Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1–C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.

Lung adenocarcinoma (LUAD) is one of the most common cancers, and patients’ prognostication is crucial for treatment decisions. Histopathological images are the most generally accessible clinical information, however they have not been employed in clinical settings for prognosis. In this study, we used WSIs and clinical data from TCGA (training and testing) and East Asian Cohort (EAS, Validation) to develop and validate DL-based prognosticator. To circumvent the need for manual ROI generation, WSIs from these patients were divided into smaller patches and scored. DeepMPS prediction model was built using the top scoring 226,383 patches. The DeepMPS model showed a C-index of 0.638 in the TCGA training cohort. The univariate and multivariate cox regression analysis identified DeepMPS as an independent predictor of survival (HR: 9.48, p-value: $<$ 0.0001) in the training cohort. The training cohort of patients was separated into low and high-risk groups at various points. Kaplan-Meier analysis showed the highest difference in survival of low and high-risk patients at the 75th percentile (HR: 3.58, 95% CI: 2.57-5.00, p-value: $<$ 0.0001). At the same cut-off as the training samples, TCGA testing cohort patients demonstrated a significant difference in survival when split into low and high-risk patients (HR: 2.30, 95% CI: 1.11-4.82, p-value: 0.044). The DeepMPS model was validated in the EAS cohort patients. DeepMPS risk score significantly segregated EAS patients into low and high-risk groups at the same cut-off point as the training cohort (HR: 2.09, 95% CI: 1.11-3.97, p-value: 0.008). In multivariate Cox regression analysis, the DeepMPS score outperformed the stage in survival prediction. We also compared the DeepMPS model with the previously developed DL-based model to show that it was the best predictor of survival with the highest C-index. In conclusion, we developed a robust DL-based prognostic model which can predict the LUAD outcome without manual intervention using histopathological images.

Cancer is a heterogeneous disease and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroup of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification irrespective of the tissue of origin. In this work, mRNA, miRNA, DNA methylation, and protein expression features from pan-cancer samples were concatenated and non-linearly projected to lower dimension using machine learning (ML) algorithm. This data was then clustered to identify multi-omics based novel subgroups. The clinical characterization of these ML subgroups indicated significant differences in overall survival (OS) and disease free survival (DFS) (p-value $<$ 0.0001). The subgroups formed by the patients from different tumors shared similar molecular alterations in terms of immune microenvironment, mutation profile, and enriched pathways. Further, decision-level and feature-level fused classification models were built to identify the novel subgroups for unseen samples. Additionally, the classification models were used to obtain the class labels for the validation samples and the molecular characteristics were verified. To summarize, this work identified novel ML subgroups using multi-omics data and showed that patients with different tumor types can be similar molecularly. We also proposed and validated the classification models for subgroup identification. The proposed classification models can be used to identify the novel multi-omics subgroups and the molecular characteristics of each subgroup can be used to design appropriate treatment regimen.