MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response.

SIGNIFICANCE

Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.

A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm)

Purpose

The present study aimed to develop and validate a preoperative model based on gadobenate-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) size of ≤5 cm. In order to provide preoperative guidance for clinicians to optimize treatment options.

Methods

164 patients with pathologically confirmed HCC and preoperative gadobenate-enhanced MRI from July 2016 to December 2020 were retrospectively included. Univariate and multivariate logistic regression (forward LR) analyses were used to determine the predictors of MVI and the model was established. Four-fold cross validation was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility.

Results

Elevated alpha-fetoprotein (HR 1.849, 95% CI: 1.193, 2.867, P=0.006), atypical enhancement pattern (HR 3.441, 95% CI: 1.523, 7.772, P=0.003), peritumoral hypointensity on HBP (HR 7.822, 95% CI: 3.317, 18.445, P<0.001), and HBP hypointensity (HR 3.258, 95% CI: 1.381, 7.687, P=0.007) were independent risk factors to MVI and constituted the HBP model. The mean area under the curve (AUC), sensitivity, specificity, and accuracy values for the HBP model were as follows: 0.830 (95% CI: 0.784, 0.876), 0.71, 0.78, 0.81 in training set; 0.826 (95% CI:0.765, 0.887), 0.8, 0.7, 0.79 in test set. The decision curve analysis (DCA) curve showed that the HBP model achieved great clinical benefits.

Conclusion

In conclusion, the HBP imaging features of Gd-BOPTA-enhanced MRI play an important role in predicting MVI for HCC. A preoperative model, mainly based on HBP imaging features of gadobenate-enhanced MRI, was able to excellently predict the MVI for HCC size of ≤5cm. The model may help clinicians preoperatively assess the risk of MVI in HCC patients so as to guide clinicians to optimize treatment options.

Hepatobiliary Phase Features of Preoperative Gadobenate-Enhanced MR can Predict Early Recurrence of Hepatocellular Carcinoma in Patients Who Underwent Anatomical Hepatectomy

Purpose

The purpose of this study was to establish a model for predicting early recurrence (≤2 years) of hepatocellular carcinoma (HCC) after anatomical hepatectomy based on the hepatobiliary phase (HBP) imaging characteristics of gadobenate-enhanced MRI.

Methods

A total of 155 patients who underwent anatomical hepatectomy HCC therapy and gadobenate-enhanced MRI were included retrospectively. The patients were divided into the early recurrence-free group (n = 103) and the early recurrence group (n = 52). Univariate and multivariate Cox regression analysis was used to determine the independent risk factors related to early recurrence, and four models were established. The preoperative model with/without HBP imaging features (HBP-pre/No HBP-pre model) and the postoperative model with/without HBP imaging features (HBP-post/No HBP-post model). Bootstrap resampling 1,000 times was used to verify the model and displayed by nomograms. The performance of nomograms was evaluated by discrimination, calibration, and clinical utility. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to evaluate the differences between models and to select the optimal model.

Results

Shape, arterial peritumoral enhancement, AFP-L3, and peritumoral hypointensity on HBP were identified as independent risk factors. Prothrombin time (PT) and r-glutamyltransferase (GGT) were selected by multivariate Cox regression. These six factors construct the HBP-pre model. Removing peritumoral hypointensity on HBP was the No HBP-pre model. Adding microvascular invasion (MVI) and microscopic capsule factors were the HBP-post and No HBP-post model. The C-index was 0.766, 0.738, 0.770, and 0.742, respectively. The NRI and IDI of the HBP-pre vs. the No HBP-pre model and the HBP-post vs. the No HBP-post model significantly increased 0.258, 0.092, 0.280, and 0.086, respectively. The calibration curve and decision curve analysis (DCA) had good consistency and clinical utility. However, the NRI and IDI of the No HBP-post vs. the No HBP-pre model and the HBP-post vs. the HBP-pre model did not increase significantly.

Conclusions

Preoperative gadobenate-enhanced MR HBP imaging features significantly improve the model performance while the postoperative pathological factors do not. Therefore, the HBP-pre model is selected as the optimal model. The strong performance of this model may help hepatologists to assess the risk of recurrence in order to guide the selection of treatment options.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography

BACKGROUND

Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC.

METHODS

A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts.

RESULTS

Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p < 0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts.

CONCLUSIONS

Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Molecular Characterization and Prospective Evaluation of Pathologic Response and Outcomes with Neoadjuvant Therapy in Metaplastic Triple-Negative Breast Cancer

PURPOSE

Metaplastic breast cancer (MpBC) is a rare subtype of breast cancer that is commonly triple-negative and poorly responsive to neoadjuvant therapy in retrospective studies.

EXPERIMENTAL DESIGN

To better define clinical outcomes and correlates of response, we analyzed the rate of pathologic complete response (pCR) to neoadjuvant therapy, survival outcomes, and genomic and transcriptomic profiles of the pretreatment tumors in a prospective clinical trial (NCT02276443). A total of 211 patients with triple-negative breast cancer (TNBC), including 39 with MpBC, received doxorubicin-cyclophosphamide-based neoadjuvant therapy.

