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Given the abundance of new research, it can be challenging to stay current on the latest advancements and findings. Oncology Times is here to help with summaries of the newest studies to ensure you are up-to-date on the latest innovations in oncology practice.



Psychosocial risks are independently associated with cancer surgery outcomes in medically comorbid patients

A new study suggests that psychological or social risk factors such as depression, limited resilience, and lack of emergency resources-along with standard medical risk factors such as high blood pressure or diabetes-are linked with higher risks of surgical complications (Ann Surg Oncol 2019;26(4):936-944). Researchers compiled a special questionnaire based on well-known terms and concepts used to measure psychosocial risk factors. For example, the questions ask a patient how well they bounce back from a difficult event, or how likely they could cope with and care for a minor infection from home after surgery. Between March and October 2017, the survey was given to 142 patients before they had abdominal cancer surgery at The Johns Hopkins Hospital or its local affiliates. Each survey took an average of 10 minutes to complete and asked patients to rank their answers to about 20 questions on a 1-5 scale. Patient outcomes were assessed 30 days after surgery using medical records to count complications such as infections, blood clots, bleeding from surgery, heart attack, kidney problems, stroke, and spending excessive time on a ventilator. One psychosocial risk factor alone did not make a person more likely to have complications after cancer surgery, according to findings. However, researchers found that if people had medical risk factors and a single psychosocial risk factor, it made them 28 percent more likely than those without those factors to have complications after surgery, even after accounting for the extra complications tied to medical risk factors. "We demonstrated a more than threefold odds of a complication in medically comorbid patients with multiple psychosocial risks," researchers noted. "These findings support the use of psychosocial risks in preoperative assessment and consideration for inclusion in preoperative optimization efforts."



Novel susceptibility variants at the ERG locus for childhood acute lymphoblastic leukemia in Hispanics

Scientists have identified genetic variations in a fourth gene that are associated with an increased risk of acute lymphoblastic leukemia (ALL) in Hispanic children (Blood 2019;133:724-729). The gene is ERG, a transcription factor that is also mutated in the leukemic cells of some ALL patients. Researchers identified inherited genetic variations in ERG that contribute to ALL risk, primarily in Hispanic children. The team compared common genetic variations in 940 genetically defined Hispanic ALL patients enrolled in COG clinical trials and 681 individuals of similar backgrounds without an ALL diagnosis. Ethnicity was assigned based on gene variations representative of European, African, and Native American ancestry. Hispanic ethnicity was defined as having more than 10 percent Native American gene variations as well as having more Native American than African gene variations. The investigators identified high-risk variations in ERG that were associated with a 1.56-fold increased risk of ALL in Hispanic children. The risk was highest for children with the highest percentage of Native American ancestry. In contrast, investigators found no significant increased ALL risk in African-American children with the high-risk ERG variations and just a 12 percent elevated risk in children of European ancestry. "Our results provide novel insights into genetic predisposition to ALL and its contribution to racial disparity in this cancer," study authors wrote.



Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

Researchers have utilized machine learning capabilities to assist with the grading of tumor patterns and subtypes of lung adenocarcinoma (Sci Rep 2019;9(3358). Using recent advances in machine learning, the team developed a deep neural network to classify different types of lung adenocarcinoma on histopathology slides and found that the model performed on par with three practicing pathologists. The model utilizes a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image, researchers explained. "We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6 percent with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7 percent on this test set." If confirmed in clinical practice, this model could assist pathologists in the improvement of the lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions before review. "Our model can potentially be used to aid pathologists in classification of these histologic patterns and ultimately contribute to more accurate grading of lung adenocarcinoma," study authors wrote.



Automated analysis of lymphocytic infiltration, tumor budding, and their spatial relationship improves prognostic accuracy in colorectal cancer

A method that integrates tumor buds, lymphocytic infiltration, and their spatial relationship could better stratify patients with stage II colorectal cancer (CRC) at high risk for disease-specific death compared with traditional methods of clinical staging, according to results published in Cancer Immunology Research (2019; doi:10.1158/2326-6066.CIR-18-0377). Data was utilized from a training cohort of 114 patients with stage II CRC who underwent surgical resection between 2002 and 2003 in Edinburgh, Scotland. The researchers analyzed cancer tissues and associated clinical data, such as TNM staging and follow-up information, to develop their prognostic signature. The method was validated in two independent cohorts (56 patients from Edinburgh in cohort 1 and 62 patients from Japan in cohort 2, respectively). Clinicopathological and image analyses data were incorporated into a machine-learning model to develop the Tumor Bud-Immuno Spatial Index (TBISI). The final model integrates the degree of lymphocytic infiltration, the number of tumor buds, and their spatial relationship to each other to stratify patients into low- and high-risk categories of disease-specific death, according to researchers. To compare TBISI with standard methods, patients from the training cohort were stratified into risk categories using TNM staging (tumor stage III or IV) and the study's version of Immunoscore. Researchers found that utilization of TBISI was more than four times and more than twice as effective in stratifying patients into high- and low-risk groups compared to TNM staging and Immunoscore, respectively. The researchers then validated the prognostic significance of TBISI in the two validation cohorts. "The investigation of the spatial relationship between lymphocytes and tumor buds within the tumor microenvironment improves accuracy of prognosis of patients with stage II colorectal cancer through an automated image analysis and machine learning workflow," study authors concluded.


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