Assessing the impact of comorbidities on autologous hematopoietic cell transplant (AHCT) outcomes using the hematopoietic cell transplant-comorbidity index (HCT-CI) in lymphoma

7102 Background: Sorror et al. has identified HCT-CI as a valid scoring of pretransplant comorbidities that predicted nonrelapse mortality and survival after allogeniec HCT. We recently reported on the validity of HCT-CI in predicting morbidity outcomes after AHCT for lymphoma (BBMT in press). High HCT-CI score predicted for prolonged hospitalization and high incidence of hospital re-admission after AHCT. The objective of this study is to evaluate the impact of HCT-CI on mortality risk after AHCT. Methods: We included pts above age of 40 with advanced HL or NHL, who underwent AHCT in our institution between 01/98 & 05/06. Median follow up was 29.4 mo. Pts were assigned scores based on the HCT-CI. Defenition of comorbidities were recently reported (Kassar et al, BBMT in press). Results: 80 pts were included (NHL: 74, HL: 6). 61 pts were male. Median age was 56 years (42–76). Comorbidities (points, prevalence%): mild hepatic (1,14), cardiac (1,15), cerebrovascular (1,4), arrhythmia (1,9), moderate pulmonary (2,11), severe pulmonary (3,8), rheumatologic (2,5), DM (1,23), inflammatory bowel disease (1,3), psychiatric (1,11), infection (1,6), obesity (1,11), and renal (2,1). Median HCT-CI was 1 (0: 37 %, 1: 26%, 2–7: 37%). 22 pts died: 15 from relapse and 7 from non relapse mortality (NRM) causes. Cumulative day-100 NRM and 1-year NRM rates are: 1.3% and 4%, respectively. Pts were categorized into 2 groups: low-risk (scores of 0–1) and high-risk (scores 2–7). Using Cox Regression model and adjusting for age and histology, low-risk group had a significantly better OS (1 wk-58.6 mo, median 34.1 mo) compared to high-risk group (5 days-23.6 mo, median 6 mo) (HR: 3.73, p = .01, 95% CI 1.32, 10.54). 1-year OS rate was 75% vs. 25%, respectively (p=.04). Conclusion: HCT-CI is a valid scoring of pre-transplant comorbidities that predicted mortality after AHCT for pts with lymphoma. The physiologic burden of comorbidities is likely to impact the tolerance to AHCT or to other therapies administered upon relapse after transplant. HCT-CI can serve as important tool for both the transplant administrator when planning for resources allocation and clinical trials, and for pts during pre-transplant evaluation and counseling. No significant financial relationships to disclose.

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Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

10.21203/rs.3.rs-877901/v1 ◽

2021 ◽

Author(s):

Shenglan Huang ◽

Jian Zhang ◽

Dan Li ◽

Xiaolan Lai ◽

Lingling Zhuang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Clinicopathological Parameters ◽

Kaplan Meier ◽

The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

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Hematopoietic Cell Transplant-Composite Risk (HCT-CR) — a Novel Prognostic Model to Predict Transplant Outcomes

Blood ◽

10.1182/blood-2018-99-111639 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2141-2141

Author(s):

Piyanuch Kongtim ◽

Simrit Parmar ◽

Denái R. Milton ◽

Jorge M. Ramos Perez ◽

Gabriela Rondon ◽

...

Keyword(s):

