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Abstract
Immunotherapy and its evaluation have shown great promise for cancer treatment. Here, a mouse subcutaneous transplantable tumor model was applied to testing therapeutic strategies. The mouse model was treated by regulating anti-PD-L1, anti-CTLA-4, cisplatin and their combined therapy. Biochemistry experiments have found that after immunotherapy, mice have more immune responses, which were manifested by an increase in the content of growth factors and the activation of T cells. Meanwhile, multimodal nonlinear optical microscopy imaging combined with algorithms was used to evaluate the treatment's effectiveness. By detecting the metabolism rate and microstructure in tissue, it was proved that combined therapies including immune checkpoint inhibitors do have a better effect on ovarian tumors. Our discovery of valid treatments for mice with ovarian tumor and provides an evaluation tool via nonlinear optics combined with algorithms offers new insights into therapeutic effect.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
1. Introduction
Ovarian cancer has become the fifth largest gynecological cancer due to its being difficult to diagnose, poor prognosis and lack of specific drugs [1]. Despite surgery and treatment with chemotherapy drugs such as paclitaxel and carboplatin, most patients with ovarian cancer still relapse [2]. Since the checkpoint inhibitors (CPIs), such as anti-programmed death-ligand 1 (PD-L1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and T-cell immunoglobulin and ITIM domain (TIGIT) were discovered, they have been increasingly used in immunotherapy [3,4]. PD-L1 is also called cluster of differentiation 274 (CD274) or B7 homolog 1 (B7-H1), which can combine with programmed cell death-ligand 1 (PD-1) in immune cells to reduce the activity of the Immune system [5]. Blocking the interaction between PD-1 and PD-L1 can increase the activity of T cells in the immune system, thereby achieving tumor suppression. CTLA-4, also known as cluster of differentiation (CD152), is a transmembrane receptor in T cells that can bind to receptors on the surface of antigen cells to stop an immune response [6]. Duraiswamy J has found that regulating PD-L1 and CTLA-4 pathways can affect the activation of tumor antigen-specific CD8 + and CD4 + effector T cells, ultimately improving long-term survival rates [7]. Zhang and Hwang WT found that the activity of CD3+/CD8 + lymphocytes is closely related to the survival rate of cancer patients [8,9]. Immunotherapy with CPIs has been shown to have a remarkable therapeutic effect in melanoma, non-small cell tumors and bladder tumors, greatly improving the overall survival (OS) and progression-free survival (PFS) in cancer patients [10–13]. However, solid tumors, like ovarian cancer, it has not achieved an expected result. Therefore, the combination of CPIs with chemotherapy was used to treat ovarian cancer in this study.
In traditional pathological diagnosis, hematoxylin and eosin need to be used for staining, so as to obtain information on the structure of the nucleus, cytoplasm, and other tissue structures. Nonlinear optical (NLO) imaging has been shown to provide similar pathological information [14–16]. Several intracellular substances have autofluorescence, such as elastic fiber, nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) making two-photon excited fluorescence (TPEF) and two-photon fluorescence lifetime imaging microscopy (TP-FLIM) possible technology for characterization of pathological conditions and cell metabolism. Moreover, due to the ordered non-centrosymmetric structure, the second harmonic generation (SHG) is commonly used to image collagen tissue [17]. In this work, we analyzed the collagen and elastin in the extracellular matrix of mice after treatment, with the aim of evaluating the pathological condition of the tumor and the effectiveness of drug treatment.
Using ID8, clonal lines established from late passaged C57BL/6 murine ovarian surface epithelial cells (MOSEC), we have developed and characterized a mouse subcutaneous transplantable tumor model (MSTTM), which is mainly used to study individual immune checkpoints, platinum drugs and their combined immunotherapy effects. Traditional biochemical analytical methods, multi-modal optical imaging and algorithms were combined to evaluate the effect of immunotherapy. Here we detected a significant treatment effect in the MSOCM after the combined therapies. Further study indicated that NOL can provide a pathological diagnosis (Fig. 1). This suggests that immunotherapy still has a lot of application space in the treatment of ovarian cancer, and optical imaging is promising as a future method for clinical diagnosis.
Fig. 1. Schematic illustration of tumor immune microenvironment of MSTTM and its therapeutic evaluation. The mouse tumor model was injected with anti-PD-L1 and/or anti-CTLA-4 to activate its own immune effects. There was the same effect between traditional clinical analysis and nonlinear optical binding algorithm to evaluate immunotherapy in mouse models.
