Cancer biology functional genomics: From small RNAs to big dreams

Abstract The year 2021 marks the 20th anniversary of the first publications reporting the discovery of the gene silencing mechanism, RNA interference (RNAi) in mammalian cells. Along with the many studies that delineated the proteins and substrates that form the RNAi pathway, this finding changed our understanding of the posttranscriptional regulation of mammalian gene expression. Furthermore, the development of methods that exploited the RNAi pathway began the technological revolution that eventually enabled the interrogation of mammalian gene function—from a single gene to the whole genome—in only a few days. The needs of the cancer research community have driven much of this progress. In this perspective, we highlight milestones in the development and application of RNAi‐based methods to study carcinogenesis. We discuss how RNAi‐based functional genetic analysis of exemplar tumor suppressors and oncogenes furthered our understanding of cancer initiation and progression and explore how such studies formed the basis of genome‐wide scale efforts to identify cancer or cancer‐type specific vulnerabilities, including studies conducted in vivo. Furthermore, we examine how RNAi technologies have revealed new cancer‐relevant molecular targets and the implications for cancer of the first RNAi‐based drugs. Finally, we discuss the future of functional genetic analysis, highlighting the increasing availability of complementary approaches to analyze cancer gene function.

Caenorhabditis elegans, and Drosophila melanogaster reporting a posttranscriptional gene silencing mechanism mediated by dsRNA that became referred to as RNA interference or RNAi. [3][4][5][6][7][8][9][10] Over the next few years, many groups defined the principal componentsenzymes, substrates, and associated proteins-of the RNAi pathway.  Critical findings demonstrating RNAi's function in regulating endogenous gene expression soon emerged, building on earlier observations of the regulation of genes in C. elegans involved in development and differentiation by small RNAs. [32][33][34][35][36][37][38][39][40][41][42] The discovery of entire classes of regulatory noncoding RNAs, particularly microRNAs (miRNAs), that control gene expression via the RNAi pathway added additional levels of complexity to the regulation of the transcriptome, [43][44][45][46][47] leading to significant insights into how disruption of miRNA-regulated gene expression contributes to disease states, including cancer. [48][49][50][51][52][53][54] Our ability to harness RNAi to study gene function represented a landmark achievement for the scientific community with immediate applicability across multiple biological contexts and disease phenotypes, especially cancer. Importantly, RNAi-based technologies enabled researchers to conduct large-scale loss-of-function (LOF) genomic screens that provided a means for unbiased discovery of analysis. In this perspective, we will discuss how the discovery of RNAi and the development of functional genomic approaches that utilize small RNA-guided protein complexes have altered our understanding of carcinogenesis and furthered the identification of new cancer therapeutic targets ( Figure 1). We will also consider the future impact of RNAi and other RNA-based technologies on our understanding of tumor biology and cancer treatment.

