Background: Synthetic fingerprints integrate clinical data within computational models allowing the identification of particular clinical subpopulations at a given moment. We here describe a deep learning strategy to detect super-responder and super-survivor patients with squamous NSCLC by setting up synthetic fingerprints and using unsupervised deep learning frameworks (UDLF).
Methods: Through www.projectdatasphere.org, we accessed the control arm clinical data (N = 548) of the randomised phase III SQUIRE trial (NCT00981058). This trial included patients with stage IV squamous NSCLC who had not received previous chemotherapy. These patients were treated with gemcitabine 1,250 mg/m2 (IV, 30-min infusion, d1/d8) and cisplatin 75 mg/m2 (IV, 120 min infusion, d1) on a 3-week cycle for a maximum of six cycles. Synthetic fingerprints resulted of the integration of 180 features collected during the first 3 cycles including demographics, medical history, physical exam, concomitant medication, histopathology, PK parameters, adverse events and common labs. These fingerprints were used as input for the UDLF. The resultant clusters were correlated with overall-response rate (ORR) and overall survival (OS).
Results: After missing data removal and feature standardization, 192 patients were eligible for the study. The UDLF was able to generate two different clusters: P0 (n = 107) and P1 (n = 84). ORR was higher in the P1 than in the P0 cluster (mean 41.6% [95% CI 31.7-52.3] vs. 28.0% [95% CI 20.4-37.2]; p = 0.04). OS was significantly longer in the P1 than in the P0 cluster (median 13.2 months vs. 9.7 months; hazard ratio 1.56 [95% CI 1.12-2.17; p = 0.008]). Feature contribution analysis showed that P1 had more patients and more events of grade III/IV neutropenia. In contrast, P0 had more patients and more events of grade III/IV nausea and vomiting. Other major differences were observed on vital signs (SBP, DBP, HR, RR, Temp), concomitant medication (osmotically-active laxatives, dexamethasone, furosemide, granisetron and ondansetron) and in hematological (RBC, HGB, HCT, MCV, WBC, neutrophils, monocytes, lymphocytes) and biochemistry (albumin, globulins, ALP, LDH, creatinine, BUN, urea, sodium, magnesium and phosphate) tests.
Conclusions: Our findings show that synthetic fingerprints and subsequent deep learning analysis can be of use to identify patients with clinical characteristics associated with high-response rate and long-term survival.
Citation: J Clin Oncol 39, 2021 (suppl 15; abstr 1548)