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Background HER2-low breast cancer (BC) is currently an area of active interest. This study evaluated the impact of low expression of HER2 on survival outcomes in HER2-negative non-metastatic breast cancer (BC). Methods Patients with HER2-negative non-metastatic BC from 6 centres within the Asian Breast Cancer Cooperative Group (ABCCG) ( n = 28,280) were analysed. HER2-low was defined as immunohistochemistry (IHC) 1+ or 2+ and in situ hybridization non-amplified (ISH−) and HER2-zero as IHC 0. Relapse-free survival (RFS) and overall survival (OS) by hormone receptor status and HER2 IHC 0, 1+ and 2+ ISH− status were the main outcomes. A combined TCGA-BRCA and METABRIC cohort ( n = 1967) was also analysed to explore the association between HER2 expression, ERBB2 copy number variation (CNV) status and RFS. Results ABCCG cohort median follow-up was 6.6 years; there were 12,260 (43.4%) HER2-low BC and 16,020 (56.6%) HER2-zero BC. The outcomes were better in HER2-low BC than in HER2-zero BC (RFS: centre-adjusted hazard ratio (HR) 0.88, 95% CI 0.82–0.93, P < 0.001; OS: centre-adjusted HR 0.82, 95% CI 0.76–0.89, P < 0.001). On multivariable analysis, HER2-low status was prognostic (RFS: HR 0.90, 95% CI 0.85–0.96, P = 0.002; OS: HR 0.86, 95% CI 0.79–0.93, P < 0.001). These differences remained significant in hormone receptor-positive tumours and for OS in hormone receptor-negative tumours. Superior outcomes were observed for HER2 IHC1+ BC versus HER2-zero BC (RFS: HR 0.89, 95% CI 0.83–0.96, P = 0.001; OS: HR 0.85, 95% CI 0.78–0.93, P = 0.001). No significant differences were seen between HER2 IHC2+ ISH− and HER2-zero BCs. In the TCGA-BRCA and METABRIC cohorts, ERBB2 CNV status was an independent RFS prognostic factor (neutral versus non-neutral HR 0.71, 95% CI 0.59–0.86, P < 0.001); no differences in RFS by ERBB2 mRNA expression levels were found. Conclusions HER2-low BC had a superior prognosis compared to HER2-zero BC in the non-metastatic setting, though absolute differences were modest and driven by HER2 IHC 1+ BC. ERBB2 CNV merits further investigation in HER2-negative BC.

Breast cancer survival has improved with significant progress in treatment and disease management. However, compliance with treatment varies. Treatment guidelines for older patients are unclear. We aim to identify predictors of noncompliance with recommended therapy in a large breast cancer population and assess the impact of noncompliance on survival. Our study included 19,241 non-metastatic female breast cancer patients, of whom 3,158 (16%) died within 10 years post-diagnosis (median survival = 5.8 years). We studied the association between treatment noncompliance and factors with logistic regression, and the impact of treatment noncompliance on survival with a flexible parametric survival model framework. The highest proportion of noncompliance was observed for chemotherapy (18%). Predictors of noncompliance with chemotherapy, radiotherapy and endocrine therapy included age, tumor size, nodal involvement and subtype (except radiotherapy). Factors associated with not receiving surgery included age and subtype. Treatment noncompliance was associated with worse overall survival for surgery (HR: 2.26 [1.80–2.83]), chemotherapy (1.25 [1.11–1.41]), radiotherapy (2.28 [1.94–2.69]) and endocrine therapy (1.70 [1.41–2.04]). Worse survival was similarly observed in older patients for whom guidelines generally do not apply. Our results highlight the importance of following appropriate treatment as recommended by current guidelines. Older patients may benefit from similar recommendations.

