Development of the semi-dry dot-blot method for intraoperative detecting micropapillary component in lung adenocarcinoma based on proteomics analysis.

BACKGROUND: Micropapillary (MIP) component was a major concern in determining surgical strategy in lung adenocarcinoma (LUAD). We sought to develop a novel method for detecting MIP component during surgery. METHODS: Differentially expressed proteins between MIP-positive and MIP-negative LUAD were identified through proteomics analysis. The semi-dry dot-blot (SDB) method which visualises the targeted protein was developed to detect MIP component. RESULTS: Cellular retinoic acid-binding protein 2 (CRABP2) was significantly upregulated in MIP-positive LUAD (P < 0.001), and the high CRABP2 expression zone showed spatial consistency with MIP component. CRABP2 expression was also associated with decreased recurrence-free survival (P < 0.001). In the prospective cohort, the accuracy and sensitivity of detecting MIP component using SDB method by visualising CRABP2 were 82.2% and 72.7%, which were comparable to these of pathologist. Pathologist with the aid of SDB method would improve greatly in diagnostic accuracy (86.4%) and sensitivity (78.2%). In patients with minor MIP component (≤5%), the sensitivity of SDB method (63.6%) was significantly higher than pathologist (45.4%). CONCLUSIONS: Intraoperative examination of CRABP2 using SDB method to detect MIP component reached comparable performance to pathologist, and SDB method had notable superiority than pathologist in detecting minor MIP component.

Photoresponsive molecularly imprinted polymers based on 4-[(4-methacryloyloxy)phenylazo] benzenesulfonic acid for the determination of sulfamethazine.

Core-shell structured photoresponsive molecularly imprinted polymers were developed for the determination of sulfamethazine in milk samples. The photoresponsive imprinted polymers were prepared with polymethyl methacrylate containing a mass of ester groups as core, sulfamethazine as template molecules, self-synthesized water-soluble 4-[(4-methacryloyloxy)phenylazo] benzenesulfonic acid as a photoresponsive monomer, and ethylene dimethacrylate as cross-linker. Interestingly, the imprinted polymer can specifically adsorb sulfamethazine under dark and 440 nm irradiation, and release it at 365 nm. A series of adsorption experiments showed that the maximum adsorption capacity reached 12.5 mg⋅g-1 , and the adsorption equilibrium was achieved within 80 min. Moreover, the imprinted polymers display excellent reusability, with almost no performance loss after four times photo-controlled adsorption-release cycles, and the imprinted polymers have excellent selectively for sulfamethazine (imprinting factor  = 3.01). In the end, the imprinted polymers realized effective separation and enrichment of sulfamethazine in milk, with a recovery rate of over 97.5%. The material can be used as a solid-phase extractant in the process of enrichment and separation for the quantitative detection of sulfamethazine in milk samples.

Spread Through Air Spaces in Residual Tumor Classification for Clinical IA Lung Adenocarcinoma.

BACKGROUND: We aimed to validate the prognostic implication of uncertain resection, R(un), proposed by International Association for the Study of Lung Cancer (IASLC) and evaluate the prognostic value of spread through air spaces (STAS) in reclassifying the R classification among patients with lung adenocarcinoma after segmentectomy. METHODS: We enrolled 1007 patients who underwent segmentectomy for c-stage IA lung adenocarcinoma between 2014 and 2017. Recurrence-free survival (RFS) and overall survival (OS) were compared to evaluate the prognostic value of IASLC-R(un) and STAS. Whether STAS would skip into complementary lobectomy was evaluated in a prospective cohort. RESULTS: The current IASLC-R(un) failed to significantly stratify the RFS (P = .078) in segmentectomy, and STAS was a stronger risk factor of poor prognosis for both RFS and OS (P < .001). Moreover, the presence of STAS was associated with increased locoregional recurrence in patients undergoing segmentectomy (P < .001) but not in those treated with lobectomy (P = .187), indicating that only STAS-positive segmentectomy was consistent with the concept of R(un) in relapse pattern. After reclassifying STAS-positive segmentectomy into the R(un) category, the proposed R(un) showed an improvement in prognosis stratification. In addition, 2 of 30 patients (6.2%) in the prospective cohort who underwent initial segmentectomy and complementary lobectomy had STAS clusters in the complementary lobectomy specimens. CONCLUSIONS: Unfavorable prognosis, relapse patterns consistent with R(un), and pathologic verification that saltatory spread of STAS observed in complementary lobectomy specimens supported reclassifying STAS-positive segmentectomy as R(un). STAS is a critical concern for the surgical completeness evaluation after segmentectomy.

Predicting therapeutic response to neoadjuvant immunotherapy based on an integration model in resectable stage IIIA (N2) non-small cell lung cancer.

