Activation of innate-adaptive immune machinery by poly(I:C) exposes a therapeutic vulnerability to prevent relapse in stroma-rich colon cancer.

OBJECTIVE: Stroma-rich tumours represent a poor prognostic subtype in stage II/III colon cancer (CC), with high relapse rates and limited response to standard adjuvant chemotherapy. DESIGN: To address the lack of efficacious therapeutic options for patients with stroma-rich CC, we stratified our human tumour cohorts according to stromal content, enabling identification of the biology underpinning relapse and potential therapeutic vulnerabilities specifically within stroma-rich tumours that could be exploited clinically. Following human tumour-based discovery and independent clinical validation, we use a series of in vitro and stroma-rich in vivo models to test and validate the therapeutic potential of elevating the biology associated with reduced relapse in human tumours. RESULTS: By performing our analyses specifically within the stroma-rich/high-fibroblast (HiFi) subtype of CC, we identify and validate the clinical value of a HiFi-specific prognostic signature (HPS), which stratifies tumours based on STAT1-related signalling (High-HPS v Low-HPS=HR 0.093, CI 0.019 to 0.466). Using in silico, in vitro and in vivo models, we demonstrate that the HPS is associated with antigen processing and presentation within discrete immune lineages in stroma-rich CC, downstream of double-stranded RNA and viral response signalling. Treatment with the TLR3 agonist poly(I:C) elevated the HPS signalling and antigen processing phenotype across in vitro and in vivo models. In an in vivo model of stroma-rich CC, poly(I:C) treatment significantly increased systemic cytotoxic T cell activity (p<0.05) and reduced liver metastases (p<0.0002). CONCLUSION: This study reveals new biological insight that offers a novel therapeutic option to reduce relapse rates in patients with the worst prognosis CC.

Experiences of running a stratified medicine adaptive platform trial: Challenges and lessons learned from 10 years of the FOCUS4 trial in metastatic colorectal cancer.

BACKGROUND: Complex innovative design trials are becoming increasingly common and offer potential for improving patient outcomes in a faster time frame. FOCUS4 was the first molecularly stratified trial in metastatic colorectal cancer and it remains one of the first umbrella trial designs to be launched globally. Here, we aim to describe lessons learned from delivery of the trial over the last 10 years. METHODS: FOCUS4 was a Phase II/III molecularly stratified umbrella trial testing the safety and efficacy of targeted therapies in metastatic colorectal cancer. It used adaptive statistical methodology to decide which sub-trial should close early, and new therapies were added as protocol amendments. Patients with newly diagnosed metastatic colorectal cancer were registered, and central laboratory testing was used to stratify their tumour into molecular subtypes. Following 16 weeks of first-line therapy, patients with stable or responding disease were eligible for randomisation into either a molecularly stratified sub-trial (FOCUS4-B, C or D) or non-stratified FOCUS4-N. The primary outcome for all studies was progression-free survival comparing the intervention with active monitoring/placebo. At the close of the trial, feedback was elicited from all investigators through surveys and interviews and consolidated into a series of recommendations and lessons learned for the delivery of similar future trials. RESULTS: Between January 2014 and October 2020, 1434 patients were registered from 88 UK hospitals. Of the 20 drug combinations that were explored for inclusion in the platform trial, three molecularly targeted sub-trials were activated: FOCUS4-D (February 2014-March 2016) evaluated AZD8931 in the BRAF-PIK3CA-RAS wildtype subgroup; FOCUS4-B (February 2016-July 2018) evaluated aspirin in the PIK3CA mutant subgroup and FOCUS4-C (June 2017-October 2020) evaluated adavosertib in the RAS+TP53 double mutant subgroup. FOCUS4-N was active throughout and evaluated capecitabine monotherapy versus a treatment break. A total of 361 (25%) registered patients were randomised into a sub-trial. Feedback on the experiences of delivery of FOCUS4 could be grouped into three main areas of challenge: funding/infrastructure, biomarker testing procedures and trial design efficiencies within which 20 recommendations are summarised. CONCLUSION: Adaptive stratified medicine platform studies are feasible in common cancers but present challenges. Our stakeholder feedback has helped to inform how these trial designs can succeed and answer multiple questions efficiently, providing resource is adequate.

Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning.

OBJECTIVE: Complex phenotypes captured on histological slides represent the biological processes at play in individual cancers, but the link to underlying molecular classification has not been clarified or systematised. In colorectal cancer (CRC), histological grading is a poor predictor of disease progression, and consensus molecular subtypes (CMSs) cannot be distinguished without gene expression profiling. We hypothesise that image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data and to resolve unclassifiable or heterogeneous cases. In this study, we present an image-based approach to predict CRC CMS from standard H&E sections using deep learning. DESIGN: Training and evaluation of a neural network were performed using a total of n=1206 tissue sections with comprehensive multi-omic data from three independent datasets (training on FOCUS trial, n=278 patients; test on rectal cancer biopsies, GRAMPIAN cohort, n=144 patients; and The Cancer Genome Atlas (TCGA), n=430 patients). Ground truth CMS calls were ascertained by matching random forest and single sample predictions from CMS classifier. RESULTS: Image-based CMS (imCMS) accurately classified slides in unseen datasets from TCGA (n=431 slides, AUC)=0.84) and rectal cancer biopsies (n=265 slides, AUC=0.85). imCMS spatially resolved intratumoural heterogeneity and provided secondary calls correlating with bioinformatic prediction from molecular data. imCMS classified samples previously unclassifiable by RNA expression profiling, reproduced the expected correlations with genomic and epigenetic alterations and showed similar prognostic associations as transcriptomic CMS. CONCLUSION: This study shows that a prediction of RNA expression classifiers can be made from H&E images, opening the door to simple, cheap and reliable biological stratification within routine workflows.

Molecular subtype-specific efficacy of anti-EGFR therapy in colorectal cancer is dependent on the chemotherapy backbone.

BACKGROUND: Patient selection for addition of anti-EGFR therapy to chemotherapy for patients with RAS and BRAF wildtype metastatic colorectal cancer can still be optimised. Here we investigate the effect of anti-EGFR therapy on survival in different consensus molecular subtypes (CMSs) and stratified by primary tumour location. METHODS: Retrospective analyses, using the immunohistochemistry-based CMS classifier, were performed in the COIN (first-line oxaliplatin backbone with or without cetuximab) and PICCOLO trial (second-line irinotecan with or without panitumumab). Tumour tissue was available for 323 patients (20%) and 349 (41%), respectively. RESULTS: When using an irinotecan backbone, anti-EGFR therapy is effective in both CMS2/3 and CMS4 in left-sided primary tumours (progression-free survival (PFS): HR 0.44, 95% CI 0.26-0.75, P = 0.003 and HR 0.12, 95% CI 0.04-0.36, P < 0.001, respectively) and in CMS4 right-sided tumours (PFS HR 0.17, 95% CI 0.04-0.71, P = 0.02). Efficacy using an oxaliplatin backbone was restricted to left-sided CMS2/3 tumours (HR 0.57, 95% CI 0.36-0.96, P = 0.034). CONCLUSIONS: The subtype-specific efficacy of anti-EGFR therapy is dependent on the chemotherapy backbone. This may provide the possibility of subtype-specific treatment strategies for a more optimal use of anti-EGFR therapy.

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning.

BACKGROUND & AIMS: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and less expensively than molecular assays. However, clinical application of this technology requires high performance and multisite validation, which have not yet been performed. METHODS: We collected H&E-stained slides and findings from molecular analyses for MSI and dMMR from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (N = 6406 specimens) and validated in an external cohort (n = 771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). RESULTS: The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound, 0.91; upper bound, 0.93) and an AUPRC of 0.63 (range, 0.59-0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC of 0.95 (range, 0.92-0.96) without image preprocessing and an AUROC of 0.96 (range, 0.93-0.98) after color normalization. CONCLUSIONS: We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using H&E-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens.

Immune status is prognostic for poor survival in colorectal cancer patients and is associated with tumour hypoxia.

