Faculty Bibliography

PURPOSE/OBJECTIVE:Hyperprogression is an atypical response pattern to immune checkpoint inhibition that has been described within non-small cell lung cancer (NSCLC). The paradoxical acceleration of tumor growth after immunotherapy has been associated with significantly shortened survival, and currently, there are no clinically validated biomarkers to identify patients at risk of hyperprogression. EXPERIMENTAL DESIGN/METHODS:=79) with the essential caveat that HPs were evenly distributed among the two sets. A total of 198 radiomic textural patterns from within and around the target nodules and features relating to tortuosity of the nodule associated vasculature were extracted from the pretreatment CT scans. RESULTS:: HR=2.66, 95% CI 1.27 to 5.55; p=0.009). CONCLUSIONS:Our study suggests that image-based radiomics markers extracted from baseline CTs of advanced NSCLC treated with PD-1/PD-L1 inhibitors may help identify patients at risk of hyperprogressions.

Background/UNASSIGNED:Lung cancer (LC) development is a process that depends on genetic mutations. The DNA methylation, an important epigenetic modification, is associated with the expression of non-coding RNAs, such as microRNAs. MicroRNAs are particularly essential for cell physiology, since they play a critical role in tumor suppressor gene activity. Furthermore, epigenetic disruptions are the primary event in cell modification, being related to tumorigenesis. In this context, microRNAs can be a useful tool in the LC suppression, consequently improving prognosis and predicting treatment. Conclusion/UNASSIGNED:This manuscript reviews the main microRNAs involved in LC and its potential clinical applications to improve outcomes, such as survival and better quality of life.

BACKGROUND:fusion-positive NSCLC, the efficacy and safety of selective RET inhibition are unknown. METHODS:fusion-positive NSCLC who had previously received platinum-based chemotherapy and those who were previously untreated separately in a phase 1-2 trial of selpercatinib. The primary end point was an objective response (a complete or partial response) as determined by an independent review committee. Secondary end points included the duration of response, progression-free survival, and safety. RESULTS:fusion-positive NSCLC who had previously received at least platinum-based chemotherapy, the percentage with an objective response was 64% (95% confidence interval [CI], 54 to 73). The median duration of response was 17.5 months (95% CI, 12.0 to could not be evaluated), and 63% of the responses were ongoing at a median follow-up of 12.1 months. Among 39 previously untreated patients, the percentage with an objective response was 85% (95% CI, 70 to 94), and 90% of the responses were ongoing at 6 months. Among 11 patients with measurable central nervous system metastasis at enrollment, the percentage with an objective intracranial response was 91% (95% CI, 59 to 100). The most common adverse events of grade 3 or higher were hypertension (in 14% of the patients), an increased alanine aminotransferase level (in 12%), an increased aspartate aminotransferase level (in 10%), hyponatremia (in 6%), and lymphopenia (in 6%). A total of 12 of 531 patients (2%) discontinued selpercatinib because of a drug-related adverse event. CONCLUSIONS:fusion-positive NSCLC who had previously received platinum-based chemotherapy and those who were previously untreated. (Funded by Loxo Oncology and others; LIBRETTO-001 ClinicalTrials.gov number, NCT03157128.).

BACKGROUND:Immune checkpoint inhibitors have been rapidly adopted for therapy of advanced non-small-cell lung cancer (aNSCLC) based on clinical trial findings. Our aim was to examine outcomes in United States oncology practice settings for patients prescribed pembrolizumab monotherapy for previously treated, programmed death ligand-1 (PD-L1)-expressing aNSCLC, thus clinically similar to patients in the KEYNOTE-010 trial. PATIENTS AND METHODS/METHODS:This retrospective observational study used a nationally representative database to identify adult patients with histologically confirmed aNSCLC and PD-L1 tumor proportion score (TPS) ≥ 1% previously treated with platinum-containing chemotherapy (and appropriate tyrosine kinase inhibitor if nonsquamous aNSCLC with EGFR/ALK genomic tumor aberration). Eligible patients initiated pembrolizumab monotherapy from January 1, 2016, to November 29, 2018; data cutoff was May 31, 2019. The Kaplan-Meier method was used to estimate real-world time on treatment (rwToT) and overall survival (OS). RESULTS:The 349 eligible patients included 199 (57%) men; the median age was 68 years (range, 37-84 years); 70 (25%) of 278 patients with known performance status had Eastern Cooperative Oncology Group score ≥ 2. The median patient follow-up was 8.1 months (range, 1 day to 39.2 months). The median rwToT was 4.9 months (95% confidence interval [CI], 3.7-5.8 months) overall and 5.8 months (95% CI, 4.2-6.6 months) for the TPS ≥ 50% cohort (n = 218). The median OS was 13.8 months (95% CI, 11.0-16.5 months) and 16.5 months (95% CI, 13.7-22.0 months) overall and for TPS ≥ 50%, respectively; 12-month survival rates were 54% and 60%, respectively. CONCLUSION/CONCLUSIONS:Patients treated at oncology practices with pembrolizumab monotherapy for previously treated PD-L1-expressing aNSCLC experienced rwToT and OS similar to treatment duration and OS in phase III clinical trial settings.

