A homology analysis system and storage medium for sporadic bilateral renal tumors

Abstract

This invention discloses a homology analysis system and storage medium for sporadic bilateral renal tumors based on genomic clonal evolution characteristics, belonging to the field of renal tumor genomic analysis technology. The system comprises seven modules: sample screening and preprocessing, whole-exome sequencing and data quality control, gene mutation detection and annotation, genomic feature analysis, clonal evolution analysis, tumor origin classification, and metastatic characteristic analysis. After excluding hereditary bilateral renal cell carcinoma through clinical screening and germline mutation detection, the system performs whole-exome sequencing on bilateral tumors of sporadic bilateral renal cell carcinoma, reconstructs the clonal structure using a Bayesian statistical model, and classifies tumor origin into bilateral primary or contralateral metastatic types based on the presence and distribution patterns of common clonal mutations. For metastatic types, the system further quantifies the clonal origin pattern and metastatic seeding time, providing molecular evidence for personalized treatment decisions.

Description

Technical Field

[0001] This invention relates to the field of kidney tumor genomic analysis technology, and in particular to a homology analysis system and storage medium for sporadic bilateral kidney tumors. Background Technology

[0002] Renal cell carcinoma is one of the most common malignant tumors of the urinary system. Approximately 1% to 5% of patients develop tumors in both kidneys, either sequentially or simultaneously, clinically termed bilateral renal cell carcinoma. Etiologically, bilateral renal cell carcinoma can be divided into two types: familial and sporadic. The former is driven by germline mutations in known pathogenic genes such as VHL, MET, FLCN, SDH series, and TSC series, and its bilateral pathogenesis has been well elucidated at the molecular genetic level. Sporadic bilateral renal cell carcinoma does not carry the aforementioned known pathogenic germline mutations, nor does it present with the clinical manifestations of hereditary renal cell carcinoma syndrome. However, whether the bilateral tumors are two independent primary lesions or a primary tumor on one side that metastasized to the contralateral kidney has long been a matter of debate.

[0003] Traditional views tend to consider sporadic bilateral renal cell carcinoma as originating from both sides independently, primarily based on retrospective clinical studies finding that the overall survival rate of such patients is similar to that of unilateral renal cell carcinoma at the same time, and that most patients do not have distant metastases to other organs at the time of bilateral renal tumor diagnosis. Under this understanding, clinical treatment plans for sporadic bilateral renal cell carcinoma are usually formulated separately according to the clinical stage of each tumor, with surgery as the primary treatment principle. However, other scholars have noted that the local recurrence rate and multifocal incidence of sporadic bilateral renal cell carcinoma are significantly higher than those of unilateral renal cell carcinoma. Furthermore, in metachronous bilateral renal cell carcinoma, the longer the time interval between the diagnosis of the two tumors, the better the prognosis. This prognostic pattern is consistent with the clinical characteristic of metastatic renal cell carcinoma, where the prognosis improves with the prolonged time since the appearance of metastatic lesions. These conflicting pieces of evidence suggest that not all sporadic bilateral renal cell carcinomas may be bilaterally independent primary, and that some may actually involve a biological process of metastasis from one tumor to the other kidney.

[0004] The clinical significance of this issue lies in the fundamental difference in treatment strategies for two distinctly different disease entities: bilateral independent primary tumors and unilateral primary tumors with contralateral metastases. Treatment for the former is based on surgical resection of the tumors on each side separately, while the latter, essentially metastatic renal cell carcinoma, requires systemic treatment according to the diagnostic and treatment guidelines for metastatic renal cell carcinoma. Treating all sporadic bilateral renal cell carcinomas with contralateral metastases as bilateral independent primary tumors risks mismatch between treatment plans and the nature of the disease. Since imaging and histopathological examinations alone cannot definitively distinguish between these two possibilities, a few studies have attempted to find answers at the genomic level using high-throughput sequencing technology. However, limited sample size and the lack of systematic clonal evolutionary analysis methods and clear rules for determining origin have resulted in the absence of a standardized analytical system for clinical reference. Therefore, current technology lacks an analytical tool capable of objectively classifying the origin of sporadic bilateral renal cell carcinomas based on bilateral tumor genomic data, through systematic clonal evolutionary analysis and standardized interpretation procedures. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides a method for homology analysis of sporadic bilateral renal tumors, executed by a computer device, the method comprising:

[0006] First and second sequencing data of bilateral renal tumors of the target object are obtained, wherein the first and second sequencing data contain tumor somatic mutation information and copy number variation information;

[0007] Based on the first sequencing data and the second sequencing data, the tumor cell fractions of each somatic cell mutation in bilateral renal tumors were calculated respectively.

[0008] Based on the tumor cell fraction, somatic mutations of bilateral renal tumors are clustered to construct a first clonal cluster set for the first tumor and a second clonal cluster set for the second tumor.

[0009] By comparing the first clonal group set with the second clonal group set, common clonal group features of bilateral renal tumors are extracted.

[0010] Based on preset homology interpretation rules, the common clonal group features are processed to output homology classification results for bilateral renal tumors of the target object, wherein the homology classification results include bilateral primary type and contralateral metastatic type.

[0011] Furthermore, based on preset homology interpretation rules, the common clonal group features are processed to output bilateral renal tumor homology classification results, including:

[0012] If there is no common clonal group containing a community cell mutation between the first clonal group set and the second clonal group set, then the homology classification result is determined to be bilateral primary type.

[0013] If there is a common clonal group between the first clonal group set and the second clonal group set, and the common clonal group satisfies a preset transfer evolution pattern, then the homology classification result is determined to be a contralateral transfer type.

[0014] Furthermore, the clone groups in each of the aforementioned clone groups are divided into primary clone groups and subclonal groups; the preset transfer evolution mode includes at least one of the following sub-modes:

[0015] First sub-pattern: The common clonal population contains a driver gene mutation, and the driver gene mutation belongs to the main clonal population in both the first and second tumors;

[0016] Second sub-mode: The common clonal group contains a driver gene mutation, and the driver gene mutation belongs to the main clonal group in the first tumor and to the subclonal group in the second tumor, or vice versa;

[0017] Third sub-mode: The co-clonal population contains a number of co-passenger mutations greater than or equal to a threshold, and the co-passenger mutations belong to the main clonal population in at least one of the first tumor or the second tumor.

[0018] Furthermore, the primary clonal population is defined as the clonal population whose upper bound of the confidence interval for the tumor cell fraction is greater than or equal to 1; the subclonal population is defined as the clonal population whose upper bound of the confidence interval for the tumor cell fraction is less than 1.

[0019] Furthermore, when the homology classification result is determined to be contralateral metastatic, the method further includes determining the clonal origin pattern of the metastatic lesions, specifically through the following steps:

[0020] The Jakarta similarity index is calculated based on the number of subclonal single nucleotide variants shared by the first tumor and the second tumor, the number of clonal single nucleotide variants unique to the first tumor, and the number of clonal single nucleotide variants unique to the second tumor.

[0021] The Jakarta similarity index is compared with a preset similarity threshold;

[0022] If the Jakarta similarity index is greater than the preset similarity threshold, the metastatic lesion is determined to be of polyclonal origin; if the Jakarta similarity index is less than or equal to the preset similarity threshold, the metastatic lesion is determined to be of monoclonal origin.

[0023] The formula for calculating the Jakarta similarity index is as follows:

[0024]

[0025] In the formula, The Jakarta similarity index. This represents the number of subclonal single nucleotide variants shared by both the first and second tumors. The number of clonal single nucleotide variants unique to the second tumor. This refers to the number of clonal single nucleotide variants unique to the first tumor.

