Treatment algorithm in 2014 for advanced non-small cell lung cancer: therapy selection by tumour histology and molecular biology

Current developments in delivering customized care: a scoping review

BACKGROUND

In recent years, there has been a growing interest in health care personalization and customization (i.e. personalized medicine and patient-centered care). While some positive impacts of these approaches have been reported, there has been a dearth of research on how these approaches are implemented and combined for health care delivery systems. The present study undertakes a scoping review of articles on customized care to describe which patient characteristics are used for segmenting care, and to identify the challenges face to implement customized intervention in routine care.

METHODS

Article searches were initially conducted in November 2018, and updated in January 2019 and March 2019, according to Prisma guidelines. Two investigators independently searched MEDLINE, PubMed, PsycINFO, Web of Science, Science Direct and JSTOR, The search was focused on articles that included "care customization", "personalized service and health care", individualized care" and "targeting population" in the title or abstract. Inclusion and exclusion criteria were defined. Disagreements on study selection and data extraction were resolved by consensus and discussion between two reviewers.

RESULTS

We identified 70 articles published between 2008 and 2019. Most of the articles (n = 43) were published from 2016 to 2019. Four categories of patient characteristics used for segmentation analysis emerged: clinical, psychosocial, service and costs. We observed these characteristics often coexisted with the most commonly described combinations, namely clinical, psychosocial and service. A small number of articles (n = 18) reported assessments on quality of care, experiences and costs. Finally, few articles (n = 6) formally defined a conceptual basis related to mass customization, whereas only half of articles used existing theories to guide their analysis or interpretation.

CONCLUSIONS

There is no common theory based strategy for providing customized care. In response, we have highlighted three areas for researchers and managers to advance the customization in health care delivery systems: better define the content of the segmentation analysis and the intervention steps, demonstrate its added value, in particular its economic viability, and align the logics of action that underpin current efforts of customization. These steps would allow them to use customization to reduce costs and improve quality of care.

Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer

Objectives

To evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images.

Materials and Methods

A total of 200 patients with radiologically defined central lung cancer were recruited. All patients underwent dual-phase chest CECT, and the histological subtypes (adenocarcinoma (ADC), squamous cell carcinoma (SCC), small cell lung cancer (SCLC)) were confirmed by histopathological samples. 107 features were used in five machine learning classifiers to perform the predictive analysis among three subtypes. Models were trained and validated in two conditions: using radiomic features alone, and combining clinical features with radiomic features. The performance of the classification models was evaluated by the area under the receiver operating characteristic curve (AUC).

Results

The highest AUCs in classifying ADC vs. SCC, ADC vs. SCLC, and SCC vs. SCLC were 0.879, 0.836, 0.783, respectively by using only radiomic features in a feedforward neural network.

Conclusion

Our study indicates that radiomic features based on the CECT images might be a promising tool for noninvasive prediction of histological subtypes in central lung cancer and the neural network classifier might be well-suited to this task.

Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-small-Cell Lung Cancer

Background: Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study, we investigated the association between radiomics features and the tumor histological subtypes, and we aimed to establish a nomogram for the classification of small cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). Methods: This was a retrospective single center study. In total, 468 cases including 202 patients with SCLC and 266 patients with NSCLC were enrolled in our study, and were randomly divided into a training set (n = 327) and a validation set (n = 141) in a 7:3 ratio. The clinical data of the patients, including age, sex, smoking history, tumor maximum diameter, clinical stage, and serum tumor markers, were collected. All patients underwent enhanced computed tomography (CT) scans, and all lesions were pathologically confirmed. A radiomics signature was generated from the training set using the least absolute shrinkage and selection operator algorithm. Independent risk factors were identified by multivariate logistic regression analysis, and a radiomics nomogram based on the radiomics signature and clinical features was constructed. The capability of the nomogram was evaluated in the training set and validated in the validation set. Results: Fourteen of 396 radiomics parameters were screened as important factors for establishing the radiomics model. The radiomics signature performed well in differentiating SCLC and NSCLC, with an area under the curve (AUC) of 0.86 (95% CI: 0.82-0.90) in the training set and 0.82 (95% CI: 0.75-0.89) in the validation set. The radiomics nomogram had better predictive performance [AUC = 0.94 (95% CI: 0.90-0.98) in the validation set] than the clinical model [AUC = 0.86 (95% CI: 0.80-0.93)] and the radiomics signature [AUC = 0.82 (95% CI: 0.75-0.89)], and the accuracy was 86.2% (95% CI: 0.79-0.92) in the validation set. Conclusion: The enhanced CT radiomics signature performed well in the classification of SCLC and NSCLC. The nomogram based on the radiomics signature and clinical factors has better diagnostic performance for the classification of SCLC and NSCLC than the simple application of the radiomics signature.

Exploratory study on classification of lung cancer subtypes through a combined K-nearest neighbor classifier in breathomics

Accurate classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) in lung cancer is critical to physicians’ clinical decision-making. Exhaled breath analysis provides a tremendous potential approach in non-invasive diagnosis of lung cancer but was rarely reported for lung cancer subtypes classification. In this paper, we firstly proposed a combined method, integrating K-nearest neighbor classifier (KNN), borderline2-synthetic minority over-sampling technique (borderlin2-SMOTE), and feature reduction methods, to investigate the ability of exhaled breath to distinguish AC from SCC patients. The classification performance of the proposed method was compared with the results of four classification algorithms under different combinations of borderline2-SMOTE and feature reduction methods. The result indicated that the KNN classifier combining borderline2-SMOTE and feature reduction methods was the most promising method to discriminate AC from SCC patients and obtained the highest mean area under the receiver operating characteristic curve (0.63) and mean geometric mean (58.50) when compared to others classifiers. The result revealed that the combined algorithm could improve the classification performance of lung cancer subtypes in breathomics and suggested that combining non-invasive exhaled breath analysis with multivariate analysis is a promising screening method for informing treatment options and facilitating individualized treatment of lung cancer subtypes patients.

Clinical profile of nonsmall cell lung carcinoma patients treated in a single unit at a tertiary cancer care center

BACKGROUND

Recent advances and understanding in the field of lung cancer and advent of newer treatments have shown a significant improvement in survival in the patients. The present study was conducted to analyze the clinical profile of nonsmall cell lung cancer (NSCLC) patients treated in a single unit at a tertiary cancer care center.

MATERIALS AND METHODS

In this retrospective analysis, 322 consecutive NSCLC patients from the year 2011 to 2012 treated in a single unit were included in the study. Patients with proven NSCLC were included in the study. The details of the patients included the demographic profile, pathological diagnosis as well as imaging data, tumor profile, details of treatment, and follow-up information.

RESULTS

The majority of the patients (95.6%) were in the age group >40 years. A large group of the patients (57.1%) were present/reformed smokers. The major histological type was adenocarcinoma (60.9%), of which 22.8% patients were found to be epidermal growth factor receptor positive. Anaplastic lymphoma kinase rearrangement positivity rate was 4.8%. Furthermore, 68% patients had Stage 4 disease. Upfront palliative chemotherapy (CT) was offered in 61.8% patients and pemetrexed with platinum compounds was the main CT regimen (46.6%). Partial response was achieved in 45.7% patients, whereas stable disease was observed in 10.9% cases. Median progression-free survival was 5 months and overall survival was 55% at 36 months.

CONCLUSION

NSCLC forms the largest subgroup of lung cancer with the patients presenting with advanced stages of disease. This area needs to be explored for the early detection and subsequently the radical treatment of the patients. Personalized approach may be considered for the management of lung cancer by identifying new predictive and prognostic biomarkers of this disease.

An international study to revise the EORTC questionnaire for assessing quality of life in lung cancer patients

BACKGROUND

The European Organization for Research and Treatment of Cancer (EORTC) QLQ-LC13 was the first module to be used in conjunction with the core questionnaire, the QLQ-C30. Since the publication of the LC13 in 1994, major advances have occurred in the treatment of lung cancer. Given this, an update of the EORTC QLQ-LC13 was undertaken.

METHODS

The study followed phases I to III of the EORTC Module Development Guidelines. Phase I generated relevant quality-of-life issues using a mix of sources including the involvement of 108 lung cancer patients. Phase II transformed issues into questionnaire items. In an international multicenter study (phase III), patients completed both the EORTC QLQ-C30 and the 48-item provisional lung cancer module generated in phases I and II. Patients rated each of the items regarding relevance, comprehensibility, and acceptance. Patient ratings were assessed against a set of prespecified statistical criteria. Descriptive statistics and basic psychometric analyses were carried out.

RESULTS

The phase III study enrolled 200 patients with histologically confirmed lung cancer from 12 centers in nine countries (Cyprus, Germany, Italy, Israel, Spain, Norway, Poland, Taiwan, and the UK). Mean age was 64 years (39 - 91), 59% of the patients were male, 82% had non-small-cell lung cancer, and 56% were treated with palliative intent. Twenty-nine of the 48 questions met the criteria for inclusion.

CONCLUSIONS

The resulting module with 29 questions, thus currently named EORTC QLQ-LC29, retained 12 of the 13 original items, supplemented with 17 items that primarily assess treatment side-effects of traditional and newer therapies.