2. Methods and materials
2.1 Mouse model
Female C57BL/6 mice, 4-5 weeks old, were purchased from Guangdong medical laboratory animal center (Guangdong, China). Animal experiments were approved by Guangdong medical laboratory animal center and animals were maintained under specific pathogen-free conditions(Ethical code: C202110-01). ID8 cell line was purchased from Procell (Wuhan, China). To evaluate the effect of immunotherapy on tumor growth, C57BL/6 mice were implanted subcutaneously on the right flank with 1×107 ID8 cells. The behavior of the mice was observed every day, weights and tumor areas were collected every week. About 3 weeks, when a significant tumor grows out, a high-precision vernier caliper was used to measure the size of tumor weekly. When the tumor reached approximately 10∼15 mm2, the mice were divided into six groups and there were six mice in each group. One week after the last treatment, mice were euthanized according to IACUC guidelines. The tumor was excised and made into paraffin slices for subsequent histological analysis and optical imaging. The spleen was taken out to detect the changes in lymphocytes in the mice.
2.2 Treatment
In the control group, mice were injected with SPSS (Stroke-physiological Saline Solution, Beyotime, Shanghai). A total of five experimental groups were set up, and the drugs used were (1) anti-mouse PD-L1 (200 µg), (2) anti-mouse CTLA-4 (200 µg), (3) DDP (5 mg/kg), (4) anti-mouse PD-L1 (200 µg) and anti-mouse CTLA-4 (200 µg), and (5) anti-mouse PD-L1 (200 µg) and DDP (5 mg/kg). The medicine in all treatment groups was injected into mice by intraperitoneal injection (i.p.). The treatment was performed every four days and Mice were killed after treating for five times.
2.3 Histological analysis
Upon euthanasia, tumors were taken out and fixed in 10% neutral buffered formalin (Solarbio, Beijing, China). After dehydrated and embedded in paraffin, tissues were cut into 4 µm sections, then hematoxylin-eosin staining was performed. For quantification of CD3, CD4 and CD8 T cells infiltration, tumor sections were marked by Immunofluorescence staining. After the tissue sections are dewaxed, they were treated with Citrate Antigen Retrieval Solution (Beyotime, Shanghai). Subsequently, tissue sections were incubated for 30 min using 5% BSA solution. Add primary antibodies to tissue slices at 4 °C overnight. After washing three times with PBS, add secondary antibody and incubate for 1 h without light. Confocal microscopy was used to image stained slices.
2.4 Flow cytometry
After the spleen of the mouse was taken out, it was ground using the back of a 10 ml syringe to prepare a cell suspension. The red blood cells in the suspension are lysed with the red blood cell lysis buffer (Beyotime, Shanghai, China). Afterward, the lymphocytes were isolated by using the murine lymphocyte separation kit (Solarbio). Then, FITC anti-mouse CD3, PE anti-mouse CD4 and PE anti-mouse CD8 were used to double-stain the CD3+, CD4 + and CD8 + cells in the suspension. Flow cytometric analysis was performed on a CytoFLEX (Beckman coulter, S.Kraemer Blvd, U.S.A) using FlowJo software.
2.5 Imaging method
Three consecutive excitation lights (405 nm, 488 nm and 640 nm) are used for single-photon imaging, and the corresponding filters: (1) BP 450/40, (2) BP 525/50, (3) BP 595/50. A femtosecond laser with a pulse width of 100 femtosecond and repetition rate of 80 MHz (Chameleon Discovery, Coherent) was selected as excitation source. The laser power was set to a level that can clearly image the fluorophore while being below the saturation of the detector. 840 nm was chosen as the excitation wavelength for TPEF (combining with BP 525/50) and SHG (combining with BP 420/10). The signal of TPEF and SHG were collected at the same time. Using confocal system, the image resolution was 1024 × 1024 pixels and the power range of laser was 5-7 mW. For TP- FLIM, 840 nm laser and BP 535/45 nm were chosen to detect the signal of FAD. 780 nm laser and BP 432/36 nm were chosen to detect the signal of NADH. The image resolution was 512×512 pixels.
2.6 Image analysis
After imaging the tissue slices, 10 different ROIs were randomly selected. Fast Fourier transform (FFT), Gray level co-occurrence matrix (GLCM) and the SHG to auto-fluorescence imaging index of dermis (SAAID) were used to analyze NLO images.
