Cytotoxic chemotherapy remains an important component of anti-cancer therapy. However, newer ‘targeted agents’ are emerging as more effective agents in certain cancer types as determined by their molecular phenotype.

Aim: We assessed the impact of comorbidity on the odds of undergoing surgical resection among non-small cell lung cancer (NSCLC) patients, and on survival among resected NSCLC and all other lung cancer patients in England. Methods: Data on 64,653 patients diagnosed with lung cancer (ICD-10 C33-C34) in England between 2008 and 2009 were extracted from the National Cancer Data Repository. Information on comorbidity for these patients was retrieved from linked Hospital Episode Statistics records and classified according to the Charlson comorbidity score (CCS 0, 1, 2, 3+). Among NSCLC patients, we calculated the odds ratio (OR) of undergoing surgical resection according CCS adjusting for case-mix (age, sex, socioeconomic deprivation, histology, performance status and clinical stage). Using Cox proportional hazard regression analyses, we calculated the case-mix adjusted mortality hazard ratio (HR) according to CCS among NSCLC patients undergoing surgical resection and among all other lung cancer patients. Among resected NSCLC patients, case-mix adjusted HRs were also assessed in three different post-surgical time periods (<30 days, 30-365 days and >365 days). Results: The likelihood of NSCLC patients undergoing surgical resection decreased with increasing severity of comorbidity [OR 0.58 (95% CI 0.46-0.74) for CCS 3+ compared with CCS 0, p-trend < 0.001]. Among resected NSCLC patients, increasing comorbidity score was associated with higher death rates [HR 1.45 (95%CI 1.07-1.98) for CCS 3+ vs. CCS 0, p-trend <0.001], and the association was most pronounced more than one year post-surgery [HR 1.67 (95% CI 1.04-2.69) for CCS 3+ compared with CCS 0, p-trend < 0.001]. Among all lung cancer patients not undergoing surgical resection, comorbidity was associated with a small increase in death rate [case-mix adjusted HR 1.07, 95% CI (1.02-1.12) for CCS 3+ vs. CCS 0, p-trend = 0.02]. Conclusions: Among resected NSCLC patients comorbidity is an independent prognostic factor for longer term survival. Among all other lung cancer patients not undergoing surgical resection, the effect of comorbidity is largely explained by performance status. Disclosure: All authors have declared no conflicts of interest.

Alkylating agents are chemically reactive drugs that react with DNA to form covalent bonds, causing single-strand or double-strand DNA breaks that lead to interstrand and intrastrand DNA cross-linking. These agents are used extensively in cancer chemotherapy. They have a steep dose- response curve and are therefore useful in dose intensification strategies(e, g.in bone marrow transplantation). The subclasses of alkylating agents are as follows.

Abstract Background: Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for oestrogen receptor (ER)-positive breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. We aimed to identify protein biomarkers associated with chemotherapy resistance, using proteomic analysis of fresh ER-positive breast cancer samples, and then to perform pilot clinical validation experiments. Materials and Methods: Chemotherapy resistant and chemotherapy sensitive tumour samples were collected from breast cancer patients who received standard anthracycline-based neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. Comparative proteomics experiments were performed using invasive ductal carcinomas which demonstrated ER-positivity (luminal subtype). Protein expression was compared between chemotherapy resistant and chemotherapy sensitive tumour samples using 2-dimensional gel electrophoresis (2-DE) with MALDI-TOF/TOF mass spectrometry (MS). In addition the Panorama XPRESS Profiler725 antibody microarray, containing 725 antibodies from a wide variety of cell signalling and apoptosis pathways, was employed in the discovery phase. Differentially expressed proteins (DEPs) were submitted to Ingenuity Pathway Analysis (IPA) to identify any canonical pathway links. A pilot series of archival breast cancer samples, from patients treated with neoadjuvant anthracycline-based chemotherapy, was used for preliminary clinical validation of putative predictive biomarkers. Results: Five datasets were generated by antibody microarray analysis, revealing 41 targets. Of these, 7 DEPs were identified in at least 2 datasets and these included 14–3-3, BID and Bcl-xL. The top canonical pathway matched in IPA was “ERK5 signaling”, which involved 6 DEPs, including 14–3-3. The “PI3/AKT” pathway also involved 6 DEPs, including 14–3-3 and Bcl-xL. Three datasets were generated using 2-DE with MALDI-TOF/TOF MS, containing over 300 DEPs. These included several isoforms of 14–3-3. Differential expression of 14–3-3, BID and Bcl-xL was confirmed by immunoblotting in samples used for the discovery phase. A pilot clinical validation using immunohistochemical analysis of archival breast cancers revealed 14–3-3 tau and tBID to be significantly associated with chemotherapy resistance. Discussion: We have successfully utilised clinical tumour samples for the discovery of putative biomarkers of chemotherapy resistance using two complementary proteomic platforms. We propose a potential role for 14–3-3 tau and BID as predictive biomarkers of chemotherapy resistance in ER-positive tumours and further validation in a larger sample series is now required. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P5-13-15.

