Artificial intelligence enhances whole-slide interpretation of PD-L1 CPS in triple-negative breast cancer: A multi-institutional ring study.

BACKGROUND AND AIMS: Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability in the consistency and accuracy of the interpretation. It is of great importance to establish an objective and effective method which is highly repeatable. METHODS: We proposed a model in a deep learning-based framework, which at the patch level incorporated cell analysis and tissue region analysis, followed by the whole-slide level fusion of patch results. Three rounds of ring studies (RSs) were conducted. Twenty-one pathologists of different levels from four institutions evaluated the PD-L1 CPS in TNBC specimens as continuous scores by visual assessment and our artificial intelligence (AI)-assisted method. RESULTS: In the visual assessment, the interpretation results of PD-L1 (Dako 22C3) CPS by different levels of pathologists have significant differences and showed weak consistency. Using AI-assisted interpretation, there were no significant differences between all pathologists (P = 0.43), and the intraclass correlation coefficient (ICC) value was increased from 0.618 [95% confidence interval (CI) = 0.524-0.719] to 0.931 (95% CI = 0.902-0.955). The accuracy of interpretation result is further improved to 0.919 (95% CI = 0.886-0.947). Acceptance of AI results by junior pathologists was the highest among all levels, and 80% of the AI results were accepted overall. CONCLUSION: With the help of the AI-assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD-L1 (Dako 22C3) CPS. Our AI-assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice.

Concordance of assessments of four PD-L1 immunohistochemical assays in esophageal squamous cell carcinoma (ESCC).

OBJECTIVE: Given real-world limitations in programmed death-ligand 1 (PD-L1) testing, concordance studies between PD-L1 assays are needed. We undertook comparisons of PD-L1 assays (DAKO22C3, Ventana SP263, Ventana SP142, E1L3N) among observers in esophageal squamous cell carcinoma (ESCC) to provide information on the analytical and clinical comparability of four PD-L1 IHC assays. METHODS: Paraffin embedded samples of 50 cases of esophageal squamous cell carcinoma were obtained, satined with all four PD-L1 assays. PD-L1 was evaluated by 68 pathologists from 19 different hospitals. PD-L1 expression was assessed for combined positive score (CPS). RESULTS: The expression sensitivity of SP263 was the highest in ESCC, followed by 22C3, E1L3N and SP142. Taking CPS 10 as the critical value, inter-observer concordance for CPS scores among 68 physicians was assessed for the 22C3, SP263, SP142, and E1L3N assays, yielding values of 0.777, 0.790, 0.758, and 0.782, respectively. In the comparison between assays, the overall CPS scores concordance rates between 22C3 and SP263, SP142, and E1L3N were 0.896, 0.833, and 0.853, respectively. 22C3 and SP263 have high concordance, with OPA of 0.896, while E1L3N and SP142 have the highest concordance, with OPA of 0.908. CONCLUSION: In ESCC, the concordance of PD-L1 evaluation among observers is good, and the immune cell score is still an important factor affecting the concordance of interpretation among observers. Cases near the specific threshold are still the difficult problem of interpretation. SP263 had the highest CPS score of the four assays. SP263 cannot identify all 22C3 positive cases, but had good concordance with 22C3.E1L3N and SP142 showed high concordance.

Effect of storage time of paraffin sections on the expression of PD-L1 (SP142) in invasive breast cancer.

BACKGROUND: PD-L1 staining using long-stored paraffin sections may not be consistent with the true PD-L1 expression of patients. Therefore, it is necessary to explore the expression of PD-L1(SP142) in paraffin sections of invasive breast cancer with different storage times and the optimal storage temperature for unstained paraffin sections. METHODS: The study included 71 cases of PD-L1(SP142) positive breast cancer. The unstained paraffin sections were stored at room temperature conditions (20-25 °C), 4 °C, -20 °C and - 80 °C, respectively. PD-L1 staining was performed at 1, 2, 3, 4, 8, 12 and 24 weeks of storage. PD-L1 expression was assessed with a continuity score. RESULTS: The PD-L1 antigen was gradually lost as the storage time of paraffin sections increased. The PD-L1 positivity rate was 97.18% at 1 week for the sections stored at room temperature, and decreased from 83.10 to 71.83% for the sections stored for 2 weeks to 4 weeks, and 61.97%, 54.93%, and 32.93% for 8, 12, and 24 weeks, respectively. When stored at low temperatures of 4 °C, -20 °C and - 80 °C, the positivity rate decreases with the same trend but more slowly compared to room temperature. The mean IC score of PD-L1 also showed a gradual decrease in all cases. In the consistency analysis, PD-L1 expression in slices stored at room temperature for 2 weeks was consistent with PD-L1 expression in fresh slices (ICC ≥ 0.9 for all slices), and PD-L1 expression in slices stored at 4 °C or -20 °C for 4 weeks was consistent with PD-L1 expression in fresh slices (ICC ≥ 0.9 for all slices). When stored under refrigeration at -80 °C, PD-L1 expression in slices stored for 3 weeks was consistent with that in fresh slices (ICC ≥ 0.9). CONCLUSIONS: To our knowledge, this is the first article on the effect of preservation time and preservation temperature of paraffin sections on PD-L1 expression in breast cancer. Long-term storage of paraffin sections of unstained invasive breast cancer can lead to antigen loss of PD-L1 (SP142). Refrigerated storage of paraffin sections can delay antigen loss, with best results at 4 °C or -20 °C, and a storage time of no more than 4 weeks is recommended.

