Breast Cancer

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility

Basyuni, S., et al., Nature Communications, Sep 2024

Abstract

Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England’s 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an “FFPEImpact” score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many.

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The PARTNER trial of neoadjuvant olaparib with chemotherapy in triple-negative breast cancer

Abraham, J. E., et al., Nature, April 2024

Abstract

PARTNER is a prospective, phase II–III, randomized controlled clinical trial that recruited patients with triple-negative breast cancer^1,2, who were germline BRCA1 and BRCA2 wild type³. Here we report the results of the trial. Patients (n = 559) were randomized on a 1:1 basis to receive neoadjuvant carboplatin–paclitaxel with or without 150 mg olaparib twice daily, on days 3 to 14, of each of four cycles (gap schedule olaparib, research arm) followed by three cycles of anthracycline-based chemotherapy before surgery. The primary end point was pathologic complete response (pCR)⁴, and secondary end points included event-free survival (EFS) and overall survival (OS)⁵. pCR was achieved in 51% of patients in the research arm and 52% in the control arm (P = 0.753). Estimated EFS at 36 months in the research and control arms was 80% and 79% (log-rank P > 0.9), respectively; OS was 90% and 87.2% (log-rank P = 0.8), respectively. In patients with pCR, estimated EFS at 36 months was 90%, and in those with non-pCR it was 70% (log-rank P < 0.001), and OS was 96% and 83% (log-rank P < 0.001), respectively. Neoadjuvant olaparib did not improve pCR rates, EFS or OS when added to carboplatin–paclitaxel and anthracycline-based chemotherapy in patients with triple-negative breast cancer who were germline BRCA1 and BRCA2 wild type. ClinicalTrials.gov ID: NCT03150576.

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Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer

Santonja, A., et al, EMBO Mol Med, May 2023

Problem

Circulating tumor DNA (ctDNA) can be used as a non-invasive liquid biopsy in cancer patients to track disease burden in blood. Different strategies have been used to quantify ctDNA, but few studies have compared the performance of different tumor-informed and tumor-naïve assays to detect ctDNA in the same patient samples.

Results

Our results demonstrate that ctDNA dynamics and tumor allele fractions were highly concordant when targeting different mutation types in serial blood samples collected from breast cancer patients undergoing treatment. Tumor-informed assays showed the highest sensitivity for detection of ctDNA at low concentrations. SNV-hybrid capture, targeting thousands of single nucleotide variants, and SV-multiplex PCR, targeting tens of structural variants, were able to detect ctDNA down to a few parts per million.

Impact

Choice of assay for ctDNA quantification depends on many factors including the required sensitivity for its intended use, the mutation type being assayed, turnaround time, and cost. This study demonstrates that personalized assays targeting patient-specific mutations identified in the tumor were the most sensitive assays to detect low levels of ctDNA in blood, and SV-multiplex PCR has potential to be used as a clinical diagnostic assay.

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Multi-omic machine learning predictor of breast cancer therapy response

Sammut, S. J., et al, Multi-omic machine learning predictor of breast cancer therapy response, Nature, Dec 2021

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment¹. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy². Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery³ were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

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Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients

Woitek, R., et al, Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients. Cancer Research, Sept 2021.

Hyperpolarized ¹³C-MRI is an emerging tool for probing tissue metabolism by measuring ¹³C-label exchange between intravenously injected hyperpolarized [1–¹³C]pyruvate and endogenous tissue lactate. Here, we demonstrate that hyperpolarized ¹³C-MRI can be used to detect early response to neoadjuvant therapy in breast cancer.

Media coverage for this publication in the Sunday Times.

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ctDNA monitoring using patient-specific sequencing and integration of variant reads

Wan, J.C.M., et al., Sci Transl Med, 17 Jun 2020

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci.

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Hyperpolarized 13C MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Woitek R. et al, Radiology: Imaging Cancer, 2020

The purpose of this research was to compare hyperpolarized carbon 13 (13C) MRI with dynamic contrast material–enhanced (DCE) MRI in the detection of early treatment response in breast cancer. In this institutional review board–approved prospective study, a woman with triple-negative breast cancer (age, 49 years) underwent 13C MRI after injection of hyperpolarized [1–carbon 13 {13C}]-pyruvate and DCE MRI at 3 T at baseline and after one cycle of neoadjuvant therapy. The 13C-labeled lactate-to-pyruvate ratio derived from hyperpolarized 13C MRI and the pharmacokinetic parameters transfer constant (Ktrans) and washout parameter (kep) derived from DCE MRI were compared before and after treatment.

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Imaging mass cytometry and multi-platform genomics define the phenogenomic landscape of breast Cancer

Ali, R. et al, Nature Cancer, 2020

Zooming in on breast cancer reveals how mutations shape the tumour landscape. Scientists have created one of the most detailed maps of breast cancer ever achieved, revealing how genetic changes shape the physical tumour landscape.

