Neoadjuvant Chemotherapy Response In Patients Research Articles

Predicting clinical outcomes in the S1314-COXEN trial using a multimodal deep learning model integrating histopathology, cell types, and gene expression.

533 Background: Accurate prediction of response to neoadjuvant chemotherapy (NAC) is essential for optimizing treatment outcomes in muscle-invasive bladder cancer (MIBC). We present a cutting-edge multimodal deep learning model aimed at predicting outcomes of NAC from accessible H & E images and molecular data. Methods: We designed a Graph-based Multi-modal Late Fusion (GMLF) deep learning model that integrates three types of data from the S1314-COXEN clinical trial: 1) Neural embeddings from 182 whole slide images (WSIs), generated via the ResNet50 architecture. 2) Cell types based on morphology, including cancer, necrotic, immune, and stromal cells, and their spatial positions within the tumor microenvironment extracted from WSIs using the HoVer-Net framework, a multiple-branch convolutional neural network for predicting cell types. 3) Patient-level RNA expression data derived from 1,071-dimensional gene expression vectors. The dataset was randomly divided into 80% for model training, using 5-fold cross-validation (5-fold CV), and 20% for internal validation. Results: Our dataset included 182 WSIs of 180 patients who were randomized to receive either gemcitabine-cisplatin or dose-dense methotrexate-vinblastine-adriamycin-doxorubicin-cisplatin. Of all patients, 30.8% demonstrated a complete pathological response (pT0 after radical cystectomy). Our GMLF model achieved an AUROC of 0.7417 ± 0.1021 for predicting whether a patient showed a complete pathologic response to NAC in a 5-fold CV. Using an 80/20 training and validation split, the model yielded an AUROC of 0.7236. Interestingly, our model emphasized the contribution of spatial information collected from WSIs. For selecting the best architecture for the histology branch, the Slidegraph+ framework, a graph neural network-based model, achieved an AUROC of 0.6938 ± 0.0565, significantly outperforming patch-level models as the second-best, which achieved AUROC of 0.5572 ± 0.1793. Shapley Additive Explanation (SHAP) analysis revealed RNA expression branch as the most influential, with a mean SHAP magnitude of 0.13, followed by the neural embeddings at 0.12. SHAP-based interpretation identified the most important expressed genes impacting model predictability, including TP63. Our gene set analysis identified the most influential pathways for predicting response, which included basal differentiation and myo-fibroblast enrichment (p-value < 0.05). Conclusions: Our interpretable GMLF model accurately predicts NAC response in patients with MIBC by integrating standard H&E images and RNA expression data. Our model interpretations not only revealed the importance of each modality but also uncovered histopathological, cellular, and molecular underpinnings of response to NAC in MIBC. These findings open the door to AI-guided development of precision therapies for MIBC. Clinical trial information: NCT02177695 .

Association of race and CA 19-9 nonproduction with limited pathologic response to neoadjuvant chemotherapy in patients with localized pancreatic cancer.

