Risk Tools Limited in Their Ability to Predict Development of Breast Cancer including Editorial Comment

Article: 
....Major Limitations Exist
According to the researchers, these models each have major limitations. Most notable is that the models rely on known risk factors, given that up to 60% of breast cancers occur in the absence of known risk factors. In addition, with the exception of the Gail model, these tools have not been well validated, and they also do not include nonhereditary risk factors. However, even the Gail model has limited ability to discriminate between individuals at risk, especially those in higher-risk groups, according to the study authors.
To date, no existing model is "totally able to discriminate between families that do and do not have mutations or between women who will and will not develop breast cancer," they write. "Steady and incremental improvement in the models are being made, but these changes require revalidation."
Other risk factors, such as mammographic density, weight gain, and serum steroid hormone measurements, are being considered for inclusion in the existing models. Studies are underway to determine if these factors are feasible and will improve breast cancer risk prediction, according to the study authors.

Editorial: Models Differ in Details
"The authors have provided a useful survey of the literature and have presented an informative summary of the risk factors used in various models," write Mitchell Gail, MD, PhD, and Phuong Mai, MD, from the National Cancer Institute, in a related editorial. Drs. Gail and Phuong caution, however, that the various models differ in important details and that physicians need to be cognizant of these differences.
"Promising directions include incorporating mammographic density, information on genotype or regulation of gene expression ... and more refined use of pathology data and biomarker data from biopsy samples," the editorialists add.

press release: Comparison of available breast cancer risk assessment tools shows room for improvement

"All of these models have major limitations, say the authors. Most important is their reliance on known risk factors. Studies have shown that up to 60% of breast cancers arise in the absence of any known risk factors. Also, except for the Gail model, none of the models has been extensively validated, and most do not include nonhereditary factors. The Gail model has limited ability to discriminate between individuals at risk, especially those in higher-risk groups, according to the authors."

Validation of the pedigree assessment tool (PAT) in families with BRCA1 and BRCA2 mutations

CONCLUSIONS: In overall performance, the PAT is at least comparable to the Myriad II and Penn II models in screening women appropriate for genetic referral. Simplicity and identification of families with non-BRCA hereditary BC syndromes suggest that the PAT is better suited for BC risk screening.

Now's the time to find biomarkers on purpose -- Annals of Oncology

"Studies need to be conducted to determine the optimal design for using genome-wide profiling to identify putative biomarkers of drug response. To date, most biomarkers of drug response identified through genome-wide profiling have occurred through retrospective analysis of available tissue. To really progress this field, realistic planning for biomarker discovery and validation in clinical trials needs to be conducted. We, as clinical scientists, need to progress from only using convenient clinical cohorts to identify biomarkers to actually planning and following through with prospective clinical trials whose aims are to discover and/or validate putative biomarkers of drug response. To initiate a study without a realistic plan for discovery and validation reflects a lack of serious desire to find robust clinical predictors.^.... Until this becomes more commonplace, the genomic revolution will be focused on manuscript generation and investigator career development, leaving the benefit to patients nothing more than an unrealized dream."

Evidence Updates: Impact of two supportive care interventions on anxiety, depression, quality of life, and unmet needs in patients (advanced cancer)

Evidence Updates: Impact of two supportive care interventions on anxiety, depression, quality of life, and unmet needs in patients
Girgis A, Breen S, Stacey F, et al. Impact of two supportive care interventions on anxiety, depression, quality of life, and unmet needs in patients with nonlocalized breast and colorectal cancers. J Clin Oncol. 2009 Dec 20;27(36):6180-90. Epub 2009 Nov 16. PMID: 19917842 (Original)
DISCIPLINE RELEVANCE TO PRACTICE IS THIS NEWS?
Oncology - Breast 3 / 7 3 / 7
Oncology - Gastrointestinal 4 / 7 4 / 7
Oncology - Palliative and Supportive Care 6 / 7 6 / 7

Abstract PURPOSE: Patients with cancer experience considerable symptom burden, psychological morbidity, and unmet psychosocial needs. Research suggests that feedback of patient-reported outcomes to clinicians or caseworkers, alongside management strategies, may result in improved patient functioning. Two intervention models were developed to test this effect in a randomized, controlled trial against usual care (UC): a telephone caseworker (TCW) model and an oncologist/general practitioner (O/GP) model. Primary end points included anxiety, depression, physical/emotional functioning, and unmet supportive care needs.  
PATIENTS AND METHODS: Participants with nonlocalized breast or colorectal cancers were surveyed by computer-assisted telephone interview (CATI) at three time points: baseline, 3 months, and 6 months. Data collected from participant CATIs in the supportive care models were used to generate feedback to either each participant`s designated TCW, or their nominated O/GPs. Data obtained from participants in the UC model were used only to assess the impact of supportive care models. In total, 356 participants consented to study participation, completed the baseline CATI, and were randomly assigned to the UC, TCW, or O/GP groups.  
RESULTS: No overall intervention effect was observed. Physical functioning was significantly improved at the third CATI for participants in the TCW model (P = .01), and there was a trend toward fewer participants with unmet needs (P = .07). TCW group participants also were more likely to have the following: identified issues of need discussed (P < .0001); referrals made (P < .0001); and strong agreement that the intervention improved communication with their health care team (P = .0005).  
CONCLUSION: The TCW model holds some promise; however, additional work in at-risk populations is required before we recommend implementation.

abstract: Review The potential of biologic network models in understanding the etiopathogenesis of ovarian cancer