Recent treatment patterns and real-world survival following first-line anti-PD-L1 treatment for extensive-stage small cell lung cancer.

BACKGROUND: The landscape of small cell lung cancer (SCLC) has changed since the 2019 and 2020 approvals of anti-PD-L1 atezolizumab and durvalumab for first-line (1L) treatment in combination with chemotherapy. We studied treatment patterns and real-world overall survival (rwOS) following 1L-3L therapy. PATIENTS AND METHODS: A nationwide electronic health record (EHR)-derived de-identified database was used to describe treatment patterns, characteristics, and survival of patients with extensive-stage (ES)-SCLC by 1L anti-PD-L1 treatment. Patients with ES-SCLC who initiated ≥1 line of systemic therapy from 2013 to 2021, with potential follow-up through 2022, were included. RESULTS: Among 9952 patients with SCLC, there were 4308 patients with ES-SCLC treated during the study period who met eligibility criteria. Etoposide + platinum (EP) chemotherapy was most common in the 1L, with addition of anti-PD-L1 therapy to most regimens by 2019. Second-line regimens varied by platinum sensitivity status and shifted from topotecan to lurbinectedin over time. Median rwOS following 1L therapy was 8.3 months (95% CI, 7.9-8.8) in those treated with 1L anti-PD-L1 and 8.0 months (95% CI, 7.8-8.2) in those who were not. Following 2L and 3L, median rwOS was 5.6 (95% CI, 4.9-6.3) and 4.9 months (95% CI, 3.4-6.0), respectively, among 1L anti-PD-L1-treated, and 4.5 (95% CI, 4.2-4.9) and 4.0 months (95% CI, 3.7-4.5), respectively, among those who were not. CONCLUSION: Despite the introduction of frontline anti-PD-L1 therapy, survival remains dismal among patients with ES-SCLC treated in the real-world setting.

Development of an algorithm to identify small cell lung cancer patients in claims databases.

Introduction: The treatment landscape of small cell lung cancer (SCLC) is evolving. Evidence generated from administrative claims is needed to characterize real-world SCLC patients. However, the current ICD-10 coding system cannot distinguish SCLC from non-small cell lung cancer (NSCLC). We developed and estimated the accuracy of an algorithm to identify SCLC in claims-only databases. Methods: We performed a cross-sectional study of lung cancer patients diagnosed from 2016-2017 using the Surveillance, Epidemiology and End Results (SEER), linked with Medicare database. The analysis included two phases - data exploration (utilizing a 25% random sample) and data validation (remaining 75% sample). The SEER definition of SCLC and NSCLC were used as the gold standard. Claims-based algorithms were identified and evaluated for their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The eligible cohort included 31,912 lung cancer patients. The mean age was 76.3 years, 44.6% were male, with 9.4% having SCLC and 90.6% identified as NSCLC using SEER. The exploration analysis identified potential algorithms based on treatment data. In the validation analysis of 7,438 lung cancer patients who received systemic treatment in the outpatient setting, an etoposide-based algorithm (etoposide use in 180 days following lung cancer diagnosis) to identify SCLC showed: sensitivity 95%, specificity 95%, PPV 82% and NPV 99%. Discussion: An etoposide treatment-based algorithm showed good accuracy in identifying SCLC patients. Such algorithms can facilitate analyses of treatment patterns, outcomes, healthcare resource and costs among treated SCLC patients, thereby bolstering the evidence-base for best patient care.

Natural history of SCLC patients treated in third-line and beyond: A retrospective real world study.