Stem Cell ◽

High Risk ◽

Research Funding ◽

Risk Group ◽

High Risk Group ◽

Hematopoietic Cell ◽

Cell Transplant ◽

Hematopoietic Cell Transplant ◽

Composite Risk ◽

Very High

Abstract Introduction Outcomes after allogeneic stem cell transplant (AHSCT) are influenced by both disease and patient related factors. We hypothesized that combining hematopoietic stem cell transplant comorbidity-age index (HCT-CI/Age) and the refined disease risk index (DRI-R) would better predict survival post-transplant and developed a hematopoietic cell Transplant-composite risk (HCT-CR) model, which we tested in a group of patients with acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) treated at MD Anderson Cancer Center (MDACC). Methods The study included consecutively treated patients, 18 years of age or older, with AML and MDS who received first AHSCT at MDACC between 2005-2016. Donors were HLA-matched related (MRD), HLA-matched unrelated (MUD), 9/10 MUD (MMUD), haploidentical (HAPLO) and 9/10 MRD (MMRD). To develop this model, patients were assigned into 4 groups: 1. Patients with low/intermediate DRI-R and HCT-CI/Age </=3 (low-risk); 2. Patients with low/intermediate DRI-R and HCT-CI/Age >3 (intermediate-risk); 3. Patients with high/very high DRI-R and HCT-CI/Age </=3 (high-risk); and 4. Patients with high/very high DRI-R and HCT-CI/Age >3 (very high-risk). Primary endpoint was 5-year overall survival (OS); other outcomes assess were progression-free survival (PFS), non-relapse mortality (NRM) and relapse rate. The stability of the HCT-CR model was tested by bootstrap resampling. The discrimination power of the HCT-CR model on OS was compared with that of the DRI-R, HCT-CI/Age and cytogenetic risk model by the Harrell C-concordance index. Results The analysis included 942 patients (492 male and 450 female) with a median age of 53 years (range 18-65 years). Cytogenetic data at diagnosis was available in 928 (98.5%) patients and was favorable, intermediate and adverse cytogenetic risk in 63 (7%), 523 (56%) and 342 (37%), respectively. Fifty-five (6%), 399 (43%), 392 (42%) and 82 (9%) patients had low, intermediate, high and very high DRI-R, respectively. The HCT-CI/Age was available in 922 (98%) patients with the median score of 3 (range 0-18). Donor types included MRD (n=377), MUD (n=416), MMUD (n=68), HAPLO (n=73) and MMRD (N=8). Using the HCT-CR model, patients were stratified into 4 risk groups: low (N=272), intermediate (N=168), high (N=284) and very high-risk (N=184), with significantly different survival. The 5-year OS rates for patients in low, intermediate, high and very high-risk group were 57%, 48%, 34%, and 26%, respectively (P<0.001) (Figure 1). The probability of 5-year PFS rates were 55%, 46%, 30% and 23% for these 4 risk groups, respectively (P<0.001). Post-transplant outcomes of all 4 HCR-CR groups are summarized in Table 1. Compared with the low HCT-CR risk group, patients with intermediate, high and very high-risk group had a significantly increased risk of death with HR of 1.42 (95%CI 1.06-1.91; P=0.02), 2.11 (95%CI 1.65-2.70; P<0.001), and 3.02 (95%CI 2.32-3.92; P<0.001), respectively. The significant association between OS and the HCT-CR groups persisted after adjusting for potential confounders with adjusted HR of 1.37 (95%CI 1.02-1.85) for intermediate, 2.08 (95%CI 1.62-2.67) for high and 2.92 (95%CI 2.23-3.82) for very high-risk group when compared with low risk group. The stability of the hematopoietic cell transplant - composite risk model was confirmed in a bootstrap resampling procedure. Among 500 new datasets, on average, patients in intermediate, high and very high-risk group had significantly increased risk of death after transplant when compared with low risk group with HR of 1.39, 2.11 and 2.98, respectively. Results from the concordance test showed that the HCT-CR model provided better discriminative capacity for prediction of OS when compared with DRI-R, HCT-CI/Age and cytogenetic risk group models with C-index of 0.62 versus 0.60, 0.54 and 0.55, respectively. The goodness of fit test showed that the HCT-CR model fit the data significantly better than the other models (P<0.001). Conclusion Combining disease and patient-related factors provides better survival stratification for patients with AML/MDS receiving AHSCT. This novel HCT-CR model will be validated in patients with all diseases undergoing allogeneic hematopoietic stem cell transplantation and results will be presented at the meeting. Disclosures Oran: ASTEX: Research Funding; Celgene: Consultancy, Research Funding; AROG pharmaceuticals: Research Funding. Champlin:Otsuka: Research Funding; Sanofi: Research Funding.

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Hematopoietic Cell Transplantation - Comorbidity Index (HCT-CI) as a Surrgoate Marker for Predition of Transplant Outcome for Pateints (pts) with Multiple Myeloma (MM) Undergoing Autologous Stem Cell Transplantation (ASCT): High HCT-CI Score Predicts for Poor Outcome.