Prognostic gene HLA-DMA associated with cell cycle and immune infiltrates in LUAD.

BACKGROUND: The dominant subclass of non-small-cell lung cancer (NSCLC) is lung adenocarcinoma (LUAD). The tumor microenvironment (TME) is a crucial feature of carcinogenesis and progression in LUAD. Furthermore, immune and stromal components of TME are crucial factors to investigating and curing LUAD. Thus, the study assessed the value of TME-related genes for LUAD prognosis and immune infiltration. METHODS: All data were downloaded from TCGA and GEO databases. The immune and stromal scores were downloaded from ESTIMATE, and the association between the scores and prognosis was explored by Kaplan-Meier survival analysis. Protein-protein interaction (PPI) network and univariate Cox regression were used to find TME-related differentially expressed genes (DEGs), and HLA-DMA was regarded as a prognostic hub gene. Western blot analyses, qRT-PCR, and immunofluorescence were applied to verify HLA-DMA expression in clinical samples. NSCLC cell lines were used to verify the effect of HLA-DMA on cell proliferation and cell cycle distribution. At last, the alteration of immunotherapy response and TME transition caused by HLA-DMA different expression were further studied. RESULTS: The immune score was positively correlated with survival. The functional analyses suggested that TME-related DEGs may be involved in the immune response. The expression level of HLA-DMA was decreased in LUAD. In addition, HLA-DMA expression was associated with several clinical features and was positively associated with survival. Furthermore, HLA-DMA may suspend cell proliferation by regulating cell cycle. HLA-DMA expression was closely associated with immune infiltration and positively correlated with TMB, indicating that patients with high HLA-DMA level were more suitable for immunotherapy. CONCLUSION: These results reveal that HLA-DMA might act as a biomarker for immune infiltration and immunotherapy response.

Tumor infiltrating lymphocytes and neutrophil-to-lymphocyte ratio in relation to pathological complete remission to neoadjuvant therapy and prognosis in triple negative breast cancer.

OBJECTIVE: To explore the correlation between the peripheral blood neutrophil-to-lymphocyte ratio (NLR) and tumor infiltrating lymphocyte (TIL) before neoadjuvant therapy (NAT) and the prognosis of patients with triple negative breast cancer. METHOD: A total of 126 patients with TNBC who received NAT were screened out. TILs, CD8+TIL and FOXP3+TIL were detected by immunohistochemistry in core needle biopsy specimens before treatment, and NLR was calculated. Kaplan-Meier analysis was used to estimate survival rates. Univariate and multivariate analyses were performed using Cox proportional hazards regression. RESULTS: NLR was negatively correlated with TILs density (p = 0.040) and FOXP3+ TIL was positively correlated with NLR (p = 0.019). Patients with low NLR/high TILs density showed the highest pCR rate (46/48, 95 %), while only 6/22 patients (21 %) with high NLR/low TILs density achieved pCR. Multivariate analysis showed that high NLR was independently associated with pCR ((HR = 5.043, 95 %CI = 1.637-15.535, p = 0.005). High T stage, lymph node involvement, lymphovascular invasion, high NLR, low TILs density and low CD8+ TIL were associated with poor OS and BCSS. Multivariate Cox regression analysis showed that high NLR (HR = 36.182, 95 %CI = 4.120-317.759, p = 0.001), high CD8+ TIL density (HR = 0.182, 95 %CI = 0.044-0.754, p = 0.019) were independently associated with poor OS. Similarly, high NLR (HR = 23.989, 95 %CI = 2.275-252.131, p = 0.008) was independently associated with worse BCSS. CONCLUSIONS: NLR may help to screen the high-risk population of TNBC patients after neoadjuvant therapy.

Fibroblast activation protein in the tumor microenvironment predicts outcomes of PD-1 blockade therapy in advanced non-small cell lung cancer.

