Analysis of 1996 Korean Breast Cancer Data

<span lang="EN-US">Statisticians in both academia and industry have encountered problems with high-dimensional data. The rapid feature increase has caused the feature count to outstrip the instance count. There are several established methods when selecting features <span>from massive amounts of breast cancer data. Even so, overfitting continues to be a problem. The challenge of choosing important features with minimum loss in a different sample size is another area with room for development. As a result, the feature selection technique is crucial for dealing with high-dimensional</span> data classification issues. This paper proposed a new architecture for high-dimensional breast cancer data using filtering techniques and a logistic regression model. Essential features are filtered out using a combination of hybrid chi–square and hybrid information gain (hybrid IG) with logistic regression as classifier. The results showed that hybrid IG performed the best for high-dimensional breast and prostate cancer data. The top 50 and 22 features outperformed the other configurations, with the highest classification accuracies of 86.96% and 82.61%, respectively, after integrating the hybrid <span>information gain and logistic function (hybrid IG+LR) with a sample size of 75. In the future, multiclass classification of multidimensional medical data to be evaluated using data from</span> a different domain<span>.</span></span>

Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer

Can detection of circulating tumor cells in peripheral blood provide prognostic data in breast cancer?

Clinical outcome in cancer patients can be determined by the hostresponse factors as well as the tumor characteristics. In the last decade, the markers of systemic inflammatory response including neutrophil, platelet and lymphocyte have been investigated in various types of malignant tumors [1]. Cancer development enhances chronic inflammation. On the other hand, the inflammation can enhance tumor growth, invasion and metastasis. Although the elevated neutrophil counts can be related to paraneoplastic activity of the tumor, the low lymphocyte counts can be associated with immune system suppression in cancer. Both the increased neutrophil and platelet, and conversely, decreased lymphocyte can be associated with poor prognosis. In addition, the increased neutrophil to lymphocyte ratio (NLR) in cancer patients can be related to the limited immune capability during carcinogenesis. One of the routinely available markers of systemic inflammatory response is NLR, which is derived from absolute neutrophil and absolute lymphocyte counts of the full blood count. The routine preoperative platelet to lymphocyte ratio (PLR) is also readily available laboratory variable without additional costs. Some studies have shown that patients with higher NLR have significantly lower survival rates than those with lower NLR in different cancer types including breast cancer. Recently, only two studies by Azab et al and Noh et al have been evaluated if NLR is predictive of breast cancer-specific mortality or disease-free survival [2, 3]. The other recent report by Seretis et al has shown that the mean NLR was significantly elevated in thyroid cancer compared with benign thyroid disorders [4]. Besides, Azab and coworkers comparing the prognostic value of NLR versus PLR in breast cancer have shown the NLR to be superior in predicting long-term breast cancer specific mortality [3]. However, to our best knowledge, the usefulness of pretreatment NLR and PLR to diagnosis in breast cancer has not been reported. Encouraged by the studies, we have retrieved data in breast cancer as well as benign breast lesions and performed an analysis to assess the usefulness of pretreatment NLR and PLR with respect to the diagnosis of breast cancer. We have also investigated if the NLR and PLR are associated with the lymph node metastasis. The study consisted of 60 breast cancer patients (group 1) and 35 cases with benign breast lesions including fibrocystic changes, sclerosing adenosis and fibroadenoma (group 2). Patients with active infection, any known other cancer history, hematologic disorders, chronic or current steroid treatment, and chronic inflammatory or autoimmune disorders were excluded from sample groups. The pretreatment NLR and PLR were calculated by total neutrophil or platelet counts divided by total lymphocyte number. Results were expressed as mean values ± standard deviation with range and were compared using Student’s t test for two groups or ANOVA test for more than two groups. A P value 0.05) (Table 1). Breast cancer group was also divided into two groups according to lymph node metastases. Among 60 breast cancer patients, 34 patients had lymph node metastases. The mean NLRs were 2.87 ± 1.5 and 2.08 ± 0.8 in patients with or without lymph node metastasis, respectively (P = 0.017). An elevated level of PLR in patient with lymph node metastasis was found, but this did not reach statistical significance (P = 0.076). The tumor/host interaction may have important influence on carcinogenesis. Cancer-associated inflammation is also an important determinant of outcome in cancer patients. Although recent studies have reported that high level of NLR is an independent risk factor for poor prognosis in patients with various types of cancer including breast cancer [1-3], there are limited data related to NLR or PLR as potential marker in the context of malignancy diagnosis. As far as we know, the presented report was the first study that has investigated the possible correlations of pretreatment NLR and PLR in breast cancer and benign breast lesions. However, there are some limitations of the study. First, we did not determine NLR and PLR in healthy women. Second limitation was the small sample Manuscript accepted for publication December 18, 2013

