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.

No abstract

Background Immunotherapy using a checkpoint inhibitor (CPI) alone or in combination with chemotherapy is the standard of care for treatment‐naive patients with advanced non–small cell lung cancer (NSCLC) without driver mutations for which targeted therapies have been approved. It is unknown whether continuing CPI treatment beyond disease progression results in improved outcomes. Methods Patients who experienced progressive disease (PD) after a clinical benefit from chemotherapy plus a CPI were enrolled. Patients received pembrolizumab (200 mg every 3 weeks) plus next‐line chemotherapy. The primary end point was progression‐free survival (PFS) according to the Response Evaluation Criteria in Solid Tumors (version 1.1). Key secondary end points included the overall survival (OS), clinical benefit rate, and toxicity. The authors’ hypothesis was that continuing pembrolizumab beyond progression would improve the median PFS to 6 months in comparison with a historical control of 3 months with single‐agent chemotherapy alone. Results Between May 2017 and February 2020, 35 patients were enrolled. The patient and disease characteristics were as follows: 51.4% were male; 82.9% were current or former smokers; and 74.3%, 20%, and 5.7% had adenocarcinoma, squamous cell carcinoma, and NSCLC not otherwise specified, respectively. The null hypothesis that the median PFS would be 3 months was rejected (p < .05). The median PFS was 5.1 months (95% confidence interval [CI], 3.6–8.0 months). The median OS was 24.5 months (95% CI, 15.6–30.9 months). The most common treatment‐related adverse events were fatigue (60%), anemia (54.3%), and nausea (42.9%). There were no treatment‐related deaths. Conclusions Pembrolizumab plus next‐line chemotherapy in patients with advanced NSCLC who experienced PD after a clinical benefit from a CPI was associated with statistically significant higher PFS in comparison with historical controls of single‐agent chemotherapy alone.

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<0.001 and HR=1.15, p<0.001, respectively) and OS (HR=2.25, p=0.001 and HR=1.29, p<0.001, respectively). Treatment with ibrutinib was associated with superior EFS (HR=0.52, p<0.001), but did not significantly impact OS (p=0.51). Del(17p)/TP53 mutation demonstrated a trend towards shortened EFS (HR=1.27, p=0.125) and significantly shorter OS (HR=1.88, p=0.008). CLL-CI≥3 and CIRS≥7 showed similar independent associations with worse EFS and OS (Table). Median EFS and OS were shorter in patients with high CLL-CI score (Fig). Results were consistent in patients treated with either ibrutinib or CIT. 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.