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Abstract Background Recurrence and metastases are still frequent outcomes after initial tumour control in women diagnosed with breast cancer. Although therapies are selected based on tumour characteristics measured at baseline, prognostic biomarkers can identify those at risk of poor outcomes. Circulating progastrin or hPG80 was found to be associated with survival outcomes in renal and hepatocellular carcinomas and was a plausible prognostic biomarker for breast cancer. Methods Women with incident breast cancers from Calgary, Alberta, Canada enrolled in the Breast to Bone (B2B) study between 2010 to 2016 and provided blood samples prior to any treatment initiation. Plasma from these baseline samples were analysed for circulating progastrin or hPG80. Participant characteristics as well as tumour ones were evaluated for their association with hPG80 and survival outcomes (time to recurrence, recurrence – free survival, breast cancer specific survival and overall survival) in Cox proportional hazards regression models. Results The 464 participants with measurable hPG80 in this study had an average age of 57.03 years (standard deviation of 11.17 years) and were predominantly diagnosed with Stage I (52.2%) and Stage II (40.1%) disease. A total of 50 recurrences and 50 deaths were recorded as of June 2022. In Cox PH regression models adjusted for chemotherapy, radiation therapy, cancer stage and age at diagnosis, log hPG80 (pmol/L) significantly increased the risks for recurrence (Hazard Ratio (HR) = 1.330, 95% Confidence Interval (CI) = (0.995 – 1.777, p = 0.054)), recurrence-free survival (HR = 1.399, 95% CI = (1.106 – 1.770), p = 0.005) and overall survival (HR = 1.385, 95% CI = (1.046 – 1.834), = 0.023) but not for breast cancer specific survival (HR = 1.015, 95% CI = (0.684 – 1.505), p = 0.942). Conclusions hPG80 levels measured at diagnosis were significantly associated with the risk of recurrence or death from any cause in women with breast cancer. Since the recurrence rates of breast cancer are still relatively high amongst women diagnosed at an early stage, identifying women at high risk of recurrence at their time of diagnosis is important. hPG80 is a promising new prognostic biomarker that could improve the identification of women at higher risk of poor outcomes.

Clinical management of papillary thyroid cancer depends on estimations of prognosis. Standard care, which relies on prognostication based on clinicopathologic features, is inaccurate. We applied a machine learning algorithm (HighLifeR) to 502 cases annotated by The Cancer Genome Atlas Project to derive an accurate molecular prognostic classifier. Unsupervised analysis of the 82 genes that were most closely associated with recurrence after surgery enabled the identification of three unique molecular subtypes. One subtype had a high recurrence rate, an immunosuppressed microenvironment, and enrichment of the EZH2-HOTAIR pathway. Two other unique molecular subtypes with a lower rate of recurrence were identified, including one subtype with a paucity of BRAFV600E mutations and a high rate of RAS mutations. The genomic risk classifier, in addition to tumor size and lymph node status, enabled effective prognostication that outperformed the American Thyroid Association clinical risk stratification. The genomic classifier we derived can potentially be applied preoperatively to direct clinical decision-making. Distinct biological features of molecular subtypes also have implications regarding sensitivity to radioactive iodine, EZH2 inhibitors, and immune checkpoint inhibitors.

Abstract Background Low muscle radiodensity on computed tomography (CT) scan, indicative of myosteatosis, is commonly observed in cancer patients and can be associated with poor prognosis. Radiodensity is typically measured at the level of the third lumbar vertebra (L3). It is unknown whether features at L3 reflect a systemic state affecting peripheral muscle groups, whether images used at different levels can be used as a surrogate if L3 images are unavailable, and how radiodensity varies between cancer types. Methods Core and extremity muscle radiodensities were measured in whole body CT images from melanoma patients to evaluate the anatomical distribution of muscle radiodensity measurements. Core muscle radiodensity was measured in 891 patients with different cancer types to study malignancy‐dependent patterns in muscle radiodensity. Results Low muscle radiodensity at L3 (

Cancer screening is an important component of a cancer control strategy. Indigenous people in Canada have higher incidence rates for many types of cancer, including those that can be detected early or prevented through organized screening programs. Increased participation and retention in cancer screening is critical to improved population health outcomes amongst Indigenous people. This rapid review evaluates cancer screening interventions published in the last six years. Included studies demonstrated increased participation in breast, colorectal, or cervical cancer screening programs in Indigenous populations or showed promise of increased participation based on the factors that influence people’s screening practices, such as knowledge, attitude, or intent to screen. The Preferred Reporting Items for Systematic Reviews guided the search strategy. The review identified 85 articles with 12 meeting the specified criteria: seven studies reported an increase in cancer screening participation and five studies reported improved knowledge, attitude, or intent to screen. The use of multiple culturally appropriate strategies in co-designed studies were the most effective. This review will be used to inform First Nations (FN) populations and Screening Programs in Alberta of potential strategies to address disparities identified through a recent data analysis comparing cancer screening and outcomes between FN and non-FN people.