RESULTS

Although not meeting the threshold for statistical significance, patients with MpBCs were less likely to experience a pCR (23% vs. 40%; P = 0.07), had shorter event-free survival (29.4 vs. 32.2 months, P = 0.15), metastasis-free survival (30.3 vs. 32.4 months, P = 0.22); and overall survival (32.6 vs. 34.3 months, P = 0.21). This heterogeneity is mirrored in the molecular profiling. Mutations in PI3KCA (23% vs. 9%, P = 0.07) and its pathway (41% vs. 18%, P = 0.02) were frequently observed and enriched in MpBCs. The gene expression profiles of each histologically defined subtype were distinguishable and characterized by distinctive gene signatures. Among nonmetaplastic (non-Mp) TNBCs, 10% possessed a metaplastic-like gene expression signature and had pCR rates and survival outcomes similar to MpBC.

CONCLUSIONS

Further investigations will determine if metaplastic-like tumors should be treated more similarly to MpBC in the clinic. The 23% pCR rate in this study suggests that patients with MpBC should be considered for NAT. To improve this rate, a pathway analysis predicted enrichment of histone deacetylase (HDAC) and RTK/MAPK pathways in MpBC, which may serve as new targetable vulnerabilities.

Abstract 2736: Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin

Introduction: Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of subsequent breast conservation surgery. There are currently no methods to accurately predict how a TNBC patient will respond to NAT before surgery. In this work, we applied a digital twin framework to address this unmet clinical need, by integrating quantitative magnetic resonance imaging (MRI) data with mechanism-based mathematical modeling.

Methods: Multiparametric MRI was acquired in patients (N = 50) before, after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C), and again after 12 cycles of Paclitaxel as part of the ARTEMIS (NCT02276433) trial. Within each imaging session, dynamic contrast-enhanced (DCE) MRI, diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. The images were processed by a pipeline consisting of motion correction, multiparametric image alignment, inter-visit image registration to align the tumor and surrounding breast tissue, tissue segmentation, and estimation of tumor cellularity from DWI. A mechanism-based mathematical model, a reaction-diffusion equation, is used to characterize the mobility of tumor cells via diffusion damped by mechanical tissue properties, tumor proliferation via logistic growth, and treatment-induced cell death via the delivery and decay of therapies. For each patient, pre-treatment images were used for model initialization. The model calibration and prediction were implemented with two strategies: 1) using images acquired during the A/C for calibration and predicting up to the end of A/C, and 2) using images acquired during and after the A/C for calibration and predicting up to the end of NAT. For strategy 1), we evaluated the model by comparing its predicted tumor volume and total tumor cellularity to the imaging measurements at the end of A/C. For strategy 2), we evaluated the model by comparing its predicted final response to the post-surgical pathological findings.

Results: For strategy 1), our framework predicted the change of tumor volume and total tumor cellularity with Pearson correlation coefficients of 0.91 and 0.89, respectively. Regarding strategy 2), our framework achieved an area under the receiver operator characteristic curve of 0.88 for distinguishing pCR from non-pCR. As a comparison, imaging measurement of tumor volume at the end of A/C achieved an AUC of 0.79.

Conclusion: Our approach successfully captures the patient-specific dynamics of TNBC response to NAT and provides an improved prediction of final response, which demonstrates the potential of a digital twin framework to be a powerful tool for predicting response to NAT. Once validated, the method will provide a unique opportunity for optimizing treatment plans on a patient-specific basis.

Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2736.

Metastatic non-Hematopoietic Neoplasms to the Breast: A Study of 238 Cases

AIMS

The aim of this study was to review the clinicopathologic characteristics of metastatic non-hematopoietic malignancies to the breast, in order to identify salient features for practicing pathologist that are useful in distinguishing metastatic lesions from primary breast neoplasms.

METHODS AND RESULTS

A total of 238 cases were identified during the period from January 2005 to January 2015. Clinicopathologic features of these cases were retrospectively reviewed. Primary tumors included melanoma (99, 42%), serous carcinoma (35, 15%), neuroendocrine neoplasm (32, 13%), sarcoma (23, 10%), and adenocarcinoma from various organs (47, 20%), among others. Most metastases were unilateral (223, 94%) and unifocal (206, 87%), and were detected radiographically (167, 70%). Concurrent ipsilateral axillary metastasis occurred in 33 (14%) patients. Among 238 cases, 41 had metastatic disease to the breast concurrently or preceding the primary cancer diagnosis. Notable, in 39 (16%) cases, breast metastasis was the first clinical presentation of disease, and 16 (41%) of these cases were initially misdiagnosed as breast primaries. In contrast, with known history of non-mammary primary tumors, only 4 of 197 (2%) cases were misdiagnosed (p<0.0001).

CONCLUSIONS

Metastatic tumors share many overlapping features with breast primary carcinomas. However, cases with a well-circumscribed tumor, lack of in situ component, ER/PR negativity, and unusual morphologic features should raise the consideration of metastatic disease. While clinical history is paramount for correct diagnosis, metastasis to the breast as the first clinical presentation is not uncommon.

