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The grading of fibrosis in myeloproliferative neoplasms (MPN) is an important component of disease classification, prognostication and monitoring. However, current fibrosis grading systems are only semi-quantitative and fail to fully capture sample heterogeneity. To improve the quantitation of reticulin fibrosis, we developed a machine learning approach using bone marrow trephine (BMT) samples (n= 107) from patients diagnosed with MPN or a reactive marrow. The resulting Continuous Indexing of Fibrosis (CIF) enhances the detection and monitoring of fibrosis within BMTs, and aids MPN subtyping. When combined with megakaryocyte feature analysis, CIF discriminates between the frequently challenging differential diagnosis of essential thrombocythemia (ET) and pre-fibrotic myelofibrosis with high predictive accuracy [area under the curve = 0.94]. CIF also shows 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 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.

•2q37 copy loss enriches significantly between newly diagnosed (∼5%), LEN-resistant (10%) and POM-resistant (16.4%) myeloma.•2q37 carries COPS7Band COPS8, required for CRBN stability; their partial loss leads to CRBN partial loss, which may blunt LEN/POM efficacy.

The acquisition of a multidrug refractory state is a major cause of mortality in myeloma. Myeloma drugs that target the cereblon (CRBN) protein include widely used immunomodulatory drugs (IMiDs), and newer CRBN E3 ligase modulator drugs (CELMoDs), in clinical trials. CRBN genetic disruption causes resistance and poor outcomes with IMiDs. Here, we investigate alternative genomic associations of IMiD resistance, using large whole-genome sequencing patient datasets (n = 522 cases) at newly diagnosed, lenalidomide (LEN)-refractory and lenalidomide-then-pomalidomide (LEN-then-POM)-refractory timepoints. Selecting gene targets reproducibly identified by published CRISPR/shRNA IMiD resistance screens, we found little evidence of genetic disruption by mutation associated with IMiD resistance. However, we identified a chromosome region, 2q37, containing COP9 signalosome members COPS7B and COPS8, copy loss of which significantly enriches between newly diagnosed (incidence 5.5%), LEN-refractory (10.0%), and LEN-then-POM-refractory states (16.4%), and may adversely affect outcomes when clonal fraction is high. In a separate dataset (50 patients) with sequential samples taken throughout treatment, we identified acquisition of 2q37 loss in 16% cases with IMiD exposure, but none in cases without IMiD exposure. The COP9 signalosome is essential for maintenance of the CUL4-DDB1-CRBN E3 ubiquitin ligase. This region may represent a novel marker of IMiD resistance with clinical utility.

The latency between acquisition of an initiating somatic driver mutation by a single-cell and clinical presentation with cancer is largely unknown. We describe a remarkable case of monozygotic twins presenting with CALRmutation-positive myeloproliferative neoplasms (MPNs) (aged 37 and 38 years), with a clinical phenotype of primary myelofibrosis. The CALRmutation was absent in T cells and dermal fibroblasts, confirming somatic acquisition. Whole-genome sequencing lineage tracing revealed a common clonal origin of the CALR-mutant MPN clone, which occurred in utero followed by twin-to-twin transplacental transmission and subsequent similar disease latency. Index sorting and single-colony genotyping revealed phenotypic hematopoietic stem cells (HSCs) as the likely MPN-propagating cell. Furthermore, neonatal blood spot analysis confirmed in utero origin of the JAK2V617Fmutation in a patient presenting with polycythemia vera (aged 34 years). These findings provide a unique window into the prolonged evolutionary dynamics of MPNs and fitness advantage exerted by MPN-associated driver mutations in HSCs.

Accurate diagnosis and classification of myeloproliferative neoplasms (MPNs) requires integration of clinical, morphological, and genetic findings. Despite major advances in our understanding of the molecular and genetic basis of MPNs, the morphological assessment of bone marrow trephines (BMT) is critical in differentiating MPN subtypes and their reactive mimics. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative, and poorly reproducible criteria. To improve the morphological assessment of MPNs, we have developed a machine learning approach for the automated identification, quantitative analysis, and abstract representation of megakaryocyte features using reactive/nonneoplastic BMT samples (n = 43) and those from patients with established diagnoses of essential thrombocythemia (n = 45), polycythemia vera (n = 18), or myelofibrosis (n = 25). We describe the application of an automated workflow for the identification and delineation of relevant histological features from routinely prepared BMTs. Subsequent analysis enabled the tissue diagnosis of MPN with a high predictive accuracy (area under the curve = 0.95) and revealed clear evidence of the potential to discriminate between important MPN subtypes. Our method of visually representing abstracted megakaryocyte features in the context of analyzed patient cohorts facilitates the interpretation and monitoring of samples in a manner that is beyond conventional approaches. The automated BMT phenotyping approach described here has significant potential as an adjunct to standard genetic and molecular testing in established or suspected MPN patients, either as part of the routine diagnostic pathway or in the assessment of disease progression/response to treatment.

