Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning

In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) in facilitating the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of convolutional neural networks (CNNs) to classify diverse tissue types from whole slide microscope images accurately. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid Deep and ensemble machine learning model that surpassed all preceding solutions for this classification task. Our model achieved 96.74% accuracy on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in advancing the task, we have made them publicly available for further research and development.Comment: 28 pages, 9 figure

H&E Multi-Laboratory Staining Variance Exploration with Machine Learning

In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists' time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue samples stained by the method routinely used in each laboratory. The samples were digitized and summarized as red, green, and blue channel histograms. Dimensions were reduced using principal component analysis. The data projected by principal components were inserted into the k-means clustering algorithm and the k-nearest neighbors classifier with the laboratories as the target. The k-means silhouette index indicated that K = 2 clusters had the best separability in all tissue types. The supervised classification result showed laboratory effects and tissue-type bias. Both supervised and unsupervised approaches suggested that tissue type also affected inter-laboratory variance. We suggest tissue type to also be considered upon choosing the staining and color-normalization approach

PD-1 and PD-L1 expression in pulmonary carcinoid tumors and their association to tumor spread

Pulmonary carcinoid (PC) tumors are rare tumors that account for approximately 1% of all lung cancers. The primary treatment option is surgery, while there is no standard treatment for metastatic disease. As the number of PCs diagnosed yearly is increasing, there is a need to establish novel therapeutic options. This study aimed to investigate programmed death protein 1 (PD-1) and programmed death ligand 1 (PD-L1) expression in PC tumors since blocking of the PD-1/PD-L1 pathway is a promising therapeutic option in various other malignancies. A total of 168 PC patients treated between 1990 and 2013 were collected from the Finnish biobanks. After re-evaluation of the tumors, 131 (78%) were classified as typical carcinoid (TC) and 37 (22%) as atypical carcinoid (AC) tumors. Primary tumor samples were immunohistochemically labeled for PD-1, PD-L1 and CD8. High PD-1 expression was detected in 16% of the tumors. PD-L1 expression was detected in 7% of TC tumors; all AC tumors were PD-L1 negative. PD-L1 expression was associated with mediastinal lymph-node metastasis at the time of diagnosis (P = 0.021) as well as overall metastatic potential of the tumor (P = 0.010). Neither PD-1 expression, PD-L1 expression nor CD8(+) T cell density was associated with survival. In conclusion, PD-1 and PD-L1 were expressed in a small proportion of PC tumors and PD-L1 expression was associated with metastatic disease. Targeting of the PD-1/PD-L1 pathway with immune checkpoint inhibitors may thus offer a treatment option for a subset of PC patients.Peer reviewe

H&E Multi-Laboratory Staining Variance Exploration with Machine Learning

In diagnostic histopathology, hematoxylin and eosin (H&E) staining is a critical process that highlights salient histological features. Staining results vary between laboratories regardless of the histopathological task, although the method does not change. This variance can impair the accuracy of algorithms and histopathologists’ time-to-insight. Investigating this variance can help calibrate stain normalization tasks to reverse this negative potential. With machine learning, this study evaluated the staining variance between different laboratories on three tissue types. We received H&E-stained slides from 66 different laboratories. Each slide contained kidney, skin, and colon tissue samples stained by the method routinely used in each laboratory. The samples were digitized and summarized as red, green, and blue channel histograms. Dimensions were reduced using principal component analysis. The data projected by principal components were inserted into the k-means clustering algorithm and the k-nearest neighbors classifier with the laboratories as the target. The k-means silhouette index indicated that K = 2 clusters had the best separability in all tissue types. The supervised classification result showed laboratory effects and tissue-type bias. Both supervised and unsupervised approaches suggested that tissue type also affected inter-laboratory variance. We suggest tissue type to also be considered upon choosing the staining and color-normalization approach.publishedVersionPeer reviewe

Does breast carcinoma belong to the Lynch syndrome tumor spectrum? - Somatic mutational profiles vs. ovarian and colorectal carcinomas

: ; ; ; ;Inherited DNA mismatch repair (MMR) defects cause predisposition to colorectal, endometrial, ovarian, and other cancers occurring in Lynch syndrome (LS). It is unsettled whether breast carcinoma belongs to the LS tumor spectrum. We approached this question through somatic mutational analysis of breast carcinomas from LS families, using established LS-spectrum tumors for comparison. Somatic mutational profiles of 578 cancer-relevant genes were determined for LS-breast cancer (LS-BC, n = 20), non-carrier breast cancer (NC-BC, n = 10), LS-ovarian cancer (LS-OC, n = 16), and LS-colorectal cancer (LS-CRC, n = 18) from the National LS Registry of Finland. Microsatellite and MMR protein analysis stratified LS-BCs into MMR-deficient (dMMR, n = 11) and MMR-proficient (pMMR, n = 9) subgroups. All NC-BCs were pMMR and all LS-OCs and LS-CRCs dMMR. All but one dMMR LS-BCs were hypermutated (> 10 non-synonymous mutations/Mb; average 174/Mb per tumor) and the frequency of MMR-deficiency-associated signatures 6, 20, and 26 was comparable to that in LS-OC and LS-CRC. LS-BCs that were pMMR resembled NC-BCs with respect to somatic mutational loads (4/9, 44%, hypermutated with average mutation count 33/Mb vs. 3/10, 30%, hypermutated with average 88 mutations/Mb), whereas mutational signatures shared features of dMMR LS-BC, LS-OC, and LS-CRC. Epigenetic regulatory genes were significantly enriched as mutational targets in LS-BC, LS-OC, and LS-CRC. Many top mutant genes of our LS-BCs have previously been identified as drivers of unselected breast carcinomas. In conclusion, somatic mutational signatures suggest that conventional MMR status of tumor tissues is likely to underestimate the significance of the predisposing MMR defects as contributors to breast tumorigenesis in LS.Peer reviewe

Ex vivo drug screening informed targeted therapy for metastatic parotid squamous cell carcinoma

The purpose of ex vivo drug screening in the context of precision oncology is to serve as a functional diagnostic method for therapy efficacy modeling directly on patient-derived tumor cells. Here, we report a case study using integrated multiomics ex vivo drug screening approach to assess therapy efficacy in a rare metastatic squamous cell carcinoma of the parotid gland. Tumor cells isolated from lymph node metastasis and distal subcutaneous metastasis were used for imaging-based single-cell resolution drug screening and reverse-phase protein array-based drug screening assays to inform the treatment strategy after standard therapeutic options had been exhausted. The drug targets discovered on the basis of the ex vivo measured drug efficacy were validated with histopathology, genomic profiling, and in vitro cell biology methods, and targeted treatments with durable clinical responses were achieved. These results demonstrate the use of serial ex vivo drug screening to inform adjuvant therapy options prior to and during treatment and highlight HER2 as a potential therapy target also in metastatic squamous cell carcinoma of the salivary glands

Impact of Age and Comorbidity on Multimodal Management and Survival from Colorectal Cancer: A Population-Based Study

This retrospective population-based study examined the impact of age and comorbidity burden on multimodal management and survival from colorectal cancer (CRC). From 2000 to 2015, 1479 consecutive patients, who underwent surgical resection for CRC, were reviewed for age-adjusted Charlson comorbidity index (ACCI) including 19 well-defined weighted comorbidities. The impact of ACCI on multimodal management and survival was compared between low (score 0–2), intermediate (score 3) and high ACCI (score ≥ 4) groups. Changes in treatment from 2000 to 2015 were seen next to a major increase of laparoscopic surgery, increased use of adjuvant chemotherapy and an intensified treatment of metastatic disease. Patients with a high ACCI score were, by definition, older and had higher comorbidity. Major elective and emergency resections for colon carcinoma were evenly performed between the ACCI groups, as were laparoscopic and open resections. (Chemo)radiotherapy for rectal carcinoma was less frequently used, and a higher rate of local excisions, and consequently lower rate of major elective resections, was performed in the high ACCI group. Adjuvant chemotherapy and metastasectomy were less frequently used in the ACCI high group. Overall and cancer-specific survival from stage I-III CRC remained stable over time, but survival from stage IV improved. However, the 5-year overall survival from stage I–IV colon and rectal carcinoma was worse in the high ACCI group compared to the low ACCI group. Five-year cancer-specific and disease-free survival rates did not differ significantly by the ACCI. Cox proportional hazard analysis showed that high ACCI was an independent predictor of poor overall survival (p < 0.001). Our results show that despite improvements in multimodal management over time, old age and high comorbidity burden affect the use of adjuvant chemotherapy, preoperative (chemo)radiotherapy and management of metastatic disease, and worsen overall survival from CRC