BACKGROUND: Immunohistochemical quantification of the immune response is prognostic for colorectal cancer (CRC). Here, we evaluate the suitability of alternative immune classifiers on prognosis and assess whether they relate to biological features amenable to targeted therapy. METHODS: Overall survival by immune (CD3, CD4, CD8, CD20 and FOXP3) and immune-checkpoint (ICOS, IDO-1 and PD-L1) biomarkers in independent CRC cohorts was evaluated. Matched mutational and transcriptomic data were interrogated to identify associated biology. RESULTS: Determination of immune-cold tumours by combined low-density cell counts of CD3, CD4 and CD8 immunohistochemistry constituted the best prognosticator across stage II-IV CRC, particularly in patients with stage IV disease (HR 1.98 [95% CI: 1.47-2.67]). These immune-cold CRCs were associated with tumour hypoxia, confirmed using CAIX immunohistochemistry (P = 0.0009), which may mediate disease progression through common biology (KRAS mutations, CRIS-B subtype and SPP1 mRNA overexpression). CONCLUSIONS: Given the significantly poorer survival of immune-cold CRC patients, these data illustrate that assessment of CD4-expressing cells complements low CD3 and CD8 immunohistochemical quantification in the tumour bulk, potentially facilitating immunophenotyping of patient biopsies to predict prognosis. In addition, we found immune-cold CRCs to associate with a difficult-to-treat, poor prognosis hypoxia signature, indicating that these patients may benefit from hypoxia-targeting clinical trials.

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 AND RESULTS: 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. Deleterious B2M mutations were detected in 39 of 121 (32%) dMMR tumours. Five contained missense B2M-variants of unknown significance, so were excluded from further analyses. With median follow-up of 7.4 years, none of the 39 B2M-mutant tumours recurred, compared with 14 of 77 (18%) B2M-wild-type 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 (three of 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.

Prospective patient stratification into robust cancer-cell intrinsic subtypes from colorectal cancer biopsies.

Colorectal cancer (CRC) biopsies underpin accurate diagnosis, but are also relevant for patient stratification in molecularly-guided clinical trials. The consensus molecular subtypes (CMSs) and colorectal cancer intrinsic subtypes (CRISs) transcriptional signatures have potential clinical utility for improving prognostic/predictive patient assignment. However, their ability to provide robust classification, particularly in pretreatment biopsies from multiple regions or at different time points, remains untested. In this study, we undertook a comprehensive assessment of the robustness of CRC transcriptional signatures, including CRIS and CMS, using a range of tumour sampling methodologies currently employed in clinical and translational research. These include analyses using (i) laser-capture microdissected CRC tissue, (ii) eight publically available rectal cancer biopsy data sets (n = 543), (iii) serial biopsies (from AXEBeam trial, NCT00828672; n = 10), (iv) multi-regional biopsies from colon tumours (n = 29 biopsies, n = 7 tumours), and (v) pretreatment biopsies from the phase II rectal cancer trial COPERNCIUS (NCT01263171; n = 44). Compared to previous results obtained using CRC resection material, we demonstrate that CMS classification in biopsy tissue is significantly less capable of reliably classifying patient subtype (43% unknown in biopsy versus 13% unknown in resections, p = 0.0001). In contrast, there was no significant difference in classification rate between biopsies and resections when using the CRIS classifier. Additionally, we demonstrated that CRIS provides significantly better spatially- and temporally- robust classification of molecular subtypes in CRC primary tumour tissue compared to CMS (p = 0.003 and p = 0.02, respectively). These findings have potential to inform ongoing biopsy-based patient stratification in CRC, enabling robust and stable assignment of patients into clinically-informative arms of prospective multi-arm, multi-stage clinical trials. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

HER2 overexpression and amplification as a potential therapeutic target in colorectal cancer: analysis of 3256 patients enrolled in the QUASAR, FOCUS and PICCOLO colorectal cancer trials.

