bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting disease progression and mortality in metastatic colorectal cancer patients (mCRC) through an artificial intelligence-based analytical tool.
Authors: Galmarini Carlos Maria , Lucius Maximiliano 
Published: June 2021

1549 Background: Predicting the clinical course of metastatic disease remains a key challenge in CRC. Estimating prognosis of these late-stage patients can avoid undertreatment or overtreatment and also guide the follow-up intensity. This study has investigated the ability of an artificial intelligence-based analytical tool to identify those mCRC patients with high risk of disease progression and mortality based on their clinical parameters. Methods: Through www.projectdatasphere.org we accessed datasets of two randomised phase III trials including chemo-naïve (NCT00364013, n = 1183 patients) and chemo-refractory (NCT00113763, N = 483) mCRC patients. We generated synthetic fingerprints (SF) for each patient through the integration of 44 clinical features (demographics, anthropometrics, medical history, blood tests and treatment characteristics) collected, respectively, during the screening phase and the first month of inclusion in each trial. These SF were then input into a deep learning framework (DLF) to identify subgroup of patients based on their similarities. The resultant clusters were correlated with progression-free survival (PFS) and overall survival (OS). Results: After discarding missing data, 861 chemo-naïve and 341 chemo-resistant mCRC patients were eligible for the study. In the chemo-naïve cohort, the SF/DLF system was able to detect two different clusters: C0 (n = 31) and C1 (n = 830). Patients in C0 had a higher risk of progression (median PFS 6.2 months vs. 9.1 months; hazard ratio 1.83, 95% CI 1.16-2.88; p = 0.008) and death (median OS 13.2 months vs. 20.1 months; hazard ratio 2.84, 95% CI 1.68-4.80; p < 0.001) compared to patients in C1. When applied to the chemo-resistant cohort, the SF/DLF system was again able to identify two different clusters: P0 (n = 159) and P1 (n = 182). Patients in P0 had a higher risk of progression (median PFS 1.7 months vs. 1.8 months; hazard ratio 1.32, 95% CI 1.05-1.67; p < 0.001) and death (median OS 6.1 months vs. 6.8 months; hazard ratio 1.34, 95% CI 1.07-1.68; p = 0.01) compared to patients in P1. In both cases, feature contribution analysis showed that major differences between clusters were related to clinical status, anthropometrics and haematological and biochemistry tests. Conclusions: Our SF/DLF system can identify mCRC subtypes based on distinct clinical features that correlate with higher risk of progression and death. Further work is required to validate this approach as a novel prognostic biomarker tool for monitoring mCRC patients.
Efficacy analysis of a remote symptom management platform.
Authors: Kosmidis Paris A. , Lagogianni Christie , Kosmidis Thanos 
Published: June 2021

1566 Background: Quality of life of cancer patients is a critical part of cancer care. Symptom management is evolving as a multidisciplinary approach, and is increasingly delivered through a combination of physical and remote interactions. CareAcross is an online platform offering personalized, guidelines-based support to cancer patients, that complements physicians’ support and enables remote monitoring. This analysis investigated the improvement in the quality of life of cancer patients delivered through such remote support. Methods: Patients engage with an online interactive platform to receive personalized support based on a variety of parameters, through algorithms incorporating their exact diagnoses, treatments and comorbidities, and more. For symptom management, patients report the presence of specific side-effects via brief questionnaires; for each side-effect reported, they receive tailored support (text and multimedia) to help overcome it. These online questionnaires are repeated periodically to capture the outcome of the supportive process, and provide additional support as necessary. A retrospective analysis evaluated the efficacy of the personalized support: each patient’s reported side-effects were compared before versus after receiving the support, hence calculating the reduced incidence. Results: 2203 patients from 8 countries, with breast, lung, prostate or colorectal cancer (1563, 404, 159 and 77, respectively) reported side-effects, received support, and updated their reports at least once. The median follow-up period was 4.9 months. The patient-reported outcomes on their quality of life revealed substantial improvement, regardless of cancer type (lowest recorded improvement = 25.7%). Commonly reported side-effects included sleep problems, dry mouth, constipation, changes in food taste, and more (see Table). Side-effects reported in specific cancer types also showed substantial improvement, including hot flushes (breast; 32.0% improvement), dyspnea (lung; 38.1%), bowel dysfunction (prostate; 80%) and others. The efficacy of the support to breast, prostate or colorectal cancer patients was similar; support to lung cancer patients exhibited the lowest efficacy (p < 0.05). Fatigue was the most common side-effect. It was also the most resistant to improvement compared to all others (p < 0.05). Conclusions: Digital remote support of cancer patients is a realistic option to improve quality of life. Randomized controlled trials can help quantify its impact on health economics, hospital admissions, resource utilization, and other aspects.[Table: see text]
Risk factors associated with skeletal-related events following denosumab cessation among patients with bone metastases from solid tumors: A real-world machine learning approach.
