Decision support systems for incurable non-small cell lung cancer: a systematic review

Exploring the impact of patient-specific clinical features on osimertinib effectiveness in a real-world cohort of patients with EGFR mutated non-small cell lung cancer

Osimertinib is prescribed to patients with metastatic non-small cell lung cancer (NSCLC) and a sensitizing EGFR mutation. Limited data exists on the impact of patient characteristics or osimertinib exposure on effectiveness outcomes. This was a Dutch, multicenter cohort study. Eligible patients were ≥18 years, with metastatic EGFRm+ NSCLC, receiving osimertinib. Primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS) and safety. Kaplan-Meier analyses and multivariate Cox proportional hazard models were performed. In total, 294 patients were included. Primary EGFR-mutations were mainly exon 19 deletions (54%) and p.L858R point mutations (30%). Osimertinib was given in first-line (40%), second-line (46%) or beyond (14%), with median PFS 14.4 (95% CI: 9.4-19.3), 13.9 (95% CI: 11.3-16.1) and 8.7 months (95% CI: 4.6-12.7), respectively. Patients with low BMI (<20.0 kg/m2 ) had significantly shorter PFS/OS compared to all other subgroups. Patients with a high plasma trough concentration in steady state (Cmin,SS ; >271 ng/mL) had shorter PFS compared to a low Cmin,SS (<163 ng/mL; aHR 2.29; 95% CI: 1.13-4.63). A significant longer PFS was seen in females (aHR = 0.61, 95% CI: 0.45-0.82) and patients with the exon 19 deletion (aHR = 0.58, 95% CI: 0.36-0.92). A trend towards longer PFS was seen for TP53 wild-type patients, while age did not impact PFS. Patients with a primary EGFR exon 19 deletion had longer PFS, while a low BMI, male sex and a high Cmin,SS were indicative for shorter PFS and/or OS. Age was not associated with effectiveness outcomes of osimertinib.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

"The challenge is the complexity" - A qualitative study about decision-making in advanced lung cancer treatment

INTRODUCTION

The value of shared decision-making and decision aids (DA) has been well documented yet remain difficult to integrate into clinical practice. We wanted to investigate needs and challenges regarding decision-making about advanced lung cancer treatment after first-line therapy, focusing on DA applicability.

METHODS

Qualitative data from separate, semi-structured focus groups with patients/relatives and healthcare professionals were analysed using systematic text condensation. 12 patients with incurable lung cancer, seven relatives, 12 nurses and 18 doctors were recruited from four different hospitals in Norway.

RESULTS

The participants described the following needs and challenges affecting treatment decisions: 1) Continuity of clinician-patient-relationships as a basic framework for decision-making; 2) barriers to information exchange; 3) negotiation of autonomy; and 4) assessment of uncertainty and how to deal with it. Some clinicians feared DA would steal valuable time and disrupt consultations, arguing that such tools could not incorporate the complexity and uncertainty of decision-making. Patients and relatives reported a need for more information and the possibility both to decline or continue burdensome therapy. Participants welcomed interventions supporting information exchange, like communicative techniques and organizational changes ensuring continuity and more time for dialogue. Doctors called for tools decreasing uncertainty about treatment tolerance and futile therapy.

CONCLUSION

Our study suggests it is difficult to develop an applicable DA for advanced lung cancer after first-line therapy that meets the composite requirements of stakeholders. Comprehensive decision support interventions are needed to address organizational structures, communication training including scientific and existential uncertainty, and assessment of frailty and treatment toxicity.

Clinician perspectives on clinical decision support systems in lung cancer: Implications for shared decision-making

Background

Lung cancer treatment decisions are typically made among clinical experts in a multidisciplinary tumour board (MTB) based on clinical data and guidelines. The rise of artificial intelligence and cultural shifts towards patient autonomy are changing the nature of clinical decision‐making towards personalized treatments. This can be supported by clinical decision support systems (CDSSs) that generate personalized treatment information as a basis for shared decision‐making (SDM). Little is known about lung cancer patients' treatment decisions and the potential for SDM supported by CDSSs. The aim of this study is to understand to what extent SDM is done in current practice and what clinicians need to improve it.