HER2 overexpression/amplification is linked to trastuzumab response in breast/gastric cancers. One suggested anti-EGFR resistance mechanism in colorectal cancer (CRC) is aberrant MEK-AKT pathway activation through HER2 up-regulation. We assessed HER2-amplification/overexpression in stage II-III and IV CRC patients, assessing relationships to KRAS/BRAF and outcome. Pathological material was obtained from 1914 patients in the QUASAR stage II-III trial and 1342 patients in stage IV trials (FOCUS and PICCOLO). Tissue microarrays were created for HER2 immunohistochemistry. HER2-amplification was assessed using FISH and copy number variation. KRAS/BRAF mutation status was assessed by pyrosequencing. Progression-free survival (PFS) and overall survival (OS) data were obtained for FOCUS/PICCOLO and recurrence and mortality for QUASAR; 29/1342 (2.2%) stage IV and 25/1914 (1.3%) stage II-III tumours showed HER2 protein overexpression. Of the HER2-overexpressing cases, 27/28 (96.4%) stage IV tumours and 20/24 (83.3%) stage II-III tumours demonstrated HER2 amplification by FISH; 41/47 (87.2%) also showed copy number gains. HER2-overexpression was associated with KRAS/BRAF wild-type (WT) status at all stages: in 5.2% WT versus 1.0% mutated tumours (p < 0.0001) in stage IV and 2.1% versus 0.2% in stage II-III tumours (p = 0.01), respectively. HER2 was not associated with OS or PFS. At stage II-III, there was no significant correlation between HER2 overexpression and 5FU/FA response. A higher proportion of HER2-overexpressing cases experienced recurrence, but the difference was not significant. HER2-amplification/overexpression is identifiable by immunohistochemistry, occurring infrequently in stage II-III CRC, rising in stage IV and further in KRAS/BRAF WT tumours. The value of HER2-targeted therapy in patients with HER2-amplified CRC must be tested in a clinical trial. © 2015 The Authors. Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Image-based consensus molecular subtyping in rectal cancer biopsies and response to neoadjuvant chemoradiotherapy.

The development of deep learning (DL) models to predict the consensus molecular subtypes (CMS) from histopathology images (imCMS) is a promising and cost-effective strategy to support patient stratification. Here, we investigate whether imCMS calls generated from whole slide histopathology images (WSIs) of rectal cancer (RC) pre-treatment biopsies are associated with pathological complete response (pCR) to neoadjuvant long course chemoradiotherapy (LCRT) with single agent fluoropyrimidine. DL models were trained to classify WSIs of colorectal cancers stained with hematoxylin and eosin into one of the four CMS classes using a multi-centric dataset of resection and biopsy specimens (n = 1057 WSIs) with paired transcriptional data. Classifiers were tested on a held out RC biopsy cohort (ARISTOTLE) and correlated with pCR to LCRT in an independent dataset merging two RC cohorts (ARISTOTLE, n = 114 and SALZBURG, n = 55 patients). DL models predicted CMS with high classification performance in multiple comparative analyses. In the independent cohorts (ARISTOTLE, SALZBURG), cases with WSIs classified as imCMS1 had a significantly higher likelihood of achieving pCR (OR = 2.69, 95% CI 1.01-7.17, p = 0.048). Conversely, imCMS4 was associated with lack of pCR (OR = 0.25, 95% CI 0.07-0.88, p = 0.031). Classification maps demonstrated pathologist-interpretable associations with high stromal content in imCMS4 cases, associated with poor outcome. No significant association was found in imCMS2 or imCMS3. imCMS classification of pre-treatment biopsies is a fast and inexpensive solution to identify patient groups that could benefit from neoadjuvant LCRT. The significant associations between imCMS1/imCMS4 with pCR suggest the existence of predictive morphological features that could enhance standard pathological assessment.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer.

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

FOCUS4 biomarker laboratories: from the benefits to the practical and logistical issues faced during 6 years of centralised testing.

