Prognostic Value of 18 F-FDG PET/CT in Diffuse Large B-Cell Lymphoma Treated with a Risk-Adapted Immunochemotherapy Regimen

Early identification of patients with diffuse large B-cell lymphoma (DLBCL) who are likely to experience disease recurrence or refractory disease after rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) would be useful for improving risk-adapted treatment strategies. We aimed to assess the prognostic value of F-FDG PET/CT parameters at baseline, interim, and end of treatment (EOT). We analyzed the prognostic impact of F-FDG PET/CT in 166 patients with DLBCL treated with a risk-adapted immunochemotherapy regimen. Scans were obtained at baseline, after 4 cycles of R-CHOP or 3 cycles of RR-CHOP (double dose of R) and 1 cycle of CHOP alone (interim) and 6 wk after completing therapy (EOT). Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan-Meier and the impact of clinical/PET factors assessed with Cox models. We also assessed the predictive ability of the recently proposed International Metabolic Prognostic Index (IMPI). The median follow-up was 7.9 y. International Prognostic Index (IPI), baseline metabolic tumor volume (MTV), and change in maximum SUV (ΔSUV ) at interim scans were statistically significant predictors for OS. Baseline MTV, interim ΔSUV , and EOT Deauville score were statistically significant predictors of PFS. Combining interim PET parameters demonstrated that patients with Deauville 4-5 and positive ΔSUV ≤ 70% at restaging (∼10% of the cohort) had extremely poor prognosis. The IMPI had limited discrimination and slightly overestimated the event rate in our cohort. Baseline MTV and interim ΔSUV predicted both PFS and OS with this sequential immunochemotherapy program. Combining interim Deauville score with interim ΔSUV may identify an extremely high-risk DLBCL population

Matched control analysis suggests R-CHOP followed by (R)-ICE may improve outcome in non-GCB DLBCL compared to R-CHOP

Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) is considered the standard-of-care for patients with advanced-stage diffuse large B-cell lymphoma (DLBCL), despite findings that non-germinal center B-cell-like (non-GCB) patients have significantly worse outcome with this regimen. We evaluated the prognostic significance of baseline risk factors, including cell of origin (COO) classified by the Hans algorithm, within an alternative chemoimmunotherapy program. At Memorial Sloan Kettering Cancer Center (MSK), 151 patients with DLBCL received sequential R-CHOP induction and (R)-ICE (rituximab, ifosfamide, carboplatin, and etoposide) consolidation. Outcome analysis based on COO was validated with a propensity score matched cohort treated with R-CHOP from the Mayo Clinic component of the Molecular Epidemiology Resource (MER). Among the GCB (n=69) and non-GCB (n=69) patients at MSK, event-free survival (EFS) of non-GCB was superior to that of GCB (HR 0.53, 95% CI 0.29-0.98). Overall survival (OS) demonstrated an association in the same direction but was not statistically significant (HR 0.68, 95% CI 0.33-1.42). Propensity score matched patients from MSK (n=108) demonstrated a small attenuation in the HRs for EFS (HR 0.57, 95% CI 0.27-1.18) and OS (HR 0.76, 95% CI 0.33-1.79) and were no longer statistically significant. In contrast, the matched MER cohort (n=108) demonstrated an EFS association (HR 1.17, 95% CI 0.70-1.95) and OS association (HR 1.13, 95% CI 0.64-2.00) in the opposite direction, but were also not statistically significant. R-CHOP induction and (R)-ICE consolidation may overcome the negative prognostic impact of the non-GCB phenotype, per the Hans algorithm, and can be preferentially selected for this population

Multidimensional Single-Cell Analysis of BCR Signaling Reveals Proximal Activation Defect As a Hallmark of Chronic Lymphocytic Leukemia B Cells