609 Background: Black patients with pancreatic ductal adenocarcinoma (PDAC) are less likely to have major pathologic response (MPR) following induction chemotherapy compared to non-Black patients. Rare reports have suggested that Black patients are more likely to be CA 19-9 non-producers. We aimed to determine if disproportionate rates of CA 19-9 nonproduction underlie racial differences in neoadjuvant chemotherapy response in patients with localized PDAC. We also explored differences in somatic mutations between CA 19-9 producers (CAprod) and non-producers (CAnonprod). Methods: Black and White patients with localized PDAC receiving ≥ 2 cycles of neoadjuvant chemotherapy followed by pancreatectomy (2010-2019) at 7 high-volume centers were reviewed. Patients were categorized as CAprod (CA 19-9 > 5 U/mL) or CAnonprod (CA 19-9 ≤ 5 U/mL). Uni- and multi-variable models evaluated differences in rates of CAnonprod by race, and the association of CAnonprod with MPR (CAP 0/1). The Pancreatic Cancer Action Network (PanCAN)’s SPARK health data platform which contains clinical and multi-omic data from the PanCAN Know Your Tumor program was queried to identify patients with CA 19-9 and genomic data. Fisher’s exact test was used to compare differentially mutated genes between CAprod and CAnonprod patients. Results: Our cohort included 385 (93.3%) CAprod and 30 (6.7%) CAnonprod. CAnonprod patients were more likely to be Black than CAprod (50.0% vs 12.5%, p<0.01). There were no other differences in demographic or clinical features between CAnonprod and CAprod. Most patients received FOLFIRINOX vs Gemcitabine based chemotherapy (60.3% CAprod vs. 56.7% CAnonprod, p=0.27) with a median number of 4.0 vs 5.0 cycles (p=0.83), respectively. Rate of MPR was significantly higher in patients with CAprod compared with CAnonprod patients (26.3% vs 7.7%, p=0.03). No CAnonprod patient had a complete pathologic response vs 28 (7.3%) of CAprod (p=0.12). CAnonprod was independently associated with decreased odds of MPR (OR 0.21, CI [0.04-0.99]). Black race was independently associated with increased odds of CAnonprod (OR 8.66, CI [3.81 – 19.7]). When stratified by production of CA 19-9, there was no significant difference between rate of MPR between Black and White patients. In the PanCAN KYT analysis, CAnonproduc had higher rates of SWI/SNF alterations (50.0% of CAnonprod vs 30.1% of CAprod, P<0.01;P-adj=ns; ARID1B was the most frequently mutated SWI/SNF gene in CAnonprod (31.0% vs 9.0% CApos, p<0.01; P-adj=ns). Conclusions: CAnonprod were more likely to be Black and have significantly worse rates of MPR following neoadjuvant chemotherapy in patients with localized PDAC. Moreover, CAnonprod was associated with higher rates of SWI/SNF alterations, noting a potential biologic basis for racial differences in chemotherapy response to curative-intent treatment for PDAC.

Development and validation of a nomogram model based on pretreatment ultrasound and contrast-enhanced ultrasound to predict the efficacy of neoadjuvant chemotherapy in patients with borderline resectable or locally advanced pancreatic cancer

ObjectivesTo develop a nomogram using pretreatment ultrasound (US) and contrast-enhanced ultrasound (CEUS) to predict the clinical response of neoadjuvant chemotherapy (NAC) in patients with borderline resectable pancreatic cancer (BRPC) or locally advanced pancreatic cancer (LAPC).MethodsA total of 111 patients with pancreatic ductal adenocarcinoma (PDAC) treated with NAC between October 2017 and February 2022 were retrospectively enrolled. The patients were randomly divided (7:3) into training and validation cohorts. The pretreatment US and CEUS features were reviewed. Univariate and multivariate logistic regression analyses were used to determine the independent predictors of clinical response in the training cohort. Then a prediction nomogram model based on the independent predictors was constructed. The area under the curve (AUC), calibration plot, C-index and decision curve analysis (DCA) were used to assess the nomogram’s performance, calibration, discrimination and clinical benefit.ResultsThe multivariate logistic regression analysis showed that the taller-than-wide shape in the longitudinal plane (odds ratio [OR]:0.20, p = 0.01), time from injection of contrast agent to peak enhancement (OR:3.64; p = 0.05) and Peaktumor/ Peaknormal (OR:1.51; p = 0.03) were independent predictors of clinical response to NAC. The predictive nomogram developed based on the above imaging features showed AUCs were 0.852 and 0.854 in the primary and validation cohorts, respectively. Good calibration was achieved in the training datasets, with C-index of 0.852. DCA verified the clinical usefulness of the nomogram.ConclusionsThe nomogram based on pretreatment US and CEUS can effectively predict the clinical response of NAC in patients with BRPC and LAPC; it may help guide personalized treatment.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer

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.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning.

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.