Objective

To explore (1) the extent to which patient preferences are taken into consideration in non‐small‐cell lung cancer (NSCLC) treatment decisions; (2) clinician perspectives on using CDSSs to support SDM.

Design

Mixed methods study consisting of a retrospective cohort study on patient deviation from MTB advice and reasons for deviation, qualitative interviews with lung cancer specialists and observations of MTB discussions and patient consultations.

Setting and Participants

NSCLC patients (N = 257) treated at a single radiotherapy clinic and nine lung cancer specialists from six Dutch clinics.

Results

We found a 10.9% (n = 28) deviation rate from MTB advice; 50% (n = 14) were due to patient preference, of which 85.7% (n = 12) chose a less intensive treatment than MTB advice. Current MTB recommendations are based on clinician experience, guidelines and patients' performance status. Most specialists (n = 7) were receptive towards CDSSs but cited barriers, such as lack of trust, lack of validation studies and time. CDSSs were considered valuable during MTB discussions rather than in consultations.

Conclusion

Lung cancer decisions are heavily influenced by clinical guidelines and experience, yet many patients prefer less intensive treatments. CDSSs can support SDM by presenting the harms and benefits of different treatment options rather than giving single treatment advice. External validation of CDSSs should be prioritized.

Patient or Public Contribution

This study did not involve patients or the public explicitly; however, the study design was informed by prior interviews with volunteers of a cancer patient advocacy group. The study objectives and data collection were supported by Dutch health care insurer CZ for a project titled ‘My Best Treatment’ that improves patient‐centeredness and the lung cancer patient pathway in the Netherlands.

A clinical scoring tool validated with machine learning for predicting severe hand-foot syndrome from sorafenib in hepatocellular carcinoma

Purpose

Sorafenib is an effective therapy for advanced hepatocellular carcinoma (HCC). Hand–foot syndrome (HFS) is a serious adverse effect associated with sorafenib therapy. This study aimed to develop an updated clinical prediction tool that allows personalized prediction of HFS following sorafenib initiation.

Methods

Individual participant data from Phase III clinical trial NCT00699374 were used in Cox proportional hazard analysis of the association between pre-treatment clinicopathological data and grade ≥ 3 HFS occurring within the first 365 days of sorafenib treatment for advanced HCC. Multivariable prediction models were developed using stepwise forward inclusion and backward deletion and internally validated using a random forest machine learning approach.

Results

Of 542 patients, 116 (21%) experienced grades ≥ 3 HFS. The prediction tool was optimally defined by sex (male vs female), haemoglobin (< 130 vs ≥ 130 g/L) and bilirubin (< 10 vs 10–20 vs ≥ 20 µmol/L). The prediction tool was able to discriminate subgroups with significantly different risks of grade ≥ 3 HFS (P ≤ 0.001). The high (score = 3 +)-, intermediate (score = 2)- and low (score = 0–1)-risk subgroups had 40%, 27% and 14% probability of developing grade ≥ 3 HFS within the first 365 days of sorafenib treatment, respectively.

Conclusion

A clinical prediction tool defined by female sex, high haemoglobin and low bilirubin had high discrimination for predicting HFS risk. The tool may enable improved evaluation of personalized risks of HFS for patients with advanced HCC initiating sorafenib.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04411-9.

Development and Evaluation of a Real-World Outcomes-Based Tool to Support Informed Clinical Decision Making in the Palliative Treatment of Patients With Metastatic NSCLC

PURPOSE

To develop and evaluate a tool for patients with stage IV non-small-cell lung cancer and their thoracic oncologists (TOs) that provides insight into real-world effectiveness of systemic treatments to support informed clinical decision making in the palliative setting.

METHODS

A participatory design approach was used to acquire insights from patients and TOs into preferences regarding the content and design of the web-based tool. Implementation was investigated by means of an adoption and usage rate. The appreciation of the tool was evaluated through a telephone survey with patients and a questionnaire for TOs.

