Ki67 expression has been a valuable prognostic variable in breast cancer, but has not seen broad adoption due to lack of standardization between institutions. Automation could represent a solution. Here we investigate the reproducibility of Ki67 measurement between three image analysis platforms with supervised classifiers performed by the same operator, by multiple operators, and finally we compare their accuracy in prognostic potential. Two breast cancer patient cohorts were used for this study. The standardization was done with the 30 cases of ER+ breast cancer that were used in phase 3 of International Ki67 in Breast Cancer Working Group initiatives where blocks were centrally cut and stained for Ki67. The outcome cohort was from 149 breast cancer cases from the Yale Pathology archives. A tissue microarray was built from representative tissue blocks with median follow-up of 120 months. The Mib-1 antibody (Dako) was used to detect Ki67 (dilution 1:100). HALO (IndicaLab), QuantCenter (3DHistech), and QuPath (open source software) digital image analysis (DIA) platforms were used to evaluate Ki67 expression. Intraclass correlation coefficient (ICC) was used to measure reproducibility. Between-DIA platform reproducibility was excellent (ICC: 0.933, CI: 0.879-0.966). Excellent reproducibility was found between all DIA platforms and the reference standard Ki67 values of Spectrum Webscope (QuPath-Spectrum Webscope ICC: 0.970, CI: 0.936-0.986; HALO-Spectrum Webscope ICC: 0.968, CI: 0.933-0.985; QuantCenter-Spectrum Webscope ICC: 0.964, CI: 0.919-0.983). All platforms showed excellent intra-DIA reproducibility (QuPath ICC: 0.992, CI: 0.986-0.996; HALO ICC: 0.972, CI: 0.924-0.988; QuantCenter ICC: 0.978, CI: 0.932-0.991). Comparing each DIA against outcome, the hazard ratios were similar. The inter-operator reproducibility was particularly high (ICC: 0.962-0.995). Our results showed outstanding reproducibility both within and between-DIA platforms, including one freely available DIA platform (QuPath). We also found the platforms essentially indistinguishable with respect to prediction of breast cancer patient outcome. Results justify multi-institutional DIA studies to assess clinical utility.

• Histones migrate into the cytoplasm of normal erythroblasts during maturation, leading to extruded nuclei largely depleted of protein.• Loss of nuclear exportin Xpo7 inhibits normal erythroid nuclear condensation and enucleation; histones remain in Xpo7-knockdown nuclei.Global nuclear condensation, culminating in enucleation during terminal erythropoiesis, is poorly understood. Proteomic examination of extruded erythroid nuclei from fetal liver revealed a striking depletion of most nuclear proteins, suggesting that nuclear protein export had occurred. Expression of the nuclear export protein, Exportin 7 (Xpo7), is highly erythroid-specific, induced during erythropoiesis, and abundant in very late erythroblasts. Knockdown of Xpo7 in primary mouse fetal liver erythroblasts resulted in severe inhibition of chromatin condensation and enucleation but otherwise had little effect on erythroid differentiation, including hemoglobin accumulation. Nuclei in Xpo7-knockdown cells were larger and less dense than normal and accumulated most nuclear proteins as measured by mass spectrometry. Strikingly, many DNA binding proteins such as histones H2A and H3 were found to have migrated into the cytoplasm of normal late erythroblasts prior to and during enucleation, but not in Xpo7-knockdown cells. Thus, terminal erythroid maturation involves migration of histones into the cytoplasm via a process likely facilitated by

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Programmed Death 1 Ligand 1 (PD-L1) Immunohistochemistry (IHC) is the key FDA-approved predictive marker to identify responders to anti-PD1 axis drugs. Multiple PD-L1 IHC assays with various antibodies and cut-points have been used in clinical trials across tumor types. Comparative performance characteristics of these assays have been extensively studied qualitatively but not quantitatively. Here we evaluate the use of a standardized PD-L1 Index TMA to objectively determine agreement between antibody assays for PD-L1 applying quantitative digital image analysis. Using a specially constructed Index Tissue Microarray (TMA) containing a panel of 10 isogenic cell lines in triplicate, we tested identical but independently grown batches of isogenic cells to prove Index TMAs can be produced in large quantities and hence serve as a standardization tool. Then the Index TMAs were evaluated using quantitative immunofluorescence (QIF) to validate the TMA itself and also to compare antibodies including E1L3N, SP142 and SP263. Next, an inter-laboratory and inter-assay comparison of 5 PD-L1 chromogenic IHC assays (US Food and Drug Administration (FDA) approved and lab developed test (LDT)) were performed at 12 sites around the USA. As previously reported, the SP142-FDA assay failed to detect low levels of PD-L1 in cell lines distinguished by the other 4 assays. The assays for 22C3-FDA, 28-8-FDA, SP263-FDA and E1L3N-LDT were highly similar across sites and all laboratories showed a high consistency over time for all assays using this Index TMA. In conclusion, we were able to objectively quantify PD-L1 expression on a standardized Index TMA using digital image analysis and we confirmed previous subjective assessments of these assays, but now in a multi-institutional setting. We envision commercial use of this Index TMA or similar smaller version as a useful standardization mechanism to compare results between institutions and to identify abnormalities while running routine clinical samples.

Purpose: Although tumor-infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple-negative breast cancer (TNBC), it is subject to inter and intraobserver variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. Experimental Design: Using the QuPath open-source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts, and “other” cells on hematoxylin–eosin (H&E)–stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation, we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets. Results: We found that all five machine TIL variables had significant prognostic association with outcomes (P ≤ 0.01 for all comparisons) but showed cell-specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathologic factors including stage, age, and histologic grade (P ≤ 0.0003 for all analyses). Conclusions: Neural net-driven cell classifier-defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open-source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility. See related commentary by Symmans, p. 5446