CONSTRUCTION METHOD OF BENIGN-MALIGNANT PULMONARY NODULE DIFFERENTIAL DIAGNOSIS MODEL BASED ON SINGLE-CELL IMMUNE ATLAS

Abstract

A construction method for constructing a benign-malignant pulmonary nodule differential diagnosis model based on a single-cell immune atlas provided. In this method, PBMCs are obtained by utilizing peripheral blood samples, followed by CyTOF analysis to generate a CyTOF dataset. Using Phenotype Analysis and Representation Clustering (PARC) algorithm, cells are categorized into distinct phenotypes based on marker expression, and the frequencies of cell subsets are employed as potential modeling features that are finally selected by using the RF method with 10-fold cross-validation strategy, thereby enabling lung cancer screening and early diagnosis. This technology is characterized by its non-invasiveness, high sensitivity, and high specificity, improving the diagnostic accuracy of lung cancer screening and providing patients with earlier treatment opportunities and more suitable surgical approaches.

Claims

1. A construction method for constructing a benign-malignant pulmonary nodule differential diagnosis model based on a single-cell immune atlas, comprising the following steps: obtaining PBMCs from peripheral blood samples by using a Ficoll isolation method, performing a CyTOF measurement on the PBMCs to obtain a CyTOF dataset, employing Phenotype Analysis and Representation Clustering algorithm to classify the cells into distinct phenotypes based on marker expression, and utilizing the frequencies of cell subsets as modeling features to obtain the benign-malignant pulmonary nodule differential diagnosis model.

2. The construction method according to claim 1, wherein the method is realized through the following: Step a. subjecting a peripheral blood sample to Ficoll isolation to obtain PBMCs, suspending the PBMCs in 5 ml pre-cooled fluorescence-activated cell sorting buffer, centrifuging at 4 C. under 400g for 5 min, discarding supernatant, and resuspending cell sediment in the buffer, performing cell counting and quality assessment of the PBMCs before a CyTOF running to ensure a count greater than 310.sup.6 and a viability rate exceeding 85%; Step b. selecting 40 metal-conjugated antibodies as markers for cell labeling; washing the PBMCs with a PBS buffer and staining with 0.5 mM cisplatin, undergoing the cells to Fc receptor block and binding with the antibodies for 30 min, removing unbound antibodies via centrifugation, and fixing the PBMCs in a 200 L intercalation solution overnight; washing the cells in distilled water and resuspending, adding into 20% EQ calibration beads solution, and performing further analysis by a mass cytometer; Step c. classifying the cells into distinct phenotypes based on marker expression by using Phenotype Analysis and Representation Clustering algorithm, utilizing the frequencies of cell subsets as potential modeling features that are finally selected by using the RF method with 10-fold cross-validation strategy; and Step d. performing modeling through the RF to obtain the benign-malignant pulmonary nodule differential diagnosis model.

3. The construction method according to claim 2, wherein in step a, for enrollment of the peripheral blood sample, randomized grouping is adopted, covering various nodule sizes, various nodule types, and samples with various degrees of adenocarcinoma invasiveness.

4. The construction method according to claim 3, wherein the various nodule sizes comprise 10 mm, 11-20 mm and 21-30 mm, the various nodule types comprise solid nodule, part-solid nodule, and pure ground-glass opacity nodule, and the samples with various degrees of adenocarcinoma invasiveness comprise AAH, AIS, MIA, and IA.

5. The construction method according to claim 2, wherein the fluorescence-activated cell sorting buffer in step a is 1PBS+0.5% BSA.

6. The construction method according to claim 2, wherein the markers of the 40 metal-conjugated antibody in step b comprise: CD45, CD3, CD56, TCR /, CD196, CD14, IgD, CD123, CD85j, CD19, CD25, CD274, CD278, CD39, CD27, CD24, CD45RA, CD86, CD28, CD197, CD11c, CD33, CD152, CD161, CD185, CD66b, CD183, CD94, CD57, CD45RO, CD127, CD279 (PD-1), CD38, CD194, CD20, CD16, HLA-DR, CD4, CD8a, CD11b.

7. The construction method according to claim 2, wherein a combination of the markers of the 40 metal-conjugated antibody is employed in step b.

