SARS-CoV-2 omicron spike simulations: broad antibody escape, weakened ACE2 binding, and modest furin cleavage

The recent emergence of the omicron variant of the SARS-CoV-2 virus with large numbers of mutations has raised concern about a potential new surge in infections. Here we use molecular dynamics to study the biophysics of the interface of the BA1 and BA2 omicron spike protein binding to (i) the ACE2 receptor protein, (ii) antibodies from all known binding regions, and (iii) the furin binding domain. Our simulations suggest that while there is a significant reduction of antibody (Ab) binding strength corresponding to escape, the omicron spikes pay a cost in terms of weaker receptor binding as measured by interfacial hydrogen bonds (H-bond). The furin cleavage domain (FCD) is the same or weaker binding than the delta variant, suggesting lower fusogenicity resulting in less viral load and disease intensity than the delta variant. IMPORTANCE The BA1 and BA2 and closely related BA2.12.2 and BA.5 omicron variants of SARS-CoV-2 dominate the current global infection landscape. Given the high number of mutations, particularly those which will lead to antibody escape, it is important to establish accurate methods that can guide developing health policy responses that identify at a fundamental level whether omicron and its variants are more threatening than its predecessors, especially delta. The importance of our work is to demonstrate that simple in silico simulations can predict biochemical binding details of the omicron spike protein that have epidemiological consequences, especially for binding to the cells and for fusing the viral membrane with the cells. In each case, we predicted weaker binding of the omicron spike, which agreed with subsequent experimental results. Future virology experiments will be needed to test these predictions further.

Systematically higher Ki67 scores on core biopsy samples compared to corresponding resection specimen in breast cancer: a multi-operator and multi-institutional study

Ki67 has potential clinical importance in breast cancer but has yet to see broad acceptance due to inter-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) investigate the comparability of Ki67 measurement across corresponding core biopsy and resection specimen cases, and (ii) assess section to section differences in Ki67 scoring. Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding resection specimens from 30 estrogen receptor-positive breast cancer patients were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and immunohistochemically (IHC) stained for Ki67 (MIB-1 antibody). The QuPath platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed as in published studies. A guideline for building an automated Ki67 scoring algorithm was sent to participating labs. Very high correlation and no systematic error (p = 0.08) was found between consecutive Ki67 IHC sections. Ki67 scores were higher for core biopsy slides compared to paired whole sections from resections (p ≤ 0.001; median difference: 5.31%). The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation). Therefore, Ki67 IHC should be tested on core biopsy samples to best reflect the biological status of the tumor.

Abstract P5-02-01: Analytical validation and prognostic potential of an automated digital scoring protocol for Ki67: An International Ki67 Working Group study

Background: The nuclear proliferation biomarker Ki67 has multiple potential roles in breast cancer, including aiding decisions based on prognosis, but has unacceptable between-laboratory variability. Here we tested an open source and calibrated automated digital image analysis (DIA) platform to: (i) Assess inter-laboratory reproducibility of automated Ki67 measurement among 17 participating labs and compare those with standardized pathologist-based visual scoring. (ii) Investigate the comparability of Ki67 measurement across corresponding core biopsy and whole section cases. (iii) Test prognostic potential of the built Ki67 scoring algorithms on an independent cohort.

Methods: Two sets of 60 previously stained slides containing 30 core-cut biopsy and 30 corresponding whole tumor sections from 30 ER+ breast cancer cases were sent to 17 participating labs for automated assessment of average Ki67 expression. The blocks were centrally cut and stained for Ki67 using the Mib-1 antibody. The QuPath (open-source software) DIA platform was used to evaluate tumoral Ki67 expression. Calibration of the DIA method was performed in our previous study (Acs et al, Lab Invest 2019). A detailed guideline for building an automated Ki67 scoring algorithm was sent to the participating labs. Visual scoring of average Ki67 expression was performed by pathologists according to published standardized methods (Leung et al, NPJ Br Cancer 2016; Leung et al, Histopath 2019). Locked down DIA Ki67 scoring algorithms were applied to a validation cohort: 222 breast cancer cases from the Karolinska University Hospital in whole section format. Sufficient reproducibility to declare analytical validity was defined as an Intra Class Correlation (ICC) with lower limit of 95% credible interval (CI) >0.80. Markov Chain Monte Carlo routines for generalized linear mixed models were used to estimate ICCs and calculate corresponding CIs.

Results: The same-section ICC was 0.902 (CI: 0.852-0.949) across 17 labs using calibrated DIA platform on core biopsy slides and 0.845 (CI: 0.778-0.912) on whole sections. The different-section ICC across the 17 labs was 0.873 (CI: 0.806-0.932) scoring on core biopsy slides and 0.777 (CI: 0.670-0.874) on whole sections. The pathologist-based visual Ki67 scoring showed ICC of 0.860 for all comparisons, respectively (CI: 0.795-0.927). Similar to what was observed for visual Ki67 scoring, the DIA scores are higher for core biopsy slides compared to paired whole sections (p≤0.001; median difference: 5.31%; IQR: 11.50%). Ki67 scores of all locked down DIA algorithms correlates significantly (p≤0.023) with outcome on the validation cohort (observed hazard ratios range: 2.518-2.922).

Conclusions: Automated Ki67 evaluation using a calibrated, open-source DIA platform (QuPath) met the pre-specified criterion of success on core biopsies but not on whole sections in the multi-institutional setting. The systematic discrepancy between core biopsy and corresponding whole sections was likely due to pre-analytical factors (tissue handling, fixation) and intratumor heterogeneity. We found that different algorithms built according to calibrated DIA methods had similar prognostic potential. Assessment of clinical utility is planned.

Citation Format: Balazs Acs, Samuel C.Y. Leung, Kelley M. Kidwell, Indu Arun, Renaldas Augulis, Sunil S. Badve, Yalai Bai, Anita L. Bane, John M.S. Bartlett, Jane Bayani, Gilbert Bigras, Annika Blank, Signe Borgquist, Henk Buikema, Martin C. Chang, Robin L. Dietz, Andrew Dodson, Anna Ehinger, Susan Fineberg, Cornelia M. Focke, Dongxia Gao, Allen M. Gown, Carolina Gutierrez, Johan Hartman, Judith C. Hugh, Zuzana Kos, Anne-Vibeke Lænkholm, Arvydas Laurinavicius, Richard M. Levenson, Rustin Mahboubi-Ardakani, Mauro G. Mastropasqua, Takuya Moriya, Sharon Nofech-Mozes, C. Kent Osborne, Liron Pantanowitz, Frédérique M. Penault-Llorca, Tammy Piper, Mary Anne Quintayo, Tilman T. Rau, Stefan Reinhard, Stephanie Robertson, Takashi Sakatani, Roberto Salgado, Melanie Spears, Jane Starczynski, Tomoharu Sugie, Bert van der Vegt, Giuseppe Viale, Shakeel Virk, Lila A. Zabaglo, Daniel F. Hayes, Mitch Dowsett, Torsten O. Nielsen, David L. Rimm, International Ki67 in Breast Cancer Working Group, BIG-NABCG. Analytical validation and prognostic potential of an automated digital scoring protocol for Ki67: An International Ki67 Working Group study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-01.