Abdur Rahman Jabir

Dr. Abdur Rahman Jabir

orcid

Research Focus & Metrics

Academic Career Length

5Years (2022-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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10 December 2022 in Chest
Verified
Journal Article
Closed access
Mahmoud Ahmad OmarMahmoud Ahmad Omar
Mahmoud Ahmad Omar
AJAbdur Rahman Jabir
Abdur Rahman Jabir
IKImadh Khan
Imadh Khan
ENEnrico M. Novelli
Enrico M. Novelli
JXJulia Z. Xu
Julia Z. Xu

31 March 2025 in Cancers
Verified
Journal Article
Open access
PDF file
MCMark J. Chang
Mark J. Chang
DSDaniel Stamos
Daniel Stamos
CUCetin Urtis
Cetin Urtis
NBNathan L. Bowers
Nathan L. Bowers
LSLauren Schmalz
Lauren Schmalz
MPMercedes Porosnicu
Mercedes Porosnicu
Background/Objectives: Cutaneous squamous cell carcinoma (cSCC) harbors one of the most mutated genomes. There are limited data on the genomic profile and its predictive potential for response to immunotherapy with PD-1 inhibitors in cSCC. Methods: This study retrospectively reviewed cSCC patients treated with PD-1 inhibitor monotherapy at a single institution. Clinical characteristics, treatment outcomes, PD-L1 expression, tumor mutation burden (TMB), and genomic profile in tumor and blood were analyzed. Logistic regression and a support vector classifier were used to validate identified biomarkers of significance. Results: Twenty-five patients were evaluable for response and had genomics tested in tumor and/or blood. Of the total, 80% of patients achieved an objective response: 40% complete response (CR), 32% partial response (PR) for more than 6 months, and 8% stable disease (SD) for more than 1 year; 20% of patients progressed on treatment. With a median follow-up of 21 months, progression-free survival (PFS) was 28 months in responders vs. 3 months in non-responders (p = 0.00001). Median PD-L1 was 25% in responders vs. 10% in non-responders (p = 0.39). There was no difference in median TMB between responders and non-responders. Eight gene mutations were significantly more frequent in non-responders than in responders: CDK12 (p = 0.005), CTCF (p = 0.033), CTNNB1 (p = 0.033), IGF1R (p = 0.038), IKBKE (p = 0.016), MLH1 (0.033), QKI (p = 0.016), and TIPARP (p = 0.033). A support vector model of these genes classified responders and non-responders with an accuracy of 0.88 in the training data and 1.0 in the testing data. Conclusions: PD-1 inhibitor monotherapy produces an impressive response. Eight gene mutations were significantly more frequent in non-responders. PD-L1 and TMB were inconclusive in predicting treatment response to anti-PD-L1.