Authors: Stopeck Alison , Higano Celestia S. , Henry David H. , Bachmann Basia A. , Rehn Marko , Jacobson Dionna , Cadieux Benoit 
Published: June 2021

1567 Background: The anti-RANKL monoclonal antibody denosumab has been shown to be superior to the bisphosphonate zoledronate for the prevention of skeletal-related events (SREs) in patients with incident bone metastases (BM) from solid tumors (ST). Clinical guidelines recommend the use of a bone-targeting agent for SRE prevention for ≥ 2 years. However, real-world treatment patterns in the U.S. suggest that the denosumab treatment duration is often < 1 year. Applying a machine learning approach, we sought to identify risk factors associated with SRE incidence following cessation of denosumab to help inform optimal clinical SRE prevention strategies. Methods: Using the Optum PanTher Electronic Health Record repository, patients diagnosed with incident BM from a primary ST between 1 Jan 2007 and 1 Sep 2019 were evaluated for inclusion in the study. Eligible patients had to receive ≥ 2 consecutive 120 mg denosumab doses on an every 4-week (± 14 days) schedule and have a minimum follow-up ≥ 1 year after the last denosumab dose or an SRE occurring between days 84 and 365 after denosumab cessation. Extreme gradient boosting was used to develop an SRE risk prediction model evaluated on a test dataset. Impact and relative importance of available medical, clinical, and treatment factors on SRE risk following denosumab cessation were extracted from the model using Shapley additive explanations (SHAP). Univariate analyses on risk factors with the highest importance from pooled and tumor-specific models were also conducted. Results: A total of 1,414 patients (breast, n = 563 [40%]; prostate, 421 [30%]; lung, 180 [13%]; other cancers, 250 [17%]) met inclusion criteria, with a median of 253 (min, 88; max, 2726) days of denosumab treatment; 490 (35%) experienced ≥ 1 SRE following denosumab cessation. With a meaningful model performance based on an area under the receiver operating characteristic (AUROC) score of 77%, SHAP identified several significant factors that predicted an increased SRE risk following denosumab cessation, including prior SREs, shorter denosumab treatment duration, and a higher number of clinic visits as the top-ranked factors (Table). Conclusions: A machine learning approach to SRE risk factor identification may help clinicians better assess the individualized patient’s need for denosumab treatment persistence and improve patient outcomes. Results from tumor-specific groups will be presented at the meeting.[Table: see text]
A Phase 0 ‘trigger’ trial of CDK4/6 plus ERK1/2 inhibitors in recurrent glioblastoma.
