2,070 research outputs found
Improving biomarker assessment in breast pathology
The accuracy of prognostic and therapy-predictive biomarker assessment in breast tumours is
crucial for management and therapy decision in patients with breast cancer. In this thesis,
biomarkers used in clinical practice with emphasise on Ki67 and HER2 were studied using
several methods including immunocytochemistry, in situ
hybridisation, gene expression assays and digital image analysis, with the overall aim to
improve routine biomarker evaluation and clarify the prognostic potential in early breast
cancer.
In paper I, we reported discordances in biomarker status from aspiration cytology and paired
surgical specimens from breast tumours. The limited prognostic potential of
immunocytochemistry-based Ki67 scoring demonstrated that immunohistochemistry on
resected specimens is the superior method for Ki67 evaluation. In addition, neither of the
methods were sufficient to predict molecular subtype. Following this in paper II, biomarker
agreement between core needle biopsies and subsequent specimens was investigated, both in
the adjuvant and neoadjuvant setting. Discordances in Ki67 and HER2 status between core
biopsies and paired specimens suggested that these biomarkers should be re-tested on all
surgical breast cancer specimens. In paper III, digital image analysis using a virtual double
staining software was used to compare methods for assessment of proliferative activity,
including mitotic counts, Ki67 and the alternative marker PHH3, in different tumour regions
(hot spot, invasive edge and whole section). Digital image analysis using virtual double staining
of hot spot Ki67 outperformed the alternative markers of proliferation, especially in
discriminating luminal B from luminal A tumours. Replacing mitosis in histological grade with
hot spot-scored Ki67 added significant prognostic information. Following these findings, the
optimal definition of a hot spot for Ki67 scoring using virtual double staining in relation to
molecular subtype and outcome was investigated in paper IV. With the growing evidence of
global scoring as a superior method to improve reproducibility of Ki67 scoring, a different
digital image analysis software (QuPath) was also used for comparison. Altogether, we found
that automated global scoring of Ki67 using QuPath had independent prognostic potential
compared to even the best virtual double staining hot spot algorithm, and is also a practical
method for routine Ki67 scoring in breast pathology. In paper V, the clinical value of HER2
status was investigated in a unique trastuzumab-treated HER2-positive cohort, on the protein,
mRNA and DNA levels. The results demonstrated that low levels of ERBB2 mRNA but neither
HER2 copy numbers, HER2 ratio nor ER status, was associated with risk of recurrence among
anti-HER2 treated breast cancer patients.
In conclusion, we have identified important clinical aspects of Ki67 and HER2 evaluation and
provided methods to improve the prognostic potential of Ki67 using digital image analysis. In
addition to protein expression of routine biomarkers, mRNA levels by targeted gene expression
assays may add further prognostic value in early breast cance
Artificial intelligence for breast cancer precision pathology
Breast cancer is the most common cancer type in women globally but is associated with a
continuous decline in mortality rates. The improved prognosis can be partially attributed to
effective treatments developed for subgroups of patients. However, nowadays, it remains
challenging to optimise treatment plans for each individual. To improve disease outcome and
to decrease the burden associated with unnecessary treatment and adverse drug effects, the
current thesis aimed to develop artificial intelligence based tools to improve individualised
medicine for breast cancer patients.
In study I, we developed a deep learning based model (DeepGrade) to stratify patients that
were associated with intermediate risks. The model was optimised with haematoxylin and eosin
(HE) stained whole slide images (WSIs) with grade 1 and 3 tumours and applied to stratify
grade 2 tumours into grade 1-like (DG2-low) and grade 3-like (DG2-high) subgroups. The
efficacy of the DeepGrade model was validated using recurrence free survival where the
dichotomised groups exhibited an adjusted hazard ratio (HR) of 2.94 (95% confidence interval
[CI] 1.24-6.97, P = 0.015). The observation was further confirmed in the external test cohort
with an adjusted HR of 1.91 (95% CI: 1.11-3.29, P = 0.019).
In study II, we investigated whether deep learning models were capable of predicting gene
expression levels using the morphological patterns from tumours. We optimised convolutional
neural networks (CNNs) to predict mRNA expression for 17,695 genes using HE stained WSIs
from the training set. An initial evaluation on the validation set showed that a significant
correlation between the RNA-seq measurements and model predictions was observed for
52.75% of the genes. The models were further tested in the internal and external test sets.
Besides, we compared the model's efficacy in predicting RNA-seq based proliferation scores.
Lastly, the ability of capturing spatial gene expression variations for the optimised CNNs was
evaluated and confirmed using spatial transcriptomics profiling.
In study III, we investigated the relationship between intra-tumour gene expression
heterogeneity and patient survival outcomes. Deep learning models optimised from study II
were applied to generate spatial gene expression predictions for the PAM50 gene panel. A set
of 11 texture based features and one slide average gene expression feature per gene were
extracted as input to train a Cox proportional hazards regression model with elastic net
regularisation to predict patient risk of recurrence. Through nested cross-validation, the model
dichotomised the training cohort into low and high risk groups with an adjusted HR of 2.1
(95% CI: 1.30-3.30, P = 0.002). The model was further validated on two external cohorts.
In study IV, we investigated the agreement between the Stratipath Breast, which is the
modified, commercialised DeepGrade model developed in study I, and the Prosigna® test.
