The impact of Oncotype DX testing on breast cancer management and chemotherapy prescribing patterns in a tertiary referral centre

Challenges and barriers for the adoption of personalized medicine in Europe: the case of Oncotype DX Breast Recurrence Score® test

Personalized medicine, aiming to tailor treatments based on individual patient characteristics, holds immense potential in oncology. However, its widespread adoption in Europe faces numerous challenges, as illustrated by the case study of the Oncotype DX Breast Recurrence Score® assay, a genomic test for breast cancer. This manuscript delineates the multifaceted obstacles encountered during the introduction of the Oncotype DX®test (Oncotype DX Breast Recurrence Score test) in Europe from 2004 to 2018. In June 2018, the TAILORx results were published in the New England Journal of Medicine (Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med 2018;379:111-21, Sparano JA, Gray RJ, Ravdin PM, Makower DF, Pritchard KI, Albain KS, et al. Clinical and genomic risk to guide the use of adjuvant therapy for breast cancer. N Engl J Med 2019;380:2395-405), and reported that among 6,711 women with hormone-receptor-positive, HER2-negative, node-negative breast cancer and a midrange recurrence score of 11-25 on the Oncotype DX assay, endocrine therapy was not inferior to chemoendocrine therapy, which provides evidence that adjuvant chemotherapy was not beneficial in these patients. Through a comprehensive analysis of clinical evidence, commercial presence, reimbursement mechanisms, guideline recommendations, regulatory pathways, and local experiences, this study sheds light on the intricate dynamics influencing the adoption of personalized medicine technologies. This article examines the various obstacles encountered during the introduction of the Oncotype DX Breast Cancer Assay in Europe from 2004 to 2018. By analyzing clinical evidence, commercial presence, reimbursement mechanisms, guideline recommendations, regulatory pathways, and local experiences, this study reveals the complex factors that influence the adoption of personalized medicine technologies. By highlighting these challenges, this article offers valuable insights into strategies to facilitate the integration of innovative diagnostic tools into clinical practice across Europe, ultimately leading to improved treatment decision-making for cancer patients.

Post-diagnosis weight trajectories and mortality among women with breast cancer

Weight gain after breast cancer diagnosis is associated with adverse health outcomes. Yet, few studies have characterized post-diagnosis weight change in the modern treatment era or populations most at risk for weight changes. Among women diagnosed with stages I-III breast cancer in the Smilow Care Network (2013-2019; N = 5441), we abstracted demographic and clinical characteristics from electronic health records and survival data from tumor registries. We assessed if baseline characteristics modified weight trajectories with nonlinear multilevel mixed-effect models. We evaluated body mass index (BMI) at diagnosis and weight change 1-year post-diagnosis in relation to all-cause and breast cancer-specific mortality with Cox proportional hazard models. Women had 34.4 ± 25.5 weight measurements over 3.2 ± 1.8 years of follow-up. Weight gain was associated with ER/PR-, HER2+ tumors, BMI ≤ 18.5 kg/m2, and age ≤ 45 years (+4.90 kg (standard error [SE] = 0.59), +3.24 kg (SE = 0.34), and +1.75 kg (SE = 0.10), respectively). Weight loss was associated with BMI ≥ 35 kg/m2 and age ≥ 70 years (-4.50 kg (SE = 0.08) and -4.34 kg (SE = 0.08), respectively). Large weight loss (≥10%), moderate weight loss (5-10%), and moderate weight gain (5-10%) 1-year after diagnosis were associated with higher all-cause mortality (hazard ratio [HR] = 2.93, 95% confidence interval [CI] = 2.28-3.75, HR = 1.32, 95% CI = 1.02-1.70 and HR = 1.39, 95% CI = 1.04-1.85, respectively). BMI ≥ 35 kg/m2 or BMI ≤ 18.5 kg/m2 at diagnosis were also associated with higher all-cause mortality. Weight change after a breast cancer diagnosis differed by demographic and clinical characteristics highlighting subgroups at-risk for weight change during a 5-year period post-diagnosis. Monitoring and interventions for weight management early in clinical care are important.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning

Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers are needed. We use whole-genomics (WGS; n = 155) with matching whole-transcriptomics (WTS; n = 113) from biopsies of ARSI-treated mCRPC patients for unbiased discovery of biomarkers and development of machine learning-based prediction models. Tumor mutational burden (q < 0.001), structural variants (q < 0.05), tandem duplications (q < 0.05) and deletions (q < 0.05) are enriched in poor responders, coupled with distinct transcriptomic expression profiles. Validating various classification models predicting treatment duration with ARSI on our internal and external mCRPC cohort reveals two best-performing models, based on the combination of prior treatment information with either the four combined enriched genomic markers or with overall transcriptomic profiles. In conclusion, predictive models combining genomic, transcriptomic, and clinical data can predict response to ARSI in mCRPC patients and, with additional optimization and prospective validation, could improve treatment guidance.

Clinicopathological Factors Affecting Breast Cancer Survival in Jamaican Women: A Retrospective Review

OBJECTIVE

Breast cancer is the leading cause of cancer affecting women worldwide. The survival rate is primarily affected by the stage of the disease and several other demographic and clinicopathological factors.

METHODS

This study is a retrospective cohort study of female patients of the University Hospital of the West Indies diagnosed with breast cancer between 2011 and 2016. The age, tumor size, SBR/Nottingham grade, tumor histologic subtype, tumor molecular subtype, and survival status of the cohort on November 1, 2019, were determined. The data were summarized. Survival across each variable was compared using univariate log-rank tests, Cox proportional hazard models, and crude and adjusted models. A second wave analysis was performed excluding patients whose survival status was presumed.

RESULTS

A total of 503 patients were analyzed. The overall survival rate at 1, 3, and 5 years were 96.4%, 84.9%, and 79.0%, respectively, for the entire cohort. The molecular subtype was the most significant clinicopathological factor affecting overall survival. A younger age < 40 years, higher histologic grade, estrogen receptor-negative breast cancers, invasive ductal type breast cancers, and T1 lesions were associated with poorer survival outcomes at 5 years. The findings were reproduced after a second wave analysis excluding patients who were presumed alive was applied.

CONCLUSIONS

Breast cancer overall survival in Jamaica is consistent with that of other developing countries in the literature. This study is an important contribution to the growing body of literature available and aids to the overall understanding of the behavior of breast cancer locally.

Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.

Comparison of the Nottingham Prognostic Index and OncotypeDX© recurrence score in predicting outcome in estrogen receptor positive breast cancer

INTRODUCTION

Traditionally, Nottingham prognostic index (NPI) informed prognosis in patients with estrogen receptor positive, human epidermal growth factor receptor-2 negative, node negative (ER+/HER2-/LN-) breast cancer. At present, OncotypeDX© Recurrence Score (RS) predicts prognosis and response to adjuvant chemotherapy (AC).

AIMS

To compare NPI and RS for estimating prognosis in ER + breast cancer.

METHODS

Consecutive patients with ER+/HER2-/LN- disease were included. Disease-free (DFS) and overall survival (OS) were determined using Kaplan-Meier and Cox regression analyses.

