Therapeutic strategies and predictive models for Xp11.2 translocation/TFE3 gene fusion renal cell carcinoma in adults based on data of two Chinese medical centers

Materials and Methods

Study design and participants

This dual-center retrospective study involved RCa patients from two independent regional medical centers in Zhejiang, China. Data for 2520 patients admitted to Zhejiang Provincial People’s Hospital from 2008-02 to 2023-05 were collected and labeled as the R cohort for nomogram construction. Then, data for 2041 patients admitted to the Second Affiliated Hospital of Zhejiang Chinese Medical University from 2005-05 to 2023-04 were collected, labeled as the H cohort, and used for validation.

Furthermore, 149 TFE3-RCC patients were identified from data from two medical centers and labeled as the T-cohort, which was used for prognostic analysis.

Pathological examination

TFE3-RCC may grossly mimic conventional clear-cell RCC in HE staining. Immunohistochemistry tests (IHC) can distinguish them from conventional clear-cell cancers. The most distinctive IHC feature of TFE3-RCC is detectable nuclear staining for chimeric (mutant) TFE3 protein, which is absent in conventional clear cells. The IHC test has a diagnostic 97.5% sensitivity and 99.6% specificity [16, 17]. Based on the unique translocation of the TFE3 gene in Xp11.2 translocation renal cell carcinoma, fluorescence in situ hybridization (FISH) was used to design a TFE3 gene separation probe, which was specifically bound to the DNA fragments at both ends of the target gene TFE3 to display its location and detect its TFE3 in the tissue, to diagnose TFE3-RCC further [17, 18].

Since only immunohistochemical examination is available at the Second Affiliated Hospital of Zhejiang Chinese Medical University, FISH pathological test is not routinely conducted; therefore, all indistinguishable pathological sections were sent back to the Laboratory Center of Zhejiang Provincial People’s Hospital for IHC or FISH for further diagnosis. When FISH assay findings contradicted the IHC results, a definitive diagnosis of Xp11.2 RCC was made through genetic investigation, which included FISH assays and other molecular biology.

Baseline data collection

Regarding the nomogram, we collected baseline information from R and H cohort, which included age, sex, body mass index, personal history (smoking, drinking, heart disease, hypertension, and diabetes), and hematological indicators (MON, EOS, BAS, Fib, D-dimer, ALB, ALT, AST, GGT, BA, GLU, BUN, UA, TC, TG, HDL, LDL, and lactate dehydrogenase).

Regarding prognostic analysis, patient information was collected from the T cohort, which included age, sex, pT staging, invasion of regional lymph nodes, location of tumors, tumor boundary (Based on preoperative imaging), tumor necrosis or bleeding, immunohistochemical staining (Cathepsin-K, CD10, CK7, CA-IX, and PAX-8), surgical method, targeted medicine (mTOR inhibitors and tyrosine kinase inhibitors), progression, and prognosis.

Telephone consultation was arranged by assistants for prognostic data, and all patient information was sent to the Zhejiang Provincial People’s Hospital for secondary reviewing and grouping (Supplementary Table 1).

A set of inclusion and exclusion criteria was strictly formulated to screen eligible patients in two medical centers: (1) Enrolled cases with complete baseline information; (2) pathological examination: All paraffin section staining reports indicated renal cell carcinoma, and ambiguous pathological results were sent to immunohistochemical or fluorescent in situ hybrid (FISH) examination; (3) clinical data were collected preoperatively; (4) enrolled cases without any preoperative treatment; (5) patients with other malignancies or family history of RCa were excluded; (6) adult patients older than 16 years. The “R,” “H,” and “T” cohorts were remarked as the “Ra,” “Ha,” and “Ta” cohorts after rigorous screening above (Figure 1).

Figure 1. Enrollment process.

Model construction and validation

The nomogram was established using the Ra cohort, and nomogram accuracy was verified using the Ha external validation cohort.

