Back

SMARCAL1 is a candidate therapeutic target for ALT-positive tumors

Taglialatela, A.; Lee, J.; Azeroglu, B.; Chen, X.; Dello Stritto, M. R.; Gouge, A.; Lama-Diaz, T.; Vaitsiankova, A.; Leuzzi, G.; Dela Cruz, F.; Khan, Z. F.; Kung, A. L.; Cejka, P.; Denchi, E. L.; Min, J.; Ciccia, A.

2026-02-16 cancer biology
10.64898/2026.02.15.704070 bioRxiv
Show abstract

A significant subset of tumors, including over 50% of osteosarcomas--an aggressive bone malignancy affecting children, adolescents, and young adults--relies on alternative lengthening of telomeres (ALT), a telomerase-independent, DNA repair-based mechanism for telomere elongation. The overall 5-year survival rate for osteosarcoma patients is [~]65%, underlying the need to develop novel targeted therapies. Through the Cancer Dependency Map, we identify SMARCAL1, a DNA translocase previously shown to remodel stalled replication forks, as a top selective dependency factor in telomerase-negative tumors. Using a panel of ALT-positive and ALT-negative cancer cell lines, as well as osteosarcoma patient-derived xenograft cells, we confirm that ALT-positive cells are uniquely sensitive to the loss of SMARCAL1, whose depletion exacerbates ALT-dependent phenotypes and telomeric DNA damage. Notably, we demonstrate that suppressing ALT abrogates their dependency on SMARCAL1. Mechanistically, we show that SMARCAL1 loss leads to telomeric ssDNA accumulation in ALT-positive cells, dependent in part on DNA repriming mediated by the DNA primase/polymerase PRIMPOL. Moreover, SMARCAL1s ssDNA annealing activity counteracts DNA unwinding by the BLM helicase, limiting telomeric ssDNA accumulation and DNA damage in ALT-positive cells. Importantly, SMARCAL1 depletion induces senescence in ALT-positive cancer cells, rendering them susceptible to treatment with senolytic agents. Together, these findings establish SMARCAL1 as a key regulator of ALT metabolism and highlight SMARCAL1 as a promising therapeutic target for ALT-positive tumors.

Published in Genes & Development (predicted rank #5) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.