Prediction Of Pathological Complete Response Research Articles

ASO Visual Abstract: Prediction of Pathologic Complete Response in Esophageal Squamous Cell Carcinoma Using Preoperative Serum Small Ribonucleic Acid Obtained After Neoadjuvant Chemoradiotherapy.

ASO Visual Abstract: Prediction of Pathologic Complete Response in Esophageal Squamous Cell Carcinoma Using Preoperative Serum Small Ribonucleic Acid Obtained After Neoadjuvant Chemoradiotherapy.

Prediction of Pathologic Complete Response in Esophageal Squamous Cell Carcinoma Using Preoperative Serum Small Ribonucleic Acid Obtained After Neoadjuvant Chemoradiotherapy.

The authors hypothesized that small ribonucleic acid (sRNA) obtained from blood samples after neoadjuvant therapy from patients treated with neoadjuvant chemoradiation therapy (NACRT) could serve as a novel biomarker for predicting pathologic complete response (pCR). This study included 99 patients treated with esophagectomy after NACRT between March 2010 and October 2021 whose blood samples were collected between the end of NACRT and surgery. Next-generation sequencing (NGS) was used to analyze sRNAs from the blood samples. A predictive model for pCR comprising micro-RNA isoforms (isomiR), transfer RNA (tRNA)-derived sRNAs (tsRNAs), and clinical factors was constructed using cross-validation. Of the 99 patients, pCR was diagnosed for 30 and non-pCR for 69 of the patients. Among sRNAs, the isomiRs of let-7b and miR-93 and the tsRNA group derived from tRNA-Gly-CCC/GCC were identified as predictive factors. The clinical factors included a decrease in the maximum standardized uptake value (SUVmax) at the primary site, clinical complete response post-NACRT, preoperative biopsy, and post-NACRT carcinoembryonic antigen levels. The combined predictive model for pCR (C-PM) was established using the three sRNAs and four clinical factors. The area under the curve for the C-PM was 0.84, which was a significant factor in the multivariate analysis (odds ratio, 89.41; 95 % confidence interval 8.1-987.5; p < 0.001). Pathologic complete response after NACRT can be predicted by a predictive model constructed from preoperative clinical factors obtained via minimally invasive procedures and sRNA identified by NGS. Preoperative pCR prediction may influence treatment decision-making after NACRT.

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk breast cancer. Previous studies suggest that pathological complete response (pCR) is a viable indicator of long-term outcomes of neoadjuvant chemotherapy for high-risk breast cancer. While pCR can be assessed during surgery after the chemotherapy, early prediction of pCR before the completion of the chemotherapy may facilitate personalized treatment management to achieve an improved outcome. Notably, the acquisition of dynamic contrast-enhanced magnetic resonance (DCEMR) images at multiple time points during the I-SPY 2 trial opens up the possibility of achieving early pCR prediction. In this study, we investigated the feasibility of the early prediction of pCR to neoadjuvant chemotherapy using multi-time point DCEMR images and clinical data acquired in the I-SPY2 trial. The prediction uncertainty was also quantified to allow physicians to make patient-specific decisions on treatment plans based on the level of associated uncertainty. The dataset used in our study included 624 patients with DCEMR images acquired at 3 time points before the completion of the chemotherapy: pretreatment (T0), after 3 cycles of treatment (T1), and after 12 cycles of treatment (T2). A convolutional long short-term memory (LSTM) network-based deep learning model, which integrated multi-time point deep image representations with clinical data, including tumor subtypes, was developed to predict pCR. The performance of the model was evaluated via the method of nested 5-fold cross validation. Moreover, we also quantified prediction uncertainty for each patient through test-time augmentation. To investigate the relationship between predictive performance and uncertainty, the area under the receiver operating characteristic curve (AUROC) was assessed on subgroups of patients stratified by the uncertainty score. By integrating clinical data and DCEMR images obtained at three-time points before treatment completion, the AUROC reached 0.833 with a sensitivity of 0.723 and specificity of 0.800. This performance was significantly superior (p<0.01) to models using only images (AUROC=0.706) or only clinical data (AUROC=0.746). After stratifying the patients into eight subgroups based on the uncertainty score, we found that group #1, with the lowest uncertainty, had a superior AUROC of 0.873. The AUROC decreased to 0.637 for group #8, which had the highest uncertainty. The results indicate that our convolutional LSTM network-based deep learning model can be used to predict pCR earlier before the completion of chemotherapy. By combining clinical data and multi-time point deep image representations, our model outperforms models built solely on clinical or image data. Estimating prediction uncertainty may enable physicians to prioritize or disregard predictions based on their associated uncertainties. This approach could potentially enhance the personalization of breast cancer therapy.

257P Spatial predictors of pathologic complete response to neoadjuvant chemotherapy using imaging mass cytometry in the IMMUcan TNBC cohort

257P Spatial predictors of pathologic complete response to neoadjuvant chemotherapy using imaging mass cytometry in the IMMUcan TNBC cohort

Predictors of pathological complete response to neoadjuvant treatment in invasive breast cancer with different human epidermal growth factor receptor 2 (HER2) subcategories.