[0026] Furthermore, when the homology classification result indicates a contralateral transfer type, the method further includes estimating the sowing time of the transferred seeds, specifically through the following steps:

[0027] The seed-planting time is calculated based on the ratio of the number of clonal single nucleotide variants unique to the first tumor to the number of clonal single nucleotide variants unique to the second tumor, combined with a preset time correction factor; the calculation formula is as follows:

[0028]

[0029] In the formula, To change the seed sowing time; The time correction factor is mentioned above; This refers to the total time interval from the appearance of tumor blast cells to the diagnosis of the primary tumor.

[0030] Furthermore, after obtaining the first and second sequencing data of the bilateral renal tumors of the target subject, and before calculating the tumor cell fractions of each somatic mutation, the method further includes a molecular data cleaning step targeting sporadic features:

[0031] Obtain reference sequencing data of normal tissue from the target object;

[0032] If germline mutations are detected in preset hereditary bilateral renal cell carcinoma-related gene loci when comparing the normal tissue reference sequencing data, the homology analysis is terminated and an abnormality alert is output; the hereditary bilateral renal cell carcinoma-related gene loci include at least the VHL, MET, FLCN, SDHB, SDHC, SDHD, TSC1, and TSC2 genes.

[0033] The present invention also provides a homology analysis system for sporadic bilateral renal tumors, the system comprising:

[0034] The data acquisition module is used to acquire first and second sequencing data of bilateral renal tumors of the target object, wherein the first and second sequencing data contain tumor somatic mutation information and copy number variation information;

[0035] The score calculation module is used to calculate the tumor cell score of each somatic cell mutation in bilateral renal tumors based on the first sequencing data and the second sequencing data, respectively.

[0036] The clonal clustering module is used to cluster somatic mutations of bilateral renal tumors based on the tumor cell fraction, respectively, to construct a first clonal cluster set for the first tumor and a second clonal cluster set for the second tumor.

[0037] The homology classification module is used to compare the first clonal group set with the second clonal group set, extract common clonal group features of bilateral renal tumors, and process the common clonal group features based on preset homology interpretation rules to output the homology classification results of bilateral renal tumors for the target object.

[0038] The present invention also provides a computer device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the methods described above.

[0039] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0040] The homology analysis system for sporadic bilateral renal tumors provided by this invention reconstructs clonal structures and compares clonal evolutionary relationships of somatic mutations in bilateral tumors from whole-exome sequencing data. Based on preset interpretation rules, it classifies sporadic bilateral renal cell carcinomas according to tumor origin into bilateral primary, contralateral metastatic, and indeterminate origin types. This allows for objective classification conclusions of bilateral tumor origin in clinically long-standing issues of uncertainty through genomic data analysis. The system incorporates both clinical and molecular screening mechanisms to exclude hereditary bilateral renal cell carcinomas during sample screening, ensuring that the analysis subjects are strictly limited to sporadic bilateral renal cell carcinomas and avoiding the impact of confusion caused by different pathogenesis types on the accuracy of classification. The system's tumor origin classification module establishes interpretation rules based on differences in the clonal levels of common clonal mutations and driver mutations, and verifies the classification conclusions from multiple perspectives through paired comparative analysis of multidimensional genomic features and TMB comparison analysis with external databases. For patients diagnosed with contralateral metastatic disease, the system further quantifies the monoclonal or polyclonal origin pattern of metastatic lesions using the Jakar similarity index, and estimates the seeding time of metastatic lesions based on the relationship between the number of clonal mutations between the primary lesion and the metastatic lesions. This provides an objective molecular basis for developing differentiated treatment plans for sporadic bilateral renal cell carcinoma of different origins in clinical practice. Attached Figure Description

[0041] Figure 1This is a schematic diagram of the screening and enrollment process for patients with sporadic bilateral renal cell carcinoma in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the bilateral tumor three-dimensional volume reconstruction process in an embodiment of the present invention, where A is the acquisition of the original Dicom data of the preoperative image, B is the delineation of the tumor outline layer by layer and color marking, and C is the three-dimensional modeling and volume calculation.

[0043] Figure 3 This is a schematic diagram of the overall technical route of the system in this embodiment of the invention;

[0044] Figure 4 This is a schematic diagram of the bilateral tumor imaging features and grouping markers of the enrolled patients in this embodiment of the invention;

[0045] Figure 5 This is a panoramic waterfall diagram of sporadic bilateral renal cell carcinoma gene mutations in an embodiment of the present invention;

[0046] Figure 6 These are typical example diagrams of two clonal evolution modes in embodiments of the present invention, wherein A is a two-dimensional CCF distribution diagram of bilateral tumors in a bilateral primary tumor patient, B is a clonal evolution fish diagram of the patient, C is a two-dimensional CCF distribution diagram of bilateral tumors in a contralateral metastatic patient, and D is a clonal evolution fish diagram of the patient.

[0047] Figure 7 This is a comparative analysis diagram of genomic characteristics between the Early and Late groups of contralateral metastatic sporadic bilateral renal cell carcinoma in this embodiment of the invention. A is a comparison of base variation spectrum, B is a comparison of COSMIC mutation signature frequency, C is a comparison of TMB, D is a comparison of CNA burden, E is a comparison of wGII, F is a comparison of ITHi, and G is a comparison of SDI.

[0048] Figure 8 This is a comparative analysis diagram of genomic characteristics of bilateral primary sporadic bilateral renal cell carcinoma in the Early and Late groups in an embodiment of the present invention. A is a comparison of base variation spectrum, B is a comparison of COSMIC mutation signature frequency, C is a comparison of TMB, D is a comparison of CNA burden, E is a comparison of wGII, F is a comparison of ITHI, and G is a comparison of SDI.

[0049] Figure 9 This is a comparison analysis of TMB between contralateral metastatic bilateral clear cell renal cell carcinoma and unilateral clear cell renal cell carcinoma from the TCGA-KIRC database in this embodiment of the invention;

[0050] Figure 10The following is a diagram showing the results of the metastatic feature analysis of contralateral sporadic bilateral renal cell carcinoma in an embodiment of the present invention. A is a scatter plot that distinguishes monoclonal and polyclonal origins by the JSI threshold; B is a distribution plot of the number of various SNVs under the two origin modes; C is a schematic diagram of the concept of metastatic seeding time and a histogram of Ts distribution; D is a comparison plot of Ts values ​​for synchronic and metachronous bilateral renal cell carcinoma; E is a scatter plot of Spearman correlation analysis between bilateral tumor diagnosis time interval and Ts.

[0051] Figure 11 This is a diagram showing the relationship between tumor origin classification and prognosis in an embodiment of the present invention. In this diagram, A is a comparison of PFS survival curves between the bilateral primary group and the contralateral metastatic group, B is a comparison of OS survival curves between the two groups, C is a comparison of postoperative distant metastasis rates between the two groups, and D is a comparison of disease-free survival rates between the two groups.

[0052] Figure 12 This is a schematic diagram illustrating the concept of the renal cell carcinoma tumor evolution timeline and metastatic seed sowing time in an embodiment of the present invention;

[0053] Figure 13 This is a schematic diagram illustrating the main conclusions of the classification of sporadic bilateral renal cell carcinoma tumor origin in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and technical effects of this invention clearer, the specific embodiments of this invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of protection of this invention.

