Tumor-infiltrating lymphocytes in breast cancer: ready for prime time?

Germinal center B-cell subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis, immunotherapy response, and treatment resistance in head and neck squamous carcinoma

Background

More than 60 % of patients with head and neck squamous carcinoma (HNSCC) are diagnosed at advanced stages and miss radical treatment. This has prompted the need to find new biomarkers to achieve early diagnosis and predict early recurrence and metastasis of tumors.

Methods

Single-cell RNA sequencing (scRNA-seq) data from HNSCC tissues and peripheral blood samples were obtained through the Gene Expression Omnibus (GEO) database (GSE164690) to characterize the B-cell subgroups, differentiation trajectories, and intercellular communication networks in HNSCC and to construct a prognostic model of the associated risks. In addition, this study analyzed the differences in clinical features, immune cell infiltration, functional enrichment, tumor mutational burden (TMB), and drug sensitivity between the high- and low-risk groups.

Results

Using scRNA-seq of HNSCC, we classified B and plasma cells into a total of four subgroups: naive B cells (NBs), germinal center B cells (GCBs), memory B cells (MBs), and plasma cells (PCs). Pseudotemporal trajectory analysis revealed that NBs and GCBs were at the early stage of B cell differentiation, while MBs and PCs were at the end. Cellular communication revealed that GCBs acted on tumor cells through the CD99 and SEMA4 signaling pathways. The independent prognostic value, immune cell infiltration, TMB and drug sensitivity assays were validated for the MEF2B+ GCB score groups.

Conclusions

We identified GCBs as B cell-specific prognostic biomarkers for the first time. The MEF2B+ GCB score fills the research gap in the genetic prognostic prediction model of HNSCC and is expected to provide a theoretical basis for finding new therapeutic targets for HNSCC.

Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists

PURPOSE

To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists.

MATERIALS AND METHODS

This multi-center study conducted a retrospective analysis of DCE-MRI images from 330 women diagnosed with breast cancer, with 138 cases categorized as benign and 192 as malignant. The training and internal testing sets comprised 270 patients from center 1, while the external testing cohort consisted of 60 patients from center 2. A fusion feature set consisting of radiomics features and deep transfer learning features was constructed from both intratumoral (ITR) and peritumoral (PTR) areas. The Least absolute shrinkage and selection operator (LASSO) based support vector machine was chosen as the classifier by comparing its performance with five other machine learning models. The diagnostic performance and clinical usefulness of fusion model were verified and assessed through the area under the receiver operating characteristics (ROC) and decision curve analysis. Additionally, the performance of the fusion model was compared with the diagnostic assessments of two experienced radiologists to evaluate its relative accuracy. The study strictly adhered to CLEAR and METRICS guidelines for standardization to ensure rigorous and reproducible methods.

RESULTS

The findings show that the fusion model, utilizing radiomics and deep transfer learning features from the ITR and PTR, exhibited exceptional performance in classifying breast tumors, achieving AUCs of 0.950 in the internal testing set and 0.921 in the external testing set. This performance significantly surpasses that of models relying on singular regional radiomics or deep transfer learning features alone. Moreover, the fusion model demonstrated superior diagnostic accuracy compared to the evaluations conducted by two experienced radiologists, thereby highlighting its potential to support and enhance clinical decision-making in the differentiation of benign and malignant breast tumors.

CONCLUSION

The fusion model, combining multi-regional radiomics with deep transfer learning features, not only accurately differentiates between benign and malignant breast tumors but also outperforms the diagnostic assessments made by experienced radiologists. This underscores the model's potential as a valuable tool for improving the accuracy and reliability of breast tumor diagnosis.

T-reg transcriptomic signatures identify response to check-point inhibitors

Regulatory T cells (Tregs) is a subtype of CD4+ T cells that produce an inhibitory action against effector cells. In the present work we interrogated genomic datasets to explore the transcriptomic profile of breast tumors with high expression of Tregs. Only 0.5% of the total transcriptome correlated with the presence of Tregs and only four transcripts, BIRC6, MAP3K2, USP4 and SMG1, were commonly shared among the different breast cancer subtypes. The combination of these genes predicted favorable outcome, and better prognosis in patients treated with checkpoint inhibitors. Twelve up-regulated genes coded for proteins expressed at the cell membrane that included functions related to neutrophil activation and regulation of macrophages. A positive association between MSR1 and CD80 with macrophages in basal-like tumors and between OLR1, ABCA1, ITGAV, CLEC5A and CD80 and macrophages in HER2 positive tumors was observed. Expression of some of the identified genes correlated with favorable outcome and response to checkpoint inhibitors: MSR1, CD80, OLR1, ABCA1, TMEM245, and ATP13A3 predicted outcome to anti PD(L)1 therapies, and MSR1, CD80, OLR1, ANO6, ABCA1, TMEM245, and ATP13A3 to anti CTLA4 therapies, including a subgroup of melanoma treated patients. In this article we provide evidence of genes strongly associated with the presence of Tregs that modulates the response to check point inhibitors.

Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer

BACKGROUND

Identifying breast cancer (BC) patients with germline breast cancer susceptibility gene (gBRCA) mutation is important. The current criteria for germline testing for BC remain controversial. This study aimed to develop a nomogram incorporating ultrasound radiomic features and clinicopathological factors to predict gBRCA mutations in patients with BC.

MATERIALS AND METHODS

In this retrospective study, 497 women with BC who underwent gBRCA genetic testing from March 2013 to May 2022 were included, including 348 for training (84 with and 264 without a gBRCA mutation) and 149 for validation(36 patients with and 113 without a gBRCA mutation). Factors associated with gBRCA mutations were identified to establish a clinicopathological model. Radiomics features were extracted from the intratumoral and peritumoral regions (3 mm and 5 mm) of each image. The least absolute shrinkage and selection operator regression algorithm was used to select the features and logistic regression analysis was used to construct three imaging models. Finally, a nomogram that combined clinicopathological and radiomics features was developed. The models were evaluated based on the area under the receiver operating characteristic curve (AUC), calibration, and clinical usefulness.

