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The purpose of the study was to create a new prognostic model for breast cancer (BC) patients that had been treated with a preoperative neoadjuvant chemotherapy, considering different parameters of regressive changes in the residual tumor (RT) respectively the tumor bed (TB). HE-stained whole-slide images of 518 patients, treated between 2001-2019 in Hamburg, Germany following current therapy standards, were analyzed regarding different variables of regressive changes in the pretherapeutic diagnosed BC. First, size of RT and TB was measured, lymph node metastasis and residual intraductal carcinoma in situ were noted. Additional parameters that were examined were cell density and cell shape of RT, heterogeneity of residual tumor cells spreading within the TB and size of the biggest cell clusters as well as changes of the stroma and inflammation within the TB. Data were used pseudo-anonymized after consent was given by the patients and a positive vote of the ethics committee. In the univariate observation of above-mentioned variables it was shown that heterogeneity, cell density and cell shape of residual tumor cells, stroma changes and the presence of lymph node metastases as well as largest diameter and largest cluster of RT have an impact on progression free survival. Using decision tree analysis, the patient cohort could be divided in three risk groups with significant different outcome regarding progression free survival with low, intermediate and high risk for an event (local recurrence of disease, metastasis, death). Our multivariate "Altona regression model", that was constructed using Cox regression, showed significantly better prognostic value concerning progression free survival for BC patients after neoadjuvant therapy and following surgery, compared to two well-known regression gradings by Sinn et al. and MD Anderson (RCB). Comparison of regression models Altona Sinn et al. RCB Altona vs. Sinn Altona vs. RCB Sinn vs. RCB iAUC 0.607 0.350 0.380 < 0.001 < 0.001 < 0.001 Concordance 0.788 0.378 0.478 < 0.001 < 0.001 < 0.001 Citation Format: Anne-Sophie Wegscheider, Katharina Lotfy-Eckmann, Bernhard Ulm, Christoph Lindner, Kay Friedrichs, Axel Niendorf. Altona regression model: a new prognostic model for breast cancer patients after neoadjuvant treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1019.

Distant metastasis is the most lethal hallmark of cancer. Nevertheless, due to a lack of suitable markers, the emerging motility of cancer cells only has a negligible impact on current diagnosis and prognosis. In cancer clusters of solid tumours, cooperative unjamming transitions generate regions of unjammed motile cells, as we could show by vital cancer cell tracking in patient-derived tumour explants. Thereby, cancer cells and their nuclei collectively deform/elongate to squeeze past each other. The cells' differing shapes and densities, and nuclei of jammed and unjammed cancer cells can serve as the first reliable motility marker in static images such as histopathological slides. A retrospective clinical study that strongly correlates unjamming with distant metastasis demonstrates that unjamming within primary tumours is part of the metastatic cascade. With the histological slides of two independent breast cancer patient collectives, we train (N=688) and validate (N=692) our quantitative prognostic index based on unjamming regarding metastatic risk. Our index corrects for false high- and low-risk predictions based on nodal status. Combining information derived from the nodal status with unjamming may reduce over- and under-treatment and significantly improve the understanding of the metastatic cascade.

PURPOSENeoadjuvant systemic treatment (NST) elicits a pathologic complete response in 40%-70% of women with breast cancer. These patients may not need surgery as all local tumor has already been eradicated by NST. However, nonsurgical approaches, including imaging or vacuum-assisted biopsy (VAB), were not able to accurately identify patients without residual cancer in the breast or axilla. We evaluated the feasibility of a machine learning algorithm (intelligent VAB) to identify exceptional responders to NST.METHODSWe trained, tested, and validated a machine learning algorithm using patient, imaging, tumor, and VAB variables to detect residual cancer after NST (ypT+ or in situ or ypN+) before surgery. We used data from 318 women with cT1-3, cN0 or +, human epidermal growth factor receptor 2–positive, triple-negative, or high-proliferative Luminal B–like breast cancer who underwent VAB before surgery (ClinicalTrials.gov identifier: NCT02948764 , RESPONDER trial). We used 10-fold cross-validation to train and test the algorithm, which was then externally validated using data of an independent trial (ClinicalTrials.gov identifier: NCT02575612 ). We compared findings with the histopathologic evaluation of the surgical specimen. We considered false-negative rate (FNR) and specificity to be the main outcomes.RESULTSIn the development set (n = 318) and external validation set (n = 45), the intelligent VAB showed an FNR of 0.0%-5.2%, a specificity of 37.5%-40.0%, and an area under the receiver operating characteristic curve of 0.91-0.92 to detect residual cancer (ypT+ or in situ or ypN+) after NST. Spiegelhalter's Z confirmed a well-calibrated model ( z score –0.746, P = .228). FNR of the intelligent VAB was lower compared with imaging after NST, VAB alone, or combinations of both.CONCLUSIONAn intelligent VAB algorithm can reliably exclude residual cancer after NST. The omission of breast and axillary surgery for these exceptional responders may be evaluated in future trials.