Background Neoadjuvant chemotherapy (NAC) plays an important role in the management of locally advanced breast cancer. It allows for downstaging of tumors, potentially allowing for breast conservation. NAC also allows for in-vivo testing of the tumors’ response to chemotherapy and provides important prognostic information. There are currently no clearly defined clinical models that incorporate imaging with clinical data to predict response to NAC. Thus, the aim of this work is to develop a predictive AI model based on routine CT imaging and clinical parameters to predict response to NAC. Methods The CT scans of 324 patients with NAC from multiple centers in Singapore were used in this study. Four different radiomics models were built for predicting pathological complete response (pCR): first two were based on textural features extracted from peri-tumoral and tumoral regions, the third model based on novel space-resolved radiomics which extract feature maps using voxel-based radiomics and the fourth model based on deep learning (DL). Clinical parameters were included to build a final prognostic model. Results The best performing models were based on space-resolved and DL approaches. Space-resolved radiomics improves the clinical AUCs of pCR prediction from 0.743 (0.650 to 0.831) to 0.775 (0.685 to 0.860) and our DL model improved it from 0.743 (0.650 to 0.831) to 0.772 (0.685 to 0.853). The tumoral radiomics model performs the worst with no improvement of the AUC from the clinical model. The peri-tumoral combined model gives moderate performance with an AUC of 0.765 (0.671 to 0.855). Conclusions Radiomics features extracted from diagnostic CT augment the predictive ability of pCR when combined with clinical features. The novel space-resolved radiomics and DL radiomics approaches outperformed conventional radiomics techniques.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor with high relapse rate. In this study, we aimed to determine if dose-escalated (DE) radiotherapy improved tumor control and survival in GBM patients. We conducted a retrospective analysis of 49 and 23 newly-diagnosed histology-proven GBM patients, treated with DE radiotherapy delivered in 70 Gy (2.33 Gy per fraction) and conventional doses (60 Gy), respectively, between 2007 and 2013. Clinical target volumes for 70 and 60 Gy were defined by 0.5 and 2.0 cm expansion of magnetic resonance imaging T1-gadolinium-enhanced tumor/surgical cavity, respectively. Bilateral subventricular zones (SVZ) were contoured on a co-registered pre-treatment magnetic resonance imaging and planning computed tomography dataset as a 5 mm wide structure along the lateral margins of the lateral ventricles. Survival outcomes of both cohorts were compared using log-rank test. Radiation dose to SVZ in the DE cohort was evaluated. Median follow-up was 13.6 and 15.1 months for the DE- and conventionally-treated cohorts, respectively. Median overall survival (OS) of patients who received DE radiotherapy was 15.2 months (95% confidence interval [CI] =11.0-18.6), while median OS of the latter cohort was 18.4 months (95% CI =12.5-31.4, P=0.253). Univariate analyses of clinical and dosimetric parameters among the DE cohort demonstrated a trend of longer progression-free survival, but not OS, with incremental radiation doses to the ipsilateral SVZ (hazard ratio [HR] =0.95, 95% CI =0.90-1.00, P=0.052) and proportion of ipsilateral SVZ receiving 50 Gy (HR =0.98, 95% CI =0.97-1.00, P=0.017). DE radiotherapy did not improve survival in patients with GBM. Incorporation of ipsilateral SVZ as a radiotherapy target volume for patients with GBM requires prospective validation.

Incidence of breast cancer is rising rapidly in Asia. Some breast cancer risk factors are modifiable. We examined the impact of known breast cancer risk factors, including body mass index (BMI), reproductive and hormonal risk factors, and breast density on the incidence of breast cancer, in Singapore. The study population was a population-based prospective trial of screening mammography - Singapore Breast Cancer Screening Project. Population attributable risk and absolute risks of breast cancer due to various risk factors were calculated. Among 28,130 women, 474 women (1.7%) developed breast cancer. The population attributable risk was highest for ethnicity (49.4%) and lowest for family history of breast cancer (3.8%). The proportion of breast cancers that is attributable to modifiable risk factor BMI was 16.2%. The proportion of breast cancers that is attributable to reproductive risk factors were low; 9.2% for age at menarche and 4.2% for number of live births. Up to 45.9% of all breast cancers could be avoided if all women had breast density <12% and BMI <25 kg/m 2 . Notably, sixty percent of women with the lowest risk based on non-modifiable risk factors will never reach the risk level recommended for mammography screening. A combination of easily assessable breast cancer risk factors can help to identify women at high risk of developing breast cancer for targeted screening. A large number of high-risk women could benefit from risk-reduction and risk stratification strategies.

ObjectiveTo examine the association between socioeconomic status (SES), financial subsidies and awareness-related factors such as age, cancer stage and family history, and the uptake of cancer genetic testing, with a focus on equitable access to care.DesignRetrospective cohort study.SettingTertiary care cancer genetics service in Singapore.ParticipantsThe study population included 2687 individuals of all ages, genders and ethnicities who attended pretest counselling between 2014 and 2020 and were eligible for genetic testing for hereditary cancer syndromes.Primary and secondary outcome measuresThe primary outcome was the uptake of genetic testing. The main explanatory variables were SES (proxied by Housing Index), subsidy status, age, cancer stage and family history. Analyses examined whether associations varied across SES and age subgroups.ResultsReceipt of financial subsidies was strongly associated with testing uptake (adjusted OR 9.15, 95% CI 2.68 to 31.20). Uptake exceeded 90% among subsidised individuals across all socioeconomic strata, compared with 56–68% among non-subsidised individuals, with the largest gains in the lowest SES group (43 vs 28 percentage points (pp) in the highest). The level of subsidy was not associated with uptake. Younger patients (18–39 years) had higher uptake than those aged 60+ (66% vs 57%); patients with advanced cancer (stage IV) had the highest uptake (82% vs 57–66% in earlier stages); and family history was associated with increased uptake, strongest for having a child with cancer (+28 pp). Interaction analysis suggested that the additive effects of subsidies were greatest in lower SES groups and in older adults.ConclusionsFinancial subsidies were strongly associated with higher genetic testing uptake. Awareness indicators like age, cancer stage and family history were associated with higher uptake. The association between subsidies and uptake varied by SES and age, suggesting that subsidies may help reduce disparities and improve equitable access to genetic testing services.

