Fegan, Chris

Scoring systems designed to improve the accuracy of prognostication in chronic lymphocytic leukemia (CLL) usually rely on discretized/dichotomic values of the clinical and biological variables. Here we analyzed the immunophenotypic and (immuno)genetic profiles of 2,243 CLL patients with Rai stage 0-I-II, by applying unsupervised machine learning methods, elaborating prognostic factors as continuous variables, to identify novel relationships and interactions likely missed in conventional hierarchical models. The study included an internal training cohort (n=863) and two external validation cohorts provided from Cardiff University/Brighton Medical School (n=455) and the Mayo Clinic (Rochester, MN; n=925). Primary endpoint was Time to First Treatment (TTFT). Laboratory-based markers evaluated in the training cohort were: surface antigen expression by flow cytometry (as % of positive cells) of CD20, FMC7, CD49d, CD49c, CD38, CD23, CD43, CD22, ZAP-70; chromosomal aberrations (% of nuclei with abnormal signal) del13q, tris12, del11q and del17p,; mutational status of TP53 by NGS as % VAF; IGHV mutational status as % mutation. 420/863 cases received treatment (median 46 months). Univariate Cox regression identified FISH del11q, del17p and tris12, TP53 and IGHV mutations, CD38 and CD49d expression, as features associated with TTFT (p-value <0.001). An unsupervised k-means algorithm partitioned cases into 6 clusters (C1-C6), as selected by the elbow method, and centroid analysis evaluated which feature most contributed to each cluster (Panel A): C1 (n=275) was heavily IGHV-mutated (mutation range 4.5-22.0%) with low representation of all other features; C2 (n=208) was mostly IGHV-unmutated/intermediate (up to 4.5%) in the absence of other features; C3 (n=169) were CD49d-expressing cases (>50% expression), with low representation of other features, except CD38; C4 (n=127) were cases with trisomy 12 (>28%), also expressing CD49d and CD38; C5 (n=48) were cases with highly clonal del11q (44-98%) and IGHV-unmutated, mutually exclusive with TP53 disruption; C6 (n=34) contained TP53-disrupted cases with high mutation burden (VAF 36-97%) and/or del17p (range 40-96%), with low importance of all the other features. Of note, alternative use of either TP53 mutation or del17p as defining features of cluster 6 did not result in changes of the classification accuracy. Clinically, clusters 3-4-5-6 showed median TTFT of 26, 22, 9, 5 months respectively whereas median TTFT was not reached for clusters 1-2. Hierarchical agglomerative clustering aggregated the 6 clusters in 3 major risk tiers: high (C5-6), intermediate (C2-3-4) and low (C1), with median TTFT of 7, 45 and not reached, respectively (Panel B). Multivariate Cox analysis (MVA) with Rai staging demonstrated that the 3-tier risk score contributed significantly and independently to TTFT estimate (p<0.0001; Harrel's c-index=0.71). To classify novel patients, we designed a hierarchical algorithm, based on each clusters' estimated risk and according to calculated cut-off, i.e. C6, del17p/ TP53>36%; C5, del11q>43%; C4, tris12>28%; C3, CD49d>50%; C2, IGHV≤4.5%; C1, the remaining patients with IGHV>4.5%. This approach was highly robust by an internal training-validation bootstrap (cluster classification accuracy = 0.96±0.02). External samples from two independent cohorts were classified into the six clusters using the cut-offs defined by the hierarchical classifier, and then aggregated into the 3-risk tiers. The classifier performed robustly in both cohorts, again adding independent prognostic information to Rai stage in MVA, with p<0.0001 and c-index of 0.76 for Cardiff/Brighton cohort and p<0.0001 and c-index of 0.74 for Mayo cohort. In conclusion, our machine-learning-driven, laboratory-based classification identifies clusters of patients who at higher risk of requiring early treatment, independent of clinical staging, and may help to better identify patients who may benefit from early treatment or more frequent disease surveillance. For some features, TP53 or IGHV mutational status, our unsupervised approach selected cut-offs not recapitulating the canonical ones, envisioning different biological activities in the TTFT setting. For example, TP53 mutations were clinically relevant only if present in the vast majority of the clone , suggesting the involvement of specific activities of the TP53 mutant in this context.

