Materiały źródłowe

• #1 Lung Cancer Prognosis | How to Understand Your Prognosis

  https://www.lungcancercenter.com/treatment/prognosis/

  When people discuss understanding a prognosis and whether a prognosis is good or bad, they are referring to the survivability of the diagnosis. As a rule, survivability (also known as the survival rate) signifies what percentage of people diagnosed with the same stage of cancer lived past a specified timeframe (typically, five years) following diagnosis. […] Among all cancers, the survival rate for lung cancer is low at 18.6 percent – compared to survival rates of 89.6 percent for breast cancer and 98.2 percent for prostate cancer. […] Nonetheless, among the different types of lung cancer, some prognoses generally have higher survival rates than others. […] Below are the average 5-year survival rates for small cell and non-small lung cancer prognoses depending on their particular stage.

• #2 Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

  https://www.mdpi.com/2072-6694/14/23/5931

  Lung cancer is the leading cause of cancer-related death worldwide. Non-small cell lung cancer (NSCLC) accounts for 80–90% of primary lung cancers, and is mostly diagnosed at an advanced stage with prognosis remaining poor despite recent therapeutic advances. The introduction of immunotherapy for the treatment of locally advanced or metastatic non-small cell lung cancer (NSCLC) has shown an improvement in terms of overall survival and progression-free survival. However, a durable clinical benefit (DCB) (>6 months) is only achieved in 20–50% of patients. Early identification of patients likely to benefit from this treatment is therefore crucial. […] Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC.

• #3 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  Non-small cell lung cancer (NSCLC) makes up 85-90% of lung cancer cases, with adenocarcinoma (AC) and squamous cell carcinoma (SCC) being the most common subtypes. […] Treatment options for NSCLC include surgery, chemotherapy, immunotherapy, and targeted therapies, which are personalized for each patient outcome. […] Targeted therapies, such as monoclonal antibodies and tyrosine kinase inhibitors, are especially effective for advanced or metastatic cases, improving patient outcomes. […] Lung cancer treatments provide a solid foundation for exploring new therapies through drug repositioning (DR) strategies, which have gained importance in recent years. […] Drug repurposing involves using existing drugs to treat diseases for which they were not originally developed. […] This approach saves time, reduces costs, and offers greater safety, as these drugs have already been evaluated in patients.

• #4 More Accurate Prognosis for Lung Cancer Possible With New Test, Study Shows < Yale School of Medicine

  https://medicine.yale.edu/news-article/more-accurate-prognosis-for-lung-cancer-possible-with-new-test/

  Lung cancer remains one of the most common and deadliest cancers; however, it is currently difficult for physicians to provide patients with an accurate prognosis. […] Tumor stage is the standard measure for assessing a lung cancer patients risk of progression and death, and is important to guide chemotherapy decisions, said Dhruva Biswas, MBBS, PhD, associate research scientist (cardiovascular medicine) and a member of the Cardiovascular Data Science (CarDS) Lab. Yet even among supposedly low-risk, Stage I patients, up to 30 percent die within five years. A validated molecular biomarker could improve these outcomes. […] In a new study published in Nature Cancer, the researchers found that ORACLE can better predict patient survival than the current clinical standard, even at the earliest stage of cancer.

• #5 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5572183/

  Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score 9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score 15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses. While there are several recognised factors that predict outcome for patients diagnosed with non-small-cell lung cancer (NSCLC), our ability to risk-stratify individual patients at the time of their diagnosis is limited. Lung cancer prognostication remains rudimentary and to date no single model has demonstrated superior performance, clinical utility, or widespread global uptake. The objectives of this study were to identify baseline patient and disease variables associated with overall survival (OS) in patients with newly diagnosed NSCLC, and to derive and subsequently validate a simple and generalisable prognostic model. Using the LCPI and m-LCPI scoring systems, each one-point increase was associated with 2-fold increased mortality risk. The ability of the LCPI to stratify patients according to prognostic group was maintained (and superior to stage alone) in subgroup analyses for early stage patients likely receiving curative surgery or curative chemoradiotherapy and for advanced stage patients likely receiving non-curative chemotherapy, radiotherapy, or molecular therapies. The proposed LCPI is a compound scoring system, including established and novel variables, for the prediction of survival following NSCLC diagnosis. The LCPI is simple and generalisable, using data easily and routinely obtained during diagnostic evaluations. Importantly, the model was developed from prospectively collected data and we provide external validation to demonstrate superior survival prediction compared to stage alone (current best practice), and consistent predictive performance across two independent cohorts.

• #6 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer | British Journal of Cancer

  https://www.nature.com/articles/bjc2017232

  Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. […] The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. […] The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses. […] The proposed LCPI is a compound scoring system, including established and novel variables, for the prediction of survival following NSCLC diagnosis. […] Importantly, the model was developed from prospectively collected data and we provide external validation to demonstrate superior survival prediction compared to stage alone (current best practice), and consistent predictive performance across two independent cohorts, c-statistic (Harrells c) 0.73 and 0.68.

• #7 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5572183/

  Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score 9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score 15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses. While there are several recognised factors that predict outcome for patients diagnosed with non-small-cell lung cancer (NSCLC), our ability to risk-stratify individual patients at the time of their diagnosis is limited. Lung cancer prognostication remains rudimentary and to date no single model has demonstrated superior performance, clinical utility, or widespread global uptake. The objectives of this study were to identify baseline patient and disease variables associated with overall survival (OS) in patients with newly diagnosed NSCLC, and to derive and subsequently validate a simple and generalisable prognostic model. Using the LCPI and m-LCPI scoring systems, each one-point increase was associated with 2-fold increased mortality risk. The ability of the LCPI to stratify patients according to prognostic group was maintained (and superior to stage alone) in subgroup analyses for early stage patients likely receiving curative surgery or curative chemoradiotherapy and for advanced stage patients likely receiving non-curative chemotherapy, radiotherapy, or molecular therapies. The proposed LCPI is a compound scoring system, including established and novel variables, for the prediction of survival following NSCLC diagnosis. The LCPI is simple and generalisable, using data easily and routinely obtained during diagnostic evaluations. Importantly, the model was developed from prospectively collected data and we provide external validation to demonstrate superior survival prediction compared to stage alone (current best practice), and consistent predictive performance across two independent cohorts.