[0055] This invention provides a homology analysis system for sporadic bilateral renal tumors based on genomic clonal evolution characteristics. This system is used to determine the genomic homology of bilateral tumors in patients with sporadic bilateral renal cell carcinoma, thereby determining whether the bilateral tumors are bilaterally independent primary tumors or unilateral primary tumors with contralateral metastasis. Figure 3 As shown, the system includes a sample screening and preprocessing module, a whole-exome sequencing and data quality control module, a gene mutation detection and annotation module, a genome feature analysis module, a clonal evolution analysis module, a tumor origin classification module, and a metastatic feature analysis module. These modules are logically connected sequentially to form a complete analysis workflow. The following sections describe each module and its specific implementation.

[0056] The sample screening and preprocessing module is used to screen patient samples that meet the definition of sporadic bilateral renal cell carcinoma from the patient population, and to preprocess the acquired tissue samples to meet the quality requirements of subsequent sequencing. For example... Figure 1As shown, the definition of sporadic bilateral renal cell carcinoma is the starting point of this system's analytical workflow, and its accuracy directly affects the reliability of all subsequent analytical conclusions. Bilateral renal cell carcinoma can be divided into familial bilateral renal cell carcinoma and sporadic bilateral renal cell carcinoma based on the presence or absence of a family history. Familial bilateral renal cell carcinoma is closely related to specific germline gene mutations, and bilateral or multifocal renal tumors often appear as a manifestation of hereditary renal cell carcinoma syndrome. Its pathogenesis has been well elucidated at the molecular genetic level. The analytical object of this invention is strictly limited to sporadic bilateral renal cell carcinoma, meaning that the patient does not have the clinical manifestations of hereditary renal cell carcinoma syndrome, nor does he / she carry any known pathogenic germline gene mutations associated with hereditary bilateral renal cell carcinoma.

[0057] In practice, the sample screening and preprocessing module performs sporadic bilateral renal cell carcinoma screening, which includes the following two levels of assessment. The first level is clinical screening, which involves collecting the patient's clinical history information to confirm that the patient explicitly denies a family history of hereditary renal cell carcinoma and does not have multisystem clinical manifestations associated with known hereditary renal cell carcinoma syndromes. Known hereditary renal cell carcinoma syndromes associated with bilateral renal tumors and their characteristic manifestations are as follows: Von Hippel-Lindau syndrome, caused by the VHL gene (located at 3p25-26), typically presents with renal cell carcinoma diagnosed between the ages of 40 and 45. Clinically, it manifests as multiple tumors in both kidneys (most commonly renal cysts and clear cell renal cell carcinoma), often accompanied by pancreatic cysts, pancreatic serous cystadenomas, pancreatic neuroendocrine tumors, pheochromocytomas, and hemangioblastomas of the central nervous system and retina. Hereditary papillary renal cell carcinoma, caused by the MET gene (located at 7q31), typically presents with type I papillary renal cell carcinoma bilaterally or multiple times, often without involvement of other systems. Birt-Hogg-Dubé syndrome, caused by the FLCN gene (located at 17p11.2), typically presents with renal tumors around the age of 50. Clinically, it manifests as multiple tumors in both kidneys (most commonly eosinophilic cysts, chromophobe cysts, and hemangioblastomas of the central nervous system and retina). Renal cell carcinoma is commonly seen in several types of kidney cancer, often accompanied by multiple fibrolipomas of the skin, multiple cysts in both lungs, and pneumothorax. Succinate dehydrogenase-deficient renal cell carcinoma, caused by genes including SDHB (located at 1p36.13), SDHC (located at 1q23.3), and SDHD (located at 11q23.1), is typically diagnosed in patients aged 30 to 40 years, and clinically presents with multiple tumors in both kidneys, pheochromocytomas, and paragangliomas. Tuberous sclerosis, caused by TSC1 (located at 9q34.3) and TSC2 (located at 16p13.3), is also typically diagnosed around age 30 years, and clinically presents with multiple tumors in both kidneys (most commonly hamartomas, renal cysts, eosinophilic tumors, or chromophobe carcinomas), often accompanied by pulmonary lymphangioleiomyomatosis, cortical tuberous sclerosis, subependymal giant cell astrocytoma, and cardiac rhabdomyomas. All of these syndromes are autosomal dominant inherited. In the implementation of this module, a systematic assessment is conducted on whether patients have typical clinical manifestations of the above syndromes. If a patient has clinically suspected manifestations or conclusive evidence of any of the above syndromes, they will not be included in the scope of analysis of this system.

[0058] The second level is molecular screening. After subsequent whole-exome sequencing, germline variation detection results from the sequencing data are used to determine whether the patient carries pathogenic germline gene mutations associated with the aforementioned hereditary bilateral renal cell carcinoma. Specifically, germline mutation analysis is performed on sequencing data from adjacent normal tissue samples, focusing on gene loci known to be associated with hereditary renal cell carcinoma syndrome, such as VHL, MET, FLCN, SDHB, SDHC, SDHD, TSC1, and TSC2 genes. If reported pathogenic or suspected pathogenic germline mutations are detected in the above genes, the patient is considered to have a hereditary renal cell carcinoma background and is not included in the analysis scope of sporadic bilateral renal cell carcinoma; the corresponding sample will be excluded. Only when the patient simultaneously meets the criteria of both levels above—that is, clinically denying a family history of hereditary renal cell carcinoma and not having clinical manifestations of hereditary renal cell carcinoma syndrome, and the sequencing data not finding pathogenic germline mutations in the above genes—can sporadic bilateral renal cell carcinoma be confirmed and included in subsequent analysis. Figure 1 As shown, in one embodiment, after excluding patients who have undergone unilateral surgery, patients with different pathological types of bilateral tumors, patients with incomplete clinicopathological data, and patients with hereditary renal cell carcinoma syndrome, the initial candidate patients enter the DNA extraction and quality control stage; after excluding samples with substandard tumor cell content or DNA concentration, as well as samples with substandard whole exome sequencing data quality, a collection of sporadic bilateral renal cell carcinoma samples that can be used for subsequent analysis is finally obtained.

[0059] Regarding sample type, the tissue samples analyzed by this system are specimens obtained from patients with sporadic bilateral renal cell carcinoma after surgical resection. Each patient includes at least three samples: tumor tissue from the left kidney, tumor tissue from the right kidney, and adjacent normal tissue. Adjacent normal tissue is used as a paired normal control for somatic mutation detection. For quality control of tissue samples, in accordance with TCGA protocol requirements, the proportion of tumor cell nuclei in tumor tissue samples should not be less than 50%, and the proportion of necrotic tissue should not be less than 20%, to ensure sufficient coverage of tumor-related signals in subsequent sequencing data. Samples can be derived from fresh frozen tissue after surgery or from formalin-fixed paraffin-embedded specimens. For paraffin-embedded specimens, the paraffin sections must be pathologically reviewed by technicians with a pathological background before DNA extraction to confirm that both tumor and normal tissue components in the sample meet quality requirements. The sample screening and preprocessing module also performs consistency determination of bilateral tumor pathological types. In one implementation of this system, patients with sporadic bilateral renal cell carcinoma included in the analysis are required to have bilateral tumors with the same pathological subtype, such as bilateral clear cell renal cell carcinoma or bilateral papillary renal cell carcinoma. The classification of pathological subtypes was based on the World Health Organization's 2016 classification criteria for renal cell carcinoma, and the nuclear grading was based on the WHO / ISUP grading system. Patients with bilateral tumor pathological types differed from the clonal analysis framework established by this system in terms of the logical inference of tumor origin, and therefore were not included in this implementation.

