Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets.</p> <p>Results</p> <p>Our method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test.</p> <p>Conclusion</p> <p>Prediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.</p

The discovery and evaluation of treatment stratification biomarkers in epithelial ovarian cancer

Background: Epithelial ovarian cancer (EOC) has the poorest survival outcomes for all gynaecological malignancies. Despite emerging knowledge on the heterogeneity of the disease the majority of patients are initially treated the same. Biomarkers which could predict treatment response would greatly enhance patient care. Results: Differential DNA methylation analysis of serous EOC tumours identified 180 loci significantly associated with residual disease status (n=297, p<0.05, FDR <5%), with 23 corresponding genes also significantly associated in gene expression array analysis (p<0.05, FDR <10%). Differential DNA methylation of 27 loci were significantly associated with overall survival in patients receiving optimal debulking (n=78, p<0.05). Patients optimally debulked but with poor prognosis markers were found to have the same survival as those suboptimally debulked suggesting that supraradical surgery is not beneficial in these patients. Hypomethylation at intragenic regions of the homeobox gene MSX1 was associated with primary platinum resistance significantly (n=61, p<0.05, FDR<5%) and these findings were validated in an independent dataset (n=252, p<0.05). DNA methylation was also significantly correlated to gene expression. Platinum resistant ovarian cancer cell lines demonstrated significantly lower gene expression of MSX1 compared to sensitive pairs. Proliferation and apoptosis cell-line assays demonstrated sensitisation to cisplatin when cisplatin resistant A2780/CP70 cells re-expressed MSX1 following gene transfection. An increase in p53 downstream transcripts, CDKN1A (p21) and BAX was also demonstrated in these MSX1 transfectants. The detection of disease at 5 distinct anatomical sites determined by preoperative computed tomography was significantly associated with surgical debulking outcomes in a test (n=111) and validation (n=70) cohort of patients with EOC (sensitivity 64.7-69.2%, specificity 67.9-71.4%, AUC 0.721-0.749). Conclusions: DNA methylation is a potential rich source of biomarkers predicting cytoreductive outcome and survival. The discovery and validation of a novel DNA methylation biomarker of chemotherapy resistance is demonstrated with exciting findings related to its biological function in cisplatin sensitive assays.Open Acces

Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer

The widespread introduction of high throughput RNA interference screening technology has revealed tumour drug sensitivity pathways to common cytotoxics such as paclitaxel, doxorubicin and 5-fluorouracil, targeted agents such as trastuzumab and inhibitors of AKT and Poly(ADP-ribose) polymerase (PARP) as well as endocrine therapies such as tamoxifen. Given the limited power of microarray signatures to predict therapeutic response in associative studies of small clinical trial cohorts, the use of functional genomic data combined with expression or sequence analysis of genes and microRNAs implicated in drug response in human tumours may provide a more robust method to guide adjuvant treatment strategies in breast cancer that are transferable across different expression platforms and patient cohorts

Investigation of intra-tumour heterogeneity to identify texture features to characterise and quantify neoplastic lesions on imaging

The aim of this work was to further our knowledge of using imaging data to discover image derived biomarkers and other information about the imaged tumour. Using scans obtained from multiple centres to discover and validate the models has advanced earlier research and provided a platform for further larger centre prospective studies. This work consists of two major studies which are describe separately: STUDY 1: NSCLC Purpose The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). Patients and methods Pre-therapy PET scans from 358 Stage I–III NSCLC patients scheduled for radical radiotherapy/chemoradiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. Using a semiautomatic threshold method to segment the primary tumors, radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients. Results Of 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information. Conclusion PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy. STUDY 2: Ovarian Cancer Purpose The 5-year survival of epithelial ovarian cancer is approximately 35-40%, prompting the need to develop additional methods such as biomarkers for personalised treatment. Patient and Methods 657 texture features were extracted from the CT scans of 364 untreated EOC patients. A 4-texture feature ‘Radiomic Prognostic Vector (RPV)’ was developed using machine learning methods on the training set. Results The RPV was able to identify the 5% of patients with the worst prognosis, significantly improving established prognostic methods and was further validated in two independent, multi-centre cohorts. In addition, the genetic, transcriptomic and proteomic analysis from two independent datasets demonstrated that stromal and DNA damage response pathways are activated in RPV-stratified tumours. Conclusion RPV could be used to guide personalised therapy of EOC. Overall, the two large datasets of different imaging modalities have increased our knowledge of texture analysis, improving the models currently available and provided us with more areas with which to implement these tools in the clinical setting.Open Acces