RESULTS

From clinical data of 2,989 patients with stage IV non-small-cell lung cancer diagnosed in one of the Santeon hospitals, an interface was developed to show treatments plus both real-world outcomes and clinical trial results after selecting patient characteristics (patients like me). This prototype of the tool was finalized after discussion in a focus group with four TOs and semi-structured interviews with six patients. The tool was implemented and used by TOs in three of six Santeon hospitals (50% adoption rate). The tool was used in 48 patients (29% usage rate), of which 17 participated in the telephone survey. Ten TOs responded to the questionnaire. The responses varied from positive reactions on the clear overview of treatment outcomes to statements that the tool rarely changed treatment decisions. Overall, the majority of patients and TOs scored the tool as of added value (71% and 83%, respectively).

CONCLUSION

Our real-world data tool in metastatic lung cancer was appreciated in clinical practice by both patients and TOs. However, the efficacy of the implementation can be improved.

Machine Learning for Prediction of Survival Outcomes with Immune-Checkpoint Inhibitors in Urothelial Cancer

Simple Summary

Machine learning (ML) is a form of artificial intelligence that could be used to enhance the efficiency of developing accurate prediction models for survival outcomes with cancer medicines, which is critical in informing disease prognosis and care planning. We used data from two recent clinical trials to develop and validate ML‐based clinical prediction models of the overall and progression‐free survival rates in patients with urothelial cancer initiating the immune checkpoint inhibitor (ICI) atezolizumab. We demonstrated that ML can efficiently develop an accurate prediction model of survival, enable an accurate prognostic risk classification, and provide realistic expectations of treatment outcomes in patients undergoing urothelial cancer-initiating ICIs therapy.

Abstract

Machine learning (ML) may enhance the efficiency of developing accurate prediction models for survival, which is critical in informing disease prognosis and care planning. This study aimed to develop an ML prediction model for survival outcomes in patients with urothelial cancer-initiating atezolizumab and to compare model performances when built using an expert-selected (curated) versus an all-in list (uncurated) of variables. Gradient-boosted machine (GBM), random forest, Cox-boosted, and penalised, generalised linear models (GLM) were evaluated for predicting overall survival (OS) and progression-free survival (PFS) outcomes. C-statistic (c) was utilised to evaluate model performance. The atezolizumab cohort in IMvigor210 was used for model training, and IMvigor211 was used for external model validation. The curated list consisted of 23 pretreatment factors, while the all-in list consisted of 75. Using the best-performing model, patients were stratified into risk tertiles. Kaplan–Meier analysis was used to estimate survival probabilities. On external validation, the curated list GBM model provided slightly higher OS discrimination (c = 0.71) than that of the random forest (c = 0.70), CoxBoost (c = 0.70), and GLM (c = 0.69) models. All models were equivalent in predicting PFS (c = 0.62). Expansion to the uncurated list was associated with worse OS discrimination (GBM c = 0.70; random forest c = 0.69; CoxBoost c = 0.69, and GLM c = 0.69). In the atezolizumab IMvigor211 cohort, the curated list GBM model discriminated 1-year OS probabilities for the low-, intermediate-, and high-risk groups at 66%, 40%, and 12%, respectively. The ML model discriminated urothelial-cancer patients with distinctly different survival risks, with the GBM applied to a curated list attaining the highest performance. Expansion to an all-in approach may harm model performance.

Value of the Lung Immune Prognostic Index in Patients with Non-Small Cell Lung Cancer Initiating First-Line Atezolizumab Combination Therapy: Subgroup Analysis of the IMPOWER150 Trial

