How does cancer treatment based on genomic data differ from standard techniques, such as chemical therapy?

First of all, to understand the reason behind the tailored therapy against cancer, you need to understand the causes of cancer. Although it is a widespread definition that Cancer is a mass of cells with infinite growth potential, but it is the genome wherein the reasons behind this potential can be found. The current decade 2005 onwards witnessed a massive revolution as the sequencing technologies developed in almost geometric progression. Genomics came a long way from Sanger sequencing in the earlier era to the current Illumina and other next generation sequencing platforms.

The Genomics approach towards the cancer has led to the understanding of the progression of several Cancer types by studying the mutations termed as Driver and Passenger mutations in the genome. We know that there are several classes of genes that need to be mutated for cancer to progress. These are tumor-suppressors, genes that protect our cells from cancer, so they need to be inactivated by mutations, and oncogenes. These genes need to be overactivated by mutations, but again these are just mutations, just changes in DNA. Or, for example, amplifications of the same gene: it’s not mutated, but now instead of one copy of the gene you get ten copies of the gene. And that’s enough to make the cancer cell. So the mutations may not really be the changes in DNA sequence, there can be certain aberrations in the intricate regulation machinery of these genes causing an inactivation of the tumor-suppressors or causing ectopic expression of the oncogenes.

But mutations cannot really hit at specific positions. That’s the essence of Darwinian theory of evolution that mutations happen at random. So that means that cells are sitting and waiting for the next mutation, and now we’re talking about cancer, so cancer cells are sitting waiting for the next mutation to happen. When this mutation happens in one of the cells, the cell will take over the population and will form sort of a new layer of cancer, if you wish. In biology we call this clonal selection,i.e. a particular population of cells becomes dominant.

However, the cells are sitting and waiting for the right mutation, so they are getting random mutations. And they are generally getting a little bit sick of this random mutations. These mutations are collectively called ‘passengers’. So those mutations that drive cancer progression are called ‘drivers’ and others are called ‘passengers’. It’s is generally "believed" that passengers are neutral, they play no role in cancer. Because drivers are usually the same in different patients, but passengers are all different.

So one of the major center of focus of current cancer genomics is to profile the cancer genome from several patients and based on the specific passenger and driver mutation, tailor a patient specific therapy. Moreover, this was needed given the highly heterogenous nature of the cancer. By far several disorders in medical history could be diagnosed and treated by particular fixed symptoms and targets, but in cancer that is not the case. Moreover it is now also observed that the differential response and reversion ability of cancer patients to the radiotherapy and chemotherapy is because certain patients are genetically predisposed to do so.

By far the 1000 genome project has given us only the exome sequence data, so this just gives us information about the driver and passenger mutations from the coding regions of the cancer genome which is far from less compared to the complete genome. Albeit, this data too has led to beliefs that genomics has lot of potential to determine the cancer therapy for every patient based on his genetic predisposition. Hence given the level of complexity Cancer possesses, Genomics by far gives the best possible approach to design very reliable therapies.

Reference: https://www.quora.com/How-does-cancer-treatment-based-on-genomic-data-differ-from-standard-techniques-such-as-chemical-therapy

If computational chemistry methods like density functional theory work well for organic chemistry, why are experimental techniques like X-ray diffraction needed to predict structure, & why isn't drug discovery using computer simulation easy?

TLDR; Highly accurate computational chemistry methods do work very well for organic chemistry - as long as the molecules are reasonably small, isolated and cold. Protein-ligand-systems under physiological conditions are large, dissolved (and perhaps “crowded”) and hot.

Quantum chemistry exhibits bad computational scaling properties, which makes calculations on condensed matter systems like e.g. a protein with a drug molecule in its binding site and dissolved in water (such systems have something like 100000 atoms) at ambient temperature extremely expensive. This is slowly changing, but we are currently barely at a point where the much cheaper molecular-mechanical forcefield calculations are fast enough to calculate the free energy of binding (the measure of binding strength) between a protein and a potential drug molecule on a timescale and with costs palatable to industrial researchers (but those are not particularly accurate…). The reason is that unless your system is ultra-cold, the atoms are moving all the time and you need not a calculation on a single structure but lots and lots of them to get a meaningfull “ensemble” of structures that truly sample and/or represent the many degrees of freedom of movement such a system has.

