Suva Lab

Such an exciting step! Huge congratulations!

Awesome work Fede and team!

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...

I thank my great mentor/boss @suvalab.bsky.social and outstanding co-PIs @itaytirosh.bsky.social, @roelverhaak.bsky.social, Antonio Iavarone and Anna Lasorella. Also appreciate all collaborators, patients and their families who generously provided the samples.

12/ Before that, I would like to say thank wonderful co-first authors/friends, @avishayspitzer.bsky.social, @kevin-johnson.bsky.social and Luciano Garofano.

11/ How this architecture evolves during progression from primary to recurrent GBM is addressed in the second paper, that will be introduced by @avishayspitzer.bsky.social in the following threads later.

10/ These three layers of heterogeneity are inter-related and partially associated with specific genetic aberrations, thereby defining three stereotypic ecosystems in GBM. This work provides an unparalleled view of the multi-layered transcriptional architecture of GBM.

9/ Third, after controlling for the frequencies of cellular states, we find that the remaining variation between GBM samples highlights three baseline gene expression programs which we labeled Neuronal, Glial, and Extracellular Matrix.

8/ Second, we describe the diversity of cellular states and their pathway-based functional activities, with an expanded set of malignant cell states, including glial progenitor cell-like, neuronal-like, and cilia-like states that were previously depleted by tumor dissociation

7/ First, GBMs can be classified by their broad cellular composition, encompassing malignant, immune, vascular, neuronal, and glial cell types.

6/ In the first paper, we intensively analyzed GBM cellular heterogeneity, irrespective of time points. Our analyses reveled three transcriptomic layers that contribute to GBM heterogeneity.

4/ We thank @mdanderson.bsky.social, @dukecancer.bsky.social, @uoft.bsky.social, @pitiesalpetriere.bsky.social, NORLUX Neuro-Oncology laboratory, Seoul National University and @utokyoofficial.bsky.social for providing precious primary and recurrent GBM cohorts.

3/ In these studies, we collected 121 longitudinal GBM samples from 6 countries under GBM CARE (Cellular Analysis of Resistance and Evolution) consortium, and profiled them by single-nucleus RNA-seq (total 420 k cells).

2/These are the fantastic collaborative effort between Suva, Tirosh, Verhaak and Iavarone/Lasorella labs. We appreciate all supports from Mass General Brigham (Cancer Center, Pathology), @broadinstitute.org, Weizmann Institute of Science, @yalepress.bsky.social and University of Miami.

The multilayered transcriptional architecture of glioblastoma ecosystems - Nature Genetics Integrated single-cell transcriptomic and genetic characterization of 121 adult glioblastomas identifies heterogeneity at cell type, cell state and baseline expression program levels associated with s...

1/ Thrilled to share our TWO back-to-back papers published in Nature Genetics today! www.nature.com/articles/s41... , www.nature.com/articles/s41...

Today, the American Association for Cancer Research (AACR) called on Congress to reject the Administration's nearly 40% cut to the NIH budget and allocate a bipartisan funding increase to $51.3 billion for the agency to accelerate progress for patients. www.aacr.org/about-the-aa...