Segmentation of patients with small cell lung cancer into responders and non-responders using the optimal cross-validation technique.

BACKGROUND: The timing of treating cancer patients is an essential factor in the efficacy of treatment. So, patients who will not respond to current therapy should receive a different treatment as early as possible. Machine learning models can be built to classify responders and nonresponders. Such classification models predict the probability of a patient being a responder. Most methods use a probability threshold of 0.5 to convert the probabilities into binary group membership. However, the cutoff of 0.5 is not always the optimal choice. METHODS: In this study, we propose a novel data-driven approach to select a better cutoff value based on the optimal cross-validation technique. To illustrate our novel method, we applied it to three clinical trial datasets of small-cell lung cancer patients. We used two different datasets to build a scoring system to segment patients. Then the models were applied to segment patients into the test data. RESULTS: We found that, in test data, the predicted responders and non-responders had significantly different long-term survival outcomes. Our proposed novel method segments patients better than the standard approach using a cutoff of 0.5. Comparing clinical outcomes of responders versus non-responders, our novel method had a p-value of 0.009 with a hazard ratio of 0.668 for grouping patients using the Cox proportion hazard model and a p-value of 0.011 using the accelerated failure time model which approved a significant difference between responders and non-responders. In contrast, the standard approach had a p-value of 0.194 with a hazard ratio of 0.823 using the Cox proportion hazard model and a p-value of 0.240 using the accelerated failure time model indicating the responders and non-responders do not differ significantly in survival. CONCLUSION: In summary, our novel prediction method can successfully segment new patients into responders and non-responders. Clinicians can use our prediction to decide if a patient should receive a different treatment or stay with the current treatment.

Rational and design of prophylactic cranial irradiation (PCI) and brain MRI surveillance versus brain MRI surveillance alone in patients with limited-stage small cell lung cancer achieving complete remission (CR) of tumor after chemoradiotherapy: a multicenter prospective randomized study.

BACKGROUND: Prophylactic cranial irradiation (PCI) is part of standard care in limited-stage small cell lung cancer (SCLC) at present. As evidence from retrospective studies increases, the benefits of PCI for limited-stage SCLC are being challenged. METHODS: A multicenter, prospective, randomized controlled study was designed. The key inclusion criteria were: histologically or cytologically confirmed small cell carcinoma, age ≥ 18 years, KPS ≥ 80, limited-stage is defined as tumor confined to one side of the chest including ipsilateral hilar, bilateral mediastinum and supraclavicular lymph nodes, patients have received definitive thoracic radiotherapy (regardless of the dose-fractionation of radiotherapy used) and chemotherapy, evaluated as complete remission (CR) of tumor 4-6 weeks after the completion of chemo-radiotherapy. Eligible patients will be randomly assigned to two arms: (1) PCI and brain MRI surveillance arm, receiving PCI (2.5 Gy qd to a total dose of 25 Gy in two weeks) followed by brain MRI surveillance once every three months for two years; (2) brain MRI surveillance alone arm, undergoing brain MRI surveillance once every three months for two years. The primary objective is to compare the 2-year brain metastasis-free survival (BMFS) rates between the two arms. Secondary objectives include 2-year overall survival (OS) rates, intra-cranial failure patterns, 2-year progression-free survival rates and neurotoxicity. In case of brain metastasis (BM) detect during follow-up, stereotactic radiosurgery (SRS) will be recommended if patients meet the eligibility criteria. DISCUSSION: Based on our post-hoc analysis of a prospective study, we hypothesize that in limited-stage SCLC patients with CR after definitive chemoradiotherapy, and ruling out of BM by MRI, it would be feasible to use brain MRI surveillance and omit PCI in these patients. If BM is detected during follow-up, treatment with SRS or whole brain radiotherapy does not appear to have a detrimental effect on OS. Additionally, this approach may reduce potential neurotoxicity associated with PCI.

Real-life clinical management patterns in extensive-stage small cell lung cancer across France: a multi-method study.