<div>Abstract<p>Evidence from observational studies for the effects of circulating antioxidants on the risk of breast and ovarian cancer was inconsistent. To elucidate the potential causal association of circulating antioxidants on the risk of breast and ovarian cancer, we carried out a two-sample Mendelian randomization (MR) study. The instrumental variables as proxies of genetic liability to circulating antioxidants were obtained from several published data. Summary-level data of breast and ovarian cancer were obtained from genome-wide association study (GWAS) conducted by the Breast (122,977 cases and 105,974 controls) and the Ovarian (25,509 cases and 40,941 controls) Cancer Association Consortiums. MR analyses were mainly performed using the inverse variance–weighted tests. Sensitivity analyses were further conducted to assess heterogeneity and horizontal pleiotropy. No evidence of causal association between genetically predicted circulating antioxidants and breast cancer and its histotypes was discovered as assessed by absolute levels [β-carotenoid: OR, 0.98; 95% confidence interval (CI), 0.92–1.05; <i>P</i> = 0.627; lycopene: OR, 0.99; 95% CI, 0.95–1.03; <i>P</i> = 0.532; retinol: OR, 0.87; 95% CI, 0.49–1.55; <i>P</i> = 0.645; ascorbate: OR, 1.00; 95% CI, 0.99–1.00; <i>P</i> = 0.123] and metabolites (α-tocopherol: OR, 0.88; 95% CI, 0.65–1.19; <i>P</i> = 0.394; γ-tocopherol: OR, 1.00; 95% CI, 0.87–1.16; <i>P</i> = 0.978; retinol: OR, 1.02; 95% CI, 1.00–1.04; <i>P</i> = 0.070; ascorbate: OR, 0.99; 95% CI, 0.91–1.06; <i>P</i> = 0.703). Similarly, no beneficial effect of genetic determinants of circulating antioxidants on ovarian cancer and its histotypes was found. Our study might not indicate a protective role of circulating antioxidants on the breast or ovarian cancer risk.</p>Prevention Relevance:<p>Although this study does not find that circulating antioxidants are protective against breast and ovarian cancer, it is still possible that a high intake of antioxidant-rich foods containing other potentially beneficial components could be cancer preventative. </p></div>