Authors: Sanai Nader , Chang Yu-Wei , Margaryan Tigran , DeSantis Anita , Elliott Mackenna , Pennington-Krygier Chelsea , Hook Garry , Yoo Wonsuk , Tien An-Chi , Tovmasyan Artak , Mehta Shwetal 
Published: June 2021

2005 Background: The RB-CDK4/6 and MAPK signaling pathways are dysregulated in glioblastoma (GBM). Our recent phase 0 study of ribociclib in recurrent GBM patients suggested that CDK4/6 inhibitor monotherapy is not durable. In this ongoing, dual-drug phase 0 study (NCT04391595), we evaluate the tumor pharmacokinetics (PK) and tumor pharmacodynamics (PD) of abemaciclib, a selective CDK4/6-inhibitor, plus LY3214996, a selective ERK1/2 inhibitor, in recurrent GBM patients. Methods: Adult patients eligible for this open-label, multi-center phase 0 protocol had recurrent GBM with (1) intact RB expression, (2) >30% pERK expression, and (3) CDKN2A/B deletion or CDK4/6 amplification. Prior to a planned resection, patients received six days of abemaciclib (150mg BID) plus LY3214996 (200mg QD). In a Time-Escalation Arm, ten patients were assigned to 3-5 hour or 7-9 hour intervals from final drug dose to tumor removal. Tumor tissue (gadolinium [Gd]-enhancing and nonenhancing regions), cerebrospinal fluid (CSF), and plasma were collected. Total and unbound drug concentrations were measured using validated LC-MS/MS methods. Tumor PD effects, including RB and RSK phosphorylation, were compared to matched archival or pre-treatment biopsied tissue. A PK ‘trigger’ ( i.e., unbound concentration > 5x biochemical IC50) was set for each drug. Gd-nonenhancing tumor tissue exhibiting abemaciclib and LY3214996 concentrations in excess of their trigger threshold qualified patients for postoperative dual-drug therapy. Results: In this interim analysis, no dose-limiting toxicities were observed. In Gd-nonenhancing tumor regions, median unbound concentrations of abemaciclib (including its equipotent M2 and M20 metabolites) were 31.2 nM (3-5 hour cohort) and 25.1 nM (7-9 hour cohort). In the same tissue, median unbound concentrations of LY3214996 were 52.0 nM (3-5 hour cohort) and 10.2 nM (7-9 hour cohort). Tumor RB and RSK phosphorylation decreased in 6/10 and 2/10 patients, respectively. Gd-enhancing tumor proliferation (MIB-1) was decreased in 8/10 patients. 5/10 patients exceeded PK thresholds for both abemaciclib (12 nM) and LY3214996 (25 nM), thereby entering the study’s therapeutic expansion phase. Conclusion: Abemaciclib and LY3214996 achieve pharmacologically-relevant concentrations in Gd-non-enhancing GBM tissue and are associated with suppression of the RB pathway and tumor proliferation. Following 6 days of presurgical drug exposure, the Optimal Time Interval (OTI) for tissue sampling was 3-5 hours after the final drug dose. Based on this interim analysis, the trial will accrue an additional 25 patients at this OTI. Clinical trial information: NCT04391595.
Comparison of immune microenvironment between primary lung tumors and paired brain metastatic tumors.
Authors: Chen Likun , Wu Lihong , Li Meichen , Chen Huan , Wu Lijia , Hao Pei , He Ji 
Published: June 2021

2020 Background: Lung cancer is one of the most common causes of brain metastases (BMs) and is always associated with poor prognosis. To evaluate the characteristics of the tumor immune microenvironment in brain metastases of non-small-cell lung cancer (NSCLC), we investigated the immunophenotype of primary NSCLC and paired brain metastases. Methods: Forty-three Chinese patients with NSCLC who had BMs at presentation or during the course of their disease were admitted to the Sun Yat-Sen University Cancer Center (Guangzhou, China) from 2000 to 2019. RNA sequencing (RNA-seq) of eighty-six formalin-fixed, paraffin embedded (FFPE) samples from primary lung tumors and paired brain metastases of 43 patients was conducted to comprehensively analyze the tumor immune microenvironment. Results: Our data revealed that brain metastases compared with primary lung tumors exhibited reduced tumor infiltrating lymphocytes (TILs) (all 28 immune cell subtypes P < 0.05), lower fraction of activated CD8 T cell and effector memory CD8 T cell in total TILs (P = 0.028, P < 0.001, respectively); higher fraction of macrophage and neutrophil in total TILs (P < 0.001, P < 0.01, respectively). Comparing with the primary lung tumors, the scores of some immune related signatures, including MHC non-class signature, IFN gamma signature and T-cell-inflamed gene-expression profile (GEP) signature, were significantly lower in brain metastases (P = 0.004, P = 0.009, P = 0.004, respectively), while the score of MHC class-II signature was higher in brain metastases (P = 0.045). We found the distributions of tumor microenvironment immune types (TMIT) in brain metastases and primary lung tumors were different. Brain metastases contained significantly lower proportion of TMIT I (high PD-L1/ high CD8A) (23%) than primary lung tumors (47%) (P < 0.05). Besides, we found three immune inhibitory checkpoint molecules, namely C10orf54 (VISTA), CTLA4 and CD274 (PD-L1) were downregulated in brain metastases than in primary lung tumors (P < 0.001, P < 0.001, P = 0.034, respectively). Moreover, there was poor correlation of PD-L1 expression between paired brain metastases and primary lung tumors (R = 0.28, P = 0.068). Unsupervised hierarchic cluster analysis revealed the primary lung tumors had two distinct patterns of immune gene signatures, namely Cluster A and Cluster B, and the tumors in Cluster B were immune rich, but associated with poor prognosis (log-rank P = 0.021). Conclusions: Our work illustrates the immune landscape of brain metastases from NSCLC, and suggests that the tumor immune microenvironment in brain metastases compared with primary lung tumors is further immunosuppressed, that may help guide immunotherapeutic strategies for NSCLC brain metastases.