Glutathione dynamics is a potential predictive and therapeutic trait for neoadjuvant chemotherapy response in bladder cancer

Radical cystectomy with preoperative cisplatin-based neoadjuvant chemotherapy (NAC) is the standard care for muscle-invasive bladder cancers (MIBCs). However, the complete response rate to this modality remains relatively low, and current clinicopathologic and molecular classifications are inadequate to predict NAC response in patients with MIBC. Here, we demonstrate that dysregulation of the glutathione (GSH) pathway is fundamental for MIBC NAC resistance. Comprehensive analysis of the multicohort transcriptomes reveals that GSH metabolism and immune-response genes are enriched in NAC-resistant and NAC-sensitive MIBCs, respectively. A machine-learning-based tumor/stroma classifier is applied for high-throughput digitalized immunohistochemistry analysis, finding that GSH dynamics proteins, including glutaminase-1, are associated with NAC resistance. GSH dynamics is activated in cisplatin-resistant MIBC cells, and combination treatment with a GSH dynamics modulator and cisplatin significantly suppresses tumor growth in an orthotopic xenograft animal model. Collectively, these findings demonstrate the predictive and therapeutic values of GSH dynamics in determining the NAC response in MIBCs.

Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer

ObjectivesMultiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT).MethodsWe retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient’s disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders.ResultsThe interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71–0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT.ConclusionThe mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC.Trial registrationRetrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).

BMI and TLC increase Neoadjuvant Chemotherapy Response in Patients with Locally Advanced Breast Cancer

Anthracycline-based neoadjuvant chemotherapy is the recommended therapy for locally advanced breast cancer (LABC) patients. Unfortunately, no study has reported the relationship between body mass index (BMI), total lymphocyte count (TLC), and responses to this type of chemotherapy. This study aimed to determine the relationship between BMI, TLC, and response to doxorubicin/epirubicin neoadjuvant chemotherapy in LABC patients. A retrospective cohort design was applied to medical records of LABC patients undergoing neoadjuvant chemotherapy at Ulin General Hospital Banjarmasin, Indonesia, from July to December 2021. BMI and TLC data were assessed based on the values before chemotherapy, while the chemotherapy response was measured using the RECIST 1.1 criteria after 3 cycles. Multinomial logistic regression test with 95% confidence level was used to analyze these data. The results showed that as many as 71% of patients experienced a Partial Response (PR), while 5% and 23% of the patients demonstrated Stable Disease (SD) and Progressive Disease (PD), respectively. Each increase in BMI of 1 kg/m2 was significantly associated with an increase in the occurrence of PR and PD by 1.26 times and 1.29 times, respectively, when compared to the occurrence of PD. Meanwhile, an increase in TLC of 100 cells/mm3 was associated with an increase in the occurrence of PR by 6.83 times and an increase in the occurrence of SD. of 6.94 when compared to the occurrence of PD. Therefore, there is a significant relationship between BMI, TLC, and response to anthracycline-based neoadjuvant chemotherapy in LABC patients

Analysis of the Clinical Molecular Characteristics and Neoadjuvant Chemotherapy Response in Patients with Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer and Axillary Lymph Node Metastasis

Objective: This study aimed to investigate the clinical molecular characteristics in patients with human epidermal growth factor receptor 2 (HER2)-negative breast cancer and axillary lymph node metastasis and explored the related factors of the neoadjuvant chemotherapy (NAC) response. Methods: The data of 185 patients with HER2-negative breast cancer and axillary lymph node metastasis who were treated in the Department of Breast Center of the Affiliated Hospital of Qingdao University from July 2017 to July 2021 were retrospectively analyzed. The clinical features and the related factors for the responses of the primary tumor and axillary lymph node metastasis to NAC were analyzed. Statistical analysis was conducted using the SPSS 26.0 statistical software. Univariate analysis was conducted using the χ2 test, and multivariate analysis was conducted using logistic regression analysis. Results: The differences in estrogen receptor (ER), progesterone receptor (PR), and Ki67 among the three HER2-negative subgroups (the immunohistochemistry (IHC)0 group, IHC1+ group, and IHC2+/in situ hybridization– group) were statistically significant (p &lt; 0.05). Univariate analysis revealed that the differences in the tumor stage, ER, PR, and Ki67 among the groups based on the response of the primary tumor to NAC were statistically significant (p &lt; 0.05), and the differences in ER, PR, and Ki67 among the groups based on the response of axillary lymph node metastasis to NAC were statistically significant (p &lt; 0.05). Multivariate analysis revealed that the difference in Ki67 among the groups based on the response of axillary lymph node metastasis to NAC was statistically significant (p &lt; 0.05). Conclusions: When the expression level of HER2-negative IHC increases, the positive rates of ER and PR increase. A smaller tumor, negative ER, negative PR, and a Ki67 level &gt;30% indicate a good effect of NAC for primary tumors. Negative ER, negative PR, and a Ki67 level &gt;30% indicate a good effect of NAC for axillary lymph node metastasis. Therefore, Ki67 may be an independent factor affecting the efficacy of NAC for axillary lymph node metastasis.