Abstract Introduction: Observation is the standard of care for asymptomatic early stage chronic lymphocytic leukemia (CLL) however these cases follow a heterogenous course. Recent studies show novel biomarkers can delineate indolent from aggressive early stage disease and current clinical trials are exploring the role of early intervention in high risk cases. Although several scoring systems have been established in CLL, most are designed for overall survival, do not circumscribe early stage disease, and require cumbersome calculations relying on extensive laboratory and clinical information. Aim: We propose a novel laboratory-based prognostic calculator to risk stratify time to first treatment (TTFT) in early stage CLL and guide candidate selection for early intervention. Methods: We included 1574 cases of early stage CLL in an international cohort from Italy, the United Kingdom and the United States using a training-validation model. Patient information was obtained from participating centers in accordance with the Declaration of Helsinki. The training cohort included 478 Rai 0 cases from a multicenter Italian cohort, all referred to a single center (Clinical and Experimental Onco-Hematology Unit of the Centro Riferimento Oncologico in Aviano, IT) for immunocytogenetic lab analyses. Considering TTFT as an endpoint, we evaluated 8 variables (age>65, WBC>32K, 17p-, 11q-, +12, IGHV status, CD49d+, gender) with univariate and multivariate Cox regression internally validated using bootstrapping procedures. FISH thresholds were 5% for 11q-, and +12 and 10% for 17p-. Cases were categorized according to the hierarchical model proposed by Dohner. IGHV status was considered unmutated at ≥98%. CD49d+ was set at >30%. WBC cutoff of >32K was established by maximally selected log rank analysis. Variables were weighted based on the proportion of their normalized hazard ratios rounded to the nearest whole integer. We used recursive partitioning for risk-category determination and Kaplan-Meier analysis to generate survival curves. We compared the concordance index (C-index) of our model with the CLL international prognostic index (CLL-IPI) for 381/478 cases in the training cohort with available beta-2-microglobulin data and for all validation cohorts. We used 3 independent single-center cohorts for external validation. Results: The training cohort had 478 cases of Rai 0 CLL with a median (95% CI) TTFT of 124 months (m) (104-183m). Five prognostic variables emerged with respect to TTFT, and each assigned a point value of 1 or 2 according to their respective normalized HR values as follows: 17p-, and UM IGHV (2 pts); 11q-, +12, and WBC>32K (1 pt). We identified three risk groups, based on point cut-offs of 0, 1-2, and 3-5 established by recursive partitioning analysis with a median (95% CI) TTFT of 216m (216-216m), 104m (93-140m) and 58m (44-68m) (p<0.0001, C-index 0.75) for the low, intermediate, and high-risk groups, respectively (figure 1). A comparison with the CLL-IPI was possible in 381 cases with available beta-2-microglobulin data. In this subset, the C-index was 0.75 compared to 0.68 when patient risk groups were split according to the CLL-IPI. The scoring system was then validated in 3 independent cohorts of early stage CLL: i) Gemelli Hospital in Rome, IT provided 144 Rai 0 cases. Median (95% CI) TTFT was 86m (80-94m, 95% CI). Median (95% CI) TTFT for the low, intermediate and high-risk groups was 239m (239-239m), 98m (92-132m) and 85m (60-109m) respectively (p=0.002 between low and intermediate groups, p=0.09 between intermediate and high groups; C-index 0.64 v 0.60 for CLL-IPI). ii) Cardiff University Hospital in Wales, UK provided 395 Binet A cases. Median (95% CI) TTFT was 74 m (67-81m) overall and NR, 111m (97-146m) and 70m (29-114m) for the low, intermediate and high-risk groups respectively (p<0.001 between low and intermediate groups, p=0.009 between intermediate and high groups; C-index 0.63 v 0.63 for CLL-IPI). iii) Mayo Clinic in Rochester, MN provided 557 Rai 0 cases. Median (95% CI) TTFT was 127m (96m-NR) overall and NR, 76m (64m-NR) and 36m (31-59m) for the low, intermediate and high-risk groups respectively (p<0.0001; C-index 0.72 v 0.68 for CLL-IPI). Conclusion: We present a novel laboratory-based scoring system for Rai 0/Binet A CLL to aid case selection in risk-adapted treatment for early disease. Further comparison to existing indices is needed to verify its utility in the clinical setting. Disclosures Zaja: Novartis: Honoraria, Research Funding; Takeda: Honoraria; Abbvie: Honoraria; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Fegan:Roche: Honoraria; Napp: Honoraria; Janssen: Honoraria; Gilead Sciences, Inc.: Honoraria; Abbvie: Honoraria. Pepper:Cardiff University: Patents & Royalties: Telomere measurement patents. Parikh:AstraZeneca: Honoraria, Research Funding; Janssen: Research Funding; MorphoSys: Research Funding; Abbvie: Honoraria, Research Funding; Gilead: Honoraria; Pharmacyclics: Honoraria, Research Funding. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees.