2.7 Statistical analysis
All measurement data are presented as the mean ± standard deviation (s.d.) from at least three independent experiments. Using Maxinmum Likelihood Estimate (MLE), TP- FLIM Images were analyzed by SPCImage. OriginPro 9.1 and GraphPad Prism 7 (La Jolla, CA) was used for data analysis. Differences were considered to be statistically significant if p < 0.01.
3. Results
3.1 Construction of the mouse model and immunotherapy
Treatment based on immune checkpoints has been proven to play a significant role in the treatment of multiple tumors, but single immune checkpoint therapy is not effective in treating ovarian tumor. Therefore, it is very important to find more treatment methods and diagnostic techniques for ovarian tumor. In this study, we sought to test the impact of immunotherapy on tumor progression in mouse subcutaneous tumor models and aimed to find useful combination therapies for ovarian tumor. First, a large number of ID8 cells were subcutaneously injected into the flank of C57BL/6 mice to construct mouse subcutaneous transplantable tumor model (MSTTM) (Fig. 2(A)). MSTTM mice were randomized into six groups for treatment. Correspondingly, five treatment plans include anti-mouse PD-L1, anti-mouse CTLA-4, DDP, anti-mouse PD-L1 combining with DDP, and anti-mouse PD-L1 combining with anti-mouse CTLA-4 were formulated for treatment. As a control, non-experimental mice were injected with saline only in each treatment. Medicines were administered by intraperitoneal (i.p.) injection every four days. These dosing strategies were repeated five times, then the mouse was euthanized (Fig. 2(B)). The weight of the mouse and the size of the tumor were recorded after each treatment (Figs. 2(C) and 2(D)).
Fig. 2. Transplantable mouse tumor model development. A: ID8 cells were injected to the flank of C57BL/6 to build the mouse subcutaneous transplantable tumor model; B: 200 µg anti-PD-L1, 200 µg anti-CTLA-4 and 5 mg/kg DDP were intraperitoneally injected into mice according to the treatment plan; C: The mice bodyweight of five treatment groups and control group; D: The mice tumor area of six groups. The displayed body weight and tumor area values are the average of all mice in their group.
It can be seen that bodyweight of the mice showed an increasing trend with the treatment (Fig. 2(C)). The weight of mice in the treatment groups other than anti-CTLA-4 (1.61 g) was significantly increased compared to the control group (2.01 g). In particular, the mice in the anti-PD-L1-containing treatment group increased in size compared to before treatment. Anti-PD-L1 group added 2.63 g, anti-PD-L1-anti-CTLA-4 group increased 2.68 g and anti-PD-L1-DDP went up 2.71 g. At the same time, the size of the tumor is showing varying degrees of shrinking trend (Fig. 2(D)). In particular, the tumor area of mice after anti-PD-L1-anti-CTLA-4 and anti-PD-L1-DDP treatment decreased by 28.6% and 21.5%, respectively. This result proves the obvious therapeutic result of the combination therapy. However, due to the small sample size these effects did not reach significance.
3.2 Variation of T-cell populations and growth factor after kinds of treatment in MSTTM
In order to further investigate the effect of treatment on the immune and tumor microenvironment in MSTTM, after five treatments, the mice were euthanized. Then, the factor content in the serum of MSTTM was detected by kit (Fig. 3(A)). Compared with the control group, the IL-10 content in the treatment group increased significantly. Among them, anti-PD-L1 and anti-CTLA-4 group has the most obvious growth, which proves that CTLA-4 can effectively regulate IL-10 in MSTTM.
Fig. 3. Characterization of growth factor and T-cell populations in MSTTM after different therapies. A: Changes of IL-10 content in serum of MSTTM after treatment; B, C: Changes in the percentage of CD3+, CD4+, and CD8 + in the lymphocytes of the spleen of MSTTM after treatment; D: Immunofluorescence staining was performed on CD3+, CD4+, and CD8 + in tumor tissues of MSTTM after DDP and anti-PD-L1-anti-CTLA-4 treatment. The fluorescent color of anti-mouse CD8 + and anti-mouse CD4 + is green, the fluorescent color of antibody anti-mouse CD3 + is red, and blue is DAPI. All scale bars were 50 µm.
