| 1. |
- Nacer, Deborah F., et al.
(författare)
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Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1477-4054 .- 1467-5463. ; 22:6
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Tidskriftsartikel (refereegranskat)abstract
- Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.
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| 2. |
- Nacer, Deborah F.
(författare)
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Decoding pan-cancer complexity. Multiomic insights from the lung and breast
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer presently represents a significant global health challenge, illustrated by the high incidence and mortality rates associated with lung and breast cancer. Technological advances and concerted large-scale initiatives have yielded vast amounts of cancer data to be used for research purposes. The objective of this thesis is to capitalize on the current state of the bioinformatics field to develop and employ tools that extract biological insights from multiomic data across a range of cancer types. Utilizing publicly available data sets from our research group and from other researchers worldwide, two of the included studies were centered on methodological development. Firstly, we successfully applied a gene-expression-based prognostic tool originally developed for lung adenocarcinoma, the most common histological subtype of lung cancer, to multiple cancer types, demonstrating the broad applicability of such classifiers (Paper I). Secondly, we refined a method for adjusting DNA methylation data based on the mixture of malignant and non-malignant cells in tumor samples, enhancing biological interpretability of such methylation data sets (Paper III). The remaining studies investigated the biological heterogeneity within lung and breast tumors. Specifically, we stratified breast cancer patients from southern Sweden according to whether they had had genes associated with increased breast cancer risk screened for variants and compared the two resulting groups, obtaining a real-world read-out of the current screening guidelines and patient selection criteria (Paper II). Lastly, we subdivided lung adenocarcinoma into four distinct subgroups based on adjusted DNA methylation data and characterized the resulting sample clusters, both showing congruence to previously proposed mRNA and protein subtypes and providing novel insights into this malignancy (Paper IV). Taken together, the findings presented in this thesis have contributed to our collective understanding of the complex cancer biology landscape.
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| 3. |
- Nacer, Deborah F., et al.
(författare)
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Molecular characteristics of breast tumors in patients screened for germline predisposition from a population-based observational study
- 2023
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Ingår i: Genome Medicine. - 1756-994X. ; 15, s. 1-20
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundPathogenic germline variants (PGVs) in certain genes are linked to higher lifetime risk of developing breast cancer and can influence preventive surgery decisions and therapy choices. Public health programs offer genetic screening based on criteria designed to assess personal risk and identify individuals more likely to carry PGVs, dividing patients into screened and non-screened groups. How tumor biology and clinicopathological characteristics differ between these groups is understudied and could guide refinement of screening criteria.MethodsSix thousand six hundred sixty breast cancer patients diagnosed in South Sweden during 2010–2018 were included with available clinicopathological and RNA sequencing data, 900 (13.5%) of which had genes screened for PGVs through routine clinical screening programs. We compared characteristics of screened patients and tumors to non-screened patients, as well as between screened patients with (n = 124) and without (n = 776) PGVs.ResultsBroadly, breast tumors in screened patients showed features of a more aggressive disease. However, few differences related to tumor biology or patient outcome remained significant after stratification by clinical subgroups or PAM50 subtypes. Triple-negative breast cancer (TNBC), the subgroup most enriched for PGVs, showed the most differences between screening subpopulations (e.g., higher tumor proliferation in screened cases). Significant differences in PGV prevalence were found between clinical subgroups/molecular subtypes, e.g., TNBC cases were enriched for BRCA1 PGVs. In general, clinicopathological differences between screened and non-screened patients mimicked those between patients with and without PGVs, e.g., younger age at diagnosis for positive cases. However, differences in tumor biology/microenvironment such as immune cell composition were additionally seen within PGV carriers/non-carriers in ER + /HER2 − cases, but not between screening subpopulations in this subgroup.ConclusionsCharacterization of molecular tumor features in patients clinically screened and not screened for PGVs represents a relevant read-out of guideline criteria. The general lack of molecular differences between screened/non-screened patients after stratification by relevant breast cancer subsets questions the ability to improve the identification of screening candidates based on currently used patient and tumor characteristics, pointing us towards universal screening. Nevertheless, while that is not attained, molecular differences identified between PGV carriers/non-carriers suggest the possibility of further refining patient selection within certain patient subsets using RNA-seq through, e.g., gene signatures.
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| 4. |
- Staaf, Johan, et al.
(författare)
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Molecular characteristics of lung adenocarcinoma with respect to patient age at diagnosis
- 2023
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Ingår i: International Journal of Cancer. - : Wiley. - 0020-7136 .- 1097-0215. ; 153:1, s. 197-209
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Tidskriftsartikel (refereegranskat)abstract
- Lung cancer is primarily a disease of the elderly, with a median age at diagnosis around 70 years. In our study we sought to address the question of whether and how clinical characteristics, molecular alterations and molecular phenotypes differ between patient populations with early-stage lung adenocarcinoma (AC) with respect to age at diagnosis. Patients were stratified based on age at diagnosis into five systematic age bins (
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| 5. |
- Veerla, Srinivas, et al.
(författare)
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Perturbation and stability of PAM50 subtyping in population-based primary invasive breast cancer
- 2023
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Ingår i: npj Breast Cancer. - 2374-4677. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- PAM50 gene expression subtypes represent a cornerstone in the molecular classification of breast cancer and are included in risk prediction models to guide therapy. We aimed to illustrate the impact of included genes and biological processes on subtyping while considering a tumor’s underlying clinical subgroup defined by ER, PR, and HER2 status. To do this we used a population-representative and clinically annotated early-stage breast tumor cohort of 6233 samples profiled by RNA sequencing and applied a perturbation strategy of excluding co-expressed genes (gene sets). We demonstrate how PAM50 nearest-centroid classification depends on biological processes present across, but also within, ER/PR/HER2 subgroups and PAM50 subtypes themselves. Our analysis highlights several key aspects of PAM50 classification. Firstly, we demonstrate the tight connection between a tumor’s nearest and second-nearest PAM50 centroid. Additionally, we show that the second-best subtype is associated with overall survival in ER-positive, HER2-negative, and node-negative disease. We also note that ERBB2 expression has little impact on PAM50 classification in HER2-positive disease regardless of ER status and that the Basal subtype is highly stable in contrast to the Normal subtype. Improved consciousness of the commonly used PAM50 subtyping scheme will aid in our understanding and interpretation of breast tumors that have seemingly conflicting PAM50 classification when compared to clinical biomarkers. Finally, our study adds further support in challenging the common misconception that PAM50 subtypes are distinct classes by illustrating that PAM50 subtypes in tumors represent a continuum with prognostic implications.
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