NDRG1 in Aggressive Breast Cancer Progression and Brain Metastasis

BACKGROUND

N-Myc downstream regulated gene 1 (NDRG1) suppresses metastasis in many human malignancies, including breast cancer, yet has been associated with worse survival in patients with inflammatory breast cancer. The role of NDRG1 in the pathobiology of aggressive breast cancers remains elusive.

METHODS

To study the role of NDRG1 in tumor growth and brain metastasis in vivo, we transplanted cells into cleared mammary fat pads or injected them in tail veins of SCID/Beige mice (n = 7-10 per group). NDRG1 protein expression in patient breast tumors (n = 216) was assessed by immunohistochemical staining. Kaplan-Meier method with 2-sided log-rank test was used to analyze the associations between NDRG1 and time-to-event outcomes. A multivariable Cox regression model was used to determine independent prognostic factors. All statistical tests were 2-sided.

RESULTS

We generated new sublines that exhibited a distinct propensity to metastasize to the brain. NDRG1-high-expressing cells produced more prevalent brain metastases (100% vs 44.4% for NDRG1-low sublines, P = .01, Fisher's exact test), greater tumor burden, and reduced survival in mice. In aggressive breast cancer cell lines, silencing NDRG1 led to reduced migration, invasion, and tumor-initiating cell subpopulations. In xenograft models, depleting NDRG1 inhibited primary tumor growth and brain metastasis. In patient breast tumors, NDRG1 was associated with aggressiveness: NDRG1-high expression was also associated with shorter overall survival (hazard ratio [HR] = 2.27, 95% confidence interval [95% CI] = 1.20 to 4.29, P = .009) and breast cancer-specific survival (HR = 2.19, 95% CI = 1.07 to 4.48, P = .03). Multivariable analysis showed NDRG1 to be an independent predictor of overall survival (HR = 2.17, 95% CI = 1.10 to 4.30, P = .03) and breast cancer-specific survival rates (HR = 2.27, 95% CI = 1.05 to 4.92, P = .04).

CONCLUSIONS

We demonstrated that NDRG1 drives tumor progression and brain metastasis in aggressive breast cancers and that NDRG1-high expression correlates with worse clinical outcomes, suggesting that NDRG1 may serve as a therapeutic target and prognostic biomarker in aggressive breast cancers.

Abstract P5-08-13: NDRG1 expression is an independent prognostic factor in inflammatory breast cancer

Background: Inflammatory breast cancer (IBC) is a rare and highly aggressive form of breast cancer. Patients with IBC still have worse clinical outcomes compared to patients with non-IBC (40% versus 63% for 5 years overall survival, respectively) despite advances in breast cancer treatment. IBC remains a poorly characterized disease lacking specific therapeutic targets and prognostic biomarkers. Our group demonstrated that N-myc downstream regulated gene 1 (NDRG1) is crucial in promoting tumorigenesis and brain metastasis in mouse models of IBC. Our hypothesis is that NDRG1 is a prognostic marker associated with poor outcome in IBC patients. Methods: 64 IBC patients in a tissue microarray were evaluated by immunohistochemical staining with anti-NDRG1 primary antibody and NDRG1 levels were quantified. Using the median value, 32 patients were grouped as NDRG1-low (≤ median), and 32 as NDRG1-high (&gt;median). Survival data were compared by Kaplan–Meier curves and log-rank test. Results: The average age of patients was 50 years. Sixty-two percent of patients had estrogen receptor (ER)-negative tumors, 83% were stage III, 80% high grade, and 67% of these patients received adjuvant radiation. The median follow-up time for the patients studied was 11.7 years, and the median overall survival (OS) time was 3.7 years. On univariate analysis, NDRG1 expression, tumor grade, disease stage, ER status, and adjuvant radiation therapy were associated with OS and disease specific survival (DSS). Patients with NDRG1-low tumors experienced better actuarial 10-year OS (p=0.0129) and DSS (p=0.0074), and showed significant higher 10-year OS and DSS rates than patients with NDRG1-high(OS, 45% vs. 19%, p=0.0278; DSS, 52% vs. 22%, p=0.0139). The median OS and DSS times were shorter for NDRG1-high patients (OS, 2.5 years; DSS, 3.1 years) than for NDRG1-low patients (OS, 5.9 years; DSS, 10.7 years). Multivariable analysis, NDRG1 was an independent predictor of OS (hazard ratio [HR]=2.449, p=0.0274) and DSS (HR=2.727, p=0.0039). ER status, disease stage and adjuvant radiation were also independent predictor for both OS and DSS. Furthermore, we observed that NDRG1-high expressing ER-negative tumors exhibit worse outcome in IBC patients (OS, p=0.0003; DSS, p=0.0003). NDRG1-high expression correlated with worse outcome in patients that received adjuvant radiation treatment (OS, p=0.0088; DSS, p=0.0093), and those with stage III (OS, p=0.0450; DSS, p=0.0239). Conclusions: Our findings demonstrated that NDRG1 is positively correlated with aggressive tumor characteristics in IBC and that it is an independent prognostic factor for OS and DSS in IBC patients, suggesting that targeting NDRG1 may provide a novel therapeutic strategy to improve outcomes for patients with IBC. Our data further suggests that NDRG1 warrants further investigation in radiation response.