Exosomal Metabolic Signatures Are Associated with Differential Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer.

Neoadjuvant chemotherapy (NAC) is commonly used in breast cancer (BC) patients to increase eligibility for breast-conserving surgery. Only 30% of patients with BC show pathologic complete response (pCR) after NAC, and residual disease (RD) is associated with poor long-term prognosis. A critical barrier to improving NAC outcomes in patients with BC is the limited understanding of the mechanisms underlying differential treatment outcomes. In this study, we evaluated the ability of exosomal metabolic profiles to predict NAC response in patients with BC. Exosomes isolated from the plasma of patients after NAC were used for metabolomic analyses to identify exosomal metabolic signatures associated with the NAC response. Among the 16 BC patients who received NAC, eight had a pCR, and eight had RD. Patients with RD had 2.52-fold higher exosome concentration in their plasma than those with pCR and showed significant enrichment of various metabolic pathways, including citrate cycle, urea cycle, porphyrin metabolism, glycolysis, and gluconeogenesis. Additionally, the relative exosomal levels of succinate and lactate were significantly higher in patients with RD than in those with pCR. These data suggest that plasma exosomal metabolic signatures could be associated with differential NAC outcomes in BC patients and provide insight into the metabolic determinants of NAC response in patients with BC.

Dual-Branch Convolutional Neural Network Based on Ultrasound Imaging in the Early Prediction of Neoadjuvant Chemotherapy Response in Patients With Locally Advanced Breast Cancer.

The early prediction of a patient’s response to neoadjuvant chemotherapy (NAC) in breast cancer treatment is crucial for guiding therapy decisions. We aimed to develop a novel approach, named the dual-branch convolutional neural network (DBNN), based on deep learning that uses ultrasound (US) images for the early prediction of NAC response in patients with locally advanced breast cancer (LABC). This retrospective study included 114 women who were monitored with US during pretreatment (NAC pre) and after one cycle of NAC (NAC1). Pathologic complete response (pCR) was defined as no residual invasive carcinoma in the breast. For predicting pCR, the data were randomly split into a training set and test set (4:1). DBNN with US images was proposed to predict pCR early in breast cancer patients who received NAC. The connection between pretreatment data and data obtained after the first cycle of NAC was considered through the feature sharing of different branches. Moreover, the importance of data in various stages was emphasized by changing the weight of the two paths to classify those with pCR. The optimal model architecture of DBNN was determined by two ablation experiments. The diagnostic performance of DBNN for predicting pCR was compared with that of four methods from the latest research. To further validate the potential of DBNN in the early prediction of NAC response, the data from NAC pre and NAC1 were separately assessed. In the prediction of pCR, the highest diagnostic performance was obtained when combining the US image information of NAC pre and NAC1 (area under the receiver operating characteristic curve (AUC): 0.939; 95% confidence interval (CI): 0.907, 0.972; F1-score: 0.850; overall accuracy: 87.5%; sensitivity: 90.67%; and specificity: 85.67%), and the diagnostic performance with the combined data was superior to the performance when only NAC pre (AUC: 0.730; 95% CI: 0.657, 0.802; F1-score: 0.675; sensitivity: 76.00%; and specificity: 68.38%) or NAC1 (AUC: 0.739; 95% CI: 0.664, 0.813; F1-score: 0.611; sensitivity: 53.33%; and specificity: 86.32%) (p<0.01) was used. As a noninvasive prediction tool, DBNN can achieve outstanding results in the early prediction of NAC response in patients with LABC when combining the US data of NAC pre and NAC1.