Circulating biomarkers as prognostic and predictive markers in rectal cancer

Standard treatment for patients with locally advanced rectal cancer (LARC) is currently preoperative radiotherapy with or without concurrent chemotherapy followed by total mesorectal excision. Approximately 10-20% of patients who receive preoperative therapy currently achieve a complete pathological or clinical response to therapy (pCR and cCR respectively). These patients have been demonstrated to have improved long-term outcomes, such as disease-free survival. At present, there are no methods available to reliably predict which patients will achieve pCR or cCR before surgical intervention or clinical examination respectively. This thesis aims to explore the technical aspects relating to a range of circulating biomarkers that might be used to facilitate this understanding for future evaluation in larger data sets. As part of this thesis, we developed an assay for the extraction and analysis of exosomederived microRNA (exoRNA) in patients with LARC. Using this assay, we detected variable levels of Mir-31, Mir-99a* and Mir-125b in longitudinal plasma samples. No significant associations were observed between microRNA levels and patient clinical outcomes. Circulating tumour DNA (ctDNA) detection in longitudinal plasma samples was lower than expected. The low rate of detection seen in patients with LARC may have been due to technical limitations. Alternatively, this may be indicative of limited ctDNA shedding in this cohort, bringing into question the potential utility of this biomarker in these patients as a future routine test. We also investigated the ability of immune and derived systemic inflammatory ratios to predict patient response to therapy in an expanded cohort of 235 patients with LARC. Again, few significant findings were observed. Overall, our findings suggest that the use of these circulating biomarkers may have limited clinical efficacy in patients with LARC

Ovarian cancer. Biomarkers, surgical outcome and survival.

Ovarian cancer is the eighth most common female cancer worldwide and the most lethal of the gynaecologic malignancies. Around 700 women are diagnosed in Sweden per year. Due to vague symptoms most of the patients are diagnosed with late-stage epithelial ovarian cancer (EOC) and prognosis is poor, with a five-year survival of 49%. However, for early-stage EOC the prognosis is excellent. Biomarkers for screening and early diagnosis have been sought for decades. To date, CA125 and HE4 are the only biomarkers in clinical use. Both lack sensitivity and specificity for early-stage EOC. The standard treatment for EOC is primary surgery with adjuvant chemotherapy. Centralisation of ovarian cancer care to high-volume hospitals with subspecialist surgeons and improved chemotherapy regimens have improved outcome and survival. Ovarian cancer surgery was centralised in Sweden in 2012.The aims of my thesis were to assess new biomarkers for their potential to improve the diagnostic performance of CA125 and HE4 in women with ovarian tumours (studies I and II), to evaluate ovarian cancer surgery after centralisation (study III) and to assess the incidence and survival in EOC in Sweden since the 1960s (study IV). Study I: CA125, HE4, B7-H4 and cleaved and intact suPAR were analysed in preoperative plasma samples from 350 women with ovarian tumours. Plasma levels of CA125, HE4 and suPAR(II-III) were found to increase from benign tumours to borderline, EOC type I and EOC type II while B7-H4 was only elevated in EOC II. Logistic regression models were fitted and a model combining CA125, HE4, suPAR(II-III) and age performed better (AUC=0.933) than the established ROMA algorithm (CA125, HE4 and menopause status) for discrimination of benign tumours from EOC in premenopausal women. The ROMA performed best in postmenopausal women (AUC=0.914). Furthermore, we correlated preoperative biomarker levels with survival after EOC diagnosis. High HE4, CA125 and suPAR(I) were prognostic for poor survival. At 12 months suPAR(I) was the only independent biomarker prognostic for poor short-term survival. In women above 75 years, high suPAR(I) indicated very poor prognosis in the first year after diagnosis (HR=8.9, p=0.01).Study II: 177 inflammation- and cancer-associated biomarkers were analysed in preoperative plasma samples from 180 women with ovarian tumour, using the proximity extension assay. HE4 was the best performing single biomarker for discrimination between benign tumours and EOC. Three-biomarker combinations of HE4, CA125 and one additional biomarker were compared to a reference model of HE4 and CA125. No biomarker significantly improved the diagnostic performance of HE4 and CA125. Study III: We analysed data from the GynOp Registry 2013-15. Out of 1108 cases of ovarian cancer surgery with curative intent, 30% were performed in regional hospitals with fewer than 20 cases per year. Four tertiary centres performed more than 25 surgeries per year. Compared with regional hospitals, tertiary centres perform more extensive surgery without an increased frequency of major complications. Large differences exist in patient selection for primary surgery and complete resection rates between the tertiary centres. Study IV: We identified all women with a diagnosis of epithelial ovarian, fallopian tube, and peritoneal cancers or undesignated abdominal/pelvic cancer from 1960 to 2014 in the Swedish Cancer Registry. Analyses of age-standardised incidence and relative survival (RS) were carried out and time trend graphs were modelled according to age, tumour site, and morphology. Since 1980 the age-standardised incidence of EOC has declined in Sweden. The age-standardised RS in EOC up to five years from diagnosis improved from 1960 to 2014. The 10-year RS has remained unchanged since 1960. In conclusion, CA125 plus HE4 continues to stand out as the best biomarker combination for assessment of cancer risk in a woman with ovarian tumours. CA125, HE4 and suPAR(I) are potential prognostic markers. Adding biomarkers to the preoperative assessment, especially in elderly women, could aid in the treatment decision on extensive primary surgery or neoadjuvant treatment. After centralisation of ovarian cancer surgery in Sweden, many women still have surgery at low-volume regional hospitals. The treatment for advanced EOC seems to differ considerably between the tertiary centres. Further centralisation as well as increased collaboration and exchange of knowledge between tertiary centres are needed to ensure equal access to care, regardless of region of living. Improved surgical and oncological treatment has prolonged life after EOC diagnosis. However, long-term survival remains poor. Most patients will die of their cancer. In order to cure EOC we need to find the patients at early stages. Better diagnostic tools are urgently needed