AIMS: FOCUS4 was a phase II/III umbrella trial, recruiting patients with advanced or metastatic colorectal cancer, between 2014 and 2020. Molecular profiling of patients' formalin-fixed, paraffin-embedded tumour blocks was undertaken at two centralised biomarker laboratories (Leeds and Cardiff), and the results fed directly to the Medical Research Council Clinical Trials Unit, and used for subsequent randomisation. Here the laboratories discuss their experiences. METHODS: Following successful tumour content assessment, blocks were sectioned for DNA extraction and immunohistochemistry (IHC). Pyrosequencing was initially used to determine tumour mutation status (KRAS, NRAS, BRAF and PIK3CA), then from 2018 onwards, next-generation sequencing was employed to allow the inclusion of TP53. Protein expression of MLH1, MSH2, MSH6, PMS2 and pTEN was determined by IHC. An interlaboratory comparison programme was initiated, allowing sample exchanges, to ensure continued assay robustness. RESULTS: 1291 tumour samples were successfully analysed. Assay failure rates were very low; 1.9%-3.3% for DNA sequencing and 0.9%-1.3% for IHC. Concordance rates of >98% were seen for the interlaboratory comparisons, where a result was obtained by both laboratories. CONCLUSIONS: Practical and logistical problems were identified, including poor sample quality and difficulties with sample anonymisation. The often last-minute receipt of a sample for testing and a lack of integration with National Health Service mutation analysis services were challenging. The laboratories benefitted from both pretrial validations and interlaboratory comparisons, resulting in robust assay development and provided confidence during the implementation of new sequencing technologies. We conclude that our centralised approach to biomarker testing in FOCUS4 was effective and successful.

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.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.

Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer.

PURPOSE: High tumor production of the EGFR ligands, amphiregulin (AREG) and epiregulin (EREG), predicted benefit from anti-EGFR therapy for metastatic colorectal cancer (mCRC) in a retrospective analysis of clinical trial data. Here, AREG/EREG IHC was analyzed in a cohort of patients who received anti-EGFR therapy as part of routine care, including key clinical contexts not investigated in the previous analysis. EXPERIMENTAL DESIGN: Patients who received panitumumab or cetuximab ± chemotherapy for treatment of RAS wild-type mCRC at eight UK cancer centers were eligible. Archival formalin-fixed paraffin-embedded tumor tissue was analyzed for AREG and EREG IHC in six regional laboratories using previously developed artificial intelligence technologies. Primary endpoints were progression-free survival (PFS) and overall survival (OS). RESULTS: A total of 494 of 541 patients (91.3%) had adequate tissue for analysis. A total of 45 were excluded after central extended RAS testing, leaving 449 patients in the primary analysis population. After adjustment for additional prognostic factors, high AREG/EREG expression (n = 360; 80.2%) was associated with significantly prolonged PFS [median: 8.5 vs. 4.4 months; HR, 0.73; 95% confidence interval (CI), 0.56-0.95; P = 0.02] and OS [median: 16.4 vs. 8.9 months; HR, 0.66 95% CI, 0.50-0.86; P = 0.002]. The significant OS benefit was maintained among patients with right primary tumor location (PTL), those receiving cetuximab or panitumumab, those with an oxaliplatin- or irinotecan-based chemotherapy backbone, and those with tumor tissue obtained by biopsy or surgical resection. CONCLUSIONS: High tumor AREG/EREG expression was associated with superior survival outcomes from anti-EGFR therapy in mCRC, including in right PTL disease. AREG/EREG IHC assessment could aid therapeutic decisions in routine practice. See related commentary by Randon and Pietrantonio, p. 4021.

Adaptor Template Oligo-Mediated Sequencing (ATOM-Seq) is a new ultra-sensitive UMI-based NGS library preparation technology for use with cfDNA and cfRNA.

Liquid biopsy testing utilising Next Generation Sequencing (NGS) is rapidly moving towards clinical adoption for personalised oncology. However, before NGS can fulfil its potential any novel testing approach must identify ways of reducing errors, allowing separation of true low-frequency mutations from procedural artefacts, and be designed to improve upon current technologies. Popular NGS technologies typically utilise two DNA capture approaches; PCR and ligation, which have known limitations and seem to have reached a development plateau with only small, stepwise improvements being made. To maximise the ultimate utility of liquid biopsy testing we have developed a highly versatile approach to NGS: Adaptor Template Oligo Mediated Sequencing (ATOM-Seq). ATOM-Seq's strengths and versatility avoid the major limitations of both PCR- and ligation-based approaches. This technology is ligation free, simple, efficient, flexible, and streamlined, and it offers novel advantages that make it perfectly suited for use on highly challenging clinical material. Using reference and clinical materials, we demonstrate detection of known SNVs down to allele frequencies of 0.1% using as little as 20-25 ng of cfDNA, as well as the ability to detect fusions from RNA. We illustrate ATOM-Seq's suitability for clinical testing by showing high concordance rates between paired cfDNA and FFPE clinical samples.

