Simona Cristea Profile picture
Mar 22, 2023 32 tweets 12 min read Read on X
🚨Our new study is out @CellReports!

We use single cell protein quantification & single cell FISH to map #spatial interactions in genetic mosaicism & tumor microenvironment in #Glioblastoma!

Wonderful collaboration w/ @janiszewska_lab @DalitEngelhardt
@Kacper_W_PhD

Deep dive👇 Image
First, some context.

Glioblastoma (GBM) is one of the deadliest, most aggressive cancers that exist, with a median survival of only 15 months.

In GBM, 'single cell heterogeneity' are not simply buzzwords.

Rather, this immense heterogeneity is a main reason of treatment failure Image
Recent work demonstrated that single GBM tumors are mosaics of cells in different states, each associated with distinct genomic driver alterations.

While transitions between cell states can occur, each genetic driver favors a particular cell state.

tinyurl.com/mrx2jpj8
Here, we aimed to better understand how genetically different GBM populations *spatially* interact with each other & further with the tumor microenvironment

We studied this via highly multiplexed spatial protein profiling & single cell spatial mapping of FISH in selected drivers
To do this, we constructed a tissue microarray (TMA) from 17 formalin-fixed paraffin-embedded (FFPE) IDH wild-type GBM tumors.

From each tumor block, we randomly selected 3–4 cores (61 in total) to investigate patterns of local intra-tumor heterogeneity within each tumor biopsy. Image
We used 3 slides of the multiregion TMA for single cell profiling of:
- EGFR, CDK4 & PDGFRA DNA by multicolor FISH (35K cells)
- specific-to-allele PCR-FISH (STAR-FISH, 26K cells)
- protein production, NanoString GeoMx digital spatial profiling (DSP, 79 proteins)

Main findings👇 Image
1. Local heterogeneity in GBM microenvironment (GeoMx DSP data)

DSP allows quantification of oligo tags released by UV-illuminated regions of interest (ROI) selected by immunofluorescent staining.

It enables simultaneous antibody staining for 10s of markers & control antibodies Image
Here, we selected 96 ROIs from the 61 TMA cores, with 2–3 ROIs in cores with heterogeneous immunofluorescent staining.

These ROIs were then subjected to DSP for 79 protein markers associated with GBM biology, neurobiology, and immunology.
We observed interesting spatial patterns:

- protein levels of immune genes (CD68, HLA-DR, CD11b, CD45, IBA1, CD163, CD14) correlated across tumors

- members of the WNT pathway correlated with TCF/LEF pathway

- clusters of immune & neuronal markers were negatively correlated Image
Since the TMA cores represent spatially distant regions of the same tumor fragment with possibly different compositions, we assessed the variability of protein production between ROIs from distinct tumors relative to the variability within tumors (Kruskal-Wallis H test). Image
The ratio of inter-to-intra tumor variability was quite different across the profiled proteins.

For example, vascular cell adhesion molecule (VCAM) & CD31, both associated with endothelial function & tumor vascularization, showed high variance between regions of the same tumor.
On the contrary, immune-related proteins s.a. CD45, as well as EGFR and p53 (often overexpressed & mutated in GBM), showed particularly high inter vs. intra-tumor variability

This may indicate that immune state & major drivers are less variable between biopsies in the same tumor
2. Single-cell genetic heterogeneity in GBM

We performed multiplexed FISH for EGFR & CDK4 on the multicore TMA (35K nuclei in 17 tumors).

We chose these 2 genes because their copy number amplifications characterize genetically distinct GBM states (+PDGFRA, but no signal here). Image
Therefore, each cell was assigned to one of the following 4 genotypes:
- E (EGFR amplified & not CDK4 amplified)
- C (CDK4 amplified & not EGFR amplified)
- EC (EGFR & CDK4 amplified)
- N/O (no amplification of either EGFR or CDK4) Image
We found variation in the frequency of cells of each genotype within each tumor.

Although many tumors had relatively high E cell frequency, C & EC cells were also present in most.

There was also variation between cores taken from the same biopsy, meaning within small regions. Image
The relative proportions of the different genotypes followed interesting correlation patterns (each point here represents the ratio of the cells with a particular genotype to the total number of cells within a tumor; beware the axes don't go all the way 0-1). Image
3. Tumor stratification by co-occurrence of EGFR & CDK4 in same vs. different cells

Our dataset of single cell genotypes allows investigating differences btwn tumors in which these alterations tend to co-occur in same cell & tumors in which they do not(different cells or absent)
For this, we calculated the odds ratio (OR) of detecting co-amplified cells within each of the 17 GBMs.