Muscle-invasive bladder cancer: pretreatment prediction of response to neoadjuvant chemotherapy with diffusion-weighted MR imaging.

To investigate the usefulness of diffusion-weighted MR imaging with ADC value and histogram analysis of ADC in the prediction of response to neoadjuvant chemotherapy (NAC) in patients with muscle-invasive bladder cancer (MIBC). Fifty-eight consecutive patients with clinical T2-4aN0M0 MIBC who underwent MRI before and after NAC were enrolled in the prospective study. The evaluation of response to NAC was based on the pathologic T (pT) stage after surgery. Patients with non-muscle-invasive residual cancer (pTa, pTis, pT1) were defined as responders, while those with muscle-invasive residual cancer (≥ pT2) were defined as non-responders. The ADC value measured from a single-section region of interest and ADC histogram parameters derived from whole-tumor volume of interest in responder and non-responder were compared using the Mann-Whitney U test or independent samples t test. ROC curve analysis was used to evaluate the diagnostic performance of ADC value and ADC histogram parameters in predicting the response to NAC. The pretreatment ADC value of responders ([1.33 (± 0.21)] × 10-3mm2/s) was significantly higher than that of non-responders ([1.09 (± 0.08)] × 10-3mm2/s) (P < .001). Most of the pretreatment ADC histogram parameters (Mean, 10th, 25th, 50th, 75th, and 90th percentiles) of responders were significantly higher than that of non-responders (P < .001). The AUC was highest for the pretreatment ADC value (0.88; 95% confidence interval: 0.77, 0.95; P < .001). Diffusion-weighted MR imaging with ADC value and histogram analysis of ADC are useful to predict NAC response in patients with MIBC.

Abstract P2-12-24: Exosomal metabolic signatures are associated with differential response to neoadjuvant chemotherapy in patients with breast cancer

Abstract Background: Neoadjuvant chemotherapy (NAC) is becoming the standard of care for aggressive breast cancer (BC). In patients with BC, NAC can increase eligibility for breast-conserving surgery, lower the excision volume, and assist in evaluating the disease course. NAC may shrink (residual disease, RD) or even eliminate (pathologic complete response, pCR) the tumor. Patients with RD often have a poor long-term prognosis. In contrast, pCR is associated with better long-term survival outcomes. Approximately 30% of patients with BC show pCR after NAC, while the rest exhibit RD. A critical barrier to improving NAC outcomes in patients with BC is the lack of precise molecular targets and limited understanding of the molecular mechanisms underlying differential treatment outcomes in patients with BC receiving NAC. Thus, there is an urgent clinical need to elucidate the underlying molecular regulation associated with NAC resistance. Studies have shown that cellular stress induces the release of small extracellular vesicles known as exosomes, contributing to drug resistance. In this study, we evaluated the ability of exosomal metabolic profiles to predict NAC response in BC patients. Methods: We collected blood samples of 16 patients with BC who received NAC at Grady Memorial Hospitals, USA. Further, total exosomes were isolated from plasma by an ultracentrifugation method and characterized for their concentration (number/ml), size distribution, and exosomal content. Exosome sizes were validated using nanoparticle tracking analysis. RNA, proteins, and metabolites were isolated from exosomes to perform deep multiplatform analyses (viz. miRNA seq, proteomics, and metabolomics) of exosomal cargo to identify various potential biological determinants of NAC response and evaluate their contributions to the risk of RD in patients with BC after NAC. KEGG and HMDB IDs were used for metabolomics data analyses. Various analyses viz. enriched metabolomic pathway analysis, MS peak to pathway analysis (using GSEA and Mummichog), and network explorer analysis were performed using Metaboanalyst online portal to reveal the metabolic signatures in exosomes of BC patients who exhibited pCR and RD after NAC treatment. Results: Among the 16 patients with BC who received NAC, eight patients attained pCR, and eight had RD. Interestingly, we found that patients with pCR secreted 61% fewer exosomes but had 24% larger-sized exosomes than patients with RD following NAC. Furthermore, significant differences in the loading of exosomal cargo were observed between the two groups. Based on these results, we proposed that the differential RNA, protein, and metabolite profile of exosomes might contribute to the higher drug sensitivity in patients with pCR. Our proteomics and miRNA-seq analyses of exosomes from patients with pCR and RD after NAC did not show any significant differences between the groups. However, metabolite set enrichment analysis using MetaboAnalyst revealed that compared to exosomes from patients with pCR, patients with RD were significantly enriched in various metabolic pathways. These pathways included valine, leucine, and isoleucine biosynthesis; phenylalanine metabolism; one-carbon pool folate; and inositol phosphate metabolism. Additionally, MS peak to pathway analysis revealed differential enrichment of various other metabolic pathways, particularly in patients with RD.Conclusions: These data strongly suggest that exosomal metabolic signatures are associated with differential NAC outcomes in patients with BC. We believe that in-depth studies of these metabolite signatures will give essential clues about differential NAC response in patients with BC. Citation Format: Shriya Joshi, Chakravarthy Garalapati, Shristi Bhattarai, Darshan Shimoga Chandrashekar, Sooryanarayana Varambally, Gagan Deep, Ritu Aneja. Exosomal metabolic signatures are associated with differential response to neoadjuvant chemotherapy in patients with 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 P2-12-24.