RESULTS

1471 patients met inclusion criteria. The mean follow-up was 110.7months. NPI was calculable for 1382 patients: 19.8% had NPI≤2.4 (291/1471), 33.0% had NPI 2.41-3.4 (486/1471), 30.0% had NPI 3.41-4.4 (441/1471), 10.9% had NPI 4.41-5.4 (160/1471), and 0.3% had NPI>5.4 (4/1471). In total, 329 patients underwent RS (mean RS: 18.7) and 82.1% had RS < 25 (270/329) and 17.9% had RS ≥ 25 (59/329). Using multivariable Cox regression analyses (n = 1382), NPI independently predicted DFS (Hazard ratio (HR): 1.357, 95% confidence interval (CI): 1.140-1.616, P < 0.001) and OS (HR: 1.003, 95% CI: 1.001-1.006, P = 0.024). When performing a focused analysis of those who underwent both NPI and RS (n = 329), neither biomarker predicted DFS or OS. Using Kaplan Meier analyses, NPI category predicted DFS (P = 0.008) and (P = 0.026) OS. Conversely, 21-gene RS group failed to predict DFS (P = 0.187) and OS (P = 0.296).

CONCLUSION

In our focused analysis, neither NPI nor RS predicted survival outcomes. However, in the entire series, NPI independently predicted both DFS and OS. On the 40th anniversary since its derivation, NPI continues to provide accurate prognostication in breast cancer, outperforming RS in the current study.

Explainable artificial intelligence for precision medicine in acute myeloid leukemia

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of “black box” in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 ex-vivo tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the FLT3, CBFβ-MYH11, and NRAS status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.

Molecular Subtyping of Invasive Breast Cancer Using a PAM50-Based Multigene Expression Test-Comparison with Molecular-Like Subtyping by Tumor Grade/Immunohistochemistry and Influence on Oncologist's Decision on Systemic Therapy in a Real-World Setting

In intermediate risk hormone receptor (HR) positive, HER2 negative breast cancer (BC), the decision regarding adjuvant chemotherapy might be facilitated by multigene expression tests. In all, 142 intermediate risk BCs were investigated using the PAM50-based multigene expression test Prosigna® in a prospective multicentric study. In 119/142 cases, Prosigna® molecular subtyping was compared with local and two central (C1 and C6) molecular-like subtypes relying on both immunohistochemistry (IHC; HRs, HER2, Ki-67) and IHC + tumor grade (IHC+G) subtyping. According to local IHC, 35.4% were Luminal A-like and 64.6% Luminal B-like subtypes (local IHC+G subtype: 31.9% Luminal A-like; 68.1% Luminal B-like). In contrast to local and C1 subtyping, C6 classified >2/3 of cases as Luminal A-like. Pairwise agreement between Prosigna® subtyping and molecular-like subtypes was fair to moderate depending on molecular-like subtyping method and center. The best agreement was observed between Prosigna® (53.8% Luminal A; 44.5% Luminal B) and C1 surrogate subtyping (Cohen’s kappa = 0.455). Adjuvant chemotherapy was suggested to 44.2% and 88.6% of Prosigna® Luminal A and Luminal B cases, respectively. Out of all Luminal A-like cases (locally IHC/IHC+G subtyping), adjuvant chemotherapy was recommended if Prosigna® testing classified as Prosigna® Luminal A at high / intermediate risk or upgraded to Prosigna® Luminal B.

A Novel Surrogate Nomogram Capable of Predicting OncotypeDX Recurrence Score©

Background: OncotypeDX Recurrence Score© (RS) is a commercially available 21-gene expression assay which estimates prognosis and guides chemoendocrine prescription in early-stage estrogen-receptor positive, human epidermal growth factor receptor-2-negative (ER+/HER2−) breast cancer. Limitations of RS testing include the cost and turnaround time of several weeks. Aim: Our aim is to develop a user-friendly surrogate nomogram capable of predicting RS. Methods: Multivariable linear regression analyses were performed to determine predictors of RS and RS > 25. Receiver operating characteristic analysis produced an area under the curve (AUC) for each model, with training and test sets were composed of 70.3% (n = 315) and 29.7% (n = 133). A dynamic, user-friendly nomogram was built to predict RS using R (version 4.0.3). Results: 448 consecutive patients who underwent RS testing were included (median age: 58 years). Using multivariable regression analyses, postmenopausal status (β-Coefficient: 0.25, 95% confidence intervals (CIs): 0.03−0.48, p = 0.028), grade 3 disease (β-Coefficient: 0.28, 95% CIs: 0.03−0.52, p = 0.026), and estrogen receptor (ER) score (β-Coefficient: −0.14, 95% CIs: −0.22−−0.06, p = 0.001) all independently predicted RS, with AUC of 0.719. Using multivariable regression analyses, grade 3 disease (odds ratio (OR): 5.67, 95% CIs: 1.32−40.00, p = 0.037), decreased ER score (OR: 1.33, 95% CIs: 1.02−1.66, p = 0.050) and decreased progesterone receptor score (OR: 1.16, 95% CIs: 1.06−1.25, p = 0.002) all independently predicted RS > 25, with AUC of 0.740 for the static and dynamic online nomogram model. Conclusions: This study designed and validated an online user-friendly nomogram from routinely available clinicopathological parameters capable of predicting outcomes of the 21-gene RS expression assay.

Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures

Breast cancer is a global cause for concern owing to its high incidence around the world. The alarming increase in breast cancer cases emphasizes the management of disease at multiple levels. The management should start from the beginning that includes stringent cancer screening or cancer registry to effective diagnostic and treatment strategies. Breast cancer is highly heterogeneous at morphology as well as molecular levels and needs different therapeutic regimens based on the molecular subtype. Breast cancer patients with respective subtype have different clinical outcome prognoses. Breast cancer heterogeneity emphasizes the advanced molecular testing that will help on-time diagnosis and improved survival. Emerging fields such as liquid biopsy and artificial intelligence would help to under the complexity of breast cancer disease and decide the therapeutic regimen that helps in breast cancer management. In this review, we have discussed various risk factors and advanced technology available for breast cancer diagnosis to combat the worst breast cancer status and areas that need to be focused for the better management of breast cancer.

Value of the 21-gene expression assay in predicting locoregional recurrence rates in estrogen receptor-positive breast cancer: a systematic review and network meta-analysis

Purpose

The Oncotype DX© 21-gene Recurrence Score (RS) estimates the risk of distant disease recurrence in early-stage estrogen receptor-positive, human epidermal growth factor receptor-2-negative (ER+/HER2− ) breast cancer. Using RS to estimate risk of locoregional recurrence (LRR) is less conclusive. We aimed to perform network meta-analysis (NMA) evaluating the RS in estimating LRR in ER+/HER2− breast cancer.

Methods

A NMA was performed according to PRISMA-NMA guidelines. Analysis was performed using R packages and Shiny.

