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Supplemental Figure 2. CLL-CI correlates with OS across CLL patient subgroups (derivation dataset).

Supplemental Figure 1. CIRS correlates with outcomes in CLL (derivation dataset).

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Purpose:Comorbid medical conditions define a subset of patients with chronic lymphocytic leukemia (CLL) with poor outcomes. However, which comorbidities are most predictive remains understudied.Experimental Design:We conducted a retrospective analysis from 10 academic centers to ascertain the relative importance of comorbidities assessed by the cumulative illness rating scale (CIRS). The influence of specific comorbidities on event-free survival (EFS) was assessed in this derivation dataset using random survival forests to construct a CLL-specific comorbidity index (CLL-CI). Cox models were then fit to this dataset and to a single-center, independent validation dataset.Results:The derivation and validation sets comprised 570 patients (59% receiving Bruton tyrosine kinase inhibitor, BTKi) and 167 patients (50% receiving BTKi), respectively. Of the 14 CIRS organ systems, three had a strong and stable influence on EFS: any vascular, moderate/severe endocrine, moderate/severe upper gastrointestinal comorbidity. These were combined to create the CLL-CI score, which was categorized into 3 risk groups. In the derivation dataset, the median EFS values were 58, 33, and 20 months in the low, intermediate, and high-risk groups, correspondingly. Two-year overall survival (OS) rates were 96%, 91%, and 82%. In the validation dataset, median EFS values were 81, 40, and 23 months (two-year OS rates 97%/92%/88%), correspondingly. Adjusting for prognostic factors, CLL-CI was significantly associated with EFS in patients treated with either chemo-immunotherapy or with BTKi in each of our 2 datasets.Conclusions:The CLL-CI is a simplified, CLL-specific comorbidity index that can be easily applied in clinical practice and correlates with survival in CLL.

Supplemental Fig. 3. CLL-CI correlates with EFS in patient subgroups (validation set).