[0060] This system also includes a bilateral tumor temporal grouping submodule, used to estimate the chronological order of bilateral tumor occurrence in patients with synchronous sporadic bilateral renal cell carcinoma. Sporadic bilateral renal cell carcinoma can be divided into synchronous and metachronous types according to the time interval between the diagnosis of bilateral tumors. Synchronous bilateral renal cell carcinoma refers to the discovery of bilateral renal lesions at the time of patient's visit, or the discovery of contralateral renal cell carcinoma within six months (inclusive) after the discovery of unilateral renal cell carcinoma at the time of patient's visit. Metachronous bilateral renal cell carcinoma refers to the discovery of contralateral renal cell carcinoma more than six months after the diagnosis of unilateral renal cell carcinoma. For metachronous patients, the chronological order of bilateral tumor occurrence can be directly determined based on the order of diagnosis, with the tumor diagnosed earlier in the Early group and the tumor diagnosed later in the Late group. For synchronous patients, since both renal tumors are discovered almost simultaneously, other methods are needed to infer the order of occurrence. This system uses a three-dimensional volume reconstruction method based on imaging data to estimate the volume of bilateral tumors. Assuming that the pathological types of the tumors on both sides are the same and there is no statistically significant difference in WHO / ISUP nuclear grade, it is assumed that the growth rates of the tumors on both sides are similar. The larger tumors occurred earlier and were classified into the Early group; the smaller tumors occurred later and were classified into the Late group.

[0061] like Figure 2As shown, the specific volume estimation process includes three steps: Step A, using medical image viewing software to acquire the raw DICOM format data of the patient's preoperative enhanced CT scan arterial phase image or MRI T2-weighted sequence image; Step B, using image segmentation software to delineate the contours of both tumors layer by layer, and marking the tumors on both sides with different colors (e.g., green for the left kidney tumor and yellow for the right kidney tumor), generating a 3D reconstruction project file; Step C, using 3D reconstruction software to read the above project file, perform 3D modeling of both tumors, and calculate the volume of each tumor. If the patient has multiple tumor lesions in one kidney, the largest tumor lesion on that side is used as the primary lesion for volume comparison. Figure 4 As shown, in one embodiment, the volume of 44 tumors from 21 patients with sporadic bilateral renal cell carcinoma was estimated and grouped using the method described above. This time-series grouping result will be used for inferring the directionality of primary and metastatic lesions in subsequent clonal evolution analysis.

[0062] The whole-exome sequencing and data quality control module is responsible for performing whole-exome sequencing on screened samples and preprocessing and quality-controlling the raw sequencing data. In one implementation, genomic DNA is extracted from formalin-fixed paraffin-embedded specimens using the QIAGEN DNA Tissue Kit, and DNA from exon regions is isolated using targeted capture pulldown technology. A whole-exome library is constructed using the xGen Exome Research Panel and TruePrep DNA Library Prep Kit V2. Paired-end sequencing is then performed on the NovaSeq 6000 high-throughput sequencing platform. For tumor tissue samples, the average sequencing depth reaches 262×; for paired adjacent normal tissue samples, the average sequencing depth reaches 174×. After sequencing, the sequencing data is aligned with the human reference genome (NCBIbuild 37) using the Burrows-Wheeler Aligner (BWA), and sambamba is used to remove repetitive alignment sequences generated during polymerase chain reaction amplification, obtaining preprocessed alignment data. This alignment data forms the basis for all subsequent analytical steps.

[0063] The gene mutation detection and annotation module is used to identify and functionally annotate tumor somatic mutations from alignment data. In one implementation, this module uses Strelka2 software (default settings) and paired adjacent normal tissue sequencing data as controls to detect single nucleotide variants (SNVs) and small insertion / deletion mutations (InDels) in each tumor tissue. For SNV identification, an effective sequencing read length covering the mutated region is required. reads, and the read length of the mutated allele. For InDel identification, the required allele read length is specified. Reads. In normal control tissue samples, the average sequencing depth at the same site must be [missing information]. Furthermore, this site supports the read length of the variant allele. Reads were analyzed to exclude interference from germline mutations. In the mutation filtering stage, minor allele frequencies (MAFs) from public population genetic variation databases such as ExAC, gnomAD, and esp6500 were used. Base mutations were considered common germline polymorphisms and eliminated. The ratio of base transitions (Ti) to transversions (Tv) was used to screen for candidate oncogenic mutations. The variants retained after the above filtering were considered candidate somatic mutations. Nucleotide variations and polymorphisms were identified and functionally annotated using Ensembl Variant EffectPredictor to determine the type of mutation's impact on protein coding, including missense mutations, nonsense mutations, frameshift deletions, frameshift insertions, splice site mutations, and multi-hit mutations.

[0064] This module also assesses the pathogenicity of mutations in known tumor driver genes. Specifically, a mutation in a driver gene is identified as a driver mutation if it meets any of the following criteria: Criterion 1: The mutation is a nonsense mutation, frameshift mutation, or splice site mutation; Criterion 2: The missense mutation has a high FATHMM-MKL score in the COSMIC database. Condition 3: The missense mutation simultaneously satisfies at least two of the following three independent prediction evaluation criteria: The prediction score in the SIFT model is... In the Polyphen2 model, it is rated as "potentially damaging" or "very likely to damage," and in the MutationAssessor model, it is rated as "moderate" or "high."

[0065] This module also includes a significant mutant gene (SMG) screening function. In one implementation, after processing the MAF file with maftools software, significant mutant genes are screened using MutSigCV software. A significant mutant gene is defined as... Mutated genes. For example... Figure 5As shown, in the sporadic bilateral renal cell carcinoma samples analyzed in this system, whole-exome sequencing was performed on 44 tumor samples from 21 patients, revealing a total of 11,781 non-synonymous somatic mutations, including 11,710 non-synonymous SNVs and 71 non-synonymous InDels. The detected significant mutated genes and their mutation frequencies are as follows: VHL (20 / 44, 45.5%), PBRM1 (9 / 44, 20.5%), SETD2 (4 / 44, 9.1%), MTOR (4 / 44, 9.1%), KDM5C (4 / 44, 9.1%), ATM (3 / 44, 6.8%), and BAP1 (3 / 44, 6.8%). These seven significant mutated genes accounted for 58.3% (7 / 12) of the reference driver mutation genes. In addition, driver mutations of CUL3 (1 / 44, 2.3%), TP53 (1 / 44, 2.3%) and PTEN (1 / 44, 2.3%) were detected. These genes play roles in the KEAP1-NRF2-CUL3 signaling pathway, the P53-related signaling pathway, and the PI3K-AKT-mTOR signaling pathway, respectively.

[0066] The genomic characterization module is used to quantify the genomic characteristics of each tumor in multiple dimensions. The characteristic parameters calculated by this module include base variation spectrum, mutation signature spectrum, tumor mutation burden (TMB), copy number variation (CNA) burden, weighted genomic instability index (wGII), intratumoral heterogeneity index (ITHi), and Shannon diversity index (SDI).

[0067] The method for calculating the spectrum of base variations is as follows: The relative frequency distribution of the six base substitution types in each tumor is statistically analyzed. Considering the base complementary pairing principle (A pairs with T, G pairs with C), point mutations of any base in the DNA double helix are summarized into the following six variation forms: C>A, C>G, C>T, T>A, T>C, and T>G. Figure 7 A and Figure 8 As shown in Figure A, in the sporadic bilateral renal cell carcinoma samples analyzed in this system, C>T was the most common base variant, with a median frequency of 40.3% (IQR 12.8%–50.7%). The median frequencies of other variant types were as follows: T>C 11.6% (IQR 2.9%–15.5%), C>A 10.1% (IQR 3.5%–13.5%), C>G 8.3% (IQR 2.0%–11.5%), T>A 7.6% (IQR 2.0%–11.5%), and T>G 6.4% (IQR 2.1%–9.9%).

