Background: Pancreatic ductal adenocarcinoma (PDAC) ranks among the deadliest types of cancer, and it will be meaningful to search for new biomarkers with prognostic value to help clinicians tailor therapeutic strategies. Methods: Here we tried to use an advanced optical imaging technique, multiphoton microscopy (MPM) combining second-harmonic generation (SHG) and two-photon excited fluorescence (TPEF) imaging, for the label-free detection of PDAC tissues from a cohort of 149 patients. An automated image processing method was used to extract collagen features from SHG images and the Kaplan-Meier survival analysis and Cox proportional hazards regression were used to assess the prognostic value of collagen signatures. Results: SHG images clearly show the different characteristics of collagen fibers in tumor microenvironment. We gained eight collagen morphological features, and a Feature-score was derived for each patient by the combination of these features using ridge regression. Statistical analyses reveal that Feature-score is an independent factor, and can predict the overall survival of PDAC patients as well as provide well risk stratification. Conclusions: SHG imaging technique can potentially be a tool for the accurate diagnosis of PDAC, and this optical biomarker (Feature-score) may help clinicians make more approximate treatment decisions.

Tumor-infiltrating immune cells and fibroblasts are significant components of the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC), and they participate in tumor progression as closely as tumor cells. However, the relationship between the features of the TME and patient outcomes and the interactions among TME components are still unclear. In this study, we evaluated the PDAC TME in terms of the quantity and location of cluster of differentiation (CD)4⁺ T cells, CD8⁺ T cells, macrophages, stromal maturity, and tumor-stroma ratio (TSR), as evaluated by immunohistochemical staining of serial whole-tissue sections from 116 patients with PDAC. The density of T cells and macrophages (mainly activated macrophages) was significantly higher at the invasive margins (IMs) than at the tumor center (TC). CD4⁺ T cells were significantly association with all the other tumor-associated immune cells (TAIs) including CD8, CD68 and CD206 positive cells. Tumors of the non-mature (intermediate and immature) stroma type harbored significantly more CD8⁺ T cells at the IMs and more CD68⁺ macrophages at the IMs and the TC. The density of CD4⁺, CD8⁺, and CD206⁺ cells at the TC; CD206⁺ cells at the IMs; and tumor-node-metastasis (TNM) staging were independent risk factors for patient outcomes, and the c-index of the risk nomogram for predicting the survival probability based on the TME features and TNM staging was 0.772 (95% confidence interval: 0.713–0.832). PDAC harbored a significantly immunosuppressive TME, of which the IMs were the hot zones for TAIs, while cells at the TC were more predictive of prognosis. Our results indicated that the model based on the features of the TME and TNM staging could predict patient outcomes.

Zou Y, et al. J Gastric Cancer. 2019 Jan;19:e9. https://doi.org/10.5230/jgc.2019.19.e9

The lysosome is an important target for realizing antitumor therapy. Lysosomal cell death exerts significant therapeutic effects on apoptosis and drug-resistance. The development of lysosome-targeting nanoparticles to obtain efficient cancer treatment is challenging. In this article, nanoparticles composed of DSPE@M-SiPc and possessing bright two-photon fluorescence, lysosome targeting ability, and photodynamic therapy multifunctionalities are prepared by encapsulating morpholinyl-substituted silicon phthalocyanine (M-SiPc) with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(poly(ethylene glycol))-2000] (DSPE). Two photon fluorescence bioimaging showed that M-SiPc and DSPE@M-SiPc mainly locate in lysosomes after cellular internalization. Upon irradiation, DSPE@M-SiPc effectively generates reactive oxygen species and damages the function of lysosome, subsequently leading to lysosomal cell death. DSPE@M-SiPc is a promising photosensitizer for cancer treatment.

The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.

Pancreatic ductal adenocarcinoma (PDAC) presents a clinical challenge due to its poor prognosis and high mortality rate. Here, we aimed to enhance the prognostic prediction of patients with PDAC by studying collagen features in tumor microenvironment using multiphoton microscopy (MPM) combining with image processing technique. We identified eight distinct tumor-associated collagen signatures (TACS1-8) from multiphoton images of PDAC tissues and developed an optical biomarker, TACS-score, based on the TACS1-8 using ridge regression analysis. Additionally, we also extracted 142 microscopic TACS (M-TACS) from second-harmonic generation (SHG) images and constructed a new robust biomarker, M-TACS-score, using the least absolute shrinkage and selection operator (LASSO) regression analysis. Our statistical results demonstrate that as two new optical biomarkers, TACS- and M-TACS-score, are independent prognostic factors and have good discriminatory ability (high AUC) as well as risk stratification (high HR) comparing with traditional clinical model (combining seven clinical risk factors, age, sex, TNM stage, tumor location and differentiation, perineural and lymph-vascular invasion) in predicting overall survival (OS) of patients with PDAC, highlighting their potential prognostic and predictive value. A combination of label-free multiphoton imaging technique and computer-aided image processing method may offer a novel and promising approach for finding new biomarkers to improve prognosis prediction and thereby tailor treatment strategies more effectively.