Introduction: Medical comorbidities influence survival in CLL. We previously reported on a simplified CLL-specific comorbidity scale, the CLL-CI (Gordon et al. 2019), which required assessment of only three organ systems and was predictive of outcomes in a heterogeneous retrospective patient cohort. Herein we analyzed CLL-CI among patients treated with ibrutinib. Methods: This retrospective study included 339 CLL patients treated with ibrutinib at 9 academic centers between 2014-2019. Vascular, endocrine and upper-gastrointestinal organ systems were assessed at the time of ibrutinib initiation. Each was scored from 0 to 3, in order of increasing severity of dysfunction to generate the CLL-CI score (range, 0-9; Figure A). As established previously, CLL-CI≥2 was deemed high-risk. Event free survival (EFS) was measured from start of ibrutinib to development of new CLL-related symptoms, disease progression, start of a new therapy or death. Overall survival (OS) was measured from treatment initiation to death. Patients with no EFS or OS events were censored at last follow up. The Kaplan-Meier method and log-rank test were used to estimate and compare survival. Multivariable Cox regression was utilized to model EFS and OS. Differences between CLL-CI groups were evaluated with Wilcoxon rank sum and Fisher's exact tests. Results: Median age was 68 years (range, 30-91), 240 (71%) were treated in the relapsed/refractory setting (range of prior therapies, 0-10). Advanced Rai stage (3-4) was present in 206 (61%) and TP53 aberrancy was present in 93 (27%) patients at the start of ibrutinib therapy. Median follow up was 23 months (range, 1-71). CLL-CI score was high (≥2) in 202/339 patients (60%). Patient characteristics were well balanced between subgroups (CLL-CI In multivariate models adjusted for age, del(17p) and relapsed disease, high CLL-CI was associated with shortened EFS (HR=1.65; p=0.014, Figure) and OS (HR=1.73; p=0.1). CIRS score≥7 also correlated with EFS (HR=1.91; p=0.002) and OS (HR=2.78; p=0.006). Ibrutinib discontinuation rates due to adverse events were more frequent in patients with CLL-CI ≥2 (25% vs 14%; p=0.014). However, dose reduction rates were similar (24% vs 20%; p=0.51). Fifty-two deaths occurred: 40 in the high CLL-CI subgroup and 12 in the low CLL-CI subgroup. Cause of death was known in 31 patients. Death due to disease progression was more frequent in the high CLL-CI subgroup (28% vs 8%; p Since some of the key ibrutinib toxicities (atrial fibrillation, hypertension) may not have been accounted for in the CLL-CI we further elucidated their possible impact. Cardiac disease was significantly more prevalent among patients with high CLL-CI (37% vs 16%, p Conclusions: Here we present the largest cohort of CLL patients treated with ibrutinib in whom comorbidities have been systematically assessed. We find that the CLL-CI (which assesses endocrine, vascular and upper gastrointestinal conditions) correlates with survival and tolerance of therapy in this population. Unexpectedly, we found that hypertension and cardiac comorbidities did not improve CLL-CI's discriminatory power. This result combined with the simplicity of scoring the CLL-CI makes it an attractive tool for clinical practice. CLL-CI needs to be explored prospectively in patients treated with ibrutinib and other targeted therapies. Disclosures Patel: Genentech: Consultancy, Speakers Bureau; BeiGene: Consultancy; Celgene/BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Speakers Bureau; Adaptive Biotechnologies: Consultancy; AstraZeneca: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics: Consultancy, Speakers Bureau; Kite: Consultancy. Cohen:Janssen, Adicet, Astra Zeneca, Genentech, Aptitude Health, Cellectar, Kite/Gilead, Loxo: Consultancy; Genentech, BMS, Novartis, LAM, BioInvent, LRF, ASH, Astra Zeneca, Seattle Genetics: Research Funding. Choi:Pharmacyclics/Abbvie: Research Funding; Genentech: Consultancy. Hill:BMS: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Research Funding; Takeda: Research Funding; Beigene: Consultancy, Honoraria, Research Funding; AstraZenica: Consultancy, Honoraria, Research Funding; Genentech: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria, Research Funding; Pharmacyclics: Consultancy, Honoraria, Research Funding. Shadman:Mustang Bio, Celgene, Pharmacyclics, Gilead, Genentech, Abbvie, TG therapeutics, Beigene, Astra Zeneca, Sunesis, Beigene: Research Funding; Abbvie, Genentech, Astra Zeneca, Sound Biologics , Pharmacyclics, Verastem, ADC therapeutics, Beigene, Cellectar, BMS, Morphosys and Atara Biotherapeutics: Consultancy. Stephens:Innate: Consultancy; Gilead: Research Funding; Verastem: Research Funding; Janssen: Consultancy; Acerta: Research Funding; Pharmacyclics: Consultancy; MingSight: Research Funding; Beigene: Consultancy; Arqule: Research Funding; Juno: Research Funding; Karyopharm: Consultancy, Research Funding. Brander:Novartis: Consultancy, Other; NCCN: Other; Tolero: Research Funding; AstraZeneca: Consultancy, Honoraria, Other, Research Funding; Ascentage: Other, Research Funding; ArQule: Consultancy, Other, Research Funding; AbbVie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Tolero: Research Funding; Teva: Consultancy, Honoraria; Novartis: Consultancy, Other; NCCN: Other; Verastem: Consultancy, Honoraria, Other, Research Funding; TG Therapeutics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Pfizer: Consultancy, Other; Pharmacyclics LLC, an AbbVie Company: Consultancy, Honoraria, Other, Research Funding; MEI Pharma: Other, Research Funding; Juno/Celgene/BMS: Other, Research Funding; Genentech: Consultancy, Honoraria, Other, Research Funding; DTRM: Other, Research Funding; BeiGene: Other, Research Funding; Teva: Consultancy, Honoraria. Danilov:BeiGene: Consultancy; Pharmacyclics: Consultancy; Abbvie: Consultancy; Bristol-Myers Squibb: Research Funding; Rigel Pharmaceuticals: Consultancy; Astra Zeneca: Consultancy, Research Funding; Aptose Biosciences: Research Funding; Verastem Oncology: Consultancy, Research Funding; Takeda Oncology: Research Funding; Gilead Sciences: Research Funding; Bayer Oncology: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Nurix: Consultancy; Celgene: Consultancy; Karyopharm: Consultancy.

