scholarly journals Prognostic Model of Successful Tyrosine Kinase Inhibitors Discontinuation Based on the Russian RU-SKI Multicenter Prospective Study

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1740-1740
Author(s):  
Oleg Shukhov ◽  
Ekaterina Chelysheva ◽  
Anna Petrova ◽  
Anastasiya Bykova ◽  
Irina Nemchenko ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Kinase Inhibitors ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Molecular Response ◽  
Validation Set ◽  
Multicenter Prospective Study ◽  
Tki Discontinuation

Abstract Background: A large number of tyrosine kinase inhibitors (TKI) discontinuation studies has shown that about 50% of chronic myeloid leukemia (CML) patients (pts) after TKI cessation lose a major molecular response (MMR) and should return to therapy. Despite the fact that, after TKI resumption, almost all patients re-achieve deep molecular remission, facilitating a more accurate selection process for therapy cessation remains topical. Aim: Develop a prognostic model for the better selection of CML patients for TKI discontinuation. Methods: The base for the training set was the Russian multicenter prospective study RU-SKI on the discontinuation of TKI in pts with CML and deep molecular response (DMR). Ninety-eight CML pts with chronic phase (CP), TKI therapy for at least three years and a stable DMR (BCR-ABL<0.01%) for at least two years were enrolled. Seven Pts with a previous history of unsuccessful treatment-free remission (TFR) were excluded from the analysis. The BCR-ABL level was evaluated by RQ-PCR according to the international scale (IS). The schedule of molecular tests was as follows: monthly during the first six months (mo) after TKI cessation, every two mo from six to 12 mo and every three mo thereafter. Treatment by the same TKI was resumed in case of MMR loss (BCR-ABL>0.1%). We used the Kaplan-Meier method for calculating the probability of TFR. Univariate analyses were performed using the log-rank test to identify prognostic factors for TFR. Variables found to be significant at the p<0.10 level were entered into a proportional hazards regression analysis. We categorized each independently significant factor into two groups, depending on the optimal cutoff level obtained by ROC analysis and the minimum P-value approach. The next step was a second multivariate analysis including categorized variables. A favorable factor from each variable was scored as 1, while the adverse factor was scored proportionally to the level of hazard ratio. The cumulative score for each patient was calculated. Patients were allocated to either a high or a low risk group concerning MMR loss after the cutoff level was determined by ROC analysis. The validation set included a series of 48 retrospective cases of discontinued patients selected according to RU-SKI inclusion criteria. Results: Baseline characteristics of the training set (n=91): male: 48%; median (Mе) age at TKI cessation 46 years (range 22 to 80); Me duration of TKI therapy 8.3 years (range three to 16.2); Me duration of DMR 3.2 years (range two to 10.7). Therapy before treatment cessation: imatinib in 63 (69%) pts, second-generation (2G) TKI in 28 (31%) pts. Me follow-up time after TKI cessation was 14 mo (range three to 36). Probability of TFR was 55% after 12 mo of follow-up. We analyzed the following factors: age, gender, history of previous resistance to imatinib, type and line of TKI, duration of therapy, length of DMR, depth of molecular response before cancellation, Sokal risk group. Age, Sokal risk group, duration of therapy and depth of molecular response were found to be independently significant factors and included in the survival prognostic model (Table 1). 73% (n=66) of pts scored 5.5 points or less and were assigned to the low risk group. The probability of TFR was 64% and 33% for the low risk and high risk groups, respectively (p=0.001) (Figure 1). Baseline characteristics of validation set (n=48): male: 36%; Ме age at TKI cessation 46 years (range 22 to 76); Me duration of TKI therapy six years (range 3.5 to 13.2); Me duration of DMR 2.8 years (range two to 10). Therapy before treatment stopped: imatinib in 31 (65%) pts, 2G TKI in 17 (35%) pts. Me follow-up time after TKI cessation was 36 mo (range six to 116). Probability of TFR was 47% after 30 mo of follow-up. In the validation set, 77% (n=37) of pts were assigned to the low risk group. The probability of TFR was 56% and 18% for the low risk and high risk groups, respectively (p=0.007) (Figure 2). Conclusion: RU-SKI prognostic model is effective in prediction of successful TFR and can be used for better selection of CML pts for TKI discontinuation. Disclosures Shukhov: Novartis: Other: provided consultations and performed lectures ; Bristol Myers Squibb: Other: provided consultations and performed lectures . Chelysheva:Novartis: Other: provided consultations and performed lectures; Fusion Pharma: Other: provided consultations ; Bristol Myers Squibb: Other: provided consultations and performed lectures. Turkina:Novartis: Other: provided consultations; Bristol Myers Squibb: Other: provided consultations; Phizer: Other: provided consultations; Fusion Pharma: Other: provided consultations.