RESULTS

Age at diagnosis, family history of BC, personal history of other BRCA-related cancers, and human epidermal growth factor receptor 2 status were independent predictors of the clinicopathological model. The AUC of the imaging radiomics model combining intratumoral and peritumoral 3 mm areas in the validation set was 0.783 (95% confidence interval [CI]: 0.702-0.862), which showed the best performance among three imaging models. The nomogram yielded better performance than the clinicopathological model in validation sets (AUC: 0.824 [0.755-0.894] versus 0.659 [0.563-0.755], p = 0.007).

CONCLUSION

The nomogram based on ultrasound images and clinicopathological factors performs well in predicting gBRCA mutations in BC patients and may help to improve clinical decisions about genetic testing.

Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI

PURPOSE

Our study aimed to investigate the potential of radiomics with DCE-MRI for predicting Ki-67 expression in invasive ductal breast cancer.

METHOD

We conducted a retrospective study including 223 patients diagnosed with invasive ductal breast cancer. Radiomics features were extracted from DCE-MRI using 3D-Slicer software. Two Ki-67 expression cutoff values (20% and 29%) were examined. Patients were divided into training (70%) and test (30%) sets. The Elastic Net method selected relevant features, and five machine-learning models were established. Radiomics models were created from intratumoral, peritumoral, and combined regions. Performance was assessed using ROC curves, accuracy, sensitivity, and specificity.

RESULT

For a Ki-67 cutoff value of 20%, the combined model exhibited the highest performance, with area under the curve (AUC) values of 0.838 (95% confidence interval (CI): 0.774-0.897) for the training set and 0.863 (95% CI: 0.764-0.949) for the test set. The AUC values for the tumor model were 0.816 (95% CI: 0.745-0.880) and 0.830 (95% CI: 0.724-0.916), and for the peritumor model were 0.790 (95% CI: 0.711-0.857) and 0.808 (95% CI: 0.682-0.910). When the Ki-67 cutoff value was set at 29%, the combined model also demonstrated superior predictive ability in both training set (AUC: 0.796; 95% CI: 0.724-0.862) and the test set (AUC: 0.823; 95% CI: 0.723-0.911). The AUC values for the tumor model were 0.785 (95% CI: 0.708-0.861) and 0.784 (95% CI: 0.663-0.882), and for the peritumor model were 0.773 (95% CI: 0.690-0.844) and 0.729 (95% CI: 0.603-0.847).

CONCLUSION

Radiomics with DCE-MRI can predict Ki-67 expression in invasive ductal breast cancer. Integrating radiomics features from intratumoral and peritumoral regions yields a dependable prognostic model, facilitating pre-surgical detection and treatment decisions. This holds potential for commercial diagnostic tools.

An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

RATIONALE AND OBJECTIVES

This study aimed to create and verify a nomogram for preoperative prediction of Ki-67 expression in breast malignancy to assist in the development of personalized treatment strategies.

MATERIALS AND METHODS

This retrospective study received approval from the institutional review board and included a cohort of 197 patients with breast malignancy who were admitted to our hospital. Ki-67 expression was divided into two groups based on a 14% threshold: low and high. A radiomics signature was built utilizing 1702 radiomics features based on an intra- and peritumoral (10 mm) regions of interest. Using multivariate logistic regression, radiomics signature, and ultrasound (US) characteristics, the nomogram was developed. To evaluate the model's calibration, clinical application, and predictive ability, decision curve analysis (DCA), the calibration curve, and the receiver operating characteristic curve were used, respectively.

RESULTS

The final nomogram included three independent predictors: tumor size (P = .037), radiomics signature (P < .001), and US-reported lymph node status (P = .018). The nomogram exhibited satisfactory performance in the training cohort, demonstrating a specificity of 0.944, a sensitivity of 0.745, and an area under the curve (AUC) of 0.905. The validation cohort recorded a specificity of 0.909, a sensitivity of 0.727, and an AUC of 0.882. The DCA showed the nomogram's clinical utility, and the calibration curve revealed a high consistency among the expected and detected values.

CONCLUSION

The nomogram used in this investigation can accurately predict Ki-67 expression in people with malignant breast tumors, helping to develop personalized treatment approaches.

Potential Antihuman Epidermal Growth Factor Receptor 2 Target Therapy Beneficiaries: The Role of MRI-Based Radiomics in Distinguishing Human Epidermal Growth Factor Receptor 2-Low Status of Breast Cancer

BACKGROUND

Multiparametric MRI radiomics could distinguish human epidermal growth factor receptor 2 (HER2)-positive from HER2-negative breast cancers. However, its value for further distinguishing HER2-low from HER2-negative breast cancers has not been investigated.

PURPOSE

To investigate whether multiparametric MRI-based radiomics can distinguish HER2-positive from HER2-negative breast cancers (task 1) and HER2-low from HER2-negative breast cancers (task 2).

STUDY TYPE

Retrospective.

POPULATION

Task 1: 310 operable breast cancer patients from center 1 (97 HER2-positive and 213 HER2-negative); task 2: 213 HER2-negative patients (108 HER2-low and 105 HER2-zero); 59 patients from center 2 (16 HER2-positive, 27 HER2-low and 16 HER2-zero) for external validation.

FIELD STRENGTH/SEQUENCE

A 3.0 T/T1-weighted contrast-enhanced imaging (T1CE), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC).

ASSESSMENT

Patients in center 1 were assigned to a training and internal validation cohort at a 2:1 ratio. Intratumoral and peritumoral features were extracted from T1CE and ADC. After dimensionality reduction, the radiomics signatures (RS) of two tasks were developed using features from T1CE (RS-T1CE), ADC (RS-ADC) alone and T1CE + ADC combination (RS-Com).

STATISTICAL TESTS

Mann-Whitney U tests, the least absolute shrinkage and selection operator, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

For task 1, RS-ADC yielded higher area under the ROC curve (AUC) in the training, internal, and external validation of 0.767/0.725/0.746 than RS-T1CE (AUC = 0.733/0.674/0.641). For task 2, RS-T1CE yielded higher AUC of 0.765/0.755/0.678 than RS-ADC (AUC = 0.706/0.608/0.630). For both of task 1 and task 2, RS-Com achieved the best performance with AUC of 0.793/0.778/0.760 and 0.820/0.776/0.711, respectively, and obtained higher clinical benefit in DCA compared with RS-T1CE and RS-ADC. The calibration curves of all RS demonstrated a good fitness.

