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Background
High response rates of metastatic melanoma have been reported upon immune checkpoint inhibition by PD-1 blockade alone or in combination with CTLA-4 inhibitors. However, the majority of patients with a primary resistance to anti-PD-1 monotherapy is also refractory to a subsequent combined checkpoint inhibition. In BRAF wildtype patients with a primary resistance to PD-1 inhibitors, therapeutic options are therefore limited and immune-related adverse events (irAE) have to be taken into consideration when discussing a subsequent immunotherapy.
Case presentation
We report the case of a 68-year-old male patient with metastatic melanoma who experienced an acute renal failure with nephrotic syndrome due to a minimal change disease developing after a single dose of the anti-PD-1 antibody pembrolizumab. A kidney biopsy revealed a podocytopathy without signs of interstitial nephritis. Renal function recovered to almost normal creatinine and total urine protein levels upon treatment with oral steroids and diuretics. Unfortunately, a disease progression (PD, RECIST 1.1) was observed in a CT scan after resolution of the irAE. In a grand round, re-exposure to a PD-1-containing regime was recommended. Consensually, a combined immunotherapy with ipilimumab and nivolumab was initiated. Nephrotoxicity was tolerable during combined immunotherapy and a CT scan of chest and abdomen showed a deep partial remission (RECIST 1.1) after three doses of ipilimumab (3 mg/kg) and nivolumab (1 mg/kg).
Conclusion
This case illustrates that a fulminant response to combined checkpoint inhibition is possible after progression after anti-PD-1 monotherapy and a severe irAE.
miR-221 is regarded as an oncogene in many malignancies, and miR-221-mediated resistance towards TRAIL was one of the first oncogenic roles shown for this small noncoding RNA. In contrast, miR-221 is downregulated in prostate cancer (PCa), thereby implying a tumour suppressive function. By using proliferation and apoptosis assays, we show a novel feature of miR-221 in PCa cells: instead of inducing TRAIL resistance, miR-221 sensitized cells towards TRAIL-induced proliferation inhibition and apoptosis induction. Partially responsible for this effect was the interferon-mediated gene signature, which among other things contained an endogenous overexpression of the TRAIL encoding gene TNFSF10. This TRAIL-friendly environment was provoked by downregulation of the established miR-221 target gene SOCS3. Moreover, we introduced PIK3R1 as a target gene of miR-221 in PCa cells. Proliferation assays showed that siRNA-mediated downregulation of SOCS3 and PIK3R1 mimicked the effect of miR-221 on TRAIL sensitivity. Finally, Western blotting experiments confirmed lower amounts of phospho-Akt after siRNA-mediated downregulation of PIK3R1 in PC3 cells. Our results further support the tumour suppressing role of miR-221 in PCa, since it sensitises PCa cells towards TRAIL by regulating the expression of the oncogenes SOCS3 and PIK3R1. Given the TRAIL-inhibiting effect of miR-221 in various cancer entities, our results suggest that the influence of miR-221 on TRAIL-mediated apoptosis is highly context- and entity-dependent.
Background
Several recent publications have demonstrated the use of convolutional neural networks to classify images of melanoma at par with board-certified dermatologists. However, the non-availability of a public human benchmark restricts the comparability of the performance of these algorithms and thereby the technical progress in this field.
Methods
An electronic questionnaire was sent to dermatologists at 12 German university hospitals. Each questionnaire comprised 100 dermoscopic and 100 clinical images (80 nevi images and 20 biopsy-verified melanoma images, each), all open-source. The questionnaire recorded factors such as the years of experience in dermatology, performed skin checks, age, sex and the rank within the university hospital or the status as resident physician. For each image, the dermatologists were asked to provide a management decision (treat/biopsy lesion or reassure the patient). Main outcome measures were sensitivity, specificity and the receiver operating characteristics (ROC).
Results
Total 157 dermatologists assessed all 100 dermoscopic images with an overall sensitivity of 74.1%, specificity of 60.0% and an ROC of 0.67 (range = 0.538–0.769); 145 dermatologists assessed all 100 clinical images with an overall sensitivity of 89.4%, specificity of 64.4% and an ROC of 0.769 (range = 0.613–0.9). Results between test-sets were significantly different (P < 0.05) confirming the need for a standardised benchmark.
Conclusions
We present the first public melanoma classification benchmark for both non-dermoscopic and dermoscopic images for comparing artificial intelligence algorithms with diagnostic performance of 145 or 157 dermatologists. Melanoma Classification Benchmark should be considered as a reference standard for white-skinned Western populations in the field of binary algorithmic melanoma classification.