scholarly journals A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Peijie Chen ◽  
Yuting Gao ◽  
Si Ouyang ◽  
Li Wei ◽  
Min Zhou ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Training Set ◽  
Non Coding Rnas ◽  
Validation Set ◽  
Immune Related Genes

Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups.Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment.Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with longer OS and low-risk group with shorter OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p &lt; 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher.Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.

scholarly journals Incidences of Deep Molecular Responses and Treatment-Free Remission in de Novo CP-CML Patients

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2521
Author(s):  
Gabriel Etienne ◽  
Stéphanie Dulucq ◽  
Fréderic Bauduer ◽  
Didier Adiko ◽  
François Lifermann ◽  
...  
Keyword(s):  
Kinase Inhibitors ◽  
De Novo ◽  
Chronic Phase ◽  
Risk Scores ◽  
Prolonged Treatment ◽  
Molecular Response ◽  
First Line ◽  
Treatment Free Remission ◽  
Tki Discontinuation

Background: Tyrosine Kinase Inhibitors (TKIs) discontinuation in patients who had achieved a deep molecular response (DMR) offer now the opportunity of prolonged treatment-free remission (TFR). Patients and Methods: Aims of this study were to evaluate the proportion of de novo chronic-phase chronic myeloid leukemia (CP-CML) patients who achieved a sustained DMR and to identify predictive factors of DMR and molecular recurrence-free survival (MRFS) after TKI discontinuation. Results: Over a period of 10 years, 398 CP-CML patients treated with first-line TKIs were included. Median age at diagnosis was 61 years, 291 (73%) and 107 (27%) patients were treated with frontline imatinib (IMA) or second- or third-generation TKIs (2–3G TKI), respectively. With a median follow-up of seven years (range, 0.6 to 13.8 years), 182 (46%) patients achieved a sustained DMR at least 24 months. Gender, BCR-ABL1 transcript type, and Sokal and ELTS risk scores were significantly associated with a higher probability of sustained DMR while TKI first-line (IMA vs. 2–3G TKI) was not. We estimate that 28% of CML-CP would have been an optimal candidate for TKI discontinuation according to recent recommendations. Finally, 95 (24%) patients have entered in a TFR program. MRFS rates at 12 and 48 months were 55.1% (95% CI, 44.3% to 65.9%) and 46.9% (95% CI, 34.9% to 58.9%), respectively. In multivariate analyses, first-line 2–3G TKIs compared to IMA and TKI duration were the most significant factors of MRFS. Conclusions: Our results suggest that frontline TKIs have a significant impact on TFR in patients who fulfill the selection criteria for TKI discontinuation.

scholarly journals Validation of Clinical Prognostic Models and Integration of Genetic Biomarkers of Drug Resistance in CLL Patients Treated with Ibrutinib

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 186-186 ◽  
Author(s):  
Inhye E. Ahn ◽  
Xin Tian ◽  
Maher Albitar ◽  
Sarah E. M. Herman ◽  
Erika M. Cook ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Prognostic Models ◽  
Discovery Cohort ◽  
Treatment Naïve ◽  
Equity Ownership