• #8 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer | British Journal of Cancer

  https://www.nature.com/articles/bjc2017232

  Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. […] The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. […] The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses. […] The proposed LCPI is a compound scoring system, including established and novel variables, for the prediction of survival following NSCLC diagnosis. […] Importantly, the model was developed from prospectively collected data and we provide external validation to demonstrate superior survival prediction compared to stage alone (current best practice), and consistent predictive performance across two independent cohorts, c-statistic (Harrells c) 0.73 and 0.68.

• #9 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5572183/

  Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score 9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score 15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses. While there are several recognised factors that predict outcome for patients diagnosed with non-small-cell lung cancer (NSCLC), our ability to risk-stratify individual patients at the time of their diagnosis is limited. Lung cancer prognostication remains rudimentary and to date no single model has demonstrated superior performance, clinical utility, or widespread global uptake. The objectives of this study were to identify baseline patient and disease variables associated with overall survival (OS) in patients with newly diagnosed NSCLC, and to derive and subsequently validate a simple and generalisable prognostic model. Using the LCPI and m-LCPI scoring systems, each one-point increase was associated with 2-fold increased mortality risk. The ability of the LCPI to stratify patients according to prognostic group was maintained (and superior to stage alone) in subgroup analyses for early stage patients likely receiving curative surgery or curative chemoradiotherapy and for advanced stage patients likely receiving non-curative chemotherapy, radiotherapy, or molecular therapies. The proposed LCPI is a compound scoring system, including established and novel variables, for the prediction of survival following NSCLC diagnosis. The LCPI is simple and generalisable, using data easily and routinely obtained during diagnostic evaluations. Importantly, the model was developed from prospectively collected data and we provide external validation to demonstrate superior survival prediction compared to stage alone (current best practice), and consistent predictive performance across two independent cohorts.

• #10 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer

  https://pmc.ncbi.nlm.nih.gov/articles/PMC5572183/

  Non-small-cell lung cancer outcomes are poor but heterogeneous, even within stage groups. To improve prognostic precision we aimed to develop and validate a simple prognostic model using patient and disease variables. The derived Lung Cancer Prognostic Index (LCPI) included stage, histology, mutation status, performance status, weight loss, smoking history, respiratory comorbidity, sex, and age. Two-year overall survival rates according to LCPI in the derivation and two validation cohorts, respectively, were 84, 77, and 68% (LCPI 1: score 9); 61, 61, and 42% (LCPI 2: score 10-13); 33, 32, and 14% (LCPI 3: score 14-16); 7, 16, and 5% (LCPI 4: score 15). Discrimination (c-statistic) was 0.74 for the derivation cohort, 0.72 and 0.71 for the two validation cohorts. The LCPI contributes additional prognostic information, which may be used to counsel patients, guide trial eligibility or design, or standardise mortality risk for epidemiological analyses. While there are several recognised factors that predict outcome for patients diagnosed with non-small-cell lung cancer (NSCLC), our ability to risk-stratify individual patients at the time of their diagnosis is limited. Lung cancer prognostication remains rudimentary and to date no single model has demonstrated superior performance, clinical utility, or widespread global uptake. The objectives of this study were to identify baseline patient and disease variables associated with overall survival (OS) in patients with newly diagnosed NSCLC, and to derive and subsequently validate a simple and generalisable prognostic model. Using the LCPI and m-LCPI scoring systems, each one-point increase was associated with 2-fold increased mortality risk. The ability of the LCPI to stratify patients according to prognostic group was maintained (and superior to stage alone) in subgroup analyses for early stage patients likely receiving curative surgery or curative chemoradiotherapy and for advanced stage patients likely receiving non-curative chemotherapy, radiotherapy, or molecular therapies. The proposed LCPI is a compound scoring system, including established and novel variables, for the prediction of survival following NSCLC diagnosis. The LCPI is simple and generalisable, using data easily and routinely obtained during diagnostic evaluations. Importantly, the model was developed from prospectively collected data and we provide external validation to demonstrate superior survival prediction compared to stage alone (current best practice), and consistent predictive performance across two independent cohorts.

• #11 Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer | British Journal of Cancer

  https://www.nature.com/articles/bjc2017232

  The ability of the LCPI to stratify patients according to prognostic group was maintained (and superior to stage alone) in subgroup analyses for early stage patients likely receiving curative surgery or curative chemoradiotherapy (stage IIIIA) and for advanced stage patients likely receiving non-curative chemotherapy, radiotherapy, or molecular therapies (stage IIIBIV). […] Lung Cancer Prognostic Index discrimination performance on external validation (Harrells c 0.72 and 0.71) was similar to a Surveillance, Epidemiology, and End Results Program (SEER) model (Harrells c 0.690.72), but overcomes several limitations. […] The role of tumour grade was considered differently in the SEER and our models, deliberately omitted from our cohorts due to reporting subjectivity and limited application in real-world clinical settings. […] The validated LCPI from our study has current real-world relevance for routine care contributing additional prognostic information, which may be utilised in conjunction with validated tools and evidence-based patient management guidelines.

• #12 A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

  https://atm.amegroups.org/article/view/81325/html

  Genomics has played a significant part in the prognosis and treatment management of patients with NSCLC by clarifying the role of driver genes and providing information on mutation and gene expression information. […] Researchers have explored the prognosis prediction value of somatic mutations, and many studies have revealed the prognostic role of some individual mutations. […] Another significant prognostic biomarker resulting from genomics analysis is tumor mutational burden (TMB), which is defined as the number of nonsynonymous mutations, especially in immunotherapy patients. […] The wide diversity of tumor mutations is a great challenge for researchers developing effective biomarkers for diagnosis or prognosis because of the large proportions of the genome needed to be examined to provide adequate sensitivity.

