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Introduction
Artificial intelligence (AI) has been applied to a wide range of daily activities to assist in decision-making. Randomized clinical trials can compare the efficacy of treatment between ...patient groups. However, the best treatment decision for each individual patient, with their own clinical and biological features, and in the context of comparable treatment options, is more difficult to predict. The integrated consideration of various prognostic features can reach the point beyond human recognition. An AI-assisted approach may help with decision-making in complex clinical situations. The aim of this study is to introduce a prototype of AI to predict outcome such as achievement of major molecular response (MMR) within 1 year of the start of tyrosine kinase inhibitor (TKI).
Methods
Response data for 630 patients with newly diagnosed CML-CP in consecutive prospective clinical trials of frontline imatinib (n=73; NCT00048672), high-dose imatinib (n=208; NCT00038469 and NCT00050531), nilotinib (n=148; NCT00129740), dasatinib (n=150; NCT00254423), and ponatinib (n=51; NCT01570868) were analyzed. After multiple imputation for missing variables, neural network analysis with a multilayer perceptron model using the statistically significant variables by stepwise multivariate analysis was performed to predict the cumulative incidence of MMR within 1 year. The hyperbolic tangent and softmax activation function were used to create the architecture of hidden layers and output layers, respectively. Batch training with scaled conjugate gradient optimization algorithm with learning parameters (initial Lambda of 0.0000005, initial Sigma of 0.00005, interval center of 0, and interval offset of ±0.5) was used to train the neural network. To evaluate the accuracy of prediction, the entire cohort was randomly divided into training dataset (70%) and test dataset (30%). The correct prediction in the test dataset was repeatedly assessed 1,000 times to validate this approach. The whole cohort was subsequently used to create the AI model for MMR prediction, and was divided into two cohorts based on the prediction by the AI; AI-predicted response, and AI-predicted nonresponse. Hypothetical choice of TKI was assumed to rank the selection of TKI among imatinib 400 mg/day, imatinib 800 mg/day, dasatinib, nilotinib, and ponatinib to calculate the estimated percentage of MMR within 1 year for each patient. The Kaplan-Meier method with a log-rank test was used for failure-free survival (FFS), transformation-free survival (TFS), event-free survival (EFS), and overall survival (OS). To balance baseline patient characteristics between cohorts, propensity score matching after propensity score calculation by logistic regression was performed with nearest neighbor matching method with a caliper of 0.20. Exact matching was used for the type of cytogenetic, transcript, and TKI.
Results
Of 630 patients treated, 464 (74%) achieved MMR within 1 year. The stepwise multivariate analysis identified the selection of TKI, type of transcript, white blood cell count, albumin, and spleen size at diagnosis were the predictors for MMR within 1 year. Neural network analysis with a multilayer perceptron model is shown in figure 1. Through repeated random selection for training set (70%) and test set (30%), the mean correct prediction for MMR within 1 year was 77.4% (95% confidence interval CI, 74.2-80.5), and 76.9% (95% CI, 71.4-82.3), respectively. Of 630 patients, the neural network model predicted 539 patients (86%) as responders, and 91 patients (14%) as nonresponders (table 1). Before propensity score matching, the AI-response cohort had higher rates of CCyR, MMR, MR4, MR4.5, and CMR as well as FFS, TFS, EFS, and OS compared to those of the AI-nonresponse cohort (figure 2). After propensity score matching, 25 patients in each cohort were identified, and the baseline differences were minimized (table 1). The AI-response cohort had higher rates of MMR, MR4, and FFS than those of AI-non-response cohort (figure 2).
Conclusion
AI with a multilayer perceptron model can predict target outcome. Incorporation of additional clinical and biological variables may improve the prediction rates to suggest the best treatment option in each patient with CML-CP. Such strategy is ongoing.
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Kantarjian:ARIAD: Research Funding; Bristol-Myers Squibb: Research Funding; Amgen: Research Funding; Pfizer Inc: Research Funding; Delta-Fly Pharma: Research Funding; Novartis: Research Funding. Jabbour:ARIAD: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy. Ravandi:BMS: Research Funding; Seattle Genetics: Consultancy, Honoraria, Research Funding. Konopleva:AbbVie: Research Funding; Genentech: Research Funding. Wierda:Novartis: Research Funding; Abbvie: Research Funding; Acerta: Research Funding; Gilead: Research Funding; Genentech: Research Funding. Daver:Pfizer: Consultancy, Research Funding; Kiromic: Research Funding; BMS: Research Funding; Otsuka: Consultancy, Honoraria; Sunesis: Consultancy, Research Funding; Karyopharm: Honoraria, Research Funding; Ariad: Research Funding.
Disease-inherent and treatment-related immune dysfunction remain leading causes for morbidity and mortality in patients with chronic lymphocytic leukemia (CLL). The advent of kinase inhibitors that ...target B cell receptor (BCR) signaling, which lack myelo- and T lymphocyte toxicity, raised hopes that these new agents may be less immunosuppressive and allow for better immune reconstitution when compared to chemo-immunotherapy (CIT). The effects of the BTK inhibitor ibrutinib or CIT with fludarabine, cyclophosphamide and rituximab (FCR) on the normal B cell repertoire have not been well characterized. Here, we used state-of-the-art immunosequencing technology to investigate how ibrutinib treatment affects the regeneration of non-malignant B-cells when compared to patients treated with FCR.