Univariate binary logistic regression analysis was used to evaluate different variables. Variables with P < 0.05 were included in multiple logistic regression analysis models, and odds ratios (OR) and 95% confidence intervals (95% CI) were also calculated. Statistically significant variables in the multivariate analysis were used to build the nomogram. The apparent performance of the nomogram was evaluated using bootstrap validation in the Ha cohort. Discrimination was measured using the C-statistic, which was equal to the area under the curve (AUC) calculated by plotting the receiver operating characteristic (ROC) curve. Finally, calibration was performed by drawing a calibration curve.

Decision curve analysis (DCA)

A DCA curve was constructed to predict the TFE3-RCC probability. R studio was used to quantify the clinical net benefit of our model under different threshold probabilities.

Statistical analysis

SPSS (version 26.0) and R (version 4.1.1) were used for the statistical analyses. We followed the STROBE guidelines for research and data analysis. The classification data are presented as numbers and percentages. Patient baseline characteristics are presented as means, interquartile range (IQR), numbers, and proportions. A log-rank test was performed to analyze the effects of each variable on progression and prognosis. Variables with statistical significance were included in the Multivariate COX survival regression model, and independent predictors derived from the model were described using the Kaplan-Meier survival curve. P < 0.05 was considered a statistically significant difference.

Results

Based on strict compliance with the inclusion criteria, 1571, 1124, and 121 patients were included in the Ra, Ha, and Ta cohorts, respectively, and all clinical parameters are summarized in Table 1. The TFE3-RCC positive rates in the R and H cohorts were 3.4% (88/2520) and 2.9% (61/2041), respectively. In this study, the sex incidence ratio was 1:1.9, and females had a considerably higher incidence than males. Age appears to be a protective factor for morbidity, and younger patients are more likely to suffer from TFE3-RCC than elder patients (HR: 5.287, p = 0.021).

Table 1. Demographic characteristics of the patients in each cohort.

Clinical parameters	R (n = 2520)	H (n = 2041)	T (n = 149)
	Ra (n = 1571)	Ha (n = 1124)	Ta (n = 121)
Median age, y (IQR)	59.9 (50.0–70.0)	60.5 (52.0–70.0)	61.9 (53.0–72.0)
Sex
Male	720 (45.8)	587 (52.2)	42 (34.7)
Female	841 (54.2)	537 (47.8)	79 (65.3)
Median BMI, kg/m2 (IQR)	23.3 (21.2–25.3)	23.4 (21.3–25.5)	24.2 (21.9–26.2)
<24	954 (60.7)	654 (58.2)	52 (43.0)
≥24	617 (39.3)	470 (41.8)	69 (57.0)
Diabetes	181 (11.5)	143 (12.7)	8 (6.6)
Hypertension	646 (41.1)	496 (44.1)	44 (36.4)
Cardiac disease	85 (5.4)	71 (6.3)	8 (6.6)
Alcoholism	514 (32.7)	399 (35.5)	31 (25.6)
Smoking	530 (33.7)	344 (30.6)	71 (58.7)
Abbreviations: IQR: interquartile range; BMI: body mass index; TFE3-RCC: Xp11.2 translocation/TFE3 gene fusion renal cell carcinoma.

Establishment of the predictable nomogram

A binary logistic regression analysis was performed to screen for predictors of RCa in the Ra cohort. Univariate analysis revealed that younger age, female, high BMI, smoking, low EOS, and high LDL were significantly linked to TFE3-RCC diagnosis (Table 2 and Supplementary Table 2). The subsequent multiple logistic regression analysis included variables related to the TFE3-RCC diagnosis in the univariate analysis (Table 2). Therefore, a predictive model containing six variables was established (Figure 2).

Table 2. Univariate and multivariate analysis for screening the predictors of outcomes of TFE3-RCC.