DATA CONCLUSION

Multiparametric MRI radiomics could noninvasively and robustly distinguish HER2-positive from HER2-negative breast cancers and further distinguish HER2-low from HER2-negative breast cancers.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

The Peritumoral CD8 + /FOXP3 + Cell Ratio Has Prognostic Value in Triple-negative Breast Cancer

Compelling data has demonstrated the prognostic significance of tumor-infiltrating lymphocytes (TILs) in triple-negative breast cancer (TNBC), a subtype generally associated with a poor clinical outcome but highly heterogeneous in nature. There have been limited studies investigating the importance of subsets of T cells in TILs. Further, the significance of intratumoral versus peritumoral TILs remains controversial. We examined the prognostic value of tumor-associated CD8 + cytotoxic T cells and FOXP3 + regulatory T cells in 35 chemotherapy-naive TNBC cases with a tumor-host interface in the tissue sections. The CD8 + and FOXP3 + cell count was expressed by immunoreactive cells per high-power field in an average of 10 high-power fields. There was a wide range of CD8 + and FOXP3 + T cells within the peritumoral and intratumoral stroma. Both CD8 + and FOXP3 + TILs were significantly higher at the former location as compared with the latter ( P <0.0001 and 0.003, respectively). The numbers of CD8 + and FOXP3 + T cells, either within peritumoral or intratumoral stroma, were not significantly associated with distant relapse-free or disease-specific survival. However, the peritumoral CD8 + /FOXP3 + ratio of TILs was significantly associated with prolonged relapse-free survival ( P =0.04) and disease-specific survival ( P =0.02). This association was not observed with the CD8 + /FOXP3 + ratio of intratumoral TILs. These observations suggest that the immunologic balance in the tumor microenvironment might determine antitumor immunity. Further, the peritumoral TILs appear to play a more important role in the progression of TNBC when compared with the intratumoral TILs, thus reaffirming the necessity of revisiting the method for the assessment of TILs.

Quantitative Indicators of Retraction Phenomenon on an Automated Breast Volume Scanner: Initial Study in the Diagnosis and Prognostic Prediction of Breast Tumors

Retraction phenomenon is a unique sign on an automated breast volume scanner coronal plane image and has high specificity in differentiating benign lesions from malignant breast cancer. The purpose of this study was to quantify the retraction phenomenon by setting different rules to describe connected regions from different dimensions. In total, six quantitative indicators (F_Ω1,F_Γ,F_S,F_Ω2,F_Ω3and F_L) were obtained. F_Ω1, F_Ω2 and F_Ω3 represent the relative areas of the connected region under different rules. F_ΓandF_S represent the number ratio and absolute area of the connected region, respectively. F_L represents the ratio of edge numbers. Two hundred fourteen patients with 214 lesions (90 benign and 124 malignant) were enrolled in this study. All quantitative indicators in the malignant group were significantly higher than those in the benign group (all p values <0.001). The indicator F_Γ achieved the highest area under the receiver operating characteristic curve (AUC) (0.701, 95% confidence interval: 0.631-0.771). Both F_Γ and F_S had significant associations with axillary lymph node metastasis (p = 0.023 and 0.049). Compared with the classic texture feature gray-level co-occurrence matrix, retraction phenomenon quantization improved the AUC by 8.3%. The results indicate that retraction phenomenon quantitative indicators have certain value in distinguishing benign and malignant breast lesions and seem to be associated with axillary lymph node metastasis.

Duration of Tumor-infiltrating Lymphocytes Assessment with Significant Overall Survival Prognostic Value in Locally Advanced Breast Cancer

Background Tumor microenvironment, represented by tumor-infiltrating lymphocytes, was dynamically evolving throughout the time. Thus, in the interpretation of TIL as a prognostic factor overall survival of breast cancer, it is important to note the duration of the TIL examination until neoadjuvant chemotherapy is performed. Aim To determine whether the assessment of TIL at a certain duration still has value as a prognostic factor for the overall survival of breast cancer and its cut off point. Methods This was a retrospective cohort study that included subjects with locally advanced breast cancer in Sanglah General Hospital, registered in Bali’s Cancer Registry. The study has been approved by the institutional review board of Udayana University, Denpasar, Bali. Data collected were age, breast cancer subtype, value of TIL, time of NAC start, and duration of survival. Missing data were obtained from electronic medical records. TIL is then grouped into groups with low TIL (negative and +1) and high TIL (+2 and +3). Data analysis were done with Statistical Package for the Social Sciences 25.0. Results As many as 150 subjects with locally advanced breast cancer who survived and died in 2011-2020 were analyzed. The mean age of subjects in this study was 48.7 (SD 9.3) years with a median survival of 47 months. The mean duration of the TIL asessment to NAC was 46.3 days (SD 24.5). The duration cut off point of TIL assessment to NAC that is valuable as breast cancer’s overall prognostic value was 31 days (AUC 0.716, Sensitivity 64.1%, Specificity 35.5%). In subjects with TIL examined for less than 31 days, it was found that TIL could significantly prognosticated the overall survival of breast cancer (p = 0.005). High TIL was associated with better overall survival and low TIL is associated with poor overall survival. Discussion Tumor immune microenvironment played an important role in tumor progression and supression.  High levels of TIL has been generally accepted as an indicator for a more robust anti-tumor immune response, thus yielding favorable outcome. Timing of TIL analysis was important to determine as microenvironment dynamically progressing. In this study, we proved that timing matters and only short-term duration of TIL assessment to NAC meaningful as a prognostic value for breast cancer’s overall survival. Conclusion The duration of TIL to NAC assessment was important in determine the meaningful of TIL value in the prognostication overall survival of locally advanced breast cancer. The short-term TIL assessment duration (<31 days) predicted well, but not in the long term assessment duration. In the future, it is hoped that clinicians will be more critical in the interpretation of TIL, especially the duration of TIL assessment before NAC.

Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer

RATIONALE AND OBJECTIVES

Peritumoral features have been suggested to be useful in improving the prediction performance of radiomic models. The aim of this study is to systematically investigate the prediction performance improvement for sentinel lymph node (SLN) status in breast cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral region sizes.

MATERIALS AND METHODS

This retrospective study was performed using dynamic contrast-enhanced MRI scans of 162 breast cancer patients. The effect of peritumoral features was evaluated in a radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions were generated by dilating the tumor regions-of-interest (ROIs) manually annotated by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction models were established in the training set (∼67% of cases) using the radiomics pipeline with and without peritumoral features derived from different peritumoral thicknesses. The prediction performance was tested in an independent validation set (the remaining ∼33%).

RESULTS

For this specific application, the accuracy in the validation set when using the two radiologists' ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral features. The choice of the peritumoral size could affect the level of improvement.

CONCLUSION

This study systematically investigates the effect of peritumoral region sizes in radiomic analysis for prediction performance improvement. The choice of the peritumoral size is dependent on the ROI drawing and would affect the final prediction performance of radiomic models, suggesting that peritumoral features should be optimized in future radiomics studies.

Features from MRI texture analysis associated with survival outcomes in triple-negative breast cancer patients

PURPOSE

The purpose of the study is to evaluate the associations between intratumoral or peritumoral textural features derived from pretreatment magnetic resonance imaging (MRI) and recurrence-free survival (RFS) in triple-negative breast cancer (TNBC) patients.

METHODS

Forty-three patients with TNBC who underwent preoperative MRI between February 2008 and March 2014 were included. We performed two-dimensional texture analysis on the intratumoral or peritumoral region of interest (ROI) on axial of T2-weighted image (T2WI), dynamic contrast-enhanced (DCE)-MRI and DCE-MRI subtraction images. We also analyzed histopathological data. Cox proportional hazards models were used to investigate associations with survival outcomes.

RESULTS

Twelve of the 43 patients (27.9%) had recurrence disease, at a median of 32.5 months follow-up (1.4-61.5 months). In univariate analysis, nine texture features in T2WI and DCE-MRI subtraction images were significantly associated with RFS. In multivariate analysis, intratumoral difference entropy in DCE-MRI subtraction images in the initial phase (hazard ratio 11.71; 95% confidence interval (CI) [1.41, 97.00]; p value 0.023) and, peritumoral difference variance in DCE-MRI subtraction images in the delayed phase (hazard ratio 9.60; 95% CI [1.98, 46.51]; p value 0.005), were both independently associated with RFS. Moreover, multivariate analysis revealed the presence of lymphovascular invasion as independently associated with RFS (hazard ratio 8.13; 95% CI [2.16, 30.30]; p value 0.002).

CONCLUSIONS

At pretreatment MRI, an intratumoral and peritumoral quantitative approach using texture analysis has the potential to serve as a prognostic marker in patients with TNBC.

CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients

Breast cancer (BC) prognosis and therapeutic sensitivity could not be predicted efficiently. Previous evidence have shown the vital roles of CDKN1C in BC. Therefore, we aimed to construct a CDKN1C‐based model to accurately predicting overall survival (OS) and treatment responses in BC patients. In this study, 995 BC patients from The Cancer Genome Atlas database were selected. Kaplan‐Meier curve, Gene set enrichment and immune infiltrates analyses were executed. We developed a novel CDKN1C‐based nomogram to predict the OS, verified by the time‐dependent receiver operating characteristic curve, calibration curve and decision curve. Therapeutic response prediction was followed based on the low‐ and high‐nomogram score groups. Our results indicated that low‐CDKN1C expression was associated with shorter OS and lower proportion of naïve B cells, CD8 T cells, activated NK cells. The predictive accuracy of the nomogram for 5‐year OS was superior to the tumour‐node‐metastasis stage (area under the curve: 0.746 vs. 0.634, p < 0.001). The nomogram exhibited excellent predictive performance, calibration ability and clinical utility. Moreover, low‐risk patients were identified with stronger sensitivity to therapeutic agents. This tool can improve BC prognosis and therapeutic responses prediction, thus guiding individualized treatment decisions.

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer.

A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study

Background

Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal in clinical practice. We aimed to develop a deep learning-based model for treatment decision in NPC.

Methods

A total of 1872 patients with stage T3N1M0 NPC were enrolled from four Chinese centres and received either ICT+CCRT or CCRT. A nomogram was constructed for predicting the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images, based on which an optimal treatment regimen was recommended. Model performance was assessed by the concordance index (C-index) and the Kaplan-Meier estimator.

Findings

The nomogram showed excellent prognostic ability for disease-free survival in both the CCRT (C-index range: 0.888-0.921) and ICT+CCRT (C-index range: 0.784-0.830) groups. According to the prognostic difference between treatments using the nomogram, patients were divided into the ICT-preferred and CCRT-preferred groups. In the ICT-preferred group, patients receiving ICT+CCRT exhibited prolonged survival over those receiving CCRT in the internal and external test cohorts (hazard ratio [HR]: 0.17, p<0.001 and 0.24, p=0.02); while the trend was opposite in the CCRT-preferred group (HR: 6.24, p<0.001 and 12.08, p<0.001). Similar results for treatment decision using the nomogram were obtained in different subgroups stratified by clinical factors and MR acquisition parameters.

Interpretation

Our nomogram could predict the prognosis of T3N1M0 NPC patients with different treatment regimens and accordingly recommend an optimal treatment regimen, which may serve as a potential tool for promoting personalized treatment of NPC.

Funding

National Key R&D Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, Strategic Priority Research Program of CAS, Project of High-Level Talents Team Introduction in Zhuhai City, Beijing Natural Science Foundation, Beijing Nova Program, Youth Innovation Promotion Association CAS.

Prognostic value of tumour-infiltrating lymphocytes based on the evaluation of frequency in patients with oestrogen receptor-positive breast cancer

PURPOSE

We investigate the prognostic value of tumour-infiltrating lymphocytes (TILs) based on the evaluation of the present frequency in patients with breast cancer rather than that of the density proposed in previous research.

