Confirmation that somatic mutations of beta-2 microglobulin correlate with a lack of recurrence in a subset of stage II mismatch repair deficient colorectal cancers from the QUASAR trial

Aims: Beta2‐Microglobulin (B2M) forms part of the HLA class I complex and plays a role in metastatic biology. B2M mutations occur frequently in mismatch repair‐deficient colorectal cancer (dMMR CRC) with limited data suggesting they may protect against recurrence. Our experimental study tested this hypothesis by investigating B2M mutation status and B2M protein expression and recurrence in patients in the stage II QUASAR clinical trial. Methods: Sanger sequencing was performed for the three coding exons of B2M on 121 dMMR and a subsample of 108 pMMR tumours; 52 with recurrence and 56 without. B2M protein expression was assessed by immunohistochemistry. Mutation status and protein expression were correlated with recurrence and compared to proficient mismatch repair (pMMR) CRCs. Results: Deleterious B2M mutations were detected in 39/121 (32%) dMMR tumours. Five contained missense B2M‐variants of unknown significance, so were excluded from further analyses. With median follow‐up 7.4 years, none of the 39 B2M‐mutant tumours recurred, compared with 14/77 (18%) B2M‐wildtype tumours (p=0.005); six at local and eight at distant sites. Sensitivity and specificity of IHC in detecting B2M mutations was 87% and 71% respectively. Significantly (p<0.0001) fewer 3/104 (2.9%) of the 108 pMMR CRCs demonstrated deleterious B2M mutations. One pMMR tumour, containing a frameshift mutation, later recurred. Conclusion: B2M mutations were detected in nearly one third of dMMR cancers, none of which recurred. B2M mutation status has potential clinical utility as a prognostic biomarker in stage II dMMR CRC. The mechanism of protection against recurrence and whether this protection extends to stage III disease remains unclear

Intra-tumoural stromal morphometry predicts disease recurrence but not response to 5-fluorouracil – results from the QUASAR trial of colorectal cancer

Introduction: The biological importance of tumour-associated stroma is increasingly apparent, yet clinical utility remains ill-defined. In stage-II / Dukes B colorectal cancer (CRC), clinical biomarkers are urgently required to direct therapeutic options. We report here prognostic/predictive analyses, and molecular associations, of stromal morphometric quantification in the Quick and Simple and Reliable (QUASAR) trial of CRC. Materials and methods: Relative proportions of tumour epithelium (PoT) or stroma (PoS) were morphometrically quantified using digitised haematoxylin and eosin sections derived from 1,800 patients enrolled in QUASAR which randomised 3,239 (91% stage II) CRC patients between adjuvant fluorouracil/folinic acid (FUFA) chemotherapy and observation. The prognostic/predictive value of PoT/PoS measures were determined by stratified log-rank analyses. Results: High tumour stroma (≥50%) was associated with increased recurrence risk: 31.3% (143/457) recurrence for ≥50% versus 21.9% (294/1,343) if <50% [Rate ratio (RR)=1.62; 95%CI 1.30-2.02, p<0.0001)]. For stromal proportions of ≥65%, 40% (46/115) of patients had recurrent disease within 10 years. The adverse prognostic effect of high stroma was independent of established prognostic variables, and maintained in stage II / Dukes B patients (RR=1.62; 95%CI=1.26-2.08; p=0.0002). KRAS mutation in the presence of high stroma augmented recurrence risk (RR=2.93; 95%CI=1.87-4.59; p=0.0005). Stromal morphometry did not predict response to FUFA chemotherapy. Discussion: Simple digital morphometry applied to a single representative H&E section identifies CRC patients with over 50% higher risk of disease recurrence. This technique can reliably partition patients into sub-populations with differential risks of tumour recurrence in a simple and cost-effective manner. Further prospective validation is warranted

Lynch syndrome screening in colorectal cancer: results of a prospective 2-year regional programme validating the NICE diagnostics guidance pathway throughout a 5.2-million population

Aims Screening all patients newly diagnosed with colorectal cancer (CRC) for possible Lynch syndrome (LS) has been recommended in the United Kingdom since the National Institute for Health and Care Excellence (NICE) released new diagnostics guidance in February 2017. We sought to validate the NICE screening pathway through a prospective regional programme throughout a 5.2-million population during a 2-year period. Methods and results Pathology departments at 14 hospital trusts in the Yorkshire and Humber region of the United Kingdom were invited to refer material from patients with newly diagnosed CRC aged 50 years or over between 1 April 2017 and 31 March 2019 for LS screening. Testing consisted of immunohistochemistry for MLH1, PMS2, MSH2 and MSH6 followed by BRAF mutation analysis ± MLH1 promoter methylation testing in cases showing MLH1 loss. A total of 3141 individual specimens were submitted for testing from 12 departments consisting of 3061 unique tumours and 2791 prospectively acquired patients with CRC. Defective mismatch repair (dMMR) was observed in 15% of cases. In cases showing MLH1 loss, 76% contained a detectable BRAF mutation and, of the remainder, 77% showed MLH1 promoter hypermethylation. Of the patients included in the final analysis, 81 (2.9%) had an indication for germline testing. Conclusion LS screening using the NICE diagnostics guidance pathway is deliverable at scale identifying significant numbers of patients with dMMR. This information is used to refer patients to regional clinical genetics services in addition to informing treatment pathways including the use of adjuvant/neoadjuvant chemotherapy and immunotherapy