| TURNING BIOLOGY INTO TECHNOLOGY
The initial descriptions of RNAi in mammalian cells led rapidly to the generation of the resources that enabled researchers to deplete protein expression with relative ease. Specifically, the field saw the development of the tools we still use today: expressed short hairpin RNAs (shRNAs) and synthetic small interfering RNAs (siRNAs) that mimic precursor or mature miRNAs [55][56][57][58][59] (Figure 2). However, the design of artificial mediators of RNAi came with challenges. In particular, the prioritization of sequences in a transcript that favor target mRNA cleavage and degradation required extensive optimization. Over time, studies that defined miRNA-mediated gene regulation and iterative empirical studies that tested many different sequences corresponding to a specific transcript resulted in an enhanced understanding of the parameters that facilitate effective RNAi. [60][61][62][63][64][65][66][67][68] Based on these results, academic groups and commercial vendors developed computational models that could predict the sequence of active and specific siRNA or shRNA with some level of accuracy. Selection parameters included the nucleotide content at the 5ʹ ends of each strand of the dsRNA, with a high A/U nucleotide content at the 5ʹ end of one strand favoring its incorporation into the enzyme complex-the RNA-induced silencing complex (RISC), responsible for the interaction with the target mRNA. 69 Our group contributed to these efforts by demonstrating the impact of targeting the coding region of protein-coding genes versus regulatory 5ʹ or 3ʹ untranslated region (UTR) regions and sequence polymorphisms when selecting siRNA or shRNA sequences. 70,71 Improved RNAi-effector design tools lead to the generation of siRNA or shRNA libraries initially targeting subsets of genes, such as gene families or genes with similar functions. These libraries progressed to become genome-wide in scale, paving the way for functional genomic studies previously impossible in mammalian cells. 72,73 The decision to use siRNA or shRNA screening platforms for functional genomic studies depended on many factors. However, in most cases, arrayed screens that involved the analysis of single or limited numbers of RNAi effectors per well of a multi-well plate typically used siRNA resources, while expressed shRNA resources frequently utilized a pooled approach (Figure 3). Parameters used to select a screening approach included the biological question that the screen aimed to address, the model system, the assay endpoint, and access to suitable infrastructure. As the scale of experimental RNAi-based workflows increased, so did the need to develop computational tools that facilitated assessment of data quality, the normalization of results across a screen, correction for sequence-biases, the statistical classification of lead candidate genes for further analysis, and the need for appropriate experimental procedures to validate the results of large-scale screens. 74 As we recognize the successes that turned RNAi biology into RNAi technology, it is also critical to acknowledge the problems of data reproducibility and inconsistency that emerged as RNAi-based studies became ubiquitous. The development of second-and even third-generation RNAi reagents helped address some of the data reproducibility issues associated with early studies. However, it is vital to consider the RNAi mechanism's intrinsic features that generate variation, including differences in the efficacy of even highly optimized reagents and that RNAi reduces gene expression; it does not eliminate it. Over time, the increased publication of results that emphasize consistency in the phenotypic effects induced by siRNAs or shRNAs targeting the same gene has helped reduce the likelihood of false-positive results. Nevertheless, many RNAi-independent factors also need attention when assessing the results of any functional genomic study. For example, the number of cell lines used to define a tumor-type or mutation-specific effect. The signal-to-noise ratio of the endpoint assay used to identify candidate genes of interest can also influence the interpretation of a study's results. Finally, the failure to include appropriate secondary assays or orthogonal approaches to validate initial findings can contribute to the pursuit of nonspecific targets. 75 However, no technology is perfect, and it is essential to remember that the advent of RNAi-based protocols for the manipulation of gene expression initiated whole new ways of examining mammalian biology, including the study of carcinogenesis.

| UNRAVELING THE MISWIRING OF CANCER SIGNALING
Most tumor cells exhibit alterations in signaling pathways that regulate the many processes required for carcinogenesis, including cell proliferation or differentiation, and environmental responses. Thus, it is not surprising that the cancer community quickly adopted RNAibased functional genetic studies, including unbiased screens to study F I G U R E 1 Cancer biology functional genomics. (A) Timeline highlighting the findings leading up to the identification of the RNAi pathway in mammalian cells and the subsequent development of RNAi-based methods for the study of cancer biology. (B) A schematic representation of the topics discussed in this perspective. The ability to manipulate gene expression via the posttranscriptional gene silencing mechanism, RNAi, has enabled interrogation of cancer signaling pathways and cancer-specific dependencies and further provided new avenues for the development of cancer therapeutics. C. elegans, Caenorhabditis elegans; CRISPR, clustered regularly interspaced short palindromic repeats; RNAi, RNA interference; shRNA, short hairpin RNA; siRNA, small interfering RNA SUNDARA RAJAN ET AL. | 1345 the genetic and epigenetic basis of cancer, and the functional consequences of alterations in specific genes and pathways. Here, we discuss selected RNAi-mediated functional genetic studies of representative cancer-associated genes and pathways.