BACKGROUND: We designed this study based on both a physician practice survey and real-world patient data to: (1) evaluate clinical management practices in extensive-stage small cell lung cancer (ES-SCLC) among medical centers located across France; and (2) describe first-line treatment patterns among patients with ES-SCLC following the introduction of immunotherapy into clinical practice. METHODS: A 50-item questionnaire was completed by physicians from 45 medical centers specialized in SCLC management. Responses were collected from June 2022 to January 2023. The survey questions addressed diagnostic workup of ES-SCLC, chemoimmunotherapy in first-line and second-line settings, and use of prophylactic cranial irradiation (PCI) and radiotherapy. In parallel, using a chart review approach, we retrospectively analyzed aggregated information from 548 adults with confirmed ES-SCLC receiving first-line treatment in the same centers. RESULTS: In ES-SCLC, treatment planning is based on chest computed tomography (CT) (as declared by 100% of surveyed centers). Mean time between diagnosis and treatment initiation was 2-7 days, as declared by 82% of centers. For detection of brain metastases, the most common imaging test was brain CT (84%). The main exclusion criteria for first-line immunotherapy in the centers were autoimmune disease (87%), corticosteroid therapy (69%), interstitial lung disease (69%), and performance status ≥ 2 (69%). Overall, 53% and 36% of centers considered that patients are chemotherapy-sensitive if they relapse within ≥ 3 months or ≥ 6 months after first-line chemoimmunotherapy, respectively. Among the 548 analyzed patients, 409 (75%) received chemoimmunotherapy as a first-line treatment, 374 (91%) of whom received carboplatin plus etoposide and 35 (9%) cisplatin plus etoposide. Overall, 340/548 patients (62%) received maintenance immunotherapy. Most patients (68%) did not receive radiotherapy or PCI. CONCLUSIONS: There is an overall alignment of practices reflecting recent clinical guidelines among medical centers managing ES-SCLC across France, and a high prescription rate of immunotherapy in the first-line setting.

Six versus four or five cycles of first-line etoposide and platinum-based chemotherapy combined with thoracic radiotherapy in patients with limited-stage small-cell lung cancer: A propensity score-matched analysis of a prospective randomized trial.

OBJECTIVES: The recommended treatment for limited-stage small-cell lung cancer (LS-SCLC) is a combination of thoracic radiotherapy (TRT) and etoposide plus cisplatin (EP) chemotherapy, typically administered over 4-6 cycles. Nonetheless, the optimal duration of chemotherapy is still not determined. This study aimed to compare the outcomes of patients with LS-SCLC who received either 6 cycles or 4-5 cycles of EP chemotherapy combined with TRT. MATERIALS AND METHODS: In this retrospective analysis, we utilized data from our prior prospective trial to analyze the outcomes of 265 LS-SCLC patients who received 4-6 courses of EP combined with concurrent accelerated hyperfractionated TRT between 2002 and 2017. Patients were categorized into two groups depending on their number of chemotherapy cycles: 6 or 4-5 cycles. To assess overall survival (OS) and progression-free survival (PFS), we employed the Kaplan-Meier method after conducting propensity score matching (PSM). RESULTS: Among the 265 LS-SCLC patients, 60 (22.6%) received 6 cycles of EP chemotherapy, while 205 (77.4%) underwent 4-5 cycles. Following PSM (53 patients for each group), the patients in the 6 cycles group exhibited a significant improvement in OS and PFS in comparison to those in the 4-5 cycles group [median OS: 29.8 months (95% confidence interval [CI], 23.6-53.1 months) vs. 22.7 months (95% CI, 20.8-29.1 months), respectively, p = 0.019; median PFS: 17.9 months (95% CI, 13.7-30.5 months) vs. 12.0 months (95% CI, 9.8-14.2 months), respectively, p = 0.006]. The two-year and five-year OS rates were 60.38% and 29.87% in the 6 cycles group, whereas 47.17% and 15.72% in the 4-5 cycles group, respectively. CONCLUSION: Patients diagnosed with LS-SCLC who were treated with EP regimen chemotherapy combined with TRT exhibited notably enhanced survival when administered 6 cycles of chemotherapy, as compared to those who underwent only 4-5 cycles.

Evolutionary trajectories of small cell lung cancer under therapy.