Citation Format: Emilly Schlee Villodre, Yun Gong, Xiaoding Hu, Lei Huo, Esther C Yoon, Naoto T Ueno, Wendy A Woodward, Debu Tripathy, Juhee Song, Bisrat G Debeb. NDRG1 expression is an independent prognostic factor in inflammatory 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 P5-08-13.

Abstract PD11-07: Integrated model for early prediction of neoadjuvant systemic therapy response in triple negative breast cancer

Background: TNBC constitutes an aggressive and heterogeneous group of tumors with variable response to neoadjuvant therapy (NAT) that currently lacks clinically available profiling strategies for prediction. We aimed to develop an integrated model based on imaging, pathological and clinical data capable to predict NAT response in TNBC early during therapy. METHOD AND MATERIALS:125 Stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433) who had DCE-MRI at baseline (BL) and post 2 cycles (C2) of NAT, and had surgery were included in this analysis. Tumor volume was calculated using 3D measurements at BL and C2 time points DCE-MRI. Percent tumor volume reduction (TVR) between BL and C2 was calculated. Demographic, clinical, and pathological data (age, T and N stage, histology, androgen receptor expression, Ki-67, stromal tumor infiltrating lymphocytes level (sTIL), and PD-L1 expression), and treatment response at surgery (pCR vs non-pCR) were documented. Recursive partitioning was used to identify TVR cutoff value. Multivariate logistic regression and ROC analysis were used to assess associations and build and evaluate predictive models. RESULTS: 61 (49%) TNBC pts showed pCR at surgery, and 64 (51%) non-pCR. Recursive partitioning analysis identified ≥ 55% TVR as the optimal cutoff values for pCR prediction at C2. TVR, N stage and sTIL were significantly associated with pCR in the multivariate analyses (p&lt;0.002, p&lt;0.01, p&lt;0.001, respectively). Integrated model containing TVR (≥55% vs &lt;55%), N stage (N0 vs N+) and sTIL (≥20% vs &lt;20%) was predictive of pCR with AUC 0.84 (95% CI:0.77-0.91). Integrated model performance was significantly better than TVR only or clinical only (sTIL, and N stage) models (p&lt;0.001). CONCLUSION: Integrated model that included imaging (DCE-MRI TVR), clinical (N stage) and pathological (sTIL) data showed high accuracy for prediction of NAT response in TNBC patients early during treatment. Validation of these results in a large prospective study is ongoing.

Citation Format: Gaiane Margishvili Rauch, Rosalind P. Candelaria, Mary Saber Guirguis, Medine Boge, Rania M. M. Mohamed, Nabil Elshafeey, Jia Sun, Gary J Whitman, Jessica Leung, Huong C Le-Petross, Lumarie Santiago, Deanna Lane, Marion Scoggins, David Spak, Miral M Patel, Frances Perez, Jason B. White, Elizabeth Ravenberg, Wei Peng, Debu Tripathy, Vicente Valero, Jennifer Litton, Lei Huo, Clinton Yam, Alastair Thompson, Jingfei Ma, Stacy L. Moulder, Wei Yang, Beatriz E. Adrada. Integrated model for early prediction of neoadjuvant systemic therapy 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 PD11-07.

Abstract PD11-06: Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer

Background and Purpose: There is currently lack of recognized imaging criteria for prediction of treatment response to NAST in breast cancer patients. And early identification of treatment response to neoadjuvant systemic therapy (NAST) in Triple Negative Breast Cancer (TNBC) patients is important for appropriate treatment selection and response monitoring. A novel MRI sequence, Magnetic Resonance Image Compilation (MagIC) is capable of simultaneous quantitation of several tissue water properties including longitudinal (T1), transverse (T2) relaxation times, and proton density (PD). In this study we evaluated the ability of a radiomic model extracted from a novel MagIC sequence acquired early during NAST to predict pathologic complete response to NAST in TNBC. Materials and Methods: This IRB approved prospective ARTEMIS trial (NCT02276443) included 184 women (122 training dataset, 62 testing dataset) diagnosed with stage I-III TNBC. All patients were scanned with MagIC on a 3T MRI scanner at baseline (184 patients), and after 4 cycles (156 Patients) of NAST. T1, T2 and PD maps were generated from the source images using SyMRI (SyntheticMR, Linkoping, Sweden). Histopathology at surgery was used to determine pathologic complete response (pCR) which was defined as absence of the invasive cancer in the breast and axillary lymph nodes. 3D contouring of the tumors was performed using an in-house toolbox. 310 (10 first-order, 300 GLCM) textural features were extracted from each map, with total of 930 features/patient. Radiomic features were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. To build a multivariate, predictive model, logistic regression with elastic net regularization was performed for texture feature selection. The tuning parameter was optimized using 5-fold cross-validation based on the average area under curve (AUC) of each fold of a cross-validation using training data. Then the testing data were used to compare model’s performance by AUC. Results: Univariate analysis found 23 PD, 17 T1 and 10 T2 radiomic features at C4 time point to be able to predict pCR status with AUC &gt;70% in both training and testing cohort. The top performing radiomic features were Entropy, Variance, Homogeneity and Energy (Tables1-2). Multivariate radiomics models from C4-PD, and C4-T1 maps showed best performance during both cross validation and independent testing. The radiomic signature of C4-T1 map that included 27features had best performance, with an AUC of 0.77, 0.70 (95% CI: 0.571-0.868) in training and testing cohort respectively. C4-PD map radiomic signature that included 6features was able to predict the pCR status with AUC of 0.73, 0.72 (95% CI: 0.571-0.868) in training and testing cohort respectively. Conclusion: Our data found that MagIC-based radiomics signature could potentially predict pathologic complete response in TNBC early during NAST. This data shows the potential application of MagIC radiomic model for improvement of response assessment in TNBC.