Blood ◽

10.1182/blood.v110.11.5104.5104 ◽

2007 ◽

Vol 110 (11) ◽

pp. 5104-5104

Author(s):

Mohamed I. Farhat ◽

Ronald Myint ◽

Stephanie A. Gregory ◽

Parameswaran Venugopal ◽

Mohamad Kassar ◽

...

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Cell Transplantation ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Heart Valve Disease ◽

Risk Scores ◽

Transplant Outcome

Abstract Background: For all “transplant eligible” pts with MM, established practice guidelines recommend ASCT as part of the front line treatment. However, the definition of “transplant eligible” remains undefined. The HCT-CI is a new tool that encapsulates pre-transplant comorbidities used in predicting transplant outcomes in pts undergoing allogeneic SCT. This scoring system has been shown to be a good predictor for non-relapse mortality (NRM) & survival in pts undergoing alloSCT. In this study, we hypothesize that HCT-CI could predict the transplant outcome on pts with MM undergoing ASCT and could potentially be utilized as a tool for selecting pts with MM for transplant. Methods: A retrospective analysis of 75 pts with multiple myeloma whom underwent ASCT in our institution between 02/99 and 12/03 with a median follow up of 30 months. Pts were assigned scores based on the HCT-CI. Definitions of comorbidities were as previously reported by Sorror et. al. (Blood2005; 106:2912). Results: Median age was 56 years (38 – 73); M:F 1:1. 51 pts received a single & 24 had tandem ASCT. The majority of pt. had IgG myeloma (IgG kappa: 45; IgG Lambda 17). Comorbidities (points, number of pts): mild hepatic (1,16), renal (2,6), cardiac (1,8), arrhythmia (1,1), heart valve disease (3,4), cerebrovascular (1,8), DM (1,11), PUD (2,2), inflammatory bowel disease (1,0), Tumor (3,6), pulmonary (2,5), psychiatric (1,8), rheumatologic (2,3), infection (1,6), and obesity (1,10). HCT-CI score of 0 seen in 32%, 1 in 28%, 2–8 in 40% of the pts, with a median score of 1.65. 20 patients died with only one due to NRM. Pts were categorized into 2 groups: low-risk (scores of 0–1) – 46 pts and high-risk (scores 2–8) – 29. Using a cox regression model, the low risk group had a survival advantage (HR = 2.55, P = 0.04). Using Kaplan Meier survival estimate comparing the low risk and high risk group (figure1), the 5 yrs overall survival were 77% & 22% respectively (P = 0.04). While the median survival for the high risk group was 3.52 years, it has not been reached for the low risk group. Conclusion: Here, we have demonstrated a survival benefit for pts with low (0–1) compared with high (≥ 2) HCT-CI score. In addition, the outcome of pts with high HCT-CI score was also similar to non-transplant pts as published in the literature. This raises the question of “benefit” of ASCT for pts with high HCT-CI score. Thus, HCT-CI may serve as a useful tool to select pts whom would benefit most from ASCT. Figure 1 Figure 1

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A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

BioMed Research International ◽

10.1155/2020/4845360 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Dakui Luo ◽

Zezhi Shan ◽

Qi Liu ◽

Sanjun Cai ◽

Qingguo Li ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Differentially Expressed ◽

Metabolic Signature ◽

Metabolic Genes ◽

Prognostic Prediction

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

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A novel signature of two long non-coding RNAs in BRCA mutant ovarian cancer to predict prognosis and efficiency of chemotherapy

Journal of Ovarian Research ◽

10.1186/s13048-020-00712-w ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Yinglian Pan ◽

Li Ping Jia ◽

Yuzhu Liu ◽

Yiyu Han ◽

Qian Li ◽

...

