Quantitative stain-free imaging and digital profiling of collagen structure reveal diverse survival of triple negative breast cancer patients

Stromal Type I Collagen in Breast Cancer: Correlation to Prognostic Biomarkers and Prediction of Chemotherapy Response

INTRODUCTION

Fibrillar collagens accumulate in the breast cancer stroma and appear as poorly defined spiculated masses in mammography imaging. The prognostic value of tissue type I collagen remains elusive in treatment-naïve and chemotherapy-treated breast cancer patients. Here, type I collagen mRNA and protein expression were analysed in 2 large independent breast cancer cohorts. Levels were related to clinicopathological parameters, prognostic biomarkers, and outcome.

METHOD

COL1A1 mRNA expression was analysed in 2509 patients with breast cancer obtained from the cBioPortal database. Type I collagen protein expression was studied by immunohistochemistry in 1395 women diagnosed with early invasive breast cancer.

RESULTS

Low COL1A1 mRNA and protein levels correlated with poor prognosis features, such as hormone receptor negativity, high histological grade, triple-negative subtype, node positivity, and tumour size. In unadjusted analysis, high stromal type I collagen protein expression was associated with improved overall survival (OS) (HR = 0.78, 95% CI = 0.61-0.99, p = .043) and trended towards improved breast cancer-specific survival (BCSS) (HR = 0.65, 95% CI = 0.42-1.01, P = 0.053), although these findings were lost after adjustment for other clinical variables. In unadjusted analysis, high expression of type I collagen was associated with better OS (HR = 0.70, 95% CI = 0.55-0.90, P = .006) and BCSS (HR = 0.55, 95% CI = 0.34-0.88, P = .014) among patients not receiving chemotherapy. Strikingly, the opposite was observed among patients receiving chemotherapy. There, high expression of type I collagen was instead associated with worse OS (HR = 1.83, 95% CI = 0.65-5.14, P = .25) and BCSS (HR = 1.72, 95% CI = 0.54-5.50, P = .357).

CONCLUSION

Low stromal type I collagen mRNA and protein expression are associated with unfavourable tumour characteristics in breast cancer. Stromal type I collagen might predict chemotherapy response.

Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis

PURPOSE

Collagen features in breast tumor microenvironment is closely associated with the prognosis of patients. We aim to explore the prognostic significance of collagen features at breast tumor border by combining multiphoton imaging and imaging analysis.

METHODS

We used multiphoton microscopy (MPM) to label-freely image human breast tumor samples and then constructed an automatic classification model based on deep learning to identify collagen signatures from multiphoton images. We recognized three kinds of collagen signatures at tumor boundary (CSTB I-III) in a small-scale, and furthermore obtained a CSTB score for each patient based on the combined CSTB I-III by using the ridge regression analysis. The prognostic performance of CSTB score is assessed by the area under the receiver operating characteristic curve (AUC), Cox proportional hazard regression analysis, as well as Kaplan-Meier survival analysis.

RESULTS

As an independent prognostic factor, statistical results reveal that the prognostic performance of CSTB score is better than that of the clinical model combining three independent prognostic indicators, molecular subtype, tumor size, and lymph nodal metastasis (AUC, Training dataset: 0.773 vs. 0.749; External validation: 0.753 vs. 0.724; HR, Training dataset: 4.18 vs. 3.92; External validation: 4.98 vs. 4.16), and as an auxiliary indicator, it can greatly improve the accuracy of prognostic prediction. And furthermore, a nomogram combining the CSTB score with the clinical model is established for prognosis prediction and clinical decision making.

CONCLUSION

This standardized and automated imaging prognosticator may convince pathologists to adopt it as a prognostic factor, thereby customizing more effective treatment plans for patients.

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning

Prostate carcinoma, a slow-growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non-neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.

Nonlinear microscopy and deep learning classification for mammary gland microenvironment studies

Tumors, their microenvironment, and the mechanisms by which collagen morphology changes throughout cancer progression have recently been a topic of interest. Second harmonic generation (SHG) and polarization second harmonic (P-SHG) microscopy are label-free, hallmark methods that can highlight this alteration in the extracellular matrix (ECM). This article uses automated sample scanning SHG and P-SHG microscopy to investigate ECM deposition associated with tumors residing in the mammary gland. We show two different analysis approaches using the acquired images to distinguish collagen fibrillar orientation changes in the ECM. Lastly, we apply a supervised deep-learning model to classify naïve and tumor-bearing mammary gland SHG images. We benchmark the trained model using transfer learning with the well-known MobileNetV2 architecture. By fine-tuning the different parameters of these models, we show a trained deep-learning model that suits such a small dataset with 73% accuracy.