Table 1.Best performing radiomic features from PD map after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valuePD-mapAngular Variance of Sum entropy1060.73820.6437-0.8328500.73240.5895-0.8752&lt;0.001Range of Sum entropy1060.73930.6446-0.834500.72120.5753-0.867&lt;0.001Angular Variance of Sum entropy1060.75960.6662-0.853500.70190.5538-0.8501&lt;0.001Average of Sum entropy1060.73470.6367-0.8327500.70990.5613-0.8585&lt;0.001Angular Variance of Sum variance1060.70160.602-0.8011500.70190.5543-0.8495&lt;0.001Range of Sum variance1060.70050.6001-0.8009500.700.5499-0.8476&lt;0.001

Table 2.Best performing radiomic features from T1-T2 maps after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valueT1-mapAngular Variance of Sum entropy1060.76530.6762-0.8544500.70510.5524-0.8579&lt;0.001Range of Sum entropy1060.76530.6759-0.8547500.70350.5503-0.8567&lt;0.001Average of Entropy1060.75250.6568-0.8482500.71630.572-0.8607&lt;0.001Average of Sum entropy1060.750.6552-0.8448500.70190.555-0.8488&lt;0.001Angular Variance of Energy1060.7450.6493-0.8407500.73080.59-0.8715&lt;0.001Range of Energy1060.74290.6466-0.8392500.72920.5885-0.8699&lt;0.001Average of Energy1060.74110.6438-0.8384500.7260.5852-0.8667&lt;0.001Average of Entropy1060.73360.635-0.8322500.74040.602-0.8787&lt;0.001Average of Maximum probability1060.70760.6054-0.8098500.71630.5704-0.8623&lt;0.001Range of Maximum probability1060.70550.6018-0.8092500.75640.6195-0.8933&lt;0.001T2-mapAngular Variance of Energy1060.74820.6531-0.8433500.70990.5644-0.8555&lt;0.001Range of Energy1060.7450.6495-0.8405500.70350.5569-0.8501&lt;0.001Average of Entropy1060.74070.6416-0.8399500.72920.585-0.8733&lt;0.001Average of Sum entropy1060.73860.6405-0.8367500.72440.5797-0.869&lt;0.001Average of Energy1060.73180.6309-0.8327500.72120.5743-0.86&lt;0.001Angular Variance of Sum entropy1060.7290.631-0.827500.72760.5857-0.8695&lt;0.001Range of Sum entropy1060.72760.6295-0.8257500.72280.5796-0.8659&lt;0.001Average of Information measure of correlation 11060.71580.6147-0.8169500.70990.5638-0.8561&lt;0.001Average of Entropy1060.700.5903-0.8028500.74360.6014-0.8858&lt;0.001

Citation Format: Nabil Elshafeey, Ken-Pin Hwang, Beatriz Elena Adrada, Rosalind Pitpitan Candelaria, Medine Boge, Rania M Mahmoud, Huiqin Chen, Jia Sun, Wei Yang, Aikaterini Kotrotsou, Benjamin C Musall, Jong Bum Son, Gary J Whitman, Jessica Leung, Huong Le-Petross, Lumarie Santiago, Deanna Lynn Lane, Marion Elizabeth Scoggins, David Allen Spak, Mary Saber Guirguis, Miral Mahesh Patel, Frances Perez, Abeer H Abdelhafez, Jason B White, Lei Huo, Elizabeth Ravenberg, Wei Peng, Alastair Thompson, Senthil Damodaran, Debu Tripathy, Stacey L Moulder, Clinton Yam, Mark David Pagel, Jingfei Ma, Gaiane Margishvili Rauch. Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy 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 PD11-06.

Abstract P3-03-06: Prediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response

