1. |
- Perez-Gracia, Jose Luis, et al.
(author)
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Strategies to design clinical studies to identify predictive biomarkers in cancer research
- 2017
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In: Cancer Treatment Reviews. - : Elsevier BV. - 0305-7372. ; 53, s. 79-97
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Research review (peer-reviewed)abstract
- The discovery of reliable biomarkers to predict efficacy and toxicity of anticancer drugs remains one of the key challenges in cancer research. Despite its relevance, no efficient study designs to identify promising candidate biomarkers have been established. This has led to the proliferation of a myriad of exploratory studies using dissimilar strategies, most of which fail to identify any promising targets and are seldom validated. The lack of a proper methodology also determines that many anti-cancer drugs are developed below their potential, due to failure to identify predictive biomarkers. While some drugs will be systematically administered to many patients who will not benefit from them, leading to unnecessary toxicities and costs, others will never reach registration due to our inability to identify the specific patient population in which they are active. Despite these drawbacks, a limited number of outstanding predictive biomarkers have been successfully identified and validated, and have changed the standard practice of oncology. In this manuscript, a multidisciplinary panel reviews how those key biomarkers were identified and, based on those experiences, proposes a methodological framework—the DESIGN guidelines—to standardize the clinical design of biomarker identification studies and to develop future research in this pivotal field.
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2. |
- Aine, Mattias, et al.
(author)
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Molecular analyses of triple-negative breast cancer in the young and elderly
- 2021
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In: Breast cancer research : BCR. - : Springer Science and Business Media LLC. - 1465-5411 .- 1465-542X. ; 23:1
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Journal article (peer-reviewed)abstract
- BACKGROUND: Breast cancer in young adults has been implicated with a worse outcome. Analyses of genomic traits associated with age have been heterogenous, likely because of an incomplete accounting for underlying molecular subtypes. We aimed to resolve whether triple-negative breast cancer (TNBC) in younger versus older patients represent similar or different molecular diseases in the context of genetic and transcriptional subtypes and immune cell infiltration.PATIENTS AND METHODS: In total, 237 patients from a reported population-based south Swedish TNBC cohort profiled by RNA sequencing and whole-genome sequencing (WGS) were included. Patients were binned in 10-year intervals. Complimentary PD-L1 and CD20 immunohistochemistry and estimation of tumor-infiltrating lymphocytes (TILs) were performed. Cases were analyzed for differences in patient outcome, genomic, transcriptional, and immune landscape features versus age at diagnosis. Additionally, 560 public WGS breast cancer profiles were used for validation.RESULTS: Median age at diagnosis was 62 years (range 26-91). Age was not associated with invasive disease-free survival or overall survival after adjuvant chemotherapy. Among the BRCA1-deficient cases (82/237), 90% were diagnosed before the age of 70 and were predominantly of the basal-like subtype. In the full TNBC cohort, reported associations of patient age with changes in Ki67 expression, PIK3CA mutations, and a luminal androgen receptor subtype were confirmed. Within DNA repair deficiency or gene expression defined molecular subgroups, age-related alterations in, e.g., overall gene expression, immune cell marker gene expression, genetic mutational and rearrangement signatures, amount of copy number alterations, and tumor mutational burden did, however, not appear distinct. Similar non-significant associations for genetic alterations with age were obtained for other breast cancer subgroups in public WGS data. Consistent with age-related immunosenescence, TIL counts decreased linearly with patient age across different genetic TNBC subtypes.CONCLUSIONS: Age-related alterations in TNBC, as well as breast cancer in general, need to be viewed in the context of underlying genomic phenotypes. Based on this notion, age at diagnosis alone does not appear to provide an additional layer of biological complexity above that of proposed genetic and transcriptional phenotypes of TNBC. Consequently, treatment decisions should be less influenced by age and more driven by tumor biology.
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3. |
- Bai, Yalai, et al.
(author)
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An Open Source, Automated Tumor Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer
- 2021
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In: Clinical Cancer Research. - 1078-0432. ; 27:20, s. 5557-5565
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Journal article (peer-reviewed)abstract
- Purpose: Although tumor infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple negative breast cancer (TNBC), it is subject to inter and intra-observer variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. Experimental Design: Using the QuPath open source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts and “other” cells on hematoxylin-eosin (H&E) stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets. Results: We found that all five machine TIL variables had significant prognostic association with outcomes (p≤0.01 for all comparisons) but showed cell specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathological factors including stage, age and histological grade (p≤0.003 for all analyses). Conclusions: Neural net driven cell classifier defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility.