Inhibition of WEE1 Is Effective in TP53- and RAS-Mutant Metastatic Colorectal Cancer: A Randomized Trial (FOCUS4-C) Comparing Adavosertib (AZD1775) With Active Monitoring.

PURPOSE: Outcomes in RAS-mutant metastatic colorectal cancer (mCRC) remain poor and patients have limited therapeutic options. Adavosertib is the first small-molecule inhibitor of WEE1 kinase. We hypothesized that aberrations in DNA replication seen in mCRC with both RAS and TP53 mutations would sensitize tumors to WEE1 inhibition. METHODS: Patients with newly diagnosed mCRC were registered into FOCUS4 and tested for TP53 and RAS mutations. Those with both mutations who were stable or responding after 16 weeks of chemotherapy were randomly assigned 2:1 between adavosertib and active monitoring (AM). Adavosertib (250 mg or 300 mg) was taken orally once on days 1-5 and days 8-12 of a 3-week cycle. The primary outcome was progression-free survival (PFS), with a target hazard ratio (HR) of 0.5 and 80% power with a one-sided 0.025 significance level. RESULTS: FOCUS4-C was conducted between April 2017 and Mar 2020 during which time 718 patients were registered; 247 (34%) were RAS/TP53-mutant. Sixty-nine patients were randomly assigned from 25 UK hospitals (adavosertib = 44; AM = 25). Adavosertib was associated with a PFS improvement over AM (median 3.61 v 1.87 months; HR = 0.35; 95% CI, 0.18 to 0.68; P = .0022). Overall survival (OS) was not improved with adavosertib versus AM (median 14.0 v 12.8 months; HR = 0.92; 95% CI, 0.44 to 1.94; P = .93). In prespecified subgroup analysis, adavosertib activity was greater in left-sided tumors (HR = 0.24; 95% CI, 0.11 to 0.51), versus right-sided (HR = 1.02; 95% CI, 0.41 to 2.56; interaction P = .043). Adavosertib was well-tolerated; grade 3 toxicities were diarrhea (9%), nausea (5%), and neutropenia (7%). CONCLUSION: In this phase II randomized trial, adavosertib improved PFS compared with AM and demonstrates potential as a well-tolerated therapy for RAS/TP53-mutant mCRC. Further testing is required in this sizable population of unmet need.

Capecitabine Versus Active Monitoring in Stable or Responding Metastatic Colorectal Cancer After 16 Weeks of First-Line Therapy: Results of the Randomized FOCUS4-N Trial.

PURPOSE: Despite extensive randomized evidence supporting the use of treatment breaks in metastatic colorectal cancer (mCRC), they are not universally offered to patients despite improvements in quality of life without detriment to overall survival (OS). FOCUS4-N was set up to explore the impact of oral maintenance therapy in patients who are responding to first-line therapy. METHODS: FOCUS4 was a molecularly stratified trial program that registered patients with newly diagnosed mCRC. The FOCUS4-N trial was offered to patients in whom a targeted subtrial was unavailable or biomarker tests failed. Patients were randomly assigned using a 1:1 ratio between maintenance capecitabine and active monitoring (AM). The primary outcome was progression-free survival (PFS) with secondary outcomes including OS toxicity and tolerability. RESULTS: Between March 2014 and March 2020, 254 patients were randomly assigned (127 to capecitabine and 127 to AM) across 88 UK sites. Baseline characteristics were balanced. There was strong evidence of efficacy for PFS (hazard ratio = 0.40; 95% CI, 0.21 to 0.75; P < .0001), but no significant improvement in OS (hazard ratio, 0.93; 95% CI, 0.69 to 1.27; P = .66) was observed. Compliance with treatment was good, and toxicity from capecitabine versus AM was as expected with grade ≥ 2 fatigue (25% v 12%), diarrhea (23% v 13%), and hand-foot syndrome (26% v 3%). Quality of life showed little difference between the groups. CONCLUSION: Despite strong evidence of disease control with maintenance therapy, OS remains unaffected and FOCUS4-N provides additional evidence to support the use of treatment breaks as safe management alternatives for patients who are stable or responding to first-line treatment for mCRC. Capecitabine without bevacizumab may be used to extend PFS in the interval after 16 weeks of first-line therapy.