[0068] In mutation signature analysis, a three-base sequence motif is formed by one base upstream and downstream of the mutation site and the mutated base. The arrangement and combination of the four bases form... Ninety-six possible mutation patterns were identified. The frequencies of 96 mutation patterns in each tumor were decomposed into linear combinations of 30 mutation signs reported in the COSMIC database using nonnegative matrix factorization. Further tumor mutation spectrum analysis was performed using MutationalPatterns software. In the sporadic bilateral renal cell carcinoma samples analyzed in this system, mutation frequencies of six signs were identified. The median frequencies are as follows: Signature 1, 24.7% (IQR 17.5%–37.9%); Signature 12, 16.1% (IQR 3.7%–29.5%); Signature 6, 15.3% (IQR 7.3%–21.5%); Signature 3, 7.4% (IQR 0.2%–15.7%); Signature 22, 3.6% (IQR 2.2%–5.9%); and Signature 11, 2.4% (IQR 0%–5.7%).

[0069] Tumor mutation burden (TMB) is defined as the number of non-synonymous somatic mutations per megabase (MB) in the coding regions of the genome. When calculating TMB, gene mutations within the homogeneous coding sequences generated by the CDS engineering are first selected, and then variant counts are removed. mutations, allele frequencies mutations and total sequencing depth After removing synonymous and splice mutations, non-synonymous mutations (including SNV and InDel) were counted. The median TMB in 21 sporadic bilateral renal cell carcinoma cases was 5.3 mut / MB (IQR 4.2–8.1 mut / MB).

[0070] Copy number variation load was obtained through genome-wide quantitative analysis of somatic copy number variations using the FACETS algorithm, calculating the proportion of regions with copy number abnormalities in the genome. The median CNA load in 21 sporadic bilateral renal cell carcinomas was 6.9% (IQR 1.3%–15.2%). The weighted genomic instability index (wGII) was used to measure the overall chromosomal instability of the tumor genome. It was calculated using a previously reported algorithm, weighted and summed for the proportions of regions on each chromosome whose copy number deviated from the normal diploid level. The median wGII in 21 sporadic bilateral renal cell carcinomas was 0.2 (IQR 0.1–0.3). Intratumoral heterogeneity was assessed using two indicators: ITHi and SDI. ITHi was calculated based on the ratio of subclonal to clonal mutations, while SDI was calculated using the information entropy formula based on the frequency distribution of each clonal population within the tumor. The median ITHi was 4 (IQR 3–5) and the median SDI was 0.7 (IQR 0.5–0.9) in 21 sporadic bilateral renal cell carcinoma cases. The calculated results of these genomic characteristic parameters will be used for subsequent comparative analysis of genomic characteristics between bilateral tumors, providing auxiliary validation for the tumor origin classification conclusions.

[0071] The clonal evolution analysis module is the core module of this system. Its function is to reconstruct the clonal structure of bilateral tumors for each patient using high-confidence somatic mutation data, and to infer the evolutionary relationship between the bilateral tumors by comparing the similarity of their clonal structures. This module receives somatic mutation data and copy number variation data output from the gene mutation detection and annotation module, and inputs both into the PyClone Bayesian statistical model for clonal population identification and frequency estimation. Specifically, parameters such as the allele frequency, local copy number status, and tumor purity of the somatic single nucleotide variants detected in each tumor are input into the PyClone model to calculate the Cancer Cell Fraction (CCF) for each mutation. The CCF reflects the proportion of tumor cells carrying the mutation in the entire tumor cell population. Based on the distribution pattern of the CCF, mutations with similar CCFs are grouped into the same clonal population. Furthermore, the PyClone output is input into an iterative version of the Citup evolutionary model, using maximum likelihood or Bayesian methods to infer the ancestral relationships between clonal populations and reconstruct the clonal evolutionary tree of the tumor.

[0072] In the classification definition of clonal populations, the upper bound of the 95% confidence interval (CI) of CCF is set. The clonal population is defined as the master clone, representing the earliest mutated group in the tumor that is shared by all or the vast majority of tumor cells; the upper bound of the 95% CI of CCF is set. The clonal group is defined as a subclone, representing a group of mutations that appears later in the tumor evolution process and exists only in some tumor cells.

[0073] like Figure 6 As shown, for each patient, this system calculates the CCF of each clonal group in both tumors and plots a two-dimensional CCF distribution map in a two-dimensional coordinate system with the CCF of one tumor as the horizontal axis and the CCF of the other tumor as the vertical axis (e.g., Figure 6 A and Figure 6 As shown in Figure C), to visually present the differences in clonal population distribution between the two tumors. Simultaneously, a clonal evolution fish diagram (e.g., using the Timescape tool) is generated. Figure 6 B and Figure 6 As shown in Figure D), the abundance changes of each clonal population in bilateral tumors over time and the clonal phylogenetic relationships are dynamically displayed. Figure 6 Two distinct models of clonal evolution are given: Figure 6 A and Figure 6 The patient shown in B has significant differences in the clonal evolution characteristics of the bilateral tumors. Each clonal group is concentrated near the two coordinate axes in the two-dimensional CCF diagram, indicating that the clonal evolution of the bilateral tumors is independent and suggesting that both tumors are primary. Figure 6 C and Figure 6 The patient shown in D exhibits highly similar bilateral tumor clonal evolutionary characteristics, with multiple clonal clusters distributed diagonally in the 2D CCF map, suggesting that the bilateral tumors are unilateral primary and contralateral metastases. The distribution of bilateral tumor clonal clusters in the 21 enrolled patients is presented using 2D CCF maps and evolutionary fish diagrams.

[0074] The tumor origin classification module, based on the output of the clonal evolution analysis module, classifies the bilateral tumor origin of each patient with sporadic bilateral renal cell carcinoma according to pre-defined interpretation rules. This system categorizes sporadic bilateral renal cell carcinoma into three types based on tumor origin: bilateral primary, contralateral metastatic, and indeterminate origin. Figure 13 As shown, the design of the interpretation rules follows the following logic.

[0075] If there are no common clonal mutations between the two tumors, meaning that all clonal mutations detected in one tumor are not detected in the other, it indicates that the clonal evolution of the two tumors is completely independent and unfolds along different pathways. In this case, the patient is diagnosed with bilateral primary tumors. As shown in Figure 6A, this type of bilateral tumor appears on a two-dimensional CCF distribution map as follows: the vast majority of mutation clusters are concentrated near the coordinate axes, meaning that there is a significant CCF only in one tumor and close to zero in the other tumor, with no shared clonal clusters in the diagonal direction between the two.

[0076] If a common clonal mutation exists between bilateral tumors, and the clonal structure pattern formed by the common clonal mutation conforms to at least one of the following three sub-patterns, the patient is considered to have contralateral metastatic disease. The first seed pattern is: both tumors have the same clonal driver gene mutation, and this driver mutation is at the master clonal level in both tumors, indicating that both tumors share the earliest driver event and have a common tumor clonal ancestor. The second seed pattern is: the common clonal driver gene mutation is at the master clonal level in one tumor and decreases to the subclonal level in the other tumor, suggesting that the driver mutation is an early event inherited by all tumor cells in the primary tumor, while it is carried by only some tumor cells in the metastatic lesion, consistent with the expected change in clonal frequency during dissemination from the primary tumor to the metastatic lesion. The third seed pattern is: there is a common clonal mutation between bilateral tumors. The presence of common passenger mutations in multiple clonal populations, with these common passenger mutations occurring at the principal clonal level in one tumor, suggests that mutational sharing between bilateral tumors encompasses multiple independently occurring passenger mutations. This broad pattern of mutational sharing probabilistically contradicts the hypothesis of independent origin and is more consistent with the evolutionary explanation of metastasis and dissemination.