Clinical data on infection rates and immunoglobulin levels was analyzed from 40 CLL patients treated with ibrutinib (median number of two pre-treatments) or frontline CIT with FCR at MD Anderson Cancer Center. In a representative subset of 20 patients, flow cytometry and next generation sequencing (NGS) of the immunoglobulin heavy chain (IGH) gene locus was used to monitor non-malignant B-cell immune reconstitution for 24 months after start of treatment with ibrutinib or FCR.
Comparison of ibrutinib treatment with CIT revealed that immunoglobulin levels remained stable and relatively low in both cohorts, except for an increase in IgA during ibrutinib treatment, as previously reported. NGS results showed that ibrutinib treatment significantly decreased the non-malignant B-cells count after 24 months of treatment, while the counts were quantitatively stable in the FCR cohort. Next, we determined the dynamics of non-malignant B-cell immune repertoire composition over treatment. Based on the mutational status of the V gene, non-malignant B-cells were classified as IGH hypermutated (<98% identity to the corresponding germline V gene, corresponding to antigen-experienced B-cells) or IGH unmutated (≥98% identity to the corresponding germline V gene, corresponding to antigen-naïve B-cells). Before treatment initiation, the mean percentage of antigen-experienced B-cells did not significantly differ between the groups (ibrutinib 39%, FCR 48%). After 24 months, a significant decrease of antigen-experienced B-cells was observed in the FCR cohort, while the ratio of antigen-experienced and antigen-naïve B-cells remained unchanged in ibrutinib treated patients (ibrutinib 39%, FCR 22%, p=0.01). Analysis of the IGH clonotype repertoire using the Shannon-Wiener and the inverse Simpson diversity indices confirmed these results, showing that the non-malignant IGH repertoire was composed of balanced numbers of antigen-experienced and antigen-naïve medium sized clones before treatment initiation in both cohorts. In line with the IGH repertoire shift towards antigen-naïve B-cells in FCR treated patients, the medium-sized clones disappeared after treatment, with large numbers of small-sized unmutated clones dominating after 24 months (p<0.0001). In ibrutinib treated patients, the repertoire diversity remained stable throughout the course of treatment.
Taken together, our data indicate that continuous treatment with ibrutinib preserves preexisting (partially antigen-experienced) B-cells but impairs de-novo generation of naive B-cells. In contrast, FCR leads to a deletion of memory B-cells but also a subsequent substantial renewal of the B-cell repertoire. Both patterns may differentially affect immune-competence towards infections.
Bokemeyer:Karyopharm: Research Funding. Jain:Pfizer: Consultancy, Honoraria, Research Funding; Incyte: Research Funding; Genentech: Research Funding; Abbvie: Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Infinity: Research Funding; Novartis: Consultancy, Honoraria; Servier: Consultancy, Honoraria; Novimmune: Consultancy, Honoraria; ADC Therapeutics: Consultancy, Honoraria, Research Funding; BMS: Research Funding; Celgene: Research Funding; Seattle Genetics: Research Funding. Wierda:Gilead: Research Funding; Abbvie: Research Funding; Novartis: Research Funding; Acerta: Research Funding; Genentech: Research Funding. Burger:Pharmacyclics: Research Funding.
Background
Prognostic factors correlate with clinical outcomes independent of treatment. Many prognostic factors have been identified in CLL. More effective treatments, like B cell receptor (BCR) ...signaling pathway inhibitors, have the potential to nullify the prognostic impact of some markers. Time-to-first treatment is an endpoint unaffected by choice of treatment. CpG-stimulated metaphase karyotype can reliably and reproducibly identify cytogenetic abnormalities in CLL that may not be seen with standard non-stimulated karyotype or by FISH. Complex cytogenetics, defined as 3 or more chromosome abnormalities identified in 2 or more metaphases was the highest-risk feature for shorter progression-free and overall survival in patients receiving ibrutinib for relapsed/refractory CLL. Complex karyotype is not uncommon among relapsed/refractory CLL cases, particularly those who previously received genotoxic chemotherapy. The frequency and impact of karyotype abnormalities have not previously been reported for treatment-naïve CLL.
Methods
We evaluated treatment-naïve patients with CLL who had their initial evaluation, which included prognostic factor assessment, at MDACC between July 2013 and June 2016. CpG-stimulated metaphase karyotype of CLL cells from blood or bone marrow was performed by culture of mononuclear cells for 72hrs in media containing CpG-685 (20ug/ml), phorbol 12-myristate 13-acetate (PMA; 0.04ug/ml) and Pokeweed mitogen (PWM; 0.1ug/ml). Banding and analyses were by standard laboratory procedures. Twenty metaphases were analyzed per culture and patients were categorized as having diploid karyotype, a single clonal chromosome abnormality present in more than 1 metaphase, two clonal chromosome abnormities present in more than 1 metaphase, or 3 or more chromosome abnormalities identified in more than 1 metaphase (complex).
Results
CpG-stimulated karyotype was obtained in 500 treatment-naïve patients with CLL. The frequency and distribution of chromosome abnormalities with other prognostic factors and time-to-first treatment were analyzed (Table). The majority (69%) of patients had diploid cytogenetics. Higher-risk prognostic features such as del(17), del(11q), unmutated IGHV and ZAP70 expression were associated with presence of complex karyotype abnormalities. Shorter time-to-first treatment from diagnosis was associated with 1, 2, and 3 or more clonal chromosome abnormalities compared to diploid karyotype (p=.0005) (Figure).