Background: Palbociclib, with endocrine therapy, has demonstrated clinical benefit in patients with ER+ve HER2-ve metastatic breast cancer in randomised controlled clinical trials. Further observational data is beneficial to illustrate how these results translate into benefit in routine practice. Furthermore, detecting predictors of response aids clinical decision-making. Our primary aim was to report the survival data and adverse event (AE) rates in a real-life population treated with palbociclib. Secondarily, we aimed to identify predictors of response. Method: We retrospectively analysed 150 patients with metastatic breast cancer treated with palbociclib and endocrine therapy in routine clinical practice, over 3 years in three sites across South East Scotland. Baseline demographics (including disease site, prior treatment, bloods), progression data, AE rates, delays/dose reduction (DR) were recorded. For statistical analysis, we calculated actuarial rates of progression and breast cancer specific survival (BCSS) for the whole cohort. Survival analysis was performed using Kaplan-Meier and cox regression statistics. Statistical associations between variables were analysed by generalised linear models with chi-square statistics. Results: The average patient age was 61.4 years and most were post-menopausal (86.7%). Around two thirds (63.3%) were previously treated adjuvantly and most were performance status 0 (51.3%) or 1 (44.0%). Commonest disease sites were bone (76.0%), liver (46.0%), lung (42.7%) or nodes (36.7%). Patients were prescribed palbociclib with either; aromatase inhibitor (AI) (74.0%) as 1st line or fulvestrant (26.0%) as 2nd line. Standard starting dose was 125mg (87.3%). Rationale for a lower starting dose was mainly due to patient age (78.9%), by physician’s choice.For the whole cohort, the actuarial progression rate at 24 months was 0.45 (95%CI±0.12) and BCSS rate was 0.69 at 24 months (95%CI±0.16). In the AI sub-group (n=111), at 12 months, the actuarial progression rate was 0.26 (95%CI±0.10) and BCSS rate was 0.86 at 12 months (95%CI±0.08). In the fulvestrant sub-group (n=39), the actuarial progression rate was 0.56 (95%CI±0.25) and BCSS was 0.82 (95%CI±0.14) at 12 months. Most common sites of progression were; visceral (84.1%), bone (40.1%), nodal (18.2%) or chest wall (6.8%). less had progressive disease (11.7% vs. 42.8%), compared to the fulvestrant group (n=28). The commonest AE was fatigue. During treatment, 67.1% had grade 3/4 neutropenia. Neutropenic sepsis rates were low (1.3%). Around half (47.9%) required DR, mainly for neutropenia (81.4%). A lower baseline ANC was associated with a lower ANC during cycle 1 (p<0.001) and increased likelihood of DR (p=0.001). Age, disease site and prior chemotherapy did not predict neutropenia. On multivariate analysis for predictors of response, patients were more likely to progress if they had liver involvement (OR 3.5, 95%CI 1.6-7.4, p=0.001) or had previous endocrine therapy in the adjuvant setting (OR 1.74, 95%CI 1.1-2.8, p=0.019). Those who had a DR were significantly less likely to progress (OR 0.23, 95%CI 0.9-0.6, p=0.001). Discussion: Our real-world clinical data of the use of palbociclib illustrates it is an effective therapy in a heterogeneous population, with a progression rate of 45% at 24 months. Neutropenia was common but neutropenic sepsis rates were very low, similarly to the pooled PALOMA safety data. The only predictor of neutropenia was baseline ANC. Those who had prior endocrine therapy or with liver involvement were more likely to progress. Interestingly, those with a DR were significantly less likely to progress. Other observational data has produced mixed results with regard to this relationship and therefore larger studies are needed to validate these findings. Citation Format: Ailsa J Oswald, Peter Hall, Arran Turnbull, Olga Oikonomidou. Palbociclib real-world data in metastatic breast cancer: A multi-site report of survival and adverse events in routine clinical practice in Scotland [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS10-55.

PurposeSince 1996, the Korean Breast Cancer Society has collected nation-wide breast cancer data and analyzed the data using their online registration program biannually. The purpose of this study was to evaluate the characteristics of Korean breast cancer from 2008 and examine chronological based patterns.MethodsData were collected from 38 medical schools (67 hospitals), 20 general hospitals, and 10 private clinics. The data on the total number, gender, and age distribution were collected through a questionnaire as well as other detailed data analyzed via the online registration program.ResultsIn 2008, there were 13,908 patients who were newly diagnosed with breast cancer. The crude incidence rate of female breast cancer was 57.3 among 100,000 and the median age was 49 years. The age distribution had not changed since the initial survey; however the proportion of postmenopausal patients had increased and median age was older than the past. In staging distribution, the proportion of early breast cancer (stage 0, I) was 47.2% with, breast-conserving surgery performed in 58% and mastectomy in 39.5%.ConclusionCompared to past data, the incidence of breast cancer in Korea continues to rise. Furthermore, the proportion of those detected by screening and breast conservation surgery has increased remarkably. To understand the patterns of Korean breast cancer, the nation-wide data should continuously investigated.

Developments in breast cancer biology over the last year have brought molecular medicine closer to the clinic. Within the past year, two major advances have taken place. First, microarray-based expression profiling has shown promise with the preliminary demonstration that clustering techniques can predict clinical outcome in lymphoma, pediatric leukemia, and breast cancer. Data in breast cancer have demonstrated the ability of microarray-based expression profiling to detect tumor cells in peripheral blood samples, to predict chemotherapy responses in fine-needle aspiration samples in neoadjuvant chemotherapy, and, most importantly, to predict disease-free survival and overall survival from profiles in breast cancer surgical specimens. Second, in breast cancer genetics, CHEK2 was identified as one of what are likely to be many low-penetrance breast cancer susceptibility genes. These studies demonstrate the transition of basic biologic research to clinical application.