[0077] In the 21 sporadic bilateral renal cell carcinoma cases analyzed in this system, the classification results are as follows: 7 cases (33.3%) had no common clonal mutations in either side of the tumor and were classified as bilateral primary type; 14 cases (66.7%) had common clonal mutations in both sides of the tumor, of which 12 cases had relatively similar clonal evolutionary structures and met one of the above three sub-patterns, and were classified as contralateral metastatic type—specifically, 3 cases (25.0%) belonged to the first seed pattern, 2 cases (16.7%) belonged to the second seed pattern, and 7 cases (58.3%) belonged to the third seed pattern; another 2 cases (9.6%) had only common clonal mutations in two clonal groups, neither of which were driver mutations, and it was impossible to make a definite determination of the origin of their bilateral tumors based on existing interpretation rules, so they were classified as indeterminate origin type. That is, in the 21 sporadic bilateral renal cell carcinoma cases, 12 cases (57.1%) were contralateral metastatic type, 7 cases (33.3%) were bilateral primary type, and 2 cases (9.6%) were indeterminate origin type.

[0078] This classification module also performs comparative analysis of bilateral tumor genomic features to validate the classification conclusions of clonal evolution analysis from another perspective. For the 12 patients diagnosed with contralateral metastatic tumors, paired statistical tests were performed on various genomic characteristic parameters of the Early group (primary lesions) and the Late group (metastatic lesions). Figure 7 As shown, there were no statistically significant differences in the frequencies of the six base variations between the Early and Late groups in patients with contralateral metastatic disease. The specific results are: C>A (10.4% vs 11.6%). C>G (10.5% vs 10.3%) C>T (46.5% vs 46.8%) T>A (8.8% vs 9.1%) T>C (12.8% vs 14.4%) T>G (8.2% vs 6.3%) ).like Figure 7 As shown in B, there were no statistically significant differences in the signature frequencies of the six significant COSMIC mutations between the two groups. ): Signature.1 (18.6% vs 23.4%, ), Signature.3 (5.7% vs 4.7%, ), Signature.6 (17.9% vs 20.7%, ), Signature.11 (4.3% vs 0.7%, ), Signature.12 (30.5% vs18.0%, ), Signature.22 (4.6% vs 3.7%, ).like Figure 7 As shown in D to 7G, the CNA load in the two groups (5.4% vs 5.9%) wGII (0.2 vs 0.1) ), ITHI (5.0 vs 3.5, ) and SDI (1.0 vs 0.7, The differences were not statistically significant, indicating that the bilateral tumors in contralateral metastatic patients were highly similar in overall genomic characteristics, consistent with the expectation of homologous evolution. Figure 7 As shown in C, the TMB in the Early group was significantly higher than that in the Late group, and the difference was statistically significant (8.2 mut / MB vs 5.1 mut / MB). This result is consistent with the biological logic that the primary lesion accumulates more somatic mutations during the disease process.

[0079] For the seven patients diagnosed with bilateral primary tumors, bilateral tumor genomic characteristics were compared and analyzed using the same method. Figure 8 As shown, the CNA load in the Early group was significantly higher than that in the Late group (15.2% vs 0.8%). ), while the base variation spectrum, COSMIC mutation signature frequency, and TMB ( ), wGII ( ), ITHI ) and SDI ( There were no statistically significant differences between the two groups. Notably, there was no significant difference in TMB between the two groups in patients with bilateral primary lesions, which contrasts with the pattern in contralateral metastatic patients where the TMB in the primary lesion was significantly higher than that in the metastatic lesions, thus indirectly supporting the rationality of the two classification models.

[0080] In a further validation implementation, the system retrieved whole-exome sequencing data from 417 cases of unilateral clear cell renal cell carcinoma (ccRCC) from the TCGA-KIRC database. A total of 36,353 somatic mutations were identified in these 417 cases of unilateral ccRCC, including 16,821 missense mutations, 6,383 silent mutations, and 2,999 InDel mutations, with a calculated mean TMB of 1.1 mut / MB. Figure 9 As shown, the mean TMB of contralateral metastatic sporadic bilateral clear cell renal cell carcinoma in this system was 3.3 mut / MB, which was significantly higher than the mean TMB of unilateral ccRCC in TCGA-KIRC. This result suggests that contralateral metastatic sporadic bilateral renal cell carcinoma is more progressive in its disease course than unilateral renal cell carcinoma, further supporting the classification conclusion that this type of tumor is not bilaterally independent primary but rather involves a metastatic evolutionary relationship.

[0081] The metastatic feature analysis module further analyzes the clonal origin pattern and seeding time of metastatic lesions in patients diagnosed with contralateral sporadic bilateral renal cell carcinoma. The input data for this module includes bilateral tumor clonal population information output from the clonal evolution analysis module and classification results from the tumor origin classification module.

[0082] In determining the clonal origin pattern, this system uses the Jaccard Similarity Index (JSI) to quantify the somatic mutation similarity between the primary and metastatic lesions. The formula for calculating JSI is:

[0083]

[0084] in The number of clonal SNVs unique to metastatic lesions, The number of clonal SNVs unique to the primary lesion. This refers to the number of subclonal SNVs shared by the primary lesion and metastatic lesions.

[0085] like Figure 10 As shown in A, As a classification threshold. When When the metastatic lesion is determined to be of polyclonal origin, that is, the metastatic lesion is formed by the dissemination of multiple different tumor cell subpopulations from the primary lesion; when At this point, metastatic lesions are determined to be of monoclonal origin, meaning they originate from a single clone or cell population within the primary tumor. This threshold was set based on a systematic review of the classification accuracy of different thresholds in existing studies; a threshold of 0.3 achieved a classification accuracy of 91.1%. In the 12 cases of contralateral sporadic bilateral renal cell carcinoma analyzed in this system, all patients had a common subclonal SNV in both tumors, further confirming that the contralateral tumor was metastatic. Of these, 11 cases (91.7%) were of polyclonal origin, and 1 case (8.3%) was of monoclonal origin. Figure 10 As shown in B, in sporadic bilateral renal cell carcinoma of polyclonal origin, the number of subclonal SNVs shared by the primary lesion and metastases was significantly greater than the number of clonal SNVs unique to the metastases. In cases of monoclonal origin, the number of clonal SNVs unique to each metastatic lesion and the primary lesion is greater than the number of subclonal SNVs shared by both.

[0086] In estimating the sowing time of transferred seeds, such as Figure 12 As shown, this system uses the clonal mutation number relationship between the primary tumor and metastatic lesions to estimate the time interval from the formation of the initial metastatic seed cells (disseminated tumor cells, DTCs) to the clinical diagnosis of the primary tumor. This time interval is the metastatic seed sowing time. The estimation process is based on the following mathematical model. Auxiliary parameters are set. :

[0087]

[0088] in The time interval between the emergence of tumor stem cells and the formation of the most recent common ancestor in the primary tumor. The total time interval from the appearance of the primitive tumor cells of renal cell carcinoma to the clinical diagnosis of the primary tumor. The approximate calculation formula is:

[0089]

[0090] in The number of clonal SNVs unique to metastatic lesions, The number of clonal SNVs unique to the primary lesion. The higher the value, the earlier the formation of metastatic seed cells, the earlier the dissemination event occurs in the tumor evolution process, and the closer the occurrence of metastatic lesions is to the occurrence of the primary lesion. The smaller the value, the later the dissemination event occurs, and the later the metastatic lesion occurs compared to the primary lesion.