OBJECTIVES/OBJECTIVE:To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT). MATERIALS AND METHODS/METHODS:) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery. RESULTS:, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62). CONCLUSION/CONCLUSIONS:Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.

Activating receptor tyrosine kinase RET (rarranged during transfection) gene alterations have been identified as oncogenic in multiple malignancies. RET gene rearrangements retaining the kinase domain are oncogenic drivers in papillary thyroid cancer, non-small-cell lung cancer, and multiple other cancers. Activating RET mutations are associated with different phenotypes of multiple endocrine neoplasia type 2 as well as sporadic medullary thyroid cancer. RET is thus an attractive therapeutic target in patients with oncogenic RET alterations. Multikinase inhibitors with RET inhibitor activity, such as cabozantinib and vandetanib, have been explored in the clinic for tumors with activating RET gene alterations with modest clinical efficacy. As a result of the nonselective nature of these multikinase inhibitors, patients had off-target adverse effects, such as hypertension, rash, and diarrhea. This resulted in a narrow therapeutic index of these drugs, limiting ability to dose for clinically effective RET inhibition. In contrast, the recent discovery and clinical validation of highly potent selective RET inhibitors (pralsetinib, selpercatinib) demonstrating improved efficacy and a more favorable toxicity profile are poised to alter the landscape of RET-dependent cancers. These drugs appear to have broad activity across tumors with activating RET alterations. The mechanisms of resistance to these next-generation highly selective RET inhibitors is an area of active research. This review summarizes the current understanding of RET alterations and the state-of-the-art treatment strategies in RET-dependent cancers.

Cyclin-dependent kinase 7 (CDK7) is a central regulator of the cell cycle and gene transcription. However, little is known about its impact on genomic instability and cancer immunity. Using a selective CDK7 inhibitor, YKL-5-124, we demonstrated that CDK7 inhibition predominately disrupts cell-cycle progression and induces DNA replication stress and genome instability in small cell lung cancer (SCLC) while simultaneously triggering immune-response signaling. These tumor-intrinsic events provoke a robust immune surveillance program elicited by T cells, which is further enhanced by the addition of immune-checkpoint blockade. Combining YKL-5-124 with anti-PD-1 offers significant survival benefit in multiple highly aggressive murine models of SCLC, providing a rationale for new combination regimens consisting of CDK7 inhibitors and immunotherapies.

Despite substantial progress in lung cancer immunotherapy, the overall response rate in KRAS-mutant lung adenocarcinoma (ADC) patients remains low. Combining standard immunotherapy with adjuvant approaches that enhance adaptive immune responses-such as epigenetic modulation of anti-tumor immunity-is therefore an attractive strategy. To identify epigenetic regulators of tumor immunity, we constructed an epigenetic-focused sgRNA library, and performed an in vivo CRISPR screen in a KrasG12D/P53-/- (KP) lung ADC model. Our data showed that loss of the histone chaperone Asf1a in tumor cells sensitizes tumors to anti-PD-1 treatment. Mechanistic studies revealed that tumor cell-intrinsic Asf1a deficiency induced immunogenic macrophage differentiation in the tumor microenvironment by upregulating GM-CSF expression and potentiated T cell activation in combination with anti-PD-1. Our results provide rationale for a novel combination therapy consisting of ASF1A inhibition and anti-PD-1 immunotherapy.

No predictive biomarkers can robustly identify non-small cell lung cancer (NSCLC) patients who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of computed tomography (CT) patterns both within and outside tumor nodules before and after 2-3 cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 NSCLC patients at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST (response evaluation criteria in solid tumors)-derived response. Association of delta-radiomic risk-score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an area under the curve (AUC) of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, 0.85 and 0.81 in D2 and D3. DRS was associated with OS (hazard ratio: 1.64, 95% CI: 1.22 - 2.21, P = 0.0011, C-Index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in NSCLC patients.

Purpose/UNASSIGNED:response to chemotherapy in patients with non-small cell lung cancer (NSCLC). Materials and Methods/UNASSIGNED:Data in a total of 125 patients who had been treated with pemetrexed-based platinum doublet chemotherapy at Cleveland Clinic were retrospectively analyzed. The patients were divided randomly into two sets with the constraint that there were an equal number of responders and nonresponders in the training set. The training set comprised 53 patients with NSCLC, and the validation set comprised 72 patients. A machine learning classifier trained with radiomic texture features extracted from intra- and peritumoral regions of non-contrast-enhanced CT images was used to predict response to chemotherapy. The radiomic risk-score signature was generated by using least absolute shrinkage and selection operator with the Cox regression model; association of the radiomic signature with TTP and OS was also evaluated. Results/UNASSIGNED:= .0011). Additionally, decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics signature had a higher overall net benefit in prediction of high-risk patients to receive treatment than the clinicopathologic measurements. Conclusion/UNASSIGNED: