Clinical impact of panel gene sequencing on therapy of advanced cancers of the digestive system: a retrospective, single center study.

BACKGROUND: Panel gene sequencing is an established diagnostic tool for precision oncology of solid tumors, but its utility for the treatment of cancers of the digestive system in clinical routine is less well documented. METHODS: We retrospectively identified patients with advanced or metastatic gastrointestinal, pancreaticobiliary or hepatic cancers who received panel gene sequencing at a tertiary university hospital from 2015 to 2022. For these cases, we determined the spectrum of genetic alterations, clinicopathological parameters and treatment courses. Assessment of actionability of genetic alterations was based on the OncoKB database, cancer-specific ESMO treatment guidelines, and recommendations of the local molecular tumor board. RESULTS: In total, 155 patients received panel gene sequencing using either the Oncomine Focus (62 cases), Comprehensive (91 cases) or Childhood Cancer Research Assay (2 cases). The mean age of patients was 61 years (range 24-90) and 37% were female. Most patients suffered from either colorectal cancer (53%) or cholangiocellular carcinoma (19%). 327 genetic alterations were discovered in 123 tumor samples, with an average number of 2.1 alterations per tumor. The most frequently altered genes were TP53, KRAS and PIK3CA. Actionable gene alterations were detected in 13.5-56.8% of tumors, according to ESMO guidelines or the OncoKB database, respectively. Thirteen patients were treated with targeted therapies based on identified molecular alterations, with a median progression-free survival of 8.8 months. CONCLUSIONS: Actionable genetic alterations are frequently detected by panel gene sequencing in patients with advanced cancers of the digestive tract, providing clinical benefit in selected cases. However, for the majority of identified actionable alterations, sufficient clinical evidence for targeted treatments is still lacking.

Cyclin A2 Expression as Predictive Biomarker in Muscle-Invasive Upper Tract Urothelial Carcinoma.

INTRODUCTION: The aim was to evaluate the prognostic value of altered Cyclin A2 (CCNA2) gene expression in upper tract urothelial carcinoma (UTUC) and to assess its predictive potential as a prognostic factor for overall survival (OS) and disease-free survival. METHODS: 62 patients who underwent surgical treatment for UTUC were included. Gene expression of CCNA2, MKI67, and p53 was analyzed by quantitative reverse transcriptase polymerase chain reaction. Survival analyses were performed using the Kaplan-Meier method and the log-rank test. For Cox regression analyses, uni- and multivariable hazard ratios were calculated. Spearman correlation was used to analyze correlation of CCNA2 expression with MKI67 and p53. RESULTS: The median age of the cohort was 73 years, and it consisted of 48 males (77.4%) and 14 females (22.6%). Patients with high CCNA2 expression levels showed longer OS (HR 0.33; 95% CI: 0.15-0.74; p = 0.0073). Multivariable Cox regression analyses identified CCNA2 overexpression (HR 0.37; 95% CI: 0.16-0.85; p = 0.0189) and grading G2 (vs. G3) (HR 0.39; 95% CI: 0.17-0.87; p = 0.0168) to be independent predictors for longer OS. CCNA2 expression correlated positively with MKI67 expression (Rho = 0.4376, p = 0.0005). CONCLUSION: Low CCNA2 expression is significantly associated with worse OS. Thus, CCNA2 might serve as a potential biomarker in muscle-invasive UTUC and may be used to characterize a subset of patients having an unfavorable outcome and for future risk assessment scores.

Perioperative Atezolizumab Plus Fluorouracil, Leucovorin, Oxaliplatin, and Docetaxel for Resectable Esophagogastric Cancer: Interim Results From the Randomized, Multicenter, Phase II/III DANTE/IKF-s633 Trial.

PURPOSE: This trial evaluates the addition of the PD-L1 antibody atezolizumab (ATZ) to standard-of-care fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT) as a perioperative treatment for patients with resectable esophagogastric adenocarcinoma (EGA). METHODS: DANTE started as multicenter, randomized phase II trial, which was subsequently converted to a phase III trial. Here, we present the results of the phase II proportion, focusing on surgical pathology and safety outcomes on an exploratory basis. Patients with resectable EGA (≥cT2 or cN+) were assigned to either four preoperative and postoperative cycles of FLOT combined with ATZ, followed by eight cycles of ATZ maintenance (arm A) or FLOT alone (arm B). RESULTS: Two hundred ninety-five patients were randomly assigned (A, 146; B, 149) with balanced baseline characteristics between arms. Twenty-three patients (8%) had tumors with microsatellite instability (MSI), and 58% patients had tumors with a PD-L1 combined positive score (CPS) of ≥1. Surgical morbidity (A, 45%; B, 42%) and 60-day mortality (A, 3%; B, 2%) were comparable between arms. Downstaging favored arm A versus arm B (ypT0, 23% v 15% [one-sided P = .044]; ypT0-T2, 61% v 48% [one-sided P = .015]; ypN0, 68% v 54% [one-sided P = .012]). Histopathologic complete regression rates (pathologic complete response or TRG1a) were higher after FLOT plus ATZ (A, 24%; B, 15%; one-sided P = .032), and the difference was more pronounced in the PD-L1 CPS ≥10 (A, 33%; B, 12%) and MSI (A, 63%; B, 27%) subpopulations. Complete margin-free (R0) resection rates were relatively high in both arms (A, 96%; B, 95%). The incidence and severity of adverse events were similar in both groups. CONCLUSION: Within the limitations of the exploratory nature of the data, the addition of ATZ to perioperative FLOT is safe and improved postoperative stage and histopathologic regression.

Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images.

BACKGROUND: Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially for hormone receptor-positive, HER2-negative cases. However, invasive lymph node staging is increasingly omitted before therapy, and studies such as the randomised Intergroup Sentinel Mamma (INSEMA) trial address the potential for further de-escalation of axillary surgery. Therefore, it would be helpful to accurately predict the pretherapeutic sentinel status using medical images. METHODS: Using a ResNet 50 architecture pretrained on ImageNet and a previously successful strategy, we trained deep learning (DL)-based image analysis algorithms to predict sentinel status on hematoxylin/eosin-stained images of predominantly luminal, primary breast tumours from the INSEMA trial and three additional, independent cohorts (The Cancer Genome Atlas (TCGA) and cohorts from the University hospitals of Mannheim and Regensburg), and compared their performance with that of a logistic regression using clinical data only. Performance on an INSEMA hold-out set was investigated in a blinded manner. RESULTS: None of the generated image analysis algorithms yielded significantly better than random areas under the receiver operating characteristic curves on the test sets, including the hold-out test set from INSEMA. In contrast, the logistic regression fitted on the Mannheim cohort retained a better than random performance on INSEMA and Regensburg. Including the image analysis model output in the logistic regression did not improve performance further on INSEMA. CONCLUSIONS: Employing DL-based image analysis on histological slides, we could not predict SLN status for unseen cases in the INSEMA trial and other predominantly luminal cohorts.

Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning.

Studies have shown that colorectal cancer prognosis can be predicted by deep learning-based analysis of histological tissue sections of the primary tumor. So far, this has been achieved using a binary prediction. Survival curves might contain more detailed information and thus enable a more fine-grained risk prediction. Therefore, we established survival curve-based CRC survival predictors and benchmarked them against standard binary survival predictors, comparing their performance extensively on the clinical high and low risk subsets of one internal and three external cohorts. Survival curve-based risk prediction achieved a very similar risk stratification to binary risk prediction for this task. Exchanging other components of the pipeline, namely input tissue and feature extractor, had largely identical effects on model performance independently of the type of risk prediction. An ensemble of all survival curve-based models exhibited a more robust performance, as did a similar ensemble based on binary risk prediction. Patients could be further stratified within clinical risk groups. However, performance still varied across cohorts, indicating limited generalization of all investigated image analysis pipelines, whereas models using clinical data performed robustly on all cohorts.

A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma.

PURPOSE: To develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC). METHODS: Digitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan-Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used. RESULTS: A significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test, p < 0.01) and validation set (n = 266, p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11-4.35; p < 0.01) and DSS (HR 4.90; 95%-CI 2.78-8.64; p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15-4.65; p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability. CONCLUSION: The DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.

Microscopic, Macroscopic and Thermal Impact of Argon Plasma, Diode Laser, and Electrocoagulation on Ovarian Tissue.

BACKGROUND/AIM: To compare the microscopic, macroscopic and thermal damage inflicted to ovarian tissue by conventional monopolar and bipolar energy, argon plasma coagulation (APC) and diode laser. MATERIALS AND METHODS: Bovine ovaries were used as a substitute for human tissue and subjected to the four aforementioned techniques and the inflicted damage was measured. Sixty fresh and morphologically similar cadaveric bovine ovaries were divided into five equal groups, each group was subjected to one of the following energy applications for both 1 and 5 s: Monopolar, bipolar electrocoagulation, diode laser, preciseAPC® and forcedAPC® Ovarian temperatures were measured at 4 and 8 s after treatment. Formalin-fixed ovarian specimens were examined by pathologists regarding macroscopic, microscopic and thermal tissue damage. RESULTS: None of the ovaries reached the temperature producing severe damage (40°C) after 1 s of energy transfer. Heating of adjacent ovarian tissue was least pronounced when preciseAPC® and monopolar electrocoagulation were applied (27.2±3.3°C and 28.2±2.9°C after 5 s of application, respectively). Conversely, 41.7% of the ovaries subjected to bipolar electrocoagulation for 5 s overheated. ForcedAPC® resulted in the most pronounced lateral tissue defects (2.8±0.3 mm after 1 s and 4.7±0.6 mm after 5 s). When the modalities were applied for 5 s, the electrosurgical instruments (mono- and bipolar) and preciseAPC® induced similar lateral tissue damage (1.3±0.6 mm, 1.1±1.6 mm and 1.2±1.3 mm, respectively). preciseAPC® created the shallowest defect of all the techniques (0.05±0.1 mm after 5 s of application). CONCLUSION: Our study hints at superior safety profiles of preciseAPC® and monopolar electrocoagulation compared to bipolar electrocoagulation, diode laser and forcedAPC® for ovarian laparoscopic surgery.

Perioperative FLOT plus ramucirumab for resectable esophagogastric adenocarcinoma: A randomized phase II/III trial of the German AIO and Italian GOIM.

This multicenter, randomized phase II/III study evaluated the addition of the vascular endothelial growth factor receptor-2 inhibitor ramucirumab to FLOT as perioperative treatment for resectable esophagogastric adenocarcinoma. Patients received either FLOT alone (Arm A) or combined with ramucirumab followed by ramucirumab monotherapy (Arm B). The primary endpoint for the phase II portion was the pathological complete or subtotal response (pCR/pSR) rate. Baseline characteristics were comparable between both arms with a high rate of tumors signet-ring cell component (A:47% B:43%). No between-arm difference in pCR/pSR rate was seen (A:29% B:26%), therefore the transition to phase III was not pursued. Nevertheless, the combination was associated with a significantly increased R0-resection rate compared with FLOT alone (A:82% B:96%; P = .009). In addition, the median disease-free survival was numerically improved in Arm B (A:21 months B:32 months, HR 0.75, P = 0.218), while the median overall survival was similar in both treatment arms (A:45 months B:46 months, HR 0.94, P = 0.803). Patients with Siewert type I tumors receiving transthoracic esophagectomy with intrathoracic anastomosis showed an increased risk of serious postoperative complications after ramucirumab treatment, therefore recruitment of those patients was stopped after the first-third of the study. Overall, surgical morbidity and mortality was comparable, whereas more non-surgical grade ≥ 3 adverse events were observed with the combination, especially anorexia (A:1% B:11%), hypertension (A:4% B:13%) and infections (A:19% B:33%). The combination of ramucirumab and FLOT as perioperative treatment shows efficacy signals, particularly in terms of R0 resection rates, for a study population with a high proportion of prognostically poor histological subtypes, and further evaluation in this subgroup seems warranted.