Abstract Introduction: We previously reported a prognostic scoring system in CLL using pre-treatment factors in patients treated with ibrutinib [Ahn et al, 2016 ASH Annual Meeting]. Here we present long-term follow-up results and validation of the prognostic models in a large independent cohort of patients. We also determine the incidence of resistance-conferring mutations in BTK and PLCG2 genes in different clinical risk groups. Methods and Patients: The discovery cohort comprised 84 CLL patients on a phase II study with either TP53 aberration (deletion 17p or TP53 mutation) or age ≥65 years (NCT01500733). The validation cohort comprised 607 patients pooled from four phase II and III studies for ibrutinib in treatment-naïve or relapsed/refractory CLL (NCT01105247; NCT01578707; NCT01722487; NCT01744691). All patients received single-agent ibrutinib 420mg once daily. We used Cox regression models to identify independent predictors of PFS, Kaplan-Meier method to estimate probabilities of PFS, log-rank test to compare PFS, and Cochran-Armitage trend test to compare the incidence of mutation among subgroups. We used R version 3.5.0 or SAS® version 9.3 for statistical analyses. For biomarker correlation, we tested cellular DNA or cell-free DNA collected from patients in the discovery cohort with the targeted sequencing of BTK and PLCG2 genes. Result: At a median follow-up of 5.2 years, 28 (33.3%) of 84 patients in the discovery cohort progressed or died. 52 (61.9%) patients had treatment-naïve CLL. Independent factors of PFS on univariate analysis were; TP53 aberration, prior treatment, and β-2 microglobulin (B2M) >4mg/L (P<0.05 for all tests). Unmutated IGHV and advanced Rai stage (III/IV) showed a trend toward inferior outcome without reaching statistical significance. Because higher levels of B2M were associated with relapsed/refractory CLL, we performed two multivariate Cox regression models to assess B2M and prior treatment status separately. Risk groups were determined by the presence of TP53 aberration, advanced Rai stage, and B2M >4mg/L for Model 1, and TP53 aberration, advanced Rai stage, and relapsed/refractory CLL for Model 2 (Table 1). The high-risk group had all three adverse risk factors; the intermediate-risk group had two risk factors; and the low-risk group, none or one. The median PFS of the high-risk group was 38.9 months for Model 1 and 38.4 months for Model 2, and was significantly shorter than those of intermediate and low-risk groups. In the validation cohort, 254 (41.8%) of 607 patients progressed or died at a median follow-up of 4.2 years. 167 (27.5%) patients had treatment-naïve CLL. Both models showed statistically significant differences in PFS by risk groups (Table 1). For the high-risk group, 4-year PFS was 30.2% in Model 1 and 30.5% in Model 2, which were inferior to those of intermediate (53.4 and 52.4%) and low-risk groups (68.7 and 73.7%). Model 1 classified 20% of patients and Model 2 classified 28% of patients to the high-risk group. BTK and PLCG2 mutations are common genetic drivers of ibrutinib resistance in CLL. To determine whether the incidence of these mutations correlates with prognostic risk groups, we performed targeted sequencing of BTK and PLCG2 of samples collected from patients in the discovery cohort. We used cell-free DNA for patients who received long-term ibrutinib (≥3 years) and had low circulating tumor burden, and cellular DNA, for samples collected within 3 years on ibrutinib or at progression. Of 84 patients, 69 (82.1%) were tested at least once, and 37 (44.0%) were tested at least twice. The frequency of testing was similar across the risk groups by two models (P>0.05). The cumulative incidences of mutations at 5 years in the low-, intermediate-, and high-risk groups were: 21.4%, 44.8% and 50%, respectively, by Model 1 (P=0.02); and 22.6%, 41.4% and 66.7%, respectively, by Model 2 (P=0.01). Conclusion: We developed and validated prognostic models to predict the risk of disease progression or death in CLL patients treated with ibrutinib. Risk groups classified by three commonly available pre-treatment factors showed statistically significant differences in PFS. The clinically-defined high-risk disease was linked to higher propensity to develop clonal evolution with BTK and/or PLCG2 mutations, which heralded ibrutinib resistance. Disclosures Albitar: Neogenomics Laboratories: Employment. Ma:Neogenomics Laboratories: Employment. Ipe:Pharmacyclics, an AbbVie Company: Employment, Other: Travel; AbbVie: Equity Ownership. Tsao:Pharmacyclics LLC, an AbbVie Company: Employment. Cheng:Pharmacyclics LLC, an AbbVie Company: Employment. Dean:CTI BioPharma Corp.: Employment, Equity Ownership; Pharmacyclics LLC, an AbbVie Company: Employment, Equity Ownership. Wiestner:Pharmacyclics LLC, an AbbVie Company: Research Funding.