Background: The risk of distant recurrence gene signature, MammaPrint (MP), together with the molecular subtyping gene signature, BluePrint (BP), stratifies breast tumors into Luminal A, Luminal B, HER2, and Basal subtypes, independent of immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH) expression. In the Neoadjuvant Breast Registry Symphony Trial (NBRST), MP and BP identified patients likely to respond to neoadjuvant treatment with higher accuracy compared to conventional methods. Here, we report 5-year follow up (FU) data in breast cancer (BC) patients from the NBRST registry with discordant clinical and genomic subtyping.Methods: This prospective study enrolled 1072 early-stage BC patients from 2009-2014 who received MP and BP testing. Patients received neoadjuvant therapy following standard of care and consented to 5 years post-surgery FU. IHC determined hormone receptor (HR) status, including ER and PR, and IHC and/or FISH determined HER2 status. Median FU for distant metastasis free survival (DMFS) and overall survival (OS) was 4.6 and 5 years, respectively. Differences in DMFS and OS was assessed by Kaplan Meier analysis and log-rank test.Results: Overall, BP reclassified 22% of tumors into different molecular subtypes compared to IHC/FISH (Table). BP reclassified 17% of ER+HER2- tumors as BP Basal, with higher pathological complete response (pCR) rates compared to ER+/BP Luminal tumors (36% vs. 4%). ER+/BP Basal patients had similar pCR rates as triple negative BC (TNBC)/BP Basal patients (36% vs. 37%) following neoadjuvant treatment, and pCR correlated with improved survival outcomes. The 5-year DMFS and OS probabilities were lower in ER+/BP Basal patients compared to TNBC/BP Basal patients and were substantially lower compared to ER+/BP Luminal patients (P < 0.001). There were 106 HR-HER2+ patients, of whom BP reclassified 23.6% to Basal and 2.8% as Luminal B; the remaining 73.6% were confirmed HER2 by BP. The 5-year DMFS and OS probabilities were worse in HER2+/BP Basal patients compared to HER2+/BP HER2 patients. Of 142 triple positive (TP, ER+PR+HER2+) patients, BP classified 55% as Luminal, 39% as HER2, and 6% as Basal, with higher pCR rates observed in BP Basal and BP HER2 tumors compared to BP Luminal. The 5-year DMFS and OS probabilities were substantially lower in TP/BP Basal patients compared to TP/BP HER2 and TP/BP Luminal patients (P < 0.05 and P < 0.04). Of clinical HER2+ patients (HR+ or HR-) that received pertuzumab, patients that reclassified as BP Basal had worse OS compared to BP HER2 patients (P < 0.04).Conclusion: ER+HER2- and HER2+ patients that reclassified as BP Basal are more likely to achieve pCR and have improved survival, demonstrating the clinical utility of BP in the neoadjuvant setting. These patients may benefit from optimized chemotherapy used for TNBC, including novel emerging treatments such as PD-1 and PARP1 inhibitors, in addition to HER2-targeted therapy. Furthermore, HER2+ tumors that were confirmed HER2 by BP may have high response rates to regimens containing TDM-1. Lastly, BP identified a subgroup of triple positive BC patients, who reclassified as BP Luminal, that may avoid overtreatment. Overall, molecular subtyping using MP and BP is more accurate in stratifying patients and predicting treatment responses and 5-year disease outcomes than conventional methods and thus, facilitates successful treatment decisions. Clinical subtypeFrequency of BP classificationBluePrint subtypepCR%5-yr DMFS (95% CI)5-yr OS (95% CI)TNBC (n=236)0.42% (1/236)Luminal A100% (1/1)N/AN/A2.54% (6/236)Luminal B16.67% (1/6)N/AN/A1.27% (3/236)HER233.33% (1/3)N/AN/A95.76% (226/236)Basal36.73% (83/226)100% (pCR)100% (pCR)60.5% (50.5-69.1)(non-PCR)64.3%(52.1-71.2) (non-PCR)ER+HER2- (n=520)28.84% (152/520)Luminal A1.97% (3/152)91.1% (84.0-95.2)94.6% (88.3-97.6)52.37% (276/520)Luminal B5.43% (15/276)75.2% (69.0-80.4)84.5% (79.0-88.7)1.33% (7/520)HER214.29% (1/7)N/AN/A17.46% (92/520)Basal35.9%(33/92)84.1% (67.8-92.5) (pCR)86.3% (70.1-94.1) (pCR)54.6% (42.0-65.5) (non-pCR)57% (43.7-68.2) (non-pCR)HR-HER2+ (n=106)2.83% (3/106)Luminal B66.67% (2/3)100%100%73.59% (78/106)HER269% (54/78)82.8% (69.9-90.5)88.6% (76.0-94.8)23.58% (25/106)Basal40% (10/25)79.0% (52.5-91.7)79.0% (52.5-91.7)Triple Positive (n=142)12.68% (18/142)Luminal A22.22% (4/18)88.8% (76.5-94.8)94.5% (83.8-98.2)42.25% (60/142)Luminal B11.67%(7/60)38.73% (55/142)HER244.44% (24/55)87.5% (72.0-94.7)97.9% (83.8-98.2)6.34% (9/142)Basal55.56%(5/9)62.5% (22.9-86.1)70.0% (22.5-91.8)HER2+ (HR+ or HR-)treated with pertuzumab (n=105)28.6%(30/105)Luminal37% (11/30)84.7% (63.8-94.1)92.2%(71.8-98.0)57%(60/105)HER282% (49/60)91.4% (78.3-96.8)92.9%(79.2-97.7)14%(15/105)Basal40% (6/15)66.0%(31.1-86.3)64.0% (29.1-85.1) Citation Format: Pat Whitworth, James Pellicane, Jr, Paul Baron, Peter Beitsch, Laura Lee, Michael Rotkis, Angela Mislowsky, Carrie Dul, Charles Nash, Bichlien Nguyen, Mary Murray, Paul Richards, Mark Gittleman, Stephanie Akbari, Shiyu Wang, Andrea Menicucci, Erin B Yoder, Lisa Blumencranz, William Audeh. Molecular subtyping by BluePrint improves prediction of treatment responses and survival outcomes in patients with discordant clinical and genomic classification [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-04.

