Background

TGD modeling using sum of longest diameters (SLD) is associated with long-term outcomes in NSCLC. Early changes in radiomics features within the tumor may also correlate with survival outcomes. We retrospectively evaluated 3 methods to assess early treatment outcomes: tumor growth rate (g) by SLD, volumetric measurements, and change in radiomics signatures to predict survival outcomes in NSCLC.

Methods

Patients with stage IV NSCLC in CheckMate 9LA treated with first-line nivolumab+ipilimumab+chemotherapy (NIVO+IPI+CHEMO) or CHEMO alone were included. TGD was modeled using radiologically-assessed SLD from ≤5 target lesions or sum of volumes (SVOL) from all measurable lesions >10 mm at baseline, 6, 12, and/or 18 weeks. Measurements were fitted to the TGD model.¹ Overall survival (OS) for each growth quartile was estimated by Kaplan–Meier curves. Changes in radiomic features from all measurable lesions >10 mm were assessed at week 6 and 12.

Results

At week 18, low SVOL- and SLD-derived g values were associated with longer median OS across both treatment arms. SVOL-derived g values were more consistent across timepoints if evaluated at week 12 and 18 than SLD-derived g values. Delta radiomics signatures to predict long-term OS at week 6 (table) and 12 performed better than RECIST 1.1 in the NIVO+IPI+CHEMO arm.

Conclusions

SVOL-derived g values correlate with longer OS and are more consistent across timepoints than SLD-derived g values at 18 weeks of treatment. Delta radiomics signatures as early as 6 weeks on-treatment were better than RECIST in identifying patients with NSCLC deriving long-term OS benefit. Both findings can potentially inform early decision making in clinical trials and real-world use.

1. Fojo AT et al. J Clin Oncol. 2022;40(16_suppl):Abst 9063.

Clinical trial identification

NCT03215706.

Editorial acknowledgement

Editorial support was provided by Keri Wellington, PhD, and Isobel Markham of Spark Medica Inc.

Legal entity responsible for the study

Bristol-Myers Squibb.

Funding

Bristol-Myers Squibb.

Disclosure

L. Schwartz: Financial Interests, Personal, Advisory Role: Roche, Novartis; Financial Interests, Personal, Research Grant: Merck, Boehringer Ingelheim. K. Aggarwal: Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb; Financial Interests, Institutional, Full or part-time Employment: Bristol-Myers Squibb. D.J. Grootendorst: Financial Interests, Institutional, Full or part-time Employment: Bristol-Myers Squibb; Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb. S. Kotapati: Financial Interests, Institutional, Full or part-time Employment: Bristol-Myers Squibb; Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb. M. Fronheiser: Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb; Financial Interests, Institutional, Full or part-time Employment: Bristol-Myers Squibb. B. Zhao: Financial Interests, Personal, Royalties: Varian Medical Systems; Financial Interests, Institutional, Sponsor/Funding: Bristol-Myers Squibb; Financial Interests, Institutional, Research Grant: National Cancer Institute. C. Coronado-Erdmann: Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb, Incyte. M. Micsinai-Balan: Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb; Financial Interests, Institutional, Full or part-time Employment: Bristol-Myers Squibb; Financial Interests, Personal, Other: Bristol-Myers Squibb. M. Karasarides: Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb; Financial Interests, Institutional, Full or part-time Employment: Bristol-Myers Squibb. A.T. Fojo: Financial Interests, Personal, Advisory Role: Akita Biomedical; Financial Interests, Personal, Other, Honoraria: Merck; Financial Interests, Institutional, Research Grant: Merck, Ipsen, Pfizer. K. Brown: Financial Interests, Institutional, Full or part-time Employment: Bristol-Myers Squibb; Financial Interests, Personal, Stocks/Shares: Bristol-Myers Squibb.

Background

Lung cancer is a global problem with an increasing incidence worldwide. Efforts have been made to adopt screening programs that can lead to earlier diagnosis. However, little is known about the resulting changes in disease distribution.