The lung immune prognostic index (LIPI) is proposed to differentiate prognosis and treatment benefit from immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (NSCLC). There is minimal information on the predictive importance with first-line, combination ICI approaches. In post-hoc analysis of IMpower150, Cox-proportional hazard analysis assessed the association between LIPI groups and overall survival (OS)/progression free survival (PFS). IMpower150 involved chemotherapy-naïve, metastatic non-squamous NSCLC participants randomized atezolizumab-carboplatin-paclitaxel (ACP), bevacizumab-carboplatin-paclitaxel (BCP), or atezolizumab-BCP (ABCP). Good (0 factors), intermediate (1 factor), and poor LIPI (2 factors) were defined via derived neutrophil-to-lymphocyte ratio >3, and lactate dehydrogenase >upper limit of normal. Of 1148 participants, 548 had good, 479 intermediate, and 121 poor LIPI. In 385 participants randomised ABCP, a significant association between LIPI and OS (HR (95%CI): intermediate LIPI = 2.16 (1.47-3.18), poor LIPI = 5.28 (3.20-8.69), p < 0.001) and PFS (HR (95%CI): intermediate LIPI = 1.47 (1.11-1.95), poor LIPI = 3.02 (2.03-4.50), p < 0.001) was identified. Median OS was 24, 16, and 7 months for good, intermediate, and poor LIPI, respectively. ACP associations were similar. Relative OS treatment effect (HR 95%CI) of ABCP vs. BCP was 0.78 (0.53-1.15), 0.67 (0.49-0.91), and 0.87 (0.51-1.47) for the good, intermediate, and poor LIPI groups, respectively (P(interaction) = 0.66), with no benefit in median OS observed in the poor LIPI group. LIPI identified subgroups with significantly different survival following ABCP and ACP initiation for chemotherapy-naïve, metastatic non-squamous NSCLC. There was insufficient evidence that LIPI identifies patients unlikely to benefit from ABCP treatment.

An Improved Self-Labeled Algorithm for Cancer Prediction

Nowadays, cancer constitutes the second leading cause of death globally. The application of an efficient classification model is considered essential in modern diagnostic medicine in order to assist experts and physicians to make more accurate and early predictions and reduce the rate of mortality. Machine learning techniques are being broadly utilized for the development of intelligent computational systems, exploiting the recent advances in digital technologies and the significant storage capabilities of electronic media. Ensemble learning algorithms and semi-supervised algorithms have been independently developed to build efficient and robust classification models from different perspectives. The former attempts to achieve strong generalization by using multiple learners, while the latter attempts to achieve strong generalization by exploiting unlabeled data. In this work, we propose an improved semi-supervised self-labeled algorithm for cancer prediction, based on ensemble methodologies. Our preliminary numerical experiments illustrate the efficacy and efficiency of the proposed algorithm, proving that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.

Evaluation of a National Comprehensive Cancer Network Guidelines-Based Decision Support Tool in Patients With Non-Small Cell Lung Cancer: A Nonrandomized Clinical Trial

IMPORTANCE

The association of guideline-based decision support with the quality of care in patients with non-small cell lung cancer (NSCLC) is not known.

OBJECTIVE

To evaluate the association of exposure to the National Comprehensive Cancer Center (NCCN) guidelines with guideline-concordant care and patients' decisional conflict.

DESIGN, SETTING, AND PARTICIPANTS

A nonrandomized clinical trial, conducted at a tertiary care academic institution, enrolled patients from February 23, 2015, to September 28, 2017. Data analysis was conducted from July 19, 2019, to April 22, 2020. A cohort of 76 patients with NSCLC seen at diagnosis or disease progression and a retrospective cohort of 157 patients treated before the trial were included. Adherence to 6 NCCN recommendations were evaluated: (1) smoking cessation counseling, (2) adjuvant chemotherapy for patients with stage IB to IIB NSCLC after surgery, (3) pathologic mediastinal staging in patients with stage III NSCLC before surgery, (4) pathologic mediastinal staging in patients with stage III NSCLC before nonsurgical treatment, (5) definitive chemoradiotherapy for patients with stage III NSCLC not having surgery, and (6) molecular testing for epidermal growth factor receptor and anaplastic lymphoma kinase alterations for patients with stage IV NSCLC. Subgroup analysis was conducted to compare the rates of guideline concordance between the prospective and retrospective cohorts. Secondary end points included decisional conflict and satisfaction.

INTERVENTIONS

An online tool customizing the NCCN guidelines to patients' clinical and pathologic features was used during consultation, facilitated by a trained coordinator.

MAIN OUTCOMES AND MEASURES

Concordance of practice with 6 NCCN treatment recommendations on NSCLC and patients' decisional conflict.