To evaluate the immune microenvironment, flow cytometry and immunofluorescence staining were used to detect the T cells in secondary lymphoid organs (SLOs) and the tumor of MSTTM (Figs. 3(B) and 3(C)). First, lymphocyte fluid of the spleen was characterized for T-cell infiltration, as well as expression of CD3+, CD4 + and CD8 + . After DDP treatment, the number of immune cells in the MSTTM were not significantly different from that in the control group. This shows that the chemotherapy process does not cause a strong response from the immune system in MSTTM mice. Compared to both the chemotherapy and the control groups, the number of CD3+, CD4 + and CD8 + cells increased significantly in the MSTTM mice after the immunotherapy group. In particular, we found that after the interventional immune checkpoint, the number of CD4 + increased the most. In addition, we found that after anti-PD-L1 treatment, the amount of CD8 + in the serum of mice increased about three times. This result further proved that the activation of CD8 + can be achieved by inhibiting PD-L1.
In addition, the tumor part of the mouse was made into tissue sections and T cells were expressed by immunofluorescence staining (Fig. 3(D)). CD3 + and CD4 + are double-stained on one slice, and CD8 + is single-stained. It can be seen from Fig. 3 that only CD3 + has expression in slices of both treatment groups. The flow cytometry data above shows that immunotherapy activates CD4 + cells in MSTTM mouse serum. This finding is of particular interest since it shows that effector T cells are significantly different in the blood and tumor sites in the MSTTM. Combined with previous studies, we now have a better understanding of the changes in the tumor microenvironment in MSTTM after immunotherapy.
3.3. Evaluating the cell metabolism with TP-FLIM
There are differences in the metabolism rate of cancer cells and normal cells due to different metabolic patterns. Therefore, it is expected that the therapeutic effect can be evaluated by detecting the concentration of substances involved in cell metabolism. Flavin adenine dinucleotide (FAD), as a coenzyme of some redox enzymes, is widely involved in various redox reactions in the body. FLIM redox rate was defined as the ratio of the fraction of bound-NADH (i.e., with a long lifetime) to the fraction of bound-FAD (i.e., with a short lifetime), increasing with higher metabolic activity. FLIM redox rate increases with higher metabolic activity.
TP-FLIM images were fitted by Maximum Likelihood Estimate (MLE) and the double index fit is selected according to the fit index. TP- FLIM images were then fitted by Phasor Plot, and a suitable ROI was selected to reduce the effect of background noise on the fitting results. The fluorescence lifetime images were shown in Figs. 4(B) and 4(C). After different treatments, the average lifetime of FAD changed in the tissues of each experimental group, indicating that non-fluorescent FADH2 was converted into unbound fluorescent FAD (i.e, long lifetime). As shown in Fig. 4(D), FLIM redox rate of different groups was 0.469 (SPSS), 0.457 (anti-PD-L1), 0.454 (anti-CTLA-4), 0.443 (DDP), 0.449 (anti-PD-L1 & DDP), 0.414 (anti-PD-L1 & anti-CTLA-4). Differences in lifetime revealed metabolic differences between tissue cells in six treatment groups. Although the differences were not very obvious, it can be seen that the metabolism of tissue cells after treatment showed a decreasing trend.
Fig. 4. TP-FLIM images of tumor slices of MSTTM mice after different therapy. A: Representative fluorescence lifetime images of Bound-NADH and Bound-FAD in the tissues; B: the Bound-NADH fluorescence lifetime fitting of A; C: the Bound-FAD fluorescence lifetime fitting of A; D: FLIM redox rate of A. All scale bars were 100 µm.
3.4. Evaluating the therapeutic effect with TPEF and SHG
Our research focuses on the use of auto-fluorescence to reveal the distribution of endogenous fluorescent substances in tumor tissues. In order to study the structure of mouse tumor tissue, TPEF and SHG were used to image the unstained-sliced tissue. A previous study showed that the endogenous fluorescent signal in fresh living tissues mainly comes from NADH and FAD. The endogenous signals of isolated tissues are mainly collagen and keratin [18]. Due to the typical non-centrosymmetric structure, collagen, microtubules and muscle fibers have strong second harmonic signals. Meanwhile, as the wavelength of the laser increases, elastic fibers have a higher fluorescence intensity than collagen fibers. Therefore, a laser with a wavelength of 840 nm was selected as the excitation light, which combined with BP 420/10 nm filter to realize the detection of collagen fibers. At the same time, the two-photon excited fluorescence signal is collected into the PMT after LP (reflect) 560 nm and BP 525/50 nm filter. To learn more about the structure of tumor tissue, edge and internal tissue were respectively selected. The TPEF and SHG images of them were shown in Fig. 5.