Quantitative image-based collagen structural features predict the reversibility of hepatitis C virus-induced liver fibrosis post antiviral therapies

The novel targeted therapeutics for hepatitis C virus (HCV) in last decade solved most of the clinical needs for this disease. However, despite antiviral therapies resulting in sustained virologic response (SVR), a challenge remains where the stage of liver fibrosis in some patients remains unchanged or even worsens, with a higher risk of cirrhosis, known as the irreversible group. In this study, we provided novel tissue level collagen structural insight into early prediction of irreversible cases via image based computational analysis with a paired data cohort (of pre- and post-SVR) following direct-acting-antiviral (DAA)-based treatment. Two Photon Excitation and Second Harmonic Generation microscopy was used to image paired biopsies from 57 HCV patients and a fully automated digital collagen profiling platform was developed. In total, 41 digital image-based features were profiled where four key features were discovered to be strongly associated with fibrosis reversibility. The data was validated for prognostic value by prototyping predictive models based on two selected features: Collagen Area Ratio and Collagen Fiber Straightness. We concluded that collagen aggregation pattern and collagen thickness are strong indicators of liver fibrosis reversibility. These findings provide the potential implications of collagen structural features from DAA-based treatment and paves the way for a more comprehensive early prediction of reversibility using pre-SVR biopsy samples to enhance timely medical interventions and therapeutic strategies. Our findings on DAA-based treatment further contribute to the understanding of underline governing mechanism and knowledge base of structural morphology in which the future non-invasive prediction solution can be built upon.

Detection of pathological response of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer using multiphoton microscopy

Neoadjuvant treatment is often considered in breast cancer patients with axillary lymph node involvement, but most of patients do not have a pathologic complete response to therapy. The detection of residual nodal disease has a significant impact on adjuvant therapy recommendations which may improve survival. Here, we investigate whether multiphoton microscopy (MPM) could identify the pathological changes of axillary lymphatic metastasis after neoadjuvant chemotherapy in breast cancer. And furthermore, we find that there are obvious differences in seven collagen morphological features between normal node and residual axillary disease by combining with a semi-automatic image processing method, and also find that there are significant differences in four collagen features between the effective and no-response treatment groups. These research results indicate that MPM may help estimate axillary treatment response in the neoadjuvant setting and thereby tailor more appropriate and personalized adjuvant treatments for breast cancer patients.

Label-free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy

Invasive micropapillary carcinoma of the breast (IMPC) is a rare form of breast cancer with unique histological features, and is associated with high axillary lymph node metastasis and poor clinical prognosis. Thus, IMPC should be diagnosed in time to improve the treatment and management of patients. In this study, multiphoton microscopy (MPM) is used to label-free visualize the morphological features of IMPC. Our results demonstrate that MPM images are well in agreement with hematoxylin and eosin staining and epithelial membrane antigen staining, indicating MPM is comparable to traditional histological analysis in identifying the tissue structure and cell morphology. Statistical analysis shows significant differences in the circumference and area of the glandular lumen and cancer nest between the different IMPC cell clusters with complete glandular lumen morphology, and also shows difference in collagen length, width, and orientation, indicating the invasive ability of different morphologies of IMPC may be different.