A previous multivariable model identified the following independent characteristics correlated with time-to-first treatment: size of the largest cervical lymph node, number of nodal sites involved, FISH cytogenetics, LDH, and IGHV mutation status (Wierda et al. JCO 29:4088, 2011). We are working to integrate stimulated cytogenetics results into this multivariable model for time-to-first treatment.
Conclusions
Clonal chromosome abnormalities were identified in nearly 30% of previously untreated patients with CLL and were associated with shorter time-to-first treatment. Analyses are ongoing to determine optimal integration into a multivariable model for time-to-first treatment.
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Jain:Celgene: Research Funding; Novimmune: Consultancy, Honoraria; Genentech: Research Funding; Seattle Genetics: Research Funding; ADC Therapeutics: Consultancy, Honoraria, Research Funding; Incyte: Research Funding; Infinity: Research Funding; Novartis: Consultancy, Honoraria; Abbvie: Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Servier: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria, Research Funding; BMS: Research Funding. Thompson:Pharmacyclics: Consultancy, Honoraria. Burger:Gilead: Research Funding; Roche: Other: Travel, Accommodations, Expenses; Janssen: Consultancy, Other: Travel, Accommodations, Expenses; Portola: Consultancy; Pharmacyclics, LLC, an AbbVie Company: Research Funding. O'Brien:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria. Wierda:Genentech: Research Funding; Gilead: Research Funding; Acerta: Research Funding; Abbvie: Research Funding; Novartis: Research Funding.
In chronic lymphocytic leukemia (CLL), it is recognized that germinal center experience by the cell-of-origin has an influence on the malignant phenotype and impacts clinical behavior. Measurement of ...the mutational status of the IGHV locus is a commonly used approach that subgroups patients according to this natural history. Other biomarkers correlating with these subgroups, such as ZAP70 expression and ZAP70 DNA methylation, are also used. Recently, we and others have found vast differences in global DNA methylation profiles among CLL cases that classifies distinct subgroups related to their natural history. Here we systematically evaluate various markers of natural history of the cell-of-origin for their ability to discriminate clinical outcomes.
IGHV mutational status and ZAP70 expression are currently the most widely used prognostic markers of germinal center experience. These markers are generally concordant, with IGHV-unmutated associating with ZAP70 positivity. To compare their relative prognostic impact, we selected CLL cases from a large sample cohort collected as a prospective study maintained by the CLL Research Consortium that were discordant (IGHV-mutated and ZAP70(+) or IGHV-unmutated and ZAP70(-); n=192). We found a significant difference in time-to-first-treatment (TTFT; P=0.002) and overall survival (OS; P=0.032) between discordant groups, with curves separating according to IGHV as opposed to ZAP70 (Fig.1A).
DNA methylation-based classification of CLL according to their natural history has not been compared to IGHV status. This classification strategy subgroups the majority of cases into low-programmed CLL (LP-CLL) or high-programmed (HP-CLL) subgroups that generally correlate with IGHV-unmutated/ZAP70(+) and IGHV-mutated/ZAP70(-), respectively, along with a minority of cases in an intermediate (IP-CLL) subgroup. We used our previously-described DNA methylation assay, based on MassARRAY interrogating 7 genomic loci including ZAP70, to group the 192 samples with discordant IGHV/ZAP70 results with 327 samples analyzed previously from the same cohort. DNA methylation (LP-CLL vs. HP-CLL) outperformed IGHV classification (unmutated vs. mutated) in terms of separating TTFT curves (n=432; LP-CLL vs. HP-CLL median difference=4.8 years, unmutated vs. mutated IGHV median difference=2.9 years) and OS curves (n=491; LP-CLL vs. HP-CLL median difference=12.9 years, unmutated vs. mutated IGHV median difference=6.8 years). To evaluate whether DNA methylation subgroups provide additional prognostic information to IGHV, we compared TTFT and OS within IGHV subtypes. We found that the ability of DNA methylation subgrouping to separate OS curves was maintained in the IGHV-unmutated subtype (P<0.001; Fig.1B) and TTFT in both IGHV-unmutated and mutated subtypes (P<0.001 and P=0.016, respectively). Conversely, IGHV subtypes did not significantly separate outcomes within DNA methylation subgroups (P>0.5 for all).
Further investigation of the features of DNA methylation subgroups as defined revealed a strong bias in the frequency of several recurrent adverse prognostic somatic mutations. In particular 17p deletions and EGR2, SF3B1 and XPO1 mutations occurred frequently within the LP-CLL subgroup. We next explored whether the presence of each mutation correlated with clinical outcome within its corresponding DNA methylation subgroup, and found heterogenous results. EGR2 mutations separated TTFT in the LP-CLL subgroup (P=0.0009), but 17p, XPO1 and SF3B1 did not (P>0.3). This suggests that the cell-of-origin should be taken into account to accurately assess the clinical impact of somatic mutations.
In summary, we demonstrate that among discordant cases for IGHV/ZAP70, IGHV appears to be a stronger prognostic factor for both TTFT and OS. When then evaluating the relative importance of DNA methylation subgrouping and IGHV status on TTFT and OS, we find that DNA methylation subgrouping is the more powerful prognostic factor. Among the markers of the cell-of-origin, DNA methylation is a strong tool for risk-stratifying patients and should be considered in development of risk prediction models.