	Univariate model			Multivariate model
	OR	95% CI	P	B	OR	95% CI	P
Younger age	5.305	3.095~9.095	<0.001	1.725	5.611	3.230~9.749	<0.001
Female	1.775	1.086~2.900	0.022	0.654	1.923	1.147~3.224	0.013
BMI	1.114	1.032~1.202	0.006	0.127	1.135	1.046~1.232	0.002
Smoking	2.849	1.745~4.650	<0.001	1.104	3.015	1.803~5.042	<0.001
Low EOS	3.208	1.468~7.009	0.003	1.328	3.773	1.604~8.879	0.002
High LDL	2.126	1.310~3.452	0.002	0.712	2.039	1.229~3.383	0.006
Abbreviations: BMI: body mass index; EOS: Eosinophils; LDL: Low-density lipoprotein.

Figure 2. Diagnostic nomogram. An accurate TFE3-RCC diagnostic nomogram constructed using age, sex, BMI, smoking, eosinophil count, and LDL level.

Model validation demonstrated that the model had good reproducibility (AUC = 0.796). Moreover, external validation showed that the prediction nomogram had excellent discrimination ability (AUC = 0.806). In both cohorts, the calibration curves indicated a high correlation between the predicted and observed TFE3-RCC incidence (Figure 3).

Figure 3. Calibration curve and ROC curve. Discrimination of the nomogram was evaluated by the ROC curve, AUC = 0.796 in the Ra cohort (A), AUC = 0.806 in the Ha cohort (B); calibration curves illuminate the agreement between the predicted risks of TFE3-RCC (C) and the observed incidence of TFE3-RCC (D). The dotted line represents the ideal flawless model.

Decision curve analysis

To evaluate the nomogram’s clinical usefulness, a DCA curve was drawn to verify that the nomogram could increase the detection rate of TFE3-RCC, which further confirmed the clinical effectiveness of the nomogram (Figure 4).

Figure 4. Decision curve analysis. DCA curve analysis of the training cohort (A) and external validation (B). Quantified net benefits were measured for different threshold probabilities. The y-axis denotes the standardized net benefit, and the x-axis denotes the threshold probabilities. The red line represents our nomogram, the blue line represents the condition of patients with TFE3-RCC, and the black line represents the condition of which none suffered from TFE3-RCC.

Pathologic analysis

As for gross specimens in the Ta cohort, 84 cases showed mainly solid pattern masses, 27 displayed cystic solid masses, and 10 displayed cystic masses. Upon observing the paraffin sections, most tissue surfaces were golden yellow, but some specimens were greyish white, greyish yellow, greyish red, or multicolored.

Under the microscope, most tumor tissues revealed a nest, papillary, acinar, sheet, or mixed morphology. Most tumor cells have significant atypia, abundant and deeply stained eosinophile cytoplasm, and large nuclear kernels. Psammoma bodies can be observed in the intercellular stroma of most sections. There were 20 cases (16.5%) of tumor necrosis or hemorrhage and 37 cases (30.5%) of invasive growth.

All immunohistochemical tests (IHC) revealed that TFE3 was strongly diffusely positive in 121 cases, Cathepsin K was positive in 43 cases (35.5%), CD10 was positive in 30 cases (24.7%), CK7 was positive in 37 cases (30.5%), CA-X was positive in 8 cases (6.6%), and PAX8 was positive in 40 cases (33.0%) (Figure 5).

Figure 5. Representative images of TFE3 immunohistochemical staining and microscopic appearance for Xp11.2 RCC. (A) Blood sinusoid and Psammoma bodies were abundant in intercellular substance. The arrow points to Psammoma bodies; (B) abundant and deeply stained eosinophile cytoplasm, similar to renal clear cell carcinoma; (C) the results of immunohistochemistry showed that TFE3 was strongly positive in cancerous tissue; (D) FISH test results: 100 cells were counted, and the number of cells with TFE3 gene breakage was more than 20. TFE3 gene probe: Broken (positive).

Clinical data and prognostic analysis

After surgery, patients were reviewed every three months; the median follow-up time for 121 cases was 37 (3~96) months, and the median progression time for 38 cases with recurrence or metastasis was 12 (3~48) months. Postoperative imaging examination found that 15 cases of local recurrence, 9 cases of post-peritoneal lymph nodes were metastasized, and 14 cases were transferred in the distance (six lung metastases, four bone metastases, three liver metastases, and one cavity vein system metastasis). All patients refused to proceed with postoperative cytoreductive surgery. The progression-free survival rates at 3 and 5 years were 75.2 and 68.5%, respectively, in the Ta cohort.