OBJECTIVE: Accurately predicting response during neoadjuvant chemoimmunotherapy for resectable non-small cell lung cancer remains clinically challenging. In this study, we investigated the effectiveness of blood-based tumor mutational burden (bTMB) and a deep learning (DL) model in predicting major pathologic response (MPR) and survival from a phase 2 trial. METHODS: Blood samples were prospectively collected from 45 patients with stage IIIA (N2) non-small cell lung cancer undergoing neoadjuvant chemoimmunotherapy. An integrated model, combining the computed tomography-based DL score, bTMB, and clinical factors, was developed to predict tumor response to neoadjuvant chemoimmunotherapy. RESULTS: At baseline, bTMB were detected in 77.8% (35 of 45) of patients. Baseline bTMB ≥11 mutations/megabase was associated with significantly greater MPR rates (77.8% vs 38.5%, P = .042), and longer disease-free survival (P = .043), but not overall survival (P = .131), compared with bTMB <11 mutations/megabase in 35 patients with bTMB available. The developed DL model achieved an area under the curve of 0.703 in all patients. Importantly, the predictive performance of the integrated model improved to an area under the curve of 0.820 when combining the DL score with bTMB and clinical factors. Baseline circulating tumor DNA (ctDNA) status was not associated with pathologic response and survival. Compared with ctDNA residual, ctDNA clearance before surgery was associated with significantly greater MPR rates (88.2% vs 11.1%, P < .001) and improved disease-free survival (P = .010). CONCLUSIONS: The integrated model shows promise as a predictor of tumor response to neoadjuvant chemoimmunotherapy. Serial ctDNA dynamics provide a reliable tool for monitoring tumor response.

The number of metastatic lymph nodes is more predictive of prognosis than location-based N stage for nonsmall cell lung cancer: a retrospective cohort study.

BACKGROUND: The eighth edition of nodal classification is defined only by the anatomical location of metastatic lymph nodes and has limited prognostic discrimination power. The authors aimed to evaluate the prognostic significance and discriminatory capability of the number of metastatic lymph nodes (nN) in resected nonsmall cell lung cancer. MATERIALS AND METHODS: Patients with stage IA to IIIB resected nonsmall cell lung cancer between 1 January 2009 and 31 December 2013 were analyzed as a Chinese cohort. The optimal thresholds for the nN classification were determined by the X-tile. The receiver operating characteristic curve, net reclassification improvement and standardized net benefit calculated by decision curve analysis was estimated to quantify the nN classification's performance in prognostic stratification. External validation in the surveillance, epidemiology, and end results database was performed to test the robustness of the nN classification. RESULTS: Both cohorts showed a stepwise prognosis deterioration with increasing nN. One to three, four to six, and more than six were selected as optimal thresholds of nN classification in the Chinese cohort, which included 4432 patients, then validated in the SEER cohort ( n =28 022 patients). Multivariate Cox analysis showed the nN classification was an independent predictive factor for overall survival in both cohorts (Chinese cohort and SEER cohort: N 0 vs. N 1-3 , P <0.001; N 0 vs. N 3-6 , P <0.001; N 0 vs. N >6 , P <0.001). And prognostic discriminatory capability was improved in the nN classification compared with location-based N classification [5-year NRI score, 0.106 (95% CI: 0.049-0.132) and 5-year time-independent AUC, 0.593 (95% CI: 0.560-0.625) vs. 0.554 (95% CI: 0.520-0.588), P <0.001]. CONCLUSIONS: The nN classification tended to be a superior prognostic indicator than the location-based N classification. The number of metastatic lymph nodes should be considered in the future revision of the TNM system.

Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas.

INTRODUCTION: The International Association for the Study of Lung Cancer (IASLC) newly proposed grading system for lung adenocarcinomas (ADC) has been shown to be of prognostic significance. Hence, intraoperative consultation for the grading system was important regarding the surgical decision-making. Here, we evaluated the accuracy and interobserver agreement for IASLC grading system on frozen section (FS), and further investigated the prognostic performance. METHODS: FS and final pathology (FP) slides were reviewed by three pathologists for tumor grading in 373 stage I lung ADC following surgical resection from January to June 2013 (retrospective cohort). A prospective multicenter cohort (January to June 2021, n = 212) were included to confirm the results. RESULTS: The overall concordance rates between FS and FP were 79.1% (κ = 0.650) and 89.6% (κ = 0.729) with substantial agreement in retrospective and prospective cohorts, respectively. Presence of complex gland was the only independent predictor of discrepancy between FS and FP (presence versus. absence: odds ratio, 2.193; P = 0.015). The interobserver agreement for IASLC grading system on FS among three pathologists were satisfactory (κ = 0.672 for retrospective cohort; κ = 0.752 for prospective cohort). Moreover, the IASLC grading system by FS diagnosis could well predict recurrence-free survival and overall survival for patients with stage I invasive lung ADC. CONCLUSIONS: Our results suggest that FS had high diagnostic accuracy and satisfactory interobserver agreement for IASLC grading system. Future prospective studies are merited to validate the feasibility of using FS to match patients into appropriate surgical type.