The COVID-19 pandemic has demonstrated the need for massively-parallel, cost-effective tests monitoring viral spread. Here we present SARSeq, saliva analysis by RNA sequencing, a method to detect SARS-CoV-2 and other respiratory viruses on tens of thousands of samples in parallel. SARSeq relies on next generation sequencing of multiple amplicons generated in a multiplexed RT-PCR reaction. Two-dimensional, unique dual indexing, using four indices per sample, enables unambiguous and scalable assignment of reads to individual samples. We calibrate SARSeq on SARS-CoV-2 synthetic RNA, virions, and hundreds of human samples of various types. Robustness and sensitivity were virtually identical to quantitative RT-PCR. Double-blinded benchmarking to gold standard quantitative-RT-PCR performed by human diagnostics laboratories confirms this high sensitivity. SARSeq can be used to detect Influenza A and B viruses and human rhinovirus in parallel, and can be expanded for detection of other pathogens. Thus, SARSeq is ideally suited for differential diagnostic of infections during a pandemic.

Next generation sequencing (NGS) together with protein expression analysis is back bone of molecularly targeted therapy in precision medicine. Our retrospective study shows our experience with NGS of 324 genes in combination with protein expression in patients with advanced breast cancer (aBC). The primary purpose was to analyze the prevalence of individual genetic alterations combined with protein expression to define potential targets for an individualized therapy. Between April 2018 and September 2019, 41 patients with aBC were offered a NGS test. The test was used to detect clinically relevant genomic alterations and to support further targeted therapy decisions. Hormone receptors, ERBB2 of tumors and PD-L1 was stained by immunohistochemistry. The data was recorded up to September 2019. After prior consent 41 results were available for further analysis. The most common BC subtypes were triple-negative (n = 16), HR+/ERBB2− (n = 15), and ERBB2+ (n = 9), with one missing data of the primary tumor. 27 patients had more than one genetic alteration. The most common alterations were PIK3CA (n = 14) and ERBB2 alterations (n = 11). Followed by ESR1 (n = 10), FGFR1 (n = 7) and PTEN (n = 7). 68% of the alterations were clinically relevant (tier I and II of ESCAT classification). The most common treatment recommendation was ERBB2-directed therapy (single or double blockade, trastuzumab emtansine and lapatinib) followed by alpelisib in combination with fulvestrant. Comprehensive genomic profiling combined with protein expression analysis in aBC allowed a guided personalized therapy for half of our patients. So far there are no well-defined tools allowing interpretations of genomic alterations detected by NGS in combination with protein expression and other factors.

Background Increasing knowledge about the genomic changes underpinning cancer development and growth has led to a rapidly expanding number of individualized therapies that specifically target these changes in a patient's tumor. Here we present a case report of a patient with metastatic esophageal carcinoma whose tumor harbored NTRK1 gene amplification and who received targeted systemic therapy with larotrectinib. At initial diagnosis, the patient presented with tumor obstruction of the middle esophagus, simultaneous liver and lung metastases, UICC IV and WHO performance status 3. Materials and Methods The solid tumor genomic profiling test FoundationOne CDx (F1CDx) was used to detect clinically relevant genomic alterations that, in turn, might identify a targeted therapeutic approach if suggested by the findings. The patient was then treated with larotrectinib and had subsequent follow‐up biopsies. Results Simultaneous biopsies of the primary tumor and liver lesions identified a metastatic squamous cell esophageal carcinoma. Comprehensive genomic profiling obtained from liver metastases identified numerous genomic alterations including amplification of NTRK1. Owing to the reduced performance status of the patient, chemotherapy could not be applied and was denied. Although larotrectinib is only approved for the treatment of cancers with NTRK gene fusions, treatment was started and led to a shrinkage of the primary tumor as well as the liver and lung metastases within 6 weeks according to RECIST criteria accompanied by tumor marker decrease. The NTRK1 gene amplification was below the limit of detection in a subsequent liver biopsy. Conclusion The use of comprehensive genomic profiling, specifically F1CDx, enabled the selection of a targeted therapy that led to a rapid reduction of the tumor and its metastases according to RECIST criteria. This case suggests that larotrectinib is not only effective in NTRK fusions but may be efficacious in cases with gene amplification. Key Points Advances in precision medicine have revolutionized the treatment of cancer and have allowed oncologists to perform more individualized therapy. This case shows that larotrectinib could also be effective in cases of NTRK amplification of cancer. Today, there is only limited knowledge about NTRK alterations in squamous epithelial carcinoma of the esophagus. Longitudinal tumor sequencing during the course of the disease may allow for the detection of a molecular genetic cause once the tumor progresses. Additional actionable gene alterations may then be identified, which may provide the rationale for a therapy switch.

Oncologic patients are regarded as the population most at risk of developing a severe course of COVID‐19 due to the fact that malignant diseases and chemotherapy often weaken the immune system. In the face of the ongoing SARS‐CoV‐2 pandemic, how particular patients deal with this infection remains an important question. In the period between the 15 and 26 April 2020, a total of 1227 patients were tested in one of seven oncologic outpatient clinics for SARS‐CoV‐2, regardless of symptoms, employing RT‐qPCR. Of 1227 patients, 78 (6.4%) were tested positive of SARS‐CoV‐2. Only one of the patients who tested positive developed a severe form of COVID‐19 with pneumonia (CURB‐65 score of 2), and two patients showed mild symptoms. Fourteen of 75 asymptomatic but positively tested patients received chemotherapy or chemo‐immunotherapy according to their regular therapy algorithm (±4 weeks of SARS‐CoV‐2 test), and 48 of 78 (61.5%) positive‐tested patients received glucocorticoids as co‐medication. None of the asymptomatic infected patients showed unexpected complications due to the SARS‐CoV‐2 infection during the cancer treatment. These data clearly contrast the view that patients with an oncologic disease are particularly vulnerable to SARS‐CoV‐2 and suggest that compromising therapies could be continued or started despite the ongoing pandemic. Moreover the relatively low appearance of symptoms due to COVID‐19 among patients on chemotherapy and other immunosuppressive co‐medication like glucocorticoids indicate that suppressing the response capacity of the immune system reduces disease severity.

Screening mammography is a widely used approach for early breast cancer detection, effectively increasing the survival rate of affected patients. According to the Food and Drug Administration’s Mammography Quality Standards Act and Program statistics, approximately 39 million mammography procedures are performed in the United States each year. Therefore, breast cancer screening is among the most common radiological tasks. Interpretation of screening mammograms by a specialist radiologist includes primarily the review of breast positioning quality, which is a key factor affecting the sensitivity of mammography and thus the diagnostic performance. Each mammogram with inadequate positioning may lead to a missed cancer or, in case of false positive signal interpretation, to follow-up activities, increased emotional burden and potential over-therapy and must be repeated, requiring the return of the patient. In this study, we have developed deep convolutional neuronal networks to differentiate mammograms with inadequate breast positioning from the adequate ones. The aim of the proposed automated positioning quality evaluation is to assist radiology technologists in detecting poorly positioned mammograms during patient visits, improve mammography performance, and decrease the recall rate. The implemented models have achieved 96.5% accuracy in cranio-caudal view classification and 93.3% accuracy in mediolateral oblique view regarding breast positioning quality. In addition to these results, we developed a software module that allows the study to be applied in practice by presenting the implemented model predictions and informing the technologist about the missing quality criteria.