Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients.

BACKGROUND: CanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast. METHODS: All potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively. RESULTS: CanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested. CONCLUSIONS: The extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust.

Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

CanAssist-Breast (CAB) is an immunohistochemistry (IHC)-based prognostic test for early-stage Hormone Receptor (HR+)-positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N-Cadherin, and Pan-Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low- or high-risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis-Free Survival (DMFS) and recurrence rates using Kaplan-Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high-risk vs low-risk patients. The results showed that Distant Metastasis-Free Survival (DMFS) was significantly different (P-0.002) between low- (DMFS: 95%) and high-risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low-risk DMFS: 95%, high-risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node-positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki-67 and IHC4 score-based stratification. CAB stratified a higher percentage of the cohort (82%) as low-risk than IHC4 score (41.6%) and could re-stratify >74% of high Ki-67 and IHC4 score intermediate-risk zone patients into low-risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high- or low-risk categorization.

Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.

Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5 years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression-based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning-based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a "CAB risk score" that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5 years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (P = .0002). In multivariate analysis, CAB risk score was the most significant independent predictor of distant recurrence with a hazard ratio of 4.3 (P = .0003). CanAssist-Breast is a precise and unique machine learning-based proteomic risk-classifier that can assist in risk stratification of patients with early-stage HR+ breast cancer.

A Circulating microRNA Signature Predicts Age-Based Development of Lymphoma.

Extensive epidemiological data have demonstrated an exponential rise in the incidence of non-Hodgkin lymphoma (NHL) that is associated with increasing age. The molecular etiology of this remains largely unknown, which impacts the effectiveness of treatment for patients. We proposed that age-dependent circulating microRNA (miRNA) signatures in the host influence diffuse large B cell lymphoma (DLBCL) development. Our objective was to examine tumor development in an age-based DLBCL system using an inventive systems biology approach. We harnessed a novel murine model of spontaneous DLBCL initiation (Smurf2-deficient) at two age groups: 3 and 15 months old. All Smurf2-deficient mice develop visible DLBCL tumor starting at 15 months of age. Total miRNA was isolated from serum, bone marrow and spleen and were collected for all age groups for Smurf2-deficient mice and age-matched wild-type C57BL/6 mice. Using systems biology techniques, we identified a list of 10 circulating miRNAs being regulated in both the spleen and bone marrow that were present in DLBCL forming mice starting at 3 months of age that were not present in the control mice. Furthermore, this miRNA signature was found to occur circulating in the blood and it strongly impacted JUN and MYC oncogenic signaling. In addition, quantification of the miRNA signature was performed via Droplet Digital PCR technology. It was discovered that a key miRNA signature circulates throughout a host prior to the formation of a tumor starting at 3 months old, which becomes further modulated by age and yielded calculation of a 'carcinogenic risk score'. This novel age-based circulating miRNA signature may potentially be leveraged as a DLBCL risk profile at a young age to predict future lymphoma development or disease progression as well as for potential innovative miRNA-based targeted therapeutic strategies in lymphoma.

Smurf2 regulates hematopoietic stem cell self-renewal and aging.

The age-dependent decline in the self-renewal capacity of stem cells plays a critical role in aging, but the precise mechanisms underlying this decline are not well understood. By limiting proliferative capacity, senescence is thought to play an important role in age-dependent decline of stem cell self-renewal, although direct evidence supporting this hypothesis is largely lacking. We have previously identified the E3 ubiquitin ligase Smurf2 as a critical regulator of senescence. In this study, we found that mice deficient in Smurf2 had an expanded hematopoietic stem cell (HSC) compartment in bone marrow under normal homeostatic conditions, and this expansion was associated with enhanced proliferation and reduced quiescence of HSCs. Surprisingly, increased cycling and reduced quiescence of HSCs in Smurf2-deficient mice did not lead to premature exhaustion of stem cells. Instead, HSCs in aged Smurf2-deficient mice had a significantly better repopulating capacity than aged wild-type HSCs, suggesting that decline in HSC function with age is Smurf2 dependent. Furthermore, Smurf2-deficient HSCs exhibited elevated long-term self-renewal capacity and diminished exhaustion in serial transplantation. As we found that the expression of Smurf2 was increased with age and in response to regenerative stress during serial transplantation, our findings suggest that Smurf2 plays an important role in regulating HSC self-renewal and aging.