Purpose: Comorbid medical conditions define a subset of patients with chronic lymphocytic leukemia (CLL) with poor outcomes. However, which comorbidities are most predictive remains understudied. Experimental Design: We conducted a retrospective analysis from 10 academic centers to ascertain the relative importance of comorbidities assessed by the cumulative illness rating scale (CIRS). The influence of specific comorbidities on event-free survival (EFS) was assessed in this derivation dataset using random survival forests to construct a CLL-specific comorbidity index (CLL-CI). Cox models were then fit to this dataset and to a single-center, independent validation dataset. Results: The derivation and validation sets comprised 570 patients (59% receiving Bruton tyrosine kinase inhibitor, BTKi) and 167 patients (50% receiving BTKi), respectively. Of the 14 CIRS organ systems, three had a strong and stable influence on EFS: any vascular, moderate/severe endocrine, moderate/severe upper gastrointestinal comorbidity. These were combined to create the CLL-CI score, which was categorized into 3 risk groups. In the derivation dataset, the median EFS values were 58, 33, and 20 months in the low, intermediate, and high-risk groups, correspondingly. Two-year overall survival (OS) rates were 96%, 91%, and 82%. In the validation dataset, median EFS values were 81, 40, and 23 months (two-year OS rates 97%/92%/88%), correspondingly. Adjusting for prognostic factors, CLL-CI was significantly associated with EFS in patients treated with either chemo-immunotherapy or with BTKi in each of our 2 datasets. Conclusions: The CLL-CI is a simplified, CLL-specific comorbidity index that can be easily applied in clinical practice and correlates with survival in CLL.

Introduction: Outcomes in CLL are highly variable and influenced by both biologic and clinical factors. The Cumulative Illness Rating Scale (CIRS) is frequently used to assess comorbidities in CLL. Our group has demonstrated that CIRS correlates with survival in patients treated with either CIT or ibrutinib. Yet, CIRS has not become part of common clinical practice due to complexities in scoring since 14 systems need to be evaluated. Furthermore, the relative contribution of individual comorbidities to patient outcomes is unknown. Here we report the impact of specific comorbidities in a large cohort of CLL patients and propose a simplified CLL-comorbidity index (CLL-CI). Methods: We conducted a retrospective analysis of patients with CLL treated with either CIT or kinase inhibitors at 10 US academic medical centers between 2000-2018. CIRS score was calculated as in Salvi et al, 2008. Patients were randomly divided into a training-set (n=381) and validation-set (n=189). Random survival forests (RSF) were constructed on the training-set to select variables for Cox regression models. Discrimination of models was tested in the validation-set. CIRS score in each organ system, relapse/refractory (R/R) disease, treatment type, age, and del(17p) were included as features for RSF modeling of event-free survival (EFS), defined as time from treatment to death, disease progression or next therapy. For each RSF, features were scored and ranked according to variable importance (VI; the decrease in prediction accuracy when the specific variable is randomly permuted) and minimal depth (MD; the minimum distance between the root node of a tree and the first node that splits on the specific variable). After 200 RSF's, VI and MD ranks were averaged. Organ system variables whose average rank for both predictive measures was ≤10 were chosen for Cox regression modeling of EFS and OS. Three sets of Cox models were fit on the training data and applied to the validation-set to compute c-statistics depicting each model's ability to predict EFS. Cox models assessed the addition of either CIRS or CLL-CI to known prognostic factors. Results: The data set contained 614 patients; 570 (93%) with complete data were included in our analysis. Median age was 67 years (range 30-91). Median CIRS was 7 (range, 0-29) with CIRS≥7 in 302 patients (53%). Median follow up was 31 months. Del(17p) and/or TP53 mutation was present in 113 patients (20%) and 299 (52%) were assessed in the R/R setting. Ibrutinib was