[0091] like Figure 10As shown in Figure C, in the contralateral metastatic sporadic bilateral renal cell carcinoma analyzed in this system, the median... It is 18.7 years (range 2.9–19.5 years). For example... Figure 10 As shown in D, the median number of synchronous sporadic bilateral renal cell carcinomas... The median duration of metachronous sporadic bilateral renal cell carcinoma was 19.0 years. The time was 18.7 years, with the seeding time of the original transferred cells in the metachronous group being slightly later than that in the synchronous group. The difference was not statistically significant. This result is consistent with clinical experience that the contralateral tumor in metachronous bilateral renal cell carcinoma appears later than in synchronous bilateral renal cell carcinoma. Figure 10 As shown in E, the Spearman correlation analysis results indicate that... It was negatively correlated with the time interval between bilateral tumor diagnoses, but the correlation was not statistically significant. , ).

[0092] This system also includes a tumor prognosis validation module, used to validate the clinical value of tumor origin classification based on clonal evolution analysis by comparing the oncological prognosis of bilateral primary and contralateral metastatic sporadic bilateral renal cell carcinoma. The main analytical indicators include overall survival (OS), progression-free survival (PFS), postoperative metastasis rate, and disease-free survival rate. Tumor treatment efficacy and prognostic outcomes are assessed according to the RECIST 1.1 criteria. For patients with metastatic renal cell carcinoma, the IMDC risk stratification model is used for assessment. This model includes six risk factors: diagnosis-to-treatment interval less than 1 year, KPS score less than 80, serum calcium level above the upper limit of the normal reference range, hemoglobin level below the lower limit of the normal reference range, neutrophil level above the upper limit of the normal reference range, and platelet level above the upper limit of the normal reference range. Patients are divided into low-risk (0 risk factors), intermediate-risk (1–2 risk factors), and high-risk (3–6 risk factors) groups based on the number of risk factors they possess.

[0093] like Figure 11 As shown, in one embodiment, after excluding patients with papillary renal cell carcinoma and patients with undetermined origin, a prognostic comparison analysis was performed on patients with bilateral primary and contralateral metastatic sporadic bilateral renal cell carcinoma. Figure 11 A and Figure 11 As shown in B, survival curve analysis indicates that both PFS and OS were superior in the bilateral primary group to the contralateral metastatic group. and ).like Figure 11 As shown in Figure C, the distant metastasis rate after surgery was higher in the contralateral metastasis group than in the bilateral primary group (50.0% vs 14.3%). ).like Figure 11As shown in Figure D, the disease-free survival rate in the bilateral primary tumor group was higher than that in the contralateral metastasis group (85.7% vs 50.0%). The above results suggest that contralateral metastatic sporadic bilateral renal cell carcinoma is more prone to local recurrence and / or distant metastasis after surgery, which corroborates the clinical application value of tumor origin classification based on clonal evolution analysis.

[0094] In a complete analytical workflow implementation, such as Figure 3 As shown, the above modules operate in a coordinated manner in the following order: The sample screening and preprocessing module first screens and confirms candidate patients for sporadic bilateral renal cell carcinoma and preprocesses tissue samples to ensure that all patients included in the analysis meet the inclusion criteria for sporadic bilateral renal cell carcinoma, i.e., patients deny a family history of hereditary renal cell carcinoma and do not exhibit hereditary renal cell carcinoma syndrome, and sequencing does not reveal pathogenic germline gene mutations associated with familial bilateral renal cell carcinoma. The whole-exome sequencing and data quality control module performs sequencing and data preprocessing on qualified samples. The gene mutation detection and annotation module detects somatic mutations from the sequencing data and completes functional annotation and driver mutation identification. The genomic feature analysis module calculates multidimensional genomic feature parameters for each tumor. The clonal evolution analysis module reconstructs the clonal structure of bilateral tumors and generates a clonal evolution map. The tumor origin classification module classifies the origin of bilateral tumors according to interpretation rules and verifies it through genomic feature comparison analysis and TMB comparison analysis with external databases. For patients diagnosed with contralateral metastatic disease, the metastatic feature analysis module further analyzes the clonal origin pattern (monoclonal / polyclonal) and seeding time of the metastatic lesions. Finally, the system outputs the bilateral tumor origin classification results for each patient along with supporting genomic evidence, including a clonal evolutionary structure map of the bilateral tumors, a two-dimensional CCF distribution map, a clonal evolutionary fish diagram, a gene mutation panorama, values ​​of various genomic characteristic parameters, and JSI values. Estimated values ​​and prognostic analysis results, etc.

[0095] The classification results of this system have clear clinical application value. For patients diagnosed with sporadic bilateral renal cell carcinoma of the primary type, the treatment strategy for each tumor can be independently formulated according to their respective clinical stages, based on surgical treatment, with postoperative follow-up plans similar to those for localized or locally advanced renal cell carcinoma. For patients diagnosed with contralateral metastatic sporadic bilateral renal cell carcinoma, indicating that it is essentially metastatic renal cell carcinoma, the treatment strategy should be comprehensively evaluated in conjunction with the diagnostic and treatment guidelines for metastatic renal cell carcinoma. Individualized systemic treatment plans can be given based on IMDC risk stratification results, and postoperative follow-up should be more stringent than for patients with bilateral primary type. In one application implementation, once the clinical diagnosis of sporadic bilateral renal cell carcinoma is established, it is recommended to perform puncture biopsy on both tumors to obtain tissue samples, perform whole-exome sequencing on the tumor tissue and adjacent normal tissue, use this system to perform homology analysis and origin classification of the bilateral tumors, and provide patients with individualized treatment plans and follow-up plans based on the classification results.

[0096] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it performs the functions of each module in the above-described system for homology analysis of sporadic bilateral renal tumors based on genomic clonal evolution characteristics. The storage medium may be, but is not limited to, a read-only memory, random access memory, a magnetic disk, an optical disk, flash memory, or any other medium suitable for storing computer program code. In one embodiment, after the computer program stored on the computer-readable storage medium is loaded and executed by the processor, it sequentially performs the following steps: receiving whole-exome sequencing data of bilateral tumor tissue samples and adjacent normal tissue samples from patients with sporadic bilateral renal cell carcinoma; performing quality control and preprocessing on the sequencing data, including sequence alignment and repetitive sequence removal; performing germline mutation detection on the sequencing data of adjacent normal tissue, focusing on screening for gene loci associated with known hereditary bilateral renal cell carcinoma syndromes such as VHL, MET, FLCN, SDHB, SDHC, SDHD, TSC1, and TSC2, to determine whether the patient carries pathogenic germline gene mutations, and terminating the analysis and outputting a prompt message if such germline mutations are detected; and simultaneously detecting somatic SNVs and InDel in each tumor using adjacent normal tissue data as a control. Functional annotation was performed; significant mutated genes were screened and driver mutations were identified; the base variation spectrum, mutation signature spectrum, TMB, CNA burden, wGII, ITHi, and SDI of each tumor were calculated; the clonal structure of each tumor was reconstructed using the PyClone Bayesian statistical model and the Citup evolutionary model, and clonal evolutionary relationships were inferred; the clonal structures of the bilateral tumors were compared, and the origin category of the bilateral tumors was determined according to the preset interpretation rules as bilateral primary, contralateral metastatic, or indeterminate; the various genomic characteristic parameters of the bilateral tumors were compared and analyzed to verify the origin classification conclusions; TMB was compared and analyzed with external databases to further verify the classification conclusions; for patients determined to be contralateral metastatic, JSI was calculated to determine whether the clonal origin pattern of the metastatic lesions was monoclonal or polyclonal, and based on the above... The formula estimates the sowing time for transferred seeds; it generates structured output data containing all the above analysis results.