• #13 A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

  https://atm.amegroups.org/article/view/81325/html

  Genomics has played a significant part in the prognosis and treatment management of patients with NSCLC by clarifying the role of driver genes and providing information on mutation and gene expression information. […] Researchers have explored the prognosis prediction value of somatic mutations, and many studies have revealed the prognostic role of some individual mutations. […] Another significant prognostic biomarker resulting from genomics analysis is tumor mutational burden (TMB), which is defined as the number of nonsynonymous mutations, especially in immunotherapy patients. […] The wide diversity of tumor mutations is a great challenge for researchers developing effective biomarkers for diagnosis or prognosis because of the large proportions of the genome needed to be examined to provide adequate sensitivity.

• #14 DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma | BMC Pulmonary Medicine | Full Text

  https://bmcpulmmed.biomedcentral.com/articles/10.1186/s12890-022-01924-0

  Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. […] Prognostic analysis showed a significant difference among the two groups (P=0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. […] The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. […] The use of DNA methylation markers can do us a better prognosis and predict therapy response, thereby extending patient survival. […] We identified 205 independents prognostic CpG loci and 237 corresponding promoter genes. […] The model can help identify novel biomarkers, predict prognosis, clinically diagnose and manage patients with different distinct subtypes of LUAD.

• #15 DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma | BMC Pulmonary Medicine | Full Text

  https://bmcpulmmed.biomedcentral.com/articles/10.1186/s12890-022-01924-0

  Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis. […] Prognostic analysis showed a significant difference among the two groups (P=0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis. […] The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD. […] The use of DNA methylation markers can do us a better prognosis and predict therapy response, thereby extending patient survival. […] We identified 205 independents prognostic CpG loci and 237 corresponding promoter genes. […] The model can help identify novel biomarkers, predict prognosis, clinically diagnose and manage patients with different distinct subtypes of LUAD.

• #16 Predictive Analysis of Lung Cancer Recurrence | SpringerLink

  https://link.springer.com/chapter/10.1007/978-3-642-22709-7_27

  The paper is about the predictive analysis of lung cancer recurrence based on non-small cell lung cancer carcinoma gene expression data using data mining and machine learning techniques. […] Prediction of cancer recurrence has been a challenging problem for many researchers. […] A hybrid method for gene selection and classification is used for statistical analysis of lung cancer recurrence. […] Wan, Y.-W., Sabbagh, E., Raese, R., Qian, Y., Luo, D., Denvir, J., Vallyathan, V., Castranova, V., Guo, N.L.: Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction. […] Beane, J., Sebastiani, P., Whitfield, T.H., Steiling, K., Dumas, Y.-M., Lenburg, M.E., Spira, A.: A Prediction Model for Lung Cancer Diagnosis that Integrates Genomic and Clinical Features.

• #17 Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012222

  Lung cancer remains the leading cause of cancer-related deaths worldwide. The recurrence rate ranges from 35-50% among early stage non-small cell lung cancer patients. To date, there is no fully-validated and clinically applied prognostic gene signature for personalized treatment. […] The results demonstrate the clinical utility of the identified gene signature in prognostic categorization. With this 12-gene risk score algorithm, early stage patients at high risk for tumor recurrence could be identified for adjuvant chemotherapy; whereas stage I and II patients at low risk could be spared the toxic side effects of chemotherapeutic drugs. […] This study presents a combinatorial gene selection system for the identification of a 12-gene lung cancer prognostic signature. This 12-gene signature is more accurate compared with previously published signatures in a multi-institutional study of lung adenocarcinoma (n=442). This 12-gene signature could identify stage I and stage II patients who might benefit from adjuvant chemotherapy and who could be spared of it.

• #18 Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012222

  The 12-gene risk score is more significant (hazard ratio=4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. […] The 12-gene signature could be used to select early stage lung adenocarcinoma patients at high risk for tumor recurrence for adjuvant chemotherapy. Meanwhile, it may spare stage I and II low-risk patients from unnecessary chemotherapy. […] The 12-gene signature has the potential to be used to inform physicians which anticancer drugs should be used in treating a particular patient.

• #19 Hybrid Models Identified a 12-Gene Signature for Lung Cancer Prognosis and Chemoresponse Prediction | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012222

  The 12-gene risk score is more significant (hazard ratio=4.19, 95% CI: [2.08, 8.46]) than other commonly used clinical factors except tumor stage (III vs. I) in multivariate Cox analyses. […] The 12-gene signature could be used to select early stage lung adenocarcinoma patients at high risk for tumor recurrence for adjuvant chemotherapy. Meanwhile, it may spare stage I and II low-risk patients from unnecessary chemotherapy. […] The 12-gene signature has the potential to be used to inform physicians which anticancer drugs should be used in treating a particular patient.

• #20 Predicting survival and trial outcome in non-small cell lung cancer integrating tumor and blood markers kinetics with machine learning | medRxiv

  https://www.medrxiv.org/content/10.1101/2023.09.26.23296135v4

  Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. […] We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics and four on-treatment simple blood markers (albumin, CRP, lactate dehydrogenase and neutrophils). […] Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase 2 trials (533 patients), kML was validated on the two arms of a phase 3 trial (ICI and chemotherapy, 377 and 354 patients). […] It outperformed the current state-of-the-art for individual predictions with a test set c-index of 0.790, a 12-months survival accuracy of 78.7% and a hazard ratio of 25.2 (95% CI: 10.4 61.3, p 0.0001) to identify long-term survivors. […] Critically, kML predicted the success of the phase 3 trial using only 25 weeks of on-study data (predicted HR = 0.814 (0.64 0.994) versus final study HR = 0.778 (0.65 0.931)). […] Our model constitutes a valuable approach to support personalized medicine and drug development.

