| 1. |
- Strandberg, Rickard, et al.
(författare)
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Tumour Growth Models of Breast Cancer for Evaluating Early Detection—A Summary and a Simulation Study
- 2023
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 15:3
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Tidskriftsartikel (refereegranskat)abstract
- With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.
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| 2. |
- Ugalde-Morales, Emilio, et al.
(författare)
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Association between breast cancer risk and disease aggressiveness : Characterizing underlying gene expression patterns
- 2021
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 148:4, s. 884-894
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Tidskriftsartikel (refereegranskat)abstract
- The association between breast cancer risk defined by the Tyrer-Cuzick score (TC) and disease prognosis is not well established. Here, we investigated the relationship between 5-year TC and disease aggressiveness and then characterized underlying molecular processes. In a case-only study (n = 2474), we studied the association of TC with molecular subtypes and tumor characteristics. In a subset of patients (n = 672), we correlated gene expression to TC and computed a low-risk TC gene expression (TC-Gx) profile, that is, a profile expected to be negatively associated with risk, which we used to test for association with disease aggressiveness. We performed enrichment analysis to pinpoint molecular processes likely to be altered in low-risk tumors. A higher TC was found to be inversely associated with more aggressive surrogate molecular subtypes and tumor characteristics (P <.05) including Ki-67 proliferation status (P < 5 × 10−07). Our low-risk TC-Gx, based on the weighted sum of 37 expression values of genes strongly correlated with TC, was associated with basal-like (P < 5 × 10−13), HER2-enriched subtype (P < 5 × 10−07) and worse 10-year breast cancer-specific survival (log-rank P < 5 × 10−04). Associations between low-risk TC-Gx and more aggressive molecular subtypes were replicated in an independent cohort from The Cancer Genome Atlas database (n = 975). Gene expression that correlated with low TC was enriched in proliferation and oncogenic signaling pathways (FDR < 0.05). Moreover, higher proliferation was a key factor explaining the association with worse survival. Women who developed breast cancer despite having a low risk were diagnosed with more aggressive tumors and had a worse prognosis, most likely driven by increased proliferation. Our findings imply the need to establish risk factors associated with more aggressive breast cancer subtypes.
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| 3. |
- Ugalde-Morales, Emilio, et al.
(författare)
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Interval breast cancer is associated with interferon immune response
- 2022
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Ingår i: European Journal of Cancer. - : Elsevier BV. - 0959-8049 .- 1879-0852. ; 162, s. 194-205
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Tidskriftsartikel (refereegranskat)abstract
- Background: The aggressive nature of breast cancers detected between planned mammographic screens, so-called interval cancers, remains elusive. Here, we aim to characterise underlying molecular features of interval cancer. Methods: From 672 patients with invasive breast cancer, we analysed gene expression differences between 90 ‘true’ interval cancer cases (i.e. women with low-dense breasts defined as per cent mammographic density <25%) and 310 screen-detected tumours while accounting for PAM50 subtypes and thus overall tumour aggressiveness. We computed an interval cancer gene expression profile (IC-Gx) in a total of 2270 breast tumours (regardless of interval cancer status) and tested for association with expression-based immune subtypes in breast cancer. In addition, we investigated the contribution of inherited and somatic genetic variants in distinct features of interval cancer. Results: We identified 8331 genes nominally associated with interval cancer (P-value < 0.05, fold-change > 1.5). Gene set enrichment analysis showed immune-related pathways as key processes altered in interval cancer. Our IC-Gx, based on 47 genes with the strongest associations (false discovery rate < 0.05), was found to be associated mainly with immune subtypes involving interferon response. We isolated an interaction network of interval cancer and interferon genes for which a significant load of somatic and germline variants in class I interferon genes was observed. Conclusion: We identified novel molecular features of interval breast cancer highlighting interferon pathways as a potential target for prevention or treatment.
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| 4. |
- Wang, Yinxi, et al.
(författare)
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Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information
- 2023
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Ingår i: European Journal of Cancer. - 0959-8049. ; 191, s. 112953-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. Methods: Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262). Results: We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22–3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR = 1.84; 95%CI:1.03–3.3; p = 3.99 × 10−2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm). Conclusion: We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. Significance: Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.
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| 5. |
- Westerberg, Marcus
(författare)
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Modelling short and long term consequences of changes in diagnostic activity and treatment
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Since the late 90’s the diagnostic activity for prostate cancer has increased in Sweden, primarily due to increased use of PSA testing, and this has led to a large increase in diagnoses. Simultaneously, there have been changes in treatment strategies, and more effective treatments have been introduced. This thesis aims to increase the understanding of short and long term consequences of these changes by use of high quality data on virtually all men diagnosed with prostate cancer in Sweden.In paper I, the survival of men with metastatic prostate cancer at diagnosis was investigatedby use of survival models, including Kaplan-Meier analyses and Cox proportional hazards regression.The median survival from diagnosis increased with 6 months when comparing mendiagnosed 1998-2001 with men diagnosed 2010-2015, and the risk of death decreased with 13%, while median levels of prostate specific antigen at diagnosis dropped with up to 50%.In paper II, the interplay between diagnostic activity, incidence and risk of death by prostate cancer was modelled using a discrete time model. Data on diagnostic activity, e.g. in termsof testing frequencies, was not available and therefore a proxy for the diagnostic activity wasused. The hazards were estimated within the framework of generalized additive models. Two simulations were performed, assuming low and high diagnostic activity respectively, to compare incidence and mortality from 2017-2060. Higher diagnostic activity, compared to lower, led to more men being diagnosed, primarily with lower risk prostate cancer, but in the long run it led to fewer men diagnosed with metastatic disease and fewer prostate cancer deaths.
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| 6. |
- Kanai, M, et al.
(författare)
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- 2023
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