RESULTS

Of the 76 patients with NSCLC, 44 were men (57.9%), median age at diagnosis was 68 years (interquartile range [IQR], 41-87 years), and 59 patients (77.6%) had adenocarcinoma. In the retrospective cohort, 91 of 157 patients (58.0%) were men, median age at diagnosis was 66 years (IQR, 61-65 years), and 105 patients (66.9%) had adenocarcinoma. After the intervention, patients received more smoking cessation counseling (4 of 5 [80.0%] vs 1 of 24 [4.2%], P < .001) and less adjuvant chemotherapy (0 of 7 vs 7 of 11 [63.6%]; P = .012). There was no significant change in mutation testing of non-squamous cell stage IV disease (20 of 20 [100%] vs 48 of 57 [84.2%]; P = .10). There was no significant change in pathologic mediastinal staging or initial chemoradiotherapy for patients with stage III disease. After consultation with the tool, decisional conflict scores improved by a median of 20 points (IQR, 3-34; P < .001).

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that exposure to the NCCN guidelines is associated with increased guideline-concordant care for 2 of 6 preselected recommendations and improvement in decisional conflict.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03982459.

The Effect of Higher Level Computerized Clinical Decision Support Systems on Oncology Care: A Systematic Review

BACKGROUND

To deal with complexity in cancer care, computerized clinical decision support systems (CDSSs) are developed to support quality of care and improve decision-making. We performed a systematic review to explore the value of CDSSs using automated clinical guidelines, Artificial Intelligence, datamining or statistical methods (higher level CDSSs) on the quality of care in oncology.

MATERIALS AND METHODS

The search strategy combined synonyms for 'CDSS' and 'cancer.' Pubmed, Embase, The Cochrane Library, Institute of Electrical and Electronics Engineers, Association of Computing Machinery digital library and Web of Science were systematically searched from January 2000 to December 2019. Included studies evaluated the impact of higher level CDSSs on process outcomes, guideline adherence and clinical outcomes.

RESULTS

11,397 studies were selected for screening, after which 61 full-text articles were assessed for eligibility. Finally, nine studies were included in the final analysis with a total population size of 7985 patients. Types of cancer included breast cancer (63.1%), lung cancer (27.8%), prostate cancer (4.1%), colorectal cancer (3.1%) and other cancer types (1.9%). The included studies demonstrated significant improvements of higher level CDSSs on process outcomes and guideline adherence across diverse settings in oncology. No significant differences were reported for clinical outcomes.

CONCLUSION

Higher level CDSSs seem to improve process outcomes and guidelines adherence but not clinical outcomes. It should be noticed that the included studies primarily focused on breast and lung cancer. To further explore the impact of higher level CDSSs on quality of care, high-quality research is required.

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

Treatment decision-making for patients with incurable non-small cell lung cancer (NSCLC) is complex due to the rapidly increasing number of treatments and discovery of new biomarkers. Decision support systems (DSS) could assist thoracic oncologists (TO) weighing of the pros and cons of treatments in order to arrive at an evidence-based and personalized treatment advice. Our aim is to inventory (1) TO's needs with regard to DSS in the treatment of incurable (stage IIIB/IV) NSCLC patients, and (2) preferences regarding the development of future tools in this field. We disseminated an online inventory questionnaire among all members of the Section of Oncology within the Society of Physicians in Chest Medicine and Tuberculosis. Telephone interviews were conducted to better contextualize the findings from the questionnaire. In total, 58 TO completed the questionnaire and expressed a need for new DSS. They reported that it is important for tools to include genetic and immune markers, to be sufficiently validated, regularly updated, and time-efficient. Also, future DSS should incorporate multiple treatment options, integrate estimates of toxicity, quality of life and cost-effectiveness of treatments, enhance communication between caregivers and patients, and use IT solutions for a clear interface and continuous updating of tools. With this inventory among Dutch TO, we summarized the need for new DSS to aid treatment decision-making for patients with incurable NSCLC. To meet the expressed needs, substantial additional efforts will be required by DSS developers, above already existing tools.