scholarly journals Profile of patients with Myelodysplastic syndrome: A report from a tertiary care teaching hospital from Eastern India

2021 ◽  
Vol 0 ◽  
pp. 1-6
Author(s):  
Ankita Sen ◽  
Arnab Chattopadhyay ◽  
Shuvra Neel Baul ◽  
Rajib De ◽  
Sumit Mitra ◽  
...  
Keyword(s):  
Myelodysplastic Syndrome ◽  
Teaching Hospital ◽  
Risk Group ◽  
Tertiary Care ◽  
Supportive Therapy ◽  
Risk Groups ◽  
Low Risk ◽  
Eastern India ◽  
Tertiary Care Teaching Hospital

Objectives: Myelodysplastic syndrome (MDS) is a group of myeloid neoplasms. The clinical manifestations and treatments vary depending on the subtype and risk stratification of the disease. There is a paucity of data on Indian patients with MDS. This study was undertaken to understand MDS with regard to their clinical presentation, pathological, cytogenetic profiles and also to assess their therapeutic outcomes and prognosis from our center in Eastern India. Material and Methods: This is a prospective observational study conducted in the department of hematology at a tertiary care teaching hospital from eastern part of India. The diagnosis of MDS was made from the peripheral blood examination, bone marrow aspirate examination, cytogenetics, and Fluorescence in situ hybridization results, according to the WHO guidelines. Patients were risk stratified using Revised International Prognostic Scoring System (R-IPSS) and subsequent therapeutic planning was done, with either supportive therapy in the form of recombinant human erythropoiesis stimulating agents, colony stimulating factors, packed red blood cell support as needed for low risk MDS patients. High risk patients were treated with hypomethylating agents such as Azacytidine, Decitabine, or Lenalidomide. Results: The mean duration of follow-up of patients with MDS from the point of diagnosis was 1.8 years (range 4 months–6 years). The median OS was 1.33 years. The median OS in the analysis of our patient cohort with low, intermediate, high, and very high R-IPSS was 1.67 years, 1.33 years, 1.67 years, and 1.67 years, respectively. No patients of very low risk group were identified in our study. Conclusion: Our findings reflect that MDS-MLD with low or intermediate R-IPSS risk groups is the most common types of MDS. Although supportive therapy was used to treat patients irrespective of other therapy given (depending on the risk group of the patient), it was used alone even in higher risk groups due to logistic reasons in some cases. Those patients who received supportive care alone also had a good survival duration. However, a longer follow-up duration is required to firmly establish this outcome. The median age of patients (55 years) was also lower than established studies with a median overall survival of 1.67 years.

scholarly journals Complete Identification of Autophagy-Related lncRNA Prognostic Signature for Breast Cancer

2021 ◽  
Author(s):  
Wenxi Wang ◽  
Na Li ◽  
Lin Shen ◽  
Qin Zhou ◽  
Zhanzhan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Cell Types ◽  
High Risk Group ◽  
Low Risk ◽  
High Morbidity

Abstract Purpose: Breast cancer (BC) has a relatively high morbidity and mortality for women. The research about BC prognosis is significant. Autophagy is an essential process for tumor progression, which could play its role with lncRNA, a kind of ncRNA that have regulatory roles in multiple tumors. Therefore, constructing an autophagy-related prognostic model for breast cancer is meaningful.Methods: We download data from the TCGA and HADb. Pearson correlation analysis was performed to excavate autophagy-related lncRNA. Then with gene expression difference analysis, etc. we explored the relationship between clinical features and the signature. We applied Cytoscape as well as KEGG, etc. to explore expression condition. And the autophagy status of our signature was investigated by GSEA, etc. We also searched the immune distinction by CIBERSORTx to extend our study and preliminarily verified our study in the end.Results: Firstly, we got an independent autophagy-related lncRNA prognostic model, by which BC patients were divided into high- and low-risk groups. We found that the OS of high-risk group is significantly lower than that of low-risk group, which was identical to those within various clinical subgroups. Then, the KEGG and GO analysis enriched several pathways including autophagy. PCA and GSEA analysis demonstrated the autophagy status. Several distinguishing immune cell types in separated groups were revealed by immunity analysis. Then the verification in the end proved the feasibility of our signature.Conclusion: In this study, we acquired an independent autophagy-related lncRNA signature involving 12 lncRNAs, which contributes to the prediction of prognosis of BC patients.