The evolutionary processes that underlie the marked sensitivity of small cell lung cancer (SCLC) to chemotherapy and rapid relapse are unknown1-3. Here we determined tumour phylogenies at diagnosis and throughout chemotherapy and immunotherapy by multiregion sequencing of 160 tumours from 65 patients. Treatment-naive SCLC exhibited clonal homogeneity at distinct tumour sites, whereas first-line platinum-based chemotherapy led to a burst in genomic intratumour heterogeneity and spatial clonal diversity. We observed branched evolution and a shift to ancestral clones underlying tumour relapse. Effective radio- or immunotherapy induced a re-expansion of founder clones with acquired genomic damage from first-line chemotherapy. Whereas TP53 and RB1 alterations were exclusively part of the common ancestor, MYC family amplifications were frequently not constituents of the founder clone. At relapse, emerging subclonal mutations affected key genes associated with SCLC biology, and tumours harbouring clonal CREBBP/EP300 alterations underwent genome duplications. Gene-damaging TP53 alterations and co-alterations of TP53 missense mutations with TP73, CREBBP/EP300 or FMN2 were significantly associated with shorter disease relapse following chemotherapy. In summary, we uncover key processes of the genomic evolution of SCLC under therapy, identify the common ancestor as the source of clonal diversity at relapse and show central genomic patterns associated with sensitivity and resistance to chemotherapy.

Comprehensive analysis of transcription factor-based molecular subtypes and their correlation to clinical outcomes in small-cell lung cancer.

BACKGROUND: Recent studies have reported the predictive and prognostic value of novel transcriptional factor-based molecular subtypes in small-cell lung cancer (SCLC). We conducted an in-depth analysis pairing multi-omics data with immunohistochemistry (IHC) to elucidate the underlying characteristics associated with differences in clinical outcomes between subtypes. METHODS: IHC (n = 252), target exome sequencing (n = 422), and whole transcriptome sequencing (WTS, n = 189) data generated from 427 patients (86.4% males, 13.6% females) with SCLC were comprehensively analysed. The differences in the mutation profile, gene expression profile, and inflammed signatures were analysed according to the IHC-based molecular subtype. FINDINGS: IHC-based molecular subtyping, comprised of 90 limited-disease (35.7%) and 162 extensive-disease (64.3%), revealed a high incidence of ASCL1 subtype (IHC-A, 56.3%) followed by ASCL1/NEUROD1 co-expressed (IHC-AN, 17.9%), NEUROD1 (IHC-N, 12.3%), POU2F3 (IHC-P, 9.1%), triple-negative (IHC-TN, 4.4%) subtypes. IHC-based subtype showing high concordance with WTS-based subtyping and non-negative matrix factorization (NMF) clusterization method. IHC-AN subtype resembled IHC-A (rather than IHC-N) in terms of both gene expression profiles and clinical outcomes. Favourable median overall survival was observed in IHC-A (15.2 months) compared to IHC-N (8.0 months, adjusted HR 2.3, 95% CI 1.4-3.9, p = 0.002) and IHC-P (8.3 months, adjusted HR 1.7, 95% CI 0.9-3.2, p = 0.076). Inflamed tumours made up 25% of cases (including 53% of IHC-P, 26% of IHC-A, 17% of IHC-AN, but only 11% of IHC-N). Consistent with recent findings, inflamed tumours were more likely to benefit from first-line immunotherapy treatment than non-inflamed phenotype (p = 0.002). INTERPRETATION: This study provides fundamental data, including the incidence and basic demographics of molecular subtypes of SCLC using both IHC and WTS from a comparably large, real-world Asian/non-Western patient cohort, showing high concordance with the previous NMF-based SCLC model. In addition, we revealed underlying biological pathway activities, immunogenicity, and treatment outcomes based on molecular subtype, possibly related to the difference in clinical outcomes, including immunotherapy response. FUNDING: This work was supported by AstraZeneca, Future Medicine 2030 Project of the Samsung Medical Center [grant number SMX1240011], the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [grant number 2020R1C1C1010626] and the 7th AstraZeneca-KHIDI (Korea Health Industry Development Institute) oncology research program.

Prognostic factors in extensive-stage small cell lung cancer patients with organ-specific metastasis: unveiling commonalities and disparities.