Methods

Small and non-small cell lung cancer cases between 2004 and 2019 were extracted using the SEER database [17 reg; Nov 2021 sub]. We excluded patients with unknown/unreported stages in stage trend analysis. We further performed an exploratory analysis using the year 2013, when the American Cancer Society issued its first recommendation for lung cancer CT screening with patients, to explore potential associations between screening and change in stage at presentation.

Results

We analyzed data from 660 532 lung cancer patients (non-small cell lung cancer [86.8%, n = 573 139]; small cell lung cancer [13.2%, n = 87 393]). Most cases were presented with the distant disease at initial presentation (54.9%, n = 362 733). Longitudinal tracking of the distribution of cases among different disease stages showed an increase in the proportion of patients diagnosed with the localized disease compared to patients diagnosed with a regional disease or those with distant spread with an increased median overall survival over time (table). Compared to patients diagnosed before 2013, patients diagnosed after 2013 had a statistically significant higher likelihood of presenting with a localized disease stage (24.1% vs. 19.2%, p = 0.001) and longer median overall survival (14 months vs. 10 months, p = 0.001).

Conclusions

There is an increase in the proportion of lung cancer cases presenting with localized disease stages with improved overall survival. This can probably be partially attributed to efforts made to implement wide screening programs.

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.

Background

Recently, results of many studies suggested that patients with squamous cell lung cancer (SqCLC) could benefit from immune checkpoint inhibitors (ICIs). However, not all patients receiving ICIs could respond well and many biomarkers were selected and employed to identify the subset of patients most likely to derive clinical benefits. Some studies investigated components of proteasome in melanoma, which showed a superior value than PD-L1, TMB and CD8+ T cell on predicting ICIs’ effect. Here, we performed a comprehensive analysis on proteasome-related genes and their correlation with immunity and immunotherapy in SqCLC.

Methods

An integrated analysis of transcriptomic data from TCGA and GEO database was performed. Gene set variation analysis (GSEA) was employed to investigate the relative activity of signal pathways. CIBERSORT, quanTIseq and single-sample GSEA were used for evaluating tumor immune microenvironment (TIME). Survival analysis and receiver operating characteristic were used to estimate the value of each proteasome-related gene on predicting ICIs’ effect.

Results

A total of 1870 SqCLC patients from 21 cohorts were analyzed in this study. In the result of pathway enrichment analysis, PSMB10, PSMB9, PSMB8, PSME1 and PSMC3IP were shown high correlation with immunity-related pathways, and there were 16, 13, 13, 9 and 8 cohorts that enriched more than 50% of all immunity-related pathways for the five genes, respectively. In terms of TIME analysis, PSMB10, PSMB9, PSMB8 and PSME1 has 47, 46, 41 and 41 statistically significant results, respectively, from totally 63 CD8+ T cell calculated by three algorithms in 21 cohorts, and all of these four genes were positively correlated with high infiltration of CD8+ T cell. As for the evaluation of predictive value on immunotherapy, only PSMB10 was statistically significant (mPFS: 7.33 months vs 0.70 month, p = 0.03; AUC: 0.89, 95%CI 0.65–1).

Conclusions

Among 54 proteasome-related genes, PSMB8, PSMB9 and PSMB10, three important catalytic subunits of immunoproteasome, can distinguish TIME and have high activity of immune-related pathways in SqCLC, in which PSMB10 has the potential as a biomarker of ICIs’ effect.

Legal entity responsible for the study

The authors.

Funding

This work was supported by the China National Major Project for New Drug Innovation (2017ZX09304015, 2019ZX09201-002).

Disclosure

All authors have declared no conflicts of interest.

Background

Pulmonologists have the complex task to select the optimal treatment for patients with advanced lung cancer to extend survival duration while minimizing side-effects. They do this mainly based on patient's demographic and clinical data, patient preferences and guidelines. Digitalization of healthcare makes it possible to support this treatment selection process, aiming at more personalized and precise medicine. This study introduces a clinical decision support tool, based on prediction models for survival and burden of treatment, explainable machine learning (ML) and a Graphical User Interface (GUI) for physicians.