Abstract P1-08-16: Using machine learning approaches to predict response to neoadjuvant chemotherapy in patients with triple-negative breast cancer

Abstract Background: Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) isassociated with a favorable prognosis and low recurrence rate, particularly in patients with triple-negative breast cancer (TNBC), an aggressive breast cancer subtype. However, only 30%-40%of patients with TNBC show pCR after standard NAC, while the remaining patients have non-pCR or residual disease. Different histological components of the tumor, including tumor stroma,polyploid giant cancer cells, and immune cells, can influence response to NAC in patients withTNBC. However, it remains unknown which histological components contribute to pCR afterNAC in some patients with TNBC but not in others. To address this, we developed a machinelearning pipeline to identify the most significant histological components contributing to NACresponse in patients with TNBC. Different representations of histological components were usedin this pipeline as a "full-slide" feature set to predict NAC response in patients with TNBC.Methods: Post-NAC tissues from 129 patients (55 pCR and 74 non-pCR) from Emory DecaturHospital (Decatur, GA) were used. The Decatur cohort was separated based on NAC responseinto training (33 pCR and 33 non-pCR) and validation sets. There were a total of 17 histologicalclasses annotated by a board-certified pathologist and two NAC labels. Our pipeline comparedfour different machine learning approaches (1NN, ensembleTree, linSVM, and rbfSMV), and thebest classifiers were used for testing. Each whole slide image (WSI) was partitioned into smallertiles. The first classifier used texture-based features from the 17 histological classes. Inaggregation, 55 texture features derived from each tile were provided to a classifier (rbfSVM) tooutput a probability for each new tile during testing. The predicted histology labels were used togenerate a tile-level classification map that recaptured NAC response to predict pCR or non-pCR. Each such tile-level classification map was represented by 80 graph-based featurescapturing the relevant spatial information across the different histological classes. These graph-based features were provided to the best patient-level classifier (rbfSVM) to predict NACresponse for each patient.Results: Our tile-level classification achieved 83% histological classification accuracy, and ourpatient-level machine learning pipeline achieved a 69% NAC classification accuracy. The keyhistological classes that helped distinguish between pCR and non-pCR were tumor &amp; immunecells, tumor &amp; stroma, and microvessel density (MVD) &amp; immune cells. Patients with thesecombinations of discriminating histological classes were 69% likely to have a pCR after NACbased on the accuracy of the rbfSVM NAC classifier. This finding suggests that a classificationmap with more pCR combinations in WSI classified by rbfSVM NAC classifier can helpdistinguish pCR from non-pCR. Notably, our pipeline required less than 24 hours to analyzeWSIs of one patient, accelerating patient selection for NAC treatment.Conclusion: Our machine learning pipeline can robustly identify the most significanthistological classes predicting NAC response and can guide patient selection for NAC treatment. Citation Format: Timothy Byron Fisher, Hongxiao Li, Rekha TS, Jayashree Krishnamurthy, Shristi Bhattarai, Emiel A.M. Janssen, Jun Kong, Ritu Aneja. Using machine learning approaches to predict response to neoadjuvant chemotherapy in patients with 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 P1-08-16.

