Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients

Abstract

The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator. Methods: We observed five large-scale tumor-associated collagen signatures (TACS4-8) obtained by multiphoton microscopy at the invasion front of the breast primary tumor, which contrasted with the three tumor-associated collagen signatures (TACS1-3) discovered by Keely and coworkers at a smaller scale. Highly concordant TACS1-8 classifications were obtained by three independent observers. Using the ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8 and established a risk prediction model based on the TACS-score. In a blind fashion, consistent retrospective prognosis was obtained from 995 breast cancer patients in both a training cohort ( n = 431) and an internal validation cohort ( n = 300) collected from one clinical center, and in an external validation cohort ( n = 264) collected from a different clinical center. Results: TACS1-8 model alone competed favorably with all reported models in predicting disease-free survival (AUC: 0.838, [0.800-0.872]; 0.827, [0.779-0.868]; 0.807, [0.754-0.853] in the three cohorts) and stratifying low- and high-risk patients (HR 7.032, [4.869-10.158]; 6.846, [4.370-10.726], 4.423, [2.917-6.708]). The combination of these factors with the TACS-score into a nomogram model further improved the prognosis (AUC: 0.865, [0.829-0.896]; 0.861, [0.816-0.898]; 0.854, [0.805-0.894]; HR 7.882, [5.487-11.323]; 9.176, [5.683-14.816], and 5.548, [3.705-8.307]). The nomogram identified 72 of 357 (~20%) patients with unsuccessful 5-year disease-free survival that might have been undertreated postoperatively. Conclusions: The risk prediction model based on TACS1-8 considerably outperforms the contextual clinical model and may thus convince pathologists to pursue a TACS-based breast cancer prognosis. Our methodology identifies a significant portion of patients susceptible to undertreatment (high-risk patients), in contrast to the multigene assays that often strive to mitigate overtreatment. The compatibility of our methodology with standard histology using traditional (non-tissue-microarray) formalin-fixed paraffin-embedded (FFPE) tissue sections could simplify subsequent clinical translation.

Machine-Learning–Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance

“…6 An abundance of explicit collagen fiber metrics (eg, density, orientation, alignment, individual fiber properties, texturebased fiber patterns, fiber network branching, and relationships of the fibers and tumor cells) have been demonstrated to provide diagnostic and prognostic value in various tumors. 40 However, despite the accumulated evidence of the informative value of both explicit and implicit collagen microarchitecture, clinical adoption of the models is delayed by the lack of affordable high-capacity workflows of histology processing, imaging, and computation. Recently, computational methods for extracting collagen characteristics from routine H&E sections were proposed by Keikhosravi et al, 41 who trained a convolutional neural network model on SHG data to synthesize SHG images from H&E images.…”

Characterizing the Invasive Tumor Front of Aggressive Uterine Adenocarcinoma and Leiomyosarcoma

“…This new associated ECM compartment, rich in cross-linked collagen III (reticular fibers), has been proposed as a prospective marker of early stromal invasion in incipient tumors such as breast cancer ( Sivridis et al, 2004 ; Acerbi et al, 2015 ). In addition, not only the ECM collagen composition but also the orientation of the fibers have been proposed as a prognostic signature for survival in breast cancer ( Conklin et al, 2011 ; Xi et al, 2021 ).…”

Single-Cell Proteomic Analysis Dissects the Complexity of Tumor Microenvironment in Muscle Invasive Bladder Cancer

“…The collagen fibers enriched in the type III region were directionally distributed, which may contribute to the unidirectional migration of tumor cells. In the type IV region, chaotically aligned collagen fibers were enriched; moreover, as these collagen fibers may contribute to the multidirectional metastasis of tumor cells, the tumor was indicated to be malignant [66,67]. Collectively, the results from the present study suggest that the MIBC TME is highly complex and comprises diverse phenotypes.…”