Early Triple-Negative Breast Cancers in a Singapore Cohort Exhibit High PIK3CA Mutation Rates Associated With Low PD-L1 Expression

Mutations in the PI3K pathway, particularly PIK3CA, were reported to be intimately associated with triple-negative breast cancer (TNBC) progression and the development of treatment resistance. We profiled PIK3CA and other genes on 166 early-stage TNBC tumors from Singapore for comparison to publicly available TNBC cohorts. These tumors were profiled transcriptionally using a NanoString panel of immune genes and multiplex immunohistochemistry, then manually scored for PD-L1-positivity using 2 clinically relevant clones, SP142 and 22C3. We discovered a higher rate of PIK3CA mutations in our TNBC cohort than in non-Asian cohorts, along with TP53, BRCA1, PTPN11, and MAP3K1 alterations. PIK3CA mutations did not affect overall or recurrence-free survival, and when compared with PIK3CA^WT tumors, there were no differences in immune infiltration. Using 2 clinically approved antibodies, PIK3CA^mut tumors were associated with PD-L1 negativity. Analysis of comutation frequencies further revealed that PIK3CA mutations tended to be accompanied by MAP kinase pathway mutation. The mechanism and impact of PIK3CA alterations on the TNBC tumor immune microenvironment and PD-L1 positivity warrant further study.

Biochemical "decoding" of breast ultrasound images with optoacoustic tomography fusion: First-in-human display of lipid and collagen signals on breast ultrasound

To date, studies which utilized ultrasound (US) and optoacoustic tomography (OT) fusion (US-OT) in biochemical differentiation of malignant and benign breast conditions have relied on limited biochemical data such as oxyhaemoglobin (OH) and deoxyhaemoglobin (DH) only. There has been no data of the largest biochemical components of breast fibroglandular tissue: lipid and collagen. Here, the authors believe the ability to image collagen and lipids within the breast tissue could serve as an important milestone in breast US-OT imaging with many potential downstream clinical applications. Hence, we would like to present the first-in-human US-OT demonstration of lipid and collagen differentiation in an excised breast tissue from a 38-year-old female.

Phosphorylation of eIF4E in the stroma drives the production and spatial organisation of collagen type I in the mammary gland

The extracellular matrix (ECM) plays critical roles in breast cancer development. Whether ECM composition is regulated by the phosphorylation of eIF4E on serine 209, an event required for tumorigenesis, has not been explored. Herein, we used proteomics and mouse modelling to investigate the impact of mutating serine 209 to alanine on eIF4E (i.e., S209A) on mammary gland (MG) ECM. The proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD028953. We discovered that S209A knock-in mice, expressing a non-phosphorylatable form of eIF4E, have less collagen-I deposition in native and tumor-bearing MGs, leading to altered tumor cell invasion. Additionally, phospho-eIF4E-deficiency impacts collagen topology; fibers at the tumor-stroma boundary in phospho-eIF4E-deficient mice run parallel to the tumor edge but radiate outwards in wild-type mice. Finally, a phospho-eIF4E-deficient tumor microenvironment resists anti-PD-1 therapy-induced collagen deposition, correlating with an increased anti-tumor response to immunotherapy. Clinically, we showed that collagen-I and phospho-eIF4E are positively correlated in human breast cancer samples, and that stromal phospho-eIF4E expression is influenced by tumor proximity. Together, our work defines the importance of phosphorylation of eIF4E on S209 as a regulator of MG collagen architecture in the tumor microenvironment, thereby positioning phospho-eIF4E as a therapeutic target to augment response to therapy.

Microfluidic Arrays of Breast Tumor Spheroids for Drug Screening and Personalized Cancer Therapies

One of the obstacles limiting progress in the development of effective cancer therapies is the shortage of preclinical models that capture the dynamic nature of tumor microenvironments. Interstitial flow strongly impacts tumor response to chemotherapy; however, conventional in vitro cancer models largely disregard this key feature. Here, a proof of principle microfluidic platform for the generation of large arrays of breast tumor spheroids that are grown under close-to-physiological flow in a biomimetic hydrogel is reported. This cancer spheroids-on-a-chip model is used for time- and labor-efficient studies of the effects of drug dose and supply rate on the chemosensitivity of breast tumor spheroids. The capability to grow large arrays of tumor spheroids from patient-derived cells of different breast cancer subtypes is shown, and the correlation between in vivo drug efficacy and on-chip spheroid drug response is demonstrated. The proposed platform can serve as an in vitro preclinical model for the development of personalized cancer therapies and effective screening of new anticancer drugs.