the most common treatment (n=338, 59%), followed by fludarabine (n=163) and bendamustine (n=116). In the training-set, four organ system variables ("musculoskeletal", "renal", "endocrine" and "upper GI"), were selected based on RFS average predictive measure ranks and summed to derive the CLL-CI score. Median CLL-CI was 2 (range, 0-11) in the training cohort with a value of 3 identified as the optimal cut-point for association with EFS; 236 (41%) had a high CLL-CI score (≥3). Cox models that included either CLL-CI or CIRS (alongside age, disease status, type of treatment, and del(17p)/TP53 mutation) yielded c-statistics of 0.68 (95% CI: 0.65-0.69) and 0.68 (95% CI: 0.65-0.70), respectively. These discrimination estimates were modestly superior to the model without a comorbidity variable (c-statistic, 0.64). In the complete data set, R/R disease and age were associated with decreased EFS (HR=2.14, p Conclusion: In this large data set, we utilized random forests to identify "musculoskeletal", "upper GI", "endocrine", and "renal" comorbidities as the most prognostic of EFS in patients with CLL. Using only these 4 CIRS variables, we developed and validated a simplified comorbidity score (CLL-CI) which performed similar to CIRS, but has lower complexity and therefore can be easily incorporated into clinical practice. Disclosures Patel: Sunesis: Consultancy; Pharmacyclics/Janssen: Consultancy, Speakers Bureau; AstraZeneca: Consultancy, Research Funding, Speakers Bureau; Celgene: Consultancy, Speakers Bureau; Genentech: Consultancy, Speakers Bureau. Persky:Sandoz: Consultancy; Morphosys: Other: Member, Independent Data Monitoring Committee; Debiopharm: Other: Member, Independent Data Monitoring Committee; Bayer: Consultancy. Cohen:Genentech, Inc.: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Gilead/Kite: Consultancy; LAM Therapeutics: Research Funding; UNUM: Research Funding; Hutchison: Research Funding; Astra Zeneca: Research Funding; Lymphoma Research Foundation: Research Funding; ASH: Research Funding. Choi:Rigel: Consultancy, Research Funding; Gilead: Consultancy, Speakers Bureau; Oncternal: Research Funding; Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Genentech: Consultancy, Speakers Bureau; Abbvie: Consultancy, Research Funding, Speakers Bureau. Hill:Pharmacyclics: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Consultancy, Honoraria; Amgen: Research Funding; Takeda: Research Funding; Celegene: Consultancy, Honoraria, Research Funding; Seattle Genetics: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Genentech: Consultancy, Research Funding; Kite: Consultancy, Honoraria; TG therapeutics: Research Funding. Shadman:Sunesis: Research Funding; Pharmacyclics: Consultancy, Research Funding; Celgene: Research Funding; ADC Therapeutics: Consultancy; Atara Biotherapeutics: Consultancy; Genentech: Consultancy, Research Funding; Gilead: Consultancy, Research Funding; Mustang Bio: Research Funding; Verastem: Consultancy; Astra Zeneca: Consultancy; AbbVie: Consultancy, Research Funding; BeiGene: Research Funding; TG Therapeutic: Research Funding; Sound Biologics: Consultancy; Acerta Pharma: Research Funding. Stephens:Acerta: Research Funding; Karyopharm: Research Funding; Gilead: Research Funding. Brander:Tolero: Research Funding; MEI: Research Funding; Acerta: Research Funding; Genentech: Consultancy, Honoraria, Research Funding; AstraZeneca: Consultancy, Research Funding; Novartis: Consultancy; Pharmacyclics LLC, an AbbVie Company: Consultancy; BeiGene: Research Funding; DTRM Biopharma: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Teva: Consultancy, Honoraria; TG Therapeutics: Consultancy, Honoraria, Research Funding. Danilov:Celgene: Consultancy; Curis: Consultancy; Bayer Oncology: Consultancy, Research Funding; Seattle Genetics: Consultancy; AstraZeneca: Consultancy, Research Funding; Gilead Sciences: Consultancy, Research Funding; Verastem Oncology: Consultancy, Other: Travel Reimbursement , Research Funding; Janssen: Consultancy; Pharmacyclics: Consultancy; Aptose Biosciences: Research Funding; Bristol-Meyers Squibb: Research Funding; TG Therapeutics: Consultancy; Takeda Oncology: Research Funding; MEI: Research Funding; Abbvie: Consultancy; Genentech: Consultancy, Research Funding.