Beneficial Effects of Cytogenetic and Molecular Response on Long-Term Outcome in Patients with Newly Diagnosed Chronic Myeloid Leukemia in Chronic Phase (CML-CP) Treated with Imatinib (IM): Update from the IRIS Study.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 166-166 ◽  
Author(s):  
Bengt Simonsson ◽  
Keyword(s):  
Blast Crisis ◽  
Chronic Phase ◽  
Risk Groups ◽  
Low Risk ◽  
Molecular Response ◽  
Newly Diagnosed ◽  
Long Term Outcome ◽  
Increased Risk

Abstract Background: IM was proven to be superior to IFN+Ara-C for newly diagnosed pts with CML-CP (O’Brien et al, NEJM 2003). At 42-months of follow-up, 75% of the 553 pts randomized to IM remain on treatment. Of the 553 pts randomized to IFN+Ara-C only 4% are still on IFN+Ara-C. This update analysis is focused on IM pts. Methods: Evaluation included complete hematologic response (CHR), major/complete cytogenetic response (MCyR/CCyR) - defined as 0-35% Ph+ and 0% Ph+ metaphases respectively, major molecular response (MMR) - defined as ≥3 log reduction of bcr-abl transcripts from the standardized baseline, time to progression - defined as loss of CHR/MCyR or evolution to accelerated phase/blast crisis (AP/BC) or death due to any cause, time to AP/BC - defined as evolution to AP/BC or death due to CML, and overall survival. Results: With an average duration of 38 months of IM treatment, the best observed rates of CHR, MCyR and CCyR are 96%, 88% and 81%, respectively. Although the majority of MCyRs were achieved within the first 3 to 9 months, some pts achieved a MCyR and some even a CCyR after more than one year of treatment (Figure 1). The estimated MMR rate at 12 months is 40%. Figure 1 - Observed CHR, MCyR and CCyR during treatment with IM Figure 1 -. Observed CHR, MCyR and CCyR during treatment with IM The estimated progression-free rate at 42 months is 84%; additionally 94% are estimated free of progression to AP/BC (97% of the pts with CCyR and 73% of the pts without CCyR during study, p<0.001). The risk of relapse remains low with no apparent increased risk over time. The yearly hazard for progression to AP/BC is about 2% in each of the 4 years. The overall estimated survival at 42 months is 91% (considering all deaths). The estimated survival was lowest in pts with high risk Sokal score (84%) as compared to 91% in the intermediate risk pts and 94% in the low risk pts (p<0.001). Similarly, the best observed CCyR in the high, intermediate, and low risk groups were 69%, 80% and 88% respectively (p=0.002). In the subset of pts with CCyR the estimated survival at 42 months was 92%, 93% and 97% in the high to low risk groups (p=0.30), indicating that once pts achieve a CCyR, their survival is not significantly different between the Sokal risk groups. Of the 509 pts who were still on treatment at 12 months and had achieved a MCyR by then (n=436), the rate without progression to AP/BC at 42 months was 97% whereas it was only 83% for the 73 pts who did not achieve a MCyR at 12 months (p<0.001). The estimated survival rates at 42 months were 95% and 83% in these two response groups, respectively (p<0.001). Furthermore, for pts who had achieved a MMR at 12 months, the probability of remaining free from progression to AP/BC was 100% at 42 months compared to 95% for pts in CCyR but not in MMR, and 91% for pts not in CCyR at 12 months (p=0.0013). Conclusions: The follow-up confirms the beneficial effect of cytogenetic and molecular responses on long-term outcomes with IM. These results will be further updated using data cut-off of 31-July 2005 to reflect additional 12-months of data (i.e., 54-month follow-up).

scholarly journals Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

2021 ◽  
Vol 18 (5) ◽  
pp. 6709-6723
Author(s):  
Xin Yu ◽  
◽  
Jun Liu ◽  
Ruiwen Xie ◽  
Mengling Chang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Cox Regression Analysis