Results

16 studies with 21,037 patients were included (mean age: 55.1 years (range: 22–96)). The mean RS was 17.1 and mean follow-up was 66.4 months. Using traditional RS cut-offs, 49.7% of patients had RS < 18 (3944/7935), 33.8% had RS 18–30 (2680/7935), and 16.5% had RS > 30 (1311/7935). Patients with RS 18–30 (risk ratio (RR): 1.76, 95% confidence interval (CI): 1.32–2.37) and RS > 30 (RR: 3.45, 95% CI: 2.63–4.53) were significantly more likely to experience LRR than those with RS < 18. Using TAILORx cut-offs, 16.2% of patients had RS < 11 (1974/12,208), 65.8% had RS 11–25 (8036/12,208), and 18.0% with RS > 30 (2198/12,208). LRR rates were similar for patients with RS 11–25 (RR: 1.120, 95% CI: 0.520–2.410); however, those with RS > 25 had an increased risk of LRR (RR: 2.490, 95% CI: 0.680–9.390) compared to those with RS < 11. There was a stepwise increase in LRR rates when applying traditional and TAILORx cut-offs (both P < 0.050).

Conclusion

RS testing accurately estimates LRR risk for patients being treated for early-stage ER+/HER2− breast cancer. Future prospective, randomized studies may validate the predictive value of RS in estimating LRR.

‘Breast Cancer Resistance Likelihood and Personalized Treatment Through Integrated Multiomics’

In recent times, enormous progress has been made in improving the diagnosis and therapeutic strategies for breast carcinoma, yet it remains the most prevalent cancer and second highest contributor to cancer-related deaths in women. Breast cancer (BC) affects one in eight females globally. In 2018 alone, 1.4 million cases were identified worldwide in postmenopausal women and 645,000 cases in premenopausal females, and this burden is constantly increasing. This shows that still a lot of efforts are required to discover therapeutic remedies for this disease. One of the major clinical complications associated with the treatment of breast carcinoma is the development of therapeutic resistance. Multidrug resistance (MDR) and consequent relapse on therapy are prevalent issues related to breast carcinoma; it is due to our incomplete understanding of the molecular mechanisms of breast carcinoma disease. Therefore, elucidating the molecular mechanisms involved in drug resistance is critical. For management of breast carcinoma, the treatment decision not only depends on the assessment of prognosis factors but also on the evaluation of pathological and clinical factors. Integrated data assessments of these multiple factors of breast carcinoma through multiomics can provide significant insight and hope for making therapeutic decisions. This omics approach is particularly helpful since it identifies the biomarkers of disease progression and treatment progress by collective characterization and quantification of pools of biological molecules within and among the cancerous cells. The scrupulous understanding of cancer and its treatment at the molecular level led to the concept of a personalized approach, which is one of the most significant advancements in modern oncology. Likewise, there are certain genetic and non-genetic tests available for BC which can help in personalized therapy. Genetically inherited risks can be screened for personal predisposition to BC, and genetic changes or variations (mutations) can also be identified to decide on the best treatment. Ultimately, further understanding of BC at the molecular level (multiomics) will define more precise choices in personalized medicine. In this review, we have summarized therapeutic resistance associated with BC and the techniques used for its management.

AKR1B10 as a Potential Novel Serum Biomarker for Breast Cancer: A Pilot Study

Background

Aldo-keto reductase 1B10 (AKR1B10) is a secretory protein that is upregulated in breast cancer.

Objective

This case-controlled pilot study evaluated the serum level of AKR1B10 in healthy women and patients with a localized or metastatic breast cancer.

Methods

AKR1B10 levels were measured by ELISA and IHC in several patient cohorts.

Results

Our data showed that serum AKR1B10 was significantly elevated in patients with localized (6.72 ± 0.92 ng/ml) or metastatic (7.79 ± 1.13 ng/ml) disease compared to cancer-free healthy women (1.69 ± 0.17 ng/ml) (p<0.001); the serum AKR1B10 was correlated with its expression in tumor tissues, but not with the tumor burden, molecular subtypes or histological stages. After surgical removal of primary tumors, the serum AKR1B10 was rapidly decreased within 3 days and plateaued at a level similar to that of healthy controls in most patients. ROC curve analysis suggested the optimal diagnostic cut-off value of serum AKR1B10 at 3.456 ng/ml with AUC 0.9045 ± 0.0337 (95% CI 0.8384 - 0.9706), sensitivity 84.75% (95% CI 73.01% to 92.78%), and specificity 93.88% (95% CI 83.13% to 98.72%).

Conclusions

These data indicate the potential value of serum AKR1B10 as a biomarker of breast cancer.

Low correlation between Ki67 assessed by qRT-PCR in Oncotype Dx score and Ki67 assessed by Immunohistochemistry

Breast cancers expressing high levels of Ki67 are associated with poor outcomes. Oncotype DX test was designed for ER+/HER2- early-stage breast cancers to help adjuvant chemotherapy decision by providing a Recurrent Score (RS). RS measures the expression of 21 specific genes from tumor tissue, including Ki67. The primary aim of this study was to assess the agreement between Ki67_RNA obtained with Oncotype DX RS and Ki67_IHC. Other objectives were to analyze the association between the event free survival (EFS) and the expression level of Ki67_RNA; and association between RS and Ki67_RNA. Herein, we report a low agreement of 0.288 by Pearson correlation coefficient test between Ki67_IHC and Ki67_RNA in a cohort of 98 patients with early ER+/HER2- breast cancers. Moreover, Ki67_RNA^high tumors were significantly associated with the occurrence of events (p = 0.03). On the other hand, we did not find any association between Ki67_IHC and EFS (p = 0.26). We observed a low agreement between expression level of Ki67_RNA and Ki67 protein labelling by IHC. Unlike Ki67_IHC and independently of the RS, Ki67_RNA could have a prognostic value. It would be interesting to better assess the prognosis and predictive value of Ki67_RNA measured by qRT-PCR. The Ki67_RNA in medical routine could be a good support in countries where Oncotype DX is not accessible.

6q deletion is frequent but unrelated to patient prognosis in breast cancer

BACKGROUND

Deletions involving the long arm of chromosome 6 have been reported to occur in breast cancer, but little is known about the clinical relevance of this alteration.

METHODS

We made use of a pre-existing tissue microarray with 2197 breast cancers and employed a 6q15/centromere 6 dual-labeling probe for fluorescence in situ (FISH) analysis RESULTS: Heterozygous 6q15 deletions were found in 202 (18%) of 1099 interpretable cancers, including 19% of 804 cancers of no special type (NST), 3% of 29 lobular cancers, 7% of 41 cribriform cancers, and 28% of 18 cancers with papillary features. Homozygous deletions were not detected. In the largest subset of NST tumors, 6q15 deletions were significantly linked to advanced tumor stage and high grade (p < 0.0001 each). 6q deletions were also associated with estrogen receptor negativity (p = 0.0182), high Ki67 proliferation index (p < 0.0001), amplifications of HER2 (p = 0.0159), CCND1 (p = 0.0069), and cMYC (p = 0.0411), as well as deletions of PTEN (p = 0.0003), 8p21 (p < 0.0001), and 9p21 (p = 0.0179). However, 6q15 deletion was unrelated to patient survival in all cancers, in NST cancers, or in subsets of cancers defined by the presence or absence of lymph-node metastases.

CONCLUSION

Our data demonstrate that 6q deletion is a frequent event in breast cancer that is statistically linked to unfavorable tumor phenotype and features of genomic instability. The absence of any prognostic impact argues against a clinical applicability of 6q15 deletion testing in breast cancer patients.