Keyword(s):

Ovarian Cancer ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Gynecologic Cancer ◽

High Risk Group ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Chemotherapy Treatment ◽

Prognostic Signature

Abstract Background In this study we aimed to identify a prognostic signature in BRCA1/2 mutations to predict disease progression and the efficiency of chemotherapy ovarian cancer (OV), the second most common cause of death from gynecologic cancer in women worldwide. Methods Univariate Cox proportional-hazards and multivariate Cox regression analyses were used to identifying prognostic factors from data obtained from The Cancer Genome Atlas (TCGA) database. The area under the curve of the receiver operating characteristic curve was assessed, and the sensitivity and specificity of the prediction model were determined. Results A signature consisting of two long noncoding RNAs(lncRNAs), Z98885.2 and AC011601.1, was selected as the basis for classifying patients into high and low-risk groups (median survival: 7.2 years vs. 2.3 years). The three-year overall survival (OS) rates for the high- and low-risk group were approximately 38 and 100%, respectively. Chemotherapy treatment survival rates indicated that the high-risk group had significantly lower OS rates with adjuvant chemotherapy than the low-risk group. The one-, three-, and five-year OS were 100, 40, and 15% respectively in the high-risk group. The survival rate of the high-risk group declined rapidly after 2 years of OV chemotherapy treatment. Multivariate Cox regression associated with other traditional clinical factors showed that the 2-lncRNA model could be used as an independent OV prognostic factor. Analyses of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) indicated that these signatures are pivotal to cancer development. Conclusion In conclusion, Z98885.2 and AC011601.1 comprise a novel prognostic signature for OV patients with BRCA1/2 mutations, and can be used to predict prognosis and the efficiency of chemotherapy.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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Prognostic Modeling of Patients with Metastatic Melanoma Based on Tumor Immune Microenvironment Characteristics

10.21203/rs.3.rs-730205/v1 ◽

2021 ◽

Author(s):

Jing Liu ◽

Ting Ye ◽

Xue fang Zhang ◽

Yong jian Dong ◽

Wen feng Zhang ◽

...

Keyword(s):

High Risk ◽

Metastatic Melanoma ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Risk Scores ◽

Immune Microenvironment ◽

Immune Infiltration ◽

Tumor Immune Microenvironment

Abstract Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs.Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes (DEGs) were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic (ROC) curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT, Xcell and ssGSEA in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 were significantly different in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes (ALOX5AP, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) from the nine-IRG prognostic model, of which the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, we analyzed the prognostic ability and expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 in metastatic melanoma. Overall, a prognostic model for metastatic melanoma based on the characteristics of the tumor immune microenvironment was established, which was helpful for further studies.It could function well in helping people to understand the characteristics of the immune microenvironment in metastatic melanoma and to find possible therapeutic targets.

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Pre-Salvage ISS and Other Important Outcome Predictors in CD34 Selected Allogeneic Hematopoietic Cell Transplant for Multiple Myeloma

Blood ◽

10.1182/blood-2018-99-118625 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 3473-3473

Author(s):

Adam Bryant ◽

Patrick Hilden ◽

Sergio Giralt ◽

Miguel-Angel Perales ◽

Guenther Koehne

Keyword(s):