Advanced analytical methods play an important role in quantifying serum disease biomarkers. The problem of separating thousands of proteins can be reduced by analyzing for a ‘sub-proteome’, such as the ‘metalloproteome’, defined as all proteins that contain bound metals. We employed size exclusion chromatography (SEC) coupled to an inductively coupled plasma atomic emission spectrometer (ICP-AES) to analyze plasma from multiple sclerosis (MS) participants (n = 21), acute ischemic stroke (AIS) participants (n = 17) and healthy controls (n = 21) for Fe, Cu and Zn-metalloproteins. Using ANOVA analysis to compare the mean peak areas among the groups revealed no statistically significant differences for ceruloplasmin (p = 0.31), α2macroglobulin (p = 0.51) and transferrin (p = 0.31). However, a statistically significant difference was observed for the haptoglobin-hemoglobin (Hp-Hb) complex (p = 0.04), being driven by the difference between the control group and AIS (p = 0.012), but not with the MS group (p = 0.13), based on Dunnes test. A linear regression model for Hp-Hb complex with the groups now adjusted for age found no statistically significant differences between the groups (p = 0.95), but was suggestive for age (p = 0.057). To measure the strength of association between the Hp-Hb complex and age without possible modifications due to disease, we calculated the Spearman rank correlation in the healthy controls. The latter revealed a positive association (r = 0.39, 95% Confidence Interval = (-0.05, 0.83), which suggests that either the removal of Hp-Hb complexes from the blood circulation slows with age or that the release of Hb from red blood cells increases with age. We also observed that the Fe-peak corresponding to the Hp-Hb complex eluted ~100 s later in ~14% of all study samples, which was not correlated with age or disease diagnosis, but is consistent with the presence of the smaller Hp (1–1) isoform in 15% of the population.

FamEvent is a comprehensive R package for simulating and modeling age-at-disease onset in families carrying a rare gene mutation. The package can simulate complex family data for variable time-to-event outcomes under three common family study designs (population, high-risk clinic and multi-stage) with various levels of missing genetic information among family members. Residual familial correlation can be induced through the inclusion of a frailty term or a second gene. Disease-gene carrier probabilities are evaluated assuming Mendelian transmission or empirically from the data. When genetic information on the disease gene is missing, an expectation-maximization algorithm is employed to calculate the carrier probabilities. Penetrance model functions with ascertainment correction adapted to the sampling design provide age-specific cumulative disease risks by sex, mutation status, and other covariates for simulated data as well as real data analysis. Robust standard errors and 95% confidence intervals are available for these estimates. Plots of pedigrees and penetrance functions based on the fitted model provide graphical displays to evaluate and summarize the models.

IntroductionEvidence suggests that the expression of certain cytokine receptors increases with lung cancer evolution. Overexpression of the cytokine receptor CXCR4 is associated with poor outcomes in stage IV non-small cell lung cancer (NSCLC), with shorter survival in females with high CXCR4 expression. This study quantifies CXCR4 expression in early stage disease and evaluates its association with gender-specific recurrence-free (RFS) and overall survival (OS) in resected stage I-III NSCLC patients.MethodsPatient characteristics and clinical outcomes were obtained from the Glans-Look Lung Cancer (G-LLC) database for early stage NSCLC patients diagnosed between 2003-2006 at the Tom Baker Cancer Centre (TBCC). CXCR4 expression was quantified on tissue microarrays (TMA). Median RFS and OS were evaluated by gender using Kaplan-Meier analyses. CXCR4 expression and outcome data were analyzed using Cox proportional hazards (PH) and multi-state models (MSM).Results176 stage I-III NSCLC patients were identified. CXCR4 expression was lower in early stage NSCLC patients, with a mean CXCR4 expression of 1729 (SD 1083) compared to 2640 (SD 1541) in stage IV patients. On Kaplan-Meier, median RFS by gender was similar (male 52.8 months vs. female 54.5 months) as was median OS (male 80.9 months vs. female 89.0 months), and there was no significant difference in RFS (p = 0.60) or OS (p = 0.30) by gender and CXCR4 groups over follow-up. By multivariable analysis, CXCR4 expression was not prognostic for RFS (Hazard Ratio (HR) = 1.00, p = 0.73) or OS (HR = 1.00, p = 0.44), and no gender difference was observed.ConclusionsCXCR4 expression increases with stage progression in NSCLC but is not prognostic in early stage NSCLC patients of either gender. Mechanisms by which CXCR4 expression increases during lung carcinogenesis warrant further exploration and testing in clinical trials.

