Clinical management of NUT carcinoma (NC) in Germany: Analysis of survival, therapy response, tumor markers and tumor genome sequencing in 35 adult patients

NUT carcinomas (NC) are very rare and highly aggressive tumors, molecularly defined by an aberrant gene fusion involving the NUTM1 gene. NCs preferentially arise intrathoracically or in the head and neck region, having a highly adverse prognosis with almost no long-term survivors. Here, we report on a cohort of 35 adult NC patients who were evaluated at University Hospital Tuebingen in an eight year time span, i.e. between 2016 and 2023. Primary objectives were overall survival (OS) and influence of primary tumor locations, fusion gene types and staging on OS. Secondary objectives were patient baseline characteristics, risk factors, tumor markers, treatment decisions and responses to therapy comparing thoracic vs non-thoracic origins. Further, data from tumor genome sequencing were analyzed. In this monocentric German cohort, 54 % of patients had thoracic tumors and 65 % harbored a BRD4-NUTM1 fusion gene. Median OS was 7.5 months, being significantly dependent on primary tumor location and nodal status. Initial misdiagnosis was a problem in 31 % of the cases. Surgery was the first treatment in most patients (46 %) and 80 % were treated with polychemotherapies, showing longer progression free survival (PFS) with ifosfamide-based than with platinum-based regimens. Patients treated with an immune checkpoint inhibitor (ICI) in addition to first-line chemotherapy tended to have longer OS. Initial LDH levels could be identified as a prognostic measure for survival prognosis. Sequencing data highlight aberrant NUTM1 fusion genes as unique tumor driver genes. This is the largest adult European cohort of this orphan tumor disease, showing epidemiologic and molecular features as well as relevant clinical data. Awareness to prevent misdiagnosis, fast contact to a specialized nation-wide center and referral to clinical studies are essential as long-term survival is rarely achieved with any of the current therapeutic regimes.

Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer.

Biologic approaches to drug selection and targeted therapy: hype or clinical reality?

Oncology remains at the forefront of the application of individualized or genomics-driven approaches to cancer care. This approach acknowledges cancer as a genetic disease, driven by alterations in oncogenes and tumor suppressors, with the strategy of using this information to guide therapy based on therapeutics capable of targeting specific alterations. Recent advances suggest a changing landscape in how management decisions are approached for the patient with non-small cell lung cancer. An expanding and functionally useful toolbox of novel targeted agents and biomarkers to drive therapeutic choices is beginning to impact patient care. This article reviews key advances, with commentary and perspective for the practicing thoracic surgical oncologist.