Among human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients who receive anti-HER2 treatment, a noteworthy correlation between pathological complete response (pCR) and longer survival has been observed. The rate of pCR varies with the tumor's degree of HER2 protein expression. The aim of this study was to assess the correlations between clinicopathological characteristics, magnetic resonance imaging (MRI) parameters, and pCR in breast cancer with different HER2 subcategories. A total of 281 invasive breast cancer patients diagnosed with HER2-positivity were included. HER2-positive translated to immunohistochemistry (IHC) 3+ or IHC 2+/fluorescence in situ hybridization (FISH)(+). All enrolled patients underwent baseline MRI examination and received neoadjuvant chemotherapy, dual anti-HER2 therapy, and subsequent therapeutic surgery from January 2021 to May 2022. A logistic regression model was used to evaluate the effects of covariates on pCR. Compared to the IHC 2+/FISH(+) group, patients with IHC 3+ tumors had a higher pCR rate (58.1% vs. 26.7%, P<0.001), clinical stage (58.6% vs. 40%, P=0.038), apparent diffusion coefficient (ADC) value (0.96 vs. 0.88 mm2/s, P=0.004), and were more likely to be estrogen receptor (ER) negative (55.9% vs. 31.1%, P=0.002) and progesterone receptor (PR) negative (72.5% vs. 46.7%, P=0.001). In both groups, univariate analysis showed that the pCR group more often had ER-negative and PR-negative status than the non-pCR group (P<0.001). The final multivariable analysis showed that ER-negativity was associated with pCR in the IHC 2+/FISH(+) group (P=0.004). ER-negativity and the longest diameter were two independent predictors of pCR in the IHC 3+ group (P<0.001 for ER, P=0.026 for longest diameter). The IHC 3+ group had a higher pCR rate than the IHC 2+/FISH(+) group. Along with clinicopathological characteristics, MRI parameters were supplemental predictors of pCR, particularly in IHC 3+ patients.

18F-FDG PET/CT for early prediction of pathological complete response in breast cancer neoadjuvant therapy: a retrospective analysis.

Neoadjuvant treatment has been developed as a systematic approach for patients with early breast cancer and has resulted in improved breast-conserving rate and survival. However, identifying treatment-sensitive patients at the early phase of therapy remains a problem, hampering disease management and raising the possibility of disease progression during treatment. In this retrospective analysis, we collected 2-deoxy-2-[F-18] fluoro-d-glucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) images of primary tumor sites and axillary areas and reciprocal clinical pathological data from 121 patients who underwent neoadjuvant treatment and surgery in our center. The univariate and multivariate logistic regression analyses were performed to investigate features associated with pathological complete response (pCR). An 18F-FDG PET/CT-based prediction model was trained, and the performance was evaluated by receiver operating characteristic curves (ROC). The maximum standard uptake values (SUVmax) of 18F-FDG PET/CT were a powerful indicator of tumor status. The SUVmax values of axillary areas were closely related to metastatic lymph node counts (R = 0.62). Moreover, the early SUVmax reduction rates (between baseline and second cycle of neoadjuvant treatment) were statistically different between pCR and non-pCR patients. The early SUVmax reduction rates-based model showed great ability to predict pCR (AUC = 0.89), with all molecular subtypes (HR+HER2-, HR+HER2+, HR-HER2+, and HR-HER2-) considered. Our research proved that the SUVmax reduction rate of 18F-FDG PET/CT contributed to the early prediction of pCR, providing rationales for utilizing PET/CT in NAT in the future.

Comparison of radiomics-based machine-learning classifiers for the pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer.

Machine learning classifiers are increasingly used to create predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). Few studies have compared the effectiveness of different ML classifiers. This study evaluated radiomics models based on pre- and post-contrast first-phase T1 weighted images (T1WI) in predicting breast cancer pCR after NAT and compared the performance of ML classifiers. This retrospective study enrolled 281 patients undergoing NAT from the Duke-Breast-Cancer-MRI dataset. Radiomic features were extracted from pre- and post-contrast first-phase T1WI images. The Synthetic Minority Oversampling Technique (SMOTE) was applied, then the dataset was randomly divided into training and validation groups (7:3). The radiomics model was built using selected optimal features. Support vector machine (SVM), random forest (RF), decision tree (DT), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM) were classifiers. Receiver operating characteristic curves were used to assess predictive performance. LightGBM performed best in predicting pCR [area under the curve (AUC): 0.823, 95% confidence interval (CI) [0.743-0.902], accuracy 74.0%, sensitivity 85.0%, specificity 67.2%]. During subgroup analysis, RF was most effective in pCR prediction in luminal breast cancers (AUC: 0.914, 95% CI [0.847-0.981], accuracy 87.0%, sensitivity 85.2%, specificity 88.1%). In triple-negative breast cancers, LightGBM performed best (AUC: 0.836, 95% CI [0.708-0.965], accuracy 78.6%, sensitivity 68.2%, specificity 90.0%). The LightGBM-based radiomics model performed best in predicting pCR in patients with breast cancer. RF and LightGBM showed promising results for luminal and triple-negative breast cancers, respectively.