Multiple Myeloma ◽

High Risk ◽

Chronic Gvhd ◽

Hematopoietic Cell ◽

Older Age ◽

Cell Transplant ◽

Hematopoietic Cell Transplant ◽

Term Survival ◽

Allogeneic Hematopoietic Cell Transplant ◽

The Impact

Abstract Introduction Despite recent therapeutic advances Multiple Myeloma (MM) remains largely incurable, and outcomes in patients who develop resistance to imid or proteasome inhibitor therapies are universally dismal.1 Allogeneic hematopoietic cell transplant (alloHCT) remains the only curative MM treatment but has been associated with historically high rates of GVHD and of non-relapse mortality (NRM), exceeding 40% in some series.2 Although these rates have decreased in recent years, the potential morbidity and mortality associated with alloHCT and the increasing availability of alternative non-transplant therapies demands a thoroughly informed pre-alloHCT assessment. Here we assess the impact of pre-alloHCT variables on clinical outcomes in a large cohort of relapsed/refractory (RR) MM patients who underwent CD34+ selected alloHCT at our institution. Methods This retrospective study included all MM patients who had CD34+ selected alloHCT from Jun 2010 to Dec 2015. Patients were conditioned with targeted dose busulfan (0.8 mg/kg x 10), melphalan (70 mg/m2 x 2) and fludarabine (25mg/m2 x 5) followed by infusion of a CD34+ selected peripheral blood stem cell graft, without post alloHCT GVHD prophylaxis. Estimates were given using the Kaplan-Meier and cumulative incidence methods. Competing risks for relapse, NRM, and GVHD were death, relapse, and relapse or death respectively. The log-rank and Gray's test were used to assess univariable associations. GVHD by 6 months was assessed via a landmark analysis. Results Our 73 patient cohort had a median age of 55 (37-66) and was mostly male (74%). Most patients had low risk MM by ISS (50/66, 76%) and intermediate risk MM by R-ISS (45/66, 68%) at pre-salvage assessment. Patients had a median of 4 (2-9) pre-alloHCT lines of therapy and were evenly split between patients in PR and in VGPR or CR at time of alloHCT (50% and 49%). Median HCT-CI score was 2 (range 0-6) with the majority of patients graded as intermediate or high risk (score ≥1; 55/73, 75%). At a median follow-up in survivors of 35 months (12-84) OS and PFS rates were 70% and 53% at 1 year (95% CI 58-79, 41-64) and 50% and 30% at 3 years, respectively (38-62, 19-41). The cumulative incidences of relapse were 25% and 47% at 1 and 3 years, respectively (16-35, 35-58), and 1 year NRM was 22% (13-32). Deaths were balanced between relapse and non-relapse causes (54% and 46% respectively). Incidence of grade II-IV acute GVHD was 7% at 100 days (3-14), and of chronic GVHD was 8% at 1 year (3-16). In univariable analysis, intermediate-high risk ISS assessed prior pre-alloHCT salvage therapy was associated with lower OS (3 year 30 v 54%, p=0.037), lower PFS (3 year 9 v 33%, p=0.013), and greater relapse incidence (3 year 72 v 39%, p=0.004). Older age and GVHD prior to 6 months were also associated with lower OS; older age, more heavily pre-treated disease, and worse disease status at alloHCT were associated with lower PFS; and heavier pre-alloHCT treatment was also associated with higher relapse (Table 1). Higher HCTCI was not associated with increased NRM (1 year 22 v 16 v 27% for HCTCT 0, 1-2, ≥3 respectively; p = 0.863). Discussion We describe a cohort of high-risk heavily pretreated RRMM patients with durable OS (50% at 3 years), comparatively low PFS (30% at 3 years), and historically improved rates of NRM (22% at 1 year). We also importantly identified numerous pre-alloHCT variables that were associated with survival, PFS, and relapse. Amongst these, poor ISS measured prior to pre-alloHCT salvage was consistently associated with worse survival and relapse outcomes and may speak to this score's utility as a dynamic measure of disease risk in patients exposed to multiple lines and therapy. Conclusions Our report reinforces that CD34+ selected alloHCT can achieve prolonged disease control and long term survival in high risk, heavily treated refractory MM populations, and newly describes certain pre-transplant variables that may help identify patients with better potential survival and relapse outcomes. Given the dismal prognosis and lack of established alternate therapies for RRMM patients, we advocate that identification of favorable or adverse pre-transplant variables during pre-alloHCT assessment be used to inform alloHCT decision-making rather than to exclude certain patient cohorts from this potentially effective and curative treatment option. Disclosures Perales: Abbvie: Other: Personal fees; Merck: Other: Personal fees; Incyte: Membership on an entity's Board of Directors or advisory committees, Other: Personal fees and Clinical trial support; Takeda: Other: Personal fees; Novartis: Other: Personal fees.

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Identification of N6-Methyladenosine-Associated Long Non-coding RNAs for Immunotherapeutic Response and Prognosis in Patients With Pancreatic Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.748442 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xinshuang Yu ◽

Peng Dong ◽

Yu Yan ◽

Fengjun Liu ◽

Hui Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Messenger Rna ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Risk Scores ◽