Smurf2 suppresses B-cell proliferation and lymphomagenesis by mediating ubiquitination and degradation of YY1.

About half of patients with diffuse large B-cell lymphoma (DLBCL) do not respond to or relapse soon after the standard chemotherapy, indicating a critical need to better understand the specific pathways perturbed in DLBCL for developing effective therapeutic approaches. Mice deficient in the E3 ubiquitin ligase Smurf2 spontaneously develop B-cell lymphomas that resemble human DLBCL with molecular features of germinal centre or post-germinal centre B cells. Here we show that Smurf2 mediates ubiquitination and degradation of YY1, a key germinal centre transcription factor. Smurf2 deficiency enhances YY1-mediated transactivation of c-Myc and B-cell proliferation. Furthermore, Smurf2 expression is significantly decreased in primary human DLBCL samples, and low levels of Smurf2 expression correlate with inferior survival in DLBCL patients. The Smurf2-YY1-c-Myc regulatory axis represents a novel pathway perturbed in DLBCL that suppresses B-cell proliferation and lymphomagenesis, suggesting pharmaceutical targeting of Smurf2 as a new therapeutic paradigm for DLBCL.

Serial transplantation of bone marrow to test self-renewal capacity of hematopoietic stem cells in vivo.

Hematopoietic stem cells (HSCs) have the ability to self-renew and replenish the blood and immune system for the life span of an individual. An age-associated decline in HSC function is responsible for the decreased immune function and increased incidence of myeloid diseases and anemia in the elderly. The changes in HSC function are thought to occur as the result of an intrinsic defect in the self-renewal potential of HSCs as they age. In this chapter, we describe a bone marrow serial transplantation protocol designed to test the self-renewal capacity of HSCs in vivo.

Smurf2 regulates the senescence response and suppresses tumorigenesis in mice.

The E3 ubiquitin ligase Smurf2 mediates ubiquitination and degradation of several protein targets involved in tumorigenesis and induces senescence in human cells. However, the functional role of Smurf2 in tumorigenesis has not been fully evaluated. In this study, we generated a mouse model of Smurf2 deficiency to characterize the function of this E3 ligase in tumorigenesis. Smurf2 deficiency attenuated p16 expression and impaired the senescence response of primary mouse embryonic fibroblasts. In support of a functional role in controlling cancer, Smurf2 deficiency increased the susceptibility of mice to spontaneous tumorigenesis, most notably B-cell lymphoma. At a premalignant stage of tumorigenesis, we documented a defective senescence response in the spleens of Smurf2-deficient mice, consistent with a mechanistic link between impaired senescence regulation and increased tumorigenesis. Taken together, our findings offer the genetic evidence of an important tumor suppressor function for Smurf2.

Regulation of senescence in cancer and aging.

Senescence is regarded as a physiological response of cells to stress, including telomere dysfunction, aberrant oncogenic activation, DNA damage, and oxidative stress. This stress response has an antagonistically pleiotropic effect to organisms: beneficial as a tumor suppressor, but detrimental by contributing to aging. The emergence of senescence as an effective tumor suppression mechanism is highlighted by recent demonstration that senescence prevents proliferation of cells at risk of neoplastic transformation. Consequently, induction of senescence is recognized as a potential treatment of cancer. Substantial evidence also suggests that senescence plays an important role in aging, particularly in aging of stem cells. In this paper, we will discuss the molecular regulation of senescence its role in cancer and aging. The potential utility of senescence in cancer therapeutics will also be discussed.