| 1. |
|
|
| 2. |
- Mansour, Mohammed S I, et al.
(författare)
-
PD-L1 Testing in Cytological Non-Small Cell Lung Cancer Specimens : A Comparison with Biopsies and Review of the Literature
- 2021
-
Ingår i: Acta Cytologica. - : S. Karger AG. - 0001-5547 .- 1938-2650. ; 65:6, s. 501-509
-
Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: Programmed death-ligand 1 (PD-L1) expression is used for treatment prediction in non-small cell lung cancer (NSCLC). While cytology may be the only available material in the routine clinical setting, testing in clinical trials has mainly been based on biopsies.METHODS: We included 2 retrospective cohorts of paired, concurrently sampled, cytological specimens and biopsies. Also, the literature on PD-L1 in paired cytological/histological samples was reviewed. Focus was on the cutoff levels ≥1 and ≥50% positive tumor cells.RESULTS: Using a 3-tier scale, PD-L1 was concordant in 40/47 (85%) and 66/97 (68%) of the paired NSCLC cases in the 2 cohorts, with kappa 0.77 and 0.49, respectively. In the former cohort, all discordant cases had lower score in cytology. In both cohorts, concordance was lower in samples from different sites (e.g., biopsy from primary tumor and cytology from pleural effusion). Based on 25 published studies including about 1,700 paired cytology/histology cases, the median (range) concordance was 81-85% (62-100%) at cutoff 1% for a positive PD-L1 staining and 89% (67-100%) at cutoff 50%.CONCLUSIONS: The overall concordance of PD-L1 between cytology and biopsies is rather good but with significant variation between laboratories, which calls for local quality assurance.
|
|
| 3. |
- Porwit, Anna, et al.
(författare)
-
Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow-Self Organizing Maps algorithm
- 2022
-
Ingår i: Cytometry Part B - Clinical Cytometry. - : Wiley. - 1552-4949 .- 1552-4957. ; 102:2, s. 134-142
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The Flow-Self Organizing Maps (FlowSOM) artificial intelligence (AI) program, available within the Bioconductor open-source R-project, allows for an unsupervised visualization and interpretation of multiparameter flow cytometry (MFC) data.METHODS: Applied to a reference merged file from 11 normal bone marrows (BM) analyzed with an MFC panel targeting erythropoiesis, FlowSOM allowed to identify six subpopulations of erythropoietic precursors (EPs). In order to find out how this program would help in the characterization of abnormalities in erythropoiesis, MFC data from list-mode files of 16 patients (5 with non-clonal anemia and 11 with myelodysplastic syndrome [MDS] at diagnosis) were analyzed.RESULTS: Unsupervised FlowSOM analysis identified 18 additional subsets of EPs not present in the merged normal BM samples. Most of them involved subtle unexpected and previously unreported modifications in CD36 and/or CD71 antigen expression and in side scatter characteristics. Three patterns were observed in MDS patient samples: i) EPs with decreased proliferation and abnormal proliferating precursors, ii) EPs with a normal proliferating fraction and maturation defects in late precursors, and iii) EPs with a reduced erythropoietic fraction but mostly normal patterns suggesting that erythropoiesis was less affected. Additionally, analysis of sequential samples from an MDS patient under treatment showed a decrease of abnormal subsets after azacytidine treatment and near normalization after allogeneic hematopoietic stem-cell transplantation.CONCLUSION: Unsupervised clustering analysis of MFC data discloses subtle alterations in erythropoiesis not detectable by cytology nor FCM supervised analysis. This novel AI analytical approach sheds some new light on the pathophysiology of these conditions.
|
|
| 4. |
- Violidaki, Despoina, et al.
(författare)
-
Analysis of erythroid maturation in the nonlysed bone marrow with help of radar plots facilitates detection of flow cytometric aberrations in myelodysplastic syndromes
- 2020
-
Ingår i: Cytometry Part B - Clinical Cytometry. - : Wiley. - 1552-4949 .- 1552-4957. ; 98:5, s. 399-411
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Accumulating data support the role of flow cytometry (FCM) in diagnostic work-up of myelodysplastic syndromes (MDS). Changes in erythropoiesis are less documented than in granulopoiesis. However, most studies were performed on bone marrow samples (BMSs) after red blood cell lysis. We have established a FCM protocol for erythropoiesis, following a no-lysis approach and live gate acquisition of nucleated cells using DNA dye DRAQ5. Methods: The ERY tube consisted of CD36, CD71, CD105, CD117, CD13, and CD45. Comparison with cytomorphological differential counts was carried out in a learning cohort of 80 BMS. To detect aberrations, we analyzed 208 BMS from 135 patients and five normal donors, divided into three cohorts: MDS (n = 68), nonclonal cytopenia (n = 43), and normal controls (n = 29). Radar plot (RP) was created for an overview of normal and aberrant patterns. Results: The proportion of erythropoiesis in the ERY tube showed better agreement with the cytomorphology, compared to FCM panels on lysed BMS. We confirmed that aberrations in coefficient of variation (CV) of CD36 fluorescence intensity (p <.001), mean fluorescence intensity of CD36 (p =.012), and CV of CD105 (p <.001) can distinguish between MDS and nonclonal cytopenia. RP facilitated evaluation of erythropoietic maturation patterns and aberrant patterns were identified in 85% of MDS patients. Conclusion: This study provides evidence that a no-lysis approach and RP analysis allow a more reliable evaluation of erythropoiesis and erythroid dysplasia, supporting the integration of FCM erythroid panels in the standard work-up of MDS.
|
|