A prognostic model based on cell-cycle control predicts outcome of breast cancer patients

Background A prognostic model combining biomarkers of metaphase-anaphase transition of the cell cycle was developed for invasive breast cancer. The prognostic value and clinical applicability of the model was evaluated in comparison with the routine prognosticators of invasive breast carcinoma. Methods The study comprised 1135 breast cancer patients with complete clinical data and up to 22-year follow-up. Regulators of metaphase-anaphase transition were detected immunohistochemically and the biomarkers with the strongest prognostic impacts were combined into a prognostic model. The prognostic value of the model was tested and evaluated in separate patient materials originating from two Finnish breast cancer centers. Results The designed model comprising immunoexpressions of Securin, Separase and Cdk1 identified 8.4-fold increased risk of breast cancer mortality (p 75%) of patients resulting with favorable as opposed to unfavorable outcome of the model. Along with nodal status, the model showed independent prognostic impact for all breast carcinomas and for subgroups of luminal, N+ and N- disease. Conclusions The impact of the proposed prognostic model in predicting breast cancer survival was comparable to nodal status. However, the model provided additional information in N- breast carcinoma in identifying patients with aggressive course of disease, potentially in need of adjuvant treatments. Concerning N+, in turn, the model could provide evidence for withholding chemotherapy from patients with favorable outcome.</div

Impact of Age and Comorbidity on Multimodal Management and Survival from Colorectal Cancer: A Population-Based Study

This retrospective population-based study examined the impact of age and comorbidity burden on multimodal management and survival from colorectal cancer (CRC). From 2000 to 2015, 1479 consecutive patients, who underwent surgical resection for CRC, were reviewed for age-adjusted Charlson comorbidity index (ACCI) including 19 well-defined weighted comorbidities. The impact of ACCI on multimodal management and survival was compared between low (score 0–2), intermediate (score 3) and high ACCI (score ≥ 4) groups. Changes in treatment from 2000 to 2015 were seen next to a major increase of laparoscopic surgery, increased use of adjuvant chemotherapy and an intensified treatment of metastatic disease. Patients with a high ACCI score were, by definition, older and had higher comorbidity. Major elective and emergency resections for colon carcinoma were evenly performed between the ACCI groups, as were laparoscopic and open resections. (Chemo)radiotherapy for rectal carcinoma was less frequently used, and a higher rate of local excisions, and consequently lower rate of major elective resections, was performed in the high ACCI group. Adjuvant chemotherapy and metastasectomy were less frequently used in the ACCI high group. Overall and cancer-specific survival from stage I-III CRC remained stable over time, but survival from stage IV improved. However, the 5-year overall survival from stage I–IV colon and rectal carcinoma was worse in the high ACCI group compared to the low ACCI group. Five-year cancer-specific and disease-free survival rates did not differ significantly by the ACCI. Cox proportional hazard analysis showed that high ACCI was an independent predictor of poor overall survival (p < 0.001). Our results show that despite improvements in multimodal management over time, old age and high comorbidity burden affect the use of adjuvant chemotherapy, preoperative (chemo)radiotherapy and management of metastatic disease, and worsen overall survival from CRC