The OR = the likelihood that a cell with EGFR amplification also harbors a CDK4 amplification, relative to the likelihood of a CDK4 amplification without EGFR amplification. Image
OR = 1: amplification in one of these 2 genes has no effect on the other being amplified.

OR above/below 1: increased/decreased tendency for EGFR & CDK4 amplifications to co-occur in same cell vs. what is expected if one amplification had no effect on the other one occurring.
Based on OR values, we separated tumors into 3 classes:
- ORlow: decreased tendency for EGFR & CDK4 same-cell co-amplification (OR <<1)
- ORhigh: increased tendency for EGFR & CDK4 same-cell co-amplification (OR >>1)
- no effect (OR ~ 1) Image
The frequencies of E cells (only EGFR amplification) & N/O cells (neither EGFR nor CDK4) were different between ORhigh & ORlow groups, while C & EC cells were similarly distributed

It can then be that low OR is driven by increased frequency of cells with only EGFR amplification Image
Both the average & the entropy of EGFR copy number values in E cells was significantly higher in ORlow tumors (unlike CDK4).

This suggests that it may be that EGFR amplifications in E cells in ORlow tumors are qualitatively different from those in E cells in ORhigh tumors. Image
A possible explanation is that EGFR amplification is generated by extrachromosomal DNA (ecDNA) fragments in ORlow tumors.

ecDNA fragments contain an oncogene & an enhancer element, allowing for increased gene expression.

Their presence has been documented in GBM.
We tested this by running AmpliconArchitect on whole genome seq. data from 3 ORlow & 3 ORhigh tumors.

ORlow tumors indeed showed a complex structure of genomic rearrangements involving EGFR amplicon, versus simpler amplification patterns in ORhigh tumors

nature.com/articles/s4146…
Coming back:

The in situ single cell genotyping dataset that we generated contains spatial coordinates for each nucleus recorded in an image.

So, based on this data, we can compute distances between spatial locations of cells with distinct genotypes in ORlow & ORhigh tumors.
Interestingly, we find that:

- E cells in ORlow tumors are closer to each other than ORhigh (same distance for C)
- C cells show a tendency towards tighter clustering in the tissue

This suggests that cells with only CDK4 amplif are less migratory & remain closer after division. Image
4. The co-occurrence of EGFR & CDK4 amplifications is associated with an immunosuppressive tumor microenvironment

ORhigh tumors were enriched in proteins associated with immune cell infiltration & an immunosuppressive microenvironment (CD163, IBA1, CD14, CD45, CD11b, HLA-DR). Image
Immunofluorescent staining of the original sections from ORlow & ORhigh tumors confirmed huge differences in infiltration of CD163+ cells.

Interestingly, this can suggest that a higher relative frequency of EGFR & CDK4 co-amplif. is associated w/ a more immunosuppressive state! Image
Phew, that's it for this long thread!

TL;DR:
- EGFR-CDK4 co-amplification corresponds to distinct alteration and expression trends. This co-amplification is subclonal & can only be recovered from single-cell data (hidden in bulk)
- it is possible that our FISH data identified tumors harboring extrachromosomal DNA EGFR as a distinct class of tumors, associated with low immune marker expression & high levels of EGFR protein

- immune cells infiltrating ORhigh tumors may be predominantly immunosuppressive
- even though preliminary, this suggests that the assessment of co-amplification of EGFR and CDK4 at the single-cell level by FISH could serve as a proxy of immune status (much easier & reliable to assess than standard immunofluorescence for immune infiltration)
Finally, links for:

paper: tinyurl.com/2wsm454m
Github: tinyurl.com/mph5uxyk
Zenodo: tinyurl.com/ye275d9v
WGS data: tinyurl.com/esfep873

It was an immense pleasure to work together with
@janiszewska_lab & all coauthors on such an important project!

Thank you all 👏

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More from @simocristea

Dec 31, 2023
To end 2023, I’ll share one of the most insightful & well-written papers I read in 2023.

This study @Nature links *spatial* tumor organization to immunotherapy response in breast cancer.