High mitochondrial content is associated with breast cancer aggressiveness

Mitochondria are relevant for cancer initiation and progression. Antibodies against mitochondrially encoded cytochrome c oxidase II (MTCO2), targeting a mitochondria specific epitope, can be used to quantitate the mitochondria content of tumor cells. The present study evaluated the impact of the cellular mitochondrial content on the prognosis of patients with breast cancer using immunohistochemical analysis on 2,197 arrayed breast cancer specimens. Results were compared with histological tumor parameters, patient overall survival, tumor cell proliferation using Ki67 labeling index (Ki67LI) and various other molecular features. Tumor cells exhibited stronger MTCO2 expression than normal breast epithelial cells. MTCO2 immunostaining was largely absent in normal breast epithelium, but was observed in 71.9% of 1,797 analyzable cancer specimens, including 34.6% tumors with weak expression, 22.3% with moderate expression and 15.0% with strong expression. High MTCO2 expression was significantly associated with advanced tumor stage, high Bloom-Richardson-Elston/Nottingham (BRE) grade, nodal metastasis and shorter overall survival (P<0.0001 each). In multivariate analysis, MTCO2 expression did not provide prognostic information independent of BRE grade, pathological tumor and pathological lymph node status. Additionally, significant associations were observed for high MTCO2 expression and various molecular features, including high Ki67LI, amplifications of HER2, MYC, CCND1 and MDM2, deletions of PTEN, 8p21 and 9p, low estrogen receptor expression (P<0.0001 each) and progesterone receptor expression (P<0.0001). The present study demonstrated that high MTCO2 expression was strongly associated with a poor prognosis and unfavorable phenotypical and molecular tumor features in patients with breast cancer. This suggests that the mitochondrial content may have a pivotal role in breast cancer progression.

Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma

BACKGROUND

Ki-67 immunohistochemistry (IHC) staining is a widely used cancer proliferation assay; however, its limitations could be improved with automated scoring. The OncotypeDXTM Recurrence Score (ORS), which primarily evaluates cancer proliferation genes, is a prognostic indicator for breast cancer chemotherapy response; however, it is more expensive and slower than Ki-67.

OBJECTIVE

To compare manual Ki-67 (mKi-67) with automated Ki-67 (aKi-67) algorithm results based on manually selected Ki-67 "hot spots" in breast cancer, and correlate both with ORS.

METHODS

105 invasive breast carcinoma cases from 100 patients at our institution (2011-2013) with available ORS were evaluated. Concordance was assessed via Cohen's Kappa (κ).

RESULTS

57/105 cases showed agreement between mKi-67 and aKi-67 (κ 0.31, 95% CI 0.18-0.45), with 41 cases overestimated by aKi-67. Concordance was higher when estimated on the same image (κ 0.53, 95% CI 0.37-0.69). Concordance between mKi-67 score and ORS was fair (κ 0.27, 95% CI 0.11-0.42), and concordance between aKi-67 and ORS was poor (κ 0.10, 95% CI -0.03-0.23).

CONCLUSIONS

These results highlight the limits of Ki-67 algorithms that use manual "hot spot" selection. Due to suboptimal concordance, Ki-67 is likely most useful as a complement to, rather than a surrogate for ORS, regardless of scoring method.

Impact of Oncotype DX testing on ER+ breast cancer treatment and survival in the first decade of use

BACKGROUND

The Oncotype DX breast recurrence score has been introduced more than a decade ago to aid physicians in determining the need for systemic adjuvant chemotherapy in patients with early-stage, estrogen receptor (ER)+, lymph node-negative breast cancer.

METHODS

In this study, we utilized data from The Surveillance, Epidemiology, and End Results (SEER) Program to investigate temporal trends in Oncotype DX usage among US breast cancer patients in the first decade after the introduction of the Oncotype DX assay.

RESULTS

We found that the use of Oncotype DX has steadily increased in the first decade of use and that this increase is associated with a decreased usage of chemotherapy. Patients who utilized the Oncotype DX test tended to have improved survival compared to patients who did not use the assay even after adjusting for clinical variables associated with prognosis. In addition, chemotherapy usage in patients with high-risk scores is associated with significantly longer overall and breast cancer-specific survival compared to high-risk patients who did not receive chemotherapy. On the contrary, patients with low-risk scores who were treated with chemotherapy tended to have shorter overall survival compared to low-risk patients who forwent chemotherapy.

CONCLUSION

We have provided a comprehensive temporal overview of the use of Oncotype DX in breast cancer patients in the first decade after Oncotype DX was introduced. Our results suggest that the use of Oncotype DX is increasing in ER+ breast cancer and that the Oncotype DX test results provide valuable information for patient treatment and prognosis.

Precision Medicine: Making It Happen for Malaysia

Precision medicine is transforming healthcare worldwide and aims to improve the effectiveness of management of many diseases including cancers, other non-communicable diseases (NCDs) and also rare diseases. Precision medicine takes into account the individual patient’s genetic, environment and lifestyle data. Developed nations are already embarking on precision medicine initiatives including the 100,000 Genomes England and the Precision Medicine Initiative in the United States (US). The Academy of Sciences Malaysia, the Ministry of Health and the Ministry of Higher Education are working together to put forward a precision medicine initiative for Malaysia. The key drivers that must be put in place include a strong policy agenda, a national large scale genome sequencing project and with it a national genome database, the implementation of the electronic medical record (EMR) system, a payment and reimbursement system to cover for the genetic testing and the targeted treatment, and putting in place an ecosystem that will support precision medicine. Relevant guidelines and Acts will also need to be developed especially with regard to privacy and confidentiality. The future of precision medicine is now and this will certainly bring better outcome and value to the patients.

Evaluating the Clinical Utility of Routine Sentinel Lymph Node Biopsy and the Value of Adjuvant Chemotherapy in Elderly Patients Diagnosed With Oestrogen Receptor Positive, Clinically Node Negative Breast Cancer

Background

Sentinel lymph node biopsy (SLNB) provides staging information and guides adjuvant therapy in early breast cancer (EBC). Routine SLNB in oncogeriatricians with low-risk EBC remains controversial.

Aims

To evaluate axillary management in elderly patients diagnosed with oestrogen receptor positive (ER+), clinically lymph node negative (cLN-) EBC, and to assess whether SLNB affects further axillary management or adjuvant chemotherapy (ACTX) decision making.

Methods

Female patients aged > 65 years, diagnosed with ER+, human epidermal growth factor receptor-2 negative (HER2-), and cLN- breast cancer (BC), who underwent surgery and SLNB were included. Clinicopathological predictors of ACTX and completion axillary lymph node dissection (CALND) were determined. Kaplan-Meier analyses assessed survival outcomes.

Results

A total of 253 patients were included (median age: 72 years, range: 66-90), all underwent SLNB; 50 (19.8%) had lymphatic metastasis on SLNB (SLNB+). Of these, 19 proceeded to CALND (38.0%), 10 (52.6%) of whom had further axillary disease (ALND+). 20 of the 50 SLNB+ patients received ACTX (40.0%) as did 31 of the 203 SLNB- patients (15.2%) (P < .001). Oncotype DX (ODX) testing was utilized in 82 cases (32.8%). Younger age (P < .001), SLNB+ (P < .001) and ODX score (P = .003) were all associated with ACTX prescription. ODX > 25 (OR: 4.37, 95% CI: 1.38-13.80, P = .012) independently predicted receiving ACTX. Receiving ACTX and proceeding to CALND did not improve disease-free (P = .485 and P = .345) or overall survival (P = .981 and P = .646).

