Outcomes of Chemoimmunotherapy Among Patients With Extensive-Stage Small Cell Lung Cancer According to Potential Clinical Trial Eligibility

Prediction of prognosis in lung cancer using machine learning with inter-institutional generalizability: A multicenter cohort study (WJOG15121L: REAL-WIND)

OBJECTIVES

Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis.

MATERIALS AND METHODS

This multicenter, retrospective, hospital-based cohort study included consecutive patients with stage IV lung cancer who were randomly categorized into the training and independent test cohorts with a 2:1 ratio, respectively. The primary metric to assess algorithm performance was the area under the receiver operating characteristic curve in the independent test cohort. To assess the inter-institutional generalizability of the algorithms, we investigated their ability to predict patient outcomes in the remaining facility after being trained using data from 15 other facilities.

RESULTS

Overall, 6,751 patients (median age, 70 years) were enrolled, and 1,515 (22 %) showed mutated epidermal growth factor receptor expression. The median overall survival was 16.6 (95 % confidence interval, 15.9-17.5) months. Algorithm performance metrics in the test cohort showed that the areas under the curves were 0.90 (95 % confidence interval, 0.88-0.91), 0.85 (0.84-0.87), 0.83 (0.81-0.85), and 0.85 (0.82-0.87) at 180, 360, 720, and 1,080 predicted survival days, respectively. The performance test of 16 algorithms for investigating inter-institutional generalizability showed median areas under the curves of 0.87 (range, 0.84-0.92), 0.84 (0.78-0.88), 0.84 (0.76-0.89), and 0.84 (0.75-0.90) at 180, 360, 720, and 1,080 days, respectively.

CONCLUSION

This study developed machine learning algorithms that could accurately predict the prognosis in patients with stage IV lung cancer with high inter-institutional generalizability. This can enhance the accuracy of prognosis prediction and support informed and shared decision-making in clinical settings.

Durvalumab plus platinum-etoposide chemotherapy for extensive-stage small cell lung cancer: a retrospective real-world study

Background

Immune checkpoint inhibitor plus platinum-etoposide (PE) improved overall survival (OS) in patients with extensive-stage small cell lung cancer (ES-SCLC). While the CASPIAN trial demonstrated the efficacy of durvalumab plus PE, the clinical trial results may not be representative of the general, real-world population because clinical trials often have strict inclusion and exclusion criteria. We herein report the efficacy and safety of durvalumab plus PE in patients with ES-SCLC in real-world, clinical practice.

Methods

The present, monocentric, retrospective study evaluated patients with ES-SCLC or recurrent, limited-stage SCLC who received durvalumab plus PE between September 2020 and February 2023. The efficacy and incidence of adverse events (AEs) were also evaluated.

Results

The study included 40 patients, of whom 17 were elderly (age >70 years), and 15 had performance status (PS) 2 or 3. The median follow-up time was 13.0 months [95% confidence interval (CI): 8.0-22.2 months]. The objective response rate was 80.0% (95% CI: 63.1-91.6%), and the disease control rate was 88.6% (95% CI: 73.3-96.8%). The median progression-free survival (PFS) was 5.9 months (95% CI: 4.9-6.9), and the median OS was 25.4 months (95% CI: 4.6-46.2). Factors such as advanced age, poor PS, and presence of brain metastases were not associated with lower PFS and OS. Twenty-six patients (65.0%) experienced grade 3 or higher AEs, mainly hematological toxicity. AEs leading to treatment discontinuation occurred in three patients (8%).

Conclusions

Durvalumab plus PE in patients with ES-SCLC showed good efficacy and safety according to our real-world data, suggesting that this treatment is well tolerated in clinical practice, even in elderly patients and those with poor PS.