Comprehensive biomarker analysis of long-term response to trastuzumab in patients with HER2-positive advanced gastric or gastroesophageal adenocarcinoma.

BACKGROUND: A subgroup of patients with HER2-positive metastatic gastric and gastroesophageal junction cancers shows long-term response under trastuzumab maintenance monotherapy. Obviously, HER2 status alone is not able to identify these patients. We performed this study to identify potential new prognostic biomarkers for this long-term responding patient group. PATIENTS AND METHODS: Tumour samples of 19 patients with HER2-positive metastatic gastric and gastroesophageal junction cancer who underwent trastuzumab treatment were retrospectively collected from multiple centres. Patients were divided into long-term responding (n = 7) or short-term responding group (n = 12) according to progression-free survival (PFS≥12 months vs. PFS < 12 months). Next-generation sequencing and microarray-based gene expression analysis were performed along with HER2 and PD-L1 immunohistochemistry. RESULTS: Long-term responding patients had significantly higher PD-L1 combined positive scores (CPS) and CPS correlated with longer progression-free survival. PD-L1 positivity (CPS ≥ 1) was further associated with an increased CD4+ memory T-cell score. The ERBB2 copy number as well as the tumour mutational burden could not discriminate between short-term and long-term responding patients. Genetic alterations and coamplifications in HER2 pathway associated genes such as EGFR, which were connected to trastuzumab resistance, were present in 10% of the patients and equally distributed between the groups. CONCLUSION: The study highlights the clinical relevance of PD-L1 testing also in the context of trastuzumab treatment and offers a biological rational by demonstrating elevated CD4+ memory T-cells scores in the PD-L1-positive group.

DNA-Methylation Analysis as a Tool for Thymoma Classification.

BACKGROUND: Thymomas are malignant thymic epithelial tumors that are difficult to diagnose due to their rarity and complex diagnostic criteria. They represent a morphologically heterogeneous class of tumors mainly defined by "organo-typical" architectural features and cellular composition. The diagnosis of thymoma is burdened with a high level of inter-observer variability and the problem that some type-specific morphological alterations are more on the continuum than clear-cut. Methylation pattern-based classification may help to increase diagnostic precision, particularly in borderline cases. METHODS AND RESULTS: We applied array-based DNA methylation analysis to a set of 113 thymomas with stringent histological annotation. Unsupervised clustering and t-SNE analysis of DNA methylation data clearly segregated thymoma samples mainly according to the current WHO classification into A, AB, B1, B2, B2/B3, B3, and micronodular thymoma with lymphoid stroma. However, methylation analyses separated the histological subgroups AB and B2 into two methylation classes: mono-/bi-phasic AB-thymomas and conventional/"B1-like" B2-thymomas. Copy number variation analysis demonstrated methylation class-specific patterns of chromosomal alterations. INTERPRETATION: Our study demonstrates that the current WHO classification is generally well reflected at the methylation level but suggests that B2- and AB-thymomas are (epi)genetically heterogeneous. Methylation-based classifications could help to refine diagnostic criteria for thymoma classification, improve reproducibility, and may affect treatment decisions.

Homologous Recombination Deficiency as an Ovarian Cancer Biomarker in a Real-World Cohort: Validation of Decentralized Genomic Profiling.

