Search

Detailed Individual Characteristics of 49 Enrolled Patients

Purpose: Decisions to continue or suspend therapy with immune checkpoint inhibitors are commonly guided by tumor dynamics seen on serial imaging. However, immunotherapy responses are uniquely challenging to interpret because tumors often shrink slowly or can appear transiently enlarged due to inflammation. We hypothesized that monitoring tumor cell death in real time by quantifying changes in circulating tumor DNA (ctDNA) levels could enable early assessment of immunotherapy efficacy.Experimental Design: We compared longitudinal changes in ctDNA levels with changes in radiographic tumor size and with survival outcomes in 28 patients with metastatic non–small cell lung cancer (NSCLC) receiving immune checkpoint inhibitor therapy. CtDNA was quantified by determining the allele fraction of cancer-associated somatic mutations in plasma using a multigene next-generation sequencing assay. We defined a ctDNA response as a >50% decrease in mutant allele fraction from baseline, with a second confirmatory measurement.Results: Strong agreement was observed between ctDNA response and radiographic response (Cohen's kappa, 0.753). Median time to initial response among patients who achieved responses in both categories was 24.5 days by ctDNA versus 72.5 days by imaging. Time on treatment was significantly longer for ctDNA responders versus nonresponders (median, 205.5 vs. 69 days; P < 0.001). A ctDNA response was associated with superior progression-free survival [hazard ratio (HR), 0.29; 95% CI, 0.09–0.89; P = 0.03], and superior overall survival (HR, 0.17; 95% CI, 0.05–0.62; P = 0.007).Conclusions: A drop in ctDNA level is an early marker of therapeutic efficacy and predicts prolonged survival in patients treated with immune checkpoint inhibitors for NSCLC. Clin Cancer Res; 24(8); 1872–80. ©2018 AACR.

Supplementary Materials

PDF file - 76K

PDF file - 63K, ctDNA levels in patients with fewer than 3 time-points

Understanding the changes in diverse molecular pathways underlying the development of breast tumors is critical for improving diagnosis, treatment, and drug development. Here, we used RNA-profiling of canine mammary tumors (CMTs) coupled with a robust analysis framework to model molecular changes in human breast cancer. Our study leveraged a key advantage of the canine model, the frequent presence of multiple naturally occurring tumors at diagnosis, thus providing samples spanning normal tissue and benign and malignant tumors from each patient. We showed human breast cancer signals, at both expression and mutation level, are evident in CMTs. Profiling multiple tumors per patient enabled by the CMT model allowed us to resolve statistically robust transcription patterns and biological pathways specific to malignant tumors versus those arising in benign tumors or shared with normal tissues. We showed that multiple histological samples per patient is necessary to effectively capture these progression-related signatures, and that carcinoma-specific signatures are predictive of survival for human breast cancer patients. To catalyze and support similar analyses and use of the CMT model by other biomedical researchers, we provide FREYA, a robust data processing pipeline and statistical analyses framework.

The authors collected data by structured interview from a convenience sample of 228 physically healthy, largely (82%) treatment-seeking, cocaine smokers with minimal histories of other smoked (other than tobacco and marijuana) or injection drug use. The vast majority of subjects also smoked either marijuana only (17.5%), tobacco only (17%), or both (61%), with onset of such smoking almost always (97%) preceding the initiation of regular cocaine smoking. There were few significant differences in sociodemographic or cocaine use characteristics among the subgroups of subjects smoking either cocaine only or cocaine and marijuana and/or tobacco. More than one-third of marijuana smokers quit (45%) or decreased (38%) their use after starting regular cocaine smoking, whereas only 5% of tobacco smokers did so. These findings suggest that marijuana smoking is more influenced by regular cocaine smoking than is tobacco smoking.

The authors investigated the occurrence of pathological gambling among cocaine-dependent outpatients, its influence on short-term outcome of treatment, and comparative characteristics of patients with and without pathological gambling.The subjects were 313 cocaine-dependent (200 also opiate-dependent) outpatients in clinical trials of medication for cocaine dependence. Pathological gambling (DSM-III-R criteria) was assessed with the Diagnostic Interview Schedule, and sociodemographic and socioeconomic characteristics were determined with the Addiction Severity Index. Outcome was defined as time in treatment (proportion of maximum scheduled time) and proportion of cocaine-positive urine samples during treatment.Pathological gambling had a lifetime occurrence rate of 8.0% and a current (past month) occurrence of 3.8%. Onset preceded the onset of cocaine dependence in 72.0% of the patients (and preceded onset of opiate dependence in 44.4%). Patients with pathological gambling (lifetime or current) did not differ significantly from other patients in length of treatment or proportion of cocaine-positive urine samples. Those with lifetime pathological gambling were significantly more likely to have tobacco dependence (84.0% versus 61.1%) and antisocial personality disorder (56.0% versus 19.8%), to be unemployed (84.0% versus 49.3%), to have recently engaged in illegal activity for profit (64.0% versus 38.5%), and to have been incarcerated (62.5% versus 33.9%).Pathological gambling is substantially more prevalent among cocaine-dependent outpatients than in the general population. Patients with pathological gambling differ from other cocaine-dependent outpatients in some sociodemographic characteristics but not in short-term outcome of treatment for cocaine dependence.