Background: Triple negative breast cancer (TNBC) has a poor prognosis. In particular, TNBC patients who have significant residual disease at the time of surgery following completion of neoadjuvant systemic therapy (NST) have an especially poor prognosis. In an effort to identify patients who are unlikely to achieve pathologic complete response (pCR), we investigated if pre-treatment breast MRI morphological characteristics and imaging response patterns during NST can predict pCR in TNBC patients. Materials and Methods: As part of a prospective IRB-approved clinical trial (ARTEMIS, NCT02276443), 199 patients with biopsy-proven stage I-III TNBC received NST and were classified as pCR or non-pCR based on histopathology at surgery. Patients underwent breast MRI at baseline (BL), after 2 cycles (C2), and 4 cycles (C4) of Adriamycin-based chemotherapy (AC). Subsequently, patients received either taxane-based NST or targeted therapy guided by mid-treatment imaging response. MRI studies were reviewed by two fellowship-trained breast radiologists who were blinded to the pathology results. ACR MRI BIRADS lexicon (5th Ed) was used to describe BL tumor morphology. Imaging response pattern at C2 and C4 MRI was classified as follows: type 0 (complete), type 1 (concentric shrinkage), type 2 (crumble), type 3 (diffuse enhancement), type 4 (stable), or type 5 (progression). Morphological baseline features and response patterns were summarized and compared to the pCR status on surgical pathology using Fisher’s exact test. P values less than 0.05 were considered statistically significant. Results: Median age was 53 years, range 24-79. Of 199 patients, 95 (48%) had pCR and 104 (52%) had non-pCR. At BL MRI, an irregularly-shaped mass and homogenous or clumped non-mass enhancement were associated with pCR (p=0.026 and p=0.013, respectively). Multifocality, peritumoral edema, and intratumoral necrosis were independent of pCR. Following NST, the most common MRI response pattern was type 1, seen with equal frequency in pCR and non-pCR at C2 (58% and 42%, respectively) and C4 (47% and 53%, respectively). The following response pattern associations were found: type 0 was associated with pCR at both C2 and C4 timepoints (p&lt;0.001), while types 4 and 5 were associated with non-pCR at C2, (p&lt;0.001). The four patterns: types 2, 3, 4, 5, were associated with non-pCR at C4 (p&lt;0.001). Conclusion: Baseline MRI tumor morphological characteristics and MRI imaging response patterns during NST may be valuable markers for pCR prediction in TNBC. Qualitative breast MRI assessment may act as an accessible tool to identify TNBC patients who are unlikely to achieve pCR and may benefit from targeted therapies.

Citation Format: Mary S Guirguis, Beatriz E Adrada, Rosalind P Candelaria, Jia Sun, Gary J Whitman, Wei T Yang, Medine Boge, Rania M Mohamed, Nabil A Elshafeey, Deanna L Lane, Huong Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral Patel, Frances Perez, Peng Wei, Debu Tripathy, Jason White, Elizabeth Ravenberg, Lei Huo, Jennifer Litton, Banu Arun, Vincente Valero, Alastair Thompson, Stacy Moulder, Clinton Yam, Gaiane M Rauch. Prediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response [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 P3-03-06.

Abstract P1-08-08: Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer viamathematical modeling and quantitative MRI

Introduction:. Patients with locally advanced triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of the subsequent breast conservation surgery. A critical unmet need is the lack of a method to accurately predict how a patient with TNBC will respond to NAT before surgery. In this work, we applied a clinical-computational framework to predict response of TNBC early in the course of NAT, by integrating quantitative MRI with mechanism-based mathematical modeling. Methods:. Patients and Data. Multiparametric quantitative MRI was acquired in patients (n = 46) before, and after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C) regimen as part of the MD Anderson Cancer Center TNBC Moonshot Program. Within each imaging session, dynamic contrast-enhanced (DCE-), diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. Image processing. The processing pipeline consisted of three components. First, the images within each visit were registered to account for patient motion, and the parametric maps from the DCE and DWI images were computed. Second, inter-visit image registration was achieved by a non-rigid registration applied on breast, with a rigid penalty applied on the tumor region to preserve its size and shape. Third, post-processing was performed for preparation of modeling, including segmentation of the breast contour and tissues, and calculation of voxel-wise cellularity within tumors. Mathematical modeling. A predictive model was developed based on a reaction-diffusion equation (Eq. 1). The mobility of tumor cells is represented by diffusion coupled to mechanical properties of the tissue (Eq. 2), and the proliferation of the tumor is described with logistic growth. The injection and decay of administered therapies, inducing tumor cell death, is also represented in the model (Eq. 3). The variables and parameters used are listed in Table 1. Eq. 1: ∂N(x,t)/∂t = ∇⋅(D(x,t) ∇N(x,t)) + k(x) (1 - N(x,t)/θ)N(x,t) - (λ1(x,t) + λ2(x,t))N(x,t). Eq. 2: D(x,t) = D0 e-γσ(x,t). Eq. 3: λn(x,t) = αne-βn t C(x,t), n = 1, 2. For each patient, the domain and initial condition were generated from the pre-treatment images, and the images acquired during NAT were used for patient-specific calibration of parameters. The calibrated model was then used to predict the response to be observed at the end of NAT. We evaluated the model by comparing its predictions of tumor volume, longest axis, voxel-wise cellularity, and total tumor cellularity to the imaging measurements at the end of A/C. Results:. Our model predicted the tumor volume, total cellularity, and longest axis with a Pearson correlation coefficient (PCC) of 0.85, 0.80, and 0.60, respectively. The accuracy of voxel-wise cellularity achieved a PCC with the median (range) of 0.89 (0.77 - 0.93) between the prediction and the actual measurement. Moreover, we set criteria of 70% shrinkage of tumor volume to define response versus non-response cases, with which our model achieved a differentiation sensitivity/specificity of 0.90/0.73. Discussion:. Preliminary results of our study demonstrate the potential of the clinical-computational framework as a powerful tool for predicting response to NAT. Once validated, the method could also assist in optimizing treatment plans on a patient specific basis, or guiding patient selection in trials for novel NAT regimens.

