Patient Experience in Clinical Trials: Quality of Life, Financial Burden, and Perception of Care in Patients With Multiple Myeloma or Lymphoma Enrolled on Clinical Trials Compared With Standard Care.

PURPOSE: Patients' concerns regarding clinical trial (CT) participation include apprehension about side effects, quality of life (QoL), financial burden, and quality of care. METHODS: We prospectively evaluated the experience of patients with multiple myeloma or lymphoma who were treated on CTs (CT group, n = 35) versus patients treated with standard approaches (non-CT group, n = 88) focusing on QoL, financial burden of care, and patients' perception of quality of care over a 1-year period. RESULTS: There were no significant differences in any of the patient-reported outcomes in CT versus non-CT groups. We observed an initial decline in overall QoL in the first 3 months across both groups, driven primarily by physical and functional well-being. QoL gradually improved and was above baseline by month 12. Patients reported highest improvement in the functional well-being subdomain. Patients in both groups reported high satisfaction with the quality of care received, and there were no differences in overall satisfaction, communication with team, or access to care. At baseline, 16%-19% of patients reported financial burden, which increased to a peak of 33% in the CT group and to 49% in the non-CT group over the course of 1 year. There was no significant difference in financial burden in the two groups overall. Most of the patients reported getting all the care that was deemed medically necessary in both groups. However, a significant proportion of patients reported having to make other kinds of financial sacrifices because of their cancer (CT group: 33% of patients at baseline and 21%-40% over 1 year; non-CT group: 19% at baseline and 25%-36% over 1 year). CONCLUSION: Patients treated on CTs reported comparable QoL and quality of care with the non-CT group. A high proportion of patients reported financial burden over time in both groups. Our findings can serve as a guide to educate patients regarding CT participation and highlight the need to address the significant financial burden experienced by patients with cancer.

Time to Second-line Treatment and Subsequent Relative Survival in Older Patients With Relapsed Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma.

BACKGROUND: Novel targeted therapies offer excellent short-term outcomes in patients with chronic lymphocytic leukemia and small lymphocytic lymphoma (CLL/SLL). However, there is disagreement over how widely these therapies should be used in place of standard chemo-immunotherapy (CIT). We investigated whether stratification on the length of the interval between first-line (T1) and second-line (T2) treatments could identify a subgroup of older patients with relapsed CLL/SLL with an expectation of normal overall survival, and for whom CIT could be an acceptable treatment choice. PATIENTS AND METHODS: Patients with relapsed CLL/SLL who received T2 were identified from the SEER-Medicare Linked Database. Five-year relative survival (RS5; ie, the ratio of observed survival to expected survival based on population life tables) was assessed after stratifying patients on the interval between T1 and T2. We then validated our findings in the Mayo Clinic CLL Database. RESULTS: Among 1974 SEER-Medicare patients (median age = 77 years) who received T2 for relapsed CLL/SLL, longer time-to-retreatment was associated with a modestly improved prognosis (P = .01). However, even among those retreated ≥ 3 years after T1, survival was poor compared with the general population (RS5 = 0.50 or lower in SEER-Medicare). Similar patterns were observed in the younger Mayo validation cohort, although prognosis was better overall among the Mayo patients, and patients with favorable fluorescence in situ hybridization retreated ≥ 3 years after T1 had close to normal expected survival (RS5 = 0.87). CONCLUSION: Further research is needed to quantify the degree to which targeted therapies provide meaningful improvements over CIT in long-term outcomes for older patients with relapsed CLL/SLL.

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors.

BACKGROUND: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women. METHODS: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. RESULTS: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10(-6)). CONCLUSIONS: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2 Future work is needed to develop powerful statistical methods able to detect these complex interactions. IMPACT: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. ©2016 AACR.

Assessment of hepatocyte growth factor in ovarian cancer mortality.

BACKGROUND: Invasive ovarian cancer is a significant cause of gynecologic cancer mortality. METHODS: We examined whether this mortality was associated with inherited variation in approximately 170 candidate genes/regions [993 single-nucleotide polymorphisms (SNPs)] in a multistage analysis based initially on 312 Mayo Clinic cases (172 deaths). Additional analyses used The Cancer Genome Atlas (TCGA; 127 cases, 62 deaths). For the most compelling gene, we immunostained Mayo Clinic tissue microarrays (TMA, 326 cases) and conducted consortium-based SNP replication analysis (2,560 cases, 1,046 deaths). RESULTS: The strongest initial mortality association was in HGF (hepatocyte growth factor) at rs1800793 (HR = 1.7, 95% CI = 1.3-2.2, P = 2.0 × 10(-5)) and with overall variation in HGF (gene-level test, P = 3.7 × 10(-4)). Analysis of TCGA data revealed consistent associations [e.g., rs5745709 (r(2) = 0.96 with rs1800793): TCGA HR = 2.4, CI = 1.4-4.1, P = 2.2 × 10(-3); Mayo Clinic + TCGA HR = 1.6, CI = 1.3-1.9, P = 7.0 × 10(-5)] and suggested genotype correlation with reduced HGF mRNA levels (P = 0.01). In Mayo Clinic TMAs, protein levels of HGF, its receptor MET (C-MET), and phospho-MET were not associated with genotype and did not serve as an intermediate phenotype; however, phospho-MET was associated with reduced mortality (P = 0.01) likely due to higher expression in early-stage disease. In eight additional ovarian cancer case series, HGF rs5745709 was not associated with mortality (HR = 1.0, CI = 0.9-1.1, P = 0.87). CONCLUSIONS: We conclude that although HGF signaling is critical to migration, invasion, and apoptosis, it is unlikely that HGF genetic variation plays a major role in ovarian cancer mortality. Furthermore, any minor role is not related to genetically-determined expression. IMPACT: Our study shows the utility of multiple data types and multiple data sets in observational studies.