<div><p>Purpose</p><p>Chronic Lymphocytic Leukemia (CLL) is defined by a perturbed B-cell receptor-mediated signaling machinery. We aimed to model differential signaling behavior between B cells from CLL and healthy individuals to pinpoint modes of dysregulation.</p><p>Experimental Design</p><p>We developed an experimental methodology combining immunophenotyping, multiplexed phosphospecific flow cytometry, and multifactorial statistical modeling. Utilizing patterns of signaling network covariance, we modeled BCR signaling in 67 CLL patients using Partial Least Squares Regression (PLSR). Results from multidimensional modeling were validated using an independent test cohort of 38 patients.</p><p>Results</p><p>We identified a dynamic and variable imbalance between proximal (pSYK, pBTK) and distal (pPLCγ2, pBLNK, ppERK) phosphoresponses. PLSR identified the relationship between upstream tyrosine kinase SYK and its target, PLCγ2, as maximally predictive and sufficient to distinguish CLL from healthy samples, pointing to this juncture in the signaling pathway as a hallmark of CLL B cells. Specific BCR pathway signaling signatures that correlate with the disease and its degree of aggressiveness were identified. Heterogeneity in the PLSR response variable within the B cell population is both a characteristic mark of healthy samples and predictive of disease aggressiveness.</p><p>Conclusion</p><p>Single-cell multidimensional analysis of BCR signaling permitted focused analysis of the variability and heterogeneity of signaling behavior from patient-to-patient, and from cell-to-cell. Disruption of the pSYK/pPLCγ2 relationship is uncovered as a robust hallmark of CLL B cell signaling behavior. Together, these observations implicate novel elements of the BCR signal transduction as potential therapeutic targets.</p></div

Statistical Analysis of CLL B-cell Phosphoprofile using Partial Least Squares Regression (PLSR) against disease status.

<p>This figure details our method for analyzing phosphoflow data by Partial Least Squares Regression (PLSR) against disease status: A Training: 1 CLL B cells or healthy B cells were isolated using the gating described in the methods section. 2 Phosphospecific antibodies allowed detection of the phosphoresponse for pPLCγ2, pSYK, pBTK, pBLNK and ppERK. The percentage of cells positive for each phosphospecific antibody was calculated based on the unstimulated histograms. These %pX⁺ provide the raw data for Partial Least Squares Regression analysis, PLSR. 3 PLSR was applied to best correlate a linear combination of these phosphoprofiles for each patient (all healthy controls and all CLL samples pooled together) with a variable defining disease status (arbitrarily set to 0 for healthy individuals and 1 for CLL patients). This step of the analysis is referred to as “Training” as the PLSR algorithm uses a subset of CLL B cells and Healthy B cells to model the covariance of each phosphoresponse. 4 The output of PLSR analysis is a linear combination V_PLSR with weights (β<i>_i</i>) to each observed “predictor” variable (%pX<i>_i</i>⁺). The PLSR algorithm output attempts to find weights that best match the differences between healthy and CLL patient phosphoprofiles with disease status. B Test: Using the model (V_PLSR) defined during the training phase, we tested the power of our model by its ability to correctly predict the disease status of independently-acquired CLL patients and healthy individuals. Phosphoresponses are measured and linearly-combined into a V_PLSR variable as specified by the training step. C BCR signaling diagram highlighting pathway-based understanding of the V_PLSR score and weights. D Example of V_PLSR predictive power: two samples (one CLL and one healthy control) were tested side-by-side. As predicted, V_PLSR helps discriminate their differences in phosphoresponses. As illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079987#pone-0079987-g003" target="_blank">Fig. 3A</a>, %pX⁺ values over an unstimulated control are calculated and linearly combined to yield V_PLSR for the sample under consideration. Sample 1119, yields V_PLSR = 0.02, while Sample 1062, yields V_PLSR = 0.84, consistently with disease status (healthy and CLL, respectively).</p

Variability in the Signaling Profile Allows Partioning of CLL Patients Into Distinct Prognostic Groups.