Abstract Background Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based selection, a new secondary selection method that is implemented in the R package BioMark. Two of these methods were evaluated using the more well-known false discovery rate (FDR) as well. Results Simulation studies varied factors relevant to biological data studies, with results based on the median values of 200 partial area under the receiver operating characteristic curve. There was no single top performing method across all factor settings, but the student t test based on stability selection or with FDR adjustment and the variable importance in projection (VIP) scores from partial least squares regression models obtained using a stability-based approach tended to perform well in most settings. Similar results were found with a real spiked-in metabolomics dataset. Group sample size, group effect size, number of significant variables and correlation structure were the most important factors whereas the percentage of significant variables was the least important. Conclusions Researchers can improve prediction scores for their study data by choosing VIP scores based on stability variable selection over the other approaches when the number of variables is small to modest and by increasing the number of samples even moderately. When the number of variables is high and there is block correlation amongst the significant variables (i.e., true biomarkers), the FDR-adjusted student t test performed best. The R package BioMark is an easy-to-use open-source program for variable selection that had excellent performance characteristics for the purposes of this study.

We describe a biomarker-based approach to delivering chemotherapy that entails monitoring treatment changes in the circulating metabolome that reflect efficacy. In-vitro, multiple tumor cell lines were exposed to numerous chemotherapeutics. Supernatants were collected at baseline and 72 hours post treatment. MTT assays were used to quantify growth inhibition. Clinical samples were derived from a phase II clinical trial of second-line axitinib in patients with advanced hepatocellular carcinoma. Sera were collected at baseline and 2-4 weeks after treatment initiation. Response to therapy was estimated by CT scan at 8 weeks. Samples were analyzed by gas chromatography-mass spectrometry to identify metabolomic changes associated with response. In vitro, we found drug-specific and generalizable patterns of change in the extracellular metabolome accompany growth inhibition. A cell death signature was also identified. This approach was also applied to clinical samples. While the in vitro signatures were detectable in vivo, a more robust signal was identified clinically that appeared within 4 weeks of administering drug that distinguished individuals with a treatment response. These changes were extinguished as tumor growth resumed. Serial monitoring of the metabolome during chemotherapy is a means to follow treatment efficacy and emergence of resistance, informing the oncologist whether to modify treatment.

In glioblastoma multiforme (GBM), brain-tumor-initiating cells (BTICs) with cancer stem cell characteristics have been identified and proposed as primordial cells responsible for disease initiation, recurrence, and therapeutic resistance. However, the extent to which individual, patient-derived BTIC lines reflect the heterogeneity of GBM remains poorly understood. Here we applied a stem cell biology approach and compared self-renewal, marker expression, label retention, and asymmetric cell division in 20 BTIC lines. Through cluster analysis, we identified two subgroups of BTIC lines with distinct precursor states, stem- or progenitor-like, predictive of survival after xenograft. Moreover, stem and progenitor transcriptomic signatures were identified, which showed a strong association with the proneural and mesenchymal subtypes, respectively, in the TCGA cohort. This study proposes a different framework for the study and use of BTIC lines and provides precursor biology insights into GBM.