OBJECTIVES: To describe treatment patterns and estimate outcomes among real-world small cell lung cancer (SCLC) patients in the US who received three or more lines of therapy. MATERIALS AND METHODS: We conducted a retrospective analysis of adult patients with SCLC who received a front-line platinum-based regimen and two additional lines of therapy (ie., a cohort of at least three lines of therapy). De-identified patients were selected from a United States Flatiron Health oncology database of electronic health records. Treatment patterns were captured by line of therapy. Outcomes evaluated by line of therapy included real-world overall survival (rwOS), real-world progression free survival (rwPFS), real-world response rate (rwRR) and real-world duration of response (rwDOR). RESULTS: The analysis included 326 3L SCLC patients, of which 103 (32 %) received 4L treatment, and 38 % (39/103) of 4L treated received 5L of therapy. Among the 3L cohort, the average age was 67 years, 49 % were male, and nearly all had a history of smoking (96 %). In the 3L setting, the median rwOS was 5.3 months (95 % Confidence Interval (CI): 4.5, 6.0), median rwPFS was 2.5 months (95 % CI: 2.1, 2.7), rwRR was 19.3 % (95 % CI: 15.2, 24.0) and median DOR was 3.4 months (95 % CI: 2.8, 4.4). No differences were seen in outcomes between the overall cohort and a subgroup of patients treated with front-line platinum-based regimen with an anti-programmed cell death ligand 1 (PD-L1) agent (atezolizumab or durvalumab), in each respective line of therapy. CONCLUSION: Results from this large, real-world study of US patients with SCLC in the 3L setting and beyond highlight the poor treatment outcomes in advanced SCLC patients with existing therapies and underscore the dire need for new therapies for SCLC patients.

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC.

Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC. The TME in AA patients was characterized by interactions between endothelial cells, macrophages, and mesenchymal-like cells, which were associated with poor patient survival. In contrast, the EA TNBC-associated niche is enriched in T-cells and neutrophils suggestive of an exhaustion and suppression of otherwise active T cell responses. Ligand-receptor and pathway analyses of race-associated niches found AA TNBC to be immune cold and hence immunotherapy resistant tumors, and EA TNBC as inflamed tumors that evolved a distinctive immunosuppressive mechanism. Our study revealed the presence of racially distinct tumor-promoting and immunosuppressive microenvironments in AA and EA patients with TNBC, which may explain the poor clinical outcomes.

Systematic Literature Review of the Prevalence and Prognostic Value of Delta-Like Ligand 3 Protein Expression in Small Cell Lung Cancer.

BACKGROUND: Delta-like ligand 3 (DLL3), a member of the Notch pathway, has been identified as a potential therapeutic target as it is highly expressed in small cell lung cancer (SCLC), a subtype accounting for 15% of lung cancer cases. OBJECTIVE: A systematic literature review (SLR) was conducted to understand the prevalence and prognostic impact of DLL3 expression on survival of patients with SCLC and treatment response. PATIENTS AND METHODS: Systematic literature searches were conducted across multiple databases to capture studies of any SCLC population that evaluated DLL3 expression. Specific outcomes of interest included prevalence of DLL3 expression, method of expression analysis, and impact on outcome, including treatment response and survival (overall, progression-free, disease-free) according to varying levels of DLL3 expression/positivity. Standard risk of bias tools were used to evaluate study quality. RESULTS: Among the 30 included studies, the most common DLL3 testing method was immunohistochemistry (N = 26, 86.7%). For comparability, results focused on the 13 (22.3%) studies that used the Ventana DLL3 (SP347) immunohistochemistry assay. The prevalence of DLL3 positivity ranged from 80.0-93.5% for studies using a threshold of ≥ 1% of tumor cells (N = 4) and 58.3-91.1% for studies with a ≥ 25% threshold (N = 4). DLL3 expression was generally categorized as high using cutoffs of ≥ 50% (prevalence range: 45.8-79.5%; N = 6) or ≥ 75% (prevalence range: 47.3-75.6%; N = 5) of cells with positivity. Two studies used an H-score of ≥ 150 to define high DLL3 expression with prevalence ranging from 33.3-53.1%. No consistent associations were seen between DLL3 expression level and patient age, sex, smoking history, or disease stage. Two studies reported change in DLL3 expression category (high versus low) before and after chemotherapy. No statistically significant differences were reported between DLL3 expression groups and survival (overall, progression-free, or disease-free) or treatment response. CONCLUSIONS: There is a high prevalence of DLL3 expression in SCLC. Further research and analytical methods may help to characterize different populations of patients with SCLC based on DLL3 expression. While no significant prognostic factor in the included studies was identified, additional cohort studies using standardized methodology, with longer follow-up, are needed to better characterize any potential differences in patient survival or response by DLL3 expression level in SCLC.

Diversity and Representation Among United States Participants in Amgen Clinical Trials.