Abstract Abstract #5074 Background: Resistance to radiotherapy may be a significant factor in the development of local recurrence following surgical resection and radiotherapy. We aimed to develop a novel in vitro model of radioresistance using a breast cancer cell line and to subsequently identify molecular biomarkers which may be associated with the radioresistant phenotype. We utilised a quantitative proteomics technique (iTRAQ) based on MALDI-TOF/TOF mass spectrometry (MS) to identify differentially expressed proteins. Material and Methods: We established 3 novel breast cancer cell sublines which were significantly resistant to radiotherapy when compared with the parental cells. The radioresistant sublines were created by irradiating cells in fractionated doses of 2Gy up to a total dose of 40Gy. Sufficient time was allowed for the cells to recover between subsequent irradiations. A dose response curve was assessed at the end of treatment to demonstrate a statistically significant increase in radioresistance for each novel cell subline when compared with parental cells. One radioresistant/parental cell pair was first analysed using in-solution digestion and liquid chromatographic separation with protein identification by MALDI-TOF/TOF (LC-MALDI analysis) on an Applied Biosystems 4800 Plus instrument. Quantitative iTRAQ (Applied Biosystems) was then performed on the same instrument for all 3 radioresistant/parental cell pairs. Results: A total of 586 and 652 proteins were identified in T47D and T47DRR cells respectively by LC-MALDI. Those proteins identified in both cell lines and any redundant entries were removed to reveal those proteins which were unique to each cell line. In total 244 unique proteins were identified in T47D cells and 311 unique proteins were identified in T47DRR cells. Comparison of the 3 pairs of radioresistant/parental cell samples by iTRAQ revealed a number of differentially expressed proteins. Using a standard ≥2-fold change in expression, these iTRAQ analyses revealed significant changes in the expression of 51 proteins in one or more of the radio-resistant derivatives. Further confirmation by immunoblotting is underway. Currently the decrease in expression of 26S proteasome associated subunits has been confirmed by this method. Conclusion: LC-MALDI and iTRAQ analysis has revealed a large number of candidate proteins which may be associated with a radioresistant phenotype. These now require further confirmatory studies. These MS-based techniques offer a powerful proteomic approach to identify candidate biomarkers which may be involved in radioresistance. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 5074.

