Unbiased and automated identification of a circulating tumour cell definition that associates with overall survival

Circulating tumour cells (CTC) in patients with metastatic carcinomas are associated with poor survival and can be used to guide therapy. Classification of CTC however remains subjective, as they are morphologically heterogeneous. We acquired digital images, using the CellSearch™ system, from blood of 185 castration resistant prostate cancer (CRPC) patients and 68 healthy subjects to define CTC by computer algorithms. Patient survival data was used as the training parameter for the computer to define CTC. The computer-generated CTC definition was validated on a separate CRPC dataset comprising 100 patients. The optimal definition of the computer defined CTC (aCTC) was stricter as compared to the manual CellSearch CTC (mCTC) definition and as a consequence aCTC were less frequent. The computer-generated CTC definition resulted in hazard ratios (HRs) of 2.8 for baseline and 3.9 for follow-up samples, which is comparable to the mCTC definition (baseline HR 2.9, follow-up HR 4.5). Validation resulted in HRs at baseline/follow-up of 3.9/5.4 for computer and 4.8/5.8 for manual definitions. In conclusion, we have defined and validated CTC by clinical outcome using a perfectly reproducing automated algorithm

Everolimus-eluting bioresorbable vascular scaffolds for treatment of patients presenting with ST-segment elevation myocardial infarction: BVS STEMI first study

AimsWe evaluated the feasibility and the acute performance of the everolimus-eluting bioresorbable vascular scaffolds (BVS) for the treatment of patients presenting with ST-segment elevation myocardial infarction (STEMI).Methods and resultsThe present investigation is a prospective, single-arm, single-centre study, reporting data after the BVS implantation in STEMI patients. Quantitative coronary angiography and optical coherence tomography (OCT) data were evaluated. Clinical outcomes are reported at the 30-day follow-up. The intent-to-treat population comprises a total of 49 patients. The procedural success was 97.9%. Pre-procedure TIMI-flow was 0 in 50.0% of the patients; after the BVS implantation, a TIMI-flow III was achieved in 91.7% of patients and the post-procedure percentage diameter stenosis was 14.7 ± 8.2%. No patients had angiographically visible residual thrombus at the end of the procedure. Optical coherence tomography analysis performed in 31 patients showed that the post-procedure mean lumen area was 8.02 ± 1.92 mm2, minimum lumen area 5.95 ± 1.61 mm2, mean incomplete scaffold apposition area 0.118 ± 0.162 mm2, mean intraluminal defect area 0.013 ± 0.017 mm2, and mean percentage malapposed struts per patient 2.80 ± 3.90%. Scaffolds with >5% malapposed struts were 7. At the 30-day follow-up, target-lesion failure rate was 0%. Non-target-vessel revascularization and target-vessel myocardial infarction (MI) were reported. A non-target-vessel non-Q-wave MI occurred. No cases of cardiac death or scaffold thrombosis were observed.ConclusionIn the present series, the BVS implantation in patients presenting with acute MI appeared feasible, with high rate of final TIMI-flow III and good scaffold apposition. Larger studies are currently needed to confirm these preliminary data

Development of automatic FISH probe counting in CTC

Introduction: Presence of Circulating Tumor Cells (CTC) in blood of patients with metastatic carcinomas has been associated with poor progression free and overall survival. Characterization of CTC can be performed by Fluorescence In Situ Hybridization (FISH), however counting of FISH dots by human reviewers can be tiring and subjective and thus likely produces variable outcomes. We investigated whether automated counting of FISH dots in CTC is comparable to the counts obtained by expert reviewers. Material and Methods: Samples processed on the CellSearchTM system for CTC counting were hybridized with fluorescent DNA probes targeting the HER2/neu gene region and the centromeric region of chromosome 17 or the centromeric regions of chromosome 1,7,8,17. (1) For optimization of the algorithm 492 Z-stacks from leukocytes carried over through the CellSearch procedure were recorded and a maximum intensity profile (MIP) was created. Five reviewers counted FISH dots in the MIP data set to create a ground truth. The automatic counting algorithm was validated in a set of stored images of CTC probed for chromosome 1,7,8,17 from castration resistant prostate cancer patients (CRPC).(1) Results: The data set with carried-over leukocytes was counted reliably by the algorithm: 97.8% of the HER2/neu FISH dots and 97.5% of the centromeric 17 dots were counted equally by the PC and the reviewers, regarding only the subset of images for which all the reviewers agreed. The mean intra-reviewer agreement was 96.5%. In the validation set copy number of chromosome 1 in carried-over leukocytes scored by an expert agreed in 50.8% with the automated count, for chromosome 7 34.4% for chromosome 8 22.8.% and for chromosome 17 55.0%. For CTC in the validation set copy number of chromosome 1 scored by an expert agreed in 71.6% with the automated count, for chromosome 7 56.2% for chromosome 8 64.8% and for chromosome 17 41.0%. For copy numbers larger than 6 both the expert and automated count were recorded as larger than 6. Agreement between the expert count and automated count did not significantly alter with the detected copy number. Over-count in the validation set was largely due to clustering of nuclei that were counted as one cell. Conclusions: Automatic FISH dot counting in CTC images is feasible for images where reviewers agree upon. The intra-reviewer variation of 3.5% shows that reviewers have ambiguous rules and are not reliable. This variation is closely related to the heterogeneity -size and shape- of the FISH dots. The PC has a reproducibility of 100% and is thus a good replacement for human reviewers

aCTC and mCTC cut-off values with HRs, median OS, for baseline (panel A) and first follow-up samples (panel B) for the validation set enrolled in Phase I/II trials of abiraterone acetate at Royal Marsden Hospital.

<p>*Overall Survival.</p>†<p>Hazard Ratio.</p

Kaplan-Meier plots of the classifier (grey lines) and the manual CellSearch (black lines) definition.

<p>The training set is shown in panel A (baseline, N = 185) and panel B (follow-up, N = 185). Kaplan-Meier plots for the validation set are shown in panel C (baseline, N = 93) and panel D (follow-up, N = 96). Censoring is indicated by vertical marks on the Kaplan-Meier plot.</p

Frequency distributions of mCTC and aCTC from patients (N = 185) and control samples (N = 68).

<p>The top row shows the mCTC frequency distribution (panel A). The next rows show the number of aCTC for the optimal aCTC definition (panel B), the classifier without the CD45 exclusion criterion (panel C), without the DAPI criterion (panel D), and of TMP objects that are EpCAM+CK+CD45- and <4 µm in diameter (panel E). The percentage of patients with 0 objects is shown numerically on the left. On the right the HRs derived by dichotomizing on the median number of objects in patients are shown, together with the 25, 50, and 75 percentiles. The percentiles are also indicated in the plot.</p

Schematic overview of the aCTC classifier development process.

<p>A: importing of images; B: object segmentation; C: feature measurements; D: classification of aCTC.</p

aCTC and mCTC cut-off values with HRs, median OS, for baseline (panel A) and first follow-up samples (panel B) for the training set enrolled in the IMMC-38 study.

<p>*Overall Survival; ^†Hazard Ratio.</p