Back

Sacituzumab Govitecan as an Effective Strategy for Sensitizing Chemoresistant HNSCC Cells to Senolytic Intervention

Luffman, N.; Hu, B.; Koblinski, J.; Gewirtz, D.; Harada, H.

2026-04-15 cancer biology
10.64898/2026.04.13.718209 bioRxiv
Show abstract

Head and neck squamous cell carcinoma (HNSCC) is currently the sixth most prevalent cancer worldwide and is marked by a high tumor relapse frequency due to acquired chemoresistance, requiring alternative strategies to sensitize resistant tumor cell populations to treatment. Sacituzumab govitecan (SG), a TROP2-targeting antibody-drug conjugate, has been successful in limiting tumor progression in pretreated patients with triple-negative and hormone-receptor positive HER2-negative breast cancer. However, it has been ineffective as a monotherapy in HNSCC. This may be attributed to the promotion of senescence that could ultimately lead to tumor relapse. Senolytics, drugs inducing cell death in senescent cell populations, have been effective in sensitizing a variety of solid tumor types to standard of care chemotherapies in preclinical studies. Consequently, we investigated the effectiveness of SG treatment followed by the senolytic, ABT-263, as a "two-hit" therapeutic strategy against cisplatin-resistant HNSCC. We established that isogenic cisplatin-sensitive and -resistant HNSCC cells express high levels of TROP2 and undergo senescence following SG treatment, and found that TROP2 expression and the SN-38 SG warhead are necessary for SG to induce senescence. SG treatment supplemented with a panel of BCL-2 family targeting senolytics revealed that both cisplatin-sensitive and -resistant senescent HNSCC cells are sensitive to BCL-XL specific inhibitors, such as ABT-263. Furthermore, we determined that ABT-263 sensitized HNSCC cells to apoptosis via a BAK and BAX-dependent mechanism. In vivo studies confirmed that SG treatment followed by ABT-263 limited tumor progression and extended survival without notable toxicity. Thus, SG in combination with senolytic treatment may be an effective strategy for suppressing the growth of cisplatin-resistant HNSCC cells.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Molecular Cancer Therapeutics
33 papers in training set
Top 0.1%
14.7%
2
Cancers
200 papers in training set
Top 0.3%
10.3%
3
Molecular Oncology
50 papers in training set
Top 0.1%
5.0%
4
Frontiers in Oncology
95 papers in training set
Top 0.6%
5.0%
5
British Journal of Cancer
42 papers in training set
Top 0.3%
4.4%
6
Scientific Reports
3102 papers in training set
Top 26%
4.4%
7
International Journal of Cancer
42 papers in training set
Top 0.4%
2.8%
8
Cell Reports Medicine
140 papers in training set
Top 2%
2.1%
9
Cell Death Discovery
51 papers in training set
Top 0.3%
2.1%
50% of probability mass above
10
eLife
5422 papers in training set
Top 35%
2.1%
11
Translational Oncology
18 papers in training set
Top 0.1%
2.1%
12
Molecular Therapy
71 papers in training set
Top 1%
1.9%
13
Nature Communications
4913 papers in training set
Top 48%
1.9%
14
Cancer Letters
32 papers in training set
Top 0.2%
1.7%
15
Aging
69 papers in training set
Top 1%
1.7%
16
Cancer Research Communications
46 papers in training set
Top 0.6%
1.4%
17
BMC Cancer
52 papers in training set
Top 2%
1.1%
18
Communications Biology
886 papers in training set
Top 15%
1.1%
19
Cancer Medicine
24 papers in training set
Top 1%
1.0%
20
Pharmaceuticals
33 papers in training set
Top 1%
1.0%
21
EMBO Molecular Medicine
85 papers in training set
Top 3%
1.0%
22
PLOS ONE
4510 papers in training set
Top 63%
0.9%
23
Clinical Cancer Research
58 papers in training set
Top 1%
0.9%
24
Biomedicine & Pharmacotherapy
43 papers in training set
Top 0.9%
0.8%
25
Cell Communication and Signaling
35 papers in training set
Top 0.9%
0.8%
26
Breast Cancer Research
32 papers in training set
Top 0.5%
0.8%
27
Antibody Therapeutics
16 papers in training set
Top 0.5%
0.8%
28
Molecular Cancer
14 papers in training set
Top 0.9%
0.8%
29
Cell Reports
1338 papers in training set
Top 32%
0.8%
30
Cell Death & Differentiation
48 papers in training set
Top 0.7%
0.8%