Background: Neoadjuvant systemic treatment (NST) elicits a pathologic complete response (pCR, ypT0, ypN0) in 40-70% of women with HER2 positive, triple-negative, and high-proliferative Luminal B breast cancer. These patients may not need surgery as all local tumor has already been eradicated by NST. However, their safe identification prior to surgery is a major challenge: imaging after NST, minimally-invasive biopsies, or combinations of both using narrow patient selection criteria are not accurate enough either because they showed high rates of missed cancer or high rates of missed pCR. Recently, the concept of an intelligent, minimally-invasive, vacuum-assisted biopsy (intelligent VAB) was introduced to identify exceptional responders to NST. The intelligent VAB is a multivariate risk algorithm that uses artificial intelligence techniques to analyze conventional VAB results alongside contextualizing patient, imaging, and tumor information. It showed great potential to reliably identify patients with a pCR in the breast (ypT0). However, the absent integration of the axillary status impairs clinical applicability. In this study, we evaluated the feasibility of an intelligent VAB to identify exceptional responders to NST in the breast and axilla. Methods: We trained, tested, and validated a machine learning algorithm (Extreme Gradient Boosting Tree) using patient, imaging, tumor, and conventional VAB variables to detect residual cancer after NST (ypT+/is or ypN+) prior to surgery. We used data from 318 women with cT1-3, cN0/+, HER2 positive, triple-negative breast or high-proliferative Luminal B breast cancer who underwent VAB before surgery (NCT02948764). We used 10-fold cross-validation to train and test the algorithm which was externally validated using data of an independent, similar trial (NCT02575612). Findings were compared to the histopathologic evaluation of the surgical specimen. False-negative rate (FNR), specificity, and area under the ROC curve (AUROC) were the main outcome measures. Results: In the development set (n=318), mean patient age was 52.5 years and 45.3% (144 of 318) achieved a pCR (ypT0 and ypN0). Using resampling methods, the intelligent VAB showed an FNR of 5.2% (9 of 174, 95% CI 2.4-9.5), a specificity of 37.5% (54 of 144, 95% CI 29.6-45.9), and an AUROC of 0.92 (95% CI 0.90-0.94) in the development set to detect residual cancer (ypT+/is or ypN+) after NST. In the external validation set (n=45), mean patient age was 48.1 years and 44.4% (20 of 45) achieved a pCR. The intelligent VAB showed an FNR of 0% (0 of 25, 95% CI 0.0-13.7), a specificity of 40.0% (8 of 20, 95% CI 19.1-63.9) and an AUROC of 0.91 (95% CI 0.82-0.97). Spiegelhalter’s Z confirmed a well-calibrated model (z score -0.746, P 0.228). FNR of the intelligent VAB was lower compared to imaging after NST, conventional VAB, or combinations of both using narrow patient selection criteria. Conclusion: An intelligent VAB can reliably exclude residual cancer after NST for women with cT1-3, cN0/+, HER2 positive, triple-negative breast or high-proliferative Luminal B breast cancer. The omission of breast and axillary surgery for these exceptional responders may be evaluated in future trials. Trial registration: NCT02948764 and NCT02575612. Funding: German Research Foundation (DFG) Diagnostic Performance ComparisonFalse-negative rate - % (95% CI); no.Specificity - % (95% CI); no.Negative predictive value - % (95% CI); no.Positive predictive value - % (95% CI); no.AUROC - value (95% CI)Development set (n=318)Imaging after NST24.4% (18.0-13.7); 40 of 16452.2% (43.4-61.0); 69 of 13263.3% (53.5-72.3); 69 of 10966.3% (59.1-73.0); 124 of 187-Conventional VAB32.8% (25.8-40.3); 57 of 174100% (97.5-100); 144 of 14471.6% (64.9-77.8); 144 of 201100% (96.9-100); 117 of 117-Imaging after NST + VAB16.7% (11.4-23.2); 28 of 16832.1% (24.4-40.6); 44 of 13761.1% (48.9-72.4); 44 of 7260.1% (56.1-69.1); 140 of 223-VAB + patient selection9.1% (5.0-14.1) 15 of 17036.3% (28.2-45.0); 49 of 13576.6% (64.3-86.2); 49 of 6464.3% (57.9-70.4); 155 of 241-Intelligent VAB (Extreme Gradient Boosting tree)5.2% (2.4-9.6); 9 of 17437.5% (29.6-45.9); 54 of 14485.7% (74.6-93.3); 54 of 6364.7% (58.5-70.6); 165 of 2550.92 (0.90-0.94)External validation (n=45)Imaging after NST24.0% (9.4-45.1%);6 of 2565.0% (40.8-84.6%);13 of 2068.4% (43.4-87.4%);13 of 1973.1% (52.2-88.4%);19 of 26-Conventional VAB28.0% (12.1-49.4%);7 of 25100% (83.2-100%);20 of 2074.1% (53.7-88.9%);20 of 27100% (81.5-100%);18 of 18-Imaging after NST + VAB12.0% (2.5-31.2); 3 of 2565.0% (40.8-84.6%);13 of 2081.3% (54.4-96.0%); 13 of 1675.9% (56.5-89.7%); 22 of 29-VAB + patient selection4.0% (1.0-2.4); 1 of 2530.0% (9.4-45.1%); 6 of 2085.7% (69.8-99.8); 6 of 763.2% (46.0-78.2); 24 of 38-Intelligent VAB (Extreme Gradient Boosting tree)0.0% (0.0-13.7%);0 of 2540.0% (19.1-63.9%);8 of 20100% (63.1-100%);8 of 867.8% (50.2-82.0%);25 of 370.91 (0.82 - 0.97)AUROC = Area under the receiver operating characteristic curve; CI = confidence interval Citation Format: André Pfob, Chris Sidey-Gibbons, Geraldine Rauch, Bettina Thomas, Benedikt Schaefgen, Sherko Kuemmel, Toralf Reimer, Markus Hahn, Marc Thill, Jens-Uwe Blohmer, John Hackmann, Wolfram Malter, Inga Bekes, Kay Friedrichs, Sebastian Wojcinski, Sylvie Joos, Stefan Paepke, Tom Degenhardt, Joachim Rom, Achim Rody, Regina Große, Marion van Mackelenbergh, Mattea Reinisch, Maria Karsten, Michael Golatta, Joerg Heil. Intelligent vacuum-assisted breast biopsy to identify breast cancer patients with pathologic complete response after neoadjuvant systemic treatment for omission of breast and axillary surgery [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD7-02.