<abstract> <sec><title>Objective</title><p>We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC).</p> </sec> <sec><title>Methods</title><p>Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Results</title><p>Seven m6A-related autophagy genes were screened to construct a prognostic model: <italic>CASP4</italic>, <italic>CDKN1A</italic>, <italic>DLC1</italic>, <italic>ITGB1</italic>, <italic>PINK1</italic>, <italic>TP63</italic>, and <italic>EIF4EBP1</italic>. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups.</p> </sec> <sec><title>Conclusions</title><p>Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.</p> </sec> </abstract>

A Risk Score Integrating BAALC, ERG and WT1 Expression Can Be Used To Improve Risk Stratification In Acute Promyelocytic Leukemia

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1323-1323
Author(s):  
Anna Hecht ◽  
Florian Nolte ◽  
Daniel Nowak ◽  
Verena Nowak ◽  
Benjamin Hanfstein ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Stratification ◽  
Risk Score ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Expression Levels ◽  
Improve Risk Stratification

Abstract Introduction With current therapy regimens over 75% of patients with de novo acute promyelocytic leukemia (APL) can be cured. Approaches to further improve patient outcome by stratifying patients at the time of initial diagnosis according to their individual risk and to adjust therapy accordingly have been based on clinical features only. Molecular markers have not been established for risk stratification as yet. Recently, we have shown that high expression levels of the genes brain and acute leukemia, cytoplasmic (BAALC) and ets related gene (ERG) are associated with inferior outcome in APL patients. In addition, data indicate that aberrant expression of the gene Wilms’ tumor 1 (WT1) is a negative prognostic factor with regard to overall survival (OS) after complete remission (CR) and relapse free survival (RFS) in APL. In this study we evaluated the prognostic relevance of a combined score integrating the expression levels of the above mentioned genes to further improve risk stratification in APL patients. Methods Expression levels of BAALC, ERG and WT1 of 62 patients with newly diagnosed APL were retrospectively analyzed in bone marrow mononuclear cells using multiplex reverse transcriptase quantitative real-time PCR (qRT-PCR). Median age of patients was 47 years (range: 19 to 82y). All patients gave informed consent. Patients were diagnosed and treated in the German AML Cooperative Group (AMLCG) study with a treatment of simultaneous ATRA and double induction chemotherapy including high-dose ara-C, consolidation and maintenance chemotherapy. The following gene expression levels were identified as negative risk factors in preceding studies: BAALC expression ≥25th percentile (BAALChigh), ERG expression >75th percentile (ERGhigh) and WT1 expression ≤25th percentile or ≥75th percentile (WT1low/high). A risk score was developed as follows: for the presence of one of the mentioned risk factors one scoring point was assigned to a respective patient, i.e. a maximum of 3 points (one point for BAALChigh, ERGhigh and WT1low/high, respectively) and a minimum of 0 points (i.e. presenting with none of the aforementioned risk factors) could be allocated to one patient. Accordingly, patients were divided into four risk groups: 7 patients scored 0 points (= low risk), 27 patients scored 1 point (= intermediate 1 risk), 19 patients scored 2 points (= intermediate 2 risk) and 9 patients scored 3 points (= high risk). Subsequently, OS, RFS and relapse free interval (RFI) were calculated using the Kaplan-Meier method and a log-rank test was used to compare differences between the four risk groups (p<0.05). Results The integrative risk score divided patients into four groups with significantly different outcome. The low risk group showed a RFS of 100% at 10 years of follow-up compared to the intermediate 1 risk group with 81%, the intermediate 2 risk group with 58% and the high risk group with a RFS of 42% only (median survival: 4.6y) (p=0.02). In accordance, the RFI differed significantly between the four groups: low risk 100%, intermediate 1 risk 100%, intermediate 2 risk 89% and high risk 71% (p=0.049). There was no statistically significant difference between the 4 groups with regard to OS in the entire patient cohort. However, there was a clear trend towards a difference in OS in patients who achieved a CR after induction therapy: low risk 100%, intermediate 1 risk 81%, intermediate 2 risk 68% and high risk 53% survival at 10 years of follow-up (p=0.09). Conclusion Integration of expression levels of the genes BAALC, ERG and WT1 into a scoring system identifies 4 risk groups with significantly different outcome with regard to RFS and RFI. It might be a promising approach to guide therapeutic decisions in patients with APL. However, multivariate analyses and validation of these data in an independent patient cohort is warranted. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Treatment-free remission in CML: who, how, and why?