“Progression” was defined as the recurrence or metastasis of the disease after surgery. Regarding disease progression, we found that stage pT1-2 patients did not benefit from different surgical modalities (HR: 0.297, 95% CI: 0.083~1.006; P = 0.063), while stage pT3-4 patients did benefit from processing nephrectomy rather than partial nephrectomy (HR: 0.069, 95% CI: 0.016~0.296; P < 0.001). The surgical methods for radical nephrectomy included five patients with lymph node dissection. Preoperative imaging revealed renal hilar lymph node invasion, and renal hilar lymph node dissection was performed intraoperatively. Five patients recovered well after surgery, and three patients received adjuvant drug therapy with TKI after surgery. Until now, none of the five patients demonstrated disease progression.

A total of 24 patients received postoperative medication-assisted therapy in the Ta cohort; six received mTOR inhibitor therapy, and 24 received TKI therapy. Due to TKI treatment failure, six patients chose an mTOR inhibitor. Progression was significantly controlled after the treatment procedure was replaced in five patients, while one did not experience an obvious treatment effect. Two patients were treated with a combination of TKI and mTOR inhibitor, but they abandoned the combination therapeutic procedure because of early side effects and continued the monotherapy therapeutic procedure (Figure 6).

Figure 6. Contrast-enhanced computed tomography results of postoperative follow-up in a patient with TFE3-RCC. (A) The results of re-examination one month after the surgery; (B) three months after surgery, the patient’s re-examination showed multiple tumor metastases in the abdominal wall, left psoas major muscle, and pelvic cavity, so the patient started sunitinib therapy; (C) the re-examination results at six months after surgery showed that the metastasis was smaller than before, and the disease progression was controlled.

Variables related to patient prognosis were included in the univariate analysis, which was performed using the log-rank method for inter-group comparison in the Ta cohort (Table 3).

Table 3. Log-rank analysis of different variables for prognosis and progression.

	n	Progression			Prognosis
		Progressive cases	x2	p-value	x2	p-value
Age
<55	26	13 (50.0)	5.287	0.021	3.027	0.082
≥55	95	25 (26.3)
Sex
Male	37	10 (27.0)	1.409	0.235	2.493	0.114
Female	84	28 (33.3)
pT staging
T1~2	72	14 (19.4)	5.187	0.023	5.557	0.018
T3~4	49	24 (48.9)
Invasion of regional lymph node
+	23	19 (82.6)	57.161	<0.001	39.003	<0.001
−	98	19 (19.4)
Location of tumors
Left	73	25 (34.2)	0.492	0.483	2.921	0.087
Right	48	13 (27.1)
Tumor boundary
Clear	84	23 (27.4)	0.812	0.368	1.356	0.244
Unclear	37	15 (40.5)
Tumor necrosis or bleeding
+	20	3 (15.0)	4.033	0.045	0.835	0.361
−	101	35 (34.6)
Cathepsin-K
+	43	14 (32.5)	0.071	0.790	0.062	0.804
−	78	24 (30.7)
CD10
+	30	9 (30.0)	0.005	0.944	3.640	0.056
−	91	29 (31.8)
CK7
+	37	16 (43.2)	2.747	0.097	1.980	0.159
−	84	22 (26.2)
CA-IX
+	8	1 (12.5)	0.982	0.322	0.841	0.359
−	113	37 (32.7)
PAX-8
+	40	11 (27.5)	0.148	0.700	<0.001	0.986
−	81	27 (33.3)
Tyrosine Kinase Inhibitors
+	24	16 (66.7)	39.758	<0.001	55.742	<0.001
−	97	22 (22.7)
mTOR inhibitors
+	6	1 (16.7)	0.273	0.601	0.646	0.422
−	115	37 (32.2)
Surgical method
Partial nephrectomy	52	26 (50.0)	22.079	<0.001	14.825	<0.001
Radical nephrectomy	69	12 (17.4)

Age, pT staging, regional lymph node invasion, tumor necrosis or bleeding, tyrosine kinase inhibitors, and surgical methods significantly affected the progression outcomes. pT staging, regional lymph node invasion, tyrosine kinase inhibitors, and surgical method significantly affected the prognosis.