[0097] In another embodiment of the present invention, the above-mentioned system is deployed as software on a server or local workstation. It receives sequencing data files and clinical parameter information input by the user through a graphical user interface or command-line interface, calls various analysis modules in the background to perform computational tasks, and presents the analysis results to the user in the form of a report. The report content includes, but is not limited to, basic patient information, confirmation information of sporadic bilateral renal cell carcinoma (including germline mutation screening results), a panoramic view and waterfall plot of gene mutations in both tumors, a list of significantly mutated genes and their frequencies, various genomic characteristic parameters, a two-dimensional CCF distribution map and clonal evolution fish diagram of both tumors, tumor origin classification conclusions and basis, comparative analysis results of bilateral tumor genomic characteristics, TMB comparison results with external databases, JSI value and clonal origin pattern determination. Estimated values ​​and prognostic reference information are provided. In terms of system architecture, data is transferred between analysis modules through standardized data interfaces, supporting independent upgrades and replacements of modules. For example, when a new or improved somatic mutation detection algorithm emerges, the detection engine in the gene mutation detection and annotation module can be replaced without affecting the operation of other modules; similarly, when a superior statistical model emerges in the field of clonal evolutionary analysis, the core algorithm in the clonal evolutionary analysis module can also be replaced.

[0098] It should be noted that the specific parameter thresholds, algorithm names, and software tools involved in the above embodiments are merely illustrative examples and should not be construed as limiting the scope of protection of this invention. Without departing from the basic technical concept of this invention, those skilled in the art can adjust and replace the specific parameter settings, algorithm selection, and implementation details according to actual application scenarios and data characteristics. For example, whole exome sequencing can be replaced with whole genome sequencing or targeted genome sequencing, and the corresponding mutation detection and analysis procedures can be adaptively adjusted; clonal structure reconstruction can employ Bayesian or maximum likelihood methods other than PyClone and Citup; the classification threshold of JSI can be optimized and adjusted according to the characteristics of different sample sets; specific software tools in base variation spectrum analysis, mutation signature analysis, and other steps can also be replaced with other functionally equivalent tools. All the above-described modified embodiments should be considered as reasonable coverage of the scope of protection of this invention.

[0099] It should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for homology analysis of sporadic bilateral renal tumors, executed by computer equipment, characterized in that, The method includes: First and second sequencing data of bilateral renal tumors of the target object are obtained, wherein the first and second sequencing data contain tumor somatic mutation information and copy number variation information; Based on the first sequencing data and the second sequencing data, the tumor cell fractions of each somatic cell mutation in bilateral renal tumors were calculated respectively. Based on the tumor cell fraction, somatic mutations of bilateral renal tumors are clustered to construct a first clonal cluster set for the first tumor and a second clonal cluster set for the second tumor. By comparing the first clonal group set with the second clonal group set, common clonal group features of bilateral renal tumors are extracted. Based on preset homology interpretation rules, the common clonal group features are processed to output homology classification results for bilateral renal tumors of the target object, wherein the homology classification results include bilateral primary type and contralateral metastatic type.

2. The method according to claim 1, characterized in that, The pre-defined homology interpretation rules are used to process the common clonal group features and output bilateral renal tumor homology classification results, including: If there is no common clonal group containing a community cell mutation between the first clonal group set and the second clonal group set, then the homology classification result is determined to be bilateral primary type. If there is a common clonal group between the first clonal group set and the second clonal group set, and the common clonal group satisfies a preset transfer evolution pattern, then the homology classification result is determined to be a contralateral transfer type.

3. The method according to claim 2, characterized in that, The clone populations in each of the aforementioned clone population sets are divided into primary clone populations and subclonal populations; the preset transfer evolution mode includes at least one of the following sub-modes: First sub-pattern: The common clonal population contains a driver gene mutation, and the driver gene mutation belongs to the main clonal population in both the first and second tumors; Second sub-mode: The common clonal group contains a driver gene mutation, and the driver gene mutation belongs to the main clonal group in the first tumor and to the subclonal group in the second tumor, or vice versa; Third sub-mode: The co-clonal population contains a number of co-passenger mutations greater than or equal to a threshold, and the co-passenger mutations belong to the main clonal population in at least one of the first tumor or the second tumor.

4. The method according to claim 3, characterized in that, The primary clonal population is defined as the clonal population whose upper bound of the confidence interval for the tumor cell fraction is greater than or equal to 1; the subclonal population is defined as the clonal population whose upper bound of the confidence interval for the tumor cell fraction is less than 1.

5. The method according to claim 2, characterized in that, When the homology classification result is determined to be contralateral metastatic, the method further includes determining the clonal origin pattern of the metastatic lesions, the specific steps of which are as follows: The Jakarta similarity index is calculated based on the number of subclonal single nucleotide variants shared by the first tumor and the second tumor, the number of clonal single nucleotide variants unique to the first tumor, and the number of clonal single nucleotide variants unique to the second tumor. The Jakarta similarity index is compared with a preset similarity threshold; If the Jakarta similarity index is greater than the preset similarity threshold, the metastatic lesion is determined to be of polyclonal origin; if the Jakarta similarity index is less than or equal to the preset similarity threshold, the metastatic lesion is determined to be of monoclonal origin. The formula for calculating the Jakarta similarity index is as follows: In the formula, The Jakarta similarity index. This represents the number of subclonal single nucleotide variants shared by both the first and second tumors. The number of clonal single nucleotide variants unique to the second tumor. This refers to the number of clonal single nucleotide variants unique to the first tumor.

6. The method according to claim 5, characterized in that, When the homology classification result indicates a contralateral transfer type, the method further includes estimating the sowing time of the transferred seeds, specifically through the following steps: The seed sowing time is calculated based on the ratio of the number of clonal single nucleotide variants unique to the first tumor to the number of clonal single nucleotide variants unique to the second tumor, combined with a preset time correction coefficient. The calculation formula is as follows: In the formula, To change the seed sowing time; The time correction factor is mentioned above; This refers to the total time interval from the appearance of tumor blast cells to the diagnosis of the primary tumor.

7. The method according to any one of claims 1-6, characterized in that, After acquiring the first and second sequencing data of bilateral renal tumors of the target subject, and before calculating the tumor cell fractions of each somatic mutation, the method further includes a molecular data cleaning step targeting sporadic features: Obtain reference sequencing data of normal tissue from the target object; If germline mutations are detected at preset hereditary bilateral renal cell carcinoma-related gene loci when comparing the normal tissue reference sequencing data, the homology analysis is terminated and an abnormality alert is output. The genetic loci associated with hereditary bilateral renal cell carcinoma include at least the VHL, MET, FLCN, SDHB, SDHC, SDHD, TSC1, and TSC2 genes.

8. A homology analysis system for sporadic bilateral renal tumors, characterized in that, The system includes: The data acquisition module is used to acquire first and second sequencing data of bilateral renal tumors of the target object, wherein the first and second sequencing data contain tumor somatic mutation information and copy number variation information; The score calculation module is used to calculate the tumor cell score of each somatic cell mutation in bilateral renal tumors based on the first sequencing data and the second sequencing data, respectively. The clonal clustering module is used to cluster somatic mutations of bilateral renal tumors based on the tumor cell fraction, respectively, to construct a first clonal cluster set for the first tumor and a second clonal cluster set for the second tumor. The homology classification module is used to compare the first clonal group set with the second clonal group set, extract common clonal group features of bilateral renal tumors, and process the common clonal group features based on preset homology interpretation rules to output the homology classification results of bilateral renal tumors for the target object.

9. A computer device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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