• #21 Predicting survival and trial outcome in non-small cell lung cancer integrating tumor and blood markers kinetics with machine learning | medRxiv

  https://www.medrxiv.org/content/10.1101/2023.09.26.23296135v4.full-text

  Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. […] We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics and four on-treatment simple blood markers (albumin, CRP, lactate dehydrogenase and neutrophils). […] It outperformed the current state-of-the-art for individual predictions with a test set c-index of 0.790, a 12-months survival accuracy of 78.7% and a hazard ratio of 25.2 (95% CI: 10.4 61.3, p 0.0001) to identify long-term survivors. […] Our model constitutes a valuable approach to support personalized medicine and drug development. […] Altogether, there is a need for better and validated predictive models of OS for both personalized health care (individual predictions) and drug development (trial predictions).

• #22 Predicting survival and trial outcome in non-small cell lung cancer integrating tumor and blood markers kinetics with machine learning | medRxiv

  https://www.medrxiv.org/content/10.1101/2023.09.26.23296135v4.full-text

  The kML model outperformed current state-of-the-art methods based on either baseline or on-treatment data alone, utilizing only routine clinical information, with a c-index of 0.79 and an accuracy of 78% for prediction of 12-month survival, on the test dataset. […] Together, these results demonstrate important predictive performances of overall survival following ATZ treatment using the kML model.

• #23 Predicting survival and trial outcome in non-small cell lung cancer integrating tumor and blood markers kinetics with machine learning | medRxiv

  https://www.medrxiv.org/content/10.1101/2023.09.26.23296135v4

  Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. […] We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics and four on-treatment simple blood markers (albumin, CRP, lactate dehydrogenase and neutrophils). […] Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase 2 trials (533 patients), kML was validated on the two arms of a phase 3 trial (ICI and chemotherapy, 377 and 354 patients). […] It outperformed the current state-of-the-art for individual predictions with a test set c-index of 0.790, a 12-months survival accuracy of 78.7% and a hazard ratio of 25.2 (95% CI: 10.4 61.3, p 0.0001) to identify long-term survivors. […] Critically, kML predicted the success of the phase 3 trial using only 25 weeks of on-study data (predicted HR = 0.814 (0.64 0.994) versus final study HR = 0.778 (0.65 0.931)). […] Our model constitutes a valuable approach to support personalized medicine and drug development.

• #24 Predicting survival and trial outcome in non-small cell lung cancer integrating tumor and blood markers kinetics with machine learning | medRxiv

  https://www.medrxiv.org/content/10.1101/2023.09.26.23296135v4.full-text

  Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. […] We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics and four on-treatment simple blood markers (albumin, CRP, lactate dehydrogenase and neutrophils). […] It outperformed the current state-of-the-art for individual predictions with a test set c-index of 0.790, a 12-months survival accuracy of 78.7% and a hazard ratio of 25.2 (95% CI: 10.4 61.3, p 0.0001) to identify long-term survivors. […] Our model constitutes a valuable approach to support personalized medicine and drug development. […] Altogether, there is a need for better and validated predictive models of OS for both personalized health care (individual predictions) and drug development (trial predictions).

• #25 Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets

  https://www.mdpi.com/2072-6694/17/2/285

  This study presents a novel semi-supervised learning (SSL) approach that improves lung cancer survival predictions by incorporating diverse datasets, including head and neck cancer (HNCa), alongside handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans. […] Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs). […] Accurate prognostic models are needed to enhance clinical decision-making, with overall survival (OS) often used to assess treatment efficacy. […] While supervised learning (SL) methods are effective, acquiring labeled data is costly and difficult.

• #26 Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets

  https://www.mdpi.com/2072-6694/17/2/285

  SSL has shown promise in LCa prediction, outperforming fully supervised models, and it has enhanced diagnostics in malignant diseases. […] This study explores the benefits of using diverse datasets, including HNCa, in an SSL approach with pseudo-labeling, alongside LCa datasets, to improve prediction performance compared to SL focused only on LCa. […] The SSL strategy outperformed the SL method (p << 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). [...] In the survival prediction tasks, the SSL approach was unsuitable due to the requirement of follow-up times, so we employed an SL approach using 199 LCa patients. [...] The HRF framework achieved an average C-index of 0.79 ± 0.08, while the DRF framework showed a slightly higher C-index of 0.80 ± 0.1, both with highly significant log rank p-values below 0.001. [...] This study showed that integrating SSL with DRFs reduces dependence on costly modalities like PET, enabling CT alone to achieve high predictive performance.

• #27 Enhanced Lung Cancer Survival Prediction Using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets

  https://www.mdpi.com/2072-6694/17/2/285

  SSL has shown promise in LCa prediction, outperforming fully supervised models, and it has enhanced diagnostics in malignant diseases. […] This study explores the benefits of using diverse datasets, including HNCa, in an SSL approach with pseudo-labeling, alongside LCa datasets, to improve prediction performance compared to SL focused only on LCa. […] The SSL strategy outperformed the SL method (p << 0.001), achieving an average accuracy of 0.85 ± 0.05 with DRFs from PET and PCA + Multi-Layer Perceptron (MLP), compared to 0.69 ± 0.06 for the SL strategy using DRFs from CT and PCA + Light Gradient Boosting (LGB). [...] In the survival prediction tasks, the SSL approach was unsuitable due to the requirement of follow-up times, so we employed an SL approach using 199 LCa patients. [...] The HRF framework achieved an average C-index of 0.79 ± 0.08, while the DRF framework showed a slightly higher C-index of 0.80 ± 0.1, both with highly significant log rank p-values below 0.001. [...] This study showed that integrating SSL with DRFs reduces dependence on costly modalities like PET, enabling CT alone to achieve high predictive performance.