Both tests sought to stratify patients with distinct prognosis. The outputs from Stratipath Breast
comprise a risk score and a two-level risk stratification whereas the outputs from Prosigna®
include the risk of recurrence score and a three-tier risk stratification. By comparing the number
of patients assigned to ‘low’ or ‘high’ risk groups, we found an overall moderate agreement
(76.09%) between the two tests. Besides, the risk scores by two tests also revealed a good
correlation (Spearman's rho = 0.59, P = 1.16E-08). In addition, a good correlation was observed
between the risk score from each test and the Ki67 index. The comparison was also carried out
in the subgroup of patients with grade 2 tumours where similar but slightly dropped correlations
were found
The Magnitude of Androgen Receptor Positivity in Breast Cancer Is Critical for Reliable Prediction of Disease Outcome
Purpose: Consensus is lacking regarding the androgen receptor (AR) as a prognostic marker in breast cancer. The objectives of this study were to comprehensively review the literature on AR prognostication and determine optimal criteria for AR as an independent predictor of breast cancer survival. Experimental Design: AR positivity was assessed by immunostaining in two clinically validated primary breast cancer cohorts [training cohort, n = 219; validation cohort, n = 418; 77% and 79% estrogen receptor alpha (ERα) positive, respectively]. The optimal AR cut-point was determined by ROC analysis in the training cohort and applied to both cohorts. Results: AR was an independent prognostic marker of breast cancer outcome in 22 of 46 (48%) previous studies that performed multivariate analyses. Most studies used cut-points of 1% or 10% nuclear positivity. Herein, neither 1% nor 10% cut-points were robustly prognostic. ROC analysis revealed that a higher AR cut-point (78% positivity) provided optimal sensitivity and specificity to predict breast cancer survival in the training (HR, 0.41; P = 0.015) and validation (HR, 0.50; P = 0.014) cohorts. Tenfold cross-validation confirmed the robustness of this AR cut-point. Patients with ERα-positive tumors and AR positivity ≥78% had the best survival in both cohorts (P 0.87) had the best outcomes (P < 0.0001). Conclusions: This study defines an optimal AR cut-point to reliably predict breast cancer survival. Testing this cut-point in prospective cohorts is warranted for implementation of AR as a prognostic factor in the clinical management of breast cancer
Transcriptional gene signatures : passing the restriction point for routine clinical implementation
Uncontrolled cell growth and cell division are central to the process of tumorigenesis and a number of gene expression signatures have been developed based on genes that are involved in the cell cycle. Notably, gene expression signatures are used extensively in breast cancer research to examine the disease at a molecular level to describe tumour progression, treatment response and patients’ survival.
The subject of this thesis is to explore the potential prognostic capacity of gene expression signatures in breast cancer and additionally, determine the prognostic capacity of a transcriptomic cell cycle activity (CCS) signature within variety of cancer types.
Several breast cancer gene expression signatures have emerged and been validated over the past two decades in large retrospective clinical trials. Although the clinical impact of these signatures has been clearly demonstrated, breast cancer therapeutic guidelines are still
established on the basis of immunohistochemical markers (IHC) such as estrogen (ER), progesterone (PR), human epidermal growth factor 2 (HER2) and the proliferation marker Ki67. In Study I, the additional prognostic information derived from the combination of gene expression signatures and IHC/Ki67 was investigated in two Swedish breast cancer cohorts. Cohort I is comprised of 621 individuals with primary breast cancer tumours diagnosed between 1997 and 2005 in Stockholm region of Sweden. Cohort II consists of 484 individuals with primary breast tumours who diagnosed and received primary therapy
in the Uppsala region of Sweden between 1987 and 1989. In Cohort I, Recurrence score
(RS) and PAM50 gene expression signatures added prognostic information beyond Ki67
and IHC subtypes while only IHC subtypes provided additional prognostic information to
all gene expression signatures with the exception of PAM50 gene signature in this cohort. Similar results were observed in Cohort II.
The ability of gene expression signatures to provide prognostic and treatment predictive information has been tested in primary breast tumours; however, their capability to provide similar information in the metastatic breast cancer (MBC) patients has not been
investigated. In Study II, the prognostic capacity of gene expression signatures in breast cancer was evaluated in the metastatic setting in a Swedish multicenter randomized clinical trial known as “TEX” with 304 patients diagnosed with advanced locoregional or distant breast cancer relapse. A large number of tumours were classified into intermediate or high4
risk groups by all gene expression signatures. PAM50 was the only gene expression signature that provided prognostic information from lymph node (LN) metastases.
In Study III, the prognostic and treatment-specific potential of CCND1 amplification was
assessed in two breast cancer cohorts with 1965 and 340 patients, respectively. In the combined cohort, patients with CCND1-amplified tumours show worse survival in ER+/HER2-/LN-, luminal A and luminal B subtypes. Moreover, luminal A subtype with
CCND1-amplified tumours shared similar gene expression changes with and luminal B
subtype.
In Study IV, the DNA mutations and chromosome arm-level aneuploidy within tumours with different cell cycle activity (CCS) were explored. We showed that cell cycle activity
varied broadly among and within different cancer types. Two well-known oncogenes (TP53
and PIK3CA) exhibit the highest rate of mutations within different CCS groups.
Furthermore, chromosomal arm level aberrations present in all CCS groups with a higher number of gains in 7p, 20q whereas deletions were more frequent within 17p and 8p arms.
In the survival analysis, patients with higher CCS tumours show worse Progression-free interval relative to low and intermediate CCS groups.
In conclusions, we have shown that PAM50 and RS gene expression signatures can add prognostic information to Ki67 and IHC subtypes; however, IHC subtypes did not add any
prognostic information to PAM50 signature. Moreover, PAM50 gene expression signature can provide prognostic information from LN metastases in MBC patients. Additionally, CCND1 gene amplification has the potential to stratify patients with worse survival outcome within good-prognosis luminal A subtype tumours. Finally, we have demonstrated
that CCS can provide independent prognostic information across cancer types
Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning
Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.</jats:p
- …