Conclusions

Routine SNLB may not be necessary in elderly patients diagnosed with ER+, cLN- EBC. Future oncogeriatric practice is likely to see genomic testing guiding ACTX prescription in this group.

Clinicopathological correlates, oncological impact, and validation of Oncotype DX™ in a European Tertiary Referral Centre

Oncotype DX™ (ODX) score estimates prognosis and predicts breast cancer recurrence. It also individualizes patient adjuvant chemotherapy prescription in breast cancer. This assay relies on genetic and molecular markers; the clinicopathological phenotype of which are tested routinely. The aim of this study was determine whether clinicopathological and immunohistochemical information predicts ODX recurrence score (RS). Secondly, to assess the impact on adjuvant chemotherapy (AC) and oncological outcome of ODX testing in patients in a European tertiary referral center. Estrogen receptor positive (ER+), human epidermal growth factor receptor-2 negative (HER2-), lymph node negative (LN-), and female breast cancer patients with ODX testing performed between 2007 and 2015 were categorized into low- (<11), intermediate- (11-25), and high-risk (>25) groups. Clinicopathological and immunohistochemical correlates of RS were determined. Predictors of RS were assessed using binary logistic regression. Oncological outcome was assessed using Kaplan-Meier and Cox regression analyses. ODX was performed in 400 consecutive ER+LN- patients. Median follow-up was 74.1 months (3.0-144.4). Low grade (odds ratio [OR]:2.39; 95% confidence interval [CI]:1.04-5.51, p = 0.041) independently predicted low ODX, while high grade (OR:2.04; 95% CI: 1.19-3.49, p = 0.009) and reduced progesterone receptor (PgR) expression (OR: 2.57, 95% CI: 1.42-4.65, p = 0.002) independently predicted high ODX. Omission of AC in intermediate- (p = 0.159) and high-risk (p = 0.702) groups did not negatively impact survival. In conclusion, tumor grade independently predicts low and high RS, while PgR negativity predicts high RS. ODX reduced AC prescription without compromising oncological outcome.

The Level of Agreement Among Medical Oncologists on Adjuvant Chemotherapy Decision for Breast Cancer in Pre and Post-Oncotype DX Settings

INTRODUCTION

The Oncotype DX assay plays an important role in the identification of the specific subset of hormone receptor (HR)-positive and node-negative breast cancer (BC) patients, who would benefit the most from adjuvant chemotherapy. The current study aimed at assessing the level of agreement among medical oncologists on adjuvant chemotherapy decisions before and after Oncotype DX, as well as the intra-observer agreement of each medical oncologist's decision of prescribing adjuvant chemotherapy based on clinicopathological and immunohistochemical parameters only and followed by Oncotype DX recurrence score (RS) results.

METHODS

A retrospective analysis of data related to clinicopathological and immunohistochemical parameters, and Oncotype DX RS result for 145 female, estrogen receptor (ER)-positive, HER2 negative, and both node-negative and positive BC patients was performed. Initially, the data without Oncotype DX RS was sent to 16 oncologists in multiple centers in the Middle East. After one week, the same data with the shuffling of cases were sent to the oncologists with the addition of the Oncotype DX RS result for each patient. The inter and intra-observer agreement (kappa and Fleiss multi-rater kappa) among oncologists' decision of prescribing adjuvant chemotherapy pre and post-Oncotype DX RS results were assessed. Oncotype DX risk scores were used as continuous variables as well as based on old RS grouping, categorized into low (0-17), intermediate (18-30), and high risk (≥ 31) groups. A test with a p-value of < 0 .05 will be considered statistically significant.

RESULTS

The mean age ± SD of the cohort was 51.9 ± 9.4 years. Sixty-nine patients (47.6%) were premenopausal whereas 76 patients (52.4%) were postmenopausal. The mean Oncotype DX RS was 17.8 ± 8.6 and 54.5% had low recurrence risk (RR), 37.9% had intermediate RR and only 7.6% had high RR. The majority of our cases were grade two (53.1%) and T stage one (49%), whereas 29.7% had positive one to three lymph nodes. The addition of Oncotype DX results improved the agreement among oncologists' decision from fair to moderate (kappa = 0.52; p <0.001). On average, an oncologist's decision of prescribing adjuvant chemotherapy pre and post-Oncotype DX had an agreement in 70.6% of the cases, with agreement observed mostly for cases where the initial decision of adjuvant chemotherapy was (no) and it was retained with post-Oncotype DX assay (46.1%), compared to 24.5% cases where the initial decision was (yes) and it was retained with post-Oncotype DX assay (kappa = 0.39; p <0.001). The addition of the Oncotype DX RS result avoided chemotherapy in 20.4% of cases and identified 9% of cases as candidates for adjuvant chemotherapy (kappa = 0.38; p <0.001). The disagreement was highest among cases with intermediate RR (33.6%) followed by high and low RR (31.3% and 21.6%) with a statistical significance of <0.001.

CONCLUSION

We conclude that the Oncotype DX RS significantly influenced the decision to prescribe adjuvant chemotherapy among HR-positive, HER2 negative, and both node-negative and positive patients, as it increased the level of agreement among oncologists and led to a decrease in the use of adjuvant chemotherapy compared to the pre-Oncotype recommendations.

An Epithelial-Mesenchymal Transition (EMT) Preoperative Nomogram for Prediction of Lymph Node Metastasis in Bladder Cancer (BLCA)

Lymph node (LN) metastasis is a lethal independent risk factor for patients with bladder cancer (BLCA). Accurate evaluation of LN metastasis is of vital importance for disease staging, treatment selection, and prognosis prediction. Several histopathologic parameters are available to predict LN metastasis postoperatively. To date, medical imaging techniques have made a great contribution to preoperatively diagnosis of LN metastasis, but it also exhibits substantial false positives. Therefore, a reliable and robust method to preoperatively predict LN metastasis is urgently needed. Here, we selected 19 candidate genes related to epithelial-mesenchymal transition (EMT) across the LN metastasis samples, which was previously reported to be responsible for the subtype transition and correlation with malignancy and prognosis of BLCA, to establish an EMT-LN signature through LASSO logistic regression analysis. The EMT-LN signature could significantly predict LN metastasis with high accuracy in the TCGA-BLCA cohort, as well as several independent cohorts. As integrating with C3orf70 mutation, we developed an individualized prediction nomogram based on the EMT-LN signature. The nomogram exhibited good discrimination on LN metastasis status, with AUC of 71.7% and 75.9% in training and testing datasets of the TCGA-BLCA cohort. Moreover, the EMT-LN nomogram displayed good calibration with p > 0.05 in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis (DCA) revealed that the EMT-LN nomogram was of high potential for clinical utility. In summary, we established an EMT-LN nomogram integrating an EMT-LN signature and C3orf70 mutation status, which acted as an easy-to-use tool to facilitate preoperative prediction of LN metastasis in BLCA individuals.