BACKGROUND: This study aimed to identify shared and distinct prognostic factors related to organ-specific metastases (liver, lung, bone, and brain) in extensive-stage small cell lung cancer (ES-SCLC) patients, then construct nomograms for survival prediction. METHODS: Patient data for ES-SCLC were from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019. Kaplan-Meier analysis was applied to estimate overall survival (OS), and Cox regression was used to identify prognostic factors. A Venn diagram was utilized to distinguish common and unique prognostic factors among the variables assessed. These identified prognostic factors were used to formulate a nomogram, and its predictive accuracy and reliability were evaluated using C-indexes, calibration curves, and receiver operating characteristic (ROC) curves. RESULTS: A total of 24,507 patients diagnosed with ES-SCLC exhibiting metastases to the liver, lung, bone, and brain were included. The 6-month, 1-year, and 2-year OS rates were 46.1%, 19.7%, and 5.0%, respectively. Patients with liver metastasis demonstrated the most unfavorable prognosis, with a 1-year OS rate of 14.5%, while those with brain metastasis had a significantly better prognosis with a 1-year OS rate of 21.6%. The study identified seven common factors associated with a poor prognosis in ES-SCLC patients with organ-specific metastases: older age, male sex, unmarried status, higher T stage, presence of other metastases, and combination radiotherapy and chemotherapy. Furthermore, specific prognostic factors were identified for patients with metastasis to the liver, bone, and brain, including paired tumors, lack of surgical treatment at the primary site, and household income, respectively. To facilitate prognostic predictions, four nomograms were developed and subsequently validated. The performance of these nomograms was assessed using calibration curves, C-indexes, and the area under the curve (AUC), all of which consistently indicated good predictive accuracy and reliability. CONCLUSIONS: Patients diagnosed with ES-SCLC with organ-specific metastases revealed shared and distinct prognostic factors. The nomograms developed from these factors demonstrated good performance and can serve valuable clinical tools to predict the prognosis of ES-SCLC patients with organ-specific metastases.

Small cells - big issues: biological implications and preclinical advancements in small cell lung cancer.

Current treatment guidelines refer to small cell lung cancer (SCLC), one of the deadliest human malignancies, as a homogeneous disease. Accordingly, SCLC therapy comprises chemoradiation with or without immunotherapy. Meanwhile, recent studies have made significant advances in subclassifying SCLC based on the elevated expression of the transcription factors ASCL1, NEUROD1, and POU2F3, as well as on certain inflammatory characteristics. The role of the transcription regulator YAP1 in defining a unique SCLC subset remains to be established. Although preclinical analyses have described numerous subtype-specific characteristics and vulnerabilities, the so far non-existing clinical subtype distinction may be a contributor to negative clinical trial outcomes. This comprehensive review aims to provide a framework for the development of novel personalized therapeutic approaches by compiling the most recent discoveries achieved by preclinical SCLC research. We highlight the challenges faced due to limited access to patient material as well as the advances accomplished by implementing state-of-the-art models and methodologies.

Nunavimmi puvakkut kaggutimik aanniaqarniq: Qanuilirqitaa? Lung cancer in Nunavik: How are we doing? A retrospective matched cohort study.

BACKGROUND: Whether Inuit in Canada experience disparities in lung cancer survival remains unknown. When requiring investigation and treatment for lung cancer, all residents of Nunavik, the Inuit homeland in Quebec, are sent to the McGill University Health Centre (MUHC), in Montréal. We sought to compare survival among patients with lung cancer at the MUHC, who were residents of Nunavik and Montréal, Quebec, respectively. METHODS: We conducted a retrospective cohort study. Using lung cancer registry data, we identified Nunavik residents with histologically confirmed lung cancer diagnosed between 2005 and 2017. We aimed to match 2 Montréal residents to each Nunavik resident on sex, age, calendar year of diagnosis, and histology (non-small cell lung cancer v. small cell lung cancer). We reviewed medical records for data on additional patient characteristics and treatment, and obtained vital status from a provincial registry. We compared survival using Kaplan-Meier analysis and Cox proportional hazards regression. RESULTS: We included 95 residents of Nunavik and 185 residents of Montréal. For non-small cell lung cancer, median survival times were 321 (95% confidence interval [CI] 184-626) days for Nunavik (n = 71) and 720 (95% CI 536-1208) days for Montréal residents (n = 141). For small cell lung cancer, median survival times were 190 (95% CI 159-308) days for Nunavik (n = 24) and 270 (95% CI 194-766) days for Montréal residents (n = 44). Adjusting for matching variables, stage, performance status, and comorbidity, Nunavik residents had a higher hazard of death (hazard ratio 1.68, 95% CI 1.17-2.41). INTERPRETATION: Nunavik residents experience disparities in survival after lung cancer diagnosis. Although studies in other Inuit Nunangat regions are needed, our findings point to an urgent need to ensure that interventions aimed at improving lung cancer survival, including lung cancer screening, are accessible to Inuit Nunangat residents.