[Artículo traducido] Efectividad y seguridad del tratamiento neoadyuvante del cáncer de mama triple negativo en la vida real

ObjectiveTriple-negative breast cancer is a subtype of aggressive breast cancer. Our aim is to evaluate the effectiveness and safety of neoadjuvant treatment in early-stage triple-negative breast cancer and to identify predictors of pathological complete response. MethodsThis is a single-center, retrospective study involving 79 patients with triple-negative breast cancer who initiated neoadjuvant treatment between January 2017 and October 2022. Descriptive analyses were performed as appropriate. Statistical analysis utilized bivariate logistic regression to explore the presence of factors related to pathological complete response, and the Kaplan–Meier method was employed for survival analysis. ResultsIn the overall population, 27 patients (n = 78; 34.6%) achieved pathological complete response in the breast and axillary lymph nodes, and 31 (n = 73; 42.5%) achieved a grade 5 pathological complete response in the breast, according to the Miller and Payne classification. The addition of platinum to standard therapy improved both breast and axillary lymph node pathological complete response rates. Age less than 40 years was identified as a predictor of pathological complete response in our study population through bivariate analysis, while Ki67 levels lower than 70% were associated with a lower pathological complete response rate. Adverse events were reported in 72 patients (91.1%), with grade 3–5 adverse events observed in 33 (41.8%). There was a particularly notable increase in gastrointestinal and hematological adverse events when platinum was added. ConclusionsIn this population, we observed moderate rates of pathological complete response with acceptable chemotherapy tolerance. Platinum-based chemotherapy appears to enhance the likelihood of achieving pathological complete response, albeit with a less favorable safety profile. Therefore, evaluating the benefit–risk balance is crucial when selecting the optimal chemotherapy regimen for individual patients.

Prediction of pathological complete response and prognosis in locally advanced rectal cancer.

Colorectal cancer is currently the third most common malignant tumor and the second leading cause of cancer-related death worldwide. Neoadjuvant chemoradiotherapy (nCRT) is standard for locally advanced rectal cancer (LARC). Except for pathological examination after resection, it is not known exactly whether LARC patients have achieved pathological complete response (pCR) before surgery. To date, there are no clear clinical indicators that can predict the efficacy of nCRT and patient outcomes. To investigate the indicators that can predict pCR and long-term outcomes following nCRT in patients with LARC. Clinical data of 128 LARC patients admitted to our hospital between September 2013 and November 2022 were retrospectively analyzed. Patients were categorized into pCR and non-pCR groups. Univariate analysis (using the χ 2 test or Fisher's exact test) and logistic multivariate regression analysis were used to study clinical predictors affecting pCR. The 5-year disease-free survival (DFS) and overall survival (OS) rates were calculated using Kaplan-Meier analysis, and differences in survival curves were assessed with the log-rank test. Univariate analysis showed that pretreatment carcinoembryonic antigen (CEA) level, lymphocyte-monocyte ratio (LMR), time interval between neoadjuvant therapy completion and total mesorectal excision, and tumor size were correlated with pCR. Multivariate results showed that CEA ≤ 5 ng/mL (P = 0.039), LMR > 2.73 (P = 0.023), and time interval > 10 wk (P = 0.039) were independent predictors for pCR. Survival analysis demonstrated that patients in the pCR group had significantly higher 5-year DFS rates (94.7% vs 59.7%, P = 0.002) and 5-year OS rates (95.8% vs 80.1%, P = 0.019) compared to the non-pCR group. Tumor deposits (TDs) were significantly correlated with shorter DFS (P = 0.002) and OS (P < 0.001). Pretreatment CEA, LMR, and time interval contribute to predicting nCRT efficacy in LARC patients. Achieving pCR demonstrates longer DFS and OS. TDs correlate with poor prognosis.

In situ HER2 RNA expression as a predictor of pathologic complete response of HER2-positive breast cancer patients receiving neoadjuvant chemotherapy and anti-HER2 targeted treatment

BackgroundImmunohistochemistry (IHC) and in situ hybridization (ISH) remain standard biomarkers for therapeutic decisions in human epidermal growth factor 2 (HER2)-positive breast cancers (BCs); however, they are insufficient to explain the heterogeneous anti-HER2 response.MethodsWe aimed to investigate the correlation of in situ HER2 RNA expression (isHRE), using RNAscope, with HER2 biomarkers and the impact of isHRE on the pathological complete response (pCR) rates of 278 patients with HER2 IHC/fluorescence ISH (FISH)-positive BC receiving neoadjuvant chemotherapy and anti-HER2 targeted treatment (NCTT).ResultsWe validated HER2 RNAscope scoring as a semiquantitative method to determine isHRE and showed a positive correlation between RNAscope scores and pCR rates, with particularly different rates between patients with a score of 5 versus 1–4 BCs (66.7% vs. 15.9%, p < 0.0001). There were higher RNAscope scores and pCR rates in patients with HER2 IHC 3 + versus IHC 2+/FISH + BCs and HER2 RNAscope scores and pCR rates showed similar non-linear positive correlations with HER2 copy numbers and HER2/centromere 17 ratios. Moreover, in each HER2-positive IHC/FISH category, higher pCR rates were observed in patients with RNAscope scores of 5 versus 1–4 BC. Patients achieving pCR had BCs with notably higher HER2 RNAscope scores. Multivariate analysis identified HER2 RNAscope 5 as a strong pCR predictor [odds ratio = 10.865, p < 0.001]. The combined impact of multivariate analysis-defined pCR predictors demonstrated that a higher pCR rate was observed in patients with a score of 5 versus a score of 1–4 BCs regardless of the status of hormone receptor and mono-or dual anti-HER2 blockade.ConcusionsOur results demonstrated that high isHRE (RNAscope score 5) is a strong pCR predictor in patients with HER2-positive BCs receiving NCTT, highlighting the complementary role of isHRE in stratifying HER2 status in tissue. Such stratification is relevant to anti-HER2 therapeutic efficacy, particularly using the cutoff of score 1–4 versus 5.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer.