Background
Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level precision. However, a statistically significant improvement compared with dermatologist classification has not been reported to date.
Methods
For this comparative study, 4204 biopsy-proven images of melanoma and nevi (1:1) were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated. For the experiment, an additional 804 biopsy-proven dermoscopic images of melanoma and nevi (1:1) were randomly presented to dermatologists of nine German university hospitals, who evaluated the quality of each image and stated their recommended treatment (19,296 recommendations in total). Three McNemar's tests comparing the results of the CNN's test runs in terms of sensitivity, specificity and overall correctness were predefined as the main outcomes.
Findings
The respective sensitivity and specificity of lesion classification by the dermatologists were 67.2% (95% confidence interval [CI]: 62.6%–71.7%) and 62.2% (95% CI: 57.6%–66.9%). In comparison, the trained CNN achieved a higher sensitivity of 82.3% (95% CI: 78.3%–85.7%) and a higher specificity of 77.9% (95% CI: 73.8%–81.8%). The three McNemar's tests in 2 × 2 tables all reached a significance level of p < 0.001. This significance level was sustained for both subgroups.
Interpretation
For the first time, automated dermoscopic melanoma image classification was shown to be significantly superior to both junior and board-certified dermatologists (p < 0.001).
Recent research revealed the superiority of artificial intelligence over dermatologists to diagnose melanoma from images. However, 30–50% of all melanomas and more than half of those in young patients evolve from initially benign lesions. Despite its high relevance for melanoma screening, neither clinicians nor computers are yet able to reliably predict a nevus’ oncologic transformation. The cause of this lies in the static nature of lesion presentation in the current standard of care, both for clinicians and algorithms. The status quo makes it difficult to train algorithms (and clinicians) to precisely assess the likelihood of a benign skin lesion to transform into melanoma. In addition, it inhibits the precision of current algorithms since ‘evolution’ image features may not be part of their decision. The current literature reveals certain types of melanocytic nevi (i.e. ‘spitzoid’ or ‘dysplastic’ nevi) and criteria (i.e. visible vasculature) that, in general, appear to have a higher chance to transform into melanoma. However, owing to the cumulative nature of oncogenic mutations in melanoma, a more fine-grained early morphologic footprint is likely to be detectable by an algorithm. In this perspective article, the concept of melanoma prediction is further explored by the discussion of the evolution of melanoma, the concept for training of such a nevi classifier and the implications of early melanoma prediction for clinical practice. In conclusion, the authors believe that artificial intelligence trained on prospective image data could be transformative for skin cancer diagnostics by (a) predicting melanoma before it occurs (i.e. pre-in situ) and (b) further enhancing the accuracy of current melanoma classifiers. Necessary prospective images for this research are obtained via free mole-monitoring mobile apps.
Downregulation of miR-221-3p expression in prostate cancer (PCa) predicted overall and cancer-specific survival of high-risk PCa patients. Apart from PCa, miR-221-3p expression levels predicted a response to tyrosine kinase inhibitors (TKI) in clear cell renal cell carcinoma (ccRCC) patients. Since this role of miR-221-3p was explained with a specific targeting of VEGFR2, we examined whether miR-221-3p regulated VEGFR2 in PCa. First, we confirmed VEGFR2/KDR as a target gene of miR-221-3p in PCa cells by applying Luciferase reporter assays and Western blotting experiments. Although VEGFR2 was mainly downregulated in the PCa cohort of the TCGA (The Cancer Genome Atlas) database, VEGFR2 was upregulated in our high-risk PCa cohort (n = 142) and predicted clinical progression. In vitro miR-221-3p acted as an escape mechanism from TKI in PC3 cells, as displayed by proliferation and apoptosis assays. Moreover, we confirmed that Sunitinib induced an interferon-related gene signature in PC3 cells by analyzing external microarray data and by demonstrating a significant upregulation of miR-221-3p/miR-222-3p after Sunitinib exposure. Our findings bear a clinical perspective for high-risk PCa patients with low miR-221-3p levels since this could predict a favorable TKI response. Apart from this therapeutic niche, we identified a partially oncogenic function of miR-221-3p as an escape mechanism from VEGFR2 inhibition.