Tissue and circulating biomarkers of benefit to immunotherapy in extensive-stage small cell lung cancer patients.

Extensive stage-Small-Cell Lung Cancer (ES-SCLC) is an aggressive cancer with dismal prognosis. The addition of immune-checkpoint inhibitors (ICIs) to platinum-based chemotherapy have been consistently demonstrated to improve outcomes and survival, becoming the new standard in first - line treatment of ES-SCLC patients. However, despite positive results reported in the pivotal trials, longer benefit appears evident only for a selected group of patients. Several predictive biomarkers have been studied so far but the prospective identification of patients more likely to experience better outcome seems to be challenging in SCLC. Indeed, classical immune predictive biomarkers as PD-L1 and tumor mutational burden (TMB) seem not to correlate with outcomes. Recently, a new molecular classification of SCLC based on differential expression of genes associated with specific clinical behaviors and therapeutic vulnerability have been presented suggesting a new field to be investigated. Despite the achievements, these studies focused mainly on inter-tumoral heterogeneity, limiting the exploration of intra-tumoral heterogeneity and cell to cell interactions. New analysis methods are ongoing in order to explore subtypes plasticity. Analysis on single biopsies cannot catch the whole genomic profile and dynamic change of disease over time and during treatment. Moreover, the availability of tissue for translational research is limited due to the low proportion of patients undergoing surgery. In this context, liquid biopsy is a promising tool to detect reliable predictive biomarkers. Here, we reviewed the current available data on predictive role of tissue and liquid biomarkers in ES-SCLC patients receiving ICIs. We assessed latest results in terms of predictive and prognostic value of gene expression profiling in SCLC. Finally, we explored the role of liquid biopsy as a tool to monitor SCLC patients over time.

Lineage-specific intolerance to oncogenic drivers restricts histological transformation.

Lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) are thought to originate from different epithelial cell types in the lung. Intriguingly, LUAD can histologically transform into SCLC after treatment with targeted therapies. In this study, we designed models to follow the conversion of LUAD to SCLC and found that the barrier to histological transformation converges on tolerance to Myc, which we implicate as a lineage-specific driver of the pulmonary neuroendocrine cell. Histological transformations are frequently accompanied by activation of the Akt pathway. Manipulating this pathway permitted tolerance to Myc as an oncogenic driver, producing rare, stem-like cells that transcriptionally resemble the pulmonary basal lineage. These findings suggest that histological transformation may require the plasticity inherent to the basal stem cell, enabling tolerance to previously incompatible oncogenic driver programs.

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer.

BACKGROUND: The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). METHODS: Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan-Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan-Meier analysis. RESULTS: A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001). CONCLUSION: Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

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.

Predictive biomarkers for immunotherapy response in extensive-stage SCLC.

BACKGROUND: Small cell lung cancer (SCLC) accounts for about 13-15% of all lung cancers, and about 70% of SCLC patients have developed extensive-stage small cell lung cancer (ES-SCLC) at the time of diagnosis because of its highgrade malignancy, easy invasion, and metastasis. In recent years, immunotherapy combined with chemotherapy has become the standard first-line treatment for ES-SCLC. However, SCLC is a relatively immune-cold lung cancer subtype with a limited number of beneficiaries and a short benefit period. Therefore, the use of biomarkers to identify populations with significant benefits from immunotherapy will help improve the efficacy and survival benefits of immunotherapy. However, predictive biomarkers suitable for clinical practice have not been established in the field of SCLC. PURPOSE: In order to find the predictive biomarkers of immunotherapy for ES-SCLC, we summarized the research progress of traditional biomarkers, such as programmed cell death ligand 1 (PD-L1) and tumor mutation burden (TMB), and summarizes the research of potential biomarkers associated with prognosis, such as molecular subtypes, special gene expression, expression of major histocompatibility complex (MHC) I and II classes, tumor immune microenvironment (TIME), and circulating tumor DNA (ctDNA) .We aim to provide new insights on biomarkers. CONCLUSION: The exploration of biomarkers for immunotherapy of SCLC is still very difficult, and it is clear that conventional predictive biomarkers are not suitable for SCLC. At present, the molecular subtypes defined from transcription factors may have some guiding significance, which still needs to be confirmed by prospective clinical studies. In addition, the ctDNA positivity rate of SCLC is higher than that of other tumor types, which can also solve the dilemma of the difficulty of obtaining specimens of SCLC tissues. And the dynamic change of ctDNA also has great potential to predict the curative effect of SCLC, which is worth further clinical exploration.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study.