To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

A nomogram to predict the pathological complete response in patients with breast cancer based on the TILs-US score.

The tumor-infiltrating lymphocytes-ultrasonography score is a calculation system for predicting lymphocyte-predominant breast cancers in surgical specimens. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score was developed to predict the pathological complete response in breast cancer treated with neoadjuvant chemotherapy. A retrospective evaluation was conducted on 118 patients with breast cancer treated with neoadjuvant chemotherapy at Hiroshima University Hospital. Tumor-infiltrating lymphocytes-ultrasonography scores ≥4 were classified as high. A nomogram was developed using a stepwise logistic regression model for pathological complete response (ypT0 ypN0), based on the smallest Akaike information criterion. The predictive ability and clinical usefulness of the nomogram were also evaluated. Among 118 patients, 34 (28.8%) achieved a pathological complete response, and 52 (44.1%) exhibited high tumor-infiltrating lymphocytes-ultrasonography. In multivariate logistic regression analysis, high tumor-infiltrating lymphocytes-ultrasonography (odds ratio, 6.01; P<0.001), clinical complete response (odds ratio, 4.83; P=0.004) and hormone receptor (odds ratio, 3.48; P=0.038) were independent predictors of pathological complete response. A nomogram based on tumor-infiltrating lymphocytes-ultrasonography score, clinical complete response, hormone receptor and clinical N status was developed. The nomogram showed an area under the curve of 0.831 and a bias-corrected area under the curve of 0.809. The calibration plot showed a good fit between the expected and actual pathological complete response values. Decision curve analysis also showed the clinical utility of the nomogram for predicting pathological complete responses. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score exhibited a favorable predictive ability for pathological complete response in patients with breast cancer, which can be useful in predicting the residual disease status after neoadjuvant chemotherapy.

Prediction of pathologic complete response to chemoimmunotherapy in triple-negative breast cancer using tumor-infiltrating lymphocytes: Exploiting cutoff values.

e12629 Background: Triple-negative breast cancer (TNBC) prognosis is significantly influenced by tumor-infiltrating lymphocytes (TILs), but the lack of validated cutoff values has limited their clinical applicability. In the era of neoadjuvant pembrolizumab plus chemotherapy (P+CT) as a new standard of care, the role of TILs as predictors of response to chemoimmunotherapy remains uncertain. Methods: This study aimed to assess TILs as predictors of pathologic complete response (pCR) within the Neo-Real study, a multicenter, real-world data investigation on neoadjuvant P+CT in TNBC. TILs were evaluated using the standardized methodology of the International TILs Working Group. Logistic regression and receiver operating characteristic (ROC) curve analysis were performed to evaluate the predictive ability of TILs expression and multivariable models for pCR. Results: The analysis included 128 patients (pts) treated with neoadjuvant P+CT (68% stage II, 26% stage III). The proportion of pts in each TILs’ category and results of ROC curve analysis are presented in the Table. A cutoff value of 10% demonstrated the highest accuracy in predicting pCR, while a high specificity was observed at a cutoff value of 50%. Thus, the probability of residual disease if TILs ≥ 50% is considerably low. Incorporating categorized clinical and pathological variables, a multivariable logistic regression model, using TILs (≥ 10% vs &lt; 10%), Ki67 (≥ 50% vs &lt; 50%), and tumor stage (III vs II), exhibited the highest AUC (0.688) for predicting pCR. Conclusions: Our study underscores the predictive value of TILs for pCR in TNBC following neoadjuvant P+CT. The cutoff value of ≥ 50% identified patients with a very high probability of pCR. The results reinforce TILs’ use as a biomarker for treatment de-escalation, especially for pts with TILs ≥ 50%. Further enhancement of TILs' predictive potential may be achieved through multivariable models. [Table: see text]

Contrast-enhanced pelvic magnetic resonance imaging (MRI) for the prediction of treatment response in mucinous rectal cancer.