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4. |
- Bosch Campos, Ana, et al.
(author)
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PI3K inhibition results in enhanced estrogen receptor function and dependence in hormone receptor-positive breast cancer
- 2015
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In: Science Translational Medicine. - : American Association for the Advancement of Science (AAAS). - 1946-6234 .- 1946-6242. ; 7:283, s. 1-11
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Journal article (peer-reviewed)abstract
- Activating mutations of PIK3CA are the most frequent genomic alterations in estrogen receptor (ER)-positive breast tumors, and selective phosphatidylinositol 3-kinase a (PI3Kα) inhibitors are in clinical development. The activity of these agents, however, is not homogeneous, and only a fraction of patients bearing PIK3CA-mutant ER-positive tumors benefit from single-agent administration. Searching for mechanisms of resistance, we observed that suppression of PI3K signaling results in induction of ER-dependent transcriptional activity, as demonstrated by changes in expression of genes containing ER-binding sites and increased occupancy by the ER of promoter regions of upregulated genes. Furthermore, expression of ESR1 mRNA and ER protein were also increased upon PI3K inhibition. These changes in gene expression were confirmed in vivo in xenografts and patient-derived models and in tumors from patients undergoing treatment with the PI3Kα inhibitor BYL719. The observed effects on transcription were enhanced by the addition of estradiol and suppressed by the anti-ER therapies fulvestrant and tamoxifen. Fulvestrant markedly sensitized ER-positive tumors to PI3Kα inhibition, resulting in major tumor regressions in vivo. We propose that increased ER transcriptional activity may be a reactive mechanism that limits the activity of PI3K inhibitors and that combined PI3K and ER inhibition is a rational approach to target these tumors.
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5. |
- Ciesla, Maciej, et al.
(author)
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Oncogenic translation directs spliceosome dynamics revealing an integral role for SF3A3 in breast cancer
- 2021
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In: Molecular Cell. - : Elsevier BV. - 1097-2765. ; 81:7
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Journal article (peer-reviewed)abstract
- Splicing is a central RNA-based process commonly altered in human cancers; however, how spliceosomal components are co-opted during tumorigenesis remains poorly defined. Here we unravel the core splice factor SF3A3 at the nexus of a translation-based program that rewires splicing during malignant transformation. Upon MYC hyperactivation, SF3A3 levels are modulated translationally through an RNA stem-loop in an eIF3D-dependent manner. This ensures accurate splicing of mRNAs enriched for mitochondrial regulators. Altered SF3A3 translation leads to metabolic reprogramming and stem-like properties that fuel MYC tumorigenic potential in vivo. Our analysis reveals that SF3A3 protein levels predict molecular and phenotypic features of aggressive human breast cancers. These findings unveil a post-transcriptional interplay between splicing and translation that governs critical facets of MYC-driven oncogenesis.
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6. |
- Kimbung, Siker, et al.
(author)
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High expression of cholesterol biosynthesis genes is associated with resistance to statin treatment and inferior survival in breast cancer
- 2016
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In: Oncotarget. - : Impact Journals, LLC. - 1949-2553. ; 7:37, s. 59640-59651
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Journal article (peer-reviewed)abstract
- There is sufficient evidence that statins have a protective role against breast cancer proliferation and recurrence, but treatment predictive biomarkers are lacking. Breast cancer cell lines displaying diverse sensitivity to atorvastatin were subjected to global transcriptional profiling and genes significantly altered by statin treatment were identified. Atorvastatin treatment strongly inhibited proliferation in estrogen receptor (ER) negative cell lines and a commensurate response was also evident on the genome-wide transcriptional scale, with ER negative cells displaying a robust deregulation of genes involved in the regulation of cell cycle progression and apoptosis. Interestingly, atorvastatin upregulated genes involved in the cholesterol biosynthesis pathway in all cell lines, irrespective of sensitivity to statin treatment. However, the level of pathway induction; measured as the fold change in transcript levels, was inversely correlated to the effect of statin treatment on cell growth. High expression of cholesterol biosynthesis genes before treatment was associated with resistance to statin therapy in cell lines and clinical biopsies. Furthermore, high expression of cholesterol biosynthesis genes was independently prognostic for a shorter recurrence-free and overall survival, especially among ER positive tumors. Dysregulation of cholesterol biosynthesis is therefore predictive for both sensitivity to anti-cancer statin therapy and prognosis following primary breast cancer diagnosis.
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