BACKGROUND: This study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis. METHODS: In the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA). RESULTS: In total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients. CONCLUSION: Our prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

Left Main Bronchus Obstruction in a Patient with Small-cell Lung Cancer Successfully Treated with Venovenous Extracorporeal Membrane Oxygenation.

Lung cancer can cause fatal central airway obstruction. Rapid airway clearance is necessary in some cases, but ventilator management may be insufficient to maintain oxygenation levels. Venovenous extracorporeal membrane oxygenation (VV-ECMO) may be an effective rescue therapy for respiratory failure, but its efficacy in treating tumor-related airway obstruction is unknown. We herein report a case of central airway obstruction and severe acute respiratory failure due to small-cell lung cancer successfully treated with VV-ECMO, bronchoscopic airway intervention, and chemotherapy. VV-ECMO can be an effective option for the treatment of central airway obstruction with acute respiratory failure due to lung cancer.

Potential subtype-specific therapeutic approaches in small cell lung cancer.

PURPOSE OF REVIEW: Small cell lung cancer (SCLC) remains one of the most aggressive thoracic malignancies with an especially dismal prognosis. While the detection of various targetable driver mutations and immune checkpoints have revolutionized the treatment of non-small cell lung cancer (NSCLC), there has been only modest therapeutic innovation over the past decades in SCLC. In this review, we aim to provide a brief summary on the clinical relevance of recent research findings, which could soon pave the way towards a more personalized and targeted management of SCLC patients. RECENT FINDINGS: Substantial research on the biological and molecular heterogeneity of SCLC has been conducted in the last years. Recent results from comprehensive profiling studies have shown that unique major SCLC subtypes can be distinguished based on the relative expression of key transcription regulators (ASCL1, NEUROD1, POU2F3) or distinct inflammatory features. Understanding the differing molecular characteristics of these distinct subtypes has resulted in the identification of specific therapeutic vulnerabilities. SUMMARY: The recently introduced molecular SCLC subtype classification represents a substantial progress towards a personalized and more efficacious approach in SCLC. The consequences of this paradigm shift provide hope for improved patient care and clinical outcomes in this exceptionally lethal thoracic malignancy.

Construction and validation of a nomogram to predict the overall survival of small cell lung cancer: a multicenter retrospective study in Shandong province, China.

BACKGROUND: Patients diagnosed with small cell lung cancer (SCLC) typically experience a poor prognosis, and it is essential to predict overall survival (OS) and stratify patients based on distinct prognostic risks. METHODS: Totally 2309 SCLC patients from the hospitals in 15 cities of Shandong from 2010 - 2014 were included in this multicenter, population-based retrospective study. The data of SCLC patients during 2010-2013 and in 2014 SCLC were used for model development and validation, respectively. OS served as the primary outcome. Univariate and multivariate Cox regression were applied to identify the independent prognostic factors of SCLC, and a prognostic model was developed based on these factors. The discrimination and calibration of this model were assessed by the time-dependent C-index, time-dependent receiver operator characteristic curves (ROC), and calibration curves. Additionally, Decision Curve Analysis (DCA) curves, Net Reclassification Improvement (NRI), and Integrated Discriminant Improvement (IDI) were used to assess the enhanced clinical utility and predictive accuracy of the model compared to TNM staging systems. RESULTS: Multivariate analysis showed that region (Southern/Eastern, hazard ratio [HR] = 1.305 [1.046 - 1.629]; Western/Eastern, HR = 0.727 [0.617 - 0.856]; Northern/Eastern, HR = 0.927 [0.800 - 1.074]), sex (female/male, HR = 0.838 [0.737 - 0.952]), age (46-60/≤45, HR = 1.401 [1.104 - 1.778]; 61-75/≤45, HR = 1.500 [1.182 - 1.902]; >75/≤45, HR = 1.869 [1.382 - 2.523]), TNM stage (II/I, HR = 1.119[0.800 - 1.565]; III/I, HR = 1.478 [1.100 - 1.985]; IV/I, HR = 1.986 [1.477 - 2.670], surgery (yes/no, HR = 0.677 [0.521 - 0.881]), chemotherapy (yes/no, HR = 0.708 [0.616 - 0.813]), and radiotherapy (yes/no, HR = 0.802 [0.702 - 0.917]) were independent prognostic factors of SCLC patients and were included in the nomogram. The time-dependent AUCs of this model in the training set were 0.699, 0.683, and 0.683 for predicting 1-, 3-, and 5-year OS, and 0.698, 0.698, and 0.639 in the validation set, respectively. The predicted calibration curves aligned with the ideal curves, and the DCA curves, the IDI, and the NRI collectively demonstrated that the prognostic model had a superior net benefit than the TNM staging system. CONCLUSION: The nomogram using SCLC patients in Shandong surpassed the TNM staging system in survival prediction accuracy and enabled the stratification of patients with distinct prognostic risks based on nomogram scores.