Poly (ADP-ribose) polymerase (PARP) inhibitors represent one of the most exciting recent developments in cancer therapy. While substantial efficacy has been shown with clinically available PARP inhibitors (PARPis), to date, in treatment of hereditary deletions of BRCA1/2 in breast and ovarian cancers, the high promise of these drugs has not yet been realized in sporadic cancers. We present here strong preclinical data for a novel, mechanistically based, combinatorial approach to using DNA methyltransferase inhibitors (DNMTis), such as decitabine (DAC) and 5-Azacytidine (5-AZA), with PARP inhibitors (PARPis) as a treatment strategy for acute myelogenous leukemias (AML) and triple negative breast cancer (TNBC). We have previously demonstrated that low doses of 5-AZA and DAC alone show efficacy in AML and TNBC, and propose treatment with PARPis to enhance sensitivity of cancer cells to DNMTis. The mechanistic rationale for our approach is based upon: 1) data from our group and others showing DNMT1 and PARP1 associate in a complex, and this association increases with DNA damage; 2) the fact that 5-AZA and DAC trap DNMTs led us to hypothesize that these drugs might also increase PARP trapping at DNA damage sites; and 3) the cytotoxicity of the most potent PARPis (e.g. BMN 673) appears to correlate with the degree of trapping of PARP1 in chromatin. We first find that in cultured human AML and TNBC cells, the DNMTis (5 to 20 nM DAC or 100 to 200nM 5-AZA) and PARPis (1 to 10 nM BMN 673) alone trap PARP into chromatin, and this effect is enhanced when the drugs are combined. In addition, the PARPi-DNMTi combination treatment of TNBC cell line MDA-MB-231 resulted in significantly enhanced retention of PARP1 and DNMT1 at sites of double strand breaks (DSBs) induced by laser microirradiation. Concomitant with this, the combined doses resulted in significant increases in cytotoxic DSBs, observed 4-24 hours after DSB induction, when compared to single-drug treatments. Homologous recombination (HR) DSB repair activity also appears decreased, as measured by GFP reporter assays. In keeping with these findings, colony survival assays demonstrated that the combination treatment, compared to either drug alone, strongly inhibited colony formation of TNBC cell lines (N=4). Notably non-tumorigenic MCF10A cells showed no significant differences in colony numbers with single or combination drug treatments. Similar to TNBCs, AML cell lines (N=3) as well as primary AML cells (N=8) showed dramatic decreases in colonies in combination vs single agent drug treatments. In the most important translational implications of the preliminary studies, in in vivo therapy TNBC and AML models in immune-deficient mice, our low dose combinations of DNMTis and PARPis provide for potent anti-tumor responses. Mouse xenograft experiments using BRCA mutant TNBC cell line SUM149PT demonstrated that the combination treatment has a significant (p<0.05) survival advantage compared to control (vehicle), AZA (0.5mg/kg) or BMN (0.3 mg/kg) alone. Importantly, mouse MDA-MB-231 xenografts with intact BRCA1 showed significant survival with the combined drug treatment. Likewise in AML xenografts of MOLM14 and MV411 cell lines treated with the drug combination show significantly decrease leukemia burden, as measured by luciferase imaging. Our data suggest a novel use of both DNMTis and PARPis in a compelling therapeutic strategy for TNBCs independent of BRCA mutations and poor prognosis AML; the latter will be investigated in a clinical trial to be based at the University of Maryland and funded by Van Andel-SU2C. Citation Format: Nidal Muvarak, Khadiza Chowdhury, Carine Robert, Xia Limin, Eun Yong Choi, Yi Cai, Marina Bellani, Michael Seidman, Maria R. Baer, Rena Lapidus, Stephen B. Baylin, Feyruz V. Rassool. Combination of DNA methyltransferase and PARP inhibitors as a novel therapy strategy for multiple cancers: Key data in AML and triple negative breast cancer [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr IA13.