Conserving the axilla in breast cancer

It is recognised that surgical conservatism is the most effective way of managing the axilla in breast cancer patients undergoing primary breast conserving surgery. The extended clinical scenarios in which a less aggressive approach can be safely adopted warrant consideration—including a group of patients who potentially could bypass surgical staging of the axilla altogether. The application of omission of further surgical management and axillary radiotherapy in the primary surgical and neoadjuvant chemotherapy settings are considered.

Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer: a systematic review and economic analysis

BACKGROUND

Breast cancer and its treatment can have an impact on health-related quality of life and survival. Tumour profiling tests aim to identify whether or not women need chemotherapy owing to their risk of relapse.

OBJECTIVES

To conduct a systematic review of the effectiveness and cost-effectiveness of the tumour profiling tests oncotype DX^® (Genomic Health, Inc., Redwood City, CA, USA), MammaPrint^® (Agendia, Inc., Amsterdam, the Netherlands), Prosigna^® (NanoString Technologies, Inc., Seattle, WA, USA), EndoPredict^® (Myriad Genetics Ltd, London, UK) and immunohistochemistry 4 (IHC4). To develop a health economic model to assess the cost-effectiveness of these tests compared with clinical tools to guide the use of adjuvant chemotherapy in early-stage breast cancer from the perspective of the NHS and Personal Social Services.

DESIGN

A systematic review and health economic analysis were conducted.

REVIEW METHODS

The systematic review was partially an update of a 2013 review. Nine databases were searched in February 2017. The review included studies assessing clinical effectiveness in people with oestrogen receptor-positive, human epidermal growth factor receptor 2-negative, stage I or II cancer with zero to three positive lymph nodes. The economic analysis included a review of existing analyses and the development of a de novo model.

RESULTS

A total of 153 studies were identified. Only one completed randomised controlled trial (RCT) using a tumour profiling test in clinical practice was identified: Microarray In Node-negative Disease may Avoid ChemoTherapy (MINDACT) for MammaPrint. Other studies suggest that all the tests can provide information on the risk of relapse; however, results were more varied in lymph node-positive (LN+) patients than in lymph node-negative (LN0) patients. There is limited and varying evidence that oncotype DX and MammaPrint can predict benefit from chemotherapy. The net change in the percentage of patients with a chemotherapy recommendation or decision pre/post test ranged from an increase of 1% to a decrease of 23% among UK studies and a decrease of 0% to 64% across European studies. The health economic analysis suggests that the incremental cost-effectiveness ratios for the tests versus current practice are broadly favourable for the following scenarios: (1) oncotype DX, for the LN0 subgroup with a Nottingham Prognostic Index (NPI) of > 3.4 and the one to three positive lymph nodes (LN1-3) subgroup (if a predictive benefit is assumed); (2) IHC4 plus clinical factors (IHC4+C), for all patient subgroups; (3) Prosigna, for the LN0 subgroup with a NPI of > 3.4 and the LN1-3 subgroup; (4) EndoPredict Clinical, for the LN1-3 subgroup only; and (5) MammaPrint, for no subgroups.

LIMITATIONS

There was only one completed RCT using a tumour profiling test in clinical practice. Except for oncotype DX in the LN0 group with a NPI score of > 3.4 (clinical intermediate risk), evidence surrounding pre- and post-test chemotherapy probabilities is subject to considerable uncertainty. There is uncertainty regarding whether or not oncotype DX and MammaPrint are predictive of chemotherapy benefit. The MammaPrint analysis uses a different data source to the other four tests. The Translational substudy of the Arimidex, Tamoxifen, Alone or in Combination (TransATAC) study (used in the economic modelling) has a number of limitations.

CONCLUSIONS

The review suggests that all the tests can provide prognostic information on the risk of relapse; results were more varied in LN+ patients than in LN0 patients. There is limited and varying evidence that oncotype DX and MammaPrint are predictive of chemotherapy benefit. Health economic analyses indicate that some tests may have a favourable cost-effectiveness profile for certain patient subgroups; all estimates are subject to uncertainty. More evidence is needed on the prediction of chemotherapy benefit, long-term impacts and changes in UK pre-/post-chemotherapy decisions.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42017059561.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Does Mode of Surgical Intervention Based on Oncotype DX Score Influence Disease Recurrence in Early Breast Cancer?

Introduction  Routine utilization of multigene assays to inform operative decision-making in early breast cancer (EBC) treatment is yet to be established. In this pilot study, we sought to establish the potential benefits of surgical intervention in EBC based on recurrence risk quantification using the Oncotype DX (ODX) assay.

Materials and Methods  Consecutive ODX tests performed over a 9-year period from October 2007 to May 2016 were evaluated. Oncotype scores were classified into high (≥31), medium (18–30), or low-risk (0–17) groups. The primary outcome was breast cancer recurrence. Subgroup analysis offered assessment of the recurrence effect of mode of surgical intervention for patient groups as defined by the oncotype score.

Results  In total 361 patients underwent ODX testing. The mean age and follow-up were 55.25 (± 10.58) years and 38.59 (± 29.1) months, respectively. The majority of patients underwent wide local excision (86.7%) with 8.9 and 4.4% patients having a mastectomy or wide local excision with completion mastectomy, respectively. Fifty-one percent of patients fell into the low risk ODX category with a further 40.2 and 8.5% deemed to be of intermediate and high risk. Five patients (1.38%) had disease recurrence. Comparative analysis of operative groups in each oncotype group revealed no difference in recurrence scores in the low- ( p  = 0.84) and high-risk groups ( p  = 0.92) with a statistically significant difference identified in the intermediate risk group ( p  = 0.002).

Conclusion  To date we have been unable to definitively identify a role for ODX in guiding surgical approach in EBC. There is, however, a need for larger studies to examine this hypothesis.

Machine learning approaches to drug response prediction: challenges and recent progress

Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.

Pseudogene-gene functional networks are prognostic of patient survival in breast cancer

BACKGROUND

Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival.

RESULTS

In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood.

CONCLUSIONS

We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions.

Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation

More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow‐up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco‐regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%‐96.86%), sensitivity (=0.35‐0.64), specificity (=0.97‐0.99), and AUC (=0.804‐0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow‐up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging.

Opportunities for improving cancer treatment using systems biology

Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.

A Pre-operative Nomogram for Prediction of Lymph Node Metastasis in Bladder Urothelial Carcinoma

The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of MLL2, and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status (p < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and MLL2 mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with p = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.

Multidisciplinary care of breast cancer patients: a scoping review of multidisciplinary styles, processes, and outcomes

Background

Clinical practice guidelines recommend a multidisciplinary approach to cancer care that brings together all relevant disciplines to discuss optimal disease management. However, the literature is characterized by heterogeneous definitions and few reviews about the processes and outcomes of multidisciplinary care. The objective of this scoping review was to identify and classify the definitions and characteristics of multidisciplinary care, as well as outcomes and interventions for patients with breast cancer.

Methods

A systematic search for quantitative and qualitative studies about multidisciplinary care for patients with breast cancer was conducted for January 2001 to December 2017 in the following electronic databases: medline, embase, PsycInfo, and cinahl. Two reviewers independently applied our eligibility criteria at level 1 (title/abstract) and level 2 (full-text) screening. Data were extracted and synthesized descriptively.

