Improved prognostication in small (pT1) breast cancers by image cytometry

Genome Instability Profiles Predict Disease Outcome in a Cohort of 4,003 Patients with Breast Cancer

PURPOSE

The choice of therapy for patients with breast cancer is often based on clinicopathologic parameters, hormone receptor status, and HER2 amplification. To improve individual prognostication and tailored treatment decisions, we combined clinicopathologic prognostic data with genome instabilty profiles established by quantitative measurements of the DNA content.

EXPERIMENTAL DESIGN

We retrospectively assessed clinical data of 4,003 patients with breast cancer with a minimum postoperative follow-up period of 10 years. For the entire cohort, we established genome instability profiles. We applied statistical methods, including correlation matrices, Kaplan-Meier curves, and multivariable Cox proportional hazard models, to ascertain the potential of standard clinicopathologic data and genome instability profiles as independent predictors of disease-specific survival in distinct subgroups, defined clinically or with respect to treatment.

RESULTS

In Cox regression analyses, two parameters of the genome instability profiles, the S-phase fraction and the stemline scatter index, emerged as independent predictors in premenopausal women, outperforming all clinicopathologic parameters. In postmenopausal women, age and hormone receptor status were the predominant prognostic factors. However, by including S-phase fraction and 2.5c exceeding rate, we could improve disease outcome prediction in pT1 tumors irrespective of the lymph node status. In pT3-pT4 tumors, a higher S-phase fraction led to poorer prognosis. In patients who received adjuvant endocrine therapy, chemotherapy or radiotherapy, or a combination, the ploidy profiles improved prognostication.

CONCLUSIONS

Genome instability profiles predict disease outcome in patients with breast cancer independent of clinicopathologic parameters. This applies especially to premenopausal patients. In patients receiving adjuvant therapy, the profiles improve identification of high-risk patients.

Methodological challenges in the evaluation of prognostic factors in breast cancer

Many studies are carried out in an effort to find factors that help explain the large unexplained variation in prognosis of breast cancer patients. The principles of good study design and analysis are less well appreciated for prognostic factor studies than for therapeutic trials. The oncology literature is full of results from studies of varying quality, often with conflicting findings. As a consequence, despite the large number of studies, there is still uncertainty about the importance of most prognostic factors. Few recently proposed prognostic factors for breast cancer have become widely accepted. This paper reviews the important methodological issues underlying such research. These issues are illustrated with examples from published studies and recent reviews of papers published in cancer journals. Guidelines are proposed for conducting and evaluating prognostic factor studies which should improve the quality of research in this important area.

p34cdc2 in invasive breast cancer: relationship to DNA content, Ki67 index and c-erbB-2 expression

AIMS

One-hundred and eighty-eight cases of human mammary carcinoma were examined immunohistochemically for their expression of Ki67, p34cdc2 and c-erbB-2. DNA image cytometry was performed to evaluate DNA ploidy, Auer type, S-phase fraction (SPF), 5c exceeding rate (5cER) and 2c deviation index (2cDI).

METHODS AND RESULTS

One-hundred and sixty-eight cases were invasive ductal carcinomas, 20 were of invasive lobular type. Routinely assessed oestrogen and progesterone receptor scores were available. The results were analysed statistically in comparison to tumour type, histopathological grade, lymph node status, menopausal status, patient age and overall survival. Ki67 (P < 0.002) and c-erbB-2 (P < 0.0001) correlated well with overall survival (P < 0.0008) and grade (P < 0.038) but not with lymph node status and tumour type. p34cdc2 showed a trend towards a positive correlation with Ki67 (P < 0.058) and a significant negative correlation with receptor status (P < 0.008) but with none of the other parameters examined.

CONCLUSIONS

No association between the DNA measured parameters (Auer type, SPF, 5cER and 2cDI) and survival was found. Our results suggest that c-erbB-2 and Ki67 are parameters which might, in combination with receptor status, help to define subgroups with different outcomes.

Computer-derived nuclear features compared with axillary lymph node status for breast carcinoma prognosis

BACKGROUND

Both axillary lymph node involvement and tumor anaplasia, as expressed by visually assessed grade, have been shown to be prognostically important in breast carcinoma outcome. In this study, axillary lymph node involvement was used as the standard against which prognostic estimations based on computer-derived nuclear features were gauged.

METHODS

The prognostic significance of nuclear morphometric features determined by computer-based image analysis were analyzed in 198 consecutive preoperative samples obtained by fine-needle aspiration (FNA) from patients with invasive breast carcinoma. A novel multivariate prediction method was used to model the time of distant recurrence as a function of the nuclear features. Prognostic predictions based on the nuclear feature data were cross-validated to avoid overly optimistic conclusions. The estimated accuracy of these prognostic determinations was compared with determinations based on the extent of axillary lymph node involvement.

RESULTS

The predicted outcomes based on nuclear features were divided into three groups representing best, intermediate, and worst prognosis, and compared with the traditional TNM lymph node stratification. Nuclear feature stratification better separated the prognostically best from the intermediate group whereas lymph node stratification better separated the prognostically intermediate from the worst group. Prognostic accuracy was not increased by adding lymph node status or tumor size to the nuclear features.

CONCLUSIONS

Computer analysis of a preoperative FNA more accurately identified prognostically favorable patients than did pathologic examination of axillary lymph nodes and may obviate the need for routine axillary lymph node dissection.

Analysis of DNA and morphometry in breast carcinoma

Feulgen-stained imprints and smears from 730 cases of invasive breast cancer were investigated using an image analysis system. From each tumor sample 100 cells were randomly scanned and several DNA and morphometrical parameters evaluated. Their prognostic value for a prediction of distant metastases within 5 years was investigated with the multivariate Cox regression analysis, which was performed for all consecutive cases, as well as for node-negative and node-positive patients separately. The multivariate analyses showed a strong prognostic value of the anisonucleosis (variation of nuclear radius) and the DNA histogram type in addition to the nodal status, the tumor size (pT), and the histological tumor grade. However, performing this analysis for both node-positive patients and for those without lymph node metastases demonstrated a different prognostic meaning of the variables. The combination of each of the group-specific variables led to a prognostic factor, which allowed an assignment of patients to several subgroups with significantly different risk for distant metastases. Thus, both a low-risk group of node-negative patients with a 5-year distant recurrence rate of only 5.8%, and a higher risk group of node-negative patients with a recurrence rate of 38.6% could be identified. Among the node-positive patients, a low-risk group with a distant recurrence rate of 8.6%, and also a high risk group with 69% distant recurrence, could be identified.