Subsequently, the variables mentioned above were subsumed within the multi-factor model, and the results depicted that invasion of regional lymph nodes, tyrosine kinase inhibitors, and surgical methods were independent factors related to progression, while tyrosine kinase inhibitors were also associated with prognosis (Table 4 and Figure 7).

Table 4. Multivariate COX survival regression analysis of prognosis and progression.

	Progression			Prognosis
	HR	95% CI	p-value	HR	95% CI	p-value
Age	1.444	0.701~2.976	0.319	–	–	–
pT staging	1.793	0.840~3.827	0.131	3.191	0.814~12.510	0.096
Invasion of regional lymph node	4.326	1.942~9.641	<0.001	3.013	0.737~12.324	0.125
Tumor necrosis or bleeding	0.398	0.111~1.430	0.158	–	–	–
Surgical method	0.374	0.163~0.856	0.020	0.165	0.019~1.397	0.098
Tyrosine Kinase Inhibitor	3.217	1.489~6.949	0.003	25.121	2.955~13.554	0.003

Figure 7. Kaplan-Meier analysis for progression-free survival and overall survival in the Ta cohort. (A) The effect of TKI on progression; (B) the effect of regional lymph node invasion on progression; (C) the effect of surgical methods on progression; (D) the effect of TKI on prognosis.

Abbreviations

TFE3-RCC: Xp11.2 translocation/TFE3 gene fusion renal cell carcinoma; RCC: Renal cell carcinoma; TKI: Tyrosine Kinase Inhibitors; BMI: Body Mass Index; AUC: Area under the receiver operating characteristic curve; IHC: Immunohistochemistry test; FISH: Fluorescence in situ hybridization; EOS: Eosinophils; LDL: Low-density lipoprotein; MON: Monocyte; BAS: Basophil; ALB: Albumin; ALT: Alanine transaminase; AST: Aspartate aminotransferase; GGT: γ-Glutamyl Transferase; BA: Bile acids; GLU: Blood glucose; BUN: Urea; UA: Uric acid; TC: Total cholesterol; TG: Triglyceride; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; LDH: Lactate dehydrogenase.

Author Contributions

Conceptualization: LF, BYC, and YHW. Methodology: YYK and ZCF. Software: YYK and BYC. Validation: YYK and BYC. Formal analysis: ZCF, WZD, and YYK. Investigation: ZCF, WZD, CJ, LXT and SM. Resources: (Clinicopathologic analysis) ZM and CXJ. Data curation: ZCF, WZD, CJ, JDX and SM. Writing – original draft: YYK. Writing – review and editing: DY and BYC. Supervision: DY and BYC. Project administration: YHW, YYK, DY, and BYC. Visualization: YYK and WZD.

Acknowledgments

We acknowledge the contributions made to this study by all patients, their families, and all nurses and doctors engaged at Zhejiang Provincial People’s Hospital and the Second Affiliated Hospital of Zhejiang Chinese Medical University. We also thank Home for Researchers editorial team (http://www.home-for-researchers.com/) for language editing service.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Ethical Statement and Consent

Approval of the research protocol by an Institutional Review Board: The study was approved by the Ethics Committee of Zhejiang Provincial People’s Hospital (QT2023311) and the Second Affiliated Hospital of Zhejiang Chinese Medical University (2023-LW-020-01). Enrolled patients are required to sign pre-operative consent forms before surgery which includes consent to use their data as the content of later clinical research.

Funding

This research was funded by the Medical Technology Plan of Zhejiang Province (grant number 2021KY051).

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