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Important progress has been made over the last decade in the classification, imaging, and treatment of neuroendocrine neoplasm (NENs), with several new agents approved for use. Although the treatment options available for patients with well-differentiated neuroendocrine tumors (NETs) have greatly expanded, the rapidly changing landscape has presented several unanswered questions about how best to optimize, sequence, and individualize therapy. Perhaps the most important development over the last decade has been the approval of 177Lu-DOTATATE for treatment of gastroenteropancreatic-NETs, raising questions around optimal sequencing of peptide receptor radionuclide therapy (PRRT) relative to other therapeutic options, the role of re-treatment with PRRT, and whether PRRT can be further optimized through use of dosimetry among other approaches. The NET Task Force of the National Cancer Institute GI Steering Committee convened a clinical trial planning meeting in 2021 with multidisciplinary experts from academia, the federal government, industry, and patient advocates to develop NET clinical trials in the era of PRRT. Key clinical trial recommendations for development included 1) PRRT re-treatment, 2) PRRT and immunotherapy combinations, 3) PRRT and DNA damage repair inhibitor combinations, 4) treatment for liver-dominant disease, 5) treatment for PRRT-resistant disease, and 6) dosimetry-modified PRRT.

Monomorphic epitheliotropic intestinal T-cell lymphoma (MEITL) is a rare type of T-cell lymphoma of the gastrointestinal tract that presents an aggressive course. Previously, this entity was named type II T-cell lymphoma associated with enteropathy. However, in later years it was classified as a different entity due to its particular characteristics. This neoplasm usually affects the jejunum and ileum and usually has a clinical presentation with intestinal perforation, intestinal obstruction or bleeding. Its diagnosis requires histopathological evaluation and differentiation from other types of T cell lymphomas that affect the intestine. Its prognosis is usually very poor and therapeutic options are limited and are based on induction chemotherapy followed by autologous bone marrow transplant. This article presents the first case of MEITL in Peru, in an elderly patient who presented at hospital emergency with intestinal perforation and that presented multiple relapses of the neoplasm.

BackgroundDetailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.MethodsWe built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.ResultsOur model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.ConclusionsA Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.