• #28 Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy

  https://www.mdpi.com/2072-6694/14/23/5931

  Lung cancer is the leading cause of cancer-related death worldwide. Non-small cell lung cancer (NSCLC) accounts for 80–90% of primary lung cancers, and is mostly diagnosed at an advanced stage with prognosis remaining poor despite recent therapeutic advances. The introduction of immunotherapy for the treatment of locally advanced or metastatic non-small cell lung cancer (NSCLC) has shown an improvement in terms of overall survival and progression-free survival. However, a durable clinical benefit (DCB) (>6 months) is only achieved in 20–50% of patients. Early identification of patients likely to benefit from this treatment is therefore crucial. […] Delta-radiomics and radiomics features extracted from baseline and follow-up PET/CT images could predict outcome in NSCLC patients treated with immunotherapy and identify patients who would benefit from this new standard. These data reinforce the rationale for the use of advanced image analysis of PET/CT scans to further improve personalized treatment management in advanced NSCLC.

• #29 Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features | Nature Communications

  https://www.nature.com/articles/ncomms12474

  Computerized image processing technology has been shown to improve efficiency, accuracy and consistency in histopathology evaluations, and can provide decision support to ensure diagnostic consistency. […] In this study, we aim to improve the prognostic prediction of lung adenocarcinoma and squamous cell carcinoma patients through objective features distilled from histopathology images. […] We next investigated the prognostic values of our quantitative feature sets. Stage I adenocarcinoma patients are known to have diverse survival outcomes. […] Our model successfully distinguished shorter-term survivors from longer-term survivors in the test set. […] Our approach for survival prediction was validated with images from an independent data set (the Stanford TMA database). […] Our prognostic methodology for squamous cell carcinoma was also confirmed in the independent Stanford TMA cohort. […] In summary, we demonstrate that histopathology image classifiers based on quantitative features can successfully predict survival outcomes of lung adenocarcinoma and lung squamous cell carcinoma patients.

• #30 Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features | Nature Communications

  https://www.nature.com/articles/ncomms12474

  Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients prognoses. […] Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. […] Prompt and meticulous inspection of tumour histomorphology is critical to patient care, and determination of relevant prognostic markers is the key to personalized cancer management. […] Currently, lung cancer samples are manually evaluated for their histological features by light microscopy. However, qualitative evaluation of well-established histopathology patterns alone (such as the classification of tumour grades) is insufficient for predicting the survival outcomes of patients with lung adenocarcinoma or lung squamous cell carcinoma.

• #31 Association of muscle strength and cardiorespiratory fitness with all-cause and cancer-specific mortality in patients diagnosed with cancer: a systematic review with meta-analysis | British Journal of Sports Medicine

  https://bjsm.bmj.com/content/59/10/722

  Cancer patients diagnosed with any form of cancer and stage with high muscle strength or cardiorespiratory fitness levels had a significant reduction in the risk of all-cause mortality compared with those with low physical fitness levels. […] Physical fitness components were significant predictors of all-cause mortality in patients with advanced cancer stages as well as in lung and digestive cancers. […] Increments in cardiorespiratory fitness were associated with a significantly reduced risk of cancer-specific mortality. […] High muscle strength and CRF were significantly associated with a lower risk of all-cause mortality. […] These fitness components were especially predictive in patients with advanced cancer stages as well as in lung and digestive cancers. […] This highlights the importance of assessing fitness measures for predicting mortality in cancer patients. […] Implementing tailored exercise prescriptions to enhance muscle strength and CRF in patients with cancer may help to reduce cancer-related mortality.

• #32 Association of muscle strength and cardiorespiratory fitness with all-cause and cancer-specific mortality in patients diagnosed with cancer: a systematic review with meta-analysis | British Journal of Sports Medicine

  https://bjsm.bmj.com/content/59/10/722

  Cancer patients diagnosed with any form of cancer and stage with high muscle strength or cardiorespiratory fitness levels had a significant reduction in the risk of all-cause mortality compared with those with low physical fitness levels. […] Physical fitness components were significant predictors of all-cause mortality in patients with advanced cancer stages as well as in lung and digestive cancers. […] Increments in cardiorespiratory fitness were associated with a significantly reduced risk of cancer-specific mortality. […] High muscle strength and CRF were significantly associated with a lower risk of all-cause mortality. […] These fitness components were especially predictive in patients with advanced cancer stages as well as in lung and digestive cancers. […] This highlights the importance of assessing fitness measures for predicting mortality in cancer patients. […] Implementing tailored exercise prescriptions to enhance muscle strength and CRF in patients with cancer may help to reduce cancer-related mortality.

• #33 Association of muscle strength and cardiorespiratory fitness with all-cause and cancer-specific mortality in patients diagnosed with cancer: a systematic review with meta-analysis | British Journal of Sports Medicine

  https://bjsm.bmj.com/content/59/10/722

  Cancer patients diagnosed with any form of cancer and stage with high muscle strength or cardiorespiratory fitness levels had a significant reduction in the risk of all-cause mortality compared with those with low physical fitness levels. […] Physical fitness components were significant predictors of all-cause mortality in patients with advanced cancer stages as well as in lung and digestive cancers. […] Increments in cardiorespiratory fitness were associated with a significantly reduced risk of cancer-specific mortality. […] High muscle strength and CRF were significantly associated with a lower risk of all-cause mortality. […] These fitness components were especially predictive in patients with advanced cancer stages as well as in lung and digestive cancers. […] This highlights the importance of assessing fitness measures for predicting mortality in cancer patients. […] Implementing tailored exercise prescriptions to enhance muscle strength and CRF in patients with cancer may help to reduce cancer-related mortality.

• #34 Lung Cancer Prognosis | How to Understand Your Prognosis

  https://www.lungcancercenter.com/treatment/prognosis/

  Below are just a few of the factors that affect a lung cancer prognosis. […] Predictive factors affect how well a patient’s cancer will respond to a particular treatment. These factors are then used by a cancer care team to develop targeted therapies. […] Hence, the presence of some genetic markers can mean a more favorable prognosis for the individual.