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Integrating Digital Pathology and Imaging data

Integrating multi-modality data acquired during neoadjuvant therapy in early breast cancer patients; TRANSNEO – DIGITAL PATHOLOGY AND IMAGING DATA:

In order to predict response to therapy the MFICM consortium has extracted features from digital pathology and dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) in TransNeo cases. Manual tumour delineation and kinetic modelling of DCE MRI as well as visual reconstruction of spatial heterogeneity using deep learning has been performed and spatial heterogeneity has been detected. By including molecular features associated with response to neoadjuvant therapy into machine learning classifiers, we will be able to predict outcome in regards to progression free survival.

Integration of ctDNA and Imaging data

Identifying minimal residual disease and early treatment response by utilising ctDNA and imaging data (CT, MRI):

CT scans are used as standard of care assessment of tumour progression; using a breast trial in the metastatic setting (DETECT) we have been able to show that ctDNA can also be used to predict with 90% accuracy if a tumour is responding to treatment and tumour content in plasma is highly predictive of survival. Next steps will include integration of ctDNA data with ¹³C-MRI, which can be used for metabolic imaging of breast cancer patients in the clinic, creating new possibilities for non-invasive cancer monitoring in this patient group.

Integration of Patient Derived Xenographs and Cell Line data

Enhanced molecular stratification of patients before treatment based on network models derived from patients, PDXT, and cell lines utilising computational network models to integrate patient, PDXT and cell line data:

A computational network model of the EGFR, p53, cell cycle and DDR signalling using publicly available TNBC cell lines data and 10 PARTNER patients’ data has been built and tested. A thorough analysis of the PARTNER patients’ mutational profiles and copy number aberrations (CNAs) to extract the functional mutations profile for each of the 31 PARTNER patients has also been undertaken. These mutational profiles have then been used to simulate the response to treatment for each PARTNER patient receiving either chemo alone or chemo + olaparib. This unique analysis will highlight which parts in the network contribute to different patients’ outcome and pave the way for better personalised treatment plans for TNBC patients.

PREDICT: Breast Cancer survival prediction tool

As part of the interpretation and visualisation of data streams for our research community, oncologists and patients, PREDICT is an online tool developed by UCAM researchers.

PREDICT helps patients and clinicians to visualise how different treatments for early invasive breast cancer might improve survival rates after surgery. We will continue enhancing this visualisation tool to guide our development of a visualisation platform. We have finished the analysis of the data from the Breast Cancer Association Consortium on the prognostic role of progesterone receptor status, which will be incorporated into the final prognostic model and a new version v3.0 of PREDICT, which will launch later this year. PREDICT is currently hosted by Public Health England as a free on-line tool and has been viewed by more than 1 million individuals around the world.

PREDICT breast was awarded the 2019 National Cancer Research Institute Research Impact prize awarded at the annual meeting on 4th November 2019.

Variational Autoencoders for Cancer Data Integration

Novel AI/ML models for more accurate diagnosis and for predicting drug toxicity from clinical and genomic data using retrospective datasets:

A computational framework that enables clinicians to investigate cancer traits and translate the results into clinical application has been developed based on retrospective data (clinical outcome data, genomics data) from the METABRIC study. This novel approach was based on novel AI algorithms and can now be used for prospective data sets.

Data integration in breast cancer within MFICM focuses on:

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility

Abstract

The PARTNER trial of neoadjuvant olaparib with chemotherapy in triple-negative breast cancer

The PARTNER trial of neoadjuvant olaparib with chemotherapy in triple-negative breast cancer

Abstract

Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer

Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer

Problem

Results

Impact

Multi-omic machine learning predictor of breast cancer therapy response

Multi-omic machine learning predictor of breast cancer therapy response

Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients

Hyperpolarized carbon-13 MRI for very early response assessment of neoadjuvant chemotherapy in breast cancer patients

ctDNA monitoring using patient-specific sequencing and integration of variant reads

ctDNA monitoring using patient-specific sequencing and integration of variant reads

Hyperpolarized 13C MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Hyperpolarized 13C MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Imaging mass cytometry and multi-platform genomics define the phenogenomic landscape of breast Cancer

Imaging mass cytometry and multi-platform genomics define the phenogenomic landscape of breast Cancer

Integrating Digital Pathology and Imaging data

Integrating Digital Pathology and Imaging data

Integration of ctDNA and Imaging data

Integration of ctDNA and Imaging data

Integration of Patient Derived Xenographs and Cell Line data

Integration of Patient Derived Xenographs and Cell Line data

PREDICT: Breast Cancer survival prediction tool

PREDICT: Breast Cancer survival prediction tool

Variational Autoencoders for Cancer Data Integration

Variational Autoencoders for Cancer Data Integration

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