<p>A Graphic representation of all five phosphoresponses for CLL patients (blue, n = 110) as compared to the healthy controls (red, n = 11). A pentagon was created for each sample by connecting the percent of responding cells (over unstimulated control) recorded on the axes. A wide variability in extent of response was observed in the CLL group, encompassing all possible level of responsiveness. Three distinct groups of CLL could be defined based on the responsiveness (inset): patients with uniformly low response (left, at least 4 of the 5 pX⁺<40%), patients with uniformly high response (right, at least 4 pX⁺>75%), and patients with intermediate, healthy-like response (middle, at least 4 pX⁺ within the range of healthy samples). This groups of patients yielded n = 28 among low-responders, n = 36 among intermediate (healthy-like) responders and n = 17 among high-responders. B Kaplan-Meyer curves showing time from diagnosis to first treatment (TTFT) was plotted for the three distinct CLL subgroups described in D. Patients whose phosphoprofile consists of uniformly high phosphoresponses had a statistically significant (p<0.01) shorter time to first treatment (TTFT). Furthermore, high responders had a larger fraction that had required treatment (p<0.04). Those patients whose %pX⁺ values were uniformly low or similar to healthy individuals across the 5 responses studied had a significantly longer time to first treatment, and fewer patients within this cohort have required treatment at the time of this analysis. C Distinct IGHV status (% deviation from germline sequence) is also observed between these three subgroups. The dashed line indicates the 2% cutoff for mutational status, mutated vs. unmutated. Mean ± Std. Error % Mutated: Healthy-like = 2.02±0.43; High = 0.73±.21; Low = 3.9±.86. D Three-dimensional visualization of three phosphoresponses ([%pPLCγ2⁺, %pBLNK⁺ and %pSYK⁺], or ([%pPLCγ2⁺, %pSYK⁺ and %ppERK⁺]) for (blue) all CLL and (red) healthy samples. Each panel demonstrates the clear separation of the healthy patient samples from the CLL patient samples solely by virtue of the combined %pX⁺ values for the three phosphoresponses.</p

Phosphoprofiling of the Proximal BCR Signaling Pathway Uncovers High Variability in BCR Signaling Pathway Behavior in CLL B cells.

<p>A. Simplified diagram of the BCR signaling pathway components investigated in this study. B. Representative histograms of phosphoresponses of B- cells for two CLL patients (a high and a low responder) and one healthy individual. CLL and healthy B cells were gated as described in the methods section. Stimulated samples (anti-IgM+H₂O₂) are indicated by a black line, unstimulated samples are shaded grey. C. Contour maps of the average B cell population: For each cohort, CLL and healthy, the cellular phosphoresponse fluorescence intensity values are averaged and viewed two dimensionally. Bimodality in the phosphoresponse of CLL B cells can be seen for the pairwise pPLCγ2 vs. pSYK) (see also Supp. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079987#pone.0079987.s003" target="_blank">Fig. S2</a> for other combinations of phosphoresponses). Healthy B cells show modest variability within the B-cell population, while the CLL B cells can be distinguished by their all-or-none response. D. Following IgM crosslinking, phosphoresponses are shown as the percentage of responding cells for each patient over an unstimulated matched control. This view of the wide, continuous range of each CLL patients' phosphoprofile highlights the high variability in the behavior of the BCR signaling pathway. Only the pPLCγ2 response showed statistical significance when comparing healthy to CLL cells (***p<0.0001).</p

Cell-to-cell variability analysis for V_PLSR as a function of CD20 and CD5 abundance.

<p>A V_PLSR analysis of high responder, low responder, and healthy-like responder groups. Three representative patient samples are shown for each responder type. All CLL B-cell samples have a majority of their cellular population exhibiting a V_PLSR>0.695 consistent with the CLL range (blue, as shown in the scale bar). Histograms represent the mean and spread of the V_PLSR values within each sample's B-cell population for low, high, and healthy-like responders. While low and high responders have a V_PLSR score centered around 1, the width of this peak is highly variable, but much more so in the healthy-like CLL. Healthy samples uniformly have a large variance of V_PLSR scores. B Evaluation of the heterogeneity within the samples as a measure of the variance of V_PLSR within the B cell population. Healthy-like responders exhibited a significantly larger variance compared to high or how responders (healthy-like vs. high: p = 0.0005; healthy-like vs. low: p = 0.0008; high vs. low: p = 0.23). The variance of V_PLSR within the B cell population of healthy samples is also shown.</p