Management of small cell lung cancer complicated with paraneoplastic Cushing's syndrome: a systematic literature review.

Paraneoplastic Cushing's syndrome (PCS) is a rare, but clinically important feature of small cell lung cancer (SCLC) that is associated with even worse prognosis. To identify key considerations in comprehensive management of SCLC patients complicated with PCS, we conducted a systematic review of relevant reports on PubMed and Web of Science, focusing on SCLC with PCS cases. The systematic review analyzed 61 reports published between 1985 and 2022 with a total of 157 SCLC patients included. Out of the 157 patients, 132 (84.1%) patients across 58 (95.1%) reports were diagnosed with ectopic Cushing's syndrome. The immunohistochemical (IHC) staining for adrenocorticotropic hormone (ACTH) was performed on 30 (19.1%) patients across 22 (36.1%) reports and demonstrated encouraging performance. For treatment, chemotherapy and ketoconazole were utilized in 50 (81.97%) and 24 (39.34%) reports, respectively. Regarding cause of death, infection and cancer were equally frequent, each being recorded in 17 (27.87%) reports. To conclude, the majority of PCS cases in SCLC patients were caused by ectopic hormone secretion. In order to make a differential diagnosis, it is recommended to utilize IHC staining for a specific hormone such as ACTH or corticotropin-releasing hormone. In the comprehensive treatment of SCLC with PCS patients, effective management of hypercortisolism and potent safeguarding against infection play two crucial roles. Ultimately, further confirmations are required regarding the specificity and accuracy of IHC staining technique as well as the efficacy and safety of immunotherapy in the treatment of SCLC with PCS patients.

Hyperinflation and reduced diffusing capacity predict prognosis in SCLC: value of extended pre-therapeutic lung function testing.

BACKGROUND: Small cell lung cancer (SCLC) is characterized by aggressive growth and poor prognosis. Although SCLC affects nearly exclusively heavy smokers and leads to frequent respiratory symptoms, the impact of pre-therapeutic lung function testing in SCLC is sparely investigated until now. Therefore, we sought to examine whether we could find prognostic markers in pre-therapeutic lung function testing of SCLC patients. PATIENTS AND METHODS: We retrospectively analysed a cohort of 205 patients with the diagnosis of SCLC between 2010 and 2018. Pre-therapeutic values of spirometry, body plethysmography and measurement of diffusing capacity was extracted from patients' charts. Comparisons between groups were performed using the Mann-Whitney U-test or by chi-square tests as appropriate. Kaplan-Meier analyses and COX-regression models were performed to correlate lung function parameters with patients' outcome. RESULTS: Airway obstruction itself, or the diagnosis chronic obstructive pulmonary disease (COPD) based on GOLD definitions did not correlate with survival in SCLC patients. Hyperinflation measured by increased residual volume and residual volume to total lung capacity ratio (log-rank p < 0.001) and reduced diffusing capacity (log-rank p = 0.007) were associated with reduced survival. Furthermore, patients with hyperinflation as well as impairments in gas exchange representing an emphysematic phenotype had the worst outcome (log-rank p < 0.001). CONCLUSION: We recommend including body plethysmography and measurement of diffusing capacity in the pre-therapeutic assessment of SCLC patients. Our findings suggest that reduction of hyperinflation may lead to better outcome in SCLC patients. Thus, in addition to effective tumour therapy, adequate therapy of the comorbidity of COPD should also be provided. In particular, measures to reduce hyperinflation by means of dual bronchodilation as well as respiratory physiotherapy should be further assessed in this setting.