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Wierda:Acerta: Research Funding; Novartis: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Abbvie: Research Funding. Brown:Infinity: Consultancy; Janssen: Consultancy; Gilead Sciences: Consultancy; Roche/Genentech: Consultancy; Celgene: Consultancy; Sun BioPharma: Consultancy; Pfizer: Consultancy; Abbvie: Consultancy. Kipps:Roche: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Gilead: Consultancy, Honoraria, Speakers Bureau; Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria, Research Funding.
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Background
TKIs are the mainstay of treatment for CML. Cardiovascular events may occur during the course of therapy with TKI. We present a single institution experience of cardiovascular events ...among CML patients receiving initial therapy with various TKIs.
Methods
We retrospectively assessed 584 CML patients enrolled on consecutive or parallel prospective frontline TKI clinical trials (imatinib, n=281; dasatinib, n=120; nilotinib, n=132, ponatinib n=51). Treatment-emergent adverse events (TEAE) under the MedRA heading "Cardiovascular" were analyzed. Over 1,080 unique terms are included under this MedRA heading. Poisson regression models assessed factors associated with occurrence of cardiovascular and arterio-thrombotic events.
Results
The median age (range) at start of treatment was 49 yrs (15 - 87); 41% were female and 79% were white. The overall median time of follow-up was 66 mos, with the longest follow-up for the imatinib (117 mos) cohort and the shortest for the ponatinib (13 mos) cohort. Co-morbid predisposing conditions were present at baseline in the majority of patients, with significant imbalance among treatment arms. Cardiovascular events occurred in 53% of all patients. Hypertension (new or worsening, 42%) was the most prevalent cardiovascular TEAE, followed by rhythm/rate disorders (11%). Arterio-thrombotic event occurred in 7% of all patients (coronary artery disease in 4%, cerebrovascular in 2%, peripheral arterial in 2%). Overall, the incidence rate (IR) of cardiovascular and arterio-thrombotic TEAEs were 13.9 and 1.1 per 100-person years respectively. Ponatinib showed the highest IR of TEAEs (85.5 and 5.1 per 100-person years respectively).
In a multivariate analysis, the incidence risk ratio (IRR) of cardiovascular and arterio-thrombotic events were higher for all 2nd-3rd generation TKIs compared to imatinib. The IRR with ponatinib for cardiovascular events was 7.1 (95% CI: 4.7-10.7; p≤0.001) and for arterio-thrombotic events 6.6 (95% CI: 1.8-23.8 p=0.004); dasatinib and nilotinib showed similar IRR among them, both higher than imatinib. In addition, older age was associated with higher IRR of cardiovascular (IRR=1.01, 95% CI: 1.0-1.02; p=0.003) and arterio-thrombotic (IRR=1.04, 95% CI: 1.00-1.07; p=0.023) events adjusted for other variables.
Conclusion
Cardiovascular TEAE are common during the course of therapy with TKI for patients with CML, predominantly for older patients and those on 2nd and 3rd generation TKI (particularly ponatinib). These patients require close monitoring during treatment. Investigation on the mechanism for cardiovascular TEAE with TKIs is required.
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Jabbour:BMS: Consultancy; Pfizer: Research Funding; ARIAD: Research Funding; ARIAD: Consultancy; Pfizer: Consultancy; Novartis: Research Funding. Wierda:Novartis: Research Funding; Abbvie: Research Funding; Acerta: Research Funding; Gilead: Research Funding; Genentech: Research Funding. Daver:Otsuka: Consultancy, Honoraria; Sunesis: Consultancy, Research Funding; Ariad: Research Funding; Kiromic: Research Funding; Pfizer: Consultancy, Research Funding; Karyopharm: Honoraria, Research Funding; BMS: Research Funding. Kadia:BMS: Research Funding; Novartis: Honoraria. Burger:Portola: Consultancy; Gilead: Research Funding; Roche: Other: Travel, Accommodations, Expenses; Janssen: Consultancy, Other: Travel, Accommodations, Expenses; Pharmacyclics, LLC, an AbbVie Company: Research Funding. DiNardo:Daiichi Sankyo: Other: advisory board, Research Funding; Novartis: Other: advisory board, Research Funding; Agios: Other: advisory board, Research Funding; Abbvie: Research Funding; Celgene: Research Funding. Ravandi:Seattle Genetics: Consultancy, Honoraria, Research Funding; BMS: Research Funding. Cortes:ARIAD: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Teva: Research Funding.
T cell exhaustion is characterized by coordinated expression of a series of negative checkpoint receptors such as programmed death-1 (PD-1), 2B4, CD160 and TIGIT, resulting in T cell dysfunction and ...immune evasion. Under physiological states, these inhibitory molecules maintain self-tolerance and prevent autoimmunity by applying a break on cytotoxic T cells. In cancer, T-cells exhibit features of T-cell exhaustion including increased expression of PD-1, 2B4 and CD160, coupled with reduced T cell proliferation, altered synapse formation and impaired cytotoxicity. Although the role of the PD-1/PD-L1 axis in mediating T cell defects in chronic lymphocytic leukemia (CLL) is well-studied, the contribution of other checkpoint molecules such as 2B4, CD160 and TIGIT in mediating tumor-induced immune dysfunction remains to be determined.
Checkpoint inhibitors have provided a paradigm-shifting approach to cancer treatment. We hypothesized that the expression levels of checkpoint receptors on T-cells, as well as the "fitness" of the T cell compartment may provide a prognostic stratification system to predict response to checkpoint inhibitors in CLL. To determine if the number of inhibitory receptors per cell and their expression level may identify patient-to-patient differences that may not be easily deciphered using conventional research tools, we performed a detailed single-cell analysis of the T-cell repertoire, using 40-parameter mass cytometry (CyTOF) in 12 untreated CLL and 12 healthy controls.