METHODS

Multiphoton microscopy (MPM) was introduced to label-freely obtain TIL images from a total of 564 patients, and then TILs were redefined as TILs-1 to TILs-3 from MPM images according to the relative positions between TILs, tumour cells and collagen fibres. More seminally, a new method, which was based on the present frequency of TILs-1 to TILs-3, was presented for assessing the predictive ability of TILs, and then a tumour-infiltrating lymphocytes score (TILs-score) for each patient was obtained by ridge regression analysis.

RESULTS

Data results from Cox proportional hazards regression analysis showed that the TILs-score was an independent prognostic factor for both disease-free survival (DFS) and overall survival (OS) in the complete cohort (n = 564), oestrogen receptor (ER)-positive subgroup (n = 352) and ER-negative subgroup (n = 212), but was more suitable for the ER-positive subgroup. Furthermore, the nomogram model combining the TILs-score with independent clinical factors further improved the predictive ability for the ER-positive subgroup: area under the curve (AUC) at 5-year DFS (OS) and hazard ratio (HR) for DFS (OS) in the training cohort increase from 0.735 (0.785) to 0.814 (0.830) and from 3.156 (5.845) to 4.643 (7.006), respectively, and in the validation cohort from 0.749 (0.748) to 0.804 (0.830) and from 3.104 (3.701) to 3.729 (5.132), respectively.

CONCLUSION

The TILs-score is an independent prognostic factor and displays a strong prognostic value for ER-positive breast cancer. To our knowledge, this is the first time to use MPM for studying the prognostic value of TILs in breast cancer.

Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions

OBJECTIVE

To conduct perilesional region radiomics analysis of contrast-enhanced mammography (CEM) images to differentiate benign and malignant breast lesions.

METHODS AND MATERIALS

This retrospective study included patients who underwent CEM from November 2017 to February 2020. Lesion contours were manually delineated. Perilesional regions were automatically obtained. Seven regions of interest (ROIs) were obtained for each lesion, including the lesion ROI, annular perilesional ROIs (1 mm, 3 mm, 5 mm), and lesion + perilesional ROIs (1 mm, 3 mm, 5 mm). Overall, 4,098 radiomics features were extracted from each ROI. Datasets were divided into training and testing sets (1:1). Seven classification models using features from the seven ROIs were constructed using LASSO regression. Model performance was assessed by the AUC with 95% CI.

RESULTS

Overall, 190 women with 223 breast lesions (101 benign; 122 malignant) were enrolled. In the testing set, the annular perilesional ROI of 3-mm model showed the highest AUC of 0.930 (95% CI: 0.882-0.977), followed by the annular perilesional ROI of 1 mm model (AUC = 0.929; 95% CI: 0.881-0.978) and the lesion ROI model (AUC = 0.909; 95% CI: 0.857-0.961). A new model was generated by combining the predicted probabilities of the lesion ROI and annular perilesional ROI of 3-mm models, which achieved a higher AUC in the testing set (AUC = 0.940).

CONCLUSIONS

Annular perilesional radiomics analysis of CEM images is useful for diagnosing breast cancers. Adding annular perilesional information to the radiomics model built on the lesion information may improve the diagnostic performance.

KEY POINTS

• Radiomics analysis of the annular perilesional region of 3 mm in CEM images may provide valuable information for the differential diagnosis of benign and malignant breast lesions. • The radiomics information from the lesion region and the annular perilesional region may be complementary. Combining the predicted probabilities of the models constructed by the features from the two regions may improve the diagnostic performance of radiomics models.

Intratumoral and Peritumoral Radiomics Based on Functional Parametric Maps from Breast DCE-MRI for Prediction of HER-2 and Ki-67 Status

BACKGROUND

Radiomics has been applied to breast magnetic resonance imaging (MRI) for gene status prediction. However, the features of peritumoral regions were not thoroughly investigated.

PURPOSE

To evaluate the use of intratumoral and peritumoral regions from functional parametric maps based on breast dynamic contrast-enhanced MRI (DCE-MRI) for prediction of HER-2 and Ki-67 status.

STUDY TYPE

Retrospective.

POPULATION

A total of 351 female patients (average age, 51 years) with pathologically confirmed breast cancer were assigned to the training (n = 243) and validation (n = 108) cohorts.

FIELD STRENGTH/SEQUENCE

3.0T, T₁ gradient echo.

ASSESSMENT

Radiomic features were extracted from intratumoral and peritumoral regions on six functional parametric maps calculated using time-intensity curves of DCE-MRI. The intraclass correlation coefficients (ICCs) were used to determine the reproducibility of feature extraction. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three radiomics signatures (RSs) were built using the least absolute shrinkage and selection operator (LASSO) logistic regression model, respectively.

STATISTICAL TESTS

Wilcoxon rank-sum test, minimum redundancy maximum relevance, LASSO, receiver operating characteristic curve (ROC) analysis, and DeLong test.

RESULTS

The intratumoral and peritumoral RSs for prediction of HER-2 and Ki-67 status achieved areas under the ROC (AUCs) of 0.683 (95% confidence interval [CI], 0.574-0.793) and 0.690 (95% CI, 0.577-0.804), and 0.714 (95% CI, 0.616-0.812) and 0.692 (95% CI, 0.590-0.794) in the validation cohort, respectively. The combined RSs yielded AUCs of 0.713 (95% CI, 0.604-0.823) and 0.749 (95% CI, 0.656-0.841), respectively. There were no significant differences in prediction performance among intratumoral, peritumoral, and combined RSs. Most (69.7%) of the features had good agreement (ICCs >0.8).

DATA CONCLUSION

Radiomic features of intratumoral and peritumoral regions on functional parametric maps based on breast DCE-MRI had the potential to identify HER-2 and Ki-67 status.

LEVEL OF EVIDENCE

3 Technical Efficacy Stage: 2.

Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT

INTRODUCTION

The purpose of this study was to predict pathologic complete response (pCR) to neoadjuvant therapy in breast cancer using radiomics based on pretreatment staging contrast-enhanced computed tomography (CECT).