OBJECTIVE: Describe the demographic profile of US participants in Amgen clinical trials over a 10-year period and variations across therapeutic areas, indications, and geographies. METHODS: Cross-sectional retrospective study including participants enrolled (2005-2020) in phase 1-3 trials completed between January 1, 2012 and June 30, 2021. RESULTS: Among 31,619 participants enrolled across 258 trials, one-fifth represented racial minority populations (Asian, 3%; Black or African American, 17%; American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, multiracial, each < 1%); fewer than one-fifth (16%) represented an ethnic minority population (Hispanic or Latino). Compared with census data, representation of racial and ethnic groups varied across US states. Across most therapeutic areas (bone, cardiovascular, hematology/oncology, inflammation, metabolic disorders, neuroscience) except nephrology, participants were predominantly White (72-81%). A similar proportion of males and females were enrolled between 2005 and 2016; male representation was disproportionately higher than female between 2016 and 2020. Across most medical indications, the majority of participants were 18-65 years of age. CONCLUSIONS AND RELEVANCE: While the clinical research community is striving to achieve diversity and proportional representation across clinical trials, certain populations remain underrepresented. Our data provide a baseline assessment of the diversity and representation of US participants in Amgen-sponsored clinical trials and add to a growing body of evidence on the importance of diversity in clinical research. These data provide a foundation for strategies aimed at supporting more equitable and representative research, and a baseline from which to assess the impact of future strategies to advance health equity.

Controlling for Differential Regression-To-The-Mean via Propensity Scores: A Simulation Study.

Purpose: Regression-to-the-mean (RTM) is a statistical phenomenon that may occur in epidemiologic studies where inclusion in the study cohort is contingent upon experiencing a laboratory/clinical measurement beyond a defined threshold. When differential across treatment groups, RTM could bias the final study estimate. This poses substantial challenges in observational studies that index patients upon experiencing extreme laboratory or clinical values. Our objective was to investigate propensity score-based methods as a tool for mitigating this source of bias via simulation. Methods: We simulated a noninterventional comparative effectiveness study, comparing treatment with romiplostim to standard-of-care therapies for immune thrombocytopenia (ITP), a disease characterized by low platelet counts. Platelet counts were generated from normal distributions according to the underlying ITP severity, a strong confounder of treatment and outcome. Patients were assigned treatment probabilities based upon ITP severity, which created varied levels of differential and non-differential RTM. Treatments were compared via the difference in median platelet counts during 23 weeks of follow-up. We calculated four summary metrics of the platelet counts measured prior to cohort entry and built six propensity score models to adjust for those variables. We adjusted for these summary metrics using inverse probability of treatment weights. Results: Across all simulated scenarios, propensity score adjustment reduced bias and increased precision of the treatment effect estimator. Adjusting for combinations of the summary metrics was most effective at reducing bias. Adjusting for the mean of prior platelet counts or the difference between the cohort-qualifying platelet count and the largest prior count eliminated the most bias when assessed individually. Conclusion: These results suggest that differential RTM could be reasonably addressed by propensity score models with summaries of historical laboratory values. This approach can be easily applied to any comparative effectiveness or safety study, though investigators should carefully consider the best summary metric for their data.

Comparability of Osteoporosis Treatment Groups Among Female Medicare Beneficiaries in the United States.

It is often difficult to obtain valid estimates of comparative treatment effectiveness and safety owing to differences across patient populations taking different medications in the real world. One approach for assessing comparability between treatment groups in effectiveness studies is to use negative control outcomes (NCOs). NCOs share similar sources of bias with the primary outcomes but have no plausible causal relationship to the treatment of interest. Observing differences in the risk of NCOs thus provides evidence for residual confounding between groups. This retrospective study assessed the comparability of postmenopausal women, treated with osteoporosis medications with various mechanisms of action such as denosumab (receptor activator of nuclear factor κB ligand [RANKL] inhibitor), zoledronic acid (bisphosphonate derivative), or oral bisphosphonates including alendronate. Administrative claims data were extracted from the US Centers for Medicare and Medicaid Services' Chronic Condition Warehouse database (May 2010-December 2016). Propensity scores were used to match denosumab patients 1:1 to comparators. Four nonfracture NCOs and three early fracture NCOs (before substantial biologic effects of treatment would be expected) were assessed over 1-year and 3-month follow-up periods, respectively. According to comparability decision rules established a priori, patients initiating denosumab were comparable to those initiating zoledronic acid or alendronate, irrespective of prior osteoporosis treatment experience. Among new users, new switchers, and in the historical fracture subgroup, no meaningful differences were observed in the cumulative incidence of the seven NCOs comparing denosumab to zoledronic acid. This empirical examination can assist in the selection of appropriate comparator groups for future comparability research using real-world data. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Association Between Granulocyte Colony-Stimulating Factor (G-CSF) Use and Myelodysplastic Syndrome (MDS) or Acute Myeloid Leukemia (AML) Among Elderly Patients with Breast, Lung, or Prostate Cancer.