The diagnostic evaluation of homologous recombination deficiency (HRD) is central to define targeted therapy strategies for patients with ovarian carcinoma. We evaluated HRD in 514 ovarian carcinoma samples by next-generation sequencing of DNA libraries, including BRCA1/BRCA2 and 26,523 single-nucleotide polymorphisms using the standardized Myriad HRD assay, with the predefined cut point of ≥42 for a positive genomic instability score (GIS). All samples were measured in the central Myriad laboratory and in an academic molecular pathology laboratory. A positive GIS was detected in 196 (38.1%) of tumors, whereas 318 (61.9%) were GIS negative. Combining GIS and BRCA mutations, a total of 200 (38.9%) of the 514 tumors were HRD positive. A positive GIS was significantly associated with high-grade serous histology (P < 0.000001), grade 3 tumors (P = 0.001), and patient age <60 years (P = 0.0003). The concordance between both laboratories for the GIS status was 96.9% (P < 0.000001), with a sensitivity of 94.6% and a specificity of 98.4%. Concordance for HRD status was 97.1% (499 of 514 tumors). The percentage of HRD-positive tumors in our real-life cohort was similar to the proportion observed in the recently published PAOLA-1 trial, with high concordance between central and local laboratories. Our results support introduction of the standardized HRD assay in academic molecular pathology laboratories, thus broadening access to personalized oncology strategies for patients with ovarian cancer worldwide.

Second-line therapy with nivolumab plus ipilimumab for older patients with oesophageal squamous cell cancer (RAMONA): a multicentre, open-label phase 2 trial.

BACKGROUND: The overall survival of patients with advanced and refractory oesophageal squamous cell carcinoma, mostly aged 65 years and older, is poor. Treatment with PD-1 antibodies showed improved progression-free survival and overall survival. We assessed the safety and efficacy of combined nivolumab and ipilimumab therapy in this population. METHODS: This multicentre, open-label, phase 2 trial done in 32 sites in Germany included patients aged 65 years and older with oesophageal squamous cell carcinoma and disease progression or recurrence following first-line therapy. Patients were treated with nivolumab (240 mg fixed dose once every 2 weeks, intravenously) in the safety run-in phase and continued with nivolumab and ipilimumab (nivolumab 240 mg fixed dose once every 2 weeks and ipilimumab 1 mg/kg once every 6 weeks, intravenously). The primary endpoint was overall survival, which was compared with a historical cohort receiving standard chemotherapy in the intention-to-treat population. This study is registered with ClinicalTrials.gov, NCT03416244. FINDINGS: Between March 2, 2018, and Aug 20, 2020, we screened 75 patients with advanced oesophageal squamous cell carcinoma. We enrolled 66 patients (50 [76%] men and 16 [24%] women; median age 70·5 years [IQR 67·0-76·0]), 44 (67%) of whom received combined nivolumab and ipilimumab therapy and 22 (33%) received nivolumab alone. Median overall survival time at the prespecified data cutoff was 7·2 months (95% CI 5·7-12·4) and significantly higher than in a historical cohort receiving standard chemotherapy (p=0·0063). The most common treatment-related adverse events were fatigue (12 [29%] of 42), nausea (11 [26%]), and diarrhoea (ten [24%]). Grade 3-5 treatment-related adverse events occurred in 13 (20%) of 66 patients. Treatment-related death occurred in one patient with bronchiolitis obliterans while on nivolumab and ipilimumab treatment. INTERPRETATION: Patients aged at least 65 years, with advanced oesophageal squamous cell carcinoma might benefit from combined nivolumab and ipilimumab therapy in second-line treatment. FUNDING: Bristol Myers Squibb.

Deep learning can predict survival directly from histology in clear cell renal cell carcinoma.

For clear cell renal cell carcinoma (ccRCC) risk-dependent diagnostic and therapeutic algorithms are routinely implemented in clinical practice. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. Multivariable logistic regression was used to combine of the CNNs prediction and clinicopathological parameters. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9-68.1%), 86.2% (95%-CI: 81.8-90.5%), 44.9% (95%-CI: 40.2-49.6%), and 0.70 (95%-CI: 0.69-0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70-8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60-5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92-4.94, p = 0.08) on external validation. The results demonstrate that the CNN's image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.

