Single-cell genomic variation induced by mutational processes in cancer

How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct 'foreground' mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.

Medication adherence in rheumatoid arthritis patients: older is wiser

OBJECTIVES

To create a profile of individuals nonadherent to their medications in an age-stratified sample (ages 34-84) of community-dwelling rheumatoid arthritis patients. The relative contributions of age, cognitive function, disability, emotional state, lifestyle, and beliefs about illness to nonadherence were assessed.

DESIGN

A direct observation approach was used in conjunction with structural equation modeling. All participants were administered a preliminary assessment battery. Medications were then transferred to vials with microelectronic caps that recorded medication events for all medications for the next 4 weeks.

PARTICIPANTS AND SETTING

A volunteer sample of 121 community-dwelling rheumatoid arthritis (RA) patients were recruited from newspaper ads, posters, and via informal physician contact from private rheumatology practices in Atlanta and Athens, Georgia. Written verification of the RA diagnosis and a disease severity rating were obtained from personal physicians before patients were enrolled in the study. Patients were tested in a private physician's office, and their medication adherence was monitored electronically for a month in their every-day work and home settings.

MEASUREMENTS AND RESULTS

Structural equation modeling techniques were used to develop a model of adherence behavior. Cognitive and psychosocial measures were used to construct latent variables to predict adherence errors. The model of medication adherence explained 39% of the variance in adherence errors. The model demonstrated that older adults made the fewest adherence errors, and middle-aged adults made the most. A busy lifestyle, age, and cognitive deficits predicted nonadherence, whereas coping with arthritis-related moods predicted adherence. Illness severity, medication load, and physical function did not predict adherence errors. Omission of medication accounted for nearly all errors.

CONCLUSION

Despite strong evidence for normal, age-related cognitive decline in this sample, older adults had sufficient cognitive function to manage medications. A busy lifestyle and middle age were more determinant of who was at risk of nonadherence than beliefs about medication or illness. Thus, practicing physicians should not assume that older adults have insufficient cognitive resources to manage medications and that they will be the most likely to make adherence errors. Very busy middle-aged adults seem to be at the greatest risk of managing medications improperly.