Avishay Spitzer, MD, PhD

15/ This of course wouldn't have been possible without the generous contribution of the patients and their families, for which we are ever thankful.

14/ Big thanks also to the institutions that provided tumor samples @dukepress.bsky.social, @mdanderson.bsky.social, Tokyo University Hospital, Pitié-Salpêtrière Hospital, St. Michael's Hospital, Seoul National University and NORLUX and funders NIH, NCI, Mark Foundation and Sontag Foundation.

13/ @weizmanninstitute.bsky.social, Mass General Brigham (Cancer Center, Pathology), @broadinstitute.org, @yalepress.bsky.social and University of Miami.

12/ Big thanks to the PIs that supervised the project @itaytirosh.bsky.social, @suvalab.bsky.social, @roelverhaak.bsky.social, Antonio Iavarone and Anna Lasorella, all collaborators and institutions involved in the CARE consortium >

11/ To sum-up, our multi-center study offers a high-resolution atlas of GBM recurrence dynamics, shaped by treatment response and TME context and underscores the tremendous heterogeneity of this devastating disease.

10/ Overall genetic divergence (SNVs + CNAs) correlated with transcriptional evolution, suggesting that GBM cell state shifts during recurrence are at least partially genomically driven.

9/ Small deletion phenotype (linked to radiotherapy) was associated with hypoxia-related states at recurrence, likely reflecting selection of radioresistant subpopulations. SBS21 (MMR-deficiency signature) also increased post-treatment in both CARE and GLASS cohorts.

8/ Interestingly, quantifying MGMT activity from the single-cell expression data outperformed promoter methylation as a prognostic marker in this cohort.

7/ However, in certain sub-groups, specific trajectories do tend to recur more often. First, MGMT-Low tumors (likely TMZ responders) tend to lose MES-like states whereas MGMT-High tumors (likely non-responders) tend to gain MES-like at recurrence.

6/ Despite global conservation, individual matched tumor pairs showed frequent divergence, with the majority switching at least one transcriptional layer. Overall, 72% of all possible transitions were observed across the cohort.

5/ Contrary to previous studies, we did not observe a significant enrichment of mesenchymal-like (MES-like) states at recurrence. Instead, recurrence trajectories were diverse and patient-specific, with no single state dominating across the cohort.

4/ And now for the results! Across the cohort the most consistent change at recurrence was reduced malignant cell fraction, with a reciprocal increase in glial and neuronal TME cells. This was observed in ~66% of patients, suggesting increased tumor integration into brain tissue.

3/ We first leveraged this large cohort to revisit the intra- and inter-tumor heterogeneity in GBM and characterized three multi-layered transcriptional ecosystems. Read more about this study in this great thread by @masashi-nomura.bsky.social →

bsky.app/profile/masa...

2/ In this study we profiled the longitudinal evolution of glioblastoma at single-cell resolution - altogether 121 treatment-naïve and exposed tumors from 59 patients, 430K nuclei, full clinical annotation and whole exome/genome sequencing.

Deciphering the longitudinal trajectories of glioblastoma ecosystems by integrative single-cell genomics - Nature Genetics Comparison of paired primary and recurrent glioblastomas at the single-cell transcriptomic level describes molecular and cellular trajectories associated with tumor recurrence, highlighting extensive ...

1/ Truly excited to share our new study that I had to privilege to co-lead during my PhD alongside great friends and collaborators @masashi-nomura.bsky.social, @kevin-johnson.bsky.social and Luciano Garofano, published at Nature Genetics @natureportfolio.nature.com!

www.nature.com/articles/s41...