Results

The search yielded 9537 unique results, of which 191 were included in the final analysis. Two main types of multidisciplinary care were identified: conferences and clinics. Most studies focused on outcomes of multidisciplinary care that could be variously grouped at the patient, provider, and system levels. Research into processes tended to focus on processes that facilitate implementation: team-working, meeting logistics, infrastructure, quality audit, and barriers and facilitators.

Summary

Approaches to multidisciplinary care using conferences and clinics are well described. However, studies vary by design, clinical context, patient population, and study outcome. The heterogeneity of the literature, including the patient populations studied, warrants further specification of multidisciplinary care practice and systematic reviews of the processes or contexts that make the implementation and operation of multidisciplinary care effective.

A 4-gene panel predicting the survival of patients with glioblastoma

BACKGROUND

To identify independently prognostic gene panel in patients with glioblastoma (GBM).

MATERIALS AND METHODS

The Cancer Genome Atlas (TCGA)-GBM was used as a training set and a test set. GSE13041 was used as a validation set. Survival associated differentially expression genes (DEGs), derived between GBM and normal brain tissue, was obtained using univariate Cox proportional hazards regression model and then was included in a least absolute shrinkage and selection operator penalized Cox proportional hazards regression model. Thus, a 4-gene prognostic panel was developed based on the risk score for each patient in that model. The prognostic role of the 4-gene panel was validated using univariate and multivariable Cox proportional hazards regression model.

RESULTS

A total of 686 patients with GBM were included in our study; 724 DEGs was identified, 133 of which was significantly correlated with the overall survival (OS) of patients with GBM. A 4-gene panel including NMB, RTN1, GPC5, and epithelial membrane protein 3 (EMP3) was developed. Kaplan-Meier survival analysis suggested that patients in the 4-gene panel low risk group had significantly better OS than those in the 4-gene panel high risk group in the training set (hazard ratio [HR] = 0.3826; 95% confidence interval [CI]: 0.2751-0.532; P < 0.0001), test set (HR = 0.718; 95% CI: 0.5282-0.9759; P = 0.033) and the independent validation set (HR = 0.6898; 95% CI: 0.4872-0.9766; P = 0.035). Both univariate and multivariable Cox proportional hazards regression analysis suggested that the 4-gene panel was independent prognostic factor for GBM in the training set.

CONCLUSION

We developed and validated 4-gene panel that was independently correlated with the survival of patients with GBM.

The role of tumor DNA as a diagnostic tool for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) represents the most common type of head and neck cancer worldwide. However, despite advances in cancer care globally there has been little progress in HNSCC, with survival remaining static and slightly worse in laryngeal squamous cell carcinoma with 5 year survivals remaining at ∼50%. Conventional analysis of tissue through cytopathology or histopathology are the mainstay of diagnosis. Furthermore there are no useful biomarkers for disease diagnosis or surveillance. With recent technological advances, particularly in next generation sequencing, here we explore the application of tumor DNA for HNSCC diagnosis and surveillance, to improve surgical margin analysis and the potential use of molecular agents aiding in the imaging of HNSCC.

Applying new Magee equations for predicting the Oncotype Dx recurrence score

BACKGROUND

Breast cancer is one of the most prevalent cancers in women. Oncotype Dx is a multi-gene assay frequently used to predict the recurrence risk for estrogen receptor-positive early breast cancer, with values < 18 considered low risk; ≥ 18 and ≤ 30, intermediate risk; and > 30, high risk. Patients at a high risk for recurrence are more likely to benefit from chemotherapy treatment.

METHODS

In this study, clinicopathological parameters for 37 cases of early breast cancer with available Oncotype Dx results were used to estimate the recurrence score using the three new Magee equations. Correlation studies with Oncotype Dx results were performed. Applying the same cutoff points as Oncotype Dx, patients were categorized into low-, intermediate- and high-risk groups according to their estimated recurrence scores.

RESULTS

Pearson correlation coefficient (R) values between estimated and actual recurrence score were 0.73, 0.66, and 0.70 for Magee equations 1, 2 and 3, respectively. The concordance values between actual and estimated recurrence scores were 57.6%, 52.9%, and 57.6% for Magee equations 1, 2 and 3, respectively. Using standard pathologic measures and immunohistochemistry scores in these three linear Magee equations, most low and high recurrence risk cases can be predicted with a strong positive correlation coefficient, high concordance and negligible two-step discordance.

CONCLUSIONS

Magee equations are user-friendly and can be used to predict the recurrence score in early breast cancer cases.

p16 overexpression and 9p21 deletion are linked to unfavorable tumor phenotype in breast cancer

Overexpression of the p16 tumor suppressor, but also deletion of its gene locus 9p21, is linked to unfavorable tumor phenotype and poor prognosis in breast cancer. To better understand these contradictory observations, and to clarify the prognostic impact of p16 expression and 9p21 deletion, a tissue microarray (TMA) with 2,197 breast cancers was analyzed by fluorescence in-situ hybridization and immunohistochemistry (FISH) for 9p21 deletion and p16 expression. p16 immunostaining was weak in 25.6%, moderate in 7.1%, and strong in 12.7% of 1,684 evaluable cancers. Strong p16 staining was linked to advanced tumor stage (p = 0.0003), high-grade (p < 0.0001), high tumor cell proliferation (p < 0.0001), negative hormone receptor (ER/PR) status (p < 0.0001 each), and shorter overall survival (p = 0.0038). 9p21 deletion was found in 15.3% of 1,089 analyzable breast cancers, including 1.7% homozygous and 13.6% heterozygous deletions. 9p21 deletion was linked to adverse tumor features, including high-grade (p < 0.0001) and nodal positive cancers (p = 0.0063), high cell proliferation (p < 0.0001), negative hormone receptor (ER/PR) status (p ≤ 0.0006), and HER2 amplification (p = 0.0078). Patient outcome was worse in 9p21 deleted than in undeleted cancers (p = 0.0720). p16 expression was absent in cancers harboring homozygous 9p21 deletions, but no difference in p16 expression was found between cancers with (59.2% p16 positive) and without heterozygous 9p21 deletion (51.3% p16 positive, p = 0.0256). In summary, p16 expression is unrelated to partial 9p21 deletion, but both alterations are linked to aggressive breast cancer phenotype. High-level p16 expression is a strong predictor of unfavorable disease course in breast cancer.

