Risk as a pattern over time: Delineation of time-dependent risk factors in biological,psychological, and social variables in cancer patients

Background

Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time.

Methods

Patients who attended the authors’ outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model.

Results

The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term.

Conclusions

The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.     

Functional estrogen receptor signal transduction pathway activity and antihormonal therapy response in low-grade ovarian carcinoma

Background

Advanced low-grade ovarian carcinoma (LGOC) is difficult to treat. In several studies, high estrogen receptor (ER) protein expression was observed in patients with LGOC, which suggests that antihormonal therapy (AHT) is a treatment option. However, only a subgroup of patients respond to AHT, and this response cannot be adequately predicted by currently used immunohistochemistry (IHC). A possible explanation is that IHC only takes the ligand, but not the activity, of the whole signal transduction pathway (STP) into account. Therefore, in this study, the authors assessed whether functional STP activity can be an alternative tool to predict response to AHT in LGOC.

Methods

Tumor tissue samples were obtained from patients with primary or recurrent LGOC who subsequently received AHT. Histoscores of ER and progesterone receptor (PR) were determined. In addition, STP activity of the ER STP and of six other STPs known to play a role in ovarian cancer was assessed and compared with the STP activity of healthy postmenopausal fallopian tube epithelium.

Results

Patients who had normal ER STP activity had a progression-free survival (PFS) of 16.1 months. This was significantly shorter in patients who had low and very high ER STP activity, with a median PFS of 6.0 and 2.1 months, respectively (p < .001). Unlike ER histoscores, PR histoscores were strongly correlated to the ER STP activity and thus to PFS.

Conclusions

Aberrant low and very high functional ER STP activity and low PR histoscores in patients with LGOC indicate decreased response to AHT. ER IHC is not representative of functional ER STP activity and is not related to PFS.     

Molecular imaging in oncology: Current impact and future directions

The authors define molecular imaging, according to the Society of Nuclear Medicine and Molecular Imaging, as the visualization, characterization, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. Although practiced for many years clinically in nuclear medicine, expansion to other imaging modalities began roughly 25 years ago and has accelerated since. That acceleration derives from the continual appearance of new and highly relevant animal models of human disease, increasingly sensitive imaging devices, high-throughput methods to discover and optimize affinity agents to key cellular targets, new ways to manipulate genetic material, and expanded use of cloud computing. Greater interest by scientists in allied fields, such as chemistry, biomedical engineering, and immunology, as well as increased attention by the pharmaceutical industry, have likewise contributed to the boom in activity in recent years. Whereas researchers and clinicians have applied molecular imaging to a variety of physiologic processes and disease states, here, the authors focus on oncology, arguably where it has made its greatest impact. The main purpose of imaging in oncology is early detection to enable interception if not prevention of full-blown disease, such as the appearance of metastases. Because biochemical changes occur before changes in anatomy, molecular imaging—particularly when combined with liquid biopsy for screening purposes—promises especially early localization of disease for optimum management. Here, the authors introduce the ways and indications in which molecular imaging can be undertaken, the tools used and under development, and near-term challenges and opportunities in oncology.