Fig. 5. TPEF and SHG images of tumor slices of MSTTM mice after different therapy. The boundaries and internals of the tumor were selected for imaging. B, C, D: The distribution of collagen and elastic fibers in MSTTM were analyzed using SAAID and GLCM. B: SAAID results of border tumor tissue; C: SAAID results of internal tumor tissue; D: GLCM results of tumor tissue. All scale bars were 100 µm.
First, the SHG to autofluorescence aging index of dermis (SAAID) parameter was used to evaluate the ratio of elastic fibers and collagen in tumor tissues of MSTTM mice after therapy. TPEF and SHG images were opened in Fiji, then transformed to 8-bit image (0 to 255 gray levels) type. After threshold calibration, the average fluorescence intensity in these pictures was calculated. The value of the SAAID index was calculated by the following definition:
where I mean the intensity of SHG and TPEF signal.As shown in Figs. 5(B) and 5(C), the distribution of elastic fibers and collagen in the edges and interior of the tumor is significantly different. Autofluorescence (TPEF images) and SHG can be used to quantify the analysis of elastic fibers and collagen. The value of SAAID approaches 1 when collagen is completely replaced by elastic fibers [19]. In addition to anti-CTLA-4 group, the collagen content in the boundary is much higher than its content inside the tumor tissue. This suggests that collagen in the tissue decreases as the tumor area deteriorates. Recently, collagen was found to be reduced in collagen in mouse models, leading to accelerated growth of pancreatic cancer [20]. It is worth noting that regulating PD-L1 can slow down collagen that decreases with cancer growth and have good to excellent outcomes in MSTTM, which may become another research direction of immunotherapy. Together, these results suggest that collagen as a marker plays an important role in cancer diagnosis. This method is expected to be able to distinguish the pathological stage of cancer tissue slices.
Then, Gray level co-occurrence matrix (GLCM), a widely used texture analysis method, was used to further study the morphology of collagen. It can provide information about the spatial relationship between the gray levels of pixels. Separations of 0 to 12 in 2-pixel increments were used for this study and the parameters for 90-degree orientation were computed [21]. As shown in Fig. 5(D), as the steps increase, the correlation between fibrils decreases rapidly. There was a different pixel distance of the GLCM curves where the correlation dropped below 50% of the initial value of different groups. It can be seen that in all treatment groups, in particular anti-PD-L1 group, the value of Corr50 was smaller than that of the control group. In addition, compared to the control group, the curve of the treatment group decreased significantly between separations of 0 to 12. Matrix stretching caused by tumor growth affects the surrounding collagen morphology, altering the correlation between them. It can be seen from the results that the cancerous degree of tumor tissue in treated mice was lower than that of the control group. Meanwhile, the correlation of collagen was higher in the tumor tissue of mice in the treatment group.
In order to obtain the amplitude of information about microscopic biological structure in MSTTM for therapeutic evaluation, Fast Fourier Transform (FFT) was used to analyze the SHG images of all treatment groups. The collagen anisotropy could be evaluated by the length-to-width ratio of the ellipse major axis and the minor axis produced by FFT. As shown in Fig. 6(A), three different areas were randomly selected according to the distance from the boundary (ROI-1 was nearest and ROI-3 was farthest). Ellipse fitting on the data processed by FFT was performed, and the value of aspect ratio was obtained by calculating the ratio of the minor axis to the major axis. When the collagen distribution is isotropic, the value of AR tends to 1. As the distance from the boundary increases, the AR decreases from ROI-1 (0.74) to ROI-3 (0.65). It can be seen that this trend is present in all experimental groups (Fig. 6(B)). These results show that in tumor tissues, as the distance from the boundary increases, collagen fibers gradually become anisotropic. Collagen fibers are the main component of the extracellular matrix (ECM) structure network. When tumor tissue grows, tumor cells will infiltrate and invade surrounding tissues through collagen fibers, resulting in anisotropy of collagen fibers. Therefore, the change in AR value from ROI-3 to ROI-2 reveals the migration path of the tumor in the tumor tissue. The average aspect ratio after fitting the FFT results for all treatment group slices is shown in Fig. 6(C). Compared to the control group, it can be seen that the average AR value of each treatment group has increased to varying degrees, thus providing rationale for assessing the impact of therapy.