PATIENTS AND METHODS

A total of 215 patients were retrospectively analyzed. Based on the intratumoral and peritumoral regions of CECT images, radiomic features were extracted and selected, respectively, to develop an intratumoral signature and a peritumoral signature with logistic regression in a training dataset (138 patients from November 2015 to October 2017). We also developed a clinical model with the molecular characterization of the tumor. A radiomic nomogram was further constructed by incorporating the intratumoral and peritumoral signatures with molecular characterization. The performance of the nomogram was validated in terms of discrimination, calibration, and clinical utility in an independent validation dataset (77 patients from November 2017 to December 2018). Stratified analysis was performed to develop a subtype-specific radiomic signature for each subgroup.

RESULTS

Compared with the clinical model (area under the curve [AUC], 0.756), the radiomic nomogram (AUC, 0.818) achieved better performance for pCR prediction in the validation dataset with continuous net reclassification improvement of 0.787 and good calibration. Decision curve analysis suggested the nomogram was clinically useful. Subtype-specific radiomic signatures showed improved AUCs (luminal subgroup, 0.936; human epidermal growth factor receptor 2-positive subgroup, 0.825; and triple negative subgroup, 0.858) for pCR prediction.

CONCLUSION

This study has revealed a predictive value of pretreatment staging-CECT and successfully developed and validated a radiomic nomogram for individualized prediction of pCR to neoadjuvant therapy in breast cancer, which could assist clinical decision-making and improve patient outcome.

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction.

Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models.

Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models.

Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Tumor-Infiltrating Lymphocytes in Breast Cancer: Evaluating Interobserver Variability, Heterogeneity, and Fidelity of Scoring Core Biopsies

OBJECTIVES

The aims were to evaluate breast cancer (BC) tumor-infiltrating lymphocytes (TILs) interobserver variability, heterogeneity, and the fidelity of scoring TILs in core needle biopsy (CNB).

METHODS

Matching CNB and two full-face sections (FFSs) of BC cases (n = 100) were independently reviewed by two pathologists. Percentage of stromal lymphocytes (TIL-str) and intratumoral lymphocytes (iTu-Ly) were recorded.

RESULTS

The weighted κ values for the degree of agreement between both raters were 0.53 to 0.71. However, there was a slight improvement in the interobserver variability for TIL-str and slight decline in iTu-Ly when ER+/HER2- cases were excluded. The intraclass correlation coefficient for FFS1 vs FFS2 was 0.91 for TIL-str and 0.96 for iTu-Ly. Spearman correlation coefficient for CNB vs FFS1/FFS2 was 0.81 for TIL-str and 0.79 for iTu-Ly.

CONCLUSIONS

We conclude that the agreement in TILs scoring between the raters is acceptable. Caution should be practiced when scoring iTu-Ly in CNBs.

Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI

BACKGROUND

Sentinel lymph node (SLN) status is an important prognostic factor for patients with breast cancer, which is currently determined in clinical practice by invasive SLN biopsy.

PURPOSE

To noninvasively predict SLN metastasis in breast cancer using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) intra- and peritumoral radiomics features combined with or without clinicopathologic characteristics of the primary tumor.

STUDY TYPE

Retrospective.

POPULATION

A total of 163 breast cancer patients (55 positive SLN and 108 negative SLN).

FIELD STRENGTH/SEQUENCE

1.5T, T₁ -weighted DCE-MRI.

ASSESSMENT

A total of 590 radiomic features were extracted for each patient from both intratumoral and peritumoral regions of interest. To avoid overfitting, the dataset was randomly separated into a training set (∼67%) and a validation set (∼33%). The prediction models were built with the training set using logistic regression on the most significant radiomic features in the training set combined with or without clinicopathologic characteristics. The prediction performance was further evaluated in the independent validation set.

STATISTICAL TESTS

Mann-Whitney U-test, Spearman correlation, least absolute shrinkage selection operator (LASSO) regression, logistic regression, and receiver operating characteristic (ROC) analysis were performed.

RESULTS

Combining radiomic features with clinicopathologic characteristics, six features were automatically selected in the training set to establish the prediction model of SLN metastasis. In the independent validation set, the area under ROC curve (AUC) was 0.869 (NPV = 0.886). Using radiomic features alone in the same procedure, 4 features were selected and the validation set AUC was 0.806 (NPV = 0.824).

DATA CONCLUSION

This is the first attempt to demonstrate the feasibility of using DCE-MRI radiomics to predict SLN metastasis in breast cancer. Clinicopathologic characteristics improved the prediction performance. This study provides noninvasive methods to evaluate SLN status for guiding further treatment of breast cancer patients, and can potentially benefit those with negative SLN, by eliminating unnecessary invasive lymph node removal and the associated complications, which is a step further towards precision medicine.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:131-140.

Evaluation of the prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers

Tumor-infiltrating lymphocytes (TILs) may be associated with clinical outcome in triple-negative breast cancers (TNBCs). However, lacking of standardized methodologies in TILs evaluation has hindered its application in clinical practice. To evaluate the prognostic role of TILs scored by methods recommended by International TILs Working Group 2014, we performed a retrospective study of TILs in 425 primary invasive TNBCs in a Chinese population with a median follow-up of 4 years. Intratumoral TILs (iTILs) and stromal TILs (sTILs) were scored respectively. The associations between TILs and disease-free survival (DFS), distant disease-free survival (DDFS) and overall survival (OS) were evaluated with COX models. ITILs were not associated with prognosis. Higher sTILs were associated with better prognosis; for every 10% increase in sTILs, a 5% reduction of risk of recurrence or death (P < 0.001), 5% reduction of risk of distant recurrence (P < 0.001), and 4% reduction of risk of death (P = 0.002) were observed. Multivariate analysis confirmed sTILs to be an independent prognostic marker. 3.5% of TNBCs had more than 50% lymphocytes (lymphocyte-predominant breast cancer, LPBC), and associations between LPBC status and prognosis were observed but did not reach statistical significance. TNBCs with more than 20% sTILs had a significantly better prognosis than the patients with no more than 20% sTILs. In conclusion, our study indicated that sTILs scored by methods recommended by International TILs Working Group 2014 were associated with the prognosis of TNBCs. STILs could be an independent prognostic biomarker in TNBCs, increasing sTILs predicting better prognosis.