Table 1. Summary of the variables and parameters in the modelQuantitiesDefinition AssignmentDomainsΩbreast tissue domainGenerated from pre-treatment MRITEnd time point of NAT procedureDetermined from NAT schedulexCoordinate in breast tissueAssociated with spatial domain, ΩttimeAssociated with temporal domain, [0, T]VariablesN(x,t)Tumor cell numberInitialized from pre-treatment ADC, computed via Eq. 1D(x,t)Diffusive mobility of tumor cellsComputed via Eq. 2λn(x,t)Death rate induced by nth type of drugComputed via Eq. 3, n = 1 and 2 for A/Cσ(x,t)Von Mises stressComputed from gradient of N(x,t), based on Hormuth et al., 2018C(x,t)Spatiotemporal distribution of drugsAssigned based on NAT schedule and DCE imagesParametersk(x)Proliferation rate of tumor cellsLocally calibratedθTumor cells carry capacityGlobally calibratedαnEfficacy rate of nth type of drugGlobally calibratedβnDecay rate of of nth type of drugGlobally calibratedD0Diffusion coefficient of tumor cells in the absence of mechanical restrictionsGlobally calibratedγStress-tumor cell diffusion coupling constantAssigned based on Hormuth et al., 2018

Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania M. Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Stacy Moulder, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer viamathematical modeling and quantitative MRI [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 P1-08-08.

Expression of angiogenic markers in jawbones and femur in a rat model treated with zoledronic acid

Objectives

This study aimed to investigate the gene expression of angiogenic marker in surgically treated jawbones and femur on a rat model administrated with zoledronic acid.

Results

No soft tissue fenestration or bone exposure was found in femur. Delayed soft tissue healing was found in both ZA group (3 in mandible, 4 in maxilla) and control group (1 in mandible, 2 in maxilla), while exposed bone was found only in the ZA group (1 in maxilla, 2 in mandible). RT-PCR analysis demonstrated no significant difference in gene expression of angiogenetic markers between ZA-treated and control groups in femur and mandible. In the maxilla, the expression of VEGFA and VEGFR-2 in medium-term ZA group was significantly down-regulated compared with that in the control. The ZA treatment does not change significantly the expression of the angiogenic factors in femur and mandible, but significantly downregulates the expression in maxilla in this rat model. The angiogenesis inhibition may contribute to the development of MRONJ but does not play a key role.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05900-5.

BI-RADS Ultrasound Lexicon Descriptors and Stromal Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer

PURPOSE

Increased levels of stromal tumor-infiltrating lymphocytes (sTILs) have recently been considered a favorable independent prognostic and predictive biomarker in triple-negative breast cancer (TNBC). The purpose of this study was to determine the relationship between BI-RADS (Breast Imaging Reporting and Data System) ultrasound lexicon descriptors and sTILs in TNBC.

MATERIALS AND METHODS

Patients with stage I-III TNBC were evaluated within a single-institution neoadjuvant clinical trial. Two fellowship-trained breast radiologists used the BI-RADS ultrasound lexicon to assess pretreatment tumor shape, margin, echo pattern, orientation, posterior features, and vascularity. sTILs were defined as low <20 or high ≥20 on the pretreatment biopsy. Fisher's exact tests were used to assess the association between lexicon descriptors and sTIL levels.

RESULTS

The 284 patients (mean age 52 years, range 24-79 years) were comprised of 68% (193/284) with low-sTIL tumors and 32% (91/284) with high-sTIL tumors. TNBC tumors with high sTILs were more likely to have the following features: (1) oval/round shape than irregular shape (p = 0.003), (2) circumscribed or microlobulated margins than spiculated, indistinct, or angular margins (p = 0.0005); (3) complex cystic and solid pattern than heterogeneous pattern (p = 0.006); and (4) posterior enhancement than shadowing (p = 0.002). There was no significant association between sTILs and descriptors for orientation and vascularity (p = 0.06 and p = 0.49, respectively).

CONCLUSION

BI-RADS ultrasound descriptors of the pretreatment appearance of a TNBC tumor can be useful in discriminating between tumors with low and high sTIL levels. Therefore, there is a potential use of ultrasound tumor characteristics to complement sTILs when used as stratification factors in treatment algorithms for TNBC.

Guideline conformity to the Stupp regimen in patients with newly diagnosed glioblastoma multiforme in China

Aims: To determine how consistently Chinese glioblastoma multiforme (GBM) patients were treated according to the Stupp regimen. Patients and methods: The proportion of treatments conforming to the Stupp regimen and reasons for nonconformity were evaluated in 202 newly diagnosed GBM patients. Results: Only 15.8% of GBM patients received treatments compliant with the Stupp regimen. The main deviations were temozolomide dosages >75 mg/m² (58/120; 48.3%) and treatment durations <42 days (84/120; 70.0%) in the concomitant phase and temozolomide dosages <150 mg/m² (89/101; 88.1%) in the maintenance phase. Median overall survival (27.09 vs 18.21 months) and progression-free survival (14.27 vs 12.10 months) were longer in patients who received Stupp regimen-compliant treatments. Conclusion: Increased conformity to the Stupp regimen is needed for GBM patients in China.