In mucinous rectal cancer, it can be difficult to differentiate between cellular and acellular mucin. The purpose of this study was to evaluate, in patients with mucinous rectal cancer, the value of static enhancement (enh) and pharmacokinetic parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) in predicting pathologic complete response. This retrospective cross-sectional study performed at Memorial Sloan Kettering Cancer Center included 43 patients (24 males and 19 females; mean age, 57 years) with mucinous rectal cancer who underwent MRI at baseline as well as after neoadjuvant chemoradiotherapy but before surgical resection between 2008 and 2019. Two radiologists independently segmented tumors on contrast-enhanced axial 3D T1-weighted images and sagittal DCE magnetic resonance images. On contrast-enhanced axial T1-weighted images, the static parameters enh and relative enhancement (renh) were estimated. On DCE images, the pharmacokinetic parameters Ktrans, kep, relative Ktrans (rKtrans), and relative kep (rkep) were estimated. Associations between all parameters with pathologic complete response were tested using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis was performed to assess the area under the curve (AUC) for each parameter. Of the 43 patients who were included in the study, 42/43 (98%) had evaluable contrast-enhanced axial T1-weighted images and 35/43 (81%) had evaluable DCE images. Of the patients with evaluable contrast-enhanced axial T1-weighted images, 9/42 (21%) had pathologic complete response and 33/42 (79%) did not have pathologic complete response. For reader 1, enh(pre-neoadjuvant chemotherapy), enh(post-neoadjuvant chemotherapy), and renh were significant predictors of pathologic complete response [P=0.045 (AUC =0.73), 0.039 (AUC =0.74), and 0.0042, respectively]. For reader 2, enh(pre-neoadjuvant chemotherapy) and renh were significant predictors [P=0.021 (AUC =0.77) and 0.002, respectively]. For renh, the AUC was 0.83 for reader 1, and 0.82 for reader 2. Meanwhile, of those patients with evaluable DCE images, 9/35 (26%) had pathologic complete response and 26/35 (74%) did not have pathologic complete response. Ktrans(pre-neoadjuvant chemotherapy), kep(pre-neoadjuvant chemotherapy), and rkep were significant predictors [P=0.016 (AUC =0.73), 0.00057 (AUC =0.81), and 0.0096 (AUC =0.74), respectively]. Static and pharmacokinetic parameters of contrast-enhanced MRI show promise to predict neoadjuvant treatment response. Static enh parameters, which are simpler to assess, showed the strongest prediction.

Pretreatment neutrophil to lymphocyte ratio as a predictor of pathological complete response to neoadjuvant immunotherapy in early breast cancer.

e12545 Background: Neoadjuvant immunotherapy has been increasingly used in early breast cancer, while lack of accessible predictive biomarkers of response remains an open question. High pretreatment neutrophil to lymphocyte ratio (NLR) was associated with worse prognosis and treatment responses to neoadjuvant chemotherapy in multiple cancers including breast cancer. However, it remains unclear whether NLR was associated with response to neoadjuvant immunotherapy in early breast cancer. Methods: We retrospectively analyzed 89 early breast cancer patients who received neoadjuvant chemotherapy in combination with at least 4 cycles of immunotherapy and subsequent breast surgery in Sun Yat-sen University Cancer Center. Pretreatment NLR was calculated, and Miller-Payne grade 5 in primary tumor site was considered pathological complete response (pCR) to neoadjuvant therapy. Univariate and multivariate analyses were used to evaluate the associations between clinicopathological markers and pCR. Results: 49.4% (44/89) of the patients achieved pCR. Compared to non-pCR patients, pCR patients had significantly lower pretreatment NLR (1.93 vs 2.87, p&lt;0.001) and lower percentage of pretreatment cT3/T4 stage (11.4% vs 37.7%, p=0.006), and received more cycles of neoadjuvant immunotherapy (6.5 vs 6.0, p&lt;0.05). More pCR patients received TCb based neoadjuvant therapy (86.4% vs 51.1%, p&lt;0.001). In multivariate analysis, the pretreatment NLR (p=0.002) and cT3/T4 (p=0.010, as compared to cT1/T2) were independent predictive factors for pCR. Conclusions: Lower pretreatment NLR was associated with higher possibility of achieving pCR in early breast cancer treated with neoadjuvant immunotherapy. [Table: see text]

An MRI-based multi-parameter clinical decision support system (NeoMDSS) for early prediction of pathological complete response after the first cycle of neoadjuvant therapy in breast cancer: A multi-center prospective observational cohort study.