Gruvberger et al. postulate, in their commentary [1] published in this issue of Breast Cancer Research, that our gene set may not be broadly applicable to other breast tumor cohorts, and they suggest that may be important to define prognostic expression profiles separately in estrogen receptor (ER) positive and negative tumors. This is based on two observations derived from our gene expression profiling data in breast cancer [2]: the overlap between reporter genes for prognosis and ER status, and Gruvberger et al.'s inability to confirm the prognosis prediction using a nonoptimal selection of 58 of our 231 prognosis reporter genes. The overlap between our prognosis reporter genes and the ER status genes is certainly very large, mainly because ~10% of all genes on our microarray contain information on the ER status (2460 out of 24,479). However, the overlap between the 70 optimal prognosis genes and the 550 optimal ER status genes is only 17% (12 out of 70). We therefore believe that there is a different subset of genes that reports prognosis as compared with ER status. Our prognosis classifier strongly predicts the risk of distant metastases (odds ratio = 15, 95% confidence interval = 4–56, P < 0.0001). Adjusted for associations with known clinical prognosticators, including ER status, in a multivariable analysis, this odds ratio slightly increases to 18 (95% confidence interval = 3.3–94, P = 0.0002) [2]. This indicates that the predictive capacity of our prognosis classifier cannot be explained by its association with, among other factors, ER status as suggested. There are a few points of concern relating to Gruvberger et al.'s inability to develop an outcome classifier on their data set. It should be pointed out that the prognosis signature is subtle both in the number of genes and the magnitude of differential regulation as compared with the ER status signature. The microarray platform sensitivity and reproducibility is a key issue in generating high quality data that ultimately determine the power of discovery. The reproducibility of our platform is clearly demonstrated by the uniform patterns related to ER status (Fig. 3b in [2]) as compared with the ER status expression patterns published by Gruvberger et al. (Fig. 2b in [3]). Our experiences with cDNA arrays and oligonucleotide arrays showed that the cDNA arrays are less sensitive in detecting small differential regulations because of the high chance of nonspecific binding. Another point concerns the reference sample used in the two-color assay and its effect on the sensitivity. A nearby reference (a tumor sample pool in our case) makes the differences between the samples easily detectable, whereas a distant reference (one cell line [1]) makes this harder to detect since the fluctuations of the large offset interferes with the measurement of small differential signals. Moreover, we always repeat the same measurement twice in fluor-reversal pairs to minimise the potential labeling biases. It is possible that the aforementioned reasons and the fact that most of our 70 optimal genes were not on their array prevented Gruvberger et al. from revealing the prognosis signature. Finally, it is of importance to note that we derived our prognosis classifier from breast cancer patients of whom the majority (93%) did not receive adjuvant systemic therapy, whereas all Gruvberger et al.'s patients received adjuvant tamoxifen treatment. So, a relatively better outcome (approximately 30%) can be expected within Gruvberger et al.'s ER-positive subgroup because of the tamoxifen treatment. The predictive power of our prognosis reporters may be reduced in an adjuvantly treated patient group. A confirmation on a large unselected 'cohort' of breast cancer patients is required to validate our findings. We have recently completed DNA microarray analyses on a cohort of 295 breast cancer samples. The results of this will be published in the near future.