Consistent with previous reports, we found that expression of 2B4 (43.7% vs 30.8%), PD1 (28.8% vs 21%) and CD160 (17% vs. 9.7%) was significantly higher on CLL CD8+ T cells compared to healthy controls. In addition, CD8+T cells in CLL expressed higher levels of TIGIT (48.2% vs 25.2%), CD57 (43.9% vs 17.9%) and KLRG1 (49.5% vs. 29.7%). We clearly distinguished 2 patterns of exhaustion marker distribution in CLL. In one group of patients, the expression of checkpoint receptors was similar to that seen in healthy controls, whereas in the second group, CD8+ T-cells expressed higher levels of PD1, 2B4, TIGIT, CD160 as well as markers of terminal differentiation such as CD57 and KLRG1.
Compared to healthy donors, CLL was characterized by an inversion in the CD4:CD8 ratio. Interestingly, CD8+ T cells in patients with a low CD4:CD8 ratio (defined as <2.5) expressed significantly higher levels of 2B4 (56.6% vs 31.25%), TIGIT (62.9% vs 37%), CD160 (22.8% vs 12.6%), CD57 (57% vs 28.7), PD-1 (34.6% vs 24.5%) and KLRG1 (62.3% vs 36.3%). In contrast, the expression levels of PD-1, 2B4 and CD160 in CLL patients with a CD4:CD8 ratio of ≥2.5 were similar to that seen in healthy controls, suggesting that the CD4:CD8 ratio may be a valuable marker of T cell exhaustion in CLL.
Next, we compared the number of checkpoint molecules expressed per CD8+ T-cells in CLL patients versus healthy donors. Whereas a similar proportion of CD8+ T-cells in CLL (mean 19.56%, range 18.34-31.73%) and healthy donors (mean 22.13%, 14.17-41.19%) expressed one inhibitory receptor, a significantly higher proportion of CLL patients expressed 2 and more inhibitory receptors (mean 28.4, range 10.52-48.78%) compared to healthy controls (mean 15.38%, range 9.67-21.94%). PD-1 was mostly co-expressed with TIGIT, although TIGIT+PD-1+CD4+ and CD8+ T-cells were higher in CLL compared to healthy controls (12.9% vs 7.1%). Interestingly the predominant population of PD-1+CD8+ T cells in CLL was also positive for 2B4 and TIGIT, whereas expression of TIGIT was more diverse and was seen in association with PD-1, 2B4, KLRG1 or CD57.
Taken together, our findings indicate a remarkable heterogeneity in the expression patterns of inhibitory molecules on CD8+ and CD4+ T-cells in CLL. While CLL patients with a normal CD4:CD8 ratio expressed comparable levels of inhibitory molecules to that seen in healthy controls, a low CD4:CD8 ratio was indicative of higher expression of checkpoint molecules. On a per cell basis, CLL CD8+ T cells expressed more inhibitory receptors compared to healthy controls, suggesting that certain patients may benefit from combinational use of checkpoint molecules. A more detailed data and analysis, including transcription and functional profile of exhausted CLL T cells, will be presented in the meeting.
Wierda:Abbvie: Research Funding; Novartis: Research Funding; Acerta: Research Funding; Gilead: Research Funding; Genentech: Research Funding. Jain:Incyte: Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding; Abbvie: Research Funding; Infinity: Research Funding; BMS: Research Funding; Genentech: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; ADC Therapeutics: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Seattle Genetics: Research Funding; Celgene: Research Funding; Servier: Consultancy, Honoraria; Novimmune: Consultancy, Honoraria.
In CLL the signal transducer and activator of transcription (STAT)-3 is constitutively phosphorylated on serine 727 residues. This phosphorylated form of STAT3 is biologically active and plays a ...pivotal role in activating several pathways that stimulate proliferation and provide CLL cells with survival advantage. However, what induces serine phosphorylation of STAT3 in CLL cells is currently unknown. Looking for potential kinases that phosphorylate STAT3 on serine residues, we performed mass spectrometry analysis of CLL cell proteins co-immunoprecipitated with anti-serine pSTAT3 antibodies. Of the 365 pulled-down proteins we identified the beta subunit of the serine/threonine kinase casein kinase (CK)-2. Using Western immunoblotting we confirmed that phosphorylated CK2 was readily detected in CLL cells from 7 randomly selected patients. Furthermore, immnoprecipitation studies showed that STAT3 and serine pSTAT3 co-immunoprecipitated with CK2 and that CK2 co-immunoprecipitated with STAT3, suggesting that CK2 binds STAT3. Therefore to determine whether CK2 phosphorylates STAT3 we incubated active CK2 with recombinant human (rh) STAT3 and found that CK2 phosphorylated rhSTAT3 on serine 727 residues in an ATP-enriched cell-free medium. Because unlike normal B cells CLL cells express CD5 and CD5 activates CK2, we wondered whether CD5 plays a role in STAT3 phosphorylation. Immunoprecipitation studies determined that CD5 co-immunoprecipitated with CK2 and pSTAT3, and transfection of CLL cells with CD5-siRNA significantly reduced the levels of serine pSTAT3. Because CD5 activates CD5, we incubated CLL cells with CD5 neutralizing antibodies and found that CD5 neutralizing antibodies significantly reduced the levels of serine pSTAT3. Because STAT3 is not constitutively in T lymphocytes although STAT3, CK2 and CD5 are ubiquitously expressed in these cells, we wondered whether a protein, not present in T lymphocytes, is required for the induction of STAT3 phosphorylation in CLL cells. The