Predictive Value of Dual-phase 18F-FDG PET/CT in the Assessment of Neoadjuvant Chemotherapy Response in Patients with Locally Advanced Breast Cancer: A prospective Comparative Study with Dynamic Contrast-enhanced Magnetic Resonance Imaging

Predictive Value of Dual-phase 18F-FDG PET/CT in the Assessment of Neoadjuvant Chemotherapy Response in Patients with Locally Advanced Breast Cancer: A prospective Comparative Study with Dynamic Contrast-enhanced Magnetic Resonance Imaging

The Role of Pretreatment 18F-FDG PET/CT for Early Prediction of Neoadjuvant Chemotherapy Response in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma

IntroductionTo evaluate the role of maximal standardized uptake values (SUVmax) and total lesion glycolysis (TLG) from serial 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) for early prediction of neoadjuvant chemotherapy (NAC) response in locoregionally advanced nasopharyngeal carcinoma (LANPC).MethodsA total of 121 LANPC patients who completed pretreatment 18F-FDG PET/CT between June 2017 and July 2020 were retrospectively included. The median age of all the participants was 50 years old (range: 19–74 years), with 94 (77.7%) males and 27 (22.3%) females. The SUVmax from the primary tumor site (SUVmax-PT) and the total lesion glycolysis from the primary tumor site (TLG-PT) were recorded. Tumor response was calculated according to the Response Evaluation Criteria in Solid Tumor (RECIST) 1.1 Criteria at two-week post-secondary NAC cycle. Patients who achieved an objectively partial or full reaction after two cycles of NAC were defined as ‘responders’, and patients who obtained stability or progression were classified as ‘non-responders’.ResultsAfter two cycles of NAC, 96 patients were categorized as “responders” and 25 patients as “non-responders”. The optimal thresholds of the SUVmax-PT were 11.8 and 38.5 for the TLG-PT. Non-responders were significantly associated with high SUVmax-PT (HR, 3.49; 95% CI, 1.17–10.36; p = 0.024) and TLG-PT (HR, 4.45; 95% CI, 1.44–13.78; p = 0.010) in multivariate analysis. Recursive partitioning analysis (RPA) categorized patients into three prognostic groups based on SUVmax-PT and TLG-PT: high-response group, intermediate-response group, and low-response group, with corresponding favorable response rates of 94%, 80%, and 55%, respectively. Moreover, a nomogram was created based on metabolic parameters that precisely projected an individual’s response of NAC (C-index, 0.787; 95% CI, 0.533–1.000).ConclusionPretreatment 18F-FDG PET/CT to measure SUVmax-PT and TLG-PT could be a useful non-invasive method for early indication of NAC efficacy. The nomogram based on PET/CT parameters may potentially provide direction for treatment decisions based on NAC levels.

Evaluation of Tumor Elasticity and Clinicopathologic Factors Affecting Neoadjuvant Chemotherapy Response in Patients with Breast Cancer

Evaluation of Tumor Elasticity and Clinicopathologic Factors Affecting Neoadjuvant Chemotherapy Response in Patients with Breast Cancer

Accuracy of Neutrophil/Lymphocyte Ratio as Predictor of Neoadjuvant Chemotherapy Response in Locally Advanced Breast Cancer at Dr. Mohammad Hoesin General Hospital Palembang