| WNT-β-catenin signaling and other developmental pathways
Several subtypes of cancer exhibit disruption of signaling pathways involved in embryonic development and cell differentiation. Because of the crucial roles that developmental pathways, including Hedgehog (Hh) and WNT-β-catenin signaling, play in carcinogenesis, their characterization in healthy or tumor cells became the primary focus of many RNAi-based studies; an effort that continues using complementary functional genetic tools. Many of these RNAi studies used a reporter assay to measure the transcriptional output of a signaling pathway. For example, one of the first genome-wide RNAi screens conducted in 2003 used a Drosophila cell-based luciferase reporter assay of Hh signaling. The screen identified known components (e.g., Frizzled-1 and Frizzled-2 and GSK3-beta) and new regulators of the Hh pathway, including a link to WNT signaling F I G U R E 2 RNAi-mediated regulation of gene expression. (A) Simplified schematic of miRNA biogenesis and their subsequent effect on gene expression. In brief, the ribonuclease III enzyme DROSHA, together with the DGCR8-microprocessor complex subunit, processes the pol II transcribed primary miRNA transcripts to release the precursor miRNA (pre-miRNA). The translocation of the pre-miRNA to the cytoplasm requires the RAN-GTP binding protein (Ran-GTP)-dependent export protein Exportin 5 (XPO5). In the cytoplasm, a second ribonuclease III enzyme, DICER further processes the pre-miRNA and together with TRBP (TARBP2 subunit of RISC loading complex) facilitates the loading of the mature miRNA guide stranded into the RNA-induced silencing complex or RISC, with a member of the argonaute (AGO) family of proteins at its core. The miRNA-RISC binds one or more complementary mRNA sequences, predominantly in the 3ʹUTR of mRNAs with a tolerance of some nucleotide mismatches, altering mRNA stability and/or the efficiency of translation. (B) An outline of the principal methods used to deliver RNAi reagents (shRNAs or synthetic siRNAs) into cells. The delivery of shRNAs typically involves the cloning of the sequences corresponding to both strands of the duplex siRNA linked through a region that will form a stem-loop into a viral vector system (usually lentiviral-based) that will facilitate the expression of the shRNA in a mammalian cell. The delivery of chemically synthesized duplex RNA molecules, synthetic siRNAs, typically involves using a lipid-based reagent that facilitates uptake into the cytoplasm. (C) A simplified schematic of shRNA and siRNA-mediated targeting of a transcript for degradation via the RNAi pathway. Following the delivery or generation of the duplex RNA, the guide strand loads into RISC with argonaute RISC catalytic component 2 (AGO2) at its core. The RNA-loaded AGO2-RISC can interact with all regions of an mRNA, and through perfect or near-perfect basepairing catalyzes the cleavage of the mRNA, resulting in transcript degradation. 3ʹUTR, 3ʹ untranslated region; miRNA, microRNA; mRNA, messenger RNA; RNAi, RNA interference; shRNA, short hairpin RNA; siRNA, small interfering RNA through the activity of CK1α. 76 79 A study published in the same year reported a genomewide siRNA screen that also used a β-catenin-responsive luciferase assay, but this study used DLD1, colon adenocarcinoma cells, that harbor inactivating mutations in the tumor suppressor APC. 80 Follow-up analysis highlighted the function of AGGF1 that the authors linked to chromatin-remodeling, though to date, this finding's significance remains unclear. A third study published in 2008 reported the results of shRNA screens (targeting~1000 genes) conducted in human colon cancer cells that assessed either cell viability or a readout of β-catenin-dependent transcription. Of the nine genes highlighted by these screens, one-CDK8-maps to a region of chromosome 13 frequently amplified in CRC. Subsequent studies showed that the function of CDK8 as part of the mediator complex contributes to the transcriptional activity of β-catenin. 81 We contributed F I G U R E 3 RNAi screening strategies. (A) A diagrammatic representation of plate-based arrayed siRNA screening strategies compatible with endpoints such as cell viability, and luminescence or fluorescence reporter assays. Synthetic siRNAs are pre-stamped into each well of a multiwell plate (1), and the siRNA-liposome complex formed by adding a pre-optimized amount of a lipid-based transfection reagent (2). Following the addition of cells (3) to the siRNA-liposome complex, the transfected cells grow for 24-96 h under standard cell culture conditions before assessing a relevant endpoint. (B) A simplified outline of bar-coded shRNA-based pooled screening using a lentiviral vector delivery system. Cell populations transduced using pooled libraries that retain a normalized representation of each shRNA form the basis of different experimental groups, for example, different time-points, Day 0 and Day 21, or two treatment conditions, vehicle or active compound. Following a selection period, the isolation of genomic DNA from each cell population allows for the assessment of the relative representation of each shRNA. RNAi, RNA interference; shRNA, short hairpin RNA; siRNA, small interfering RNA SUNDARA RAJAN ET AL.
| 1347 to another study that used RNAi to study candidate genes found within the region of chromosome 13 amplified in CRC. Interestingly, we found that silencing LNX2 in CRC cell lines results in the downregulation of NOTCH-regulated genes and TCF7L2-target genes.
Furthermore, the examination of tumor-derived expression profiles found an LNX2-TCF7L2 gene expression module in CRC samples. 82 A further study of β-catenin transcriptional activity involved the correlation of data from a series of shRNA screens conducted as part of Project Achilles (discussed in further detail below in Section 4.4) with a cancer cell line's relative β-catenin activity. 83 84 The same group also recently described using a positive-selection assay to identify genes needed for β-catenin activity in cells harboring APC mutations. 85 The screen identified BCL9L, a known co-transcriptional activator of β-catenin, and IPO11, a protein not previously linked to β-catenin. Follow-up studies demonstrated that IPO11 regulates the nuclear transport of β-catenin in APC mutant cells that the activity of IPO11 could represent a mutation-specific vulnerability.