Impact of immune-related adverse events on survival outcomes in extensive-stage small cell lung cancer patients treated with immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors have recently become the standard of care in the first-line treatment of extensive-stage small cell lung cancer. Although immune-related adverse events have been reported to influence prognosis in non-small cell lung cancer patients, few studies have investigated the prognostic value of immune-related adverse events in small cell lung cancer patients. In this study, we evaluated the prognosis of patients who developed immune-related adverse events after first-line treatment with immune checkpoint inhibitor-based chemotherapy for extensive-stage small cell lung cancer.

METHODS

We enrolled 90 patients with extensive-stage small cell lung cancer who received immune checkpoint inhibitor-based chemotherapy as first-line treatment from September 2019 to December 2022 in six hospitals in Japan. The patients were categorized into groups with and without immune-related adverse events.

RESULTS

There were 23 patients with and 67 without immune-related adverse events. Seventeen patients had grade 1-2 immune-related adverse events, and nine (including overlapping cases) had grade ≥3. The most frequent immune-related adverse event was a skin rash. The median survival time was 22 months in patients with immune-related adverse events and 9.3 months in patients without immune-related adverse events. The hazard ratio was 0.40 (95% confidence interval: 0.19-0.83, p = 0.013).

CONCLUSIONS

The results of this study show that immune-related adverse events are associated with improved survival outcomes in patients with extensive-stage small cell lung cancer.

A Multicenter Study Assessing the Real-World Use and Effectiveness of First-Line Chemotherapy Plus Immunotherapy in Advanced Small-Cell Lung Cancer (SCLC) Patients

BACKGROUND

First-line chemotherapy plus immunotherapy (CT-IO) has recently demonstrated survival benefits over CT alone in extensive-stage small-cell lung cancer (ES-SCLC), based on randomized phase III studies. This retrospective multicenter study assessed the real-world use and effectiveness of CT-IO in ES-SCLC patients.

PATIENTS AND METHODS

All newly diagnosed ES-SCLC patients from 4 French hospitals treated with CT alone or CT-IO between May 2020 and December 2021 were included. Overall survival (OS) and real-world progression-free survival (rwPFS) were estimated using the Kaplan-Meier method. Cox proportional hazard models were performed to estimate hazard ratios (HRs) with 95 % confidence intervals (CIs) in univariate and multivariate models. The aim was not to compare efficacy between groups.

RESULTS

Among 104 patients, 75 (72.1%) received CT-IO. Brain metastases were diagnosed in 28.3% of patients, and 29.8% were performance status (PS) ≥ 2. At a median follow-up of 16.8 months (95%CI, 14.9-23.4), the median OS was 11.4 months (95%CI, 7.7-14.7) in the CT-IO group, and the 12-month OS rate was 43.6% (95%CI, 33.3-57.2). In the CT group, the median OS was 7.8 months (95%CI, 5.4-11.8) and the 12-month OS rate was 15.3% (95%CI, 5.7-41.0). In multivariate analyses, baseline brain and liver metastases were associated with a shorter OS for patients treated in the CT-IO group (HR, 3.80 [95%CI, 1.90-7.60] and 3.12 [95%CI, 1.60-6.08] respectively; P < 0.001 for both).

CONCLUSION

We showed that clinicians have chosen to use IO beyond the specific criteria defined in guidelines. Survival data appeared promising with a median OS comparable to the one previously demonstrated in clinical trials.

Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer

BACKGROUND

In small-cell lung cancer (SCLC), the tumor immune microenvironment (TIME) could be a promising biomarker for immunotherapy, but objectively evaluating TIME remains challenging. Hence, we aimed to develop a predictive biomarker of immunotherapy efficacy through a machine learning analysis of the TIME.

METHODS

We conducted a biomarker analysis in a prospective study of patients with extensive-stage SCLC who received chemoimmunotherapy as the first-line treatment. We trained a model to predict 1-year progression-free survival (PFS) using pathological images (H&E, programmed cell death-ligand 1 (PD-L1), and double immunohistochemical assay (cluster of differentiation 8 (CD8) and forkhead box P3 (FoxP3)) and patient information. The primary outcome was the mean area under the curve (AUC) of machine learning models in predicting the 1-year PFS.