Systematic biobanking, novel imaging techniques, and advanced molecular analysis for precise tumor diagnosis and therapy: The Polish MOBIT project

Personalized and precision medicine is gaining recognition due to the limitations by standard diagnosis and treatment; many areas of medicine, from cancer to psychiatry, are moving towards tailored and individualized treatment for patients based on their clinical characteristics and genetic signatures as well as novel imaging techniques. Advances in whole genome sequencing have led to identification of genes involved in a variety of diseases. Moreover, biomarkers indicating severity of disease or susceptibility to treatment are increasingly being characterized. The continued identification of new genes and biomarkers specific to disease subtypes and individual patients is essential and inevitable for translation into personalized medicine, in estimating both, disease risk and response to therapy. Taking into consideration the mostly unsolved necessity of tailored therapy in oncology the innovative project MOBIT (molecular biomarkers for individualized therapy) was designed. The aims of the project are: (i) establishing integrative management of precise tumor diagnosis and therapy including systematic biobanking, novel imaging techniques, and advanced molecular analysis by collecting comprehensive tumor tissues, liquid biopsies (whole blood, serum, plasma), and urine specimens (supernatant; sediment) as well as (ii) developing personalized lung cancer diagnostics based on tumor heterogeneity and integrated genomics, transcriptomics, metabolomics, and radiomics PET/MRI analysis. It will consist of 5 work packages. In this paper the rationale of the Polish MOBIT project as well as its design is presented. (iii) The project is to draw interest in and to invite national and international, private and public, preclinical and clinical initiatives to establish individualized and precise procedures for integrating novel targeted therapies and advanced imaging techniques.

MiR-205 as a promising biomarker in the diagnosis and prognosis of lung cancer

MicroRNA-205 (miR-205) was revealed as a novel diagnostic and prognostic biomarker for lung cancer, but the results in the published papers were inconsistent. This meta-analysis aimed to investigate the diagnostic and prognostic roles of miR-205 in patients with lung cancer. Totally, 16 eligible articles were included, among which 10 articles investigated the diagnostic value of miR-205, 5 articles examined its prognostic values, and 1 article studied both diagnostic and prognostic values. For the diagnostic meta-analysis, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the overall area under the curve of miR-205 for patients with lung cancer were 0.88 (95% CI = 0.78 – 0.94), 0.78 (95% CI = 0.66 – 0.86), 4.00 (95% CI = 2.47 – 6.49), 0.16 (95% CI = 0.08 – 0.30), 25.86 (95% CI = 9.29 – 71.95), and 0.90 (95% CI = 0.87 – 0.92), respectively, indicating that miR-205 is a useful biomarker for diagnostic of lung cancer. The subgroup analysis further demonstrated that miR-205 had an excellent overall accuracy for detection with tissue samples compare with blood samples. For the prognostic meta-analysis, the pooled outcome of the disease-free survival and recurrence-free survival analyses revealed that increased miR-205 levels had a protective role in the prognosis of patients with lung cancer (pooled HR = 0.86, 95% CI: 0.78-0.96, z = 2.83, P = 0.005). In conclusion, miR-205 may be a promising biomarker for detection, predicting the recurrence of patients with lung cancer.

Randomised Phase 2 study of maintenance linsitinib (OSI-906) in combination with erlotinib compared with placebo plus erlotinib after platinum-based chemotherapy in patients with advanced non-small cell lung cancer

Background:

Maintenance therapy is important in advanced/metastatic non-small cell lung cancer (NSCLC). Erlotinib as switch maintenance following platinum-based chemotherapy increases survival. Cross-talk between the epidermal growth factor receptor and insulin-like growth factor receptor (IGFR) pathways mediate resistance to individual receptor blockade. This study compared maintenance linsitinib plus erlotinib vs erlotinib plus placebo in patients with NSCLC.

Methods:

In this Phase II randomised trial, patients without progression following four cycles of first-line platinum-based chemotherapy (N=205) received continuous schedule maintenance oral linsitinib 150 mg or placebo BID combined with erlotinib 150 mg QD for 21-day cycles. The primary endpoint was progression-free survival (PFS).

Results:

The study was unblinded early due to linsitinib non-superiority. No difference was found between the two treatment groups in median PFS of 125 days linsitinib vs 129 days placebo (P=0.601); no difference in overall survival (OS) was observed. Tolerability was similar, although in the linsitinib group, treatment-related adverse events and discontinuations were more frequent. No drug–drug interaction was implicated.

Conclusions:

Linsitinib maintenance therapy added to erlotinib did not improve PFS or OS in non-progressing NSCLC patients. This highlights the need for robust biomarkers of response for combinations that incorporate IGFR-targeted therapies in maintenance or other therapeutic settings.