Structure prediction for organic crystals (small molecules) is more tractable. However, this is still a global optimization problem that again requires thousands of calculations. Nevertheless it nowadays seems to work reasonably well, see e.g. the Report on the sixth blind test of organic crystal structure prediction methods. Now if you again mean predicting the structure of protein-ligand-systems, this is again currently out of reach due to the computational expense.

Reference: https://www.quora.com/If-computational-chemistry-methods-like-density-functional-theory-work-well-for-organic-chemistry-why-are-experimental-techniques-like-X-ray-diffraction-needed-to-predict-structure-why-isnt-drug-discovery-using-computer-simulation-easy 

Which pipeline products are available for protein and peptide-based therapeutics?

Protein and peptide-based therapeutics have been in use for more than three decades since the approval of recombinant human insulin, the first protein therapy, in 1982. Earlier, most biologic drugs were delivered through the subcutaneous route. However, over time, advances in delivery formulations have enabled the development of orally administrable versions of therapeutic proteins / peptides. Owing to numerous compelling reasons, the concept of oral delivery has gained significant traction. The first oral protein / peptide-based product candidate, Linzess®, was launched in 2012 in the US and EU. Recently, Trulance®, another orally administrable product was approved in the US (January 2017) for the treatment of chronic idiopathic constipation (CIC). In fact, in January 2018, Trulance® was approved for another indication, namely irritable bowel syndrome with constipation (IBS-C).

Around 100 oral protein / peptide therapeutics are currently being developed across various preclinical / clinical stages for a diverse range of indications. Two products, namely Linzess® (Ironwood Pharmaceuticals) and Trulance® (Synergy Pharmaceuticals), are commercially available; of these, Trulance® was approved in January 2017. Nearly 41% of the pipeline molecules are under clinical development; of these, 8 molecules are being investigated in phase III and phase II/III, 18 molecules in phase II, 5 molecules in phase II (planned), 1 in phase I/II, and 9 molecules in phase I and phase I (planned) clinical trials. However, majority (57%) of the product candidates in the pipeline are still in the preclinical and discovery stages.

To overcome the challenges related to the effective administration of oral biologics, several innovative technologies to formulate and deliver oral biologics are being developed. Notable examples of advanced drug delivery technologies include (in decreasing order of number of pipeline products) Robotic Pill Maker technology (Rani Therapeutics), Peptelligence® (Enteris BioPharma), Axcess

TM oral drug delivery technology (Proxima Concepts), Oral Peptide Utility System (OPUS) technology (Biolingus), Sublingual Immunotherapy (SLIT) technology (Biolingus), and Oramed Protein Oral Delivery (POD) technology (Oramed Pharmaceuticals).

According to a recent report published by Roots Analysis on ‘Oral Proteins and Peptides Market (3rd Edition), 2018-2030’, 42% of the products in the development pipeline are designed to treat various metabolic disorders, including (in decreasing order of number of pipeline products) diabetes, obesity and non-alcoholic steatohepatitis (NASH). Nearly 15% of therapy candidates are being developed for the treatment of digestive and gastrointestinal disorders, including (in decreasing order of number of pipeline products) chronic idiopathic constipation (CIC), irritable bowel syndrome with constipation (IBS-C), inflammatory bowel disease (IBD) (Crohn's disease and ulcerative colitis), opioid-induced constipation (OIC) and short bowel syndrome (SBS). Products are also being developed for other therapeutic areas, such as autoimmune disorders (9), infectious diseases (9), hormonal disorders (5), bone disorders (4), blood disorders (3), various oncological indications (2), respiratory disorders (2), urogenital disorders (2), genetic disorders (1) and neurodegenerative disorders (1).

Reference:
https://www.rootsanalysis.com/reports/view_document/oral-proteins-and-peptides-market-3rd-edition-2018-2030/192.html