Functional antagonism between CagA and DLC1 in gastric cancer.

Helicobacter (H.) pylori-induced gastritis is a risk factor for gastric cancer (GC). Deleted-in-liver-cancer-1 (DLC1/ARHGAP7) inhibits RHOA, a downstream mediator of virulence factor cytotoxin-A (CagA) signalling and driver of consensus-molecular-subtype-2 diffuse GC. DLC1 located to enterochromaffin-like and MIST1+ stem/chief cells in the stomach. DLC1+ cells were reduced in H. pylori gastritis and GC, and in mice infected with H. pylori. DLC1 positivity inversely correlated with tumour progression in patients. GC cells retained an N-terminal truncation variant DLC1v4 in contrast to full-length DLC1v1 in non-neoplastic tissues. H. pylori and CagA downregulated DLC1v1/4 promoter activities. DLC1v1/4 inhibited cell migration and counteracted CagA-driven stress phenotypes enforcing focal adhesion. CagA and DLC1 interacted via their N- and C-terminal domains, proposing that DLC1 protects against H. pylori by neutralising CagA. H. pylori-induced DLC1 loss is an early molecular event, which makes it a potential marker or target for subtype-aware cancer prevention or therapy.

FLOT Versus FLOT/Trastuzumab/Pertuzumab Perioperative Therapy of Human Epidermal Growth Factor Receptor 2-Positive Resectable Esophagogastric Adenocarcinoma: A Randomized Phase II Trial of the AIO EGA Study Group.

PURPOSE: High pathologic complete response (pCR) rates and comparably good survival data were seen in a phase II trial combining perioperative fluorouracil, leucovorin, oxaliplatin, and docetaxel (FLOT) chemotherapy with trastuzumab for resectable, esophagogastric adenocarcinoma (EGA). The current trial evaluates the addition of trastuzumab and pertuzumab to FLOT as perioperative treatment for human epidermal growth factor receptor 2-positive resectable EGA. METHODS: In this multicenter, randomized phase II/III trial, patients with human epidermal growth factor receptor 2-positive, resectable EGA (≥ clinical tumor 2 or clinical nodal-positive) were assigned to four pre- and postoperative cycles of either FLOT alone (arm A) or combined with trastuzumab and pertuzumab, followed by nine cycles of trastuzumab/pertuzumab (arm B). The primary end point for the phase II part was the rate of pCR. RESULTS: The trial was closed prematurely, without transition into phase III, after results of the JACOB trial were reported. Eighty-one patients were randomly assigned (A: 41/B: 40) during the phase II part. The pCR rate was significantly improved with the trastuzumab/pertuzumab treatment (A: 12%/B: 35%; P = .02). Similarly, the rate of pathologic lymph node negativity was higher with trastuzumab/pertuzumab (A: 39%/B: 68%), whereas the R0 resection rate (A: 90%/B: 93%) and surgical morbidity (A: 43%/B: 44%) were comparable. Moreover, the inhouse mortality was equal in both arms (overall 2.5%). The median disease-free survival was 26 months in arm A and not yet reached in arm B (hazard ratio, 0.58; P = .14). After a median follow-up of 22 months, the median overall survival was not yet reached (hazard ratio, 0.56; P = .24). Disease-free survival and overall survival rates at 24 months were 54% (95% CI, 38 to 71) and 77% (95% CI, 63 to 90) in arm A and 70% (95% CI, 55 to 85) and 84% (95% CI, 72 to 96) in arm B, respectively. More ≥ grade 3 adverse events were reported with trastuzumab/pertuzumab, especially diarrhea (A: 5%/B: 41%) and leukopenia (A: 13%/B: 23%). CONCLUSION: The addition of trastuzumab/pertuzumab to perioperative FLOT significantly improved pCR and nodal negativity rates at the price of higher rates of diarrhea and leukopenia.

The drug-induced phenotypic landscape of colorectal cancer organoids.