Significant Difference ◽

Selection Operator

Pancreatic cancer is a highly aggressive disease with poor prognosis. N6-methyladenosine (m6A) is critical for post-transcriptional modification of messenger RNA (mRNA) and long non-coding RNA (lncRNA). However, the m6A-associated lncRNAs (m6A-lncRNA) and their values in predicting clinical outcomes and immune microenvironmental status in pancreatic cancer patients remain largely unexplored. This study aimed to evaluate the importance of m6A-lncRNA and established a m6A-lncRNA signature for predicting immunotherapeutic response and prognosis of pancreatic cancer. The m6A-lncRNA co-expression networks were constructed using data from the TCGA and GTEx database. Based on the least absolute shrinkage and selection operator (LASSO) analysis, we constructed an 8 m6A-lncRNA signature risk model, and selection operator (LASSO) analysis, and stratified patients into the high- and low-risk groups with significant difference in overall survival (OS) (HR = 2.68, 95% CI = 1.74–4.14, P < 0.0001). Patients in the high-risk group showed significantly reduced OS compared to patients in the low-risk group (P < 0.001). The clinical characteristics and m6A-lncRNA risk scores were used to construct a nomogram which accurately predicted the OS in pancreatic cancer. TIMER 2.0 were used to investigate tumor immune infiltrating cells and its relationship with pancreatic cancer. CIBERSORT analysis revealed increased higher infiltration proportions of M0 and M2 macrophages, and lower infiltration of naive B cell, CD8+ T cell and Treg cells in the high-risk group. Compared to the low-risk group, functional annotation using ssGSEA showed that T cell infiltration and the differential immune-related check-point genes are expressed at low level in the high-risk group (P < 0.05). In summary, our study constructed a novel m6A-associated lncRNAs signature to predict immunotherapeutic responses and provided a novel nomogram for the prognosis prediction of pancreatic cancer.

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Identification of an Autophagy-Related Pair Signature for Predicting Prognoses and Immune Activity in Pancreatic Adenocarcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.743938 ◽

2021 ◽

Vol 12 ◽

Author(s):

Qian Zhang ◽

Liping Lv ◽

Ping Ma ◽

Yangyang Zhang ◽

Jiang Deng ◽

...

Keyword(s):

High Risk ◽

Pancreatic Adenocarcinoma ◽

Small Molecule ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

M2 Macrophage ◽

Related Gene ◽

Significant Difference

BackgroundPancreatic adenocarcinoma (PAAD) spreads quickly and has a poor prognosis. Autophagy research on PAAD could reveal new biomarkers and targets for diagnosis and treatment.MethodsAutophagy-related genes were translated into autophagy-related gene pairs, and univariate Cox regression was performed to obtain overall survival (OS)-related IRGPs (P<0.001). LASSO Cox regression analyses were performed to construct an autophagy-related gene pair (ARGP) model for predicting OS. The Cancer Genome Atlas (TCGA)-PAAD cohort was set as the training group for model construction. The model predictive value was validated in multiple external datasets. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Tumor microenvironments and immune infiltration were compared between low- and high-risk groups with ESTIMATE and CIBERSORT. Differentially expressed genes (DEGs) between the groups were further analyzed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used to identify potential small-molecule compounds in L1000FWD.ResultsRisk scores were calculated as follows: ATG4B|CHMP4C×(-0.31) + CHMP2B|MAP1LC3B×(0.30) + CHMP6|RIPK2 ×(-0.33) + LRSAM1|TRIM5×(-0.26) + MAP1LC3A|PAFAH1B2×(-0.15) + MAP1LC3A|TRIM21×(-0.08) + MET|MFN2×(0.38) + MET|MTDH×(0.47) + RASIP1|TRIM5×(-0.23) + RB1CC1|TPCN1×(0.22). OS was significantly shorter in the high-risk group than the low-risk group in each PAAD cohort. The ESTIMATE analysis showed no difference in stromal scores but a significant difference in immune scores (p=0.0045) and ESTIMATE scores (p=0.014) between the groups. CIBERSORT analysis showed higher naive B cell, Treg cell, CD8 T cell, and plasma cell levels in the low-risk group and higher M1 and M2 macrophage levels in the high-risk group. In addition, the results showed that naive B cells (r=-0.32, p<0.001), Treg cells (r=-0.31, p<0.001), CD8 T cells (r=-0.24, p=0.0092), and plasma cells (r=-0.2, p<0.026) were statistically correlated with the ARGP risk score. The top 3 enriched GO-BPs were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched KEGG pathways were the insulin secretion, dopaminergic synapse, and NF-kappa B signaling pathways. Several potential small-molecule compounds targeting ARGs were also identified.ConclusionOur results demonstrate that the ARGP-based model may be a promising prognostic indicator for identifying drug targets in patients with PAAD.

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