Abstract The characteristic clinical heterogeneity observed in chronic lymphocytic leukemia (CLL) reflects its biological diversity and as a consequence, better understanding of the biology of this disease has led to the identification of a number of useful prognostic tools. Advances in massively parallel sequencing technologies have unearthed genetic and epigenetic heterogeneity and reinforced the concept that specific genetic lesions and the development of genomic complexity can modulate the pathology of CLL. In addition, DNA methylation profiling has shown that CLL patients can be grouped into at least three clinically relevant epigenetic subgroups with distinct outcomes. However, despite this explosion in knowledge, defining the prognosis of individual CLL patients remains a significant clinical challenge. Consequently, there is a need to identify prognostic tests that can provide reliable personalized risk assessments. We have recently shown that a subset of CLL patients manifest telomere dysfunction, which drives genomic instability and clonal evolution. We went on to show an association between short, dysfunctional telomeres and 'high-risk' genetic lesions1,2. Importantly, longitudinal analysis confirmed that tumor cell telomere length remained remarkably stable throughout the course of the disease suggesting that the telomere length is fixed at an early point in disease pathogenesis making it a reliable predictor of both clinical progression and overall survival (OS) regardless of when the measurement is made. In this study, we generated telomere length profiles in 224 newly diagnosed, early stage CLL patients seen at the Mayo clinic in Rochester using high-throughput single telomere length analysis (HT-STELA). We then tested the ability of telomere length to predict time to treatment (TTT) and OS and compared its predictive power with DNA methylation profiling and CLL-IPI scores. Patients with a mean telomere length inside the fusogenic range (IFR) had a significantly shorter time to treatment (P<0.0001; HR = 3.2) and OS (P = 0.0027; HR = 2.3) when compared to patients with a mean telomere length outside the fusogenic range (OFR) (Figure 1A and B). When considering epigenetic subgroups, we found a strong association between telomere length and 'epitype'; high programmed (HP) CLL cells had significantly longer telomeres than intermediate programmed (IP), and low programmed (LP) CLL cells (P = 0.0002 and P<0.0001 respectively). Furthermore, bifurcation of the HP and IP groups into telomere length OFR and IFR subsets identified patients with distinctly different TTT (P = 0.0008; HR = 2.74) and OS (P = 0.004; HR = 2.4). 79/81 (97.5%) of the LP group had short telomere length IFR, but when we divided the LP group above and below the mean telomere length of the fusogenic range (≤/>2.17kb), patients with the shortest telomeres had a significantly worse OS (P = 0.034; HR = 2.83). Telomere length was also strongly associated with CLL-IPI score; the CLL-IPI low-risk group (CLL-IPI 1) had the longest telomeres and the CLL-IPI high-risk group (CLL-IPI 4) had the shortest telomeres (P<0.0001). More heterogeneity in telomere length was observed in the CLL-IPI 1 and 2 