Outcomes of immunotherapy (ICI) alone vs tyrosine kinase inhibitors (TKI) alone versus ICI and TKI combined in renal cell carcinoma brain metastasis.
Authors: O'Shea Patrick Joseph , Tatineni Vineeth , Rauf Yasmeen , Jia Xuefei , Murphy Erin Sennett , Chao Samuel T. , Suh John H. , Peereboom David M. , Ahluwalia Manmeet Singh 
Published: June 2021

2030 Background: Renal cancer is the fourth most common cause of metastatic tumors to the brain. Tyrosine kinase inhibitors (TKIs) targeting VEGFR and other receptors, such as sunitinib, pazopanib, etc., have been used as first line for renal cell carcinoma brain metastasis (RCCBM). Immune Checkpoint Inhibitors (ICIs) targeting PD-L1 and CTLA-4 interactions, such as nivolumab and ipilimumab respectively, have also been used as first line treatment for RCCBM. However, the efficacy of TKIs alone, ICIs alone, or TKIs and ICIs combined as first line treatment has emerged as a topic of interest. Methods: Patients with RCCBM treated with either TKIs, ICIs, or both at our tertiary care center from 2010-2019 were evaluated. Overall Survival (OS) was measured from initiation of either TKI or ICI therapy to date of death or last follow up. The Cox proportional hazard model was used to determine differences in OS. Results: 218 patients with RCCBM were included. Of these, 32 were treated with ICIs alone, 112 were treated with TKIs alone, and 76 were treated with a combination of ICIs and TKIs. For ICI treatment alone the median age at diagnosis was 61 years (Interquartile range (IQR) 38-82), 72% of the patients were male, and 97% were white. For TKI treatment alone the median age at diagnosis was 58 years (IQR 37-82), 70% of the patients were male, and 92% were white. For the combination cohort the median age at diagnosis was 63 years (IQR 45-79), 69% of the patients were male, and 97% were white. OS for patients receiving ICI, TKI, and combination treatment had a median of 69.1, 42.7, and 126.0 months and a 2-year rate of 77%, 69%, and 93%, respectively. With ICI treatment as a reference, TKI treated patients had an OS hazard ratio of 1.32 (95% CI = 0.78 - 2.21, p = 0.30) and ICI/TKI combination had an OS hazard ratio of 0.52 (95% CI = 0.30 - 0.92, p = 0.024). Conclusions: A combination treatment of ICIs and TKIs was associated with an increase in OS when compared to treatment with either TKIs or ICIs alone in patients with RCCBM. These results should be interpreted cautiously due to treatment selection bias. Further studies need to be done to control for other patient variables such as performance status, number of intracranial lesions, and extra-cranial metastasis.[Table: see text]
Outcomes of first-generation versus third-generation epidermal growth factor receptor (EGFR) inhibitors in non-small cell lung cancer with brain metastases (NSCLCBM).
