Correlation of the combination of CT-derived tumor texture and vessel tortuosity on survival outcomes for immunotherapy but not for chemotherapy in metastatic non-small cell lung cancer (mNSCLC): Results from a CheckMate227 (CM227) subset.

Authors

null

Pushkar Mutha

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA

Pushkar Mutha , Mohammadhadi Khorrami , Babajide Sonuga , Kshitij Aggarwal , Wiem Safta , Diederik J. Grootendorst , David Paulucci , Oana Mustatea , Brook Nepon Sixt , David Witt , Hilmi Al-Shakhshir , Vidya Sankar Viswanathan , Jeremy Callahan , Vamsidhar Velcheti , Anant Madabhushi

Organizations

Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, Atlanta, GA, Bristol Myers Squibb, Princeton, NJ, Bristol Myers Squibb, Bucharest, Romania, Atlanta Veterans Administration Medical Center, Atlanta, GA, Emory University Hospital, Decatur, GA, NYU Perlmutter Cancer Center, New York, NY

Research Funding

No funding sources reported

Background: CM227 (NCT02477826) is a large multi-center phase 3 trial that evaluated the benefit of immunotherapy (IO) over chemotherapy (Ch) as first line therapy in stage IV NSCLC. While PD-L1 and tumor mutational burden (TMB) initially emerged as promising biomarkers, CM227 showed benefit of IO over Ch regardless of PD-L1 or TMB status. There is an unmet clinical need for predictive biomarkers to identify patients (pts) who will respond to IO. In this study, we report initial blinded validation results of a CT-derived biomarker combining change in textural radiomics (Δ-Rad) and quantitative vessel tortuosity (Δ-QVT) between baseline and 6-week post-treatment for predicting response and survival outcome of IO and Ch alone in a subset of patients enrolled in CM227. Methods: This retrospective study included baseline (B) and 6-week post-treatment (TP1) CT scans from (a) a multi-center training set (Dtr, N=110) of first line IO-treated mNSCLC pts and (b) a validation set consisting of a subset of mNSCLC from CM227 (Dv, N=224), of which 178 pts were treated with IO (DvIO) and 36 pts (DvCh) with Ch. Intra-tumoral, peri-tumoral texture radiomic (Khorrami et al., Cancer Immunol Res 2020) and QVT (Alilou et al., Sci Adv 2022) features were extracted from up to the two largest measurable lung lesions on each CT using an in-house MATLAB pipeline. Δ-Rad and Δ-QVT features were computed as the feature difference between B and TP1. A best objective IO response classifier, MCombo was trained on Dtr using a combination of Δ-Rad and Δ-QVT features. Kaplan-Meier analyses with log rank p-values, hazard ratio (HR) and its confidence interval (CI) were computed to assess the predictive benefit of MCombo with overall survival (OS) and progression free survival (PFS) in Dv, DvIO and DvCh. We also report area under the receiver operating characteristic (AUC) of IO response predictions compared against best overall response in Dv, DvIO and DvCh. Results: MCombo predicted best overall response with an AUC of 0.67, 0.68, 0.74 in Dv, DvIO, and DvCh, respectively. MCombo was statistically significantly associated with OS and PFS in Dv and DvIO but not in DvCh (Table). Conclusions: Our preliminary findings reveal that combination of CT-based textural and vessel tortuosity features are predictive of IO response over chemotherapy in a subset of CM227 pts. Validation on the entire CM277 cohort is warranted.

DatasetOverall Survival
HR (CI), p-value
Progression Free Survival
HR (CI), p-value
Dv0.56 (0.38, 0.83), p = 0.003520.48 (0.33, 0.71), p = 0.00015
DvIO0.46 (0.29, 0.74), p = 0.001080.40 (0.25, 0.63), p = 6e-5
DvCh1.51 (0.68, 3.42), p = 0.31931.04 (0.46, 2.35), p = 0.9328

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Abstract Details

Meeting

2024 ASCO Annual Meeting

Session Type

Poster Session

Session Title

Lung Cancer—Non-Small Cell Metastatic

Track

Lung Cancer

Sub Track

Metastatic Non–Small Cell Lung Cancer

Citation

J Clin Oncol 42, 2024 (suppl 16; abstr 8610)

DOI

10.1200/JCO.2024.42.16_suppl.8610

Abstract #

8610

Poster Bd #

474

Abstract Disclosures