EGFR Amplification in Metastatic Colorectal Cancer.

BACKGROUND: EGFR amplification occurs in about 1% of metastatic colorectal cancers (mCRCs) but is not routinely tested as a prognostic or predictive biomarker for patients treated with anti-EGFR monoclonal antibodies. Herein, we aimed to characterize the clinical and molecular landscape of EGFR-amplified mCRC. METHODS: In this multinational cohort study, we compared clinical data of 62 patients with EGFR-amplified vs 1459 EGFR nonamplified mCRC, as well as comprehensive genomic data of 35 EGFR-amplified vs 439 EGFR nonamplified RAS/BRAF wild-type and microsatellite stable (MSS) tumor samples. All statistical tests were 2-sided. RESULTS: EGFR amplification was statistically significantly associated with left primary tumor sidedness and RAS/BRAF wild-type status. All EGFR-amplified tumors were MSS and HER2 nonamplified. Overall, EGFR-amplified samples had higher median fraction of genome altered compared with EGFR-nonamplified, RAS/BRAF wild-type MSS cohort. Patients with EGFR-amplified tumors reported longer overall survival (OS) (median OS = 71.3 months, 95% confidence interval [CI] = 50.7 to not available [NA]) vs EGFR-nonamplified ones (24.0 months; 95% CI = 22.8 to 25.6; hazard ratio [HR] = 0.30, 95% CI = 0.20 to 0.44; P < .001; adjusted HR = 0.46, 95% CI = 0.30 to 0.69; P < .001). In the subgroup of patients with RAS/BRAF wild-type mCRC exposed to anti-EGFR-based therapy, EGFR amplification was again associated with better OS (median OS = 54.0 months, 95% CI = 35.2 to NA, vs 29.1 months, 95% CI = 27.0 to 31.9, respectively; HR = 0.46, 95% CI = 0.28 to 0.76; P = .002). CONCLUSION: Patients with EGFR-amplified mCRC represent a biologically defined subgroup and merit dedicated clinical trials with novel and more potent EGFR-targeting strategies beyond single-agent monoclonal antibodies.

Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in RAS Wild-Type Metastatic Colorectal Cancer.

PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice due to issues with analytic precision and practicality. We investigated whether AREG/EREG IHC could predict benefit from the anti-EGFR agent panitumumab. EXPERIMENTAL DESIGN: Artificial intelligence algorithms were developed to assess AREG/EREG IHC in 274 patients from the PICCOLO trial of irinotecan with or without panitumumab (Ir vs. IrPan) in RAS wild-type mCRC. The primary endpoint was progression-free survival (PFS). Secondary endpoints were RECIST response rate (RR) and overall survival (OS). Models were repeated adjusting separately for BRAF mutation status and primary tumor location (PTL). RESULTS: High ligand expression was associated with significant PFS benefit from IrPan compared with Ir [8.0 vs. 3.2 months; HR, 0.54; 95% confidence interval (CI), 0.37-0.79; P = 0.001]; whereas low ligand expression was not (3.4 vs. 4.4 months; HR, 1.05; 95% CI, 0.74-1.49; P = 0.78). The ligand-treatment interaction was significant (P interaction = 0.02) and remained significant after adjustment for BRAF-mutation status and PTL. Likewise, RECIST RR was significantly improved in patients with high ligand expression (IrPan vs. Ir: 48% vs. 6%; P < 0.0001) but not those with low ligand expression (25% vs. 14%; P = 0.10; P interaction = 0.01). The effect on OS was similar but not statistically significant. CONCLUSIONS: AREG/EREG IHC identified patients who benefitted from the addition of panitumumab to irinotecan chemotherapy. IHC is a practicable assay that may be of use in routine practice.