Hematology ◽  
2017 ◽  
Vol 2017 (1) ◽  
pp. 102-109 ◽  
Author(s):  
Francois-Xavier Mahon
Keyword(s):  
Clinical Trials ◽  
Myeloid Leukemia ◽  
Kinase Inhibitors ◽  
Chronic Phase ◽  
Healthy Population ◽  
Molecular Response ◽  
Treatment Free Remission ◽  
Tki Discontinuation

Abstract Chronic myeloid leukemia (CML) is the best example of successful targeted therapy. Today, the overall survival of patients with CML treated by using tyrosine kinase inhibitors (TKIs) is very close to that of the healthy population. The current question is: how can we further ameliorate the clinical outcome of patients with CML? Clinical trials have shown that some patients with CML in the chronic phase who achieve sustained deep molecular responses on TKI therapy can safely suspend therapy with no evidence of relapse. The long follow-up studies and the number of eligible patients have now validated the concept of treatment-free remission (ie, the ability to maintain a molecular response after stopping therapy). It should be considered as the future criterion to evaluate the success of clinical trials, especially if we want to take into account the quality of life of patients in addition to the economic aspect. Because post-TKI discontinuation follow-ups have been increasing over time with no evidence of relapse in some patients, the next step for the coming decade will be to address the topic of CML cure.

scholarly journals An Immune-Related Long Noncoding RNA Signature as a Prognostic Biomarker for Human Endometrial Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ziwei Wang ◽  
Yan Liu ◽  
Jun Zhang ◽  
Rong Zhao ◽  
Xing Zhou ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Noncoding Rna ◽  
Prognostic Model ◽  
Risk Group ◽  
Risk Groups ◽  
Figo Stage ◽  
Low Risk ◽  
Pathological Grade ◽  
Immune Related Genes

Background. Endometrial cancer is among the most common malignant tumors threatening the health of women. Recently, immunity and long noncoding RNA (lncRNA) have been widely examined in oncology and shown to play important roles in oncology. Here, we searched for immune-related lncRNAs as prognostic biomarkers to predict the outcome of patients with endometrial cancer. Methods. RNA sequencing data for 575 endometrial cancer samples and immune-related genes were downloaded from The Cancer Genome Atlas (TCGA) database and gene set enrichment analysis (GSEA) gene sets, respectively. Immune-related lncRNAs showing a coexpression relationship with immune-related genes were obtained, and Cox regression analysis was performed to construct the prognostic model. Survival, independent prognostic, and clinical correlation analyses were performed to evaluate the prognostic model. Immune infiltration of endometrial cancer samples was also evaluated. Functional annotation of 12 immune-related lncRNAs was performed using GSEA software. Prognostic nomogram and survival analysis for independent prognostic risk factors were performed to evaluate the prognostic model and calculate the survival time based on the prognostic model. Results. Twelve immune-related lncRNAs (ELN-AS1, AC103563.7, PCAT19, AF131215.5, LINC01871, AC084117.1, NRAV, SCARNA9, AL049539.1, POC1B-AS1, AC108134.4, and AC019080.5) were obtained, and a prognostic model was constructed. The survival rate in the high-risk group was significantly lower than that in the low-risk group. Patient age, pathological grade, the International Federation of Gynecology and Obstetrics (FIGO) stage, and risk status were the risk factors. The 12 immune-related lncRNAs correlated with patient age, pathological grade, and FIGO stage. Principal component analysis and functional annotation showed that the high-risk and low-risk groups separated better, and the immune status of the high-risk and low-risk groups differed. Nomogram and receiver operating characteristic (ROC) curves effectively predicted the prognosis of endometrial cancer. Additionally, age, pathological grade, FIGO stage, and risk status were all related to patient survival. Conclusion. We identified 12 immune-related lncRNAs affecting the prognosis of endometrial cancer, which may be useful as therapeutic targets and molecular biomarkers.

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