Tumor DNA: an emerging biomarker in head and neck cancer

Head and neck cancer (HNC) includes a diverse range of malignancies arising commonly from mucosal epithelia of the upper aerodigestive tract. Head and neck squamous cell carcinoma (HNSCC), the most common form of HNC, develops in the oral cavity, pharynx, and larynx and is associated with tobacco exposure, alcohol abuse, and infection with oncogenic viruses. Despite global advances in cancer care, HNSCC often presents with advanced disease and is associated with poor 5-year survival of ~50%. Genotyping tumor tissue to guide clinical decision-making is becoming commonplace in modern oncology, but in the management of HNSCC, tissue biopsies with cytopathology or histopathology remain the mainstay for diagnosis. Furthermore, conventional biopsies are temporally and spatially limited, often providing a brief snapshot of a single region of a heterogeneous tumor. In the absence of a useful biomarker, both primary and recurrent HNSCCs are diagnosed with conventional imaging and clinical examination. As a result, many patients are diagnosed with advanced disease. Tumor DNA is an emerging biomarker in HNSCC. DNA fragments are constantly being shed from tumors and metastatic lesions, and can therefore be detected in blood and other bodily fluids. Utilizing next-generation sequencing techniques, these tumor DNA can be characterized and quantified. This can serve as a minimally invasive liquid biopsy allowing for specific tumor profiling, dynamic tumor burden monitoring, and active surveillance for disease recurrences. In HNSCC, analysis of tumor DNA has the potential to enhance tumor profiling, aid in determining patient prognosis, and guide treatment decisions.

Axillary Ultrasound Accurately Excludes Clinically Significant Lymph Node Disease in Patients With Early Stage Breast Cancer

OBJECTIVE

Assess the performance characteristics of axillary ultrasound (AUS) for accurate exclusion of clinically significant axillary lymph node (ALN) disease.

BACKGROUND

Sentinel lymph node biopsy (SLNB) is currently the standard of care for staging the axilla in patients with clinical T1-T2, N0 breast cancer. AUS is a noninvasive alternative to SLNB for staging the axilla.

METHODS

Patients were identified using a prospectively maintained database. Sensitivity, specificity, and negative predictive value (NPV) were calculated by comparing AUS findings to pathology results. Multivariate analyses were performed to identify patient and/or tumor characteristics associated with false negative (FN) AUS. A blinded review of FN and matched true negative cases was performed by 2 independent medical oncologists to compare treatment recommendations and actual treatment received. Recurrence-free survival was described using Kaplan-Meier product limit methods.

RESULTS

A total of 647 patients with clinical T1-T2, N0 breast cancer underwent AUS between January 2008 and March 2013. AUS had a sensitivity of 70%, NPV of 84%, and PPV of 56% for the detection of ALN disease. For detection of clinically significant disease (>2.0 mm), AUS had a sensitivity of 76% and NPV of 89%. FN AUS did not significantly impact adjuvant medical decision making. Patients with FN AUS had recurrence-free survival equivalent to patients with pathologic N0 disease.

CONCLUSIONS

AUS accurately excludes clinically significant ALN disease in patients with clinical T1-T2, N0 breast cancer. AUS may be an alternative to SLNB in these patients, where axillary surgery is no longer considered therapeutic, and predictors of tumor biology are increasingly used to make adjuvant therapy decisions.

Clinical use of the Oncotype DX genomic test to guide treatment decisions for patients with invasive breast cancer

Implementation of the Oncotype DX assay has led to a change in the manner in which chemotherapy is utilized in patients with early stage, estrogen receptor (ER)-positive, node-negative breast cancer; ensuring that patients at highest risk of recurrence are prescribed systemic treatment, while at the same time sparing low-risk patients potential adverse events from therapy unlikely to influence their survival. This test generates a recurrence score between 0 and 100, which correlates with probability of distant disease recurrence. Patients with low-risk recurrence scores (0–17) are unlikely to derive significant survival benefit with adjuvant chemotherapy and hormonal agents derived from using adjuvant hormonal therapy only. Conversely, adjuvant chemotherapy has been shown to significantly improve survival in patients with high-risk recurrence scores (≥31). Trials are ongoing to determine how best to manage patients with recurrence scores in the intermediate range. This review outlines the introduction and impact of Oncotype DX testing on practice; ongoing clinical trials investigating its utility; and challenging clinical scenarios where the absolute recurrence score may require careful interpretation. We also performed a bibliometric analysis of publications on the topics of breast cancer and Oncotype DX as a surrogate marker of acceptability and incorporation of the assay into the management of patients with breast cancer.

Impact of Oncotype DX breast Recurrence Score testing on adjuvant chemotherapy use in early breast cancer: Real world experience in Greater Manchester, UK

BACKGROUND

The National Institute for Health and Clinical Excellence (NICE) recommended the Oncotype DX^® Breast Recurrence Score^® (RS) assay as an option for informing adjuvant chemotherapy decisions in node-negative, oestrogen receptor (ER)+, human epidermal growth factor receptor 2 (HER2)-negative early breast cancer assessed to be at intermediate risk of recurrence based on clinicopathological factors. We evaluated the impact of RS testing on adjuvant chemotherapy decision-making in routine clinical practice in a UK Cancer Network.

METHODS

RS testing was performed in 201 females with newly diagnosed, ER+, HER2-negative, invasive breast cancer who underwent breast surgery with curative intent, were calculated to have a >3% overall survival benefit at 10 years from adjuvant chemotherapy based on PREDICT, and were considered for adjuvant chemotherapy. The impact of RS testing on adjuvant treatment decisions/associated cost was assessed.

RESULTS

In all patients, the multi-disciplinary team recommended chemotherapy but the RS result allowed 127/201 patients (63.2%) to avoid unnecessary adjuvant chemotherapy. Amongst ER+, HER2-negative, node-negative patients (eligible for Oncotype DX testing in UK guidelines), 60.3% were spared chemotherapy. In node-positive patients, the assay reduced the use of chemotherapy by 69.2%. The use of RS testing to guide treatment in these 201 patients was associated with significant cost saving (when considering the cost of RS testing for all patients plus chemotherapy and its associated cost for 74 patients).

CONCLUSIONS

Incorporating RS testing into routine clinical practice for selected node-negative and node-positive breast cancer patients significantly reduces the use of chemotherapy (p < 0.001) with its associated morbidity and costs.

Antibody-Guided In Vivo Imaging for Early Detection of Mammary Gland Tumors

BACKGROUND: Earlier detection of transformed cells using target-specific imaging techniques holds great promise. We have developed TAB 004, a monoclonal antibody highly specific to a protein sequence accessible in the tumor form of MUC1 (tMUC1). We present data assessing both the specificity and sensitivity of TAB 004 in vitro and in genetically engineered mice in vivo. METHODS: Polyoma Middle T Antigen mice were crossed to the human MUC1.Tg mice to generate MMT mice. In MMT mice, mammary gland hyperplasia is observed between 6 and 10 weeks of age that progresses to ductal carcinoma in situ by 12 to 14 weeks and adenocarcinoma by 18 to 24 weeks. Approximately 40% of these mice develop metastasis to the lung and other organs with a tumor evolution that closely mimics human breast cancer progression. Tumor progression was monitored in MMT mice (from ages 8 to 22 weeks) by in vivo imaging following retro-orbital injections of the TAB 004 conjugated to indocyanine green (TAB-ICG). At euthanasia, mammary gland tumors and normal epithelial tissues were collected for further analyses. RESULTS: In vivo imaging following TAB-ICG injection permitted significantly earlier detection of tumors compared with physical examination. Furthermore, TAB-ICG administration in MMT mice enabled the detection of lung metastases while sparing recognition of normal epithelia. CONCLUSIONS: The data highlight the specificity and the sensitivity of the TAB 004 antibody in differentiating normal versus tumor form of MUC1 and its utility as a targeted imaging agent for early detection, tumor monitoring response, as well as potential clinical use for targeted drug delivery.