Jeremy Thomas

Residual Cancer Burden (RCB) after neoadjuvant chemotherapy (NAC) is validated to predict event-free survival (EFS) in breast cancer but has not been studied for invasive lobular carcinoma (ILC). We studied patient-level data from a pooled cohort across 12 institutions. Associations between RCB index, class, and EFS were assessed in ILC and non-ILC with mixed effect Cox models and multivariable analyses. Recursive partitioning was used in an exploratory model to stratify prognosis by RCB components. Of 5106 patients, the diagnosis was ILC in 216 and non-ILC in 4890. Increased RCB index was associated with worse EFS in both ILC and non-ILC (p = 0.002 and p < 0.001, respectively) and remained prognostic when stratified by receptor subtype and adjusted for age, grade, T category, and nodal status. Recursive partitioning demonstrated residual invasive cancer cellularity as most prognostic in ILC. These results underscore the utility of RCB for evaluating NAC response in those with ILC.

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 byERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery³were 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.

The risk of recurrence for endocrine-treated breast cancer patients persists for many years or even decades following surgery and apparently successful adjuvant therapy. This period of dormancy and acquired resistance is inherently difficult to investigate; previous efforts have been limited to in-vitro or in-vivo approaches. In this study, sequential tumour samples from patients receiving extended neoadjuvant aromatase inhibitor therapy were characterised as a novel clinical model. Consecutive tumour samples from 62 patients undergoing extended (4–45 months) neoadjuvant aromatase inhibitor therapy with letrozole were subjected to transcriptomic and proteomic analysis, representing before (≤ 0), early (13–120 days), and long-term (> 120 days) neoadjuvant aromatase inhibitor therapy with letrozole. Patients with at least a 40% initial reduction in tumour size by 4 months of treatment were included. Of these, 42 patients with no subsequent progression were classified as “dormant”, and the remaining 20 patients as “acquired resistant”. Changes in gene expression in dormant tumours begin early and become more pronounced at later time points. Therapy-induced changes in resistant tumours were common features of treatment, rather than being specific to the resistant phenotype. Comparative analysis of long-term treated dormant and resistant tumours highlighted changes in epigenetics pathways including DNA methylation and histone acetylation. The DNA methylation marks 5-methylcytosine and 5-hydroxymethylcytosine were significantly reduced in resistant tumours compared with dormant tissues after extended letrozole treatment. This is the first patient-matched gene expression study investigating long-term aromatase inhibitor-induced dormancy and acquired resistance in breast cancer. Dormant tumours continue to change during treatment whereas acquired resistant tumours more closely resemble their diagnostic samples. Global loss of DNA methylation was observed in resistant tumours under extended treatment. Epigenetic alterations may lead to escape from dormancy and drive acquired resistance in a subset of patients, supporting a potential role for therapy targeted at these epigenetic alterations in the management of resistance to oestrogen deprivation therapy.

Background: The risk of recurrence for endocrine-treated breast cancer patients persists for many years or even decades following surgery and apparently successful adjuvant therapy. This period of dormancy and acquired resistance is inherently difficult to investigate, previous efforts have been limited to in vitro or in vivo approaches. In this study, sequential tumour samples from patients receiving extended neoadjuvant endocrine treatment were characterised as a novel clinical model. Methods: Consecutive tumour samples from 62 patients undergoing extended (4-45 months) neoadjuvant aromatase inhibitor, letrozole, therapy were subjected to transcriptomic and proteomic analysis, representing before (≤0), early-on (13-120 days) and long-term (>120 days) neoadjuvant letrozole treatment. Patients with at least a 40% initial reduction in tumour size by 4 months of treatment were included. Of these, 42 patients with no subsequent progression were classified as “dormant”, and the remaining 20 patients as “acquired resistant”. Results: Changes in gene expression in dormant tumours begin early and become more pronounced at later timepoints. Therapy-induced changes in resistant tumours were common features of treatment, rather than being specific to resistant phenotype. Comparative analysis of long-term treated dormant and resistant tumours highlighted changes in epigenetics pathways including DNA methylation and histone acetylation. DNA methylation marks 5-methylcytosine and 5-hydroxymethylcytosine were significantly reduced in resistant tumours compared to dormant tissues after extended letrozole treatment. Conclusions: This is the first patient-matched gene expression study investigating long-term aromatase inhibitor-induced dormancy and acquired resistance in breast cancer. Dormant tumours continue to change during treatment whereas acquired resistant tumours more closely resemble their diagnostic samples. Global loss of DNA methylation was observed in resistant tumours under extended treatment. Epigenetic alterations may lead to escape from dormancy and drive acquired resistance in a subset of patients supporting a potential role for therapy targeted at these epigenetic alterations in the management of endocrine resistant breast cancer.

Introduction SUPREMO is a phase 3 randomised trial evaluating radiotherapy post-mastectomy for intermediate-risk breast cancer. 1688 patients were enrolled from 16 countries between 2006 and 2013. We report the results of central pathology review carried out for quality assurance. Patients and methods A single recut haematoxylin and eosin (H&E) tumour section was assessed by one of two reviewing pathologists, blinded to the originally reported pathology and patient data. Tumour type, grade and lymphovascular invasion were reviewed to assess if they met the inclusion criteria. Slides from potentially ineligible patients on central review were scanned and reviewed online together by the two pathologists and a consensus reached. A subset of 25 of these cases was double-reported independently by the pathologists prior to the online assessment. Results The major contributors to the trial were the UK (75%) and the Netherlands (10%). There is a striking difference in lymphovascular invasion (LVi) rates (41.6 vs. 15.1% (UK); p = p = p = 0.40) but a significant difference in the reviewed frequency (16.9 vs. 9.9%; p = 0.004). Of the N− cases, 104 (25.1%) would have been ineligible by initial central review by virtue of grade and/or lymphovascular invasion status. Following online consensus review, this fell to 70 cases (16.3% of N− cases, 4.1% of all cases). Conclusions These data have important implications for the design, powering and interpretation of outcomes from this and future clinical trials. If critical pathology criteria are determinants for trial entry, serious consideration should be given to up-front central pathology review.

Aims To critically evaluate HER2 testing data for 3500 consecutive cases over 28 months for a single laboratory and review these findings in the light of current UK reporting guidelines. Methods We have reviewed all data relating to the HER2 testing service including reagents and analytical machine, HER2 immunohistochemistry (IHC) and fluorescent in situ hybridisation (FISH) scoring profiles. We have examined the place of double counting and rapid screening of FISH and when it is and is not safe to do so. This analysis has been facilitated greatly by the inclusion of custom scripts embedded in a spreadsheet used for recording the data. Results There were no differences in scoring profiles in relation to testing machine or reagent batch for both HER2 IHC and FISH. There was excellent concordance in scoring by the biomedical scientists and pathologists providing the service. There is a significant difference between the proximity of scores in double-scored cases with HER2 copy number >6.0 compared with those with copy number Conclusions Single counting of HER2 FISH supported by a second rapid screen is safe and practicable within strict parameters. Any assessment of proximity of scores in double-scored cases should take into account the HER2 copy number, with greater spreads experienced at higher copy numbers. Centralising HER2 testing in a regional centre allows detailed audit of the entire analytical process.