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient

Swarm learning for decentralized artificial intelligence in cancer histopathology

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer

Histological intratumoral heterogeneity in pretreatment esophageal cancer biopsies predicts survival benefit from neoadjuvant chemotherapy: results from the UK MRC OE02 trial

Despite the use of multimodal treatment, survival of esophageal cancer (EC) patients remains poor. One proposed explanation for the relatively poor response to cytotoxic chemotherapy is intratumor heterogeneity. The aim was to establish a statistical model to objectively measure intratumor heterogeneity of the proportion of tumor (IHPoT) and to use this newly developed method to measure IHPoT in the pretreatment biopsies from from EC patients recruited to the OE02 trial. A statistical mixed effect model (MEM) was established for estimating IHPoT based on variation in hematoxylin/eosin (HE) stained pretreatment biopsy pieces from the same individual in 218 OE02 trial patients (103 treated by chemotherapy and surgery (chemo+surgery); 115 patients treated by surgery alone). The relationship between IHPoT, prognosis, chemotherapy survival benefit, and clinicopathological variables was assessed. About 97 (44.5%) and 121 (55.5%) ECs showed high and low IHPoT, respectively. There was no significant difference in IHPoT between surgery (median [range], 0.1637 [0–3.17]) and chemo+surgery (median [range], 0.1692 [0–2.69]) patients (P = 0.43). Chemo+surgery patients with low IHPoT had a significantly longer survival than surgery patients (HR = 1.81, 95% CI: 1.20–2.75, P = 0.005). There was no survival difference between chemo+surgery and surgery patients with high IHPoT (HR = 1.15, 95% CI: 0.72–1.81, P = 0.566). This is the first study suggesting that IHPoT measured in the pretreatment biopsy can predict chemotherapy survival benefit in EC patients. IHPoT may represent a clinically useful biomarker for patient treatment stratification. Future studies should determine if pathologists can reliably estimate IHPoT

Intra-tumoural stromal morphometry predicts disease recurrence but not response to 5-fluorouracil - results from the QUASAR trial of colorectal cancer.

INTRODUCTION: The biological importance of tumour-associated stroma is increasingly apparent, yet clinical utility remains ill-defined. In stage-II / Dukes B colorectal cancer (CRC), clinical biomarkers are urgently required to direct therapeutic options. We report here prognostic/predictive analyses, and molecular associations, of stromal morphometric quantification in the Quick and Simple and Reliable (QUASAR) trial of CRC MATERIALS AND METHODS: Relative proportions of tumour epithelium (PoT) or stroma (PoS) were morphometrically quantified using digitised haematoxylin and eosin sections derived from 1,800 patients enrolled in QUASAR which randomised 3,239 (91% stage II) CRC patients between adjuvant fluorouracil/folinic acid (FUFA) chemotherapy and observation. The prognostic/predictive value of PoT/PoS measures were determined by stratified log-rank analyses RESULTS: High tumour stroma (≥50%) was associated with increased recurrence risk: 31.3% (143/457) recurrence for ≥50% versus 21.9% (294/1,343) if &lt;50% [Rate ratio (RR)=1.62; 95%CI 1.30-2.02, p&lt;0.0001)]. For stromal proportions of ≥65%, 40% (46/115) of patients had recurrent disease within 10 years. The adverse prognostic effect of high stroma was independent of established prognostic variables, and maintained in stage II / Dukes B patients (RR=1.62; 95%CI=1.26-2.08; p=0.0002). KRAS mutation in the presence of high stroma augmented recurrence risk (RR=2.93; 95%CI=1.87-4.59; p=0.0005). Stromal morphometry did not predict response to FUFA chemotherapy DISCUSSION: Simple digital morphometry applied to a single representative H&amp;E section identifies CRC patients with over 50% higher risk of disease recurrence. This technique can reliably partition patients into sub-populations with differential risks of tumour recurrence in a simple and cost-effective manner. Further prospective validation is warranted. This article is protected by copyright. All rights reserved.</p