groups and patients with telomere length IFR in these 'low-risk' groups showed significantly shorter TTT (P<0.0001; HR = 4.24) and overall survival (P<0.0001; HR = 10.1) (Figure 1C and 1D). Finally, multivariate modelling, using Cox proportional hazards with forward selection, revealed that telomere length retained independent prognostic significance when CLL-IPI score was entered in the model for time to treatment (HR = 1.42; P = 0.023) and OS (HR = 1.68; P = 0.008). In conclusion, telomere length analysis provides more accurate prognostic information about the progression and outcome of CLL patients and can augment the prognostic power of the CLL-IPI for early stage CLL. References Lin TT et al. Blood. 2010;116(11):1899-907. Britt-Compton et al. Leukemia. 2012; 26(4):826-30. Disclosures Norris: Cardiff University: Patents & Royalties: Telomere measurement patent. Shanafelt:GlaxoSmithKline: Research Funding; Genentech: Research Funding; Pharmacyclics: Research Funding; Jansen: Research Funding. Kay:Acerta: Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Agios Pharm: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees. Fegan:Napp: Honoraria; Gilead Sciences, Inc.: Honoraria; Roche: Honoraria; Abbvie: Honoraria; Janssen: Honoraria. Baird:Cardiff University: Patents & Royalties: Telomere measurement patents. Pepper:Cardiff University: Patents & Royalties: Telomere measurement patents.

Abstract Purpose Although CD49d is an unfavorable prognostic marker in chronic lymphocytic leukemia (CLL), definitive validation evidence is lacking. A worldwide multi-center analysis was performed using published and unpublished CLL series to evaluate the impact of CD49d as overall survival (OS) and treatment free survival (TFS) predictor. Patients and Methods A training/validation strategy was chosen to find the optimal CD49d cut-off. The hazard ratio (HR) for death and treatment imposed by CD49d was estimated by pooled analysis of 2,972 CLL, and Cox analysis stratified by center and stage was used to adjust for confounding variables. The importance of CD49d over other flow cytometry-based prognosticators (CD38, ZAP-70) was ranked by recursive partitioning. Results Patients with ≥30% of neoplastic cells expressing CD49d were considered CD49d+. The decrease in OS at 5 and 10-years among CD49d+ cases was 7% and 23% (decrease in TFS 26% and 25% respectively). The pooled HR of CD49d for OS was 2.5 (2.3 for TFS) in univariate analysis. This HR remained significant and of similar magnitude (HR=2.0) in a Cox model adjusted for clinical and biological prognosticators. Hierarchical trees including all cases, or restricted to early stage or patients ≤65 years, always selected CD49d as the most important flow-cytometry-based biomarker, with negligible additional prognostic information added by CD38 or ZAP-70. Consistently, by bivariate analysis, CD49d reliably identified patients' subsets with poorer outcome independent of CD38 and ZAP-70. Conclusions In this analysis of ∼3000 patients, CD49d emerged as the strongest flow cytometry-based predictor of OS and TFS in CLL. Disclosures: Shanafelt: Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding. Burger:Pharmacyclics: Research Funding.