Authors: Tatineni Vineeth , O'Shea Patrick Joseph , Rauf Yasmeen , Jia Xuefei , Murphy Erin Sennett , Chao Samuel T. , Suh John H. , Peereboom David M. , Ahluwalia Manmeet Singh 
Published: June 2021

2031 Background: Non-small cell lung cancer (NSCLC) is the most common cause of brain metastases, with 10-30% of patients developing brain metastases. EGFR is a transmembrane glycoprotein that is mutated in up to 50% of NSCLCs. First-generation EGFR tyrosine kinase inhibitors (TKI), such as erlotinib and gefitinib, are limited by blood-brain barrier (BBB) penetration and exon 20 (T790M) tumor mutations. Third-generation EGFR TKIs, such as osimertinib, have shown better BBB penetration and efficacy against T790M mutations. In this retrospective study, we evaluated the overall survival (OS) and progression-free survival (PFS) in NSCLCBM patients treated with first and third-generation EGFR TKIs. Methods: NSCLCBM patients diagnosed between 2010 and 2019 at our tertiary care center were investigated. Information regarding molecular marker status, systemic therapies, and date of progression were collected. OS was defined as the start date of systemic therapy to the date of last follow-up or death. OS and PFS were estimated by the Cox proportional model. Results: A total of 193 NSCLCBM patients with an EGFR mutation were identified. 33 EGFR mutant patients received first-generation EGFR TKIs, of which 56.7% were females, 82.1% were white, and had a median age of 63.2 years. 22 patients received third-generation EGFR TKIs, 64.1% being female, 76.9% being white, and with a median age of 71.5 years. The median OS (mOS) in patients who received first and third-generation EGFR TKIs was 59.8 months and 65.9 months respectively (p-value (p) = 0.06). The median PFS (mPFS) between the first and third-generation EGFR TKI cohorts was 44.3 months and 66.9 months respectively (p= 0.048, hazard ratio (HR) = 0.50 (95% confidence interval (CI) = 0.25, 0.99). Conclusions: Newer generation of targeted therapies in NSCLCBM have focused on overcoming previous efficacy hurdles, including BBB penetration and resistant mutations. We determined that there was a significant mPFS benefit in osimertinib compared to erlotinib or gefitinib, and a trend towards significant mOS benefit in osimertinib compared to erlotinib or gefitinib in patients with NSCLCBM. However, these results should be interpreted cautiously due to treatment selection bias, and further studies need to be conducted on brain metastases lesion size and response rates.[Table: see text]
An investigation into the impact of next generation sequencing on the use of targeted treatments in glioblastoma.
Authors: John Jordan , Lukas Rimas Vincas , Kumthekar Priya 
Published: June 2021

2035 Background: Next-generation sequencing (NGS) provides clinicians immense amounts of information about a patient’s cancer. NGS allows the detection of a wide range of gene alterations undetectable by older techniques. This has the promise to help guide clinicians in their decision-making as genetic alterations can be both prognostic and predictive. NGS may reveal new treatment options for patients with glioblastoma. The goal of this project was to evaluate how often NGS results influence the use of targeted treatment agents in glioblastoma. This study did not investigate the impact of NGS on reclassification of cancers, prognosis, or treatment decisions outside of the use of targeted therapy. Methods: We conducted a retrospective chart review to see if adult glioblastoma patients received treatments for potentially actionable gene alterations and for fusion variants because of NGS. Here we looked at alterations labeled as actionable from the Tempus company’s NGS results. We collected diagnosis and treatment information from the Electronic Medical Record and from the Electronic Data Warehouse. We examined if patients after receiving NGS would receive targeted treatment, as that likely indicated these treatments were given due to NGS indication. We excluded cytotoxic chemotherapy agents and bevacizumab as these therapies are utilized regardless of a targeted gene indication. This analysis excluded treatments received in clinical trials in which enrollment was independent of NGS. This analysis looked at the proportion of patients with actionable alterations that were treated with targeted agents. Results: 261 glioblastoma patients were found on NGS to have mutations which were potentially actionable. Thirty-three of these patients (12.6%) received a respective targeted therapy, and the other 228 patients (87.4%) did not receive an NGS guided targeted therapy. 97 patients had EGFR copy number gains or EGFR gain of function mutations of which 21 were treated with depatuxizumab mafodotin, and one additional patient who was treated with ABBV 321. Of the NGS treatment guided therapies, depatuxizumab mafodotin was the most used targeted agent in our sample set. Additionally, there were 30 patients with fusion variants and 11 of them were FGFR3-TACC3 fusions. Of these 11, 4 received targeted regimens (36.4%) with either TAS-120, pemigatinib, erdafitinib, or erdafitinib with ponatinib. The other fusion variants were not acted upon. Conclusions: These preliminary results suggest that NGS data currently does not frequently impact treatment decisions for glioblastoma. Although this study is limited by being a single institution study, future efforts include expanding this analysis to multiple institutions. With improvements in therapeutics as well as with wider availability of NGS testing, sequencing data may impact a larger percentage of glioblastoma patients.