Reovirus is a ubiquitous RNA virus that exploits aberrant signaling pathways for its replication. The oncolytic potential of reovirus against numerous cancers under pre-clinical/clinical conditions has been documented by us and others. Despite its proven clinical activity, the underlying mechanisms of reovirus oncolysis is still not well elucidated. If reovirus therapy is to be optimized for cancer, including breast cancer patients, it is imperative to understand the mechanisms of reovirus oncolysis, especially in treatment of resistant tumour.In the present study global gene expression profiling was utilized as a preliminary roadmap to tease-out pivotal molecules involved in reovirus induced apoptosis in breast cancer. Reovirus treated HTB133 and MCF7 breast cancer cells revealed transcriptional alteration of a defined subset of apoptotic genes and members of the nuclear factor-kappa B (NF-kB) family and p53 upregulated modulator of apoptosis (PUMA) were prominent. Since NF-kB can paradoxically suppress or promote apoptosis in cancer, the significance of NF-kB in reovirus oncolysis of breast cancer was investigated. Real time PCR analysis indicated a 2.9-4.3 fold increase in NF-kB p65 message levels following reovirus infection of MCF7 and HTB133, respectively. Nuclear translocation of NF-kB p65 protein was also dramatically augmented post reovirus treatment and correlated with enhanced DNA binding. Pharmacologic inhibition of NF-kB lead to oncolytic protection and significant down regulation of PUMA message levels. PUMA down regulation using siRNA suppressed reovirus oncolysis via significantly repressed apoptosis in p53 mutant HTB133 cells.This study demonstrates for the first time that a prominent pathway of reovirus oncolysis of breast cancer is mediated through NF-kB and that PUMA upregulation is dependent on NF-kB activation. These findings represent potential therapeutic indicators of reovirus treatment in future clinical trials.

Treatment and clinical-outcomes were described in a sub-cohort of non-small-cell lung cancer (NSCLC) patients with disease-progression (PD) after epidermal growth factor tyrosine kinase inhibitors (EGFR-TKIs) treatment.We retrospectively analyzed a single-institutional EGFR mutation positive (EGFRmut+) NSCLC cohort for post-TKI-PD management, and assessed overall survival (OS) and post-progression survival (PPS). All de-novo (first lung-cancer occurrence) stage IIIA-IV patients, as well as de-novo stage IV subset was analyzed. Multi-state modeling (MSM) and a Cox PH regression model with propensity score weights adjusted for clinicopathological variables between: diagnosis and PD and PD to death.123 stage IIIA-IV patients were identified with 104 meeting RECIST-1.1-PD criteria. This RECIST-1.1-PD criteria subset included females (64.6%), Asians (39.4%), never/non-smokers (55.8%), and exon 19 deletion carriers (44.2%). Commonest treatment beyond initial-PD was continuing TKI alone (46/104), with another 21 patients continuing TKI plus additional systemic therapy. The median OS for patients who continued TKI treatment at initial-PD was 21.1 months versus 15.6 months for patients who discontinued TKI, p = 0.006. Via MSM analysis, continuing TKI at initial-PD followed by other systemic therapy was associated with an 83% reduced death risk, adjusted HR: 0.17 (95% CI: 0.07, 0.39). In the Cox PH model, ever-smokers with an exon 19 deletion had increased risk of death after PD (adjusted HR: 3.19, 95% CI: 1.54, 6.58), as did exon 21 mutation carriers, (adjusted HR: 2.10, 95% CI: 1.10, 4.00) and females (adjusted HR: 3.19, 95% CI: 1.54, 6.58).Subsequent systemic therapy after continuing TKI at initial-PD reduced the risk of death. Additionally, our data suggest that positive smoking history increases death risk for some EGFR mutation types and females.

Genome-wide association studies (GWASs) have identified low-penetrance common variants (i.e., single nucleotide polymorphisms, SNPs) associated with breast cancer susceptibility. Although GWASs are primarily focused on single-locus effects, gene-gene interactions (i.e., epistasis) are also assumed to contribute to the genetic risks for complex diseases including breast cancer. While it has been hypothesized that moderately ranked (P value based) weak single-locus effects in GWASs could potentially harbor valuable information for evaluating epistasis, we lack systematic efforts to investigate SNPs showing consistent associations with weak statistical significance across independent discovery and replication stages. The objectives of this study were i) to select SNPs showing single-locus effects with weak statistical significance for breast cancer in a GWAS and/or candidate-gene studies; ii) to replicate these SNPs in an independent set of breast cancer cases and controls; and iii) to explore their potential SNP-SNP interactions contributing to breast cancer susceptibility. A total of 17 SNPs related to DNA repair, modification and metabolism pathway genes were selected since these pathways offer a priori knowledge for potential epistatic interactions and an overall role in breast carcinogenesis. The study design included predominantly Caucasian women (2,795 cases and 4,505 controls) from Alberta, Canada. We observed two two-way SNP-SNP interactions (APEX1-rs1130409 and RPAP1-rs2297381; MLH1-rs1799977 and MDM2-rs769412) in logistic regression that conferred elevated risks for breast cancer (P(interaction)