| NF-κB signaling
The transcription factor complex NF-κB regulates various biological processes, including B-cell development and inflammation, and so alterations in proteins that form part of the NF-κB signaling cascade contribute to the initiation and progression of several tumor types.
An early focused shRNA screen used a cell-based reporter of tumor necrosis factor (TNF) activation, an endpoint of NF-κB signaling, to identify negative regulators of NF-κB signaling by targeting members of the deubiquitinating family of proteins. 86 This study identified CYLD (CYLD lysine 63 deubiquitinase) as a negative regulator of NF-κB that removes ubiquitin from the TNF receptor-associated factor TRAF2; loss of CYLD drives a proliferative signal via the core NF-κB regulator, the IKK complex. Interestingly, individuals with cylindromatosis harbor mutations in CYLD that results in multi-focal skin lesions, and so this RNAi-based study prompted the initiation of a clinical trial of the IKKβ signaling inhibitor salicylic acid. 87 Overall, treatment with salicylic acid resulted in the complete remission of 2 out of 12 cylindromatosis tumors, with some response in 8 other tumors.
In a hematologic tumor-type, several RNAi screens performed by Staudt and co-workers have probed the NF-κB signaling pathway in a subtype of Diffuse Large B-cell Lymphoma (DLBCL). In an initial study using a pooled shRNA screening strategy targeting 2500 genes performed in cell lines representing the two subgroups of DLBCLactivated B-cell (ABC) and germinal B-cell (GBC) and an IKKβ reporter assay, revealed the dependence of the ABC-DLBCL on NF-κB signaling. 88 Specifically, the screens identified CARD11 and two interacting proteins, MALT1 and BCL10, as critical regulators of the ABC-DLBCL. A follow-up shRNA screen in cells representing the ABC-subtype of DLBCL identified Myd88 as a consistent vulnerability across multiple ABC-DLBCL cell lines. 89 This same screening project also identified IRAK1 as an essential gene for the viability of these cancer cells. More recently, the same group has used CRISPR-Cas9 functional genomic screens to identify a multi-protein supercomplex in the DLBCL-ABC subtype that drives JAK-STAT3 signaling via NF-κB regulated IL-10 and MyD88. 90  pronounced systematic disease. In Section 5, we will discuss additional TP53-related in vivo RNAi studies.