Cancer-Testis Antigens in Triple-Negative Breast Cancer: Role and Potential Utility in Clinical Practice

Breast cancer cells commonly express tumour-associated antigens that can induce immune responses to eradicate the tumour. Triple-negative breast cancer (TNBC) is a form of breast cancer lacking the expression of hormone receptors and cerbB2 (HER2) and tends to be more aggressive and associated with poorer prognoses due to the limited treatment options. Characterisation of biomarkers or treatment targets is thus of great significance in revealing additional therapeutic options. Cancer-testis antigens (CTAs) are tumour-associated antigens that have garnered strong attention as potential clinical biomarkers in targeted immunotherapy due to their cancer-restricted expressions and robust immunogenicity. Previous clinical studies reported that CTAs correlated with negative hormonal status, advanced tumour behaviour and a poor prognosis in a variety of cancers. Various studies also demonstrated the oncogenic potential of CTAs in cell proliferation by inhibiting cell death and inducing metastasis. Multiple clinical trials are in progress to evaluate the role of CTAs as treatment targets in various cancers. CTAs hold great promise as potential treatment targets and biomarkers in cancer, and further research could be conducted on elucidating the mechanism of actions of CTAs in breast cancer or combination therapy with other immune modulators. In the current review, we summarise the current understandings of CTAs in TNBC, addressing the role and utility of CTAs in TNBC, as well as discussing the potential applications and advantage of incorporating CTAs in clinical practise.

A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients

BACKGROUND

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.

METHODS

In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.

RESULTS

The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.

CONCLUSIONS

The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

Canine mammary cancer tumour behaviour and patient survival time are associated with collagen fibre characteristics

Precise diagnosis and prognosis are key in prevention and reduction of morbidity and mortality in all types of cancers. Here we show that changes in the collagen fibres in the main histological subtypes of canine mammary gland carcinomas are directly associated with the tumour behaviour and the animal survival time and could become a useful tool in helping with diagnosis. Imaging by second harmonic generation and multiphoton excited fluorescence microscopy were performed to evaluate the collagen and cellular segment parameters in cancer biopsies. We present a retrospective study of 45 cases of canine mammary cancer analysing 836 biopsies regions including normal mammary gland tissue, benign mixed tumours, carcinoma in mixed tumour, carcinosarcoma, micropapillary carcinoma and solid carcinoma. The image analyses and the comparison between the tumour types allowed to assess the collagen fibre changes during tumour progression. We demonstrate that the collagen parameters correlate with the clinical and pathological data, the results show that in neoplastic tissues, the collagen fibres are more aligned and shorter as compared to the normal tissues. There is a clear association of the mean fibre length with the dogs survival times, the carcinomas presenting shorter collagen fibres indicate a worse survival rate.

Navigating the Collagen Jungle: The Biomedical Potential of Fiber Organization in Cancer

Recent research has highlighted the importance of key tumor microenvironment features, notably the collagen-rich extracellular matrix (ECM) in characterizing tumor invasion and progression. This led to great interest from both basic researchers and clinicians, including pathologists, to include collagen fiber evaluation as part of the investigation of cancer development and progression. Fibrillar collagen is the most abundant in the normal extracellular matrix, and was revealed to be upregulated in many cancers. Recent studies suggested an emerging theme across multiple cancer types in which specific collagen fiber organization patterns differ between benign and malignant tissue and also appear to be associated with disease stage, prognosis, treatment response, and other clinical features. There is great potential for developing image-based collagen fiber biomarkers for clinical applications, but its adoption in standard clinical practice is dependent on further translational and clinical evaluations. Here, we offer a comprehensive review of the current literature of fibrillar collagen structure and organization as a candidate cancer biomarker, and new perspectives on the challenges and next steps for researchers and clinicians seeking to exploit this information in biomedical research and clinical workflows.

The Tumor Microenvironment of Primitive and Metastatic Breast Cancer: Implications for Novel Therapeutic Strategies

Breast cancer evolves thanks to a dense and close interaction with the surrounding tumor microenvironment (TME). Fibroblasts, leukocytes, blood and lymphatic endothelial cells and extracellular matrix are the constituents of this entity, and they synergistically play a pivotal role in all of the stages of breast cancer development, from its onset to its metastatic spread. Moreover, it has been widely demonstrated that variations to the TME can correspond to prognosis variations. Breast cancer not only modulates the transformation of the environment within the mammary gland, but the same process is observed in metastases as well. In this minireview, we describe the features of TME within the primitive breast cancer, throughout its evolution and spread into the main metastatic sites.