INTRODUCTION: Patients diagnosed with cancer have an increased risk both for myelodysplastic syndromes (MDS) and for acute myeloid leukemia (AML) following treatment. METHODS: Using SEER-Medicare data, we selected patients aged 66 years and older who completed systemic therapy between 2002 and 2014 for breast (stage I-III), lung (stage I-III), or prostate (stage I-IV) cancer. For each cancer, we estimated the risk of a composite endpoint of MDS or AML in patients receiving granulocyte colony-stimulating factor (G-CSF) vs. not. RESULTS: The 10-year cumulative risk difference (granulocyte colony-stimulating factor [G-CSF] - no G-CSF) for MDS-AML was 0.45% (95% CI 0.13-0.77%) in breast cancer and 0.39% (95% CI 0.15-0.62%) in lung cancer. G-CSF use was associated with a hazard ratio of 1.60 (95% CI 1.07-2.40) in breast cancer and 1.50 (95% CI 0.99-2.29) in lung cancer. Filgrastim use was associated with a hazard ratio of 1.01 (95% CI 1.00-1.03) per administration in breast cancer and 1.02 (95% CI 0.99-1.05) per administration in lung cancer. Pegfilgrastim was associated with a hazard ratio of 1.08 (95% CI 1.01-1.15) per administration in breast cancer and 1.12 (95% CI 1.00-1.25) per administration in lung cancer. Analyses in prostate cancer were limited because of the low number of events. CONCLUSIONS: The use of G-CSF in patients diagnosed with breast and lung cancer is associated with an increased risk of MDS-AML. However, the MDS-AML absolute risk difference is very low.

The Design and Validation of a New Algorithm to Identify Incident Fractures in Administrative Claims Data.

Our study validated a claims-based algorithm for the identification of incident and recurrent fractures in administrative data. We used Centers for Medicare and Medicaid (CMS) claims from 2005 to 2014 linked to the Reasons for Geographic and Racial Differences in Stroke (REGARDS) database. Case qualifying (CQ) fractures were identified among participants with ≥12 months of fee-for-service coverage before first fracture claim and ≥6 months after. Recurrent fractures were defined as the first CQ fracture that occurred following a clean period of at least 90 days from the last claim associated with the preceding incident fracture. We used medical records (discharge summary, imaging, and surgical report) to adjudicate fractures. We calculated positive predictive values (PPVs) for incident and recurrent fractures. Our study was not designed to assess the algorithm sensitivity or negative predictive value. We identified 2049 potential incident fractures from claims among 1650 participants. Record retrieval was attempted for 728 (35.5%) suspected incident fractures (prioritizing more recent CQ fractures associated with osteoporosis, but without explicitly requiring any osteoporosis ICD-9 diagnosis code). Our final sample included 520 claims-identified fractures with medical records, of which 502 (96.5%) were confirmed. The PPVs (95% CI) of the hip, wrist, humerus, and clinical vertebra-all exceeded 95%. We identified 117 beneficiaries with 292 ≥2 CQ fracture episodes at the same site, and attempted retrieval on 105 (36.0%) episodes. Our analytic sample included 72 (68.5%) CQ episodes from 33 participants. The PPVs for identifying recurrent clinical vertebral, hip/femur, and nonvertebral fractures with a 90-day clean period exceeded 95%. Although we could not ascertain sensitivity, our updated fracture identification algorithms had high PPV for the identification of incident and recurrent fractures of the same site. Although medical record review and clinical adjudication remain a gold standard, our claims-based algorithm provides an alternative approach to fracture ascertainment when high PPV is desired. © 2019 American Society for Bone and Mineral Research.