| Fusion oncogenes
Since the publication of these early studies (pre-2006), hundreds of reports have described application of RNAi-based strategies to probe TP53 function. Here, we will highlight one further study, focused on the bone tumor, osteosarcoma (OS). 113 The genomes of OS tumor samples are complex, with almost no consistent genetic changes, except for mutations altering TP53 expression or function. 114  To date, the rarity and genetic complexity of OS have meant clinical translation of this finding has proven a challenge, but this is a targe-

| The Ras oncoprotein family
Activating mutations in KRAS, NRAS, and HRAS, which encode for members of the RAS family of proteins, occur at high prevalence in many cancer types, particularly subtypes of lung and pancreatic cancers. 115 One of the earliest shRNA screens first focused on identifying genes that may alter RAS activity in the absence of a mutation using modified primary fibroblasts, with enhanced potential to transform. The screen identified the transcription factor PITX1 as a putative suppressor of RAS activity, 116 a finding subsequent studies of hepatocarcinogenesis confirmed. 117 Another study conducted a genome-wide RNAi screen in K-Ras-transformed NIH-3T3 cells to investigate the suppression of a proapoptotic pathway by RAS proteins. 118 The screen implicated over 25 candidate genes in the regulation of FAS expression, a proapoptotic molecule. A follow-up study by the same group described how several of these candidate genes form a RAS-regulated gene network and suppresses FAS expression by altering DNA methyltransferase, DNMT1 activity. 119 An early attempt to identify proteins that mutant RAS cells are selectively dependent on for cell survival employed a 4000 gene siRNA library to screen the colon cancer cell line DLD-1 that harbors one mutant and one wild-type KRAS allele. 120   The publication of these comprehensive shRNA screening efforts came at the same time as reports of the first wave of genome-wide CRISPR-Cas9 screens designed to examine RAS biology that broadly used the same approaches as used for earlier RNAi studies. 131,132 For example, Wang et al. 131  web-based sites that enable the community to search standardized and regularly updated LOF data sets and retrieve gene-specific or whole-genome data.

| Early siRNA screens
An early study that tested the hypothesis that RNAi screens conducted in a panel of cell lines could identify pan or mutation-specific gene dependencies employed siRNAs targeting 700 kinases and kinase-related genes. 134  | 1351 effects. For example, one study used a genome-wide RNAi screening data set to determine the effect of siRNAs with identical seed sequences, developing a common seed analysis (CSA) score for each siRNA in the screen. Such analysis allows for the de-prioritizations of siRNAs with a high CSA score due to a promiscuous seed sequence. 145 An extension of this study used the heptamer seed sequence at the 5ʹ end of the guide strand to identify previously unidentified candidate genes of interest within genome-wide siRNAs screens. 146 Finally, with the increased availability of large-scale RNAi screening data using the same reagents in different screens, it was possible to devise computational methods that encompassed several features of these earlier efforts, in an easier to apply fashion. One  16,000 genes to screen breast, pancreatic, and ovarian cancer cell lines (72 lines in total). 153 In this case, the integration of expression profiles that correlate with different subtypes of breast cancer and RNAi screening data identified potential breast cancer subtype-specific vulnerabilities; findings supported and extended by a follow-up study that included the results of shRNA screens performed in 77 breast cancer cell lines. 154 Most of these studies used statistical analysis to reduce the false-negative and positive rates, but others also took the approach of increasing the number of RNAi effectors per gene. For example, a study by Hoffman et al. 155 showed that applying the RSA method 144 157 and drug sensitivity data. 158 Importantly, this represents an ongoing collaborative effort involving multiple institutions and funding sources that continues to employ pooled CRISPR-Cas9 gene-editing screens to define cancer-specific vulnerabilities. 149,159,160 The reader is encouraged to explore the DepMap portal interface and make use of the regularly updated results available at https://depmap.org/portal/download/. However, it is essential to remember that any hypotheses generated using these, and other large-scale cancer cell line data, will always need extensive experimental investigation and validation using orthogonal approaches.