Mammary-specific expression of Trim24 establishes a mouse model of human metaplastic breast cancer

Conditional overexpression of histone reader Tripartite motif containing protein 24 (TRIM24) in mouse mammary epithelia (Trim24COE) drives spontaneous development of mammary carcinosarcoma tumors, lacking ER, PR and HER2. Human carcinosarcomas or metaplastic breast cancers (MpBC) are a rare, chemorefractory subclass of triple-negative breast cancers (TNBC). Comparison of Trim24COE metaplastic carcinosarcoma morphology, TRIM24 protein levels and a derived Trim24COE gene signature reveals strong correlation with human MpBC tumors and MpBC patient-derived xenograft (PDX) models. Global and single-cell tumor profiling reveal Met as a direct oncogenic target of TRIM24, leading to aberrant PI3K/mTOR activation. Here, we find that pharmacological inhibition of these pathways in primary Trim24COE tumor cells and TRIM24-PROTAC treatment of MpBC TNBC PDX tumorspheres decreased cellular viability, suggesting potential in therapeutically targeting TRIM24 and its regulated pathways in TRIM24-expressing TNBC.

A Nomogram in Predicting Risks of Intrahepatic Cholangiocarcinoma After Partial Hepatectomy for Hepatolithiasis

BACKGROUND

Hepatolithiasis is associated with the development of intrahepatic cholangiocarcinoma (ICC). This study sought to investigate risk factors of ICC for hepatolithiasis after partial hepatectomy (PH) and to develop a model for predicting ICC risk.

METHODS

Data on consecutive patients who underwent PH for hepatolithiasis at the Eastern Hepatobiliary Surgery Hospital between January 2009 and December 2011 were reviewed. Independent risk factors of ICC identified by Cox regression model were used to develop a nomogram in predicting ICC after PH for hepatolithiasis.

RESULTS

Of 2056 patients, 168 developed ICC at a median follow-up of 7.2 years. The cumulative incidences of ICC at 3, 5, and 8 years after PH for hepatolithiasis were 3.0%, 6.5%, and 12.9%, respectively. Independent risk factors of ICC were identified to be a long duration of hepatolithiasis-related symptoms (hazard ratio, 1.088 [95% confidence interval, 1.057-1.120]), metabolic syndrome (2.036 [1.210-3.425]), a high neutrophil-to-lymphocyte ratio (1.250 [1.009-2.816] for 3-5 vs ≤3; 1.538 [1.048-2.069] for ≥5 vs ≤3), hepatic atrophy (1.711 [1.189-2.462]), segmental intensity differences (1.513 [1.052-2.176]), persistent biliary strictures (2.825 [1.480-5.391]), and residual stone disease (2.293 [1.511-3.481]). By incorporating these factors, a constructed nomogram showed a concordance index of 0.721 to predict ICC. The calibration plots demonstrated good agreement between observed and predicted morbidities. The optimal cutoff point for the nomogram was 48 in differentiating between high and low-risk of ICC.

CONCLUSIONS

A nomogram for predicting ICC after PH for hepatolithiasis was constructed based on risk factors of developing ICC. Patients with a nomogram point of ≥48 were predicted to have a high risk of ICC.

Immune Phenotype and Response to Neoadjuvant Therapy in Triple-Negative Breast Cancer

PURPOSE

Increasing tumor-infiltrating lymphocytes (TIL) is associated with higher rates of pathologic complete response (pCR) to neoadjuvant therapy (NAT) in patients with triple-negative breast cancer (TNBC). However, the presence of TILs does not consistently predict pCR, therefore, the current study was undertaken to more fully characterize the immune cell response and its association with pCR.

EXPERIMENTAL DESIGN

We obtained pretreatment core-needle biopsies from 105 patients with stage I-III TNBC enrolled in ARTEMIS (NCT02276443) who received NAT from Oct 22, 2015 through July 24, 2018. The tumor-immune microenvironment was comprehensively profiled by performing T-cell receptor (TCR) sequencing, programmed death-ligand 1 (PD-L1) IHC, multiplex immunofluorescence, and RNA sequencing on pretreatment tumor samples. The primary endpoint was pathologic response to NAT.

RESULTS

The pCR rate was 40% (42/105). Higher TCR clonality (median = 0.2 vs. 0.1, P = 0.03), PD-L1 positivity (OR: 2.91, P = 0.020), higher CD3⁺:CD68⁺ ratio (median = 14.70 vs. 8.20, P = 0.0128), and closer spatial proximity of T cells to tumor cells (median = 19.26 vs. 21.94 μm, P = 0.0169) were associated with pCR. In a multivariable model, closer spatial proximity of T cells to tumor cells and PD-L1 expression enhanced prediction of pCR when considered in conjunction with clinical stage.

CONCLUSIONS

In patients receiving NAT for TNBC, deep immune profiling through detailed phenotypic characterization and spatial analysis can improve prediction of pCR in patients receiving NAT for TNBC when considered with traditional clinical parameters.