592 Background: Currently, there is no reliable and convenient way to predict response rates in early stages of neoadjuvant therapy (NAT) for breast cancer, which can affect the prognosis of non-responsive patients. Methods: In this prospective multi-center observational study, eligible participants who underwent NAT followed by radical surgery were recruited. All patients underwent magnetic resonance imaging (MRI) examination before and after the first cycle of NAT (1st-NAT). We analyzed the effects of clinicopathological features, such as histological grade, hormone receptor (HR) and HER2 status, Ki-67 expression, blood cell analysis, and MRI parameters including the reduction ratio of tumor diameter (ΔD%), apparent diffusion coefficient (ADC), and early enhancement ratio (EER) on NAT. By incorporating independent influencing factors, we developed a multiparametric clinical decision support system (NeoMDSS) model based on a retrospective cohort and validated its accuracy in both a prospective single-center internal cohort and a prospective multi-center external cohort. Relevant clinical trial details can be found under the identifier NCT04909554. Results: A total of 301 breast cancer patients were enrolled between January 2019 and December 2023, including 140 patients in the training cohort, 120 patients in the internal validation cohort, and 41 patients in the external validation cohort. The NeoMDSS model demonstrated excellent performance in predicting pathological complete response (pCR) at 1st-NAT, with an AUC of 0.874 (95% CI 0.813-0.935) in the training cohort, 0.845 (95% CI 0.771-0.919) in the internal validation cohort, and 0.867 (95% CI 0.742-0.992) in the external validation cohort. The NeoMDSS model exhibited a sensitivity, specificity, and accuracy of 80.0%, 81.3%, and 80.5%, respectively, in the internal validation cohort, and 87.0%, 83.3%, and 85.4%, respectively, in the external validation cohort. Calibration curves and decision curve analysis (DCA) further supported the clinical value of the NeoMDSS model. To facilitate the clinical application of the NeoMDSS model, a nomogram and an online website ( www.gdphneomdss.com ) were developed to calculate the probability of pCR, which the sensitivity, specificity, and accuracy of the NeoMDSS model were all 80.0% in the internal validation cohort and 80.0%, 81.3%, and 80.5% in the external validation cohort. Conclusions: The NeoMDSS model serves as a convenient tool in clinical practice at 1st-NAT to calculate and accurately predict the probability of pCR after NAT. This assists clinicians in determining whether to modify the treatment plan and improving personalized treatment for patients with poor response to NAT. Clinical trial information: NCT04909554 .

Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity

BackgroundEarly prediction of pathological complete response (pCR) is important for deciding appropriate treatment strategies for patients. In this study, we aimed to quantify the dynamic characteristics of dynamic contrast-enhanced magnetic resonance images (DCE-MRI) and investigate its value to improve pCR prediction as well as its association with tumor heterogeneity in breast cancer patients.MethodsThe DCE-MRI, clinicopathologic record, and full transcriptomic data of 785 breast cancer patients receiving neoadjuvant chemotherapy were retrospectively included from a public dataset. Dynamic features of DCE-MRI were computed from extracted phase-varying radiomic feature series using 22 CAnonical Time-sereis CHaracteristics. Dynamic model and radiomic model were developed by logistic regression using dynamic features and traditional radiomic features respectively. Various combined models with clinical factors were also developed to find the optimal combination and the significance of each components was evaluated. All the models were evaluated in independent test set in terms of area under receiver operating characteristic curve (AUC). To explore the potential underlying biological mechanisms, radiogenomic analysis was implemented on patient subgroups stratified by dynamic model to identify differentially expressed genes (DEGs) and enriched pathways.ResultsA 10-feature dynamic model and a 4-feature radiomic model were developed (AUC = 0.688, 95%CI: 0.635–0.741 and AUC = 0.650, 95%CI: 0.595–0.705) and tested (AUC = 0.686, 95%CI: 0.594–0.778 and AUC = 0.626, 95%CI: 0.529–0.722), with the dynamic model showing slightly higher AUC (train p = 0.181, test p = 0.222). The combined model of clinical, radiomic, and dynamic achieved the highest AUC in pCR prediction (train: 0.769, 95%CI: 0.722–0.816 and test: 0.762, 95%CI: 0.679–0.845). Compared with clinical-radiomic combined model (train AUC = 0.716, 95%CI: 0.665–0.767 and test AUC = 0.695, 95%CI: 0.656–0.714), adding the dynamic component brought significant improvement in model performance (train p < 0.001 and test p = 0.005). Radiogenomic analysis identified 297 DEGs, including CXCL9, CCL18, and HLA-DPB1 which are known to be associated with breast cancer prognosis or angiogenesis. Gene set enrichment analysis further revealed enrichment of gene ontology terms and pathways related to immune system.ConclusionDynamic characteristics of DCE-MRI were quantified and used to develop dynamic model for improving pCR prediction in breast cancer patients. The dynamic model was associated with tumor heterogeniety in prognostic-related gene expression and immune-related pathways.

A clinicopathological-imaging nomogram for the prediction of pathological complete response in breast cancer cases administered neoadjuvant therapy

