Velcheti, Vamsidhar

Abstract Background Real-world first-line maintenance (1LM) treatment data are limited for advanced/metastatic non-small cell lung cancer (a/mNSCLC). Materials and Methods In this electronic health record-derived, deidentified database study, eligible patients (≥18 years; diagnosed with stage III/IV non-small cell lung cancer [June 1, 2017-September 30, 2021]) initiated pembrolizumab-based 1LM after 4-6 cycles of first-line (1L) platinum-based chemotherapy-pembrolizumab ± pemetrexed. Study outcomes were real-world time to next treatment or death (rwTTNTD), overall survival (rwOS), and predictors of outcomes. Results Of 1944 patients analyzed (median follow-up, 12.2 months), 51.9% received 1LM pembrolizumab-pemetrexed and 48.1% pembrolizumab monotherapy. Median rwTTNTD and rwOS were 9.2 (95% CI: 8.5-9.8) and 18.7 (95% CI: 17.7-20.3) months, respectively. In multivariable analyses, factors significantly associated with shorter rwTTNTD included 5%-<10% (hazard ratio [HR], 1.34; 95% CI: 1.17-1.54) or ≥10% (HR, 1.69; 95% CI: 1.42-2.01) weight loss during 1L versus 0% or <5% weight loss. Programmed death-ligand 1 (PD-L1) expression 1%–49% (HR, 0.81; 95% CI: 0.71-0.93) or ≥50% (HR, 0.55; 95% CI: 0.47-0.64) and female sex (HR, 0.85; 95% CI: 0.75-0.95) were significantly associated with longer rwTTNTD. These variables were also significantly associated with shorter (weight loss 5%-<10%: HR, 1.52; 95% CI: 1.30-1.77; ≥10% HR, 2.06; 95% CI: 1.71-2.48) and longer rwOS (PD-L1 expression: 1%-49% HR, 0.84; 95% CI: 0.72-0.98; ≥ 50% HR, 0.57; 95% CI: 0.48-0.68; female sex: HR, 0.81; 95% CI: 0.71-0.92). Conclusions Predictors of real-world clinical outcomes included 1L treatment, weight loss, PD-L1 status, and sex. Poor outcomes persisted despite immunotherapy-based 1LM availability, revealing an unmet need in this population.

Abstract Background Multiple preclinical studies have shown cytotoxic synergy involving combinations of poly (ADP-ribose) polymerase (PARP) inhibitors and topoisomerase 1 (TOP1) inhibitors, but such combinations have proven too toxic in clinical trials. Liposomal irinotecan (nal-IRI) achieved similar intratumoral exposure with better antitumor activity than the conventional TOP1 inhibitor irinotecan in preclinical models. Tumor targeted delivery of TOP1 inhibitor using nal-IRI and an intermittent schedule of administration of PARP inhibitor may provide a tolerable combination. Methods A phase I study was performed to evaluate the safety and tolerability of escalating doses of nal-IRI and the PARP inhibitor veliparib in patients with solid tumors resistant to standard treatments. Nal-IRI was administered on days 1 and 15 and veliparib on days 5-12 and 19-25 in 28-day cycles. Results Eighteen patients were enrolled across 3 dose levels. Five patients encountered dose–limiting toxicities, including grade 3 diarrhea lasting more than 72 h in 3 patients and 1 patient each with grade 4 diarrhea and grade 3 hyponatremia. The most common grade 3 or 4 toxicities included diarrhea (50% of patients), nausea (16.6%), anorexia, and vomiting (11.1% each) (Table 1). There was no difference in frequencies of adverse events based on UGT1A1*28 status or prior opioid use (Table 1). Conclusion The clinical trial was terminated due to high frequency of unacceptable gastrointestinal toxicities, which precluded dose escalation of veliparib in combination with nal-IRI (ClinicalTrials.gov Identifier: NCT02631733).

The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (St, N = 145), validation (Sv, N = 145), and independent validation (Siv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in Sv that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on Sv.