  The acquisition of different data modalities can enhance our knowledge and
understanding of various diseases, paving the way for a more personalized
healthcare. Thus, medicine is progressively moving towards the generation of
massive amounts of multi-modal data (\emph{e.g,} molecular, radiology, and
histopathology). While this may seem like an ideal environment to capitalize
data-centric machine learning approaches, most methods still focus on exploring
a single or a pair of modalities due to a variety of reasons: i) lack of ready
to use curated datasets; ii) difficulty in identifying the best multi-modal
fusion strategy; and iii) missing modalities across patients. In this paper we
introduce a real world multi-modal dataset called MMIST-CCRCC that comprises 2
radiology modalities (CT and MRI), histopathology, genomics, and clinical data
from 618 patients with clear cell renal cell carcinoma (ccRCC). We provide
single and multi-modal (early and late fusion) benchmarks in the task of
12-month survival prediction in the challenging scenario of one or more missing
modalities for each patient, with missing rates that range from 26$\%$ for
genomics data to more than 90$\%$ for MRI. We show that even with such severe
missing rates the fusion of modalities leads to improvements in the survival
forecasting. Additionally, incorporating a strategy to generate the latent
representations of the missing modalities given the available ones further
improves the performance, highlighting a potential complementarity across
modalities. Our dataset and code are available here:
https://multi-modal-ist.github.io/datasets/ccRCC