B cell linker protein (BLNK) is an adaptor protein that is expressed in B-lymphocytes and is required for LPS-induced STAT3 phosphorylation. To determine whether BLNK is required for STAT3 phosphorylation in CLL cells we transfected the cells with BLNK-siRNA and found that the levels of serine pSTAT3 were significantly reduced. Furthermore, we found that BLNK co-immunoprecipitated with CK2, CD5, STAT3, and phosphorylated STAT3. Taken together these findings suggested that both CD5 and BLNK are required for the induction of STAT3 phosphorylation by CK2 in CLL cells. Because CD5 is a cell membrane protein we wondered whether its role in the assebly of the STAT3-phosphorylation complex would alter its localization. Therefore, we performed confocal microscopy studies that revealed that CD5 is cell bound. Then we prepared CLL cell cytoplasmic and nuclear extracts and using Western immunoblotting found that the STAT3-phosphorylation complex proteins including CD5, CK2, BLNK, STAT3, and serine pSTAT3 were detected in the cytoplasmic extracts whereas only STAT3 and phosphoserine STAT3 were detected in the nuclear extract, suggesting that following its phosphorylation, pSTAT3 detaches from the complex and shuttles to the nucleus. In conclusion, our data unraveled the role of intracellular proteins that participate in the induction of constitutive phosphorylation of STAT3 on serine 727 residues in CLL cells. Whether disruption of this protein complex might benefit patients with CLL remains to be determined.
O'Brien:Janssen: Consultancy, Honoraria; Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding. Jain:Abbvie: Research Funding; Infinity: Research Funding; ADC Therapeutics: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Servier: Consultancy, Honoraria; BMS: Research Funding; Novimmune: Consultancy, Honoraria; Celgene: Research Funding; Genentech: Research Funding; Incyte: Research Funding; Seattle Genetics: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding. Wierda:Novartis: Research Funding; Abbvie: Research Funding; Acerta: Research Funding; Gilead: Research Funding; Genentech: Research Funding.
Background: Burkitt leukemia/lymphoma is a highly aggressive B-cell neoplasm that is potentially curable with chemoimmunotherapy. While patients (pts) who are refractory to initial therapy or ...subsequently relapse are considered to have a poor prognosis, few studies have systematically evaluated the outcomes of these pts.
Methods: We retrospectively identified 146 adult pts with Burkitt or Burkitt-like leukemia or lymphoma treated at our institution between 1992 and 2013 with frontline hyper-CVAD-based regimens. 12 pts died before initial response assessment and 4 were unevaluable due to inadequate records; these pts were therefore excluded from the analysis. Among the 130 pts evaluable for response, 36 pts (28%) had relapsed (n=33) or refractory (n=3) Burkitt or Burkitt-like leukemia/lymphoma and are the subject of this analysis. Early and late relapse were defined as relapse <6 months and ≥6 months from the time of first remission, respectively. Overall response rate (ORR) defined as the composite of complete remission (CR) and partial remission (PR), relapse-free survival (RFS), and overall survival (OS) were evaluated.
Results: The median age of the evaluable population was 51 years (range, 18-76 years). Among the 36 relapsed/refractory pts, initial presentation was leukemia in 29 pts and lymphoma in 7 pts. Initial treatment was hyper-CVAD alone in 11 pts, hyper-CVAD plus rituximab (R-hyper-CVAD) in 24 pts, and hyper-CVAD plus ofatumumab in 1 pt. Among the 33 relapsed pts, 30 had achieved CR to frontline treatment and 3 had only achieved PR. The median time to first relapse was 6.6 months (range 0.7-75.3 months). Twelve pts (36%) and 21 pts (64%) experienced early and late relapse, respectively.
Thirty relapsed/refractory pts received at least one salvage regimen, 29 of whom were evaluable for response. The backbone salvage regimens received were hyper-CVAD in 13 pts (43%), ICE in 6 (14%), EPOCH in 2 (7%), MOAD in 2 (7%), and miscellaneous regimens in 7 (23%). Subsequent stem cell transplant (SCT) was performed in 6 pts (allogeneic, n=3; autologous, n=3). The ORR to salvage chemotherapy was 38% (CR, n=8; PR, n=3); 18 pts were refractory. Among the 18 pts with late relapse who received salvage therapy, the ORR was 61%. In contrast, of the 11 pts who were refractory to frontline therapy or had early relapse, none responded to salvage therapy (P<0.001 vs. pts with remission duration ≥6 months).
The median OS for the entire cohort (measured from time of relapse or treatment failure) was 2.7 months, with a 1-year OS rate of 10% (Figure 1A). Among responding pts, the median RFS was 3.3 months, with a 1-year RFS rate of 18% (Figure 1B). Only 2 pts were still alive without relapse at last follow-up, one who had undergone salvage autologous SCT and one who had a late relapse 75 months after initial diagnosis and was treated with R-EPOCH without SCT consolidation. Pts who responded to salvage treatment had superior OS compared to those who did not (median OS: 7.5 months vs. 1.5 months, respectively; P<0.001). Pts with late relapse had a median OS of 5.0 months, compared to only 1.4 months for those who were refractory to frontline therapy or experienced early relapse (P<0.001).