Fig. 6. The anisotropy of collagen in MSTTM was analyzed using FFT. A: The FFT analysis result of the tumor of anti- PD-L1 and anti-CTLA-4 group. Three ROIs were randomly selected. Ellipse fitting was performed on the FFT analyzed results to obtain the value of AR; B: The AR value difference between ROI-3 and ROI-1 randomly selected in each treatment group; C: The value of AR in each treatment group. All scale bars were 100 µm.
4. Discussion
Up to now, the cure rate of ovarian cancer is still very low and there is a very high recurrence. Ovarian cancer shows obvious symptoms until the final stages, and patients will miss the best time for treatment at this time. Many mouse models, such as the transplantable mouse tumor model, carcinogen-induced mouse tumor model, genetically engineered mouse model, and humanized mouse model have been designed and used to study the relationship between the tumor and the immune system, especially the immune-regulatory mechanism in the tumor microenvironment. Besides, by inoculating histocompatibility tumor cell lines into immunocompetent inbred mice, major revolutions have happened in treatment-oriented tumor immunology [22–24]. However, few studies have elaborated on the interaction between the subcutaneous tumors of mice and the immune system. Immunotherapy with PD-L1 has revolutionized the treatment of different types of tumors. However, the therapeutic effect of single anti-PD-L1 treatment in ovarian cancer is not obvious. Therefore, our research aims to study the host immunity on cancer progression and the results of immunotherapy and test the therapeutic effects of different treatments on subcutaneous ovarian tumors in mice, especially the combination therapy based on immune checkpoints. In order to characterize the changes in the immune system of the MSTTM mice after each treatment, the serum was used to detect the concentration of growth factor IL-10. Flow cytometry and immunofluorescence staining were performed to evaluate T cell (CD3+, CD4 + and CD8+) expression after different treatments. It was found that after combined treatment with anti-mouse PD-L1 and anti-mouse CTLA-4, IL-10 in mice increased significantly and CD4 + cells were activated. At the same time, the results of immunofluorescence revealed the activation of CD3 + cells in tumor tissues. The combination of anti-mouse PD-L1 and chemotherapy also caused a certain degree of an immune response. The above results indicate that compared with monotherapy, combined therapy can achieve better treatment effects. At the same time, treatment based on immune checkpoints can cause different immune responses in the tumor microenvironment and immune system.
Next, nonlinear optical imaging (TP-FLIM, TPEF and SHG) combined with algorithms were used to evaluate the treatment results and tumor morphology. TP-FLIM fitting of bound-FAD revealed that the treatment group had lower cell metabolism than the control group. The results of SAAID revealed differences in the distribution of collagen and elastic fibers in tumor tissue boundaries and internal tissues. As the tumor tissue continues to deteriorate, the internal collagen tissue is reduced. It can be seen from the GLCM results, the Corr50 value of all treatment groups is smaller than that of contrast group. These results indicate that the degree of serious degree of the pathology of tumor in the treated group is lower than that in the control group. The analysis results of FFT proved that the degree of cancerous tumors in the same tissue spread from the inside to the outside. The above results further verified the therapeutic effects of mouse tumors after different therapies and found that the treatment based on immune checkpoints has obvious therapeutic effects. At the same time, the FFT results also revealed that collagen gradually tends to anisotropy during tumor growth and the migration path of the tumor from the inside to the outside. Deeper longitudinal studies will aid in understanding the underpinnings of immune checkpoint therapies in mouse models, however, it may be expected that regulating immune checkpoints to mediate the activation of T cells in the immune system to achieve an immune response is an effective method for the treatment of ovarian cancer. In addition to the information such as collagen trend and metabolism have been analyzed, NOL combined with algorithmic analysis can also obtain information such as the collagen angle in the tissue and the orientation of the collagen fiber. These results will provide a new perspective for the use of NOL to evaluate the treatment results and detect morphology associated with the progression of ovarian cancer.
Funding
National Natural Science Foundation of China (61935012, 61961136005, 62175163, 62127819, 61835009); Shenzhen Key projects (JCYJ20200109105404067); Shenzhen International Cooperation Project (GJHZ20190822095420249).
Disclosures
The authors declare that there are no conflicts of interest related to this article.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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