Background Most patients with chronic lymphocytic leukemia (CLL) present with multiple comorbidities. Although comorbidities negatively affect outcomes for patients treated with chemoimmunotherapy, their impact on patients who receive targeted therapies is unknown. Methods This multicenter, retrospective analysis evaluated the significance of comorbidities, as assessed by the Cumulative Illness Rating Scale (CIRS), among patients with CLL treated with ibrutinib. Results One hundred forty-five patients received ibrutinib (80% in a relapsed/refractory setting). A high burden of comorbidities (CIRS score ≥ 7) was associated with inferior median event-free survival (EFS; 24 vs 37 months; P = .003) and 2-year overall survival (OS; 79% vs 100%; P = .005). In an adjusted Cox model, both EFS and OS worsened with an incremental increase in the CIRS score. Furthermore, comorbidities were associated with an increased risk of ibrutinib dose reduction and therapy discontinuation. CIRS was predictive in both frontline and relapsed CLL, regardless of patient age. Conclusions Comorbidities portend a poor prognosis among patients with CLL treated with ibrutinib. Prospective studies are needed to optimize the treatment of patients with CLL who have comorbidities. Cancer 2018. © 2018 American Cancer Society.

Introduction: Chronic lymphocytic leukemia (CLL) is a common leukemia which tends to occur late in life. Comorbidities are common, and the iwCLL guidelines recommend their assessment in patients (pts) enrolled on clinical trials. The Cumulative Illness Rating Scale (CIRS) is a rigorous tool designed to evaluate the burden of comorbidities, which has been employed in therapeutic studies. Our group and others demonstrated that CIRS score predicts survival in pts with CLL treated with either chemo-immunotherapy (CIT) or novel kinase inhibitors (KI; ibrutinib) (Manda et al, 2016 & Gordon et al, 2018). However, CIRS has not become part of common clinical practice, in part due to complexities in scoring. It is also unknown whether all of the 14 organ systems included in the score carry equal weight to determine prognosis. Here we report the impact of specific comorbidities from a multicenter retrospective cohort of CLL pts treated with either CIT or KI. Methods: We conducted a retrospective analysis of pts with CLL treated at five US academic medical centers between 2000 and 2017. CIRS score was calculated as in Salvi et al, 2008. Random forest (RF) was used to assess specific comorbidities' impact on overall survival (OS) and event-free survival (EFS, defined as time to new therapy, disease progression or death). We adapted two separate approaches to investigate the RF variable selection process: variable Importance (VIMP), a property related to variable misspecification, and Minimal Depth (MD), a property derived from the construction of trees within the forest. Best variables were those selected consistently as top 3 in both VIMP and MD on the 500 RF repetitions. Because hepatic and renal comorbidities were rare they were excluded. OS and EFS were assessed by Kaplan-Meier estimates and Cox proportional hazard model adjusted for performance status and age. Significance was assessed with log-rank test. Results: 398 pts were included in the final analysis. The median age was 63 years (range, 30-93). 50% of pts (n=198) had a high CIRS score (≥7). 184 pts (46%) had comorbidities assessed in relapsed setting. For all pts, the most common treatments included ibrutinib (n=145; 37%), fludarabine-containing regimens (n=104; 26%) and bendamustine (n=39; 10%). Complex karyotype was observed in 3.5% (n=14) and 10.6% (n=42) of pts had del(17p). Pts with comorbidities (CIRS ≥7) demonstrated shortened survival following therapy, with 5-year OS of 64% vs 89% (p Random forest variable selections identified vascular comorbidities (e.g. DVT/PE) as the most influential risk factor for OS with CIT treatment, while HEENT and cardiac comorbidities were most impactful to OS for patients treated with KI. For EFS, the most influential comorbidities were cardiac and vascular for the CIT treatment group and endocrine and HEENT for patients treated with KI. Across EFS and OS, the most frequently selected variables in CIT were cardiac, hypertension, vascular and neurologic. We constructed a simplified scoring system assigning 1 point for each category. Comparing scores of 0, 1 and 2-4 (n=100, n=82, n=60), 5-year OS was 87%, 82% and 66%, respectively (p Conclusion: Comorbidities impact survival in CLL whether treated with CIT or KI. Which comorbidities are most prognostic may vary by treatment type. Vascular and cardiac comorbidities appear to be the most relevant in CLL pts treated with CIT. Meanwhile, cardiac, endocrine and HEENT had greater impact when pts were treated with KI. A simplified CIRS score is predictive of outcomes in both treatment subgroups. Disclosures Choi: Gilead: Speakers Bureau; AbbVie, Inc: Consultancy, Speakers Bureau; Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Rigel: Consultancy; Genentech: Speakers Bureau. Cohen:Takeda: Research Funding; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees; Infinity Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Millennium: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Research Funding; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioInvent: Consultancy. Persky:Genentech: Honoraria; Morphosys (IDMC): Consultancy; Spectrum: Research Funding; Merck: Research Funding. Danilov:Aptose Biosciences: Research Funding; Verastem: Consultancy, Research Funding; Astra Zeneca: Consultancy; Gilead Sciences: Consultancy, Research Funding; Takeda Oncology: Research Funding; Genentech: Consultancy, Research Funding; TG Therapeutics: Consultancy; Bayer Oncology: Consultancy, Research Funding.

e19514Background: Concurrent use of strong CYP3A4 inhibitors increases ibrutinib exposure 20-fold. Hence, use of strong CYP3A4 inhibitors/inducers is not recommended, and moderate inhibitors warran...