Immunotherapy is our strongest weapon against cancer. We need to understand it better.
🧵🧵 Image
Long thread ahead, going deep into the molecular workings of breast cancer immunotherapy.

TL;DR:
1. Cancer–immune interactions & proliferative fractions predict immunotherapy response
2. Both pre-treatment & on-treatment predictors
3. Immunotherapy remodels the microenvironment
The paper is about triple-negative breast cancer (TNBC).

TNBC lacks ER & PR hormone receptors and human epidermal growth factor 2 (HER2) activity.

It is the most aggressive of the 4 breast cancer subtypes.

Responds poorest to treatment & has higher prevalence in younger women. Image
Read 41 tweets
Oct 31, 2023
New: the monthly roller-coaster through October’s coolest life science papers is here 🚀🧬

3-sentence summaries of papers on evolution, single cell methodologies, genetic screens & more.

And, only for October, an educational video on fighting cancer🤺 as a bonus.

Enjoy 3x10! Image
1. Assembly theory (Sharma et al., Nature)

The most (in)famous paper I read this month proposes a new framework (assembly theory, that is) to explain basically everything… or, more specifically, “to unify descriptions of evolutionary selection across physics and biology” 1/3 Image
This paper is not an easy read for anybody (in particular evolutionary biologists), but, to its merit, it sparked scientific discussions by being different than what is expected for a scientific paper describing evolution. 2/3
Read 38 tweets
Sep 22, 2023
The human genome is gradually unravelling its secrets 🎁

AlphaMissense model @ScienceMagazine: one more path lit up by deep learning in exploring the code of life 🧬

We now know with high confidence if 89% of ALL missense variants are benign or pathogenic

Key contributions🧵🧵 Image
First things first:

Missense variants = genetic variants (i.e DNA bases) that change the amino acid sequence (i.e groups of 3 bases, building blocks of proteins) in proteins.

Missense variants are more important than non-missense ones, as more likely to have functional impact. Image
Now, even if a variant changes the amino acid structure of a protein (i.e it is missense), it is not necessarily that the variant also impacts the function of its corresponding protein.

Further, even if protein function gets impacted, it isn't clear in which way or by how much.
Read 41 tweets
Jul 21, 2023
3 amazing papers just out @Nature, the kind worth giving up sleep for🦉

Spatial multi-omics human maps:
-placenta: MIBI & DSP
-intestine: CODEX & snRNAseq & snATACseq
-kidney: Visium & scRNA & scATAC

After sequencing single cells, we are now finally putting them back together🧵 Image
1. Placenta 1/5

Understanding the mysterious maternal processes that sustain embryo development is fascinating.

Mapping those spatially with proteins & mRNA to describe the maternal-fetal interface in the first half of pregnancy is really mind-blowing.

https://t.co/j14fWp8dZznature.com/articles/s4158…
Image
1. Placenta 2/5

- 500,000 cells with MIBI of 37 antibody panel
- 66 individuals (6-20 weeks gestation)

Immune tolerance model proposed for how the structure & function of the maternal endometrium transforms to promote the regulated invasion of genetically dissimilar fetal cells Image
Read 19 tweets
Jun 20, 2023
Twitter messed up my previous thread, but this is too important to let it slide:

Here are (again) my summary & thoughts on early detection & an amazing work

Deep learning model trained on 9 million patient records in Denmark & US finds people at risk for pancreatic cancer
🧵🧵 Image
In this thread, we'll discuss:

1. Context & significance of study
2. Datasets
3. Deep learning model
4. Model performance
5. Feature interpretability
6. Thoughts

And here's the link to the paper:

nature.com/articles/s4159…
1. Context & Significance of Study
========================

Pancreatic cancer is a terrible disease.

Despite impressive progress, its 5-year survival rate in the US is currently no more than 12%. Image
Read 56 tweets
Jun 1, 2023
Cancer is a terrible disease, and also one that we all know too well.

It is not a new problem, rather one that exists since thousands of years & is studied in unimaginable detail.

Then why do people still die of cancer?

Let's start understanding this by taking a step back. Image
It’s 1938, and Public Health Services are advising people that detecting and treating cancers early will save their lives. Image
Now fast-forward nowadays. We hear the exact same core message from the Public Health Services of our times, gradually and consistently backed up by more and more data. Image
Read 46 tweets

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