8. The construction method according to claim 2, wherein frequencies of 34 immune cell subsets and markers are selected in step c as modeling features, which comprise 19 features of benign-malignant pulmonary nodule differential diagnosis model comprising: CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+CD28.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+CD274.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+CD197.sup.+CD45RA.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+HLA-DR.sup.+CD38.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+CXCR5.sup.CD183.sup.CCR6, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+CD25.sup.+CD127.sup.CD161.sup.CD45RA.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD8.sup.+CD197.sup.+CD45RA.sup.+, CD33.sup.CD14.sup.CD3.sup.CD19.sup.+CD24.sup.+CD38.sup.+, CD33.sup.CD14.sup.CD3.sup.CD20.sup.CD38.sup.+CD27.sup.+, CD33.sup.CD14.sup.CD3.sup.CD56.sup.+CD16.sup.+CD94.sup.+, CD33.sup.CD14.sup.CD3.sup.CD56.sup.+CD16.sup.+CD161.sup.+, CD3.sup.CD19.sup.CD56.sup.CD14.sup.CD123.sup.+CD11c.sup.+, CD33.sup.CD14.sup.CD3.sup.CD56.sup.+CD16, CD86, CD11c, CD183, CD94, CD4, CD11b; and 15 features of lung cancer invasiveness assessment model comprising: CD33.sup.CD14.sup.CD3.sup.+CD8.sup.+CD85j.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD8.sup.+CD161.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+CD197.sup.CD45RA.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+HLA-DR.sup.+CD38.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD4.sup.+HLA-DR.sup.+CD38.sup.+, CD33.sup.CD14.sup.CD3.sup.+CXCR5.sup.+, CD33.sup.CD14.sup.CD3.sup.+CD8.sup.+CD197.sup.+CD45RA; CD33.sup.CD14.sup.CD3.sup.CD19.sup.+CD24.sup.+CD38.sup.+, CD33.sup.CD14.sup.CD3.sup.CD56.sup.+CD16.sup.+CD57.sup.+, CD33.sup.CD14.sup.CD3.sup.CD56.sup.+CD16.sup.+HLA-DR.sup.+, CD3.sup.CD19.sup.CD56.sup.CD14.sup.HLA-DR.sup., CD56.

9. The construction method according to claim 2, wherein a training set and a validation set are used in step c, wherein the samples are classified into the training set and validation set according to chronological order of enrollment.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0024] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following briefly introduces the accompanying drawings required for describing the embodiments or the prior art. Obviously, the drawings in the following description are merely examples of the present disclosure. For those skilled in the art, other drawings can be obtained from the provided ones without creative effort.

[0025] FIG. 1 is a flowchart of the lung cancer screening based on CyTOF cellular immune profiling in the present disclosure.

[0026] FIG. 2 is a box plot of risk scores for CA and non-CA group samples in the training set and validation set of the lung cancer diagnostic model in the present disclosure.

[0027] FIG. 3 is an AUC plot of the training set and validation set of the lung cancer diagnostic model in the present disclosure.

[0028] FIG. 4 is a box plot of risk scores for the most challenging pathological non-CA group and CA group samples in the training set and validation set of the lung cancer diagnostic model in the present disclosure.

[0029] FIG. 5 is an AUC plot of the most challenging pathological non-CA group and CA group samples in the training set and validation set of the lung cancer diagnostic model in the present disclosure.

[0030] FIG. 6 is a box plot of risk scores for MIA and non-IA group samples in the training set and validation set of the surgical decision model in the present disclosure.

[0031] FIG. 7 is an AUC plot of the training set and validation set of the surgical decision model in the present disclosure.

DESCRIPTION OF EMBODIMENTS

[0032] The technical solutions of the present disclosure are described clearly and completely below in conjunction with the accompanying drawings and embodiments. It is evident that the described embodiments are only a part of the embodiments of the present disclosure, rather than all of them. Based on the embodiments of the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort shall fall within the scope of protection of the present disclosure.

Example 1: a Method for Constructing a Benign-Malignant Pulmonary Nodule Differential Diagnosis Model Based on Single-Cell Immune Atlas Using CyTOF

[0033] The specific steps are as follows:

[0034] 1. Peripheral blood samples (5 ml per case) were collected and delivered to the laboratory for further processing within 12 hours at room temperature or 48 hours at 4 C. low-temperature conditions. Subjects were required to meet the following inclusion criteria: age 18 years; (patients with pulmonary nodules scheduled for surgical resection confirmed by histopathology, those with pulmonary nodules showing no changes after 3-year follow-up, or pulmonary nodules 4 mm in diameter); and signed informed consent. Exclusion criteria were defined as: history of cancer treatment; acute infection phase; blood transfusion within 6 months prior to sampling; use of medications affecting peripheral blood components within 2 weeks prior to sampling; locally recurrent tumors; organ decompensation; immunodeficiency syndrome; hematologic precancerous conditions; immunosuppressive therapy; or coagulation disorders. A total of 1,032 peripheral blood samples were obtained for this project. As shown in FIG. 1, the collected peripheral blood samples were processed as follows upon transfer to the laboratory.

[0035] 2. Sample preprocessing, including: PBMCs were isolated from the blood by Ficoll-Paque density gradient centrifugation. The cells were suspended in a 5 ml pre-cooled FACS buffer (1PBS+0.5% BSA), centrifuged at 4 C. under 400g for 5 mins, discarded supernatant, and resuspended cell sediment in the buffer. Cell counting and quality assessment of the PBMCs were performed before a CyTOF analysis to ensure a count greater than 310.sup.6 and a viability rate above 85%.