Methods

ML models were trained on cohorts ranging in size of 89–405 lung cancer patients with stage IIIB and IV, to predict the survival probability for different treatment options, 6 weeks, 3 months, 6 months and 1 year after the start of the first treatment. Additional models were trained on the evolution of two symptom scales, dysphagia and alopecia, of the EORTC-QLQ-LC13 questionnaire to forecast the burden of treatment. Patient demographics, laboratory results, comorbidities, tumour characteristics and treatment regimens were used as features. All models combined allow the pulmonary oncologist to simulate different therapy responses via an in-house developed GUI.

Results

The classification prediction models achieved good performance results with area under the curve values ranging from 0.78 to 0.86. In practice, a physician enters a patient identifier in the GUI. Then, the tool automatically collects all required features of the patient, flagging divergent values. After selecting a treatment schedule, the model probability outcomes are depicted, as well as the importance of each feature (based on Shapley scores) which enables the pulmonologist to understand the rationale behind the ML model's predictions.

Conclusions

We have proven that models trained on real world hospital data are capable of making reliable outcome predictions. This GUI can be used in clinical practice, provided that extra data is collected and an extensive validation procedure takes place. This will enable physicians to use data-driven predictions based on patient and disease characteristics as support in their treatment decision process.

Legal entity responsible for the study

The authors.

Funding

Flanders Innovation and Entrepreneurship.

Disclosure

All authors have declared no conflicts of interest.

Background

Platinum-based chemotherapy plus immune check point inhibitors (ICIs) have become a front-line standard treatment in NSCLC, but no prospective data of DTX plus RAM following front-line chemotherapy plus ICIs are available. Previous research has proven residual ICIs efficacy beyond 20 weeks after termination of ICIs, and VEGF-R2 blockade could enhance antitumor immunity by improving T-cell function. Here, we report the results of multicenter, phase II study of DTX plus RAM following front-line chemotherapy plus ICIs.

Methods

The primary end point of the study was objective response rate (ORR), and secondary endpoints were disease control rate (DCR), progression-free survival (PFS), and safety etc. Patients were treated with 60 mg/m² of DTX and 10 mg/kg of RAM on day 1 with strong recommendation of pegfilgrastim on day 2 every 3 weeks. A null and alternative hypothesis of ORR were set as 10% and 30% with α error of 0.1 and β error of 0.1.

Results

Thirty-three patients were recruited from 8 institutions. Patient characteristics were as follows: median age (range): 66 (42–79) y; ECOG-PS 1, n = 13 (39%); interval after last administration of ICIs<6 weeks, n = 21 (64%). In the efficacy analysis population (n = 32), the primary endpoint was met as 11 patients achieved PR with ORR at 34.4% (80%CI, 23.1–47.2%). Another 15 patients achieved SD and the DCR was 81.3% (95%CI, 63.6–92.8%). Median PFS was 6.5 months. Grade≥3 anemia and febrile neutropenia was observed in 2 (6%) and 3 patients (9%). No treatment-related deaths and no new safety signals were observed.

Conclusions

DTX plus RAM demonstrated encouraging antitumor activity with a manageable safety profile in patients who have failed with front-line chemotherapy plus ICIs.

Clinical trial identification

jCRTs041190077.

Legal entity responsible for the study

M. Morise.

Funding

Eli Lilly.

Disclosure

M. Morise: Financial Interests, Institutional, Research Grant: Eli Lilly, Boehringer Ingelheim; Financial Interests, Institutional, Principal Investigator: Roche, Chugai, AstraZeneca, Taiho, Merck Serono, AbbVie, Ono. K. Ito: Financial Interests, Personal, Invited Speaker: Eli Lilly. All other authors have declared no conflicts of interest.