A literature review of treatment-specific clinical prediction models in patients with breast cancer

Despite advances in the breast cancer treatment, significant variability in patient outcomes remain. This results in significant stress to patients and clinicians. Treatment-specific clinical prediction models allow patients to be matched against historical outcomes of patients with similar characteristics; thereby reducing uncertainty by providing personalised estimates of benefits, harms, and prognosis. To achieve this objective, models need to be clinical-grade with evidence of accuracy, reproducibility, generalizability, and be user-friendly. A structured search was undertaken to identify treatment-specific clinical prediction models for therapeutic or adverse outcomes in breast cancer using clinicopathological data. Significant gaps in the presence of validated models for available treatments was identified, along with gaps in prediction of therapeutic and adverse outcomes. Most models did not have user-friendly tools available. With the aim being to facilitate the selection of the best medicine for a specific patient and shared-decision making, future research will need to address these gaps.

Predicting Thrombocytopenia in Patients With Breast Cancer Treated With Ado-trastuzumab Emtansine

INTRODUCTION

Thrombocytopenia is a common and potentially serious adverse event of ado-trastuzumab emtansine (T-DM1) use in patients with advanced breast cancer. However, the risk factors have been minimally explored. Our aim was to develop a clinical prediction model from the clinicopathologic data that would allow for quantification of the personalized risks of thrombocytopenia from T-DM1 usage.

MATERIALS AND METHODS

Data from 3 clinical trials, EMILIA (a study of trastuzumab emtansine versus capecitabine + lapatinib in participants with HER2 [human epidermal growth factor receptor 2]-positive locally advanced or metastatic breast cancer), TH3RESA (a study of trastuzumab emtansine in comparison with treatment of physician's choice in participants with HER2-positive breast cancer who have received at least two prior regimens of HER2-directed therapy), and MARIANNE [a study of trastuzumab emtansine (T-DM1) plus pertuzumab/pertuzumab placebo versus trastuzumab (Herceptin) plus a taxane in participants with metastatic breast cancer], were pooled. Cox proportional hazard analysis was used to assess the association between the pretreatment clinicopathologic data and grade ≥ 3 thrombocytopenia occurring within the first 365 days of T-DM1 use. A multivariable clinical prediction model was developed using a backward elimination process.

RESULTS

Of the 1620 participants, 141 (9%) had experienced grade ≥ 3 thrombocytopenia. On univariable analysis, the body mass index, race, presence of brain metastasis, platelet count, white blood cell count, and concomitant corticosteroid use were significantly associated with the occurrence of grade ≥ 3 thrombocytopenia (P < .05). The multivariable prediction model was optimally defined by race (Asian vs. non-Asian) and platelet count (100-220 vs. 220-300 vs. >300 × 10⁹/L). A large discrimination between the prognostic subgroups was observed. The highest risk subgroup (Asian and platelet count of 100-220 cells ×10⁹/L) had a 40% probability of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy compared with 2% for the lowest risk subgroup (non-Asian and platelet count > 300 × 10⁹/L).

CONCLUSION

A clinical prediction model, defined by race and pretreatment platelet count, was able to discriminate subgroups with a significantly different risk of grade ≥ 3 thrombocytopenia after T-DM1 initiation. The model allows for improved interpretation of the personalized risks and risk/benefit ratio of T-DM1.

External validation of a prognostic model for mortality among patients with non-small-cell lung cancer using the Veterans Precision Oncology Data Commons

BACKGROUND

There is wide interest in developing prognostic models in non-small-cell lung cancer (NSCLC) due to the heterogeneity of the disease. Models developed at other healthcare institutions may not be directly applicable for patients treated at the Department of Veterans Affairs (VA). External validation of a candidate prognostic model among VA patients would be crucial before it can be implemented to aid clinical decision-making.

METHODS

A prognostic model for mortality developed in the Military Health System (MHS) was applied to data from the VA Precision Oncology Data Repository (VA-PODR), which is available to researchers inside and outside the VA at the Veterans Precision Oncology Data Commons (VPODC). Measures of discrimination and calibration were calculated for the MHS model. The MHS model was also refitted in VA-PODR data using the same risk factors to compare the effect of specific factors and predictive performance when the model is developed using VA data.