Guidelines for the adjuvant treatment of postmenopausal women with endocrine-responsive breast cancer: Past, present and future recommendations

Introduction: Delivering maximum dose to tumor and minimum dose to normal tissues is the most important goal in radiotherapy. According to ICRU, the maximum acceptable uncertainty in the delivered dose compared to the prescribed dose should be lower than 5%, and this is because of the relationship between absorbed dose, tumor control and normal tissue damage. Absorbed dose accuracy is investigated by an in vivo dosimetry method. In this paper, we compared absorbed dose in the tumors of the breast and pelvic region against the calculated dose. The amount of deviations and the factors that cause this deviation in dose delivery to patients and some methods for decreasing them were evaluated. Materials and methods: The entrance and exit doses of 36 pelvic-region cancer patients and 38 breast cancer patients who were treated by cobalt-60 teletherapy were measured using p-type diodes. It should be noted that the transmission method was used to assess the dose at isocenter. Two ionization chambers (0.6 cc and 0.3 cc) were used for calibration and determination of the correction coefficients in water and slab phantoms. Deviations between calculated and measured doses of entrance, exit and midline points were calculated and the results were shown using histograms. Results: The average and standard deviation for entrance, exit and midline points for pelvis cancer were assessed to be about 0.10%, -1.86% and -1.35% for mean deviation and 5.03%, 7.32% and 5.86% for standard deviation, respectively. The corresponding data for breast cancer were 0.78%, 5.29% and 3.59% for mean deviation and 5.97%, 10.23% and 9.86%, respectively. There was no significant difference between the calculated and measured doses (p > 0.1), except exit dose in breast cancer (p < 0.05). The temperature and angle of incidence correction factors were neglected due to their less than 1% deviations. Discussion and Conclusions: Some error sources are patient setup error, patient motion and dose calculation algorithm error (due to ignoring inhomogeneity and patient curvature). As no significant deviations were found in midline dose, the method used has an acceptable accuracy. In vivo dosimetry can perform a basic role in the quality control of radiotherapy departments.