• #35 Cuproptosis-related lncRNA predict prognosis and immune response of lung adenocarcinoma | World Journal of Surgical Oncology | Full Text

  https://wjso.biomedcentral.com/articles/10.1186/s12957-022-02727-7

  We found a significant decrease in the survival of patients with high TMB (P0.05) and an increase in TP53 and TTN expression in patients at high risk. […] Our results are consistent with those of previous studies, suggesting that TP53 and TTN can act as targets for cancer immunotherapy. […] In the present study, we found that the TIDE score in the low-risk group was higher than that in the high-risk group; however, the difference in the effect of immunotherapy on high-risk patients with LUAD and low-risk patients with LUAD still needs to be further explored. […] This study provides new insights into the prediction of survival of patients with LUAD and the efficacy of clinical treatment.

• #36 Cuproptosis-related lncRNA predict prognosis and immune response of lung adenocarcinoma | World Journal of Surgical Oncology | Full Text

  https://wjso.biomedcentral.com/articles/10.1186/s12957-022-02727-7

  We found a significant decrease in the survival of patients with high TMB (P0.05) and an increase in TP53 and TTN expression in patients at high risk. […] Our results are consistent with those of previous studies, suggesting that TP53 and TTN can act as targets for cancer immunotherapy. […] In the present study, we found that the TIDE score in the low-risk group was higher than that in the high-risk group; however, the difference in the effect of immunotherapy on high-risk patients with LUAD and low-risk patients with LUAD still needs to be further explored. […] This study provides new insights into the prediction of survival of patients with LUAD and the efficacy of clinical treatment.

• #37 Lung Cancer Survival Rates | 5-Year Survival Rates for Lung Cancer | American Cancer Society

  https://www.cancer.org/cancer/types/lung-cancer/detection-diagnosis-staging/survival-rates.html

  Survival rates can give you an idea of what percentage of people with the same type and stage of cancer are still alive a certain amount of time (usually 5 years) after they were diagnosed. […] Survival rates cant tell you how long you will live, but they may help give you a better understanding of how likely it is that your treatment will be successful. […] Keep in mind that survival rates are estimates and are often based on previous outcomes of large numbers of people who had a specific cancer, but they cant predict what will happen in any particular persons case. […] A relative survival rate compares people with the same type and stage of cancer to people in the overall population. […] The SEER database tracks 5-year relative survival rates for non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in the United States, based on how far the cancer has spread.

• #38 Lung Cancer Survival Rates | 5-Year Survival Rates for Lung Cancer | American Cancer Society

  https://www.cancer.org/cancer/types/lung-cancer/detection-diagnosis-staging/survival-rates.html

  Survival rates can give you an idea of what percentage of people with the same type and stage of cancer are still alive a certain amount of time (usually 5 years) after they were diagnosed. […] Survival rates cant tell you how long you will live, but they may help give you a better understanding of how likely it is that your treatment will be successful. […] Keep in mind that survival rates are estimates and are often based on previous outcomes of large numbers of people who had a specific cancer, but they cant predict what will happen in any particular persons case. […] A relative survival rate compares people with the same type and stage of cancer to people in the overall population. […] The SEER database tracks 5-year relative survival rates for non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in the United States, based on how far the cancer has spread.

• #39 Lung Cancer Survival Rates | 5-Year Survival Rates for Lung Cancer | American Cancer Society

  https://www.cancer.org/cancer/types/lung-cancer/detection-diagnosis-staging/survival-rates.html

  Survival rates can give you an idea of what percentage of people with the same type and stage of cancer are still alive a certain amount of time (usually 5 years) after they were diagnosed. […] Survival rates cant tell you how long you will live, but they may help give you a better understanding of how likely it is that your treatment will be successful. […] Keep in mind that survival rates are estimates and are often based on previous outcomes of large numbers of people who had a specific cancer, but they cant predict what will happen in any particular persons case. […] A relative survival rate compares people with the same type and stage of cancer to people in the overall population. […] The SEER database tracks 5-year relative survival rates for non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in the United States, based on how far the cancer has spread.

• #40 Lung Cancer Survival Rates | 5-Year Survival Rates for Lung Cancer | American Cancer Society

  https://www.cancer.org/cancer/types/lung-cancer/detection-diagnosis-staging/survival-rates.html

  Survival rates can give you an idea of what percentage of people with the same type and stage of cancer are still alive a certain amount of time (usually 5 years) after they were diagnosed. […] Survival rates cant tell you how long you will live, but they may help give you a better understanding of how likely it is that your treatment will be successful. […] Keep in mind that survival rates are estimates and are often based on previous outcomes of large numbers of people who had a specific cancer, but they cant predict what will happen in any particular persons case. […] A relative survival rate compares people with the same type and stage of cancer to people in the overall population. […] The SEER database tracks 5-year relative survival rates for non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) in the United States, based on how far the cancer has spread.

• #41 Lung Cancer Survival Rates | 5-Year Survival Rates for Lung Cancer | American Cancer Society

  https://www.cancer.org/cancer/types/lung-cancer/detection-diagnosis-staging/survival-rates.html

  These numbers apply only to the stage of the cancer when it is first diagnosed. They do not apply later on if the cancer grows, spreads, or comes back after treatment. […] These numbers dont take everything into account. Survival rates are grouped based on how far the cancer has spread. But other factors, such as the subtype of NSCLC, gene changes in the cancer cells, your age and overall health, and how well the cancer responds to treatment, can also affect your outlook. […] People now being diagnosed with NSCLC or SCLC may have a better outlook than these numbers show. Treatments have improved over time, and these numbers are based on people who were diagnosed and treated at least 5 years earlier.

• #42 Lung Cancer Survival Rates | 5-Year Survival Rates for Lung Cancer | American Cancer Society

  https://www.cancer.org/cancer/types/lung-cancer/detection-diagnosis-staging/survival-rates.html

  These numbers apply only to the stage of the cancer when it is first diagnosed. They do not apply later on if the cancer grows, spreads, or comes back after treatment. […] These numbers dont take everything into account. Survival rates are grouped based on how far the cancer has spread. But other factors, such as the subtype of NSCLC, gene changes in the cancer cells, your age and overall health, and how well the cancer responds to treatment, can also affect your outlook. […] People now being diagnosed with NSCLC or SCLC may have a better outlook than these numbers show. Treatments have improved over time, and these numbers are based on people who were diagnosed and treated at least 5 years earlier.