Breast cancer is a heterogeneous disease representing a number of different histopathologic and molecular types which should be taken into consideration if prognostic or predictive models are to be developed. The aim of the present study was to demonstrate the validity of the long-known Nottingham prognostic index (NPI) in a large retrospective study (n = 6654 women with a first primary unilateral and unifocal invasive breast cancer diagnosed and treated between April 1996 and October 2018, median follow-up time of breast cancer cases was 15.5 years [14.9–16.8]) from a single pathological institution. Furthermore, it was intended to develop an even superior risk stratification model considering an additional variable, namely the patient’s age at the time of diagnosis. Heterogeneity of these cases was addressed by focusing on estrogen receptor-positive as well as Her2-negative cases and taking the WHO-defined different tumor types into account. Calculating progression free survival Cox-regression and CART-analysis revealed significantly superior iAUC as well as concordance values in comparison to the NPI based stratification, leading to an alternative, namely the Altona prognostic index (API). The importance of the histopathological tumor type was corroborated by the fact that when calculated separately and in contrast to the most frequent so-called “No Special Type” (NST) carcinomas, neither NPI nor API could show valid prognostic stratification.

The receptors for estrogen (ESR1) and progesterone (PGR) are both part of the same signaling pathway and routinely used for breast cancer stratification. We tested the hypothesis if a coordinated analysis could add extra information for prognostic stratification.ESR1 and PGR gene expression was first investigated by quantitative reverse transcription polymerase chain reaction in fresh-frozen invasive ductal breast cancer samples (Hamburg collective, case-control, n=317). Our results were then tested using two datasets generated by different technical approaches: i) a public DNA-chip data set (GSE3494, n=251) and ii) semiquantitative protein expression data based on immunohistochemistry (Stuttgart collective, n=18,528).The PGR/ESR1 gene-expression ratio was a prognostic indicator in those with ESR1/PGR-positive breast cancer (Hamburg collective), with a high PGR/ESR1 expression ratio indicating a favorable outcome. In all three collectives, the PGR/ESR1 mRNA ratio or its protein equivalent was a univariate prognostic factor and also a multivariate prognostic factor in the Hamburg and Stuttgart collectives.Calculation of the PGR/ESR1 gene-expression ratio and its immunohistochemical surrogate could be a useful and simple addition to routine breast cancer diagnostics. A high PGR/ESR1 ratio could be indicative of a favorable clinical outcome.