RESULTS

Time-dependent AUC of the MHS prognostic model was 0.788, 0.806, 0.780, and 0.779 for predicting survival at 1, 2, 3, and 5 years following diagnosis, respectively. Significant discrepancies were found between predicted and observed rates of survival, particularly for later years. When the model is refit in VA-PODR data, it achieved cross-validated AUCs of 0.739, 0.773, 0.769, and 0.807 at the same time points, and discrepancies between predicted and observed survival were reduced.

CONCLUSIONS

Validation of the MHS prognostic model in VA-PODR demonstrates that its discrimination remains strong when applied to VA patients. Nevertheless, further calibration to VA data may be needed to improve its risk estimation performance. This study highlights the utility of VA-PODR and the VPODC as a national resource for developing analytic tools that are well adapted to the Veteran population.

miR-5582-5p inhibits cell proliferation of non-small cell lung cancer through targeting FGF-10

Accumulating evidence has suggested that microRNAs (miRNAs) play important roles in regulating the progression of cancerby acting as tumor suppressors or oncogenes. Here, our results demonstrated that miR-5582-5p was significantly down-regulated in non-small cell lung cancer (NSCLC) tissues and cell lines compared with normal controls. Overexpression of miR-5582-5p markedly inhibited the proliferation and migration of NSCLC cells. Consistently, the apoptosis of NSCLC cells was also significantly promoted by overexpressed miR-5582-5p. Functional study uncovered that miR-5582-5p bound the 3'-untranslated region (UTR) of fibroblastic growth factor-10 (FGF-10) and decreased the expression of FGF-10 in NSCLC cells. FGF-10 was up-regulated in NSCLC tissues and inversely correlated with the level of miR-5582-5p in NSCLC tissues. Overexpression of FGF-10 significantly reversed the inhibitory effect of miR-5582-5p on the proliferation of NSCLC cells. Taken together, our results demonstrated the functional mechanism of miR-5582-5p in suppressing malignant behaviors of NSCLC cells by targeting FGF-10. These findings demonstrated that miR-5582-5p might be a novel therapeutic target in the treatment of NSCLC.

Effect of TW37 on the growth of H1975 EGFR‑TKI‑resistant lung cancer cells and its underlying mechanisms

Previous studies have suggested that the B‑cell lymphoma 2 (Bcl‑2) inhibitor, TW37, may induce apoptosis of the non‑small cell lung cancer cell line, H1975/epidermal growth factor receptor‑tyrosine kinase inhibitor (EGFR‑TKI), which exhibits secondary resistance to EGFR‑TKI. However, the effects of TW37 on H1975/EGFR‑TKI cells remain unclear. The aim of the present study was to investigate the effects of TW37 on the growth of H1975/EGFR‑TKI cells and explore the underlying mechanisms. An in vitro study was performed, whereby H1975/EGFR‑TKI cells were treated with serially increasing concentrations of TW37. MTT, flow cytometry, migration and transwell invasion assays were preformed to investigate the proliferation, apoptosis, migration and invasion of H1975/EGFR‑TKI cells, respectively. In addition, reverse transcription‑polymerase chain reaction and western blot analyses were performed to detect the mRNA and protein expression levels of apoptosis‑associated factors, respectively. An enzyme‑linked immunosorbent assay was performed to detect phosphatidylinositol [3,4,5] tris‑phosphate (PIP3) expression. The results suggested that the mRNA and protein expression levels of Bcl‑2 were significantly decreased in TW37‑treated cells when compared with the untreated control group. Following treatment with TW37, the proliferation, migration and invasion ability of H1975/EGFR‑TKI cells decreased in a dose‑dependent manner, while the percentage of apoptotic cells increased. In addition, the results demonstrated that TW37 reduced the expression of PIP3 and the phosphorylation of AKT serine/threonine kinase 1 (AKT) in H1975/EGFR‑TKI cells in a dose‑dependent manner. In conclusion, TW37 inhibited H1975/EGFR‑TKI cell growth and induced cell apoptosis potentially via suppression of AKT signaling pathway activation.