RESULTS

We analyzed 100,544 patches of pathological images from 78 patients. The mean AUC values of patient information, pathological image, and combined models were 0.789 (range 0.571-0.982), 0.782 (range 0.750-0.911), and 0.868 (range 0.786-0.929), respectively. The PFS was longer in the high efficacy group than in the low efficacy group in all three models (patient information model, HR 0.468, 95% CI 0.287 to 0.762; pathological image model, HR 0.334, 95% CI 0.117 to 0.628; combined model, HR 0.353, 95% CI 0.195 to 0.637). The machine learning analysis of the TIME had better accuracy than the human count evaluations (AUC of human count, CD8-positive lymphocyte: 0.681, FoxP3-positive lymphocytes: 0.626, PD-L1 score: 0.567).

CONCLUSIONS

The spatial analysis of the TIME using machine learning predicted the immunotherapy efficacy in patients with SCLC, thus supporting its role as an immunotherapy biomarker.

Treatment patterns and clinical outcomes in 157 patients with extensive-stage small cell lung cancer: real-world evidence from a single-center retrospective study

Background

Immune checkpoint inhibitors (ICIs) have changed the therapeutic options for extensive-stage small-cell lung cancer (ES-SCLC). In this real-world study, we analyzed the treatment patterns in patients with ES-SCLC and evaluated the efficacy of chemotherapy combined with immunotherapy as first-line therapy.

Methods

A retrospective analysis was performed on patients with ES-SCLC who received treatment at China-Japan Friendship Hospital (Beijing, China) between August 1, 2020, and April 30, 2023. The treatment patterns appeared in the form of Sunburst Chart and Sankey diagram. The survival analyses were conducted by Kaplan-Meier curves.

Results

A total of 157 patients with ES-SCLC were retrospectively included. According to first-line therapy, patients were divided into the chemotherapy (CT) group (n=82) and chemo-immunotherapy (CIT) group (n=75). The median treatment lines were 2[1, 2] and cycles were 8[5, 12], respectively. 82 patients received the second line of therapy, followed by 37 for the third, 15 for the fourth, 11 for the fifth, and 5 for the sixth. Overall, the treatment patterns involved 11 options including 12 chemotherapy regimens, 11 ICIs, and 4 targeted agents. The second-line treatment pattern had the most options (9) and regimens (43). In the first 3 lines, chemotherapy was the largest proportion of treatment options. The addition of ICIs prolonged progression-free survival from 6.77 (95% confidence interval [CI], 6.00-7.87) to 7.33 (95% CI, 6.03-9.80) months (hazard ratio [HR]=0.67, 95% CI, 0.47-0.95; P=0.025), overall survival from 12.97 (10.90-23.3) to 14.33 (12.67-NA) months without statistically significant difference (HR=0.86, 95% CI, 0.55-1.34; P=0.505).

Conclusion

The treatment options of patients with ES-SCLC are more diversified. Combination therapy is the current trend, where chemotherapy is the cornerstone. Meanwhile, ICIs participate in almost all lines of treatment. However, the clinical efficacy remains barely satisfactory. We are urgently expecting more breakthrough therapies except immunology will be applied in the clinic.

Role of chemotherapeutic drugs in immunomodulation of cancer

The immune system has a variety of potential effects on a tumor microenvironment and the course of chemotherapy may vary according to that. Anticancer treatments can encourage the release of unwanted signals from senescent tumor cells or the removal of immune-suppressive cells, which can lead to immune system activation. Hence, by inducing an immunological response and conversely making cancer cells more vulnerable to immune attack, chemotherapeutic agents can destroy cancer cells. Furthermore, chemotherapy can activate anticancer immune effectors directly or indirectly by thwarting immunosuppressive pathways. Therefore, in this review, we discuss how chemotherapeutic agents take part in immunomodulation and the molecular mechanisms underlying them. We also focus on the importance of carefully addressing the conflicting effects of chemotherapy on immune responses when developing successful combination treatments based on chemotherapy and immune modulators.