In January 2007, the ‘AGO Kommission Mamma’ for the 5th time updated the ‘AGO Recommendations for the Diagnostic and Therapy of Breast Cancer’ that were established and published for the first time

Purpose: The predictive value of topoisomerase-IIα (topo-II) has been evaluated in advanced breast cancer patients randomly treated with single-agent doxorubicin or docetaxel. Experimental design: Primary tumor samples from patients enrolled in a randomized, phase III clinical trial comparing single-agent doxorubicin (75 mg/m2 q3wks) with docetaxel (100 mg/m2 q3wks) were collected and topo-II status was evaluated by immunohistochemistry (clone KiS1). Results: Topo-II status was evaluated in 108 samples, 55 (51%) in the doxorubicin arm and 53 (49%) in the docetaxel arm. An increment of 10% in cells expressing topo-II is associated with a statistically significant odds ratio (OR; 95% confidence interval) of 1.09 (1.03–1.15; P = 0.002) for overall response to doxorubicin versus 1.002 (0.94–1.07; P = 0.95) in the docetaxel arm. With increasing topo-II, the favorable OR for overall response to docetaxel compared with doxorubicin decreases to become not significant in patients with topo-II tumor content >10%. In a multivariate analysis, (a) HER-2 status seems positively correlated with overall response to chemotherapy (OR, 2.34; 95% confidence interval, 0.87–6.27; P = 0.09). (b) Overall response to doxorubicin is significantly lower than overall response to docetaxel (OR, 0.17; 95% confidence interval, 0.04–0.64; P = 0.009) but with a significant interaction term for doxorubicin-treated patients with topo-II tumor content >10% (OR, 8.31; 95% confidence interval, 1.86–37.03; P = 0.05). Conclusions: (a) Topo-II overexpression confers a higher probability of response in the doxorubicin arm only. (b) Despite being a small retrospective study, this study is in line with previously reported studies and the hypotheses raised are now being tested in a prospective neoadjuvant trial.

Background Scalp cooling is a long known method to reduce chemotherapy-induced alopecia in cancer patients with solid tumors. Due to a progress in this method, a medical device enabling individual feedback-controlled temperature regulation was evaluated. Between June 2011 and December 2012, 83 breast cancer patients were included. Evaluation was focussed on the quantification of alopecia, satisfaction and side effects of the scalp cooling system in (neo-) adjuvant chemotherapy regimens. Alopecia quantification was done by patient evaluation and experts rating. Findings Based on patient hair loss evaluations, the mean overall success rate of scalp cooling (

The well-characterized tubular-type of breast tumors is classified as low-risk breast cancer.We report on the results of a retrospective analysis on clinical and biological features of 248 tubular breast tumors including follow-up and treatment data from two German series of 21,065 breast cancer cases. The majority of tumors were stage I or stage II, ER- and PR-positive and c-erbB2-negative with a 5-year survival-rate of 96.3%. 51.3% of patients received hormonal treatment, 75.5% had post-operative radiotherapy and 11.8% were treated with a chemotherapeutical regimen.Our retrospective analysis showed no treatment benefit for either anti-hormonal or chemotherapeutical regimens. Post-operative radiotherapy, however, improved the survival rate of patients with tubular carcinoma (log-rank=5, p=0.025). Our data suggest that post-operative radiotherapy is an important treatment to prolong survival for patients suffering from tubular breast cancer.

Background. Assessment of prognostic markers in breast cancer independent of the axillary lymph node status is of major concern for the application of adjuvant treatment regimens. The current treatment decision is based mainly on the axillary lymph node status. Because of improved screening methods, the number and proportion of patients with node-negative disease are increasing, which warrants a search for reliable prognostic parameters. The application of tumor suppressor gene expression appears to be especially suited as a marker of the progress in malignant cellular dedifferentiation. Methods. Tumor tissues of 156 patients with primary invasive breast cancer were analyzed immunohistochemically for the presence of p53 protein in paraffin-embedded material. The reaction to monoclonal antibody PAb1801 yielded better results than did reactions to monoclonal antibody DO1 and polyclonal antibody CM-1. The significance of the immunohistochemical data was compared with a panel of established risk factors. Results. Nuclear accumulation of p53 protein proved to be an independent marker of dedifferentiation, regardless of the lymph node status. Tumors showing p53 immunoreactivity were significantly more often related with histological Grade 3 and the absence of steroid hormone receptors. Kaplan–Meier estimation and multivariate analysis of disease-free and overall survival rate corroborated the importance of p53 as a prognostic parameter. Conclusion. Overexpression of p53 protein emerged as a reliable and independent predictor for disease recurrence and reduced survival rates in patients with breast cancer.