Introduction: Breast cancer is one of the most common neoplasm occurs in women. Neoadjuvant Chemotherapy (NACT) is widely used in Locally Advanced Breast Cancer (LABC) and Inflammatory Breast Cancer (IBC), to reduce tumor size (down-sizing), and further allowing breast-conserving surgery to be performed. Accurate markers are needed to prevent impractical chemotherapy that allows patients to receive definitive surgery in a timely manner. Previous study showed neutrophil/lymphocyte ratio (NLR) was superior in predicting long-term outcomes over PLR. This study aims to evaluate the relationship between the NLR value and neoadjuvant chemotherapy response in patients with locally advanced breast cancer at Dr. Mohammad Hoesin General Hospital Palembang.&#x0D; Methods: This study is a prognostic test of the NLR accuracy to predict the response of neoadjuvant chemotherapy in patients with locally advanced breast cancer. The study included 113 patients as study subjects.&#x0D; Results: The obtained NLR cut off point value was ≤ 2.92 with an area under the ROC curve (AUC) of 92.2%. The diagnostic measures of the NLR prognostic test had sensitivity of 93%, specificity of 93%, Positive Predictive Value of 97%, Negative Predictive Value of 82%, and accuracy of 93%. There is a significant relationship between NLR and chemotherapy response in patients with locally advanced breast cancer (p = 0.000).&#x0D; Conclusion: There is a significant relationship between NLTR and chemotherapy response. NLR can be used as a predictor of neoadjuvant chemotherapy response with a good degree of accuracy.

Platelet/Lymphocyte Ratio Accuracy as Predictor of Neo-Adjuvant Chemotherapy Response in Locally Advanced Breast Cancer at Dr. Mohammad Hoesin General Hospital Palembang

Background: Platelet/lymphocyte ratio (PLR) is considered as predictor for chemotherapy response in locally advanced breast cancer undergoing neo-adjuvant chemotherapy. This study aims to evaluate the relationship between the platelet/lymphocyte ratio and neo-adjuvant chemotherapy response in patients with locally advanced breast cancer at Dr. Mohammad Hoesin General Hospital Palembang.&#x0D; Methods: This study is a prognostic test of the platelet/lymphocyte ratio accuracy to predict the response of neo-adjuvant chemotherapy in patients with locally advanced breast cancer. The study included 113 subjects.&#x0D; Results: From 113 patients, the PLR cut off point value was 206.83 with an under curve area (AUC) of 91.8%. The results of the PLR prognostic test were sensitivity of 98%, specificity of 87%, Positive Predictive Value of 95%, Negative Predictive Value of 93%, and 95% of accuracy. There was a significant relationship between PLR and chemotherapy response in patients with locally advanced breast cancer (p = 0.000).&#x0D; Conclusion: There was a significant relationship between PLR on chemotherapy response. PLR can be used as a predictor of neo-adjuvant chemotherapy response with a good degree of accuracy.

Contrast enhanced ultrasound quantitative parameters for assessing neoadjuvant chemotherapy response in patients with locally advanced breast cancer.

To evaluate the role of contrast-enhanced ultrasound (CEUS) quantitative parameters in predicting neoadjuvant chemotherapy (NACT) response in patients with locally advanced breast cancer (LABC). 30 patients with histologically proven LABC scheduled for NACT were recruited. CEUS was performed using a contrast bolus of 4.8 ml and time intensity curves (TICs) were obtained by contrast dynamics software. CEUS quantitative parameters assessed were peak enhancement (PE), time-to-peak (TTP), area under the curve (AUC) and mean transit time (MTT). The parameters were documented on four consecutive instances: before NACT and 3 weeks after each of the three cycles. The gold-standard was pathological response using Miller Payne Score obtained pre NACT and post-surgery. A decrease in mean values of PE and an increase in mean values of TTP and MTT was observed with each cycle of NACT among responders. Post each cycle of NACT (compared with baseline pre-NACT), there was a statistically significant difference in % change of mean values of PE, TTP and MTT between good responders and poor responders (p-value < 0.05). The diagnostic accuracy of TTP post-third cycle was 87.2% (p = 0.03), and MTT post--second and third cycle was 76.7% (p = 0.004) and 86.7% (p = 0.006) respectively. In responders, a decrease in the tumor vascularity was reflected in the CEUS quantitative parameters as a reduction in PE, and a prolongation in TTP, MTT. Prediction of NACT response by CEUS has the potential to serve as a diagnostic modality for modification of chemotherapy regimens during ongoing NACT among patients with LABC, thus affecting patient prognosis.