Abstract Abstract #5072 Background: Resistance to radiotherapy may be a significant factor in the development of local recurrence following surgical resection and radiotherapy. In addition, if patients with radioresistant breast cancers can be identified, harmful side effects from exposure to unnecessary ionizing radiation could be prevented. We aimed to develop a novel in vitro model of radioresistance using breast cancer cell lines and to subsequently identify molecular biomarkers which may be associated with the radioresistant phenotype. Antibody microarrays offer a complementary approach for proteomic analysis in conjunction with standard screening methods such as two dimensional gel electrophoresis/ mass spectrometry and other quantitative proteomic techniques. We previously utilised the Panorama Cell Signalling Antibody Microarray Kit (Sigma-Aldrich) consisting of 224 antibodies (Smith et al Mol Cancer Ther 5:2115-20, 2006). In this study we utilised a novel high density 725 antibody microarray to screen for proteins associated with radioresistance. Material and Methods: We established novel breast cancer cell sublines which were significantly resistant to radiotherapy when compared with the parental cells (T47D; MCF-7). The radioresistant sublines were created by irradiating cells in fractionated doses of 2Gy up to a total dose of 40Gy. Sufficient time was allowed for the cells to recover between subsequent irradiations. A dose response curve was assessed at the end of treatment to demonstrate a statistically significant increase in radioresistance for the novel cell subline when compared with parental cells. The radioresistant/parental cell pairs were analysed using the Panorama Antibody Microarray XPRESS Profiler725 Kit (Sigma-Aldrich). The microarray comprised 725 different antibodies on nitrocellulose coated microscope slides. The antibodies were selected from a wide variety of pathways, including apoptotic and cell signalling pathways. Results: Utilising a Cy3/Cy5 labelling strategy the antibody microarray approach yielded a number of a total of 28 targets for further study. Of these, three proteins were identified independently from both of the radioresistant cell lines. These were GFI1 (Growth Factor Independence-1), DR4 (Death Receptor 4) and Importin a1. Immunoblotting and other proteomic approaches have confirmed the identities and differential expression of some candidate protein targets. Conclusion: High density antibody microarrays potentially offer a powerful new proteomic technique to allow the global analysis of many proteins simultaneously. This analysis has produced both complementary and confirmatory data in our proteomic screening for putative biomarkers associated with radiotherapy resistance. We successfully identified a number of protein targets which may be associated with a radioresistant phenotype. Further confirmatory and validation studies are ongoing. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 5072.

Tumour markers have a limited role, if any, in initial investigations, but they can be important in following up patients with known malignancy

Background Previous epidemiological studies have investigated the relationship between individual nutrients such as vitamin D and vitamin B 12 and mammographic density, a strong marker of breast cancer risk [1], with varied results.There has been limited research on overall dietary patterns and most studies have focused on adult dietary patterns [2].We examine prospective data to determine whether dietary patterns from childhood to adult life affect mammographic density.Methods The Medical Research Council National Survey of Health and Development is a national representative sample of 2,815 men and 2,547 women followed since their birth in March 1946 [3].A wealth of medical and social data has been collected in over 25 follow-ups by home visits, medical examinations and postal questionnaires.Dietary intakes at age 4 years were determined by 24-hour recalls and in adulthood (ages 36, 43 years) by 5-day food records.Copies of the mammograms (two views for each breast) taken when the women were closest to age 50 years were obtained from the relevant NHS centres.A total of 1,319 women were followed up since birth in 1946 for whom a mammogram at age 50 years was retrieved, and the percentage mammographic density was measured using the computer-assisted threshold method for all 1,161 women.Breast cancer incidence for the whole cohort is being ascertained through the National Health Service Central Register.Statistical analysis Reduced rank regression analysis, a relatively new approach to dietary pattern analysis, is being used to identify dietary patterns associated with mammographic density [4].This approach identifies patterns in food intake that are predictive of an intermediate outcome of the disease process, such as mammographic density, and subsequently examines the relationship between the identified dietary patterns and breast cancer risk. ResultsPreliminary analyses so far suggest that variations in dietary patterns in adulthood might explain more than 10% of the variation in percentage mammographic density at age 50 years (age 36 years: 13%; age 43 years: 14%), with variations in patterns in childhood explaining slightly less.Further work is being carried out on the characteristics of these dietary patterns and their effects on percentage mammographic density and its two components (that is, absolute areas of dense and nondense tissues) and on breast cancer risk, after adjusting for socioeconomic status, anthropometric variables and reproductive factors. ConclusionThe present study will provide for the first time information on the relationship between dietary patterns across the life course and mammographic density, and will help to clarify the pathways through which diet may affect breast cancer risk.