Amplification of genomic DNA encoding oncogenes such as HER-2 (syn. c-erbB2/c-neu) may be substantially involved in the initiation and progression of breast cancer. In order to refine and facilitate the quantitative analysis of HER-2 amplification in breast cancer, differential polymerase chain reaction (PCR) was performed on DNA derived from single cryosections of tumor tissue. This technique is based on the simultaneous amplification of a potentially amplified oncogene (HER-2) and a reference gene (IFN-gamma). Differential PCR yielded reproducible results that were in agreement with gene copy quantification using the dot blot technique. Thus we suggest differential PCR to be a reliable and rapid method for determining relative gene dosage in a minute amount of tumour tissue.

NY-CO-58/KIF2C has been identified as a tumor antigen by screening antibody responses in patients with colorectal cancer. However, expression had not consequently been examined, and nothing was known about its ability to induce spontaneous T cell responses, which have been suggested to play a role in the development of colorectal cancer. We analyzed 5 colorectal cancer cell lines, and tumor samples and adjacent healthy tissues from 176 patients with epithelial cancers for the expression of NY-CO-58/KIF2C by RT-PCR and Western Blot. T cell responses of 43 colorectal cancer patients and 35 healthy donors were evaluated by ELISpot following stimulation with 30mer peptides or full-length protein. All cell lines and tumor samples from colorectal cancer patients expressed NY-CO-58/KIF2C on the protein and RNA level, and expression levels correlated strongly with Ki-67 expression (r = 0.69; p = 0.0003). Investigating NY-CO-58/KIF2C-specific T cell responses, CD8(+) T cells directed against 1 or more peptides were found in less than 10% of patients, whereas specific CD4(+) T cells were detected in close to 50% of patients. These T cells were of high avidity, recognized the naturally processed antigen and secreted IFN-gamma and TNF-alpha. Depletion of CD4(+)CD25(+) T cells before stimulation significantly increased the intensity of the preexisting response. NY-CO-58/KIF2C is significantly overexpressed in colorectal and other epithelial cancers and expression levels correlate with the proliferative activity of the tumor. Importantly, NY-CO-58/KIF2C was able to induce spontaneous CD4(+) T cell responses of the Th1-type, which were tightly controlled by peripheral T regulatory cells.

Recently, amplification of PPFIA1, encoding a member of the liprin family located about 600 kb telomeric to CCND1 on chromosome band 11q13, was described in squamous cell carcinoma of head and neck. Because 11q13 amplification is frequent in breast cancer, and PPFIA1 has been suggested to contribute to mammary gland development, we hypothesized that PPFIA1 might also be involved in the 11q13 amplicon in breast cancer and contribute to breast cancer development. A tissue microarray containing more than 2000 human breast cancers was analyzed for gene copy numbers of PPFIA1 and CCND1 by means of fluorescence in situ hybridization. PPFIA1 amplification was found in 248/1583 (15.4%) of breast cancers. Coamplification with CCND1 was found in all (248/248, 100%) PPFIA1-amplified cancers. CCND1 amplification without PPFIA1 coamplification was found in additional 117 (4.7%) tumors. Amplification of both PPFIA1 and CCND1 were significantly associated with high-grade phenotype (P = 0.0002) but were unrelated to tumor stage (P = 0.7066) or nodal stage (P = 0.5807). No difference in patient prognosis was found between 248 CCND1/PPFIA1 coamplified tumors and 117 tumors with CCND1 amplification alone (P = 0.6419). These data show that PPFIA1 amplification occurs frequently in breast cancer. The higher incidence of CCND1 amplification when compared with PPFIA1, the lack of prognostic relevance of coamplifications, and the fact that PPFIA1 amplification was found exclusively in CCND1-amplified cancers suggest that PPFIA1 gene copy number changes represent concurrent events of CCND1 amplification rather than specific biological incidents.

SiR-Kaufmann and colleagues (March 11, p 615) assessthe expression of CD44 isoforms in breast cancer andcorrelate the data with outlook. With antibodies againsthuman CD44 variant isoforms (CD44v), they showsignificant correlations between presence of v6 epitopes andoverall survival. Reactivity against DIII, a polyclonal serum,emerged as an independent prognostic factor in multivariateanalyses, surpassing in risk even such established factors aslymph-node status, tumour size, and histological grading.These workers claim the necessity for further studiesincluding more patients.