Patient-derived organoids resemble the biology of tissues and tumors, enabling ex vivo modeling of human diseases. They have heterogeneous morphologies with unclear biological causes and relationship to treatment response. Here, we use high-throughput, image-based profiling to quantify phenotypes of over 5 million individual colorectal cancer organoids after treatment with >500 small molecules. Integration of data using multi-omics modeling identifies axes of morphological variation across organoids: Organoid size is linked to IGF1 receptor signaling, and cystic vs. solid organoid architecture is associated with LGR5 + stemness. Treatment-induced organoid morphology reflects organoid viability, drug mechanism of action, and is biologically interpretable. Inhibition of MEK leads to cystic reorganization of organoids and increases expression of LGR5, while inhibition of mTOR induces IGF1 receptor signaling. In conclusion, we identify shared axes of variation for colorectal cancer organoid morphology, their underlying biological mechanisms, and pharmacological interventions with the ability to move organoids along them.

Artificial intelligence to predict oncological outcome directly from hematoxylin and eosin-stained slides in urology.

INTRODUCTION: Artificial intelligence (AI) has been successfully applied for automatic tumor detection and grading in histopathological image analysis in urologic oncology. The aim of this review was to assess the applicability of these approaches in image-based oncological outcome prediction. EVIDENCE ACQUISITION: A systematic literature search was conducted using the databases MEDLINE through PubMed and Web of Science up to April 20, 2021. Studies investigating AI approaches to determine the risk of recurrence, metastasis, or survival directly from H&E-stained tissue sections in prostate, renal cell or urothelial carcinoma were included. Characteristics of the AI approach and performance metrics were extracted and summarized. Risk of bias (RoB) was assessed using the PROBAST tool. EVIDENCE SYNTHESIS: 16 studies yielding a total of 6658 patients and reporting on 17 outcome predictions were included. Six studies focused on renal cell, six on prostate and three on urothelial carcinoma while one study investigated renal cell and urothelial carcinoma. Handcrafted feature extraction was used in five, a convolutional neural network (CNN) in six and a deep feature extraction in four studies. One study compared a CNN with handcrafted feature extraction. In seven outcome predictions, a multivariable comparison with clinicopathological parameters was reported. Five of them showed statistically significant hazard ratios for the AI's model's-prediction. However, RoB was high in 15 outcome predictions and unclear in two. CONCLUSIONS: The included studies are promising but predominantly early pilot studies, therefore primarily highlighting the potential of AI approaches. Additional well-designed studies are needed to assess the actual clinical applicability.

Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review.

BACKGROUND: Over the past decade, the development of molecular high-throughput methods (omics) increased rapidly and provided new insights for cancer research. In parallel, deep learning approaches revealed the enormous potential for medical image analysis, especially in digital pathology. Combining image and omics data with deep learning tools may enable the discovery of new cancer biomarkers and a more precise prediction of patient prognosis. This systematic review addresses different multimodal fusion methods of convolutional neural network-based image analyses with omics data, focussing on the impact of data combination on the classification performance. METHODS: PubMed was screened for peer-reviewed articles published in English between January 2015 and June 2021 by two independent researchers. Search terms related to deep learning, digital pathology, omics, and multimodal fusion were combined. RESULTS: We identified a total of 11 studies meeting the inclusion criteria, namely studies that used convolutional neural networks for haematoxylin and eosin image analysis of patients with cancer in combination with integrated omics data. Publications were categorised according to their endpoints: 7 studies focused on survival analysis and 4 studies on prediction of cancer subtypes, malignancy or microsatellite instability with spatial analysis. CONCLUSIONS: Image-based classifiers already show high performances in prognostic and predictive cancer diagnostics. The integration of omics data led to improved performance in all studies described here. However, these are very early studies that still require external validation to demonstrate their generalisability and robustness. Further and more comprehensive studies with larger sample sizes are needed to evaluate performance and determine clinical benefits.