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Additional file 1: Table S1. All treatment emergent adverse events categorised and graded according to CTCAE version 4.03. Table S2. HR23B score data for all treated patients. Figure S1. Progression-free Survival on CXD101.

Patients with relapsed or refractory diffuse large B cell lymphoma (DLBCL) who are unfit for or relapsed post autologous stem‐cell transplantation have poor outcomes. Historically, mTORC1 inhibitors have produced responses in approximately 30% of patients in this setting. mTORC1 inhibitor efficacy may be limited by resistance mechanisms including AKT activation by mTORC2. To date, dual mTORC1/2 inhibitors targeting both the TORC1 and TORC2 complexes have not been investigated in DLBCL. This phase II trial investigated the oral dual mTORC1/2 inhibitors vistusertib in an intermittent dosing schedule of 125 mg b.d. for two days per week. Thirty patients received vistusertib and six received vistusertib‐rituximab for up to six cycles (28‐day cycles). Two partial responses were achieved on monotherapy. Duration of response were 57 and 62 days respectively for these patients. 19% had stable disease within six cycles. In the monotherapy arm, the median progression‐free survival was 1.69 (95% confidence interval 1.61‐2.14) months and median overall survival was 6.58 (95% CI 3.81‐not reached) months respectively. The median duration of response or stable disease across the trial duration was 153 days (95% confidence interval 112‐not reached). Tumour responses according to PET/CT versus CT were concordant. There were no differences noted in tumour volume response according to cell of origin by either gene expression profiling or immunohistochemistry. Vistusertib +/‐ rituximab was well tolerated; across 36 patients 86% of adverse events were grade (G) 1‐2. Common vistusertib‐related adverse events were similar to those described with mTORC1 inhibitors: nausea (47% G1‐2), diarrhoea (27% G1‐2, 6% G3), fatigue (30% G1‐2, 3% G3), mucositis (25% G1‐2, 6% G3), vomiting (17% G1‐2) and dyspepsia (14% G1‐2). Dual mTORC1/2 inhibitors do not clearly confer an advantage over mTORC1 inhibitors in relapsed or refractory DLBCL. Potential resistance mechanisms are discussed within.

The detection and grading of fibrosis in myeloproliferative neoplasms (MPN) is an important component of disease classification, prognostication and disease monitoring. However, current fibrosis grading systems are only semi-quantitative and fail to capture sample heterogeneity. To improve the detection, quantitation and representation of reticulin fibrosis, we developed a machine learning (ML) approach using bone marrow trephine (BMT) samples (n = 107) from patients diagnosed with MPN or a reactive / nonneoplastic marrow. The resulting Continuous Indexing of Fibrosis (CIF) enhances the detection and monitoring of fibrosis within BMTs, and aids the discrimination of MPN subtypes. When combined with megakaryocyte feature analysis, CIF discriminates between the frequently challenging differential diagnosis of essential thrombocythemia (ET) and pre-fibrotic myelofibrosis (pre-PMF) with high predictive accuracy [area under the curve = 0.94]. CIF also shows significant promise in the identification of MPN patients at risk of disease progression; analysis of samples from 35 patients diagnosed with ET and enrolled in the Primary Thrombocythemia-1 (PT-1) trial identified features predictive of post-ET myelofibrosis (area under the curve = 0.77). In addition to these clinical applications, automated analysis of fibrosis has clear potential to further refine disease classification boundaries and inform future studies of the micro-environmental factors driving disease initiation and progression in MPN and other stem cell disorders. The image analysis methods used to generate CIF can be readily integrated with those of other key morphological features in MPNs, including megakaryocyte morphology, that lie beyond the scope of conventional histological assessment.Key PointsMachine learning enables an objective and quantitative description of reticulin fibrosis within the bone marrow of patients with myeloproliferative neoplasms (MPN),Automated analysis and Continuous Indexing of Fibrosis (CIF) captures heterogeneity within MPN samples and has utility in refined classification and disease monitoringQuantitative fibrosis assessment combined with topological data analysis may help to predict patients at increased risk of progression to post-ET myelofibrosis, and assist in the discrimination of ET and pre-fibrotic PMF (pre-PMF)