The CD4/CD8 ratio of tumor-infiltrating lymphocytes at the tumor-host interface has prognostic value in triple-negative breast cancer

Compelling evidence has demonstrated the prognostic value of tumor-infiltrating lymphocytes (TILs), especially in triple-negative breast cancer (TNBC). However, only a limited number of studies to investigate the importance of the subsets of T cells in TILs have been carried out, less so the significance of the location of these TILs. In this study, we explored in a cohort of 42 consecutive TNBC cases the prognostic significance of TIL subsets at the tumor-host interface (within 1 high-power field [0.5 mm] of the invasive front) and compared them with TILs within the intratumoral stroma. Given the reported importance of TILs in HER2-overexpressing breast cancer, a subset of such tumors was also included for comparison. The range was wide in both locations; nevertheless, the mean CD4⁺ and CD8⁺ T cell count was significantly higher at the tumor-host interface than that found within the intratumoral stroma (both P<.0001). The number of CD4⁺ or CD8⁺ T cells at either location was not significantly associated with distant relapse-free or overall survival. However, the CD4/CD8 ratio at the tumor-host interface was significantly associated with both relapse-free survival (hazard ratio 0.2, P=.002) and overall survival (hazard ratio 0.13, P=.002), whereas this association was not seen for the CD4/CD8 ratio within the intratumoral stroma. As expected, both tumor size and nodal status were significantly associated with survival outcomes. The findings further support the contention that TILs, as markers of regional immune escape, are of prognostic importance in TNBC progression and that the CD4/CD8 ratio of TILs at the tumor-host interface plays a distinctive role, thus appearing to be of clinical relevance.

The prognostic value of pretreatment neutrophil-to-lymphocyte ratio in breast cancer: Deleterious or advantageous?

Breast cancer is one of the leading malignant tumors that endanger women's health worldwide. Despite the rapid progress on the therapies, including chemotherapy, surgical resection, and other auxiliary methods, there were still numerous people died of breast cancer, which promoted the researchers to concentrate on the prognostic factor of breast cancer. In recent years, an increasing number of studies have been focused on the prognostic value of pretreatment neutrophil-to-lymphocyte ratio in breast cancer. This article is a brief review of the associations between neutrophil-to-lymphocyte ratio and the prognosis of breast cancer patients, which may give a greater insight into the development of breast cancer and enable clinicians to cure it completely.

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

Background

In this study, we evaluated the ability of radiomic textural analysis of intratumoral and peritumoral regions on pretreatment breast cancer dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).

Methods

A total of 117 patients who had received NAC were retrospectively analyzed. Within the intratumoral and peritumoral regions of T1-weighted contrast-enhanced MRI scans, a total of 99 radiomic textural features were computed at multiple phases. Feature selection was used to identify a set of top pCR-associated features from within a training set (n = 78), which were then used to train multiple machine learning classifiers to predict the likelihood of pCR for a given patient. Classifiers were then independently tested on 39 patients. Experiments were repeated separately among hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR⁺, HER2⁻) and triple-negative or HER2⁺ (TN/HER2⁺) tumors via threefold cross-validation to determine whether receptor status-specific analysis could improve classification performance.

Results

Among all patients, a combined intratumoral and peritumoral radiomic feature set yielded a maximum AUC of 0.78 ± 0.030 within the training set and 0.74 within the independent testing set using a diagonal linear discriminant analysis (DLDA) classifier. Receptor status-specific feature discovery and classification enabled improved prediction of pCR, yielding maximum AUCs of 0.83 ± 0.025 within the HR⁺, HER2⁻ group using DLDA and 0.93 ± 0.018 within the TN/HER2⁺ group using a naive Bayes classifier. In HR⁺, HER2⁻ breast cancers, non-pCR was characterized by elevated peritumoral heterogeneity during initial contrast enhancement. However, TN/HER2⁺ tumors were best characterized by a speckled enhancement pattern within the peritumoral region of nonresponders. Radiomic features were found to strongly predict pCR independent of choice of classifier, suggesting their robustness as response predictors.

Conclusions

Through a combined intratumoral and peritumoral radiomics approach, we could successfully predict pCR to NAC from pretreatment breast DCE-MRI, both with and without a priori knowledge of receptor status. Further, our findings suggest that the radiomic features most predictive of response vary across different receptor subtypes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0846-1) contains supplementary material, which is available to authorized users.

Transcriptomic immunologic signature associated with favorable clinical outcome in basal-like breast tumors

Background

Most patients with early stage triple negative breast cancer (TNBC) receive adjuvant chemotherapy. Activation of the immune system is associated with tumor response and may help identify TNBC with favorable outcome.

Methods

Gene expression data were obtained from the GEO Dataset GDS2250/GSE3744. Affymetrix CEL files were downloaded and analyzed with Affymetrix Transcriptome Analysis Console 3.0. Functional genomics was implemented with David Bioinformatics Resources 6.8. Data contained at Oncomine were used to identify genes upregulated in basal-like cancer compared to normal breast tissue. Data contained at cBioportal were used to assess for molecular alterations. The KMPlotter online tool, METABRIC and GSE25066 datasets were used to associate gene signatures with clinical outcome.

Results

1564 upregulated genes were identified as differentially expressed between normal and basal-like tumors. Of these, 16 genes associated with immune function were linked with clinical outcome. HLA-C, HLA-F, HLA-G and TIGIT were associated with both improved relapse-free survival (RFS) and overall survival (OS). The combination of HLA-F/TIGIT and HLA-C/HLA-F/TIGIT showed the most favorable outcome (HR for RFS 0.44, p<0.001; HR for OS 0.22, p<0.001; and HR for RFS 0.46, p<0.001; HR for OS 0.15, p<0.001; respectively). The association of HLA-C/HLA-F with outcome was confirmed using the METABRIC and GSE25066 datasets. No copy number alterations of these genes were identified.

Conclusion

We describe a gene signature associated with immune function and favorable outcome in basal-like breast cancer. Incorporation of this signature in prospective studies may help to stratify risk of early stage TNBC.

