Bibliographic record
Abstract
Scientists gathered in San Francisco in October to discuss the latest findings in breast cancer research at the 2009 Breast Cancer Symposium. Below, Funda Meric-Bernstam, MD, professor of surgical oncology at M. D. Anderson Cancer Center in Houston, Texas, and breast disease section editor for Cancer, offers the bottom line on 3 of the featured studies. Trastuzumab May Improve Survival in High-Risk HER2i- Breast Cancer Finding: Women with small node-negative, HER2-positive (HER2+), breast tumors appear to benefit from adjuvant trastuzumab (Herceptin) treatment. (Lead author: Heather L. McArthur MD, MPH, Memorial Sloan-Kettering Cancer Center in New York). Commentary: Randomized clthical trials of trastuzumab chemotherapy have shown a significant improvement recurrence and survival rates in women with node positive “high-risk” node-negative, HER2+ tumors. However, the treatment of small HER2+ tumors is controversial. In the study by McArthur et al, investigators evaluated the outcome of women with HER2+ tumors 2 cm or smaller who were treated at Memorial Sloan-Kettering before and after the adjuvant trastuzumab era. The authors reported a significant improvement in disease-free survival in the post-trastuzumab era. Although retrospective studies have limitations due to potential additional changes in management over that time (eg, imaging and adjuvant endocrine therapy), the findings are compelling. These results need to Nt considered in the context of 2 recent studies—one from M. D. Anderson, the other from the Instituto Europeo di Oncologia in Milan, Italy. Both showed that women with small (International Union Against Cancer [UICCI] Tla,Tlb) HER2+ tumors are at significantly increased risk of recurrence1, 2 Thus, HER2+ womcn, cvcn with smallcr tumors, arc at high enough risk to warrant inclusion in future HER2-targeted therapy trials. Although level 1 evidence [as measured by the US Preventive Services Task Force evidence-based medicine standards] is lacking, the results from McArthur et al suggest that adjuvant anti-HER2 therapy is worth considering in with HER2+T1 tumors. Novel Tool May Predict Tamoxifen Response Finding: PAM5O gene assay is highly prognostic for estrogen receptor (ER)-positive, tamoxifen-treated, breast cancer. (Lead author: Matthew j Ellis, PhD, Washington University School of Medicine in St. Louis, Missouri). Commentary: PAM5O is a novel 50-gene qRT-PCRbased predictor that can be used on formalin-fixed, paraf fin-embedded tissue. This gene expression- based system differentiates “intrinsic subtypes” (luminal A and B, HER2-enriched, and basal-like) and previously has been shown to predict response to neoadjuvant chemotherapy.3 Ellis et al demonstrate that PAM5O is highly prognostic among women with ER-positive disease who received tamoxifen alone. PAM5O appeared to have superiority over both clinical-pathologic predictors and immunohistochemistry-based predictors. If validated, PAM5O holds promise as a valuable prognostic tool. It would be of interest to see how PAM5O compares with Oncotype Dx, which is commonly used in this patient population. Immunohistochemistry (IHC) Classification May Help Select Breast Cancer Treatment Finding: Tissue microarrays identify biological subtype of breast cancer and predict value of adjuvant chemotherapy. (Lead author Torsten 0. Nielsen, MD, PhD, University of British Columbia in Vancouvei Canada). Commentary: Nielson et al used IHC for ER, HER2, Ki67, cytokeratin 5/6, and epidermal growth factor receptor (EGFR) to categorize patients treated in the Cancer and Leukemia Group B (CALGB)-9344 study into intrinsic subset types. This study demonstrated that intrinsic biologic subtype is independently prognostic. It also predicted the benefit of adding paclitaxel to adjuvant doxorubicin-cyclophosphamide chemotherapy. Clcarly, we are entering an era where we will be able to classify breast tumors better, and one hopes that we can rapidly use this information to stratify patients better on the basis of risk of recurrence and relative benefit of additional therapies. An IHC-based panel may be advantageous because of its low cost, because it adds only 3 additional IHC markers (Ki67, cytokcratin 5/6, and EGFR) to markers already in routine use (ER and HER2). It will be interesting to see how reproducible this THC-based classification will be between laboratories and how it compares with genomics-based predictors like PAM5O.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".