.

Introduction. The standard treatment for non-metastatic triple-negative breast cancer (TNBC) is neoadjuvant chemotherapy (NAC) and nearly 50% exhibit pathological complete response (pCR). However, patients with residual disease after NAC are at increased risk for recurrence and death. Prior studies examining the transcriptome of TNBC pre/post-NAC have examined a limited number of genes (&amp;lt;500) in heterogeneous subgroups of TNBC (e.g. LAR and non-LAR). We explored the transcriptome of androgen-receptor (AR) negative (non-LAR) TNBC subtype both pre/post NAC to identify pathways associated with NAC response. Methods. Tumors obtained pre/post NAC from TNBC patients enrolled in the Breast Cancer Genome Guided therapy study (BEAUTY) underwent RNA sequencing and reverse-phase protein array (RPPA). EdgeR was applied for differentially expressed (DE) analysis and regression methods for RPPA. Digital deconvolution method (CIBERSORTx) and TNBC single-cell data were used to obtain cell types. Pathway analysis was carried out using 2972 gene sets and gene set variation analysis (GSVA). Functional enrichment analysis was conducted with significant genes. Results. Of the 44 TNBC patients, 32 patients were excluded from the analysis cohort due to: LAR tumor (6 pts.), non-LAR tumor with pCR (23 pts.), and cell type issues with RNA-seq data (3 pt.). Paired RNA-Seq data were available for 12 TNBC patients (4 with progression &amp;lt;2 years [EP]) and 8 who were progression-free &amp;gt; 4 years [NP]) and paired RPPA data were available for 9 of these 12 patients. Differentially expressed genes, proteins and cell types between EP and NP in post-NAC. We identified 489 genes differentially expressed (DE) between EP and NP (logFC=|2|, FDR &amp;lt; 0.05). Analysis of cytobands from these 489 genes showed an enrichment of genes on chromosome 6p22.1-2 and 17q25.3 regions (enrichment ratio &amp;gt;5; p-value &amp;lt;10E-4). Critical genes identified in the AR- network (p-value &amp;lt; 10E-3) were IL1RN, SLAMF9, KRT81, BHLHE22, B3GALT5, PCP4, TREM1, AQP9, NRTN, and COL2A1.In addition, preliminary results from RPPA data of post-NAC tumors showed astrocytic phosphoprotein (PEA-15), involved in apoptosis, proliferation, glucose metabolism, as well as cell proliferation and Y box binding (YB1) proteins (involved in metastases), were more DE in EP than NP (p &amp;lt; 0.05). CIBERSORTx was applied to estimate the proportions of different cell types in post-NAC tumors. Cancer-associated fibroblasts iCAFs were low and myCAFs are high in EP vs NP. It is known that the cross-talk between CAFs and tumor cells may induce tumor resistance to chemotherapy. Differentially expressed pathways in post and pre-NAC EP tumors. Using genome-wide expression data from the paired 12 tumors and the GSVA method, we obtained individual pathway scores for 2972 pathways. One hundred ninety pathways were downregulated and 61 pathways were upregulated (p-value &amp;lt;= 0.05) in the post-NAC residual disease of EP relative to NP. We further examined these 190 pathways in the paired EPs and found 71% of those pathways were upregulated in the pre-NAC. These 190 downregulated pathways were enriched with FOXO, TGF-beta, PI3k, FGFR1, insulin and others. The 61 upregulated pathways in post-NAC EP tumors were enriched with mismatch repair, purine, tubulin, telomere, polymerase and gap-junction related pathways; 77% of those 61 pathways were downregulated in pre-NAC. Conclusions. Using a comprehensive “omics” approach, we have identified novel cancer and drug response pathways associated with recurrence in AR-TNBC disease. Further work to evaluate these as markers of outcome and potential drug targets is warranted. Citation Format: Krishna R Kalari, Vera J Suman, Xiaojia Tang, Jason P Sinnwell, Kevin J Thompson, Peter T Vedell, Jodi M Carter, Sarah A McLaughlin, Alvaro Moreno Aspitia, Donald W Northfelt, Richard J Gray, Richard Weinshilboum, Liewei Wang, Judy C Boughey, Matthew Goetz. Multi-omics data shows downregulation of mismatch repair, purin and tublin pathways in AR-negative triple-negative chemotherapy-resistant tumors [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-01-05.

Background: Triple-negative breast cancer (TNBC) is known for its highly aggressive nature and poor prognosis. However, it has been noted to exhibit high immunogenicity compared to other breast cancer types, making it more responsive to immunotherapy. Recently, neoadjuvant chemotherapy (NAC) has become the standard treatment for early high-risk TNBC. Higher stromal tumor-infiltrating lymphocytes (TIL) predict increased pathologic complete response (pCR) and superior clinical outcomes in TNBC treated with NAC. Despite this general trend, high TIL does not always predict a favorable response, nor does low TIL always predict a poor response. This suggests that individuals with varying treatment outcomes might have different immune dynamics, even under similar TIL levels. Objective: This study aimed to examine the tumor immune microenvironment (TIME) of pretreated TNBC samples that subsequently underwent NAC using multiplex immunofluorescence (mIHC). Our investigation explored the role of TIME in NAC response and elucidated differences in TIME among patients with similar TIL levels but varying treatment outcomes, as well as those with differing TIL levels but similar treatment responses. Methods: We retrospectively collected medical records of 16 patients with stage II-III early TNBC treated with NAC at Gangnam Severance Hospital, Yonsei University, between January 2019 and August 2021. Two pathologists (SSJ and YJC) evaluated stromal TIL levels, tumor-stroma ratio, and PD-L1 (22C3) status (CPS). mIHC, including CK, CD4, CD8, CD20, and FOXP3, was performed for tumor cells (TC) and immune cells (IC). Spatial metrics, including the cellular densities of TC and IC, and cell-cell distances between IC, were analyzed. Results: A total of 1,183,644 cells (761,976 TC and 421,668 IC) from sixteen patients (six pCR [4 high- and 2 low TIL] and ten non-pCR [4 high- and 6 low TIL]) were analyzed. There were no significant differences in baseline characteristics or IC densities between the pCR and non-pCR groups. In the pCR group, CD20+IC and CD8+IC were predominant IC subtypes, particularly in high TIL cases, whereas CD4+IC was the dominant fraction in the non-pCR group. Density plots of different IC types showed dense clustering of CD8+IC and CD20+IC near the TC in the pCR group. Regarding IC-IC interaction, the CD4 IC pair was the largest fraction overall. Within the pCR group, high TIL cases had a high proportion of CD20 IC pairs, whereas low TIL cases had IC pairs, including CD8+IC. Conclusion: This study observed differences in IC composition and their distribution in pretreated TNBC samples based on pCR status and TIL levels. The findings suggest that tumoral CD8+IC and CD20+IC play crucial roles in determining the response to NAC in eTNBC. Particularly, significant infiltration of CD20+IC in high TIL conditions suggests the presence of B-T cell interaction. Citation Format: Jee Hung Kim, Inho Park, Su-Jin Shin, Heounjeong Go, Jiwon KO, Yangkyu Lee, Soong June Bae, Sung Gwe Ahn, Joon Jeong, Yoon Jin Cha. Comparison of tumor immune microenvironment of TNBC with different treatment outcome with neoadjuvant chemotherapy: Spatial analysis using multiplex fluorescent immunohistochemistry [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P1-04-28.