Targeting oncogenic vulnerabilities in triple negative breast cancer: biological bases and ongoing clinical studies

Triple negative breast cancer (TNBC) is still an incurable disease despite the great scientific effort performed during the last years. The huge heterogeneity of this disease has motivated the evaluation of a great number of therapies against different molecular alterations. In this article, we review the biological bases of this entity and how the known molecular evidence supports the current preclinical and clinical development of new therapies. Special attention will be given to ongoing clinical studies and potential options for future drug combinations.

Metastatic human epidermal growth factor receptor 2-positive breast cancer: Management, challenges, and future directions

HER2 is over-expressed or amplified in 15-20% of breast cancer. Significant progress has been made in the treatment of metastatic HER2+ breast cancer. This is largely due to successful targeting of the HER2 pathway. There are several approved agents in the metastatic setting. However, treatment resistance frequently develops and tumors eventually progress. In recent years, our understanding of mechanisms of resistance has evolved. It is generally accepted now that HER2-positive breast cancer is not one disease. New therapeutic strategies and a tailored approach to management are necessary to maximize patient outcomes and minimize toxicity.

Increased CD4 and CD8-positive T cell infiltrate signifies good prognosis in a subset of triple-negative breast cancer

Tumour-infiltrating lymphocytes (TILs) signify immune response to tumour in a variety of cancers including breast cancer. However, earlier studies examining the clinical significance of TILs in breast cancers have generated mixed results. There are only a few that address the relationship between TILs and clinical outcomes in triple-negative breast cancers (TNBC). The aim of this study is to evaluate the clinical significance of TILs that express CD4 + and CD8 + , in TNBC. Immunohistochemical staining of CD4 and CD8 was performed on tissue microarrays of 164 cases of TNBC. TILs were counted separately as intratumoral when within the cancer cell nests (iTILs) and as stromal when within cancer stroma (sTILs). High CD8 + iTILs and sTILs, and CD4 + iTILs correlated with histologic grade. On Kaplan-Meier analysis, a significantly better survival rate was observed in high CD8 + iTIL (disease-free survival, DFS: P = 0.004, overall survival, OS: P = 0.02) and both high CD4 + iTILs (DFS: P = 0.025, OS: P = 0.023) and sTILs (DFS: P = 0.01, OS: P = 0.002). In multivariate analysis, CD8 + iTILs (DFS: P = 0.0095), CD4 + sTILs (DFS: P = 0.0084; OS: P = 0.0118), and CD4 (high) CD8 (high) CD8 iTILs (DFS: P = 0.0121; OS: P = 0.0329) and sTILs (DFS: P = 0.0295) showed significantly better survival outcomes. These results suggest that high levels of both CD8 + iTILs and CD4 + sTILs as well as CD4 (high) CD8 (high) iTILs and sTILs are independent prognostic factors in TNBC.

Computational pathology of pre-treatment biopsies identifies lymphocyte density as a predictor of response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

There is a need to improve prediction of response to chemotherapy in breast cancer in order to improve clinical management and this may be achieved by harnessing computational metrics of tissue pathology. We investigated the association between quantitative image metrics derived from computational analysis of digital pathology slides and response to chemotherapy in women with breast cancer who received neoadjuvant chemotherapy.

METHODS

We digitised tissue sections of both diagnostic and surgical samples of breast tumours from 768 patients enrolled in the Neo-tAnGo randomized controlled trial. We subjected digital images to systematic analysis optimised for detection of single cells. Machine-learning methods were used to classify cells as cancer, stromal or lymphocyte and we computed estimates of absolute numbers, relative fractions and cell densities using these data. Pathological complete response (pCR), a histological indicator of chemotherapy response, was the primary endpoint. Fifteen image metrics were tested for their association with pCR using univariate and multivariate logistic regression.

RESULTS

Median lymphocyte density proved most strongly associated with pCR on univariate analysis (OR 4.46, 95 % CI 2.34-8.50, p < 0.0001; observations = 614) and on multivariate analysis (OR 2.42, 95 % CI 1.08-5.40, p = 0.03; observations = 406) after adjustment for clinical factors. Further exploratory analyses revealed that in approximately one quarter of cases there was an increase in lymphocyte density in the tumour removed at surgery compared to diagnostic biopsies. A reduction in lymphocyte density at surgery was strongly associated with pCR (OR 0.28, 95 % CI 0.17-0.47, p < 0.0001; observations = 553).

CONCLUSIONS

A data-driven analysis of computational pathology reveals lymphocyte density as an independent predictor of pCR. Paradoxically an increase in lymphocyte density, following exposure to chemotherapy, is associated with a lack of pCR. Computational pathology can provide objective, quantitative and reproducible tissue metrics and represents a viable means of outcome prediction in breast cancer.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00070278 ; 03/10/2003.

Breast cancer stem cells phenotype and plasma cell-predominant breast cancer independently indicate poor survival

PURPOSE

Cancer stem cell-tumor microenvironment ecosystem is proposed to drive tumor heterogeneity. Tumor-infiltrating lymphocytes (TILs) in breast cancer ecosystem were demonstrated to indicate better prognosis and benefit from chemotherapy. This study sought to detect the association between breast cancer stem cells and TILs.

METHODS

92 patients with breast cancer were enrolled. Matched cancerous and paracancerous tissues were assembled in a tissue microarray and immunohistochemistry was employed to test expression of breast cancer stem cells (BCSCs) markers. TILs counts were estimated with global hematoxylin-eosin staining. The association between TILs and BCSCs phenotypes was analysed by multivariate analysis.

RESULTS

Although it was unable to find direct significant association between BCSCs phenotypes and TILs, the BCSCs phenotype with CD44(+)CD24(-)ALDH1A1(+)EpCAM(+)CD49f(+) was proved to be associated with worse DFS and OS (P=0.037 and 0.001). This result was confirmed by cox proportional-hazards regression model (for DFS and OS respectively, HR=2.438 and 3.383, P=0.019 [95%CI 1.418-3.457] and 0.025 [95%CI 1.162-9.843]). Additionally, in results of TILs, plasma cell-predominant breast cancer (PPBC) was unexpectedly found to indicate worse OS and HR was 2.686 (P=0.038 [95%CI 1.582-3.789]).

CONCLUSIONS

The BCSCs phenotype and PPBC may be helpful stratified factors in future clinical trials. The underlying mechanism needs further research.