Accurate diagnosis and classification of myeloproliferative neoplasms (MPNs) requires integration of clinical, morphological, and genetic findings. Despite major advances in our understanding of the molecular and genetic basis of MPNs, the morphological assessment of bone marrow trephines (BMT) is critical in differentiating MPN subtypes and their reactive mimics. However, morphological assessment is heavily constrained by a reliance on subjective, qualitative, and poorly reproducible criteria. To improve the morphological assessment of MPNs, we have developed a machine learning approach for the automated identification, quantitative analysis, and abstract representation of megakaryocyte features using reactive/nonneoplastic BMT samples (n = 43) and those from patients with established diagnoses of essential thrombocythemia (n = 45), polycythemia vera (n = 18), or myelofibrosis (n = 25). We describe the application of an automated workflow for the identification and delineation of relevant histological features from routinely prepared BMTs. Subsequent analysis enabled the tissue diagnosis of MPN with a high predictive accuracy (area under the curve = 0.95) and revealed clear evidence of the potential to discriminate between important MPN subtypes. Our method of visually representing abstracted megakaryocyte features in the context of analyzed patient cohorts facilitates the interpretation and monitoring of samples in a manner that is beyond conventional approaches. The automated BMT phenotyping approach described here has significant potential as an adjunct to standard genetic and molecular testing in established or suspected MPN patients, either as part of the routine diagnostic pathway or in the assessment of disease progression/response to treatment.

Quantitative image analysis has potential to transform the interpretation of bone marrow trephine (BMT) samples in myeloproliferative neoplasms (MPN) and improve the evaluation of anti-fibrotic therapies. To investigate the utility of recently developed algorithms evaluating reticulin fibrosis and megakaryocyte features in myelofibrosis (MF), we analysed samples from a multi-center, phase II study of zinpentraxin alfa (ZPN, PRM-151). ZPN is a recombinant form of human pentraxin-2 (PTX2) that has shown clinical activity as monotherapy and in combination with ruxolitinib (RUX) in a phase II trial in patients with Int-1/-2 or high risk MF (NCT01981850). In stage 2 of this study, patients ineligible for, intolerant of, or with an inadequate response to RUX were randomised to receive 0.3, 3.0, or 10.0 mg/kg ZPN on Days 1, 3, and 5 of Cycle 1, and every 4 weeks thereafter for up to 9 cycles. We demonstrate the potential of quantitative image analysis to augment and refine conventional expert histological assessment of MF.

Clonality studies greatly assist in the diagnosis of challenging haematopathology cases. These robust and standardised tests aid the detection of clonal lymphoid populations and may assist in lymphocyte subtyping. In this case report, a gentleman presented with a high-grade transformation of a B cell neoplasm which histologically and immunophenotypically mimicked a T cell anaplastic large-cell lymphoma. With the aid of T cell and B cell receptor clonality studies, it was demonstrated that this tumour was in fact of B cell lineage. This report exemplifies the role of these increasingly used and relatively new molecular tests in unusual and difficult lymphoma presentations and highlights potential pitfalls in the interpretation of their results.

Patients with inflammatory bowel disease (IBD) have an increased risk of colon cancer. However, the immune cells and cytokines that mediate the transition from intestinal inflammation to cancer are poorly understood. We show that bacteria-induced colon cancer is accompanied by differential accumulation of IL-17+IL-22+ colonic innate lymphoid cells (cILCs), which are phenotypically distinct from LTi and NK-22 cells, and that their depletion in mice with dysplastic inflammation blocks the development of invasive colon cancer. Analysis of the functional role of distinct Type 17 cytokines shows that although blockade of IL-17 inhibits some parameters of intestinal inflammation, reduction in dysplasia and colorectal cancer (CRC) requires neutralization of IL-22 indicating a unique role for IL-22 in the maintenance of cancer in this model. Mechanistic analyses showed that IL-22 selectively acts on epithelial cells to induce Stat3 phosphorylation and proliferation. Importantly, we could detect IL-22+CD3+ and IL-22+CD3− cells in human CRC. Our results describe a new activity of IL-22 in the colon as a nonredundant mediator of the inflammatory cascade required for perpetuation of CRC, highlighting the IL-22 axis as a novel therapeutic target in colon cancer.