ObjectiveTo construct a user-friendly nomogram with MRI and clinicopathological parameters for the prediction of pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC). MethodsWe retrospectively enrolled consecutive female patients pathologically confirmed with breast cancer who received NAT followed by surgery between January 2018 and December 2022 as the development cohort. Additionally, we prospectively collected eligible candidates between January 2023 and December 2023 as an external validation group at our institution. Pretreatment MRI features and clinicopathological variables were collected, and the pre- and post-treatment background parenchymal enhancement (BPE) and the changes in BPE on two MRIs were compared between patients who achieved pCR and those who did not. Multivariable logistic regression analysis was used to identify independent variables associated with pCR in the development cohort. These independent variables were combined into a predictive nomogram for which performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis, and external validation. ResultsIn the development cohort, there were a total of 276 female patients with a mean age of 48.3 ± 8.7 years, while in the validation cohort, there were 87 female patients with a mean age of 49.0 ± 9.5 years. Independent prognostic factors of pCR included small tumor size, HER2(+), high Ki-67 index,high signal enhancement ratio (SER), low minimum value of apparent diffusion coefficient (ADCmin), and significantly decreased BPE after NAT(change of BPE). The nomogram, which incorporates the above parameters, demonstrated excellent predictive performance in both the development and external validation cohorts, with AUC values of 0.900 and 0.850, respectively. Additionally, the nomogram showed excellent calibration capacities, as indicated by Hosmer-Lemeshow test p values of 0.508 and 0.423 in the two cohorts. Furthermore, the nomogram provided greater net benefits compared to the default simple schemes in both cohorts. ConclusionA nomogram constructed using tumor size, HER2 status, Ki-67 index, SER, ADCmin, and changes in pre- and post-NAT BPE demonstrated strong predictive performance, calibration ability, and greater net benefits for predicting pCR in patients with BC after NAT. This suggests that the user-friendly nomogram could be a valuable imaging biomarker for identifying suitable candidates for NAT.

Abstract PS09-01: Individual patient data meta-analysis of clinical and translational biomarkers for prediction of pathological complete response (pCR) after de-escalated therapy in HER2+ breast cancer in four trials of the West German Study Group

Abstract Background Introduction of anti-HER2 therapies substantially improved outcomes for HER2-positive early breast cancer (HER2+ eBC). However, a considerable proportion of patients (pts) may still be overtreated with systemic chemotherapy (CTx) combinations. Establishing safe de-escalation strategies to avoid toxicities requires precise patient selection. Therefore, we set out to determine predictors for efficacy and survival in the unique setting of four de-escalation trials investigating short (12-week) neoadjuvant treatments (NAT) in HER2+ eBC. We investigated the prognostic ability of clinical and translational biomarkers for pCR to identify pts with the best prognosis after de-escalated systemic CTx-free NAT. Methods 756 pts who received de-escalated NAT were analyzed: trastuzumab + pertuzumab (T + P, n=92) and T + P + paclitaxel (pac, n=42) in ADAPT-HR-/HER2+ (NCT01817452); trastuzumab emtansine (T-DM1, n=118), T-DM1 + endocrine therapy (ET, n=125), and T + ET (n=129) in ADAPT-HR+/HER2+ (NCT01779206), T + P + pac (n=107) and T + P + ET (n=100) in TP-II (NCT03272477); and T + P + pembrolizumab (n=43) in Keyriched-1 (NCT03988036) in HER2-enriched (HER2-E) subtype by PAM50. The primary endpoint of each trial was pCR (ypT0/is ypN0). Survival was a secondary endpoint in all trials but Keyriched-1; survival data for TP-II are pending. Baseline gene expression was analyzed by BC360 assay; intrinsic molecular subtypes were determined using the PAM50 predictor. Stromal tumor infiltrating lymphocytes (sTILs) were evaluated at baseline and at week 3 of NAT. sTILs were categorized using a 30% threshold and the low cellularity category ( &amp;lt; 500 invasive tumor cells) at week 3. Prognostic markers for pCR were identified with univariate and multivariable logistic regression models with backstep algorithm excluding variables with p≥0.1; considered variables included age, sTILs (baseline, 3-weeks), cT, cN, grade, hormone receptor and HER2 status, intrinsic subtype, and standardized ERBB2 and ESR1 gene expression. These analyses were performed separately for NAT involving systemic CTx (i.e. containing pac, n=149) and systemic CTx-free NAT (n=607). Results Overall pCR rate was 39.1% (CTx: 66.4%; CTx-free: 32.5%). Multivariable analysis identified predictors of pCR after CTx-free NAT: low cellularity at week 3 (vs &amp;lt; 30% sTILs: OR 3.21, 95%CI 1.85-5.57), ERBB2 (OR 1.90, 95%CI 1.41-2.55), HER2 3+ (vs 1+/2+ and ISH+: OR 8.01, 95%CI 1.65-38.99), LumA/LumB/Basal subtype (vs HER2-E: OR 0.57, 95%CI 0.34-0.97), cT2 (vs 1: OR 0.54, 95%CI 0.33-0.89), and cN1-3 (vs 0, OR 0.46, 95%CI 0.27-0.78). In CTx-containing NAT, only ERBB2 (OR 2.08, 95%CI 1.28-3.40) and additionally ESR1 (OR 0.38, 95%CI 0.23-0.61) were prognostic for pCR. Updated analysis including expanded gene expression data from ADAPT-HR+/HER2+ and a prognostic score for pCR after CTx-free NAT developed using machine learning methods will be presented at the meeting. Conclusions This pooled analysis demonstrated a 66% pCR rate after a short (12-week) de-escalated NAT in HER2+ eBC. In one-third of patients (with mainly stage I cancer, higher HER2 expression by immunohistochemistry and gene expression analysis, and low cellularity at 3 weeks), pCR can be achieved without systemic CTx. Identified biomarkers could pave the way for developing new patient selection strategies to spare CTx-associated acute and late toxicities in a clinically meaningful number of patients. Citation Format: Monika Graeser, Oleg Gluz, Christine zu Eulenburg, Sherko Küemmel, Raquel von Schumann, Matthias Christgen, Rachel Wuerstlein, Enrico Pelz, Hans-Heinrich Kreipe, Peter Schmid, Marc Thill, Michael Braun, Jochem Potenberg, Claudia Schumacher, Joke Tio, Andreas Hartkopf, Marianne Just, Christian Schem, Kerstin Lüdtke-Heckenkamp, Eva-Maria Grischke, Felix Hilpert, Angela Kentsch, Ronald Kates, Ulrike Nitz, Nadia Harbeck. Individual patient data meta-analysis of clinical and translational biomarkers for prediction of pathological complete response (pCR) after de-escalated therapy in HER2+ breast cancer in four trials of the West German Study Group [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS09-01.