GATA3 has been identified as one of the most frequently mutated genes in breast cancer. In the METABRIC cohort, among 1,980 patient cases, 230 breast cancers harbored GATA3 mutations (~12%). 75% of the GATA mutations were observed in luminal breast tumors (47% in luminal A, 28% in luminal B). The recent genomic data in metastatic breast cancer also showed that the frequency of GATA3 somatic mutations was even higher in the metastatic breast cancer cohort. Lung, lymph nodes, and brain metastases were observed in the GATA3 mutant breast cancer patients. Based on these patient genomic data, GATA3 mutations have been considered as cancer drivers, yet the functional consequences of GATA3 mutations are underexplored. We and other groups previously identified that patients carrying GATA3 mutations have diverse clinical features. More than 70% of cases are small nucleotide deletions or insertions (indel), while less than 30% are missense mutations. By classifying the GATA3 indel mutations into 4 groups, we observed distinct clinical features. Somatic mutations found in the GATA3 second zinc-finger domain (ZnFn2) are significantly associated with poorer patient outcomes including worse patient survival. ZnFn2 mutations are predominantly found in luminal B breast tumors, while splice site mutations are frequently found in luminal A breast tumors and associated with better patient survival. These distinct clinical features clearly suggest the differential impacts of GATA3 mutations on breast cancer cells. To dissect the function of GATA3 mutants, we developed a GATA3 mutant cell line (R330fs/WT T47D cells) by CRISPR, which endogenously expresses a heterozygous R330 frame-shift (R330fs) mutant. R330fs mutations are found in multiple data cohorts. The majority (&amp;gt;90%) of GATA3 mutations are heterozygous. Therefore, this R330fs mutant T47D cell line mimics the type of alteration found in patients. Importantly, we found that the R330fs mutation induces transcriptional reprogramming in T47D luminal breast cancer cells leading to more aggressive phenotypes such as faster tumor growth. In the mutant cells, many epithelial marker genes (such as progesterone receptor and TFF1) were down-regulated while mesenchymal marker genes (such as TWIST1 and SNAI2) are up-regulated suggesting that R330fs induces a partial EMT in T47D cells. We also found that transcriptional changes in the R330fs mutant cells were strongly associated with redistribution of GATA3, Estrogen Receptor alpha, and FOXA1. Gene expression profiles in the other GATA3 mutant breast cancer cell lines also suggest similar genomic alterations. These results suggest the active roles of GATA3 mutations during breast cancer development. Citation Format: Motoki Takaku, Mika Saotome, Renju Nair. Breast cancer derived GATA3 mutations disrupt luminal transcriptional network [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 P3-09-12.

Male breast cancer is a rare disease, occurring in about 1% of all breast cancers. Can we learn from this uncommon illness? The answer should be a resounding yes, but we are far from understanding the whole story. This issue of the Asia–Pacific Journal of Clinical Oncology features a comprehensive review on male breast cancer by Field et al.1 We learn that the incidence of breast cancer in men may be increasing with elusive etiologies. We also learn that the prognosis is the same as female breast cancer patients, matching the stage by stage and other prognostic factors. However, we are told that 90% or more male breast cancers have positive estrogen receptors and very few (1–2%) have HER-2/NEU gene amplification expression. Therefore, common sense would have suggested that male breast cancer sufferers should have better prognosis than their female counterparts. But that is not the case by literature review. What are the possible explanations? The following speculations may shed some clues for future research: The proliferative drive or signal transduction pathway in male breast cancer may be quite different in that they may have shorter duration of response or early development of drug resistance. However, we do not have any clinical or laboratory evidence to substantiate this notion. Male patients may have more difficulties to accept the diagnosis of a female disease. Hence, they are more prone to be lost for follow-up or stop treatment early. That is, they may not get treated as vigorously as female patients. Again, we need more epidemiological data to support this idea. Or, the existing data on survival are not reliable as most if not all the data are retrospective. There is hardly any clinical trial on female breast cancers that included male patients. Therefore, a fair comparison is not possible. From their review, we understand that there are substantial differences between male and female breast cancers, such as genetic predisposing factors (BRCA2 mutation is the most common form in men as compared to BRCA1 in female patients), histopathology (much less lobular carcinoma in male than in female), hormonal receptors (male breast cancers have more frequent positive estrogen receptor than female patients) and response to aromatase inhibitor (aromatase inhibitors increase testosterone levels in male not in female and less effective in suppressing estrogen in male volunteers). This review paper also pointed out many missing pieces of information in all aspects of male breast cancer. There are hardly any prospective clinical phase II trials with aromatase inhibitors or chemotherapy agents such as taxanes, gemcitabine, vinorelbine which are commonly used in female breast cancer. Rightly so, the authors cautioned the routine use of aromatase inhibitors in men due to differences in endocrine response between both sexes. We are still lacking the essential genomic data in male breast cancer which may provide clues to explain this phenomenon. Although it is of importance and interest to know all the differences and similarities between male and female breast cancer patients, we need to consider what we can do to utilize this information to elicit clues to solve some of the unknown mysteries and advance our knowledge. Unfortunately, not much can be done by a single institution or study group for the time being. Perhaps, one immediate step to be taken should be to form an international study group. Then, prospective phase II studies may become a reality in the near future, as there are too few cases of male breast cancer in any single institution. In essence, clues may be discovered among controversies and from insufficient data, but it requires confirmation and validation. In this case, we need worldwide concerted efforts to study this rare disease: male breast cancer.