Chronic inflammation of the intestine has been associated with an elevated risk of developing colorectal cancer. Recent association studies have highlighted the role of genetic predisposition in the etiology of colitis and started to unravel its complexity. However, the genetic factors influencing the progression from colon inflammation to tumorigenesis are not known. We report the identification of a genetic interval Hiccs that regulates Helicobacter hepaticus–induced colitis and associated cancer susceptibility in a 129.RAG−/− mouse model. The 1.7-Mb congenic interval on chromosome 3, containing eight genes and five microRNAs, renders susceptible mice resistant to colitis and reduces tumor incidence and multiplicity. Bone marrow chimera experiments showed that resistance is conferred by the hematopoietic compartment. Moreover, the Hiccs locus controls the induction of the innate inflammatory response by regulating cytokine expression and granulocyte recruitment by Thy1+ innate lymphoid cells. Using a tumor-promoting model combining chronic Helicobacter hepaticus infection and the carcinogen azoxymethane, we found that Hiccs also regulates the frequency of colitis-associated neoplasia. Our study highlights the importance of innate immune cells and their genetic configuration in driving progression from inflammation toward cancer and opens the door for analysis of these pathways in human inflammatory disorders and associated cancers.

A burgeoning family of p53-related genes have been described recently, including p73 and p63. Both these genes encode proteins with many similarities to p53 but also with the potential for forming a range of related species by alternative promoter usage and alternative splicing. In order to begin the characterization of p63, we generated a polyclonal serum (designated SC1) that recognizes the C-terminus of p63α. We have shown that this reagent recognizes p63α but not p53 nor p73. By western blot analysis both p63α and the N-terminal truncated form of p63α (ΔNp63α) were found in a range of cell lines. Similar immunoblot analysis of tissues reveals considerable complexity with at least four SC1-immunoreactive isoforms being identified. In immunohistological studies SC1 immunoreactivity is widely detectable, being predominantly associated with proliferative compartments in epithelia. However, non-proliferative populations can also show SC1 immunostaining. No simple relationship between the isoforms identified by immunoblotting of tissue lysates and the tissue immunostaining characteristics was identified. A previously unrecognized species intermediate in mobility between p63α and ΔNp63α was found in several tissues, including nerve and peripheral blood lymphocytes. Interestingly, there is suppression of p63α expression in HaCat cells in a time- and concentration-dependent manner after UV and MMS treatment. Our data provide further information about the complexity of p63 and the SC1 serum will prove to be a useful tool in further studies of this p53 homologue.

Philadelphia negative myeloproliferative neoplasms (MPN) are an excellent tractable disease model of a number of aspects of human cancer biology, including genetic evolution, tissue associated fibrosis and cancer stem cells. In this review we discuss recent insights into MPN biology gained from the application of a number of new single cell technologies to study human disease, with a specific focus on single cell genomics, single cell transcriptomics and digital pathology.

Palomba: Janssen: Consultancy. Gerecitano:Genentech: Consultancy, Other: Advisory Board; AbbVie: Consultancy, Other: Advisory Board. Matasar:Spectrum: Consultancy; Genentech: Consultancy. Straus:Millenium Pharmaceuticals: Research Funding. He:Foundation Medicine, Inc.: Employment, Equity Ownership. Balasubramanian:Foundation Medicine: Employment, Equity Ownership. Stephens:Foundation Medicine, Inc.: Employment, Equity Ownership. Miller:Foundation Medicine: Employment. Levine:Loxo Oncology: Membership on an entity's Board of Directors or advisory committees; CTI BioPharma: Membership on an entity's Board of Directors or advisory committees; Foundation Medicine: Consultancy. Younes:Celgene: Honoraria; Johnson and Johnson: Research Funding; Novartis: Research Funding; Bayer: Honoraria; Bristol Meyer Squibb: Honoraria; Sanofi-Aventis: Honoraria; Seattle Genetics: Honoraria, Research Funding; Curis: Research Funding; Janssen: Honoraria; Takeda Millenium: Honoraria; Incyte: Honoraria.

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