The full study is already available www.biorxiv.org/content/10.1101/2025.05.29.656750

I would also like to acknowledge the @evenflowproject.bsky.social for their support

I would like to thank Davide Cirillo and @alfonsovalencia.bsky.social for their supervision

Studying cancer progression with DRL is challenging due to limited longitudinal data and the absence of methods for real-time tracking. Still, solving these problems could lead to major breakthroughs in personalized treatment

Single cell omics data is commonly used to infer pseudo-time trajectories of cancer cells. However, these trajectories lack actual temporal information from either real time or stages.

It is also not clear how to study cancer in a temporal manner, given cancer progression differs from patient to patient. From the literature, we see that stages may be used as a proxy time-unit to study cancer’s time dimension.

Given the difficulties of performing a longitudinal study in human patients, there is a lack of follow-up data, especially in cancer.

We found that the most common uses of VAEs to study cancer include diagnosis, prognosis, and subtyping.

Cancer is a highly complex and dynamic disease, making it well-suited for analysis using DRL methods and VAEs.

We wanted to elucidate the most common uses of DRL and the VAE in the study of cancer, paying special attention to the temporal component of cancer, which remains understudied.

DRL methods are used to learn a low-dimensional embedding from data. Specifically, the VAE can learn said representation, the latent space, keeping non-linear relationships in the original data. Moreover, it is also a generative method, as it can create new, synthetic data from the original data.

References
[1] link.springer.com/article/10.1... [2] doi.org/10.3171/2019... [3] academic.oup.com/neuro-oncolo... [4] doi.org/10.1016/j.ce... [5] jhoonline.biomedcentral.com/articles/10.... [6] linkinghub.elsevier.com/retrieve/pii... [7] doi.org/10.1016/j.cc...
[8] arxiv.org/abs/1312.6114

Shout out to all the authors: Alejandro Tejada-Lapuerta, Beatriz Urda-García, Iker Núñez-Carpintero, @alfonsovalencia.bsky.social, and Davide Cirillo. Thanks to the #Evenflow project, and my institution @bsc-cns.bsky.social

5. How is this helpful for MB research?

We believe our contributions will help develop better treatments for MB: labeling patients’ subgroups leads to different treatment strategies, so elucidating the most adequate is essential for an optimal recovery.

4.2. We have seen there are about 2,500 genes’ expressions that are unique to the G3-G4 subgroup, some of which are commonly mutated in MB: KMT2C, MYC, SNCAIP, SYNCRIP, and TP53.

4. What do we find?

4.1. By identifying and augmenting the patients in the G3-G4 subgroup, we achieved high classification performance, reinforcing that this intermediate group displays distinct features in comparison to G3 and G4.

3. How can we study a rare occurrence of a rare disease?

We have obtained the data from the largest repository available on MB [7] and used the VAE's [8] generative ability to amplify the G3-G4 subgroup. This means we can learn from real patient data to generate new, synthetic patients.

2.2. Research has suggested the possibility to deem G3 and G4 as a continuum [4] but also the existence of an additional subgroup between G3 and G4 [5, 6] sometimes referred to as G3-G4. However, the limited number of patients in this intermediate case have made gaining relevant insights a challenge

2. Why study Medulloblastoma Subgroups?

2.1. G3 and G4 subgroups tend to be closely clustered. This tight relationship is reflected in the latest consensus classification of MB, dividing the disease into WNT, SHH, and non-WNT/non-SHH subgroups [3].