Conclusions: Pts with relapsed/refractory Burkitt leukemia/lymphoma have very poor outcomes, with an ORR to salvage therapy of 38% and a median OS of only 2.7 months. Pts with a remission duration after frontline therapy of ≥6 months have superior outcomes, although the OS of this group is still poor. Novel treatment strategies are needed for pts with relapsed/refractory disease.
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Jabbour:ARIAD: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy. Cortes:ARIAD: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Teva: Research Funding. Wierda:Abbvie: Research Funding; Acerta: Research Funding; Novartis: Research Funding; Gilead: Research Funding; Genentech: Research Funding. Thompson:Pharmacyclics: Consultancy, Honoraria.
Introduction
Initial treatment with tyrosine kinase inhibitors (TKI) induces excellent response in the majority of patients with CML-CP. Current guidelines recommend periodic monitoring of BCR-ABL1 ...levels to monitor response. During the course of treatment, recognizing early predictors of deeper response and longer-term outcomes can help guide treatment. This is relevant not only at the specified fixed time point typically reported (i.e., 3, 6, 12 months) but at any other time point when an assessment is made. Achievement of sustained deep molecular response is a goal of increasing relevance as it opens the possibility of treatment discontinuation. The objective of this study is suggest optimal BCR-ABL transcript levels at any given time, and to suggest a prediction model for sustained molecular response 4.5 (MR4.5) (BCR-ABL ≤0.0032%) for at least 2 years according to BCR-ABL levels achieved within the first 12 months of TKI therapy.
Methods
Response data for 630 patients with newly diagnosed CML-CP in consecutive prospective clinical trials of frontline imatinib (n=73; NCT00048672), high-dose imatinib (n=208; NCT00038469 and NCT00050531), nilotinib (n=148; NCT00129740), dasatinib (n=150; NCT00254423), and ponatinib (n=51; NCT01570868) were analyzed. Real-time PCR analysis was performed at approximately 3 month intervals during the first year and 6 month intervals thereafter. The "best fit average" molecular response was defined by robust linear regression models, with which the estimated molecular level in patients with complete cytogenetic response (CCyR) within 1 year, major molecular response (MMR) within 1 year, and sustained MR4.5 at any point were defined. The acceptable molecular response was defined by quantile regression for the 95th percentile, with which the worst 5% BCR-ABL levels in patients with CCyR within 1 year, MMR within 1 year, and sustained MR4.5 at any point were identified.
Results
In 630 patients, 2512 data points of BCR-ABL levels within 1 year of TKI were identified. The median follow-up for the entire cohort was 106 months (range, 0.3-177.8). The regression equations for best fit average PCR for CCyR within 1 year was Log10(PCR) = -0.2159 x (Months) + 0.1957; for MMR within 1 year, Log10(PCR) = -0.2304 x (Months) + 0.1046; for sustained MR4.5 at any point, Log10(PCR) = -0.2154 x Months -0.1161. The regression equations for acceptable PCR for CCyR within 1 year was Log10(PCR) = -0.15796 x (Months) + 1.54839; for MMR within 1 year, Log10(PCR) = -0.20999 x (Months) + 1.54839; for sustained MR4.5, Log10(PCR) = -0.22476 x (Months) + 1.50516 (Figure 1). The best fit average PCR (i.e., estimated levels achieved by the average responder in each category) for CCyR within 1 year was 0.353%, 0.079%, 0.017%, and 0.004% at 3, 6, 9, and 12 months, respectively; for MMR within 1 year was 0.259%, 0.053%, 0.011%, and 0.002% at 3, 6, 9, and 12 months, respectively; for sustained MR4.5 at any point was 0.295%, 0.067%, 0.015%, and 0.003% at 3, 6, 9, and 12 months, respectively (Table 1). To achieve CCyR within 1 year, the acceptable PCR (i.e., levels achieved by 95% of all those who eventually reach the said endpoint) response was 11.872%, 3.987%, 1.339%, and 0.450% at 3, 6, 9, and 12 months, respectively; to achieve MMR within 1 year, 8.287%, 1.943%, 0.455%, and 0.107% at 3, 6, 9, and 12 months, respectively; to achieve sustained MR4.5 at any time, 6.774%, 1.434%, 0.304%, and 0.064% at 3, 6, 9, and 12 months, respectively. Of 289 patients who eventually achieved sustained MR4.5, 288 (99%) achieved CCyR within 1 year; 268 (93%), MMR within 1 year; 201 (70%), MR4 within 1 year; 162 (56%), MR4.5 within 1 year; 72 (25%), CMR within 1 year. Of 359 patients who achieved MMR within 1 year with a minimum follow-up of 48 months, 256 (71%) achieved sustained MR4.5; of 180 patients who achieved MR4.5 within 1 year, 151 (84%); of 72 patients who achieved CMR within 1 year, 65 (90%).
Conclusion
Proper interpretation of early transcript levels at any time during the course of therapy may help predict later response and outcome. Such models can be built to guide therapy for patients in a continuous basis. To achieve sustained MR4.5 for at least 2 years, deeper responses are required at each time point. Our model proposes optimal values that predict the highest probability of reaching such goal. At a minimum, CCyR within 1 year is required to achieve sustained MR4.5.