Abstract PS09-06: Tumor infiltrating lymphocytes as a predictor of pathologic complete response to neoadjuvant therapy in HER2 positive breast cancer

Abstract Background The presence of tumor infiltrating lymphocytes (TILs) has shown positive prognostic relevance for certain subtypes of breast cancer, although its value as a predictive biomarker is still uncertain. The purpose of this study was to study the association between TILs expression with the pathologic complete response (pCR) following neoadjuvant therapy in Mexican patients with HER2+ breast cancer. Methods Retrospective cohort of patients with stage I-III HER2+ breast cancer who received neoadjuvant therapy between 2017 and 2020 at Instituto Nacional de Cancerología (INCan). We collected demographic and clinico-pathological characteristics. Stage was assigned using AJCC 7th criteria; hormone receptor (HR) and HER2 status were determined according to ASCO-CAP guidelines. Quantification of TILs was made by microscopy and stained with hematoxylin-eosin. TIL levels were defined as low (1-10%) intermediate (11-40%) and high ( &amp;gt;40%). Complete pathologic response was defined as the absence of invasive disease in breast and axilla (ypT0/is N0). pCR rates were compared using X2 and Fisher's exact tests according to demographic and clinicopathologic characteristics. Univariate logistic regression analysis was performed to estimate the probability of pCR according to relevant characteristics. Those parameters with p ≤0.10 on univariate analysis were included in a multivariate logistic regression model. A p&amp;lt; 0.05 was considered as statistically significant. Results We included 164 patients (mean age 51.0 years, SD 11.4). 86 patients (54.1%) were postmenopausal, 75 patients (47.2%) had T3-T4 tumors, 129 patients (81.1%) had positive lymph nodes, 133 patients (83.6%) presented Ki67 ≥30%, 59 patients (37.1%) had negative hormone receptors, 136 patients (85.5%) were treated with anthracyclines and 20 patients (12.6%) received dual anti-HER2 therapy with trastuzumab/pertuzumab. TIL expression was low in 84 patients (52.8%), intermediate in 47 (29.6%), and high in 28 patients (17.6%). The pathologic complete response (pCR) rate in the overall population was 50.9%. pCR rates according to the demographic and clinico-pathological features are listed in Table 1. In patients with intermediate-high TILs, pCR rates were 73.1% with trastuzumab, and 75.0% with dual HER2 blockade (p=0.91). Among patients with low TILs, pCR rates were 23.6% and 75.0%, respectively (p &amp;lt; 0.001). On univariate analysis, intermediate TILs (OR 3.59; 95% C.I 1.70-7.59; p= &amp;lt; 0.001), high TILs (OR 29.00; 95% C.I 6.40-131.37; p= &amp;lt; 0.001), Ki67 ≥30% (OR 2.73, 95% C.I 1.11-6.73; p=0.02), and dual anti-HER2 therapy (OR 3.31; 95% C.I 1.14-9.63 p= 0.021) were associated with a higher probability of pCR. T3/T4 tumor size (OR 0.43; 95% C.I 0.22-0.81; p = 0.009) was associated with a lower probability of pCR. Positive lymph nodes, HR status , and use of anthracyclines were not significantly associated. On multivariate analysis, intermediate TILs (OR 3.28; 95% C.I 1.46-7.32 p=.004), high TILs (OR 32.37; 95% C.I 6.84-153.04 p= &amp;lt; 0.001), and dual anti-HER2 therapy (OR 6.58; 95% CI 2.01-21.47 p= .002) remained significantly associated with pCR. Conclusions Intermediate/high TIL expression was significantly associated with pCR. Our results suggest that TIL expression could help select patients with locally advanced HER2+ breast cancer for treatment intensification with dual HER2 blockade in resource-limited settings. However, these findings require confirmation in larger, prospective studies before implementation into routine practice. TABLE 1. Pathologic complete response (pCR) rates according to demographic and clinical pathological features (n=164). Citation Format: Alberto Monroy Chargoy, Haydee Cristina Verduzco-Aguirre, Javier Monroy Chargoy, Jose Rodrigo Espinosa-Fernandez, Jesus Antonio Martinez Ojeda, Paula Cabrera-Galeana. Tumor infiltrating lymphocytes as a predictor of pathologic complete response to neoadjuvant therapy in HER2 positive breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS09-06.