PURPOSE: The identification of robust predictive biomarkers of the response to programmed cell death-1 (PD-1) blockade remains a critical concern. Here, we investigated on fibroblast activation protein (FAP) as a microenvironment-derived biomarker of clinical outcomes of PD-1 blockade therapy, and the correlation between FAP expression and T cell infiltration in advanced non-small cell lung cancer (NSCLC). METHODS: A total of 135 patients with advanced NSCLC who received PD-1 blockade therapy were retrospectively analyzed. The potential associations among FAP expression, CD3 + T cell and CD8 + T cell infiltration, and clinical outcomes of immunotherapy were validated by immunohistochemistry, bioinformatic analyses, and statistical measurements. RESULTS: FAP was widely expressed in advanced NSCLC tissues. FAP was correlated with decreased density of CD8 + T cells (Spearman's rho - 0.32, p < 0.001) and immunosuppressive tumor microenvironment (TME) status. No correlations were detected between FAP and PD-L1 expression or with the density of CD3 + T cells. The patients with higher expression of FAP showed worse response rate (16.4% vs. 38.7%, p < 0.001) and worse progression-free survival (HR = 2.56, 95% CI 1.69-3.87, p < 0.001). In addition, FAP contributed to shortened overall survival in subgroups of the patients with squamous cell lung cancer (p = 0.020), PD-1 blockade monotherapy (p = 0.017), and first-line therapy (p = 0.028). CONCLUSION: FAP is a potential predictive biomarker of resistance to PD-1 blockade. Further investigation is warranted to identify a strategy for targeting FAP to alleviate the immunosuppressive TME and broaden the clinical effectiveness of PD-1 blockade therapy.

Towards carbon neutrality: A study on China's long-term low-carbon transition pathways and strategies.

As the world's biggest carbon dioxide (CO2) emitter and the largest developing country, China faces daunting challenges to peak its emissions before 2030 and achieve carbon neutrality within 40 years. This study fully considered the carbon-neutrality goal and the temperature rise constraints required by the Paris Agreement, by developing six long-term development scenarios, and conducting a quantitative evaluation on the carbon emissions pathways, energy transformation, technology, policy and investment demand for each scenario. This study combined both bottom-up and top-down methodologies, including simulations and analyses of energy consumption of end-use and power sectors (bottom-up), as well as scenario analysis, investment demand and technology evaluation at the macro level (top-down). This study demonstrates that achieving carbon neutrality before 2060 translates to significant efforts and overwhelming challenges for China. To comply with the target, a high rate of an average annual reduction of CO2 emissions by 9.3% from 2030 to 2050 is a necessity, which requires a huge investment demand. For example, in the 1.5 °C scenario, an investment in energy infrastructure alone equivalent to 2.6% of that year's GDP will be necessary. The technological pathway towards carbon neutrality will rely highly on both conventional emission reduction technologies and breakthrough technologies. China needs to balance a long-term development strategy of lower greenhouse gas emissions that meets both the Paris Agreement and the long-term goals for domestic economic and social development, with a phased implementation for both its five-year and long-term plans.

How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies.

Programmed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936-0.962) from moderate in RS1 (0.674, 95% CI: 0.614-0.735) and RS2 (0.736, 95% CI: 0.683-0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953-0.964) and 13% (0.815, 95% CI: 0.803-0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% "fully accepted" and 91% "almost accepted". The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

Stable quantum key distribution using a silicon photonic transceiver.

The wider deployment of commercial quantum key distribution (QKD) may benefit from an integrated system with reduced cost, small form-of-factor and high robutness. Silicon photonic circuits are good candidates while their performance stability in some contexts remains a challenge. We demonstrate a silicon photonic QKD transceiver based on time-bin protocol. The stability of the transceiver is investigated and a feedback function is proposed to improve the temperature-dependent performance of the transceiver. With the help of a faster data-processing ability, such scheme can facilitate more application scenarios, therefore achieving wider implementation of QKD in the future.

Developed and developing world responsibilities for historical climate change and CO2 mitigation.

At the United Nations Framework Convention on Climate Change Conference in Cancun, in November 2010, the Heads of State reached an agreement on the aim of limiting the global temperature rise to 2 °C relative to preindustrial levels. They recognized that long-term future warming is primarily constrained by cumulative anthropogenic greenhouse gas emissions, that deep cuts in global emissions are required, and that action based on equity must be taken to meet this objective. However, negotiations on emission reduction among countries are increasingly fraught with difficulty, partly because of arguments about the responsibility for the ongoing temperature rise. Simulations with two earth-system models (NCAR/CESM and BNU-ESM) demonstrate that developed countries had contributed about 60-80%, developing countries about 20-40%, to the global temperature rise, upper ocean warming, and sea-ice reduction by 2005. Enacting pledges made at Cancun with continuation to 2100 leads to a reduction in global temperature rise relative to business as usual with a 1/3-2/3 (CESM 33-67%, BNU-ESM 35-65%) contribution from developed and developing countries, respectively. To prevent a temperature rise by 2 °C or more in 2100, it is necessary to fill the gap with more ambitious mitigation efforts.