• #43 More Accurate Prognosis for Lung Cancer Possible With New Test, Study Shows < Yale School of Medicine

  https://medicine.yale.edu/news-article/more-accurate-prognosis-for-lung-cancer-possible-with-new-test/

  Lung cancer remains one of the most common and deadliest cancers; however, it is currently difficult for physicians to provide patients with an accurate prognosis. […] Tumor stage is the standard measure for assessing a lung cancer patients risk of progression and death, and is important to guide chemotherapy decisions, said Dhruva Biswas, MBBS, PhD, associate research scientist (cardiovascular medicine) and a member of the Cardiovascular Data Science (CarDS) Lab. Yet even among supposedly low-risk, Stage I patients, up to 30 percent die within five years. A validated molecular biomarker could improve these outcomes. […] In a new study published in Nature Cancer, the researchers found that ORACLE can better predict patient survival than the current clinical standard, even at the earliest stage of cancer.

• #44 More Accurate Prognosis for Lung Cancer Possible With New Test, Study Shows < Yale School of Medicine

  https://medicine.yale.edu/news-article/more-accurate-prognosis-for-lung-cancer-possible-with-new-test/

  The study found that ORACLE more accurately predicted which patients cancer was likely to spread and how well a patients tumor would respond to certain chemotherapy drugs. […] While more validation is needed, we hope that doctors could one day use ORACLE to help develop a more accurate surveillance schedule, select targeted cancer therapies, and ultimately help patients live longer. […] To further validate the test, the researchers plan to conduct a randomized controlled trial to determine if the test improves patient survival.

• #45 More Accurate Prognosis for Lung Cancer Possible With New Test, Study Shows < Yale School of Medicine

  https://medicine.yale.edu/news-article/more-accurate-prognosis-for-lung-cancer-possible-with-new-test/

  The study found that ORACLE more accurately predicted which patients cancer was likely to spread and how well a patients tumor would respond to certain chemotherapy drugs. […] While more validation is needed, we hope that doctors could one day use ORACLE to help develop a more accurate surveillance schedule, select targeted cancer therapies, and ultimately help patients live longer. […] To further validate the test, the researchers plan to conduct a randomized controlled trial to determine if the test improves patient survival.

• #46 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  Proteins are fundamental biomolecules composed of one or more chains of amino acids. […] The proposed methodology was applied to proteins targeted by drugs used in lung cancer treatment, a disease that remains the leading cause of cancer-related mortality worldwide. […] Significant sequence patterns were identified, establishing connections between drug-target proteins and proteins associated with lung cancer. […] By employing this approach, relationships between lung cancer drug-target proteins and proteins associated with four additional cancer types were uncovered. […] Furthermore, validation through an extensive literature review confirmed biological links between lung cancer drug-target proteins and proteins related to other malignancies, reinforcing the potential of this methodology for identifying new therapeutic applications.

• #47 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  Non-small cell lung cancer (NSCLC) makes up 85-90% of lung cancer cases, with adenocarcinoma (AC) and squamous cell carcinoma (SCC) being the most common subtypes. […] Treatment options for NSCLC include surgery, chemotherapy, immunotherapy, and targeted therapies, which are personalized for each patient outcome. […] Targeted therapies, such as monoclonal antibodies and tyrosine kinase inhibitors, are especially effective for advanced or metastatic cases, improving patient outcomes. […] Lung cancer treatments provide a solid foundation for exploring new therapies through drug repositioning (DR) strategies, which have gained importance in recent years. […] Drug repurposing involves using existing drugs to treat diseases for which they were not originally developed. […] This approach saves time, reduces costs, and offers greater safety, as these drugs have already been evaluated in patients.

• #48 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  Non-small cell lung cancer (NSCLC) makes up 85-90% of lung cancer cases, with adenocarcinoma (AC) and squamous cell carcinoma (SCC) being the most common subtypes. […] Treatment options for NSCLC include surgery, chemotherapy, immunotherapy, and targeted therapies, which are personalized for each patient outcome. […] Targeted therapies, such as monoclonal antibodies and tyrosine kinase inhibitors, are especially effective for advanced or metastatic cases, improving patient outcomes. […] Lung cancer treatments provide a solid foundation for exploring new therapies through drug repositioning (DR) strategies, which have gained importance in recent years. […] Drug repurposing involves using existing drugs to treat diseases for which they were not originally developed. […] This approach saves time, reduces costs, and offers greater safety, as these drugs have already been evaluated in patients.

• #49 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  Non-small cell lung cancer (NSCLC) makes up 85-90% of lung cancer cases, with adenocarcinoma (AC) and squamous cell carcinoma (SCC) being the most common subtypes. […] Treatment options for NSCLC include surgery, chemotherapy, immunotherapy, and targeted therapies, which are personalized for each patient outcome. […] Targeted therapies, such as monoclonal antibodies and tyrosine kinase inhibitors, are especially effective for advanced or metastatic cases, improving patient outcomes. […] Lung cancer treatments provide a solid foundation for exploring new therapies through drug repositioning (DR) strategies, which have gained importance in recent years. […] Drug repurposing involves using existing drugs to treat diseases for which they were not originally developed. […] This approach saves time, reduces costs, and offers greater safety, as these drugs have already been evaluated in patients.

• #50 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  Non-small cell lung cancer (NSCLC) makes up 85-90% of lung cancer cases, with adenocarcinoma (AC) and squamous cell carcinoma (SCC) being the most common subtypes. […] Treatment options for NSCLC include surgery, chemotherapy, immunotherapy, and targeted therapies, which are personalized for each patient outcome. […] Targeted therapies, such as monoclonal antibodies and tyrosine kinase inhibitors, are especially effective for advanced or metastatic cases, improving patient outcomes. […] Lung cancer treatments provide a solid foundation for exploring new therapies through drug repositioning (DR) strategies, which have gained importance in recent years. […] Drug repurposing involves using existing drugs to treat diseases for which they were not originally developed. […] This approach saves time, reduces costs, and offers greater safety, as these drugs have already been evaluated in patients.

