The single-stranded DNA–binding factor SUB1/PC4 alleviates replication stress at telomeres and is a vulnerability of ALT cancer cells
Bibliographic record
Abstract
To achieve replicative immortality, cancer cells must activate telomere maintenance mechanisms. In 10 to 15% of cancers, this is enabled by recombination-based alternative lengthening of telomeres pathways (ALT). ALT cells display several hallmarks including heterogeneous telomere length, extrachromosomal telomeric repeats, and ALT-associated PML bodies. ALT cells also have high telomeric replication stress (RS) enhanced by fork-stalling structures (R-loops and G4s) and altered chromatin states. In ALT cells, telomeric RS promotes telomere elongation but above a certain threshold becomes detrimental to cell survival. Manipulating RS at telomeres has thus been proposed as a therapeutic strategy against ALT cancers. Through analysis of genome-wide CRISPR fitness screens, we identified ALT-specific vulnerabilities and describe here our characterization of the roles of SUB1, a ssDNA-binding protein, in telomere stability. SUB1 depletion increases RS at ALT telomeres, profoundly impairing ALT cell growth without impacting telomerase-positive cells. During RS, SUB1 is recruited to stalled forks and ALT telomeres via its ssDNA-binding domain. This recruitment is potentiated by RPA depletion, suggesting that these factors may compete for ssDNA. The viability of ALT cells and their resilience toward RS also requires ssDNA binding by SUB1. SUB1 depletion accelerates cell death induced by FANCM depletion, triggering unsustainable levels of telomeric damage in ALT cells. Finally, combining SUB1 depletion with RS-inducing drugs rapidly induces replication catastrophe in ALT cells. Altogether, our work identifies SUB1 as an ALT susceptibility with roles in the mitigation of RS at ALT telomeres and suggests advanced therapeutic strategies for a host of still poorly managed cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".