Abstract Abstract 3871 Background. A number of investigators have provided data supporting the value of CD49d expression on B leukemic cells in chronic lymphocytic leukemia (CLL) as independent prognostic variable. These studies used a variety of clinical end points including overall survival (OS), time to treatment (TTT) or treatment free survival (TFS). The factors included in multivariate analyses of these studies also differed. Unresolved issues regarding the prognostic value of CD49d assessment in CLL included the choice of the optimal cut-off to define positivity and CD49d prognostic value in patients subsets defined by other standard prognostic factors. Aim. To perform meta-analysis using individual patient level data from studies of CD49d expression to evaluate its ability to predict OS (primary end-point) and TTT/TFS (secondary end-point). Methods. Studies published by 30 April 2011, reporting an association of high CD49d expression on B CLL cells, measured by flow cytometry, and end-points were identified by Medline search. Additionally, we performed a manual review of abstracts presented at the congress of the American Society of Hematology from 2006 to 2010. CD49d was used as a categorical variable, as coded in original studies. Results. We identified 6 published studies and one abstract for inclusion. All authors agreed to provide individual data on the patients in these publications as well as unpublished data on additional patients to be used for replication analysis. Four authors also submitted updated follow-up data. We collected the following variables: date of diagnosis, OS, TTT/TFS, CD49d, CD38, ZAP-70, immunoglobulin mutational (IGHV) status, del17p and del11q chromosomal aberrations, age, stage, ALC, and β2 microglobulin concentration. Data from 3146 individual patients was available with 261 subsequently excluded due to missing end point and/or CD49d data. Of 2771 patients with valid data, 1405 (51%) were included in the previous publications and 1366 (49%) were unpublished. Before starting analyses a decision was made to perform meta-analysis on published data and use data from previously unpublished data as a validation cohort. Pooled CD49d hazard ratio for OS was 2.62 (1.88–3.64). In bivariate analysis, the prognostic value of CD49d was confirmed and of comparable value in patients subsets defined by CD38 and ZAP-70 expression, IGHV status, unfavorable chromosomal aberrations. Finally, we performed a multivariate analysis including CD49d, CD38, ZAP-70, IGHV status, del17p and del11q. CD49d was significantly associated with shorter overall survival in this adjusted model, with a hazard ratio of 2.47(1.17–5.21). Inspection of Martingale residuals plots of CD49d in each study failed to show a recognizable cut-point. CD49d cut-points previously adopted in published studies (30% and 45%) were subjected to validation in the cohort of 1366 unpublished patients. The Cox model with 45% cut-off had a slight greater hazard ratio (2.73 vs 2.65, log-rank p<0.0001). Only 4% of patients changed prognostic category after swapping the cut-off from 45% to 30%. Conclusions. Preliminary results of a meta-analysis using individual patient data from >1400 patients confirm the association of high CD49d expression with short OS independent of other prognostic parameters. While the 45% cut-off has a marginal better prognostic power, the 30% cut-off has the advantage of being already in use for CD38 and generally adopted for other surface antigens in chronic leukemia immunophenotyping. These findings may have implication for patient's stratification in future prospective studies and potential therapeutic efforts to target CD49d or CD49d signaling. Disclosures: Shanafelt: Genentech: Research Funding; Glaxo-Smith-Kline: Research Funding; Cephalon: Research Funding; Hospira: Research Funding; Celgene: Research Funding; Polyphenon E International: Research Funding. Burger:Pharmacyclics: Consultancy, Research Funding.

Human cytomegalovirus (HCMV) is a widely prevalent herpes virus which establishes a state of chronic infection. The establishment of CMV‐specific immunity controls viral reactivation and leads to the accumulation of very large numbers of virus‐specific T cells which come to dominate the immune repertoire. There is concern that this may reduce the immune response to heterologous infections and HCMV infection has been associated with reduced survival in elderly people. Patients with chronic lymphocytic leukemia (B‐CLL) suffer from a state of immune suppression but have a paradoxical increase in the magnitude of the CMV‐specific T cell and humoral immune response. As such, there is now considerable interest in how CMV infection impacts on the clinical outcome of patients with B‐CLL. Utilizing a large prospective cohort of patients with B‐CLL (n = 347) we evaluated the relationship between HCMV seropositivity and patient outcome. HCMV seropositive patients had significantly worse overall survival than HCMV negative patients in univariate analysis (HR = 2.28, 95% CI: 1.34–3.88; P = 0.002). However, CMV seropositive patients were 4 years older than seronegative donors and this survival difference was lost in multivariate modeling adjusted for age and other validated prognostic markers (P = 0.34). No significant difference was found in multivariate modeling between HCMV positive and negative patients in relation to the time to first treatment (HR = 1.12, 95% CI: 0.68–1.84; P = 0.65). These findings in a second independent cohort of 236 B‐CLL patients were validated. In conclusion no evidence that HCMV impacts on the clinical outcome of patients with B‐CLL was found. Am. J. Hematol. 91:776–781, 2016. © 2016 Wiley Periodicals, Inc.