• #51 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  Non-small cell lung cancer (NSCLC) makes up 85-90% of lung cancer cases, with adenocarcinoma (AC) and squamous cell carcinoma (SCC) being the most common subtypes. […] Treatment options for NSCLC include surgery, chemotherapy, immunotherapy, and targeted therapies, which are personalized for each patient outcome. […] Targeted therapies, such as monoclonal antibodies and tyrosine kinase inhibitors, are especially effective for advanced or metastatic cases, improving patient outcomes. […] Lung cancer treatments provide a solid foundation for exploring new therapies through drug repositioning (DR) strategies, which have gained importance in recent years. […] Drug repurposing involves using existing drugs to treat diseases for which they were not originally developed. […] This approach saves time, reduces costs, and offers greater safety, as these drugs have already been evaluated in patients.

• #52 Finding patterns in lung cancer protein sequences for drug repurposing | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322546

  It has a success rate of over 30%, compared to just 2% in new drug development. […] The primary aim of this study is to propose that the key patterns identified in drug target proteins for NSCLC treatment can be used as a foundation for discovering new opportunities for drug repurposing. […] The relevant patterns found within the drugs target proteins for NSCLC were first searched for within the sequences of proteins specific to NSCLC. […] This analysis aimed to determine whether the patterns relevant to lung cancer treatments also applied to the disease itself. […] The identification of characteristic patterns in the target proteins of NSCLC treatments paves the way for a novel drug repurposing strategy. […] This study proposes that patterns occurring with frequencies of 5% or 10% in the amino acid sequences of NSCLC drug target proteins could be crucial in facilitating the use of these drugs for treating other diseases.

• #53 “How Long Have I Got?” in Stage IV NSCLC Patients With at Least 3 Months Up to 10 Years Survival, Accuracy of Long-, Intermediate-, and Short-Term Survival Prediction Is Not Good Enough to Answer This Question

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8724440/

  Most lung cancer patients worldwide [stage IV nonsmall cell lung cancer (NSCLC)] have a poor survival: 25%30% die 3 months. […] We evaluated in a large group of 737 stage IV NSCLC patients surviving 3.2120.0 months, the accuracies of short- and long-term survival predictive values of baseline factors, radiotherapy (RT), platinum-based chemotherapy (PBT), and tyrosine kinase inhibitor targeted therapy (TKI-TT). […] The median survival (16.1 months) of 47 patients who refused PBT, RT, and TKI-TT was significantly worse than those with RT, PBT, and/or TKI-TT (23.3 months, HR = 1.60, 95% CI = 1.062.42, p = 0.04). […] In stage IV NSCLC patients with 3 months survival, baseline features, and systemic therapeutic modalities have strong survival predictive value but do not accurately identify short- and long-term survivors.

• #54 “How Long Have I Got?” in Stage IV NSCLC Patients With at Least 3 Months Up to 10 Years Survival, Accuracy of Long-, Intermediate-, and Short-Term Survival Prediction Is Not Good Enough to Answer This Question

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8724440/

  The predictive value of other features and interventions discussed should be investigated in the worldwide very large group of stage IV NSCLC patients with 3 months survival. […] However, surprisingly, truly little scientific attention has been paid to the factors causing the good prognosis in the worldwide 35,000105,000 stage IV NSCLC long survivors. […] The question from individual stage IV NSCLC patients surviving at least 3 months: How long do I still have?, currently cannot be accurately answered. […] Despite the strongly significant predictive value of several of the baseline and treatment modalities, none of the features (neither alone nor in combination) can accurately identify subgroups with a short survival (11.7 months), and relatively long survival (38 months). […] We conclude that the question from stage IV NSCLC patients with 3 months survival Doctor, how long do I have? cannot yet be answered reliably.

• #55 “How Long Have I Got?” in Stage IV NSCLC Patients With at Least 3 Months Up to 10 Years Survival, Accuracy of Long-, Intermediate-, and Short-Term Survival Prediction Is Not Good Enough to Answer This Question

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8724440/

  The predictive value of other features and interventions discussed should be investigated in the worldwide very large group of stage IV NSCLC patients with 3 months survival. […] However, surprisingly, truly little scientific attention has been paid to the factors causing the good prognosis in the worldwide 35,000105,000 stage IV NSCLC long survivors. […] The question from individual stage IV NSCLC patients surviving at least 3 months: How long do I still have?, currently cannot be accurately answered. […] Despite the strongly significant predictive value of several of the baseline and treatment modalities, none of the features (neither alone nor in combination) can accurately identify subgroups with a short survival (11.7 months), and relatively long survival (38 months). […] We conclude that the question from stage IV NSCLC patients with 3 months survival Doctor, how long do I have? cannot yet be answered reliably.

• #56 “How Long Have I Got?” in Stage IV NSCLC Patients With at Least 3 Months Up to 10 Years Survival, Accuracy of Long-, Intermediate-, and Short-Term Survival Prediction Is Not Good Enough to Answer This Question

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8724440/

  The predictive value of other features and interventions discussed should be investigated in the worldwide very large group of stage IV NSCLC patients with 3 months survival. […] However, surprisingly, truly little scientific attention has been paid to the factors causing the good prognosis in the worldwide 35,000105,000 stage IV NSCLC long survivors. […] The question from individual stage IV NSCLC patients surviving at least 3 months: How long do I still have?, currently cannot be accurately answered. […] Despite the strongly significant predictive value of several of the baseline and treatment modalities, none of the features (neither alone nor in combination) can accurately identify subgroups with a short survival (11.7 months), and relatively long survival (38 months). […] We conclude that the question from stage IV NSCLC patients with 3 months survival Doctor, how long do I have? cannot yet be answered reliably.

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