[0036] 3. CyTOF staining and data analysis, including: 40 metal-conjugated antibodies were selected as markers for cell labeling; the PBMCs were washed with a PBS buffer and stained with 0.5 mM cisplatin, the cells were blocked with Fc receptors and bound with the antibodies for 30 min. Unbound antibodies were removed via centrifugation. The PBMCs were fixed in a 200 L intercalation solution. The cells were washed in distilled water and resuspended, added into 20% EQ calibration beads solution, and performed further analyzed by a mass cytometer. FCS files were normalized using the bead normalization method. Each sample dataset was debarcoded using a dual-state filtering scheme with unique mass-tag barcodes. FlowJo software was employed to exclude debris, dead cells, and doublets, retaining only live single immune cells.

[0037] 4. Feature selection: the samples were classified into a training set and validation set according to chronological order of enrollment. The training set included 178 lung cancer samples and 218 non-CA controls. First, the negative and positive expression of markers on each cell in the training set were evaluated. Then, based on the expression profiles of these markers, the Random Forest (RF) algorithm and 10-fold cross-validation were used to select characteristic cell subsets. Characteristics with an importance level exceeding 0.01 in each successful random forest model constructions were recorded. If a characteristic appeared over 350 times in 1000 cross-validation iterations, it was counted. Ultimately, 19 characteristic cell subsets were screened for model construction.

[0038] 5. Model construction: using the 178 lung cancer samples and 218 non-CA controls from the training set, the characteristics selected above were employed to build a lung cancer diagnostic model via the RF. Risk scores for each participants were calculated by the modeling, which representing the average probability of a sample being judged positive by each decision tree in the random forest model, ranging from 0 to 1.

Example 2: a Validation Method for a Benign-Malignant Pulmonary Nodule Differential Diagnosis Model Based on Single-Cell Immune Atlas Using CyTOF Technology

[0039] 1. Following the protocol provided in Example 1, CyTOF staining and data analysis were performed on the validation set, which included 251 untrained non-CA samples and 283 lung cancer samples.

[0040] 2. New peripheral blood samples were input into the lung cancer diagnostic model constructed with the 19 cell subsets for prediction and evaluation of the blood samples. It was determined whether the sample was from a lung cancer patient based on the prediction results of the lung cancer diagnostic model.

Example 3: Construction and Validation of a Model for Assessing the Invasive Degree of Lung Cancer Based on Single-Cell Immune Atlas Using CyTOF

[0041] 1. Following the method provided in Example 1, CyTOF staining and data analysis were performed by using 113 pulmonary nodule samples pathologically diagnosed as MIA and 105 pulmonary nodule samples pathologically diagnosed as IA in the training set.

[0042] 2. Following the method provided in Example 1, the RF and 10-fold cross-validation were employed to select characteristic cell subsets. A total of 15 cell subsets were screened as modeling features to construct a new model for determining the invasive degree of malignant pulmonary nodules.

[0043] 3. Following the method provided in Example 2, model validation was conducted by using 111 pulmonary nodule samples pathologically diagnosed as MIA and 106 pulmonary nodule samples pathologically diagnosed as IA in the validation set.

[0044] Clearly, the above Examples of the present disclosure are merely illustrative examples provided to explain the present disclosure more clearly and do not limit the implementation of the present disclosure. For those skilled in the art, other variations or modifications in different forms may be made based on the above description. It is impossible to exhaust all implementation methods here, and any obvious variations or modifications derived from the technical solutions of the present disclosure still fall within the protection scope of the present disclosure.

[0045] Referring to FIGS. 2-3, it can be seen that the benign-malignant pulmonary nodule differential diagnosis model disclosed in the present disclosure exhibits excellent diagnostic performance in distinguishing lung cancer (pathologically confirmed CA) from non-CA samples (including imaging-confirmed non-CA and pathologically confirmed non-CA). The AUCs of the training set and the validation set reached 0.95 and 0.96, respectively, outperforming existing clinically used models: the Mayo model (with the AUCs of the training set and validation set being 0.75 and 0.70 respectively), the Veterans Association (VA) model (with the AUCs of the training set and validation set being 0.73 and 0.65 respectively), and the BROCK model (with the AUCs of the training set and validation set being 0.84 and 0.85 respectively).

[0046] Referring to FIGS. 4-5, it can be seen that the benign-malignant pulmonary nodule differential diagnosis model disclosed in the present disclosure demonstrates outstanding performance in the most challenging differentiation between pathologically non-CA and lung cancer groups (with the AUCs of the training set and validation set being 0.92 and 0.90 respectively), surpassing existing clinically used models: the Mayo model, the Veterans Association (VA) model, and the BROCK model.

[0047] Referring to FIGS. 6-7, the model constructed in this solution can effectively distinguish MIA from IA preoperatively, with the AUCs of the training set and validation set being 0.97 and 0.93, respectively.

[0048] The various Examples in this specification are described in a progressive manner, with each Example focusing on the differences from other embodiments. Similar or identical parts between the Examples can be cross-referenced. As for the steps disclosed in the embodiments, since they correspond to the methods disclosed in the Examples, the description is relatively brief, and relevant details can be referred to in the method section.

[0049] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present disclosure. Various modifications to these Examples will be apparent to those skilled in the art, and the general principles defined herein may be applied to other Examples without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not limited to the Examples shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.