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Kantarjian:Bristol-Myers Squibb: Research Funding; ARIAD: Research Funding; Amgen: Research Funding; Pfizer Inc: Research Funding; Delta-Fly Pharma: Research Funding; Novartis: Research Funding. Ravandi:Seattle Genetics: Consultancy, Honoraria, Research Funding; BMS: Research Funding. Konopleva:AbbVie: Research Funding; Genentech: Research Funding. Wierda:Novartis: Research Funding; Abbvie: Research Funding; Acerta: Research Funding; Gilead: Research Funding; Genentech: Research Funding. Daver:Ariad: Research Funding; Karyopharm: Honoraria, Research Funding; Sunesis: Consultancy, Research Funding; BMS: Research Funding; Kiromic: Research Funding; Otsuka: Consultancy, Honoraria; Pfizer: Consultancy, Research Funding. Jabbour:ARIAD: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Research Funding; BMS: Consultancy.
Background: T-cell prolymphocytic leukemia (T-PLL) is a rare and aggressive mature T-cell lymphoid leukemia. T-PLL is diagnosed based on characteristic immunophenotype, cytogenetic, and molecular ...aberrations (including members of the JAK-STAT signaling pathway) which may explain its aggressive clinical course. Cytogenetic features include chromosome (chr) 14 aberrations, accompanied by gene rearrangement involving proto-oncogenes TCL1 or MTCP1. In this analysis, we report our single-institution experience with T-PLL over the last 25 years.
Methods: We reviewed the clinico-pathologic records of 101 consecutive patients (pts) with T-PLL, who presented to our institution between 1990 and 2015. The survival (OS) of pts was calculated from the date of initial presentation to the date of last follow up. Kaplan-Meier product limit method was used to estimate the median OS.
Results: Median age of pts was 64 years (range 35-87 years). Eighteen pts (18%) presented with a performance status of 2 or higher. Lymphadenopathy, splenomegaly, skin lesions, hepatomegaly and pleural effusion were seen in 58%, 37%, 30%, 8%, and 7% of pts, respectively. Complex karyotype and aberrations in chr 14 were seen in 54 (63%) and 42 (49%) pts, respectively. TCL1 expression by immunohistochemistry was performed in 29 pts. The overexpression of TCL1 was detected in 23/29 pts (79%). Among untreated pts, 18/47 (38%) had a chr abnormality involving TCL-1. At initial presentation to our center, 59 pts (58%) were untreated and 42 pts (42%) had relapsed/refractory disease with a median of 2 prior treatments (range 1-6). Seventy-one pts (70%) were treated at our institution. Median overall OS from diagnosis in all pts was 21.7 mos (Range 1.5-107.7 mos). Median OS was longer in untreated pts than in pts with relapsed/refractory disease (27 mos vs. 17 mos; P=0.08). In a multivariate analysis for assessing the prognostic factors for OS in 43 untreated pts in whom all variables were available, we found that the presence of a pleural effusion HR (95% CI) 5.21 (1-27; P <0.04), high absolute lymphocyte count 1.009 (1.002-1.016; P = 0.006) and complex karyotype 4.6 (1.2-16.9; P = 0.02) predicted for increased risk of death. Fifty four evaluable pts (32 untreated and 22 with relapsed disease) received treatment with alemtuzumab based regimen, 36 as monotherapy and 18 in combination with pentostatin. Seventeen evaluable pts (1 untreated and 16 with relapsed disease) were treated with nelarabine. Overall response rate (ORR), complete remission rate (CR), median OS and progression free survival (PFS) are summarized in Table-1. Among 39 previously untreated pts who received treatment at our institution, the distribution of treatment regimen was alemtuzumab-based (n=32), nelarabine (n=1), other nucleoside analogues (n=5) and combination chemotherapy (n=1). Twenty two pts (56%) achieved complete remission (CR) after frontline treatment. Ten pts underwent allogeneic stem cell transplantation (SCT) after achieving a remission after initial treatment with alemtuzumab either as a monotherapy or in combination with pentostatin. We did not observe any significant difference in OS (Figure-1A) or PFS among pts who did/did not undergo stem cell transplantation after achieving an initial remission with the frontline therapies. Furthermore, we observed that there was no significant difference in OS among pts with or without a chr abnormality involving TCL-1 after frontline therapy (Figure-1B).
Conclusions: In this retrospective analysis, we have observed that outcomes in T-PLL remain poor. We have shown that alemtuzumab has some therapeutic activity in pts with T-PLL but durable remissions are uncommon. Rarity of this disease limits the conduct of large scale clinical trials; hence multicenter collaborative effort is required to conduct prospective studies. In pts who achieve a CR, consolidation treatment with SCT did not improve survival in our series. Studies to further define the genomic imbalances in pts with T-PLL and identify potential therapeutic targets and pathways providing drug resistance in T-PLL are underway.
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O'Brien:Pharmacyclics, LLC, an AbbVie Company: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria. Wierda:Acerta: Research Funding; Novartis: Research Funding; Gilead: Research Funding; Abbvie: Research Funding; Genentech: Research Funding. Jain:Novartis: Consultancy, Honoraria; Pharmacyclics: Consultancy, Honoraria, Research Funding; Novimmune: Consultancy, Honoraria; Abbvie: Research Funding; Servier: Consultancy, Honoraria; Seattle Genetics: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; ADC Therapeutics: Consultancy, Honoraria, Research Funding; Infinity: Research Funding; Incyte: Research Funding; BMS: Research Funding. Thompson:Pharmacyclics: Consultancy, Honoraria. Kantarjian:BMS, Pfizer, Amgen, Novartis: Research Funding.
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