The transcription factor NRF2 is the major mediator of oxidative stress responses and is closely connected to therapy resistance in tumors harboring activating mutations in the NRF2 pathway. In melanoma, such mutations are rare, and it is unclear to what extent melanomas rely on NRF2. Here we show that NRF2 suppresses the activity of the melanocyte lineage marker MITF in melanoma, thereby reducing the expression of pigmentation markers. Intriguingly, we furthermore identified NRF2 as key regulator of immune-modulating genes, linking oxidative stress with the induction of cyclooxygenase 2 (COX2) in an ATF4-dependent manner. COX2 is critical for the secretion of prostaglandin E2 and was strongly induced by H\(_2\)O\(_2\) or TNFα only in presence of NRF2. Induction of MITF and depletion of COX2 and PGE2 were also observed in NRF2-deleted melanoma cells in vivo. Furthermore, genes corresponding to the innate immune response such as RSAD2 and IFIH1 were strongly elevated in absence of NRF2 and coincided with immune evasion parameters in human melanoma datasets. Even in vitro, NRF2 activation or prostaglandin E2 supplementation blunted the induction of the innate immune response in melanoma cells. Transcriptome analyses from lung adenocarcinomas indicate that the observed link between NRF2 and the innate immune response is not restricted to melanoma.
The COVID‐19 pandemic caused by SARS‐CoV‐2 has far‐reaching direct and indirect medical consequences. These include both the course and treatment of diseases. It is becoming increasingly clear that infections with SARS‐CoV‐2 can cause considerable immunological alterations, which particularly also affect pathogenetically and/or therapeutically relevant factors.
Against this background we summarize here the current state of knowledge on the interaction of SARS‐CoV‐2/COVID‐19 with mediators of the acute phase of inflammation (TNF, IL‐1, IL‐6), type 1 and type 17 immune responses (IL‐12, IL‐23, IL‐17, IL‐36), type 2 immune reactions (IL‐4, IL‐13, IL‐5, IL‐31, IgE), B‐cell immunity, checkpoint regulators (PD‐1, PD‐L1, CTLA4), and orally druggable signaling pathways (JAK, PDE4, calcineurin). In addition, we discuss in this context non‐specific immune modulation by glucocorticosteroids, methotrexate, antimalarial drugs, azathioprine, dapsone, mycophenolate mofetil and fumaric acid esters, as well as neutrophil granulocyte‐mediated innate immune mechanisms.
From these recent findings we derive possible implications for the therapeutic modulation of said immunological mechanisms in connection with SARS‐CoV‐2/COVID‐19. Although, of course, the greatest care should be taken with patients with immunologically mediated diseases or immunomodulating therapies, it appears that many treatments can also be carried out during the COVID‐19 pandemic; some even appear to alleviate COVID‐19.
The approval of BRAF and MEK inhibitors has signifi-cantly improved treatment outcomes for patients with BRAF-mutated metastatic melanoma. The 3 first-line targeted therapy trials have provided similar results, and thus the identification of predictive biomarkers may generate a more precise basis for clinical deci-sion-making. Elevated baseline lactate dehydrogenase (LDH) has already been determined as a strong prog-nostic factor. Therefore, this indirect analysis compa-red subgroups with elevated baseline LDH across the pivotal targeted therapy trials co-BRIM, COMBI-v and COLUMBUS part 1. The Bucher method was used to compare progression-free survival, objective response rate and overall survival indirectly. The results show a non-significant risk reduction for progression in the subgroup with elevated baseline LDH receiving vemu-rafenib plus cobimetinib compared with dabrafenib plus trametinib and encorafenib plus binimetinib. Al-though an indirect comparison, these data might pro-vide some guidance for treatment recommendations in melanoma patients with elevated LDH.
Approximately 50% of all melanomas harbor an activating BRAF mutation. In patients suffering from an advanced melanoma with such a somatic alteration, combined targeted therapy with a BRAF and MEK inhibitor can be applied to significantly increase the survival probability. Nevertheless, resistance mechanisms, as well as negative predictive biomarkers (elevated lactate dehydrogenase levels, high number of metastatic organ disease sites, brain metastasis), remain a major problem in treating melanoma patients. Recently, a landmark overall survival (OS) rate of 34% after 5 years of combined targeted therapy in treatment-naïve patients was reported. On the other hand, patients harboring a BRAF mutation and receiving first-line immune checkpoint blockade with ipilimumab plus nivolumab showed a 5-year OS rate of 60%. As indicated by these data, long-term survival can be reached in melanoma patients but it remains unclear if this is equivalent to reaching a true cure for metastatic melanoma. In this review, we summarize the recent results for combined targeted therapy and immunotherapy in advanced melanoma harboring an activating BRAF mutation and discuss the impact of baseline characteristics on long-term outcome.