This article aims to provide a detailed description of the Singapore Breast Cancer Cohort (SGBCC), an ongoing multi-ethnic cohort established with the overarching goal to identify genetic markers for breast cancer risk, prognosis and treatment response, as well as to understand the ethnic differences in disease risk and outcome in an Asian setting. The cohort comprises of breast cancer patients aged 21 years and above from six public hospitals which diagnose and treat nearly 76% breast cancer cases in Singapore. Self-reported data on sociodemographic and lifestyle, reproductive risk factors, medical history and family history of breast or ovarian cancer is collected using a structured questionnaire. Clinical data on tumour characteristics, and treatment modalities are obtained through medical record. Bio-specimens (blood or saliva) is collected at recruitment. Follow-up on survival information is done through routine linkage with the Registry of Births and Deaths. As of 31 December 2016, 7,768 subjects have been recruited to the study with 76% subjects contributed bio-specimens. The SGBCC provides a valuable platform which offers a unique, large and rich resource for new research ideas on breast cancer related phenotypic risk factors and genetic markers.

Background Family history, and genetic and non-genetic risk factors can stratify women according to their individual risk of developing breast cancer. The extent of overlap between these risk predictors is not clear. Methods In this case-only analysis involving 7600 Asian breast cancer patients diagnosed between age 30 and 75 years, we examined identification of high-risk patients based on positive family history, the Gail model 5-year absolute risk [5yAR] above 1.3%, breast cancer predisposition genes (protein-truncating variants [PTV] in ATM , BRCA1 , BRCA2 , CHEK2 , PALB2 , BARD1 , RAD51C , RAD51D , or TP53 ), and polygenic risk score (PRS) 5yAR above 1.3%. Results Correlation between 5yAR (at age of diagnosis) predicted by PRS and the Gail model was low ( r =0.27). Fifty-three percent of breast cancer patients ( n =4041) were considered high risk by one or more classification criteria. Positive family history, PTV carriership, PRS, or the Gail model identified 1247 (16%), 385 (5%), 2774 (36%), and 1592 (21%) patients who were considered at high risk, respectively. In a subset of 3227 women aged below 50 years, the four models studied identified 470 (15%), 213 (7%), 769 (24%), and 325 (10%) unique patients who were considered at high risk, respectively. For younger women, PRS and PTVs together identified 745 (59% of 1276) high-risk individuals who were not identified by the Gail model or family history. Conclusions Family history and genetic and non-genetic risk stratification tools have the potential to complement one another to identify women at high risk.

Background: Patients with bilateral breast cancers are uncommon and are associated with a poorer prognosis. While metastatic staging guidelines in patients with unilateral cancer were established, the indication of metastatic staging in patients with bilateral breast cancers is unclear. We aimed to determine which patients with synchronous bilateral breast cancers require metastatic staging at diagnosis. This is the first such reported study, to the best of our knowledge. Methods: A retrospective review of newly diagnosed synchronous bilateral invasive breast cancer patients at our institution was performed. We excluded patients with malignant phyllodes or no metastatic staging. Patients’ demographics and pathological and staging results were analysed to determine the group of bilateral breast cancer patients who required metastatic staging. Results: A total of 92 patients with synchronous bilateral invasive cancers were included. The mean age was 58 years old, and 64.1% had bilateral invasive ductal carcinoma. 23.9% had systemic metastasis. Nodal status was statistically significant for systemic metastasis on staging (p = 0.0081), with only three patients (3.3%) having negative nodal status and positive metastatic staging. These three patients, however, showed symptoms of distant metastasis. 92.3% of patients with negative nodes also had negative metastatic staging. Using negative nodal status as a guide avoided metastatic staging in 40.4% of all patients. Conclusions: Negative nodal status was the most predictive factor for no systemic metastasis on staging in patients with synchronous bilateral invasive breast cancers. Hence, metastatic staging could be reserved for patients with symptoms of systemic metastasis and/or metastatic nodes. This finding could be validated in larger studies.

Background Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. Methods Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: “Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior.” The model was trained with 75% of the dataset ( n  = 39,416 outpatient encounters) and validated on a 25% hold-out dataset ( n  = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. Results In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856–0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. Conclusion Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care.