Old and new markers for breast cancer prognosis: the need for integrated research on quantitative issues

A Prediction Model for Breast Cancer Recurrence after Adjuvant Hormone Therapy

Hormone therapy with tamoxifen has long been the established adjuvant treatment for node-positive, estrogen–receptor-positive breast cancer in postmenopausal women. Since 30–40% of these patients fail to respond, reliable outcome prediction is necessary for successful treatment allocation. Using pathobiological variables (available in most clinical records: tumor size, nodal involvement, estrogen and progesterone receptor content) from 596 patients recruited at a comprehensive cancer center, we developed a prediction model which we validated in an independent cohort of 175 patients recruited at a general hospital. Calculated at 3 and 4 years of follow-up, the discrimination indices were 0.716 [confidence limits (CL) 0.641, 0.752] and 0.714 (CL 0.650, 0.750) for the training data, and 0.726 (CL 0.591, 0.769) and 0.677 (CL 0.580, 0.745) for the testing data. Waiting for more effective approaches from genomic and proteomic studies, a model based on consolidated pathobiological variables routinely assessed at relatively low costs may be considered as the reference for assessing the gain of new markers over traditional ones, thus substantially improving the conventional use of prognostic criteria.

Psychosocial intervention effects on adaptation, disease course and biobehavioral processes in cancer

A diagnosis of cancer and subsequent treatments place demands on psychological adaptation. Behavioral research suggests the importance of cognitive, behavioral, and social factors in facilitating adaptation during active treatment and throughout cancer survivorship, which forms the rationale for the use of many psychosocial interventions in cancer patients. This cancer experience may also affect physiological adaptation systems (e.g., neuroendocrine) in parallel with psychological adaptation changes (negative affect). Changes in adaptation may alter tumor growth-promoting processes (increased angiogenesis, migration and invasion, and inflammation) and tumor defense processes (decreased cellular immunity) relevant for cancer progression and the quality of life of cancer patients. Some evidence suggests that psychosocial intervention can improve psychological and physiological adaptation indicators in cancer patients. However, less is known about whether these interventions can influence tumor activity and tumor growth-promoting processes and whether changes in these processes could explain the psychosocial intervention effects on recurrence and survival documented to date. Documenting that psychosocial interventions can modulate molecular activities (e.g., transcriptional indicators of cell signaling) that govern tumor promoting and tumor defense processes on the one hand, and clinical disease course on the other is a key challenge for biobehavioral oncology research. This mini-review will summarize current knowledge on psychological and physiological adaptation processes affected throughout the stress of the cancer experience, and the effects of psychosocial interventions on psychological adaptation, cancer disease progression, and changes in stress-related biobehavioral processes that may mediate intervention effects on clinical cancer outcomes. Very recent intervention work in breast cancer will be used to illuminate emerging trends in molecular probes of interest in the hope of highlighting future paths that could move the field of biobehavioral oncology intervention research forward.

When will more useful predictive factors be ready for use?

Adjuvant chemotherapy is widely used, but its performance is not optimal. Two subgroups of patients do not get any benefit from adjuvant chemotherapy: the first one comprises patients who are already cured by locoregional treatment alone and the second one patients who do not profit from adjuvant chemotherapy because of resistance to the regimens employed. To improve the cost/benefit of this treatment strategy, we have two means: one is to improve the sensitivity of prognostic factors to be able to select a specific group with a good signature that does not need adjuvant treatment; the second is to identify predictive factors that may help us to select the optimal therapeutic strategy or the optimal regimen or drug for individual patients. New technologies of microarray revealed several genetic profiles. A large randomized trial (Microarray In Node-negative Disease may Avoid ChemoTherapy, MINDACT) will compare the information obtained with the genomic profiling and the classical clinico-pathologic index (St Gallen); the objective is to allow women not to be treated with adjuvant chemotherapy if their genomic signature is good. Another trial (EORTC 10994) is conducted in order to show that in cases of p53 mutated tumors, neoadjuvant chemotherapy with docetaxel is more efficient than an anthracycline-containing regimen. A supplementary study will evaluate gene profile predicting for p53 status. So, new genomic prognostic factors are still in development and seem very promising for optimizing the indications for adjuvant chemotherapy.