| RNAi SCREENING IN THE MOUSE
The examination of gene function through the application of small RNA-guided functional genetic tools has accelerated our ability to delineate the activity of complex transcriptional and signaling cascades and identify new candidate target proteins for the treatment of cancer and other diseases. However, most such studies have involved the use of cell lines established years or even decades ago and grown as monolayers on treated plastic. While informative, such experiments lack the biological context that a whole-organism study can offer. For this reason, many groups sought to adapt RNAi and now CRISPR-based technologies for application in vivo. C. elegans proved a remarkably straightforward organism for the study of gene function in vivo by RNAi because there is no need for a carrier molecule to introduce the dsRNA, 161-163 but mouse models have and remain the principal whole-organism used for the study of cancer biology. As discussed above, an early in vivo RNAi study that investigated the function of TP53 in B-cell lymphomagenesis used shRNAs targeting Tp53 to generate an epi-allelic cell series and showed a correlation between the levels of Tp53 protein and disease state. 112 However, difficulties in predicting and controlling the exact level of silencing by a given RNAi effector that emerged over the next few years meant that few others adopted this approach. The study by Hemann et al. 112 did though, along with others (e.g., [164][165][166] 176 A separate shRNA-screen (347 mouse genes) also used an in utero approach, but this time using embryos derived from TGFβ-Receptor-II conditional knockout mice, the same group identified Myh9 as a putative tumor suppressor in SCC. 177 Further studies suggested that Myh9 acts as a regulator of Tp53 stability and cellular localization. 177

| WHEN TWO (OR MORE) ARE BETTER THAN ONE
Combination chemotherapy remains the backbone of cancer care and is likely to remain so for many tumor-types for years to come.
For this reason, determining the optimal genetic background in which a drug acts or identifying more effective drug combinations was an early focus of RNAi studies and screens. Many of these studies built on the concept of synthetic lethality that had derived from results in fruit flies and yeast, which showed that in some cases, the function of one gene could compensate for the LOF of another, resulting in no discernable phenotype after genetic disruption of one or other of these genes. However, mutation of both genes results in organismal demise, or synthetic lethality. [178][179][180][181] In cancer, synthetic lethality typically refers to a gene that is nonessential when depleted ARID1B. We will also discuss another aspect of synthetic lethal interactions of interest to the cancer community, that of gene-drug interactions, focusing on genes that modulate chemotherapy agents.

| MTAP and PRMT5
The    [205][206][207][208][209] Here, in brief, we will discuss two clinically focused efforts testing the use of siRNAmediated gene silencing to treat solid tumors-the targeting of mutant-KRAS transcripts (siG12D-LODER) and the silencing of the gene encoding the EPHA2 receptor tyrosine kinase.
As discussed in Section 3.4, mutations in KRAS are present in many tumors, and several early RNAi studies assessed the feasibility of selectively targeting the transcripts encoding mutant KRAS. [210][211][212][213][214] The last of these studies, authored by Zorde Khvalevsky et al. including the ability to make increasingly precise single-nucleotide changes (please see the following recent review 224 ). Alternatively, we can perturb gene expression using either modified Cas9 (e.g., the catalytically inactivated version of Cas9 and dCas9) or an alternative enzyme such as Cas13 to inhibit gene expression (CRISPRi) or, through a further modification of dCas9, activate expression (CRISPRa). For further descriptions of CRISPR-based functional genetic tools, we direct the reader to recent in-depth reviews or commentaries and the citations and links therein. [225][226][227][228][229][230][231] We also see the emergence of systems that mediate the selective degradation of a protein. 232 The access to this diversity of functional genetic tools offers the cancer community the opportunity to address myriad questions. As a starting point, gene-editing tools may enhance our ability to study the early steps of carcinogenesis by generating model systems that better mimic the accumulation of mutations observed SUNDARA RAJAN ET AL.
| 1355 in many tumors. It may also be possible to generate better metastasis models that require the concurrent activation of some genes and the inactivation of others. Furthermore, we now have the potential to examine the temporal effects of depleting genes over a broader time frame, from minutes using protein degradation tools through to hours or days using CRISPR-or RNAi-based approaches and the ability to reverse this effect. Employment of sophisticated temporal control over gene expression may also allow us to mimic better the dynamic and heterogeneous patterns of gene expression present in a growing tumor and thus improve insights into how we can target a higher proportion of cells within a tumor.