Ramucirumab in the treatment of non-small cell lung cancer

INTRODUCTION

Therapeutic options for treating Non-Small Cell Lung Cancer (NSCLC) have recently increased. Ramucirumab (Cyramza), an anti-angionenic agent was approved in 2014 for treatment of several malignancies, including second-line treatment of patients with NSCLC with disease progression on or after platinum-based chemotherapy. Areas covered: We performed a comprehensive search of the literature focused on clinical trials with use of ramucirumab, targeting its evolution in the treatment of NSCLC. This review summarizes the results regarding its safety and efficacy. Expert opinion: Angiogenesis has been widely recognized as a quintessential feature in cancer, intrinsically mediating tumor survival and progression. Ramucirumab, an anti-VEGFR2 agent, combined with docetaxel, was FDA-approved for NSCLC patients. Results from a phase III trial have demonstrated the usefulness of this combination, with benefits in progression free survival and overall survival for NSCLC patients. A greater magnitude of benefit is seen in patients with aggressive tumor behavior. Treatment with ramucirumab is generally tolerable, however, there is potential for severe toxicity. Adverse events reported with this combination include neutropenia, febrile neutropenia and hypertension. Also, there is the intrinsic risk of bleeding resulting from the mechanism of action. As such, adverse events should be identified timely, so drug-related complications can be prevented.

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non-small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

Background

Radiomics can quantify tumor phenotypic characteristics non-invasively by applying feature algorithms to medical imaging data. In this study of lung cancer patients, we investigated the association between radiomic features and the tumor histologic subtypes (adenocarcinoma and squamous cell carcinoma). Furthermore, in order to predict histologic subtypes, we employed machine-learning methods and independently evaluated their prediction performance.

Methods

Two independent radiomic cohorts with a combined size of 350 patients were included in our analysis. A total of 440 radiomic features were extracted from the segmented tumor volumes of pretreatment CT images. These radiomic features quantify tumor phenotypic characteristics on medical images using tumor shape and size, intensity statistics, and texture. Univariate analysis was performed to assess each feature’s association with the histological subtypes. In our multivariate analysis, we investigated 24 feature selection methods and 3 classification methods for histology prediction. Multivariate models were trained on the training cohort and their performance was evaluated on the independent validation cohort using the area under ROC curve (AUC). Histology was determined from surgical specimen.

Results

In our univariate analysis, we observed that fifty-three radiomic features were significantly associated with tumor histology. In multivariate analysis, feature selection methods ReliefF and its variants showed higher prediction accuracy as compared to other methods. We found that Naive Baye’s classifier outperforms other classifiers and achieved the highest AUC (0.72; p-value = 2.3 × 10⁻⁷) with five features: Stats_min, Wavelet_HLL_rlgl_lowGrayLevelRunEmphasis, Wavelet_HHL_stats_median, Wavelet_HLL_stats_skewness, and Wavelet_HLH_glcm_clusShade.

Conclusion

Histological subtypes can influence the choice of a treatment/therapy for lung cancer patients. We observed that radiomic features show significant association with the lung tumor histology. Moreover, radiomics-based multivariate classifiers were independently validated for the prediction of histological subtypes. Despite achieving lower than optimal prediction accuracy (AUC 0.72), our analysis highlights the impressive potential of non-invasive and cost-effective radiomics for precision medicine. Further research in this direction could lead us to optimal performance and therefore to clinical applicability, which could enhance the efficiency and efficacy of cancer care.

Validation for histology-driven diagnosis in non-small cell lung cancer using hsa-miR-205 and hsa-miR-21 expression by two different normalization strategies

Targeted therapy of non-small cell lung cancer (NSCLC) demands a more accurate tumor classification that is crucial for patient selection in personalized treatment. MicroRNAs constitute a promising class of biomarkers and a helpful tool for the distinction between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC). The aim of this study was to evaluate the impact of two different normalization strategies, using U6 snRNA and hsa-miR-103 as reference genes, on hsa-miR-205 and hsa-miR-21 expression levels, in terms of the classification of subtypes of NSCLC. By means of a quantitative real-time polymerase chain reaction (qRT-PCR) microRNA expression levels were evaluated in a classification set of 98 surgically resected NSCLC fresh-frozen samples, and validated findings in an independent set of 42 NSCLC samples. The microRNA expression levels were exploited to develop a diagnostic test using two data normalization strategies. The performance of microRNA profiling in different normalization methods was compared. We revealed the microRNA-based qRT-PCR tests to be appropriate measures for distinguishing between AC and SCC (the concordance of histologic diagnoses and molecular methods greater than 88%). Performance evaluation of microRNA tests, based on the two normalization strategies, showed that the procedure using hsa-miR-103 as reference target has a slight advantage (sensitivity 83.33 and 100% in classification and validation set, respectively) compared to U6 snRNA. Molecular tests based on microRNA expression allow a reliable classification of subtypes for NSCLC and can constitute a useful diagnostic strategy in patient selection for targeted therapy.